title: Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System

abstract: Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available. It is also a very challenging task mainly because of the lower amount of information in the visual articulations compared to the audible utterance. In this work, principle component analysis is applied to the image patches — extracted from the video data — to learn the weights of a two-stage convolutional network. Block histograms are then extracted as the unsupervised learning features. These features are employed to learn a recurrent neural network with a set of long short-term memory cells to obtain spatiotemporal features. Finally, the obtained features are used in a tandem GMM-HMM system for speech recognition. Our results show that the proposed method has outperformed the baseline techniques applied to the OuluVS2 audiovisual database for phrase recognition with the frontal view cross-validation and testing sentence correctness reaching 79% and 73%, respectively, as compared to the baseline of 74% on cross-validation.


title: Quality of life among parents seeking treatment for their child’s functional abdominal pain

abstract: PURPOSE ::: Dealing with a child who suffers from functional abdominal pain (FAP) is a major challenge for the child's parents. However, little is known about the quality of life (QoL) of this group of parents. Therefore, this cross-sectional study aimed to provide a comprehensive analysis of parental QoL among parents seeking treatment for their child's abdominal pain. ::: ::: ::: METHODS ::: 133 parents of 7-13-year-old children diagnosed with FAP reported on their health-related QoL (HRQoL), as assessed by the SF-12, and on caregiver-related QoL, as assessed by two CHQ-PF50 scales (emotional impact, time impact). T tests were used to compare the parents' scores on these measures with reference scores. Subgroups which were at risk of impairment were defined by cut-off scores. Determinants of parental QoL were identified by hierarchical regression analyses. ::: ::: ::: RESULTS ::: While the parents showed significantly poorer mental health compared to population-based reference samples (d = 0.33-0.58), their physical health did not differ. However, parents were severely strained with respect to the time impact and emotional impact of their child's health (d = 0.33-1.58). While 12.7-27.9% of the parents were at risk of poor HRQoL, 60.6-70.1% were highly strained due to the demands of their role as caregivers. Physical and mental health were best explained by parents' psychiatric symptoms, while parents' perception of their child's impairment additionally determined the high time and emotional impact. ::: ::: ::: CONCLUSIONS ::: Physical HRQoL is not impaired in the majority of parents seeking treatment for their child's functional abdominal pain. However, the time demands and worries due to the child's pain deserve specific attention. Psychosocial interventions for a child's FAP should include information provided to the parents about coping with time constraints and emotional impact. Further prospective studies are warranted.


title: Towards scalable and accurate item-oriented recommendations

abstract: Most recommenders research aims at personalized systems, which suggest items based on user profiles. However, in reality many systems deal with item-oriented recommendations. In such setups, given a single item of interest, the system needs to provide other related items, following patterns like "people who liked this also liked...". While item-oriented systems are central in their importance, they have been approached so far using very basic tools. We identify several hurdles faced by standard approaches to the item-oriented task. First, the sparseness of observed activities prevents establishing reliable similarity relations for many item pairs. Second, we address a scalability challenge at the retrieval stage present in many real-world systems: Given an item inventory, which may encompass millions of items, it is desired to identify the most related item pairs in a sub-quadratic time. This work addresses these two challenges, thereby improving both accuracy and scalability of item-oriented recommenders. Additionally, we propose an empirical evaluation scheme for comparing the quality of different solutions with encouraging results.


title: SecureSMS: prevention of SMS interception on Android platform

abstract: Damage caused by smishing has been rapidly increased. A major problem resulted from the smishing is to induce overcharging via micropayment service without interaction with a user. The overcharging is based on the interception of SMS that contains one-time password for the micropayments. Almost all the damage has occurred on android platform. Various solutions to prevent the smishing have been launched recently. However, none of them are able to completely protect it against SMS interception. In this paper, we address the limitations of the existing solutions and suggest a method which thoroughly can prevent SMS interception by means of the modified android platform. We demonstrate the proposed method by implementing it and developing an app which is replaced with the native SMS app. In addition, we suggest an additional idea to protect our mechanism against expected threats on the rooted environment.


title: Achieving 100% Throughput in an Input-Queued Switch

abstract: It is well known that head-of-line blocking limits the throughput of an input-queued switch with first-in-first-out (FIFO) queues. Under certain conditions, the throughput can be shown to be limited to approximately 58.6%. It is also known that if non-FIFO queueing policies are used, the throughput can be increased. However, it has not been previously shown that if a suitable queueing policy and scheduling algorithm are used, then it is possible to achieve 100% throughput for all independent arrival processes. In this paper we prove this to be the case using a simple linear programming argument and quadratic Lyapunov function. In particular, we assume that each input maintains a separate FIFO queue for each output and that the switch is scheduled using a maximum weight bipartite matching algorithm. We introduce two maximum weight matching algorithms: longest queue first (LQF) and oldest cell first (OCF). Both algorithms achieve 100% throughput for all independent arrival processes. LQF favors queues with larger occupancy, ensuring that larger queues will eventually be served. However, we find that LQF can lead to the permanent starvation of short queues. OCF overcomes this limitation by favoring cells with large waiting times.


title: Robust graph SLAM back-ends: A comparative analysis

abstract: In this work, we provide an in-depth analysis of several recent robust Simultaneous Localization And Mapping (SLAM) back-end techniques that aim to recover the correct graph estimate in the presence of outliers in loop closure constraints. We present a benchmark dataset for evaluation of such methods by augmenting the KITTI Vision Benchmark with ground truth as well as generated loop closure hypotheses and present a detailed analysis of recently proposed robust SLAM methods using this benchmark. We also look into how these methods achieve the desired robustness and what are the implications for the SLAM problem. We discuss the issues involved in using the output of these robust back-ends for tasks such as path planning and how they can be addressed. The problem of robustness needs to be addressed adequately in order to have a complete and reliable solution to the SLAM problem.


title: Stress Levels and Adaptability in Parents of Toddlers With and Without Autism Spectrum Disorders

abstract: The toddler years can be a particularly stressful time for all parents, however, parents of children with disabilities may experience additional sources of stress. Recent literature on early education for children with disabilities promotes inclusion with typical peers with increases in the availability of inclusive programs. However, little is known about early intervention inclusion programs and parental factors such as stress and adaptability. The current study expands the research for children with disabilities by investigating the associations of having a young child with an Autism Spectrum Disorder (ASD) on multiple dimensions of parental stress for mothers and fathers and how participation in an inclusive toddler program may be related to these stress levels. Results for this community sample are consistent with previous research indicating that that both mothers and fathers of children with ASD report significantly elevated levels of both child and parent related stress in comparison with parents ...


title: Post-adoption switching behavior for online service substitutes: A perspective of the push-pull-mooring framework

abstract: The post-adoption behaviors of online service users are critical performance factors for online service providers. To fill an academic gap that persists regarding bloggers' switching behavior across online service substitutes, this empirical study investigates which factors affect bloggers who switch social network sites, in an attempt to understand specifically how push, pull, and mooring factors shape their switching intentions. The data to test the hypotheses come from an online survey of 319 bloggers, analyzed using partial least squares techniques. The results confirm positive influences of push and pull effects, a negative influence of mooring effects, and an interactive effect of push and mooring on switching intentions. The push-pull-mooring framework thus is a useful tool for comprehending the competing forces that influence the use of online service substitutes. In particular, perceptions of weak connections and writing anxiety push bloggers away, whereas relative enjoyment and usefulness pull bloggers to social network sites; switching cost and past experience also inhibit a change. These findings offer key insights and implications for the competitive strategy choices of online service providers.


title: A holistic view of knowledge management strategy

abstract: Purpose – The purpose of this research is to seek to address the way in which knowledge is being transmitted among the members of a large corporation and suggests a realistic strategy that promises to be most appropriate for effective knowledge transfer in the new knowledge economy.Design/methodology/approach – The research involves an in‐depth case study of knowledge transfer strategies used in a UK‐based group within a high‐tech global corporation. which was purposively selected for data collection. A semi‐structured instrument was developed based on the review of the knowledge management literature. The data analysis procedure within the case study employed in this research was based on the approach proposed by Miles and Huberman.Findings – Drawing on an empirical work in a large high‐tech corporation, this paper suggests a hybrid strategy which recognises the interplay between the soft and hard mechanisms, and falls somewhere between the rather divergent mechanisms.Research limitations/implications – ...


