01 05 03 2 v 2 1 5 N ov 2 00 1 Quantum Digital Signatures. We present a quantum digital signature scheme whose security is based on fundamental principles of quantum physics. It allows a sender (Alice) to sign a message in such a way that the signature can be validated by a number of different people, and all will agree either that the message came from Alice or that it has been tampered with. To accomplish this task, each recipient of the message must have a copy of Alice's "public key," which is a set of quantum states whose exact identity is known only to Alice. Quantum public keys are more difficult to deal with than classical public keys: for instance, only a limited number of copies can be in circulation, or the scheme becomes insecure. However, in exchange for this price, we achieve unconditionally secure digital signatures. Sending an m-bit message uses up O(m) quantum bits for each recipient of the public key. We briefly discuss how to securely distribute quantum public keys, and show the signature scheme is absolutely secure using one method of key distribution. The protocol provides a model for importing the ideas of classical public key cryptography into the quantum world.

Question: What is a quantum public key?


CARDIAC: An Intelligent Conversational Assistant for Chronic Heart Failure Patient Heath Monitoring. We describe CARDIAC, a prototype for an intelligent conversational assistant that provides health monitoring for chronic heart failure patients. CARDIAC supports user initiative through its ability to understand natural language and connect it to intention recognition. The natural language interface allows patients to interact with CARDIAC without special training. The system is designed to understand information that arises spontaneously in the course of the interview. If the patient gives more detail than necessary for answering a question, the system updates the user model accordingly. CARDIAC is a first step towards developing cost-effective, customizable, automated in-home conversational assistants that help patients manage their care and monitor their health using natural language.

Question: What is a conversational assistant?


Predicting Knowledge Sharing Intention Based on Theory of Reasoned Action Framework : An Empirical Study on Higher Education Institution. Based on theory of reasoned action (TRA) theoretical framework, this study try to predict individual knowledge sharing intention on higher education institution context. This study also integrate some potential factors (channel richness, psychological forces and organizational climate factors) which supporting individuals' knowledge-sharing intentions Through a field survey of 242 lecturer on public and privateuniversity at Purwokerto and Yogyakarta city, this study confirm our hypothesis that attitudes toward knowledge sharing and subjective norms with regard to knowledge sharing as well as organizational climate affect individuals' intentions to share knowledge. This study showed thatPerceived Reciprocal Benefits, Perceived Enjoyment in Helping Others, Perceived Reputation Enhancement and channel richness affect individuals'attitudes toward knowledge sharing while organizational climate affect subjective norms. Overall, the results of the study advance prior research in the area of knowledge sharing by shedding light on the determinants of knowledge sharing behaviors of knowledge workers. These insights could be used by university in developing work environments that are conducive for knowledge worker to share their knowledge. Keyword: Knowledge sharing intention, TRA, Knowledge worker, attitude, university.

Question: What is the difference between knowledge sharing and knowledge sharing intention?


Water Quality Measurement and Control from Remote Station for Pisiculture Using NI myRIO Beena. A Wireless sensor network is designed and implemented to automatically and remotely monitor and control the water quality parameters such as temperature, turbidity, pH and dissolved oxygen required for pisiculture. The measurements of the system which is at base station are communicated to the remote farmer through built-in Wi-Fi of NI-myRIO and displayed on user's computer screen from where he can monitor and change the settings of the system. An audio output is also produced from speakers of the computer. SMS alert is sent to farmer's mobile for variations in the monitored parameters of the system at the base station via GSM modem. The programming of the entire system is done using LabVIEW software and NI myRIO which has inbuilt processor, FPGA, Wi-Fi and web servicing capabilities. Web publishing tool of LabVIEW allows internet access to the front panel of the system to any remote user.

Question: How to measure water quality?


Quantum generalisation of feedforward neural networks. We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e. unitary. (The classical networks we generalise are called feedforward, and have step-function activation functions.) The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

Question: What is a quantum autoencoder?


Mutation Spectrum and Phenotypic Features in Noonan Syndrome with PTPN11 Mutations: Definition of Two Novel Mutations.. OBJECTIVES To evaluate the spectrum of PTPN11 gene mutations in Noonan syndrome patients and to study the genotype-phenotype associations.   METHODS In this study, twenty Noonan syndrome patients with PTPN11 mutations were included. The patients underwent a detailed clinical and physical evaluation. To identify inherited cases, parents of all mutation positive patients were analyzed.   RESULTS Thirteen different PTPN11 mutations, two of them being novel, were detected in the study group. These mutations included eleven missense mutations: p.G60A, p.D61N, p.Y62D, p.Y63C, p.E69Q, p.Q79R, p.Y279C,p.N308D, p.N308S, p.M504V, p.Q510R and two novel missense mutations: p.I56V and p.I282M. The frequency of cardiac abnormalities and short stature were found to be 80 % and 80 %, respectively. Mental retardation was not observed in patients having exon 8 mutations. No significant correlations were detected between other phenotypic features and genotypes.   CONCLUSIONS By identifying genotype-phenotype correlations, this study provides information on phenotypes observed in NS patients with different PTPN11 mutations.

Question: What is the frequency of cardiac abnormalities in Noonan syndrome?


A Markerless Augmented Reality Approach Based on Real-Time 3 D Reconstruction using Kinect. In this paper we present a markerless augmented reality (MAR) approach based on real-time 3D reconstruction using a low-cost depth camera, the Kinect. We intend to use this MAR approach for 3D medical visualization. In the actual state of developing, our approach is based on the KinectFusion algorithm with some minor adaptations, as long as our goal is not reconstruct scenes but use the reconstructed model as a reference to MAR. Keywords-Augmented Reality; 3D Reconstruction; Kinect;

Question: What is the difference between augmented reality and virtual reality?


Social Choice for Partial Preferences Using Imputation. Within the field of multiagent systems, the area of computational social choice considers the problems arising when decisions must be made collectively by a group of agents. Usually such systems collect a ranking of the alternatives from each member of the group in turn, and aggregate these individual rankings to arrive at a collective decision. However, when there are many alternatives to consider, individual agents may be unwilling, or unable, to rank all of them, leading to decisions that must be made on the basis of incomplete information. While earlier approaches attempt to work with the provided rankings by making assumptions about the nature of the missing information, this can lead to undesirable outcomes when the assumptions do not hold, and is ill-suited to certain problem domains. In this thesis, we propose a new approach that uses machine learning algorithms (both conventional and purpose-built) to generate plausible completions of each agent's rankings on the basis of the partial rankings the agent provided (imputations), in a way that reflects the agents' true preferences. We show that the combination of existing social choice functions with certain classes of imputation algorithms, which forms the core of our proposed solution, is equivalent to a form of social choice. Our system then undergoes an extensive empirical validation under 40 different test conditions, involving more than 50,000 group decision problems generated from real-world electoral data, and is found to outperform existing competitors significantly, leading to better group decisions overall. Detailed empirical findings are also used to characterize the behaviour of the system, and illustrate the circumstances in which it is most advantageous. A general testbed for comparing solutions using real-world and artificial data (Prefmine) is then described, in conjunction with results that justify its design decisions. We move on to prop

Question: What is a social choice function?


