title: Process Implanting: A New Active Introspection Framework for Virtualization

abstract: Previous research on virtual machine introspection proposed "out-of-box" approach by moving out security tools from the guest operating system. However, compared to the traditional "in-the-box" approach, it remains a challenge to obtain a complete semantic view due to the semantic gap between the guest VM and the hypervisor. In this paper, we present Process Implanting, a new active VM introspection framework, to narrow the semantic gap by implanting a process from the host into the guest VM and executing it under the cover of an existing running process. With the protection and coordination from the hypervisor, the implanted process can run with a degree of stealthiness and exit gracefully without leaving negative impact on the guest operating system. We have designed and implemented a proof-of-concept prototype on KVM which leverages hardware virtualization. We also propose and demonstrate application scenarios for Process Implanting in the area of VM security


title: A tutorial on hidden Markov models and selected applications in speech recognition

abstract: This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >


title: Design of an artificial muscle continuum robot

abstract: This paper introduces a novel continuum manipulator consisting of two flexible sections connected by rigid base plates. Each section uses 6-8 opposing contracting and extending McKibben actuators to provide two-axis bending. The contracting/extending actuator pairs are sized and positioned to provide matched rotation and torque. The experimental manipulator generates large rotation (30 - 35 degrees per section and 50 degrees for the entire manipulator) and load capacity (25 lb vertical lift) at 60 psi. With a large strength to weight ratio, this manipulator also is highly flexible, simple to manufacture, and is cost-effective.


title: Feeding value of pastures for horses.

abstract: The feeding value of fresh pasture grazed in situ is determined by animal performance or productivity and could be relatively easily established for growing and lactating horses. Despite this, there is a lack of published information on the relative feeding value of different pastures and forages grazed by horses in New Zealand and the world. In addition, for adult breeding or non-breeding and young or adult sport or performance horses, the definition of feeding value and its determination remain problematic. Limited information suggests that the feeding value of perennial ryegrass-based pasture in New Zealand for young growing horses is high, and growth rates for Thoroughbred horses fed solely on pasture in New Zealand are similar to those reported from the Northern Hemisphere where grain-based supplements are fed in addition to pasture or other forages. Attempts to assess the ability of fresh pastures to meet the nutrient requirements of horses are hampered by problems associated with determination of feed intake by grazing horses and lack of knowledge of the digestibility and utilisation of digested nutrients, including the relative bioavailability of macro- and micro-minerals in pasture. A further challenge for future research is to determine the effect of herbage allowance and grazing behaviour, including pasture species preferences, on voluntary feed intake by grazing horses. Grazing pasture has benefits for equine health and well-being including reduced risk of some nutrition-related disorders and reduced prevalence of stereotypic behaviour. Pastured horses have greater freedom for expression of natural behaviours including social interaction and exercise. However, grazing pasture is also associated with animal health problems, particularly parasitism and diseases related to pasture-associated toxins.


Tu1738 Glucose Breath Test in Patients With Overlapping FD and IBS. Is There Any Difference Compared to Patients With Only FD or Only IBS


title: The Effects of Functional Training on Pain, Function, and Performance in Taekwondo Players with Mechanical Low Back Pain

abstract: Background: The aim of this study was to assess the effects of functional training (FT) on pain, function, and performance in elite male taekwondo players with CMLBP. Method: 27 elite male taekwondo players (mean ± standard deviation: age 24.4 ± 4.9 years, body mass 75.7 ± 15.1 kg, height 181.4 ± 7.8 cm) were allocated to two experimental (n = 15) and control (n = 12) groups. The eight-week FT was divided into four distinct phases for the subjects of the experimental group. Variables of pain and function were assessed by Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI), respectively. The movement time of Ap Douliou Chagi kick (MTAK) and speed of Ap Douliou Chagi kick (SAK) was used for the variable of performance. Results: Considering the results of pretest before the implementation of the scores protocol, after FT intervention, VAS (p = 0.004), ODI (p = 0.005), MTAK on the right leg (p = 0.029) and the left leg (p = 0.013) decreased and SAK on both legs (p = 0.000) increased significantly in comparison with the control group. Conclusion: It is assumed that FT can reduce the pain intensity and improve the function and performance in taekwondo players.


title: Impact ionization MOS (I-MOS)-Part I: device and circuit simulations

abstract: One of the fundamental problems in the continued scaling of transistors is the 60 mV/dec room temperature limit in the subthreshold slope. In part I this work, a novel transistor based on the field-effect control of impact-ionization (I-MOS) is explored through detailed device and circuit simulations. The I-MOS uses gated-modulation of the breakdown voltage of a p-i-n diode to switch from the OFF state to the ON state and vice-versa. Device simulations using MEDICI show that the I-MOS has a subthreshold slope of 5 mV/dec or lower and I/sub ON/>1 mA//spl mu/m at 400 K. Simulations were used to further explore the characteristics of the I-MOS including the transients of the turn-on mechanism, the short-channel effect, scalability, and other important device attributes. Circuit mode simulations were also used to explore circuit design using I-MOS devices and the design of an I-MOS inverter. These simulations indicated that the I-MOS has the potential to replace CMOS in high performance and low power digital applications. Part II of this work focuses on I-MOS experimental results with emphasis on hot carrier effects, germanium p-i-n data and breakdown in recessed structure devices.


title: Fetal facial profile markers of Down syndrome in the second and third trimesters of pregnancy.

abstract: OBJECTIVES ::: To investigate the use of the maxilla-nasion-mandible (MNM) angle and fetal profile (FP) line to assess the degree of midfacial hypoplasia in Down-syndrome fetuses in the second and third trimesters of pregnancy. ::: ::: ::: METHODS ::: The MNM angle and FP line were measured retrospectively in stored two-dimensional images or three-dimensional volumes of fetuses with Down syndrome. Data collected from January 2006 to July 2013 were retrieved from the digital databases of participating units. The MNM angle was expressed as a continuous variable (degrees) and the FP line as positive, negative or zero. Measurements were obtained from stored images in the midsagittal plane by two experienced examiners and compared with our previously reported normal ranges for euploid fetuses. A MNM angle below the 5(th) centile of the reference range and a positive or negative FP line were considered as abnormal. ::: ::: ::: RESULTS ::: A total of 133 fetuses with Down syndrome were available for analysis, eight of which were subsequently excluded because of inadequate images. The MNM angle was not influenced by gestational age (P = 0.48) and was significantly smaller in Down-syndrome fetuses than in euploid fetuses (mean, 12.90° vs 13.53°, respectively; P = 0.015). The MNM angle was below the 5th centile for euploid fetuses in 16.8% of fetuses with Down syndrome (P < 0.01). In the cohort of Down-syndrome fetuses, a positive FP line was present in 41.6% of cases (with a false-positive rate (FPR) of 6.3%) and was positively correlated with Down syndrome and gestational age (P < 0.01). There was no case with a negative FP line. In cases of Down syndrome, a positive FP line was correlated with a small MNM angle (P < 0.01). ::: ::: ::: CONCLUSIONS ::: A small MNM angle and a positive FP line can be regarded as novel markers for Down syndrome. The FP line is an easy marker to measure, has a low FPR, does not require knowledge of normal reference values and has the potential to differentiate between Down syndrome and trisomy 18, as, in the latter, the FP line is often negative.


title: Stacked Semantic-Guided Attention Model for Fine-Grained Zero-Shot Learning

abstract: Zero-Shot Learning (ZSL) is achieved via aligning the semantic relationships between the global image feature vector and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-to-end. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.


title: Event2Mind: Commonsense Inference on Events, Intents, and Reactions

abstract: We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("X wants to stay awake") and reactions ("X feels alert") of the event's participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people's intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.


Which Healthy Eating Nudges Work Best? A Meta-Analysis of Field Experiments


Standardizing reuse


title: Industrial communication intrusion detection algorithm based on improved one-class SVM

abstract: Anomaly detection based on communication behavior is one of difficult problems of industrial control systems for intrusion detection. A normal communication behavior control model is established by using improved one-class SVM and a PSO-OCSVM algorithm based on particle swarm algorithm is designed to optimize parameters in this paper. This method established an intrusion detection model to identify abnormal Modbus TCP traffic according to the normal Modbus function code sequence. And the efficiency, reliability and real-time of the proposed method met the industrial control system for anomaly detection are proved by simulation results.


