title: Toward principles for the design of ontologies used for knowledge sharing?

abstract: Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.


title: Classifier ensembles for changing environments

abstract: We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble. Individual classifier models capable of online learning are reviewed. The concept of ”forgetting” is discussed. Online ensembles and strategies suitable for changing environments are summarized.


title: A Dynamic Secure Group Sharing Framework in Public Cloud Computing

abstract: With the popularity of group data sharing in public cloud computing, the privacy and security of group sharing data have become two major issues. The cloud provider cannot be treated as a trusted third party because of its semi-trust nature, and thus the traditional security models cannot be straightforwardly generalized into cloud based group sharing frameworks. In this paper, we propose a novel secure group sharing framework for public cloud, which can effectively take advantage of the cloud servers’ help but have no sensitive data being exposed to attackers and the cloud provider. The framework combines proxy signature, enhanced TGDH and proxy re-encryption together into a protocol. By applying the proxy signature technique, the group leader can effectively grant the privilege of group management to one or more chosen group members. The enhanced TGDH scheme enables the group to negotiate and update the group key pairs with the help of cloud servers, which does not require all of the group members been online all the time. By adopting proxy re-encryption, most computationally intensive operations can be delegated to cloud servers without disclosing any private information. Extensive security and performance analysis shows that our proposed scheme is highly efficient and satisfies the security requirements for public cloud based secure group sharing.


title: A new learning paradigm: Learning using privileged information

abstract: In the Afterword to the second edition of the book "Estimation of Dependences Based on Empirical Data" by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM γ + method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm 1 and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas.


title: Self-Serving Justifications Doing Wrong and Feeling Moral

abstract: Unethical behavior by “ordinary” people poses significant societal and personal challenges. We present a novel framework centered on the role of self-serving justification to build upon and advance the rapidly expanding research on intentional unethical behavior of people who value their morality highly. We propose that self-serving justifications emerging before and after people engage in intentional ethical violations mitigate the threat to the moral self, enabling them to do wrong while feeling moral. Pre-violation justifications lessen the anticipated threat to the moral self by redefining questionable behaviors as excusable. Post-violation justifications alleviate the experienced threat to the moral self through compensations that balance or lessen violations. We highlight the psychological mechanisms that prompt people to do wrong and feel moral, and suggest future research directions regarding the temporal dimension of self-serving justifications of ethical misconduct.


title: Large area, continuous, few-layered graphene as anodes in organic photovoltaic devices

abstract: Large-area, continuous, transparent, and highly conducting few-layered graphene films produced by chemical vapor deposition method were used as anode for application in photovoltaic devices. The noncovalent modification of the graphene films with pyrene buanoic acid succidymidyl ester improved the power conversion efficiency (PCE) to 1.71%. This performance corresponds to ∼55.2% of the PCE of a control device based on indium tin oxide (ITO)/poly(3,4–ethylenedioxythiophene):poly(styrenesulfonate)/poly(3-hexyl)thiophene: phenyl-C61-butyric acid methyl ester/LiF/Al electrodes (PCE=3.1%). This finding paves the way for the substitution of ITO in photovoltaic and electroluminescent devices with low cost graphene films.


title: StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

abstract: Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.


title: A Statistical Approach for RF Exposure Compliance Boundary Assessment in Massive MIMO Systems

abstract: Massive multiple-input multiple-output (MIMO) is a fundamental enabler to provide high data throughput in next generation cellular networks. By equipping the base stations (BSs) with tens or hundreds of antenna elements, narrow and high gain beams can be used to spatially multiplex several user equipment (UE) devices. While increasing the achievable performance, focusing the transmit power into specific UE directions also poses new issues when performing the radio frequency (RF) exposure assessment. In fact, the spatial distribution of the actual BS transmit power strongly depends on the deployment scenario and on the position of the UEs. Traditional methods for assessing the RF exposure compliance boundaries around BS sites are generally based on maximum transmit power and static beams. In massive MIMO systems, these approaches tend to be very conservative, in particular when time averaging is properly considered. In this work, we propose to leverage the three dimensional spatial channel model standardized by the Third Generation Partnership Project in order to assess reasonably foreseeable compliance boundaries of massive MIMO BSs. The analysis is performed by considering BSs fully loaded and different configurations of active UEs per cell. Numerical results show that the statistical approach developed in this paper allows reducing to nearly half the compliance distance when compared to the traditional method.


title: Using Text Analytics to Derive Customer Service Management Benefits from Unstructured Data

abstract: Deriving value from structured data is now commonplace. The value of unstructured textual data, however, remains mostly untapped and often unrecognized. This article describes the text analytics journeys of three organizations in the customer service management area. Based on their experiences, we provide four lessons that can guide other organizations as they embark on their text analytics journeys.


title: VP-ReID: Vehicle and Person Re-Identification System

abstract: With the capability of locating and tracking specific suspects or vehicles in a large camera network, person Re-Identification (ReID) and vehicle ReID show potential to be a key technology in smart surveillance system. They have been drawing lots of attentions from both academia and industry. To demonstrate our recent research progresses on those two tasks, we develop a robust and efficient person and video ReID system named as VP-ReID. This system is build based on our recent works including Deep Convolutional Neural Network design for discriminative feature extraction, efficient off-line indexing, as well as distance metric optimization for deep feature learning. Constructed upon those algorithms, VP-ReID identifies query vehicle and person efficiently and accurately from a large gallery set.


title: The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem

abstract: We describe a general methodology for the design of large-scale recursive neural network architectures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connections to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dynamics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. The protein prediction servers are available over the Web at: www.igb.uci.edu/tools.htm .


title: “The Internet is a Mask”: High School Students' Suggestions for Preventing Cyberbullying

abstract: INTRODUCTION ::: Interactions through technology have an important impact on today's youth. While some of these interactions are positive, there are concerns regarding students engaging in negative interactions like cyberbullying behaviors and the negative impact these behaviors have on others. The purpose of the current study was to explore participant suggestions for both students and adults for preventing cyberbullying incidents. ::: ::: ::: METHODS ::: Forty high school students participated in individual, semi-structured interviews. Participant experiences and perceptions were coded using constant comparative methods to illustrate ways in which students and adults may prevent cyberbullying from occurring within their school and community. ::: ::: ::: RESULTS ::: Students reported that peers would benefit from increasing online security, as well as becoming more aware of their cyber-surroundings. Regarding adult-provided prevention services, participants often discussed that there is little adults can do to reduce cyberbullying. Reasons included the difficulties in restricting online behaviors or providing effective consequences. However, some students did discuss the use of in-school curricula while suggesting that adults blame people rather than technology as potential ways to prevent cyberbullying. ::: ::: ::: CONCLUSION ::: Findings from the current study indicate some potential ways to improve adult efforts to prevent cyberbullying. These strategies include parent/teacher training in technology and cyberbullying, interventions focused more on student behavior than technology restriction, and helping students increase their online safety and awareness.


title: An augmented reality simulator for ultrasound guided needle placement training

abstract: Details are presented of a low cost augmented-reality system for the simulation of ultrasound guided needle insertion procedures (tissue biopsy, abscess drainage, nephrostomy etc.) for interventional radiology education and training. The system comprises physical elements; a mannequin, a mock ultrasound probe and a needle, and software elements; generating virtual ultrasound anatomy and allowing data collection. These two elements are linked by a pair of magnetic 3D position sensors. Virtual anatomic images are generated based on anatomic data derived from full body CT scans of live humans. Details of the novel aspects of this system are presented including; image generation, registration and calibration.