An improved NSGA-III algorithm for feature selection used in intrusion detection. Feature selection can improve classification accuracy and decrease the computational complexity of classification. Data features in intrusion detection systems (IDS) always present the problem of imbalanced classification in which some classifications only have a few instances while others have many instances. This imbalance can obviously limit classification efficiency, but few effort s have been made to address it. In this paper, a scheme for the many-objective problem is proposed for feature selection in IDS, which uses two strategies, namely, a special domination method and a predefined multiple targeted search, for population evolution. It can differentiate traffic not only between normal and abnormal but also by abnormality type. Based on our scheme, NSGA-III is used to obtain an adequate feature subset with good performance. An improved many-objective optimization algorithm (I-NSGA-III) is further proposed using a novel niche preservation procedure. It consists of a bias-selection process that selects the individual with the fewest selected features and a fit-selection process that selects the individual with the maximum sum weight of its objectives. Experimental results show that I-NSGA-III can alleviate the imbalance problem with higher classification accuracy for classes having fewer instances. Moreover, it can achieve both higher classification accuracy and lower computational complexity. © 2016 Published by Elsevier B.V.

Question: What is the difference between NSGA-III and I-NSGA-III?


The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea. We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective. © 2013 Elsevier Ltd. All rights reserved.

Question: What is the effect of site quality on repurchase intention?


Multilevel inverters: a survey of topologies, controls, and applications. Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. This paper presents the most important topologies like diode-clamped inverter (neutral-point clamped), capacitor-clamped (flying capacitor), and cascaded multicell with separate dc sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. This paper also presents the most relevant control and modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation. Special attention is dedicated to the latest and more relevant applications of these converters such as laminators, conveyor belts, and unified power-flow controllers. The need of an active front end at the input side for those inverters supplying regenerative loads is also discussed, and the circuit topology options are also presented. Finally, the peripherally developing areas such as high-voltage high-power devices and optical sensors and other opportunities for future development are addressed.

Question: What is a multilevel inverter?


Dynamic relief mapping1. The best texture mapping techniques that focus on the microrelief effect use iterative algorithms. The number of iterations must be determined in advance, but for real-time rendering, the number of iterations must be chosen dynamically to take into account the change of the depth scale and the viewing angle (viewing direction). In this paper, we presented a real-time rendering technique that allows a combined synchronization between depth scaling, viewing angle and number of iterations.

Question: What is the best texture mapping technique?


Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network. Controller Area Network (CAN) bus in the vehicles is a de facto standard for serial communication to provide an efficient, reliable and economical link between Electronic Control Units (ECU). However, CAN bus does not have enough security features to protect itself from inside or outside attacks. Intrusion Detection System (IDS) is one of the best ways to enhance the vehicle security level. Unlike the traditional IDS for network security, IDS for vehicle requires light-weight detection algorithm because of the limitations of the computing power of electronic devices reside in cars. In this paper, we propose a light-weight intrusion detection algorithm for in-vehicle network based on the analysis of time intervals of CAN messages. We captured CAN messages from the cars made by a famous manufacturer and performed three kinds of message injection attacks. As a result, we find the time interval is a meaningful feature to detect attacks in the CAN traffic. Also, our intrusion detection system detects all of message injection attacks without making false positive errors.

Question: What is the CAN bus?


Application of Data Mining Techniques in IoT: A Short Review. Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Interfacing an every object together through internet looks very difficult, but within a frame of time Internet of Things will drastically change our life. The enormous data captured by the Internet of Things (IoT) are considered of high business as well as social values and extracting hidden information from raw data, various data mining algorithm can be applied to IoT data. In this paper, We survey systematic review of various data mining models as well as its application in Internet of Thing (IoT) field along with its merits and demerits. At last, we discussed challenges in IoT.

Question: What is the size of the Internet of Things?


User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest. Platforms like Reddit have attracted large and vibrant communities, but the individuals in those communities are free to migrate to other platforms at any time. History has borne this out with the mass migration from Slashdot to Digg. The underlying motivations of individuals who migrate between platforms, and the conditions that favor migration online are not well-understood. We examine Reddit during a period of community unrest affecting millions of users in the summer of 2015, and analyze large-scale changes in user behavior and migration patterns to Reddit-like alternative platforms. Using self-reported statements from user comments, surveys, and a computational analysis of the activity of users with accounts on multiple platforms, we identify the primary motivations driving user migration. While a notable number of Reddit users left for other platforms, we found that an important pull factor that enabled Reddit to retain users was its long tail of niche content. Other platforms may reach critical mass to support popular or "mainstream" topics, but Reddit's large userbase provides a key advantage in supporting niche top-

Question: What is the difference between a niche and a mainstream topic?


Deep Learning for Intelligent Video Analysis. Analyzing videos is one of the fundamental problems of computer vision and multimedia content analysis for decades. The task is very challenging as video is an information-intensive media with large variations and complexities. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now able to boost the performance of video analysis significantly and initiate new research directions to analyze video content. This tutorial will present recent advances under the umbrella of video understanding, which start from a unified deep learning toolkit--Microsoft Cognitive Toolkit (CNTK) that supports popular model types such as convolutional nets and recurrent networks, to fundamental challenges of video representation learning and video classification, recognition, and finally to an emerging area of video and language.

Question: What is deep learning?


Enriching Wayfinding Instructions with Local Landmarks. Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method.

Question: How to enrich wayfinding instructions with local landmarks?


Identity Authentication and Capability Based Access Control (IACAC) for the Internet of Things. In the last few years the Internet of Things (IoT) has seen widespread application and can be found in each field. Authentication and access control are important and critical functionalities in the context of IoT to enable secure communication between devices. Mobility, dynamic network topology and weak physical security of low power devices in IoT networks are possible sources for security vulnerabilities. It is promising to make an authentication and access control attack resistant and lightweight in a resource constrained and distributed IoT environment. This paper presents the Identity Authentication and Capability based Access Control (IACAC) model with protocol evaluation and performance analysis. To protect IoT from man-in-the-middle, replay and denial of service (Dos) attacks, the concept of capability for access control is introduced. The novelty of this model is that, it presents an integrated approach of authentication and access control for IoT devices. The results of other related study have also been analyzed to validate and support our findings. Finally, the proposed protocol is evaluated by using security protocol verification tool and verification results shows that IACAC is secure against aforementioned attacks. This paper also discusses performance analysis of the protocol in terms of computational time compared to other Journal of Cyber Security and Mobility, Vol. 1, 309–348. c © 2013 River Publishers. All rights reserved. 310 P.N. Mahalle et al. existing solutions. Furthermore, this paper addresses challenges in IoT and security attacks are modelled with the use cases to give an actual view of IoT networks.

Question: What is the difference between authentication and access control?


Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation. Multi-label propagation aims to transmit the multi-label information from labeled examples to unlabeled examples based on a weighted graph. Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagation in an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. To address this problem, this paper associates each possible label with a "teacher", and proposes a "Multi-Label Teaching-to-Learn and Learning-toTeach" (ML-TLLT) algorithm, so that the entire propagation process is guided by the teachers and manipulated from simple examples to more difficult ones. In the teaching-to-learn step, the teachers select the simplest examples for the current propagation by investigating both the definitiveness of each possible label of the unlabeled examples, and the dependencies between labels revealed by the labeled examples. In the learning-to-teach step, the teachers reversely learn from the learner's feedback to properly select the simplest examples for the next propagation. Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets.

Question: What is multi-label propagation?


