Resource allocation for device-to-device communications underlaying LTE-advanced networks. The Long Term Evolution-Advanced (LTEAdvanced) networks are being developed to provide mobile broadband services for the fourth generation (4G) cellular wireless systems. Deviceto- device (D2D) communications is a promising technique to provide wireless peer-to-peer services and enhance spectrum utilization in the LTE-Advanced networks. In D2D communications, the user equipments (UEs) are allowed to directly communicate between each other by reusing the cellular resources rather than using uplink and downlink resources in the cellular mode when communicating via the base station. However, enabling D2D communications in a cellular network poses two major challenges. First, the interference caused to the cellular users by D2D devices could critically affect the performances of the cellular devices. Second, the minimum quality-of-service (QoS) requirements of D2D communications need to be guaranteed. In this article, we introduce a novel resource allocation scheme (i.e. joint resource block scheduling and power control) for D2D communications in LTE-Advanced networks to maximize the spectrum utilization while addressing the above challenges. First, an overview of LTE-Advanced networks, and architecture and signaling support for provisioning of D2D communications in these networks are described. Furthermore, research issues and the current state-of-the-art of D2D communications are discussed. Then, a resource allocation scheme based on a column generation method is proposed for D2D communications. The objective is to maximize the spectrum utilization by finding the minimum transmission length in terms of time slots for D2D links while protecting the cellular users from harmful interference and guaranteeing the QoS of D2D links. The performance of this scheme is evaluated through simulations.

Question: What is the minimum transmission length in terms of time slots for D2D links?


Reducing the human overhead in text categorization. Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe away to reduce this effort, while retaining the methods' accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.

Question: How to reduce the human overhead in text categorization?


Development and design of a robotic manta ray featuring flexible pectoral fins. This paper showed our preliminary work in probing into fish robot with pectoral fin propulsion. The aim of this project is to develop a fish robot mimicking manta rays that swim with flexible pectoral fins in oscillation motion. Based on the analysis of motion feature and skeletal structure of pectoral fin of manta rays, a kind of flexible pectoral fin made of silicone rubber was designed. Experiments on the propulsion of fin prototype were carried out in tank. Experiment results showed that flexible pectoral fin in oscillation motion could generate great thrust and the thrust would increase with fin-beat frequency and amplitude increasing. In the end, a robot fish with flexible pectoral fins was built. The speed of robot fish could reach the maximum of 1.4 times BL (body length) per second. Experiments proved that it was feasible to develop a robot fish featuring flexible pectoral fins in oscillation motion and the method to mimic manta rays was a great idea to improve performance of underwater vehicles.

Question: What is the maximum speed of a robot fish?


Cranial Impalement of a Falling Fence Spike in a Child: A Case Report. Cranial impalement injuries are rare. They occur from a variety of objects, and via different mechanisms. We describe the case of a 5-year old boy who suffered cranial impalement injury via a unique mechanism. He presented to our centre with an impacted 17.8cm long metallic rod (a fence spike) in the vertex of his cranium, just off the midline. The spike penetrated his head and broke off its supporting frame as the frame was falling off a collapsing brick fence. He was transported as soon as possible to the hospital by relatives, without any attempt to remove the impaled spike. An urgent cranial computerized tomogram was done, and the object was removed under general anaesthesia in the operating theatre. The patient had complete recovery and was subsequently discharged from the hospital, with no residual neurological deficit. This case demonstrates a rare mechanism of cranial impalement. It also highlights the importance of following basic principles in the management of such injuries.

Question: How rare is cranial impalement?


ECDSA Security in Bitcoin and Ethereum : a Research Survey. This survey discusses the security level of the signature scheme implemented in Bitcoin and Ethereum.

Question: What is the security level of the signature scheme implemented in Bitcoin and Ethereum?


An LTE Base-Station Magnetoelectric Dipole Antenna with Anti-Interference Characteristics and Its MIMO System Application. In this research, a wideband long-term evolution (LTE) base-station antenna with flexible band-notch characteristics is introduced by two window-type slots etched on radiation patches of the magnetoelectric dipole. The presented antenna can provide a band-notch range with an impedance bandwidth of 2.39-2.52 GHz in the band of 1.825-2.925 GHz, which well applies the wideband antenna for LTE with the good anti-interference from wireless local area network narrow communication band. The typical parameters are investigated to show that the antenna with anti-interference characteristics exhibits large scope band-notch adjustability in the whole wideband region. Based on above element, an LTE base station antenna multiple-input, multiple-output system with anti-interference characteristics is investigated and measured to show its good performances: anti-interference, high isolation, high gain, unidirectional radiation, low back radiation, and low cross-polarization.

Question: What is the LTE base-station antenna with anti-interference characteristics?


Dual Path Networks. In this work, we present a simple, highly efficient and modularized Dual Path Network (DPN) for image classification which presents a new topology of connection paths internally. By revealing the equivalence of the state-of-the-art Residual Network (ResNet) and Densely Convolutional Network (DenseNet) within the HORNN framework, we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations. To enjoy the benefits from both path topologies, our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures. Extensive experiments on three benchmark datasets, ImagNet-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. In particular, on the ImagNet-1k dataset, a shallow DPN surpasses the best ResNeXt-101(64× 4d) with 26% smaller model size, 25% less computational cost and 8% lower memory consumption, and a deeper DPN (DPN-131) further pushes the state-of-the-art single model performance with about 2 times faster training speed. Experiments on the Places365 large-scale scene dataset, PASCAL VOC detection dataset, and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet, ResNet and the latest ResNeXt model over various applications.

Question: What is the difference between ResNet and DenseNet?


Some Basic Cryptographic Requirements for Chaos-Based Cryptosystems. In recent years, a large amount of work on chaos-based cryptosystems have been published. However many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, every new cryptosystem would benefit from. The suggested guidelines address three main issues: implementation, key management, and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith, and attenuation.

Question: What is a cryptosystem?


Effects of quiz-style information presentation on user understanding. This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keep their attention on receiving information, it is expected that information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. This indicates that quiz-style information presentation promotes engagement in the interaction with the system, leading to the improved user understanding.

Question: How can we improve user understanding?


An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a "base" learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.

Question: What is the difference between bagging and boosting?


Fast End-to-End Trainable Guided Filter. Image processing and pixel-wise dense prediction have been advanced by harnessing the capabilities of deep learning. One central issue of deep learning is the limited capacity to handle joint upsampling. We present a deep learning building block for joint upsampling, namely guided filtering layer. This layer aims at efficiently generating the high-resolution output given the corresponding low-resolution one and a high-resolution guidance map. The proposed layer is composed of a guided filter, which is reformulated as a fully differentiable block. To this end, we show that a guided filter can be expressed as a group of spatial varying linear transformation matrices. This layer could be integrated with the convolutional neural networks (CNNs) and jointly optimized through end-to-end training. To further take advantage of end-to-end training, we plug in a trainable transformation function that generates task-specific guidance maps. By integrating the CNNs and the proposed layer, we form deep guided filtering networks. The proposed networks are evaluated on five advanced image processing tasks. Experiments on MIT-Adobe FiveK Dataset demonstrate that the proposed approach runs 10-100× faster and achieves the state-of-the-art performance. We also show that the proposed guided filtering layer helps to improve the performance of multiple pixel-wise dense prediction tasks. The code is available at https://github.com/wuhuikai/DeepGuidedFilter.

Question: What is the difference between guided filtering and guided convolution?


Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal. We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.

Question: How does the network work?


