title: TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections

abstract: Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.


title: Natural language processing in mental health applications using non-clinical texts †

abstract: Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.


title: Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations

abstract: Knowledge graphs are a versatile framework to encode richly structured data relationships, but it not always apparent how to combine these with existing entity representations. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity. However, useful knowledge graphs often contain diverse entities and relations (with potentially disjoint underlying corpora) which do not accord with these assumptions. To overcome these limitations, we present Functional Retrofitting, a framework that generalizes current retrofitting methods by explicitly modeling pairwise relations. Our framework can directly incorporate a variety of pairwise penalty functions previously developed for knowledge graph completion. We present both linear and neural instantiations of the framework. Functional Retrofitting significantly outperforms existing retrofitting methods on complex knowledge graphs and loses no accuracy on simpler graphs (in which relations do imply similarity). Finally, we demonstrate the utility of the framework by predicting new drug--disease treatment pairs in a large, complex health knowledge graph.


title: SPACLE: investigating learning across virtual and physical spaces using spatial replays

abstract: Classroom experiments that evaluate the effectiveness of educational technologies do not typically examine the effects of classroom contextual variables (e.g., out-of-software help-giving and external distractions). Yet these variables may influence students' instructional outcomes. In this paper, we introduce the Spatial Classroom Log Explorer (SPACLE): a prototype tool that facilitates the rapid discovery of relationships between within-software and out-of-software events. Unlike previous tools for retrospective analysis, SPACLE replays moment-by-moment analytics about student and teacher behaviors in their original spatial context. We present a data analysis workflow using SPACLE and demonstrate how this workflow can support causal discovery. We share the results of our initial replay analyses using SPACLE, which highlight the importance of considering spatial factors in the classroom when analyzing ITS log data. We also present the results of an investigation into the effects of student-teacher interactions on student learning in K-12 blended classrooms, using our workflow, which combines replay analysis with SPACLE and causal modeling. Our findings suggest that students' awareness of being monitored by their teachers may promote learning, and that "gaming the system" behaviors may extend outside of educational software use.


IT-BPO INDUSTRY PROFILE, PROSPECTS, CHALLENGES AND ISSUES FOR GROWTH AND EMPLOYMENT


title: Improving Phoneme segmentation with Recurrent Neural Networks.

abstract: Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural network. Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.


title: Deep learning of support vector machines with class probability output networks

abstract: Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated.


title: Herbal Therapy: What Every Facial Plastic Surgeon Must Know

abstract: Herbal medicine (phytomedicine) uses remedies possessing significant pharmacological activity and, consequently, potential adverse effects and drug interactions. The explosion in sales of herbal therapies has brought many products to the marketplace that do not conform to the standards of safety and efficacy that physicians and patients expect. Unfortunately, few surgeons question patients regarding their use of herbal medicines, and 70% of patients do not reveal their use of herbal medicines to their physicians and pharmacists. All surgeons should question patients about the use of the following common herbal remedies, which may increase the risk of bleeding during surgical procedures: feverfew, garlic, ginger, ginkgo, and Asian ginseng. Physicians should exercise caution in prescribing retinoids or advising skin resurfacing in patients using St John's wort, which poses a risk of photosensitivity reaction. Several herbal medicines, such as aloe vera gel, contain pharmacologically active ingredients that may aid in wound healing. Practitioners who wish to recommend herbal medicines to patients should counsel them that products labeled as supplements have not been evaluated by the US Food and Drug Administration and that no guarantee of product quality can be made.


Digitalization of Business Processes in Human Resources Management of Russian Industrial Enterprises


title: A deep learning approach to fetal-ECG signal reconstruction

abstract: Fetal electrocardiogram (FECG) monitoring has become essential due to the current increase in the relative number of cardiac patients worldwide. This paper proposes to use a deep learning approach to compress/recover FECG signals, improving the computation speed in a telemonitoring system. The problem is analogous to the reconstruction of a non-sparse signal in compressive sensing (CS) framework. The architecture incorporates a non-linear mapping using a stacked denoising autoencoder (SDAE). The compression of the raw non-sparse FECG data takes place at the transmitter side using a deep neural network. After pre-training, the whole deep SDAE can be further fine tuned by the mini-batch gradient descent-based back-propagation algorithm. Although the training for SDAE is usually time-consuming, it does not affect the performance due to the one-time off-line training process. The real-time FECG reconstruction is faster due to a few matrix-vector multiplications at the receiver end. The simulations performed by employing standard non-invasive FECG databases shows promising results in terms of the reconstruction quality.


title: Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss

abstract: Bidirectional long short-term memory (bi-LSTM) networks have recently proven successful for various NLP sequence modeling tasks, but little is known about their reliance to input representations, target languages, data set size, and label noise. We address these issues and evaluate bi-LSTMs with word, character, and unicode byte embeddings for POS tagging. We compare bi-LSTMs to traditional POS taggers across languages and data sizes. We also present a novel bi-LSTM model, which combines the POS tagging loss function with an auxiliary loss function that accounts for rare words. The model obtains state-of-the-art performance across 22 languages, and works especially well for morphologically complex languages. Our analysis suggests that bi-LSTMs are less sensitive to training data size and label corruptions (at small noise levels) than previously assumed.


title: A Large $\sigma $V$_{\rm TH}$/VDD Tolerant Zigzag 8T SRAM With Area-Efficient Decoupled Differential Sensing and Fast Write-Back Scheme

abstract: Nanometer SRAM cannot achieve lower VDDmin due to read-disturb, half-select disturb and write failure. This paper demonstrates quantitative performance advantages of a zigzag 8T-SRAM (Z8T) cell over the decoupled single-ended sensing 8T-SRAM (DS8T) with write-back schemes, which was previously recognized as the most area-efficient cell under large σVTH/VDD conditions. Since Z8T uses only 1T for each decoupled read-port, faster 2T differential sensing (D2S) can be implemented within the same area as the single-ended DS8T. Thanks to D2S, Z8T cell enables much faster R/W speed at VDDmin than DS8T. For the same VDDmin/speed, Z8T reduces the cell area by 15%. The Z8T 32 Kb macro is 14% smaller area and 53% faster than DS8T cells. Three macros were fabricated using foundry provided 65 nm low-power and 90 nm generic processes. The measured VDDmin for a 65 nm 256-row 32 Kb and a 32-row 4 Kb macro are 430 mV and 250 mV respectively. The measured VDDmin for a 90 nm 256-row 64 Kb macro is 230 mV.


title: A Model Towards Using Evidence from Security Events for Network Attack Analysis

abstract: Constructing an efficient and accurate model from security events to determine an attack scenario for an enterprise network is challenging. In this paper, we discuss how to use the information obtained from security events to construct an attack scenario and build an evidence graph. To achieve the accuracy and completeness of the evidence graph, we use Prolog inductive and abductive reasoning to correlate evidence by reasoning the causality, and use an anti-forensics database and a corresponding attack graph to find the missing evidence.


title: A new miniaturized antenna for ISM 433 MHz frequency band

abstract: A new miniaturized antenna for 433MHz frequency band is introduced for sensor networks. The antenna is a magnetic monopole loaded with a capacitor, which can significantly decrease the resonant frequency of the structure. It is designed to operate in the ISM 433MHz band on a small PCB (103mm⋆55mm) and has at least 3MHz bandwidth. Moreover, its radiation efficiency is suitable for the possible applications and its pattern is omnidirectionnal.


title: Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems

abstract: Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper a new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance based CNN approaches, achieving better eye gaze accuracy with significantly fewer computational requirements. A brief updated literature review is also provided.


