title: Toward personalized care management of patients at risk: the diabetes case study

abstract: Chronic diseases constitute the leading cause of mortality in the western world, have a major impact on the patients' quality of life, and comprise the bulk of healthcare costs. Nowadays, healthcare data management systems integrate large amounts of medical information on patients, including diagnoses, medical procedures, lab test results, and more. Sophisticated analysis methods are needed for utilizing these data to assist in patient management and to enhance treatment quality at reduced costs. In this study, we take a first step towards better disease management of diabetic patients by applying state-of-the art methods to anticipate the patient's future health condition and to identify patients at high risk. Two relevant outcome measures are explored: the need for emergency care services and the probability of the treatment producing a sub-optimal result, as defined by domain experts. By identifying the high-risk patients our prediction system can be used by healthcare providers to prepare both financially and logistically for the patient needs. To demonstrate a potential downstream application for the identified high-risk patients, we explore the association between the physician treating these patients and the treatment outcome, and propose a system that can assist healthcare providers in optimizing the match between a patient and a physician. Our work formulates the problem and examines the performance of several learning models on data from several thousands of patients. We further describe a pilot system built on the results of this analysis. We show that the risk for the two considered outcomes can be evaluated from patients' characteristics and that features of the patient-physician match improve the prediction accuracy for the treatment's success. These results suggest that personalized medicine can be valuable for high risk patients and raise interesting questions for future improvements.


title: Why Facebook Reactions are good news for evaluating social media campaigns

abstract: Evaluating any aspect of a marketing communications campaign is challenging, especially when the campaign includes social media. In the past, social media and paid social activity such as Facebook have been measured with metrics such as clicks, impressions, likes, comments and fans. Although these measurements provide an insight into the numbers of viewers exposed to content and some indication of how well the content is ‘liked’, they are less efficient at providing an understanding about how consumers engage with the content. The trial of Facebook Reactions provides an opportunity for marketers to measure such emotional engagement.


title: Hand gesture recognition using view projection from point cloud

abstract: In this paper we propose a multi-view method to recognize hand gestures using point cloud. The main idea of this paper is to project point cloud into view images and hand gestures are described by extracting and fusing features in view images. The conversion of feature space increases the inner-class similarity and meanwhile reduces the inter-class similarity. The features of view images are extracted in parallel so the scale of each feature extractor can be reduced to converge easily. In our method we perform a refined hand segmentation to segment hand form background firstly. Then the segmented hand point cloud is projected into different view planes to form view images. Next we use convolutional neural networks as feature extractors to extract features of view images. The extracted view image features are fused to form the features of hand gestures. Finally a SVM is trained for hand gesture recognition. The experimental results show that our multiview method achieves higher recognition rate and more robust to the challenging rotation changes especially out-plane rotations.


title: HACCP in the chocolate industry

abstract: Abstract Over the last 20 years chocolate products have been at the origin of several salmonellosis outbreaks involving mainly children. One of the main characteristics are the very low levels of Salmonella normally found in such contaminated products. Due to the low water activity, the pathogens are not able to grow but these conditions provide a good protection leading to increased heat resistance and survival. It is therefore necessary to take preventive measures during processing to avoid recontamination of the product after the roasting step which represents the only barrier for Salmonella . The particular aspects of the HACCP system for chocolate are described and outlined.


title: Towards Enhanced Usability of Natural Language Interfaces to Knowledge Bases

abstract: Many Natural Language Interfaces (NLIs) to knowledge bases have been developed in order to provide easy access to structured data for casual users. However, those that have reasonable performance are domain-specific and tend to require customisation for each new domain, which, from a developer’s perspective, makes them expensive to maintain and unattractive for practical applications spanning different domains. This paper explores how the performance of existing NLI systems to knowledge bases can be improved without the extra cost of extensive customisation. Additionally, usability of NLIs to knowledge bases is explored from two aspects: that of the developer who is customising the system and that of the end-user who is querying it. We discuss existing methods for increasing the usability of NLI systems and their impact on the overall retrieval performance.


title: Learning Actionable Representations from Visual Observations

abstract: In this work we explore a new approach for robots to teach themselves about the world simply by observing it. In particular we investigate the effectiveness of learning task-agnostic representations for continuous control tasks. We extend Time-Contrastive Networks (TCN) that learn from visual observations by embedding multiple frames jointly in the embedding space as opposed to a single frame. We show that by doing so, we are now able to encode both position and velocity attributes significantly more accurately. We test the usefulness of this self-supervised approach in a reinforcement learning setting. We show that the representations learned by agents observing themselves take random actions, or other agents perform tasks successfully, can enable the learning of continuous control policies using algorithms like Proximal Policy Optimization (PPO) using only the learned embeddings as input. We also demonstrate significant improvements on the real-world Pouring dataset with a relative error reduction of 39.4% for motion attributes and 11.1% for static attributes compared to the single-frame baseline. Video results are available at https://sites.google.com/view/actionablerepresentations .


A female with the 48,XXXX karyotype.


Large dataset for keyphrases extraction


title: Qualitative research methods in mental health.

abstract: As the evidence base for the study of mental health problems develops, there is a need for increasingly rigorous and systematic research methodologies. Complex questions require complex methodological approaches. Recognising this, the MRC guidelines for developing and testing complex interventions place qualitative methods as integral to each stage of intervention development and implementation. However, mental health research has lagged behind many other healthcare specialities in using qualitative methods within its evidence base. Rigour in qualitative research raises many similar issues to quantitative research and also some additional challenges. This article examines the role of qualitative methods within mental heath research, describes key methodological and analytical approaches and offers guidance on how to differentiate between poor and good quality qualitative research.


title: Interpretable Machine Learning Models for the Digital Clock Drawing Test

abstract: The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possible the analysis of both the drawing process and final product. We developed methodology to analyze pen stroke data from these drawings, and computed a large collection of features which were then analyzed with a variety of machine learning techniques. The resulting scoring systems were designed to be more accurate than the systems currently used by clinicians, but just as interpretable and easy to use. The systems also allow us to quantify the tradeoff between accuracy and interpretability. We created automated versions of the CDT scoring systems currently used by clinicians, allowing us to benchmark our models, which indicated that our machine learning models substantially outperformed the existing scoring systems.


title: Effects of Internal Control Systems on Financial Performance of Sugarcane outgrowercompanies inKenya

abstract: This study was designed to investigate the effect of internal control systems on the financial performance of sugarcane outgrower companies in Kenya.The study adopted a descriptive correlational survey design. All the sugarcane outgrower companies were studied. Both the primary and secondary data was collected. Primary data was collected from the key informants from all the nine outgrower companies in Kenya using questionnaires. Secondary data was extracted from annual reports, publications and document analysis. The key informant's method was used, hence, all the Finance Managers and heads of internal audit for every outgrower company were selected to take part in the study. The data collection instruments were administered to all the nine sugarcane outgrower institutions. The data was analysed using statistical package for social scientists (SPSS) computer software version 19.0 to generate cumulative frequencies and percentages. The study found a positive significant effect of internal control system on the financial performance (R = 0.682), and R square 0.428 thus internal control components accounts for 42,8% variance in performance The findings are expected to be of value to the sugarcane outgrower stakeholders and form a basis for improving financial performance of sugarcane outgrower companies.


title: MERC: Match at Edge and Route intra--Cluster for Content-based Publish/Subscribe Systems

abstract: Despite suffering from inefficiency and flexibility limitations, the filter-based routing (FBR) algorithm is widely used in content-based publish/subscribe (pub/sub) systems. To address its limitations, we propose a dynamic destination-based routing algorithm called D-DBR, which decomposes pub/sub into two independent parts: Content-based matching and destination-based multicasting. D-DBR exhibits low event matching cost and high efficiency, flexibility, and robustness for event routing in small scale overlays. To boost scalability, we further complement D-DBR with a new routing algorithm called MERC. MERC divides the overlay into interconnected clusters and applies content-based and destination-based mechanisms to route events inter- and intra-cluster, respectively. We implemented all algorithms in the PADRES pub/sub system. Experimental results show that our algorithms outperform FBR in terms of improving event dissemination throughput by up to 700% and reducing the end-to-end latency by up to 55%.


title: Analysis of a low-dimensional bottleneck neural network representation of speech for modelling speech dynamics.

abstract: This paper presents an analysis of a low-dimensional representation of speech for modelling speech dynamics, extracted using bottleneck neural networks. The input to the neural network is a set of spectral feature vectors. We explore the effect of various designs and training of the network, such as varying the size of context in the input layer, size of the bottleneck and other hidden layers, and using input reconstruction or phone posteriors as targets. Experiments are performed on TIMIT. The bottleneck features are employed in a conventional HMMbased phoneme recognition system, with recognition accuracy of 70.6% on the core test achieved using only 9-dimensional features. We also analyse how the bottleneck features fit the assumptions of dynamic models of speech. Specifically, we employ the continuous-state hidden Markov model (CS-HMM), which considers speech as a sequence of dwell and transition regions. We demonstrate that the bottleneck features preserve well the trajectory continuity over time and can provide a suitable representation for CS-HMM.


