title: Low Cost Attacks on Tamper Resistant Devices

abstract: There has been considerable recent interest in the level of tamper resistance that can be provided by low cost devices such as smart-cards. It is known that such devices can be reverse engineered using chip testing equipment, but a state of the art semiconductor laboratory costs millions of dollars. In this paper, we describe a number of attacks that can be mounted by opponents with much shallower pockets. Three of them involve special (but low cost) equipment: differential fault analysis, chip rewriting, and memory remanence. There are also attacks based on good old fashioned protocol failure which may not require any special equipment at all. We describe and give examples of each of these. Some of our attacks are significant improvements on the state of the art; others are useful cautionary tales. Together, they show that building tamper resistant devices, and using them effectively, is much harder than it looks.


title: Advantages of the Root Submergence Technique for Pontic Site Development in Esthetic Implant Therapy

abstract: A single-tooth implant in the esthetic region has good potential for success, but it is still challenging to restore multiple-tooth defects with implant-supported prostheses that resemble the natural dentition. This article suggests a strategy to provide a more predictable protocol for esthetic implant treatment for multiple-tooth defects using the root submergence technique (RST). By maintaining the natural tooth root with the RST, a much greater amount of surrounding tissue may be preserved than with the commonly used socket preservation technique, which almost always leads to crestal bone resorption and thus reduction of the height of the interdental papillae and width of the edentulous ridge. RST instead maintains the natural attachment apparatus of the tooth in the pontic site, which in turn allows for complete preservation of the alveolar bone frame and assists in the creation of an esthetic result in adjacent multiple-tooth-replacement cases. In situations with periodontal bone loss, orthodontic extrusion is required to create the underlying bone support for the papilla that is necessary to guarantee predictability. (Int J Periodontics Restorative Dent 2007;27:521‐527.)


Data-Efficient Policy Search using PILCO and Directed-Exploration


title: Submodular Optimization under Noise

abstract: We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: ::: - For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; ::: - For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; ::: -If the noise is adversarial, no non-trivial approximation guarantee can be obtained.


title: A comparative review of tone-mapping algorithms for high dynamic range video

abstract: Tone-mapping constitutes a key component within the field of high dynamic range HDR imaging. Its importance is manifested in the vast amount of tone-mapping methods that can be found in the literature, which are the result of an active development in the area for more than two decades. Although these can accommodate most requirements for display of HDR images, new challenges arose with the advent of HDR video, calling for additional considerations in the design of tone-mapping operators TMOs. Today, a range of TMOs exist that do support video material. We are now reaching a point where most camera captured HDR videos can be prepared in high quality without visible artifacts, for the constraints of a standard display device. In this report, we set out to summarize and categorize the research in tone-mapping as of today, distilling the most important trends and characteristics of the tone reproduction pipeline. While this gives a wide overview over the area, we then specifically focus on tone-mapping of HDR video and the problems this medium entails. First, we formulate the major challenges a video TMO needs to address. Then, we provide a description and categorization of each of the existing video TMOs. Finally, by constructing a set of quantitative measures, we evaluate the performance of a number of the operators, in order to give a hint on which can be expected to render the least amount of artifacts. This serves as a comprehensive reference, categorization and comparative assessment of the state-of-the-art in tone-mapping for HDR video.


title: Incremental probabilistic latent semantic analysis for automatic question recommendation

abstract: With the fast development of web 2.0, user-centric publishing and knowledge management platforms, such as Wiki, Blogs, and Q & A systems attract a large number of users. Given the availability of the huge amount of meaningful user generated content, incremental model based recommendation techniques can be employed to improve users' experience using automatic recommendations. In this paper, we propose an incremental recommendation algorithm based on Probabilistic Latent Semantic Analysis (PLSA). The proposed algorithm can consider not only the users' long-term and short-term interests, but also users' negative and positive feedback. We compare the proposed method with several baseline methods using a real-world Question & Answer website called Wenda. Experiments demonstrate both the effectiveness and the efficiency of the proposed methods.


title: Image-Based Localization Using LSTMs for Structured Feature Correlation

abstract: In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output, which play the role of a structured dimensionality reduction on the feature vector, leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based and SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces, where classic SIFT-based methods fail.


Ladder to Success – Eliciting Project Managers’ Perceptions of IS Project Success Criteria


title: Vehicular Ad-Hoc Networks (VANETs) - An Overview and Challenges

abstract: Vehicu lar ad-hoc networks (VANETs) technology has emerged as an important research area over the last few years. Being ad-hoc in nature, VA NET is a type of networks that is created from the concept of establishing a network of cars for a specific need or situation. VA NETs have now been established as reliable networks that vehicles use for co mmunication purpose on highways or urban environments. Along with the benefits, there arise a large number of challenges in VANET such as provisioning of QoS, h igh connectivity and bandwidth and security to vehicle and individual privacy. This article presents state-of-the-art of VANET and discusses the related issues. Network architecture, signal modeling and propagation mechanis m, mobility modeling, routing protocols and network security are discussed in detail. Main findings of this paper are that an efficient and robust VANET is one wh ich satisfies all design parameters such as QoS, minimu m latency, low BER and high PDR. So me key research areas and challenges in VANET are presented at the end of the paper.


title: An introduction to intertask transfer for reinforcement learning

abstract: Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difﬁcult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.


title: Fuzzy decision trees: issues and methods

abstract: Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. We present another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation. The merger utilizes existing methodologies in both areas to full advantage, but is by no means trivial. In particular, knowledge inferences must be newly defined for the fuzzy tree. We propose a number of alternatives, based on rule-based systems and fuzzy control. We also explore capabilities that the new framework provides. The resulting learning method is most suitable for stationary problems, with both numerical and symbolic features, when the goal is both high knowledge comprehensibility and gradually changing output. We describe the methodology and provide simple illustrations.


title: Smart Car Parking Management System Using IoT

abstract: Proliferation in the number of vehicles is leading to problems of vehicles parking at an appropriate place especially the car parking. This indirectly leads to traffic congestion. This is because of the fact that current transportation infrastructure and car park facility are unable to cope with the arrival of large number of vehicles on the road. To alleviate the aforementioned problem, authors proposed a Smart Parking Management System that helps users to automatically find a free parking space with a smaller amount. Smart Parking involves use of Ultrasonic sensor, Arduino Uno, ESP8266-01 Wi-Fi Module, Cloud server. IOT based new parking platform enable to connect, analyze and automate data gathered from devices and execute smart parking possible. Smart parking would enable vehicle occupancy, monitoring and managing of available parking space in real-time that reducing the environmental pollution. Proposed system provides optimize usage of parking space and get considerable revenue generation.


title: Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

abstract: With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.


title: Utilization Bounds for N-Processor Rate Monotone Scheduling with Static Processor Assignment

abstract: We consider the schedulability of a set of independent periodic tasks under fixed priority preemptive scheduling on homogeneous multiprocessor systems. Assuming there is no task migration between processors and each processor schedules tasks preemptively according to fixed priorities assigned by the Rate Monotonic policy, the scheduling problem reduces to assigning the set of tasks to disjoint processors in such a way that the Monotonic policy, the scheduling problem reduces to assigning the set of tasks to disjoint processors in such a way that the schedulability of the tasks on each processor can be guaranteed. In this paper we show that the worst case achievable utilization for such systems is between n(2^{1/2}-1) and (n+1)/(1+2^{1/(n+1)}), where n stands for the number of processors. The lower bound represents 41 percent of the total system capacity and the upper bound represents 50 to 66 percent depending on n. Practicality of the lower bound is demonstrated by proving it can be achieved using a First Fit scheduling algorithm.


title: Robust Smartphone App Identification via Encrypted Network Traffic Analysis

abstract: The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. In addition, the presence or absence of particular apps on a smartphone can inform an adversary, who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data, such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices, and across different versions of apps. In addition, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps, such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions.


