title: A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects

abstract: Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.


title: A Taxonomy and Survey on Distributed File Systems

abstract: Applications that process large volumes of data (such as, search engines, grid computing applications, data mining applications, etc.) require a backend infrastructure for storing data. The distributed file system is the central component for storing data infrastructure. There have been many projects focused on network computing that have designed and implemented distributed file systems with a variety of architectures and functionalities. In this paper, we develop a comprehensive taxonomy for describing distributed file system architectures and use this taxonomy to survey existing distributed file system implementations in very large-scale network computing systems such as Grids, Search Engines, etc. We use the taxonomy and the survey results to identify architectural approaches that have not been fully explored in the distributed file system research.


DART: Dense Articulated Real-Time Tracking


SuperAgent: A Customer Service Chatbot for E-commerce Websites


title: Land use planning and national soils policies

abstract: Abstract Land is used for many purposes. It is also the focus of conflict between a wide range of land uses including agriculture, mining, forestry and nature protection, leisure, and urban and industrial development. Competition between users grows more under increasing population pressure and in countries with a mixed economy. Decision making on land use options is therefore a current problem of modern societies. Adequate planning and decision making about land use is facilitated by a national soils policy framework. This is a set of guidelines, aimed at ensuring a sustainable utilization of the land—either for agricultural or non-agricultural uses—and limiting direct or indirect damage to the environment. The policy is integrated into a national development plan, and addresses the natural diversity and specific physical and socio-economic problems of the region concerned. National soils policies are therefore very country-specific; they rely on clearly defined long-term objectives and involve strategies and tools for implementation. A number of examples are given to illustrate the most relevant issues in national soils policies.


title: Microstrip Patch Antenna With Defected Ground Structure for Cross Polarization Suppression

abstract: A defected ground structure (DGS) is proposed to reduce the cross-polarized (XP) radiation of a microstrip patch antenna. The proposed DGS pattern is simple and easy to etch on a commercial microstrip substrate. This will only reduce the XP radiation field without affecting the dominant mode input impedance and co-polarized radiation patterns of a conventional antenna. The new concept has been examined and verified experimentally for a particular DGS pattern employing a circular patch as the radiator. Both simulation and experimental results are presented.


title: Automatic Detection of Online Jihadist Hate Speech

abstract: We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.


title: A study of mobile device utilization

abstract: Mobile devices are becoming more powerful and versatile than ever, calling for better embedded processors. Following the trend in desktop CPUs, microprocessor vendors are trying to meet such needs by increasing the number of cores in mobile device SoCs. However, increasing the number does not translate proportionally into performance gain and power reduction. In the past, studies have shown that there exists little parallelism to be exploited by a multi-core processor in desktop platform applications, and many cores sit idle during runtime. In this paper, we investigate whether the same is true for current mobile applications. We analyze the behavior of a broad range of commonly used mobile applications on real devices. We measure their Thread Level Parallelism (TLP), which is the machine utilization over the non-idle runtime. Our results demonstrate that mobile applications are utilizing less than 2 cores on average, even with background applications running concurrently. We observe a diminishing return on TLP with increasing the number of cores, and low TLP even with heavy-load scenarios. These studies suggest that having many powerful cores is over-provisioning. Further analysis of TLP behavior and big-little core energy efficiency suggests that current mobile workloads can benefit from an architecture that has the flexibility to accommodate both high performance and good energy-efficiency for different application phases.


An Approach to Web Query Recommendation System


Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks


title: Analyzing student performance in distance learning with genetic algorithms and decision trees. Applied Artificial Intelligence 20(8): 655-674. Page 11 of 14

abstract: Students that enroll in the undergraduate program on informatics at the Hellenic Open University (HOU) demonstrate significant difficulty in advancing beyond the introductory course. We have embarked in an effort to analyze their academic performance throughout the academic year, as measured by homework assignments, and attempt to derive short rules that explain and predict success or failure in the final exams. In this paper we review previous approaches, compare them with genetic algorithm-based induction of decision trees, and argue why our approach has a potential for developing into an alert tool.


title: A new vehicle detection method

abstract: This paper presents a new vehicle detection method ::: from images acquired by cameras embedded in a moving vehicle. ::: Given the sequence of images, the proposed algorithms should ::: detect out all cars in realtime. Related to the driving direction, ::: the cars can be classified into two types. Cars drive in the same ::: direction as the intelligent vehicle (IV) and cars drive in the ::: opposite direction. Due to the distinct features of these two types, ::: we suggest to achieve this method in two main steps. The first one detects all obstacles from images using the so-called association combined with corner detector. The second step is applied to validate each vehicle using AdaBoost classifier. The new method has been applied to different images data and the experimental results validate the efficacy of our method.


title: Systematic review automation technologies

abstract: Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.


Interactions of pH, Carbon Dioxide, Alkalinity and Hardness in Fish Ponds


title: Objectivity and subjectivity in knowledge management: a review of 20 top articles

abstract: In recent years knowledge management (KM) has received increased attention from academics and practitioners. There are several challenges to establishing KM as a separate discipline one of the most central being conceptual plurality. The purpose of this paper is to review and position 20 of the most frequently cited KM articles in management journals. More specifically, this paper classifies the KM publications on the subjectivity‐objectivity continuum, discusses the strengths and weaknesses of the publications drawing from positivism and interpretative philosophies, and further discusses the challenges in KM, and how objectivity and subjectivity can be used to provide both product and process orientations in future research. Copyright # 2006 John Wiley & Sons, Ltd.


title: FaceDate: a mobile cloud computing app for people matching

abstract: This paper presents FaceDate, a novel mobile app that matches persons based on their facial looks. Each FaceDate user uploads their profile face photo and trains the app with photos of faces they like. Upon user request, FaceDate detects other users located in the proximity of the requester and performs face matching in real-time. If a mutual match is found, the two users are notified and given the option to start communicating. FaceDate is implemented over our Moitree middleware for mobile distributed computing assisted by the cloud. The app is designed to scale with the number of users, as face recognition is done in parallel at different users. FaceDate can be configured for (i) higher performance, in which case the face recognition is done in the cloud or (ii) higher privacy, in which case the face recognition is done on the mobiles. The experimental results with Android-based phones demonstrate that FaceDate achieves promising performance.


title: International Workshop on Big Data and Data Mining Challenges on IoT and Pervasive Systems ( BigD 2 M 2015 ) New Security Architecture for IoT Network

abstract: Abstract We explain the notion of security architecture for Internet of Things (IoT) based on software-defined networking (SDN). In this context, the SDN-based architecture works with or without infrastructure, that we call SDN-Domain. This work describes the operation of the proposed architecture and summarizes the opportunity to achieve network security in a more efficient and flexible with SDN. An overview of existing SDN security applications were discussed and tackles its issues, presenting a new IoT system's architecture. In this paper we considered the network access control and global traffic monitoring for ad-hoc networks. Finally, we point out architectural design choices for SDN using OpenFlow and discuss their performance implications.


title: An anchor-free region proposal network for Faster R-CNN-based text detection approaches

abstract: The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its Intersection-over-Union-based matching criterion between anchors and ground-truth boxes. In order to better enclose scene text instances of various shapes, it requires to design anchors of various scales, aspect ratios and even orientations manually, which makes anchor-based methods sophisticated and inefficient. In this paper, we propose a novel anchor-free region proposal network (AF-RPN) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem. Compared with the anchor-based region proposal generation approaches (e.g., RPN, FPN–RPN, RRPN and FPN–RRPN), AF-RPN can get rid of complicated anchor design and achieves higher recall rate on both horizontal and multi-oriented text detection benchmark tasks. Owing to the high-quality text proposals, our Faster R-CNN-based two-stage text detection approach achieves the state-of-the-art results on ICDAR-2017 MLT, COCO-Text, ICDAR-2015 and ICDAR-2013 text detection benchmark tasks by only using single-scale and single-model testing.