title: Gravity compensation and compliance based force control for auxiliarily easiness in manipulating robot arm

abstract: The objective of this paper is to present the gravity compensation and compliance based force control for auxiliarily easiness in manipulating robot arm. Haptical application of the safety-priority robot arm technique which interacts with people must reduce the gear ratio and design necessary algorithm which can provide auxiliarily easiness in moving the robot arm especially during the teach and learning mode. In this study, we discuss the effects of two aspects and propose a control algorithm to improve efficiency of carrying heavy item. Firstly, the gear ratio of motor is bounded so that robot can be more flexibly compliant while user take grip on it. However, robot manipulator control algorithms will suffer greater gravity downward pulling issue due to low gear ratio. To solve this problem of gravity compensation, we propose a method that based on the concept of vector projection to calculate a general solution which can construct a gravity model of multi-DOF robot arm. Furthermore, we define a virtual mode that is proposed to compensate the deficiency of inertia's physical phenomenon. Secondly, we propose an approach which we call it force counterbalance control (FCC) that not only balances external load variation in addition to robot weight itself, but also keeps the property of dexterous easiness in manipulating the multi DOF robot arm. The FCC algorithm can be applied on several applications such as carrying heavy item or being auxiliarily easinese in manipulating robot arm. Our experimental result demonstrates the benefit of the proposed effect.


title: Enabling Flexible Network FPGA Clusters in a Heterogeneous Cloud Data Center

abstract: We present a framework for creating network FPGA clusters in a heterogeneous cloud data center. The FPGA clusters are created using a logical kernel description describing how a group of FPGA kernels are to be connected (independent of which FPGA these kernels are on), and an FPGA mapping file. The kernels within a cluster can be replicated with simple directives within this framework. The FPGAs can communicate to any other network device in the data center, including CPUs, GPUs, and IoT devices (such as sensors). This heterogeneous cloud manages these devices with the use of OpenStack. We observe that our infrastructure is limited due to the physical infrastructure such as the 1~Gb Ethernet connection. Our framework however can be ported to other physical infrastructures. We tested our infrastructure with a database acceleration application. This application was replicated six times across three FPGAs within our cluster and we observed a throughput increase of six times as this scaled linearly. Our framework generates the OpenStack calls needed to reserve the compute devices, creates the network connections (and retrieve MAC addresses), generate the bitstreams, programs the devices, and configure the devices with the appropriate MAC addresses, creating a ready-to-use network device that can interact with any other network device in the data center.


title: Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language

abstract: Whissell's Dictionary of Affect in Language, originally designed to quantify the Pleasantness and Activation of specifically emotional words, was revised to increase its applicability to samples of natural language. Word selection for the revision privileged natural language, and the matching rate of the Dictionary, which includes 8,742 words, was increased to 90%. Dictionary scores were available for 9 of every 10 words in most language samples. A third rated dimension (Imagery) was added, and normative scores were obtained for natural English. Evidence supports the reliability and validity of ratings. Two sample applications to very disparate instances of natural language are described. The revised Dictionary, which contains ratings for words characteristic of natural language, is a portable tool that can be applied in almost any situation involving language.


title: Recognizing Decentered Intersubjectivity in Social Experience

abstract: This article will argue that a decentering process occurs in the intersubjective connections between individuals, and that through the acknowledgement of this process researchers can better understand the potential for distortions to occur in the development of self-understanding. The concept of decentered intersubjectivity discussed in this article is the result of prior research on happiness and contentment, yet a range of emotions such as trust, guilt, shame, and disappointment could also be considered. In each case, the concept of a decentered intersubjectivity can connect the personal and emotional experiences of the individual, with a shared social experience that is deeply sociological in nature.


title: Page Fault Support for Network Controllers

abstract: Direct network I/O allows network controllers (NICs) to expose multiple instances of themselves, to be used by untrusted software without a trusted intermediary. Direct I/O thus frees researchers from legacy software, fueling studies that innovate in multitenant setups. Such studies, however, overwhelmingly ignore one serious problem: direct memory accesses (DMAs) of NICs disallow page faults, forcing systems to either pin entire address spaces to physical memory and thereby hinder memory utilization, or resort to APIs that pin/unpin memory buffers before/after they are DMAed, which complicates the programming model and hampers performance. We solve this problem by designing and implementing page fault support for InfiniBand and Ethernet NICs. A main challenge we tackle---unique to NICs---is handling receive DMAs that trigger page faults, leaving the NIC without memory to store the incoming data. We demonstrate that our solution provides all the benefits associated with "regular" virtual memory, notably (1) a simpler programming model that rids users from the need to pin, and (2) the ability to employ all the canonical memory optimizations, such as memory overcommitment and demand-paging based on actual use. We show that, as a result, benchmark performance improves by up to 1.9x.


title: Microstrip bandpass filter using degenerate modes of a novel meander loop resonator

abstract: A novel type of dual-mode microstrip bandpass filter using degenerate modes of a meander loop resonator has been developed for miniaturization of high selectivity narrow-band microwave bandpass filters. A filter of this type having a 2.5% bandwidth at 1.58 GHz was designed and fabricated. The measured filter performance is presented. >


title: REVIEW Five Rules for the Evolution of Cooperation

abstract: Cooperation is needed for evolution to construct new levels of organization. Genomes, cells, multicellular organisms, social insects, and human society are all based on cooperation. Cooperation means that selfish replicators forgo some of their reproductive potential to help one another. But natural selection implies competition and therefore opposes cooperation unless a specific mechanism is at work. Here I discuss five mechanisms for the evolution of cooperation: kin selection, direct reciprocity, indirect reciprocity, network reciprocity, and group selection. For each mechanism, a simple rule is derived that specifies whether natural selection can lead to cooperation.


title: A Spectral Learning Approach to Knowledge Tracing

abstract: Knowledge Tracing (BKT) is a common way of determining student knowledge of skills in adaptive educational systems and cognitive tutors. The basic BKT is a Hidden Markov Model (HMM) that models student knowledge based on five parameters: prior, learn rate, forget, guess, and slip. Expectation Maximization (EM) is often used to learn these parameters from training data. However, EM is a time-consuming process, and is prone to converging to erroneous, implausible local optima depending on the initial values of the BKT parameters. In this paper we address these two problems by using spectral learning to learn a Predictive State Representation (PSR) that represents the BKT HMM. We then use a heuristic to extract the BKT parameters from the learned PSR using basic matrix operations. The spectral learning method is based on an approximate factorization of the estimated covariance of windows from students' sequences of correct and incorrect responses; it is fast, local-optimum-free, and statistically consistent. In the past few years, spectral techniques have been used on real-world problems involving latent variables in dynamical systems, computer vision, and natural language processing. Our results suggest that the parameters learned by the spectral algorithm can replace the parameters learned by EM; the results of our study show that the spectral algorithm can improve knowledge tracing parameter- fitting time significantly while maintaining the same prediction accuracy, or help to improve accuracy while still keeping parameter-fitting time equivalent to EM.


title: CAOPLE: A Programming Language for Microservices SaaS

abstract: The microservices architecture is widely regarded as a promising approach to service-oriented systems. However, developing applications in the microservices architecture presents three main challenges: (a) how to program systems that consists of a large number of services running in parallel and distributed over a cluster of computers, (b) how to reduce the communication overhead caused by executing a large number of small services, (c) how to support the flexible deployment of services to a network to achieve system load balance. This paper presents a programming language called CAOPLE and reports the implementation of the language on a virtual machine called CAVM-2. The paper demonstrates how this approach meets these challenges.


title: Historical Factors, Discrimination and Oral Health among Aboriginal Australians.

abstract: Discrimination is a very real facet of Australian Aboriginal and Torres Strait Islander (ATSI) life. Paradies has detailed the strong links between racism and chronic stress and the influence this may have on general health, confounding the pre-supposed notion that ATSI populations are more genetically predisposed to chronic diseases. For example a genetic predisposition promoting central adipose storage in populations with recent (in evolutionary terms) changes to hunter-gatherer dietary patterns is thought to contribute to the higher rates of diabetes seen in ATSI and other Native populations. This relationship, however, is far from causal in any straight-forward way. In support of the work by Paradies, research from the U.S. also shows that racism, both explicit and subtle, contributes to chronic disease and suffering among ethnic minorities. While the exploration of the perceived or self-reported racial discrimination is recent, this concept has increasing evidence to support its relationship to poor health outcomes.