Visual Question Answering using Natural Language Object Retrieval and Saliency Cues


title: A methodology for constructing of philosophy ontology based on philosophical texts

abstract: An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.


title: Building a Concept-Level Sentiment Dictionary Based on Commonsense Knowledge

abstract: Sentiment dictionaries are essential for research in the sentiment analysis field. A two-step method integrates iterative regression and random walk with in-link normalization to build a concept-level sentiment dictionary. The approach uses ConceptNet as a framework to propagate sentiment values, based on the assumption that semantically related concepts share a common sentiment.


title: Human Orbitofrontal Cortex Represents a Cognitive Map of State Space

abstract: Summary Although the orbitofrontal cortex (OFC) has been studied intensely for decades, its precise functions have remained elusive. We recently hypothesized that the OFC contains a "cognitive map" of task space in which the current state of the task is represented, and this representation is especially critical for behavior when states are unobservable from sensory input. To test this idea, we apply pattern-classification techniques to neuroimaging data from humans performing a decision-making task with 16 states. We show that unobservable task states can be decoded from activity in OFC, and decoding accuracy is related to task performance and the occurrence of individual behavioral errors. Moreover, similarity between the neural representations of consecutive states correlates with behavioral accuracy in corresponding state transitions. These results support the idea that OFC represents a cognitive map of task space and establish the feasibility of decoding state representations in humans using non-invasive neuroimaging.


title: Boosting and Rocchio applied to text filtering

abstract: We discuss two learning algorithms for text filtering: modified Rocchio and a boosting algorithm called AdaBoost. We show how both algorithms can be adapted to maximize any general utility matrix that associates cost (or gain) for each pair of machine prediction and correct label. We first show that AdaBoost significantly outperforms another highly effective text filtering algorithm. We then compare AdaBoost and Rocchio over three large text filtering tasks. Overall both algorithms are comparable and are quite effective. AdaBoost produces better classifiers than Rocchio when the training collection contains a very large number of relevant documents. However, on these tasks, Rocchio runs much faster than AdaBoost.


title: Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks

abstract: Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.


title: The Effects of Information Format and Shopping Task on Consumers' Online Shopping Behavior: A Cognitive Fit Perspective

abstract: A feature central to the success of e-commerce Web sites is the design of an effective interface to present product information. However, the suitability of the prevalent information formats in supporting various online shopping tasks is not known. Using the cognitive fit theory as the theoretical framework, we developed a research model to investigate the fit between information format and shopping task, and examine its influence on consumers' online shopping performance and perceptions of shopping experience. The competition for attention theory from the marketing literature and the scanpath theory from vision research were employed to support the analyses. An experiment was conducted to examine the effects of two types of information formats (list versus matrix) in the context of two types of shopping tasks (searching versus browsing). The results show that when there is a match between the information format and the shopping task, consumers can search the information space more efficiently and have better recall of product information. Specifically, the list format better supports browsing tasks, and the matrix format facilitates searching tasks. However, a match between the information format and the shopping task has no effect on cognitive effort or attitude toward using the Web site. Overall, this research supports the application of the cognitive fit theory to the study of Web interface design. It also demonstrates the value in integrating findings from cognitive science and vision research to understand the processes involved. As the information format has been shown to affect consumers' online shopping behavior, even when the information content is held constant, the practical implications for Web site designers include providing both types of information format on their Web sites and matching the appropriate information format to the individual consumer's task.


title: Citizen Noise Pollution Monitoring

abstract: In this paper we present a new approach to monitor noise pollution involving citizens and built upon the notions of participatory sensing and citizen science. We enable citizens to measure their personal exposure to noise in their everyday environment by using GPS-equipped mobile phones as noise sensors. The geo-localised measures and user-generated meta-data can be automatically sent and shared online with the public to contribute to the collective noise mapping of cities. Our prototype, called Noise Tube, can be found online.


title: An fMRI Investigation of Spontaneous Mental State Inference for Moral Judgment

abstract: Human moral judgment depends critically on "theory of mind," the capacity to represent the mental states of agents. Recent studies suggest that the right TPJ (RTPJ) and, to lesser extent, the left TPJ (LTPJ), the precuneus (PC), and the medial pFC (MPFC) are robustly recruited when participants read explicit statements of an agent's beliefs and then judge the moral status of the agent's action. Real-world interactions, by contrast, often require social partners to infer each other's mental states. The current study uses fMRI to probe the role of these brain regions in supporting spontaneous mental state inference in the service of moral judgment. Participants read descriptions of a protagonist's action and then either (i) "moral" facts about the action's effect on another person or (ii) "nonmoral" facts about the situation. The RTPJ, PC, and MPFC were recruited selectively for moral over nonmoral facts, suggesting that processing moral stimuli elicits spontaneous mental state inference. In a second experiment, participants read the same scenarios, but explicit statements of belief preceded the facts: Protagonists believed their actions would cause harm or not. The response in the RTPJ, PC, and LTPJ was again higher for moral facts but also distinguished between neutral and negative outcomes. Together, the results illuminate two aspects of theory of mind in moral judgment: (1) spontaneous belief inference and (2) stimulus-driven belief integration.


title: The Delphi method as a research tool: an example, design considerations and applications

abstract: The Delphi method has proven a popular tool in information systems research for identifying and prioritizing issues for managerial decision-making. However, many past studies have not adopted a systematic approach to conduct a Delphi study. This article provides rigorous guidelines for the process of selecting appropriate experts for the study and gives detailed principles for making design choices during the process that ensure a valid study. A detailed example of a study to identify key factors affecting the diffusion of e-commerce in Sub-Saharan Africa illustrates the design choices that may be involved. We conclude with suggestions for theoretical applications.


title: Deep Reinforcement Learning: A Brief Survey

abstract: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.


title: Black soldier fly (Hermetia illucens) larvae enhances immune activities and increases survivability of broiler chicks against experimental infection of Salmonella Gallinarum

abstract: Black soldier fly (Hermetia illucens) larvae (BSFL) are rich in protein and have the potential to be used in animal feed. The aim of the present study was to determine the immunoprophylactic effect of BSFL against Salmonella Gallinarum in broiler chicks as an alternative feed additive. Results showed that BSFL improved body weight gain and increased frequency of CD4+ T lymphocyte, serum lysozyme activity, and spleen lymphocyte proliferation. Moreover, BSFL reinforced bacterial clearance and increased survivability of broiler chicks against S. Gallinarum. These data suggested that BSFL has prophylactic properties with stimulating non-specific immune responses, as well as reduced bacterial burden against S. Gallinarum.


title: Disruption of posterior brain systems for reading in children with developmental dyslexia

abstract: Abstract Background: Converging evidence indicates a functional disruption in the neural systems for reading in adults with dyslexia. We examined brain activation patterns in dyslexic and nonimpaired children during pseudoword and real-word reading tasks that required phonologic analysis (i.e., tapped the problems experienced by dyslexic children in sounding out words). Methods: We used functional magnetic resonance imaging (fMRI) to study 144 right-handed children, 70 dyslexic readers, and 74 nonimpaired readers as they read pseudowords and real words. Results: Children with dyslexia demonstrated a disruption in neural systems for reading involving posterior brain regions, including parietotemporal sites and sites in the occipitotemporal area. Reading skill was positively correlated with the magnitude of activation in the left occipitotemporal region. Activation in the left and right inferior frontal gyri was greater in older compared with younger dyslexic children. Conclusions: These findings provide neurobiological evidence of an underlying disruption in the neural systems for reading in children with dyslexia and indicate that it is evident at a young age. The locus of the disruption places childhood dyslexia within the same neurobiological framework as dyslexia, and acquired alexia, occurring in adults.


title: Real time pollution monitoring using Wireless Sensor Networks

abstract: Wireless Sensor Networks are gaining importance in the area of research, mainly of their wide potential applications. Atmospheric contamination in the various elements of air leading to hazardous effects of global warming and acid rains which have become a crucial apprehension for the wellness of the population. An atmosphere pollution monitoring system is extremely important to keep away from such adverse imbalance in nature. In the proposed paper, an attempt is made to develop a real-time pollution monitoring using wireless sensor networks (WSN). With the rapid growth in the industries which are the main sources of air pollutants, the problem of air pollution is becoming a serious concern for the health of the population. The concentration of major air pollutant gases like Carbon dioxide (CO 2 ), Nitrogen dioxide (NO 2 ), carbon monoxide (CO) and oxygen (O 2 ) from the air are sensed by using the commercially available gas sensors. Each of these sensors is properly calibrated as per the standard methods and these gas sensors are then incorporated with the wireless sensor motes using multi-hop data aggregation algorithm. Air pollutant data are collected from the developed test beds in the form numbers and this data is made available on the Internet through the combination of light weight middleware and a web interface. Air pollution monitoring with Wasp mote is simple and economical due to its features of wireless communication among the sensors.


