Real-Time Head Orientation from a Monocular Camera Using Deep Neural Network. [1] J. Xiao, S. Baker, I. Matthews, T. Kanade, "Real-Time Combined 2D + 3D Active Appearance Models," CVPR, pp.535-542, 2004. [2] N. Gourier, D. Hall, J. L. Crowley, "Estimating Face Orientation from Robust Detection of Salient Facial Features", Proceedings of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures, 2004. [3] G. Fanelli, M. Dantone, J. Gall, A. Jssati, L. Van Gool, "Random Forests for Real Time 3D Face Analysis," IJCV, pp.437-458, 2013. [4] Y. LeCun, R. Bottou, Y. Bengio, P. Haffner, "Gradient-based Learning Applied to Document Recognition," Proc. IEEE, vol. 86, no. 11, pp.2278-2324, 1998. [5] A. Krizhevsky, I. Sutskever, G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," NIPS, pp.1106-1114, 2012. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2010-0028680). Proposed Method  Conventional Head Pose Estimation Approaches

Question: What is the difference between a deep neural network and a convolutional neural network?


Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method. We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.

Question: How to simplify the model?


An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems. Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.

Question: What is the difference between matrix manipulation and single-element-based approach?


DC and RF breakdown voltage characteristics of SiGe HBTs for WiFi PA applications. Breakdown voltage and RF characteristics relevant for RF power amplifiers (PA) are presented in this paper. Typically, DC collector-to-emitter breakdown voltage with base open (BVCEO) or DC collector-to-base breakdown with emitter open (BVCBO) has been presented as the metric for voltage limit of PA devices. In practical PA circuits, the RF envelope voltage can swing well beyond BVCEO without causing a failure. An analysis of output power swing limitations and DC breakdown is presented with attention to biasing and temperature.

Question: What is the breakdown voltage of a transistor?


Hidradenitis suppurativa in children and adolescents: a review of treatment options.. Hidradenitis suppurativa (HS) is a burdensome disease and has the potential to affect the life course of patients. It is a rare disease in children, and the recorded literature is correspondingly scarce. This article reviews the therapeutic options for HS in children and adolescents, and highlights particular differences or challenges with treating patients in this age group compared with adults. The work-up of paediatric patients with HS should include considerations of possible endocrine co-morbidities and obesity. Medical therapy of lesions may include topical clindamycin. Systemic therapy may include analgesics, clindamycin and rifampicin, finasteride, corticosteroids or tumour necrosis factor alpha (TNFα) blockers. Superinfections should be appropriately treated. Scarring lesions generally require surgery.

Question: What is the treatment of hidradenitis suppurativa in children?


Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment. Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far. Specifically, (a) the regressors are learnt independently, (b) the descent directions may cancel one another out and (c) handcrafted features (e.g., HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.

Question: How does cascaded regression work?


Duration prediction using multi-level model for GPR-based speech synthesis. This paper introduces frame-based Gaussian process regression (GPR) into phone/syllable duration modeling for Thai speech synthesis. The GPR model is designed for predicting framelevel acoustic features using corresponding frame information, which includes relative position in each unit of utterance structure and linguistic information such as tone type and part of speech. Although the GPR-based prediction can be applied to a phone duration model, the use of phone duration model only is not always sufficient to generate natural sounding speech. Specifically, in some languages including Thai, syllable durations affect the perception of sentence structure. In this paper, we propose a duration prediction technique using a multi-level model which includes syllable and phone levels for prediction. In the technique, first, syllable durations are predicted, and then they are used as additional contexts in phone-level model to generate phone duration for synthesizing. Objective and subjective evaluation results show that GPR-based modeling with multi-level model for duration prediction outperforms the conventional HMM-based speech synthesis.

Question: How to predict the duration of a phone?


Semantic audio content-based music recommendation and visualization based on user preference examples. Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user's musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user's musical preferences by creating a humanoid cartoon-like character – the Musical Avatar – automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-ofthe-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems. 2012 Elsevier Ltd. All rights reserved.

Question: What is the size of the canadian military?


A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques. In the era of information overload, text summarization can be defined as the process of extracting useful information from a large space of available content using traditional filtering methods. One of the major challenges in the domain of extraction based summarization is that a single statistical measure is not sufficient to produce efficient summaries which would be close to human-made 'gold standard', since each measure suffers from individual weaknesses. We deal with this problem by proposing a text summarization model that combines various statistical measures so that the pitfalls of an individual technique could be compensated by the strengths of others. Experimental results are presented to demonstrate the effectiveness of the proposed method using the TAC 2011 Multiling pilot dataset for English language and ROUGE summary evaluation tool.

Question: What is the text summarization?


A new active clamping zero-voltage switching PWM current-fed half-bridge converter. A new active clamping zero-voltage switching (ZVS) pulse-width modulation (PWM) current-fed half-bridge converter (CFHB) is proposed in this paper. Its active clamping snubber (ACS) can not only absorb the voltage surge across the turned-off switch, but also achieve the ZVS of all power switches. Moreover, it can be applied to all current-fed power conversion topologies and its operation as well as structure is very simple. Since auxiliary switches in the snubber circuit are switched in a complementary way to main switches, an additional PWM IC is not necessary. In addition, it does not need any clamp winding and auxiliary circuit besides additional two power switches and one capacitor while the conventional current-fed half bridge converter has to be equipped with two clamp windings, two ZVS circuits, and two snubbers. Therefore, it can ensure the higher operating frequency, smaller-sized reactive components, lower cost of production, easier implementation, and higher efficiency. The operational principle, theoretical analysis, and design considerations are presented. To confirm the operation, validity, and features of the proposed circuit, experimental results from a 200-W, 24-200Vdc prototype are presented.

Question: What is a current-fed half-bridge converter?


Metaphor in the Mind : The Cognition of Metaphor. The most sustained and innovative recent work on metaphor has occurred in cognitive science and psychology. Psycholinguistic investigation suggests that novel, poetic metaphors are processed differently than literal speech, while relatively conventionalized and contextually salient metaphors are processed more like literal speech. This conflicts with the view of "cognitive linguists" like George Lakoff that all or nearly all thought is essentially metaphorical. There are currently four main cognitive models of metaphor comprehension: juxtaposition, category-transfer, feature-matching, and structural alignment. Structural alignment deals best with the widest range of examples; but it still fails to account for the complexity and richness of fairly novel, poetic metaphors. 1. General Issues in the Study of Metaphor Philosophers have often adopted a dismissive attitude toward metaphor. Hobbes (ch. 8) advocated excluding metaphors from rational discourse because they "openly profess deceit," while Locke (Bk. 3, ch. 10) claimed that figurative uses of language serve only "to insinuate wrong ideas, move the passions, and thereby mislead the judgment; and so indeed are perfect cheats." Later, logical positivists like Ayer and Carnap assumed that because metaphors like (1) How sweet the moonlight sleeps upon this bank!2 involve category mistakes, they have no real meaning or verification conditions. Thus, they too mentioned metaphor only to place it beyond the pale of rational discourse. Starting in the 1960s and 70s, philosophers and linguists began to take more positive interest in metaphor. Black argued forcefully that metaphors do have a distinctive, essentially non-propositional meaning or cognitive significance, which is produced by the "interaction" of the "systems of associated commonplaces" for the metaphor's "primary" and "secondary" subjects (e.g., with moonlight and sleeping sweetly). Other theorists were more fr

Question: What is the cognitive significance of metaphor?


