title: Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses

abstract: Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition‐based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent recognition performance; this strategy should be considered for future real-time experiments.


title: Linear regression-based adaptation of music emotion recognition models for personalization

abstract: Personalization techniques can be applied to address the subjectivity issue of music emotion recognition, which is important for music information retrieval. However, achieving satisfactory accuracy in personalized music emotion recognition for a user is difficult because it requires an impractically huge amount of annotations from the user. In this paper, we adopt a probabilistic framework for valence-arousal music emotion modeling and propose an adaptation method based on linear regression to personalize a background model in an online learning fashion. We also incorporate a component-tying strategy to enhance the model flexibility. Comprehensive experiments are conducted to test the performance of the proposed method on three datasets, including a new one created specifically in this work for personalized music emotion recognition. Our results demonstrate the effectiveness of the proposed method.


title: FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments

abstract: In this paper we have proposed and designed Finding HuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. Finding HuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, Finding Humo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.


title: Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites

abstract: Abstract Social Networking Sites (SNSs) such as Facebook are one of the latest examples of communications technologies that have been widely-adopted by students and, consequently, have the potential to become a valuable resource to support their educational communications and collaborations with faculty. However, faculty members have a track record of prohibiting classroom uses of technologies that are frequently used by students. To determine how likely higher education faculty are to use Facebook for either personal or educational purposes, higher education faculty (n = 62) and students (n = 120) at a mid-sized southern university were surveyed on their use of Facebook and email technologies. A comparison of faculty and student responses indicate that students are much more likely than faculty to use Facebook and are significantly more open to the possibility of using Facebook and similar technologies to support classroom work. Faculty members are more likely to use more “traditional” technologies such as email.


title: Contact Inertial Odometry: Collisions are your Friends

abstract: Autonomous exploration of unknown environments with aerial vehicles remains a challenge, especially in perceptually degraded conditions. Dust, fog, or a lack of visual or LiDAR-based features results in severe difficulties for state estimation algorithms, which failure can be catastrophic. In this work, we show that it is indeed possible to navigate in such conditions without any exteroceptive sensing by exploiting collisions instead of treating them as constraints. To this end, we present a novel contact-based inertial odometry (CIO) algorithm: it uses estimated external forces with the environment to detect collisions and generate pseudo-measurements of the robot velocity, enabling autonomous flight. To fully exploit this method, we first perform modeling of a hybrid ground and aerial vehicle which can withstand collisions at moderate speeds, for which we develop an external wrench estimation algorithm. Then, we present our CIO algorithm and develop a reactive planner and control law which encourage exploration by bouncing off obstacles. All components of this framework are validated in hardware experiments and we demonstrate that a quadrotor can traverse a cluttered environment using an IMU only. This work can be used on drones to recover from visual inertial odometry failure or on micro-drones that do not have the payload capacity to carry cameras, LiDARs or powerful computers.


title: Solving Weighted Constraint Satisfaction Problems with Memetic/Exact Hybrid Algorithms

abstract: A weighted constraint satisfaction problem (WCSP) is a constraint satisfaction problem in which preferences among solutions can be expressed. Bucket elimination is a complete technique commonly used to solve this kind of constraint satisfaction problem. When the memory required to apply bucket elimination is too high, a heuristic method based on it (denominated mini-buckets) can be used to calculate bounds for the optimal solution. Nevertheless, the curse of dimensionality makes these techniques impractical on large scale problems. In response to this situation, we present a memetic algorithm for WCSPs in which bucket elimination is used as a mechanism for recombining solutions, providing the best possible child from the parental set. Subsequently, a multi-level model in which this exact/metaheuristic hybrid is further hybridized with branch-and-bound techniques and mini-buckets is studied. As a case study, we have applied these algorithms to the resolution of the maximum density still life problem, a hard constraint optimization problem based on Conway's game of life. The resulting algorithm consistently finds optimal patterns for up to date solved instances in less time than current approaches. Moreover, it is shown that this proposal provides new best known solutions for very large instances.


title: Full-Capacity Unitary Recurrent Neural Networks

abstract: Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural networks (uRNNs), which use unitary recurrence matrices, have recently been proposed as a means to avoid these issues. However, in previous experiments, the recurrence matrices were restricted to be a product of parameterized unitary matrices, and an open question remains: when does such a parameterization fail to represent all unitary matrices, and how does this restricted representational capacity limit what can be learned? To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix. Our contribution consists of two main components. First, we provide a theoretical argument to determine if a unitary parameterization has restricted capacity. Using this argument, we show that a recently proposed unitary parameterization has restricted capacity for hidden state dimension greater than 7. Second, we show how a complete, full-capacity unitary recurrence matrix can be optimized over the differentiable manifold of unitary matrices. The resulting multiplicative gradient step is very simple and does not require gradient clipping or learning rate adaptation. We confirm the utility of our claims by empirically evaluating our new full-capacity uRNNs on both synthetic and natural data, achieving superior performance compared to both LSTMs and the original restricted-capacity uRNNs.


title: Participation in a Platform Ecosystem: Appropriability, Competition, and Access to the Installed Base *

abstract: In this study we examine the antecedents of small independent software vendor (ISV) decisions to join a platform ecosystem. Using data on the history of partnering activities from 1201 ISVs from 1996 to 2004, we find that appropriability strategies based on intellectual property rights and the possession of downstream complementary capabilities by ISVs are positively related to partnership formation, and ISVs use these two mechanisms as substitutes to prevent expropriation by the platform owner. In addition, we show that greater competition in downstream product markets between the ISV and the platform owner is associated with a lower likelihood of partnership formation, while the platform’s penetration into the ISV’s target industries is positively associated with the propensity to partner. The results highlight the role of innovation appropriation, downstream complementary capabilities, and collaborative competition in the formation of a platform ecosystem.


title: A Remote Wireless Sensor Networks for Water Quality Monitoring

abstract: To resolve real-time the traditional water quality detection method, a novel system of remote water quality measuring and monitoring based on wireless sensor network (WSN) and Code Division Multiple Access (CDMA) technology is proposed. The WSNs can monitor the targets and water quality information of the waters through the cooperation of a large amount of sensors. The optimization of node distribution is studied with the intention of reducing the energy consumption and ensuring the effective information acquisition in wireless sensor network. The functions of remote detection and real-time monitoring of natural water are implemented through the CDMA wireless data transmission. This system has a simple architecture, and isn’t confined by the geographical position. The experimental results show that this system can run stably, and its operation is convenient.


title: Multi-level Coordination Plan Model Based on Constrained Adjustment

abstract: The same load equilibrium region containing three different scheduling organs,i.e.the national,regional,provincial electric power dispatching centers of power plant are fairly common in China.Because of the relative independence at all levels of scheduling,it is hard to directly apply economical dispatch technique to the multilevel joint scheduling,resulting in cumbersome coordination.In view of the above problems,this paper presents a multistage plan coordination model for hierarchical processing based on constraint adjustment.The basic idea of the model is independent formulation and combined adjustment.First,all levels of scheduling should calculate the initial output plans with their,respective load balance to relax part of the constraints as the premise.The price for adjusting different constraints is set by making the feasibility analysis of the initial plans within the balanced region,while a constrained processing-oriented multi-level planning model is developed with minimum adjustment as the objective.Application of the model in a provincial power network has validated the models capability of multi-level coordination,security constrained,economical dispatch with less data shared,and thus improving the planning efficiency.


title: Rehabilitative Soft Exoskeleton for Rodents

abstract: Robotic exoskeletons provide programmable, consistent and controllable active therapeutic assistance to patients with neurological disorders. Here we introduce a prototype and preliminary experimental evaluation of a rehabilitative gait exoskeleton that enables compliant yet effective manipulation of the fragile limbs of rats. To assist the displacements of the lower limbs without impeding natural gait movements, we designed and fabricated soft pneumatic actuators (SPAs). The exoskeleton integrates two customizable SPAs that are attached to a limb. This configuration enables a 1 N force load, a range of motion exceeding 80 mm in the major axis, and speed of actuation reaching two gait cycles/s. Preliminary experiments in rats with spinal cord injury validated the basic features of the exoskeleton. We propose strategies to improve the performance of the robot and discuss the potential of SPAs for the design of other wearable interfaces.


title: Linguistic Knowledge and Transferability of Contextual Representations

abstract: Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced by several recent pretrained contextualizers (variants of ELMo, the OpenAI transformer language model, and BERT) with a suite of seventeen diverse probing tasks. We find that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge (e.g., conjunct identification). To investigate the transferability of contextual word representations, we quantify differences in the transferability of individual layers within contextualizers, especially between recurrent neural networks (RNNs) and transformers. For instance, higher layers of RNNs are more task-specific, while transformer layers do not exhibit the same monotonic trend. In addition, to better understand what makes contextual word representations transferable, we compare language model pretraining with eleven supervised pretraining tasks. For any given task, pretraining on a closely related task yields better performance than language model pretraining (which is better on average) when the pretraining dataset is fixed. However, language model pretraining on more data gives the best results.


Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation


title: Problematic Use of Social Network Sites: The Interactive Relationship Between Gratifications Sought and Privacy Concerns

abstract: Abstract Problematic Internet use has long been a matter of concern; however, few studies extend this line of research from general Internet use to the use of social network sites (SNSs), or explicate the problematic use of SNSs by understanding what factors may enhance or reduce users' compulsive behaviors and excessive form of use on SNSs. Building on literature that found a positive relationship between gratifications sought from the Internet and problematic Internet use, this study first explores the types of gratifications sought from SNSs and examines their relationship with problematic SNS use. It found that three types of gratifications—diversion, self-presentation, and relationship building—were positively related to problematic SNS use. In addition, with a growing body of research on SNS privacy, a moderating role of privacy concerns on SNSs has been proposed to understand how it can influence the relationship between gratifications sought from SNSs and problematic SNS use. The findings suggest ...


title: Blockchain network slice broker in 5G: Slice leasing in factory of the future use case

abstract: 5G Network Slice Broker concept aims to enable mobile virtual network operators, over-the-top providers, and industry vertical market players to request and lease resources from infrastructure providers dynamically according to needs. In the future digital factory, the leasing of resources could also happen autonomously by manufacturing equipment. This paper presents Blockchain Slice Leasing Ledger Concept and analysis of its applicability in the Factory of the Future. The novel concept utilizes 5G Network Slice Broker in a Blockchain to reduce service creation time and enable manufacturing equipment autonomously and dynamically acquire the slice needed for more efficient operations.


title: Adversarial Network Embedding

abstract: Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other high-order proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks.


Exploiting Emotion on Reviews for Recommender Systems


title: CERIAS Tech Report 2004-56 ON THE ROLE OF FILE SYSTEM METADATA IN DIGITAL FORENSICS

abstract: Most of the effort in today's digital forensics community lies in the retrieval and analysis of existing information from computing systems. Little is being done to increase the quantity and quality of the forensic information on today's computing systems. In this paper we pose the question of what kind of information is desired on a system by a forensic investigator. We give an overview of the information that exists on current systems and discuss its shortcomings. We then examine the role that file system metadata play in digital forensics and analyze what kind of information is desirable for different types of forensic investigations, how feasible it is to obtain it, and discuss issues about storing the information.


title: Dual winding method of a BLDC motor for large starting torque and high speed

abstract: This paper presents a novel winding method and its inverter circuit to drive a brushless dc (BLDC) motor to high speed with large starting torque. The proposed winding coils are composed of dual windings, i.e., main and auxiliary windings, and the proposed inverter circuit has nine switches. In the starting period, it energizes both main and auxiliary windings in parallel connection to generate large starting torque. After the motor is accelerated, the proposed inverter circuit is automatically switched to energize main winding, which generates small back-electromotive force, to reach high speed. The proposed method is applied to the spindle motor of a hard disk drive and its effectiveness is verified through experiment.


title: Design and analysis of a novel planar robotic leg for high-speed locomotion

abstract: This paper presents the mechanical design and analysis of a novel leg mechanism that has only one active degree of freedom (DOF). The proposed mechanism is intended towards simplifying the mechanical and control complexity identified with the robotic legs implemented on quadrupedal platforms capable of dynamic locomotion. First, a survey of high-speed and reduced DOF legged robotic systems is presented to elucidate the design challenges and determine system requirements. Drawing from these requirements, a novel design of a six-bar leg mechanism with a single DOF is proposed. The novelty of the mechanism lies in its ability to trace a path that accommodates the execution of trot-gait by the quadrupedal platform realized by integrating the proposed leg. The kinematics of the mechanism is formulated and a multi-body model is used to perform a series of case studies on the sensitivity of the foot trajectory to the leg's dimensional parameters. Preliminary work on optimization of the foot trajectory is then performed. This research will ultimately assist the future design of quadrupedal robots to test the ability of spatial robotic tails in stabilizing and maneuvering the platform.


title: A Classifier Ensemble of Binary Classifier Ensembles

abstract: This paper proposes an innovative combinational algorithm to improve the performance in multiclass classification domains. Because the more accurate classifier the better performance of classification, the researchers in computer communities have been tended to improve the accuracies of classifiers. Although obtaining the more accurate classifier is often aimed, there is an alternative option to reach for it. Indeed one can use many inaccurate classifiers each of which is specialized for a subspace in the problem space and then s/he can consider their consensus vote as the classification. This paper proposes a new ensembles methodology that uses ensemble of binary classifiers as elements of an ensemble. These ensembles of binary classifiers jointly work using majority weighted voting. The results of these ensembles are in weighted manner combined to decide the final vote of the classification. In empirical result, these weights in final classifier are determined with using a series of genetic algorithms. We evaluate the proposed framework on a very large scale Persian digit handwritten dataset and the results show effectiveness of the algorithm.


title: Edge computing enabling the Internet of Things

abstract: Mobile Edge Computing (MEC), a new concept that emerged about a year ago, integrating the IT and the Telecom worlds will have a great impact on the openness of the Telecom market. Furthermore, the virtualization revolution that has enabled the Cloud computing success will benefit the Telecom domain, which in turn will be able to support the IaaS (Infrastructure as a Service). The main objective of MEC solution is the export of some Cloud capabilities to the user's proximity decreasing the latency, augmenting the available bandwidth and decreasing the load on the core network. On the other hand, the Internet of Things (IoT), the Internet of the future, has benefited from the proliferation in the mobile phones' usage. Many mobile applications have been developed to connect a world of things (wearables, home automation systems, sensors, RFID tags etc.) to the Internet. Even if it is not a complete solution for a scalable IoT architecture but the time sensitive IoT applications (e-healthcare, real time monitoring, etc.) will profit from the MEC architecture. Furthermore, IoT can extend this paradigm to other areas (e.g. Vehicular Ad-hoc NETworks) with the use of Software Defined Network (SDN) orchestration to cope with the challenges hindering the IoT real deployment, as we will illustrate in this paper.


title: Patterns, Hypergraphs and Embodied General Intelligence

abstract: It is proposed that the creation of artificial general intelligence (AGI) at the human level and ultimately beyond is a problem addressable via integrating computer science algorithms and data structures within a cognitive architecture oriented toward experiential learning. A general conceptual framework for AGI is presented, beginning with a philosophy of mind based on the concept of pattern, then moving to a general mathematical and conceptual framework for modeling intelligent systems, self-modifying evolving probabilistic hypergraphs (SMEPH), and finally to an overview of a specific design for AGI, the Novamente AI engine. The problem of teaching an AGI system is discussed, in the context of Novamente's embodiment in the AGI-SIM simulation world. An educational program based loosely on Piaget's developmental stages is outlined, followed by more detailed consideration of the learning by Novamente in AGI-SIM of the Piagetan infant-level capability of "object permanence".