title: A pheromone-based utility model for collaborative foraging

abstract: Multi-agent research often borrows from biology, where remarkable examples of collective intelligence may be found. One interesting example is ant colonies? use of pheromones as a joint communication mechanism. In this paper we propose two pheromone-based algorithms for artificial agent foraging, trail-creation, and other tasks. Whereas practically all previous work in this area has focused on biologically-plausible but ad-hoc single pheromone models, we have developed a formalism which uses multiple pheromones to guide cooperative tasks. This model bears some similarity to reinforcement learning. However, our model takes advantage of symmetries common to foraging environments which enables it to achieve much faster reward propagation than reinforcement learning does. Using this approach we demonstrate cooperative behaviors well beyond the previous ant-foraging work, including the ability to create optimal foraging paths in the presence of obstacles, to cope with dynamic environments, and to follow tours with multiple waypoints.We believe that this model may be used for more complex problems still.


title: The evaluation of normalized cross correlations for defect detection

abstract: Normalized cross correlation (NCC) has been used extensively for many machine vision applications, but the traditional normalized correlation operation does not meet speed requirements for time-critical applications. In this paper, we propose a fast NCC computation for defect detection. A sum-table scheme is utilized, which allows the calculations of image mean, image variance and cross-correlation between images to be invariant to the size of template window. For an image of size M × N and a template window of size m × n, the computational complexity of the traditional NCC involves 3 ċ m ċ n ċ M ċ N additions/subtractions and 2 ċ m ċ n ċ M ċ N multiplications. The required numbers of computations of the proposed sum-table scheme can be significantly reduced to only 18 ċ M ċ N additions/subtractions and 2 ċ M ċ N multiplications.


title: A hybrid machine learning approach to network anomaly detection

abstract: Zero-day cyber attacks such as worms and spy-ware are becoming increasingly widespread and dangerous. The existing signature-based intrusion detection mechanisms are often not sufficient in detecting these types of attacks. As a result, anomaly intrusion detection methods have been developed to cope with such attacks. Among the variety of anomaly detection approaches, the Support Vector Machine (SVM) is known to be one of the best machine learning algorithms to classify abnormal behaviors. The soft-margin SVM is one of the well-known basic SVM methods using supervised learning. However, it is not appropriate to use the soft-margin SVM method for detecting novel attacks in Internet traffic since it requires pre-acquired learning information for supervised learning procedure. Such pre-acquired learning information is divided into normal and attack traffic with labels separately. Furthermore, we apply the one-class SVM approach using unsupervised learning for detecting anomalies. This means one-class SVM does not require the labeled information. However, there is downside to using one-class SVM: it is difficult to use the one-class SVM in the real world, due to its high false positive rate. In this paper, we propose a new SVM approach, named Enhanced SVM, which combines these two methods in order to provide unsupervised learning and low false alarm capability, similar to that of a supervised SVM approach. We use the following additional techniques to improve the performance of the proposed approach (referred to as Anomaly Detector using Enhanced SVM): First, we create a profile of normal packets using Self-Organized Feature Map (SOFM), for SVM learning without pre-existing knowledge. Second, we use a packet filtering scheme based on Passive TCP/IP Fingerprinting (PTF), in order to reject incomplete network traffic that either violates the TCP/IP standard or generation policy inside of well-known platforms. Third, a feature selection technique using a Genetic Algorithm (GA) is used for extracting optimized information from raw internet packets. Fourth, we use the flow of packets based on temporal relationships during data preprocessing, for considering the temporal relationships among the inputs used in SVM learning. Lastly, we demonstrate the effectiveness of the Enhanced SVM approach using the above-mentioned techniques, such as SOFM, PTF, and GA on MIT Lincoln Lab datasets, and a live dataset captured from a real network. The experimental results are verified by m-fold cross validation, and the proposed approach is compared with real world Network Intrusion Detection Systems (NIDS).


title: A Survey of Rendering Techniques in Computer Augmented Reality

abstract: Augmented Reality is a field of Virtual Reality in which virtual information is combined with real information. AR allows synthetic data to be composited with the real world. The data can be in the form of 3D geometry enabling users with special hardware devices to see a mixture of synthetic and real world objects using. The potential and applications of Augmented Reality are boundless, from the entertainment industry, training, manufacturing, robotics, aircraft maintenance, medicine, military, to tourism. The research and development of Augmented Reality software can be broadly classified into four mainstream areas: tracking and registration; timing issues; rendering techniques; and finally human ergonomics issues. This paper focuses solely on the third area - rendering techniques. Research into rendering techniques aims to achieve seamless integration of synthetic objects with a real environment such that these synthetic objects are indistinguishable from the real world environment. The aim of this paper is to review the current state of the art in Augmented Reality rendering. This includes such issues as shadows, interreflection and occlusion, and also the use of illumination techniques for producing more realistic 3D objects and providing appropriate visual cues. This review will enable researchers to target the areas which still need further work.


title: A High-Performance, Pipelined, FPGA-Based Genetic Algorithm Machine

abstract: Accelerating a genetic algorithm (GA) by implementing it in a reconfigurable field programmable gate array (FPGA) is described. The implemented GA features: random parent selection, which conserves selection circuitry; a steady-state memory model, which conserves chip area; survival of fitter child chromosomes over their less-fit parent chromosomes, which promotes evolution. A net child chromosome generation rate of one per clock cycle is obtained by pipelining the parent selection, crossover, mutation, and fitness evaluation functions. Complex fitness functions can be further pipelined to maintain a high-speed clock cycle. Fitness functions with a pipeline initiation interval of greater than one can be plurally implemented to maintain a net evaluated-chromosome throughput of one per clock cycle. Two prototypes are described: The first prototype (c. 1996 technology) is a multiple-FPGA chip implementation, running at a 1 MHz clock rate, that solves a 94-row × 520-column set covering problem 2,200× faster than a 100 MHz workstation running the same algorithm in C. The second prototype (Xilinx XVC300) is a single-FPGA chip implementation, running at a 66 MHZ clock rate, that solves a 36-residue protein folding problem in a 2-d lattice 320× faster than a 366 MHz Pentium II. The current largest FPGA (Xilinx XCV3200E) has circuitry available for the implementation of 30 fitness function units which would yield an acceleration of 9,600× for the 36-residue protein folding problem.


title: Robust Order Scheduling in the Discrete Manufacturing Industry: A Multiobjective Optimization Approach

abstract: Order scheduling is of vital importance in discrete manufacturing industries. This paper takes fashion industry as an example and discusses the robust order scheduling problem in the fashion industry. In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as preproduction events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this paper, by considering the preproduction events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multiobjective evolutionary algorithm called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider preproduction events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.


title: Beyond the filter bubble: interactive effects of perceived threat and topic involvement on selective exposure to information

abstract: We investigated participants' preferential selection of information and their attitude moderation in an online environment. Results showed that even when opposing views were presented side-to-side, people would still preferentially select information that reinforced their existing attitudes. Preferential selection of information was, however, influenced by both situational (e.g., perceived threat) and personal (e.g., topic involvement) factors. Specifically, perceived threat induced selective exposure to attitude consistent information for topics that participants had low involvement. Participants had a higher tendency to select peer user opinions in topics that they had low than high involvement, but only when there was no perception of threat. Overall, participants' attitudes were moderated after being exposed to diverse views, although high topic involvement led to higher resistance to such moderation. Perceived threat also weakened attitude moderation, especially for low involvement topics. Results have important implication to the potential effects of "information bubble" - selective exposure can be induced by situational and personal factors even when competing views are presented side-by-side.


title: Active Object Localization with Deep Reinforcement Learning

abstract: We present an active detection model for localizing objects in scenes. The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object. This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects following top-down reasoning. The proposed localization agent is trained using deep reinforcement learning, and evaluated on the Pascal VOC 2007 dataset. We show that agents guided by the proposed model are able to localize a single instance of an object after analyzing only between 11 and 25 regions in an image, and obtain the best detection results among systems that do not use object proposals for object localization.