A Survey on the Security of Stateful SDN Data Planes. Software-defined networking (SDN) emerged as an attempt to introduce network innovations faster, and to radically simplify and automate the management of large networks. SDN traditionally leverages OpenFlow as device-level abstraction. Since OpenFlow permits the programmer to "just" abstract a static flow-table, any stateful control and processing intelligence is necessarily delegated to the network controller. Motivated by the latency and signaling overhead that comes along with such a two-tiered SDN programming model, in the last couple of years several works have proposed innovative switch-level (data plane) programming abstractions capable to deploy some smartness directly inside the network switches, e.g., in the form of localized stateful flow processing. Furthermore, the possible inclusion of states and state maintenance primitives inside the switches is currently being debated in the OpenFlow standardization community itself. In this paper, after having provided the reader with a background on such emerging stateful SDN data plane proposals, we focus our attention on the security implications that data plane programmability brings about. Also via the identification of potential attack scenarios, we specifically highlight possible vulnerabilities specific to stateful in-switch processing (including denial of service and saturation attacks), which we believe should be carefully taken into consideration in the ongoing design of current and future proposals for stateful SDN data planes.

Question: What is the security implications of stateful SDN data planes?


Analysis Enhanced Particle-based Flow Visualization. Particle-based fluid simulation (PFS), such as Smoothed Particle Hydrodynamics (SPH) and Position-based Fluid (PBF), is a mesh-free method that has been widely used in various fields, including astrophysics, mechanical engineering, and biomedical engineering for the study of liquid behaviors under different circumstances. Due to its meshless nature, most analysis techniques that are developed for mesh-based data need to be adapted for the analysis of PFS data. In this work, we study a number of flow analysis techniques and their extension for PFS data analysis, including the FTLE approach, Jacobian analysis, and an attribute accumlation framework. In particular, we apply these analysis techniques to free surface fluids. We demonstrate that these analyses can reveal some interesting underlying flow patterns that would be hard to see otherwise via a number of PFS simulated flows with different parameters and boundary settings. In addition, we point out that an in-situ analysis framework that performs these analyses can potentially be used to guide the adaptive PFS to allocate the computation and storage power to the regions of interest during the simulation.

Question: What is the difference between a mesh-based and mesh-free fluid simulation?


CMT-bone: A Mini-App for Compressible Multiphase Turbulence Simulation Software. Designed with the goal of mimicking key features of real HPC workloads, mini-apps have become an important tool for co-design. An investigation of mini-app behavior can provide system designers with insight into the impact of architectures, programming models, and tools on application performance. Mini-apps can also serve as a platform for fast algorithm design space exploration, allowing the application developers to evaluate their design choices before significantly redesigning the application codes. Consequently, it is prudent to develop a mini-app alongside the full blown application it is intended to represent. In this paper, we present CMT-bone a mini-app for the compressible multiphase turbulence (CMT) application, CMT-nek, being developed to extend the physics of the CESAR Nek5000 application code. CMT-bone consists of the most computationally intensive kernels of CMT-nek and the communication operations involved in nearest-neighbor updates and vector reductions. The mini-app represents CMT-nek in its most mature state and going forward it will be developed in parallel with the CMT-nek application to keep pace with key new performance impacting changes. We describe these kernels and discuss the role that CMT-bone has played in enabling interdisciplinary collaboration by allowing application developers to work with computer scientists on performance optimization on current architectures and performance analysis on notional future systems.

Question: What is a mini-app?


Exploiting different word clusterings for class-based RNN language modeling in speech recognition. We propose to exploit the potential of multiple word clusterings in class-based recurrent neural network (RNN) language models for ensemble RNN language modeling. By varying the clustering criteria and the space of word embedding, different word clusterings are obtained to define different word/class factorizations. For each such word/class factorization, several base RNNLMs are learned, and the word prediction probabilities of the base RNNLMs are then combined to form an ensemble prediction. We use a greedy backward model selection procedure to select a subset of models and combine these models for word prediction. The proposed ensemble language modeling method has been evaluated on Penn Treebank test set as well as Wall Street Journal (WSJ) Eval 92 and 93 test sets, where it improved test set perplexity and word error rate over the state-of-the-art single RNNLMs as well as multiple RNNLMs produced by varying RNN learning conditions.

Question: How to exploit different word clusterings for class-based RNN language modeling in speech recognition.


Ethernet-Based Real-Time and Industrial Communications. Despite early attempts to use Ethernet in the industrial context, only recently has it attracted a lot of attention as a support for industrial communication. A number of vendors are offering industrial communication products based on Ethernet and TCP/IP as a means to interconnect field devices to the first level of automation. Others restrict their offer to communication between automation devices such as programmable logic controllers and provide integration means to existing fieldbuses. This paper first details the requirements that an industrial network has to fulfill. It then shows how Ethernet has been enhanced to comply with the real-time requirements in particular in the industrial context. Finally, we show how the requirements that cannot be fulfilled at layer 2 of the OSI model can be addressed in the higher layers adding functionality to existing standard protocols.

Question: What is the difference between industrial communication and automation?


Towards the enhancement of e-democracy: identifying the notion of the 'middleman paradox'. The challenge towards e-democracy, through the electronic transformation of political systems, has become increasingly evident within developed economies. It is regarded as an approach for increased and better quality citizen participation in the democratic processes. E-democracy forms a component of overall e-government initiatives where technology adoption and diffusion, to enhance wider access to, and the delivery of, government services, are apparent. However, previous research demonstrates that very few e-democracy proposals survive the stage of formal political decision-making to become substantive egovernment projects within national or international agendas. Furthermore, the implementation of e-democracy projects is undertaken at a much slower pace and with dramatically less support than the implementation of other, so-called e-administration, activities in the public sector. The research in this paper considers the notion of the 'middleman paradox', presenting theoretical and empirical evidence that further investigates the phenomenon associated with potential e-democracy improvements. Specifically, the paper adds a new dimension to existing theories on the hesitant evolution of e-democracy that clearly identifies politicians as an inhibiting factor. Proposals are made for an enhancement of these processes, and suggestions for further applicable research are demonstrated.

Question: What is the middleman paradox?


Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities. This keynote paper: presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.

Question: What is the vision of computing?


Segmentation of White Blood Cells Image Using Adaptive Location and Iteration. Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and subimage segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multiscale cues to extract a rectangular region is proposed. In this manner, the location window not only covers the whole WBC completely, but also achieves adaptive adjustment. In the subimage segmentation, the subimages preprocessed from the location window with a replace procedure are taken as initialization, and the GrabCut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.

Question: How to segment a white blood cell image?


Rationalizing Neural Predictions. Prediction without justification has limited applicability. As a remedy, we learn to extract pieces of input text as justifications – rationales – that are tailored to be short and coherent, yet sufficient for making the same prediction. Our approach combines two modular components, generator and encoder, which are trained to operate well together. The generator specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction. Rationales are never given during training. Instead, the model is regularized by desiderata for rationales. We evaluate the approach on multi-aspect sentiment analysis against manually annotated test cases. Our approach outperforms attention-based baseline by a significant margin. We also successfully illustrate the method on the question retrieval task.1

Question: What is the approach called?


WAVELET BASED WATERMARKING ON DIGITAL IMAGE. Safeguarding creative content and intellectual property in a digital form has become increasingly difficult as technologies, such as the internet, broadband availability and mobile access advance. It has grown to be progressively easier to copy, modify and redistribute digital media, resulting in great declines in business profits. Digital watermarking is a technique which has been proposed as a possible solution to this problem. Digital Watermarking is a technology which is used to identify the creator, owner, distributor of a given video or image by embedding copyright marks into the digital content, hence digital watermarking is a powerful tool used to check the copy right violation. In this paper a robust watermarking technique based on DWT (Discrete Wavelet Transform) is presented. In this technique the insertion and extraction of the watermark in the grayscale image is found to be simpler than other transform techniques. This paper successfully explains the digital watermarking technique on digital images based on discrete wavelet transform by analyzing various values of PSNR's and MSE's..