title: How traffic conditions affect driver behavior in passing maneuver

abstract: The paper reports the outcome of an experimental study carried out using an interactive foxed-based driving simulator. The research was aimed at analyzing the driver behavior in passing maneuver for different traffic conditions on two lane rural roads. A two-lane rural road of more than 8 km in length was designed and implemented in the driving simulator. The alignment was designed with tangents from 200 to 1000 m long, clothoids and curves with radii from 215 to 1000 m. Four different traffic conditions in terms of traffic volume and speeds of opposing vehicles and impeding vehicle were simulated. Thirty-two drivers drove in the simulator on the four scenarios. The data collected during the driving was analyzed in order to determine the following parameters of passing behavior: (a) following gap between the passing vehicle and the impeding vehicle at the beginning of the passing; (b) distance of passing; and (c) Time to Collision between the passing vehicle and the oncoming vehicle at the end of the maneuver. The influence of the traffic conditions on such parameters was evaluated. Potential applications of the obtained results include the modeling of driver behavior and the passing sight distance design criteria.


title: A Recommender System for Youtube Based on its Network of Reviewers

abstract: Social network studies are becoming increasingly popular and have been applied to several fields of study such as law enforcement, marketing, spread of disease, as well as in the improvement of organizational performance. One area that is yet to be explored relates to harnessing the power of social networks as recommender systems. The idea that users may provide other users with recommendations that are more relevant than naive approaches is long known. However, the approaches currently implemented are based on the creation of simple relationships such as co-purchase of similar items. Similarly, video websites would like to suggest related videos to users to maximize the time they spend on their sites. Ergo it is crucial that sites like YouTube provide users with recommendations that are relevant to them. Moreover, given the large amount of videos on YouTube, a good recommender system may alleviate users' efforts on finding videos that interest them the most. Existing recommender systems for YouTube are typically based on finding similarities between the videos' textual features (video title or tags annotations) and matching these features to tags in the user's profiles or the videos they are currently watching. This approach is very limited because it restricts the users interests to the current theme of the video being played or the preferences in their profiles. This means that if one is watching a movie about football, it is assumed that the user would like recommendations on other videos about football. In this paper, we attempt to extract information about video relationship using a network formed from reviews left as comments in YouTube videos. We create a network of videos called YouTube Recommender Network (YRN) and use complex network analysis on this network as the basis of a recommender system. Our results show that our list of recommended videos is more diverse than the ones based on textual information. Our YRN provides diversity and captures other important characteristics such as high rating and most-viewed count.


title: On the Efficiency of FHE-Based Private Queries

abstract: Private query processing is a very attractive problem in the fields of both cryptography and databases. In this work, we restrict our attention to the efficiency aspect of the problem, particularly for basic queries with conditions on various combinations of equality. Without loss of generality, these conditions can be regarded as a Boolean function, and this Boolean function can then be evaluated at ciphertexts produced by a fully homomorphic encryption (FHE) scheme without decryption. From the efficiency perspective, the remaining concern is to efficiently test the equality function without severely downgrading the performance of FHE-based querying solutions. To this end, we first analyze the multiplicative depth required for an equality test algorithm with respect to the plaintext space inhabited by general FHE schemes. The primary reason for this approach is that given an equality test algorithm, its efficiency is measured in terms of the multiplicative depth required to construct its arithmetic circuit expression. Indeed, the implemented equality test algorithm dominates the entire performance of FHE-based query solutions, apart from the performance of the underlying FHE scheme. Then, we measure the multiplicative depth considering an FHE scheme that takes an extension field as its plaintext space and that supports the depth-free evaluation of Frobenius maps. According to our analysis, when the plaintext space of an FHE scheme is a field of characteristic 2, the equality test algorithm for `-bit messages requires the lowest multiplicative depth dlog`e. Furthermore, we design a set of private query protocols for conjunctive, disjunctive, and threshold queries based on the equality test algorithm. Similarly, applying the equality test algorithm over F ::: 2 ::: l ::: , our querying protocols require the minimum depths. More specifically, a multiplicative depth of [log l] + [log (1 + ρ)] is required for conjunctive and disjunctive queries, and a depth of [log l] + 2[log (1+ρ )] is required for threshold conjunctive queries, when their query conditions have p attributes to be compared. Finally, we provide a communication-efficient version of our solutions, though with additional computational costs, when an upper bound δ (0 ≤ δ ≤ 1) on the selectivity of a database is given. Consequently, we reduce the communication cost from n to approximately [δn] ciphertexts with [log n] additional depth when the database consists of n tuples.


title: Image denoising in the wavelet domain using a new adaptive thresholding function

abstract: In this paper, a new thresholding function is proposed for image denoising in the wavelet domain. The proposed function is further used in a new subband-adaptive thresholding neural network to improve the efficiency of the denoising procedure. Some new adaptive learning types are also proposed. In these learning methods, the threshold and the thresholding function effects are considered simultaneously. These methods are used to suppress two types of important noises, Gaussian and speckle, ranging from natural images to ultrasound and SAR pictures. The simulation results show that the proposed thresholding function has superior features compared to conventional methods when used with the proposed adaptive learning types. This makes it an efficient method in image denoising applications.


title: Adaptive Learning Algorithms for Transferable Visual Recognition

abstract: Author(s): Hoffman, Judith | Advisor(s): Darrell, Trevor | Abstract: Understanding visual scenes is a crucial piece in many artificial intelligence applications ranging from autonomous vehicles and household robotic navigation to automatic image captioning for the blind. Reliably extracting high-level semantic information from the visual world in real-time is key to solving these critical tasks safely and correctly. Existing approaches based on specialized recognition models are prohibitively expensive or intractable due to limitations in dataset collection and annotation. By facilitating learned information sharing between recognition models these applications can be solved; multiple tasks can regularize one another, redundant information can be reused, and the learning of novel tasks is both faster and easier.In this thesis, I present algorithms for transferring learned information between visual data sources and across visual tasks - all with limited human supervision. I will both formally and empirically analyze the adaptation of visual models within the classical domain adaptation setting and extend the use of adaptive algorithms to facilitate information transfer between visual tasks and across image modalities.Most visual recognition systems learn concepts directly from a large collection of manually annotated images/videos. A model which detects pedestrians requires a human to manually go through thousands or millions of images and indicate all instances of pedestrians. However, this model is susceptible to biases in the labeled data and often fails to generalize to new scenarios — a detector trained in Palo Alto may have degraded performance in Rome, or a detector trained in sunny weather may fail in the snow. Rather than require human supervision for each new task or scenario, this work draws on deep learning, transformation learning, and convex-concave optimization to produce novel optimization frameworks which transfer information from the large curated databases to real world scenarios.


title: Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach

abstract: We present a novel framework for applying deep neural networks (DNN) to soft decoding of linear codes at arbitrary block lengths. Unlike other approaches, our framework allows unconstrained DNN design, enabling the free application of powerful designs that were developed in other contexts. Our method is robust to overfitting that inhibits many competing methods, which follows from the exponentially large number of codewords required for their training. We achieve this by transforming the channel output before feeding it to the network, extracting only the syndrome of the hard decisions and the channel output reliabilities. We prove analytically that this approach does not involve any intrinsic performance penalty, and guarantees the generalization of performance obtained during training. Our best results are obtained using a recurrent neural network (RNN) architecture combined with simple preprocessing by permutation. We provide simulation results that demonstrate performance that sometimes approaches that of the ordered statistics decoding (OSD) algorithm.