title: Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination

abstract: Most of the current face hallucination methods, whether they are shallow learning-based or deep learning-based, all try to learn a relationship model between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a training set. They mainly focus on modeling image prior through either model-based optimization or discriminative inference learning. However, when the input LR face is tiny, the learned prior knowledge is no longer effective and their performance will drop sharply. To solve this problem, in this paper we propose a general face hallucination method that can integrate model-based optimization and discriminative inference. In particular, to exploit the model based prior, the Deep Convolutional Neural Networks (CNN) denoiser prior is plugged into the super-resolution optimization model with the aid of image-adaptive Laplacian regularization. Additionally, we further develop a high-frequency details compensation method by dividing the face image to facial components and performing face hallucination in a multi-layer neighbor embedding manner. Experiments demonstrate that the proposed method can achieve promising super-resolution results for tiny input LR faces.


title: Robust Neural Malware Detection Models for Emulation Sequence Learning

abstract: Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their ability to run malicious command sequences. Malware authors even use polymorphism to reorder these commands and create several malicious variations. However, if executed in a secure environment, one can perform early malware detection on emulated command sequences. The models presented in this paper leverage this sequential data derived via emulation in order to perform Neural Malware Detection. These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences. Our models can capture entire event sequences and be trained directly using the known target labels. These end-to-end learning models are powered by two commonly used structures - Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). Previously proposed sequential malware classification models process no more than 200 events. Attackers can evade detection by delaying any malicious activity beyond the beginning of the file. We present specialized models that can handle extremely long sequences while successfully performing malware detection in an efficient way. We present an implementation of the Convoluted Partitioning of Long Sequences approach in order to tackle this vulnerability and operate on long sequences. We present our results on a large dataset consisting of 634,249 file sequences, with extremely long file sequences.


title: AMaturityModel based Roadmap for Implementing TOGAF

abstract: two third of the enterprise projects result in failure. The fact that the supporting architecture frameworks are overly complex is one of the main reasons for this. In this paper we try to reduce the initial complexity of adopting The Open Group Architecture Framework (TOGAF), the de facto standard, by prioritizing and tailoring its implementation based on insights from architecture maturity models. This should make the initial dive into the world of enterprise architecture and TOGAF specifically less daunting. KeywordsArchitecture, Maturity models, Maturity assessment,


title: The Synergy between Voting and Acceptance of Answers on StackOverflow - Or the Lack Thereof

abstract: Stack Overflow's primary goal is to serve as a platform for users to solicit answers regarding programming questions, though its archives are often used by other users who face similar issues and thus it serves a secondary purpose of documenting common problems. The two driving mechanisms for filtering out low quality posts and highlighting the best answers are community votes and the mark of acceptance by the original question asker. But does the asker's choice always match the popular vote? If so, is the asker's choice influenced by the community vote or is the community vote biased towards the accepted answer? And if the asker and community disagree, then can we determine any particular characteristics of posts that influence the choice of the asker and community differently, such as its size, readability, presence of code snippets and external links as well as similarity to the original question? In this paper, we explore the answers to these questions by studying a data-set of all posts on Stack Overflow from its launch in September 2008 to September 2014.


title: Conductor-backed coplanar waveguide to microstrip transition on BCB polymer thin film with bandwidth over 60 GHz

abstract: A conductor-backed coplanar waveguide to microstrip via-hole less transition realized on a commercial BCB (Benzocyclobutene) polymer thin film is studied in this paper. By using thin substrates with a low-permittivity, the achieved experimental bandwidth covers the frequency band from 1 GHz to 69 GHz. This transition, using electromagnetic coupling between the bottom and top ground planes, is very useful and requested in component packaging, on-wafer measurements of microstrip based monolithic microwave integrated circuit and for interconnections in hybrid circuits including both microstrip and coplanar structures.


title: Artificial evil and the foundation of computer ethics

abstract: Moral reasoning traditionally distinguishes two types of evil: moral (ME) and natural (NE). The standard view is that ME is the product of human agency and so includes phenomena such as war, torture and psychological crueltys that NE is the product of nonhuman agency, and so includes natural disasters such as earthquakes, floods, disease and famines and finally, that more complex cases are appropriately analysed as a combination of ME and NE. Recently, as a result of developments in autonomous agents in cyberspace, a new class of interesting and important examples of hybrid evil has come to light. In this paper, it is called artificial evil (AE) and a case is made for considering it to complement ME and NE to produce a more adequate taxonomy. By isolating the features that have led to the appearance of AE, cyberspace is characterised as a self-contained environment that forms the essential component in any foundation of the emerging field of Computer Ethics (CE). It is argued that this goes some way towards providing a methodological explanation of why cyberspace is central to so many of CE's concernss and it is shown how notions of good and evil can be formulated in cyberspace. Of considerable interest is how the propensity for an agent's action to be morally good or evil can be determined even in the absence of biologically sentient participants and thus allows artificial agents not only to perpetrate evil (and for that matter good) but conversely to `receive' or `suffer from' it. The thesis defended is that the notion of entropy structure, which encapsulates human value judgement concerning cyberspace in a formal mathematical definition, is sufficient to achieve this purpose and, moreover, that the concept of AE can be determined formally, by mathematical methods. A consequence of this approach is that the debate on whether CE should be considered unique, and hence developed as a Macroethics, may be viewed, constructively, in an alternative manner. The case is made that whilst CE issues are not uncontroversially unique, they are sufficiently novel to render inadequate the approach of standard Macroethics such as Utilitarianism and Deontologism and hence to prompt the search for a robust ethical theory that can deal with them successfully. The name Information Ethics (IE) is proposed for that theory. It is argued that the uniqueness of IE is justified by its being non-biologically biased and patient-oriented: IE is an Environmental Macroethics based on the concept of data entity rather than life. It follows that the novelty of CE issues such as AE can be appreciated properly because IE provides a new perspective (though not vice versa). In light of the discussion provided in this paper, it is concluded that Computer Ethics is worthy of independent study because it requires its own application-specific knowledge and is capable of supporting a methodological foundation, Information Ethics.


title: PERGAMON Abstract Contributed Article On the momentum term in gradient descent learning algorithms

abstract: A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.


title: ACyDS: An adaptive cyber deception system

abstract: In this paper we describe ACyDS, an adaptive cyber deception system. ACyDS provides a unique virtual network view to each host in an enterprise network. That is, a host's view of its network, including subnet topology and IP address assignments of reachable hosts and servers, does not reflect physical network configurations and is different than the view of any other host in the network. ACyDS generates network views with the desired properties dynamically; it also changes every host's network view on-the-fly. ACyDS's deception approach (i) deters reconnaissance if an intruder has compromised a host in the network, (ii) prevents collusion if multiple hosts have been compromised, and (iii) increases the likelihood and confidence of detecting the presence of intruders.


title: Transfer learning for biomedical named entity recognition with neural networks

abstract: Motivation ::: The explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER. ::: ::: ::: Results ::: We demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target datasets with a small number of labels (approximately 6000 or less). ::: ::: ::: Availability and implementation ::: Source code for the LSTM-CRF is available at https://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available at https://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/. ::: ::: ::: Supplementary information ::: Supplementary data are available at Bioinformatics online.


title: SPARK: A New VANET-Based Smart Parking Scheme for Large Parking Lots

abstract: Searching for a vacant parking space in a congested area or a large parking lot and preventing auto theft are major concerns to our daily lives. In this paper, we propose a new smart parking scheme for large parking lots through vehicular communication. The proposed scheme can provide the drivers with real-time parking navigation service, intelligent anti- theft protection, and friendly parking information dissemination. Performance analysis via extensive simulations demonstrates its efficiency and practicality. Keywords— Vehicular communications; smart parking; navi- gation; anti-theft; information dissemination; security & privacy


title: Maximizing SINR in DL MU-MIMO system on SDP algorithm

abstract: In this paper a semi-definite programming (SDP) algorithm is considered for solving a non-convex quadratic optimization problem with inequality constraints. This algorithm is used for a model of maximizing signal to interference and noise ratio (SINR) with the constraint of each user's transmitted power in the downlink multiuser multiple-input and multiple-output (DL MU-MIMO) system that can get the optimal value of each user's SINR. Compared with the block diagonalization (BD) method and the Generalized Zero-Forcing Channel Inversion (GZI) method, the SDP algorithm shows better performance.