title: Enhancing Reliability of Workflow Execution Using Task Replication and Spot Instances

abstract: Cloud environments offer low-cost computing resources as a subscription-based service. These resources are elastically scalable and dynamically provisioned. Furthermore, cloud providers have also pioneered new pricing models like spot instances that are cost-effective. As a result, scientific workflows are increasingly adopting cloud computing. However, spot instances are terminated when the market price exceeds the users bid price. Likewise, cloud is not a utopian environment. Failures are inevitable in such large complex distributed systems. It is also well studied that cloud resources experience fluctuations in the delivered performance. These challenges make fault tolerance an important criterion in workflow scheduling. This article presents an adaptive, just-in-time scheduling algorithm for scientific workflows. This algorithm judiciously uses both spot and on-demand instances to reduce cost and provide fault tolerance. The proposed scheduling algorithm also consolidates resources to further minimize execution time and cost. Extensive simulations show that the proposed heuristics are fault tolerant and are effective, especially under short deadlines, providing robust schedules with minimal makespan and cost.


title: Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies

abstract: Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single course. This indicates the existence of further avenues of research and a critical need for validation and replication to better understand the various contributing factors and the reasons why certain results occur. We introduce a novel taxonomy to analyse replicating studies and discuss the importance of replicating and reproducing previous work. We describe what is the state of the art in collecting and sharing programming data. To better understand the challenges involved in replicating or reproducing existing studies, we report our experiences from three case studies using programming data. Finally, we present a discussion of future directions for the education and research community.


title: UIUC: A Knowledge-rich Approach to Identifying Semantic Relations between Nominals

abstract: This paper describes a state-of-the-art supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs a combination of rich and varied sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources such as WordNet and additional annotated corpora. The system ranked first at the third most popular SemEval 2007 Task -Classification of Semantic Relations between Nominals and achieved an F-measure of 72.4% and an accuracy of 76.3%. We also show that some semantic relations are better suited for WordNet-based models than other relations. Additionally, we make a distinction between out-of-context (regular) examples and those that require sentence context for relation identification and show that contextual data are important for the performance of a noun-noun semantic parser. Finally, learning curves show that the task difficulty varies across relations and that our learned WordNet-based representation is highly accurate so the performance results suggest the upper bound on what this representation can do.


title: Collaborative Detection of DDoS Attacks over Multiple Network Domains

abstract: This paper presents a new distributed approach to detecting DDoS (distributed denial of services) flooding attacks at the traffic-flow level The new defense system is suitable for efficient implementation over the core networks operated by Internet service providers (ISPs). At the early stage of a DDoS attack, some traffic fluctuations are detectable at Internet routers or at the gateways of edge networks. We develop a distributed change-point detection (DCD) architecture using change aggregation trees (CAT). The idea is to detect abrupt traffic changes across multiple network domains at the earliest time. Early detection of DDoS attacks minimizes the floe cling damages to the victim systems serviced by the provider. The system is built over attack-transit routers, which work together cooperatively. Each ISP domain has a CAT server to aggregate the flooding alerts reported by the routers. CAT domain servers collaborate among themselves to make the final decision. To resolve policy conflicts at different ISP domains, a new secure infrastructure protocol (SIP) is developed to establish mutual trust or consensus. We simulated the DCD system up to 16 network domains on the Cyber Defense Technology Experimental Research (DETER) testbed, a 220-node PC cluster for Internet emulation experiments at the University of Southern California (USC) Information Science Institute. Experimental results show that four network domains are sufficient to yield a 98 percent detection accuracy with only 1 percent false-positive alarms. Based on a 2006 Internet report on autonomous system (AS) domain distribution, we prove that this DDoS defense system can scale well to cover 84 AS domains. This security coverage is wide enough to safeguard most ISP core networks from real-life DDoS flooding attacks.


title: One-Pot Terpolymerization of CO2, Propylene Oxide and Lactide Using Rare-earth Ternary Catalyst

abstract: A convenient one-pot terpolymerization of CO2, propylene oxide (PO), and L-lactide (L-LA) in short polymerization time (10 h or shorter) to afford poly(propylene carbonate-lactide) with excellent mechanical property and thermal stability using Y(CCl3COO)3-ZnEt2-glycerin rare-earth ternary catalyst is reported. The yield of the copolymerization was between 69.7 and 111.7 g/(g Zn), corresponding to L-LA/PO molar feed ratio varying from 0 to 0.1, and the number average molecular weight was between 5.5x104 and 11.9x104. The L-LA content in the terpolymer increased from 1.1% to 34.7% when L-LA/PO molar feed ratio changed from 0.01 to 0.1. Introducing L-LA as the third comonomer could significantly improve the mechanical strength and thermal stability of PPC. For the terpolymer obtained from L-LA/PO molar feed ratio of 1:50, the elongation at break reached 40.5%, which is 3 times of that of pure PPC, and the thermal decomposition temperature increased by 32 degrees C compared with pure PPC.


title: New Insights into Human Nostril Microbiome from the Expanded Human Oral Microbiome Database (eHOMD): a Resource for the Microbiome of the Human Aerodigestive Tract

abstract: ABSTRACT The expanded Human Oral Microbiome Database (eHOMD) is a comprehensive microbiome database for sites along the human aerodigestive tract that revealed new insights into the nostril microbiome. The eHOMD provides well-curated 16S rRNA gene reference sequences linked to available genomes and enables assignment of species-level taxonomy to most next-generation sequences derived from diverse aerodigestive tract sites, including the nasal passages, sinuses, throat, esophagus, and mouth. Using minimum entropy decomposition coupled with the RDP Classifier and our eHOMD V1-V3 training set, we reanalyzed 16S rRNA V1-V3 sequences from the nostrils of 210 Human Microbiome Project participants at the species level, revealing four key insights. First, we discovered that Lawsonella clevelandensis, a recently named bacterium, and Neisseriaceae [G-1] HMT-174, a previously unrecognized bacterium, are common in adult nostrils. Second, just 19 species accounted for 90% of the total sequences from all participants. Third, 1 of these 19 species belonged to a currently uncultivated genus. Fourth, for 94% of the participants, 2 to 10 species constituted 90% of their sequences, indicating that the nostril microbiome may be represented by limited consortia. These insights highlight the strengths of the nostril microbiome as a model system for studying interspecies interactions and microbiome function. Also, in this cohort, three common nasal species (Dolosigranulum pigrum and two Corynebacterium species) showed positive differential abundance when the pathobiont Staphylococcus aureus was absent, generating hypotheses regarding colonization resistance. By facilitating species-level taxonomic assignment to microbes from the human aerodigestive tract, the eHOMD is a vital resource enhancing clinical relevance of microbiome studies. IMPORTANCE The eHOMD (http://www.ehomd.org) is a valuable resource for researchers, from basic to clinical, who study the microbiomes and the individual microbes in body sites in the human aerodigestive tract, which includes the nasal passages, sinuses, throat, esophagus, and mouth, and the lower respiratory tract, in health and disease. The eHOMD is an actively curated, web-based, open-access resource. eHOMD provides the following: (i) species-level taxonomy based on grouping 16S rRNA gene sequences at 98.5% identity, (ii) a systematic naming scheme for unnamed and/or uncultivated microbial taxa, (iii) reference genomes to facilitate metagenomic, metatranscriptomic, and proteomic studies and (iv) convenient cross-links to other databases (e.g., PubMed and Entrez). By facilitating the assignment of species names to sequences, the eHOMD is a vital resource for enhancing the clinical relevance of 16S rRNA gene-based microbiome studies, as well as metagenomic studies.


title: Partial 3-D Correspondence from Shape Extremities

abstract: We present a 3-D correspondence method to match the geometric extremities of two shapes which are partially isometric. We consider the most general setting of the isometric partial shape correspondence problem, in which shapes to be matched may have multiple common parts at arbitrary scales as well as parts that are not similar. Our rank-and-vote-and-combine algorithm identifies and ranks potentially correct matches by exploring the space of all possible partial maps between coarsely sampled extremities. The qualified top-ranked matchings are then subjected to a more detailed analysis at a denser resolution and assigned with confidence values that accumulate into a vote matrix. A minimum weight perfect matching algorithm is finally iterated to combine the accumulated votes into an optimal partial mapping between shape extremities, which can further be extended to a denser map. We test the performance of our method on several data sets and benchmarks in comparison with state of the art.