title: Character-Focused Narrative Generation for Execution in Virtual Worlds

abstract: Because narrative plays such a key role in the understanding of events in our daily lives, the ability to generate narrative can be of great use in virtual reality systems whose purpose is to entertain, train, or educate their users. Narrative generation, however, is complicated by the conflicting goals of plot coherence – the appearance that events in the narrative lead towards the narrative’s outcome – and character believability – the appearance that events in the narrative are driven by the traits of the story world characters. Many systems are capable of achieving either one or the other; we present a new approach to narrative generation in the Actor Conference system, which is capable of generating narratives with both plot coherence and character believability. These narratives are declarative in nature, readily lending themselves to execution by embodied agents in virtual reality environments.


title: Security Considerations in Minutiae-Based Fuzzy Vaults

abstract: The fuzzy vault scheme is a cryptographic primitive that can be used to protect human fingerprint templates where stored. Analyses for most implementations account for brute-force security only. There are, however, other risks that have to be consider, such as false-accept attacks, record multiplicity attacks, and information leakage from auxiliary data, such as alignment parameters. In fact, the existing work lacks analyses of these weaknesses and are even susceptible to a variety of them. In view of these vulnerabilities, we redesign a minutiae-based fuzzy vault implementation preventing an adversary from running attacks via record multiplicity. Furthermore, we propose a mechanism for robust absolute fingerprint prealignment. In combination, we obtain a fingerprint-based fuzzy vault that resists known record multiplicity attacks and that does not leak information about the protected fingerprints from auxiliary alignment data. By experiments, we evaluate the performance of our security-improved implementation that, even though it has slight usability merits as compared with other minutiae-based implementations, provides improved security. However, despite heavy efforts spent in improving security, our implementation is, like all other implementations based on a single finger, subjected to a fundamental security limitation related to the false acceptance rate, i.e., false-accept attack. Consequently, this paper supports the notion that a single finger is not sufficient to provide acceptable security. Instead, implementations for multiple finger or even multiple modalities should be deployed the security of which may be improved by the technical contributions of this paper.


title: Heterogeneous memory architectures: A HW/SW approach for mixing die-stacked and off-package memories

abstract: Die-stacked DRAM is a technology that will soon be integrated in high-performance systems. Recent studies have focused on hardware caching techniques to make use of the stacked memory, but these approaches require complex changes to the processor and also cannot leverage the stacked memory to increase the system's overall memory capacity. In this work, we explore the challenges of exposing the stacked DRAM as part of the system's physical address space. This non-uniform access memory (NUMA) styled approach greatly simplifies the hardware and increases the physical memory capacity of the system, but pushes the burden of managing the heterogeneous memory architecture (HMA) to the software layers. We first explore simple (and somewhat impractical) schemes to manage the HMA, and then refine the mechanisms to address a variety of hardware and software implementation challenges. In the end, we present an HMA approach with low hardware and software impact that can dynamically tune itself to different application scenarios, achieving performance even better than the (impractical-to-implement) baseline approaches.


title: GraphPIM: Enabling Instruction-Level PIM Offloading in Graph Computing Frameworks

abstract: With the emergence of data science, graph computing has become increasingly important these days. Unfortunately, graph computing typically suffers from poor performance when mapped to modern computing systems because of the overhead of executing atomic operations and inefficient utilization of the memory subsystem. Meanwhile, emerging technologies, such as Hybrid Memory Cube (HMC), enable the processing-in-memory (PIM) functionality with offloading operations at an instruction level. Instruction offloading to the PIM side has considerable potentials to overcome the performance bottleneck of graph computing. Nevertheless, this functionality for graph workloads has not been fully explored, and its applications and shortcomings have not been well identified thus far. In this paper, we present GraphPIM, a full-stack solution for graph computing that achieves higher performance using PIM functionality. We perform an analysis on modern graph workloads to assess the applicability of PIM offloading and present hardware and software mechanisms to efficiently make use of the PIM functionality. Following the real-world HMC 2.0 specification, GraphPIM provides performance benefits for graph applications without any user code modification or ISA changes. In addition, we propose an extension to PIM operations that can further bring performance benefits for more graph applications. The evaluation results show that GraphPIM achieves up to a 2.4X speedup with a 37% reduction in energy consumption.


title: A Magnetic Membrane Actuator in Composite Technology Utilizing Diamagnetic Levitation

abstract: We present the application of diamagnetic levitation of a small floater magnet for the realization of a magnetically actuated membrane actuator in polymer composite technology. Following this approach the neutral position of the actuating membrane can be adjusted by diamagnetically stabilized levitation and the actuation of the membrane is induced by an integrated coil which is driven by an ac-signal. This setup holds the advantage that the neutral position of the flexible membrane can be stabilized with no external energy input, and the separation of the off-position from the driving mechanism allows for many possible applications of the device in, for instance, microfluidic systems.


title: Learning from Bullying Traces in Social Media

abstract: We introduce the social study of bullying to the NLP community. Bullying, in both physical and cyber worlds (the latter known as cyberbullying), has been recognized as a serious national health issue among adolescents. However, previous social studies of bullying are handicapped by data scarcity, while the few computational studies narrowly restrict themselves to cyberbullying which accounts for only a small fraction of all bullying episodes. Our main contribution is to present evidence that social media, with appropriate natural language processing techniques, can be a valuable and abundant data source for the study of bullying in both worlds. We identify several key problems in using such data sources and formulate them as NLP tasks, including text classification, role labeling, sentiment analysis, and topic modeling. Since this is an introductory paper, we present baseline results on these tasks using off-the-shelf NLP solutions, and encourage the NLP community to contribute better models in the future.


title: Music and emotion: Electrophysiological correlates of the processing of pleasant and unpleasant music

abstract: Human emotion and its electrophysiological correlates are still poorly understood. The present study examined whether the valence of perceived emotions would differentially influence EEG power spectra and heart rate (HR). Pleasant and unpleasant emotions were induced by consonant and dissonant music. Unpleasant (compared to pleasant) music evoked a significant decrease of HR, replicating the pattern of HR responses previously described for the processing of emotional pictures, sounds, and films. In the EEG, pleasant (contrasted to unpleasant) music was associated with an increase of frontal midline (Fm) theta power. This effect is taken to reflect emotional processing in close interaction with attentional functions. These findings show that Fm theta is modulated by emotion more strongly than previously believed.


title: Unmanned aerial vehicle smart device ground control station cyber security threat model

abstract: The Department of the Defense has transitioned smart devices into the battlefield as a portable hand-held unmanned aerial vehicle ground control station without adequate cyber security protections, putting critical mission data at risk to cyber security attacks. Industry has developed software apps for smart phones and tablets that allows soldiers to not only pilot unmanned aerial vehicles (UAVs) but to share and receive intelligence and reconnaissance videos and images remotely from the ground control station (GCS) or directly from the UAV. The Department of Defense has not developed a secure communication network that will support a large quantity of smart devices, nor certification, standards or policies for operation of secure smart devices. Therefore, mission critical information will be shared through unsecured, mobile and wireless networks and through unclassified, unsecure smart technology that are vulnerable to cyber security risks. Lack of security of the mobile and wireless networks and smart devices could result in the unintentional sharing of data as well as loss of control of the UAV to enemies. The Department of Defense has failed to develop a threat model and risk assessment to identify the cyber security threats and ensure the proper security countermeasures are in place. This paper will analyze the cyber security vulnerabilities within the communication links, smart devices hardware, specifically smart phones and tablets, and software applications to develop a risk model of the threat profile of the GCS networking hub. This model will help designers and users of the military and civilian UAV communities to understand the threat profile of the GCS networking hub to develop a secure communication network based upon the vulnerabilities identified for smart phones and tablets.


title: Quantification Methods in Microbiology

abstract: The quantitative evaluation of microorganisms isolated and identified in different biological samples is important in microbiology. Since 1956, the determination of bacterial concentration in urine of patients has been used in diagnosing urinary tract infections (Kass, 1956). In alimentary and industrial microbiology, the evaluation of bacterial concentrations in water and food may give important information about potability and edibility (International Commission on Microbiological Specification for Foods, 1978). Likewise, quantitative evaluations of microorganisms in operating rooms, drug preparation laboratories, intensive care units, and other environments can also be invaluable. In such settings, the comparison of quantitative results acquired from different samples during the same day and in subsequent days allows environmental contamination and in some cases its origin to be documented.