title: Comparison of RNA and cDNA transfection methods for rescue of infectious bursal disease virus

abstract: Abstract Specific alterations in the genetic material of RNA viruses rely on a technique known as reverse genetics. Transfection of cells with the altered generic material is a critical step of this procedure. In this report we have compared RNA and cDNA transfection methods for the efficiency of transient protein expression and rescue of (recombinant) infectious bursal disease virus (IBDV). Quantitative expression analysis of the secreted alkaline phosphatase reporter protein, and qualitative expression levels of an IBDV protein showed both that cDNA transfection results in a much higher level of protein expression than RNA transfection. Because the rescue of a crippled variant of IBDV was achieved consistently using the cDNA transfection method, but failed when we used the RNA transfection method, we favor the cDNA transfection method for the rescue of (recombinant) IBDV from cloned cDNA.


title: Learning a Neural-network-based Representation for Open Set Recognition

abstract: Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation instances from the same class are close to each other while instances from different classes are further apart, resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains.


title: Best practice recommendations for data screening

abstract: Summary ::: Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has the potential to identify different types of insufficient effort. An example dataset is provided to illustrate these differences and familiarize readers with the computation and implementation of the screening techniques. Researchers are encouraged to consider data screening when designing a survey, select screening techniques on the basis of theoretical considerations (or empirical considerations when pilot testing is an option), and report the results of an analysis both before and after employing data screening techniques. Copyright © 2014 John Wiley & Sons, Ltd.


title: Distributed Scrum for Large-Scale and Mission-Critical Projects

abstract: Distributed software development (DSD) is becoming popular due to the potential of delivering highquality software products in a timely and economical manner. Recently, practitioners started to apply agile software development methods to their DSD environment. However, there are not many empirical researches that have reported whether the agile methods are really viable for distributed software development. This paper illustrates the issues of the Scrum software development method in a geographically distributed software development team setting as it applies to large-scale and missioncritical projects. Through an in-depth case study, we examine how communication, coordination, and control issues are managed and what technologies are expected to mitigate the difficulties arising in distributed Scrum (DScrum). Finally, we suggest several ideas to overcome the issues discovered in this study.


Effect of unsteady aerodynamic admittances on life cycle of guyed masts


title: Convolutional neural networks with low-rank regularization

abstract: Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decomposition technique. We propose a new algorithm for computing the low-rank tensor decomposition for removing the redundancy in the convolution kernels. The algorithm finds the exact global optimizer of the decomposition and is more effective than iterative methods. Based on the decomposition, we further propose a new method for training low-rank constrained CNNs from scratch. Interestingly, while achieving a significant speedup, sometimes the low-rank constrained CNNs delivers significantly better performance than their non-constrained counterparts. On the CIFAR-10 dataset, the proposed low-rank NIN model achieves $91.31\%$ accuracy (without data augmentation), which also improves upon state-of-the-art result. We evaluated the proposed method on CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet, NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is reduced by half while the performance is still comparable. Empirical success suggests that low-rank tensor decompositions can be a very useful tool for speeding up large CNNs.


title: A load balancing scheme based on deep-learning in IoT

abstract: Extending the current Internet with interconnected objects and devices and their virtual representation has been a growing trend in recent years. The Internet of Things (IoT) contribution is in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge for the benefit of society. Benefit due to the service controlled by communication between objects is now being increased by people who use these services in real life. The sensors are deployed to monitor one or more events in an unattended environment. A large number of the event data will be generated over a period of time in IoT. Hence, the load balancing protocol is critical considerations in the design of IoT. Therefore, we propose an agent Loadbot that measures network load and process structural configuration by analyzing a large amount of user data and network load, and applying Deep Learning's Deep Belief Network method in order to achieve efficient load balancing in IoT. Also, we propose an agent Balancebot that processes a neural load prediction algorithm based on Deep Learning's Q-learning method and neural prior ensemble. We address the key functions for our proposed scheme and simulate the efficiency of our proposed scheme using mathematical analysis.


title: A Branch-Cut-and-Price Algorithm for the Energy Minimization Vehicle Routing Problem

abstract: We study a variant of the capacitated vehicle routing problem where the cost over each arc is defined as the product of the arc length and the weight of the vehicle when it traverses that arc. We propose two new mixed-integer linear programming formulations for the problem: an arc-load formulation and a set partitioning formulation based on q-routes with additional constraints. A family of cycle elimination constraints are derived for the arc-load formulation. We then compare the linear programming LP relaxations of these formulations with the two-index one-commodity flow formulation proposed in the literature. In particular, we show that the arc-load formulation with the new cycle elimination constraints gives the same LP bound as the set partitioning formulation based on 2-cycle-free q-routes, which is stronger than the LP bound given by the two-index one-commodity flow formulation. We propose a branch-and-cut algorithm for the arc-load formulation, and a branch-cut-and-price algorithm for the set partitioning formulation strengthened by additional constraints. Computational results on instances from the literature demonstrate that a significant improvement can be achieved by the branch-cut-and-price algorithm over other methods.


title: Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning

abstract: We introduce a framework for learning regrasping behaviors based on tactile data. First, we present a grasp stability predictor that uses spatio-temporal tactile features collected from the early-object-lifting phase to predict the grasp outcome with a high accuracy. Next, the trained predictor is used to supervise and provide feedback to a reinforcement learning algorithm that learns the required grasp adjustments based on tactile feedback. Our results gathered over more than 50 hours of real robot experiments indicate that the robot is able to predict the grasp outcome with 93% accuracy. In addition, the robot is able to improve the grasp success rate from 42% when randomly grasping an object to up to 97% when allowed to regrasp the object in case of a predicted failure.


title: Cross-platform mobile development: a study on apps with animations

abstract: The paper presents a comparison between different frameworks for cross-platform mobile development, MoSync, Titanium, jQuery Mobile and Phonegap, with particular attention to development of applications with animations. We define a set of criteria for the evaluation and we develop the same game as case study app, with the aim to provide an unbias judgement. Our analysis recommends Titanium as the best framework to develop mobile applications with animations.


title: SHA-3 proposal BLAKE

abstract: BLAKE is our proposal for SHA-3. BLAKE entirely relies on previously analyzed components: it uses the HAIFA iteration mode and builds its compression function on the ChaCha core function. BLAKE resists generic second-preimage attacks, length extension, and sidechannel attacks. Theoretical and empirical security guarantees are given, against structural and differential attacks. BLAKE hashes on a Core 2 Duo at 12 cycles/byte, and on a 8-bit PIC microcontroller at 400 cycles/byte. In hardware BLAKE can be implemented in less than 9900 gates, and reaches a throughput of 6 Gbps. FHNW, Windisch, Switzerland, jeanphilippe.aumasson@gmail.com ETHZ, Zurich, Switzerland, henzen@iis.ee.ethz.ch FHNW, Windisch, Switzerland, willi.meier@fhnw.ch Loughborough University, UK, r.phan@lboro.ac.uk


title: Very deep multilingual convolutional neural networks for LVCSR

abstract: Abstract : Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCSR have not kept pace with recent advances in other domains where deeper neural networks provide superior performance. In this paper we propose a number of architectural advances in CNNs for LVCSR. First, we introduce a very deep convolutional network architecture with up to 14 weight layers. There are multiple convolutional layers before each pooling layer, with small 3x3 kernels, inspired by the VGG Imagenet 2014 architecture. Then, we introduce multilingual CNNs with multiple untied layers. Finally, we introduce multi-scale input features aimed at exploiting more context at negligible computational cost. We evaluate the improvements first on a Babel task for low resource speech recognition, obtaining an absolute 5.77 WER improvement over the baseline PLP DNN by training our CNN on the combined data of six different languages. We then evaluate the very deep CNNs on the Hub500 benchmark (using the 262 hours of SWB-1 training data) achieving a word error rate of 11.8 after cross-entropy training, a 1.4 WER improvement (10.6 relative) over the best published CNN result so far.