title: Motor-driven motility of fungal nuclear pores organizes chromosomes and fosters nucleocytoplasmic transport

abstract: Exchange between the nucleus and the cytoplasm is controlled by nuclear pore complexes (NPCs). In animals, NPCs are anchored by the nuclear lamina, which ensures their even distribution and proper organization of chromosomes. Fungi do not possess a lamina and how they arrange their chromosomes and NPCs is unknown. Here, we show that motor-driven motility of NPCs organizes the fungal nucleus. In Ustilago maydis, Aspergillus nidulans, and Saccharomyces cerevisiae fluorescently labeled NPCs showed ATP-dependent movements at ∼1.0 µm/s. In S. cerevisiae and U. maydis, NPC motility prevented NPCs from clustering. In budding yeast, NPC motility required F-actin, whereas in U. maydis, microtubules, kinesin-1, and dynein drove pore movements. In the latter, pore clustering resulted in chromatin organization defects and led to a significant reduction in both import and export of GFP reporter proteins. This suggests that fungi constantly rearrange their NPCs and corresponding chromosomes to ensure efficient nuclear transport and thereby overcome the need for a structural lamina.


title: Inductive Semi-supervised Multi-Label Learning with Co-Training

abstract: In multi-label learning, each training example is associated with multiple class labels and the task is to learn a mapping from the feature space to the power set of label space. It is generally demanding and time-consuming to obtain labels for training examples, especially for multi-label learning task where a number of class labels need to be annotated for the instance. To circumvent this difficulty, semi-supervised multi-label learning aims to exploit the readily-available unlabeled data to help build multi-label predictive model. Nonetheless, most semi-supervised solutions to multi-label learning work under transductive setting, which only focus on making predictions on existing unlabeled data and cannot generalize to unseen instances. In this paper, a novel approach named COINS is proposed to learning from labeled and unlabeled data by adapting the well-known co-training strategy which naturally works under inductive setting. In each co-training round, a dichotomy over the feature space is learned by maximizing the diversity between the two classifiers induced on either dichotomized feature subset. After that, pairwise ranking predictions on unlabeled data are communicated between either classifier for model refinement. Extensive experiments on a number of benchmark data sets show that COINS performs favorably against state-of-the-art multi-label learning approaches.


title: A Phosphorus Index transport factor based on variable source area hydrology for New York State

abstract: The Phosphorus (P) Index concept is used in many states to help develop nutrient management plans for livestock agriculture to protect water quality. Although many P indices conceptually incorporate variable source area (VSA) runoff processes, they generally assume proximity to a water course is an adequate proxy of runoff risk. Here we propose a VSA-based transport factor that uses the topographic index concept to indicate runoff risk. We compared both transport factors based on the current New York State P Index and our VSA-based P Index to field measures of runoff probability across an abandoned agricultural site in upstate New York. We also compared transport factors and P indices using the current and VSA- based approaches on a farm in the Catskill Mountains of New York to evaluate differences at whole-field and farm scales. Field runoff-risk measurements were better correlated with VSA-based transport factor (r 2 = 0.63, α = 0.05) than with the current dissolved-P transport factor (r 2 = 0.40, α = 0.05). Although these point-scale differences in transport factor values translated into field-scale differences in transport factor, the net differences at the farm scale and in P Index were not very large. On a field-by-field basis, 12 out of 21 fields had differ- ent transport factor categories with the VSA method. However, the total land area classified as high risk changed very little between the two methods. There was more land classified as moderate risk using the VSA-based approach than using the current methods, due to some low risk areas being classified as higher risk and some high-risk areas being classified as lower risk. The VSA approach allows managers and producers to more easily manage farm units (e.g., fields) at finer resolutions both spatially and temporally, which will increase the options for managing nutrients on fields. These types of more rigorous links between management tools and physical hydrology provide valuable, more scientifically defensible information for improving our ability to control nonpoint source pollution.


title: Preemptively scheduling hard-real-time sporadic tasks on one processor

abstract: Consideration is given to the preemptive scheduling of hard-real-time sporadic task systems on one processor. The authors first give necessary and sufficient conditions for a sporadic task system to be feasible (i.e., schedulable). The conditions cannot, in general, be tested efficiently (unless P=NP). They do, however, lead to a feasibility test that runs in efficient pseudo-polynomial time for a very large percentage of sporadic task systems. >


title: Dependence of biodiesel fuel properties on the

abstract: Biodiesel, defined as the mono-alkyl esters of vegetable oils or animal fats, is an “alternative” diesel fuel that is becoming accepted in a steadily growing number of countries around the world. Since the source of biodiesel varies with the location and other sources such as recycled oils are continuously gaining interest, it is important to possess data on how the various fatty acid profiles of the different sources can influence biodiesel fuel properties. The properties of the various individual fatty esters that comprise biodiesel determine the overall fuel properties of the biodiesel fuel. In turn, the properties of the various fatty esters are determined by the structural features of the fatty acid and the alcohol moieties that comprise a fatty ester. Structural features that influence the physical and fuel properties of a fatty ester molecule are chain length, degree of unsaturation, and branching of the chain. Important fuel properties of biodiesel that are influenced by the fatty acid profile and, in turn, by the structural features of the various fatty esters are cetane number and ultimately exhaust emissions, heat of combustion, cold flow, oxidative stability, viscosity, and lubricity.


title: Climbing Strategy for a Flexible Tree Climbing Robot—Treebot

abstract: In this paper, we propose an autonomous tree climbing strategy for a novel tree climbing robot that is named Treebot. The proposed algorithm aims to guide Treebot in climbing along an optimal path by the use of minimal sensing resources. Inspired by inchworms, the algorithm reconstructs the shape of a tree simply by the use of tactile sensors. It reveals how the realization of an environment can be achieved with limited tactile information. An efficient nonholonomic motion planning strategy is also proposed to make Treebot climb on an optimal path. This is accomplished by the prediction of the future shape of the tree. The study that is presented in this paper also includes the formulation of Treebot kinematics and an analysis of the workspace of Treebot on different shapes of a tree. Numerous experiments have been conducted to evaluate the proposed autonomous climbing algorithm and to unveil the ability of Treebot.


title: Domestic food and sustainable design: a study of university student cooking and its impacts

abstract: In four university student kitchens over twenty-one days, we captured participants' food preparation activity, quantified the greenhouse gas emissions and direct energy connected to the food and cooking, and talked to participants about their food practices. Grounded in this uniquely detailed micro-account, our findings inform sustainable design for cooking and eating at home and quantify the potential impacts. We outline the relation of the impacts to our participants' approaches to everyday food preparation, the organisation of their time, and the role of social meals. Our technique allows evaluation of opportunities for sustainable intervention design: at the appliance, in the digitally-mediated organisation of meals and inventory management, and more broadly in reflecting upon and reshaping diet.


title: False-belief understanding in 2.5-year-olds: Evidence from two novel verbal spontaneous-response tasks

abstract: Recent research indicates that toddlers and infants succeed at various non-verbal spontaneous-response false-belief tasks; here we asked whether toddlers would also succeed at verbal spontaneous-response false-belief tasks that imposed significant linguistic demands. We tested 2.5-year-olds using two novel tasks: a preferential-looking task in which children listened to a false-belief story while looking at a picture book (with matching and non-matching pictures), and a violation-of-expectation task in which children watched an adult ‘Subject’ answer (correctly or incorrectly) a standard false-belief question. Positive results were obtained with both tasks, despite their linguistic demands. These results (1) support the distinction between spontaneousand elicited-response tasks by showing that toddlers succeed at verbal false-belief tasks that do not require them to answer direct questions about agents’ false beliefs, (2) reinforce claims of robust continuity in early false-belief understanding as assessed by spontaneous-response tasks, and (3) provide researchers with new experimental tasks for exploring early false-belief understanding in neurotypical and autistic populations.


title: Timbre-specific enhancement of auditory cortical representations in musicians

abstract: Neural imaging studies have shown that the brains of skilled musicians respond differently to musical stimuli than do nonmusicians, particularly for musicians who commenced practice at an early age. Whether brain attributes related to musical skill are attributable to musical practice or are hereditary traits that influence the decision to train musically is a subject of controversy, owing to its pedagogic implications. Here we report that auditory cortical representations measured neuromagnetically for tones of different timbre (violin and trumpet) are enhanced compared to sine tones in violinists and trumpeters, preferentially for timbres of the instrument of training. Timbre specificity is predicted by a principle of usedependent plasticity and imposes new requirements on nativistic accounts of brain attributes associated with musical skill. NeuroReport 12:1‐6 & 2001 Lippincott Williams & Wilkins.


