title: SAGNet:Structure-aware Generative Network for 3D-Shape Modeling

abstract: We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure) are jointly learned and embedded in a latent space by an autoencoder. The encoder intertwines the geometry and structure features into a single latent code, while the decoder disentangles the features and reconstructs the geometry and structure of the 3D model. Our autoencoder consists of two branches, one for the structure and one for the geometry. The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code. This explicit intertwining of information enables separately controlling the geometry and the structure of the generated models. We evaluate the performance of our method and conduct an ablation study. We explicitly show that encoding of shapes accounts for both similarities in structure and geometry. A variety of quality results generated by SAGNet are presented. The data and code are at https://github.com/zhijieW-94/SAGNet.


title: Calibration between Color Camera and 3D LIDAR Instruments with a Polygonal Planar Board

abstract: Calibration between color camera and 3D Light Detection And Ranging (LIDAR) equipment is an essential process for data fusion. The goal of this paper is to improve the calibration accuracy between a camera and a 3D LIDAR. In particular, we are interested in calibrating a low resolution 3D LIDAR with a relatively small number of vertical sensors. Our goal is achieved by employing a new methodology for the calibration board, which exploits 2D-3D correspondences. The 3D corresponding points are estimated from the scanned laser points on the polygonal planar board with adjacent sides. Since the lengths of adjacent sides are known, we can estimate the vertices of the board as a meeting point of two projected sides of the polygonal board. The estimated vertices from the range data and those detected from the color image serve as the corresponding points for the calibration. Experiments using a low-resolution LIDAR with 32 sensors show robust results.


title: Multi-agent quadrotor testbed control design: integral sliding mode vs. reinforcement learning

abstract: The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.


title: Joint routing, MAC and physical layer protocol for wireless sensor networks

abstract: In the last few years, saving of energy in the wireless sensor network (WSNs) world has been, and continue to be now an important challenge in the area of WSNs. In this paper, energy aspect has been investigated, with specific regard to routing, MAC and physical layer. We propose a novel joint routing, medium access control (MAC) and physical layer protocol for wireless sensor network (WSNs) based on basic novelty feature of IH-MAC [4]. This is referred to as a Joint Routing, MAC and Physical layer protocol (RMP). The concept ran by blending the classical layered approach and combining routing, MAC and physical layer functions, the proposed RMP protocol achieves a solution for energy efficiency in WSNs. Conventionally, the problem of energy efficient protocol design is considered independently at respective layer of protocol stack. This paper has taken a holistic approach of finding solution by addressing the possible energy gain in Routing, MAC and Physical layer together. Firstly, it is seen that communication path with the large number of short hop substantially minimizes energy consumption. This phenomenon is used while instantaneous network information is collected with minimum overhead through the control packets for selecting next hop. Based on the updated network knowledge the next hop neighbour is chosen with reduced control overhead. Besides, the RMP protocol uses the approach for finding the constrained shortest path for forwarding packets [1], which results in load balancing in WSNs. We compare the RMP with other two existing protocols by network simulator ns-2. The simulation outcome of RMP supports the theatrical idea, and confirm the efficiency of proposed RMP because the energy consumption is reduced through applying the theory within simulation application.


title: Context adaptive deep neural networks for fast acoustic model adaptation

abstract: Deep neural networks (DNNs) are widely used for acoustic modeling in automatic speech recognition (ASR), since they greatly outperform legacy Gaussian mixture model-based systems. However, the levels of performance achieved by current DNN-based systems remain far too low in many tasks, e.g. when the training and testing acoustic contexts differ due to ambient noise, reverberation or speaker variability. Consequently, research on DNN adaptation has recently attracted much interest. In this paper, we present a novel approach for the fast adaptation of a DNN-based acoustic model to the acoustic context. We introduce a context adaptive DNN with one or several layers depending on external factors that represent the acoustic conditions. This is realized by introducing a factorized layer that uses a different set of parameters to process each class of factors. The output of the factorized layer is then obtained by weighted averaging over the contribution of the different factor classes, given posteriors over the factor classes. This paper introduces the concept of context adaptive DNN and describes preliminary experiments with the TIMIT phoneme recognition task showing consistent improvement with the proposed approach.


title: Active Temporal Action Detection in Untrimmed Videos Via Deep Reinforcement Learning

abstract: Existing action detection algorithms usually generate action proposals through an extensive search over the video at multiple temporal scales, which brings about huge computational overhead and deviates from the human perception procedure. We argue that the process of detecting actions in the video should be naturally one of observation and refinement: observe the current temporal window and refine the span of attended window to cover true action regions. In this paper, we propose an active action detection model that learns to search actions through continuously adjusting the bounds of temporal attended window in a self-adaptive way. The whole process can be deemed as an exploring procedure, where an agent is first placed at the beginning of the video and then traverses the whole video by adopting a sequence of transformations on the current attended window to discover actions according to a learned policy. We utilize reinforcement learning, especially the deep Q-learning algorithm to learn the agent’s decision policy. Actually, we construct an end-to-end trainable framework for the action detection task, which includes a proposal generation network based on deep Q-learning, and the classification and regression networks responsible for the action category prediction and the action location adjustment, respectively. In addition, we design a long short-term memory structure upon extracted convolutional neural network features of sparsely sampled frames to generate the effective feature representations for video sequences of various durations. We evaluate the action proposal performance of our approach on THUMOS’14 and assess the generalization ability for unseen action categories on ActivityNet . We also compare the action detection performance of ours with other state-of-the-art methods on both data sets. Experiment results validate the effectiveness of the proposed approach, which can achieve comparative or superior performance than other action detection methods via much fewer proposals.


title: Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets

abstract: Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users’ QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price.


title: Sustainable Retailing in the Fashion Industry: A Systematic Literature Review

abstract: Although sustainability in the fashion industry has gained prominence from both business practices and academic research, retailing, a vital part of the supply chain, has not yet been fairly explored in academia. The interest in this area has increased lately, mainly due to the growing complexity within this dynamic context. Therefore, it is meaningful to conduct a systematic review of the relevant published literature in this field. This study aims to identify the main perspectives of research on sustainable retailing in the fashion industry. The content analysis results indicate that the most prominent areas in the field are sustainable retailing in disposable fashion, fast fashion, slow fashion, green branding and eco-labeling; retailing of secondhand fashion; reverse logistics in fashion retailing; and emerging retailing opportunities in e-commerce. The results from this review also indicate that there is a lack of research on sustainable retailing in the fashion industry in the developing market.


title: Predictable 802.11 packet delivery from wireless channel measurements

abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (


title: Viewpoints and keypoints

abstract: We characterize the problem of pose estimation for rigid objects in terms of determining viewpoint to explain coarse pose and keypoint prediction to capture the finer details. We address both these tasks in two different settings - the constrained setting with known bounding boxes and the more challenging detection setting where the aim is to simultaneously detect and correctly estimate pose of objects. We present Convolutional Neural Network based architectures for these and demonstrate that leveraging viewpoint estimates can substantially improve local appearance based keypoint predictions. In addition to achieving significant improvements over state-of-the-art in the above tasks, we analyze the error modes and effect of object characteristics on performance to guide future efforts towards this goal.


title: Non-Uniform Metasurface Luneburg Lens Antenna Design

abstract: A metasurfing concept is demonstrated and applied in the design of Luneburg lens antennas. Using an array of size-varying circular patches on a dielectric substrate inside a parallel-plate waveguide (PPW) structure variable surface impedance is obtained, which realizes an equivalent refraction index as that of a Luneburg lens. The obtained lens has good bandwidth characteristics and significant fabrication advantages with respect to conventional dielectric lenses. Based on this PPW lens, an H-plane antenna has been designed and simulated.