EdgeChain: An Edge-IoT Framework and Prototype Based on Blockchain and Smart Contracts. The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On the one hand, IoT devices are "weak" and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively "vulnerable" facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named "EdgeChain" based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or "coin" system to link the edge cloud resource pool with each IoT device' account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on pre-defined rules on priority, application types and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a non-deniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.

Question: What is the edge computing?


A Balanced Filtering Branch-Line Coupler. A balanced filtering branch-line coupler is proposed for the first time. The proposed design is realized by coupled microstrip lines and coupled-line-fed coupling structures. Based on this, the proposed design not only exhibits the functions of power dividing and filtering for differential-mode signals, but also can suppress the common-mode noise/signal and be easily connected with other balanced circuits. The dimension of the proposed design is similar to that of the traditional single-ended design. A prototype of the balanced filtering branch-line coupler centered at 1.87 GHz with the size of 0.33 λg × 0.42 λg is fabricated, where λg is the guided wavelength at the center frequency. The measured results exhibit the maximum insertion loss of 1.4 dB with a 3-dB fractional bandwidth of 3.5%.

Question: What is a balanced filtering branch-line coupler?


A 60 V Auto-Zero and Chopper Operational Amplifier With 800 kHz Interleaved Clocks and Input Bias Current Trimming. An auto-zero and chopper operational amplifier with a 4.5-60 V supply voltage range is realized, using a 0.18 μm CMOS process augmented by 5 V CMOS and 60 V DMOS transistors. It achieves a maximum offset voltage drift of 0.02 μV/°C, a minimum CMRR of 145 dB, a noise PSD of 6.8 nV/√Hz, and a 3.1 MHz unity gain bandwidth, while dissipating 840 μA of current. Up-modulated chopper ripple is suppressed by auto- zeroing. Furthermore, glitches from the charge injection of the input switches are mitigated by employing six parallel input stages with 800 kHz interleaved clocks. This moves the majority of the glitch energy up to 4.8 MHz, while leaving little energy at 800 kHz. As a result, the requirements on an external low-pass glitch filter is relaxed, and a wider usable signal bandwidth can be obtained. Maximum input bias current due to charge injection mismatch is reduced from 1.5 nA to 150 pA by post production trimming with an on-chip charge mismatch compensation circuit.

Question: What is the maximum input bias current?


Role of mask pattern in intelligibility of ideal binary-masked noisy speech.. Intelligibility of ideal binary masked noisy speech was measured on a group of normal hearing individuals across mixture signal to noise ratio (SNR) levels, masker types, and local criteria for forming the binary mask. The binary mask is computed from time-frequency decompositions of target and masker signals using two different schemes: an ideal binary mask computed by thresholding the local SNR within time-frequency units and a target binary mask computed by comparing the local target energy against the long-term average speech spectrum. By depicting intelligibility scores as a function of the difference between mixture SNR and local SNR threshold, alignment of the performance curves is obtained for a large range of mixture SNR levels. Large intelligibility benefits are obtained for both sparse and dense binary masks. When an ideal mask is dense with many ones, the effect of changing mixture SNR level while fixing the mask is significant, whereas for more sparse masks the effect is small or insignificant.

Question: How does the mask pattern affect intelligibility of ideal binary-masked noisy speech?


Continuous Queries over Append-Only Databases. In a database to which data is continually added, users may wish to issue a permanent query and be notified whenever data matches the query. If such continuous queries examine only single records, this can be implemented by examining each record as it arrives. This is very efficient because only the incoming record needs to be scanned. This simple approach does not work for queries involving joins or time. The Tapestry system allows users to issue such queries over a database of mail and bulletin board messages. The user issues a static query, such as "show me all messages that have been replied to by Jones," as though the database were fixed and unchanging. Tapestry converts the query into an incremental query that efficiently finds new matches to the original query as new messages are added to the database. This paper describes the techniques used in Tapestry, which do not depend on triggers and thus be implemented on any commercial database that supports SQL. Although Tapestry is designed for filtering mail and news messages, its techniques are applicable to any append-only database.

Question: How do you implement a continuous query over an append-only database?


Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty. With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks.

Question: What is the largest graph convolutional network?


Active learning for online bayesian matrix factorization. The problem of large-scale online matrix completion is addressed via a Bayesian approach. The proposed method learns a factor analysis (FA) model for large matrices, based on a small number of observed matrix elements, and leverages the statistical model to actively select which new matrix entries/observations would be most informative if they could be acquired, to improve the model; the model inference and active learning are performed in an online setting. In the context of online learning, a greedy, fast and provably near-optimal algorithm is employed to sequentially maximize the mutual information between past and future observations, taking advantage of submodularity properties. Additionally, a simpler procedure, which directly uses the posterior parameters learned by the Bayesian approach, is shown to achieve slightly lower estimation quality, with far less computational effort. Inference is performed using a computationally efficient online variational Bayes (VB) procedure. Competitive results are obtained in a very large collaborative filtering problem, namely the Yahoo! Music ratings dataset.

Question: What is the bayesian matrix factorization?


Survey of Pedestrian Detection for Advanced Driver Assistance Systems. Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.

Question: What is the difference between a pedestrian and a person?


Adding Semantic Role Annotation to a Corpus of Written Dutch. We present an approach to automatic semantic role labeling (SRL) carried out in the context of the Dutch Language Corpus Initiative (D-Coi) project. Adapting earlier research which has mainly focused on English to the Dutch situation poses an interesting challenge especially because there is no semantically annotated Dutch corpus available that can be used as training data. Our automatic SRL approach consists of three steps: bootstrapping from a syntactically annotated corpus by means of a rulebased tagger developed for this purpose, manual correction on the basis of the PropBank guidelines which have been adapted to Dutch and training a machine learning system on the manually corrected data.

Question: How to automatically annotate a corpus of written Dutch?


Towards Enhancing the Security of OAuth Implementations in Smart Phones. With the roaring growth and wide adoption of smart mobile devices, users are continuously integrating with culture of the mobile applications (apps). These apps are not only gaining access to information on the smartphone but they are also able gain users' authorization to access remote servers on their behalf. The Open standard for Authorization (OAuth) is widely used in mobile apps for gaining access to user's resources on remote service providers. In this paper, we analyze the different OAuth implementations adopted by the SDKs of the popular resource providers on smartphones and demonstrate possible attacks on most OAuth implementations. By analyzing source code of more than 430 popular Android apps we summarized the trends followed by the service providers and by the OAuth development choices made by application developers. In addition, we propose an application-based OAuth Manager framework, that provides a secure OAuth flow in smartphones that is based on the concept of privilege separation and does not require high overhead.

Question: What is the difference between OAuth and OpenID?


State of the art of Trust and Reputation Systems in E-Commerce Context. This article proposes in depth comparative study of the most popular, used and analyzed Trust and Reputation System (TRS) according to the trust and reputation literature and in terms of specific trustworthiness criteria. This survey is realized relying on a selection of trustworthiness criteria that analyze and evaluate the maturity and effectiveness of TRS. These criteria describe the utility, the usability, the performance and the effectiveness of the TRS. We also provide a summary table of the compared TRS within a detailed and granular selection of trust and reputation aspects.

Question: What is the state of the art of trust and reputation systems in e-commerce context?