title: Unconscious intuition or conscious analysis? Critical questions for the Deliberation-Without-Attention paradigm

abstract: The Deliberation without Attention (DWA) effect refers to apparent improvements in decision-making following a period of distraction. It has been presented as evidence for beneficial unconscious cognitive processes. We identify two major concerns with this claim: first, as these demonstrations typically involve subjective preferences, the effects of distraction cannot be objectively assessed as beneficial; second, there is no direct evidence that the DWA manipulation promotes unconscious decision processes. We describe two tasks based on the DWA paradigm in which we found no evidence that the distraction manipulation led to decision processes that are subjectively unconscious, nor that it reduced the influence of presentation order upon performance. Crucially, we found that a lack of awareness of decision process was associated with poorer performance, both in terms of subjective preference measures used in traditional DWA paradigm and in an equivalent task where performance can be objectively assessed. Therefore, we argue that reliance on conscious memory itself can explain the data. Thus the DWA paradigm is not an adequate method of assessing beneficial unconscious thought.


title: Performance capture from sparse multi-view video

abstract: This paper proposes a new marker-less approach to capturing human performances from multi-view video. Our algorithm can jointly reconstruct spatio-temporally coherent geometry, motion and textural surface appearance of actors that perform complex and rapid moves. Furthermore, since our algorithm is purely meshbased and makes as few as possible prior assumptions about the type of subject being tracked, it can even capture performances of people wearing wide apparel, such as a dancer wearing a skirt. To serve this purpose our method efficiently and effectively combines the power of surface- and volume-based shape deformation techniques with a new mesh-based analysis-through-synthesis framework. This framework extracts motion constraints from video and makes the laser-scan of the tracked subject mimic the recorded performance. Also small-scale time-varying shape detail is recovered by applying model-guided multi-view stereo to refine the model surface. Our method delivers captured performance data at high level of detail, is highly versatile, and is applicable to many complex types of scenes that could not be handled by alternative marker-based or marker-free recording techniques.


title: Character-based feature extraction with LSTM networks for POS-tagging task

abstract: In this paper we describe a work in progress on designing the continuous vector space word representations able to map unseen data adequately. We propose a LSTM-based feature extraction layer that reads in a sequence of characters corresponding to a word and outputs a single fixed-length real-valued vector. We then test our model on a POS tagging task on four typologically different languages. The results of the experiments suggest that the model can offer a solution to the out-of-vocabulary words problem, as in a comparable setting its OOV accuracy improves over that of a state of the art tagger.


title: Circuit Techniques for 1.5-V Power Supply Flash Memory

abstract: We describe circuit techniques for a Flash memory which operates with a V/sub DD/ of 1.5 V. For the interface between the peripheral circuits and the memory core circuits, two types of level shifter circuits are proposed which convert a V/sub DD/ level signal into the high voltage signals needed for high performance. In order to improve the read performance at a low V/sub DD/, a new self-bias bitline voltage sensing scheme is described. This circuit greatly reduces the delay's dependence on bitline capacitance and achieves 19 ns reduction of the sense delay at low voltages. Multilevel storage sensing with this circuit is also discussed.


title: A 64-bit decimal floating-point adder

abstract: Due to rapid growth in financial, commercial, and Internet-based applications, there is an increasing desire to allow computers to operate on both binary and decimal floating-point numbers. Consequently, specifications for decimal floating-point support are being added to the IEEE-754 Standard for Floating-Point Arithmetic. In this paper, we present the design and implementation of a decimal floating-point adder that is compliant with the current draft revision of this standard. The adder supports operations on 64-bit (16-digit) decimal floating-point operands. We provide synthesis results indicating the estimated area and delay for our design when it is pipelined to various depths.


title: A review of non-isolated bidirectional dc-dc converters for energy storage systems

abstract: There is a growing interest in bidirectional dc-dc converters for interface battery with energy source and load. This paper provides a comprehensive review of non-isolated bidirectional dc-dc converter topologies. The classification and description of each type presented is based on the features and applications. This review paper is intended as a convenient reference to future non-isolated converter users. The most promising converters in terms of their simplicity, flexibility, and high efficiency are singled out.


Crop assessment using remote sensing - Part-II: Crop condition and yield assessment.


title: Phylogenetic Grammar Induction

abstract: We present an approach to multilingual grammar induction that exploits a phylogeny-structured model of parameter drift. Our method does not require any translated texts or token-level alignments. Instead, the phylogenetic prior couples languages at a parameter level. Joint induction in the multilingual model substantially outperforms independent learning, with larger gains both from more articulated phylogenies and as well as from increasing numbers of languages. Across eight languages, the multilingual approach gives error reductions over the standard monolingual DMV averaging 21.1% and reaching as high as 39%.


title: Breaking Lander-Waterman’s Coverage Bound

abstract: Lander-Waterman’s coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collector’s problem which proves that for such genome, the total number of bases to be sequenced should be O(G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Waterman’s result and prove that by combining the sequencing and computing processes, one can re-sequence the whole genome with as low as O(G) sequenced bases in total. Our approach also dramatically reduces the required computational power for the combined process. Simulation results are performed on real genomes with different sequencing error rates. The results support our theory predicting the log G improvement on coverage bound and corresponding reduction in the total number of bases required to be sequenced.


title: Three-Phase Three-Level DC/DC Converter for High Input Voltage and High Power Applications-Adopting Symmetrical Duty Cycle Control

abstract: Three-phase dc/dc converters have the superior characteristics including lower current rating of switches, the reduced output filter requirement, and effective utilization of transformers. To further reduce the voltage stress on switches, three-phase three-level (TPTL) dc/dc converters have been investigated recently; however, numerous active power switches result in a complicated configuration in the available topologies. Therefore, a novel TPTL dc/dc converter adopting a symmetrical duty cycle control is proposed in this paper. Compared with the available TPTL converters, the proposed converter has fewer switches and simpler configuration. The voltage stress on all switches can be reduced to the half of the input voltage. Meanwhile, the ripple frequency of output current can be increased significantly, resulting in a reduced filter requirement. Experimental results from a 540-660-V input and 48-V/20-A output are presented to verify the theoretical analysis and the performance of the proposed converter.


title: Pedestrian recognition using high-definition LIDAR

abstract: Pedestrian detection is one of the key technologies for autonomous driving systems and driving assistance systems. To predict the possibility of a future collision, these systems have to accurately recognize pedestrians as far away as possible. Moreover, the function to detect not only people walking but also people who are standing near the road is also required. This paper proposes a method for recognizing pedestrians by using a high-definition LIDAR. Two novel features are introduced to improve the classification performance. One is the slice feature, which represents the profile of a human body by widths at the different height levels. The other is the distribution of the reflection intensities of points measured on the target. This feature can contribute to the pedestrian identification because each substance has its own unique reflection characteristics in the near-infrared region of the laser beam. Our approach applies a support vector machine (SVM) to train a classifier from these features. The classifier discriminates the clusters of the laser range data that are the pedestrian candidates, generated by pre-processing. A quantitative evaluation in a road environment confirms the effectiveness of the proposed method.


title: An early fire-detection method based on image processing

abstract: The paper presents an early fire-alarm raising method based on video processing. The basic idea of the proposed of fire-detection is to adopt a RGB (red, green, blue) model based chromatic and disorder measurement for extracting fire-pixels and smoke-pixels. The decision function of fire-pixels is mainly deduced by the intensity and saturation of R component. The extracted fire-pixels will be verified if it is a real fire by both dynamics of growth and disorder, and further smoke. Based on iterative checking on the growing ratio of flames, a fire-alarm is given when the alarm-raising condition is met. Experimental results show that the developed technique can achieve fully automatic surveillance of fire accident with a lower false alarm rate and thus is very attractive for the important military, social security, commercial applications, and so on, at a general cost.