title: IXA pipeline: Efficient and Ready to Use Multilingual NLP tools

abstract: IXA pipeline is a modular set of Natural Language Processing tools (or pipes) which provide easy access to NLP technology. It offers robust and efficient linguistic annotation to both researchers and non-NLP experts with the aim of lowering the barriers of using NLP technology either for research purposes or for small industrial developers and SMEs. IXA pipeline can be used “as is” or exploit its modularity to pick and change different components. Given its open-source nature, it can also be modified and extended for it to work with other languages. This paper describes the general data-centric architecture of IXA pipeline and presents competitive results in several NLP annotations for English and Spanish.


title: Selection of Snubbers and Clamps to Optimize the Design of Transistor Switching Converters

abstract: In power transistor switching circuits, shunt snubbers (dv/dt limiting capacitors) are often used to reduce the turn-off switching loss or prevent reverse-biased second breakdown. Similarly, series snubbers (di/dt limiting inductors) are used to reduce the turn-on switching loss or prevent forward-biased second breakdown. In both cases energy is stored in the reactive element of the snubber and is dissipated during its discharge. If the circuit includes a transformer, a voltage clamp across the transistor may be needed to absorb the energy trapped in the leakage inductance. The action of these typical snubber and clamp arrangements is analyzed and applied to optimize the design of a flyback converter used as a battery charger.


title: Cross-examining dissociative identity disorder: Neuroimaging and etiology on trial

abstract: Dissociative identity disorder (DID) is probably the most disputed of psychiatric diagnoses and of psychological forensic evaluations in the legal arena. The iatrogenic proponents assert that DID phenomena originate from psychotherapeutic treatment while traumagenic proponents state that DID develops after severe and chronic childhood trauma. In addition, DID that is simulated with malingering intentions, but not stimulated by psychotherapeutic treatment, may be called pseudogenic. With DID gaining more interest among the general public it can be expected that the number of pseudogenic cases will grow and the need to distinguish between traumagenic, iatrogenic or pseudogenic DID will increase accordingly. This paper discusses whether brain imaging studies can inform the judiciary and/or distinguish the etiology of DID.


title: [Dioxin emissions and soft-tissue sarcoma: results of a population-based case-control study].

abstract: BACKGROUND ::: In 1998, the French Ministry of Environment revealed that of 71 French municipal solid waste incinerators processing more than 6 metric tons of material per hour, dioxin emission from 15 of them was above the 10 ng international toxic equivalency factor/m3 (including Besançon, emitting 16.3 ng international toxic equivalency factor/m3) which is substantially higher than the 0.1 international toxic equivalency factor/m3 prescribed by a European directive of 1994. In 2000, a macrospatial epidemiological study undertaken in the administrative district of Doubs, identified two significant clusters of soft-tissue sarcoma and non Hodgkin lymphoma in the vicinity of the municipal solid waste incinerator of Besançon. This microspatial study (at the Besançon city scale), was designed to test the association between the exposure to dioxins emitted by the municipal solid waste incinerator of Besançon and the risk of soft-tissue sarcoma. ::: ::: ::: METHODS ::: Ground-level concentrations of dioxin were modeled with a dispersion model (Air Pollution Control 3 software). Four increasing zones of exposure were defined. For each case of soft tissue sarcoma, ten controls were randomly selected from the 1990 census database and matched for gender and age. A geographic information system allowed the attribution of a dioxin concentration category to cases and controls, according to their place of residence. ::: ::: ::: RESULTS ::: Thirty-seven cases of soft tissue sarcoma were identified by the Doubs cancer registry between 1980 and 1995, corresponding to a standardized incidence (French population) of 2.44 per 100,000 inhabitants. Compared with the least exposed zone, the risk of developing a soft tissue sarcoma was not significantly increased for people living in the more exposed zones. ::: ::: ::: CONCLUSION ::: Before definitely concluding that there is no relationship between the exposure to dioxin released by a solid waste incinerator and soft tissue sarcoma, a nationwide investigation based on other registries should be conducted.


title: Improving FET Switch Linearity

abstract: RF field-effect transistors, especially pseudomorphic high-electron mobility transistors (pHEMTs), are commonly used as switches in communication applications. These small high-speed devices are vital for routing and conveying signals in such uses. The important characteristics of pHEMTs, besides their small size, are their high-frequency capability, insertion loss, isolation, power handling, switching speed, and linearity. A topology using a pair of simple but modified series and shunt elements was designed to improve upon the linearity of an RF switch. Each element of the switch was composed of a single, unbiased, but relatively long pHEMT, which was designed for the test. By shifting the position of the gate asymmetrically toward the source terminal in these transistors, it was found that the linearity was improved without cost to other performance parameters


title: An NLP-based Reading Tool for Aiding Non-native English Readers

abstract: This paper describes a text-reading tool that makes extensive use of widelyavailable NLP tools and resources to aid non-native English speakers overcome language related hindrances while reading a text. It is a web-based tool, that can be accessed from browsers running on PCs or tablets, and provides the reader with an intelligent e-book functionality.


title: A framework of wound segmentation based on deep convolutional networks

abstract: Quantification of the chronic wound size is important in clinical wound treatment as the care for each individual patient is largely based on this assessment. Recently, clinicians usually measure wound area using standardized scale mainly based on visual inspection, which is often subjective and potentially inaccurate. Unfortunatly, automated segmentation of wound area is a challenge task owing to the diverse wound characteristics and the haziness over where wound boundaries lie. To address this problem, we propose a deep convolutional networks based framework named WoundSeg to locate and segment wound areas automatically. Firstly, we establish a newly annotated dataset containing 950 digital images of different chronic wounds, and propose an annotation tool based on watershed algorithm to mask wound regions conveniently for expert clinicians. Then, a light-weight network is built specifically for wound segmentation with limited training data. Finally, morphology operation and skin detection based verification are used to postprocess the predictions of the network. We estimate the generalization error of WoundSeg with 5-fold cross-validation on the above dataset and also on various latest clinic images taken of patients at the wound clinic. Extensive experiments demonstrate the efficiency and effectiveness of the proposed framework which can be considered as a promising approach to replace empirical and imprecise manual measurement for wound areas.


title: AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

abstract: We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet is well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code is available at this https URL


title: A Novel Multilevel Inverter Based on Switched DC Sources

abstract: This paper presents a multilevel inverter that has been conceptualized to reduce component count, particularly for a large number of output levels. It comprises floating input dc sources alternately connected in opposite polarities with one another through power switches. Each input dc level appears in the stepped load voltage either individually or in additive combinations with other input levels. This approach results in reduced number of power switches as compared to classical topologies. The working principle of the proposed topology is demonstrated with the help of a single-phase five-level inverter. The topology is investigated through simulations and validated experimentally on a laboratory prototype. An exhaustive comparison of the proposed topology is made against the classical cascaded H-bridge topology.


title: APAC: Augmented PAttern Classification with Neural Networks

abstract: Deep neural networks have been exhibiting splendid accuracies in many of visual pattern classification problems. Many of the state-of-the-art methods employ a technique known as data augmentation at the training stage. This paper addresses an issue of decision rule for classifiers trained with augmented data. Our method is named as APAC: the Augmented PAttern Classification, which is a way of classification using the optimal decision rule for augmented data learning. Discussion of methods of data augmentation is not our primary focus. We show clear evidences that APAC gives far better generalization performance than the traditional way of class prediction in several experiments. Our convolutional neural network model with APAC achieved a state-of-the-art accuracy on the MNIST dataset among non-ensemble classifiers. Even our multilayer perceptron model beats some of the convolutional models with recently invented stochastic regularization techniques on the CIFAR-10 dataset.


title: Optimization of Data-intensive Flows: Is it Needed? Is it Solved?

abstract: Modern data analysis is increasingly employing data-intensive flows for processing very large volumes of data. As the data flows become more and more complex and operate in a highly dynamic environment, we argue that we need to resort to automated cost-based optimization solutions rather than relying on efficient designs by human experts. We further demonstrate that the current state-of-the-art in flow optimizations needs to be extended and we propose a promising direction for optimizing flows at the logical level, and more specifically, for deciding the sequence of flow tasks.