title: On the requirements for successful GPS spoofing attacks

abstract: An increasing number of wireless applications rely on GPS signals for localization, navigation, and time synchronization. However, civilian GPS signals are known to be susceptible to spoofing attacks which make GPS receivers in range believe that they reside at locations different than their real physical locations. In this paper, we investigate the requirements for successful GPS spoofing attacks on individuals and groups of victims with civilian or military GPS receivers. In particular, we are interested in identifying from which locations and with which precision the attacker needs to generate its signals in order to successfully spoof the receivers. We will show, for example, that any number of receivers can easily be spoofed to one arbitrary location; however, the attacker is restricted to only few transmission locations when spoofing a group of receivers while preserving their constellation. In addition, we investigate the practical aspects of a satellite-lock takeover, in which a victim receives spoofed signals after first being locked on to legitimate GPS signals. Using a civilian GPS signal generator, we perform a set of experiments and find the minimal precision of the attacker's spoofing signals required for covert satellite-lock takeover.


title: Compact hashing with joint optimization of search accuracy and time

abstract: Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results demonstrate that hashing with compact codes can achieve promising performance for large scale similarity search. However, most of the previous hashing methods with compact codes only model and optimize the search accuracy. Search time, which is an important factor for hashing in practice, is usually not addressed explicitly. In this paper, we develop a new scalable hashing algorithm with joint optimization of search accuracy and search time simultaneously. Our method generates compact hash codes for data of general formats with any similarity function. We evaluate our method using diverse data sets up to 1 million samples (e.g., web images). Our comprehensive results show the proposed method significantly outperforms several state-of-the-art hashing approaches.


title: Maternal and Paternal Risk Factors for Cryptorchidism and Hypospadias: A Case–Control Study in Newborn Boys

abstract: Little is known on environmental risk factors for cryptorchidism and hypospadias, which are among the most frequent congenital abnormalities. The aim of our study was to identify risk factors for cryptorchidism and hypospadias, with a focus on potential endocrine disruptors in parental diet and occupation. In a case–control study nested within a cohort of 8,698 male births, we compared 78 cryptorchidism cases and 56 hypospadias cases with 313 controls. The participation rate was 85% for cases and 68% for controls. Through interviews, information was collected on pregnancy aspects and personal characteristics, lifestyle, occupation, and dietary phytoestrogen intake of both parents. Occupational exposure to potential endocrine disruptors was classified based on self-reported exposure and ratings of occupational hygienists based on job descriptions. Our findings indicate that paternal pesticide exposure was associated with cryptorchidism [odds ratio (OR) = 3.8; 95% confidence interval (95% CI), 1.1–13.4]. Smoking of the father was associated with hypospadias (OR = 3.8; 95% CI, 1.8–8.2). Maternal occupational, dietary, and lifestyle exposures were not associated with either abnormality. Both abnormalities were associated with suboptimal maternal health, a lower maternal education, and a Turkish origin of the parents. Being small for gestational age was a risk factor for hypospadias, and preterm birth was a risk factor for cryptorchidism. Because paternal pesticide exposure was significantly associated with cryptorchidism and paternal smoking was associated with hypospadias in male offspring, paternal exposure should be included in further studies on cryptorchidism and hypospadias risk factors.


title: Faster and Smaller N-Gram Language Models

abstract: N-gram language models are a major resource bottleneck in machine translation. In this paper, we present several language model implementations that are both highly compact and fast to query. Our fastest implementation is as fast as the widely used SRILM while requiring only 25% of the storage. Our most compact representation can store all 4 billion n-grams and associated counts for the Google n-gram corpus in 23 bits per n-gram, the most compact lossless representation to date, and even more compact than recent lossy compression techniques. We also discuss techniques for improving query speed during decoding, including a simple but novel language model caching technique that improves the query speed of our language models (and SRILM) by up to 300%.


title: Autism as a Disorder of Biological and Behavioral Rhythms: Toward New Therapeutic Perspectives

abstract: There is a growing interest in the role of biological and behavioral rhythms in typical and atypical development. Recent studies in cognitive and developmental psychology have highlighted the importance of rhythmicity and synchrony of motor, emotional, and interpersonal rhythms in early development of social communication. The synchronization of rhythms allows tuning and adaptation to the external environment. The role of melatonin in the ontogenetic establishment of circadian rhythms and the synchronization of the circadian clocks network suggests that this hormone might be also involved in the synchrony of motor, emotional, and interpersonal rhythms. Autism provides a challenging model of physiological and behavioral rhythm disturbances and their possible effects on the development of social communication impairments and repetitive behaviors and interests. This article situates autism as a disorder of biological and behavioral rhythms and reviews the recent literature on the role of rhythmicity and synchrony of rhythms in child development. Finally, the hypothesis is developed that an integrated approach focusing on biological, motor, emotional, and interpersonal rhythms may open interesting therapeutic perspectives for children with autism. More specifically, promising avenues are discussed for potential therapeutic benefits in autism spectrum disorder of melatonin combined with developmental behavioral interventions that emphasize synchrony, such as the Early Start Denver Model.


title: A Statistical Model for Credit Scoring

abstract: We derive a model for consumer loan default and credit card expenditure.The default model is based on statistical models for discrete choice, incontrast to the usual procedure of linear discriminant analysis. Themodel is then extended to incorporate the default probability in a modelof expected profit. The technique is applied to a large sample ofapplications and expenditure from a major credit card company. Thenature of the data mandates the use of models of sample selection forestimation. The empirical model for expected profit produces an optimalacceptance rate for card applications which is far higher than theobserved rate used by the credit card vendor based on the discriminantanalysis. I am grateful to Terry Seaks for valuable comments on anearlier draft of this paper and to Jingbin Cao for his able researchassistance. The provider of the data and support for this project hasrequested anonymity, so I must thank them as such. Their help andsupport are gratefully acknowledged. Participants in the appliedeconometrics workshop at New York University also provided useful commentary.


title: DEBVisDic - First Version of New Client-Server Wordnet Browsing and Editing Tool

abstract: In this paper, we present the new wordnet development tool ::: called DEBVisDic. It is built on the recently developed ::: platform for client-server XML databases, called DEB II. This ::: platform is able to cover many possible applications, from ::: which we concentrate on the new, complete reimplementation of ::: one of the most-spread wordnet editor and browser - VisDic. We ::: argue for the benefits the new DEB II platform brings to ::: wordnet editing and to XML databases in general. In the paper, ::: we describe the state of the implementation, the insides and ::: interfaces of the DEBVisDic tool. We also discuss its ::: functionality and some distinctions in comparison with other ::: dictionary writing systems.


A Low Oversampling Ratio 14-b 500-kHz ADC with a Self-Calibrated Multibit DAC


title: Spectral Inference Networks: Unifying Deep and Spectral Learning

abstract: We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.


title: ARbis Pictus: A Study of Language Learning with Augmented Reality

abstract: This paper describes "ARbis Pictus" --a novel system for immersive language learning through dynamic labeling of real-world objects in augmented reality. We describe a within-subjects lab-based study (N=52) that explores the effect of our system on participants learning nouns in an unfamiliar foreign language, compared to a traditional flashcard-based approach. Our results show that the immersive experience of learning with virtual labels on real-world objects is both more effective and more enjoyable for the majority of participants, compared to flashcards. Specifically, when participants learned through augmented reality, they scored significantly better by 7% (p=0.011) on productive recall tests performed same-day, and significantly better by 21% (p=0.001) on 4-day delayed productive recall post tests than when they learned using the flashcard method. We believe this result is an indication of the strong potential for language learning in augmented reality, particularly because of the improvement shown in sustained recall compared to the traditional approach.