title: Virtual Machine Customization and Task Mapping Architecture for Efficient Allocation of Cloud Data Center Resources

abstract: Energy usage of large-scale data centers has become a major concern for cloud providers. There has been an active effort in techniques for the minimization of the energy consumed in the data centers. However, most approaches lack the analysis and application of real cloud backend traces. In existing approaches, the variation of cloud workloads and its effect on the performance of the solutions are not investigated. Furthermore, the focus of existing approaches is on virtual machine migration and placement algorithms, with little regard to tailoring virtualmachine configuration to workload characteristics, which can further reduce the energy consumption and resource wastage in a typical data center. To address these weaknesses and challenges, we propose a new architecture for cloud resource allocation that maps groups of tasks to customized virtual machine types. This mapping is based on the task usage patterns obtained from the analysis of the historical data extracted from utilization traces. In our work, the energy consumption is decreased via efficient resource allocation based on the actual resource usage of tasks. Experimental results show that, when resources are allocated based on the discovered usage patterns, significant energy saving can be achieved.


title: Addressing the unmet needs of patients with persistent negative symptoms of schizophrenia: emerging pharmacological treatment options

abstract: The negative symptoms of schizophrenia represent an impairment of normal emotional responses, thought processes and behaviors, and include blunting or flattening of affect, alogia/aprosody, avolition/apathy, anhedonia, and asociality. Negative symptoms contribute to a reduced quality of life, increased functional disability, increased burden of illness, and poorer long-term outcomes, to a greater degree than positive symptoms. Primary negative symptoms are prominent and persistent in up to 26% of patients with schizophrenia, and they are estimated to occur in up to 58% of outpatients at any given time. Negative symptoms respond less well to medications than positive symptoms, and to date treatment options for negative symptoms have been limited, with no accepted standard treatment. Modest benefits have been reported with a variety of different agents, including second-generation antipsychotics and add-on therapy with antidepressants and other pharmacological classes. Recent clinical research focusing on negative symptoms target novel biological systems, such as glutamatergic neurotransmission. Different approaches include: enhancing N-methyl-D-aspartate receptor function with agents that bind directly to the glycine ligand site or with glycine reuptake inhibitors; influencing the metabotropic glutamate receptor (mGluR2/3) with positive allosteric modulators; and stimulating nicotinic acetylcholine receptors. In conclusion, the lack of clearly efficacious pharmacological treatments for the management of negative symptoms represents a significant unmet need, especially considering the importance of these symptoms on patient outcomes. Hence, further research to identify and characterize novel pharmacological treatments for negative symptoms is greatly needed.


title: Neural-network-based adaptive matched filtering for QRS detection

abstract: The authors have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. They use an ANN adaptive whitening filter to model the lower frequencies of the electrocardiogram (ECG) which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. The authors developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. The detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5% with this approach, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method. >


The Effects of Using Games to Reinforce Vocabulary Learning


title: Development of Academic Attendence Monitoring System Using Fingerprint Identification

abstract: In this paper we propose a system that automates the whole process of taking attendance and maintaining its records in an academic institute. Managing people is a difficult task for most of the organizations, and maintaining the attendance record is an important factor in people management. When considering academic institutes, taking the attendance of students on daily basis and maintaining the records is a major task. Manually taking the attendance and maintaining it for a long time adds to the difficulty of this task as well as wastes a lot of time. For this reason an efficient system is designed. This system takes attendance electronically with the help of a fingerprint sensor and all the records are saved on a computer server. Fingerprint sensors and LCD screens are placed at the entrance of each room. In order to mark the attendance, student has to place his/her finger on the fingerprint sensor. On identification student’s attendance record is updated in the database and he/she is notified through LCD screen. No need of all the stationary material and special personal for keeping the records. Furthermore an automated system replaces the manual system.


title: Energy-Efficient Hybrid Precoding Design for Millimeter-Wave Massive MIMO Systems Via Coordinate Update Algorithms

abstract: This paper considers an energy-efficient, hybrid digital, and analog precoding design problem in millimeter-wave massive multiple-input multiple-output systems, in which the analog precoder is realized with a small number of energy-efficient switches and inverters rather than with a large number of high-resolution phase shifters. However, finding the optimal weights of such energy-efficient hybrid precoding requires solving a challenging combinatorial problem. A known solver, namely “adaptive cross-entropy (ACE),” can provide a reasonable solution but its computational complexity is still high. Thus, a simple yet effective coordinate update algorithm (CUA) is proposed in this paper to reduce the computational complexity while maintaining and even improving the achievable rate performance. In addition, we propose a new hybrid precoding architecture that introduces the antenna selection mechanism into the conventional energy-efficient-based hybrid precoding architecture to further reduce energy consumption. Although, the resulting design problem of this new architecture becomes more complex in that the traditional ACE-based solver cannot be applied directly, the proposed CUA-based solver can still handle this problem. The simulation results demonstrate that the proposed CUA-based solver provides the same or even an improved achievable rate performance than the ACE-based solver at a lower complexity. Furthermore, the energy consumption of the proposed hybrid precoding architecture is lower than that of the conventional energy-efficient hybrid precoding architecture.


Convolutional Neural Networks Detect Local Infiltration of Lung Cancer Primary Lesions on Baseline FDG-PET/CT


title: Sentence Entailment in Compositional Distributional Semantics

abstract: Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.


title: Rule-Based Anomaly Pattern Detection for Detecting Disease Outbreaks

abstract: This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.


title: Globally Consistent Multi-People Tracking using Motion Patterns

abstract: Many state-of-the-art approaches to people tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then further grouping them into full trajectories. This grouping typically relies on imposing local smoothness constraints but almost never on enforcing more global constraints on the trajectories. In this paper, we propose an approach to imposing global consistency by first inferring behavioral patterns from the ground truth and then using them to guide the tracking algorithm. When used in conjunction with several state-of-the-art algorithms, this further increases their already good performance. Furthermore, we propose an unsupervised scheme that yields almost similar improvements without the need for ground truth.


title: A novel three dimensional fluid-structure interaction finite element model of wave propagation in SAW device: Application to biosensing & microfluidics

abstract: The key issues related to biosensor technology include selectivity, sensitivity, response and recovery times, and detection limit; most of these limitations stem from biofouling resulting from the binding of undesirable moieties such as non-specific proteins to the sensor surface. Thus, removal of non-specifically bound (NSB) proteins remains a significant challenge in biosensing applications. Operation of biosensors in liquid media necessitates an investigation of the fluid-device interaction to understand the mechanisms of biofouling elimination. In this study, we report for the first time, a fully coupled three dimensional transient finite element fluid-solid interaction (FSI) model of the SAW device subject to liquid loading to investigate the streaming velocity fields and forces induced by SAW device. Our simulation results suggest that the SAW-fluid interaction creates a pressure gradient in the direction of acoustic wave propagation in the fluid, leading to an acoustically driven streaming phenomenon known as SAW streaming which can be used for removal of non-specifically bound (NSB) proteins. Computed velocity fields indicate that the normal component of fluid velocity is smaller than the tangential component along the propagation direction. Thus, the SAW induced drag force, arising from the tangential component of fluid velocity and leading to particle advection is an important mechanism in biofouling removal from the SAW device surface and the normal component would prevent the reattachment of the particles to the device surface. Apart from microfluidic applications, this work broadly applies to all transducers used for biological species sensing that suffer from fouling and non-specific binding of protein molecules to the device surface.