Question: What is the difference between the discrete wavelet transform and the discrete wavelet transform?


A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network. Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified endto-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time).

Question: What is a fast and compact saliency score regression network based on fully convolutional network?


Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds. The proliferation of mobile devices and ubiquitous access of the wireless network enables many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby mobile edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study joint computation partitioning and resource allocation problem for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), to solve the problem. We compares MDSA with the classic list scheduling method and the SearchAdjust algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency.

Question: What is the difference between the classic list scheduling method and the SearchAdjust algorithm?


Enumerating the Non-Isomorphic Assembly Configurations of Modular Robotic Systems. A \modular" robotic system consists of joint and link modules that can be assembled in a variety of conngurations to meet diierent or changing task requirements. However, due to typical symmetries in module design, diierent assembly conngurations may lead to robotic structures which are kinematically identical, or isomorphic. This paper considers how to enumerate the non-isomorphic assembly conngurations of a modular robotic system. We introduce an Assembly Incidence Matrix (AIM) to represent a modular robot assembly con-guration. Then we use symmetries of the module geometry and graph isomorphisms to deene an equivalence relation on the AIMs. Equivalent AIMs represent isomorphic robot assembly conngurations. Based on this equivalence relation, we propose an algorithm to generate non-isomorphic assembly conngurations of an n-link tree-like robot with diierent joint and link module types. Examples demonstrate that this method is a signiicant improvement over a brute force enumeration process.

Question: How to enumerate the non-isomorphic assembly conngurations of a modular robotic system?


Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies. In some real-world areas, it is important to enrich the data with external background knowledge so as to provide context and to facilitate pattern recognition. These areas may be described as data rich but knowledge poor. There are two challenges to incorporate this biological knowledge into the data mining cycle: (1) generating the ontologies; and (2) adapting the data mining algorithms to make use of the ontologies. This chapter presents the state-of-the-art in bringing the background ontology knowledge into the pattern recognition task for biomedical data.

Question: What is the state of the art in bringing the background ontology knowledge into the pattern recognition task for biomedical data?


Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier. The most prevailing approach now for parking lot vacancy detecting system is to use sensor-based techniques. The main impediments to the camera-based system in applying to parking lots on rooftop and outside building are the glaring sun light and dark shadows in the daytime, and low-light intensity and back-lighting in the nighttime. To date, no camera-based detecting systems for outdoor parking lots have been in practical use. A few engineering firms provide the camera-based system, which is only for underground and indoor parking lots. This paper reports on the new camera based system called ParkLotD for detecting vacancy/occupancy in parking lots. ParkLotD uses a classifier based on fuzzy c-means (FCM) clustering and hyper-parameter tuning by particle swarm optimization (PSO). The test result of the detection error rate for the indoor multi-story parking lot has improved by an order of magnitude compared to the current system based on the edge detection approach. ParkLotD demonstrates high detection performance and enables the camera-based system to achieve the practical use in outdoor parking lots.

Question: How does the camera-based system work?


SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation. Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis. Experiments on a NIST SRE 2005 corpus demonstrate the effectiveness of the new technique

Question: What is the difference between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis?


A Task-based Approach for Ontology Evaluation. The need for the establishment of evaluation methods that can measure respective improvements or degradations of ontological models e.g. yielded by a precursory ontology population stage is undisputed. We propose an evaluation scheme that allows to employ a number of different ontologies and to measure their performance on specific tasks. In this paper we present the resulting task-based approach for quantitative ontology evaluation, which also allows for a bootstrapping approach to ontology population. Benchmark tasks commonly feature a so-called gold-standard defining perfect performance. By selecting ontology-based approaches for the respective tasks, the ontology-dependent part of the performance can be measured. Following this scheme, we present the results of an experiment for testing and incrementally augmenting ontologies using a well-defined benchmark problem based on a evaluation goldstandard.

Question: What is a task-based approach for ontology evaluation?


Towards String-to-Tree Neural Machine Translation. We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. Experiments on the WMT16 German-English news translation task shown improved BLEU scores when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A smallscale human evaluation also showed an advantage to the syntax-aware system. "accepted..." header

Question: What is the difference between a syntax-aware and a syntax-agnostic neural machine translation system?


Author Disambiguation in PubMed: Evidence on the Precision and Recall of Author-ity among NIH-Funded Scientists. We examined the usefulness (precision) and completeness (recall) of the Author-ity author disambiguation for PubMed articles by associating articles with scientists funded by the National Institutes of Health (NIH). In doing so, we exploited established unique identifiers-Principal Investigator (PI) IDs-that the NIH assigns to funded scientists. Analyzing a set of 36,987 NIH scientists who received their first R01 grant between 1985 and 2009, we identified 355,921 articles appearing in PubMed that would allow us to evaluate the precision and recall of the Author-ity disambiguation. We found that Author-ity identified the NIH scientists with 99.51% precision across the articles. It had a corresponding recall of 99.64%. Precision and recall, moreover, appeared stable across common and uncommon last names, across ethnic backgrounds, and across levels of scientist productivity.

Question: How many articles are there in PubMed?


User Association for Load Balancing in Heterogeneous Cellular Networks. For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

Question: How does the user association algorithm work?


A federated Edge Cloud-IoT architecture. The provisioning of powerful decentralized services in the Internet of Things (IoT), as well as the integration of innovative IoT platforms, requires ubiquity, reliability, high-performance, efficiency, and scalability. Towards the accomplishment of the above requirements, the contemporary technological trends leaded to the IoT and Cloud convergence. This combination created the approach of federated Cloud-IoT architectures. The Cloud-IoT architectural vision is paving the way to the vital step, beyond the IoT cognitive capabilities, that refers to the IoT world leveraging cloud principles. For the success of the deployment of such innovative and high-performance solutions, the introduction and the exploitation of 5G technological principles will empower the way forward. Towards this direction, this paper presents a federated architecture combined by distributed Edge Cloud-IoT platforms, that enables the semantic-based service combination and provisioning. The 5G technological features, such as low-latency, zero-losses, high-bitrates are studied and proposed for the empowerment of the federation of the Edge Cloud-IoT architectures. A smart health use case is presented for the evaluation of the proposed solution.

Question: What is the difference between Edge Cloud and Cloud?


SURVEY OF BIOMETRIC RECOGNITION SYSTEMS AND THEIR. The term Biometrics is becoming highly important in computer security world. The human physical characteristics like fingerprints, face, hand geometry, voice and iris are known as biometrics. These features are used to provide an authentication for computer based security systems. The existing computer security systems used at various places like banking, passport, credit cards, smart cards, PIN , access control and network security are using username and passwords for person identification. The username and passwords can be replaced and/or provide double authentication by using any one of the biometric features. In this paper, the main focus is on the various biometrics, their applications and the existing biometrics recognition systems.

Question: What is the difference between biometrics and biometrics?