An adaptive contextual quantum language model Journal Item


title: Recent Research on Team and Organizational Diversity : SWOT Analysis and Implications

abstract: Sixty-three studies published in the years 1997–2002 are reviewed to assess the effects of workplace diversity on teams and organizations. Four major questions are considered: Which personal attributes have diversity researchers studied in recent years? What has been learned about the consequences of diversity for teams and organizations? What has been learned about the role of context in shaping the effects of diversity? How has research addressed the multi-level complexities inherent in the phenomenon of diversity? For each question, we consider the strengths and weaknesses of recent diversity research, point out opportunities for new research, and identify threats to continued advancement. The review concludes by considering practical implications of the accumulated evidence.


Cultural Reformations: Lydia Maria Child and the Literature of Reform


title: AnonRep: towards tracking-resistant anonymous reputation

abstract: Reputation systems help users evaluate information quality and incentivize civilized behavior, often by tallying feedback from other users such as "likes" or votes and linking these scores to a user's long-term identity. This identity linkage enables user tracking, however, and appears at odds with strong privacy or anonymity. This paper presents AnonRep, a practical anonymous reputation system offering the benefits of reputation without enabling long-term tracking. AnonRep users anonymously post messages, which they can verifiably tag with their reputation scores without leaking sensitive information. AnonRep reliably tallies other users' feedback (e.g., likes or votes) without revealing the user's identity or exact score to anyone, while maintaining security against score tampering or duplicate feedback. A working prototype demonstrates that AnonRep scales linearly with the number of participating users. Experiments show that the latency for a user to generate anonymous feedback is less than ten seconds in a 10,000-user anonymity group.


title: Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing

abstract: Abstract Automatic Vehicles Counting and Recognizing (AVCR) is a very challenging topic in transport engineering having important implications for the modern transport policies. Implementing a computer-assisted AVCR in the most vital districts of a country provides a large amount of measurements which are statistically processed and analyzed, the purpose of which is to optimize the decision-making of traffic operation, pavement design, and transportation planning. Since, the advent of computer vision technology, video-based surveillance of road vehicles has become a key component in developing autonomous intelligent transportation systems. In this context, this paper proposes a Pattern Recognition system which employs an unsupervised clustering algorithm with the objective of detecting, counting and recognizing a number of dynamic objects crossing a roadway. This strategy defines a virtual sensor, whose aim is similar to that of an inductive-loop in a traditional mechanism, i.e. to extract from the traffic video streaming a number of signals containing anarchic information about the road traffic. Then, the set of signals is filtered with the aim of conserving only motion’s significant patterns. Resulted data are subsequently processed by a statistical analysis technique so as to estimate and try to recognize a number of clusters corresponding to vehicles. Finite mixture models fitted by the EM algorithm are used to assess such clusters, which provides important information about the traffic such as the instantaneous number of vehicles, their weights, velocities and intervehicular distances.


title: Saliency Optimization from Robust Background Detection

abstract: Recent progresses in salient object detection have exploited the boundary prior, or background information, to assist other saliency cues such as contrast, achieving state-of-the-art results. However, their usage of boundary prior is very simple, fragile, and the integration with other cues is mostly heuristic. In this work, we present new methods to address these issues. First, we propose a robust background measure, called boundary connectivity. It characterizes the spatial layout of image regions with respect to image boundaries and is much more robust. It has an intuitive geometrical interpretation and presents unique benefits that are absent in previous saliency measures. Second, we propose a principled optimization framework to integrate multiple low level cues, including our background measure, to obtain clean and uniform saliency maps. Our formulation is intuitive, efficient and achieves state-of-the-art results on several benchmark datasets.


title: Cultural competence in outpatient substance abuse treatment: measurement and relationship to wait time and retention.

abstract: a b s t r a c t Background: Culturally competent practice is broadly acknowledged to be an important strategy to increase the quality of services for racial/ethnic minorities in substance abuse treatment. However, few empirically derived measures of organizational cultural competence exist, and relatively little is known about how these measures affect treatment outcomes. Method: Using a nationally representative sample of outpatient substance abuse treatment (OSAT) programs, this study used item response theory to create two measures of cultural competence—organizational practices and managers' culturally sensitive beliefs—and examined their relationship to client wait time and retention using Poisson regression modeling. Results: The most common and precisely measured organizational practices reported by OSAT managers included matching providers and clients based on language/dialect; offering cross-cultural training; and fostering connections with community and faith-based organizations connected to racial and ethnic minority groups. The most culturally sensitive belief among OSAT managers was support for lan- guage/dialect matching for racial and ethnic minority clients. Results of regression modeling indicate that organizational practices were not related to either outcome. However, managers' culturally sensi- tive beliefs were negatively associated with average wait time (p < 0.05), and positively associated with average retention (p < 0.01). Conclusions: Managers' culturally sensitive beliefs—considered to be influential for effective implementa- tion of culturally competent practices—may be particularly relevant in influencing wait time and retention in OSAT organizations that treat Latinos and African American clients. © 2011 Elsevier Ireland Ltd. All rights reserved.


title: Real-Time Adaptive Camera Tamper Detection for Video Surveillance

abstract: Criminals often resort to camera tampering to prevent capture of their actions. Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering is generally done by obstructing the camera view by a foreign object, displacing the camera and changing the focus of the camera lens. In automated camera tamper detection systems, low false alarm rates are important as reliability of these systems is compromised by unnecessary alarms and consequently the operators start ignoring the warnings. We propose adaptive algorithms to detect and identify such cases with low false alarms rates in typical surveillance scenarios where there is significant activity in the scene.


title: From the Computer Incident Taxonomy to a Computer Forensic Examination Taxonomy

abstract: Forensic investigations are usually conducted to solve crimes committed using IT-systems as pertetrator and/or victim. However, depending on the size of IT-system, also non-malicious incidents can be investigated using the same, methodological and proven techniques. Based upon the principles contained in the well-known Computer Incident Taxonomy [1], this paper proposes the establishment a common language for the description of computer forensic examinations, both in malicious and non-malicious incidents. Additionally this taxonomy helps performing a forensic examination in establishing answers to a set of well-definied questions during such an examination. The usefulness of the proposed Forensic Examination Taxonomy is shown using a malicious and a non-malicious example.


title: Development of a Wearable-Sensor-Based Fall Detection System

abstract: Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls.This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient's location.


title: Visualization of movie features in collaborative filtering

abstract: In this paper we will describe a modification of the matrix factorization (MF) algorithm which allows visualizing the user and item characteristics. When applying MF for collaborative filtering, we get a model that represents the attributes of users and items by feature vectors. Some elements of these vectors may have understandable meaning for humans but due to the lack of internal connections between the feature vectors, these are difficult to visualize. In this paper we give a detailed description of a MF method enabling better visualization of features by arranging them into a 2D map, where via the calculation of the feature values we try to position features with similar “meaning” close to each other. To achieve this first we define a neighborhood relation on features, then we modify the MF so that we introduce a new term in the error function which penalize the difference between the neighbor features. We show that this modification slightly decrease the accuracy of the model but we get well visualized feature maps. On the feature maps meanings can be associated with regions, and so we can provide an interesting explanation for the user why he/she was recommended the movie. Such plausible explanations may result in that users will better understand how the system works, which can also increase customer loyalty towards the service provider.


title: Events: Insider Threat Prediction Tool: Evaluating the probability of IT misuse

abstract: Despite the well documented and emerging insider threat to information systems, there is currently no substantial effort devoted to addressing the problem of internal IT misuse. In fact, the great majority of misuse counter measures address forms of abuse originating from external factors (i.e. the perceived threat from unauthorized users). This paper suggests a new and innovative approach of dealing with insiders that abuse IT systems. The proposed solution estimates the level of threat that is likely to originate from a particular insider by introducing a threat evaluation system based on certain profiles of user behaviour. However, a substantial amount of work is required, in order to materialize and validate the proposed solutions.