title: ISSARS: An integrated software environment for structure-specific earthquake ground motion selection

abstract: Current practice enables the design and assessment of structures in earthquake prone areas by performing time history analysis with the use of appropriately selected strong ground motions. This study presents a Matlab-based software environment, which is integrated with a finite element analysis package, and aims to improve the efficiency of earthquake ground motion selection by accounting for the variability of critical structural response quantities. This additional selection criterion, which is tailored to the specific structure studied, leads to more reliable estimates of the mean structural response quantities used in design, while fulfils the criteria already prescribed by the European and US seismic codes and guidelines. To demonstrate the applicability of the software environment developed, an existing irregular, multi-storey, reinforced concrete building is studied for a wide range of seismic scenarios. The results highlight the applicability of the software developed and the benefits of applying a structure-specific criterion in the process of selecting suites of earthquake motions for the seismic design and assessment.


title: An Examination of Undergraduate Student's Perceptions and Predilections of the Use of YouTube in the Teaching and Learning Process

abstract: Pervasive social networking and media sharing technologies have augmented perceptual understanding and information gathering and, while text-based resources have remained the standard for centuries, they do not appeal to the hyper-stimulated visual learners of today. In particular, the research suggests that targeted YouTube videos enhance student engagement, depth of understanding, and overall satisfaction in higher education courses. In order to investigate student perceptions and preferences regarding the implications of YouTube, a study was conducted at a Mid-Atlantic minority serving institution that examined student opinions regarding the usage of YouTube videos to augment instruction in online and classroombased courses. According to the findings, use of YouTube in the teaching and learning process enhances instruction with students most likely to visit video sharing services from mobile devices. Further, length has an impact on student decisions whether or not to watch a video, and course delivery format impacts length and audio preferences. Finally, there is no relationship between personal use of social media and the perceived value of the use of YouTube in the instructional process.


title: Human Emotions: An Evolutionary Psychological Perspective

abstract: Evolutionary approaches to the emotions have traditionally focused on a subset of emotions that are shared with other species, characterized by distinct signals, and designed to solve a few key adaptive problems. By contrast, an evolutionary psychological approach (a) broadens the range of adaptive problems emotions have evolved to solve, (b) includes emotions that lack distinctive signals and are unique to humans, and (c) synthesizes an evolutionary approach with an information-processing perspective. On this view, emotions are superordinate mechanisms that evolved to coordinate the activity of other programs in the solution of adaptive problems. We illustrate the heuristic value of this approach by furnishing novel hypotheses for disgust and sexual arousal and highlighting unexplored areas of research.


title: Bird Call Identification Using Dynamic Kernel Based Support Vector Machines and Deep Neural Networks

abstract: In this paper, we apply speech and audio processing techniques to bird vocalizations and for the classification of birds found in the lower Himalayan regions. Mel frequency cepstral coefficients (MFCC) are extracted from each recording. As a result, the recordings are now represented as varying length sets of feature vectors. Dynamic kernel based support vector machines (SVMs) and deep neural networks (DNNs) are popularly used for the classification of such varying length patterns obtained from speech signals. In this work, we propose to use dynamic kernel based SVMs and DNNs for classification of bird calls represented as sets of feature vectors. Results of our studies show that both approaches give comparable performance.


title: Triacylglycerol hydrolase: role in intracellular lipid metabolism

abstract: Recent scientific advances have revealed the identity of several enzymes involved in the synthesis, storage and catabolism of intracellular neutral lipid storage droplets. An enzyme that hydrolyzes stored triacylglycerol (TG), triacylglycerol hydrolase (TGH), was purified from porcine, human and murine liver microsomes. In rodents, TGH is highly expressed in liver as well as heart, kidney, small intestine and adipose tissues, while in humans TGH is mainly expressed in the liver, adipose and small intestine. TGH localizes to the endoplasmic reticulum and lipid droplets. The TGH genes are located within a cluster of carboxylesterase genes on human and mouse chromosomes 16 and 8, respectively. TGH hydrolyzes stored TG, and in the liver, the lipolytic products are made available for VLDL-TG synthesis. Inhibition of TGH activity also inhibits TG and apolipoprotein B secretion by primary hepatocytes. A role for TGH in basal TG lipolysis in adipocytes has been proposed. TGH expression and activity is both developmentally and hormonally regulated. A model for the function of TGH is presented and discussed with respect to tissue specific functions.


title: Wireless vehicular Accident Detection and Reporting System

abstract: In this paper, we suggest a method to intelligently detect an accident at any place and any time and report the same to the nearby ‘service provider’. The service provider arranges for the necessary help. Accident Detection and Reporting System (ADRS) which can be placed in any vehicle uses a sensor to detect the accident. The sensor output is monitored and processed by the PIC16F877A microcontroller. The microcontroller takes decision on the traffic accident based on the input from the sensors. The RF transmitter module which is interfaced with the microcontroller will transmit the accident information to the nearby Emergency Service Provider (ESP). This information is received by the RF receiver module at the ‘service provider’ control room in the locality. The RF transceiver module used has a range up to 100 meters under ideal conditions. The service provider can use this information to arrange for ambulance and also inform police and hospital. We used low cost RF modules, a microcontroller by Microchip, LCD module and an accelerometer. This system can be installed at accident prone areas to detect and report the same. MPLAB IDE and Proteus software are used to simulate part of the system. ADRS also implements an intelligent Accident Detection and Reporting Algorithm (ADRA) for the purpose.


title: A trust-risk perspective on social commerce use: an examination of the biasing role of habit

abstract: Purpose ::: ::: ::: ::: ::: Social commerce websites have emerged as new platforms which integrate social media features with traditional commerce aspects to enhance users’ purchasing experience. The purpose of this paper is to examine the role of social factors such as trust toward site members in determining users’ trust and risk evaluations, and the role of social commerce use habit in attenuating users’ rational risk and trust considerations for developing purchase intentions. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Relying on the risk deterrence perspective and rational decision-making models involving trust and habit, this study proposes a set of hypotheses which are tested through analyzing survey data using structural equation modeling techniques. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Results show that commerce risk deters purchasing intentions; trust toward the social commerce website increases users’ purchasing intentions; and trust toward the site members indirectly increases purchasing intentions. Moreover, trust toward site members reduces perceived commerce risk. Findings also show that habit modulates trust and risk effects on use decisions in this context; habit moderates (weakens) the relationships between commerce risk and purchase intentions and between trust toward the social commerce site and purchase intentions. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study extends theories on decision making in social settings such as in the case of social commerce. It does so by accounting for unique modulating effects of habit in social settings in which social aspects such as trust in other members and risk are unique and important.