title: Crohn's-like clinical and pathological manifestations of giant inflammatory polyposis in IBD: A potential diagnostic pitfall

abstract: Abstract Background & aims Giant inflammatory polyposis (GIP), characterized by mass-like agglomerations of inflammatory polyps, is a rare complication of inflammatory bowel disease (IBD). We reviewed a series of cases of GIP to determine its diagnostic impact on the clinical and pathologic distinction between ulcerative colitis (UC) and colonic Crohn's disease (CD). Methods All colons with GIP resected over a 13-year period were identified prospectively and the corresponding clinical and pathologic records were reviewed. Results Twelve cases of GIP were identified, accounting for 0.8% of colectomies for IBD during the same time interval. Preoperatively, 6 (50%) patients were diagnosed with UC, 2 (17%) with CD and 4 (33%) with indeterminate colitis (IC). Postoperatively, 6 of the diagnoses (50%) were revised based on strict histopathologic criteria: all 4 diagnoses of IC to UC, one diagnosis of CD to UC, and one diagnosis of UC to CD, for a total of 10 diagnoses of UC (83%) and two of CD (17%). Significantly, 7 of 10 cases with postoperative diagnoses of UC (70%) had Crohn's-like transmural inflammation exclusively within the polyposis segments attributed to fecal entrapment and stasis and accounting for the Crohn's-like clinical complications in these cases. Conclusions This case series of GIP, the largest reported from a single center, highlights the high rate of Crohn's-like clinical and pathological manifestations of GIP and their potential to confound the accurate classification of patients with IBD. A diagnosis of UC should not be amended to CD based on the findings of the polyposis segment alone.


title: Cross-lingual ontology mapping – an investigation of the impact of machine translation

abstract: Ontologies are at the heart of knowledge management and make use of information that is not only written in English but also in many other natural languages. In order to enable knowledge discovery, sharing and reuse of these multilingual ontologies, it is necessary to support ontology mapping despite natural language barriers. This paper examines the soundness of a generic approach that involves machine translation tools and monolingual ontology matching techniques in cross-lingual ontology mapping scenarios. In particular, experimental results collected from case studies which engage mappings of independent ontologies that are labeled in English and Chinese are presented. Based on findings derived from these studies, limitations of this generic approach are discussed. It is shown with evidence that appropriate translations of conceptual labels in ontologies are of crucial importance when applying monolingual matching techniques in cross-lingual ontology mapping. Finally, to address the identified challenges, a semantic-oriented cross-lingual ontology mapping (SOCOM) framework is proposed and discussed.


title: 15 GHz 25 dBm multigate-cell stacked CMOS power amplifier with 32 % PAE and ≥ 30 dB gain for 5G applications

abstract: A three-stage stacked FET CMOS power amplifier (PA) for the 12.7 to 15.3 GHz frequency range is presented. The PA achieves more than 30 dB linear gain with saturated output power of 25.1 dBm (320 mW) and peak power added efficiency (PAE) of 32.4 % at 13.5 GHz. The PA is implemented in 45 nm CMOS SOI technology. High gain is achieved with two cascode pre-driver stages and a final high power stage. The output stage comprises 512 four-stack multigate-cell devices to allow high voltage swings and correspondingly high output power. The effective gate width of the output device is 614 μm. To the authors knowledge, combination of power and efficiency achieved in this work are the highest reported for CMOS PAs in the 15 GHz band. The amplifier occupies an area of 1 × 1 mm2 including pads.


title: Improved and Simplified Inapproximability for k-means

abstract: The k-means problem consists of finding k centers in the d-dimensional Euclidean space that minimize the sum of the squared distances of all points in an input set P to their closest respective center. Awasthi et. al. recently showed that there exists a constant c>1 such that it is NP-hard to approximate the k-means objective within a factor of c. We establish that the constant c is at least 1.0013.


title: Sentiment Embeddings with Applications to Sentiment Analysis

abstract: We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore the sentiment of texts. It is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarity, such as good and bad , are mapped to neighboring word vectors. We address this issue by encoding sentiment information of texts (e.g., sentences and words) together with contexts of words in sentiment embeddings. By combining context and sentiment level evidences, the nearest neighbors in sentiment embedding space are semantically similar and it favors words with the same sentiment polarity. In order to learn sentiment embeddings effectively, we develop a number of neural networks with tailoring loss functions, and collect massive texts automatically with sentiment signals like emoticons as the training data. Sentiment embeddings can be naturally used as word features for a variety of sentiment analysis tasks without feature engineering. We apply sentiment embeddings to word-level sentiment analysis, sentence level sentiment classification, and building sentiment lexicons. Experimental results show that sentiment embeddings consistently outperform context-based embeddings on several benchmark datasets of these tasks. This work provides insights on the design of neural networks for learning task-specific word embeddings in other natural language processing tasks.


title: Shape Memory Polymer Composites of Poly(styrene-b-butadiene-b-styrene) Copolymer/Liner Low Density Polyethylene/Fe3O4 Nanoparticles for Remote Activation

abstract: Magnetically sensitive shape memory poly(styrene-b-butadiene-b-styrene) copolymer (SBS)/liner low density polyethylene (LLDPE) composites filled with various contents of Fe3O4 nanoparticles were prepared. The influence of the Fe3O4 nanoparticles content on the thermal properties, mechanical properties, fracture morphology, magnetic behavior, and shape memory effect of SBS/LLDPE/Fe3O4 composites was systematically studied in this paper. The results indicated that homogeneously dispersed Fe3O4 nanoparticles ensured the uniform heat generation and transfer in the alternating magnetic field, and endowed the SBS/LLDPE/Fe3O4 composites with an excellent magnetically responsive shape memory effect. When the shape memory composites were in the alternating magnetic field (f = 60 kHz, H = 21.21 kA·m−1), the best shape recovery ratio reached 99%, the shape retention ratio reached 99.4%, and the shape recovery speed increased significantly with the increment of Fe3O4 nanoparticles. It is anticipated that tagging products with this novel shape memory composite is helpful for the purpose of an intravascular delivery system in Micro-Electro-Mechanical System (MEMS) devices.


title: Numerical Aerodynamic Optimization Incorporating Laminar-Turbulent Transition Prediction

abstract: Nmethod, which are then used in Optima2D, a Newton–Krylov discrete-adjoint optimization algorithm based on the compressible Reynolds-averaged Navier– Stokes equations. The algorithm is applied to the design of airfoils with maximum lift-to-drag ratio, endurance factor, and lift coefficient. The design examples demonstrate that the optimizer is able to control the transitionpoint locations to provide optimum performance, often producing pressure distributions with laminar rooftops followed by concave pressure recovery. In particular, the optimization algorithm is able to design an airfoil that is very similar, in terms of both shape and performance, to one of the high-lift airfoils designed by Liebeck (Liebeck, R. H., “A Class of Airfoils Designed for High Lift in Incompressible Flow,” Journal of Aircraft, Vol. 10, No. 10, 1973, pp. 610–617) in the 1970s. The results provide a striking demonstration of the capability of the Newton– Krylov aerodynamic optimization algorithm to design airfoils with characteristics that previously required a great deal of expertise to achieve.


title: Random forest models for identifying motorway Rear-End Crash Risks using disaggregate data

abstract: This paper presents an approach to develop motorway Rear-End Crash Risk Identification Models (RECRIM) using disaggregate traffic data, meteorological data and crash database for a study site at a two-lane-per-direction section on Swiss (right-hand driving) motorway A1. Traffic data collected from inductive double loop detectors provide instant vehicle information such as speed, time headway, etc. We define traffic situations (TS) characterized by 22 variables representing traffic status for 5-minute intervals. Our goal is to develop models that can separate TS under non-crash conditions and TS under pre-crash conditions using Random Forest - an ensemble learning method. Non-crash TS were clustered into groups that we call traffic regimes (TR). Precrash TS are classified into TR so that a RECRIM for each TR is developed. Interpreting results of the models suggests that speed variance on the right lane and speed difference between two lanes are the two main causes of the rear-end crashes. The applicability of RECRIM in a real-time framework is also discussed.


title: First year student performance in a test for computational thinking

abstract: Computational thinking, a form of thinking and problem solving within computer science, has become a popular focus of research on computer science education. In this paper, we attempt to investigate the role that computational thinking plays in the experience of introductory computer science students at a South African university. To this end, we have designed and administered a test for computational thinking ability, and contrasted the results of this test with the class marks for the students involved. The results of this test give us an initial view of the abilities that students possess when entering the computer science course. The results indicate that students who performed well in the assessment have a favourable pass rate for their class tests, and specific areas of weakness have been identified. Finally, we describe the plan for a follow-up test to take place at the end of the course to determine how students' abilities have changed over a semester of studies.


title: Multiple trajectory search for unconstrained/constrained multi-objective optimization

abstract: Many real-world optimization problems involve multiple conflicting objectives. Therefore, multi-objective optimization has attracted much attention of researchers and many algorithms have been developed for solving multi-objective optimization problems in the last decade. In this paper the multiple trajectory search (MTS) is presented and successfully applied to thirteen unconstrained and ten constrained multi-objective optimization problems. These problems constitute a test suite provided for competition in the Special Session & Competition on Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms in CEC 2009. In the multiple trajectory search, a set of uniformly distributed solutions is first generated. These solutions will be separated into foreground solutions and background solutions. The search is focuses mainly on foreground solutions and partly on background solutions. The MTS chooses and applies one of the three local search methods on solutions iteratively. The three local search methods begin their search in a very large “neighborhood”. Then the neighborhood contracts step by step until it reaches a pre-defined tiny size, after then, it is reset to its original size. By utilizing such size-varied neighborhood searches, the MTS effectively solves the multi-objective optimization problems.