title: Learning Complementary Saliency Priors for Foreground Object Segmentation in Complex Scenes

abstract: Object segmentation is widely recognized as one of the most challenging problems in computer vision. One major problem of existing methods is that most of them are vulnerable to the cluttered background. Moreover, human intervention is often required to specify foreground/background priors, which restricts the usage of object segmentation in real-world scenario. To address these problems, we propose a novel approach to learn complementary saliency priors for foreground object segmentation in complex scenes. Different from existing saliency-based segmentation approaches, we propose to learn two complementary saliency maps that reveal the most reliable foreground and background regions. Given such priors, foreground object segmentation is formulated as a binary pixel labelling problem that can be efficiently solved using graph cuts. As such, the confident saliency priors can be utilized to extract the most salient objects and reduce the distraction of cluttered background. Extensive experiments show that our approach outperforms 16 state-of-the-art methods remarkably on three public image benchmarks.


title: Resilience of LTE networks against smart jamming attacks

abstract: LTE/LTE-A networks have been successfully providing advanced broadband services to millions of users worldwide. Lately, it has been suggested to use LTE networks for mission-critical applications like public safety, smart grid and military communications. We have previously shown that LTE networks are vulnerable to Denial-of-Service (DOS) and loss of service attacks from smart jammers. In this paper, we extend our previous work on resilience of LTE networks to wideband multipath fading channel, SINR estimation in frequency domain and computation of utilities based on observable parameters under the framework of single-shot and repeated games with asymmetric information. In a single-shot game formulation, network utility is severely compromised at its solutions, i.e. at the Nash Equilibria (NE). We propose evolved repeated-game strategy algorithms to combat smart jamming attacks that can be implemented in existing deployments using current technology.


title: Distributed Prioritized Experience Replay

abstract: We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.


title: Missing Data Recovery Based on Tensor-CUR Decomposition

abstract: Tensor completion is a higher way analog of matrix completion, which has proven to be a powerful tool for data analysis. In this paper, we formulate the missing data recovery problem of a three-way tensor as a tensor completion problem. We propose a novel tensor completion method based on tensor-CUR decomposition to estimate the missing data from limited samples. Computational experiments demonstrate that the proposed method yields a superior performance over other existing approaches.


title: A New Proposed Dynamic Quantum with Re-Adjusted Round Robin Scheduling Algorithm and Its Performance Analysis

abstract: Scheduling is the central concept used frequently in Operating System. It helps in choosing the processes for execution. Round Robin (RR) is one of the most widely used CPU scheduling algorithm. But, its performance degrades with respect to context switching, which is an overhead and it occurs during each scheduling. Overall performance of the system depends on choice of an optimal time quantum, so that context switching can be reduced. In this paper, we have proposed a new variant of RR scheduling algorithm, known as Dynamic Quantum with Readjusted Round Robin (DQRRR) algorithm. We have experimentally shown that performance of DQRRR is better than RR by reducing number of context switching, average waiting time and average turn around time.


title: Study of the performance of RSSI based Bluetooth Smart indoor positioning

abstract: Recently, there is a high interest for appropriate systems and methods to localize the position of people and their devices in indoor environments (e.g. home, office, hospital). From technical and economical points of view, Bluetooth Low Energy (BLE) is one of the promising technologies in this field. This paper deals with the study of performances of this technology for these purposes. For the indoor localization, a received signal strength indication (RSSI) method is used. It was tested in ideal (anechoic chamber) and real (in the office) environment conditions. For the measurement campaign and evaluation of the obtained results a generic framework is proposed. Experimental results (simulation vs. measurement) show similar behaviour of the BLE technology in ideal (no signal reflection) and real (multipath propagation) transmission environments.


title: Defining, Assessing, and Promoting E‐Learning Success: An Information Systems Perspective*

abstract: This research advances the understanding of how to define, evaluate, and promote e-learning success from an information systems perspective. It introduces the E-Learning Success Model, which posits that the overall success of an e-learning initiative depends on the attainment of success at each of the three stages of e-learning systems development: system design, system delivery, and system outcome. To study this model, an online version of an undergraduate quantitative methods core course for business students is developed using a prototyping strategy. Four cycles of development are traced, each comprised analysis, design, implementation, testing, and enhancement. Findings from the study confirm the validity of using the proposed success model for e-learning success assessment. In addition, an action research methodology is also found to be a valuable impetus for promoting e-learning success through an iterative process of diagnosing, action planning, action taking, evaluating, and learning.


title: Real time bid optimization with smooth budget delivery in online advertising

abstract: Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.


title: SpiroSmart: using a microphone to measure lung function on a mobile phone

abstract: Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.


title: Alternating least squares for personalized ranking

abstract: Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.


title: From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process

abstract: Zero-shot learning (ZSL) is concerned with the recognition of previously unseen classes. It relies on additional semantic knowledge for which a mapping can be learned with training examples of seen classes. While classical ZSL considers the recognition performance on unseen classes only, generalized zero-shot learning (GZSL) aims at maximizing performance on both seen and unseen classes. In this paper, we propose a new process for training and evaluation in the GZSL setting; this process addresses the gap in performance between samples from unseen and seen classes by penalizing the latter, and enables to select hyper-parameters well-suited to the GZSL task. It can be applied to any existing ZSL approach and leads to a significant performance boost: the experimental evaluation shows that GZSL performance, averaged over eight state-of-the-art methods, is improved from 28.5 to 42.2 on CUB and from 28.2 to 57.1 on AwA2.


title: Can Personality Be Changed? The Role of Beliefs in Personality and Change

abstract: Using recent research, I argue that beliefs lie at the heart of personality and adaptive functioning and that they give us unique insight into how personality and functioning can be changed. I focus on two classes of beliefs—beliefs about the malleability of self-attributes and expectations of social acceptance versus rejection—and show how modest interventions have brought about important real-world changes. I conclude by suggesting that beliefs are central to the way in which people package their experiences and carry them forward, and that beliefs should play a more central role in the study of personality.


title: RFID and Face Recognition Based Security and Access Control System

abstract: In the past, most of the companies were using a magnetic stripe card which is a type of card capable of storing data by modifying the magnetism of tiny iron-based magnetic particles on a band of magnetic material on the card. Card has a Personal Identification Number (PIN) allow to access account electronically. These days, RFID card has been used widely. In this paper the researcher used RFID card instead of a magnetic stripe card because it does not need a physical contact. The researcher combine a face recognition system with RFID system to support the security system. If the PIN in the RFID card matches the recognized face then a response is send to a remote station by a modem. This paper is important because it enhance security. In this paper the researcher mix two type of security (FR and RFID) which make the security stronger and reliable. In turn, this give the user more control. The matlab program is developed and it met the design criteria and solve the paper problem


title: Matching Composite Sketches to Face Photos: A Component-Based Approach

abstract: The problem of automatically matching composite sketches to facial photographs is addressed in this paper. Previous research on sketch recognition focused on matching sketches drawn by professional artists who either looked directly at the subjects (viewed sketches) or used a verbal description of the subject's appearance as provided by an eyewitness (forensic sketches). Unlike sketches hand drawn by artists, composite sketches are synthesized using one of the several facial composite software systems available to law enforcement agencies. We propose a component-based representation (CBR) approach to measure the similarity between a composite sketch and mugshot photograph. Specifically, we first automatically detect facial landmarks in composite sketches and face photos using an active shape model (ASM). Features are then extracted for each facial component using multiscale local binary patterns (MLBPs), and per component similarity is calculated. Finally, the similarity scores obtained from individual facial components are fused together, yielding a similarity score between a composite sketch and a face photo. Matching performance is further improved by filtering the large gallery of mugshot images using gender information. Experimental results on matching 123 composite sketches against two galleries with 10,123 and 1,316 mugshots show that the proposed method achieves promising performance (rank-100 accuracies of 77.2% and 89.4%, respectively) compared to a leading commercial face recognition system (rank-100 accuracies of 22.8% and 52.0%) and densely sampled MLBP on holistic faces (rank-100 accuracies of 27.6% and 10.6%). We believe our prototype system will be of great value to law enforcement agencies in apprehending suspects in a timely fashion.