A generalized hidden Markov model with discriminative training for query spelling correction. Query spelling correction is a crucial component of modern search engines. Existing methods in the literature for search query spelling correction have two major drawbacks. First, they are unable to handle certain important types of spelling errors, such as concatenation and splitting. Second, they cannot efficiently evaluate all the candidate corrections due to the complex form of their scoring functions, and a heuristic filtering step must be applied to select a working set of top-K most promising candidates for final scoring, leading to non-optimal predictions. In this paper we address both limitations and propose a novel generalized Hidden Markov Model with discriminative training that can not only handle all the major types of spelling errors, including splitting and concatenation errors, in a single unified framework, but also efficiently evaluate all the candidate corrections to ensure the finding of a globally optimal correction. Experiments on two query spelling correction datasets demonstrate that the proposed generalized HMM is effective for correcting multiple types of spelling errors. The results also show that it significantly outperforms the current approach for generating top-K candidate corrections, making it a better first-stage filter to enable any other complex spelling correction algorithm to have access to a better working set of candidate corrections as well as to cover splitting and concatenation errors, which no existing method in academic literature can correct.

Question: What is the difference between a generalized hidden Markov model and a hidden Markov model?


Human Motion Reconstruction from Action Video Data Using a 3-Layer-LSTM. Data driven motion generation is a challenging problem. Typically the model is trained using motion capture (MOCAP) data, which is limited in scope and diversity. Solving this problem using motion data extracted from Youtube videos would enable motion generation pipelines to feed from a wider range of action data, allowing for greater variability in the actions generated. Human action is expressed through a sequence of bodily movements. Hence, in this work we explore three stages for the motion synthesis pipeline: first by extracting human pose from video data with Part Affinity Fields method by [1], and then by training a 3-Layer Recurrent Neural Network (RNN) that learns to generate human-like motion for a given class from annotated video data, and finally we explore a simple synthesis of motion from multiple classes. Ultimately, this approach could enable robust generation of a wide range of motions, capturing the the subtle and fine variations of human movement expressed in videos that populate the Internet to artificially produce life like motion.

Question: How to generate human-like motion from video data?


An Improved Topology of SEPIC Converter With Reduced Output Voltage Ripple. An improved version of a single-ended primary inductor converter (SEPIC) is presented. The converter consists of a conventional SEPIC converter plus an additional high-frequency transformer and diode to maintain a freewheeling mode of the DC inductor currents during the switch on state. The voltage conversion ratio characteristics and semiconductor device voltage and current stresses are characterized. The main advantages of this converter are the continuous output current, smaller output voltage ripple, and lower semiconductors current stress compared with the conventional SEPIC converter. The design and simulation of the concept is verified by an experiment with a 48-V input and 12-V/3.75-A output converter.

Question: What is the voltage conversion ratio of this converter?


of Analytical Chemistry Wearable and Implantable Sensors for Biomedical Applications Hatice. Mobile health technologies offer great promise for reducing healthcare costs and improving patient care. Wearable and implantable technologies are contributing to a transformation in the mobile health era in terms of improving healthcare and health outcomes and providing real-time guidance on improved health management and tracking. In this article, we review the biomedical applications of wearable and implantable medical devices and sensors, ranging from monitoring to prevention of diseases, as well as the materials used in the fabrication of these devices and the standards for wireless medical devices and mobile applications. We conclude by discussing some of the technical challenges in wearable and implantable technology and possible solutions for overcoming these difficulties.

Question: What is the difference between wearable and implantable sensors?


Profile hidden Markov models. The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.

Question: What is a profile HMM?


PERFORMANCE ANALYSIS OF CHAOTIC CHIRP SPREAD SPECTRUM SYSTEM IN MULTIPATH ENVIRONMENT. Wireless channels often include multipath propagation, in which the signal has more than one path from the transmitter to the receiver. Atmospheric reflection or refraction, as well as reflection from the ground or from objects such as buildings may cause this phenomena. The aim of this research is to study the effects of multipath environment on the performance of a proposed chaotic chirp spread spectrum system (CCSSS). The benchmark performance of the proposed system is investigated in term of bit error rate (BER) under multipath environment. A Quadrature Phase Shift Keying (QPSK) modulation scheme and a matched filter based on adaptive threshold decision were used in this work. To assess the performance of this system, simulations were performed to compare the proposed system with the traditional frequency modulation differential chaotic shift keying (DCSK) spread spectrum system. The proposed system may be considered as a well qualified communication system in multipath environment.

Question: What is the difference between a chaotic chirp spread spectrum system and a traditional frequency modulation differential chaotic shift keying spread spectrum system?


A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis. Because facial wrinkle is a representative feature of facial expression, automatic wrinkle detection has been an important and challenging topic for expression simulation, recognition, and animation. Recently, most works about wrinkle detection have focused on permanent wrinkles (e.g., age wrinkles), which are usually linear shapes, whereas the detection of transient wrinkles (e.g., expression wrinkles) has not been sufficiently studied because of their shape diversity and complexity. In this work, a novel algorithm for automatic detection of transient wrinkles with linear, fixed, and chaotic shapes is proposed, which largely consists of edge pair matching, active-appearance-model-based wrinkle structure location, and support-vector-machine-based wrinkle classification. The proposed wrinkle detector is applied for expression synthesis and an improved Poisson wrinkle mapping approach is proposed. Experimental results illustrate the competitiveness of the proposed wrinkle detector in detecting different transient wrinkles. Compared with state-of-the-art algorithms, the proposed approach yields complete and accurate wrinkle centers. The expression synthesized by the improved wrinkle mapping is also much more realistic.

Question: What is the difference between a permanent and a transient wrinkle?


Eventually Returning to Strong Consistency. Eventually and weakly consistent distributed systems have emerged in the past decade as an answer to scalability and availability issues associated with strong consistency semantics, such as linearizability. However, systems offering strong consistency semantics have an advantage over systems based on weaker consistency models, as they are typically much simpler to reason about and are more intuitive to developers, exhibiting more predictable behavior. Therefore, a lot of research and development effort is being invested lately into the re-engineering of strongly consistent distributed systems, as well as into boosting their scalability and performance. This paper overviews and discusses several novel directions in the design and implementation of strongly consistent systems in industries and research domains such as cloud computing, data center networking and blockchain. It also discusses a general trend of returning to strong consistency in distributed systems, when system requirements permit so.

Question: What is the difference between strong and weak consistency?


The somatic marker hypothesis: A critical evaluation. The somatic marker hypothesis (SMH; [Damasio, A. R., Tranel, D., Damasio, H., 1991. Somatic markers and the guidance of behaviour: theory and preliminary testing. In Levin, H.S., Eisenberg, H.M., Benton, A.L. (Eds.), Frontal Lobe Function and Dysfunction. Oxford University Press, New York, pp. 217-229]) proposes that emotion-based biasing signals arising from the body are integrated in higher brain regions, in particular the ventromedial prefrontal cortex (VMPFC), to regulate decision-making in situations of complexity. Evidence for the SMH is largely based on performance on the Iowa Gambling Task (IGT; [Bechara, A., Tranel, D., Damasio, H., Damasio, A.R., 1996. Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex 6 (2), 215-225]), linking anticipatory skin conductance responses (SCRs) to successful performance on a decision-making paradigm in healthy participants. These 'marker' signals were absent in patients with VMPFC lesions and were associated with poorer IGT performance. The current article reviews the IGT findings, arguing that their interpretation is undermined by the cognitive penetrability of the reward/punishment schedule, ambiguity surrounding interpretation of the psychophysiological data, and a shortage of causal evidence linking peripheral feedback to IGT performance. Further, there are other well-specified and parsimonious explanations that can equally well account for the IGT data. Next, lesion, neuroimaging, and psychopharmacology data evaluating the proposed neural substrate underpinning the SMH are reviewed. Finally, conceptual reservations about the novelty, parsimony and specification of the SMH are raised. It is concluded that while presenting an elegant theory of how emotion influences decision-making, the SMH requires additional empirical support to remain tenable.