Cleaning Crowdsourced Labels Using Oracles For Statistical Classification


title: Implementation of SHA-2 hash function for a digital signature System-on-Chip in FPGA

abstract: The widespread adoption of Internet as a secure medium for communication and e-commerce has made cryptography a vital part of today's information systems. However, to achieve a more pervasive deployment, the supporting cryptographic (crypto) systems should exhibit processing power of high performance and efficiency. These demanding requirements can be achieved by integrating the cryptosystems into designs based on System-on-Chip (SoC). In this paper, the design and implementation of a crypto hash SHA-2 logic core in reconfigurable hardware is presented. We also discuss a public-key crypto SoC, which uses the SHA-2 hash core in conjunction with a 2048-bit RSA co-processor to perform a digital signature security scheme. We use Verilog to model the hardware, and C to code the embedded software. With the crypto SoC implemented in an Altera Nios II Stratix FPGA-based prototyping system running on a 50 MHz system clock, we obtained a throughput of 644 Mbits/sec for our SHA-512 hardware core.


title: Networking in E-textiles

abstract: The abundance of fabrics in our life offers immense possibilities for ubiquitous electronic integration. Electronic textiles (e-textiles) represent the merging of fabric and electronic technologies to be used in wearable and large-scale applications. The communication requirements of the computing elements of these systems will be presented along with a fault tolerant solution. The operation of the communication scheme will be studied analytically and compared to simulation results. The graceful deterioration of the performance with increasing faults will also be reported. Multiple e-textile application examples utilizing the networking scheme will be presented.


title: Clustering Stock Market Companies via K- Means Algorithm

abstract: One of the main concepts in pattern recognition is clustering. This technique is used as important knowledge discovery tools in modern machine learning process. Clustering of high-performance companies is very important not only for investors, but also for the creditors, financial creditors, stockholders, etc. Hence, firms’ clustering is considered as one of the important issues in Tehran Stock Exchange (TSE). To this end, we have used financial statement data of three industries in TSE for the year 2012. After selecting profit criteria (attributes) and prioritizing them using AHP, k means clustering algorithm is used to classify these companies. Also, to obtain the optimal number of clusters, different validity measures are presented. The identification of clusters of companies of TSE can be exploited to improve planning and get to more comprehensive decision making about companies.


title: Synchronization and Deduplication in Coded Distributed Storage Networks

abstract: We consider the problem of synchronizing coded data in distributed storage networks undergoing insertion and deletion edits. We present modifications of distributed storage codes that allow updates in the parity-check values to be performed with one round of communication at low bit rates and with small storage overhead. Our main contributions are novel protocols for synchronizing frequently updated and semi-static data based on functional intermediary coding involving permutation and Vandermonde matrices.


title: Abstract Tail call elimination on the Java Virtual Machine

abstract: A problem that often has to be solved by compilers for functional languages targeting the Java Virtual Machine is the elimination of tail calls. This paper explains how we solved it in our Funnel compiler and presents some experimental results about the impact our technique has on both performance and size of the compiled programs.


title: Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review

abstract: Undertaking a review of the literature is an important part of any research project. The researcher both maps and assesses the relevant intellectual territory in order to specify a research question which will further develop the knowledge hase. However, traditional 'narrative' reviews frequently lack thoroughness, and in many cases are not undertaken as genuine pieces of investigatory science. Consequently they can lack a means for making sense of what the collection of studies is saying. These reviews can he hiased by the researcher and often lack rigour. Furthermore, the use of reviews of the available evidence to provide insights and guidance for intervention into operational needs of practitioners and policymakers has largely been of secondary importance. For practitioners, making sense of a mass of often-contrad ictory evidence has hecome progressively harder. The quality of evidence underpinning decision-making and action has heen questioned, for inadequate or incomplete evidence seriously impedes policy formulation and implementation. In exploring ways in which evidence-informed management reviews might be achieved, the authors evaluate the process of systematic review used in the medical sciences. Over the last fifteen years, medical science has attempted to improve the review process hy synthesizing research in a systematic, transparent, and reproducihie manner with the twin aims of enhancing the knowledge hase and informing policymaking and practice. This paper evaluates the extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research. The paper highlights the challenges in developing an appropriate methodology.


title: Big healthcare data: preserving security and privacy

abstract: Big data has fundamentally changed the way organizations manage, analyze and leverage data in any industry. One of the most promising fields where big data can be applied to make a change is healthcare. Big healthcare data has considerable potential to improve patient outcomes, predict outbreaks of epidemics, gain valuable insights, avoid preventable diseases, reduce the cost of healthcare delivery and improve the quality of life in general. However, deciding on the allowable uses of data while preserving security and patient’s right to privacy is a difficult task. Big data, no matter how useful for the advancement of medical science and vital to the success of all healthcare organizations, can only be used if security and privacy issues are addressed. To ensure a secure and trustworthy big data environment, it is essential to identify the limitations of existing solutions and envision directions for future research. In this paper, we have surveyed the state-of-the-art security and privacy challenges in big data as applied to healthcare industry, assessed how security and privacy issues occur in case of big healthcare data and discussed ways in which they may be addressed. We mainly focused on the recently proposed methods based on anonymization and encryption, compared their strengths and limitations, and envisioned future research directions.


title: Neurophysiological predictor of SMR-based BCI performance

abstract: Abstract Brain–computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30 years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the ‘BCI-illiteracy phenomenon’, and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a ‘relax with eyes open’ condition using two Laplacian EEG channels. A correlation of r = 0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N = 80 BCI-naive participants in their first session with the Berlin brain–computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).


title: Hybrid Solar-Wind Energy Harvesting for Embedded Applications: Supercapacitor-Based System Architectures and Design Tradeoffs

abstract: To enable off-grid deployments of autonomous systems for extended operational durations, robust energy harvesting in the medium power range (1-10 W) is essential. Supercapacitor-based solar energy harvesters have emerged as a popular alternative due to their long lifetime under repeated charge-discharge cycles, low maintenance, environmental friendliness, and energy predictability and scalability. Despite their advantages, such systems are not well matched with applications that require power continuously over their operational lifetime because solar power is unavailable during nights and severely reduced on cloudy days. For such applications, it is beneficial to combine solar power with another power source-such as wind-that exhibits complementary availability. In this paper, we present multiple solar/wind (hybrid) supercapacitor-based harvesters, leveraging existing open-source solar-only harvester designs. Our designs center around three main categories that i) add wind harvesting capability to create a wind-only harvesting system, ii) use multiple harvesters for utilizing hybrid sources of power and for providing fault tolerance, or iii) use a single harvester in a time multiplexed configuration to simultaneously harvest from multiple power sources. We provide extensive experimental results to document the functionality and operational performance of a representative set of these designs.


title: Single-shot high dynamic range imaging via deep convolutional neural network

abstract: We propose a single-shot high dynamic range (HDR) imaging algorithm with row-wise varying exposures in a single image using a deep convolutional neural network (CNN). We first convert an input raw Bayer image into irradiance values by calibrating rows with different exposures. Then, we develop a new CNN model to restore missing information resulting from under- or over-exposed pixels and reconstruct the raw radiance map. Finally, we obtain the HDR image by applying a demosaicing algorithm to the raw radiance map. Experimental results on simulated images demonstrate that the proposed algorithm provides higher quality HDR images, with more details and less artifacts, than conventional algorithms.


title: TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

abstract: We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system based on the abstract syntax description language for the target MR, which gives it two major advantages: (1) it is highly accurate, using information from the syntax of the target MR to constrain the output space and model the information flow, and (2) it is highly generalizable, and can easily be applied to new types of MR by just writing a new abstract syntax description corresponding to the allowable structures in the MR. Experiments on four different semantic parsing and code generation tasks show that our system is generalizable, extensible, and effective, registering strong results compared to existing neural semantic parsers.


title: A Convolutional Fuzzy Neural Network for Image Classification

abstract: A model of Convolutional Fuzzy Neural Network for real world objects and scenes images classification is proposed. The Convolutional Fuzzy Neural Network consists of convolutional, pooling and fully-connected layers and a Fuzzy Self Organization Layer. The model combines the power of convolutional neural networks and fuzzy logic and is capable of handling uncertainty and impreciseness in the input pattern representation. The Training of The Convolutional Fuzzy Neural Network consists of three independent steps for three components of the net.


title: Classification students with learning disabilities using Naïve Bayes Classifier and Decision Tree

abstract: The Objective of this study is to preliminarily classify the student with learning disabilities before diagnostic physician using two classification techniques, Naive Bayes Classifier and Decision Tree with Model C4.5. In manual classification, the students in the school are observed by teachers who relate in study and recorded the data with specific class of student, appear or disappear learning disabilities. In experimental classification following these processes, first is generating the model by using training data set, next is predicting by testing the model with testing data set without attribute class. As a result of the study, the Decision Tree classifier can classify the student with learning disabilities better than the Naive Bayes classifier, 96.15% and 94.23% respectively. Nevertheless, this study result fit for preliminary classification for school before transfer the students who appear learning disabilities to physician.


title: The YAP Prolog System

abstract: Yet Another Prolog (YAP) is a Prolog system originally developed in the mid-eighties and that has been under almost constant development since then. This paper presents the general structure and design of the YAP system, focusing on three important contributions to the Logic Programming community. First, it describes the main techniques used in YAP to achieve an efficient Prolog engine. Second, most Logic Programming systems have a rather limited indexing algorithm. YAP contributes to this area by providing a dynamic indexing mechanism, or just-in-time indexer. Third, a important contribution of the YAP system has been the integration of both or-parallelism and tabling in a single Logic Programming system.