title: Multi-target detection algorithm for FMCW radar

abstract: FMCW (Frequency Modulation Continuous Wave) radar has been a popular method for automotive applications. The typical FMCW radar, however, has serious problems in multi-target situations. That is, range-velocity processing gives rise to so-called ghost targets due to Doppler shift in the received beat-frequency. In this paper, we propose a new transmit wave and detection algorithm. In the proposed method, the rough range is detected in the first period, and the fine range and velocity are obtained in the second period.


title: TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings

abstract: This paper revisits the problem of analyzing multiple ratings given by different judges. Different from previous work that focuses on distilling the true labels from noisy crowdsourcing ratings, we emphasize gaining diagnostic insights into our in-house well-trained judges. We generalize the well-known DAWIDSKENE model (Dawid & Skene, 1979) to a spectrum of probabilistic models under the same "TrueLabel + Confusion" paradigm, and show that our proposed hierarchical Bayesian model, called HYBRIDCONFUSION, consistently outperforms DAWIDSKENE on both synthetic and real-world data sets.


title: Segmenting a deforming mesh into near-rigid components

abstract: Given a deforming mesh in an animation, we propose a new method to segment this mesh into several near-rigid sub-meshes. From this deforming mesh over all frames of an animation, we can analyze the degree of deformation between two nearby faces on the mesh. Then, our algorithm partitions the given deforming mesh into near-rigid components where the segmentation boundaries always pass at regions of large deformation. As a result, the mesh segmentation is invariant to all frames of the given animation and the motion of faces in each near-rigid-component can be represented by the same approximate affine transformation. To demonstrate the usefulness of the algorithm, we solve the restriction of deformation transfer for triangle meshes [31] which requires similar reference poses between source mesh and target mesh.


title: Monotonic Calibrated Interpolated Look-Up Tables

abstract: Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable. In particular, guaranteed monotonicity of the learned function can be critical to user trust. We propose meeting these goals for low-dimensional machine learning problems by learning flexible, monotonic functions using calibrated interpolated look-up tables. We extend the structural risk minimization framework of lattice regression to train monotonic look-up tables by solving a convex problem with appropriate linear inequality constraints. In addition, we propose jointly learning interpretable calibrations of each feature to normalize continuous features and handle categorical or missing data, at the cost of making the objective non-convex. We address large-scale learning through parallelization, mini-batching, and propose random sampling of additive regularizer terms. Experiments on seven real-world problems with five to sixteen features and thousands to millions of training samples demonstrate the proposed monotonic functions can achieve state-of-the-art accuracy on practical problems while providing greater transparency to users.


title: A model of the perception of facial expressions of emotion by humans: Research overview and perspectives

abstract: In cognitive science and neuroscience, there have been two leading models describing how humans perceive and classify facial expressions of emotion--the continuous and the categorical model. The continuous model defines each facial expression of emotion as a feature vector in a face space. This model explains, for example, how expressions of emotion can be seen at different intensities. In contrast, the categorical model consists of C classifiers, each tuned to a specific emotion category. This model explains, among other findings, why the images in a morphing sequence between a happy and a surprise face are perceived as either happy or surprise but not something in between. While the continuous model has a more difficult time justifying this latter finding, the categorical model is not as good when it comes to explaining how expressions are recognized at different intensities or modes. Most importantly, both models have problems explaining how one can recognize combinations of emotion categories such as happily surprised versus angrily surprised versus surprise. To resolve these issues, in the past several years, we have worked on a revised model that justifies the results reported in the cognitive science and neuroscience literature. This model consists of C distinct continuous spaces. Multiple (compound) emotion categories can be recognized by linearly combining these C face spaces. The dimensions of these spaces are shown to be mostly configural. According to this model, the major task for the classification of facial expressions of emotion is precise, detailed detection of facial landmarks rather than recognition. We provide an overview of the literature justifying the model, show how the resulting model can be employed to build algorithms for the recognition of facial expression of emotion, and propose research directions in machine learning and computer vision researchers to keep pushing the state of the art in these areas. We also discuss how the model can aid in studies of human perception, social interactions and disorders.


title: Fully-Convolutional Siamese Networks for Object Tracking

abstract: The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object’s appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.


title: Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation

abstract: This study is one of the few attempts to investigate students' acceptance of an Internet-based learning medium (ILM). By integrating a motivational perspective into the technology acceptance model, our model captured both extrinsic (perceived usefulness and ease of use) and intrinsic (perceived enjoyment) motivators for explaining students' intention to use the new learning medium. Data collected from 544 undergraduate students were examined through the LISREL VIII framework. The results showed that both perceived usefulness and perceived enjoyment significantly and directly impacted their intention to use ILM. Surprisingly, perceive ease of use did not posit a significant impact on student attitude or intention towards ILM usage. Implications of this study are important for both researchers and practitioners.


title: The taste of cutlery: how the taste of food is affected by the weight, size, shape, and colour of the cutlery used to eat it

abstract: Background ::: Recent evidence has shown that changing the plateware can affect the perceived taste and flavour of food, but very little is known about visual and proprioceptive influences of cutlery on the response of consumers to the food sampled from it. In the present study, we report three experiments designed to investigate whether food tastes different when the visual and tactile properties of the plastic cutlery from which it is sampled are altered. We independently varied the weight, size, colour, and shape of cutlery. We assessed the impact of changing the sensory properties of the cutlery on participants’ ratings of the sweetness, saltiness, perceived value, and overall liking of the food tasted from it.


title: Parents' education and children's achievement : The role of personality

abstract: The reasons for the positive association between families' background variables, such as parents' education, and children's academic achievement have not fully been clarified yet. The present study investigates children's intelligence and personality as potential mediators. A sample of 580 German high school students (mean age: M = 17.0; SD = 0.7) indicated the highest education of their parents and completed measures assessing their own personality and intelligence. Children's academic achievement was operationalized by grade point average. Children's intelligence, openness to experience and, marginally, conscientiousness partially mediated the association between parents' education and children's academic achievement. Even after controlling for children's intelligence, the mediating effects of those personality traits held. Results are discussed with regard to potential underlying processes. Copyright © 2010 John Wiley & Sons, Ltd.


title: Neurocognitive Habilitation Therapy for Children With Fetal Alcohol Spectrum Disorders: An Adaptation of the Alert Program(R)

abstract: OBJECTIVE. This study evaluated the effectiveness of neurocognitive habilitation, a group therapy intervention for foster and adoptive caregivers and their children who were prenatally exposed to alcohol. ::: ::: METHOD. Participants were recruited from clients seeking evaluation for fetal alcohol syndrome (FAS) and alcohol-related neurodevelopmental disorder (ARND) and were randomly assigned to treatment and no-treatment control groups. Forty children participated in the treatment program and were compared with 38 control participants using the Behavior Rating Inventory of Executive Function (BRIEF) and the Roberts Apperception Test for Children (RATC). ::: ::: RESULTS. Significant differences between the treatment and control groups were demonstrated on the BRIEF and on the RATC, suggesting that the intervention improved executive functioning and emotional problem-solving skills. ::: ::: CONCLUSION. These findings yield promising evidence of the effectiveness of the neurocognitive habilitation intervention in improving executive functioning and emotional problem solving in children with FAS or ARND.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Self-Esteem Development From Young Adulthood to Old Age: A Cohort-Sequential Longitudinal Study

abstract: The authors examined the development of self-esteem from young adulthood to old age. Data came from the Americans’ Changing Lives study, which includes 4 assessments across a 16-year period of a nationally representative sample of 3,617 individuals aged 25 years to 104 years. Latent growth curve analyses indicated that self-esteem follows a quadratic trajectory across the adult life span, increasing during young and middle adulthood, reaching a peak at about age 60 years, and then declining in old age. No cohort differences in the self-esteem trajectory were found. Women had lower self-esteem than did men in young adulthood, but their trajectories converged in old age. Whites and Blacks had similar trajectories in young and middle adulthood, but the self-esteem of Blacks declined more sharply in old age than did the self-esteem of Whites. More educated individuals had higher self-esteem than did less educated individuals, but their trajectories were similar. Moreover, the results suggested that changes in socioeconomic status and physical health account for the decline in self-esteem that occurs in old age.


title: Trans-gram, Fast Cross-lingual Word-embeddings

abstract: We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language. We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language. We also achieve state of the art results on standard cross-lingual text classification and word translation tasks.