title: Toward an Ontology Architecture for Cyber-Security Standards

abstract: The rapid growth in magnitude and complexity of cyber-security information and event management (CSIEM) has ignited a trend toward security automation and information exchange standards. Making Security Measurable (MSM) references a collection of open community standards for the common enumeration, expression and reporting of cyber-security-related information. While MSM-related standards are valuable for enabling security automation; insufficient vocabulary management and data interoperability methods as well as domain complexity that exceeds current representation capabilities impedes the adoption of these important standards. This paper describes an Agile, ontology architecture-based approach for improving the ability to represent, manage, and implement MSM-related standards. Initial cross-standard analysis revealed enough common concepts to warrant four ontologies that are reusable across standards. This reuse will simplify standards-based data interoperability. Further, early prototyping enabled us to streamline vocabulary management processes and demonstrate the ability to represent complex domain semantics in OWL ontologies.


title: Image steganography method using K-means clustering and encryption techniques

abstract: Steganography involves hiding of text, image or any sensitive information inside another image, video or audio in such a way that an attacker will not be able to detect its presence. Steganography is, many times, confused with cryptography as both the techniques are used to secure information. The difference lies in the fact that steganography hides the data so that nothing appears out of ordinary while cryptography encrypts the text, making it difficult for an outsider to infer anything from it even if they do attain the encrypted text. Both of them are combined to increase the security against various malicious attacks. Image Steganography uses an image as the cover media to hide the secret message. In this paper, we propose an image steganography method which clusters the image into various segments and hides data in each of the segment. Various clustering algorithms can be used for image segmentation. Segmentation involves huge set of data in the form of pixels, where each pixel further has three components namely red, green and blue. K-means clustering technique is used to get accurate results. Therefore, we use K-means clustering technique to get accurate results in a small time period.


title: Unsupervised word segmentation and lexicon discovery using acoustic word embeddings

abstract: In settings where only unlabeled speech data is available, speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text. A similar problem is faced when modeling infant language acquisition. In these cases, categorical linguistic structure needs to be discovered directly from speech audio. We present a novel unsu-pervised Bayesian model that segments unlabeled speech and clusters the segments into hypothesized word groupings. The result is a complete unsupervised tokenization of the input speech in terms of discovered word types. In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional acoustic vector space. The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this space while jointly performing segmentation. We report word error rates in a small-vocabulary connected digit recognition task by mapping the unsupervised decoded output to ground truth transcriptions. The model achieves around 20% error rate, outperforming a previous HMM-based system by about 10% absolute. Moreover, in contrast to the baseline, our model does not require a pre-specified vocabulary size.


title: Learning to rank in person re-identification with metric ensembles

abstract: We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-1 recognition rates from 40% to 50% on the iLIDS benchmark, 16% to 18% on the PRID2011 benchmark, 43% to 46% on the VIPeR benchmark, 34% to 53% on the CUHK01 benchmark and 21% to 62% on the CUHK03 benchmark.


title: Generating Natural-Language Video Descriptions Using Text-Mined Knowledge

abstract: We present a holistic data-driven technique that generates natural-language descriptions for videos. We combine the output of state-of-the-art object and activity detectors with "real-world" knowledge to select the most probable subject-verb-object triplet for describing a video. We show that this knowledge, automatically mined from web-scale text corpora, enhances the triplet selection algorithm by providing it contextual information and leads to a four-fold increase in activity identification. Unlike previous methods, our approach can annotate arbitrary videos without requiring the expensive collection and annotation of a similar training video corpus. We evaluate our technique against a baseline that does not use text-mined knowledge and show that humans prefer our descriptions 61% of the time.


title: Active Inductor Current Balancing for Interleaving Multi-Phase Buck-Boost Converter

abstract: In this paper, active inductor current balancing method for multi-phase four-switch buck-boost converter is presented. The multi-phase four-switch buck-boost converter is proposed for battery powered applications, such as automotive/truck applications, where high power dc-dc converters capable of stepping up and stepping down voltages are required. Inductor current balancing circuit based on the average current sharing bus approach is used to ensure that the inductor currents are evenly distributed. A prototype of 4-phase buck-boost converter was implemented to verify the concept. Experimental results are included to show the inductor current balancing during steady state and load transient.


title: Motion sickness: a synthesis and evaluation of the sensory conflict theory

abstract: "Motion sickness" is the general term describing a group of common nausea syndromes originally attributed to motion-induced cerebral ischemia, stimulation of abdominal organ afferents, or overstimulation of the vestibular organs of the inner ear. Seasickness, car sickness, and airsickness are commonly experienced examples. However, the identification of other variants such as spectacle sickness and flight simulator sickness in which the physical motion of the head and body is normal or even absent has led to a succession of "sensory conflict" theories that offer a more comprehensive etiologic perspective. Implicit in the conflict theory is the hypothesis that neural and (or) humoral signals originate in regions of the brain subserving spatial orientation, and that these signals somehow traverse to other centers mediating sickness symptoms. Unfortunately, our present understanding of the neurophysiological basis of motion sickness is incomplete. No sensory conflict neuron or process has yet been physiologi...


title: A fistful of bitcoins: characterizing payments among men with no names

abstract: Bitcoin is a purely online virtual currency, unbacked by either physical commodities or sovereign obligation; instead, it relies on a combination of cryptographic protection and a peer-to-peer protocol for witnessing settlements. Consequently, Bitcoin has the unintuitive property that while the ownership of money is implicitly anonymous, its flow is globally visible. In this paper we explore this unique characteristic further, using heuristic clustering to group Bitcoin wallets based on evidence of shared authority, and then using re-identification attacks (i.e., empirical purchasing of goods and services) to classify the operators of those clusters. From this analysis, we characterize longitudinal changes in the Bitcoin market, the stresses these changes are placing on the system, and the challenges for those seeking to use Bitcoin for criminal or fraudulent purposes at scale.


Asymmetric Active Learning with Imbalanced Data


title: An interpretable framework for clustering single-cell RNA-Seq datasets

abstract: BackgroundWith the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering.ResultsIn this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues. DendroSplit offers a novel perspective on the single-cell RNA-Seq clustering problem motivated by the definition of “cell type”, allowing us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. We analyze several landmark single-cell datasets, demonstrating both the method’s efficacy and computational efficiency.ConclusionDendroSplit offers a clustering framework that is comparable to existing methods in terms of accuracy and speed but is novel in its emphasis on interpretabilty. We provide the full DendroSplit software package at https://github.com/jessemzhang/dendrosplit.


title: A cross-cultural study of a circumplex model of affect.

abstract: In the quest to understand how people conceptualize emotional feelings, one approach has been to seek the dimensions by which they perceive the similarities and differences among feelings. A circumplex model of affect represents this set of mutual relations by placing feeling-related concepts in a circular order in a space formed by two bipolar dimensions: pleasure-displeasure and arousalsleepiness. This article offers evidence that the circumplex structure, rather than being somehow dependent on the English-speaking student population in which it was originally obtained, occurs in different languages and cultures. In Study 1, Estonian, Greek, and Polish Ss judged the similarity between feelings described by 28 words in their respective native languages. In Study 2, Greek and Chinese Ss judged the similarity between feelings conveyed by 10 facial expressions. In all cases, multidimensional scaling of pairwise similarity scores yielded the circular order and underlying dimensions predicted by the circumplex.