Block Chain Technologies & The Semantic Web : A Framework for Symbiotic Development


A Comprehensive Study of Text Mining Approach


title: Magnetic Resonance Spectroscopy Quantification using Deep Learning

abstract: Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (metabolites) of scanned organs. In the literature, the quantification (the extraction of the potential biomarkers from the MRS signals) involves the resolution of an inverse problem based on a parametric model of the metabolite signal. However, poor signal-to-noise ratio (SNR), presence of the macromolecule signal or high correlation between metabolite spectral patterns can cause high uncertainties for most of the metabolites, which is one of the main reasons that prevents use of MRS in clinical routine. In this paper, quantification of metabolites in MR Spectroscopic imaging using deep learning is proposed. A regression framework based on the Convolutional Neural Networks (CNN) is introduced for an accurate estimation of spectral parameters. The proposed model learns the spectral features from a large-scale simulated data set with different variations of human brain spectra and SNRs. Experimental results demonstrate the accuracy of the proposed method, compared to state of the art standard quantification method (QUEST), on concentration of 20 metabolites and the macromolecule.


title: Sub-trajectory- and Trajectory-Neighbor-based Outlier Detection over Trajectory Streams

abstract: Precisely and efficiently anomaly detection over trajectory streams is critical for many real-time applications. However, due to the uncertainty and complexity of behaviors of objects over trajectory streams, this problem has not been well solved. In this paper, we propose a novel detection algorithm, called STN-Outlier, for real time applications, where a set of fine-grained behavioral features are extracted from the sub-trajectory instead of point and a novel distance function is designed to measure the behavior similarity between two trajectories. Additionally, an optimized framework(TSX) is introduced to reduce the CPU resources cost of STN-Outlier. The performance experiments demonstrate that STN-Outlier successfully captures more fine-grained behaviors than the state-of-the-art methods; besides, the TSX framework outperforms the baseline solutions in terms of the CPU time in all cases.


title: Real-Time Low-Cost Omni-Directional Stereo Vision via Bi-polar Spherical Cameras

abstract: With the rise of consumer-grade spherical cameras, offering full omni-directional 360\(^{\circ }\) image capture, the potential for low-cost omni-directional stereo vision is ever present. Whilst this potentially offers novel low-cost omni-directional depth sensing without the need for active range sensing, it presents a number of challenges associated with the realization of contemporary dense stereo matching across this spherical imaging format. In this work, we outline an approach for omni-directional dense stereo vision, based on imagery from two consumer-grade spherical cameras mounted in a bi-polar configuration, offering 360\(^{{\circ }}\) depth recovery at 5.5 fps. Specifically we outline the required aspects of inter-camera calibration and equirectangular image correction before detailing the required angular disparity correction that is unique to this stereo sensing formulation. Furthermore we illustrate the disparity and synchronization error achievable with the use of such consumer-grade spherical camera units, in addition to the quality of disparity (depth) available, within the context of on-road sensing for future vehicle autonomy.


title: Self-adaptive Privacy Concern Detection for User-generated Content

abstract: To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual's sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).


title: The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News

abstract: A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at http://web.cs.wpi.edu/\~kmlee/data/gau.html.


title: Confidence Trigger Detection: An Approach to Build Real-time Tracking-by-detection System

abstract: With deep learning based image analysis getting popular in recent years, a lot of multiple objects tracking applications are in demand. Some of these applications (e.g., surveillance camera, intelligent robotics, and autonomous driving) require the system runs in real-time. Though recent proposed methods reach fairly high accuracy, the speed is still slower than real-time application requirement. In order to increase tracking-by-detection system's speed for real-time tracking, we proposed confidence trigger detection (CTD) approach which uses confidence of tracker to decide when to trigger object detection. Using this approach, system can safely skip detection of images frames that objects barely move. We had studied the influence of different confidences in three popular detectors separately. Though we found trade-off between speed and accuracy, our approach reaches higher accuracy at given speed.


title: Detecting Android Malwares by Mining Statically Registered Broadcast Receivers

abstract: Android has a large share in the mobile apps market which makes it attractive for both malicious and good developers. Online apps markets, despite their vetting procedures, still admit malicious apps that could be downloaded mistakenly by mobile users. Detecting Android malwares has been studied by many researchers using different approaches and techniques. The vast majority of them though were focused on using the requested permissions that are declared in the AndroidManifest. xml files. A number of researchers have considered other components of the Android applications besides the permissions such as package info, activities, and process name. Some researchers pointed out Android Broadcast receivers' component but it was not discussed thoroughly like other components. In this paper, we are conducting an empirical study to investigate the usage patterns of the Broadcast receivers component by malicious and benign Android applications. In addition to processing the AndroidManifest. xml files, the source code of malware samples, in particular the onReceive() are manually analyzed. We also propose a data mining malware detection mechanism based on the statically registered Broadcast receivers. Our research shows that Android Broadcast receivers are intensively used by malware compared to benign applications. Our Java code analysis shows that malware samples fully utilize Broadcast receivers compared to benign apps. Finally, our experiments showed that using the Broadcast receivers with permissions improves the malwares prediction accuracy.


title: Let Your Preference Be Your Guide? Preferences and Choices Are More Tightly Linked for North Americans Than for Indians

abstract: Using experimental paradigms from economics and social psychology, the authors examined the cross-cultural applicability of 3 widely held assumptions about preference and choice: People (a) recruit or construct preferences to make choices; (b) choose according to their preferences; and (c) are motivated to express their preferences in their choices. In 6 studies, they compared how middle-class North American and Indian participants choose among consumer products. Participants in both contexts construct nonrandom preferences at similar speeds. Those in Indian contexts, however, are slower to make choices, less likely to choose according to their personal preferences, and less motivated to express their preferences in their choices. The authors infer that the strong link between preferences and choices observed among North Americans is not a universal feature of human nature. Instead, this link reflects the disjoint model of agency, which prescribes that people should choose freely on the basis of their preferences. In contrast, Indian contexts reflect and promote a conjoint model of agency, according to which agency is responsive to the desires and expectations of important others and may require restraining one’s preferences.


title: A Dual-Polarized Leaky Lens Antenna for Wideband Focal Plane Arrays

abstract: This paper presents the design and measurements of a wideband dual polarized leaky lens antenna suitable for tightly spaced focal plane arrays. The antenna is composed of two crossed leaky slots fed by two orthogonal microstrips to realize the dual-polarization operation. The crossed microstrips are fed differentially in order to couple the radiation into the slots. The slots are then coupled to a dielectric lens to achieve directive patterns suited for feeding large Focal distance to Diameter ratio reflectors. In this paper, the proposed leaky lens antenna is optimized to achieve high aperture efficiency with clean symmetric patterns in both polarizations exceeding an octave bandwidth. The concept is validated by the measurements of the primary fields inside the lens and with GRASP simulations of the focal plane array.


title: Random generation of combinatorial structures from a uniform

abstract: Abstract The class of problems involving the random generation of combinatorial structures from a uniform distribution is considered. Uniform generation problems are, in computational difficulty, intermediate between classical existence and counting problems. It is shown that exactly uniform generation of ‘efficiently verifiable’ combinatorial structures is reducible to approximate counting (and hence, is within the third level of the polynomial hierarchy). Natural combinatorial problems are presented which exhibit complexity gaps between their existence and generation, and between their generation and counting versions. It is further shown that for self-reducible problems, almost uniform generation and randomized approximate counting are inter-reducible, and hence, of similar complexity.


title: Radio frequency (RF) time-of-flight ranging for wireless sensor networks

abstract: Position information of nodes within Wireless Sensor Networks (WSNs) is often a requirement in order to make use of the data recorded by the sensors themselves. On deployment the nodes normally have no prior knowledge of their position and thus a locationing mechanism is required to determine their positions. In this paper, we describe a method to determine the point-to-point range between sensor nodes as part of the locationing process. A two-way Time-of-Flight (TOF) ranging scheme is presented using narrow-band RF. The frequency difference between the transceivers involved with the point-to-point measurement is used to obtain a sub-clock TOF phase offset measurement in order to achieve high resolution TOF measurements. The ranging algorithm has been developed and prototyped on a TI CC2430 development kit with no additional hardware being required. Performance results have been obtained for the line-of-sight (LOS), non-line-of-sight (NLOS) and indoor condition. Accuracy is typically better than 7.0m RMS for the LOS condition over 250.0m and 15.8m RMS for the NLOS condition over 120.0m using a one-hundred sample average. Indoor accuracy is measured to 1.7m RMS using a 1000 sample average over 8.0m. Ranging error is linear and does not increase with increased transmitter-receiver distance. Our TOA ranging scheme demonstartes a novel system where resolution and accuracy are time dependent in comparison to alternative frequency dependent methods using narrowband RF.


title: To reduce mutual coupling in microstrip patch antenna arrays elements using electromagnetic band gap structures for X-band

abstract: The demand for today is to have a Compact and reduced size devices, hence requires reduced sized antenna. For reduced sized, array elements can be placed closer to each other. However, the problem of mutual coupling, depending on interelement separation and their relative orientation, becomes a challenge [3][4]. To overcome this, we proposed an EBG structured antenna. The most used characteristics of Electromagnetic Band Gap (EBG) structure are the surface wave suppression effect within its band gap. Hence, they can reduce the mutual coupling due to surface wave propagation [2][9][10]. EBG provides better compactness, easy integrated feature and 2-D band gap properties. Also, by using EBG structure, antenna array characteristics like total size and radiation efficiency can also be improvised [1].