title: Qualitative data analysis using data displays

abstract: The amount of data generated in qualitative research can be difficult to manage. In this paper Tracey Williamson and Andrew Long discuss how the use of data displays can improve data management and also how the process can help to make the routes from raw data to research findings in qualitative research more transparent. Data displays can take several forms but share the benefit of helping to condense large amounts of data into more manageable forms. They can also help to convey information in a visually stimulating format where presentation time or column space may be limited.


title: Rising STAR: A Highly Reconfigurable Sprawl Tuned Robot

abstract: This letter presents Rising STAR (RSTAR) a novel highly reconfigurable robot. RSTAR belongs to the family of the STAR robots with similar sprawling capabilities allowing it to run in a planar configuration, either upright or inverted and change its mechanics from the lateral to the sagittal planes. However, RSTAR is fitted with a new four bar extension mechanism (FBEM) allowing it to extend the distance between its body and legs. This combination of sprawling and extension mechanisms enables RSTAR to overcome extremely challenging obstacles, crawl over flexible and slippery surfaces and even climb vertically in a tube or between two walls. The robot can extend its height and width three-fold and move its center of mass both in the fore-aft and vertical directions. We first describe a kinematic model and the dynamical analysis conducted to improve the design of the robot and evaluate its strength and motor requirements. Based on this analysis, we designed and built a three-dimensional printed prototype and experimentally tested it. The robot can run upside down and climb over obstacles that are even higher than the diameter of its wheeled legs using a turtle-like gait. To increase its mobility, RSTAR can be fitted with wheels or spoked legs or a combination of the two, giving it superiorability to engage different terrains (see video).


title: Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines

abstract: Vector representations and vector space modeling (VSM) play a central role in modern machine learning. We propose a novel approach to `vector similarity searching' over dense semantic representations of words and documents that can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. ::: We show that this approach allows the indexing and querying of dense vectors in text domains. This opens up exciting avenues for major efficiency gains, along with simpler deployment, scaling and monitoring. ::: The end result is a fast and scalable vector database with a tunable trade-off between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch. ::: We empirically demonstrate its querying performance and quality by applying this solution to the task of semantic searching over a dense vector representation of the entire English Wikipedia.


title: Hybreed: A software framework for developing context-aware hybrid recommender systems

abstract: This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed's functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.


title: Scripts, Plans and Knowledge

abstract: We describe a theoretical system intended to facilitate the use of knowledge In an understanding system. The notion of script is introduced to account for knowledge about mundane situations. A program, SAM, is capable of using scripts to understand. The notion of plans is introduced to account for general knowledge about novel situations.


title: Do we need Asimov's Laws?

abstract: In this essay the stance on robots is discussed. The attitude against robots in history, starting in Ancient Greek culture until the industrial revolution is described. The uncanny valley and some possible explanations are given. Some differences in Western and Asian understanding of robots are listed and finally we answer the question raised with the title.


title: Contact Pressure Distribution in Joints Formed by V-Band Clamps

abstract: V-band clamps offer an efficient clamping solution in diverse applications including process equipment, exhaust systems and air handling. This paper studies the distribution of interface contact pressure between the V-band and flange when the coupling is established. The determination of the contact area and pressure distribution in a joint is essential information, as it determines the integrity of the coupling. A three dimensional finite element model has been developed for this purpose. Contrary to the previous assumption in developing axisymmetric models, the 3D results showed that the contact pressure is non-uniform around the circumference of V-band with maximum contact pressure near the T-bolt area. This is in agreement with the theory in the literature. The presence and magnitude of friction has a noticeable influence on the form of the interface pressure distribution curve. It is also shown that the diameter of the band interacts with the effect of friction.


title: BotOrNot: A System to Evaluate Social Bots

abstract: While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots. Since its release in May 2014, BotOrNot has served over one million requests via our website and APIs.


title: Towards faster and better retrieval models for question search

abstract: Community question answering (cQA) has become an important service due to the popularity of cQA archives on the web. This paper is concerned with the problem of question search. Question search in cQA aims to find the historical questions that are semantically equivalent or similar to the queried questions. In this paper, we propose a faster and better retrieval model for question search by leveraging user chosen category. After introducing the question category, we can filter certain amount of irrelevant historical questions under a wide range of leaf categories. Experimental results conducted on real cQA data set demonstrate that the proposed techniques are more effective and efficient than a variety of baseline methods.


title: A Single-Stage Single-Switch LED Driver Based on Class-E Converter

abstract: This paper proposes a single-stage single-switch light-emitting diode (LED) driver that integrates a buck–boost circuit with a Class-E resonant converter by sharing single-switch device. The buck–boost circuit, working as a power-factor correction (PFC) stage, operates in the discontinuous conduction mode (DCM) to shape the input current. The Class-E converter steps down the voltage to drive the LED. For specific component parameters, the Class-E converter can achieve soft-switching on the power switch and two rectifier diodes, and reduce switch losses at high frequencies. Electrolytic capacitors are used in the proposed converter to achieve a lower cost, but the system reliability decreases. To overcome this disadvantage, film capacitors can be used, but the current ripple increases. Neglecting the cost, multilayered film capacitors are the best option, if higher reliability and lower current ripple are required. The proposed driver features high efficiency (90.8%), and a high power factor (PF) (0.995). In this paper, analytical results and design considerations at 100 kHz are presented, and a 100-W prototype with 110 $\text{V}_{\text{AC}}$ input was built to validate the theoretical analysis.


A plant-based diet for the prevention and treatment of type 2 diabetes


eyond virtual museums : Experiencing immersive virtual reality in eal museums


Fingerprint Liveness Detection in the Presence of Capable Intruders


title: A practical approach to reconciling availability, performance, and capacity in provisioning extreme-scale storage systems

abstract: The increasing data demands from high-performance computing applications significantly accelerate the capacity, capability and reliability requirements of storage systems. As systems scale, component failures and repair times increase, significantly impacting data availability. A wide array of decision points must be balanced in designing such systems. We propose a systematic approach that balances and optimizes both initial and continuous spare provisioning based on a detailed investigation of the anatomy and field failure data analysis of extreme-scale storage systems. We consider the component failure characteristics and its cost and impact at the system level simultaneously. We build a tool to evaluate different provisioning schemes, and the results demonstrate that our optimized provisioning can reduce the duration of data unavailability by as much as 52% under a fixed budget. We also observe that non-disk components have much higher failure rates than disks, and warrant careful considerations in the overall provisioning process.


title: Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

abstract: Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.


title: Review of clustering algorithms for microgrid formation

abstract: Transitioning from a traditional distribution network grid or diesel only systems to microgrids, offers end-users economic benefits and higher power quality at a reduced environmental cost. Particularly, an upcoming research area, multi-microgrids, aims to provide a more reliable network capable of self-healing. The aim of this paper is to assess well-known clustering algorithms for cost effective microgrid formation and develop a planning framework for uncoupled multi-microgrid networks. In each microgrid, a minimum spanning tree represented the network, resulting in a linear relationship between the microgrid cost and the transmission/power demand. In addition, a diversity factor was introduced to showcase the ability of larger microgrids to more reliably meet peak power demands. Simulation results from three real life datasets suggested that hierarchical clustering algorithms were more suited for microgrid planning due to their adaptability to any datasets, complete solution space search guaranteeing global optimum networks and relative computational efficiency.


title: Smart Choice for the Smart Grid: Narrowband Internet of Things (NB-IoT)

abstract: The low power wide area network (LPWAN) technologies, which is now embracing a booming era with the development in the Internet of Things (IoT), may offer a brand new solution for current smart grid communications due to their excellent features of low power, long range, and high capacity. The mission-critical smart grid communications require secure and reliable connections between the utilities and the devices with high quality of service (QoS). This is difficult to achieve for unlicensed LPWAN technologies due to the crowded license-free band. Narrowband IoT (NB-IoT), as a licensed LPWAN technology, is developed based on the existing long-term evolution specifications and facilities. Thus, it is able to provide cellular-level QoS, and henceforth can be viewed as a promising candidate for smart grid communications. In this paper, we introduce NB-IoT to the smart grid and compare it with the existing representative communication technologies in the context of smart grid communications in terms of data rate, latency, range, etc. The overall requirements of communications in the smart grid from both quantitative and qualitative perspectives are comprehensively investigated and each of them is carefully examined for NB-IoT. We further explore the representative applications in the smart grid and analyze the corresponding feasibility of NB-IoT. Moreover, the performance of NB-IoT in typical scenarios of the smart grid communication environments, such as urban and rural areas, is carefully evaluated via Monte Carlo simulations.


title: A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms

abstract: Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.