A Qualitative Account of The Nature and Use of Self-Regulated Learning (SRL) Strategies Employed by University Students


title: Deep convolutional neural networks as generic feature extractors

abstract: Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task. However, the long time needed to train such deep networks is a major drawback. We tackled this problem by reusing a previously trained network. For this purpose, we first trained a deep convolutional network on the ILSVRC-12 dataset. We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68% on CIFAR-100, compared to the previous state-of-the-art result of 65.43%. Furthermore, our findings indicate that convolutional networks are able to learn generic feature extractors that can be used for different tasks.


title: Modeling the scaling properties of human mobility

abstract: While the fat tailed jump size and the waiting time distributions characterizing individual human trajectories strongly suggest the relevance of the continuous time random walk (CTRW) models of human mobility, no one seriously believes that human traces are truly random. Given the importance of human mobility, from epidemic modeling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model not only accounts for the empirically observed scaling laws but also allows us to analytically predict most of the pertinent scaling exponents.


title: Food image classification using local appearance and global structural information

abstract: Abstract This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance.


title: QoS-Aware Service Composition for Converged Network-Cloud Service Provisioning

abstract: The crucial role of networking in Cloud computing calls for federated management of both computing and networking resources for end-to-end service provisioning. Application of the Service-Oriented Architecture (SOA) in both Cloud computing and networking enables a convergence of network and Cloud service provisioning. One of the key challenges to network -- Cloud convergence lies in QoS-aware composition of network and Cloud services. In this paper, we propose a QoS-aware service composition method to tackle this challenging issue. We first present a system model for network -- Cloud service composition and formulate the service composition problem as a variant of Multi-Constrained Optimal Path (MCOP) problem. We then develop an algorithm to solve the problem and give theoretical analysis on properties of the algorithm to show its effectiveness and efficiency for QoS-aware network-Cloud service composition. Performance of the proposed algorithm is evaluated through extensive simulation experiments and the obtained results indicate that the proposed method achieves better performance in service composition than the best currently available MCOP approach.


title: Morphology-based and sub-word language modeling for Turkish speech recognition

abstract: We explore morphology-based and sub-word language modeling approaches proposed for morphologically rich languages, and evaluate and contrast them for Turkish broadcast news transcription task. In addition, as a morphology-based model, we improve our previously proposed morphology-integrated model for automatic speech recognition. This model is built by composing the finite-state transducer of the morphological parser with a language model over lexical morphemes. This approach provides a morphology-integrated search network with an unlimited vocabulary, generating only valid word forms while reducing the out-of-vocabulary rate and hence improving the word error rate. We also analyze the effect of morpho-tactics and morphological disambiguation on the speech recognition accuracy for the morphology-integrated model. The improved morphology-integrated model performs better than statistically derived sub-word models with added benefit of generating morpho-syntactic and semantic features.


title: Ranking the strategies for Indian medical tourism sector through the integration of SWOT analysis and TOPSIS method.

abstract: Purpose ::: ::: ::: ::: ::: Organizations have to evaluate their internal and external environments in this highly competitive world. Strengths, weaknesses, opportunities and threats (SWOT) analysis is a very useful technique which analyzes the strengths, weaknesses, opportunities and threats of an organization for taking strategic decisions and it also provides a foundation for the formulation of strategies. But the drawback of SWOT analysis is that it does not quantify the importance of individual factors affecting the organization and the individual factors are described in brief without weighing them. Because of this reason, SWOT analysis can be integrated with any multiple attribute decision-making (MADM) technique like the technique for order preference by similarity to ideal solution (TOPSIS), analytical hierarchy process, etc., to evaluate the best alternative among the available strategic alternatives. The paper aims to discuss these issues. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: In this study, SWOT analysis is integrated with a multicriteria decision-making technique called TOPSIS to rank different strategies for Indian medical tourism in order of priority. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: SO strategy (providing best facilitation and care to the medical tourists at par to developed countries) is the best strategy which matches with the four elements of S, W, O and T of SWOT matrix and 35 strategic indicators. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: This paper proposes a solution based on a combined SWOT analysis and TOPSIS approach to help the organizations to evaluate and select strategies. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Creating a new technology or administering a new strategy always has some degree of resistance by employees. To minimize resistance, the author has used TOPSIS as it involves group thinking, requiring every manager of the organization to analyze and evaluate different alternatives and average measure of each parameter in final decision matrix.


title: Anomalies: The endowment effect, loss aversion, and status quo bias

abstract: most (all?) behavior can be explained by assuming that agents have stable, well-defined preferences and make rational choices consistent with those preferences in markets that (eventually) clear. An empirical result qualifies as an anomaly if it is difficult to "rationalize," or if implausible assumptions are necessary to explain it within the paradigm. This column presents a series of such anomalies. Readers are invited to suggest topics for future columns by


title: Solution of the Generalized Noah's Ark Problem.

abstract: The phylogenetic diversity (PD) of a set of species is a measure of the evolutionary distance among the species in the collection, based on a phylogenetic tree. Such a tree is composed of a root, internal nodes, and leaves that correspond to the set of taxa under study. With each edge of the tree is associated a non-negative branch length (evolutionary distance). If a particular survival probability is associated with each taxon, the PD measure becomes the expected PD measure. In the Noah's Ark Problem (NAP) introduced by Weitzman (1998), these survival probabilities can be increased at some cost. The problem is to determine how best to allocate a limited amount of resources to maximize the expected PD of the considered species. It is easy to formulate the NAP as a (difficult) nonlinear 0-1 programming problem. The aim of this article is to show that a general version of the NAP (GNAP) can be solved simply and efficiently with any set of edge weights and any set of survival probabilities by using standard mixed-integer linear programming software. The crucial point to move from a nonlinear program in binary variables to a mixed-integer linear program, is to approximate the logarithmic function by the lower envelope of a set of tangents to the curve. Solving the obtained mixed-integer linear program provides not only a near-optimal solution but also an upper bound on the value of the optimal solution. We also applied this approach to a generalization of the nature reserve problem (GNRP) that consists of selecting a set of regions to be conserved so that the expected PD of the set of species present in these regions is maximized. In this case, the survival probabilities of different taxa are not independent of each other. Computational results are presented to illustrate potentialities of the approach. Near-optimal solutions with hypothetical phylogenetic trees comprising about 4000 taxa are obtained in a few seconds or minutes of computing time for the GNAP, and in about 30 min for the GNRP. In all the cases the average guarantee varies from 0% to 1.20%.


title: Which Noun Phrases Denote Which Concepts?

abstract: Resolving polysemy and synonymy is required for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to. ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. Domain knowledge (the ontology) guides concept creation by defining a set of possible semantic types for concepts. Word sense induction is performed by inferring a set of semantic types for each noun phrase. Synonym detection exploits redundant information to train several domain-specific synonym classifiers in a semi-supervised fashion. When ConceptResolver is run on NELL's knowledge base, 87% of the word senses it creates correspond to real-world concepts, and 85% of noun phrases that it suggests refer to the same concept are indeed synonyms.


title: Security Events and Vulnerability Data for Cybersecurity Risk Estimation.