Gamification for Machine Learning: The Classification Game


title: An Introduction to Agile Methods

abstract: Abstract Agile Methods are creating a buzz in the software development community, drawing their fair share of advocates and opponents. While some people consider agile methods the best thing that has happened to software development in recent years, other people view them as a backlash to software engineering and compare them to hacking. The aim of this chapter is to introduce the reader to agile methods allowing him/her to judge whether or not agile methods could be useful in modern software development. The chapter discusses the history behind agile methods as well as the agile manifesto, a statement from the leaders of the agile movement. It looks at what it means to be agile, discusses the role of management, describes and compares some of the more popular agile methods, provides a guide for deciding where an agile approach is applicable, and lists common criticisms. It summarizes empirical studies, anecdotal reports, and lessons learned from applying agile methods and concludes with an analysis of various agile methods. The target audiences for this chapter include practitioners, who will be interested in the discussion of the different methods and their applications, researchers who may want to focus on the empirical studies and lessons learned, and educators looking to teach and learn more about agile methods.


title: An Attack Strategy based on Hyperbolic Coordinates

abstract: In this paper, from the view of community structure and information flow, a new attack strategy NCLIC is proposed based on hyperbolic coordinates in the complex network. Firstly, a centrality index LIC is introduced in the hyperbolic coordinates which can reflect the amount of information flow of a node, and the performance of LIC is analyzed under different hyperbolic mapping methods. Then some nodes in the network are named "bridge node" which play an important role in the community structure of network. Based on the LIC and the bridge node, a new attack strategy NCLIC is proposed. To verify that the NCLIC can significantly decompose a network, it has been compared with other existing attack strategies. In the experiments, and it has been found that the attack effects of NCLIC is better than others when the destroyed nodes reach to a certain amount, especially for networks with obvious community structure.


title: The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength

abstract: Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as possible to visit their websites using clickbait messages. Machine learning technology may help to handle this problem, giving rise to automatic clickbait detection. To accelerate progress in this direction, we organized the Clickbait Challenge 2017, a shared task inviting the submission of clickbait detectors for a comparative evaluation. A total of 13 detectors have been submitted, achieving significant improvements over the previous state of the art in terms of detection performance. Also, many of the submitted approaches have been published open source, rendering them reproducible, and a good starting point for newcomers. While the 2017 challenge has passed, we maintain the evaluation system and answer to new registrations in support of the ongoing research on better clickbait detectors.


title: A general framework for comparing automatic testing techniques of Android mobile apps

abstract: Abstract As an increasing number of new techniques are developed for quality assurance of Android applications (apps), there is a need to evaluate and empirically compare them. Researchers as well as practitioners will be able to use the results of such comparative studies to answer questions such as, “What technique should I use to test my app?” Unfortunately, there is a severe lack of rigorous empirical studies on this subject. In this paper, for the first time, we present an empirical study comparing all existing fully automatic “online” testing techniques developed for the Android platform. We do so by first reformulating each technique within the context of a general framework. We recognize the commonalities between the techniques to develop the framework. We then use the salient features of each technique to develop parameters of the framework. The result is a general recasting of all existing approaches in a plug-in based formulation, allowing us to vary the parameters to create instances of each technique, and empirically evaluate them on a common set of subjects. Our results show that (1) the proposed general framework abstracts all the common characteristics of online testing techniques proposed in the literature, (2) it can be exploited to design experiments aimed at performing objective comparisons among different online testing approaches and (3) some parameters that we have identified influence the performance of the testing techniques.


title: Elastic Registration of Medical Images With GANs

abstract: Conventional approaches to image registration consist of time consuming iterative methods. Most current deep learning (DL) based registration methods extract deep features to use in an iterative setting. We propose an end-to-end DL method for registering multimodal images. Our approach uses generative adversarial networks (GANs) that eliminates the need for time consuming iterative methods, and directly generates the registered image with the deformation field. Appropriate constraints in the GAN cost function produce accurately registered images in less than a second. Experiments demonstrate their accuracy for multimodal retinal and cardiac MR image registration.


Aggregate loan quality assessment in the search for a related credit-scoring model.


title: Staging a professional participatory design practice: moving PD beyond the initial fascination of user involvement

abstract: Use and users have an important and acknowledged role to most designers of interactive systems. Nevertheless any touch of user hands does not in itself secure development of meaningful artifacts. In this article we stress the need for a professional PD practice in order to yield the full potentiality of user involvement. We suggest two constituting elements of such a professional PD practice. The existence of a shared ‘where-to’ and ‘why’ artifact and an ongoing reflection and off-loop reflection among practitioners in the PD process.


title: Literature Review of Automatic Multiple Documents Text Summarization

abstract: For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abundant. So, the automatic text summarization is demanded for salient information retrieval. Automatic text summarization is a system of summarizing text by computer where a text is given to the computer as input and the output is a shorter and less redundant form of the original text. An informative pr


title: Reuse Intention of Third-Party Online Payments: A Focus on the Sustainable Factors of Alipay

abstract: An anonymous transaction environment and the advantage of virtual property have resulted in trust playing an important role in the rapid growth of online shopping in China. To satisfy this trust issue, Alibaba (China) Co., Ltd. (Hangzhou, China) invented Alipay, the largest third-party online payment service. Using a structural equation model (SEM), this paper attempts to determine whether Alipay’s service quality factors are truly sustainable. The results indicate that only two of five factors—convenience and security—are significantly mediated by the sustainable performance of customer satisfaction as a mediator. The other three factors—usefulness, responsiveness and economy—were rejected for the role of customer satisfaction, even if they are accepted regarding the direct effect on reuse intention. This result implies that Chinese web companies need to make greater efforts not to ensure initial success, but instead to ensure sustainable performance.


title: User-Oriented Context Suggestion

abstract: Recommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' preferences across contexts, and suggesting items that are appropriate in different contexts. In this paper, we present a novel recommendation task, "Context Suggestion", whereby the system recommends contexts in which items may be selected. We introduce the motivations behind the notion of context suggestion and discuss several potential solutions. In particular, we focus specifically on user-oriented context suggestion which involves recommending appropriate contexts based on a user's profile. We propose extensions of well-known context-aware recommendation algorithms such as tensor factorization and deviation-based contextual modeling and adapt them as methods to recommend contexts instead of items. In our empirical evaluation, we compare the proposed solutions to several baseline algorithms using four real-world data sets.


title: Validating ontologies with OOPS

abstract: Ontology quality can be affected by the difficulties involved in ontology modelling which may imply the appearance of anomalies in ontologies. This situation leads to the need of validating ontologies, that is, assessing their quality and correctness. Ontology validation is a key activity in different ontology engineering scenarios such as development and selection. This paper contributes to the ontology validation activity by proposing a web-based tool, called OOPS!, independent of any ontology development environment, for detecting anomalies in ontologies. This tool will help developers to improve ontology quality by automatically detecting potential errors.


title: Forensic detection of image manipulation using statistical intrinsic fingerprints

abstract: As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image forensic techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed forensic technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.


title: Attributable costs of differentiated thyroid cancer in the elderly Medicare population.

abstract: BACKGROUND ::: Little is known about costs associated with differentiated thyroid cancer (DTC) and follow-up care. This study used data from the Surveillance Epidemiology and End Results (SEER) database to examine cumulative costs attributable to disease stage and treatment options of DTC in elderly patients over 5 years. ::: ::: ::: METHODS ::: We identified 2,823 patients aged >65 years with DTC and 5,646 noncancer comparison cases from SEER Medicare data between 1995 and 2005. Cumulative costs were obtained by estimating average costs/patient in each month up to 60 months after diagnosis. We performed multivariate analyses of costs by fitting each monthly cost to linear models, controlling for demographics and comorbidities. Marginal effects of covariates were obtained by summing coefficients over 60 months. ::: ::: ::: RESULTS ::: Cumulative costs were $17,669/patient the first year and $48,989/patient 5 years after diagnosis. Regional disease was associated with higher costs at 1 year ($9,578) and 5 years ($8,902). Distant disease was associated with 1-year costs of $28,447 and 5-year costs of $20,103. Patients undergoing surgery and radiation had a decrease in cost of $722 at 5 years. ::: ::: ::: CONCLUSION ::: DTC in the elderly is associated with significant economic burden largely attributable to patient demographics, stage of disease, and treatment modalities.


title: High-Resolution Talking Face Generation via Mutual Information Approximation

abstract: Given an arbitrary speech clip and a facial image, talking face generation aims to synthesize a talking face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video speech. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, speech audio and video often have cross-modality coherence that has not been well addressed during synthesis. Therefore, this paper proposes a novel high-resolution talking face generation model for arbitrary person by discovering the cross-modality coherence via Mutual Information Approximation (MIA). By assuming the modality difference between audio and video is larger that of real video and generated video, we estimate mutual information between real audio and video, and then use a discriminator to enforce generated video distribution approach real video distribution. Furthermore, we introduce a dynamic attention technique on the mouth to enhance the robustness during the training stage. Experimental results on benchmark dataset LRW transcend the state-of-the-art methods on prevalent metrics with robustness on gender, pose variations and high-resolution synthesizing.