title: Sociocultural critique of Piaget and Vygotsky

abstract: Piaget has su!ered a great deal of criticism that his theory of psychological development neglects the social nature of human development. Much of this criticism has come from researchers following a Vygotskian approach and comparing Piaget's approach unfavorably with that of Vygotsky. Smith (1995) refers us to Piaget's collected articles on sociology (Piaget, 1995) to argue convincingly that it is oversimpli"cation and misunderstanding to assume Piaget's neglect of the social nature of human development. We want to o!er our own critique of both Piaget and Vygotsky from a new, sociocultural perspective, recently emerging in several disciplines of social sciences (Heath, 1983; Latour, 1987; Lave & Wenger, 1991; McDermott, 1993; Rogo!, 1990). We do not consider ourselves followers of Vygotsky's theory but of a sociocultural approach, despite the fact that the sociocultural approach is itself heavily built on and in#uenced by Vygotsky's work. For a while, a sociocultural approach was an invisible by-product of e!orts by mainly US psychologists like Cole (1978), Wertsch (1985), Scribner (1984), Rogo! and Wertsch (1984) and others to reconstruct and continue Vygotsky's paradigm. Just as the medieval endeavor to bring a renaissance of ancient Greek art and culture gave birth to a new art and a new culture, we argue that the renaissance of Vygotsky has gradually produced a new theoretical approach* namely the sociocultural. Initial critiques of Piaget from a Vygotskyian perspective came when the sociocultural approach was into the earlier phases of development, when `psychologistsa2 were `increasingly interested in the e!ects of the social context of individuals' cognitive developmenta (Tudge & Rogo!, 1989, p. 17). In contrast, from a current sociocultural perspective, cognitive development is embedded in social contexts and their separation is considered impossible and, thus, cannot have `e!ectsa. Like Smith, we claim that there is an overlooked similarity between Piaget and Vygotsky. However, from a recent sociocultural perspective, we associate these similarities with a shared failure to recognize the unity of cognition and social context. Our paper is primarily


title: From Buildings to Smart Buildings—Sensing and Actuation to Improve Energy Efficiency

abstract: A holistic approach to reducing the energy footprint of large commercial buildings is proposed. A detailed energy use breakdown within a modern building is presented, leading to the key insight that, in addition to the HVAC system, the energy used by miscellaneous plug loads which include IT equipment must also be addressed. An actuation framework is proposed to control plug-loads and the HVAC system for energy savings.


title: Hierarchical and Dynamic k-Path Covers

abstract: A metric-independent data structure for spatial networks called k-all-path cover (k-APC) has recently been proposed. It involves a set of vertices that covers all paths of size k, and is a general indexing technique that can accelerate various path-related processes on spatial networks, such as route planning and path subsampling to name a few. Although it is a promising tool, it currently has drawbacks pertaining to its construction and maintenance. First, k-APCs, especially for large values of k, are computationally too expensive. Second, an important factor related to quality is ignored by a prevalent construction algorithm. Third, an existing algorithm only focuses on static networks. To address these issues, we propose novel k-APC construction and maintenance algorithms. Our algorithms recursively construct the layers of APCs, which we call the k-all-path cover hierarchy, by using vertex cover heuristics. This allows us to extract k-APCs for various values of k from the hierarchy. We also devise an algorithm to maintain k-APC hierarchies on dynamic networks. Our experiments showed that our construction algorithm can yield high solution quality, and has a short running time for large values of k. They also verified that our dynamic algorithm can handle an edge weight change within 40 ms.


title: ABY3: A Mixed Protocol Framework for Machine Learning

abstract: Machine learning is widely used to produce models for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and prediction stages. In this paper, we design and implement a general framework for privacy-preserving machine learning and use it to obtain new solutions for training linear regression, logistic regression and neural network models. Our protocols are in a three-server model wherein data owners secret share their data among three servers who train and evaluate models on the joint data using three-party computation (3PC). Our main contribution is a new and complete framework ($\textABY ^3$) for efficiently switching back and forth between arithmetic, binary, and Yao 3PC which is of independent interest. Many of the conversions are based on new techniques that are designed and optimized for the first time in this paper. We also propose new techniques for fixed-point multiplication of shared decimal values that extends beyond the three-party case, and customized protocols for evaluating piecewise polynomial functions. We design variants of each building block that is secure against \em malicious adversaries who deviate arbitrarily. We implement our system in C++. Our protocols are up to \em four orders of magnitude faster than the best prior work, hence significantly reducing the gap between privacy-preserving and plaintext training.


title: Deep learning for fixed model reuse

abstract: Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and videos. There are several studies have the sense of model reuse, by trying to reuse pre-trained deep networks architectures or deep model features to train a new deep model. They, however, neglect the fact that there are many other fixed models or features available. In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse). FMR utilizes the learning power of deep models to implicitly grab the useful discriminative information from fixed model/features that have been widely used in general tasks. We firstly arrange the convolution layers of a deep network and the provided fixed model/features in parallel, fully connecting to the output layer nodes. Then, the dependencies between the output layer nodes and the fixed model/features are knockdown such that only the raw feature inputs are needed when the model is being used for testing, though the helpful information in the fixed model/features have already been incorporated into the model. On one hand, by the FMR scheme, the required amount of training data can be significantly reduced because of the reuse of fixed model/features. On the other hand, the fixed model/features are not explicitly used in testing, and thus, the scheme can be quite useful in applications where the fixed model/features are protected by patents or commercial secrets. Experiments on five real-world datasets validate the effectiveness of FMR compared with state-of-the-art deep methods.


title: Improving Agricultural Science Teachers’ Work Attitude in Akwa Ibom State of Nigeria: The Financial Initiative Perspective

abstract: This research study carried out to investigate the influence of financial incentive initiatives on agricultural teachers’work attitude in Akwa Ibom State, Nigeria. One hypothesis was formulated to guide the study. Ex-post factoresearch design was adopted for the study. A total sample of 150 agricultural science teachers and 150 studentsdrawn from public secondary schools in the state were involved in the study. The financial Incentive InitiativeOpinion Inventory (FIIOI) and Agricultural Teachers’ Work Disposition (ATWD) questionnaires developed by theresearcher were the instruments used for data collection. The data collected were statistically analyzed using themean and independent t-test at 0.05 level of significance. The finding of the study indicated others, which financialincentive initiatives associated with adequate salary, and prompt payment, significantly influenced agriculturalscience teachers’ work attitude. Based on these finding, it was recommended among others that the governmentshould provide financial incentives to agricultural science teachers by paying their salaries adequately andpromptly. This will motivate them towards improved work attitude.


Factors Affecting Consumers ’ purchasing Decision through E-Commerce


title: Robust 1-bit Compressive Sensing via Gradient Support Pursuit

abstract: This paper studies a formulation of 1-bit Compressed Sensing (CS) problem based on the maximum likelihood estimation framework. In order to solve the problem we apply the recently proposed Gradient Support Pursuit algorithm, with a minor modification. Assuming the proposed objective function has a Stable Restricted Hessian, the algorithm is shown to accurately solve the 1-bit CS problem. Furthermore, the algorithm is compared to the state-of-the-art 1-bit CS algorithms through numerical simulations. The results suggest that the proposed method is robust to noise and at mid to low input SNR regime it achieves the best reconstruction SNR vs. execution time trade-off.


Chat-App Decryption Key Extraction


title: Early Pest Identification in Agricultural Crops using Image Processing Techniques

abstract: Early disease detection is a major challenge in agriculture field. Hence proper measures has to be taken to fight bioagressors of crops while minimizing the use of pesticides. The techniques ofmachine vision are extensively applied to agricultural science,and it has great perspective especially in the plant protection field,which ultimately leads to crops management. Our goal is early detection of bioagressors. The paper describes a software prototype system for pest detection on the infected images of different leaves. Images of the infected leaf are captured bydigital camera and processed using image growing, image segmentation techniques to detect infected parts of the partic ular plants.Then the detected part is been processed for futher feature extraction which gives general idea about pests. This proposes automatic detection and calculating area of infection on leaves of a whitefly (Trialeurodes vaporariorum Westwood) at a mature stage. I. INTRODUCTION A lot of research has been done on greenhouse agrosystems and more generally on protected crops to control pests and diseases by biological means instead of pesticides. Research in agriculture is aimed towards increase of productivity and food quality at reduced expenditure and with increased profit, which has received importance in recent time. A strong demand now exists in many countries for non-chemical control methods for pests or diseases. Greenhouses are considered as biophysical systems with inputs, outputs and control process loops. Most of these control loops are automatized (e.g., climate and fertirrigation control). However noautomatic methods are available which precisely and periodically detect the pests on plants. In fact, in production conditions, greenhouse staff periodically observes plants and search for pests. This manual method is to time consuming. Diagnosis is a most difficult task to perform manually as it is a function of a number of parameters such as environment, nutrient, organism etc. With the recent advancement in image processing and pattern recognition techniques, it is possible to develop an autono mous system for disease classification of crops. (2) In this paper, we focus on early pest detection. First, this implies to regularly observe the plants. Disease images are acquired using cameras or scanners. Then the acquired image has to be processed to interpret the