abstract: Current industry standards for estimating cybersecurity risk are based on qualitative risk matrices as opposed to quantitative risk estimates. In contrast, risk assessment in most other industry sectors aims at deriving quantitative risk estimations (e.g., Basel II in Finance). This article presents a model and methodology to leverage on the large amount of data available from the IT infrastructure of an organization's security operation center to quantitatively estimate the probability of attack. Our methodology specifically addresses untargeted attacks delivered by automatic tools that make up the vast majority of attacks in the wild against users and organizations. We consider two-stage attacks whereby the attacker first breaches an Internet-facing system, and then escalates the attack to internal systems by exploiting local vulnerabilities in the target. Our methodology factors in the power of the attacker as the number of "weaponized" vulnerabilities he/she can exploit, and can be adjusted to match the risk appetite of the organization. We illustrate our methodology by using data from a large financial institution, and discuss the significant mismatch between traditional qualitative risk assessments and our quantitative approach.


title: The discrete wavelet transform: wedding the a trous and Mallat algorithms

abstract: Two separately motivated implementations of the wavelet transform are brought together. It is observed that these algorithms are both special cases of a single filter bank structure, the discrete wavelet transform, the behavior of which is governed by the choice of filters. In fact, the a trous algorithm is more properly viewed as a nonorthonormal multiresolution algorithm for which the discrete wavelet transform is exact. Moreover, it is shown that the commonly used Lagrange a trous filters are in one-to-one correspondence with the convolutional squares of the Daubechies filters for orthonormal wavelets of compact support. A systematic framework for the discrete wavelet transform is provided, and conditions are derived under which it computes the continuous wavelet transform exactly. Suitable filter constraints for finite energy and boundedness of the discrete transform are also derived. Relevant signal processing parameters are examined, and it is observed that orthonormality is balanced by restrictions on resolution. >


title: Performance analysis of data security algorithms used in the railway traffic control systems

abstract: First of all, the railway traffic control process should ensure the safety. One of the current research areas is to ensure the security of data in the distributed rail traffic control systems using wireless networks. Emerging security threats are the result of, among others, an unknown number of users who may want to access the network, and an unknown number and type of equipment that can be connected to the network. It can cause potential threats resulting from unknown format of data and hacker attacks. In order to counteract these threats, it is necessary to apply safety functions. These functions include the use of data integrity code and encryption methods. Additionally, due to character of railway traffic control systems, it is necessary to keep time determinism while sending telegrams. Exceeding the maximum execution time of a cryptographic algorithm and creating too large blocks of data constitute two critical factors that should be taken into account while developing the system for data transmission. This could result in the inability to transmit data at a given throughput of the transmission channel (bandwidth) at a certain time. The paper presents analysis of delays resulting from the realization of safety functions: such as to prepare the data for transfer and their later decoding. Following block encryption algorithms have been analyzed: Blowfish, Twofish, DES, 3DES, AES-128, AES-192 and AES-256 for modes: ECB, CBC, PCBC, CFB, OFB, CTR and data integrity codes: MD-5, SHA-1, SHA-224, SHA-256, SHA-384, SHA-512, SHA-512/224 and SHA-512/256. The obtained results can be very helpful in the development of new rail traffic control systems in which wireless data transmission is planned.


title: Automatic Classification of Power Quality Events Using Balanced Neural Tree

abstract: This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.


title: Joint adaptive loss and l2/l0-norm minimization for unsupervised feature selection

abstract: Unsupervised feature selection is a useful tool for reducing the complexity and improving the generalization performance of data mining tasks. In this paper, we propose an Adaptive Unsupervised Feature Selection (AUFS) algorithm with explicit l 2 /l 0 -norm minimization. We use a joint adaptive loss for data fitting and a l 2 /l 0 minimization for feature selection. We solve the optimization problem with an efficient iterative algorithm and prove that all the expected properties of unsupervised feature selection can be preserved. We also show that the computational complexity and memory use is only linear to the number of instances and square to the number of clusters. Experiments show that our algorithm outperforms the state-of-the-arts on seven different benchmark data sets.


Proof of Stake Made Simple with Casper


title: Cytoarchitectural reorganization of rabbit colonic goblet cells during baseline secretion.

abstract: Light and electron microscopy were coupled with point counting methods to quantitate shape and volume changes of goblet cells during their migration and maturation from the base of the crypt to the colonic surface epithelium in the rabbit. After differentiation, goblet cells attain a broad pyramidal configuration in the basal third of the crypt. The cells elongate and dramatically decrease in volume as they move into the surface epithelium. The distributions and volume fractions of organelles were found to vary considerably, depending on the location of the goblet cell in the epithelium. Mucin granules are initially synthesized throughout the cytoplasm, but become increasingly concentrated as the cell matures. Organelles involved in synthesis such as the Golgi apparatus and rough endoplasmic reticulum (RER) similarly attain a more concentrated arrangement as the cell moves up in the crypt. The mean cell volume decreases from 1,228.8 microns3 for cells in the basal third of the crypt to 541.3 microns3 for goblet cells on the surface. Most organelles decrease in proportion to this decrease, although a disproportionately large decrease in the RER was measured. When actual subcellular volumes are calculated, a net decrease in several subcellular compartments is detected. This loss of granules and organelles is accomplished by the continual synthesis and secretion of mucin granules. Cytoplasm and organelles become entrapped in the upward movement of granules towards the cell apex, become irretrievably isolated, and are sloughed into the crypt lumen. This process accounts for the decrease in cell volume and contributes to the altered cytoarchitecture of the cell.


title: Outlier Detection in Stream Data by Clustering Method

abstract: The fundamental and active research problem in a lot of fields is outlier detection. It is involved many applications. A lot of these methods based on distance measure. But for stream data these methods are not efficient. Most of the previous work on outlier detection declares online outlier and these have less accuracy and it may be lead to a wrong decision. moreover the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. The aim of this study is to present an algorithm to detect outlier in stream data by clustering method that concentrate to find real outlier in period of time. It is considered some outlier that has received in previous time and find out real outlier in stream data. The accuracy of this method is more than other methods.


title: Botnet Tracking : Exploring a Root-Cause Methodology to Prevent Distributed Denial-of-Service Attacks

abstract: Denial-of-Service (DoS) attacks pose a significant threat to the Internet today especially if they are distributed, i.e., launched simultaneously at a large number of systems. Reactive techniques that try to detect such an attack and throttle down malicious traffic prevail today but usually require an additional infrastructure to be really effective. In this paper we show that preventive mechanisms can be as effective with much less effort: We present an approach to (distributed) DoS attack prevention that is based on the observation that coordinated automated activity by many hosts needs a mechanism to remotely control them. To prevent such attacks, it is therefore possible to identify, infiltrate and analyze this remote control mechanism and to stop it in an automated fashion. We show that this method can be realized in the Internet by describing how we infiltrated and tracked IRC-based botnets which are the main DoS technology used by attackers today.


title: Single Image Super-resolution Using Deformable Patches

abstract: We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the energy function with slow, smooth and flexible prior for deformation model. During example-based super-resolution, we develop the deformation similarity based on the minimized energy function for basic patch matching. For robustness, we utilize multiple deformed patches combination for the final reconstruction. Experiments evaluate the deformation effectiveness and super-resolution performance, showing that the deformable patches help improve the representation accuracy and perform better than the state-of-art methods.


title: Learning Probabilistic Networks

abstract: A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.


title: An Algebraic Representation of Calendars

abstract: This paper uses an algebraic approach to define temporal granularities and calendars. All the granularities in a calendar are expressed as algebraic expressions based on a single “bottom” granularity. The operations used in the algebra directly reflect the ways with which people construct new granularities from existing ones, and hence yield more natural and compact granularities definitions. Calendar is formalized on the basis of the algebraic operations, and properties of calendars are studied. As a step towards practical applications, the paper also presents algorithms for granule conversions between granularities in a calendar.


title: Gender Differences in Automatic In-Group Bias: Why Do Women Like Women More Than Men Like Men?