title: A Comparison of Study Strategies for Passages: Rereading, Answering Questions, and Generating Questions

abstract: Students are often encouraged to generate and answer their own questions on to-be-remembered material, because this interactive process is thought to enhance memory. But does this strategy actually work? In three experiments, all participants read the same passage, answered questions, and took a test to get accustomed to the materials in a practice phase. They then read three passages and did one of three tasks on each passage: reread the passage, answered questions set by the experimenter, or generated and answered their own questions. Passages were 575-word (Experiments 1 and 2) or 350-word (Experiment 3) texts on topics such as Venice, the Taj Mahal, and the singer Cesaria Evora. After each task, participants predicted their performance on a later test, which followed the same format as the practice phase test (a short-answer test in Experiments 1 and 2, and a free recall test in Experiment 3). In all experiments, best performance was predicted after generating and answering questions. We show, however, that generating questions led to no improvement over answering comprehension questions, but that both of these tasks were more beneficial than rereading. This was the case on an immediate short-answer test (Experiment 1), a short-answer test taken 2 days after study (Experiment 2), and an immediate free recall test (Experiment 3). Generating questions took at least twice as long as answering questions in all three experiments, so although it is a viable alternative to answering questions in the absence of materials, it is less time-efficient.


title: Fog Computing and the Internet of Things: A Review

abstract: With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.


title: Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective

abstract: Iris recognition refers to the automated process of recognizing individuals based on their iris patterns. The seemingly stochastic nature of the iris stroma makes it a distinctive cue for biometric recognition. The textural nuances of an individual’s iris pattern can be effectively extracted and encoded by projecting them onto Gabor wavelets and transforming the ensuing phasor response into a binary code - a technique pioneered by Daugman. This textural descriptor has been observed to be a robust feature descriptor with very low false match rates and low computational complexity. However, recent advancements in deep learning and computer vision indicate that generic descriptors extracted using convolutional neural networks (CNNs) are able to represent complex image characteristics. Given the superior performance of CNNs on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and a large number of other computer vision tasks, in this paper, we explore the performance of state-of-the-art pretrained CNNs on iris recognition.We show that off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand. We also discuss the challenges and future research directions in leveraging deep learning methods for the problem of iris recognition.


title: Testing for Subsidiarity

abstract: In the EU circuit (especially the European Parliament, the Council and Coreper) as well as in national parliaments of the EU Member States, one observes a powerful tendency to regard 'subsidiarity' as a 'political' issue. Moreover, subsidiarity is frequently seen as a one-way street : powers going 'back to' Member States. Both interpretations are at least partly flawed and less than helpful when looking for practical ways to deal with subsidiarity at both EU and Member states' levels. The present paper shows that subsidiarity as a principle is profoundly 'functional' in nature and, hence, is and must be a two-way principle. A functional subsidiarity test is developed and its application is illustrated for a range of policy issues in the internal market in its widest sense, for equity and for macro-economic stabilisation questions in European integration. Misapplications of 'subsidiarity' are also demonstrated. For a good understanding, subsidiarity being a functional, two-way principle neither means that elected politicians should not have the final (political!) say (for which they are accountable), nor that subsidiarity tests, even if properly conducted, cannot and will not be politicised once the results enter the policy debate. Such politicisation forms a natural run-up to the decision-making by those elected for it. But the quality and reasoning of the test as well as structuring the information in a logical sequence ( in accordance with the current protocol and with the one in the constitutional treaty) is likely to be directly helpful for decision-makers, confronted with complicated and often specialised proposals. EU debates and decision-making is therefore best served by separating the functional subsidiarity test (prepared by independent professionals) from the final political decision itself. If the test were accepted Union-wide, it would also assist national parliaments in conducting comparable tests in a relatively short period, as the basis for possible joint action (as suggested by the constitutional treaty). The core of the paper explains how the test is formulated and applied. A functional approach to subsidiarity in the framework of European representative democracy seeks to find the optimal assignment of regulatory or policy competences to the various tiers of government. In the final analysis, this is about structures facilitating the highest possible welfare in the Union, in the fundamental sense that preferences and needs are best satisfied. What is required for such an analysis is no less than a systematic cost/benefit framework to assess the (de)merits of (de)centralisation in the EU.


title: A statistical study of magnetic tunnel junctions for high-density spin torque transfer-MRAM (STT-MRAM)

abstract: We have demonstrated a robust magnetic tunnel junction (MTJ) with a resistance-area product RA=8 Omega-mum2 that simultaneously satisfies the statistical requirements of high tunneling magnetoresistance TMR > 15sigma(Rp), write threshold spread sigma(Vw)/ <7.1%, breakdown-to-write voltage margin over 0.5 V, read-induced disturbance rate below 10-9, and sufficient write endurance, and is free of unwanted write-induced magnetic reversal. The statistics suggest that a 64 Mb chip at the 90-nm node is feasible.


title: Next generation arc welding machines based on Silicon Carbide MOSFETS and high frequency planar magnetics

abstract: Silicon Carbide (SiC) Power MOSFET devices are rapidly being introduced in different applications due their advantages over their silicon (Si) counterparts. Higher switching frequency (ƒsw) coupled with planar magnetics opens new possibilities in terms of increasing the power density of converter, while also improving their efficiency and lowering costs. In this paper, SiC MOSFETS and planar magnetics are employed to demonstrate new possibilities in arc welding machines technology. Design details of high power density power source for a multi-process welding machine are presented. Test results for 3.9kW/150A and 200 kHz switching frequency working at rated load and under over load condition are included.


title: Data-Driven Water Quality Analysis and Prediction: A Survey

abstract: Water quality becomes one of the important quality factors for the quality life in smart cities. Recently, water quality has been degraded due to diverse forms of pollution caused by disposal of human wastes, industrial wastes, automobile wastes. The increasing pollution affects water quality and the quality of people's life. Hence, water quality evaluation, monitoring, and prediction become an important and hot research subject. In the past, many environmental researchers have dedicated their research efforts on this subject using conventional approaches. Recently, many researchers begin to use the big data analytics approach to studying, evaluating, and predicting water quality due to the advances of big data applications and the availability of environmental sensing networks and sensor data. This paper reviews the published research results relating to water quality evaluation and prediction. Moreover, the paper classifies and compares the applied big data analytics approaches and big data based prediction models for water quality assessment. Furthermore, the paper also discusses the future research needs and challenges.


title: Spectral Norm Regularization for Improving the Generalizability of Deep Learning

abstract: We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spectral norm regularization, which penalizes the high spectral norm of weight matrices in neural networks. We provide supportive evidence for the abovementioned hypothesis by experimentally confirming that the models trained using spectral norm regularization exhibit better generalizability than other baseline methods.


title: Cloud based shopping guide system using qrcode

abstract: With the improvement of living standards, shops are growing bigger after constructing with more abundant goods and more variety of wares. Therefore, building a simple, fast and convenient shopping guide system has become a mutual concern of merchants and customers. In recent times mobile phone has become a popular consumer products, a simple optimization method was given to design shopping guide system run on smart phones, with the help of QR code generation and recognition technology. For efficient shopping system, unique QR codes are created to record the article name, number, location of goods placed. Phone recognizes the QR Code through the camera. After being recognized and converted, the code will be compared with the data in the server that is placed in the cloud. It provides different services according to customer's choice Ccustomer receives the latest promotions of businesses, find the best route from his current location to the destination.