title: Complexity vs. performance: empirical analysis of machine learning as a service

abstract: Machine learning classifiers are basic research tools used in numerous types of network analysis and modeling. To reduce the need for domain expertise and costs of running local ML classifiers, network researchers can instead rely on centralized Machine Learning as a Service (MLaaS) platforms. In this paper, we evaluate the effectiveness of MLaaS systems ranging from fully-automated, turnkey systems to fully-customizable systems, and find that with more user control comes greater risk. Good decisions produce even higher performance, and poor decisions result in harsher performance penalties. We also find that server side optimizations help fully-automated systems outperform default settings on competitors, but still lag far behind well-tuned MLaaS systems which compare favorably to standalone ML libraries. Finally, we find classifier choice is the dominating factor in determining model performance, and that users can approximate the performance of an optimal classifier choice by experimenting with a small subset of random classifiers. While network researchers should approach MLaaS systems with caution, they can achieve results comparable to standalone classifiers if they have sufficient insight into key decisions like classifiers and feature selection.


title: A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data

abstract: Big data is a big business, and effective modeling of this data is key. This paper provides a comprehensive multidimensional analysis of various open source tools for machine learning with big data. An evaluation standard is proposed along with detailed comparisons of the frameworks discussed, with regard to algorithm availability, scalability, speed, and more. The major tools profiled are Mahout, MLlib, H2O, and SAMOA, along with the big data processing engines they utilize, including Hadoop MapReduce, Apache Spark, and Apache Storm. There is not yet one framework that "does it all", but this paper provides insight into each tool's strengths and weaknesses along with guidance on tool choice for specific needs.


title: A comparison of linear skinning techniques for character animation

abstract: Character animation is the task of moving a complex, artificial character in a life-like manner. A widely used method for character animation involves embedding a simple skeleton within a character model and then animating the character by moving the underlying skeleton. The character's skin is required to move and deform along with the skeleton. Research into this problem has resulted in a number of skinning frameworks. There has, however, been no objective attempt to compare these methods. We compare three linear skinning frameworks that are computationally efficient enough to be used for real-time animation: Skeletal Subspace Deformation, Animation Space and Multi-Weight Enveloping. These create a correspondence between the points on a character's skin and the underlying skeleton by means of a number of weights, with more weights providing greater flexibility. The quality of each of the three frameworks is tested by generating the skins for a number of poses for which the ideal skin is known. These generated skin meshes are then compared to the ideal skins using various mesh comparison techniques and human studies are used to determine the effect of any temporal artefacts introduced. We found that Skeletal Subspace Deformation lacks flexibility while Multi-Weight Enveloping is prone to overfitting. Animation Space consistently outperforms the other two frameworks.


title: Review and classification of gain cell eDRAM implementations

abstract: With the increasing requirement of a high-density, high-performance, low-power alternative to traditional SRAM, Gain Cell (GC) embedded DRAMs have gained a renewed interest in recent years. Several industrial and academic publications have presented GC memory implementations for various target applications, including high-performance processor caches, wireless communication memories, and biomedical system storage. In this paper, we review and compare the recent publications, examining the design requirements and the implementation techniques that lead to achievement of the required design metrics of these applications.


Editorial: Impression Management and Faking in Job Interviews


title: Potent Potassium-competitive Acid Blockers: A New Era for the Treatment of Acid-related Diseases

abstract: Conventional proton pump inhibitors (PPIs) are used as a first-line therapy to treat acid-related diseases worldwide. However, they have a number of limitations including slow onset of action, influence by cytochrome P450 polymorphisms, unsatisfactory effects at night, and instability in acidic conditions. Alternative formulations of conventional PPIs have been developed to overcome these problems; however, these drugs have only introduced small advantages for controlling acid secretion compared to conventional PPIs. Potassium-competitive acid blockers (P-CABs) were developed and have beneficial effects including rapid, long-lasting, and reversible inhibition of the gastric hydrogen potassium ATPase, the proton pump of the stomach. Vonoprazan was recently innovated as a novel, orally active P-CAB. It is currently indicated for the treatment of gastric and duodenal ulcers, reflux esophagitis, and prevention of low-dose aspirin- or nonsteroidal anti-inflammatory drug-related gastric and duodenal ulcer recurrence in Japan. Vonoprazan does not require enteric coating as it is acid-stable, and it can be taken without food because it is quickly absorbed. Vonoprazan accumulates in parietal cells under both acidic and neutral conditions. It does not require an acidic environment for activation, has long-term stability at the site of action, and has satisfactory safety and tolerability. Thus, vonoprazan may address the unmet medical need for the treatment of acid-related diseases.


title: Adversarial Contrastive Estimation

abstract: Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.


Summarization And Sentiment Analysis For Understanding Socially-Generated Content


Prevention of system deadlocks


title: File Encryption using Fisher-Yates Shuffle Shuffling scheme employing pseudo-random permutations

abstract: A permutation cipher is a very old form of cryptography. It works by moving letters around in a random pattern. In this paper we have implemented a new method of pseudorandom permutation. Along with this, a working algorithm has been provided that produces a block cipher using the mentioned procedure. The discussed algorithm operates on a file containing a stream of integers delimited by a space. The file is first converted to a two dimensional array. This array is randomly shuffled. Thereafter the encrypted cipher is obtained by recombining the keys with the shuffled array. For decryption, the cipher is again read into a two dimensional array. This time, the entire array is sub-divided into three parts. The first and the final part consist of the shuffled message. The second part consists of the keys. The elements of the shuffled array are then rearranged in accordance to the keys. The recombination results in retrieving the original file containing the stream of integers.


title: Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling

abstract: Chambers et al. present an improved method for neural differentiation of human pluripotent stem cells that avoids the use of stromal feeder cells and embryoid bodies. By combining two inhibitors of SMAD signaling, the protocol generates neural cells with an efficiency of >80%.


title: Design guidelines and tools for creating believable motion for personal robots

abstract: Robots use their movement to interact with the user. Their movement is a crucial part in designing the interfaces of personal robots. It has a major impact on how a robot is perceived by the user interacting with it. Subtle flaws in the movement can create a repelling feeling and therefore affect negatively the interaction. On the other hand, carefully designed little quirks can give the robot personality and make it more interesting. A possible relationship between appearance and motion on how a device is perceived is described by the 'Uncanny Valley' conceived by Prof. M. Mori in 1970. It is widely used as a design guideline for creating humanoid interface robots. It does not, however, describe the impact that the quality of movements has on the perception and the believability of a character. Conclusive frameworks for the animation of robots are rare and are often adapted from cartoon or computer animation. This paper presents a refined design guideline for the motion of robotic characters with respect to the design requirements naturalness, adequateness and development over time. Furthermore, we created a set of tools that eases the process of designing the movement by improving on common animation techniques such as key-framed or scripted animations. The tools preserve the freedom of the animator to define specific expressive motions while at the same time offering the full power of scripting technology.


title: Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study

abstract: Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.


title: Novel Through-Silicon-Via Inductor-Based On-Chip DC-DC Converter Designs in 3D ICs

abstract: There has been a tremendous research effort in recent years to move DC-DC converters on chip for enhanced performance. However, a major limiting factor to implementing on-chip inductive DC-DC converters is the large area overhead induced by spiral inductors. Thus, we propose using through-silicon-vias (TSVs), a critical enabling technique in three-dimensional (3D) integrated systems, to implement on-chip inductors for DC-DC converters. While existing literature show that TSV inductors are inferior compared with conventional spiral inductors due to substrate loss for RF applications, in this article, we demonstrate that it is not the case for DC-DC converters, which operate at relatively low frequencies. Experimental results show that by replacing conventional spiral inductors with TSV inductors, with almost the same efficiency and output voltage, up to 4.3× and 3.2× inductor area reduction can be achieved for the single-phase buck converter and the interleaved buck converter with magnetic coupling, respectively.