Mixed Reality Serious Games and Gamification for smart education


Successful approaches to teaching introductory computer science courses with python


title: Permanent-Magnet Optimization in Permanent-Magnet-Assisted Synchronous Reluctance Motor for a Wide Constant-Power Speed Range

abstract: A permanent-magnet (PM)-assisted synchronous reluctance (PMASR) machine exhibits both high efficiency and high flux-weakening (FW) range. However, the best performance is achieved after a machine design optimization. In industry applications, the design of PMASR machines requires to satisfy an increasing number of limitations. The key points are lamination geometry, material property, and control strategy. This paper analyzes the influence of PM volume (flux level) on the motor performance, although lamination geometry and stack length are kept fixed. Thus, the PM volume inset in the rotor is optimized. The considered PMASR motor is designed for a very high FW speed range. The study is based on a finite-element (FE) analysis. The accuracy of the FE simulations is verified comparing their results with measurements on a prototype. The FE model is then used to study the different cases.


title: Face recognition using eigenface with naive Bayes

abstract: One of the lack of eigenface for prediction the face recogniton is not good accuracy. This paper uses naive Bayes for classifying the result of eigenface feature extraction to predict the face. The normalization z-score is added for sharping the accuracy. To see the performance of proposed method, the 200 datasets are divided into data training and testing by using cross validation (k=10). The results show that the proposed method can predict the face image up to 70%. Moreover by adding normalization Z-Score, the accuration of prediction raise up to 89.5% (in average).


title: Preventing software architecture erosion through static architecture conformance checking

abstract: Software architecture erosion is a problem faced by many organizations in the software industry. It happens when ‘as-implemented’ architecture does not conform to the ‘as-intended’ architecture, which results in low quality, complex, hard to maintain software. Architecture conformance checking refers to assessing the conformity of the implemented architecture to the intended architecture and can provide a strategy for detecting software architecture erosion and thereby prevent its negative consequences. When considering the current state-of-the-art of software architecture research and popular industry practices on architectural erosion, it obviously appears that such solution strategy is much needed to address the ever increasing demands for large scale complex software systems. In this paper an analysis of existing static architecture conformance checking is undertaken. Extending previously conducted research, we are in the process of developing a static architecture conformance checking tool for Java, based on GRASP ADL as a mean to overcome the challenges of software architecture erosion. Early design/implementation details of this tool are also presented.


title: An ontology based approach for activity recognition from video

abstract: Representation and recognition of human activities is an important problem for video surveillance and security applications. Considering the wide variety of settings in which surveillance systems are being deployed, it is necessary to create a common knowledge-base or ontology of human activities. Most current attempts at ontology design in computer vision for human activities have been empirical in nature. In this paper, we present a more systematic approach to address the problem of designing ontologies for visual activity recognition. We draw on general ontology design principles and adapt them to the specific domain of human activity ontologies. Then, we discuss qualitative evaluation principles and provide several examples from existing ontologies and how they can be improved upon. Finally, we demonstrate quantitatively in terms of recognition performance, the efficacy and validity of our approach for bank and airport tarmac surveillance domains.


title: Nutrients and Wound Healing: Still Searching for the Magic Bullet

abstract: Clinicians in nearly all practice areas are confronted with the challenges associated with delayed and impaired wound healing. Although nutrition plays a critical role in the healing process, controversy exists regarding the optimal nutrition regimen. This article reviews literature related to nutrition interventions that facilitate wound healing. The limitations of the research that forms the scientific basis of many nutrition recommendations are also examined. The limited availability of rigorously performed clinical studies to develop evidence-based guidelines for nutrition support in wound care emphasizes the need for further research and underscores the importance of individualizing the nutrition care plan for each patient.


title: Gaining vehicle-to-grid benefits with unidirectional electric and plug-in hybrid vehicle chargers

abstract: The goal of this paper is to confirm that unidirectional power flow, given a significant penetration of electric vehicles (EVs), is sufficient to provide a utility with reactive power support and other vehicle-to-grid benefits. This work addresses the reactive power support capability of unidirectional chargers and power demand scheduling schemes aimed at minimizing peak loading on a distribution feeder due to multiple charging EVs. Reactive power capacity available from a unidirectional charger is quantified, and the impact of a few distinct utility-controlled power-draw scheduling strategies on peak loading of a distribution feeder model is discussed.


title: Dynamic Data Selection for Neural Machine Translation

abstract: Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT). With the recent increase in popularity of neural machine translation (NMT), we explore in this paper to what extent and how NMT can also benefit from data selection. While state-of-the-art data selection (Axelrod et al., 2011) consistently performs well for PBMT, we show that gains are substantially lower for NMT. Next, we introduce dynamic data selection for NMT, a method in which we vary the selected subset of training data between different training epochs. Our experiments show that the best results are achieved when applying a technique we call gradual fine-tuning, with improvements up to +2.6 BLEU over the original data selection approach and up to +3.1 BLEU over a general baseline.


title: Solubility of radon in selected perfluorocarbon compounds and water

abstract: The solubility of radon in a series of perfluorocarbon compounds has been measured in the temperature range 5-40/sup 0/C. From the resulting data, a Hildebrand solubility parameter for radon of 8.42 +- 0.11 cal/sup 1/2/ cm/sup -3/2/ was determined. In order to provide reliable values of these parameters for radon in water, measurements were made for deionized, medium-hardness, and high-hardness water. The thermodynamic functions, ..delta..G/sup 0/, ..delta..H/sup 0/, ..delta..S/sup 0/, and ..delta..C/sub p//sup 0/, characterizing the solubilization process have also been determined.


title: An End-To-End Siamese Convolutional Neural Network for Loop Closure Detection in Visual Slam System

abstract: Loop closure detection is essential and important in visual simultaneous localization and mapping (SLAM) systems. Most existing methods typically utilize a separate feature extraction part and a similarity metric part. Compared to these methods, an end-to-end network is proposed in this paper to jointly optimize the two parts in a unified framework for further enhancing the interworking between these two parts. First, a two-branch siamese network is designed to learn respective features for each scene of an image pair. Then a hierarchical weighted distance (HWD) layer is proposed to fuse the multi-scale features of each convolutional module and calculate the distance between the image pair. Finally, by using the contrastive loss in the training process, the effective feature representation and similarity metric can be learned simultaneously. Experiments on several open datasets illustrate the superior performance of our approach and demonstrate that the end-to-end network is feasible to conduct the loop closure detection in real time and provides an implementable method for visual SLAM systems.


title: An integrated information system for snowmelt flood early-warning based on internet of things

abstract: Floods and water resource management are major challenges for human in present and the near future, and snowmelt floods which usually break out in arid or semi-arid regions often cause tremendous social and economic losses, and integrated information system (IIS) is valuable to scientific and public decision-making. This paper presents an integrated approach to snowmelt floods early-warning based on geoinformatics (i.e. remote sensing (RS), geographical information systems (GIS) and global positioning systems (GPS)), Internet of Things (IoT) and cloud services. It consists of main components such as infrastructure and devices in IoT, cloud information warehouse, management tools, applications and services, the results from a case study shows that the effectiveness of flood prediction and decision-making can be improved by using the IIS. The prototype system implemented in this paper is valuable to the acquisition, management and sharing of multi-source information in snowmelt flood early-warning even in other tasks of water resource management. The contribution of this work includes developing a prototype IIS for snowmelt flood early-warning in water resource management with the combination of IoT, Geoinformatics and Cloud Service, with the IIS, everyone could be a sensor of IoT and a contributor of the information warehouse, professional users and public are both servers and clients for information management and services. Furthermore, the IIS provides a preliminary framework of e-Science in resources management and environment science. This study highlights the crucial significance of a systematic approach toward IISs for effective resource and environment management.