title: Online social trust reinforced personalized recommendation

abstract: Recommendation techniques greatly promote the development of online service in the interconnection environment. Personalized recommendation has attracted researchers' special attention because it is more targeted to individual tasks with the characteristics of diversification and novelty. However, the data sets that personalized recommendation process usually possess the characteristics of data sparseness and information loss, which is more likely to have problems such as cognitive deviation and interest drift. To solve these issues, in recent years people gradually notice the important role in which trust factor plays in promoting the development of personalized recommendation. Given the difference between online social trust and traditional offline social trust in facilitating personalized recommendation, this paper proposes a novel technique of online social trust reinforced personal recommendation to improve the recommendation performance. Compared with traditional offline social trust-based personal recommendation, our work synthesizes both factors of online social trust and offline social trust to identify private and public trusted user communities. The trusted degree or the accredited degree can be deduced by Bayesian network probabilistic inferences. In this way, the performance of personalized recommendation can be improved by avoiding excessive interest deviation. Moreover, we also get time sequence into personal recommendation model to effectively track how user's interest changes over time. Accordingly, the recommendation accuracy can be improved by eliminating the unfavorable effect of interest drift caused by temporal variation. Empirical experiments on typical Yelp testing data set illustrate the effectiveness of the proposed recommendation technique.


title: Acceleration Analysis of 3-RPS Parallel Manipulators by Means of Screw Theory

abstract: According to the notation proposed by the International Federation for the Theory of Mechanisms and Machines IFToMM (Ionescu, 2003); a parallel manipulator is a mechanism where the motion of the end-effector, namely the moving or movable platform, is controlled by means of at least two kinematic chains. If each kinematic chain, also known popularly as limb or leg, has a single active joint, then the mechanism is called a fully-parallel mechanism, in which clearly the nominal degree of freedom equates the number of limbs. Tire-testing machines (Gough & Whitehall, 1962) and flight simulators (Stewart, 1965), appear to be the first transcendental applications of these complex mechanisms. Parallel manipulators, and in general mechanisms with parallel kinematic architectures, due to benefits --over their serial counterparts-- such as higher stiffness and accuracy, have found interesting applications such as walking machines, pointing devices, multi-axis machine tools, micro manipulators, and so on. The pioneering contributions of Gough and Stewart, mainly the theoretical paper of Stewart (1965), influenced strongly the development of parallel manipulators giving birth to an intensive research field. In that way, recently several parallel mechanisms for industrial purposes have been constructed using the, now, classical hexapod as a base mechanism: Octahedral Hexapod HOH-600 (Ingersoll), HEXAPODE CMW 300 (CMW), Cosmo Center PM-600 (Okuma), F-200i (FANUC) and so on. On the other hand one cannot ignore that this kind of parallel kinematic structures have a limited and complex-shaped workspace. Furthermore, their rotation and position capabilities are highly coupled and therefore the control and calibration of them are rather complicated. It is well known that many industrial applications do not require the six degrees of freedom of a parallel manipulator. Thus in order to simplify the kinematics, mechanical assembly and control of parallel manipulators, an interesting trend is the development of the so called defective parallel manipulators, in other words, spatial parallel manipulators with fewer than six degrees of freedom. Special mention deserves the Delta robot, invented by Clavel (1991); which proved that parallel robotic manipulators are an excellent option for industrial applications where the accuracy and stiffness are fundamental characteristics. Consider for instance that the Adept Quattro robot, an application of the Delta robot, developed by Francois Pierrot in collaboration with Fatronik (Int. patent appl. WO/2006/087399), has a


title: The SocioMetric Badges Corpus: A Multilevel Behavioral Dataset for Social Behavior in Complex Organizations

abstract: This paper presents the SocioMetric Badges Corpus, a new corpus for social interaction studies collected during a 6 weeks contiguous period in a research institution, monitoring the activity of 53 people. The design of the corpus was inspired by the need to provide researchers and practitioners with: a) raw digital trace data that could be used to directly address the task of investigating, reconstructing and predicting people's actual social behavior in complex organizations, b) information about participants' individual characteristics (e.g., personality traits), along with c) data concerning the general social context (e.g., participants' social networks) and the specific situations they find themselves in.


title: A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes

abstract: We present a tone reproduction operator that preserves visibility in high dynamic range scenes. Our method introduces a new histogram adjustment technique, based on the population of local adaptation luminances in a scene. To match subjective viewing experience, the method incorporates models for human contrast sensitivity, glare, spatial acuity and color sensitivity. We compare our results to previous work and present examples of our techniques applied to lighting simulation and electronic photography.


title: GPUvm: why not virtualizing GPUs at the hypervisor?

abstract: Graphics processing units (GPUs) provide orders-of-magnitude speedup for compute-intensive data-parallel applications. However, enterprise and cloud computing domains, where resource isolation of multiple clients is required, have poor access to GPU technology. This is due to lack of operating system (OS) support for virtualizing GPUs in a reliable manner. To make GPUs more mature system citizens, we present an open architecture of GPU virtualization with a particular emphasis on the Xen hypervisor. We provide design and implementation of full- and para-virtualization, including optimization techniques to reduce overhead of GPU virtualization. Our detailed experiments using a relevant commodity GPU show that the optimized performance of GPU para-virtualization is yet two or three times slower than that of pass-through and native approaches, whereas full-virtualization exhibits a different scale of overhead due to increased memory-mapped I/O operations. We also demonstrate that coarse-grained fairness on GPU resources among multiple virtual machines can be achieved by GPU scheduling; finer-grained fairness needs further architectural support by the nature of non-preemptive GPU workload.


title: Speech recognition and keyword spotting for low-resource languages: Babel project research at CUED.

abstract: Recently there has been increased interest in Automatic Speech Recognition (ASR) and Key Word Spotting (KWS) systems for low resource languages. One of the driving forces for this research direction is the IARPA Babel project. This paper describes some of the research funded by this project at Cambridge University, as part of the Lorelei team co-ordinated by IBM. A range of topics are discussed including: deep neural network based acoustic models; data augmentation; and zero acoustic model resource systems. Performance for all approaches is evaluated using the Limited (approximately 10 hours) and/or Full (approximately 80 hours) language packs distributed by IARPA. Both KWS and ASR performance figures are given. Though absolute performance varies from language to language, and keyword list, the approaches described show consistent trends over the languages investigated to date. Using comparable systems over the five Option Period 1 languages indicates a strong correlation between ASR performance and KWS performance.


title: Generating Natural Language from Linked Data: Unsupervised template extraction

abstract: We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.


title: Exploring millions of footprints in location sharing services

abstract: Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of user-driven footprints (i.e., "checkins"). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.


title: Data Vocalization: Optimizing Voice Output of Relational Data

abstract: Research on data visualization aims at finding the best way to present data via visual interfaces. We introduce the complementary problem of "data vocalization". Our goal is to present relational data in the most efficient way via voice output. This problem setting is motivated by emerging tools and devices (e.g., Google Home, Amazon Echo, Apple's Siri, or voice-based SQL interfaces) that communicate data primarily via audio output to their users. ::: ::: We treat voice output generation as an optimization problem. The goal is to minimize speaking time while transmitting an approximation of a relational table to the user. We consider constraints on the precision of the transmitted data as well as on the cognitive load placed on the listener. We formalize voice output optimization and show that it is NP-hard. We present three approaches to solve that problem. First, we show how the problem can be translated into an integer linear program which enables us to apply corresponding solvers. Second, we present a two-phase approach that forms groups of similar rows in a pre-processing step, using a variant of the apriori algorithm. Then, we select an optimal combination of groups to generate a speech. Finally, we present a greedy algorithm that runs in polynomial time. Under simplifying assumptions, we prove that it generates near-optimal output by leveraging the sub-modularity property of our cost function. We compare our algorithms experimentally and analyze their complexity.


title: Feature Engineering for Knowledge Base Construction

abstract: Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student. ::: Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user "think about features---not algorithms." We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.