title: Multilevel Supply-Modulated Chireix Outphasing With Continuous Input Modulation

abstract: This paper presents a dynamic characterization of a multilevel (ML) Chireix outphasing (ML-CO) power amplifier (PA) with modulated signals. The ML-CO technique combines the advantages of envelope tracking and outphasing architectures by limiting the supply modulation to discrete levels with an efficient power-DAC modulator and using outphasing for fine amplitude control. We describe an experimental test bench that supplies the required phase- and time-aligned modulated signals for outphasing and supply modulation simultaneously. Pulsed characterization is used to design an ML memoryless polynomial DPD. The linearized ML-CO GaN X-band MMIC PA is demonstrated with fixed input drive levels for 9.3-dB peak-to-average power ratio (PAPR), 1.4-MHz and 11.3-dB PAPR, 10-MHz LTE signals with a 9.7-GHz carrier. For both signals, the average total power consumption is reduced by a factor of two when supply modulation is used. An investigation of control signal generation strategies is presented, including combinations of discrete supply and continuous input modulation. A simple exponential function tracks the optimal efficiency trajectory of the ML-CO PA, enabling overall efficiency improvements within 9 and 27 percentage points. This control strategy is applied to radar pulses with amplitude shaping and frequency chirp, with the efficiency improvement of up to 11.8 percentage points compared with constant-supply operation, with 29-dB improvement in spectral confinement.


title: Multilevel Voltage-Source-Converter Topologies for Industrial Medium-Voltage Drives

abstract: This paper presents a technology review of voltage-source-converter topologies for industrial medium-voltage drives. In this highly active area, different converter topologies and circuits have found their application in the market. This paper covers the high-power voltage-source inverter and the most used multilevel-inverter topologies, including the neutral-point-clamped, cascaded H-bridge, and flying-capacitor converters. This paper presents the operating principle of each topology and a review of the most relevant modulation methods, focused mainly on those used by industry. In addition, the latest advances and future trends of the technology are discussed. It is concluded that the topology and modulation-method selection are closely related to each particular application, leaving a space on the market for all the different solutions, depending on their unique features and limitations like power or voltage level, dynamic performance, reliability, costs, and other technical specifications.


title: Language-based multimodal displays for the handover of control in autonomous cars

abstract: This paper presents a first evaluation of multimodal language-based warnings for handovers of control in autonomous cars. A set of possible handover situations varying in urgency is described. A set of multimodal, language-based warnings for these situations is then introduced. All combinations of audio, tactile and visual warnings for handovers were evaluated in terms of perceived urgency, annoyance and alerting effectiveness. Results showed clear recognition of the warning urgency in this new context, as well as low perceived annoyance overall, and higher perceived effectiveness for critical warnings. The time of transition from self-driving to manual mode in the presence of the warnings was then evaluated. Results showed quicker transitions for highly urgent warnings and poor driving performance for unimodal visual warnings. These results provide a novel set of guidelines for an effective transition of control between car and driver in an autonomous vehicle.


title: Graph-Structured Sparse Optimization for Connected Subgraph Detection

abstract: Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy. Although a number of structured sparsity models have been explored, such as trees, groups, clusters, and paths, connected subgraphs have been rarely explored in the current literature. One of the main technical challenges is that there is no structured sparsity-inducing norm that can directly model the space of connected subgraphs, and there is no exact implementation of a projection oracle for connected subgraphs due to its NP-hardness. In this paper, we explore efficient approximate projection oracles for connected subgraphs, and propose two new efficient algorithms, namely, Graph-IHT and Graph-GHTP, to optimize a generic nonlinear objective function subject to connectivity constraint on the support of the variables. Our proposed algorithms enjoy strong guarantees analogous to several current methods for sparsity-constrained optimization, such as Projected Gradient Descent (PGD), Approximate Model Iterative Hard Thresholding (AM-IHT), and Gradient Hard Thresholding Pursuit (GHTP) with respect to convergence rate and approximation accuracy. We apply our proposed algorithms to optimize several well-known graph scan statistics in several applications of connected subgraph detection as a case study, and the experimental results demonstrate that our proposed algorithms outperform state-of-the-art methods.


title: Steady-state pharmacokinetics of rilpivirine under different meal conditions in HIV-1-infected Ugandan adults

abstract: OBJECTIVES ::: To investigate the effect of food on the steady-state pharmacokinetics of rilpivirine when administered as a fixed-dose combination tablet containing tenofovir disoproxil fumarate, emtricitabine plus rilpivirine (TDF/FTC/RPV) in HIV-1-infected Ugandan patients. ::: ::: ::: METHODS ::: This was an open-label, three-period, longitudinal pharmacokinetic study with patients serving as their own controls. Fifteen consenting and virologically suppressed HIV-1-infected adults were switched from an efavirenz-based regimen to TDF/FTC/RPV for 56 days. Enrolled patients underwent 24 h blood sampling with TDF/FTC/RPV dosing in the fasted state (day 42), with a low-fat meal (11 g of fat/353 kcal, day 49) and with a moderate-fat meal (19 g of fat/589 kcal, day 56; reference). A viral load assessment was performed on day 56. ::: ::: ::: RESULTS ::: Rilpivirine AUC0-24 was significantly decreased by 16% (geometric mean ratio, 90% CI: 0.84, 0.73-0.96) during administration in the fasted state when compared with AUC0-24 during administration with a moderate-fat meal. Similarly, rilpivirine C24 was significantly decreased by 21% (0.79, 0.65-0.97) in the fasted state compared with a moderate-fat meal. Pharmacokinetic parameters were unchanged during administration with a low-fat meal, except for C24, which was significantly increased by 15% (1.15, 1.01-1.31) when compared with the moderate-fat meal. Rilpivirine Cmax was similar under the three meal conditions. Virological suppression was unchanged at the end of the study. ::: ::: ::: CONCLUSIONS ::: A food effect was observed for steady-state pharmacokinetic parameters of rilpivirine (AUC0-24 and C24) when TDF/FTC/RPV was administered in the fasted state compared with the moderate-fat meal. The TDF/FTC/RPV formulation can be administered with either a low-fat or moderate-fat meal.


title: A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC

abstract: Split-merge moves are a standard component of MCMC algorithms for tasks such as multi-target tracking and fitting mixture models with unknown numbers of components. Achieving rapid mixing for split-merge MCMC has been notoriously difficult, and state-of-the-art methods do not scale well. We explore the reasons for this and propose a new split-merge kernel consisting of two sub-kernels: one combines a "smart" split move that proposes plausible splits of heterogeneous clusters with a "dumb" merge move that proposes merging random pairs of clusters; the other combines a dumb split move with a smart merge move. We show that the resulting smart-dumb/dumb-smart (SDDS) algorithm outperforms previous methods. Experiments with entity-mention models and Dirichlet process mixture models demonstrate much faster convergence and better scaling to large data sets.


title: Generalizing and Improving Weight Initialization

abstract: We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence.


title: GPU Scheduling on the NVIDIA TX2: Hidden Details Revealed

abstract: The push towards fielding autonomous-driving capabilities in vehicles is happening at breakneck speed. Semi-autonomous features are becoming increasingly common, and fully autonomous vehicles are optimistically forecast to be widely available in just a few years. Today, graphics processing units (GPUs) are seen as a key technology in this push towards greater autonomy. However, realizing full autonomy in mass-production vehicles will necessitate the use of stringent certification processes. Currently available GPUs pose challenges in this regard, as they tend to be closed-source “black boxes” that have features that are not publicly disclosed. For certification to be tenable, such features must be documented. This paper reports on such a documentation effort. This effort was directed at the NVIDIA TX2, which is one of the most prominent GPU-enabled platforms marketed today for autonomous systems. In this paper, important aspects of the TX2’s GPU scheduler are revealed as discerned through experimental testing and validation.