Question: What is the somatic marker hypothesis?


Rumors Detection in Sina Weibo Based on Text and User Characteristics. M414 such as Twitter and Sina Weibo have brought convenience to people in recent years, but there are a large number of internet rumors bring serious harm to us. Because of the fuzziness and concealment of rumors, the automatic identification of microblog rumors is very difficult, which brings great challenges to microblog's supervision. In this paper, for the text information of rumors, we construct a new emotional classification method, and use this method to analyze the emotional orientation of comments, and then calculate the proportion of negative emotional comments. Combined with user information at the same time, we analyze retweet-list and comment-list, and then judge whether there are authoritative users who participated in the spread of rumors. Moreover, we extract features from the information of user and calculate the user's reputation value. Finally, we use the training classifier to detect microblog rumors. Experimental results illustrate the efficacy and efficiency of the proposed new features in microblog rumors detection.

Question: How to detect rumors in microblog?


Human Activity Recognition Using Recurrent Neural Networks. Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.

Question: What is the difference between a recurrent neural network and a feedforward neural network?


SURREAL: SUbgraph Robust REpresentAtion Learning. The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhood notions. However, many existing algorithms generate embeddings that fail to properly preserve the network structure, or lead to unstable representations due to random processes (e.g., random walks to generate context) and, thus, cannot generate to multi-graph problems. In this paper, we propose a robust graph embedding using connection subgraphs algorithm, entitled "SURREAL," a novel, stable graph embedding algorithmic framework. SURREAL learns graph representations using connection subgraphs by employing the analogy of graphs with electrical circuits. It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes. Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines. The experiments show that SURREAL outperforms state-of-the-art algorithms by up to 36.85% on multi-label classification problem. Further, in contrast to baselines, SURREAL, being deterministic, is completely stable.

Question: What is the difference between SURREAL and other graph embedding algorithms?


I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. Social media technologies collapse multiple audiences into single contexts, making it difficult for people to use the same techniques online that they do to handle multiplicity in face-to-face conversation. This article investigates how content producers navigate 'imagined audiences' on Twitter. We talked with participants who have different types of followings to understand their techniques, including targeting different audiences, concealing subjects, and maintaining authenticity. Some techniques of audience management resemble the practices of 'micro-celebrity' and personal branding, both strategic self-commodification. Our model of the networked audience assumes a manyto-many communication through which individuals conceptualize an imagined audience evoked through their tweets.

Question: How do people manage their audiences on twitter?


A note on the maximum flow through a network. -This note discusses the problem of maximizing the rate of flow from one terminal to another, through a network which consists of a number of branches, each of which has a !imited capacity. The main result is a theorem: The maximum possible flow from left to right through a network is equal to the minimum value among all simple cut-sets. This theorem is applied to solve a more general problem, in which a number of input nodes and a number of output nodes are used.

Question: What is a simple cut-set?


Information Extraction Using Web Usage Mining, Web Scrapping and Semantic Annotation. Extracting useful information from the web is the most significant issue of concern for the realization of semantic web. This may be achieved by several ways among which Web Usage Mining, Web Scrapping and Semantic Annotation plays an important role. Web mining enables to find out the relevant results from the web and is used to extract meaningful information from the discovery patterns kept back in the servers. Web usage mining is a type of web mining which mines the information of access routes/manners of users visiting the web sites. Web scraping, another technique, is a process of extracting useful information from HTML pages which may be implemented using a scripting language known as Prolog Server Pages(PSP) based on Prolog. Third, Semantic annotation is a technique which makes it possible to add semantics and a formal structure to unstructured textual documents, an important aspect in semantic information extraction which may be performed by a tool known as KIM(Knowledge Information Management). In this paper, we revisit, explore and discuss some information extraction techniques on web like web usage mining, web scrapping and semantic annotation for a better or efficient information extraction on the web illustrated with examples.

Question: What is the difference between web usage mining and web scraping?


Super-Scalar RAM-CPU Cache Compression. High-performance data-intensive query processing tasks like OLAP, data mining or scientific data analysis can be severely I/O bound, even when high-end RAID storage systems are used. Compression can alleviate this bottleneck only if encoding and decoding speeds significantly exceed RAID I/O bandwidth. For this purpose, we propose three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs. We compare these algorithms with compression techniques used in (commercial) database and information retrieval systems. Our experiments on the MonetDB/X100 database system, using both DSM and PAX disk storage, show that these techniques strongly accelerate TPC-H performance to the point that the I/O bottleneck is eliminated.

Question: What is the monetdb database?


A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases.. The discovery of various protein/receptor targets from genomic research is expanding rapidly. Along with the automation of organic synthesis and biochemical screening, this is bringing a major change in the whole field of drug discovery research. In the traditional drug discovery process, the industry tests compounds in the thousands. With automated synthesis, the number of compounds to be tested could be in the millions. This two-dimensional expansion will lead to a major demand for resources, unless the chemical libraries are made wisely. The objective of this work is to provide both quantitative and qualitative characterization of known drugs which will help to generate "drug-like" libraries. In this work we analyzed the Comprehensive Medicinal Chemistry (CMC) database and seven different subsets belonging to different classes of drug molecules. These include some central nervous system active drugs and cardiovascular, cancer, inflammation, and infection disease states. A quantitative characterization based on computed physicochemical property profiles such as log P, molar refractivity, molecular weight, and number of atoms as well as a qualitative characterization based on the occurrence of functional groups and important substructures are developed here. For the CMC database, the qualifying range (covering more than 80% of the compounds) of the calculated log P is between -0.4 and 5.6, with an average value of 2.52. For molecular weight, the qualifying range is between 160 and 480, with an average value of 357. For molar refractivity, the qualifying range is between 40 and 130, with an average value of 97. For the total number of atoms, the qualifying range is between 20 and 70, with an average value of 48. Benzene is by far the most abundant substructure in this drug database, sl

Question: What is the average number of atoms in a drug molecule?


ReLiDSS: Novel lie detection system from speech signal. Lying is among the most common wrong human acts that merits spending time thinking about it. The lie detection is until now posing a problem in recent research which aims to develop a non-contact application in order to estimate physiological changes. In this paper, we have proposed a preliminary investigation on which relevant acoustic parameter can be useful to classify lie or truth from speech signal. Our proposed system in is based on the Mel Frequency Cepstral Coefficient (MFCC) commonly used in automatic speech processing on our own constructed database ReLiDDB (ReGIM-Lab Lie Detection DataBase) for both cases lie detection and person voice recognition. We have performed on this database the Support Vector Machines (SVM) classifier using Linear kernel and we have obtained an accuracy of Lie and Truth detection of speech audio respectively 88.23% and 84.52%.

Question: What is the accuracy of lie detection?