title: LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

abstract: Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by (i) developing a novel Long Short-Term Memorized Context Fusion (LSTM-CF) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and (ii) incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training. Specifically, contexts in photometric and depth channels are, respectively, captured by stacking several convolutional layers and a long short-term memory layer; the memory layer encodes both short-range and long-range spatial dependencies in an image along the vertical direction. Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts. At last, the fused contextual representation is concatenated with the convolutional features extracted from the photometric channels in order to improve the accuracy of fine-scale semantic labeling. Our proposed model has set a new state of the art, i.e., \({\mathbf{48.1}}\%\) and \({\mathbf{49.4}}\%\) average class accuracy over 37 categories (\({\mathbf{2.2}}\%\) and \({\mathbf{5.4}}\%\) improvement) on the large-scale SUNRGBD dataset and the NYUDv2 dataset, respectively.


title: Compact Tri-Band Bandpass Filter Based on $\lambda/4$ Resonators With U-Folded Coupled-Line

abstract: This letter presents a novel compact microstrip tri-band bandpass filter (BPF) based on $\lambda/4$ resonators. The first and third passbands are realized by coupling the two dual-band $\lambda/4$ stepped impedance resonators (SIRs) with synthesized frequency response. Meanwhile, the second passband in the middle is achieved by introducing the two coupled $\lambda/4$ uniform impedance resonators (UIRs). A non-uniform coupled-line with the U-folded shape on one line is then proposed to independently achieve the dissimilar external couplings as desired in the tri-passbands. In parallel, the quadruplet topology for each band is formulated by introducing an additional weak I/O cross coupling, thereby bringing one pair of transmission zeros at each side of all the triple passbands, In final, a tri-band BPF with the central frequencies of 1.8, 3.5, and 5.8 GHz, and respective fractional bandwidths of 7.0%, 5.0%, and 3.5% is designed and fabricated. The measured results show good consistence with the simulated ones.


title: Sampling, Filtering and Sparse Approximations on Combinatorial Graphs

abstract: In this paper we address sampling and approximation of functions on combinatorial graphs. We develop filtering on graphs by using Schr\"odinger's group of operators generated by combinatorial Laplace operator. Then we construct a sampling theory by proving Poincare and Plancherel-Polya-type inequalities for functions on graphs. These results lead to a theory of sparse approximations on graphs and have potential applications to filtering, denoising, data dimension reduction, image processing, image compression, computer graphics, visualization and learning theory.


title: A scalable approach for data-driven taxi ride-sharing simulation

abstract: As urban population grows, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. Despite great promise, researchers and policy makers lack adequate tools to assess tradeoffs and benefits of various ride-sharing strategies. Existing approaches either make unrealistic modeling assumptions or do not scale to the sizes of existing data sets. In this paper, we propose a real-time, data-driven simulation framework that supports the efficient analysis of taxi ride sharing. By modeling taxis and trips as distinct entities, our framework is able to simulate a rich set of realistic scenarios. At the same time, by providing a comprehensive set of parameters, we are able to study the taxi ride-sharing problem from different angles, considering different stakeholders' interests and constraints. To address the computational complexity of the model, we describe a new optimization algorithm that is linear in the number of trips and makes use of an efficient indexing scheme, which combined with parallelization, makes our approach scalable. We evaluate our framework and algorithm using real data — 360 million trips taken by 13,000 taxis in New York City during 2011 and 2012. The results demonstrate that our framework is effective and can provide insights into strategies for implementing city-wide ride-sharing solutions. We describe the findings of the study as well as a performance analysis of the model.


“ Should I Stay Or Should I Go ” : Clash of Opinions in the Brexit Twitter Debate


title: Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition

abstract: This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.


title: Selection of Negative Samples for One-class Matrix Factorization.

abstract: Many recommender systems have only implicit user feedback. The two possible ratings are positive and negative, but only part of positive entries are observed. One-class matrix factorization (MF) is a popular approach for such scenarios by treating some missing entries as negative. Two major ways to select negative entries are by sub-sampling a set with similar size to that of observed positive entries or by including all missing entries as negative. They are referred to as “subsampled” and “full” approaches in this work, respectively. Currently detailed comparisons between these two selection schemes on large-scale data are still lacking. One important reason is that the “full” approach leads to a hard optimization problem after treating all missing entries as negative. In this paper, we successfully develop efficient optimization techniques to solve this challenging problem so that the “full” approach becomes practically viable. We then compare in detail the two approaches “subsampled” and “full” for selecting negative entries. Results show that the “full” approach of including much more missing entries as negative yields better results.


title: Ritual Faults , Sins , and Legal Offences A Discussion about Two Patterns of Justice in Contemporary India

abstract: In India the religious dangers attendant upon judging had been mentioned in Sanskrit texts since the early centuries of the common era. This chapter shows how, though temple mediums and judges may appear to have nothing in common, they share some similarities: they both arbitrate cases, interpret or establish tfactst and ttrutht, and they pronounce judgments and verdicts. It demonstrates that although Indian courts appear to be secularized contexts of judgment and of decision-making as far as the trial proceedings are concerned, a religious or moral understanding of sin emerges, particularly in judicial rulings. The chapter examines the judgment of the village medium, which not unexpectedly is all about "sin" or wrongdoing, discusses judicial pronouncements, which, surprisingly may also be very much about sin. It also provide details of consultations with deities to show how sin or errors are defined within this ritual context. Keywords:India; ritual faults; Sanskrit texts; sin


title: Code search with input / output queries : Generalizing , ranking , and assessment

abstract: Generalize a semantic code search approach using input/output examples as queries.Approach extension utilizes symbolic execution to find relevant code with many paths.Rank source code results based on match strength between a specification and the code.Evaluate search results against state-of-the-practice approaches, Google and Merobase.Based on opinions of 30 programmers, our search returns the most relevant results. In this work we generalize, improve, and extensively assess our semantic source code search engine through which developers use an input/output query model to specify what behavior they want instead of how it may be implemented. Under this approach a code repository contains programs encoded as constraints and an SMT solver finds encoded programs that match an input/output query. The search engine returns a list of source code snippets that match the specification.The initial instantiation of this approach showed potential but was limited. It only encoded single-path programs, reported just complete matches, did not rank the results, and was only partly assessed. In this work, we explore the use of symbolic execution to address some of these technical shortcomings. We implemented a tool, Satsy, that uses symbolic execution to encode multi-path programs as constraints and a novel ranking algorithm based on the strength of the match between an input/output query and the program paths traversed by symbolic execution. An assessment about the relevance of Satsy's results versus other search engines, Merobase and Google, on eight novice-level programming tasks gathered from StackOverflow, using the opinions of 30 study participants, reveals that Satsy often out-performs the competition in terms of precision, and that matches are found in seconds.


title: Chinese License Plate Localization in Multi-Lane with Complex Background Based on Concomitant Colors

abstract: Color is an important feature of the Chinese license plate. In this paper, a license plate location method totally based on color information for the most common blue and yellow plates is proposed. First, use a special way to generate a grayscale image, highlighting the blue and yellow regions of the original image. Then by using the color concomitant property and transitions between the background and the characters, fake plates are removed, and real plates reserved. Finally, use the color information again to get a more accurate location result and remove the fake plates by using the relationship between the stroke width and the character size. The proposed method adopts two kinds of threshold methods: global threshold method and adaptive block threshold method. By automatically judging the illumination distribution of an image, the algorithm can decide which to use. The proposed method can be used to locate license plates in multi-lane with complex background, and works well even on images with uneven illumination. 2207 images from different traffic videos are used to do experiments, including 1314 images of daytime and 893 images of nighttime. Each image contains 0-7 plates, and the location rate achieves 95.01%.


title: Is Music an Evolutionary Adaptation?

abstract: Abstract: In contemplating the function and origin of music, a number of scholars have considered whether music might be an evolutionary adaptation. This article reviews the basic arguments related to evolutionary claims for music. Although evolutionary theories about music remain wholly speculative, musical behaviors satisfy a number of basic conditions, which suggests that there is indeed merit in pursuing possible evolutionary accounts.


title: A Formal Framework to Characterize Interpretability of Procedures

abstract: We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability.