title: Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease

abstract: Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features. Feature selection is the problem of selecting informative features among all features. Materials and Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We obtained an accuracy of 80.63% using reduced 11 feature set. The proposed method compare to the accuracy of 5 machine learning feature selections. The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks


title: Current and future trends in AI

abstract: During the past 70+ years of research and development in the domain of Artificial Intelligence (AI) we observe three principal, historical waves: embryonic, embedded and embodied AI. As the first two waves have demonstrated huge potential to seed new technologies and provide tangible business results, we describe likely developments of embodied AI in the next 25-35 years. We postulate that the famous Turing Test was a noble goal for AI scientists, making key, historical inroads - while we believe that Biological Systems Intelligence and the Insect/Swarm Intelligence analogy/mimicry, though largely disregarded, represents the key to further developments. We describe briefly the key lines of past and ongoing research, and outline likely future developments in this remarkable field.


title: Treatment of menorrhagia with two levonorgestrel intra-uterine systems in congenital uterine anomalies

abstract: A 29-year-old lady had a transvaginal scan that showed a septum in the uterus. There were no fibroids and the endometrial echo in each cornu was linear. The septum extended from the level of the cervical isthmus to the uterine fundus. She did not plan a pregnancy for a few years but wished to conserve her fertility. After counselling she opted to try the LNG-IUS. In view of the two uterine cavities, two IUSs were inserted, one in each cavity. The insertion was ultrasound guided and confirmed. The procedure was done under a cervical anaesthetic block.


title: Perinatal risk factors for developmental dysplasia of the hip

abstract: AIMS ::: To identify perinatal risk factors for developmental dysplasia of the hip (DDH) and define the risk for each factor. ::: ::: ::: METHODS ::: In this case control study, using logistic regression analysis, all 1127 cases of isolated DDH live born in South Australia in 1986-93 and notified to the South Australian Birth Defects Register were included; controls comprised 150130 live births in South Australia during the same period without any notified congenital abnormalities. ::: ::: ::: RESULTS ::: Breech presentation, oligohydramnios, female sex and primiparity were confirmed as risk factors for DDH. Significant findings were an increased risk for vaginal delivery over caesarean section for breech presentation (as well as an increased risk for emergency section over elective section), high birthweight (> or = 4000 g), postmaturity and older maternal age; multiple births and preterm births had a reduced risk. There was no increased risk for caesarean section in the absence of breech presentation. For breech presentation, the risk of DDH was estimated to be at least 2.7% for girls and 0.8% for boys; a combination of factors increased the risk. ::: ::: ::: CONCLUSIONS ::: It is suggested that the risk factors identified be used as indications for repeat screening at 6 weeks of age and whenever possible in infancy. Other indications are family history and associated abnormalities.


title: Automated person recognition by walking and running via model-based approaches

abstract: Gait enjoys advantages over other biometrics in that it can be perceived from a distance and is di cult to disguise.Current approaches are mostly statistical and concentrate on walking only.By analysing leg motion we show how we can recognise people not only by the walking gait,but also by the running gait.This is achieved by either of two new modelling approaches which employ coupled oscillators and the biomechanics of human locomotion as the underlying concepts.These models give a plausible method for data reduction by providing estimates of the inclination of the thigh and of the leg,from the image data. Both approaches derive a phase-weighted Fourier description gait signature by automated non-invasive means.One approach is completely automated whereas the other requires speci cation of a single parameter to distinguish between walking and running.Results show that both gaits are potential biometrics,with running being more potent.By its basis in evidence gathering,this new technique can tolerate noise and low resolution.


title: Attitudes toward online shopping and the Internet

abstract: Since the explosion of the Web as a business medium, one of its primary uses has been for marketing. Soon, the Web will become a critical distribution channel for the majority of successful enterprises. The mass media, consumer marketers and advertising agencies seem to be in the midst of Internet discovery and exploitation. Before a company can envision what might sell online in the coming years, it must first understand the attitudes and behaviour of its potential customers. Hence, this study examines attitudes toward various aspects of online shopping and provides a better understanding of the potential of electronic commerce for both researchers and practitioners.


title: A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction

abstract: In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet), to understand how spatiotemporal memories might be learned and encoded in the recurrent circuits in the visual cortical hierarchy for predicting future video frames. This neurally inspired model operates in the analysis-by-synthesis framework. It contains a feed-forward path that computes and encodes spatiotemporal features of successive complexity and a feedback path for the successive levels to project their interpretations to the level below. Within each level, the feed-forward path and the feedback path intersect in a recurrent gated circuit, instantiated in a LSTM module, to generate a prediction or explanation of the incoming signals. The network learns its internal model of the world by minimizing the errors of its prediction of the incoming signals at each level of the hierarchy. We found that hierarchical interaction in the network increases semantic clustering of global movement patterns in the population codes of the units along the hierarchy, even in the earliest module. This facilitates the learning of relationships among movement patterns, yielding state-of-the-art performance in long range video sequence predictions in the benchmark datasets. The network model automatically reproduces a variety of prediction suppression and familiarity suppression neurophysiological phenomena observed in the visual cortex, suggesting that hierarchical prediction might indeed be an important principle for representational learning in the visual cortex.


title: Performance of Groundplane Shaping in Four-Element Dualband MIMO Antenna

abstract: This work presents performance of groundplane shaping and its effect in four element dualband multiple input multiple output (MIMO) antenna. This proposed four element dualband MIMO antenna consists of four bowtie dipole antenna which operates at 1800 MHz (low frequency) and 2300 MHz (high frequency). This proposed four element dualband MIMO antenna occupies a 270 x 210 x 100 mm 3 of FR 4 substrate. We use four types of groundplane pattern i.e. full groundplane, cornered spatial groundplane,crossed middle groundplane, and spiral groundplane. These various grounplane patterns influence the performance of main parameters of dualband MIMO antenna. Cornered spatial groundplane pattern yields a largest bandwidth (VSWR ≤ 2) 282 MHz or 15.24% of center frequency at low frequency. Full groundplane pattern creates 135.2 MHz at high frequency. In addition, cornered spatial groundplane pattern also generates a lowest VSWR that is valued 1.21 at both low frequency and high frequency. The S parameters, basically both cornered spatial and full groundplane pattern produce a better return loss than two others. All four groundplane patterns deliver equally a mutual coupling parameter.The last, this proposed four element dualband MIMO with various groundplane patterns gives a good farfield properties i.e. gain, radiation pattern, H-E field.


title: Determining Uses and Gratifications for the Internet

abstract: Uses and gratifications (U&G) is a media use paradigm from mass communications research that guides the assessment of consumer motivations for media usage and access. It has been used previously in research and decision making related to the promotion of emerging radio and television media. Recent adaptations of U&G research to the Internet are incomplete and have not identified important new Internet-specific gratifications. This paper empirically derives dimensions of consumer Internet use and usage gratifications among customers of a prominent Internet Service Provider (ISP). Results describe three key dimensions related to consumer use of the Internet, including process and content gratifications as previously found in studies of television, as well as an entirely new social gratification that is unique to Internet use. All three dimensions of gratification are relevant to managing the Internet as a commercial medium, and measures developed from the gratification profiles identified here can serve as trait-valid scales in future Internet and e-commerce research.


title: Security issues and challenges of Vehicular Ad Hoc Networks (VANET)

abstract: The security of Vehicular Ad Hoc Networks (VANET) has mostly directed the attention of today research efforts, while comprehensive solutions to protect the network from adversary and attacks still need to be improved, trying to reach a satisfactory level, for the driver and manufacturer to achieve safety of life and infotainment. The need for a robust VANET networks is strongly dependent on their security and privacy features, which will discussed in this paper. VANET facing many challenges that been addressed in this research, we also discuss a set of solutions presented for these challenges and problems; and we made critics for these solutions and proposed suitable solutions for some of these problems.


title: Hybrid DC/DC converter for EV OBC with single transformer

abstract: This paper suggests hybrid PWM DC/DC converter comprised of FB converter and PWM resonant converter for EV OBC. It has hybrid operational characteristics that switching characteristic follow PWM resonant converter and output characteristic are similar to those of FB converter. They are coupled in single transformer so that power distribution problem that causes size increase of transformer can be solved. Also, the proposed structure helps to simplify the circuit by removing dissipative snubber circuitry and output current filter because transformer leakage inductance is used for resonant parameter and the output inductor current ripple is very small. Top switches can be operated with zero-voltage-switching (ZVS) and bottom switching devices can have ZVS and zero-current-switching (ZCS) by proper design of the resonant tank. The feasibility of the proposed charger is verified with a 6.6kW prototype implemented by design procedure.