SPEA2: Improving the Strength Pareto Evolutionary Algorithm


title: An approach to improvement of HDD spindle motor efficiency

abstract: To improve the efficiency of HDD spindle motor, reducing air gap length (AGL) and raising magnetic energy product (MEP) of magnetic material are considered to be efficient methods. However some disadvantages are introduced concomitantly, such as the increase of cogging torque (CT) and unbalanced magnetic pull (UMP). This paper investigates both positive and negative effects of reduction of AGL and improvement of MEP on motor characteristics and proposes methods to decrease or avoid the negative effects. Then, an optimal design which can achieve a trade-off between high efficiency and increase of CT and UMP is proposed.


title: Design and development of smart phone based ECG monitoring system

abstract: Heart diseases have caused millions of death worldwide in the past years because of the increase in the population and rising of health care costs. The main causes of heart attacks are a lack of pre-requisite care and also un-availability of medical care in emergency situations. Treatment and prevention of heart attack is very important. Periodic checkup is important in health maintenance. Electrocardiography(ECG), can be monitor anytime, anywhere and send the report of Electrocardiography(ECG), to the medical expert with this portable device and can effectively communicate with them. In case of any abnormality, doctor will effectively communicate with the patient. For regular checkup and home based checkup, there is need of low cost, portable, low power and time-saving ECG monitoring device. The main requirement of any portable device is low power supply and in this project, there is no need of external power supply because power requirement is completed by harvesting power from mobile phone through audio jack. This paper intends towards developing a device which monitors cardiac activity and will be compact, simple to handle, non-invasive, and reliable. Headphone jack interface could be used to develop modular platforms to monitor vital signs such as Pulse rate, SpO2, EKG.


title: Multimodal sentiment analysis with word-level fusion and reinforcement learning

abstract: With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we propose a novel deep architecture for multimodal sentiment analysis that is able to perform modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules. The Gated Multimodal Embedding allows us to alleviate the difficulties of fusion when there are noisy modalities. The LSTM with Temporal Attention can perform word level fusion at a finer fusion resolution between the input modalities and attends to the most important time steps. As a result, the GME-LSTM(A) is able to better model the multimodal structure of speech through time and perform better sentiment comprehension. We demonstrate the effectiveness of this approach on the publicly-available Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving state-of-the-art sentiment classification and regression results. Qualitative analysis on our model emphasizes the importance of the Temporal Attention Layer in sentiment prediction because the additional acoustic and visual modalities are noisy. We also demonstrate the effectiveness of the Gated Multimodal Embedding in selectively filtering these noisy modalities out. These results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion.


title: A Unified Unsupervised Gaussian Mixture Variational Autoencoder for High Dimensional Outlier Detection

abstract: Paradigm-shifting systems such as cyber-physical systems, collect data of high- or ultrahigh- dimensionality tremendously. Detecting outliers in this type of systems provides indicative understanding in wide-ranging domains such as system health monitoring, information security, etc. Previous dimensionality reduction based outlier detection methods suffer from the incapability of well preserving the critical information in the low-dimensional latent space, mainly because they generally assume an isotropic Gaussian distribution as prior and fail to mine the intrinsic multimodality in high dimensional data. Moreover, most of the schemes decouple the model learning process, resulting in suboptimal performance. To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture Variational Autoencoder for outlier detection. Specifically, a variational autoencoder firstly trains a generative distribution and extracts reconstruction based features. Then we adopt a deep brief network to estimate the component mixture probabilities by the latent distribution and extracted features, which is further used by the Gaussian mixture model to estimate sample densities with the Expectation-Maximization (EM) algorithm. The inference model is optimized jointly with the variational autoencoder, the deep brief network, and the Gaussian mixture model. Afterwards, the proposed detector identifies outliers when the estimated sample density exceeds a learned threshold. Extensive simulations on six public benchmark datasets show that the proposed framework outperforms state-of-the-art outlier detection schemes and achieves, on average, 27% improvements in F1 score.


title: On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems

abstract: The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect. This problem can be mitigated if the user's intent is known in advance or data is available to pre-train a task success predictor off-line. In practice neither of these apply for most real world applications. Here we propose an on-line learning framework whereby the dialogue policy is jointly trained alongside the reward model via active learning with a Gaussian process model. This Gaussian process operates on a continuous space dialogue representation generated in an unsupervised fashion using a recurrent neural network encoder-decoder. The experimental results demonstrate that the proposed framework is able to significantly reduce data annotation costs and mitigate noisy user feedback in dialogue policy learning.


title: Energy Efficiency Maximization for Device-to-Device Communication Underlaying Cellular Networks on Multiple Bands

abstract: As green communication becomes an inevitable trend for future 5G wireless networks, how to maximize the energy efficiency (EE) of device-to-device (D2D) communication has drawn extensive attention recently. However, most of existing works only optimize the EE in the single-cell scenario, while little attention is paid to maximizing the EE of the whole cellular network underlaid with D2D communication with randomly distributed users on multiple bands. In this paper, we first consider the whole cellular network underlaid with D2D communication on multiple bands and derive the exact expressions of the successful transmission probabilities, the average sum rate and the EE based on stochastic geometry theory. Then, we formulate the optimization problem of maximizing the EE subject to four constraints regarding to transmission power and outage probabilities, and the non-convexity of this problem is also verified. After that, by exploiting the objective function property of being the sum of several functions, we propose a derivative-based algorithm to solve this non-convex optimization problem. Our theoretical analysis shows that the computational complexity of the proposed algorithm is significantly lower than that of the conventional branch and bound algorithm. Finally, simulation results demonstrate that the proposed algorithm can achieve the near-optimal EE with much better performance than the conventional algorithm.


title: Physiological response to the ambulatory performance of hand-rim and arm-crank propulsion systems.

abstract: Two types of propulsion systems—the hand rim (HR) and the arm crank (AC)—are commonly used in wheelchair ambulation. The purpose of this study was to investigate the physiological response of the two propulsion systems under actual locomotive condition by the actual users. The energetics of locomotion manual wheelchair (HR propulsion) and armpropelled three-wheeled chairs (AC-propelled) at their free chosen speed (FCS) were studied and compared. Thirty-four male subjects with dysfunctioning lower limbs; 17 manual wheelchair users and 17 arm-propelled three-wheeled chairs regular users volunteered to participate in the study. Speed (m.min21), oxygen uptake (l.min21) and heart rate (b.min21) were monitored during steady-state ambulation at FCS for 5 min. Oxygen consumption (VO2, ml.kg21min21), oxygen cost (VO2, ml.kg21m21), net locomotive energy cost (kcal kg21km21) and physiological cost index (b.m21) were derived. The FCS of the AC propelled device is remarkably higher than the HR system, and the magnitude of the physiological variables of the AC propulsion system was significantly lower (p<0.001) in relation to the HR propulsion system, as revealed from the results of t test for two sample means at a significance level p50.001. It can be inferred from the result that the AC 391 Journal of Rehabilitation Research and Development Vol. 38 No. 4, July/August 2001 Pages 391–399 Physiological response to the ambulatory performance of hand-rim and arm-crank propulsion systems Goutam Mukherjee, MSc, DPt, and Amalendu Samanta, MSc, PhD Department of Occupational Health, All India Institute of Hygiene and Public Health, Calcutta – 700 073, India propulsion system could be used for long distance rides with a higher speed required for outdoor ambulation and that the HR propulsion system is suitable only for indoor use, because of its excellent maneuverability where short-duration low-velocity ambulation is required.