title: A Compact Wideband Circularly Polarized Antenna Array for $C$ -band Applications

abstract: In this letter, we present a compact single-layer microstrip antenna array for applications in the $C$ -band. The device is designed and fabricated with an overall volume of $45~\hbox{mm} \times 45~\hbox{mm} \times 1.6~\hbox{mm}$ , (0.86 ${\lambda _0} \times 0.86 {\lambda _0} \times 0.03 {\lambda _0}$ at a frequency of 5.75 GHz), a suitable dimension for compact handhelds. The proposed array is based on a modified sequential phase network, composed of nonuniform curved transmission lines, feeding a set of $2 \times 2$ disc based patch elements. The proposed network has space-filling properties, and is arranged in the unoccupied area between the elements, minimizing the overall dimensions without sacrificing the operating bandwidth. Adequate agreements between the simulated and measured results validate the proposed design: a 3-dB axial ratio band of 900 MHz, meaning 15.5%, a peak gain of 8.25 dB maintained within 3 dB for 1.2 GHz, and a very wide 10-dB return loss bandwidth of 29%. This performance is unmatched for the given dimensional constraint with a single-layer via-less technology .


title: DFuzzy: a deep learning-based fuzzy clustering model for large graphs

abstract: Graph clustering is successfully applied in various applications for finding similar patterns. Recently, deep learning- based autoencoder has been used efficiently for detecting disjoint clusters. However, in real-world graphs, vertices may belong to multiple clusters. Thus, it is obligatory to analyze the membership of vertices toward clusters. Furthermore, existing approaches are centralized and are inefficient in handling large graphs. In this paper, a deep learning-based model ‘DFuzzy’ is proposed for finding fuzzy clusters from large graphs in distributed environment. It performs clustering in three phases. In first phase, pre-training is performed by initializing the candidate cluster centers. Then, fine tuning is performed to learn the latent representations by mining the local information and capturing the structure using PageRank. Further, modularity is used to redefine clusters. In last phase, reconstruction error is minimized and final cluster centers are updated. Experiments are performed over real-life graph data, and the performance of DFuzzy is compared with four state-of-the-art clustering algorithms. Results show that DFuzzy scales up linearly to handle large graphs and produces better quality of clusters when compared to state-of-the-art clustering algorithms. It is also observed that deep structures can help in getting better graph representations and provide improved clustering performance.


title: Discrete-time orthogonal spline collocation methods for the nonlinear Schrödinger equation with wave operator

abstract: In this paper, discrete-time orthogonal spline collocation schemes are proposed for the nonlinear Schrodinger equation with wave operator. These schemes are constructed by using orthogonal spline collocation approaches combined with finite difference methods. The conservative property, the convergence, and the stability of these methods are theoretically analyzed and also verified by extensive numerical experiments. In addition, some interesting phenomena which require further theoretical analysis are discussed numerically.


title: Integrating Stance Detection and Fact Checking in a Unified Corpus

abstract: A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim's factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. In this paper, we support the interdependencies between these tasks as annotations in the same corpus. We implement this setup on an Arabic fact checking corpus, the first of its kind.


A Memory Encryption Engine Suitable for General Purpose Processors.


title: In Search of the Dream Team: Temporally Constrained Multi-Armed Bandits for Identifying Effective Team Structures

abstract: Team structures---roles, norms, and interaction patterns---define how teams work. HCI researchers have theorized ideal team structures and built systems nudging teams towards them, such as those increasing turn-taking, deliberation, and knowledge distribution. However, organizational behavior research argues against the existence of universally ideal structures. Teams are diverse and excel under different structures: while one team might flourish under hierarchical leadership and a critical culture, another will flounder. In this paper, we present DreamTeam: a system that explores a large space of possible team structures to identify effective structures for each team based on observable feedback. To avoid overwhelming teams with too many changes, DreamTeam introduces multi-armed bandits with temporal constraints: an algorithm that manages the timing of exploration--exploitation trade-offs across multiple bandits simultaneously. A field experiment demonstrated that DreamTeam teams outperformed self-managing teams by 38%, manager-led teams by 46%, and teams with unconstrained bandits by 41%. This research advances computation as a powerful partner in establishing effective teamwork.


title: Stability analysis of a DC microgrid with master-slave control structure

abstract: Nowadays there is an increasing interest on dc microgrid for its higher energy efficiency and higher reliability as compared to the ac system. This paper addresses the stability problem in a dc microgrid with master-slave control structure. Considering the discrete sampling and control algorithm in a digital system, the small-signal models of this dc microgrid have been derived in the z plane. Eigenvalue analysis results show that the system stability and transient response will be affected by the variation of the dc load parameters, the number of slave distributed generations (DGs), and the type of dc load. A centralized oscillation suppression controller, which is composed of a proportional compensator followed by a bandpass filter, is proposed to overcome the unstable oscillations caused by different eigenvalues. The validity of the proposed approach is confirmed by simulations.


title: Content-based recommendation systems

abstract: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.


Autonomously Communicating Conceptual Knowledge Through Visual Art


Long-term stability, catalytic upgrading, and applicaton of pyrolysis oils : improving the properties of a potential substitute for fossil fuels


title: Providing Task Allocation and Secure Deduplication for Mobile Crowdsensing via Fog Computing

abstract: Mobile crowdsensing enables a crowd of individuals to cooperatively collect and share information with customers with common interest using their mobile devices. The success of mobile crowdsensing strongly depends on the participating mobile users. The broader participation, the more sensing reports are collected; on the other hand, the more replicate data may be generated, which brings heavy communication overhead. Data deduplication can improve communication efficiency, but meanwhile disclose the sensing data. In this paper, we propose a fog-assisted mobile crowdsensing framework that enables fog nodes to allocate tasks based on user mobility for improving the accuracy of task assignment, and a fog-assisted secure data deduplication scheme (Fo-SDD) to improve communication efficiency while maintaining high data confidentiality. Specifically, a BLS-oblivious pseudo-random function is designed to enable fog nodes to detect and delete the replicate data in sensing reports without exposing the content of reports, except the equality of the underlying sensing data. To further prevent privacy leakage of mobile users, we extend the Fo-SDD to hide the identities of mobile users in data collection. Chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous mobile users. Finally, we demonstrate that both schemes achieve secure data deduplication with low computational and communication overhead.


title: On First-Order Meta-Learning Algorithms

abstract: This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates. This family includes and generalizes first-order MAML, an approximation to MAML obtained by ignoring second-order derivatives. It also includes Reptile, a new algorithm that we introduce here, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. We expand on the results from Finn et al. showing that first-order meta-learning algorithms perform well on some well-established benchmarks for few-shot classification, and we provide theoretical analysis aimed at understanding why these algorithms work.


title: Efficient techniques for document sanitization

abstract: Sanitization of a document involves removing sensitive information from the document, so that it may be distributed to a broader audience. Such sanitization is needed while declassifying documents involving sensitive or confidential information such as corporate emails, intelligence reports, medical records, etc. In this paper, we present the ERASE framework for performing document sanitization in an automated manner. ERASE can be used to sanitize a document dynamically, so that different users get different views of the same document based on what they are authorized to know. We formalize the problem and present algorithms used in ERASE for finding the appropriate terms to remove from the document. Our preliminary experimental study demonstrates the efficiency and efficacy of the proposed algorithms.


title: Whirlpool hash function: architecture and VLSI implementation

abstract: New encryption algorithms have to operate in a variety of current and future applications demanding both high speed and high security. An architecture and VLSI implementation of the newest standard in the hash families, Whirlpool that achieves high-speed performance is presented. The architecture permits a wide variety of implementation tradeoffs. The design was coded using VHDL language and for the hardware implementation a FPGA device was used. While no other previous Whirlpool implementation exist, the comparison with previous hash families' implementations such as MD5, SHA-1, SHA-2 etc are given. These comparisons prove that the Whirlpool implementation is much faster compared with these previous implementations.


title: The Distinguishing Characteristics of Narrative Identity in Adults with Features of Borderline Personality Disorder: An Empirical Investigation

abstract: While identity disturbance has long been considered one of the defining features of Borderline Personality Disorder (BPD), the present study marks only the third empirical investigation to assess it and the first to do so from the perspective of research on narrative identity. Drawing on the rich tradition of studying narrative identity, the present study examined identity disturbance in a group of 40 mid-life adults, 20 with features of BPD and a matched sample of 20 without BPD. Extensive life story interviews were analyzed for a variety of narrative elements and the themes of agency, communion fulfillment (but not communion), and narrative coherence significantly distinguished the stories of those people with features of BPD from those without the disorder. In addition, associations between the theme of agency and psychopathology were evident six and twelve months following the life story interview. This study seeks to bridge the mutually-informative fields of research on personality disorders and norm...