title: Commutativity, Associativity, and Public Key Cryptography

abstract: In this paper, we will study some possible generalizations of the famous Diffie-Hellman algorithm. As we will see, at the end, most of these generalizations will not be secure or will be equivalent to some classical schemes. However, these results are not always obvious and moreover our analysis will present some interesting connections between the concepts of commutativity, associativity, and public key cryptography.


title: Containers and Cloud: From LXC to Docker to Kubernetes

abstract: This issue's "Cloud Tidbit" focuses on container technology and how it's emerging as an important part of the cloud computing infrastructure. It looks at Docker, an open source project that automates the faster deployment of Linux applications, and Kubernetes, an open source cluster manager for Docker containers.


title: Tag-Latent Dirichlet Allocation: Understanding Hashtags and Their Relationships

abstract: A hash tag is defined to be a word or phrase prefixed with the symbol "#". It is widely used in current social media sites including Twitter and Google+, and serves as a significant meta tag to categorize users' messages, to propagate ideas and topic trends. The use of hash tags has become an integral part of the social media culture. However, the free-form nature and the varied contexts of hash tags bring challenges: how to understand hash tags and discover their relationships? In this paper, we propose Tag-Latent Dirichlet Allocation (TLDA), a new topic modeling approach to bridge hash tags and topics. TLDA extends Latent Dirichlet Allocation by incorporating the observed hash tags in the generative process. In TLDA, a hash tag is mapped into the form of a mixture of shared topics. This representation further enables the analysis of the relationships between the hash tags. Applying our model to tweet data, we first illustrate the ability of our approach to explain hard-to-understand hash tags with topics. We also demonstrate that our approach enables users to further analyze the relationships between the hash tags.


title: Using Undergraduate Researchers to Build Vector and West Nile Virus Surveillance Capacity

abstract: Vector surveillance for infectious diseases is labor intensive and constantly threatened by budget decisions. We report on outcomes of an undergraduate research experience designed to build surveillance capacity for West Nile Virus (WNV) in Montana (USA). Students maintained weekly trapping stations for mosquitoes and implemented assays to test for WNV in pools of Culex tarsalis. Test results were verified in a partnership with the state health laboratory and disseminated to the ArboNET Surveillance System. Combined with prior surveillance data, Cx. tarsalis accounted for 12% of mosquitoes with a mean capture rate of 74 (±SD = 118) Cx. tarsalis females per trap and a minimum infection rate of 0.3 infected mosquitoes per 1000 individuals. However, capture and infection rates varied greatly across years and locations. Infection rate, but not capture rate, was positively associated with the number of WNV human cases (Spearman’s rho = 0.94, p < 0.001). In most years, detection of the first positive mosquito pool occurred at least a week prior to the first reported human case. We suggest that undergraduate research can increase vector surveillance capacity while providing effective learning opportunities for students.


On Foundation and Construction of Physical Unclonable Functions.


title: Semantic object removal with convolutional neural network feature-based inpainting approach

abstract: Object removal is a popular image manipulation technique, which mainly involves object segmentation and image inpainting two technical problems. In the conventional object removal framework, the object segmentation part needs a mask or artificial pre-processing; and the inpainting technique still requires further improving the quality. In this paper, we propose a new framework of object removal using the techniques of deep learning. Conditional random fields as recurrent neural networks (CRF-RNN) is used to segment the target in sematic, which can avoid the trouble of mask or artificial pre-processing for object segmentation. In inpainting part, a new method for inpainting the missing region is proposed. Besides, the representation features are calculated from the convolutional neural network (CNN) feature maps of the neighbor regions of the missing region. Then, large-scale bound-constrained optimization (L-BFGS) is used to synthesize the missing region based on the CNN representation features of similarity neighbor regions. We evaluate the proposed method by applying it to different kinds of images and textures for object removal and inpainting. Experimental results demonstrate that our method is better than the conventional method in terms of inpainting applications and object removal.


title: Risk-Sensitive Optimal Control for Markov Decision Processes with Monotone Cost

abstract: The existence of an optimal feedback law is established for the risk-sensitive optimal control problem with denumerable state space. The main assumptions imposed are irreducibility and anear monotonicity condition on the one-step cost function. A solution can be found constructively using either value iteration or policy iteration under suitable conditions on initial feedback law.


title: Cross-Selling the Right Product to the Right Customer at the Right Time:

abstract: Abstract Firms are challenged to improve the effectiveness of cross-selling campaigns. the authors propose a customer-response model that recognizes the evolvement of customer demand for various products; the possible multifaceted roles of cross-selling solicitations for promotion, advertising, and education; and customer heterogeneous preference for communication channels. they formulate cross-selling campaigns as solutions to a stochastic dynamic programming problem in which the firm's goal is to maximize the long-term profit of its existing customers while taking into account the development of customer demand over time and the multistage role of cross-selling promotion. the model yields optimal cross-selling strategies for how to introduce the right product to the right customer at the right time using the right communication channel. applying the model to panel data with cross-selling solicitations provided by a national bank, the authors demonstrate that households have different preferences and res...


title: Classifying streaming of Twitter data based on sentiment analysis using hybridization

abstract: Twitter is a social media that developed rapidly in today’s modern world. As millions of Twitter messages are sent day by day, the value and importance of developing a new technique for detecting spammers become significant. Moreover, legitimate users are affected by means of spams in the form of unwanted URLs, irrelevant messages, etc. Another hot topic of research is sentiment analysis that is based on each tweet sent by the user and opinion mining of the customer reviews. Most commonly natural language processing is used for sentiment analysis. The text is collected from user’s tweets by opinion mining and automatic sentiment analysis that are oriented with ternary classifications, such as “positive,” “neutral,” and “negative.” Due to limited size, unstructured nature, misspells, slangs, and abbreviations, it is more challenging for researchers to find sentiments for Twitter data. In this paper, we collected 600 million public tweets using URL-based security tool and feature generation is applied for sentiment analysis. The ternary classification is processed based on preprocessing technique, and the results of tweets sent by the users are obtained. We use a hybridization technique using two optimization algorithms and one machine learning classifier, namely particle swarm optimization and genetic algorithm and decision tree for classification accuracy by sentiment analysis. The results are compared with previous works, and our proposed method shows a better analysis than that of other classifiers.


title: Alors: An algorithm recommender system

abstract: Algorithm selection (AS), selecting the algorithm best suited for a particular problem instance, is acknowledged to be a key issue to make the best out of algorithm portfolios. This paper presents a collaborative filtering approach to AS. Collaborative filtering, popularized by the Netflix challenge, aims to recommend the items that a user will most probably like, based on the previous items she liked, and the items that have been liked by other users. As first noted by Stern et al. [47], algorithm selection can be formalized as a collaborative filtering problem, by considering that a problem instance “likes better“ the algorithms that achieve better performance on this particular instance. ::: ::: Two merits of collaborative filtering (CF) compared to the mainstream algorithm selection (AS) approaches are the following. Firstly, mainstream AS requires extensive and computationally expensive experiments to learn a performance model, with all algorithms launched on all problem instances, whereas CF can exploit a sparse matrix, with a few algorithms launched on each problem instance. Secondly, AS learns a performance model as a function of the initial instance representation, whereas CF builds latent factors to describe algorithms and instances, and uses the associated latent metrics to recommend algorithms for a specific problem instance. A main contribution of the proposed algorithm recommender Alors system is to handle the cold start problem – emitting recommendations for a new problem instance – through the non-linear modeling of the latent factors based on the initial instance representation, extending the linear approach proposed by Stern et al. [47]. ::: ::: The merits and generality of Alors are empirically demonstrated on the ASLib [6] and OpenML [53] benchmarks.