title: On GitHub's Programming Languages

abstract: GitHub is the most widely used social, distributed version control system. It has around 10 million registered users and hosts over 16 million public repositories. Its user base is also very active as GitHub ranks in the top 100 Alexa most popular websites. In this study, we collect GitHub's state in its entirety. Doing so, allows us to study new aspects of the ecosystem. Although GitHub is the home to millions of users and repositories, the analysis of users' activity time-series reveals that only around 10% of them can be considered active. The collected dataset allows us to investigate the popularity of programming languages and existence of pattens in the relations between users, repositories, and programming languages. ::: By, applying a k-means clustering method to the users-repositories commits matrix, we find that two clear clusters of programming languages separate from the remaining. One cluster forms for "web programming" languages (Java Script, Ruby, PHP, CSS), and a second for "system oriented programming" languages (C, C++, Python). Further classification, allow us to build a phylogenetic tree of the use of programming languages in GitHub. Additionally, we study the main and the auxiliary programming languages of the top 1000 repositories in more detail. We provide a ranking of these auxiliary programming languages using various metrics, such as percentage of lines of code, and PageRank.


title: A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data

abstract: The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1- score between 0.9 - 1.0). Indicating its potential application in the detection of a range of age-related motion impairments.


title: The cost of nurse-sensitive adverse events.

abstract: OBJECTIVE ::: The aim of this study was to describe the methodology for nursing leaders to determine the cost of adverse events and effective levels of nurse staffing. ::: ::: ::: BACKGROUND ::: The growing transparency of quality and cost outcomes motivates healthcare leaders to optimize the effectiveness of nurse staffing. Most hospitals have robust cost accounting systems that provide actual patient-level direct costs. These systems allow an analysis of the cost consumed by patients during a hospital stay. By knowing the cost of complications, leaders have the ability to justify the cost of improved staffing when quality evidence shows that higher nurse staffing improves quality. ::: ::: ::: METHODS ::: An analysis was performed on financial and clinical data from hospital databases of 3,200 inpatients. The purpose was to establish a methodology to determine actual cost per case. Three diagnosis-related groups were the focus of the analysis. Five adverse events were analyzed along with the costs. ::: ::: ::: RESULTS ::: A regression analysis reported that the actual direct cost of an adverse event was dollars 1,029 per case in the congestive heart failure cases and dollars 903 in the surgical cases. There was a significant increase in the cost per case in medical patients with urinary tract infection and pressure ulcers and in surgical patients with urinary tract infection and pneumonia. The odds of pneumonia occurring in surgical patients decreased with additional registered nurse hours per patient day. ::: ::: ::: CONCLUSION ::: Hospital cost accounting systems are useful in determining the cost of adverse events and can aid in decision making about nurse staffing. Adverse events add costs to patient care and should be measured at the unit level to adjust staffing to reduce adverse events and avoid costs.


title: Type prediction in noisy RDF knowledge bases using hierarchical multilabel classification with graph and latent features

abstract: Semantic Web knowledge bases, in particular large cross-domain data, are often noisy, incorrect, and incomplete with respect to type information. This incompleteness can be reduced, as previous work shows, with automatic type prediction methods. Most knowledge bases contain an ontology defining a type hierarchy, and, in general, entities are allowed to have multiple types (classes of an instance assigned with the rdf:type relation). In this paper, we exploit these characteristics and formulate the type prediction problem as hierarchical multi classification, where the labels are types. We evaluate different sets of features, including entity embeddings, which can be extracted from the knowledge graph exclusively. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier with the objective of improving scalability. Furthermore, we explore different variants of creating features for the classi...


title: Design of Efficient BCD Adders in Quantum-Dot Cellular Automata

abstract: Among the emerging technologies recently proposed as alternatives to the classic CMOS, quantum-dot cellular automata (QCA) is one of the most promising solutions to design ultralow-power and very high speed digital circuits. Efficient QCA-based implementations have been demonstrated for several binary and decimal arithmetic circuits, but significant improvements are still possible if the logic gates inherently available within the QCA technology are smartly exploited. This brief proposes a new approach to design QCA-based BCD adders. Exploiting innovative logic formulations and purpose-designed QCA modules, computational speed significantly higher than existing counterparts is achieved without sacrificing either the occupied area or the cell count.


title: Fiber-reinforced soft robotic anthropomorphic finger

abstract: Robotic fingers have attracted considerable attentions of researchers from multidisciplinary fields. Most of the existing devices apply rigid components such as springs, joints, gears etc., to achieve the locomotor performance of the human finger. However, high-cost, complexity hold back their practical application. Meanwhile, the material properties of these rigid components are significantly different from the biological tissues to bring in considerable risk and difficulty in human-robot interaction. In this paper, a novel soft robotic anthropomorphic finger was presented, which was purely made of low cost soft hyperelastic materials (around 1.5 dollars) and was pneumatically actuated. The morphology of the robotic finger was a replica of an adult-human finger. The chambers, fiber orientation and the skin layers were designed in concert therefore to achieve human finger-like movement. The kinematic and dynamic model of the soft robotic finger were built based on experimental empirical method. While operating underwater, DPIV experiments revealed that the underwater pinch performance of a two-finger gripper prototype is significantly affect by the position relative to the gripped object.


STOCK MARKET FORECASTING TECHNIQUES : A


title: Proposal of a Methodology for Bridge Condition Assessment

abstract: Due to the substantial role of bridges in transportation networks and in accordance with the limited funding for bridge management, remediation strategies have to be prioritised. A conservative bridge assessment will result in unnecessary actions, such as costly bridge strengthening or repairs. On the other hand, any bridge maintenance negligence and delayed actions may lead to heavy future costs or degraded assets. The accuracy of decisions developed by any manager or bridge engineer relies on the accuracy of the bridge condition assessment which emanates from visual inspection. Many bridge rating systems are based on a very subjective procedure and are associated with uncertainty and personal bias. The developing condition rating method described herein is an important step in adding more holism and objectivity to the current approaches. Structural importance and material vulnerability are the two main factors that should be considered in the evaluation of element structural index and the causal factor as the representative of age, environment, road class and inspection is implemented as a coefficient to the overall structural index. The Analytical Hierarchy Process (AHP) has been applied to evaluate the priority vector of the causal parameters.


Estimating flight departure delay distributions a statistical approach with long-term trend and short-term pattern


Diversity in Seed Storage Protein Profile of Oilseed Crop Plukenetia volubilis L . from Peruvian Amazon


Contextual Code Completion Using Machine Learning


Science education through Problem-Based Learning: a review of research focused on students


title: Context-Aware Representations for Knowledge Base Relation Extraction

abstract: We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. ::: We combine the context representations with an attention mechanism to make the final prediction. ::: Compared to a baseline system, our approach results in an average error reduction of 24% on a held-out set of relations.


title: As-rigid-as-possible image registration for hand-drawn cartoon animations

abstract: We present a new approach to deformable image registration suitable for articulated images such as hand-drawn cartoon characters and human postures. For such type of data state-of-the-art techniques typically yield undesirable results. We propose a novel geometrically motivated iterative scheme where point movements are decoupled from shape consistency. By combining locally optimal block matching with as-rigid-as-possible shape regularization, our algorithm allows us to register images undergoing large free-form deformations and appearance variations. We demonstrate its practical usability in various challenging tasks performed in the cartoon animation production pipeline including unsupervised inbetweening, example-based shape deformation, auto-painting, editing, and motion retargeting.


title: A Typology Of Social Engineering Attacks - An Information Science Perspective.

abstract: Hackers are increasingly exploiting the social movement on the Internet, which is responsible for domestication of the web and its associated technologies, by using novel methods of online social engineering. However, there is not enough support in the form of published research that can help us gain a holistic understanding of human vulnerabilities that are central to online social engineering attacks. This paper extends prior published classifications and presents a new typology of online social engineering methods that manifest during the various information seeking contexts that users engage while online. Concepts borrowed from the field of information science help us to build this typology that groups attack vectors with different human information seeking modes. The typology can be readily used as educational material to improve end user awareness about online social engineering. In addition, the typology can be used as a conceptual starting point for future empirical research on human vulnerabilities in different information seeking contexts which in turn can inform systems designers to design more effective solutions that can help mitigate the effects of such attacks.


