title: An Anatomical Study of the Middle Temporal Vein and the Drainage Vascular Networks to Assess the Potential Complications and the Preventive Maneuver During Temporal Augmentation Using Both Anterograde and Retrograde Injections

abstract: Background ::: Non-thrombotic pulmonary embolism has recently been reported as a remote complication of filler injections to correct hollowing in the temporal region. The middle temporal vein (MTV) has been identified as being highly susceptible to accidental injection.


title: Use of point-of-care testing and early assessment model reduces length of stay for ambulatory patients in an emergency department

abstract: Background ::: To assess whether the use of point-of-care testing (POCT) and early assessment team (EAT) model shortens emergency department (ED) length of stay (LOS).


title: One-Shot-Learning Gesture Recognition using HOG-HOF Features

abstract: The purpose of this paper is to describe one-shot-learning gesture recognition systems developed on the ChaLearn Gesture Dataset (ChaLearn). We use RGB and depth images and combine appearance (Histograms of Oriented Gradients) and motion descriptors (Histogram of Optical Flow) for parallel temporal segmentation and recognition. The Quadratic-Chi distance family is used to measure differences between histograms to capture cross-bin relationships. We also propose a new algorithm for trimming videos--to remove all the unimportant frames from videos. We present two methods that use a combination of HOG-HOF descriptors together with variants of a Dynamic Time Warping technique. Both methods outperform other published methods and help narrow the gap between human performance and algorithms on this task. The code is publicly available in the MLOSS repository.


title: Radio Link Planning made easy with a Telegram Bot

abstract: Traditional radio planning tools present a steep learning curve. We present BotRf, a Telegram Bot that facilitates the process by guiding non-experts in assessing the feasibility of radio links. Built on open source tools, BotRf can run on any smartphone or PC using Telegram. Running it on a smartphone has the added value that the Bot can leverage the internal GPS to capture the current coordinates. BotRf can be used in low bandwidth environments as the generated data traffic is very limited. We present an example of its use in Venezuela.


title: Character-level Intra Attention Network for Natural Language Inference

abstract: Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. ::: In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.


title: Cluster analysis for gene expression data: a survey

abstract: DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.


title: IDS alert visualization and monitoring through heuristic host selection

abstract: Traversing through multiple pages of log entries, trying to detect malicious and anomalous behavior and being able to correlate events to address multiple use cases is a non trivial task for a security administrator. It requires resources, expert knowledge and time. In this paper, we present a novel security visualization system entitled Avisa. It accentuates fundamental matters of information visualization, namely interaction and animation and synthesizes it with intrusion detection audit traces. Visual constraints inspired the use of heuristic metrics to select and display hosts with irregular and variant behaviors. We thoroughly describe the ideas behind the heuristic metrics and perform an empirical analysis to individually evaluate each metric's functionality. Avisa's intuitive interface, accompanied by the power of the heuristic functions, allows the perception of patterns and emergent properties, facilitating in understanding the underlying data.


title: Proposing New Blockchain Challenges in eHealth

abstract: The blockchain technology has reached a great boom in the health sector, due to its importance to overcome interoperability and security challenges of the EHR and EMR systems in eHealth. The main objective of this work is to show a review of the existing research works in the literature, referring to the new blockchain technology applied in ehealth and exposing the possible research lines and trends in which this technology can be focused. The search for blockchain studies in eHealth field was carried out in the following databases: IEEE Xplore, Google Scholar, Science Direct, PubMed, Web of Science and ResearchGate from 2010 to the present. Different search criteria were established such as: “Blockchain” AND (“eHealth” OR “EHR” OR “electronic health records” OR “medicine”) selecting the papers considered of most interest. A total of 84 publications on blockchain in eHealth were found, of which 18 have been identified as relevant works, 5.56% correspond to the year 2016, 22.22% to 2017 and 72.22% to 2018. Many of the publications found show how this technology is being developed and applied in the health sector and the benefits it provides. The new blockchain technology applied in eHealth identifies new ways to share the distributed view of health data and promotes the advancement of precision medicine, improving health and preventing diseases.


title: Boosting bottom-up and top-down visual features for saliency estimation

abstract: Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. Majority of models are based on low-level visual features and the importance of top-down factors has not yet been fully explored or modeled. Here, we combine low-level features such as orientation, color, intensity, saliency maps of previous best bottom-up models with top-down cognitive visual features (e.g., faces, humans, cars, etc.) and learn a direct mapping from those features to eye fixations using Regression, SVM, and AdaBoost classifiers. By extensive experimenting over three benchmark eye-tracking datasets using three popular evaluation scores, we show that our boosting model outperforms 27 state-of-the-art models and is so far the closest model to the accuracy of human model for fixation prediction. Furthermore, our model successfully detects the most salient object in a scene without sophisticated image processings such as region segmentation.