title: Accounting for the Contradictory Organizational Consequences of Information Technology: Theoretical Directions and Methodological Implications

abstract: Although much contemporary thought considers advanced information technologies as either determinants or enablers of radical organizational change, empirical studies have revealed inconsistent findings to support the deterministic logic implicit in such arguments. This paper reviews the contradictory empirical findings both across studies and within studies, and proposes the use of theories employing a logic of opposition to study the organizational consequences of information technology. In contrast to a logic of determination, a logic of opposition explains organizational change by identifying forces both promoting change and impeding change. Four specific theories are considered: organizational politics, organizational culture, institutional theory, and organizational learning. Each theory is briefly described to illustrate its usefulness to the problem of explaining information technology's role in organizational change. Four methodological implications of using these theories are also discussed: empirical identification of opposing forces, statement of opposing hypotheses, process research, and employing multiple interpretations.


title: A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images

abstract: In recent years, deep learning especially deep convolutional neural networks (DCNN) has made great progress. Many researchers take advantage of different DCNN models to do object detection in remote sensing. Different DCNN models have different advantages and disadvantages. But in the field of remote sensing, many scholars usually do comparison between DCNN models and traditional machine learning. In this paper, we compare different state-of-the-art DCNN models mainly over two publicly available remote sensing datasets—airplane dataset and car dataset. Such comparison can provide guidance for related researchers. Besides,we provide suggestions for fine-tuning different DCNN models. Moreover, for DCNN models including fully connected layers, we provide a method to save storage space.


title: Promising Future of Metamaterials

abstract: This article summarized and reviewed the realization and applications of the waveguide-based metamaterial structures. In particular, an overview of the existing metamaterial elements was presented. We discussed the possible solutions to enable wave propagation below the waveguide cutoff frequency by loading these elements. Then a detailed analysis on those waveguide unit-cells loaded with different metamaterial elements was provided based on HFSS simulation. Various guided and radiated microwave applications based on these novel metamaterial-based waveguide structures were presented. Those obtained simulation and experimental results were found to be in good agreement with the theoretical prediction. These applications also demonstrate that the combination of waveguide structure and metamaterials offers an excellent alternative approach to the design of miniaturized and high-performance microwave components. Waveguide has high-power handling capability and high Q-factor. It also provides a uniform and lossless e-negative (μ-negative) environment when operated based on the TE modes (TM modes) below the cutoff frequency. This is a promising and potentially rewarding research topic. We are looking forward to achieving more truly high-performance metamaterial devices by taking advantages of these features in the near future.


title: Organizational culture and leadership in ERP implementation

abstract: This paper theorizes how leadership affects ERP implementation by fostering the desired organizational culture. We contend that ERP implementation success is positively related with organizational culture along the dimensions of learning and development, participative decision making, power sharing, support and collaboration, and tolerance for risk and conflicts. In addition, we identify the strategic and tactical actions that the top management can take to influence organizational culture and foster a culture conducive to ERP implementation. The theoretical contributions and managerial implications of this study are discussed.


title: Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions

abstract: In this paper we present the results of a roleplay survey instrument administered to 1001 online survey respondents to study both the relationship between demographics and phishing susceptibility and the effectiveness of several anti-phishing educational materials. Our results suggest that women are more susceptible than men to phishing and participants between the ages of 18 and 25 are more susceptible to phishing than other age groups. We explain these demographic factors through a mediation analysis. Educational materials reduced users' tendency to enter information into phishing webpages by 40% percent; however, some of the educational materials we tested also slightly decreased participants' tendency to click on legitimate links.


title: Multimodal classification of Alzheimer's disease and mild cognitive impairment

abstract: Abstract Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers.


title: Collaborative Multifeature Fusion for Transductive Spectral Learning

abstract: Much existing work of multifeature learning relies on the agreement among different feature types to improve the clustering or classification performance. However, as different feature types could have different data characteristics, such a forced agreement among different feature types may not bring a satisfactory result. We propose a novel transductive learning approach that considers multiple feature types simultaneously to improve the classification performance. Instead of forcing different feature types to agree with each other, we perform spectral clustering in different feature types separately. Each data sample is then described by a co-occurrence of feature patterns among different feature types, and we apply these feature co-occurrence representations to perform transductive learning, such that data samples of similar feature co-occurrence pattern will share the same label. As the spectral clustering results in different feature types and the formed co-occurrence patterns influence each other under the transductive learning formulation, an iterative optimization approach is proposed to decouple these factors. Different from co-training that need to iteratively update individual feature type, our method allows all feature types to collaborate simultaneously. It can naturally handle multiple feature types together and is less sensitive to noisy feature types. The experimental results on synthetic, object, and action recognition datasets all validate the advantages of our method compared to state-of-the-art methods.


title: On-chip ESD protection design by using polysilicon diodes in CMOS technology for smart-card applications

abstract: A novel on-chip electrostatic discharge (ESD) protection design by using polysilicon diodes as the ESD clamp devices in CMOS process is proposed in this paper. Different process splits have been experimentally evaluated to find a suitable doping concentration for optimizing the polysilicon diodes for both on-chip ESD protection design and the application requirements of the smart-card ICs. The secondary breakdown current (It2) of the polysilicon diodes under the forward- and reverse-bias conditions has been measured by the transmission-line-puIse (TLP) generator to investigate its ESD robustness. Moreover, by adding an efficient VDD-to-VSS clamp circuit into the IC, the human-body-model (HBM) ESD robustness of the IC with polysilicon diodes as the ESD clamp devices has been successfully improved from the original /spl sim/300 V to become /spl ges/3 kV. This design has been practically applied in a mass-production smart-card IC.


Computer mediated communication and hypertext: An approach to building structure into distance semin


title: Standing Still and Cutting in Line. The Culture of the Queue in India

abstract: The queue, or the line, is a defining feature of modern life. This paper examines this ubiquitous form in India as a species of comportment. It offers an ethnographic assessment of queues in Delhi. They are grouped into two types: the ‘massified’ queue, drawing on Jean-Paul Sartre’s work; and the ‘interactional’ queue, framed by Erving Goffman’s writings. The paper discusses heavily policed queues, whose linear and serial character is produced by institutional authority. These forms suggest that elites engineer such comportment as a sign of rational distribution and civic order. The paper then illustrates the interactional dynamics of queues from within. Queues are shown to be sites of subtle signalling, where social entitlements are contextually processed. The queue’s enforcement via external authority is thus leavened by auto-generated adaptations. By offering a fine-grained description of how queuing unfolds, the paper seeks to illuminate political norms and public conduct in a postcolonial democratic milieu.


title: Application Of Neural Network To Technical Analysis Of Stock Market Prediction

abstract: This paper presents a neural network model for technical analysis of stock market, and its application to a buying and selling timing prediction system for stock index. When the numbers of learning samples are uneven among categories, the neural network with normal learning has the problem that it tries to improve only the prediction accuracy of most dominant category. In this paper, a learning method is proposed for improving prediction accuracy of other categories, controlling the numbers of learning samples by using information about the importance of each category. Experimental simulation using actual price data is carried out to demonstrate the usefulness of the method.


title: Automated Facial Expressions Analysis in Schizophrenia: A Continuous Dynamic Approach

abstract: Facial expressions play a major role in psychiatric diagnosis, monitoring and treatment adjustment. We recorded 34 schizophrenia patients and matched controls during a clinical interview, and extracted the activity level of 23 facial Action Units (AUs), using 3D structured light cameras and dedicated software. By defining dynamic and intensity AUs activation characteristic features, we found evidence for blunted affect and reduced positive emotional expressions in patients. Further, we designed learning algorithms which achieved up to 85 % correct schizophrenia classification rate, and significant correlation with negative symptoms severity. Our results emphasize the clinical importance of facial dynamics, and illustrate the possible advantages of employing affective computing tools in clinical settings.