On the Philosophy of Bitcoin/Blockchain Technology: Is it a Chaotic, Complex System?. The Philosophy of Blockchain Technology is concerned, among other things, with its ontology, how might it be characterised, how is it being created, implemented, and adopted, how does it operate in the world, and how does it evolve over time. Here, we concentrate on whether Bitcoin/blockchain can be considered a complex system and, if so, whether a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010-FlashCrash-type of chaotic regime, with catastrophic consequences for financial systems based on it. This paper starts by enhancing the relevant details of Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end-users (payers and payees), capital gain seekers, miners, full nodes maintainers, and developers, and their interactions. Secondly, the Information Theory of Complex Systems is briefly discussed for later use. Finally, blockchain is investigated with the help of Crutchfield"s Statistical Complexity measure. The low non-null statistical complexity value obtained suggests that blockchain may be considered algorithmically complicated, but hardly a complex system and unlikely to enter a chaotic regime.

Question: What is the philosophy of bitcoin?


Looking at Vehicles in the Night : Detection & Dynamics of Rear Lights. Existing nighttime vehicle detection methods use color as the primary cue for detecting vehicles. However, complex road and ambient lighting conditions, and camera configurations can influence the effectiveness of such explicit rule and threshold based methods. In this paper, there are three main contributions. First, we present a novel method to detect vehicles during nighttime involving both learned classifiers and explicit rules, which can operate in the presence of varying ambient lighting conditions. The proposed method which is titled as VeDANt (Vehicle Detection using Active-learning during Nighttime) employs a modified form of active learning for training Adaboost classifiers with Haar-like features using gray-level input images. The hypothesis windows are then further verified using the proposed techniques involving perspective geometries and color information of the taillights. Second, VeDANt is extended to analyze the dynamics of the vehicles during nighttime by detecting three taillight activities braking, turning left and turning right. Third, we release three new and fully annotated LISA-Night datasets with over 5000 frames for evaluation and benchmarking, which capture a variety of complex traffic and lighting conditions. Such comprehensively annotated and complex public datasets is a first in the area of nighttime vehicle detection. We show that VeDANt is able to detect vehicles during nighttime with over 98% accuracy and less than 1% false detections.

Question: What is the difference between a vehicle and a vehicle?


On the Use of Automated Text Summarization Techniques for Summarizing Source Code. During maintenance developers cannot read the entire code of large systems. They need a way to get a quick understanding of source code entities (such as, classes, methods, packages, etc.), so they can efficiently identify and then focus on the ones related to their task at hand. Sometimes reading just a method header or a class name does not tell enough about its purpose and meaning, while reading the entire implementation takes too long. We study a solution which mitigates the two approaches, i.e., short and accurate textual descriptions that illustrate the software entities without having to read the details of the implementation. We create such descriptions using techniques from automatic text summarization. The paper presents a study that investigates the suitability of various such techniques for generating source code summaries. The results indicate that a combination of text summarization techniques is most appropriate for source code summarization and that developers generally agree with the summaries produced.

Question: What is source code summarization?


Double-Hypergraph Based Sentence Ranking for Query-Focused Multi-document Summarizaton. Traditional graph based sentence ranking approaches modeled the documents as a text graph where vertices represent sentences and edges represent pairwise similarity relationships between two sentences. Such modeling cannot capture complex group relationships shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose two different group relationships (sentence-topic relationship and document-topic relationship) shared among sentences, and construct a double-hypergraph integrating these relationships into a unified framework. Then, a double-hypergraph based sentence ranking algorithm is developed for query-focused multi-document summarization, in which Markov random walk is defined on each hypergraph and the mixture Markov chains are formed so as to perform transductive learning in the double-hypergraph. When evaluated on DUC datasets, performance of the proposed approach is remarkable.

Question: What is the double-hypergraph based sentence ranking for query-focused multi-document summarization?


Measuring and Analyzing Write Amplification Characteristics of Solid State Disks. Write amplification brings endurance challenges to NAND Flash-based solid state disks (SSDs) such as impacts upon their write endurance and lifetime. A large write amplification degrades program/erase cycles (P/Es) of NAND Flashes and reduces the endurance and performance of SSDs. The write amplification problem is mainly triggered by garbage collections, wear-leveling, metadata updates, and mapping table updates. Write amplification is defined as the ratio of data volume written by an SSD controller to data volume written by a host. In this paper, we propose a four-level model of write amplification for SSDs. The four levels considered in our model include the channel level, chip level, die level, and plane level. In light of this model, we design a method of analyzing write amplification of SSDs to trace SSD endurance and performance by incorporating the Ready/Busy (R/B) signal of NAND Flash. Our practical approach aims to measure the value of write amplification for an entire SSD rather than NAND Flashes. To validate our measurement technique and model, we implement a verified SSD (vSSD) system and perform a cross-comparison on a set of SSDs, which are stressed by micro-benchmarks and I/O traces. A new method for SSDs is adopted in our measurements to study the R/B signals of NAND Flashes in an SSD. Experimental results show that our model is accurate and the measurement technique is generally applicable to any SSDs.

Question: What is the difference between a SSD and a hard disk drive?


A First Look at Android Malware Traffic in First Few Minutes. With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and viruswriters are paying close attention to the mobile terminals, especially the Android platform. The growing of malwares on the Android system has drawn attentions from both the academia and security industry. Recently, mobile network traffic analysis has been used to identify the malware. But due to the lack of a large-scale malware repository and a systematic analysis of network traffic features, the existing research mostly remain in theory. In this paper, we design an Android malware traffic behavior monitoring scheme to capture traffic data generated by malware samples in a real Internet environment. We capture the network traffic from 5560 malware samples in the first 5 minutes, and analyze the major compositions of the traffic data. We discover that HTTP and DNS traffic are accounted for more than 99% on the application layer traffic. We then present an analysis of related network features: DNS query, HTTP packet length, ratio of downlink to uplink traffic amount, HTTP request and Ad traffic feature. Our statistical results illustrate that: (1) more than 70% malwares generate malicious traffic in the first 5 minutes, (2) DNS query and HTTP request can be used to identify the malware, and the detection rate reaches 69.55% and 40.89% respectively, (3) Ad traffic can greatly affect the malware detection. We believe our research provides an in-depth analysis into mobile malwares' network behaviors.

Question: What is the first large-scale Canadian peacekeeping mission?


An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification ata mining unctional link artificial neural networks ulti-layer perception article swarm optimization mproved particle swarm optimization VM capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN = ISO − FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN). SN

Question: What is the difference between a particle swarm optimization and a swarm optimization?


1 Unsupervised Feature Learning in Time Series 2 Prediction Using Continuous Deep Belief Network 3. A continuous Deep Belief Network (cDBN) with two hidden layers is proposed in this 18 paper, focusing on the problem of weak feature learning ability when dealing with continuous data. 19 In cDBN, the input data is trained in an unsupervised way by using continuous version of transfer 20 functions, the contrastive divergence is designed in hidden layer training process to raise 21 convergence speed, an improved dropout strategy is then implemented in unsupervised training to 22 realize features learning by de-cooperating between the units, and then the network is fine-tuned 23 using back propagation algorithm. Besides, hyper-parameters are analysed through stability 24 analysis to assure the network can find the optimal. Finally, the experiments on Lorenz chaos series, 25 CATS benchmark and other real world like CO2 and waste water parameters forecasting show that 26 cDBN has the advantage of higher accuracy, simpler structure and faster convergence speed than 27 other methods. 28

Question: What is the difference between a continuous deep belief network and a deep belief network?