title: Seasonal Variation in Length, Weight, and Sex Distribution of Turbot (Scophthalmus maeoticus Pallas, 1811) in the Sinop Region (Black Sea) of Turkey

abstract: The length, weight and sex distribution of turbot caught by bottom turbot gill nets were investigated in relation with seasons in the Sinop region. The size ranges were 23.9-69 cm and 31.8-81 cm for male and female turbot, respectively. Mean total length and weight were 50.37 ± 0.42 cm and 2483.2 ± 74.7 g for females, 45.93 ± 0.26 cm and 1711.2 ± 31.2 g for males, and 47.79 ± 0.24 cm and 2034.6 ± 38.5 g for all individuals. Significant differences were found both in length and weight values of turbot between seasons. The highest mean length and weight values were 49.56 ± 0.35 cm and 2286.5 ± 59.6 g in the spring, respectively. The length frequencies of turbot differed significantly between spring and other seasons (P < 0.001). Significant differences were found for sex ratios of turbot in spring. The length-weight relationship was estimated as W = 0.0074 L3.22.


title: Operational Modal Analysis - Another Way of Doing Modal Testing

abstract: In the past, the vibration design process has been driven by the requirements for an acceptable ambient vibration environment for vibration-sensitive production equipment. However, the newest generation of photolithography tools, the scanners or step-and-scan systems, impose an additional requirement for the dynamic resistance properties of the tool's support points. This article discusses the current scanner support criteria in terms of receptance spectra and compares them with receptance measurements carried out in several semiconductor fabrication facilities (fabs). Design philosophies are discussed for both floor structures and tool support pedestals.


title: Nonspecific low back pain and return to work.

abstract: As many as 90 percent of persons with occupational nonspecific low back pain are able to return to work in a relatively short period of time. As long as no "red flags" exist, the patient should be encouraged to remain as active as possible, minimize bed rest, use ice or heat compresses, take anti-inflammatory or analgesic medications if desired, participate in home exercises, and return to work as soon as possible. Medical and surgical intervention should be minimized when abnormalities on physical examination are lacking and the patient is having difficulty returning to work after four to six weeks. Personal and occupational psychosocial factors should be addressed thoroughly, and a multidisciplinary rehabilitation program should be strongly considered to prevent delayed recovery and chronic disability. Patient advocacy should include preventing unnecessary and ineffective medical and surgical interventions, prolonged work loss, joblessness, and chronic disability.


title: Social engineering as an attack vector for ransomware

abstract: Constant ransomware attacks achieve to evade multiple security perimeters, because its strategy is based on the exploitation of users-the weakest link in the security chain-getting high levels of effectiveness. In this paper, an in-depth analysis of the anatomy of the ransomware is develop, and how they combine technology with manipulation, through social engineering, to meet their goals and compromise thousands of computers. Finally, simulations of the phases that it follows are carried out, leaving the necessary guidelines to evaluate the security of the information, and thus, generate policies that minimize the risks of loss of information.


title: A Planar Wideband Dual-Polarized Array for Active Antenna System

abstract: A ±45° polarized wideband slot array antenna is proposed for active antenna system (AAS). The proposed array has a planar structure that integrates all the elements on a single substrate. Metal columns are used to reduce the coupling between adjacent elements. A prototype of a 2 × 2 array antenna is fabricated and measured. The measured results show that the proposed array covers the bands LTE2300 (2300-2400 MHz) and LTE2500 (2500-2690 MHz), and the polarization isolation is higher than 30 dB while the coupling between elements is less than -22 dB.


title: Active Learning from Peers

abstract: This paper addresses the challenge of learning from peers in an online multitask setting. Instead of always requesting a label from a human oracle, the proposed method first determines if the learner for each task can acquire that label with sufficient confidence from its peers either as a task-similarity weighted sum, or from the single most similar task. If so, it saves the oracle query for later use in more difficult cases, and if not it queries the human oracle. The paper develops the new algorithm to exhibit this behavior and proves a theoretical mistake bound for the method compared to the best linear predictor in hindsight. Experiments over three multitask learning benchmark datasets show clearly superior performance over baselines such as assuming task independence, learning only from the oracle and not learning from peer tasks.