title: Von Neumann Normalisation and Symptoms of Randomness: An Application to Sequences of Quantum Random Bits

abstract: Due to imperfections in measurement and hardware, the flow of bits generated by a quantum random number generator (QRNG) contains bias and correlation, two symptoms of non-randomness. There is no algorithmic method to eliminate correlation as this amounts to guaranteeing incomputability. However, bias can be mitigated: QRNGs use normalisation techniques such as von Neumann's method-the first and simplest technique for reducing bias-and other more efficient modifications. ::: ::: In this paper we study von Neumann un-biasing normalisation for an ideal QRNG operating 'to infinity', i.e. producing an infinite bitsequence. We show that, surprisingly, von Neumann un-biasing normalisation can both increase or decrease the (algorithmic) randomness of the generated sequences. The impact this has on the quality of incomputability of sequences of bits from QRNGs is discussed. ::: ::: A successful application of von Neumann normalisation-in fact, any un-biasing transformation-does exactly what it promises, un-biasing, one (among infinitely many) symptoms of randomness; it will not produce 'true' randomness, a mathematically vacuous concept.


title: Superresolution of License Plates in Real Traffic Videos

abstract: In this paper, a novel method to enhance license plate numbers of moving vehicles in real traffic videos is proposed. A high-resolution image of the number plate is obtained by fusing the information derived from multiple, subpixel shifted, and noisy low-resolution observations. The image to be superresolved is modeled as a Markov random field and is estimated from the observations by a graduated nonconvexity optimization procedure. A discontinuity adaptive regularizer is used to preserve the edges in the reconstructed number plate for improved readability. Experimental results are given on several traffic sequences to demonstrate the robustness of the proposed method to potential errors in motion and blur estimates. The method is computationally efficient as all operations can be implemented locally in the image domain


title: An Integrated Approach to Filling Holes in Meshes

abstract: Data obtained by scanning 3D models typically contains missing pieces and holes. This paper describes an integrated approach for filling holes in triangular meshes obtained during the process of surface reconstruction, which consists of three steps: triangulation, refinement and smoothing. The approach first generates a minimal triangulation to the hole, and then iteratively subdivides the patching meshes making it assort with the density of surrounding meshes. Finally, geometric optimization is enforced to the patching meshes for improving regularity. The approach can deal with arbitrary holes in oriented connected manifold meshes. Experimental results on several triangular meshes are presented.


title: Bundle Adjustment - A Modern Synthesis

abstract: This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than restricting attention to traditional nonlinear least squares.


title: Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training

abstract: To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire because labeled medical images are not usually available due to privacy issues, lack of experts available for annotation, underrepresentation of rare conditions and poor standardization. Lack of annotated data has been addressed in conventional vision applications using synthetic images refined via unsupervised adversarial training to look like real images. However, this approach is difficult to extend to general medical imaging because of the complex and diverse set of features found in real human tissues. We propose an alternative framework that uses a reverse flow, where adversarial training is used to make real medical images more like synthetic images, and hypothesize that clinically-relevant features can be preserved via self-regularization. These domain-adapted images can then be accurately interpreted by networks trained on large datasets of synthetic medical images. We test this approach for the notoriously difficult task of depth-estimation from endoscopy. We train a depth estimator on a large dataset of synthetic images generated using an accurate forward model of an endoscope and an anatomically-realistic colon. This network predicts significantly better depths when using synthetic-like domain-adapted images compared to the real images, confirming that the clinically-relevant features of depth are preserved.


title: Hierarchical Ray Tracing for Fast Volumetric Next-Best-View Planning

abstract: A mobile robot must have the ability of building a representation of its environment and the objects in it. To build a three-dimensional (3D) model of a physical object, several scans must be taken at different locations. Selecting each location is the next-best-view problem. Search based methods, where candidate views are generated and evaluated by a utility function, are a solution. However, such methods are slow for high resolution models given that the evaluation requires visibility computation in 3D. We propose a scene representation by octrees with a hierarchical ray tracing that reduces the visibility computation time. Such method performs a coarse ray tracing, except for the interesting volumes where a finer resolution is applied. The method decreases the computation time at least one order of magnitude. Saving time with this method leads to evaluate more constraints and more candidate views in high resolution models.


title: Memristor Crossbar-based Hardware Implementation of IDS Method

abstract: Ink drop spread (IDS) is the engine of an active learning method, which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system that is subjected to modeling. In spite of its excellent potential to solve problems such as classification and modeling compared with other soft-computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of the IDS method that is based on the memristor crossbar structure. In addition to simplicity, being completely real time, having low latency, and the ability to continue working properly after the occurrence of power failure are some of the advantages of our proposed circuit. Moreover, some of operations in the IDS method have fuzzy nature, and as we will show at the end of this paper, updation of rules in the IDS structure and spiky neural networks are very similar. Therefore, IDS can be considered as a new fuzzy implementation of artificial spiky neural networks as well.


Reactive power optimization with artificial bee colony algorithm


title: Adaptation of Word Vectors using Tree Structure for Visual Semantics

abstract: We propose a framework of word-vector adaptation, which makes vectors of visually similar concepts close to each other. Here, word vectors are real-valued vector representation of words, e.g., word2vec representation. Our basic idea is to assume that each concept has some hypernyms that are important to determine its visual features. For example, for a concept Swallow with hypernyms Bird, Animal and Entity, we believe Bird is the most important since birds have common visual features with their feathers etc. Adapted word vectors are obtained for each word by taking a weighted sum of a given original word vector and its hypernym word vectors. Our weight optimization makes vectors of visually similar concepts close to each other, by giving a large weight for such important hypernyms. We apply the adapted word vectors to zero-shot learning on the TRECVID 2014 semantic indexing dataset. We achieved 0.083 of Mean Average Precision, which is the best performance without using TRECVID training data to the best of our knowledge.


title: Crowd Density Estimation Using Texture Analysis and Learning

abstract: This paper presents an automatic method to detect abnormal crowd density by using texture analysis and learning, which is very important for the intelligent surveillance system in public places. By using the perspective projection model, a series of multi-resolution image cells are generated to make better density estimation in the crowded scene. The cell size is normalized to obtain a uniform representation of texture features. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris-Laplacian space is also applied. The texture feature vectors are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem of calculating the crowd density. Finally, based on the estimated density vectors, the SVM method is used again to solve the classification problem of detecting abnormal density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.


title: Polarity Loss for Zero-shot Object Detection

abstract: Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behaviour, zero-shot object detection aims to recognize and localize 'unseen' object instances by using only their semantic information. The model is first trained to learn the relationships between visual and semantic domains for seen objects, later transferring the acquired knowledge to totally unseen objects. This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes. In this paper, we propose a novel loss function called 'Polarity loss', that promotes correct visual-semantic alignment for an improved zero-shot object detection. On one hand, it refines the noisy semantic embeddings via metric learning on a 'Semantic vocabulary' of related concepts to establish a better synergy between visual and semantic domains. On the other hand, it explicitly maximizes the gap between positive and negative predictions to achieve better discrimination between seen, unseen and background objects. Our approach is inspired by embodiment theories in cognitive science, that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary) and visual perception (seen/unseen object images). We conduct extensive evaluations on MS-COCO and Pascal VOC datasets, showing significant improvements over state of the art.


title: Interpretable 3D Human Action Analysis with Temporal Convolutional Networks

abstract: The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent. In conjunction with the recent resurgence of 3D human action representation with 3D skeletons, the quality and the pace of recent progress have been significant. However, the inner workings of state-of-the-art learning based methods in 3D human action recognition still remain mostly black-box. In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition. TCN provides us a way to explicitly learn readily interpretable spatio-temporal representations for 3D human action recognition. Through this work, we wish to take a step towards a spatio-temporal model that is easier to understand, explain and interpret. The resulting model, Res-TCN, achieves state-of-the-art results on the largest 3D human action recognition dataset, NTU-RGBD.