title: Insight and reduction of MapReduce stragglers in heterogeneous environment

abstract: Speculative and clone execution are existing techniques to overcome the problems of task stragglers and performance degradation in heterogeneous clusters for big data processing. In this paper, we propose an alternative approach to solving the problems based on analysis results of profiling and the relations of the system parameters. Our approach adjusts the amount of task slots of nodes dynamically to match the processing power of the nodes, according to current task progress rate and resource utilization. It contrasts with the existing techniques by attempting to prevent task stragglers from occurring in the first place through maintaining a balance between resource supply and demand. We have implemented this method in the Hadoop MapReduce platform, and the TPC-H benchmark results show that it achieves 20-30% performance improvement and 35-88% less stragglers than existing techniques.


title: FPGAs in Industrial Control Applications

abstract: The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.


title: Building agent teams using an explicit teamwork model and learning

abstract: Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup—the most popular of the RoboCup leagues so far. ::: ::: To address the challenge of teamwork, we investigate a novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships. This general approach to teamwork, shown to be applicable in other domains beyond RoboCup, both reduces development time and improves teamwork flexibility. We also demonstrate the application of off-line and on-line learning to improve and specialize agents' individual skills in RoboCup. These capabilities enabled our soccer-playing team, ISIS, to successfully participate in the first international RoboCup soccer tournament (RoboCup'97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league.


Image Segmentation : A Comparative Study


title: The adoption of software-as-a-service (SaaS): ranking the determinants

abstract: Purpose – Software-as-a-Service (SaaS) has the potential to provide substantial opportunities for organizations to improve their information technology without cost and management concerns. However, organizations have not utilized it to the desired level because it is very challenging for them to completely transform their basic conventional methods of running software into SaaS as a high-tech method. On the other hand, organizations have doubt which factors should be mostly considered if they want to move to SaaS. Therefore, investigating the adoption of SaaS can contribute organizations to benefit from this technology. The purpose of this paper is to provide a good insight into SaaS technology adoption. Design/methodology/approach – Considering Technology, Organization and Environment (TOE) framework and diffusion of innovation (DOI) theory as the basis, 22 university experts expressed their idea about the proposed model of SaaS adoption. Then, 30 IT professional in 15 IT enterprises that had adopted Sa...


title: The Firm's Management of Social Interactions

abstract: Consumer choice is influenced in a direct and meaningful way by the actions taken by others. These “actions” range from face-to-face recommendations from a friend to the passive observation of what a stranger is wearing. We refer to the set of such contexts as “social interactions” (SI). We believe that at least some of the SI effects are partially within the firm’s control and that this represents an exciting research opportunity. We present an agenda that identifies a list of unanswered questions of potential interest to both researchers and managers. In order to appreciate the firm’s choices with respect to its management of SI, it is important to first evaluate where we are in terms of understanding the phenomena themselves. We highlight five questions in this regard: (1) What are the antecedents of word of mouth (WOM)? (2) How does the transmission of positive WOM differ from that of negative WOM? (3) How does online WOM differ from offline WOM? (4) What is the impact of WOM? (5) How can we measure WOM? Finally, we identify and discuss four principal, non-mutually exclusive, roles


title: Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes

abstract: Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database search operators with an information theoretic ranking function called predictive relevance. Predictive relevance can be calculated by a fast sparse matrix algorithm based on posterior samples from CrossCat, a nonparametric Bayesian model for high-dimensional, heterogeneously-typed data tables. The result is a flexible search technique that applies to a broad class of information retrieval problems, which we integrate into BayesDB, a probabilistic programming platform for probabilistic data analysis. This paper demonstrates applications to databases of US colleges, global macroeconomic indicators of public health, and classic cars. We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.


title: Autonomous intelligent cruise control

abstract: Vehicle following and its effects on traffic flow has been an active area of research. Human driving involves reaction times, delays, and human errors that affect traffic flow adversely. One way to eliminate human errors and delays in vehicle following is to replace the human driver with a computer control system and sensors. The purpose of this paper is to develop an autonomous intelligent cruise control (AICC) system for automatic vehicle following, examine its effect on traffic flow, and compare its performance with that of the human driver models. The AICC system developed is not cooperative; i.e., it does not exchange information with other vehicles and yet is not susceptible to oscillations and "slinky" effects. The elimination of the "slinky" effect is achieved by using a safety distance separation rule that is proportional to the vehicle velocity (constant time headway) and by designing the control system appropriately. The performance of the AICC system is found to be superior to that of the human driver models considered. It has a faster and better transient response that leads to a much smoother and faster traffic flow. Computer simulations are used to study the performance of the proposed AICC system and analyze vehicle following in a single lane, without passing, under manual and automatic control. In addition, several emergency situations that include emergency stopping and cut-in cases were simulated. The simulation results demonstrate the effectiveness of the AICC system and its potentially beneficial effects on traffic flow. >


A stackable file system interface for Linux


title: Information Flow Control Models in Peer-to-Peer Publish/Subscribe Systems

abstract: A publish/subscribe (PS) model is an event-driven model of a distributed system. In this paper, we consider a peer-to-peer (P2P) type of PS model where each peer (process) can publish and subscribe events. Here, a peer publishes an event message and then the event message is notified to a target peer which is interested in the event. Publications and subscriptions are specified in terms of topics as discussed in topic-based PS systems. In this paper, we newly discuss a topic-based access control (TBAC) model to prevent illegal information flow among peers in PS systems. Here, an access right is a pair "t, op" of a topic t and an operation op which is publish or subscribe. A peer is allowed to publish an event message with topics and subscribe topics only if the topics are granted to the peer. An event message e is notified to a peer pi if the publication of e and subscription of pi include some common topic. If a peer pi publishes an event message e2 after receiving an event message e1, the event message e2 may bring the event of e1, which the peer pi is not allowed to publish. Here, information in the peer pi illegally flow to another peer. We define the legal flow relation among the peers. Then, we newly propose a subscription-based synchronization (SBS) protocol to prevent illegal information flow. Here, a notification is banned if the notification may cause illegal information flow. We evaluate the SBS protocol in terms of number of notifications banned.


Gujarati Script Recognition: A Review


title: Modulation of Connectivity in Visual Pathways by Attention: Cortical Interactions Evaluated with Structural Equation Modelling anf fMRI

abstract: Electrophysiological and neuroimaging studies have shown that attention to visual motion can increase the responsiveness of the motion-selective cortical area V5 and the posterior parietal cortex (PP). Increased or decreased activation in a cortical area is often attributed to attentional modulation of the cortical projections to that area. This leads to the notion that attention is associated with changes in connectivity. We have addressed attentional modulation of effective connectivity using functional magnetic resonance imaging (fMRI). Three subjects were scanned under identical stimulus conditions (visual motion) while varying only the attentional component of the task. Haemodynamic responses defined an occipito-parieto-frontal network, including the, primary visual cortex (V1), V5 and PP. A structural equation model of the interactions among these dorsal visual pathway areas revealed increased connectivity between V5 and PP related to attention. On the basis of our analysis and the neuroanatomical pattern of projections from the prefrontal cortex to PR we attributed the source of modulatory influences, on the posterior visual pathway, to the prefrontal cortex (PFC). To test this hypothesis we included the PFC in our model as a 'modulator' of the pathway between V5 and PR using interaction terms in the structural equation model. This analysis revealed a significant modulatory effect of prefrontal regions on V5 afferents to posterior parietal cortex.