title: Topology Aware Fully Convolutional Networks for Histology Gland Segmentation

abstract: The recent success of deep learning techniques in classification and object detection tasks has been leveraged for segmentation tasks. However, a weakness of these deep segmentation models is their limited ability to encode high level shape priors, such as smoothness and preservation of complex interactions between object regions, which can result in implausible segmentations. In this work, by formulating and optimizing a new loss, we introduce the first deep network trained to encode geometric and topological priors of containment and detachment. Our results on the segmentation of histology glands from a dataset of 165 images demonstrate the advantage of our novel loss terms and show how our topology aware architecture outperforms competing methods by up to 10 % in both pixel-level accuracy and object-level Dice.


title: Mining performance specifications

abstract: Functional testing is widespread and supported by a multitude of tools, including tools to mine functional specifications. In contrast, non-functional attributes like performance are often less well understood and tested. While many profiling tools are available to gather raw performance data, interpreting this raw data requires expert knowledge and a thorough understanding of the underlying software and hardware infrastructure. In this work we present an approach that mines performance specifications from running systems autonomously. The tool creates performance models during runtime. The mined models are analyzed further to create compact and comprehensive performance assertions. The resulting assertions can be used as an evidence-based performance specification for performance regression testing, performance monitoring, or as a foundation for more formal performance specifications.


title: On Predicting Geolocation of Tweets using Convolutional Neural Networks

abstract: In many Twitter studies, it is important to know where a tweet came from in order to use the tweet content to study regional user behavior. However, researchers using Twitter to understand user behavior often lack sufficient geo-tagged data. Given the huge volume of Twitter data there is a need for accurate automated geolocating solutions. Herein, we present a new method to predict a Twitter user’s location based on the information in a single tweet. We integrate text and user profile meta-data into a single model using a convolutional neural network. Our experiments demonstrate that our neural model substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1% accuracy on city-level and country-level prediction respectively.


title: Learning Informative Edge Maps for Indoor Scene Layout Prediction

abstract: In this paper, we introduce new edge-based features for the task of recovering the 3D layout of an indoor scene from a single image. Indoor scenes have certain edges that are very informative about the spatial layout of the room, namely, the edges formed by the pairwise intersections of room faces (two walls, wall and ceiling, wall and floor). In contrast with previous approaches that rely on area-based features like geometric context and orientation maps, our method attempts to directly detect these informative edges. We learn to predict 'informative edge' probability maps using two recent methods that exploit local and global context, respectively: structured edge detection forests, and a fully convolutional network for pixelwise labeling. We show that the fully convolutional network is quite successful at predicting the informative edges even when they lack contrast or are occluded, and that the accuracy can be further improved by training the network to jointly predict the edges and the geometric context. Using features derived from the 'informative edge' maps, we learn a maximum margin structured classifier that achieves state-of-the-art performance on layout prediction.


Algorithms for Generating Fundamental Cycles in a Graph


title: Electrostatic Energy-Harvesting and Battery-Charging CMOS System Prototype

abstract: The self-powering, long-lasting, and functional features of embedded wireless microsensors appeal to an ever-expanding application space in monitoring, control, and diagnosis for military, commercial, industrial, space, and biomedical applications. Extended operational life, however, is difficult to achieve when power-intensive functions like telemetry draw whatever little energy is available from energy-storage microdevices like thin-film lithium-ion batteries and/or microscale fuel cells. Harvesting ambient energy overcomes this deficit by continually replenishing the energy reservoir and indefinitely extending system lifetime. In this paper, a prototyped circuit that precharges, detects, and synchronizes to a variable voltage-constrained capacitor verifies experimentally that harvesting energy electrostatically from vibrations is possible. Experimental results show that, on average (excluding gate-drive and control losses), the system harvests 9.7 nJ/cycle by investing 1.7 nJ/cycle, yielding a net energy gain of approximately 8 nJ/cycle at an average of 1.6 ?W (in typical applications) for every 200 pF variation. Projecting and including reasonable gate-drive and controller losses reduces the net energy gain to 6.9 nJ/cycle at 1.38 ?W.


title: Elastic extreme learning machine for big data classification

abstract: Extreme Learning Machine (ELM) and its variants have been widely used for many applications due to its fast convergence and good generalization performance. Though the distributed ELM* based on MapReduce framework can handle very large scale training dataset in big data applications, how to cope with its rapidly updating is still a challenging task. Therefore, in this paper, a novel Elastic Extreme Learning Machine based on MapReduce framework, named Elastic ELM (E2LM), is proposed to cover the shortage of ELM* whose learning ability is weak to the updated large-scale training dataset. Firstly, after analyzing the property of ELM* adequately, it can be found out that its most computation-expensive part, matrix multiplication, can be incrementally, decrementally and correctionally calculated. Next, the Elastic ELM based on MapReduce framework is developed, which first calculates the intermediate matrix multiplications of the updated training data subset, and then update the matrix multiplications by modifying the old matrix multiplications with the intermediate ones. Then, the corresponding new output weight vector can be obtained with centralized computing using the update the matrix multiplications. Therefore, the efficient learning of rapidly updated massive training dataset can be realized effectively. Finally, we conduct extensive experiments on synthetic data to verify the effectiveness and efficiency of our proposed E2LM in learning massive rapidly updated training dataset with various experimental settings.