abstract: Four experiments confirmed that women's automatic in-group bias is remarkably stronger than men's and investigated explanations for this sex difference, derived from potential sources of implicit attitudes (L. A. Rudman, 2004). In Experiment 1, only women (not men) showed cognitive balance among in-group bias, identity, and self-esteem (A. G. Greenwald et al., 2002), revealing that men lack a mechanism that bolsters automatic own group preference. Experiments 2 and 3 found pro-female bias to the extent that participants automatically favored their mothers over their fathers or associated male gender with violence, suggesting that maternal bonding and male intimidation influence gender attitudes. Experiment 4 showed that for sexually experienced men, the more positive their attitude was toward sex, the more they implicitly favored women. In concert, the findings help to explain sex differences in automatic in-group bias and underscore the uniqueness of gender for intergroup relations theorists.


title: Conté: multimodal input inspired by an artist's crayon

abstract: Conte is a small input device inspired by the way artists manipulate a real Conte crayon. By changing which corner, edge, end, or side is contacting the display, the operator can switch interaction modes using a single hand. Conte's rectangular prism shape enables both precise pen-like input and tangible handle interaction. Conte also has a natural compatibility with multi-touch input: it can be tucked in the palm to interleave same-hand touch input, or used to expand the vocabulary of bimanual touch. Inspired by informal interviews with artists, we catalogue Conte's characteristics, and use these to outline a design space. We describe a prototype device using common materials and simple electronics. With this device, we demonstrate interaction techniques in a test-bed drawing application. Finally, we discuss alternate hardware designs and future human factors research to study this new class of input.


title: Real time hand pose estimation using depth sensors

abstract: This paper describes a depth image based real-time skeleton fitting algorithm for the hand, using an object recognition by parts approach, and the use of this hand modeler in an American Sign Language (ASL) digit recognition application. In particular, we created a realistic 3D hand model that represents the hand with 21 different parts. Random decision forests (RDF) are trained on synthetic depth images generated by animating the hand model, which are then used to perform per pixel classification and assign each pixel to a hand part. The classification results are fed into a local mode finding algorithm to estimate the joint locations for the hand skeleton. The system can process depth images retrieved from Kinect in real-time at 30 fps. As an application of the system, we also describe a support vector machine (SVM) based recognition module for the ten digits of ASL based on our method, which attains a recognition rate of 99.9% on live depth images in real-time1.


title: A GPU-Adapted Structure for Unstructured Grids

abstract: A key advantage of working with structured grids e.g., images is the ability to directly tap into the powerful machinery of linear algebra. This is not much so for unstructured grids where intermediate bookkeeping data structures stand in the way. On modern high performance computing hardware, the conventional wisdom behind these intermediate structures is further challenged by costly memory access, and more importantly by prohibitive memory resources on environments such as graphics hardware. In this paper, we bypass this problem by introducing a sparse matrix representation for unstructured grids which not only reduces the memory storage requirements but also cuts down on the bulk of data movement from global storage to the compute units. In order to take full advantage of the proposed representation, we augment ordinary matrix multiplication by means of action maps, local maps which encode the desired interaction between grid vertices. In this way, geometric computations and topological modifications translate into concise linear algebra operations. In our algorithmic formulation, we capitalize on the nature of sparse matrix-vector multiplication which allows avoiding explicit transpose computation and storage. Furthermore, we develop an efficient vectorization to the demanding assembly process of standard graph and finite element matrices.


title: An energy-efficient 64-QAM MIMO detector for emerging wireless standards

abstract: A power/area aware design is mandatory for the MIMO (Multi-Input Multi-Output) detectors used in LTE and WiMAX standards. The 64-QAM modulation used in the MIMO detector requires more detection effort compared to the smaller constellation sizes widely implemented in the literature. In this work we propose a new architecture for the K-best detector, which unlike the popular multi-stage architecture used for K-best detectors, implements just one core. Also, we introduce a slight modification to the K-best algorithm that reduces the number of multiplications by 44%, and reduces the total power consumption by 27%, without any noticeable performance degradation. The overall architecture consumes only 24KGate, which is the smallest area compared to the other implementations in the literature. It also results in an at least 4-fold greater throughput-efficiency (Mbps/KiloGate) compared to the other detectors, while consuming a small power. The decoder implemented in a commercial 130nm process provides a data-rate of 107Mbps, and consumes 54.4mW.


title: Reading movies: an integrated DVD player for browsing movies and their scripts

abstract: We have built over the last few years an integrated browser and query interface for watching a movie synchronized with its script. The system is demonstrated with the movie 'The Wizard of Oz', which was fully synchronized with its script using automated tools described elsewhere. Searchable textual descriptions and synchronization metadata (hyperlinks) are generated dynamically on the server side. Video content navigation runs in a DVD player embedded in a web browser on the client side. Thus, the owner of the DVD can connect to our site and view, search and navigate the movie shot by shot.


title: The MultiLis Corpus – Dealing with Individual Differences in Nonverbal Listening Behavior

abstract: Computational models that attempt to predict when a virtual human should backchannel are often based on the analysis of recordings of face-to-face conversations between humans. Building a model based on a corpus brings with it the problem that people differ in the way they behave. The data provides examples of responses of a single person in a particular context but in the same context another person might not have provided a response. Vice versa, the corpus will contain contexts in which the particular listener recorded did not produce a backchannel response, where another person would have responded. Listeners can differ in the amount, the timing and the type of backchannels they provide to the speaker, because of individual differences - related to personality, gender, or culture, for instance. To gain more insight in this variation we have collected data in which we record the behaviors of three listeners interacting with one speaker. All listeners think they are having a one-on-one conversation with the speaker, while the speaker actually only sees one of the listeners. The context, in this case the speaker's actions, is for all three listeners the same and they respond to it individually. This way we have created data on cases in which different persons show similar behaviors and cases in which they behave differently. With the recordings of this data collection study we can start building our model of backchannel behavior for virtual humans that takes into account similarities and differences between persons.


title: Stabilizing Integrators for Real-Time Physics

abstract: We present a new time integration method featuring excellent stability and energy conservation properties, making it particularly suitable for real-time physics. The commonly used backward Euler method is stable but introduces artificial damping. Methods such as implicit midpoint do not suffer from artificial damping but are unstable in many common simulation scenarios. We propose an algorithm that blends between the implicit midpoint and forward/backward Euler integrators such that the resulting simulation is stable while introducing only minimal artificial damping. We achieve this by tracking the total energy of the simulated system, taking into account energy-changing events: damping and forcing. To facilitate real-time simulations, we propose a local/global solver, similar to Projective Dynamics, as an alternative to Newton’s method. Compared to the original Projective Dynamics, which is derived from backward Euler, our final method introduces much less numerical damping at the cost of minimal computing overhead. Stability guarantees of our method are derived from the stability of backward Euler, whose stability is a widely accepted empirical fact. However, to our knowledge, theoretical guarantees have so far only been proven for linear ODEs. We provide preliminary theoretical results proving the stability of backward Euler also for certain cases of nonlinear potential functions.


title: Aspect-Level Sentiment Analysis Based on a Generalized Probabilistic Topic and Syntax Model

abstract: A number of topic models have been proposed for sentiment analysis in recent years, which rely on extensions of the basic LDA model. In this paper, we apply a generalized topic and syntax model called Part-of-Speech LDA (POSLDA) to sentiment analysis, and propose several feature selection methods that separate entities from the modifiers that describe the entities. Along with a Maximum Entropy classifier, we can use the selected features to conduct sentiment analysis at both document and aspect levels. The advantage of using POSLDA is that we can automatically separate semantic and syntactic classes, and easily extend it to aspect level sentiment analysis by mapping topics to aspects. However, words in the noun-related classes, which are also treated as semantic classes, should be removed as much as possible to reduce their impact on sentiment analysis. To evaluate the effectiveness of our solutions, we conducted experiments on two collections of review documents and obtained the accuracy results competitive to the previous work on sentiment analysis.