Virtual Machine Migration in Cloud Computing


title: Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs

abstract: The goal of imitation learning is to match example expert behavior, without access to a reinforcement signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are not explicitly modeled. We introduce an extension to the Generative Adversarial Imitation Learning method that can infer the latent structure of human decision-making in an unsupervised way. Our method can not only imitate complex behaviors, but also learn interpretable and meaningful representations. We demonstrate that the approach is applicable to high-dimensional environments including raw visual inputs. In the highway driving domain, we show that a model learned from demonstrations is able to both produce different driving styles and accurately anticipate human actions. Our method surpasses various baselines in terms of performance and functionality.


title: Delta-Hedged Gains and the Negative Market Volatility Risk Premium

abstract: We investigate whether the volatility risk premium is negative by examining the statistical properties of delta-hedged option portfolios (buy the option and hedge with stock). Within a stochastic volatility framework, we demonstrate a correspondence between the sign and magnitude of the volatility risk premium and the mean delta-hedged portfolio returns. Using a sample of S&P 500 index options, we provide empirical tests that have the following general results. First, the delta-hedged strategy underperforms zero. Second, the documented underperformance is less for options away from the money. Third, the underperformance is greater at times of higher volatility. Fourth, the volatility risk premium significantly affects delta-hedged gains, even after accounting for jump fears. Our evidence is supportive of a negative market volatility risk premium. Copyright 2003, Oxford University Press.


title: The Feature Selection Method based on Genetic Algorithm for Efficient of Text Clustering and Text Classification

abstract: Big Data means a very large amount of data and includes a range of methodologies such as big data collection, processing, storage, management, and analysis. Since Big Data Text Mining extracts a lot of features and data, clustering and classification can result in high computational complexity and the low reliability of the analysis results. In particular, a TDM (Term Document Matrix) obtained through text mining represents term-document features but features a sparse matrix. In this paper, the study focuses on selecting a set of optimized features from the corpus. A Genetic Algorithm (GA) is used to extract terms (features) as desired according to term importance calculated by the equation found. The study revolves around feature selection method to lower computational complexity and to increase analytical performance.We designed a new genetic algorithm to extract features in text mining. TF-IDF is used to reflect document-term relationships in feature extraction. Through the repetitive process, features are selected as many as the predetermined number. We have conducted clustering experiments on a set of spammail documents to verify and to improve feature selection performance. And we found that the proposal FSGA algorithm shown better performance of Text Clustering and Classification than using all of features.


title: Data stream mining in ubiquitous environments: state-of-the-art and current directions

abstract: In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.


title: A large-scale empirical study of just-in-time quality assurance

abstract: Defect prediction models are a well-known technique for identifying defect-prone files or packages such that practitioners can allocate their quality assurance efforts (e.g., testing and code reviews). However, once the critical files or packages have been identified, developers still need to spend considerable time drilling down to the functions or even code snippets that should be reviewed or tested. This makes the approach too time consuming and impractical for large software systems. Instead, we consider defect prediction models that focus on identifying defect-prone (“risky”) software changes instead of files or packages. We refer to this type of quality assurance activity as “Just-In-Time Quality Assurance,” because developers can review and test these risky changes while they are still fresh in their minds (i.e., at check-in time). To build a change risk model, we use a wide range of factors based on the characteristics of a software change, such as the number of added lines, and developer experience. A large-scale study of six open source and five commercial projects from multiple domains shows that our models can predict whether or not a change will lead to a defect with an average accuracy of 68 percent and an average recall of 64 percent. Furthermore, when considering the effort needed to review changes, we find that using only 20 percent of the effort it would take to inspect all changes, we can identify 35 percent of all defect-inducing changes. Our findings indicate that “Just-In-Time Quality Assurance” may provide an effort-reducing way to focus on the most risky changes and thus reduce the costs of developing high-quality software.


title: The Ripple Effects of Vulnerability: The Effects of a Robot's Vulnerable Behavior on Trust in Human-Robot Teams

abstract: Successful teams are characterized by high levels of trust between team members, allowing the team to learn from mistakes, take risks, and entertain diverse ideas. We investigated a robot's potential to shape trust within a team through the robot's expressions of vulnerability. We conducted a between-subjects experiment (N = 35 teams, 105 participants) comparing the behavior of three human teammates collaborating with either a social robot making vulnerable statements or with a social robot making neutral statements. We found that, in a group with a robot making vulnerable statements, participants responded more to the robot's comments and directed more of their gaze to the robot, displaying a higher level of engagement with the robot. Additionally, we discovered that during times of tension, human teammates in a group with a robot making vulnerable statements were more likely to explain their failure to the group, console team members who had made mistakes, and laugh together, all actions that reduce the amount of tension experienced by the team. These results suggest that a robot's vulnerable behavior can have "ripple effects" on their human team members' expressions of trust-related behavior.


title: Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes

abstract: Are words read visually (by means of a direct mapping from orthography to semantics) or phonologically (by mapping from orthography to phonology to semantics)? The authors addressed this long-standing debate by examining how a large-scale computational model based on connectionist principles would solve the problem and comparing the model's performance to people's. In contrast to previous models, the present model uses an architecture in which meanings are jointly determined by the 2 components, with the division of labor between them affected by the nature of the mappings between codes. The model is consistent with a variety of behavioral phenomena, including the results of studies of homophones and pseudohomophones thought to support other theories, and illustrates how efficient processing can be achieved using multiple simultaneous constraints.


title: Image information and visual quality

abstract: Measurement of image quality is crucial for many image-processing algorithms. Traditionally, image quality assessment algorithms predict visual quality by comparing a distorted image against a reference image, typically by modeling the human visual system (HVS), or by using arbitrary signal fidelity criteria. We adopt a new paradigm for image quality assessment. We propose an information fidelity criterion that quantifies the Shannon information that is shared between the reference and distorted images relative to the information contained in the reference image itself. We use natural scene statistics (NSS) modeling in concert with an image degradation model and an HVS model. We demonstrate the performance of our algorithm by testing it on a data set of 779 images, and show that our method is competitive with state of the art quality assessment methods, and outperforms them in our simulations.


title: An iterative maximum-likelihood polychromatic algorithm for CT

abstract: A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.


title: The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain

abstract: Abstract Little is known about the function of the retrosplenial cortex and until recently, there was no evidence that it had any involvement in emotional processes. Surprisingly, recent functional neuroimaging studies show that the retrosplenial cortex is consistently activated by emotionally salient words. A review of the functional neuroimaging literature reveals a previously overlooked pattern of observations: the retrosplenial cortex is the cortical region most consistently activated by emotionally salient stimuli. Evidence that this region is also involved in episodic memory suggests that it might have a role in the interaction between emotion and episodic memory. Recognition that the retrosplenial cortex has a prominent role in the processing of emotionally salient stimuli invites further studies to define its specific functions and its interactions with other emotion-related brain regions.


title: A Compact Dual-Band Antenna With Omnidirectional Radiation Pattern

abstract: In this letter, a low-profile planar microstrip antenna is proposed to operate at lower WLAN (2.4 GHz) and WiMAX (3.5 GHz) frequency bands. The antenna has good omnidirectional radiation pattern for both the bands with compact form-factor. The obtained result shows that the proposed dual-band omnidirectional microstrip antenna has better impedance bandwidth and high radiation efficiency while maintaining the structural compactness. The electromagnetic model of the proposed antenna is developed in CST Microwave Studio for analysis and optimization. The fabricated antenna and measured results are presented.


title: Large-scale spatial join query processing in Cloud

abstract: The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial joins based on point-in-polygon test and point-to-polyline distance computation. Experiments on the pickup locations of ∼170 million taxi trips in New York City and ∼10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.


title: Gaussian-based Filters for Elementary Motion Detector Delay Element: Modeling Spatio-Temporal Information Pathways with Elementary Motion Detection

abstract: The Hassenstein-Reichardt elementary motion detection (HR-EMD) model has been demonstrated to show its validity for modeling insect vision and its usefulness for machine vision applications. The HR-EMD uses signals from adjacent receptors with a delayed sample of the same signals to determine simple left-to-right or right-to-left motion based on the polarity of the HR-EMD output. Variations of the HR-EMD have used low-pass filters (LPFs) for the delay elements. Additional LPFs and high-pass filters (HPFs) combined with various techniques have been added to the HR-EMD to address its inability to provide accurate image velocity estimation. Proposed here is using the delays inherent in natural Gaussian-based spatio-temporal information processing pathways for the required delay element. Since such pathways are known to exist in duality in natural systems, a combination of dual EMDs is proposed as an alternate approach for precision in image velocity estimation.