Unsupervised Detection and Promotion of Authoritative Domains for Medical Queries in Web Search.


title: Sectored Random Projections for Cancelable Iris Biometrics

abstract: Privacy and security are essential requirements in practical biometric systems. In order to prevent the theft of biometric patterns, it is desired to modify them through revocable and non invertible transformations called Cancelable Biometrics. In this paper, we propose an efficient algorithm for generating a Cancelable Iris Biometric based on Sectored Random Projections. Our algorithm can generate a new pattern if the existing one is stolen, retain the original recognition performance and prevent extraction of useful information from the transformed patterns. Our method also addresses some of the drawbacks of existing techniques and is robust to degradations due to eyelids and eyelashes.


title: Modeling Buying Motives for Personalized Product Bundle Recommendation

abstract: Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers’ preferences and buying motives for product bundles. This article investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the proposed model can explain consumers’ buying motives for product bundles in terms of different node-types and edge-types.


title: Microfocused ultrasound for skin tightening.

abstract: The demand for noninvasive skin tightening procedures is increasing as patients seek safe and effective alternatives to aesthetic surgical procedures of the face, neck, and body. Over the past decade, radiofrequency and infrared laser devices have been popularized owing to their ability to deliver controlled heat to the dermis, stimulate neocollagenesis, and effect modest tissue tightening with minimal recovery. However, these less invasive approaches are historically associated with inferior efficacy so that surgery still remains the treatment of choice to address moderate to severe tissue laxity. Microfocused ultrasound was recently introduced as a novel energy modality for transcutaneous heat delivery that reaches the deeper subdermal connective tissue in tightly focused zones at consistent programmed depths. The goal is to produce a deeper wound healing response at multiple levels with robust collagen remodeling and a more durable clinical response. The Ulthera device (Ulthera, Inc, Meza, AZ), with refined microfocused ultrasound technology, has been adapted specifically for skin tightening and lifting with little recovery or risk of complications since its introduction in 2009. As clinical parameters are studied and optimized, enhanced efficacy and consistency of clinical improvement is expected.


title: Review Towards a science of magic

abstract: It is argued here that cognitive science currently neglects an important source of insight into the human mind: the effects created by magicians. Over the centuries, magicians have learned how to perform acts that are perceived as defying the laws of nature, and that induce a strong sense of wonder. This article argues that the time has come to examine the scientific bases behind such phenomena, and to create a science of magic linked to relevant areas of cognitive science. Concrete examples are taken from three areas of magic: the ability to control attention, to distort perception, and to influence choice. It is shown how such knowledge can help develop new tools and indicate new avenues of research into human perception and cognition.


title: Minimalistic CNN-based ensemble model for gender prediction from face images

abstract: Obtained the record gender recognition performance of 97.31% on the LFW dataset.Used about 10 times fewer training images than the previous state-of-the-art.Only publicly available training images are used.The trained model is optimized in terms of running time and required memory.The trained model is made public for download. It can be also tested via a web demo. Despite being extensively studied in the literature, the problem of gender recognition from face images remains difficult when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose a convolutional neural network ensemble model to improve the state-of-the-art accuracy of gender recognition from face images on one of the most challenging face image datasets today, LFW (Labeled Faces in the Wild). We find that convolutional neural networks need significantly less training data to obtain the state-of-the-art performance than previously proposed methods. Furthermore, our ensemble model is deliberately designed in a way that both its memory requirements and running time are minimized. This allows us to envision a potential usage of the constructed model in embedded devices or in a cloud platform for an intensive use on massive image databases.


title: www.elsevier.com/locate/ssresearch Commentary Best news yet on the six-factor model of well-being q

abstract: This study provides a response to Springer and Hauser’s claim that the Ryff Scales of Psychological Well-Being do not comprise six dimensions. We show that their analyses support the theoryguided six-factor model, although their interpretations of the data reveal a lack of understanding of the construct-oriented approach to personality assessment. We also review evidence from five categories of studies (i.e., factorial validity, psychological correlates, sociodemographic correlates, biological correlates, and intervention studies) that document the distinctiveness of the six dimensions of well-being, including among subscales that are highly correlated. We conclude with an examination of the methodological corrections (negative items, adjacent items, and redundant items) employed by Springer and Hauser, finding all to be problematic, either in terms of scientific rationale or method of implementation, or both. Correlations among latent constructs resulting from these analyses are thus highly questionable.


title: STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences

abstract: This paper presents Space-Time Occupancy Patterns (STOP), a new visual representation for 3D action recognition from sequences of depth maps. In this new representation, space and time axes are divided into multiple segments to define a 4D grid for each depth map sequence. The advantage of STOP is that it preserves spatial and temporal contextual information between space-time cells while being flexible enough to accommodate intra-action variations. Our visual representation is validated with experiments on a public 3D human action dataset. For the challenging cross-subject test, we significantly improved the recognition accuracy from the previously reported 74.7% to 84.8%. Furthermore, we present an automatic segmentation and time alignment method for online recognition of depth sequences.


title: UNIT SELECTION VOICE FOR AMHARIC USING FESTVOX

abstract: In this paper, we try to describe the issues to be considered in developing a concatenative speech synthesizer for Amharic language. The complexity of the syllable structure of the language, the phonetic nature of the language and the result of the perceptual test of the synthesizer will be discussed. Comments and recommendations for further research are included.


title: A two-stage methodology for sequence classification based on sequential pattern mining and optimization

abstract: We present a methodology for sequence classification, which employs sequential pattern mining and optimization, in a two-stage process. In the first stage, a sequence classification model is defined, based on a set of sequential patterns and two sets of weights are introduced, one for the patterns and one for classes. In the second stage, an optimization technique is employed to estimate the weight values and achieve optimal classification accuracy. Extensive evaluation of the methodology is carried out, by varying the number of sequences, the number of patterns and the number of classes and it is compared with similar sequence classification approaches.


title: Unified Camera Tamper Detection Based on Edge and Object Information

abstract: In this paper, a novel camera tamper detection algorithm is proposed to detect three types of tamper attacks: covered, moved and defocused. The edge disappearance rate is defined in order to measure the amount of edge pixels that disappear in the current frame from the background frame while excluding edges in the foreground. Tamper attacks are detected if the difference between the edge disappearance rate and its temporal average is larger than an adaptive threshold reflecting the environmental conditions of the cameras. The performance of the proposed algorithm is evaluated for short video sequences with three types of tamper attacks and for 24-h video sequences without tamper attacks; the algorithm is shown to achieve acceptable levels of detection and false alarm rates for all types of tamper attacks in real environments.


title: A fuzzy K-nearest neighbor algorithm

abstract: Classification of objects is an important area of research and application in a variety of fields. In the presence of full knowledge of the underlying probabilities, Bayes decision theory gives optimal error rates. In those cases where this information is not present, many algorithms make use of distance or similarity among samples as a means of classification. The K-nearest neighbor decision rule has often been used in these pattern recognition problems. One of the difficulties that arises when utilizing this technique is that each of the labeled samples is given equal importance in deciding the class memberships of the pattern to be classified, regardless of their `typicalness'. The theory of fuzzy sets is introduced into the K-nearest neighbor technique to develop a fuzzy version of the algorithm. Three methods of assigning fuzzy memberships to the labeled samples are proposed, and experimental results and comparisons to the crisp version are presented.


title: Experimental Validation of Energy Storage System Management Strategies for a Local DC Distribution System of More Electric Aircraft

abstract: The local DC power distribution system of More Electric Aircraft is one of the cores of the electric power transmission. Because the mechanical source (turbine engine) is unidirectional, the system is not totally reversible. Some storage and dissipation systems are necessary to store or/and dissipate the returned energy to maintain the dc bus voltage. Different power-management strategies are proposed and compared. The development of experimental test benches is necessary to validate these strategies. In this paper, we present a 3-kW test bench, emulating a unidirectional source that supplies a bidirectional load with the help of a hybrid system using supercapacitors and a dissipation system. The experimental validation of the power-management strategies is presented in this paper, too.