title: Three-Pole 1.3–2.4-GHz Diplexer and 1.1–2.45-GHz Dual-Band Filter With Common Resonator Topology and Flexible Tuning Capabilities

abstract: In this paper, a third-order tunable diplexer and a third-order tunable dual-band bandpass filter with flexible tuning capabilities and high selectivity are proposed. Both the diplexer and dual-band filter consist of two passbands that share a common resonator for size reduction. By adding tuning elements in the middle and ends of the common resonator, the resonant frequencies of the even and odd modes can be independently tuned, thus offering flexible tuning for multi-band operations. Also, by adding extra resonators next to the common resonator, high-order tunable multi-band filters can be obtained with higher stopband suppression. The 1.3-2.4-GHz tunable diplexer shows that the two channels can be either independently tuned or simultaneously tuned as desired. The two channels cover a fractional tuning range of 34% and 18.6%, respectively, while keeping high stopband suppressions, higher isolation, and good input impedance matching. Similarly, the 1.1-2.45-GHz tunable dual-band filter shows independent and simultaneous tuning capability operating with good impedance matching and high stopband suppression.


title: High-Speed Electric Drive for Exhaust Gas Energy Recovery Applications

abstract: High-speed electric drives play an important role in the field of power generating units on board vehicles and aircrafts. This paper deals with the solutions for developing the direct coupled electric drive to be used in combination with a radial turbo-expander for exhaust energy recovery in automotive applications. The descriptions of prototypal realization of both the axial-flux permanent-magnet (PM) generator and the three-level boost-rectifier converter, which results as the preferred topology for the controlled rectifier, are given. The high rotational speed of the direct-driven PM generator results in high electric fundamental frequency also, which is challenging for the electric drive control issues. Results of the electric drive prototype experimental activity are finally presented.


A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems


title: The Use of a Bayesian Neural Network Model for Classification Tasks

abstract: This thesis deals with a Bayesian neural network model. The focus is on how to use the model for automatic classification, i.e. on how to train the neural network to classify objects from some domain, given a database of labeled examples from the domain. The original Bayesian neural network is a onelayer network implementing a naive Bayesian classifier. It is based on the assumption that different attributes of the objects appear independently of each other. This work has been aimed at extending the original Bayesian neural network model, mainly focusing on three different aspects. First the model is extended to a multi-layer network, to relax the independence requirement. This is done by introducing a hidden layer of complex columns, groups of units which take input from the same set of input attributes. Two different types of complex column structures in the hidden layer are studied and compared. An information theoretic measure is used to decide which input attributes to consider together in complex columns. Also used are ideas from Bayesian statistics, as a means to estimate the probabilities from data which are required to set up the weights and biases in the neural network. The use of uncertain evidence and continuous valued attributes in the Bayesian neural network are also treated. Both things require the network to handle graded inputs, i. e. probability distributions over some discrete attributes given as input. Continuous valued attributes can then be handled by using mixture models. In effect, each mixture model converts a set of continuous valued inputs to a discrete number of probabilities for the component densities in the mixture model. Finally a query-reply system based on the Bayesian neural network is described. It constitutes a kind of expert system shell on top of the network. Rather than requiring all attributes to be given at once, the system can ask for the attributes relevant for the classification. Information theory is used to select the attributes to ask for. The system also offers an explanatory mechanism, which can give simple explanations of the state of the network, in terms of which inputs mean the most for the outputs. These extensions to the Bayesian neural network model are evaluated on a set of different databases, both realistic and synthetic, and the classification results are compared to those of various other classification methods on the same databases. The conclusion is that the Bayesian neural network model compares favorably to other methods for classification. In this work much inspiration has been taken from various branches of machine learning. The goal has been to combine the different ideas into one consistent and useful neural network model. A main theme throughout is to utilize independencies between attributes, to decrease the number of free parameters, and thus to increase the generalization capability of the method. Significant contributions are the method used to combine the outputs from mixture models over different subspaces of the domain, and the use of Bayesian estimation of parameters in the expectation maximization method during training of the mixture models.


title: Examining the critical success factors of mobile website adoption

abstract: Purpose – The purpose of this research is to examine the critical success factors of mobile web site adoption.Design/methodology/approach – Based on the valid responses collected from a questionnaire survey, the structural equation modelling technique was employed to examine the research model.Findings – The results indicate that system quality is the main factor affecting perceived ease of use, whereas information quality is the main factor affecting perceived usefulness. Service quality has significant effects on trust and perceived ease of use. Perceived usefulness, perceived ease of use and trust determine user satisfaction.Practical implications – Mobile service providers need to improve the system quality, information quality and service quality of mobile web sites to enhance user satisfaction.Originality/value – Previous research has mainly focused on e‐commerce web site success and seldom examined the factors affecting mobile web site success. This research fills the gap. The research draws on inf...


title: Voice of the customers: mining online customer reviews for product feature-based ranking

abstract: Increasingly large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. As the number of products being sold online increases, it is becoming increasingly difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, customer reviews, particularly the text describing the features, comparisons and experiences of using a particular product provide a rich source of information to compare products and make purchasing decisions. Online retailers like Amazon.com1 allow customers to add reviews of products they have purchased. These reviews have become a diverse and reliable source to aid other customers. Traditionally, many customers have used expert rankings which rate limited a number of products. Existing automated ranking mechanisms typically rank products based on their overall quality. However, a product usually has multiple product features, each of which plays a different role. Different customers may be interested in different features of a product, and their preferences may vary accordingly. In this paper, we present a feature-based product ranking technique that mines thousands of customer reviews. We first identify product features within a product category and analyze their frequencies and relative usage. For each feature, we identify subjective and comparative sentences in reviews. We then assign sentiment orientations to these sentences. By using the information obtained from customer reviews, we model the relationships among products by constructing a weighted and directed graph. We then mine this graph to determine the relative quality of products. Experiments on Digital Camera and Television reviews from real-world data on Amazon.com are presented to demonstrate the results of the proposed techniques.


title: A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks

abstract: We use a supervised multi-spike learning algorithm for spiking neural networks (SNNs) with temporal encoding to simulate the learning mechanism of biological neurons in which the SNN output spike trains are encoded by firing times. We first analyze why existing gradient-descent-based learning methods for SNNs have difficulty in achieving multi-spike learning. We then propose a new multi-spike learning method for SNNs based on gradient descent that solves the problems of error function construction and interference among multiple output spikes during learning. The method could be widely applied to single spiking neurons to learn desired output spike trains and to multilayer SNNs to solve classification problems. By overcoming learning interference among multiple spikes, our method has high learning accuracy when there are a relatively large number of output spikes in need of learning. We also develop an output encoding strategy with respect to multiple spikes for classification problems. This effectively improves the classification accuracy of multi-spike learning compared to that of single-spike learning.


title: Metabolomics in the study of kidney diseases

abstract: Metabolomics—the nontargeted measurement of all metabolites produced by the body—holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease. This Review discusses the techniques used in metabolomics and explores the limitations and future applications of this process.


title: Predicting driver maneuvers by learning holistic features

abstract: In this work, we propose a framework for the recognition and prediction of driver maneuvers by considering holistic cues. With an array of sensors, driver's head, hand, and foot gestures are being captured in a synchronized manner together with lane, surrounding agents, and vehicle parameters. An emphasis is put on real-time algorithms. The cues are processed and fused using a latent-dynamic discriminative framework. As a case study, driver activity recognition and prediction in overtaking situations is performed using a natu- ralistic, on-road dataset. A consequence of this work would be in development of more effective driver analysis and assistance systems. I. INTRODUCTION On-road driving behavior consists of intricate, multi- dimensional dynamics. It is the outcome of many variables that interact at a certain place and time. This motivates the study in this paper, which pursues a holistic approach using multiple cues in the scene. When fusing the different modal- ities in a temporal modeling framework, activity recognition and critical situation prediction can be made more effectively. Furthermore, through studying of temporally discriminative cues, we gain insight into the processes that characterize driver behavior.