title: A Green Routing Algorithm for IoT-Enabled Software Defined Wireless Sensor Network

abstract: Rapid growth in the domain of Internet of Things (IoT) leads to massive deployment of sensors and therefore the need to develop automatically reconfigurable complex wireless sensor network. Software defined networking (SDN) is a promising and widely adopted technique to automatic reconfigure the wireless sensor network. In SDN, control nodes are dynamically selected (to activate the functioning of the sensor network) in order to assign the tasks to other nodes and for routing data packets to control server. For residual energy of sensor nodes, and transmission distance among sensor nodes, the problem of control node selection has been formulated as an NP-hard problem. Usually, smart sensing devices of IoT suffers from low battery which are not frequently rechargeable. Therefore, an energy efficient routing mechanism is required to operate software defined wireless sensor network (SDWSN). In this paper, a green routing algorithm using fork and join adaptive particle swarm optimization (FJAPSO) is proposed to maximize the lifetime of sensor network. FJAPSO acts at two levels for auto optimization: optimal number of control nodes and optimal clustering of control nodes. Experimental results evidenced that FJAPSO outperforms other state of the art and significantly maximizes the lifetime of the sensor network.


title: CMOS cascode structure current reference for low power temperature sensor

abstract: This paper proposes a low power CMOS temperature-to-current generator (TCG) for voltage-mode smart temperature sensor which is composed of bias circuit, proportional-to-absolute-temperature (PTAT) current generator, and differential current generator. The multiple-block system is to obtain a reference voltage which is dependent on temperature and is applied for a temperature sensor. The current references and cascode structure are applied in PTAT current generator. In differential current generator, current mirror and reference current are used to obtain a temperature sensing current. Low power TCG is designed with 0.35-μm CMOS process. Simulation test shows that the proposed TCG is operated up to 120 μA with power dissipation of 2 mW. The sensing current shows to increase linearly with increase of temperature in the ranges of −30 ∼ 80°C.


title: PE-BPMN: Privacy-Enhanced Business Process Model and Notation

abstract: Privacy Enhancing Technologies (PETs) play an important role in preventing privacy leakage of data along information flows. Although business process modelling is well-suited for expressing stakeholder collaboration and process support by technical solutions, little is done to visualise and analyse privacy leakages in the processes. We propose PE-BPMN – privacy-enhanced extensions to the BPMN language for capturing data leakages. We demonstrate its feasibility in the mobile app scenario where private data leakages are determined. Our approach helps system builders make decisions on the privacy solutions at the early stages of development and lets auditors analyse existing systems.


title: Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-Fuzzy Inference System

abstract: Prognostics and health management (PHM) play a key role in increasing the reliability and safety of systems especially in key sectors (military, aeronautical, aerospace, nuclear, etc.). This paper presents a new methodology which combines data-driven and experience-based approaches for the PHM of roller bearings. The proposed methodology uses time domain features extracted from vibration signals as health indicators. The degradation states in bearings are detected by an unsupervised classification technique called artificial ant clustering. The imminence of the next degradation state in bearings is given by hidden Markov models, and the estimation of the remaining time before the next degradation state is given by the multistep time series prediction and the adaptive neuro-fuzzy inference system. A set of experimental data collected from bearing failures is used to validate the proposed methodology. Experimental results show that the use of data-driven and experience-based approaches is a suitable strategy to improve the PHM of roller bearings.


title: In-Store Gamification: Testing a Location-Based Treasure Hunt App in a Real Retailing Environment

abstract: Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.


title: Closing the Loop: Evaluating a Measurement Instrument for Maturity Model Design

abstract: To support the systematic improvement of business intelligence (BI) in organizations, we have designed and refined a BI maturity model (BIMM) and a respective measurement instrument (MI) in prior research. In this study, we devise an evaluation strategy, and evaluate the validity of the designed measurement artifact. Through cluster analysis of maturity assessments of 92 organizations, we identify characteristic BI maturity scenarios and representative cases for the relevant scenarios. For evaluating the designed instrument, we compare its results with insights obtained from in-depth interviews in the respective companies. A close match between our model's quantitative maturity assessments and the maturity levels from the qualitative analyses indicates that the MI correctly assesses BI maturity. The applied evaluation approach has the potential to be re-used in other design research studies where the validity of utility claims is often hard to prove.


title: Accelerated Magnetic Resonance Imaging by Adversarial Neural Network

abstract: A main challenge in Magnetic Resonance Imaging (MRI) for clinical applications is speeding up scan time. Beyond the improvement of patient experience and the reduction of operational costs, faster scans are essential for time-sensitive imaging, where target movement is unavoidable, yet must be significantly lessened, e.g., fetal MRI, cardiac cine, and lungs imaging. Moreover, short scan time can enhance temporal resolution in dynamic scans, such as functional MRI or dynamic contrast enhanced MRI. Current imaging methods facilitate MRI acquisition at the price of lower spatial resolution and costly hardware solutions.


title: Automatic number plate recognition system for vehicle identification

abstract: The proposed system will be designed as for the automatic vehicle identification for the security purpose. The number plate of the vehicle is identified by image identification technology. This system can be implemented for the security checking in the area of the parliament and defence military area and also used in the toll plaza, parking fee collection etc. The images of the vehicles will be captured by cameras and those captured images will be processed by MAT LAB. Here the MaTLAB is used for the image identification and vehicle number extraction. The system contains embedded section for the automatic control of the gate and communication purpose. A LCD screen will be provided at the entry of the gate to gives the instruction to the driver and whether he is permitted to this area or not and the message will be passed to security cabin. The communication between MATLAB and microcontroller is done by using Zigbee. We can also extend to get the information of the vehicle owner, by incorporating the RTO database. The embedded section is programmed by using embedded c.


title: Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints

abstract: This paper presents a trajectory planning method for automated parking. The proposed method constructs a state roadmap in which each node contains not only position but also orientation information of the vehicle. The roadmap is constructed by dividing the orientation space in multiple resolutions considering the non-holonomic constraints of the vehicle and the collision-avoidance constraints between the vehicle and the boundary of the parking environment. Using the state roadmap, a complex parking trajectory composed of both forward and reverse motions can be computed with small online computation cost. The proposed method is evaluated in both numerical simulations and an experiment using an electric vehicle.


title: Modelling and design of PM retention sleeves for High-Speed PM Synchronous Machines

abstract: An analytical procedure for designing mechanical sleeves in surface-mounted High-Speed Permanent Magnet Synchronous Machines (HS-PMSMs) is presented in this paper. It is based on the theory of elastic mechanics and enables the computation of contact pressures between rotor layers (shaft, back-iron, permanent magnets and sleeve) and the Von Mises equivalent stress. The proposed procedure can be combined with the electromagnetic machine analysis, leading to fast and effective HS-PMSM design. This can be accomplished in accordance with different Permanent Magnets (PMs) and/or sleeve materials. Particularly, NdFeB and Ferrite-based PMs are alternatively considered, as well as the employment of Titanium and Carbon-Fiber as sleeve materials. The proposed approach is validated through Finite Element Analysis (FEA), which regards four different HS-PMSM configurations characterized by the same rated power and speed.


It's Time to Think About an Operating System for Near Data Processing Architectures


title: An analysis of electromagnetic characteristics and mechanical load characteristics of actuator for 4 pole MC

abstract: MC(Magnetic Contactor) is a widely used switch in power distribution systems for controlling electrical loads by making and breaking electric circuits using electromagnetic force of an actuator. MCs are classified as 3 and 4 pole MC according to the number of main contacts. 4 pole MC consists of 4 main contacts, which controls a three phase four wire system or two single phase systems. The type of the main contact affects not only the electric characteristics but also the mechanical load characteristics of the MC. In order to design the actuator for the 4 pole MC, it is important to analyze the mechanical load characteristics and the electromagnetic characteristics of the actuator precisely. In this paper, the analysis of mechanical load characteristics according to the formation types of main contacts is conducted and the electromagnetic characteristics are analyzed using 2-D FEM(Finite Element Method). Furthermore, the dynamic characteristics analysis of the actuator is conducted and its validity is verified by experiments.