Android anti-forensics through a local paradigm. Mobile devices are among the most disruptive technologies of the last years, gaining even more diffusion and success in the daily life of a wide range of people categories. Unfortunately, while the number of mobile devices implicated in crime activities is relevant and growing, the capability to perform the forensic analysis of such devices is limited both by technological and methodological problems. In this paper, we focus on Anti-Forensic techniques applied to mobile devices, presenting some fully automated instances of such techniques to Android devices. Furthermore, we tested the effectiveness of such techniques versus both the cursory examination of the device and some acquisition tools. a 2010 Digital Forensic Research Workshop. Published by Elsevier Ltd. All rights reserved.

Question: What is the size of the android anti-forensics?


OCCUPATIONAL THERAPY FOR THE POLY- TRAUMA CASUALTY WITH LIMB LOSS.

Question: What is occupational therapy?


Adaptive energy-aware free viewpoint video transmission over wireless networks. As consumer-level cameras are becoming cheaper and cheaper, the scene of interest can now be captured by multiple cameras simultaneously from different viewpoints. By transmitting texture and depth maps of the views captured using two adjacent cameras, users can synthesize any view in between using depth image based rendering (DIBR) technique such as 3D-warping. This provides users with continuous view angles and is key technique for Free Viewpoint TV (FTV), etc. In this scenario, multiple views must be delivered through bandwidth-limited wireless networks, how to adaptive reduce the source coding rate to fit the bandwidth becomes an important issue. The standard multi-view video codec H.264 MVC explores the inter-view and intra-view correlation to reduce source encoding rate. But all views are required at the client side for decoding any single view, which is apparently not transmission efficient. A better solution is using H.264 to differentially encode the two adjacent views separately, but still this requires double-folder bandwidth comparing to the traditional video streaming. Multiple description coding (MDC) encodes one view into multiple descriptions for robust transmission. This inspires another option: sending only part of each view. This can greatly reduce the source encoding rate, but interpolating skipped frames before synthesizing the middle view consumes mobile devices' precious battery life. Whether to send at a lower source encoding rate but with extra energy consumption or send at a high source encoding rate is nontrivial, and has not been studied in any formal way. In this paper, we investigated both source encoding rate reduction and energy consumption with the bandwidth constraint. We formulated the problem into an optimization problem, i.e., maximizing the system utility, which is defined as the received video quality minus the weighted energy consumption. Simulatio

Question: How to reduce the source encoding rate?


INDUCING THE MORPHOLOGICAL LEXICON OF A NATURAL LANGUAGE FROM UNANNOTATED TEXT. This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology of a natural language. A probabilistic maximum a posteriori model is utilized, which builds hierarchical representations for a set of morphs, which are morpheme-like units discovered from unannotated text corpora. The induced morph lexicon stores parameters related to both the "meaning" and "form" of the morphs it contains. These parameters affect the role of the morphs in words. The model is implemented in a task of unsupervised morpheme segmentation of Finnish and English words. Very good results are obtained for Finnish and almost as good results are obtained in the English task.

Question: What is the meaning of the word "morph"?


The ' dark side ' of leadership personality and transformational leadership : An exploratory study. There is growing interest in dysfunctional dispositions in the workplace, in particular relating to leaders and managers within organizations. This paper reports an exploratory study investigating the relationship between the 'dark side' of leadership personality and transformational leadership. Data were collected from a sample of New Zealand business leaders and senior managers (n = 80) from scores on the Hogan Development Survey (HDS; Hogan & Hogan, 1997) and the Multifactor Leadership Questionnaire (MLQ; Bass & Avolio, 1995). Regression analyses revealed the 'Colorful' (histrionic) dimension of the HDS to be a positive predictor of transformational leadership, while the 'Cautious' (avoidant) and 'Bold' (narcissistic) dimensions of the HDS were revealed as negative predictors of transformational leadership. This is one of the first reports to suggest a relationship between histrionic personality and transformational leadership, in a literature preoccupied with the relationship between leadership and narcissism. 2007 Elsevier Ltd. All rights reserved.

Question: What is the dark side of leadership personality?


Robot Programming by Demonstration with Crowdsourced Action Fixes. Programming by Demonstration (PbD) can allow endusers to teach robots new actions simply by demonstrating them. However, learning generalizable actions requires a large number of demonstrations that is unreasonable to expect from end-users. In this paper, we explore the idea of using crowdsourcing to collect action demonstrations from the crowd. We propose a PbD framework in which the end-user provides an initial seed demonstration, and then the robot searches for scenarios in which the action will not work and requests the crowd to fix the action for these scenarios. We use instance-based learning with a simple yet powerful action representation that allows an intuitive visualization of the action. Crowd workers directly interact with these visualizations to fix them. We demonstrate the utility of our approach with a user study involving local crowd workers (N=31) and analyze the collected data and the impact of alternative design parameters so as to inform a real-world deployment of our system.

Question: What is the name of the robot?


Design Of Ternary Logic Gates Using CNTFET. This paper presents a novel design of ternary logic gates like STI,PTI,NTI,NAND and NOR using carbon nanotube field effect transistors. Ternary logic is a promising alternative to the conventional binary logic design technique, since it is possible to accomplish simplicity and energy efficiency in modern digital design due to the reduced circuit overhead such as interconnects and chip area. In this paper novel design of basic logic gates for ternary logic based on CNTFET, is proposed. Keywords— Carbon nano-tube Field Effect Transistor(CNTFET),MVL(multi valued logic),Ternary logic,STI,NTI,PTI

Question: What is the design of ternary logic gates using CNTFET?


Litz wire design for wireless power transfer in electric vehicles. Eddy current losses in wireless charging systems for electric vehicles must be minimized in consideration of constraints like the available space, the strand diameter in litz wire and the cost. A mathematical model for eddy current losses is needed to find the optimum geometrical design for a given application. This paper gives the fundamental equations to calculate skin effect, proximity effect and dc losses in litz wire. Litz wire consists of strands, which are twisted to form multiple bundles. Therefore eddy current losses occur not only on the strand level but the bundle level as well. The losses caused by different effects and bundle levels are compared, and the influence of strand diameter and strand number on the total loss is examined. Finally, aluminum litz wire is analyzed. It becomes apparent that the overall loss can be reduced with the aid of replacing copper litz wire by aluminum in some areas of application. Material choice and geometrical design require detailed knowledge of the systems nominal operation point.

Question: What is the difference between litz wire and twisted pair?


Hybrid PWM Strategy of SVPWM and VSVPWM for NPC Three-Level Voltage-Source Inverter. Neutral-point (NP) voltage drift is the main technical drawback of NP-clamped (NPC) three-level inverters. Traditional space vector pulsewidth modulation (SVPWM) is incapable of controlling the NP voltage for high modulation indexes and low power factors. Virtual SVPWM (VSVPWM) is capable of controlling the NP voltage under full modulation indexes and full power factors. However, this modulation strategy is more complex than SVPWM, increases the switching frequency, and deteriorates the output waveforms of the inverter. A novel PWM concept that includes NP voltage-balancing conditions is proposed. Based on this concept, a hybrid modulation scheme that uses both SVPWM and VSVPWM is presented for complete control of the NP voltage in NPC three-level inverters. The performance of this new modulation approach and its benefits over SVPWM and VSVPWM are verified by simulation and experiments.

Question: How to control the NP voltage drift in NPC three-level inverters?