title: Reconciling high server utilization and sub-millisecond quality-of-service

abstract: The simplest strategy to guarantee good quality of service (QoS) for a latency-sensitive workload with sub-millisecond latency in a shared cluster environment is to never run other workloads concurrently with it on the same server. Unfortunately, this inevitably leads to low server utilization, reducing both the capability and cost effectiveness of the cluster. In this paper, we analyze the challenges of maintaining high QoS for low-latency workloads when sharing servers with other workloads. We show that workload co-location leads to QoS violations due to increases in queuing delay, scheduling delay, and thread load imbalance. We present techniques that address these vulnerabilities, ranging from provisioning the latency-critical service in an interference aware manner, to replacing the Linux CFS scheduler with a scheduler that provides good latency guarantees and fairness for co-located workloads. Ultimately, we demonstrate that some latency-critical workloads can be aggressively co-located with other workloads, achieve good QoS, and that such co-location can improve a datacenter's effective throughput per TCO-$ by up to 52%.


title: An efficient active ripple filter for use in DC-DC conversion

abstract: When low ripple is required from a switched mode dc-dc converter, dissipative active filters offer an alternative to passive LC and coupled inductor filters. Analytical and experimental results are presented for a simple active ripple filter. The filter employs a pair of current transformers as sensors for feedforward and feedback control and two metal-oxide-semiconductor field-effect transistors (MOSFETs) as cancellation current drivers. Measurements demonstrate good ripple attenuation up to 5 MHz, more than 70 dB being obtained at 100 kHz, the switching frequency of a test converter. The overall efficiency was measured as 95%, with room for further improvement. The filter is suitable for input and output smoothing in dc-dc converters for aerospace and other critical applications.


title: Adsorption of concanavalin a on human platelets

abstract: A physiological cell surface adsorption system approach is investigated on human platelets utilizing mathematical modeling. Monodispersed washed platelets are freshly collected in an isotonic buffer as a suspension utilizing a gel filtration technique. Concanavalin A is used as a glycoprotein receptor adsorbate in the adsorption studies. Three mathematical models are proposed based on simple chemical equilibrium reactions between adsorbate and cell surface receptors in an effort to explain concanavalin A - platelet surface glycoprotein interaction. Model I assumes that all receptors are undergoing simultaneous surface reactions with the adsorbate and without correlation. Model II reflects a strong correlation between the receptors, when only one receptor is active and the second receptor(s) is nothing but the combination of first receptor-adsorbate complex. Model III assumes the presence of multiple receptors on the cell surface. Only when a specific fraction of the total number of one receptor have reacted, will the other receptor(s) initiate reaction with the adsorbate. The results suggest the existence of at least three major glycoprotein receptors interacting with the lectin, and having different equilibrium constants as indicated in the adsorption isotherm. Model III seems to support best the experimental data of concanavalin A interaction with platelet surface glycoproteins.


title: Effective User Relevance Feedback for Image Retrieval with Image Signatures

abstract: Content-based image retrieval (CBIR) has attracted much attention due to the exponential growth of digital image collections that have become available in recent years. Relevance feedback (RF) in the context of search engines is a query expansion technique, which is based on relevance judgments about the top results that are initially returned for a given query. RF can be obtained directly from end users, inferred indirectly from user interactions with a result list, or even assumed (aka pseudo relevance feedback). RF information is used to generate a new query, aiming to re-focus the query towards more relevant results. This paper presents a methodology for use of signature based image retrieval with a user in the loop to improve retrieval performance. The significance of this study is twofold. First, it shows how to effectively use explicit RF with signature based image retrieval to improve retrieval quality and efficiency. Second, this approach provides a mechanism for end users to refine their image queries. This is an important contribution because, to date, there is no effective way to reformulate an image query; our approach provides a solution to this problem. Empirical experiments have been carried out to study the behaviour and optimal parameter settings of this approach. Empirical evaluations based on standard benchmarks demonstrate the effectiveness of the proposed approach in improving the performance of CBIR in terms of recall, precision, speed and scalability.


title: PWM Converter Power Density Barriers

abstract: A pulley guard for a slidable fence gate having elongated horizontal rods supported on a plurality of pulleys rotatably mounted on fixed horizontal stub shafts, comprises a housing surrounding the pulley and preventing inadvertant finger access to the periphery of the pulley. Appropriate notches are provided in the walls of the housing to provide clearance for the stub shaft and for the elongated horizontal gate rod with which the pulley cooperates.


title: Electrochemically assisted deposition of strontium modified magnesium phosphate on titanium surfaces.

abstract: Electrochemically assisted deposition was utilized to produce ceramic coatings on the basis of magnesium ammonium phosphate (struvite) on corundum-blasted titanium surfaces. By the addition of defined concentrations of strontium nitrate to the coating electrolyte Sr(2+) ions were successfully incorporated into the struvite matrix. By variation of deposition parameters it was possible to fabricate coatings with different kinetics of Sr(2+) into physiological media, whereas the release of therapeutically relevant strontium doses could be sustained over several weeks. Morphological and crystallographic examinations of the immersed coatings revealed that the degradation of struvite and the release of Sr(2+) ions were accompanied by a transformation of the coating to a calcium phosphate based phase similar to low-crystalline hydroxyapatite. These findings showed that strontium doped struvite coatings may provide a promising degradable coating system for the local application of strontium or other biologically active metal ions in the implant-bone interface.


The New 'Code of Ethics for Nurses With Interpretive Statements' (2015): Practical Clinical Application, Part I


title: On the Reconstruction of Face Images from Deep Face Templates

abstract: State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this paper, we study the vulnerabilities of a state-of-the-art face recognition system based on template reconstruction attack. We propose a neighborly de-convolutional neural network ( NbNet ) to reconstruct face images from their deep templates. In our experiments, we assumed that no knowledge about the target subject and the deep network are available. To train the NbNet reconstruction models, we augmented two benchmark face datasets (VGG-Face and Multi-PIE) with a large collection of images synthesized using a face generator. The proposed reconstruction was evaluated using type-I (comparing the reconstructed images against the original face images used to generate the deep template) and type-II (comparing the reconstructed images against a different face image of the same subject) attacks. Given the images reconstructed from NbNets , we show that for verification, we achieve TAR of 95.20 percent (58.05 percent) on LFW under type-I (type-II) attacks @ FAR of 0.1 percent. Besides, 96.58 percent (92.84 percent) of the images reconstructed from templates of partition fa ( fb ) can be identified from partition fa in color FERET. Our study demonstrates the need to secure deep templates in face recognition systems.


title: Bilingual Experiments on Automatic Recovery of Capitalization and Punctuation of Automatic Speech Transcripts

abstract: This paper focuses on the tasks of recovering capitalization and punctuation marks from texts without that information, such as spoken transcripts, produced by automatic speech recognition systems. These two practical rich transcription tasks were performed using the same discriminative approach, based on maximum entropy, suitable for on-the-fly usage. Reported experiments were conducted both over Portuguese and English broadcast news data. Both force aligned and automatic transcripts were used, allowing to measure the impact of the speech recognition errors. Capitalized words and named entities are intrinsically related, and are influenced by time variation effects. For that reason, the so-called language dynamics have been addressed for the capitalization task. Language adaptation results indicate, for both languages, that the capitalization performance is affected by the temporal distance between the training and testing data. In what regards the punctuation task, this paper covers the three most frequent punctuation marks: full stop, comma, and question marks. Different methods were explored for improving the baseline results for full stop and comma. The first uses punctuation information extracted from large written corpora. The second applies different levels of linguistic structure, including lexical, prosodic, and speaker related features. The comma detection improved significantly in the first method, thus indicating that it depends more on lexical features. The second method provided even better results, for both languages and both punctuation marks, best results being achieved mainly for full stop. As for question marks, there is a small gain, but differences are not very significant, due to the relatively small number of question marks in the corpora.


title: Proactive vs reactive approaches to failure resilient routing

abstract: Dealing with network failures effectively is a major operational challenge for Internet service providers. Commonly deployed link state routing protocols such as OSPF react to link failures through global (i.e., network-wide) link state advertisements and routing table recomputations, causing significant forwarding discontinuity after a failure. The drawback with these protocols is that they need to trade off routing stability and forwarding continuity. To improve failure resiliency without jeopardizing routing stability, we propose a proactive local rerouting based approach called failure insensitive routing (FIR). The proposed approach prepares for failures using interface-specific forwarding, and upon a failure, suppresses the link state advertisement and instead triggers local rerouting using a backwarding table. In this paper, we prove that when no more than one link failure notification is suppressed, FIR always finds a loop-free path to a destination if one such path exists. We also formally analyze routing stability and network availability under both proactive and reactive approaches, and show that FIR provides better stability and availability than OSPF.