title: Knowledge-based approaches in software documentation: A systematic literature review

abstract: Context: Software documents are core artifacts produced and consumed in documentation activity in the software lifecycle. Meanwhile, knowledge-based approaches have been extensively used in software development for decades, however, the software engineering community lacks a comprehensive understanding on how knowledge-based approaches are used in software documentation, especially documentation of software architecture design. Objective: The objective of this work is to explore how knowledge-based approaches are employed in software documentation, their influences to the quality of software documentation, and the costs and benefits of using these approaches. Method: We use a systematic literature review method to identify the primary studies on knowledge-based approaches in software documentation, following a pre-defined review protocol. Results: Sixty studies are finally selected, in which twelve quality attributes of software documents, four cost categories, and nine benefit categories of using knowledge-based approaches in software documentation are identified. Architecture understanding is the top benefit of using knowledge-based approaches in software documentation. The cost of retrieving information from documents is the major concern when using knowledge-based approaches in software documentation. Conclusions: The findings of this review suggest several future research directions that are critical and promising but underexplored in current research and practice: (1) there is a need to use knowledge-based approaches to improve the quality attributes of software documents that receive less attention, especially credibility, conciseness, and unambiguity; (2) using knowledge-based approaches with the knowledge content in software documents which gets less attention in current applications of knowledge-based approaches in software documentation, to further improve the practice of software documentation activity; (3) putting more focus on the application of software documents using the knowledge-based approaches (knowledge reuse, retrieval, reasoning, and sharing) in order to make the most use of software documents; and (4) evaluating the costs and benefits of using knowledge-based approaches in software documentation qualitatively and quantitatively.


title: HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

abstract: In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI [8] with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.


title: Bargaining and Fixed Price Offers: How Online Intermediaries are Changing New Car Transactions

abstract: This paper develops a model of oligopolistic price competition to analyze the impact of online intermediaries such as Autobytel.com on the price setting process in the automobile market. The roles of dealer search costs, the fraction of buyers using the intermediary, the value of the item being sold, and heterogeneity in buyer bargaining abilities are explored. The model provides theoretical insights relevant to the empirical literature addressing the role that intermediaries like Autobytel play in online markets. For example, we present conditions under which the price offered through the intermediary is either higher or lower than offline (bargained) prices.


title: Spider Monkey Optimization algorithm for numerical optimization

abstract: Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.


title: Technology as Moral Proxy: Autonomy and Paternalism by Design

abstract: It is often said that technological artifacts are morally neutral, that they are bereft of morality save for whatever we might say about their designers and users. Designers and users, being people, tend to be considered the proper and exclusive focus of our moral attention in the designer-technology-user trio, while technological artifacts, mere tools, cannot contribute anything morally. Technological neutrality, a common term for describing this perspective, gives rise to trite statements like, guns don?t kill people, people kill people. According to technological neutrality, whatever morality is pinned on the artifact is done so in error. Instead, one must refer to the people surrounding an artifact to get an accurate read on the moral claims that can be associated with its use. Thus, technological neutrality supports a strict kind of delineation between people and things: people can be the subjects of a moral analysis; things cannot.


title: Performance of PMASynRM With Ferrite Magnets for EV/HEV Applications Considering Productivity

abstract: Although motors that use rare-earth permanent magnets typically exhibit high performance, their cost is high, and there are concerns about the stability of the raw material supply. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with a ferrite magnet that does not use rare-earth materials considering productivity. The performance of the proposed PMASynRM is evaluated based on the finite-element method and an experiment using a prototype machine. The analysis results reveal that the proposed PMASynRM has the same power density and an equivalent efficiency as rare-earth permanent-magnet synchronous motors for hybrid electric vehicles (2003 Toyota Prius). Furthermore, some experimental results are presented in order to validate the analytical results. As a result, the proposed PMASynRM was found to achieve high-power-density and high-efficiency performance.


Summarization with Adaptation to Media Attention


Hashtag Recommendation with Topical Attention-Based LSTM


title: 3D pedestrian tracking based on overhead cameras

abstract: This paper proposes a method to track pedestrians in crowded scenes based on the detection of the 3D head position of a person using two overhead cameras. A possible head area in one frame acquired from one of the overhead cameras is determined by evaluating a head area existence probability based on the integral polar mapped image, where a foreground pixel is assigned a probability to belong to the head area. A segment passing through the head top is estimated for each clustered head area. The disparities along each segment are calculated using the synchronized frame from the other overhead camera. The center of the points with the largest disparity on the segment is determined as the head point and its 3D position is computed using triangulation. It is then tracked using common assumptions on motion direction and velocity. This is efficiently done notwithstanding the fact that several segments may exist in a single foreground blob with each segment corresponding to a different person. The approach is tested using a publicly available visual surveillance simulation test bed. The experiments show that the 3D tracking errors are around 5 cm. The method allows for the capture of high quality close-up facial images.


title: Generating Sequences With Recurrent Neural Networks

abstract: This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.


title: Shorthand writing on stylus keyboard

abstract: We propose a method for computer-based speed writing, SHARK (shorthand aided rapid keyboarding), which augments stylus keyboarding with shorthand gesturing. SHARK defines a shorthand symbol for each word according to its movement pattern on an optimized stylus keyboard. The key principles for the SHARK design include high efficiency stemmed from layout optimization, duality of gesturing and stylus tapping, scale and location independent writing, Zipf's law, and skill transfer from tapping to shorthand writing due to pattern consistency. We developed a SHARK system based on a classic handwriting recognition algorithm. A user study demonstrated the feasibility of the SHARK method.


Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms


title: Sonic Cradle: designing for an immersive experience of meditation by connecting respiration to music

abstract: Sonic Cradle is a chamber of complete darkness where users shape a peaceful soundscape using only their respiration. This interactive system was designed to foster a meditative experience by facilitating users' sense of immersion while following a specific attentional pattern characteristic of mindfulness. The goal of Sonic Cradle is twofold: first, to trigger the proven effects of mindfulness on stress, and second, to help teach and demystify the concept of meditation for users' long-term benefit. This paper presents the design phase of the project, starting by theoretically grounding the initial concept. We then discuss 15 co-design sessions which provided informal conceptual validation and led to several concrete design iterations aimed at balancing users' perceived sense of control. The presented approach to designing an interactive stress management system can be considered research through design, as it also resulted in a novel theoretical framework for the psychology of media immersion which has implications for a wide range of research areas.


title: Artificial Intelligence: The Next Twenty-Five Years

abstract: Through this collection of programmatic statements from key figures in the field, we chart the progress of AI and survey current and future directions for AI research and the AI community.


title: Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease

abstract: Chronic obstructive pulmonary disease (COPD) is a global health problem, and since 2001, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) has published its strategy document for the diagnosis and management of COPD. This executive summary presents the main contents of the second 5-year revision of the GOLD document that has implemented some of the vast knowledge about COPD accumulated over the last years. Today, GOLD recommends that spirometry is required for the clinical diagnosis of COPD to avoid misdiagnosis and to ensure proper evaluation of severity of airflow limitation. The document highlights that the assessment of the patient with COPD should always include assessment of (1) symptoms, (2) severity of airflow limitation, (3) history of exacerbations, and (4) comorbidities. The first three points can be used to evaluate level of symptoms and risk of future exacerbations, and this is done in a way that splits patients with COPD into four categories—A, B, C, and D. Nonpharmacologic a...


title: Neural systems for tactual memories

abstract: 1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulte...


title: Induction of late cutaneous reaction by kallikrein injection: comparison with allergic-like late response to compound 48/80.