A rule based bengali stemmer. One of the biggest challenges in doing word lookups is to derive the appropriate base word for any given word in Bengali. The basic concept to the solution of the problem is to eliminate inflections from a given word to derive its stem word. Stemmers attempt to reduce a word to its root form using stemming process, which reduces an inflected or derived word to its stem or root form. Existing works in the literature use lookup tables either for stem words or suffixes, increasing the overheads in terms of memory and time. This paper develops a rule-based algorithm that eliminates inflections stepwise without continuously searching for the desired root in the dictionary. To the best of our knowledge, this paper first investigates that, in Bengali morphology, for a large set of inflections, the stems can be computed algorithmically cutting down the inflections step by step. The proposed algorithm is independent of inflected word lengths and our evaluation shows around 88% accuracy.

Question: What is the bengali stemmer?


Author ' s personal copy Narcissism and recollections of early life experiences. Recent studies have found associations between narcissistic personality features and retrospective accounts of early experiences. The current study sought to extend these previous findings by examining whether adaptive and maladaptive features of narcissism were associated with recollections of early life experiences in a non-clinical sample of undergraduate students (N = 334). Results revealed that the Entitlement/Exploitativeness feature of narcissism was associated with low security, high parental discipline, and high threats of separation. Narcissistic Grandiosity was positively associated with peer affectional support and parental discipline, whereas Narcissistic Vulnerability was not uniquely associated with memories of early life experiences. The results provide partial support for models of narcissism in which parents are recalled as failing to provide a secure base while inducing threats of separation and discipline. 2011 Elsevier Ltd. All rights reserved.

Question: What is the difference between narcissism and narcissism?


The radiation properties of electrically small folded spherical helix antennas. The radiation properties of several electrically small, folded spherical helix antennas are presented. The primary variables considered in the design of these antennas are the number of helical turns and the number of helical arms. The principle design objectives are to achieve self resonance, a low quality factor (Q), and a practical radiation resistance for small values of ka. Designs are presented for ka less than 0.5, where the antennas are self resonant, exhibiting an efficiency in excess of 95%, a Q within 1.5 times the fundamental limit, and a radiation resistance near 50 /spl Omega/. The relationship between the number of helical turns, the number of helical arms, and achieving self resonance at low frequencies is discussed.

Question: What is the radiation resistance of an antenna?


Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices. Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing unsupervised learning. They learn using a simplified and customized "spike timing dependent plasticity" rule. In the network, neurons' threshold is adjusted following a homeostasis-type rule. We perform system level simulations with an experimentally verified model of the memristive devices' behavior. They show, on the textbook case of character recognition, that performance can compare with traditional supervised networks of similar complexity. They also show that the system can retain functionality with extreme variations of various memristive devices' parameters (a relative standard dispersion of more than 50% is tolerated on all device parameters), thanks to the robustness of the scheme, its unsupervised nature, and the capability of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes, is particularly robust to read disturb effects and does not require unrealistic control on the devices' conductance. These results open the way for a novel design approach for ultraadaptive electronic systems.

Question: What is the difference between a spiking neural network and a traditional neural network?


IEEE 802.11ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi [Invited Paper]. With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave band. However, multi-gigabit-per-second throughput and small interference footprint come at the price of adverse signal propagation characteristics, and require a fundamental rethinking of Wi-Fi communication principles. This article describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular, we study the transition from omnidirectional to highly directional communication and its impact on the design of IEEE 802.11ad.

Question: What is the IEEE 802.11ad standard?


Antimicrobial agents from plants: antibacterial activity of plant volatile oils.. The volatile oils of black pepper [Piper nigrum L. (Piperaceae)], clove [Syzygium aromaticum (L.) Merr. & Perry (Myrtaceae)], geranium [Pelargonium graveolens L'Herit (Geraniaceae)], nutmeg [Myristica fragrans Houtt. (Myristicaceae), oregano [Origanum vulgare ssp. hirtum (Link) Letsw. (Lamiaceae)] and thyme [Thymus vulgaris L. (Lamiaceae)] were assessed for antibacterial activity against 25 different genera of bacteria. These included animal and plant pathogens, food poisoning and spoilage bacteria. The volatile oils exhibited considerable inhibitory effects against all the organisms under test while their major components demonstrated various degrees of growth inhibition.

Question: What is the difference between a volatile oil and a volatile?


The Case for Energy-Proportional Computing. Energy-proportional designs would enable large energy savings in servers, potentially doubling their efficiency in real-life use. Achieving energy proportionality will require significant improvements in the energy usage profile of every system component, particularly the memory and disk subsystems.

Question: What is the case for energy-proportional computing?


Agile Model-Driven Modernization to the Service Cloud. Migration of legacy systems to more advanced technologies and platforms is a current issue for many software organizations. Model-Driven Modernization combined with Software as a Service delivery model is a very promising approach, which possesses a lot of advant ges, including reduced costs, automation of migration ac tivities and reuse of system functionality. However, a drawback of such an innovative modernization approach is that it lacks mature software process models to guide its adoption. Thus , a methodology for seamless execution of different mig ration and deployment activities is quite needed. On the other hand, agile development methods have been successfully adopted in various projects, which partly or thoroughly use the engineering and delivery models exploited in the modernization process. This paper presents how a pa rticular methodology for Model-Driven Modernization with deployment to the Cloud is enriched with agile tech niques to address different challenging issues. The extended agile methodology could be used by organizations which ha ve already applied agile software development as well as by organizations that plan to introduce it in their work. KeywordsCloud computing; Agile Methodology; Modeldriven Modernization; Software as a service

Question: What is the difference between agile and model-driven modernization?


Games and Agents: Designing Intelligent Gameplay. There is an attention shift within the gaming industry toward more natural (long-term) behavior of nonplaying characters (NPCs). Multiagent system research offers a promising technology to implement cognitive intelligent NPCs. However, the technologies used in game engines and multiagent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central control, multiagent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state-of-the-art multiagent system technology. In this paper, we will focus on three specific problem areas that arise from this difference of view: synchronization, information representation, and communication. We argue that the current attempts for integration still fall short on some of these aspects. We show that to fully integrate intelligent agents in games, one should not only use a technical solution, but also a design methodology that is amenable to agents. The game design should be adjusted to incorporate the possibilities of agents early on in the process.

Question: What is the difference between a game engine and a multiagent platform?


Personal Recommendation as Link Prediction using a Bipartite Graph Kernel. We implement a personal recommendation system on the Yelp academic dataset using a novel algorithm proposed by [Li and Chen., 2012]. By constructing a bipartite userbusiness graph where positive reviews are edges between nodes, we pose the recommendation problem as link prediction. More specifically, we construct a kernel which combines the information from both the node features and local graph structure. This kernel is then put into a one-class support vector machine, which generates an ordering of likely user-business links. We downsample the graph and tune the parameters of our algorithm (via cross validation). In what follows we provide a summary of the Yelp data, describe the recommendation algorithm in detail, and present the results of our implementation.

Question: What is the yelp academic dataset?