A novel compact printed ACS fed dual-band antenna for Bluetooth/WLAN/WiMAX applications. In this research article, a compact dual band asymmetric coplanar strip-fed printed antenna is designed and presented for Bluetooth, WLAN/WiMAX and public safety applications. The dual frequency operating bands (2.45 GHz and 5.25 GHz) have been achieved by attaching two simple meander shaped radiating strips to the ACS feed line. The proposed antenna geometry is printed on a low cost FR4 substrate having thickness of 1.6mm with overall dimensions of 13 × 21.3m including uniplanar ground plane. The -10 dB impedance bandwidth of the meandered ACS-fed dual band monopole antenna is about 140MHz from 2.36-2.5 GHz, and 2500MHz from 4.5-7.0 GHz respectively, which can cover 2.4 GHz Bluetooth/WLAN, 5.2/5.8 GHz WLAN, 5.5 GHz WiMAX and 4.9 GHz US public safety bands. In addition to the simple geometry and wide impedance bandwidth features, proposed structure perform bidirectional and omnidirectional radiation pattern in both E and H-plane respectively.

Question: What is a dual band antenna?


Image-based visual navigation for mobile robots. We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.

Question: How to navigate a robot in an unknown environment?


A Metric for Performance Evaluation of Multi-Target Tracking Algorithms. Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.

Question: What is the difference between tracking and filtering?


Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving. Two important requirements when aggregating the preferences of multiple agents are that the outcome should be economically efficient and the aggregation mechanism should not be manipulable. In this article, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms. More precisely, we show that every efficient aggregation mechanism can be manipulated for all expected utility representations of the agents' preferences. This settles an open problem and strengthens several existing theorems, including statements that were shown within the special domain of assignment. Our proof is obtained by formulating the claim as a satisfiability problem over predicates from real-valued arithmetic, which is then checked using a satisfiability modulo theories (SMT) solver. To verify the correctness of the result, a minimal unsatisfiable set of constraints returned by the SMT solver was translated back into a proof in higher-order logic, which was automatically verified by an interactive theorem prover. To the best of our knowledge, this is the first application of SMT solvers in computational social choice.

Question: What is the difference between efficiency and strategyproofness?


Resilient Cell-Based Architecture for Time-to-Digital Converter. This paper proposes a resilient Time-to-Digital Converter (TDC) that lends itself to cell-based design automation. We adopt a shrinking-based architecture with a number of distinctive techniques. First of all, a specialized on-chip re-calibration scheme is developed so that the real-time transfer function of the TDC in silicon (which maps an input pulse-width to its corresponding output code) can be derived on the chip and thereby the absolute value (instead of just a relative code) of an input pulse-width under measurement can be reported. Secondly, the sampling errors stemming from the jitters of training clocks used in the calibration scheme are mitigated by the principle of multi sampling. Thirdly, a flexible coarse-shrinking block is adopted and an automatic adjustment scheme is employed so that the coarse-shrinking block can adjust itself when operated under different input pulse-width ranges.

Question: What is a time-to-digital converter?


Transaction Repair for Multi-Version Concurrency Control. The optimistic variants of Multi-Version Concurrency Control (MVCC) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even more conflicts.  In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, when encountering conflicts among transactions, our mechanism quickly detects the conflict locations in the program and partially re-executes the conflicting transactions. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput.

Question: What is the difference between optimistic and pessimistic concurrency control?


Modeling the Detection of Textual Cyberbullying. The scourge of cyberbullying has assumed alarming proportions with an ever-increasing number of adolescents admitting to having dealt with it either as a victim or as a bystander. Anonymity and the lack of meaningful supervision in the electronic medium are two factors that have exacerbated this social menace. Comments or posts involving sensitive topics that are personal to an individual are more likely to be internalized by a victim, often resulting in tragic outcomes. We decompose the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems. We experiment with a corpus of 4500 YouTube comments, applying a range of binary and multiclass classifiers. We find that binary classifiers for individual labels outperform multiclass classifiers. Our findings show that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers.

Question: How can we detect cyberbullying?


Vocabulary learning by mobile-assisted authentic content creation and social meaning-making: two case studies. In recent years, we have witnessed the concomitant rise of communicative and contextualised approaches as well as the paradigmatic development of the Mobile Assisted Language Learning (MALL) framework in analyzing language learning. The focus of MALL research has gradually shifted from content-based (delivery of learning content through mobile devices) to design-oriented (authentic and/or social mobile learning activities) study. In this paper, we present two novel case studies of MALL that emphasize learner-created content. In learning English prepositions and Chinese idioms respectively, the primary school students used the mobile devices assigned to them on a one-to-one basis to take photos in real-life contexts so as to construct sentences with the newly acquired prepositions or idioms. Subsequently, the learners were voraciously engaged in classroom or online discussion of their semantic constructions, thereby enhancing their understanding of the proper usage of the prepositions or idioms. This work shows the potential of transforming language learning into an authentic seamless learning experience.

Question: What is the difference between authentic and social learning?


A quantitative analysis of the speedup factors of FPGAs over processors. The speedup over a microprocessor that can be achieved by implementing some programs on an FPGA has been extensively reported. This paper presents an analysis, both quantitative and qualitative, at the architecture level of the components of this speedup. Obviously, the spatial parallelism that can be exploited on the FPGA is a big component. By itself, however, it does not account for the whole speedup.In this paper we experimentally analyze the remaining components of the speedup. We compare the performance of image processing application programs executing in hardware on a Xilinx Virtex E2000 FPGA to that on three general-purpose processor platforms: MIPS, Pentium III and VLIW. The question we set out to answer is what is the inherent advantage of a hardware implementation over a von Neumann platform. On the one hand, the clock frequency of general-purpose processors is about 20 times that of typical FPGA implementations. On the other hand, the iteration level parallelism on the FPGA is one to two orders of magnitude that on the CPUs. In addition to these two factors, we identify the efficiency advantage of FPGAs as an important factor and show that it ranges from 6 to 47 on our test benchmarks. We also identify some of the components of this factor: the streaming of data from memory, the overlap of control and data flow and the elimination of some instruction on the FPGA. The results provide a deeper understanding of the tradeoff between system complexity and performance when designing Configurable SoC as well as designing software for CSoC. They also help understand the one to two orders of magnitude in speedup of FPGAs over CPU after accounting for clock frequencies.

Question: What is the inherent advantage of a hardware implementation over a von Neumann platform?


Thin Images Reflected in the Water: Narcissism and Girls' Vulnerability to the Thin-Ideal.. The purpose of this research is to test how adolescent girls' narcissistic traits-characterized by a need to impress others and avoid ego-threat-influence acute adverse effects of thin-ideal exposure. Participants (11-15 years; total N = 366; all female) reported their narcissistic traits. Next, in two experiments, they viewed images of either very thin or average-sized models, reported their wishful identification with the models (Experiment 2), and tasted high-calorie foods in an alleged taste test (both experiments). Narcissism kept girls from wishfully identifying with thin models, which is consistent with the view that narcissistic girls are prone to disengage from thin-ideal exposure. Moreover, narcissism protected vulnerable girls (those who experience low weight-esteem) from inhibiting their food intake, and led other girls (those who consider their appearance relatively unimportant) to increase their food intake. These effects did not generalize to conceptually related traits of self-esteem and perfectionism, and were not found for a low-calorie foods outcome, attesting to the specificity of findings. These experiments demonstrate the importance of narcissism at reducing girls' thin-ideal vulnerability. Girls high in narcissism disengage self-protectively from threats to their self-image, a strategy that renders at least subsets of them less vulnerable to the thin-ideal.

Question: How does narcissism protect girls from thin-ideal exposure?