title: Neuropsychological Impairments and Changes in Emotional and Social Behaviour Following Severe Traumatic Brain Injury

abstract: Changes in emotional and social behaviour are relatively common following severe traumatic brain injury (TBI). Despite the serious consequences of these changes, little is known about the underlying neuropsychological deficits. In this study, we investigated which deficits might underlie these behavioural changes. The emotional and social behaviour of 17 patients with severe TBI was assessed with questionnaires, completed by the patient and a relative. Neuropsychological tests assessed recognition of emotional expressions, understanding of other people's mental states and cognitive fluency. Ratings from patients and relatives revealed changes in emotional and social behaviour after injury. Compared to matched healthy controls, the patients were impaired at recognising facial and vocal expressions of emotions, detecting social faux pas and nonverbal fluency. None of these impairments was significantly associated with the relatives' ratings of behavioural problems following TBI, although the correlation wit...


title: Sgx-Lapd: Thwarting Controlled Side Channel Attacks via Enclave Verifiable Page Faults

abstract: To make outsourcing computing more practical, Intel recently introduced SGX, a hardware extension that creates secure enclaves for the execution of client applications. With SGX, instruction execution and data access inside an enclave are invisible to the underlying OS, thereby achieving both confidentiality and integrity for outsourced computing. However, since SGX excludes the OS from its trusted computing base, now a malicious OS can attack SGX applications, particularly through controlled side channel attacks, which can extract application secrets through page fault patterns. This paper presents Sgx-Lapd, a novel defense that uses compiler instrumentation and enclave verifiable page fault to thwart malicious OS from launching page fault attacks. We have implemented Sgx-Lapd atop Linux kernel 4.2.0 and LLVM 3.6.2. Our experimental results show that it introduces reasonable overhead for SGX-nbench, a set of SGX benchmark programs that we developed.


title: Calibration, validation, and sensitivity analysis : What's what

abstract: Abstract One very simple interpretation of calibration is to adjust a set of parameters associated with a computational science and engineering code so that the model agreement is maximized with respect to a set of experimental data. One very simple interpretation of validation is to quantify our belief in the predictive capability of a computational code through comparison with a set of experimental data. Uncertainty in both the data and the code are important and must be mathematically understood to correctly perform both calibration and validation. Sensitivity analysis, being an important methodology in uncertainty analysis, is thus important to both calibration and validation. In this paper, we intend to clarify the language just used and express some opinions on the associated issues. We will endeavor to identify some technical challenges that must be resolved for successful validation of a predictive modeling capability. One of these challenges is a formal description of a “model discrepancy” term. Another challenge revolves around the general adaptation of abstract learning theory as a formalism that potentially encompasses both calibration and validation in the face of model uncertainty.


title: A giant with feet of clay: on the validity of the data that feed machine learning in medicine

abstract: This paper considers the use of machine learning in medicine by focusing on the main problem that it has been aimed at solving or at least minimizing: uncertainty. However, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of this class of computational models, thus undermining the clinical significance of their output. Recognizing this can motivate researchers to pursue different ways to assess the value of these decision aids, as well as alternative techniques that do not “sweep uncertainty under the rug” within an objectivist fiction (which doctors can come up by trusting).


title: Real time video fire detection using spatio-temporal consistency energy

abstract: In this paper a new algorithm is proposed for detecting fire from video data in real time based on a combination of features, including a new spatio-temporal consistency feature. A significant challenge in flame detection systems is to discriminate between actual fire and false alarms caused by fire colored objects. Towards this aim, we propose an algorithm that has significant advantages: a) it is fast since rectangular non-overlapping blocks are used as basic elements (instead of arbitrary-shaped regions) b) a spatio-temporal consistency feature is used in addition to color probability, spatial energy, flickering and spatio-temporal energy features and c) an improved rule-based classification approach is proposed, after evaluating seven different classification approaches. Experimental results are presented that confirm the efficiency of the proposed approach.