A Probabilistic Model for Estimating Real-valued Truth from Conflicting Sources


title: Multi-camera multi-object tracking by robust hough-based homography projections

abstract: Recently, several approaches have been introduced for incorporating the information from multiple cameras to increase the robustness of tracking. This allows to handle problems of mutually occluding objects - a reasonable scenario for many tasks such as visual surveillance or sports analysis. However, these methods often ignore problems such as inaccurate geometric constraints and violated geometric assumptions, requiring complex methods to resolve the resulting errors. In this paper, we introduce a new multiple camera tracking approach that inherently avoids these problems. We build on the ideas of generalized Hough voting and extend it to the multiple camera domain. This offers the following advantages: we reduce the amount of data in voting and are robust to projection errors. Moreover, we show that using additional geometric information can help to train more specific classifiers drastically improving the tracking performance. We confirm these findings by comparing our approach to existing (multi-camera) tracking methods.


title: Sample Efficient Policy Search for Optimal Stopping Domains

abstract: Arising naturally in many fields, optimal stopping problems consider the question of deciding when to stop an observation-generating process. We examine the problem of simultaneously learning and planning in such domains, when data is collected directly from the environment. We propose GFSE, a simple and flexible model-free policy search method that reuses data for sample efficiency by leveraging problem structure. We bound the sample complexity of our approach to guarantee uniform convergence of policy value estimates, tightening existing PAC bounds to achieve logarithmic dependence on horizon length for our setting. We also examine the benefit of our method against prevalent model-based and model-free approaches on 3 domains taken from diverse fields.


title: Understanding performance of I/O intensive containerized applications for NVMe SSDs

abstract: Our cloud-based IT world is founded on hyper-visors and containers. Containers are becoming an important cornerstone, which is increasingly used day-by-day. Among different available frameworks, docker has become one of the major adoptees to use containerized platform in data centers and enterprise servers, due to its ease of deploying and scaling. Further more, the performance benefits of a lightweight container platform can be leveraged even more with a fast back-end storage like high performance SSDs. However, increase in number of simultaneously operating docker containers may not guarantee an aggregated performance improvement due to saturation. Thus, understanding performance bottleneck in a multi-tenancy docker environment is critically important to maintain application level fairness and perform better resource management. In this paper, we characterize the performance of persistent storage option (through data volume) for I/O intensive, dockerized applications. Our work investigates the impact on performance with increasing number of simultaneous docker containers in different workload environments. We provide, first of its kind study of I/O intensive containerized applications operating with NVMe SSDs. We show that 1) a six times better application throughput can be obtained, just by wise selection of number of containerized instances compared to single instance; and 2) for multiple application containers running simultaneously, an application throughput may degrade upto 50% compared to a stand-alone applications throughput, if good choice of application and workload is not made. We then propose novel design guidelines for an optimal and fair operation of both homogeneous and heterogeneous environments mixed with different applications and workloads.


title: RDBMS, NoSQL, Hadoop: A Performance-Based Empirical Analysis

abstract: The relational data model has been dominant and widely used since 1970. However, as the need to deal with big data grows, new data models, such as Hadoop and NoSQL, were developed to address the limitation of the traditional relational data model. As a result, determining which data model is suitable for applications has become a challenge. The purpose of this paper is to provide insight into choosing the suitable data model by conducting a benchmark using Yahoo! Cloud Serving Benchmark (YCSB) on three different database systems: (1) MySQL for relational data model, (2) MongoDB for NoSQL data model, and (3) HBase for Hadoop framework. The benchmark was conducted by running four different workloads. Each workload is executed using a different increasing operation and thread count, while observing how their change respectively affects throughput, latency, and runtime.


Friction Stir Welding of Aluminum Alloys: A Review


title: Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments

abstract: Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we focus on SA of the Tunisian dialect. We use Machine Learning techniques to determine the polarity of comments written in Tunisian dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian dialect corpus of 17.000 comments from Facebook. This corpus shows a significant improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas


Active Learning for Black-Box Semantic Role Labeling with Neural Factors


title: Designing the Business Models for Circular Economy—Towards the Conceptual Framework

abstract: Switching from the current linear model of economy to a circular one has recently attracted increased attention from major global companies e.g., Google, Unilever, Renault, and policymakers attending the World Economic Forum. The reasons for this are the huge financial, social and environmental benefits. However, the global shift from one model of economy to another also concerns smaller companies on a micro-level. Thus, comprehensive knowledge on designing circular business models is needed to stimulate and foster implementation of the circular economy. Existing business models for the circular economy have limited transferability and there is no comprehensive framework supporting every kind of company in designing a circular business model. This study employs a literature review to identify and classify the circular economy characteristics according to a business model structure. The investigation in the eight sub-domains of research on circular business models was used to redefine the components of the business model canvas in the context of the circular economy. Two new components—the take-back system and adoption factors—have been identified, thereby leading to the conceptualization of an extended framework for the circular business model canvas. Additionally, the triple fit challenge has been recognized as an enabler of the transition towards a circular business model. Some directions for further research have been outlined, as well.


title: Racism as a stressor for African Americans: A biopsychosocial model.

abstract: Various authors have noted that interethnic group and intraethnic group racism are significant stressors for many African Americans. As such, intergroup and intragroup racism may play a role in the high rates of morbidity and mortality in this population. Yet, although scientific examinations of the effects of stress have proliferated, few researchers have explored the psychological, social, and physiological effects of perceived racism among African Americans. The purpose of this article was to outline a biopsychosocial model for perceived racism as a guide for future research. The first section of this article provides a brief overview of how racism has been conceptualized in the scientific literature. The second section reviews research exploring the existence of intergroup and intragroup racism. A contextual model for systematic studies of the biopsychosocial effects of perceived racism is then presented, along with recommendations for future research.


title: Analysis and design of an LCL-T resonant converter as a constant-current power supply

abstract: An LCL-T resonant converter (LCL-T RC) is shown to behave as a current source when operated at resonant frequency. A detailed analysis of the LCL-T RC for this property is presented. Closed-form expressions for converter gain, component stresses, and the condition for converter design optimized for minimum size of resonant network is derived. A design procedure is illustrated with a prototype 200-W 20-A current-source power supply and experimental results are presented. The LCL-T RC as a current source offers many advantages such as easy parallel operation and low circulating currents at light load. Additionally, with appropriate phase shift in paralleled modules, the peak-peak ripple in output current is reduced and the ripple frequency is increased, reducing filtering requirements. The leakage inductance of a transformer can be advantageously integrated into the resonant network. These merits make the topology applicable in various applications such as magnet power supply, capacitor charging power supply, laser diode drivers, etc.


title: Valuation of IT Investments Using Real Options Theory

abstract: Real Options Theory is often applied to the valuation of IT investments. The application of Real Options Theory is generally accompanied by a monetary valuation of real options through option pricing models which in turn are based on restrictive assumptions and thus subject to criticism. Therefore, this paper analyzes the application of option pricing models to the valuation of IT investments. A structured literature review reveals the types of IT investments which are valued with Real Options Theory in scientific literature. These types of IT investments are further investigated and their main characteristics are compared to the restrictive assumptions of traditional option pricing models. This analysis serves as a basis for further discussion on how the identified papers address these assumptions. The results show that a lot of papers do not account for critical assumptions, although it is known that the assumptions are not fulfilled. Moreover, the type of IT investment determines the criticality of the assumptions. Additionally, several extensions or adaptions of traditional option pricing models can be found which provide the possibility to relax critical assumptions. Researchers can profit from the results derived in this paper in two ways: First, it is demonstrated which assumptions can be critical for various types of IT investments. Second, extensions of option pricing models that relax critical assumptions are introduced.


title: WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse

abstract: We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw, unstructured text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.


title: Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment

abstract: Network embeddings, which learn low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. We propose to incorporate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. We introduce a word-by-word alignment framework that measures the compatibility of embeddings between word pairs, and then adaptively accumulates these alignment features with a simple yet effective aggregation function. In experiments, we evaluate the proposed framework on three real-world benchmarks for downstream tasks, including link prediction and multi-label vertex classification. Results demonstrate that our model outperforms state-of-the-art network embedding methods by a large margin.


title: Image stitching with single-hidden layer feedforward neural networks

abstract: In this paper, a novel image stitching method is proposed, which utilizes scale-invariant feature transform (SIFT) feature and single-hidden layer feedforward neural network (SLFN) to get higher precision of parameter estimation. In this method, features are extracted from the image sets by the SIFT descriptor and form into the input vector of the SLFN. The output of the SLFN is those translation, rotation and scaling parameters with respect to reference and registered image sets. We also apply a fast learning scheme, called pseudoinverse learning, to train SLFN to get higher training efficiency. Comparative experiments are performed between our proposed method and the traditional random sample consensus (RANSAC) based method. The results show that our method has the advantage not only at accuracy but also remarkably at fast speed.