Diffusion maps for high-dimensional single-cell analysis of differentiation data. MOTIVATION Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.   RESULTS Here, we propose the use of diffusion maps to deal with the problem of defining differentiation trajectories. We adapt this method to single-cell data by adequate choice of kernel width and inclusion of uncertainties or missing measurement values, which enables the establishment of a pseudotemporal ordering of single cells in a high-dimensional gene expression space. We expect this output to reflect cell differentiation trajectories, where the data originates from intrinsic diffusion-like dynamics. Starting from a pluripotent stage, cells move smoothly within the transcriptional landscape towards more differentiated states with some stochasticity along their path. We demonstrate the robustness of our method with respect to extrinsic noise (e.g. measurement noise) and sampling density heterogeneities on simulated toy data as well as two single-cell quantitative polymerase chain reaction datasets (i.e. mouse haematopoietic stem cells and mouse embryonic stem cells) and an RNA-Seq data of human pre-implantation embryos. We show that diffusion maps perform considerably better than Principal Component Analysis and are advantageous over other techniques for non-linear dimension reduction such as t-distributed Stochastic Neighbour Embedding for preserving the global structures and pseudotemporal order

Question: What is the difference between diffusion maps and t-distributed stochastic neighbour embedding?


For Whom Is a Picture Worth a Thousand Words ? Extensions of a Dual-Coding Theory of Multimedia Learning. In 2 experiments, highand low-spatial ability students viewed a computer-generated animation and listened simultaneously (concurrent group) or successively (successive group) to a narration that explained the workings either of a bicycle tire pump (Experiment 1) or of the human respiratory system (Experiment 2). The concurrent group generated more creative solutions to subsequent transfer problems than did the successive group; this contiguity effect was strong for highbut not for low-spatial ability students. Consistent with a dual-coding theory, spatial ability allows high-spatial learners to devote more cognitive resources to building referential connections between visual and verbal representations of the presented material, whereas low-spatial ability learners must devote more cognitive resources to building representation connections between visually presented material and its visual representation.

Question: How does spatial ability affect the learning of multimedia?


Full dimension MIMO for LTE-Advanced and 5G. Elevation beamforming and Full Dimension MIMO (FD-MIMO) has been an active area of research and standardization in 3GPP LTE-Advanced. In an FD-MIMO system, a base station with 2-dimensional (2D) active array supports multi-user joint elevation and azimuth beamforming (a.k.a. 3D beamfoming), which results in much higher cell capacity compared to conventional systems. Recent study has shown that with these new FD-MIMO technologies, we can achieve promising 3-5× gain in both cell capacity as well as cell-edge throughput. In this paper, we will provide a brief summary of recent 3GPP activities, including the recently completed 3D channel model, ongoing study on FD-MIMO scenarios, antenna/RF (radio frequency) transceiver architectures, as well as potential network performance benefits. In addition, we also discuss some methods for reducing CSI (channel state information) feedback overhead and ensuring efficient operation of large size FD-MIMO for both TDD and FDD systems.

Question: What is the difference between 3D beamforming and FD-MIMO?


EDUCATIONAL DATA MINING. Computer-based learning systems can now keep detailed logs of user-system interactions, including key clicks, eye-tracking, and video data, opening up new opportunities to study how students learn with technology. Educational Data Mining (EDM; Romero, Ventura, Pechenizkiy, & Baker, 2010) is concerned with developing, researching, and applying computerized methods to detect patterns in large collections of educational data – patterns that would otherwise be hard or impossible to analyze due to the enormous volume of data they exist within. Data of interest is not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input to quizzes and interactive exercises) but might also include data from collaborating students (e.g., text chat), administrative data (e.g., school, school district, teacher), and demographic data (e.g., gender, age, school grades). Data on student affect (e.g., motivation, emotional states) has also been a focus, which can be inferred from physiological sensors (e.g., facial expression, seat posture and perspiration). EDM uses methods and tools from the broader field of Data Mining (Witten & Frank, 2005), a sub-field of Computer Science and Artificial Intelligence that has been used for purposes as diverse as credit card fraud detection, analysis of gene sequences in bioinformatics, or the analysis of purchasing behaviors of customers. Distinguishing EDM features are its particular focus on educational data and problems, both theoretical (e.g., investigating a learning hypothesis) and practical (e.g., improving a learning tool). Furthermore, EDM makes a methodological contribution by developing and researching data mining techniques for educational applications. Typical steps in an EDM project include data acquisition, data preprocessing (e.g., data "cleaning"), data mining, and validation of results.

Question: What is the difference between data mining and data analysis?


Design of a 1.8V-input 6.5V-output digital level shifter for trimming application. A 1.8V input, 6.5V output step-up digital level shifter is proposed. It is based on contention mitigated digital level shifter (CMLS) but it uses single supply only instead of two. It uses a complete, stand-alone, and unregulated voltage charge pump circuit that generates a 6.5V output voltage from a 1.8V input. Charge pump output is then used as high-side supply (VDDH) of the step-up level shifter, while the low-side supply (VDDl) is externally-supplied 1.8V. Compared to the conventional level shifter where two separate supply voltages (VDDl and VDDH) are required, the proposed design uses a single supply to shifted-up a digital signals. This technique simplifies the user-end power supply requirement of the system, thus the proposed level shifter design is applicable in many low-power and power-constraint applications such as in mobile and portable devices application. The proposed design is implemented using 0.35-μm TSMC CMOS technology. The schematic and the nestlist have been designed and extracted using LTSPICE tool; while the simulation has been carried out using the HSPICE circuit simulator.

Question: How to design a digital level shifter?


Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait. An active ankle-foot orthoses (AAFO) is presented where the impedance of the orthotic joint is modulated throughout the walking cycle to treat drop-foot gait. During controlled plantar flexion, a biomimetic torsional spring control is applied where orthotic joint stiffness is actively adjusted to minimize forefoot collisions with the ground. Throughout late stance, joint impedance is minimized so as not to impede powered plantar flexion movements, and during the swing phase, a torsional spring-damper control lifts the foot to provide toe clearance. To assess the clinical effects of variable-impedance control, kinetic and kinematic gait data were collected on two drop-foot participants wearing the AAFO. For each participant, zero, constant, and variable impedance control strategies were evaluated and the results were compared to the mechanics of three age, weight, and height matched normals. We find that actively adjusting joint impedance reduces the occurrence of slap foot allows greater powered plantar flexion and provides for less kinematic difference during swing when compared to normals. These results indicate that a variable-impedance orthosis may have certain clinical benefits for the treatment of drop-foot gait compared to conventional ankle-foot orthoses having zero or constant stiffness joint behaviors.

Question: What is the clinical benefits of a variable-impedance ankle-foot orthosis?


Business Process Design by Reusing Business Process Fragments from the Cloud. The constant development of technologies forces companies to be more innovative in order to stay competitive. In fact, designing a process from scratch is time consuming, error prone and costly. In this context, companies are heading to reuse process fragments when designing a new process to ensure a high degree of efficiency, with respect to delivery deadlines. However, reusing these fragments may disclose sensitive business activities, especially if these latter are deployed in an untrusted environment. In addition, companies are concerned about their user's privacy. To address these issues, we investigate how to build a new business process by reusing the safest existing fragments coming from various cloud servers, i.e. The ones that comply at best with company's preferences and policies, and offer an appropriate level of safety.

Question: How to design a business process by reusing business process fragments from the cloud?


Thin skin elastodynamics. We present a novel approach for simulating thin hyperelastic skin. Real human skin is only a few millimeters thick. It can stretch and slide over underlying body structures such as muscles, bones, and tendons, revealing rich details of a moving character. Simulating such skin is challenging because it is in close contact with the body and shares its geometry. Despite major advances in simulating elastodynamics of cloth and soft bodies for computer graphics, such methods are difficult to use for simulating thin skin due to the need to deal with non-conforming meshes, collision detection, and contact response. We propose a novel Eulerian representation of skin that avoids all the difficulties of constraining the skin to lie on the body surface by working directly on the surface itself. Skin is modeled as a 2D hyperelastic membrane with arbitrary topology, which makes it easy to cover an entire character or object. Unlike most Eulerian simulations, we do not require a regular grid and can use triangular meshes to model body and skin geometry. The method is easy to implement, and can use low resolution meshes to animate high-resolution details stored in texture-like maps. Skin movement is driven by the animation of body shape prescribed by an artist or by another simulation, and so it can be easily added as a post-processing stage to an existing animation pipeline. We provide several examples simulating human and animal skin, and skin-tight clothes.