Morphological and LSU rDNA sequence variation within the Gonyaulax spinifera-Spiniferites group (Dinophyceae) and proposal of G-elongata comb. nov and G-membranacea comb. nov. Cultures were established from cysts of the cyst-based taxa Spiniferites elongatus and S. membranaceus. Motile cells and cysts from both cultures and sediment samples were examined using light and scanning electron microscopy. The cyst theca relationship was established for S. elongatus. The motile cells have the tabulation pattern 2 pr, 4', 6", 6c, � 4s, 6"', I p, Inn, but they remain unattributable to previously described Gonyaulax species. There was large variation in process length and process morphology in cysts from both cultures and wild samples and there was variation in ornamentation and in the development of spines and flanges in motile cells. A new combination, G. elongata (Reid) Ellegaard et al. comb. nov. is proposed, following new rules of the International Code of Botanical Nomenclature that give genera based on extant forms priority over genera based on fossil forms. Extreme morphological variation in the cyst and motile stages of S. membranaceus is described and this species is also transferred to the genus Gonyaulax, as G. membranacea (Rossignol) Ellegaard et al. comb. nov. Approximately 1500 bp of large subunit (LSU) rDNA were determined for these two species and for G. baltica, G. cf. spinifera ( = S. ramosus) and G. digitalis ( = Bitectatodinium tepikiense). LSU rDNA showed sequence divergences similar to those estimated between species in other genera within the Gonyaulacales; a phylogeny for the Gonyaulacales was established, including novel LSU rONA sequences for Alexandrium margalefii, A. pseudogonyaulax and Pyrodinium bahamense var. compressum. Our results show that motile stages obtained from the germination of several cysts of the 'fossil-based' Spiniferites and B. tepikiense, which were previously attributed to 'Gonyaulax spinifera group undifferentiated', belong

Question: What is the difference between fossil and extant forms?


Pregnancy-related low back pain.. Pregnancy related low back pain is a common complaint among pregnant women. It can potentially have a negative impact on their quality of life. The aim of this article is to present a current review of the literature concerning this issue.By using PubMed database and low back pain, pelvic girdle pain, pregnancy as keywords, abstracts and original articles in English investigating the diagnosis treatment of back pain during pregnancy were searched and analyzedLow back pain could present as either a pelvic girdle pain between the posterior iliac crest and the gluteal fold or as a lumbar pain over and around the lumbar spine. The source of the pain should be diagnosed and differentiated early.The appropriate treatment aims to reduce the discomfort and the impact on the pregnant womans quality of life. This article reveals the most common risk factors, as well as treatment methods, which may help to alleviate the pain. Some suggestions for additional research are also discussed.

Question: What is the most common risk factor for low back pain during pregnancy?


Programming language evolution. Programming languages are the way developers communicate with computers—just like natural languages let us communicate with one another. In both cases multiple languages have evolved. However, the programming language ecosystem changes at a much higher rate compared to natural languages. In fact, programming languages need to constantly evolve in response to user needs, hardware advances and research developments or they are otherwise displaced by newcomers. As a result, existing code written by developers may stop working with a newer language version. Consequently, developers need to search (analyse) and replace (refactor) code in their code bases to support new language versions. Traditionally, tools have focused on the replace aspect (refactoring) to support developers evolving their code bases. This dissertation argues that developers also need machine support focused on the search aspect. This dissertation starts by characterising factors driving programming language evolution based on external versus internal forces. Next, it introduces a classification of changes introduced by language designers that affect developers. It then contributes three techniques to support developers in analysing their code bases. First, we show a source code query system based on graphs that express code queries at a mixture of syntax-tree, type, control-flow graph and data-flow levels. We demonstrate how this system can support developers and language designers in locating various code patterns relevant in evolution. Second, we design an optional run-time type system for Python, that lets developers manually specify contracts to identify semantic incompatibilities between Python 2 and Python 3. Third, recognising that existing codebases do not have such contracts, we describe a dynamic analysis to automatically generate them.

Question: What is the difference between a programming language and a programming language evolution?


A compact dual-band (L1/L2) GPS antenna design. A small slot-loaded patch antenna design developed for receiving both L1 and L2 bands GPS signals is discussed. The dual band coverage is achieved by using a patch mode at L2 band and a slot mode at L1 band. High dielectric material and meandered slot line are employed to reduce the antenna size down to 25.4 mm in diameter. The RHCP is achieved by combining two orthogonal modes via a small 0°-90° hybrid chip. Both patch and slot modes share a single proximity probe conveniently located on the side of the antenna (Fig.1). This paper discusses about the design procedure as well as simulated antenna performance.

Question: What is a proximity probe?


Design and Synthesis of Synchronization Skeletons Using Branching Time Temporal Logic. We propose a method of constructing concurrent programs in which the synchronization skeleton of the program ~s automatically synthesized from a high-level (branching time) Temporal Logic specification. The synchronization skeleton is an abstraction of the actual program where detail irrelevant to synchronization is suppressed. For example, in the synchronization skeleton for a solution to the critical section problem each process's critical section may be viewed as a single node since the internal structure of the critical section is unimportant. Most solutions to synchronization problems in the literature are in fact given as synchronization skeletons. Because synchronization skeletons are in general finite state, the propositional version of Temporal Logic can be used to specify their properties. Our synthesis method exploits the (bounded) finite model property for an appropriate propositional Temporal Logic which asserts that if a formula of the logic is satisfiable, it is satisfiable in a finite model (of size bounded by a function of the length of the formula). Decision procedures have been devised which, given a formula of Temporal Logic, f, will decide whether f is satisfiable or unsatisfiable. If f is satisfiable, a finite model of f is constructed, in our application, unsatisfiability of f means that the specification is inconsistent (and must be reformulated). If the formula f is satisfiable, then the specification it expresses is consistent. A model for f with a finite number of states is constructed by the decision procedure. The synchronization skeleton of a program meeting the specification can be read from this model. The finite model property ensures that any program whose synchronization properties can be expressed in propositional Temporal Logic can be realized by a system of concurrently running processes, each of which is a finite state machine. initially, the

Question: What is the difference between a synchronization skeleton and a synchronization graph?


The importance of rehabilitation concerning upper extremity amputees: A Systematic review. OBJECTIVE To evaluate and point out the importance of prosthetic rehabilitation of upper extremity.   METHODS A systematic literature search was performed to identify studies concerning prosthetic rehabilitation in upper extremity. The PRISMA Statement 2009 was used to establish the study and the methodological quality was assessed.   RESULTS The literature search identified 620 studies. Of these 620, 9 studies fulfilled the inclusion criteria and were included for data extraction. The studies pointed out the upper limb prosthetic rehabilitation protocols consist of general exercise programme, motor tasks, phantom exercises, Muscle Training System, edema control, functional activities, signal strengthening, prosthetic education exercises, neuromuscular reeducation, virtual image and virtual reality exercises.   CONCLUSIONS The current systematic literature review has shown that the prosthetic rehabilitation seems promising especially for upper extremity amputees.

Question: What is the importance of rehabilitation concerning upper extremity amputees?


Induction and synchronous reluctance motors comparison. The aim of this paper is to investigate and compare the performances of two induction motors and two transverse laminated synchronous reluctance motors (induction motor nameplate data: 2.2 and 4 kW, 380 V, 50 Hz, 4 pole). Each induction motor is compared with a synchronous reluctance motor, that has the same stator lamination and winding, but, obviously, different rotor. The analysis has been based both on an analytical and experimental approach. The results have shown that the synchronous reluctance motor, compared to the induction motor, is capable of around a 10% to 15% larger rated torque for a given frame size. The direct comparison of the performances of the motor types has been made both for constant load and constant dissipated power conditions.