title: Semi-Supervised Novelty Detection

abstract: A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard (inductive) approach is to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated sample is also available at learning time. We argue that novelty detection in this semi-supervised setting is naturally solved by a general reduction to a binary classification problem. In particular, a detector with a desired false positive rate can be achieved through a reduction to Neyman-Pearson classification. Unlike the inductive approach, semi-supervised novelty detection (SSND) yields detectors that are optimal (e.g., statistically consistent) regardless of the distribution on novelties. Therefore, in novelty detection, unlabeled data have a substantial impact on the theoretical properties of the decision rule. We validate the practical utility of SSND with an extensive experimental study. We also show that SSND provides distribution-free, learning-theoretic solutions to two well known problems in hypothesis testing. First, our results provide a general solution to the general two-sample problem, that is, the problem of determining whether two random samples arise from the same distribution. Second, a specialization of SSND coincides with the standard p-value approach to multiple testing under the so-called random effects model. Unlike standard rejection regions based on thresholded p-values, the general SSND framework allows for adaptation to arbitrary alternative distributions in multiple dimensions.


title: A study of a video analysis framework using Kafka and spark streaming

abstract: As the use of various sensors and cloud computing technologies has spread, many life-log analysis applications for safety services for the elderly and children have been developed. However, it is difficult to perform real-time large data processing in clouds due to the computational complexity of the analysis because efficient deployment schemes of streaming computing components over cloud resources have not been well-investigated. In this study, we propose a video analysis framework that collects videos from multiple cameras and analyzes them using Apache Kafka and Apache Spark Streaming. We first investigate the data transfer performance of Apache Kafka and examine efficient cluster configuration and parameter settings. We then apply this configuration to the proposed framework and measure the data analysis throughput. The experimental results show that the overall throughput varies depending on the number of broker nodes that store data, the number of topic partitions of data, and the number of nodes that conduct analysis processing. In addition, it is confirmed that the number of cores is needed to consider for the efficient cluster configuration, and that the network bandwidth between the nodes becomes a bottleneck as the amount of data and the number of components increase.


title: Revisiting the social enhancement hypothesis: Extroversion indirectly predicts number of Facebook friends operating through Facebook usage

abstract: Extroversion directly predicts number of Facebook friends.Extroversion directly predicts number of months people are active on Facebook.Extroversion indirectly predicts number of Facebook friends, mediated through Facebook usage. An online survey of college-age Facebook users (N=209) found that extroversion, narcissism, openness, and agreeableness predicted friending more people on Facebook. However, only extroversion continued to exert an effect when these and other personality variables were examined together in one regression model, while controlling for frequency of Facebook usage and gender. Also, a path analysis model showed that extroversion directly predicted number of Facebook friends and the number of months people were active on Facebook. In addition, extroversion indirectly influenced number of Facebook friends, operating through months active on Facebook and hours per week spent on Facebook in a parallel mediation effect. Findings offer support for the social enhancement hypothesis, which argues that extroverted people benefit the most from social media.


title: Neural Predictive Control for a Car-like Mobile Robot

abstract: This paper presents a new path-tracking scheme for a car-like mobile robot based on neural predictive c ontrol. A multi-layer back-propagation n eural network is employed to model non-linear kinematics of the robot i nstead o f a linear r egression estimator in o rder to adapt t he robot t o a large operating range. The neural predictive c ontrol for path tracking is a model-based p redictive c ontrol based on neural network modelling, which can generate its output in term of the robot kinematics and a desired p ath. The desired p ath for the robot i s produced b y a polar polynomial with a simple c losed form. The multi-layer back-propagation n eural network is constructed b y a wavelet orthogonal decomposition to form a wavelet neural network that can o vercome the problem caused b y the local minima when training the neural network. The wavelet neural network has the a dvantage of using an explicit way to d etermine the number of the hidden nod es and initial value of weights. Simulation results for the modelling and control are provided to justify the proposed scheme.


title: GraphMP: An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine

abstract: Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform state-of-the-art systems such as GraphChi, X-Stream and GridGraph by 31.6x, 54.5x and 23.1x respectively, when running popular graph applications on a billion-vertex graph.


title: Usability Heuristics for Transactional Web Sites

abstract: One of the most recognized methods to evaluate usability in software applications is the heuristic evaluation. In this inspection method, Nielsen's heuristics, are the most widely used evaluation instrument. However, there is evidence in the literature which establishes that these heuristics are not appropriate when they are used to measure the level of usability of new emerging categories of software applications. This paper presents a case study where this concept is verified for Transactional Web Sites. Therefore, given the present limitations, a new set of usability heuristics was proposed following a structured and systematic methodology. Finally, fifteen new usability heuristics were obtained as final product of this research. A validation phase allowed to contrast this new proposal with the Nielsen's principles under a real context, where a heuristic evaluation was performed to a Transactional Web Site. The results established that the new set of usability heuristics, which is presented in this study, provides more accurate and promising results than the current proposal of Nielsen.


title: Puberty Suppression in Adolescents With Gender Identity Disorder: A Prospective Follow‐Up Study

abstract: ABSTRACT Introduction Puberty suppression by means of gonadotropin‐releasing hormone analogues (GnRHa) is used for young transsexuals between 12 and 16 years of age. The purpose of this intervention is to relieve the suffering caused by the development of secondary sex characteristics and to provide time to make a balanced decision regarding actual gender reassignment. Aim To compare psychological functioning and gender dysphoria before and after puberty suppression in gender dysphoric adolescents. Methods Of the first 70 eligible candidates who received puberty suppression between 2000 and 2008, psychological functioning and gender dysphoria were assessed twice: at T0, when attending the gender identity clinic, before the start of GnRHa; and at T1, shortly before the start of cross‐sex hormone treatment. Main Outcome Measures Behavioral and emotional problems (Child Behavior Checklist and the Youth‐Self Report), depressive symptoms (Beck Depression Inventory), anxiety and anger (the Spielberger Trait Anxiety and Anger Scales), general functioning (the clinician's rated Children's Global Assessment Scale), gender dysphoria (the Utrecht Gender Dysphoria Scale), and body satisfaction (the Body Image Scale) were assessed. Results Behavioral and emotional problems and depressive symptoms decreased, while general functioning improved significantly during puberty suppression. Feelings of anxiety and anger did not change between T0 and T1. While changes over time were equal for both sexes, compared with natal males, natal females were older when they started puberty suppression and showed more problem behavior at both T0 and T1. Gender dysphoria and body satisfaction did not change between T0 and T1. No adolescent withdrew from puberty suppression, and all started cross‐sex hormone treatment, the first step of actual gender reassignment. Conclusion Puberty suppression may be considered a valuable contribution in the clinical management of gender dysphoria in adolescents. de Vries ALC, Steensma TD, Doreleijers TAH, and Cohen‐Kettenis PT. Puberty suppression in adolescents with gender identity disorder: A prospective follow‐up study. J Sex Med 2011;8:2276–2283.


title: The Fundamental Limit of Flash Random Write Performance: Understanding, Analysis and Performance Modelling

abstract: The understanding, analysis and modelling of the fundamental limit of the sustained random write performance and endurance of Flash solid state drives (SSDs) are critical for Flash SSD vendors and storage system designers and practitioners. This not only helps design high-performance Flash SSDs, but also dictates how Flash can be integrated into today’s memory and storage hierarchy. This paper analyzes the fundamental limit of the sustained random write performance of Flash SSDs. An empirical model is developed to compute the write amplification and performance slowdown factor for the greedy garbage collection policy under a pure random write workload. Potential causes of the commonly observed performance slowdown are investigated, and remedies suggested. In particular, we quantitatively demonstrate the potential for enhancing the random write performance and endurance by separating long-lived data from short-lived data inside Flash SSDs. Moreover, our theoretical results suggest a tiered storage system in which cold (i.e., infrequently used) data blocks are moved to a hard disk drive (HDD) to improve cost effectiveness and to reduce Flash memory utilization, which improves garbage collection performance.


title: © 2006 INFORMS The Distribution of Order Statistics for Discrete Random Variables with Applications to Bootstrapping

abstract: An algorithm for computing the PDF of order statistics drawn from discrete parent populations is presented, along with an implementation of the algorithm in a computer algebra system. Several examples and applications, including exact bootstrapping analysis, illustrate the utility of this algorithm. Bootstrapping procedures require that B bootstrap samples be generated in order to perform statistical inference concerning a data set. Although the requirements for the magnitude of B are typically modest, a practitioner would prefer to avoid the resampling error introduced by choosing a finite B, if possible. The part of the order-statistic algorithm for sampling with replacement from a finite sample can be used to perform exact bootstrapping analysis in certain applications, eliminating the need for replication in the analysis of a data set.