Question: How to simulate thin skin elastodynamics?


A Low-Profile Third-Order Bandpass Frequency Selective Surface. We demonstrate a new class of low-profile frequency selective surfaces (FSS) with an overall thickness of lambda/24 and a third-order bandpass frequency response. The proposed FSS is a three layer structure composed of three metal layers, separated by two electrically thin dielectric substrates. Each layer is a two-dimensional periodic structure with sub-wavelength unit cell dimensions and periodicity. The unit cell of the proposed FSS is composed of a combination of resonant and non-resonant elements. It is shown that this arrangement acts as a spatial bandpass filter with a third-order bandpass response. However, unlike traditional third-order bandpass FSSs, which are usually obtained by cascading three identical first-order bandpass FSSs a quarter wavelength apart from one another and have thicknesses in the order of lambda/2 , the proposed structure has an extremely low profile and an overall thickness of about lambda/24 , making it an attractive choice for conformal FSS applications. As a result of the miniaturized unit cells and the extremely small overall thickness of the structure, the proposed FSS has a reduced sensitivity to the angle of incidence of the EM wave compared to traditional third-order frequency selective surfaces. The principles of operation along with guidelines for the design of the proposed FSS are presented in this paper. A prototype of the proposed third-order bandpass FSS is also fabricated and tested using a free space measurement system at C band.

Question: What is a third-order bandpass frequency selective surface?


A framework analysis of the open source software development paradigm. Open Source Software (OSS) has become the subject of much commercial interest of late. Certainly, OSS seems to hold much promise in addressing the core issues of the software crisis, namely that of software taking too long to develop, exceeding its budget, and not working very well. Indeed, there have been several examples of significant OSS success stories—the Linux operating system, the Apache web server, the BIND domain name resolution utility, to name but a few. However, little by way of rigorous academic research on OSS has been conducted to date. In this study, a framework was derived from two previous frameworks which have been very influential in the IS field, namely that of Zachman's IS architecture (ISA) and Checkland's CATWOE framework from Soft Systems Methodology (SSM). The resulting framework is used to analyze the OSS approach in detail. The potential future of OSS research is also discussed.

Question: What is the open source software development paradigm?


Prosthetic gingival reconstruction in a fixed partial restoration. Part 1: introduction to artificial gingiva as an alternative therapy.. The Class III defect environment entails a vertical and horizontal deficiency in the edentulous ridge. Often, bone and soft tissue surgical procedures fall short of achieving a natural esthetic result. Alternative surgical and restorative protocols for these types of prosthetic gingival restorations are presented in this three-part series, which highlights the diagnostic and treatment aspects as well as the lab and maintenance challenges. A complete philosophical approach involves both a biologic understanding of the limitations of the hard and soft tissue healing process as well as that of multiple adjacent implants in the esthetic zone. These limitations may often necessitate the use of gingiva-colored "pink" restorative materials and essential preemptive planning via three-dimensional computer-aided design/computer-assisted manufacture to achieve the desired esthetic outcome. The present report outlines a rationale for consideration of artificial gingiva when planning dental prostheses. Prosthetic gingiva can overcome the limitations of grafting and should be a consideration in the initial treatment plan. (Int J Periodontics Restorative Dent 2009;29:471-477.).

Question: What is the difference between a fixed partial denture and a fixed complete denture?


An efficient deep neural networks training framework for robust face recognition. In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization. A deep architecture based on the softmax loss function is designed to initialize the DNN. 2) The adaptive fine-tuning. Based on the trained model, a set of high-quality triplet samples is generated and used to fine-tune the network, where an adaptive triplet loss function is introduced to improve the discriminative ability of DNN. Experimental results show that, the model obtained by the proposed DNN training framework achieves 97.3% accuracy on the LFW benchmark with low training complexity, which verifies the efficiency and effectiveness of the proposed framework.

Question: How to train a deep neural network for face recognition?


The Influence of Context on Dialogue Act Recognition. This article presents a deep analysis of the influence of context information on dialogue act recognition. We performed experiments on the annotated subsets of three different corpora: the widely explored Switchboard Dialog Act Corpus, as well as the unexplored LEGO and Cambridge Restaurant corpora. In contrast with previous work, especially in what concerns the Switchboard corpus, we used an event-based classification approach, using Support Vector Machines (SVMs), instead of the more common sequential approaches, such as Hidden Markov Models (HMMs). We opted for such an approach so that we could control the amount of provided context information and, thus, explore its range of influence. Our base features consist of n-grams, punctuation, and whwords. Context information is obtained from previous utterances and provided in three ways – n-grams, n-grams tagged with relative position, and dialogue act classifications. A comparative study was conducted to evaluate the performance of the three approaches. From it, we were able to assess the importance of context information on dialogue act recognition, as well as its range of influence for each of the three selected representations. In addition to the conclusions originated by the analysis, this work also produced results that advance the state-of-the-art, especially considering previous work on the Switchboard corpus. Furthermore, since, to our knowledge, the Email addresses: eugenio.ribeiro@l2f.inesc-id.pt (Eugénio Ribeiro), ricardo.ribeiro@inesc-id.pt (Ricardo Ribeiro), david.matos@inesc-id.pt (David Martins de Matos) Preprint submitted to Computer Speech and Language June 3, 2015 ar X iv :1 50 6. 00 83 9v 1 [ cs .C L ] 2 J un 2 01 5 remaining datasets had not been previously explored for this task, our experiments can be used as baselines for future work on those corpora.

Question: What is the influence of context information on dialogue act recognition?


Elephant herding optimization algorithm for support vector machine parameters tuning. Classification is part of various applications and it is an important problem that represents active research topic. Support vector machine is one of the widely used and very powerful classifier. The accuracy of support vector machine highly depends on learning parameters. Optimal parameters can be efficiently determined by using swarm intelligence algorithms. In this paper, we proposed recent elephant herding optimization algorithm for support vector machine parameter tuning. The proposed approach is tested on standard datasets and it was compared to other approaches from literature. The results of computational experiments show that our proposed algorithm outperformed genetic algorithms and grid search considering accuracy of classification.

Question: What is the accuracy of support vector machine?


Describing Videos by Exploiting Temporal Structure. Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.

Question: How to describe videos using recurrent neural networks?


Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour. Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.

Question: How to identify learning styles?


Edge Agreement: Graph-Theoretic Performance Bounds and Passivity Analysis. This work explores the properties of the edge variant of the graph Laplacian in the context of the edge agreement problem. We show that the edge Laplacian, and its corresponding agreement protocol, provides a useful perspective on the well-known node agreement, or the consensus algorithm. Specifically, the dynamics induced by the edge Laplacian facilitates a better understanding of the role of certain subgraphs, e.g., cycles and spanning trees, in the original agreement problem. Using the edge Laplacian, we proceed to examine graph-theoretic characterizations of the H2 and H∞ performance for the agreement protocol. These results are subsequently applied in the contexts of optimal sensor placement for consensus-based applications. Finally, the edge Laplacian is employed to provide new insights into the nonlinear extension of linear agreement to agents with passive dynamics.