Question: What is the difference between induction and synchronous reluctance motors?


DPIL@FIRE2016: Overview of the Shared task on Detecting Paraphrases in Indian language. This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.

Question: What is the shared task on detecting paraphrases in Indian languages?


TABLA: A unified template-based framework for accelerating statistical machine learning. A growing number of commercial and enterprise systems increasingly rely on compute-intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FPGAs) provide a promising path forward to accommodate the needs of machine learning algorithms and represent an intermediate point between the efficiency of ASICs and the programmability of general-purpose processors. However, acceleration with FPGAs still requires long development cycles and extensive expertise in hardware design. To tackle this challenge, instead of designing an accelerator for a machine learning algorithm, we present TABLA, a framework that generates accelerators for a class of machine learning algorithms. The key is to identify the commonalities across a wide range of machine learning algorithms and utilize this commonality to provide a high-level abstraction for programmers. TABLA leverages the insight that many learning algorithms can be expressed as a stochastic optimization problem. Therefore, learning becomes solving an optimization problem using stochastic gradient descent that minimizes an objective function over the training data. The gradient descent solver is fixed while the objective function changes for different learning algorithms. TABLA provides a template-based framework to accelerate this class of learning algorithms. Therefore, a developer can specify the learning task by only expressing the gradient of the objective function using our high-level language. Tabla then automatically generates the synthesizable implementation of the accelerator for FPGA realization using a set of hand-optimized templates. We use Tabla to generate accelerators for ten different learning tasks targeted at a Xilinx Zynq FPGA platform. We rigorously compare the benef

Question: What is a template-based framework for accelerating statistical machine learning?


Specific language impairment: a convenient label for whom?. BACKGROUND The term 'specific language impairment' (SLI), in use since the 1980s, describes children with language impairment whose cognitive skills are within normal limits where there is no identifiable reason for the language impairment. SLI is determined by applying exclusionary criteria, so that it is defined by what it is not rather than by what it is. The recent decision to not include SLI in DSM-5 provoked much debate and concern from researchers and clinicians.   AIMS To explore how the term 'specific language impairment' emerged, to consider how disorders, including SLI, are generally defined and to explore how societal changes might impact on use the term.   METHODS & PROCEDURES We reviewed the literature to explore the origins of the term 'specific language impairment' and present published evidence, as well as new analyses of population data, to explore the validity of continuing to use the term.   OUTCOMES & RESULTS AND CONCLUSIONS & IMPLICATIONS We support the decision to exclude the term 'specific language impairment' from DSM-5 and conclude that the term has been a convenient label for researchers, but that the current classification is unacceptably arbitrary. Furthermore, we argue there is no empirical evidence to support the continued use of the term SLI and limited evidence that it has provided any real benefits for children and their families. In fact, the term may be disadvantageous to some due to the use of exclusionary criteria to determine eligibility for and access to speech pathology services. We propose the following recommendations. First, that the word 'specific' be removed and the label 'language impairment' be used. Second, that the exclusionary criteria be relaxed and in their place inclusionary criteria be adopted that take into account the fluid nature of language development particularly in the preschool period. Building on the goodwill and collaborations between the clin

Question: What is the difference between SLI and language impairment?


Local-Learning-Based Feature Selection for High-Dimensional Data Analysis. This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature-selection algorithm that addresses several major issues with prior work, including problems with algorithm implementation, computational complexity, and solution accuracy. The key idea is to decompose an arbitrarily complex nonlinear problem into a set of locally linear ones through local learning, and then learn feature relevance globally within the large margin framework. The proposed algorithm is based on well-established machine learning and numerical analysis techniques, without making any assumptions about the underlying data distribution. It is capable of processing many thousands of features within minutes on a personal computer while maintaining a very high accuracy that is nearly insensitive to a growing number of irrelevant features. Theoretical analyses of the algorithm's sample complexity suggest that the algorithm has a logarithmical sample complexity with respect to the number of features. Experiments on 11 synthetic and real-world data sets demonstrate the viability of our formulation of the feature-selection problem for supervised learning and the effectiveness of our algorithm.

Question: What is the difference between a local learning based feature selection and a global learning based feature selection?


A study on LIWC categories for opinion mining in Spanish reviews. With the exponential growth of social media i.e. blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language, is positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC. LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we have conducted a comparative assessment of the performance of various classification techniques: J48, SMO and BayesNet, using precision, recall and F-measure metrics. All in all, findings have revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4% and 87.2% in each domain.

Question: What is opinion mining?


Identifying HTTPS-Protected Netflix Videos in Real-Time. After more than a year of research and development, Netflix recently upgraded their infrastructure to provide HTTPS encryption of video streams in order to protect the privacy of their viewers. Despite this upgrade, we demonstrate that it is possible to accurately identify Netflix videos from passive traffic capture in real-time with very limited hardware requirements. Specifically, we developed a system that can report the Netflix video being delivered by a TCP connection using only the information provided by TCP/IP headers. To support our analysis, we created a fingerprint database comprised of 42,027 Netflix videos. Given this collection of fingerprints, we show that our system can differentiate between videos with greater than 99.99% accuracy. Moreover, when tested against 200 random 20-minute video streams, our system identified 99.5% of the videos with the majority of the identifications occurring less than two and a half minutes into the video stream.

Question: How can I identify a Netflix video?


A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks. Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use mobile anchor nodes (MANs), which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. A considerable body of research has addressed the mobile anchor node assisted localization (MANAL) problem. However, to the best of our knowledge, no updated surveys on MAAL reflecting recent advances in the field have been presented in the past few years. This survey presents a review of the most successful MANAL algorithms, focusing on the achievements made in the past decade, and aims to become a starting point for researchers who are initiating their endeavors in MANAL research field. In addition, we seek to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful advances in this research field.

Question: What is the most successful localization algorithm?


Automatic Cloud I/O Configurator for I/O Intensive Parallel Applications. As the cloud platform becomes a promising alternative to traditional HPC (high performance computing) centers or in-house clusters, the I/O bottleneck problem is highlighted in this new environment, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing the cloud I/O system configurations, such as choices of file systems, number of I/O servers and their placement strategies, etc., will lead to a considerable variation in the performance and cost efficiency of I/O intensive parallel applications. However, storage system configuration is tedious and error-prone to do manually, even for expert users, leading to solutions that are grossly over-provisioned (low cost inefficiency), substantially under-performing (poor performance) or, in the worst case, both. This paper proposes ACIC, a system which automatically searches for optimized I/O system configurations from many candidates for each individual application running on a given cloud platform. ACIC takes advantage of machine learning models to perform performance/cost predictions. To tackle the high-dimensional parameter exploration space, we enable affordable, reusable, and incremental training on cloud platforms, guided by the Plackett and Burman Matrices for experiment design. Our evaluation results with four representative parallel applications indicate that ACIC consistently identifies optimal or near-optimal configurations among a large group of candidate settings. The top ACIC-recommended configuration is capable of improving the applications' performance by a factor of up to 10.5 (3.1 on average), and cost saving of up to 89 percent (51 percent on average), compared with a commonly used baseline I/O configuration. In addition, we carried out a small-scale user study for one of the test applications, which found that ACIC consistently beat the user and even the app

Question: What is the cloud I/O system configuration?


HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT. Recent developments in memory-augmented neural networks allowed sequential problems requiring long-term memory to be solved, which were intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to an external memory component can be encoded geometrically through a novel HyperNEAT-based Neural Turing Machine (HyperNTM). The indirect HyperNEAT encoding allows for training on small memory vectors in a bit vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy nine bit vectors can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, the HyperNTM approach could now allow memory-augmented neural networks to scale to problems requiring large memory vectors and sequence lengths.