Generalized Distillation Framework for Speaker Normalization


title: Bitwise Neural Networks

abstract: Based on the assumption that there exists a neural network that efficiently represents a set of Boolean functions between all binary inputs and outputs, we propose a process for developing and deploying neural networks whose weight parameters, bias terms, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the feedforward pass. The proposed Bitwise Neural Network (BNN) is especially suitable for resource-constrained environments, since it replaces either floating or fixed-point arithmetic with significantly more efficient bitwise operations. Hence, the BNN requires for less spatial complexity, less memory bandwidth, and less power consumption in hardware. In order to design such networks, we propose to add a few training schemes, such as weight compression and noisy backpropagation, which result in a bitwise network that performs almost as well as its corresponding real-valued network. We test the proposed network on the MNIST dataset, represented using binary features, and show that BNNs result in competitive performance while offering dramatic computational savings.


title: Please Biofeed the Zombies: Enhancing the Gameplay and Display of a Horror Game Using Biofeedback

abstract: This paper describes an investigation into how real-time but low-cost biometric information can be interpreted by computer games to enhance gameplay without fundamentally changing it. We adapted a cheap sensor, (the Lightstone mediation sensor device by Wild Divine), to record and transfer biometric information about the player (via sensors that clip over their fingers) into a commercial game engine, Half-Life 2. During game play, the computer game was dynamically modified by the player’s biometric information to increase the cinematically augmented “horror” affordances. These included dynamic changes in the game shaders, screen shake, and the creation of new spawning points for the game’s non-playing characters (zombies), all these features were driven by the player’s biometric data. To evaluate the usefulness of this biofeedback device, we compared it against a control group of players who also had sensors clipped on their fingers, but for the second group the gameplay was not modified by the biometric information of the players. While the evaluation results indicate biometric data can improve the situated feeling of horror, there are many design issues that will need to be investigated by future research, and the judicious selection of theme and appropriate interaction is vital. Author Keywords Biofeedback, gameplay, horror, cinematics, shaders.


title: Integrated Seat and Suspension Control for a Quarter Car With Driver Model

abstract: In this paper, an integrated vehicle seat and suspension control strategy for a quarter car with driver model is proposed to improve suspension performance on driver ride comfort. An integrated seat and suspension model that includes a quarter-car suspension, a seat suspension, and a 4-degree-of-freedom (DOF) driver body model is presented first. This integrated model provides a platform to evaluate ride comfort performance in terms of driver head acceleration responses under typical road disturbances and to develop an integrated control of seat and car suspensions. Based on the integrated model, an H∞ state feedback controller is designed to minimize the driver head acceleration under road disturbances. Considering that state variables for a driver body model are not measurement available in practice, a static output feedback controller, which only uses measurable state variables, is designed. Further discussion on robust multiobjective controller design, which considers driver body parameter uncertainties, suspension stroke limitation, and road-holding properties, is also provided. Last, numerical simulations are conducted to evaluate the effectiveness of the proposed control strategy. The results show that the integrated seat and suspension control can effectively improve suspension ride comfort performance compared with the passive seat suspension, active seat suspension control, and active car suspension control.


title: Active modular elastomer sleeve for soft wearable assistance robots

abstract: A proposed adaptive soft orthotic device performs motion sensing and production of assistive forces with a modular, pneumatically-driven, hyper-elastic composite. Wrapping the material around a joint will allow simultaneous motion sensing and active force response through shape and rigidity control. This monolithic elastomer sheet contains a series of miniaturized pneumatically-powered McKibben-type actuators that exert tension and enable adaptive rigidity control. The elastomer is embedded with conductive liquid channels that detect strain and bending deformations induced by the pneumatic actuators. In addition, the proposed system is modular and can be configured for a diverse range of motor tasks, joints, and human subjects. This modular functionality is accomplished with a decentralized network of self-configuring nodes that manage the collection of sensory data and the delivery of actuator feedback commands. This paper mainly describes the design of the soft orthotic device as well as actuator and sensor components. The characterization of the individual sensors, actuators, and the integrated device is also presented.


title: A Review of Success Factors and Challenges of Public Sector BPR Implementations

abstract: Pressures to cut budgets and increase efficiency while maintaining performance prompted public managers to turn to the private sector for solutions. As a means of rightsizing government, cutting red tape, and reducing bureaucracy, the idea of BPR found its way into the realm of public organizations. The differing characteristics of private and public organizations introduce various challenges when transferring BPR methodologies between the sectors. With this research paper we consolidate the ample literature on BPR success from the private and public sector. We identify the relevant success factors and show that a surprising similarity exists between private and public BPR related success factors. In addition, we determine the specific characteristics and requirements of the public sector highlighting gaps in current literature. Finally, we structure the research gap in five propositions and provide directions for further research.


title: Low voltage and high power DC-AC inverter topologies for electric vehicles

abstract: In electric vehicles, systems with distributed low voltage batteries and DC bus voltage below 60 V could be an alternative to systems with a single high voltage battery. In this work, different low voltage DC-AC inverter topologies are investigated and compared in terms of power losses and component requirements. For a given driving cycle of an electric vehicle the operating points and the required size of the system components are calculated by using an iterative method. This will be used to estimate and to compare the motor losses (by using a characteristic map of the motor) and the DC-AC inverter losses (based on a loss and thermal model of the switches) for each inverter topology. Finally, the simulation is performed and the results are compared with each other. It turns out that the system with a bidirectional DC-DC converter with controlled DC-link voltage inverter has the best performance.


title: PNeuro: A scalable energy-efficient programmable hardware accelerator for neural networks

abstract: Artificial intelligence and especially Machine Learning recently gained a lot of interest from the industry. Indeed, new generation of neural networks built with a large number of successive computing layers enables a large amount of new applications and services implemented from smart sensors to data centers. These Deep Neural Networks (DNN) can interpret signals to recognize objects or situations to drive decision processes. However, their integration into embedded systems remains challenging due to their high computing needs. This paper presents PNeuro, a scalable energy-efficient hardware accelerator for the inference phase of DNN processing chains. Simple programmable processing elements architectured in SIMD clusters perform all the operations needed by DNN (convolutions, pooling, non-linear functions, etc.). An FDSOI 28 nm prototype shows an energy efficiency of 700 GMACS/s/W at 800 MHz. These results open important perspectives regarding the development of smart energy-efficient solutions based on Deep Neural Networks.


title: Contextual boost for pedestrian detection

abstract: Pedestrian detection from images is an important and yet challenging task. The conventional methods usually identify human figures using image features inside the local regions. In this paper we present that, besides the local features, context cues in the neighborhood provide important constraints that are not yet well utilized. We propose a framework to incorporate the context constraints for detection. First, we combine the local window with neighborhood windows to construct a multi-scale image context descriptor, designed to represent the contextual cues in spatial, scaling, and color spaces. Second, we develop an iterative classification algorithm called contextual boost. At each iteration, the classifier responses from the previous iteration across the neighborhood and multiple image scales, called classification context, are incorporated as additional features to learn a new classifier. The number of iterations is determined in the training process when the error rate converges. Since the classification context incorporates contextual cues from the neighborhood, through iterations it implicitly propagates to greater areas and thus provides more global constraints. We evaluate our method on the Caltech benchmark dataset [11]. The results confirm the advantages of the proposed framework. Compared with state of the arts, our method reduces the miss rate from 29% by [30] to 25% at 1 false positive per image (FPPI).