abstract: Although IgE antibody is able to provoke a late cutaneous allergic response (LCAR), the mechanism of its development is not clear. It seems to involve vasoactive and clotting mediators released from mast cells and basophils. Substances biologically similar to plasma kallikrein (KK), a Hageman factor activator, have been shown to be released by basophils in the course of an IgE-dependent reaction. Because compound 48/80 induces LCAR-like responses in the skin, we compared the ability of tissue KK and compound 48/80 to induce late cutaneous reactions (LCRs). All 40 test subjects showed an immediate wheal and flare reaction (WF) after both KK and 48/80. The WF was followed by an LCR characterized by diffuse edema, pain, and erythema in 36 of 40 subjects with 48/80 and 26 of 40 with KK. The LCRs evoked by the two agents did not differ in their appearance or in their time of development. These reactions increased until the 5 hr mark, began to decrease at the 10 hr mark, and were gone after 24 hr. Histologic study showed edema and a mixed cell infiltration with fibrin deposition. Challenge of the original injection site and a new site after 1 or 2 wk showed a local refractory state lasting 2 wk. Prednisone almost totally suppressed the LCRs induced by both KK and 48/80.


title: Blockchain government - a next form of infrastructure for the twenty-first century

abstract: Today, more than 100 blockchain projects created to transform government systems are being conducted in more than 30 countries. What leads countries rapidly initiate blockchain projects? I argue that it is because blockchain is a technology directly related to social organization; Unlike other technologies, a consensus mechanism form the core of blockchain. Traditionally, consensus is not the domain of machines but rather humankind. However, blockchain operates through a consensus algorithm with human intervention; once that consensus is made, it cannot be modified or forged. Through utilization of Lawrence Lessig’s proposition that “Code is law,” I suggest that blockchain creates “absolute law” that cannot be violated. This characteristic of blockchain makes it possible to implement social technology that can replace existing social apparatuses including bureaucracy. In addition, there are three close similarities between blockchain and bureaucracy. First, both of them are defined by the rules and execute predetermined rules. Second, both of them work as information processing machines for society. Third, both of them work as trust machines for society. Therefore, I posit that it is possible and moreover unavoidable to replace bureaucracy with blockchain systems. In conclusion, I suggest five principles that should be adhered to when we replace bureaucracy with the blockchain system: 1) introducing Blockchain Statute law; 2) transparent disclosure of data and source code; 3) implementing autonomous executing administration; 4) building a governance system based on direct democracy and 5) making Distributed Autonomous Government(DAG).


title: Consumers' online information adoption behavior: Motives and antecedents of electronic word of mouth communications

abstract: Electronic word of mouth influences the consumer buying behavior through the online exchange of customer opinions and experiences about commodities using social networking sites. Reviews or comments posted on the Internet by consumers and experts are based on their personal experiences and the evaluation and expert analysis of a product. The current study explains an overview of key elements of eWOM communication antecedents, addresses eWOM motives to analyze food products purchasing factors associated with consumer engagement. We found certain factors of eWOM that should be considered in decision making according to the model for information adoption. It revealed that consumers need for social interaction, economic incentives, and self-worth reinforcement are the primary drivers of eWOM involvement. However, argument quality was found to be more appealing then eWOM source credibility for food product purchasing. Practitioners should make their strategies to follow the credibility standards, trustworthy vehicles, perceived values, informative and digital channels for the enhancement of eWOM communications. eWOM communication is an emerging area, further research can be focused on the meta-analysis of relative factors, such as responses, moderators, and credibility to eWOM communication. Electronic word-of-mouth is an online exchange of customer opinions and experiences.The current study addresses eWOM readership from food product purchasing perspective.Self-worth reinforcement is significantly effective compared to other involvements.eWOM factors for information adoption should be considered in decision-making process.


title: Down-regulation of IL-7Ralpha expression in human T cells via DNA methylation.

abstract: IL-7 is critical for the development and survival of T cells. Recently, we found two subsets of human CD8+ T cells expressing IL-7Ralpha(high) and IL-7Ralpha(low) with different cell survival responses to IL-7. Although these CD8+ T cell subsets have differential IL-7Ralpha gene expression, the mechanism for this is unknown. DNA methylation is an important gene regulatory mechanism and is associated with the inactivation of gene expression. Thus, we investigated a role for DNA methylation in differentially regulating IL-7Ralpha gene expression in human CD8+ T cells and Jurkat T cells. IL-7Ralpha(high)CD8+ T cells had decreased methylation in the IL-7Ralpha gene promoter compared with IL-7Ralpha(low)CD8+ T cells and Jurkat T cells with low levels of IL-7Ralpha. Treating Jurkat T cells with 5-aza-2'-deoxycytidine, which reduced DNA methylation, increased IL-7Ralpha expression. Plus, the unmethylated IL-7Ralpha gene promoter construct had higher levels of promoter activity than the methylated one as measured by a luciferase reporter assay. These findings suggest that DNA methylation is involved in regulating IL-7Ralpha expression in T cells via affecting IL-7Ralpha gene promoter activity, and that the methylation of this gene promoter could be a potential target for modifying IL-7-mediated T cell development and survival.


title: Personalized Filtering of the Twitter Stream

abstract: With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, filtering Twitter stream based on the generated profiles and delivering them in real-time. Given that users interests and personalization needs change with time, we also discuss how our application can adapt with these changes.


title: A wafer map yield model based on deep learning for wafer productivity enhancement

abstract: In semiconductor manufacturing, evaluating the productivity of wafer maps prior to fabrication for designing an optimal wafer map is one of the most effective solutions for enhancing productivity. However, a yield prediction model is required to accurately evaluate the productivity of wafer maps since the design of a wafer map affects yield. In this paper, we propose a novel yield prediction model based on deep learning algorithms. Our approach exploits spatial relationships among positions of dies, sizes of dies, and die-level yield variations collected from a wafer test. By modeling these spatial features, the accuracy of yield prediction significantly increased. Furthermore, experimental results showed that the proposed yield model and approach help to design a wafer map with higher productivity nearly 13%.


title: ReBNet: Residual Binarized Neural Network

abstract: This paper proposes ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA. Binary neural networks offer an intriguing opportunity for deploying large-scale deep learning models on resource-constrained devices. Binarization reduces the memory footprint and replaces the power-hungry matrix-multiplication with light-weight XnorPopcount operations. However, binary networks suffer from a degraded accuracy compared to their fixed-point counterparts. We show that the state-of-the-art methods for optimizing binary networks accuracy, significantly increase the implementation cost and complexity. To compensate for the degraded accuracy while adhering to the simplicity of binary networks, we devise the first reconfigurable scheme that can adjust the classification accuracy based on the application. Our proposition improves the classification accuracy by representing features with multiple levels of residual binarization. Unlike previous methods, our approach does not exacerbate the area cost of the hardware accelerator. Instead, it provides a tradeoff between throughput and accuracy while the area overhead of multi-level binarization is negligible.


Introducing Topological Field Information into CCG


title: Data Morphing: An Adaptive, Cache-Conscious Storage Technique

abstract: The number of processor cache misses has a critical impact on the performance of DBMSs running on servers with large main-memory configurations. In turn, the cache utilization of database systems is highly dependent on the physical organization of the records in main-memory. A recently proposed storage model, called PAX, was shown to greatly improve the performance of sequential file-scan operations when compared to the commonly implemented N-ary storage model. However, the PAX storage model can also demonstrate poor cache utilization for other common operations, such as index scans. Under a workload of heterogenous database operations, neither the PAX storage model nor the N-ary storage model is optimal. ::: ::: In this paper, we propose a flexible data storage technique called Data Morphing. Using Data Morphing, a cache-efficient attribute layout, called a partition, is first determined through an analysis of the query workload. This partition is then used as a template for storing data in a cache-efficient way. We present two algorithms for computing partitions, and also present a versatile storage model that accommodates the dynamic reorganization of the attributes in a file. Finally, we experimentally demonstrate that the Data Morphing technique provides a significant performance improvement over both the traditional N-ary storage model and the PAX model.


title: Automatic Filling of Hidden Web Forms: A Survey

abstract: A significant part of the information available on the Web is stored in online databases which compose what is known as Hidden Web or Deep Web. In order to access information from the Hidden Web, one must fill an HTML form that is submitted as a query to the underlying database. In recent years, many works have focused on how to automate the process of form filling by creating methods for choosing values to fill the fields in the forms. This is a challenging task since forms may contain fields for which there are no predefined values to choose from. This article presents a survey of methods for Web Form Filling, analyzing the existing solutions with respect to the type of forms that they handle and the filling strategy adopted. We provide a comparative analysis of 15 key works in this area and discuss directions for future research.