Question: How large is the memory of a neural network?


A massively parallel architecture for a self-organizing neural pattern recognition machine. A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture self-organizes and self-stabilizes its recognition codes in response to arbitrary orderings of arbitrarily many and arbitrarily complex binary input patterns. Top-down attentional and matching mechanisms are critical in self-stabilizing the code learning process. The architecture embodies a parallel search scheme which updates itself adaptively as the learning process unfolds. After learning self-stabilizes, the search process is automatically disengaged. Thereafter input patterns directly access their recognition codes without any search. Thus recognition time does not grow as a function of code complexity. A novel input pattern can directly access a category if it shares invariant properties with the set of familiar exemplars of that category. These invariant properties emerge in the form of learned critical feature patterns, or prototypes. The architecture possesses a context-sensitive self-scaling property which enables its emergent critical feature patterns to form. They detect and remember statistically predictive configurations of featural elements which are derived ~ ..from the set of all input patterns that are ever experienced. Four types of attentional

Question: What is a neural network architecture?


An efficient active ripple filter for use in DC-DC conversion. When low ripple is required from a switched mode dc-dc converter, dissipative active filters offer an alternative to passive LC and coupled inductor filters. Analytical and experimental results are presented for a simple active ripple filter. The filter employs a pair of current transformers as sensors for feedforward and feedback control and two metal-oxide-semiconductor field-effect transistors (MOSFETs) as cancellation current drivers. Measurements demonstrate good ripple attenuation up to 5 MHz, more than 70 dB being obtained at 100 kHz, the switching frequency of a test converter. The overall efficiency was measured as 95%, with room for further improvement. The filter is suitable for input and output smoothing in dc-dc converters for aerospace and other critical applications.

Question: What is a ripple filter?


D2D for Intelligent Transportation Systems: A Feasibility Study. Intelligent transportation systems (ITS) are becoming a crucial component of our society, whereas reliable and efficient vehicular communications consist of a key enabler of a well-functioning ITS. To meet a wide variety of ITS application needs, vehicular-to-vehicular and vehicular-to-infrastructure communications have to be jointly considered, configured, and optimized. The effective and efficient coexistence and cooperation of the two give rise to a dynamic spectrum management problem. One recently emerged and rapidly adopted solution of a similar problem in cellular networks is the so-termed device-to-device (D2D) communications. Its potential in the vehicular scenarios with unique challenges, however, has not been thoroughly investigated to date. In this paper, we for the first time carry out a feasibility study of D2D for ITS based on both the features of D2D and the nature of vehicular networks. In addition to demonstrating the promising potential of this technology, we will also propose novel remedies necessary to make D2D technology practical as well as beneficial for ITS.

Question: What is the potential of D2D for ITS?


Wireless Geophone Network for remote monitoring and detection of landslides. Recent years have shown an alarmous increase in rain fall induced landslides. This has facilitated the need for having a monitoring system to predict the landslides which could eventually reduce the loss of human life. We have developed and deployed a Wireless Sensor Network to monitor rainfall induced landslide, in Munnar, South India. A successful landslide warning was issued in June 2009 using this system. The system is being enhanced by incorporating a Wireless Geophone Network to locate the initiation of landslide. The paper discusses an algorithm that was developed to analyze the geophone data and automatically detect the landslide signal. A novel method to localize the landslide initiation point is detailed. The algorithm is based on the time delay inherent in the transmission of waves through the surface of the earth. The approach detailed here does not require additional energy since the geophones are self excitatory. The error rate of the approach is much less when compared to the other localization methods like RSSI. The proposed algorithm is being tested and validated, in the landslide laboratory set up at our university.

Question: How does a wireless geophone network work?


Ten-year research update review: child sexual abuse.. OBJECTIVE To provide clinicians with current information on prevalence, risk factors, outcomes, treatment, and prevention of child sexual abuse (CSA). To examine the best-documented examples of psychopathology attributable to CSA. METHOD Computer literature searches of and for key words. All English-language articles published after 1989 containing empirical data pertaining to CSA were reviewed. RESULTS CSA constitutes approximately 10% of officially substantiated child maltreatment cases, numbering approximately 88,000 in 2000. Adjusted prevalence rates are 16.8% and 7.9% for adult women and men, respectively. Risk factors include gender, age, disabilities, and parental dysfunction. A range of symptoms and disorders has been associated with CSA, but depression in adults and sexualized behaviors in children are the best-documented outcomes. To date, cognitive-behavioral therapy (CBT) of the child and a nonoffending parent is the most effective treatment. Prevention efforts have focused on child education to increase awareness and home visitation to decrease risk factors. CONCLUSIONS CSA is a significant risk factor for psychopathology, especially depression and substance abuse. Preliminary research indicates that CBT is effective for some symptoms, but longitudinal follow-up and large-scale "effectiveness" studies are needed. Prevention programs have promise, but evaluations to date are limited.

Question: What is the best documented example of psychopathology attributable to child sexual abuse?


Rhythms of social interaction: messaging within a massive online network. We have analyzed the fully-anonymized headers of 362 million messages exchanged by 4.2 million users of Facebook, an online social network of college students, during a 26 month interval. The data reveal a number of strong daily and weekly regularities which provide insights into the time use of college students and their social lives, including seasonal variations. We also examined how factors such as school affiliation and informal online "friend" lists affect the observed behavior and temporal patterns. Finally, we show that Facebook users appear to be clustered by school with respect to their temporal messaging patterns.

Question: What is the time use of college students?


Microstrip-fed circular substrate integrated waveguide (SIW) cavity resonator and antenna. Substrate integrated waveguide (SIW) cavity resonators are among the emerging group of SIW-based circuit components that are gaining popularity and increasingly employed in integrated microwave and mm-wave circuits. The SIW cavities offer a significantly enhanced performance in comparison to the previously available planar microwave resonators. The high quality factor of the waveguide-based cavity resonators enables designing microwave oscillators with very low phase noise as well as compact high-gain antennas [1–4]. The SIW-cavity-based antennas also show much promise for implementation of low-cost lightweight fully-integrated high-gain antennas that find application in ultra-light communication satellites, low payload spacecrafts, high-frequency radar, and sensors. In this paper, a circular SIW cavity resonator, which is fed by a microstrip and via probe, is presented. The microstrip feed is optimized to achieve a return loss of better than 20dB from both simulation and measurements. A resonance frequency of 16.79GHz and quality factor of 76.3 are determined from vector network analyzer (VNA) measurements. Due to the folded slot on the upper conductor layer, the resonator is an efficient cavity backed antenna. A maximum gain of over 7dB is measured in an anechoic chamber at the resonance frequency of 16.79 GHz.

Question: What is a microstrip-fed circular substrate integrated waveguide (SIW) cavity resonator and antenna?


Question: What is a self-rating depression scale?


circRNA biogenesis competes with pre-mRNA splicing.. Circular RNAs (circRNAs) are widely expressed noncoding RNAs. However, their biogenesis and possible functions are poorly understood. Here, by studying circRNAs that we identified in neuronal tissues, we provide evidence that animal circRNAs are generated cotranscriptionally and that their production rate is mainly determined by intronic sequences. We demonstrate that circularization and splicing compete against each other. These mechanisms are tissue specific and conserved in animals. Interestingly, we observed that the second exon of the splicing factor muscleblind (MBL/MBNL1) is circularized in flies and humans. This circRNA (circMbl) and its flanking introns contain conserved muscleblind binding sites, which are strongly and specifically bound by MBL. Modulation of MBL levels strongly affects circMbl biosynthesis, and this effect is dependent on the MBL binding sites. Together, our data suggest that circRNAs can function in gene regulation by competing with linear splicing. Furthermore, we identified muscleblind as a factor involved in circRNA biogenesis.