Question: What is the edge Laplacian?


Normalizing Flows on Riemannian Manifolds. We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as used in protein folding, robot limbs, gene-expression) and in general directional statistics. In spite of the multitude of algorithms available for density estimation in the Euclidean spaces R that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds. We revisit techniques related to homeomorphisms from differential geometry for projecting densities to sub-manifolds and use it to generalize the idea of normalizing flows to more general Riemannian manifolds. The resulting algorithm is scalable, simple to implement and suitable for use with automatic differentiation. We demonstrate concrete examples of this method on the n-sphere S. In recent years, there has been much interest in applying variational inference techniques to learning large scale probabilistic models in various domains, such as images and text [1, 2, 3, 4, 5, 6]. One of the main issues in variational inference is finding the best approximation to an intractable posterior distribution of interest by searching through a class of known probability distributions. The class of approximations used is often limited, e.g., mean-field approximations, implying that no solution is ever able to resemble the true posterior distribution. This is a widely raised objection to variational methods, in that unlike MCMC, the true posterior distribution may not be recovered even in the asymptotic regime. To address this problem, recent work on Normalizing Flows [7], Inverse Autoregressive Flows [8], and others [9, 10] (referred collectively as normalizing flows), focused on developing scalable methods o

Question: What is a normalizing flow?


Automatic Retrieval and Clustering of Similar Words. Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.

Question: How to evaluate a thesaurus?


Blurred boundaries: the therapeutics and politics of medical marijuana.. For 5 millennia, Cannabis sativa has been used throughout the world medically, recreationally, and spiritually. From the mid-19th century to the 1930s, American physicians prescribed it for a plethora of indications, until the federal government started imposing restrictions on its use, culminating in 1970 with the US Congress classifying it as a Schedule I substance, illegal, and without medical value. Simultaneous with this prohibition, marijuana became the United States' most widely used illicit recreational drug, a substance generally regarded as pleasurable and relaxing without the addictive dangers of opioids or stimulants. Meanwhile, cannabis never lost its cachet in alternative medicine circles, going mainstream in 1995 when California became the first of 16 states to date to legalize its medical use, despite the federal ban. Little about cannabis is straightforward. Its main active ingredient, δ-9-tetrahydrocannabinol, was not isolated until 1964, and not until the 1990s were the far-reaching modulatory activities of the endocannabinoid system in the human body appreciated. This system's elucidation raises the possibility of many promising pharmaceutical applications, even as draconian federal restrictions that hamstring research show no signs of softening. Recreational use continues unabated, despite growing evidence of marijuana's addictive potential, particularly in the young, and its propensity for inducing and exacerbating psychotic illness in the susceptible. Public approval drives medical marijuana legalization efforts without the scientific data normally required to justify a new medication's introduction. This article explores each of these controversies, with the intent of educating physicians to decide for themselves whether marijuana is panacea, scourge, or both. PubMed searches were conducted using the following keywords: medical marijuana, medical cannabis, endocannabinoi

Question: What is the difference between medical marijuana and medical cannabis?


Design, fabrication and preliminary results of a novel anthropomorphic hand for humanoid robotics: RCH-1. Among social infrastructure technologies, robot technology (RT) is expected to play an important role in solving the problems of both decrease of birth rate and increase of elderly people in the 21st century, specially but not only in Japan. In order to achieve this objective, the new generation of personal robots should be capable of a natural communication with humans by expressing human-like emotion. In this sense, human hands play a fundamental role in communication, because they have grasping, sensing and emotional expression ability. This paper presents the recent results of the collaboration between the Takanishi Lab of Waseda University, Tokyo, Japan, and the Arts Lab of Scuola Superiore Sant'Anna, Pisa, Italy, and ROBOCASA. In this paper, the development of a novel anthropomorphic hand for humanoid robotics RCH-1 (ROBOCASA Hand No.1) and its integration into a humanoid robotic platform, named WE-4RII (Waseda Eye No.4 Refined II) is presented.

Question: What is the name of the robot?


Friends and neighbors on the Web. The Internet has become a rich and large repository of information about us as individuals. Anything from the links and text on a user's homepage to the mailing lists the user subscribes to are reflections of social interactions a user has in the real world. In this paper we devise techniques to mine this information in order to predict relationships between individuals. Further we show that some pieces of information are better indicators of social connections than others, and that these indicators vary between user populations and provide a glimpse into the social lives of individuals in different communities. Our techniques provide potential applications in automatically inferring real-world connections and discovering, labeling, and characterizing communities.

Question: What is the size of the internet?


Text Segmentation with Topic Modeling and Entity Coherence. This paper describes a system which uses entity and topic coherence for improved Text Segmentation (TS) accuracy. First, Linear Dirichlet Allocation (LDA) algorithm was used to obtain topics for sentences in the document. We then performed entity mapping across a window in order to discover the transition of entities within sentences. We used the information obtained to support our LDA-based boundary detection for proper boundary adjustment. We report the significance of the entity coherence approach as well as the superiority of our algorithm over existing works.

Question: How to segment text?


VHDL Implementation of 8-Bit ALU. In this paper VHDL implementation of 8-bit arithmetic logic unit (ALU) is presented. The design was implemented using VHDL Xilinx Synthesis tool ISE 13.1 and targeted for Spartan device. ALU was designed to perform arithmetic operations such as addition and subtraction using 8-bit fast adder, logical operations such as AND, OR, XOR and NOT operations, 1's and 2's complement operations and compare. ALU consist of two input registers to hold the data during operation, one output register to hold the result of operation, 8-bit fast adder with 2's complement circuit to perform subtraction and logic gates to perform logical operation. The maximum propagation delay is 13.588ns and power dissipation is 38mW. The ALU was designed for controller used in network interface card.

Question: What is the maximum propagation delay of the ALU?


RFID Systems: A Survey on Security Threats and Proposed Solutions. Low-cost Radio Frequency Identification (RFID) tags affixed to consumer items as smart labels are emerging as one of the most pervasive computing technology in history. This can have huge security implications. The present article surveys the most important technical security challenges of RFID systems. We first provide a brief summary of the most relevant standards related to this technology. Next, we present an overview about the state of the art on RFID security, addressing both the functional aspects and the security risks and threats associated to its use. Finally, we analyze the main security solutions proposed until date.

Question: What is the state of the art on RFID security?


Valid items for screening dysphagia risk in patients with stroke: a systematic review.. BACKGROUND AND PURPOSE Screening for dysphagia is essential to the implementation of preventive therapies for patients with stroke. A systematic review was undertaken to determine the evidence-based validity of dysphagia screening items using instrumental evaluation as the reference standard.   METHODS Four databases from 1985 through March 2011 were searched using the terms cerebrovascular disease, stroke deglutition disorders, and dysphagia. Eligibility criteria were: homogeneous stroke population, comparison to instrumental examination, clinical examination without equipment, outcome measures of dysphagia or aspiration, and validity of screening items reported or able to be calculated. Articles meeting inclusion criteria were evaluated for methodological rigor. Sensitivity, specificity, and predictive capabilities were calculated for each item.   RESULTS Total source documents numbered 832; 86 were reviewed in full and 16 met inclusion criteria. Study quality was variable. Testing swallowing, generally with water, was the most commonly administered item across studies. Both swallowing and nonswallowing items were identified as predictive of aspiration. Neither swallowing protocols nor validity were consistent across studies.   CONCLUSIONS Numerous behaviors were found to be associated with aspiration. The best combination of nonswallowing and swallowing items as well as the best swallowing protocol remains unclear. Findings of this review will assist in development of valid clinical screening instruments.