title: The relational self: An interpersonal social-cognitive theory

abstract: The authors propose an interpersonal social– cognitive theory of the self and personality, the relational self, in which knowledge about the self is linked with knowledge about significant others, and each linkage embodies a self– other relationship. Mental representations of significant others are activated and used in interpersonal encounters in the social– cognitive phenomenon of transference (S. M. Andersen & N. S. Glassman, 1996), and this evokes the relational self. Variability in relational selves depends on interpersonal contextual cues, whereas stability derives from the chronic accessibility of significant-other representations. Relational selves function in if–then terms (W. Mischel & Y. Shoda, 1995), in which ifs are situations triggering transference, and thens are relational selves. An individual’s repertoire of relational selves is a source of interpersonal patterns involving affect, motivation, self-evaluation, and self-regulation. The nature of the self has long perplexed and intrigued scholars across a broad spectrum of academic disciplines. In psychology alone, well over a century of inquiry has translated into a virtual explosion of theory and research in recent decades, especially in social psychology, all aiming to chart the contours of the self. Sharing this aim, we propose an interpersonal social– cognitive theory of the self that draws on theory and research in social cognition, personality psychology, and clinical psychology. Our central argument is that the self is relational— or even entangled— with significant others and that this has implications for selfdefinition, self-evaluation, self-regulation, and, most broadly, for personality functioning, expressed in relation to others. The theory clearly subscribes to the long-standing view that the self is fundamentally interpersonal (e.g., James, 1890). Indeed, we maintain that an individual’s overall repertoire of relational selves, stemming from all his or her relationships, is a major source of the interpersonal patterns that the individual enacts and experiences in the course of everyday interpersonal life—whether at work, at play, or in therapy. The proposed theory focuses on the ways in which the self is related to specific other individuals—namely, the significant oth


The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets: A Reply


title: HDTLR: A CNN based Hierarchical Detector for Traffic Lights

abstract: Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection systems are not able to provide such information. Within this work we present a hierarchical traffic light detection algorithm, which is able to detect traffic lights and determine their state and contained direction information within one CNN forward pass. This Hierarchical DeepTLR (HDTLR) outperforms current state of the art traffic light detection algorithms in state aware detection and can detect traffic lights with direction information down to a size of 4 pixel in width at a frequency of 12 frames per second.


title: Detecting, Measuring, and Testing Dyadic Patterns in the Actor―Partner Interdependence Model

abstract: Family researchers have used the actor-partner interdependence model (APIM) to study romantic couples, parent- child dyads, and siblings. We discuss a new method to detect, measure, and test different theoretically important patterns in the APIM: equal actor and partner effect (couple pattern); same size, but different signs of actor and partner effects (contrast pattern); and zero partner effects (actor-only pattern). To measure these different patterns, as well as others, we propose the estimation of the parameter k, which equals the partner effect divided by the actor effect. For both indistinguishable dyad members (e.g., twins) and distinguishable dyad members (e.g., heterosexual couples), we propose strategies for estimating and testing different models. We illustrate our new approach with four data sets. The actor-partner interdependence model (APIM; Kashy & Kenny, 2000; Kenny, 1996; Kenny & Cook, 1999) is increasingly used to analyze dyadic data from families. It has been used in studies of interactions between parent and child (Pesonen, Raikkonen, & Heinonen, 2006), romantic partners (Peterson, Pirritano, Christensen, & Schmidt, 2008), and siblings (Kenny & Cook, 1999). Researchers have found the APIM to be very useful in the study of dyadic relationships. The appeal of the APIM is that allows for the study of the influence of a person's own causal variable on his or her own outcome variable, which is called the actor effect, and on the outcome variable of the partner, which is called the partner effect. These two effects can be measured while proper statistical allowances are made for the nonindependence in the two persons' responses. Using interdependence theory (Kelley et al., 2003), Kenny and Cook (1999) discussed four general APIM pat- terns: the actor-only, the partner-only, the couple, and the contrast patterns. 1 The actor-only pattern is indicated by a nonzero actor effect, but the partner effect is zero. That is, a person's causal variable has an effect on his or her outcome variable but not on the partner's outcome variable. The partner-only pattern occurs if the partner effect is nonzero, but the actor effect is zero. The partner-only pat- tern is relatively rare, and so the new approach developed in this article does not initially consider this possibility, al- though we do return to this pattern later in the article. The couple pattern is indicated if actor and partner effects are equal. That is, a person's outcome variable is equally af- fected by his or her causal variable and by the causal variable of the partner. For instance, Campbell, Simpson, Boldry, and Kashy (2005) found that negative behaviors were affected by one's own anxious attachment style and one's partner. The contrast pattern occurs if actor and part- ner effects are equal in size but have opposite signs. For example, a person's outcome variable is positively affected by his or her causal variable and negatively affected by the partner's causal variable. One example of this pattern was found by Klumb, Hoppmann, and Staats (2006): More housework leads to higher levels of one's own cortisol, but housework done by the spouse leads to lower levels of cortisol.


title: Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant.

abstract: What is the structure of emotion? Emotion is too broad a class of events to be a single scientific category, and no one structure suffices. As an illustration, core affect is distinguished from prototypical emotional episode. Core affect refers to consciously accessible elemental processes of pleasure and activation, has many causes, and is always present. Its structure involves two bipolar dimensions. Prototypical emotional episode refers to a complex process that unfolds over time, involves causally connected subevents (antecedent; appraisal; physiological, affective, and cognitive changes; behavioral response; self-categorization), has one perceived cause, and is rare. Its structure involves categories (anger, fear, shame, jealousy, etc.) vertically organized as a fuzzy hierarchy and horizontally organized as part of a circumplex.


title: An abstract framework for argumentation with structured arguments

abstract: An abstract framework for structured arguments is presented that instantiates Dung’s (1995) abstract argumentation frameworks. Arguments are defined as inference trees formed by applying two kinds of inference rules: strict and defeasible rules. This naturally leads to three ways of attacking an argument: attacking a premise, attacking a conclusion and attacking an inference. To resolve such attacks, preferences may be used, which leads to three corresponding kinds of defeat: undermining, rebutting and undercutting defeat. The nature of the inference rules, the structure of the logic al language on which they operate and the origin of the preferences are, apart from some basic assumptions, left unspecified. The resulting framework integrates work of Pollock, Vreeswijk and others on the structure of arguments and the nature of defeat, and extends it in several respects. Various rationality postulates are proven to be satisfied by the framework, and several existing approaches a re proven to be a special case of the framework, including assumption-based argumentation and DefLog. In 1995 Phan Minh Dung introduced an abstract formalism for argumentation-based inference (Dung 1995), which assumes as input nothing else but a set (of arguments) ordered by a binary relation (of attack). Although he thus fully abst racted from the structure of arguments and the nature of the attack relation, he was still able to develop an extremely interesting theory. His article was a breakthrough in three ways: it provided a general and intuitive semantics for the consequence notions of argumentation logics (and for nonmonotonic logics in general); it made a precise comparison possible between different systems (by translating them into his abstract format); and it made a general study of formal properties of systems possible, which are inherited by instantiations of his framework. In consequence, Dung’s work has given an enormous boost to re search in computational argumentation. Yet it has also been criticised for not speci fying the structure of arguments and the nature of the attack relation, which makes it less sui table for modelling specific argumentation problems. I believe that such criticism fails t o appreciate the nature of Dung’s formalism. It is best seen not as a formalism for directly rep resenting argumentation-based inference problems but as a tool for analysing particular argumentation systems and for developing a metatheory of such systems. As such it has been very successful: differences between particular systems can be characterised in terms of some simple notions, and formal results established for the framework are inherited by its instantiations. This was already illustrated by Dung (1995) with reconstructions of Pollock’s (1987) system, various logic-programming semantics and Reiter’s (1980) d efault logic in his formalism.


title: Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis

abstract: SenticNet is currently one of the most comprehensive freely available semantic resources for opinion mining. However, it only provides numerical polarity scores, while more detailed sentiment-related information for its concepts is often desirable. Another important resource for opinion mining and sentiment analysis is WordNet-Affect, which in turn lacks quantitative information. We report a work on automatically merging these two resources by assigning emotion labels to more than 2700 concepts.


title: Lessons from Natural Language Inference in the Clinical Domain

abstract: State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.