title: Contovir - A New Adjuvant Therapy in Recurrent Respiratory Papillomatosis: A Case Study

abstract: Background: Contovir is a mixture of herbal extracts (Tanacetum vulgare, Rossa canina, Urtica dioica) that is supplemented with selenium. Objectives: This study aimed to add Contovir to the classic treatment of recurrent respiratory papillomatosis (RRP) in order to decrease the severity and extent of the disease, elongate the surgical intervals and improve the sense of patient well-being. Furthermore, we had to adjust the prescribed drug dosage, since there were no previous findings available. Patients and Methods: This is a case study of RRP patients treated with Contovir as an adjuvant to the classic treatment, from March 2011 to February 2013, at an academic tertiary hospital (Rasoul-e-Akram hospital). All patients underwent surgical removal of papilloma and then were prescribed Contovir. Disease severity was quantified based on Derkay’s staging system. Results: Eight patients were enrolled in this study. The extent and severity of the disease improved in six cases. One had no response, and the severity of disease increased in one patient. Patients with supraglottic lesions had better responses to Contovir adjuvant therapy. No immediate or long-term side effects were reported. Conclusions: Although Contovir has been found to be an advantageous adjuvant for RRP treatment, further studies are called for to verify these findings.


title: An Artificially Intelligent Jazz Performer

abstract: This paper presents an intelligent agent model for simulating the behavior of a jazz bass player during live performance. In jazz performance, there is a strikingly large gap between the instructions given in a chord grid and the music actually being played. To bridge this gap, we integrate Artificial Intelligence (AI) methods within the intelligent agent paradigm, focusing on two critical aspects. First, the experience acquired by musicians in terms of previously heard or played melodic fragments, which are stored in the agent's ''musical memory''. Second, the use of these known fragments within the evolving context of live improvisation. In previous papers, we have presented a model for an improvising bass player, emphasizing the underlying problem solving method. In this paper, we describe the fully implemented model and give more details on how melodic fragments are represented, indexed and retrieved.


title: A Theory of Regret Regulation 1.0

abstract: We propose a theory of regret regulation that distinguishes regret from related emotions, specifies the conditions under which regret is felt, the aspects of the decision that are regretted, and the behavioral implications. The theory incorporates hitherto scattered findings and ideas from psychology, economics, marketing, and related disciplines. By identifying strategies that consumers may employ to regulate anticipated and experienced regret, the theory identifies gaps in our current knowledge and thereby outlines opportunities for future research.


A SWOT Analysis of the Field of VR Rehabilitation and Therapy


title: Optimization of Taxiway Routing and Runway Scheduling

abstract: This paper describes a mixed-integer linear programming optimization method for the coupled problems of airport taxiway routing and runway scheduling. The receding-horizon formulation and the use of iteration in the avoidance constraints allows the scalability of the baseline algorithm presented, with examples based on Heathrow Airport, London, U.K., which contains up to 240 aircraft. The results show that average taxi times can be reduced by half, compared with the first-come-first-served approach. The main advantage is shown with the departure aircraft flow. Comparative testing demonstrates that iteration reduces the computational demand of the required separation constraints while introducing no loss in performance.


title: Refactoring support for class library migration

abstract: As object-oriented class libraries evolve, classes are occasionally deprecated in favor of others with roughly the same functionality. In Java's standard libraries, for example, class Hashtable has been superseded by HashMap, and Iterator is now preferred over Enumeration. Migrating client applications to use the new idioms is often desirable, but making the required changes to declarations and allocation sites can be quite labor-intensive. Moreover, migration becomes complicated---and sometimes impossible---if an application interacts with external components, if a legacy class is not completely equivalent to its replacement, or if multiple interdependent classes must be migrated simultaneously. We present an approach in which mappings between legacy classes and their replacements are specified by the programmer. Then, an analysis based on type constraints determines where declarations and allocation sites can be updated. The method was implemented in Eclipse, and evaluated on a number of Java applications. On average, our tool could migrate more than 90% of the references to legacy classes.


title: CPW Center-Fed Single-Layer SIW Slot Antenna Array for Automotive Radars

abstract: A compact co-planar waveguide (CPW) center-fed substrate-integrated-waveguide (SIW) slot antenna array is proposed to achieve narrow H-plane beamwidth and low sidelobe levels for automotive radars such as across-the-road (ATR) radar. The antenna consists of an array of SIW slot elements, a CPW-SIW transition, and a CPW power divider. A new type of CPW-SIW transition is proposed to minimize the blockage aperture of the slot antenna array for suppressing the sidelobe levels of the array. A parallel center feeding configuration is applied to avoid beam squinting. The antenna prototype printed onto a single-layer Rogers 5880 is with 32 ×4 slot elements and an overall size of 195 mm×40 mm×0.79 mm. Measured results show that the proposed antenna exhibits a gain of > 22.8 dBi, an efficiency of > 67%, a return loss of > 10 dB, a sidelobe level of <;-21 dB, and a fixed boresight beam of <;4.6° in H-planes over 24.05-24.25 GHz, suitable for ATR radar.


title: Long-Term GABA Administration Induces Alpha Cell-Mediated Beta-like Cell Neogenesis

abstract: The recent discovery that genetically modified α cells can regenerate and convert into β-like cells in vivo holds great promise for diabetes research. However, to eventually translate these findings to human, it is crucial to discover compounds with similar activities. Herein, we report the identification of GABA as an inducer of α-to-β-like cell conversion in vivo. This conversion induces α cell replacement mechanisms through the mobilization of duct-lining precursor cells that adopt an α cell identity prior to being converted into β-like cells, solely upon sustained GABA exposure. Importantly, these neo-generated β-like cells are functional and can repeatedly reverse chemically induced diabetes in vivo. Similarly, the treatment of transplanted human islets with GABA results in a loss of α cells and a concomitant increase in β-like cell counts, suggestive of α-to-β-like cell conversion processes also in humans. This newly discovered GABA-induced α cell-mediated β-like cell neogenesis could therefore represent an unprecedented hope toward improved therapies for diabetes.


Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis


title: Clustering-based forecasting method for individual consumers electricity load using time series representations

abstract: Abstract This paper presents a new method for forecasting a load of individual electricity consumers using smart grid data and clustering. The data from all consumers are used for clustering to create more suitable training sets to forecasting methods. Before clustering, time series are efficiently preprocessed by normalisation and the computation of various model-based time series representation methods. Final centroid-based forecasts are scaled by saved normalisation parameters to create the forecast for every consumer. Our method is compared with the approach that creates forecasts for every consumer separately. Evaluation and experiments were conducted on three smart meter datasets from residences of Ireland and Australia, and factories of Slovakia. The achieved results proved that our clustering-based method improves forecasting accuracy mainly for residential consumers.We can also proclaim that it can be found such time series representation and clustering setting that will our forecasting method perform more accurately than fully disaggregated approach. Our method is also more scalable since it is necessary to train the model only on clusters and not for every consumer separately


title: Rule Based Part of Speech Tagging of Sindhi Language

abstract: Part of Speech (POS) tagging is a process of assigning correct syntactic categories to each word in the text. Tag set and word disambiguation rules are fundamental parts of any POS tagger. No work has hitherto been published of tag set in Sindhi language. The Sindhi lexicon for computational processing is also not available. In this study, the tag set for Sindhi POS, lexicon and word disambiguation rules are designed and developed. The Sindhi corpus is collected from a comprehensive Sindhi Dictionary. The corpus is based on the most recent available vocabulary used by local people. In this paper, preliminary achievements of rule based Sindhi Part of Speech (SPOS) tagger are presented. Tagging and tokenization algorithms are also designed for the implementation of SPOS. The outputs of SPOS are verified by Sindhi linguist. The development of SPOS tagger may have an important milestone towards computational Sindhi language processing.


title: Strategies for implementing knowledge management: role of human resources management

abstract: The successful implementation of new technologies is dependent on many factors including the efficient management of human resources. Furthermore, recent research indicates that intellectual assets and resources can be utilised much more efficiently and effectively if organisations apply knowledge management techniques for leveraging their human resources and enhancing their personnel management. The human resources departments are well positioned to ensure the success of knowledge management programs, which are directed at capturing, using and re‐using employees’ knowledge. Through human resources management a culture that encourages the free flow of knowledge for meeting organisational goals can be created. The strategic role of the human resources department in identifying strategic and knowledge gaps using knowledge mapping is discussed in this paper. In addition, the drivers and implementation strategies for knowledge management programs are proposed.