Question: circRNA biogenesis competes with pre-mRNA splicing.


Robust Curb Detection with Fusion of 3D-Lidar and Camera Data. Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes.

Question: How to detect curbs?


Fast learning of scale-free networks based on Cholesky factorization. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia Mathematical Institute of the Serbian Academy of Sciences and Arts, Belgrade, Serbia Center for Data Analytics & Biomedical Informatics, Temple University, Philadelphia, USA Correspondence Vladisav Jelisavcic, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. E-mail: vladisav@mi.sanu.ac.rs Funding information NSF BIGDATA, Grant/Award Numbers: 1447670, 1659998, CNS-1625061; CURE Abstract Recovering network connectivity structure from highdimensional observations is of increasing importance in statistical learning applications. A prominent approach is to learn a Sparse Gaussian Markov Random Field by optimizing regularized maximum likelihood, where the sparsity is induced by imposing L1 norm on the entries of a precision matrix. In this article, we shed light on an alternative objective, where instead of precision, its Cholesky factor is penalized by the L1 norm. We show that such an objective criterion possesses attractive properties that allowed us to develop a very fast Scale-Free Networks Estimation Through Cholesky factorization (SNETCH) optimization algorithm based on coordinate descent, which is highly parallelizable and can exploit an active set approach. The approach is particularly suited for problems with structures that allow sparse Cholesky factor, an important example being scale-free networks. Evaluation on synthetically generated examples and high-impact applications from a biomedical domain of up to more than 900,000 variables provides evidence that for such tasks the SNETCH algorithm can learn the underlying structure more accurately, and an order of magnitude faster than state-of-the-art approaches based on the L1 penalized precision matrix.

Question: What is the difference between the L1 and L2 norms?


Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples. In recent years, deep learning has shown performance breakthroughs in many prominent problems. However, this comes with a major concern: deep networks have been found to be vulnerable to adversarial examples. Adversarial examples are modified inputs designed to cause the model to err. In the domains of images and speech, the modifications are untraceable by humans, but affect the model's output. In binary files, small modifications to the byte-code often lead to significant changes in its functionality/validity, therefore generating adversarial examples is not straightforward. We introduce a novel approach for generating adversarial examples for discrete input sets, such as binaries. We modify malicious binaries by injecting a small sequence of bytes to the binary file. The modified files are detected as benign, while preserving their malicious functionality. We applied this approach to an end-to-end convolutional neural malware detector and present a high evasion rate. Moreover, we showed that our payload is transferable within different positions of the same file and across different files and file families.

Question: How to generate adversarial examples for discrete input sets?


The development and malleability of executive control abilities. Executive control (EC) generally refers to the regulation of mental activity. It plays a crucial role in complex cognition, and EC skills predict high-level abilities including language processing, memory, and problem solving, as well as practically relevant outcomes such as scholastic achievement. EC develops relatively late in ontogeny, and many sub-groups of developmental populations demonstrate an exaggeratedly poor ability to control cognition even alongside the normal protracted growth of EC skills. Given the value of EC to human performance, researchers have sought means to improve it through targeted training; indeed, accumulating evidence suggests that regulatory processes are malleable through experience and practice. Nonetheless, there is a need to understand both whether specific populations might particularly benefit from training, and what cortical mechanisms engage during performance of the tasks used in the training protocols. This contribution has two parts: in Part I, we review EC development and intervention work in select populations. Although promising, the mixed results in this early field make it difficult to draw strong conclusions. To guide future studies, in Part II, we discuss training studies that have included a neuroimaging component - a relatively new enterprise that also has not yet yielded a consistent pattern of results post-training, preventing broad conclusions. We therefore suggest that recent developments in neuroimaging (e.g., multivariate and connectivity approaches) may be useful to advance our understanding of the neural mechanisms underlying the malleability of EC and brain plasticity. In conjunction with behavioral data, these methods may further inform our understanding of the brain-behavior relationship and the extent to which EC is dynamic and malleable, guiding the development of future, targeted interventions to promote executive functioning in both heal

Question: What is the malleability of executive control?


Recognition and volume estimation of food intake using a mobile device. We present a system that improves accuracy of food intake assessment using computer vision techniques. Traditional dietetic method suffers from the drawback of either inaccurate assessment or complex lab measurement. Our solution is to use a mobile phone to capture images of foods, recognize food types, estimate their respective volumes and finally return quantitative nutrition information. Automated and accurate food recognition presents the following challenges. First, there exist a large variety of food types that people consume in everyday life. Second, a single category of food may contain large variations due to different ways of preparation. Also, diverse lighting conditions may lead to varying visual appearance of foods. All of these pose a challenge to the state of the art recognition approaches. Moreover, the low quality images captured using cellphones make the task of 3D reconstruction difficult. In this paper, we combine several vision techniques (visual recognition and 3D reconstruction) to achieve quantitative food intake estimation. Evaluation of both recognition and reconstruction is provided in the experimental results.

Question: How to estimate the volume of food?


The Ideology of Interactivity (or Video Games and Taylorization of Leisure). Interactivity is one of the key conceptual apparatuses through which video games have been theorized thus far. As many writers have noted, video games are distinct from other forms of media because player actions seem to have direct, immediate consequences in the world depicted onscreen. But in many ways, this "interactive" feature of video games tends to manifest itself as a relentless series of demands, or a way of disciplining player behavior. In this sense, it seems more accurate to describe the human-machine interface made possible by gaming as an aggressive form of "interpellation" or hailing. Drawing primarily upon the work of Louis Althusser, I argue that traditional theories of interactivity fail to acknowledge the work of video games—in other words, the extent to which video games define and reconstitute players as subjects of ideology.

Question: What is the ideology of interactivity?


Measuring Word Significance using Distributed Representations of Words. Distributed representations of words as real-valued vectors in a relatively lowdimensional space aim at extracting syntactic and semantic features from large text corpora. A recently introduced neural network, named word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic information in the direction of the word vectors. In this brief report, it is proposed to use the length of the vectors, together with the term frequency, as measure of word significance in a corpus. Experimental evidence using a domain-specific corpus of abstracts is presented to support this proposal. A useful visualization technique for text corpora emerges, where words are mapped onto a two-dimensional plane and automatically ranked by significance.

Question: What is the length of the vectors?


On sequential Monte Carlo sampling methods for Bayesian filtering. In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.

Question: What is sequential Monte Carlo sampling?


Kernel auto-encoder for semi-supervised hashing. Hashing-based approaches have gained popularity for large-scale image retrieval in recent years. It has been shown that semi-supervised hashing, which incorporates similarity/dissimilarity information into hash function learning could improve the hashing quality. In this paper, we present a novel kernel-based semi-supervised binary hashing model for image retrieval by taking into account auxiliary information, i.e., similar and dissimilar data pairs in achieving high quality hashing. The main idea is to map the data points into a highly non-linear feature space and then map the non-linear features into compact binary codes such that similar/dissimilar data points have similar/dissimilar hash codes. Empirical evaluations on three benchmark datasets demonstrate the superiority of the proposed method over several existing unsupervised and semi-supervised hash function learning methods.

Question: What is kernel auto-encoder for semi-supervised hashing?