title: On the accuracy of personality judgment: A realistic approach

abstract: The "accuracy paradigm" for the study of personality judgment provides an important, new complement to the "error paradigm" that dominated this area of research for almost 2 decades. The present article introduces a specific approach within the accuracy paradigm called the Realistic Accuracy Model (RAM). RAM begins with the assumption that personality traits are real attributes of individuals. This assumption entails the use of a broad array of criteria for the evaluation of personality judgment and leads to a model that describes accuracy as a function of the availability, detection, and utilization of relevant behavioral cues. RAM provides a common explanation for basic moderators of accuracy, sheds light on how these moderators interact, and outlines a research agenda that includes the reintegration of the study of error with the study of accuracy.


title: PAM4 receiver with adaptive threshold voltage and adaptive decision feedback equalizer

abstract: A novel PAM4 receiver with adaptive threshold voltage, adaptive decision feedback equalizer and fixed linear equalizer has been presented. The proposed techniques enable threshold voltage to be adjusted automatically depending on data rate, signal swing and loss of channel. Consequently the receiver can be used in various situations without being manually calibrated. Adaptive decision feedback equalizer for PAM4 signaling is proposed, incorporated with sign-sign least mean square algorithm. Simulation results across lossy channel show proper convergence of threshold voltage and decision feedback equalizer values with the proposed receiver.


title: Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation

abstract: Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems. In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation. In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network utilizes virtual image projections of the 3D point clouds for efficient inference. Further, we propose an automated process for large-scale cross-modal training data generation called Autolabeling, in order to boost semantic labeling performance while keeping the manual annotation effort low. The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset. LiLaNet is shown to significantly outperform current state-of-the-art CNN architectures for LiDAR data. Applying our automatically generated large-scale training data yields a boost of up to 14 percentage points compared to networks trained on manually annotated data only.


title: ManiWordle: Providing Flexible Control over Wordle

abstract: Among the multifarious tag-clouding techniques, Wordle stands out to the community by providing an aesthetic layout, eliciting the emergence of the participatory culture and usage of tag-clouding in the artistic creations. In this paper, we introduce ManiWordle, a Wordle-based visualization tool that revamps interactions with the layout by supporting custom manipulations. ManiWordle allows people to manipulate typography, color, and composition not only for the layout as a whole, but also for the individual words, enabling them to have better control over the layout result. We first describe our design rationale along with the interaction techniques for tweaking the layout. We then present the results both from the preliminary usability study and from the comparative study between ManiWordle and Wordle. The results suggest that ManiWordle provides higher user satisfaction and an efficient method of creating the desired "art work," harnessing the power behind the ever-increasing popularity of Wordle.


title: A reduced actuation mecanum wheel platform for pipe inspection

abstract: This paper focuses on the design, development and assessment of a novel, 2 degrees-of-freedom magnetic pipe inspection robot. It consists of 4 mecanum wheels, with the diagonals functionally coupled and the system rotation constrained by the surface geometry, maintaining full translational mobility with reduced control and actuation requirements. The system uses positional encoding that is decoupled from the transmission system to overcome the main sources of positional/positioning errors when using mecanum wheels. The kinematic and dynamic models of the system are derived and integrated within the controller. The prototype robot is then tested and shown to follow a scan path at 20mm/s within ±1.5mm whilst correcting for gravitational drift and slip events.


title: Stability and performance of the compliance controller of the quadruped robot HyQ

abstract: A legged robot has to deal with environmental contacts every time it takes a step. To properly handle these interactions, it is desirable to be able to set the foot compliance. For an actively-compliant legged robot, in order to ensure a stable contact with the environment the robot leg has to be passive at the contact point. In this work, we asses some passivity and stability issues of the actively-compliant leg of the quadruped robot HyQ, which employs a highperformance cascade compliance controller. We demonstrate that both the nested torque loop performance as well as the actuator bandwidth have a strong influence in the range of virtual impedances that can be passively rendered by the robot leg. Based on the stability analyses and experimental results, we propose a procedure for designing cascade compliance controllers. Furthermore, we experimentally demonstrate that the HyQ's actively-compliant leg is able to reproduce the compliant behavior presented by an identical but passively-compliant version of the same leg.


title: Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests

abstract: Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common lab tests measured over time in a cohort of 298K patients derived from 8 years of administrative claims data. We compare the neural networks to a logistic regression with several hand-engineered, clinically relevant features. We find that the representation-based learning approaches significantly outperform this baseline. We believe that our work suggests a new avenue for patient risk stratification based solely on lab results.


title: A recovery model for combinational disruptions in logistics delivery: Considering the real-world participators

abstract: The existence of uncertainties may result in various unexpected disruption events in logistics delivery, which often makes actual delivery operations deviate from intended plans. The purpose of the paper is to develop a combinational disruption recovery model for vehicle routing problem with time windows (VRPTW), trying to handle a variety and a combination of delivery disruption events. Firstly, a novel approach to measure new-adding customer disruption, which considers the real-world participators (mainly including customers, drivers and logistics providers) in VRPTW, is developed. Then the paper proposes methods of transforming various delivery disruptions into the new-adding customer disruption, and determines the optimal starting times of delivery vehicles from the depot to provide a new rescue strategy (called starting later policy) for disrupted VRPTW. Based on the above, a combinational disruption recovery model for VRPTW is constructed and nested partition method (NPM) is designed to solve the proposed model. Finally, computational results are reported and compared with those of previous works, which verifies the effectiveness of the proposed solution and draws some interesting conclusions.


The ESA Human Arm Exoskeleton for Space Robotics Telepresence


title: Applying and Improving Monte-Carlo Tree Search in a Fighting Game AI

abstract: This paper evaluates the performance of Monte-Carlo Tree Search (MCTS) in a fighting game AI and proposes an improvement for the algorithm. Most existing fighting game AIs rely on rule bases and react to every situation with predefined actions, making them predictable for human players. We attempt to overcome this weakness by applying MCTS, which can adapt to different circumstances without relying on predefined action patterns or tactics. In this paper, an AI based on Upper Confidence bounds applied to Trees (UCT) and MCTS is first developed. Next, the paper proposes improving the AI with Roulette Selection and a rule base. Through testing and evaluation using FightingICE, an international fighting game AI competition platform, it is proven that the aforementioned MCTS-based AI is effective in a fighting game, and our proposed improvement can further enhance its performance.


title: Streaming Algorithms for k-Means Clustering with Fast Queries

abstract: We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. When compared with the current state-of-the-art, our methods provide a substantial improvement in the time to answer a query for cluster centers, while retaining the desirable properties of provably small approximation error, and low space usage. Our algorithms are based on a novel idea of "coreset caching" that reuses coresets (summaries of data) computed for recent queries in answering the current clustering query. We present both provable theoretical results and detailed experiments demonstrating their correctness and efficiency.


title: Robotic cognitive map building based on biology-inspired memory

abstract: This paper describes a cognitive map building system based on episodic memory for mobile robots. Inspired by the biological findings on the mammalian hippocampus, we proposed a novel method of episodic memory modeling which including a sequence of experienced observations, neutral states, behaviors and pose perceptions. The presented method can autonomously and incrementally learn the environmental experiences to construct cognitive map and realize the accumulation, integration and updating of experiences. Based on the memory model, we applied a new approach to the problem of Simultaneous Localization and Map (SLAM), inspired by computational models of hippocampus of rodents, we built the cognitive map inspired by biological memory in an office. Experimental results show that the system can work very well.


title: Radiation-Pattern-Reconfigurable Antenna Using Monopole-Loop for Fitbit Flex Wristband

abstract: This letter presents a radiation-pattern-reconfigurable antenna for a Fitbit Flex wristband, which provides health information about the human body. The advantages of this antenna are improvements in antenna performance and controlling beam directions compared to the existing antenna for the Fitbit Flex. We proposed the placement of an antenna on the upper side of the Fitbit Flex wristband as a monopole antenna. We compared the existing Fitbit Flex antenna to the proposed antennas by several antenna performance factors. Total radiation power (TRP) and total isotropic sensitivity (TIS) are measured, and the specific absorption rate (SAR) result is simulated. The proposed antennas show improvement in performance when compared to the existing Fitbit Flex existing antenna.


title: Defeating script injection attacks with browser-enforced embedded policies

abstract: Web sites that accept and display content such as wiki articles or comments typically filter the content to prevent injected script code from running in browsers that view the site. The diversity of browser rendering algorithms and the desire to allow rich content make filtering quite difficult, however, and attacks such as the Samy and Yamanner worms have exploited filtering weaknesses. This paper proposes a simple alternative mechanism for preventing script injection called Browser-Enforced Embedded Policies (BEEP). The idea is that a web site can embed a policy in its pages that specifies which scripts are allowed to run. The browser, which knows exactly when it will run a script, can enforce this policy perfectly. We have added BEEP support to several browsers, and built tools to simplify adding policies to web applications. We found that supporting BEEP in browsers requires only small and localized modifications, modifying web applications requires minimal effort, and enforcing policies is generally lightweight.


THE RESEARCH AND IMPLEMENTATION OF DYNAMIC EPIPOLAR REARRANGEMENT