title: Subregions of the human superior frontal gyrus and their connections

abstract: The superior frontal gyrus (SFG) is located at the superior part of the prefrontal cortex and is involved in a variety of functions, suggesting the existence of functional subregions. However, parcellation schemes of the human SFG and the connection patterns of each subregion remain unclear. We firstly parcellated the human SFG into the anteromedial (SFGam), dorsolateral (SFGdl), and posterior (SFGp) subregions based on diffusion tensor tractography. The SFGam was anatomically connected with the anterior and mid-cingulate cortices, which are critical nodes of the cognitive control network and the default mode network (DMN). The SFGdl was connected with the middle and inferior frontal gyri, which are involved in the cognitive execution network. The SFGp was connected with the precentral gyrus, caudate, thalamus, and frontal operculum, which are nodes of the motor control network. Resting-state functional connectivity analysis further revealed that the SFGam was mainly correlated with the cognitive control network and the DMN; the SFGdl was correlated with the cognitive execution network and the DMN; and the SFGp was correlated with the sensorimotor-related brain regions. The SFGam and SFGdl were further parcellated into three and two subclusters that are well corresponding to Brodmann areas. These findings suggest that the human SFG consists of multiple dissociable subregions that have distinct connection patterns and that these subregions are involved in different functional networks and serve different functions. These results may improve our understanding on the functional complexity of the SFG and provide us an approach to investigate the SFG at the subregional level. (C) 2013 Elsevier Inc. All rights reserved.


title: Automated Inspection of Railcar Underbody Structural Components Using Machine Vision Technology 09-2863

abstract: Monitoring the structural health of railcars is important to ensure safe and efficient railroad operation. The structural integrity of freight cars depends on the health of certain structural components within their underframes. These components serve two principal functions: supporting the car body and lading and transmitting longitudinal buff and draft forces. Although railcars are engineered to withstand large static, dynamic and cyclical loads, they can still develop a variety of structural defects. As a result, Federal Railroad Administration (FRA) regulations and individual railroad mechanical department practices require periodic inspection of railcars to detect mechanical and structural damage or defects. These inspections are primarily a manual process that relies on the acuity, knowledge and endurance of qualified inspection personnel. Enhancements to the process are possible through machine vision technology, which uses computer algorithms to process digital image data of railcar underframes into diagnostic information. This paper describes research investigating the feasibility of an automated inspection system capable of detecting structural defects in freight car underframes and presents an inspection approach using machine vision techniques including multi-scale image segmentation. A preliminary image acquisition system has been developed, field trials conducted and algorithms developed that can analyze the images and identify certain underframe components, assessing aspects of their condition. The development of this technology, in conjunction with additional preventive maintenance systems, has the potential to provide more objective information on railcar structural condition, improved utilization of railcar inspection and repair resources, increased train and employee safety, and improvements to overall railroad network efficiency.


title: Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments

abstract: Large-scale digitisation has led to a number of new possibilities with regard to adaptive and learning based methods in the field of Document Image Analysis and OCR. For ground truth production of large corpora, however, there is still a gap in terms of productivity. Ground truth is not only crucial for training and evaluation at the development stage of tools but also for quality assurance in the scope of production workflows for digital libraries. This paper describes Aletheia, an advanced system for accurate and yet cost-effective ground truthing of large amounts of documents. It aids the user with a number of automated and semi-automated tools which were partly developed and improved based on feedback from major libraries across Europe and from their digitisation service providers which are using the tool in a production environment. Novel features are, among others, the support of top-down ground truthing with sophisticated split and shrink tools as well as bottom-up ground truthing supporting the aggregation of lower-level elements to more complex structures. Special features have been developed to support working with the complexities of historical documents. The integrated rules and guidelines validator, in combination with powerful correction tools, enable efficient production of highly accurate ground truth.


title: Correction of Facial Deformity in Sturge–Weber Syndrome

abstract: BACKGROUND ::: Although previous studies have reported soft-tissue management in surgical treatment of Sturge-Weber syndrome (SWS), there are few reports describing facial bone surgery in this patient group. The purpose of this study is to examine the validity of our multidisciplinary algorithm for correcting facial deformities associated with SWS. To the best of our knowledge, this is the first study on orthognathic surgery for SWS patients. ::: ::: ::: METHODS ::: A retrospective chart review included 2 SWS patients who completed the surgical treatment algorithm. Radiographic and clinical data were recorded, and a treatment algorithm was derived. ::: ::: ::: RESULTS ::: According to the Roach classification, the first patient was classified as type I presenting with both facial and leptomeningeal vascular anomalies without glaucoma and the second patient as type II presenting only with a hemifacial capillary malformation. Considering positive findings in seizure history and intracranial vascular anomalies in the first case, the anesthetic management was modified to omit hypotensive anesthesia because of the potential risk of intracranial pressure elevation. Primarily, both patients underwent 2-jaw orthognathic surgery and facial bone contouring including genioplasty, zygomatic reduction, buccal fat pad removal, and masseter reduction without major complications. In the second step, the volume and distribution of facial soft tissues were altered by surgical resection and reposition. Both patients were satisfied with the surgical result. ::: ::: ::: CONCLUSIONS ::: Our multidisciplinary algorithm can systematically detect potential risk factors. Correction of the asymmetric face by successive bone and soft-tissue surgery enables the patients to reduce their psychosocial burden and increase their quality of life.


title: Saliency ratio optimization in an IPM machine with fractional-slot concentrated windings

abstract: The implementation of concentrated windings in interior permanent magnet (IPM) machines has numerous advantages over distributed windings, with the disadvantage being mainly the decrease in saliency ratio. This paper presents a proposed finite element (FE) method in which the d- and q-axis inductances (Ld and Lq) of the IPM machine with fractional-slot concentrated windings can be accurately determined. This method is used to determine Ld and Lq of various winding configurations and to determine the optimum saliency ratio for a 12-slot 14-pole model with fractional-slot concentrated windings. FE testing were carried out by the use of Flux2D.


title: Blending and metaphor

abstract: A more recent framework, proposed by Fauconnier and Turner (1994; 1998) seeks to explain much of the same linguistic data, and also to unify the analysis of metaphor with the analysis of a variety of other linguistic and conceptual phenomena. This framework—referred to variously as the theory of 'blending', 'conceptual blending', and 'conceptual integration'—shares many aspects of conceptual metaphor theory (CMT). For instance, both approaches treat metaphor as a conceptual rather than a purely linguistic phenomenon; both involve systematic projection of language, imagery and inferential structure between conceptual domains; both propose constraints on this projection; and so forth. However, there are also important differences between the approaches: CMT posits relationships between pairs of mental representations, while blending theory (BT) allows for more than two; CMT has defined metaphor as a strictly directional phenomenon, while BT has not; and, whereas CMT analyses are typically concerned with entrenched conceptual relationships (and the ways in which they may be elaborated), BT research often focuses on novel conceptualizations which may be short-lived. In this article we explore the relationship between BT, CMT and the phenomena they address, arguing that the two approaches are complementary. In particular, the cross-domain relationships which have been identified by CMT researchers shape and constrain the more complex process of conceptual blending. The nature of this relationship has relevance for anyone interested in the conceptual analysis of language and, more broadly, for anyone interested in conceptual structure. We begin with an overview of the BT framework, focusing on similarities and differences with the CMT framework. 2. Blending theory and conceptual metaphor theory


title: Combining visual and acoustic features for audio classification tasks

abstract: Coupling texture descriptors and acoustic features.Different methods for representing an audio as an image are compared.Heterogeneous ensemble of different classifiers improves performance. Display Omitted In this paper a novel and effective approach for automated audio classification is presented that is based on the fusion of different sets of features, both visual and acoustic. A number of different acoustic and visual features of sounds are evaluated and compared. These features are then fused in an ensemble that produces better classification accuracy than other state-of-the-art approaches. The visual features of sounds are built starting from the audio file and are taken from images constructed from different spectrograms, a gammatonegram, and a rhythm image. These images are divided into subwindows from which a set of texture descriptors are extracted. For each feature descriptor a different Support Vector Machine (SVM) is trained. The SVMs outputs are summed for a final decision. The proposed ensemble is evaluated on three well-known databases of music genre classification (the Latin Music Database, the ISMIR 2004 database, and the GTZAN genre collection), a dataset of Bird vocalization aiming specie recognition, and a dataset of right whale calls aiming whale detection. The MATLAB code for the ensemble of classifiers and for the extraction of the features will be publicly available (https://www.dei.unipd.it/node/2357 +Pattern Recognition and Ensemble Classifiers).