title: Link prediction: the power of maximal entropy random walk

abstract: Link prediction is a fundamental problem in social network analysis. The key technique in unsupervised link prediction is to find an appropriate similarity measure between nodes of a network. A class of wildly used similarity measures are based on random walk on graph. The traditional random walk (TRW) considers the link structures by treating all nodes in a network equivalently, and ignores the centrality of nodes of a network. However, in many real networks, nodes of a network not only prefer to link to the similar node, but also prefer to link to the central nodes of the network. To address this issue, we use maximal entropy random walk (MERW) for link prediction, which incorporates the centrality of nodes of the network. First, we study certain important properties of MERW on graph $G$ by constructing an eigen-weighted graph G. We show that the transition matrix and stationary distribution of MERW on G are identical to the ones of TRW on G. Based on G, we further give the maximal entropy graph Laplacians, and show how to fast compute the hitting time and commute time of MERW. Second, we propose four new graph kernels and two similarity measures based on MERW for link prediction. Finally, to exhibit the power of MERW in link prediction, we compare 27 various link prediction methods over 3 synthetic and 8 real networks. The results show that our newly proposed MERW based methods outperform the state-of-the-art method on most datasets.


title: Wideband GaN FET based limiter MMICs

abstract: The design and performance of wideband FET limiter MMICs utilizing GaN on SiC technology are presented. Two different single pole single throw (SPST) switch based circuit architectures are investigated, both of which are designed to cover the 1–6 GHz frequency range. Measured on-wafer s-parameter data demonstrates a maximum insertion loss of 0.6dB and minimum return loss of 14dB for both design variants. Continuous wave (CW) power data measured from 2–4GHz shows a linear transfer characteristic until about 16dBm input power. The feedback limiter exhibited less than 25dBm of flat leakage for a 40W input signal level while the feedforward design demonstrated less than 22dBm of leakage for a 50W CW input.


title: Genetic Algorithm Solution of the TSP Avoiding Special Crossover and

abstract: Abstract Ordinary representations of permutations in Genetic Algorithms (GA) is handicapped with producing offspring which aze not permutations at all. The conventional solution for crossover and mutation operations of permutations is to device ‘special’ operators. Unfortunately these operators suffer from violating the nature of crossover. Namely, considering the gene positions on the chromosome, these methods do not allow n-point crossover techniques which are known to favour building-block formations. In this work, an inversion sequence is proposed as the representation of a permutation. This sequence allows repetitive values and hence is robust under ordinary (n-point) crossover. There is a one-to-one mapping from ordinary permutation representation to the inversion sequence representation. The proposed method is used for solving TSPs and is compared to the well known PMX special crossover method. It is observed that this method outperforms PMX in convergence rate by a factor which can be as high as 1...


title: Large-scale vehicle re-identification in urban surveillance videos

abstract: Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re-identification (Re-Id) is an important yet frontier topic, which not only faces the challenges of enormous intra-class and subtle inter-class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re-Id: 1) massive vehicles captured in real-world traffic environment; and 2) applicable recurrence rate to give cross-camera vehicle search for vehicle Re-Id. To facilitate vehicle Re-Id research, we propose a large-scale benchmark dataset for vehicle Re-Id in the real-world urban surveillance scenario, named “VeRi”. It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2∼18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re-Id. Finally, we evaluate six competitive vehicle Re-Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.


title: Full 3D reconstruction of transparent objects

abstract: This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects.


VISUAL PERCEPTION IN JAPANESE ROCK GARDEN DESIGN


title: Novelty based Ranking of Human Answers for Community Questions

abstract: Questions and their corresponding answers within a community based question answering (CQA) site are frequently presented as top search results forWeb search queries and viewed by millions of searchers daily. The number of answers for CQA questions ranges from a handful to dozens, and a searcher would be typically interested in the different suggestions presented in various answers for a question. Yet, especially when many answers are provided, the viewer may not want to sift through all answers but to read only the top ones. Prior work on answer ranking in CQA considered the qualitative notion of each answer separately, mainly whether it should be marked as best answer. We propose to promote CQA answers not only by their relevance to the question but also by the diversification and novelty qualities they hold compared to other answers. Specifically, we aim at ranking answers by the amount of new aspects they introduce with respect to higher ranked answers (novelty), on top of their relevance estimation. This approach is common in Web search and information retrieval, yet it was not addressed within the CQA settings before, which is quite different from classic document retrieval. We propose a novel answer ranking algorithm that borrows ideas from aspect ranking and multi-document summarization, but adapts them to our scenario. Answers are ranked in a greedy manner, taking into account their relevance to the question as well as their novelty compared to higher ranked answers and their coverage of important aspects. An experiment over a collection of Health questions, using a manually annotated gold-standard dataset, shows that considering novelty for answer ranking improves the quality of the ranked answer list.


title: Inductive Supervised Quantum Learning.

abstract: In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.


Incidental Supervision: Moving beyond Supervised Learning.


title: Deformed Graph Laplacian for Semisupervised Learning

abstract: Graph Laplacian has been widely exploited in traditional graph-based semisupervised learning (SSL) algorithms to regulate the labels of examples that vary smoothly on the graph. Although it achieves a promising performance in both transductive and inductive learning, it is not effective for handling ambiguous examples (shown in Fig. 1). This paper introduces deformed graph Laplacian (DGL) and presents label prediction via DGL (LPDGL) for SSL. The local smoothness term used in LPDGL, which regularizes examples and their neighbors locally, is able to improve classification accuracy by properly dealing with ambiguous examples. Theoretical studies reveal that LPDGL obtains the globally optimal decision function, and the free parameters are easy to tune. The generalization bound is derived based on the robustness analysis. Experiments on a variety of real-world data sets demonstrate that LPDGL achieves top-level performance on both transductive and inductive settings by comparing it with popular SSL algorithms, such as harmonic functions, AnchorGraph regularization, linear neighborhood propagation, Laplacian regularized least square, and Laplacian support vector machine.


title: A review of overview+detail, zooming, and focus+context interfaces

abstract: There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.


title: Affect and Engagement in Game-BasedLearning Environments

abstract: The link between affect and student learning has been the subject of increasing attention in recent years. Affective states such as flow and curiosity tend to have positive correlations with learning while negative states such as boredom and frustration have the opposite effect. Student engagement and motivation have also been shown to be critical in improving learning gains with computer-based learning environments. Consequently, it is a design goal of many computer-based learning environments to encourage positive affect and engagement while students are learning. Game-based learning environments offer significant potential for increasing student engagement and motivation. However, it is unclear how affect and engagement interact with learning in game-based learning environments. This work presents an in-depth analysis of how these phenomena occur in the game-based learning environment, Crystal Island. The findings demonstrate that game-based learning environments can simultaneously support learning and promote positive affect and engagement.


title: Generic torque-maximizing design methodology of surface permanent-magnet vernier machine

abstract: A surface permanent-magnet vernier machine (SPMVM) has a toothed-pole structure, and its highly nonlinear relationship between its dimensions and magnetic field makes its design cumbersome. This paper presents a generic design methodology for the machine which brings out its torque-maximizing structure in a convenient manner. Various suggestions concerning the design of the SPMVM are also set forth.


title: Sequence Matching for Suspicious Activity Detection in Anti-Money Laundering

abstract: Developing effective suspicious activity detection methods has become an increasingly critical problem for governments and financial institutions in their efforts to fight money laundering. Previous anti-money laundering (AML) systems were mostly rule-based systems which suffered from low efficiency and could can be easily learned and evaded by money launders. Recently researchers have begun to use machine learning methods to solve the suspicious activity detection problem. However nearly all these methods focus on detecting suspicious activities on accounts or individual level. In this paper we propose a sequence matching based algorithm to identify suspicious sequences in transactions. Our method aims to pick out suspicious transaction sequences using two kinds of information as reference sequences: 1) individual account's transaction history and 2) transaction information from other accounts in a peer group. By introducing the reference sequences, we can combat those who want to evade regulations by simply learning and adapting reporting criteria, and easily detect suspicious patterns. The initial results show that our approach is highly accurate.