title: Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

abstract: We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence classification tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state-of-the-art for topic-based sentiment analysis.


title: Offline A/B Testing for Recommender Systems

abstract: Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an estimator of the potential uplift in revenue that could generate this new technology. It helps to iterate faster and to avoid losing money by detecting poor policies. These estimators are known as counterfactual or off-policy estimators. We show that traditional counterfactual estimators such as capped importance sampling and normalised importance sampling are experimentally not having satisfying bias-variance compromises in the context of personalised product recommendation for online advertising. We propose two variants of counterfactual estimates with different modelling of the bias that prove to be accurate in real-world conditions. We provide a benchmark of these estimators by showing their correlation with business metrics observed by running online A/B tests on a commercial recommender system.


title: WeText: Scene Text Detection under Weak Supervision

abstract: The requiring of large amounts of annotated training data has become a common constraint on various deep learning systems. In this paper, we propose a weakly supervised scene text detection method (WeText) that trains robust and accurate scene text detection models by learning from unannotated or weakly annotated data. With a "light" supervised model trained on a small fully annotated dataset, we explore semi-supervised and weakly supervised learning on a large unannotated dataset and a large weakly annotated dataset, respectively. For the unsupervised learning, the light supervised model is applied to the unannotated dataset to search for more character training samples, which are further combined with the small annotated dataset to retrain a superior character detection model. For the weakly supervised learning, the character searching is guided by high-level annotations of words/text lines that are widely available and also much easier to prepare. In addition, we design an unified scene character detector by adapting regression based deep networks, which greatly relieves the error accumulation issue that widely exists in most traditional approaches. Extensive experiments across different unannotated and weakly annotated datasets show that the scene text detection performance can be clearly boosted under both scenarios, where the weakly supervised learning can achieve the state-of-the-art performance by using only 229 fully annotated scene text images.


title: X-Ray Radiation Effect in DRAM Retention Time

abstract: For quality verification, an X-ray inspection process is commonly being used for evaluating obscured and defective solder joints in surface-mount technologies, such as ball grid arrays and flip chips. Integrated circuits subjected to any form of radiation, i.e., ionizing or nonionizing, may incur some amount of damage depending on the absorbed dose. Though most X-ray inspections for high-quality imaging require ionizing dose amounts that are considered inconsequential for device failure or non-functionality, the degree of latent damage must be carefully considered. This paper discusses X-ray-induced vulnerabilities of high-density dynamic random access memory exposed to low ionizing radiation levels typical in X-ray inspection systems. We look at critical parameters and their sensitivity in relation to varying dose amounts of X-ray irradiation. In consideration of different methodologies of reducing radiation dose amounts and limiting device exposure, we propose a procedure for attenuating potentially harmful X-ray radiation levels while preserving quality images.


title: When generalized eating detection machine learning models fail in the field

abstract: Problematic eating behaviors are a major cause of obesity. To improve our understanding of these eating behaviors, we need to be able to first reliably detect them. In this paper we use a wrist-worn sensor to test a generalized machine learning models' reliability in detecting eating episodes through data processing. We process data from a 6-axis inertial sensor. Since most eating episodes do not occur while moving, we filter out periods of physical activity, and then use an advanced motif-based time-point fusion technique to detect feeding gestures. We also cluster each of the false alarms into four categories in an effort to identify the main behaviors that confound feeding gesture detection. We tested our system on eight participants performing various activities in the wild while wearing a sensing suite: a neck- and a wrist-worn sensor, along with a wearable video camera continuously recording to capture ground truth. Trained annotators further validated the algorithms by identifying feeding gestures, and categorized the false alarms. All eating episodes were detected; however, many false alarms were also detected, yielding a 61% average F-measure in detecting feeding gestures. This result shows clear challenges in characterizing eating episodes by using a single inertial-based wrist-worn sensor.


title: Form-Oriented annotation for building a functionally independent dictionary of synthetic movement

abstract: Non-verbal behavior performed by embodied conversational agents still appears "wooden" and sometimes even "unnatural". Annotated corpora and high resolution annotations capturing the expressive details of movement, may improve the gradualness of synthetic behavior. This paper presents a non-functional, form-oriented annotation scheme based on informal corpora involving multi-speaker dialogues. This annotation scheme allows annotators to capture the expressive details of movement in high-resolutions. The expressive domains it captures are: spatial domain (movement-pose configuration on the level of articulators), fluidity (translations between movement-phases and phrases), temporal domain (movement variation in the form of movement phases), repetitivity (repetitive features of movement), and power (level of exposure). The presented annotation scheme can transform the encoded data into movement templates that can be directly reproduced by an embodied conversational agent.


title: Implementation of inverse perspective mapping algorithm for the development of an automatic lane tracking system

abstract: Vision based automatic lane tracking system requires information such as lane markings, road curvature and leading vehicle be detected before capturing the next image frame. Placing a camera on the vehicle dashboard and capturing the forward view results in a perspective view of the road image. The perspective view of the captured image somehow distorts the actual shape of the road, which involves the width, height, and depth. Respectively, these parameters represent the x, y and z components. As such, the image needs to go through a pre-processing stage to remedy the distortion using a transformation technique known as an inverse perspective mapping (IPM). This paper outlines the procedures involved.


title: Sparse and Constrained Attention for Neural Machine Translation

abstract: In NMT, words are sometimes dropped from the source or generated repeatedly in the translation. We explore novel strategies to address the coverage problem that change only the attention transformation. Our approach allocates fertilities to source words, used to bound the attention each word can receive. We experiment with various sparse and constrained attention transformations and propose a new one, constrained sparsemax, shown to be differentiable and sparse. Empirical evaluation is provided in three languages pairs.


title: Static Zero-Power-Consumption Coplanar Waveguide Embedded DC-to-RF Metal-Contact MEMS Switches in Two-Port and Three-Port Configuration

abstract: This paper reports on novel electrostatically actuated dc-to-RF metal-contact microelectromechanical systems (MEMS) switches, featuring a minimum transmission line discontinuity since the whole switch mechanism is completely embedded inside the signal line of a low-loss 3-D micromachined coplanar waveguide. Furthermore, the switches are based on a multistable interlocking mechanism resulting in static zero-power consumption, i.e., both the onstate and the offstate are maintained without applying external actuation energy. Additionally, the switches provide with active opening capability, potentially improving the switch reliability, and enabling the usage of soft low-resistivity contact materials. Both two-port single-pole-single-throw (SPST) switches featuring mechanical bistability and three-port single-pole-double-throw (SPDT) T-junction switches with four mechanically stable states are presented. The switches, together with the transmission lines, are fabricated in a single photolithography process. The loss created by the discontinuity of the switch mechanism alone is 0.08 dB at 20 GHz. Including a 500 m long transmission line with less than 0.4 dB/mm loss up to 20 GHz, the total insertion loss of the two-port devices is 0.15 and 0.3 dB at 2 and 20 GHz, and the isolation is 45 and 25 dB at 2 and 20 GHz. The three-port switches, including their T-junction transmission line, have an insertion loss of 0.31 and 0.68 dB, and an isolation of 43 and 22 dB, at 1 and 10 GHz, respectively. Actuation voltages are 23-39 V for the two-port switches and 39-89 V for the three-port switches. The microwave propagation in the micromachined transmission line and the influence of the different switch designs were analyzed by finite-element method (FEM) simulations of electromagnetic energy and volume current distributions, proving the design advantages of the proposed concept.