Question: What is the best combination of nonswallowing and swallowing items as well as the best swallowing protocol?


Stretching Agile to fit CMMI Level 3 - the story of creating MSF for CMMI Process Improvement at Microsoft Corporation. Agile practitioners pride themselves on highly productive, responsive, low ceremony, lightweight, tacit knowledge processes with little waste, adaptive planning and frequent iterative delivery of value. It is often assumed that CMMI compliant processes need to be heavyweight, bureaucratic, slow moving, high ceremony and plan driven. Agile developers often skeptically perceive formal process improvement initiatives as management generated inefficiency that gets in the way of productivity. At Microsoft, we've adopted the teachings of W. Edwards Deming and stretched our MSF for Agile Software Development method to fit the requirements for CMMI Level 3. The resultant MSF for CMMI Process Improvement is a highly iterative, adaptive planning method, light on documentation, and heavily automated through tooling. It enables management and organization of software engineering through use of agile metrics such as velocity and cumulative flow but with an added dimension of an understanding of variation – adapted from Deming's teachings. This is the story of how mixing Deming with Agile produced a lightweight CMMI solution for .Net developers everywhere.

Question: What is CMMI?


A machine learning approach for medication adherence monitoring using body-worn sensors. One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.

Question: What is a medication adherence monitoring system?


Topology Aware Fully Convolutional Networks for Histology Gland Segmentation. [1] Sirinukunwattana et al. Gland Segmentation in colon histology images: the GlaS contest, arXiv 2016 [2] Long et al. Fully convolutional networks for semantic segmentation, CVPR 2015 [3] Chend et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs, arXiv 2014 [4] Zheng et al. Conditional random fields as recurrent neural networks, ICCV 2015 Alexnet-FCN (6 layers) FCN-8s (16 layers) UNet (23 layers) DN (26 layers) 0.93 0.79 0.86 0.83

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


Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain. Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

Question: How does learning from demonstration work?


Unsupervised image segmentation using convolutional autoencoder with total variation regularization as preprocessing. Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation and enhance their performance. Our approach relies on a convolutional autoencoder (CAE) with the total variation loss (TVL) for unsupervised learning. We show that, after our CAE-TVL preprocessing step, the over-segmentation effect is significantly reduced using the same unsupervised image segmentation methods. We evaluate our approach using the BSDS500 image segmentation benchmark dataset and show the performance enhancement introduced by our approach in terms of both increased segmentation accuracy and reduced computation time. We examine the robustness of the trained CAE and show that it is directly applicable to other natural scene images.

Question: How to segment an image using convolutional autoencoder with total variation regularization as preprocessing?


Learner Engagement Measurement and Classification in 1:1 Learning. We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.

Question: What is the accuracy of the classifier?


Incremental Algorithms for Hierarchical Classification. We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function, the H-loss, implementing the simple intuition that additional mistakes in the subtree of a mistaken class should not be charged for. Based on a probabilistic data model introduced in earlier work, we derive the Bayes-optimal classifier for the H-loss. We then empirically compare two incremental approximations of the Bayes-optimal classifier with a flat SVM classifier and with classifiers obtained by using hierarchical versions of the Perceptron and SVM algorithms. The experiments show that our simplest incremental approximation of the Bayes-optimal classifier performs, after just one training epoch, nearly as well as the hierarchical SVM classifier (which performs best). For the same incremental algorithm we also derive an H-loss bound showing, when data are generated by our probabilistic data model, exponentially fast convergence to the H-loss of the hierarchical classifier based on the true model parameters.

Question: Incremental Algorithms for Hierarchical Classification


An Interactive Web Mapping Visualization of Urban Air Quality Monitoring Data of China. In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this dataset, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations.

Question: What is the air quality in china?


Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures. Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.

Question: How to improve sentiment analysis?


Key Challenges for the Smart City: Turning Ambition into Reality. Smart city is a label internationally used by cities, researchers and technology providers with different meanings. As a popular concept it is widely used by city administrators and politicians to promote their efforts. It is hard enough to find a good definition for smart cities, but even harder to find a trustworthy description of what it takes to become a smart city and how a city administration is impacted. This paper sets out to investigate how a city, aspiring to become a 'smart city', can manage the organization to realize that ambition. Specifically, the paper describes the case of the City of Ghent, Belgium, and the key challenges it has been facing in its ongoing efforts to be a smart city. Based on in depth interviews with city representatives six key challenges for smart city realization were identified and tested with a panel of representatives from five European cities that are in the process of becoming a smart city. This way, the study contributes to a more professional pursuit of the smart city concept.

Question: What are the key challenges for the smart city?


Focus on Formative Feedback. This paper reviews the corpus of research on feedback, with a particular focus on formative feedback—defined as information communicated to the learner that is intended to modify the learner's thinking or behavior for the purpose of improving learning. According to researchers in the area, formative feedback should be multidimensional, nonevaluative, supportive, timely, specific, credible, infrequent, and genuine (e.g., Brophy, 1981; Schwartz & White, 2000). Formative feedback is usually presented as information to a learner in response to some action on the learner's part. It comes in a variety of types (e.g., verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (e.g., immediately following an answer, after some period of time has elapsed). Finally, there are a number of variables that have been shown to interact with formative feedback's success at promoting learning (e.g., individual characteristics of the learner and aspects of the task). All of these issues will be discussed in this paper. This review concludes with a set of guidelines for generating formative feedback.

Question: What is formative feedback?


Machine Learning for the Internet of Things Security: A Systematic Review. Internet of things (IoT) is nowadays one of the fastest growing technologies for both private and business purposes. Due to a big number of IoT devices and their rapid introduction to the market, security of things and their services is often not at the expected level. Recently, machine learning algorithms, techniques, and methods are used in research papers to enhance IoT security. In this paper, we systematically review the state-of-the art to classify the research on machine learning for the IoT security. We analysed the primary studies, identify types of studies and publication fora. Next, we have extracted all machine learning algorithms and techniques described in primary studies, and identified the most used ones to tackle IoT security issues. We classify the research into three main categories (intrusion detection, authentication and other) and describe the primary studies in detail to analyse existing relevant works and propose topics for future research.

Question: What is the state of the art in machine learning for the internet of things security?


Reporting and Methods in Clinical Prediction Research: A Systematic Review. BACKGROUND We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures.   METHODS AND FINDINGS We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%). Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies). The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%). A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%). Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively.   CONCLUSIONS The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.

Question: What is the difference between a prediction model and a prediction study?


H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes. Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

Question: How large is the canadian military?


Automated classification of security requirements. Requirement engineers are not able to elicit and analyze the security requirements clearly, that are essential for the development of secure and reliable software. Proper identification of security requirements present in the Software Requirement Specification (SRS) document has been a problem being faced by the developers. As a result, they are not able to deliver the software free from threats and vulnerabilities. Thus, in this paper, we intend to mine the descriptions of security requirements present in the SRS document and thereafter develop the classification models. The security-based descriptions are analyzed using text mining techniques and are then classified into four types of security requirements viz. authentication-authorization, access control, cryptography-encryption and data integrity using J48 decision tree method. Corresponding to each type of security requirement, a prediction model has been developed. The effectiveness of the prediction models is evaluated against requirement specifications collected from 15 projects which have been developed by MS students at DePaul University. The result analysis indicated that all the four models have performed very well in predicting their respective type of security requirements.

Question: What is the classification of security requirements?