Towards Finding Optimal Differential Characteristics for ARX : Application to Salsa 20 ⋆


title: A Semantic Extraction and Sentimental Assessment of Risk Factors (SESARF): An NLP Approach for Precision Medicine: A Medical Decision Support Tool for Early Diagnosis from Clinical Notes

abstract: Clinical notes contain information that is crucial for the diagnosis process. However, it is usually not properly manually analyzed due to the tremendous efforts and time it takes. Hence, an automated approach is eagerly needed to maximize clinical knowledge management and reduce cost. In this paper, we propose a framework SESARF: a Semantic Extractor to identify hidden risk factors in clinical notes and a Sentimental Analyzer to assess the severity levels associated with the identified Risk Factors. This tool can be customized to any disease using Linked Open Data (LOD) by selecting a specific disease and collecting its risk factors list from medical ontologies. The extracted knowledge can serve two purposes: 1) a feature vector is prepared, for any classifier in machine learning, containing risk factors and their weights based on our semantic enrichment and sentimental analyzer and 2) a proper comparison of the extracted information with wearable body sensors that can alert any major changes in a patient's health status to personalize treatment.


title: A Genetic NewGreedy Algorithm for Influence Maximization in Social Network

abstract: A user may be influenced by the other users of a social network by sharing information. Influence maximization is one of the critical research topics aimed at knowing the current circumstances of a social network, such as the general mood of the society. The goal of this problem is to find a seed set which has a maximum influence with respect to a propagation model. For the influence maximization problem is NP-Hard, it is obvious that an exhausted search algorithm is not able to find the solution in a reasonable time. It is also obvious that a greedy algorithm may not find a solution that satisfies all the requirements. Hence, a high-performance algorithm for solving the influence maximization problem, which leverages the strength of the greedy method and the genetic algorithm (GA) is presented in this paper. Experimental results show that the proposed algorithm can provide a better result than simple GA by about 10% in terms of the quality.


title: BRAIN: BehavioR Based Adaptive Intrusion Detection in Networks: Using Hardware Performance Counters to Detect DDoS Attacks

abstract: Denial-of-Service (DoS) and Distributed Denial-of Service (DDoS) attacks account for one third of all service downtime incidents. Current DoS/DDoS attacks are not only limited to knocking down online services, but they also disguise other malicious attacks such as delivering malware, data-theft, wire fraud and even extortion. Detection of these attacks is predominantly based on the packet data and metrics derived only from packets. This work proposes a host based DDoS detection framework called BRAIN: BehavioR based Adaptive Intrusion detection in Networks. BRAIN leverages already available Hardware Performance Counters in modern processors to model the application behavior using low-level hardware events. BRAIN combines network statistics and modeled application behavior to detect DDoS attacks using machine learning. Our experiments show that BRAIN can detect multiple types of DDoS attacks, including those are undetectable by existing tools with an accuracy of 99.8% and a false alarm rate of 0%.


title: A Research on Carrier-grade SDN Controller

abstract: Software Defined Networking (SDN) is a new programmable network construction technology that enables centrally management and control, which is considered to be the future evolution trend of networks. A modularized carrier-grade SDN controller according to the characteristics of carrier-grade networks is designed and proposed, resolving the problem of controlling large-scale networks of carrier. The modularized architecture offers the system flexibility, scalability and stability. Functional logic of modules and core modules, such as link discovery module and topology module, are designed to meet the carrier's need. Static memory allocation, multi-threads technique and stick-package processing are used to improve the performance of controller, which is C programming language based. Processing logic of the communication mechanism of the controller is introduced, proving that the controller conforms to the OpenFlow specification and has a good interaction with OpenFlow-based switches. A controller cluster management system is used to interact with controllers through the east-west interface in order to manage large-scale networks. Furthermore, the effectiveness and high performance of the work in this paper has been verified by the testing using Cbench testing program. Moreover, the SDN controller we proposed has been running in China Telecom's Cloud Computing Key Laboratory, which showed the good results is achieved.


Tandem deep features for text-dependent speaker verification.


title: An all pixel PDAF CMOS image sensor with 0.64μmx1.28μm photodiode separated by self-aligned in-pixel deep trench isolation for high AF performance

abstract: We present a CMOS image sensor (CIS) with phase detection auto-focus (PDAF) in all pixels. The size of photodiode (PD) is 0.64μm by 1.28μm, the smallest ever reported and two PDs compose a single pixel. Inter PD isolation was fabricated by deep trench isolation (DTI) process in order to obtain an accurate AF performance. The layout and depth of DTI was optimized in order to eliminate side effects and maximize the performance even at extremely low light condition up to 1lux. In particular the AF performance remains comparable to that of 0.70μm dual PD CIS. By using our unique technology, it seems plausible to scale further down the size of pixels in dual PD CIS without sacrificing AF performance.


title: OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments

abstract: This paper describes the OpenViBE software platform which enables researchers to design, test, and use brain--computer interfaces (BCIs). BCIs are communication systems that enable users to send commands to computers solely by means of brain activity. BCIs are gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). The key features of the platform are (1) high modularity, (2) embedded tools for visualization and feedback based on VR and 3D displays, (3) BCI design made available to non-programmers thanks to visual programming, and (4) various tools offered to the different types of users. The platform features are illustrated in this paper with two entertaining VR applications based on a BCI. In the first one, users can move a virtual ball by imagining hand movements, while in the second one, they can control a virtual spaceship using real or imagined foot movements. Online experiments with these applications together with the evaluation of the platform computational performances showed its suitability for the design of VR applications controlled with a BCI. OpenViBE is a free software distributed under an open-source license.


title: Blossom: A Tensile Social Robot Design with a Handcrafted Shell

abstract: We describe the design and implementation of Blossom, a social robot that diverges from convention by being flexible both internally and externally. Internally, Blossom»s actuation mechanism is a compliant tensile structure supporting a free-floating platform. This design enables organic and lifelike movements with a small number of motors and simple software control. Blossom»s exterior is also flexible in that it is made of fabric and not rigidly attached to the mechanism, enabling it to freely shift and deform. The handcrafted nature of the shell allows Blossom to take on a variety of forms and appearances. Blossom is envisioned as an open platform for user-crafted human-robot interaction.


title: Generic Battery model covering self-discharge and internal resistance variation

abstract: Design and analysis of battery powered systems depend on effective battery model. Hardware-in-the-loop testing of Battery Management System (BMS) extensively uses battery models. Simulation of Hybrid Electric Vehicles highly rely on the battery models which provides actual characteristics. This paper proposes a performance enhanced battery model which can be included in electric circuit design and analysis. Combined electrical and thermal model replicates the real world performance of battery. Self-discharge is the characteristic of a battery which reduces the stored energy without external connection. Effect of self-discharge on State of Charge (SOC) of battery is considered in the paper. Internal resistance variation with SOC of a battery included in the model further enhances its exactness. Proposed model is validated for Sony US 18650 Li-ion battery in MATLAB Simulink.


An Endorsement-based Key Management System for Decentralized NDN Chat Application


title: Signal processing on graphs: Performance of graph structure estimation

abstract: A class of models for describing sets of time series generated by interacting agents using directed, weighted graphs is introduced. A computationally tractable algorithm for estimating the graph adjacency matrix of this model from observed time series data is presented. The performance guarantees of this algorithm for prediction are outlined under several assumptions on the properties of the dynamics of the system of agents and on the true values of the parameters. These guarantees are tested empirically through simulation studies using several random graph models.


title: Space vector pulse width modulation of three-level inverter extending operation into overmodulation region

abstract: Multilevel voltage-fed inverters with space vector pulse width modulation have established their importance in high power high performance industrial drive applications. The paper proposes an overmodulation strategy of space vector PWM of a three-level inverter with linear transfer characteristic that easily extends from the undermodulation strategy previously developed by the authors for neural network implementation. The overmodulation strategy is very complex because of large number of inverter switching states, and hybrid in nature, that incorporates both undermodulation and overmodulation algorithms. The paper describes systematically the algorithm development, system analysis, DSP based implementation, and extensive evaluation study to validate the modulator performance. The modulator takes the command voltage and angle information at the input and generates symmetrical PWM waves for the three phases of an IGBT inverter that operates at 1.0 kHz switching frequency. The switching states are distributed such that the neutral point voltage always remains balanced. An open loop volts/Hz controlled induction motor drive has been evaluated extensively by smoothly varying the voltage and frequency in the whole speed range that covers both undermodulation and overmodulation (nearest to square-wave) regions, and performance was found to be excellent. The PWM algorithm can be easily extended to vector-controlled drive. The algorithm development is again fully compatible for implementation by a neural network.