title: Open Domain Question Answering with Character-Level Deep Learning Models

abstract: Single-relation factoid question answering (QA) is strongly supported by rich sources of facts from knowledge bases (KB). However, there are many irrelevant information in questions and overwhelming number of facts in knowledge bases, mak-ing it difficult to capture goal entity and relation involved in a question. In order to settle these issues, firstly, a state-of-the-art sequence tagging model (BiLSTM-CRF) is adopted to detect the entity mention in a question. Then, we propose a n-gram match (NGM) algorithm with Chinese-specific rules and an attention-based siamese bidirectional long-short term memory (ASBLSTM) model to measure the lexical and se-mantic similarity between questions and candidate facts. Our whole method requires no hand-crafted template or feature engineering. In addition, character-level models are proved to be effective in solving the out of vocabulary (OOV) issue and improving the accuracy in Chinese KBQA task. Experiment results show that our system outperforms the best system with deep learning models in the KBQA share task of the Confer-ence on Natural Language Processing and Chinese Compu-ting (NLPCC) 2016 and our system achieves an AverageF1 measure of 80.97% and 37.18% on test dataset in NLPCC 2016 and 2017 respectively.


title: Habitat Loss and Extinction in the Hotspots of Biodiversity

abstract: Nearly half the world's vascular plant species and one-third of terrestrial vertebrates are endemic to 25 "hotspots" of biodiversity, each of which has at least 1500 endemic plant species. None of these hotspots have more than one-third of their pristine habitat remaining. Historically, they covered 12% of the land's sur- face, but today their intact habitat covers only 1.4% of the land. As a result of this habitat loss, we expect many of the hotspot endemics to have either become extinct or—because much of the habitat loss is recent— to be threatened with extinction. We used World Conservation Union (IUCN) Red Lists to test this expectation. Overall, between one-half and two-thirds of all threatened plants and 57% of all threatened terrestrial verte- brates are hotspot endemics. For birds and mammals, in general, predictions of extinction in the hotspots based on habitat loss match numbers of species independently judged extinct or threatened. In two classes of hotspots the match is not as close. On oceanic islands, habitat loss underestimates extinction because intro- duced species have driven extinctions beyond those caused by habitat loss on these islands. In large hotspots, conversely, habitat loss overestimates extinction, suggesting scale dependence (this effect is also apparent for plants). For reptiles, amphibians, and plants, many fewer hotspot endemics are considered threatened or ex- tinct than we would expect based on habitat loss. This mismatch is small in temperate hotspots, however, sug- gesting that many threatened endemic species in the poorly known tropical hotspots have yet to be included on the IUCN Red Lists. We then asked in which hotspots the consequences of further habitat loss (either abso- lute or given current rates of deforestation) would be most serious. Our results suggest that the Eastern Arc and Coastal Forests of Tanzania-Kenya, Philippines, and Polynesia-Micronesia can least afford to lose more habitat and that, if current deforestation rates continue, the Caribbean, Tropical Andes, Philippines, Me- soamerica, Sundaland, Indo-Burma, Madagascar, and Choco-Darien-Western Ecuador will lose the most spe- cies in the near future. Without urgent conservation intervention, we face mass extinctions in the hotspots.


title: High-Voltage CMOS ESD and the Safe Operating Area

abstract: Established methods for testing ESD robustness of high-voltage pins in smart power CMOS can lead to erroneous results. This paper investigates both LDNMOS and certain types of SCRLDMOS (SCRs embedded in LDNMOS) high-voltage clamps for safe-operating-area collapse due to trigger voltage (V t1) walk-in after transmission-line pulsing (TLP) corresponding to leakage-current increase below I t2. For the first time, the evolution of V t1 as a function of the number of TLP pulses and their magnitude is shown. Furthermore, some high-voltage clamps that are robust on their own are shown to walk-in in certain circuit environments that present high capacitance. The physical mechanism for this leakage enhancement and trigger-voltage walk-in has been established through failure analysis to be consistent with melt filament growth perhaps aided by metal migration. A simple phenomenological model is proposed to explain this behavior. Finally, the ability to avoid such V t1 collapse in SCRLDMOS through simple layout changes is shown.


title: FIN10K: A Web-based Information System for Financial Report Analysis and Visualization

abstract: In this demonstration, we present FIN10K, a web-based information system that facilitates the analysis of textual information in financial reports. The proposed system has three main components: (1) a 10-K Corpus, including an inverted index of financial reports on Form 10-K, several numerical finance measures, and pre-trained word embeddings; (2) an information retrieval system; and (3) two data visualizations of the analyzed results. The system can be of great help in revealing valuable insights within large amounts of textual information. The system is now online available at http: //clip.csie.org/10K/.


title: Influence of power converters on PV maximum power point tracking efficiency

abstract: Photovoltaic maximum power point tracker (MPPT) systems are commonly employed to maximize the photovoltaic output power, since it is strongly affected in accordance to the incident solar radiation, surface temperature and load-type changes. Basically, a MPPT system consists on a dc-dc converter (hardware) controlled by a tracking algorithm (software) and the combination of both, hardware and software, defines the tracking efficiency. This paper shows that even when the most accurate algorithm is employed, the maximum power point cannot be found, since its imposition as operation point depends on the dc-dc converter static feature and the load-type connected to the system output. For validating the concept, the main dc-dc converters, i.e., Boost, Buck-Boost, Cuk, SEPIC and Zeta are analyzed considering two load-types: resistive voltage regulated dc bus. Simulation and experimental results are included for validating the theoretical analysis.


title: Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator

abstract: Triangle is a robust implementation of two-dimensional constrained Delaunay triangulation and Ruppert's Delaunay refinement algorithm for quality mesh generation. Several implementation issues are discussed, including the choice of triangulation algorithms and data structures, the effect of several variants of the Delaunay refinement algorithm on mesh quality, and the use of adaptive exact arithmetic to ensure robustness with minimal sacrifice of speed. The problem of triangulating a planar straight line graph (PSLG) without introducing new small angles is shown to be impossible for some PSLGs, contradicting the claim that a variant of the Delaunay refinement algorithm solves this problem.


title: A Review Paper on Big Data and Hadoop

abstract: The term 'Big Data' describes innovative techniques and technologies to capture, store, distribute, manage and analyze petabyte- or larger-sized datasets with high-velocity and different structures. Big data can be structured, unstructured or semi-structured, resulting in incapability of conventional data management methods. Data is generated from various different sources and can arrive in the system at various rates. In order to process these large amounts of data in an inexpensive and efficient way, parallelism is used. Big Data is a data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it. Hadoop is the core platform for structuring Big Data, and solves the problem of making it useful for analytics purposes. Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance.


title: Deep Relative Tracking

abstract: Most existing tracking methods are direct trackers, which directly exploit foreground or/and background information for object appearance modeling and decide whether an image patch is target object or not. As a result, these trackers cannot perform well when target appearance changes heavily and becomes different from its model. To deal with this issue, we propose a novel relative tracker, which can effectively exploit the relative relationship among image patches from both foreground and background for object appearance modeling. Different from direct trackers, the proposed relative tracker is robust to localize target object by use of the best image patch with the highest relative score to the target appearance model. To model relative relationship among large-scale image patch pairs, we propose a novel and effective deep relative learning algorithm through the convolutional neural network. We test the proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our method consistently outperforms the state-of-the-art trackers due to the powerful capacity of the proposed deep relative model.


title: A Fault Tolerant Wired/Wireless Sensor Network Architecture for Monitoring Pipeline Infrastructures

abstract: This paper proposes a new fault-tolerant sensor network architecture for monitoring pipeline infrastructures. This architecture is an integrated wired and wireless network. The wired part of the network is considered the primary network while the wireless part is used as a backup among sensor nodes when there is any failure in the wired network. This architecture solves the current reliability issues of wired networks for pipelines monitoring and control. This includes the problem of disabling the network by disconnecting the network cables due to artificial or natural reasons. In addition, it solves the issues raised in recently proposed network architectures using wireless sensor networks for pipeline monitoring. These issues include the issues of power management and efficient routing for wireless sensor nodes to extend the life of the network. Detailed advantages of the proposed integrated network architecture are discussed under different application and fault scenarios.