title: Measurement of Quality of Life III. From the IQOL Theory to the Global, Generic SEQOL Questionnaire

abstract: The Danish Quality of Life Survey is based on the philosophy of life known as the integrative quality-of-life (IQOL) theory. It consists of eight different quality-of-life concepts, ranging from the superficially subjective via the deeply existential to the superficially objective (well being, satisfaction with life, happiness, meaning in life, biological order, realizing life potential, fulfillment of needs, and objective factors [ability of functioning and fulfilling societal norms]). This paper presents the work underlying the formulation of the theories of a good life and how these theories came to be expressed in a comprehensive, multidimensional, generic questionnaire for the evaluation of the global quality of life — SEQOL (self-evaluation of quality of life) — presented in full length in this paper. The instruments and theories on which the Quality of Life Survey was based are constantly being updated. It is an on-going process due to aspects such as human development, language, and culture. We arrived at eight rating scales for the quality of life that, guided by the IQOL theory, were combined into a global and generic quality-of-life rating scale. This was simplified to the validated QOL5 with only five questions, made for use in clinical databases. Unfortunately, the depth of human existence is to some extent lost in QOL5. We continue to aim towards greater simplicity, precision, and depth in the questions in order to explore the depths of human existence. We have not yet found a final form that enables us to fully rate the quality of life in practice. We hope that the several hundred questions we found necessary to adequately implement the theories of the Quality of Life Survey can be replaced by far fewer; ideally, only eight questions representing the eight component theories. These eight ideal questions have not yet been evaluated, and therefore they should not form the basis of a survey. However, the perspective is clear. If eight simple questions can accurately rate the quality of life as well as its depth, we have found an instrument of immense practical scope.


title: Virtual reality exposure therapy for Vietnam veterans with posttraumatic stress disorder

abstract: Background: Virtual reality (VR) integrates real-time computer graphics, body-tracking devices, visual displays, and other sensory input devices to immerse a participant in a computer-generated virtual environment that changes in a natural way with head and body motion. VR exposure (VRE) is proposed as an alternative to typical imaginal exposure treatment for Vietnam combat veterans with posttraumatic stress disorder (PTSD). Method: This report presents the results of an open clinical trial using VRE to treat Vietnam combat veterans who have DSM-IV PTSD. In 8 to 16 sessions, 10 male patients were exposed to 2 virtual environments: a virtual Huey helicopter flying over a virtual Vietnam and a clearing surrounded by jungle. Results: Clinician-rated PTSD symptoms as measured by the Clinician Administered PTSD Scale, the primary outcome measure, at 6-month follow-up indicated an overall statistically significant reduction from baseline (p = .0021) in symptoms associated with specific reported traumatic experiences. All 8 participants interviewed at the 6-month follow-up reported reductions in PTSD symptoms ranging from 15% to 67%. Significant decreases were seen in all 3 symptom clusters (p <.02). Patient self-reported intrusion symptoms as measured by the Impact of Event Scale were significantly lower (p <.05) at 3 months than at baseline but not at 6 months, although there was a clear trend toward fewer intrusive thoughts and somewhat less avoidance. Conclusion: Virtual reality exposure therapy holds promise for treating PTSD in Vietnam veterans.


title: Energy Harvesting and Wireless Transfer in Sensor Network Applications: Concepts and Experiences

abstract: Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems’ lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite, yet intermittent, energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about.


title: The Development of the C Language

abstract: The C programming language was devised in the early 1970s as a system implementation language for the nascent Unix operating system. Derived from the typeless language BCPL, it evolved a type structure; created on a tiny machine as a tool to improve a meager programming environment, it has become one of the dominant languages of today. This paper studies its evolution.


title: Using Lossless Data Compression in Data Storage Systems: Not for Saving Space

abstract: Lossless data compression for data storage has become less popular as mass data storage systems are becoming increasingly cheap. This leaves many files stored on mass data storage media uncompressed although they are losslessly compressible. This paper proposes to exploit the lossless compressibility of those files to improve the underlying storage system performance metrics such as energy efficiency and access speed, other than saving storage space as in conventional practice. The key idea is to apply runtime lossless data compression to enable an opportunistic use of a stronger error correction code (ECC) with more coding redundancy in data storage systems, and trade such opportunistic extra error correction capability to improve other system performance metrics in the runtime. Since data storage is typically realized in the unit of equal-sized sectors (e.g., 512 B or 4 KB user data per sector), we only apply this strategy to each individual sector independently in order to be completely transparent to the firmware, operating systems, and users. Using low-density parity check (LDPC) code as ECC in storage systems, this paper quantitatively studies the effectiveness of this design strategy in both hard disk drives and NAND flash memories. For hard disk drives, we use this design strategy to reduce average hard disk drive read channel signal processing energy consumption, and results show that up to 38 percent read channel energy saving can be achieved. For NAND flash memories, we use this design strategy to improve average NAND flash memory write speed, and results show that up to 36 percent write speed improvement can be achieved for 2 bits/cell NAND flash memories.


title: Multitasking: Productivity Effects and Gender Differences

abstract: We examine how multitasking affects performance and check whether women are indeed better at multitasking. Subjects in our experiment perform two different tasks according to three treatments: one where they perform the tasks sequentially, one where they are forced to multitask, and one where they can freely organize their work. Subjects who are forced to multitask perform significantly worse than those forced to work sequentially. Surprisingly, subjects who can freely organize their own schedule also perform significantly worse. Finally, our results do not support the stereotype that women are better at multitasking. Women suffer as much as men when forced to multitask and are actually less inclined to multitask when being free to choose. See also the article 'Multitasking' in Experimental Economics , 15(4), 641-655. 10.1007/s10683-012-9318-8


title: Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

abstract: Due to a major shortage of nurses in the U.S., future healthcare service robots are expected to be used in tasks involving direct interaction with patients. Consequently, there is a need to design nursing robots with the capability to detect and respond to patient emotional states and to facilitate positive experiences in healthcare. The objective of this study was to develop a new computational algorithm for accurate patient emotional state classification in interaction with nursing robots during medical service. A simulated medicine delivery experiment was conducted at two nursing homes using a robot with different human-like features. Physiological signals, including heart rate (HR) and galvanic skin response (GSR), as well as subjective ratings of valence (happy-unhappy) and arousal (excited-bored) were collected on elderly residents. A three-stage emotional state classification algorithm was applied to these data, including: (1) physiological feature extraction; (2) statistical-based feature selection; and (3) a machine-learning model of emotional states. A pre-processed HR signal was used. GSR signals were nonstationary and noisy and were further processed using wavelet analysis. A set of wavelet coefficients, representing GSR features, was used as a basis for current emotional state classification. Arousal and valence were significantly explained by statistical features of the HR signal and GSR wavelet features. Wavelet-based de-noising of GSR signals led to an increase in the percentage of correct classifications of emotional states and clearer relationships among the physiological response and arousal and valence. The new algorithm may serve as an effective method for future service robot real-time detection of patient emotional states and behavior adaptation to promote positive healthcare experiences.


title: Security analysis and implementation of web-based telemedicine services with a four-tier architecture

abstract: Security of telemedicine applications is not often given adequate importance by the developers and healthcare administrators primarily to reduce cost. Though some security safeguards are employed by these applications to comply with existing medical data security and privacy regulations, these are not adequate in todaypsilas context. Moreover, in a Web-based application environment not only the data but also the application itself is vulnerable to attackers. Keeping these concerns in mind, we present the design of a Web-based, four-tier telemedicine system named iMedik which is accessible over desktops as well as handheld devices. We have illustrated how the proposed system differs from existing three-tier Web applications. The compliance status of the application with HIPAA Security Guidelines has also been noted. The security measures described in our approach look into the four-tier architecture from an attackerpsilas viewpoint and present a simple road map for developing secure e-health application with anywhere, anytime availability.