Comparison of CoAP and MQTT Performance Over Capillary Radios. The IoT protocols used in the application layer, namely the Constraint Application Protocol (CoAP) and Message Queue Telemetry Transport (MQTT) have dependencies to the transport layer. The choice of transport, Transmission Control Protocol (TCP) or the User Datagram Protocol(UDP), on the other hand, has an impact on the Internet of Things (IoT) application level performance, especially over a wireless medium. The motivation of this work is to look at the impact of the protocol stack on performance over two different wireless medium realizations, namely Bluetooth Low Energy and Wi-Fi. The use case studied is infrequent small reports sent from the sensor device to a central cloud storage over a last mile radio access link. We find that while CoAP/UDP based transport performs consistently better both in terms of latency and power consumption over both links, MQTT/TCP may also work when the use requirements allow for longerlatency providing better reliability. All in all, the full connectivity stack needs to be considered when designing an IoT deployment.

Question: What is the difference between CoAP and MQTT?


Quality, productivity and economic benefits of software reuse: a review of industrial studies. Systematic software reuse is proposed to increase productivity and software quality and lead to economic benefits. Reports of successful software reuse programs in industry have been published. However, there has been little effort to organize the evidence systematically and appraise it. This review aims to assess the effects of software reuse in industrial contexts. Journals and major conferences between 1994 and 2005 were searched to find observational studies and experiments conducted in industry, returning eleven papers of observational type. Systematic software reuse is significantly related to lower problem (defect, fault or error) density in five studies and to decreased effort spent on correcting problems in three studies. The review found evidence for significant gains in apparent productivity in three studies. Other significant benefits of software reuse were reported in single studies or the results were inconsistent. Evidence from industry is sparse and combining results was done by vote-counting. Researchers should pay more attention to using comparable metrics, performing longitudinal studies, and explaining the results and impact on industry. For industry, evaluating reuse of COTS or OSS components, integrating reuse activities in software processes, better data collection and evaluating return on investment are major challenges.

Question: What is the economic benefit of software reuse?


Data and Code for "Automatic Identification of Narrative Diegesis and Point of View". The style of narrative news affects how it is interpreted and received by readers. Two key stylistic characteristics of narrative text are point of view and diegesis: respectively, whether the narrative recounts events personally or impersonally, and whether the narrator is involved in the events of the story. Although central to the interpretation and reception of news, and of narratives more generally, there has been no prior work on automatically identifying these two characteristics in text. We develop automatic classifiers for point of view and diegesis, and compare the performance of different feature sets for both. We built a goldstandard corpus where we double-annotated to substantial agreement (κ > 0.59) 270 English novels for point of view and diegesis. As might be expected, personal pronouns comprise the best features for point of view classification, achieving an average F1 of 0.928. For diegesis, the best features were personal pronouns and the occurrences of first person pronouns in the argument of verbs, achieving an average F1 of 0.898. We apply the classifier to nearly 40,000 news texts across five different corpora comprising multiple genres (including newswire, opinion, blog posts, and scientific press releases), and show that the point of view and diegesis correlates largely as expected with the nominal genre of the texts. We release the training data and the classifier for use by the community.

Question: What is the average F1 score for the classifier?


Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91% on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.

Question: What is the utility of stylistic and syntactic features for sentiment classification?


Image analysis of blood microscopic images for acute leukemia detection. Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3–7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of stained blood smear or bone marrow aspirate is the only way to effective diagnosis of leukemia. Techniques such as fluorescence in situ hybridization (FISH), immunophenotyping, cytogenetic analysis and cytochemistry are also employed for specific leukemia detection. The need for automation of leukemia detection arises since the above specific tests are time consuming and costly. Morphological analysis of blood slides are influenced by factors such as hematologists experience and tiredness, resulting in non standardized reports. A low cost and efficient solution is to use image analysis for quantitative examination of stained blood microscopic images for leukemia detection. A fuzzy clustering based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia. In the present paper two novel shape features i.e., Hausdorff Dimension and contour signature is implemented for classifying a lymphocytic cell nucleus. Support Vector Machine (SVM) is employed for classification. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.

Question: What is leukemia?


Holistic Multi-modal Memory Network for Movie Question Answering. Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial interactions among data sources in one attention hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop. In addition, it takes answer choices into consideration during the context retrieval stage. Therefore, the proposed framework effectively integrates multi-modal context, question, and answer information, which leads to more informative context retrieved for question answering. Our HMMN framework achieves state-of-the-art accuracy on MovieQA dataset. Extensive ablation studies show the importance of holistic reasoning and contributions of different attention strategies.

Question: What is the state-of-the-art accuracy on MovieQA?


Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging. We present a system to assist users in dietary logging habits, which performs food recognition from pictures snapped on their phone in two different scenarios. In the first scenario, called "Food in context", we exploit the GPS information of a user to determine which restaurant they are having a meal at, therefore restricting the categories to recognize to the set of items in the menu. Such context allows us to also report precise calories information to the user about their meal, since restaurant chains tend to standardize portions and provide the dietary information of each meal. In the second scenario, called "Foods in the wild" we try to recognize a cooked meal from a picture which could be snapped anywhere. We perform extensive experiments on food recognition on both scenarios, demonstrating the feasibility of our approach at scale, on a newly introduced dataset with 105K images for 500 food categories.

Question: What is the name of the dataset?


Deep Learning for Sensorless 3D Freehand Ultrasound Imaging. 3D freehand ultrasound imaging is a very promising imaging modality but its acquisition is often neither portable nor practical because of the required external tracking hardware. Building a sensorless solution that is fully based on image analysis would thus have many potential applications. However, previously proposed approaches rely on physical models whose assumptions only hold on synthetic or phantom datasets, failing to translate to actual clinical acquisitions with sufficient accuracy. In this paper, we investigate the alternative approach of using statistical learning to circumvent this problem. To that end, we are leveraging the unique modeling capabilities of convolutional neural networks in order to build an end-to-end system where we directly predict the ultrasound probe motion from the images themselves. Based on thorough experiments using both phantom acquisitions and a set of 100 in-vivo long ultrasound sweeps for vein mapping, we show that our novel approach significantly outperforms the standard method and has direct clinical applicability, with an average drift error of merely 7% over the whole length of each ultrasound clip.

Question: How does deep learning work?


Midpalatal implants vs headgear for orthodontic anchorage--a randomized clinical trial: cephalometric results.. INTRODUCTION The purpose of this study was to compare the clinical effectiveness of the midpalatal implant as a method of reinforcing anchorage during orthodontic treatment with that of conventional extraoral anchorage. This was a prospective, randomized, clinical trial at Chesterfield and North Derbyshire Royal Hospital NHS Trust and the Charles Clifford Dental Hospital, Sheffield, in the United Kingdom.   METHODS Fifty-one orthodontic patients between the ages of 12 and 39, with Class II Division 1 malocclusion and absolute anchorage requirements, were randomly allocated to receive either a midpalatal implant or headgear to reinforce orthodontic anchorage. The main outcome was to compare the mesial movement of the molars and the incisors of the 2 treatment groups between the start and the end of anchorage reinforcement as measured from cephalometric radiographs.   RESULTS The reproducibility of the measuring technique was acceptable. There were significant differences between T1 and T2 in the implant group for the positions of the maxillary central incisor (P <.001), the maxillary molar (P = .009), and the mandibular molar (P <.001). There were significant differences between T1 and T2 in the headgear group for the positions of the mandibular central incisor (P <.045), the maxillary molar (P <.001), and the mandibular molar (P <.001). All skeletal and dental points moved mesially more in the headgear group during treatment than in the implant group. These ranged from an average of 0.5 mm more mesially for the mandibular permanent molar to 1.5 mm more mesially for the maxillary molar and the mandibular base. No treatment changes between the groups were statistically significant.   CONCLUSIONS Midpalatal implants are an acceptable technique for reinforcing anchorage in orthodontic patients.