Applied Robot Coverage Path Planning with Multiple Decision Making Capability under Uncertainty using Knowledge Inference with Hedge Algebras


Laser-visual-inertial odometry and mapping with high robustness and low drift1


title: What limits working memory capacity?

abstract: We review the evidence for the 3 principal theoretical contenders that vie to explain why and how working memory (WM) capacity is limited. We examine the possibility that capacity limitations arise from temporal decay; we examine whether they might reflect a limitation in cognitive resources; and we ask whether capacity might be limited because of mutual interference of representations in WM. We evaluate each hypothesis against a common set of findings reflecting the capacity limit: The set-size effect and its modulation by domain-specificity and heterogeneity of the memory set; the effects of unfilled retention intervals and of distractor processing in the retention interval; and the pattern of correlates of WM tests. We conclude that-at least for verbal memoranda-a decay explanation is untenable. A resource-based view remains tenable but has difficulty accommodating several findings. The interference approach has its own set of difficulties but accounts best for the set of findings, and therefore, appears to present the most promising approach for future development. (PsycINFO Database Record


title: Video Object Segmentation without Temporal Information

abstract: Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video frames. When the temporal smoothness is suddenly broken, such as when an object is occluded, or some frames are missing in a sequence, the result of these methods can deteriorate significantly or they may not even produce any result at all. This paper explores the orthogonal approach of processing each frame independently, i.e disregarding the temporal information. In particular, it tackles the task of semi-supervised video object segmentation: the separation of an object from the background in a video, given its mask in the first frame. We present Semantic One-Shot Video Object Segmentation (OSVOS-S), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one shot). We show that instance level semantic information, when combined effectively, can dramatically improve the results of our previous method, OSVOS. We perform experiments on two recent video segmentation databases, which show that OSVOS-S is both the fastest and most accurate method in the state of the art.


title: A hybrid neural network and ARIMA model for water quality time series prediction

abstract: Accurate predictions of time series data have motivated the researchers to develop innovative models for water resources management. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks. The proposed approach consists of an ARIMA methodology and feed-forward, backpropagation network structure with an optimized conjugated training algorithm. The hybrid approach for time series prediction is tested using 108-month observations of water quality data, including water temperature, boron and dissolved oxygen, during 1996-2004 at Buyuk Menderes river, Turkey. Specifically, the results from the hybrid model provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions. The correlation coefficients between the hybrid model predicted values and observed data for boron, dissolved oxygen and water temperature are 0.902, 0.893, and 0.909, respectively, which are satisfactory in common model applications. Predicted water quality data from the hybrid model are compared with those from the ARIMA methodology and neural network architecture using the accuracy measures. Owing to its ability in recognizing time series patterns and nonlinear characteristics, the hybrid model provides much better accuracy over the ARIMA and neural network models for water quality predictions.


title: Security models for web-based applications

abstract: Using traditional and emerging access control approaches to develop secure applications for the Web.


title: Computational neuroimaging strategies for single patient predictions

abstract: Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.


title: Causality from Cz to C3/C4 or between C3 and C4 revealed by granger causality and new causality during motor imagery

abstract: Interaction between different brain regions has received wide attention recently. Granger causality (GC) is one of the most popular methods to explore causality relationship between different brain regions. In 2011, Hu et. al [1] pointed out shortcomings and/or limitations of GC by using a large of number of illustrative examples and showed that GC is only a causality definition in the sense of Granger and does not reflect real causality at all, and meanwhile proposed a new causality (NC) which is shown to be more reasonable and understandable than GC by those examples. Motor imagery (MI) is an important mental process in cognitive neuroscience and cognitive psychology and has received growing attention for a long time. However, there is few work about causality flow so far during MI based on scalp EEG. In this paper, we use scalp EEG to study causality flow during MI. The scalp EEGs are from 9 subjects in BCI competition IV held in 2008 [2] and provided by Graz University of Technology. We are interested in three regions: Cz (the centre of cerebral cortex), C3 (the left of cerebral cortex) and C4 (the right of cerebral cortex) which are considered to be optimal locations for recognizing MI states in literature. We apply GC and NC to scalp EEG and find that i) there is strong directional connectivity from Cz to C3/C4 during left hand and right hand MI based on GC and NC. ii) During left hand MI, there is directional connectivity from C4 to C3 based on GC and NC. iii) During right hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC method than by GC method. iv) Our results suggest that NC method in time and frequency domains is demonstrated to be much better to reveal causal influence between different brain regions than GC method. Thus, we deeply believe that NC method will shed new light on causality analysis in economics and neuroscience.


title: Discovering Endophenotypes for Major Depression

abstract: The limited success of genetic studies of major depression has raised questions concerning the definition of genetically relevant phenotypes. This paper presents strategies to improve the phenotypic definition of major depression by proposing endophenotypes at two levels: First, dissecting the depressive phenotype into key components results in narrow definitions of putative psychopathological endophenotypes: mood bias toward negative emotions, impaired reward function, impaired learning and memory, neurovegetative signs, impaired diurnal variation, impaired executive cognitive function, psychomotor change, and increased stress sensitivity. A review of the recent literature on neurobiological and genetic findings associated with these components is given. Second, the most consistent heritable biological markers of major depression are proposed as biological endophenotypes for genetic studies: REM sleep abnormalities, functional and structural brain abnormalities, dysfunctions in serotonergic, catecholaminergic, hypothalamic-pituitary-adrenocortical axis, and CRH systems, and intracellular signal transduction endophenotypes. The associations among the psychopathological and biological endophenotypes are discussed with respect to specificity, temporal stability, heritability, familiality, and clinical and biological plausibility. Finally, the case is made for the development of a new classification system in order to reduce the heterogeneity of depression representing a major impediment to elucidating the genetic and neurobiological basis of this common, severe, and often life-threatening illness.


title: Understanding Ohm's law: enlightenment through augmented reality

abstract: This paper proposes a new interactive environment for learning the basics of electricity in series and parallel circuits using augmented reality (AR). The system allows the student to design and study electric circuits by placing symbolic cards representing resistors on a board. Then this board is overlaid by a 3D virtual circuit with its electrical components. Finally, this virtual circuit is coupled back to reality by lighting up a real bulb with intensity depending on the chosen layout. In addition, the mathematical relationship between current, resistance and voltage (Ohm's law) is presented in an information window.


title: The human severe acute respiratory syndrome coronavirus (SARS-CoV) 8b protein is distinct from its counterpart in animal SARS-CoV and down-regulates the expression of the envelope protein in infected cells

abstract: The severe acute respiratory syndrome coronavirus (SARS-CoV), isolated from humans infected during the peak of epidemic, encodes two accessory proteins termed as 8a and 8b. Interestingly, the SARS-CoV isolated from animals contains an extra 29-nucleotide in this region such that these proteins are fused to become a single protein, 8ab. Here, we compared the cellular properties of the 8a, 8b and 8ab proteins by examining their cellular localizations and their abilities to interact with other SARS-CoV proteins. These results may suggest that the conformations of 8a and 8b are different from 8ab although nearly all the amino acids in 8a and 8b are found in 8ab. In addition, the expression of the structural protein, envelope (E), was down-regulated by 8b but not 8a or 8ab. Consequently, E was not detectable in SARS-CoV-infected cells that were expressing high levels of 8b. These findings suggest that 8b may modulate viral replication and/or pathogenesis.


Six Phase Permanent Magnet Machine with Fractional Slot Concentrated Winding