title: Hearing and saying The functional neuro-anatomy of auditory word processing

abstract: Summary The neural systems involved in hearing and repeating single words were investigated in a series of experiments using PET. Neuropsychological and psycholinguistic studies implicate the involvement of posterior and anterior left perisylvian regions (Wernicke's and Broca's areas). Although previous functional neuroimaging studies have consistently shown activation of Wernicke's area, there has been only variable implication of Broca's area. This study demonstrates that Broca's area is involved in both auditory word perception and repetition but activation is dependent on task (greater during repetition than hearing) and stimulus presentation (greater when hearing words at a slow rate). The peak of


title: A survey of multicore processors

abstract: General-purpose multicore processors are being accepted in all segments of the industry, including signal processing and embedded space, as the need for more performance and general-purpose programmability has grown. Parallel processing increases performance by adding more parallel resources while maintaining manageable power characteristics. The implementations of multicore processors are numerous and diverse. Designs range from conventional multiprocessor machines to designs that consist of a "sea" of programmable arithmetic logic units (ALUs). In this article, we cover some of the attributes common to all multicore processor implementations and illustrate these attributes with current and future commercial multicore designs. The characteristics we focus on are application domain, power/performance, processing elements, memory system, and accelerators/integrated peripherals.


title: A practical algorithm for automatic chessboard corner detection

abstract: Chessboard corner detection is a fundamental work of the popular chessboard pattern-based camera calibration technique. In this paper, a fast and robust algorithm for chessboard corner detection is presented. In our method, an initial corner set is obtained with an improved Hessian corner detector. And then, a novel strategy which takes both textural and geometrical characteristics of a chessboard into consideration is employed to eliminate fake corners in the initial corner set. The proposed algorithm only requires a user-input of the total number of chessboard inner corners, while all the other parameters can be adaptively calculated with a statistical approach. Experimental results on two public data sets demonstrate that the proposed method can outperform the most commonly used OpenCV method in terms of both detection rate and computational efficiency.


title: Scalable Inference for Nested Chinese Restaurant Process Topic Models

abstract: Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical structure is automatically determined by the data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of nCRP topic models. However, hLDA has only been evaluated at small scale, because the existing collapsed Gibbs sampling and instantiated weight variational inference algorithms either are not scalable or sacrifice inference quality with mean-field assumptions. Moreover, an efficient distributed implementation of the data structures, such as dynamically growing count matrices and trees, is challenging. ::: In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS) algorithm, which combines the advantages of collapsed and instantiated weight algorithms to achieve good scalability as well as high model quality. An initialization strategy is presented to further improve the model quality. Finally, we propose an efficient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy. ::: Empirical studies show that our algorithm is 111 times more efficient than the previous open-source implementation for hLDA, with comparable or even better model quality. Our distributed implementation can extract 1,722 topics from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders of magnitude larger than the previous largest corpus, with 50 machines in 7 hours.


title: Confronting machine-learning with neuroscience for neuromorphic architectures design

abstract: Artificial neural networks are experiencing today an unprecedented interest thanks to two main changes: the explosion of open data that is necessary for their training, and the increasing computing power of today’s computers that makes the training part possible in a reasonable time. The recent results of deep neural networks on image classification has given neural networks the leading role in machine learning algorithms and artificial intelligence research. However, most applications such as smart devices or autonomous vehicles require an embedded implementation of neural networks. Their implementation in CPU/GPU remains too expensive, mostly in energy consumption, due to the non-adaptation of the hardware to the computation model, which becomes a limit to their use. It is therefore necessary to design neuromorphic architectures, i.e. hardware accelerators that fit to the parallel and distributed computation paradigm of neural networks for reducing their hardware cost implementation. We mainly focus on the optimization of energy consumption to enable integration in embedded systems. For this purpose, we implement two models of artificial neural networks coming from two different scientific domains: the multi- layer perceptron derived from machine learning and the spiking neural network inspired from neuroscience. We compare the performances of both approaches in terms of accuracy and hardware cost to find out the most attractive architecture for the design of embedded artificial intelligence.


title: Inequality and Violent Crime

abstract: Abstract We investigate the robustness and causality of the link between income inequality and violent crime across countries. First, we study the correlation between the Gini index and homicide and robbery rates within and between countries. Second, we examine the partial correlation by considering other crime determinants. Third, we control for the endogeneity of inequality by isolating its exogenous impact on these crime rates. Fourth, we control for measurement error in crime rates by modeling it as both unobserved country effects and random noise. Finally, we examine the robustness of this partial correlation to alternative measures of inequality. The panel data consist of nonoverlapping 5‐year averages for 39 countries during 1965–95 for homicides and 37 countries during 1970–94 for robberies. Crime rates and inequality are positively correlated within countries and, particularly, between countries, and this correlation reflects causation from inequality to crime rates, even after controlling for ot...


title: A comparison of soft-switched DC-to-DC converters for electrolyser application

abstract: An Electrolyser is part of a renewable energy system (RES) and generates hydrogen from water electrolysis that is used in fuel cells. A dc-to-dc converter is required to couple the Electrolyser to the system DC bus. This paper presents the design of three soft-switched high-frequency transformer isolated dc-to-dc converters for this application based on the given specifications. It is shown that LCL-type series resonant converter with capacitive output filter is suitable for this application. Detailed theoretical and simulation results are presented. Due to the wide variation in input voltage and load current, no converter can maintain zero-voltage switching for the complete operating range. Therefore, a two stage approach (ZVT boost converter followed by LCL SRC with capacitive output filter) will be studied in the future.


title: Analysis on robust H∞ performance and stability for linear systems with interval time-varying state delays via some new augmented Lyapunov-Krasovskii functional

abstract: In this paper, the problem of H"~ performance and stability analysis for linear systems with interval time-varying delays is considered. First, by constructing a newly augmented Lyapunov-Krasovskii functional which has not been proposed yet, an improved H"~ performance criterion with the framework of linear matrix inequalities (LMIs) is introduced. Next, the result and method are extended to the problem of a delay-dependent stability criterion. Finally, numerical examples are given to show the superiority of proposed method.


title: A Portable SDR Non-Orthogonal Multiple Access Testbed for 5G Networks

abstract: Non-orthogonal multiple access (NOMA) is envisioned to be one of the promising radio access techniques for the fifth generation (5G) mobile networks. In this paper, a portable NOMA testbed based on software defined radio (SDR) is developed by transporting our NOMA system to mini personal computers (PCs). Moreover, the NOMA testbed has been enhanced from 5 MHz bandwidth to 10 MHz bandwidth. As the computation complexity grows higher with the increase of system bandwidth, the portable NOMA testbed is not competent to fully demonstrate the performance of our NOMA system due to the limitation of mini PC. For a better understanding of the NOMA testbed, the system architecture and scenario are introduced in brief. Then, the implementation of NOMA transceiver and the protocol stack are described separately. Finally, a series of experiments are carried out to evaluate its performance loss compared with the original NOMA system based on desktops. The experimental results indicate that the performance of processors may be a short slab for the development of portable SDR-based testbeds towards 5G networks.


title: FRAUD DETECTION IN MOBILE COMMUNICATIONS NETWORKS USING USER PROFILING AND CLASSIFICATION TECHNIQUES

abstract: Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. The intentions of mobile phone users cannot be well observed except through the call data. The call data is used in describing behavioural patterns of users. Neural networks and probabilistic models are employed in learning these usage patterns from call data by detecting changes in established usage patterns or to recognize typical usage patterns of fraud. The methods are shown to be effective in detecting fraudulent behaviour by empirically testing the methods with data from real mobile communications networks. Keywords: Call data, fraud detection, neural networks, probabilistic models, user profiling


title: A SPAD-based pixel linear array for high-speed time-gated fluorescence lifetime imaging

abstract: A monolithic 64-pixel linear array for Fluorescence Lifetime Imaging applications is presented. Each pixel includes four actively quenched Single Photon Avalanche Diodes, four gated 8-bit counters and is capable of measuring single and double exponential decays. The array has a 34% fill factor, a maximum throughput rate of 320 Mbps, and has been tested up to 40 MHz laser repetition rate. Preliminary fluorescence measurements have been performed obtaining a 6% lifetime precision in 660 µs accumulation time and a very good uniformity among the pixels.