Question: What is the difference between midpalatal implants and headgear?


SeiSIM : Structural Similarity Evaluation for Seismic Data Retrieval. Structural similarity evaluation is a critical step for retrieving existing databases to find matching records for a given seismic data set. The objective is to enable the re-use of historical findings to assist exploration with new survey data. Currently there are very few structural similarity metrics specifically designed for seismic data, especially seismic survey maps. In this paper, we propose a metric that combines texture similarity and geological similarity, which is derived from discontinuity maps. We test the seismic similarity metric in a retrieval application. The successful results indicate that our approach is promising.

Question: What is the objective of structural similarity evaluation for seismic data retrieval?


Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland. With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland's electricity demand forecasting.

Question: What is the improved neural network algorithm?


ARSA: a sentiment-aware model for predicting sales performance using blogs. Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.

Question: What is sentiment analysis?


Performance Evaluation and Comparative Analysis of SubCarrier Modulation Wake-up Radio Systems for Energy-Efficient Wireless Sensor Networks. Energy-efficient communication is one of the main concerns of wireless sensor networks nowadays. A commonly employed approach for achieving energy efficiency has been the use of duty-cycled operation of the radio, where the node's transceiver is turned off and on regularly, listening to the radio channel for possible incoming communication during its on-state. Nonetheless, such a paradigm performs poorly for scenarios of low or bursty traffic because of unnecessary activations of the radio transceiver. As an alternative technology, Wake-up Radio (WuR) systems present a promising energy-efficient network operation, where target devices are only activated in an on-demand fashion by means of a special radio signal and a WuR receiver. In this paper, we analyze a novel wake-up radio approach that integrates both data communication and wake-up functionalities into one platform, providing a reconfigurable radio operation. Through physical experiments, we characterize the delay, current consumption and overall operational range performance of this approach under different transmit power levels. We also present an actual single-hop WuR application scenario, as well as demonstrate the first true multi-hop capabilities of a WuR platform and simulate its performance in a multi-hop scenario. Finally, by thorough qualitative comparisons to the most relevant WuR proposals in the literature, we state that the proposed WuR system stands out as a strong candidate for any application requiring energy-efficient wireless sensor node communications.

Question: What is the difference between a WuR system and a WuR platform?


Wound healing activity of the fruit skin of Punica granatum.. The skin of the fruit and the bark of Punica granatum are used as a traditional remedy against diarrhea, dysentery, and intestinal parasites. The fruit skin extract of P. granatum was tested for its wound healing activity in rats using an excision wound model. The animals were divided into three groups of six each. The experimental group of animals was topically treated with P. granatum at a dose of 100 mg/kg every day for 15 days, while the controls and standard group animals were treated with petroleum jelly and mupirocin ointment, respectively. Phytochemical analysis of the extract revealed the presence of saponins, triterpenes, tannins, alkaloids, flavonoids, and cardiac glycosides. Extract-treated animals exhibited 95% reduction in the wound area when compared with controls (84%), which was statistically significant (P<.01). The extract-treated wounds were found to epithelize faster compared with controls. The hydroxyproline content of extract-treated animals was significantly higher than controls (P<.05). The fruit skin extract did not show any antimicrobial activity against the microrganisms tested. P. granatum promotes significant wound healing in rats and further evaluation of this activity in humans is suggested.

Question: What is the wound healing activity of the fruit skin of Punica granatum?


Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.. For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen-bonding patterns "turn" and "bridge." Repeating turns are "helices," repeating bridges are "ladders," connected ladders are "sheets." Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain "chirality" is the torsional handedness of four consecutive Ca positions and is positive for right-handed helices and negative for ideal twisted @-sheets. Curved pieces are defined as "bends." Solvent "exposure" is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10,925 residues. The dictionary is also available in computer-readable form for protein structure prediction work.

Question: What is the dictionary of protein secondary structure?


The huggable: a therapeutic robotic companion for relational, affective touch. Numerous studies have shown the positive benefits of companion animal therapy. Unfortunately, companion animals are not always available. The Huggable is a new type of robotic companion being designed specifically for such cases. It features a full body "sensitive skin" for relational affective touch, silent, muscle-like, voice coil actuators, an embedded PC with data collection and networking capabilities. In this paper we briefly describe the Huggable and propose a live demonstration of the robot.

Question: What is a therapeutic robotic companion?


Voiced-Unvoiced Classification of Speech Using a Neural Network Trained with LPC Coefficients. Voiced-Unvoiced classification (V-UV) is a well understood but still not perfectly solved problem. It tackles the problem of determining whether a signal frame contains harmonic content or not. This paper presents a new approach to this problem using a conventional multi-layer perceptron neural network trained with linear predictive coding (LPC) coefficients. LPC is a method that results in a number of coefficients that can be transformed to the envelope of the spectrum of the input frame. As a spectrum is suitable for determining the harmonic content, so are the LPC-coefficients. The proposed neural network works reasonably well compared to other approaches and has been evaluated on a small dataset of 4 different speakers.

Question: How to classify speech using neural networks?


Impact of interleaving on common-mode EMI filter weight reduction of paralleled three-phase voltage-source converters. This paper presents a detailed analysis on the impact of the interleaving angle on EMI filter weight reduction in a paralleled three-phase VSI motor drive system. The EMI noise analysis equivalent circuits are given in frequency domain and EMI filter design methods for a motor drive system are discussed. Based on the analysis, design examples are given for DC and AC common-mode (CM) EMI filters showing that minimum corner frequency in this case cannot ensure minimum EMI filter weight. In effect, the EMI filter weight is also determined by the volt-seconds on the inductor. With this consideration, the impact of interleaving is analyzed based on the equivalent circuit developed. Specifically, it is shown that interleaving can either reduce the volt-second on the inductor or help reduce the inductance needed, which has a different impact on the filter weight when the design condition is different. EMI filters are designed for both AC and DC sides as examples, showing the impact that different interleaving angles can have on filter weight reduction. Verifications are carried out both in simulation and experimentally on a 2 kW converter system.

Question: How to reduce the weight of the EMI filter?


"If It Matters, I Can Explain It": Social Desirability of Knowledge Increases the Illusion of Explanatory Depth. This paper explores whether social desirability affects the illusion of explanatory depth (IEOD) by comparing the magnitude of this illusion in topics with different levels of social desirability within several domains. This question was chosen because prior literature shows that social expectations about how much a person should know about a certain topic affect the magnitude of the IOED. Previous research shows also that social desirability has an effect on a similar illusion related to argumentation, and that the IOED is affected by the way a person thinks knowledge is distributed in his or her social group. In order to do so, 184 participants were assigned randomly to three knowledge domains (history, economics, and devices) and in each domain they rated their understanding of a high-desirability and a low-desirability topic following a standard IOED procedure. Results show that social desirability has an effect on the IOED magnitude and that overestimation of understanding varies among domains. Particularly, participants tend to overestimate their understanding of high desirability topics only. This effect was stronger in the historical domain.

Question: How does social desirability affect the illusion of explanatory depth?


Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients. Construct validity of the Self-Compassion ScaleShort Form among psychotherapy clients Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke To cite this article: Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke (2016): Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients, Counselling Psychology Quarterly, DOI: 10.1080/09515070.2016.1138397 To link to this article: http://dx.doi.org/10.1080/09515070.2016.1138397

Question: How to cite this article: Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke (2016): Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients, Counselling Psychology Quarterly, DOI: 10.1080/09515070.2016


Automatic Feature Engineering for Answer Selection and Extraction. This paper proposes a framework for automatically engineering features for two important tasks of question answering: answer sentence selection and answer extraction. We represent question and answer sentence pairs with linguistic structures enriched by semantic information, where the latter is produced by automatic classifiers, e.g., question classifier and Named Entity Recognizer. Tree kernels applied to such structures enable a simple way to generate highly discriminative structural features that combine syntactic and semantic information encoded in the input trees. We conduct experiments on a public benchmark from TREC to compare with previous systems for answer sentence selection and answer extraction. The results show that our models greatly improve on the state of the art, e.g., up to 22% on F1 (relative improvement) for answer extraction, while using no additional resources and no manual feature engineering.

Question: What is the difference between a tree kernel and a tree kernel?


Model-Agnostic Interpretability of Machine Learning. Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as blackbox functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.

Question: What is the difference between model-agnostic and model-specific explanations?


Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship. Spoken dialogue systems have received increased interest because they are potentially much more natural and powerful methods of communicating with machines than are current graphics-based interfaces. Wired for Speech presents basic research in the psychological and sociological aspects of voice synthesis and recognition. Its major lesson is that people attribute human characteristics to spoken dialogue systems for reasons related to human evolution. But although it contains interesting basic research, the book is mainly aimed at giving technological or marketing advice to those seeking to use voice interfaces when creating commercial applications. The book is oriented around a series of simple experiments designed to show just how pervasive psychological and social influences can be on the opinions and behaviors of people confronted with voice interfaces. Each chapter describes a basic research hypothesis, introduces an experiment to test it, and discusses its implications for designing voice interfaces: gender, personality, accent, ethnicity, emotion, number of distinct voices, use of " I " by the system, voices in concert with faces, mixed synthetic and recorded voices, context, and the effects of errors in human–computer cooperation. Although Wired for Speech is very accessible , especially to the non-scientist, it is written with an unusual bibliography style for an academic book: All references and details are given in a notes section at the end of the book, making up one third of the content. This narrative exposition style will probably not satisfy either type of reader: Scientists will be frustrated at the imprecision in argumen-tation, lack of detail in the book itself, and continually having to refer to the notes. The lack of detail also prevents the book from serving as a reference work. Meanwhile those needing advice when implementing voice interfaces will be puzzled at r

Question: How to design a voice interface?


Multiband antenna for Bluetooth/ZigBee/ Wi-Fi/WiMAX/WLAN/X-band applications: Partial ground with periodic structures and EBG. A compact in size microstrip antenna with multiband resonance features is described in this paper. A circular antenna with the partial ground is used as a primary antenna which resonated for WiMAX band (3.3–3.7 GHz). Further, primary antenna embedded with mushroom shape EBG (Electromagnetic Band Gap) and periodic DGS (Defected Ground Structure) to operate over WLAN band (5.1–5.8 GHz) and X-band from 8–12 GHz. Proposed antenna covers 2.4/3.5/5.5 GHz bands and complete X-band which also reflects that it has novel suitability to use for a ZigBee/Wi-Fi/Bluetooth applications. The proposed antenna is etched on FR-4 substrate and overall dimension is 20×26×1.6 mm3. The projected antenna has wide-ranging bandwidth with VSWR < 2. It provides favorable agreement between measured and simulated results.

Question: What is a multiband antenna?


Seizure classification in EEG signals utilizing Hilbert-Huang transform. BACKGROUND Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.   METHOD Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.   RESULTS The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.   CONCLUSION An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, ti

Question: How to classify EEG signals?


Vehicle model recognition from frontal view image measurements. Available online 28 June 2010

Question: How to recognize a vehicle model from a frontal view image?


CuriOS: Improving Reliability through Operating System Structure. An error that occurs in a microkernel operating system service can potentially result in state corruption and service failure. A simple restart of the failed service is not always the best solution for reliability. Blindly restarting a service which maintains client-related state such as session information results in the loss of this state and affects all clients that were using the service. CuriOS represents a novel OS design that uses lightweight distribution, isolation and persistence of OS service state to mitigate the problem of state loss during a restart. The design also significantly reduces error propagation within client-related state maintained by an OS service. This is achieved by encapsulating services in separate protection domains and granting access to client-related state only when required for request processing. Fault injection experiments show that it is possible to recover from between 87% and 100% of manifested errors in OS services such as the file system, network, timer and scheduler while maintaining low performance overheads.

Question: What is the difference between a microkernel and a monolithic kernel?


Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking. The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second, we propose a complete perception fusion architecture based on the evidential framework to solve the detection and tracking of moving objects problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project, which includes three main sensors: radar, lidar, and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car, and truck.

Question: What is the name of the project?


Shaping particle simulations with interaction forces. classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. SIGGRAPH 2014, August 10 – 14, 2014, Vancouver, British Columbia, Canada. 2014 Copyright held by the Owner/Author. ACM 978-1-4503-2960-6/14/08 Shaping Particle Simulations with Interaction Forces

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On the Use of Logic Trees for Ground-Motion Prediction Equations in Seismic-Hazard Analysis. Logic trees are widely used in probabilistic seismic hazard analysis as a tool to capture the epistemic uncertainty associated with the seismogenic sources and the ground-motion prediction models used in estimating the hazard. Combining two or more ground-motion relations within a logic tree will generally require several conversions to be made, because there are several definitions available for both the predicted ground-motion parameters and the explanatory parameters within the predictive ground-motion relations. Procedures for making conversions for each of these factors are presented, using a suite of predictive equations in current use for illustration. The sensitivity of the resulting ground-motion models to these conversions is shown to be pronounced for some of the parameters, especially the measure of source-to-site distance, highlighting the need to take into account any incompatibilities among the selected equations. Procedures are also presented for assigning weights to the branches in the ground-motion section of the logic tree in a transparent fashion, considering both intrinsic merits of the individual equations and their degree of applicability to the particular application.

Question: What is the difference between a logic tree and a probabilistic seismic hazard analysis?


Towards Continuous Sign Language Recognition with Deep Learning. Humans communicate with each other using abstract signs and symbols. While the cooperation between humans and machines can be a powerful tool for solving complex or difficult tasks, the communication must be at the abstract enough level that is both natural to the humans and understandable to the machines. Our paper focuses on natural language and in particular on sign language recognition. The approach described here combines heuristics for segmentation of the video stream by identifying the epenthesis with stacked LSTMs for automatic classification of the derived segments. This approach segments continuous stream of video data with the accuracy of over 80% and reaches accuracies of over 95% on segmented sign recognition. We compare results in terms of the number of signs being recognised and the utility of various features used for the recognition. We aim to integrate the models into a single continuous sign language recognition system and to learn policies for specific domains that would map perception of a robot to its action. This will improve the accuracy of understanding the common task within the shared activity between a human and a machine. Such understanding, in turn, will foster meaningful cooperation.

Question: What is the accuracy of sign language recognition?


Twitter Sentiment Analysis: A Bootstrap Ensemble Framework. Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.

Question: How to build a sentiment time series?