title: Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition

abstract: Unsupervised domain adaptation of speech signal aims at adapting a well-trained source-domain acoustic model to the unlabeled data from target domain. This can be achieved by adversarial training of deep neural network (DNN) acoustic models to learn an intermediate deep representation that is both senone-discriminative and domain-invariant. Specifically, the DNN is trained to jointly optimize the primary task of senone classification and the secondary task of domain classification with adversarial objective functions. In this work, instead of only focusing on learning a domain-invariant feature (i.e. the shared component between domains), we also characterize the difference between the source and target domain distributions by explicitly modeling the private component of each domain through a private component extractor DNN. The private component is trained to be orthogonal with the shared component and thus implicitly increases the degree of domain-invariance of the shared component. A reconstructor DNN is used to reconstruct the original speech feature from the private and shared components as a regularization. This domain separation framework is applied to the unsupervised environment adaptation task and achieved 11.08% relative WER reduction from the gradient reversal layer training, a representative adversarial training method, for automatic speech recognition on CHiME-3 dataset.


title: Gender Attitudes in Early Childhood: Behavioral Consequences and Cognitive Antecedents.

abstract: This study examined factors that predicted children's gender intergroup attitudes at age 5 and the implications of these attitudes for intergroup behavior. Ethnically diverse children from low-income backgrounds (N = 246; Mexican-, Chinese-, Dominican-, and African American) were assessed at ages 4 and 5. On average, children reported positive same-gender and negative other-gender attitudes. Positive same-gender attitudes were associated with knowledge of gender stereotypes. In contrast, positive other-gender attitudes were associated with flexibility in gender cognitions (stereotype flexibility, gender consistency). Other-gender attitudes predicted gender-biased behavior. These patterns were observed in all ethnic groups. These findings suggest that early learning about gender categories shape young children's gender attitudes and that these gender attitudes already have consequences for children's intergroup behavior at age 5.


title: Automatic Knowledge Base Construction from Scholarly Documents

abstract: The continuing growth of published scholarly content on the web ensures the availability of the most recent scientific findings to researchers. Scholarly documents, such as research articles, are easily accessed by using academic search engines that are built on large repositories of scholarly documents. Scientific information extraction from documents into a structured knowledge graph representation facilitates automated machine understanding of a document's content. Traditional information extraction approaches, that either require training samples or a preexisting knowledge base to assist in the extraction, can be challenging when applied to large repositories of digital documents. Labeled training examples for such large scale are difficult to obtain for such datasets. Also, most available knowledge bases are built from web data and do not have sufficient coverage to include concepts found in scientific articles. In this paper we aim to construct a knowledge graph from scholarly documents while addressing both these issues. We propose a fully automatic, unsupervised system for scientific information extraction that does not build on an existing knowledge base and avoids manually-tagged training data. We describe and evaluate a constructed taxonomy that contains over 15k entities resulting from applying our approach to 10k documents.


title: Adaptive power gating of 32-bit Kogge Stone adder

abstract: Static power consumes a significant portion of the available power budget. Consequently, leakage current reduction techniques such as power gating have become necessary. Standard global power gating approaches are an effective method to reduce idle leakage current, however, global power gating does not consider partially idle circuits and imposes significant delay and routing constraints. An adaptive power gating technique is applied locally to a 32-bit Kogge Stone adder, and evaluated at the 16nm FinFET technology node. This high granularity adaptive power gating approach employs a local controller to lower energy use and reduce circuit overhead. The controller conserves additional power when the circuit is partially idle (based on the inputs to the adder) by adaptively powering down inactive blocks. Moreover, the local controller reduces routing complexity since a global power gating signal is not required. The proposed adaptive power gating technique exhibits significant energy savings, ranging from 8% to 21%. This technique targets partially idle circuits, and therefore complements rather than replaces global power gating techniques. A 12% delay overhead results in a 5% area overhead. This delay overhead is reduced to 5% by increasing the area overhead to 16%, and can be further reduced by trading off additional area. HighlightsAn adaptive power gating technique is applied locally to a 32-bit Kogge Stone adder, and evaluated at the 16nm FinFET technology node.Local controller conserves additional power when the circuit is partially idle (based on the inputs to the adder) by adaptively powering down inactive blocks.Significant energy savings, ranging from 8% to 21%, are enabled by this adaptive power gating methodology.This technique targets partially idle circuits, and therefore complements rather than replaces global power gating techniques.


title: Teamwork and patient safety in dynamic domains of healthcare: a review of the literature

abstract: AIMS/BACKGROUND ::: This review examines current research on teamwork in highly dynamic domains of healthcare such as operating rooms, intensive care, emergency medicine, or trauma and resuscitation teams with a focus on aspects relevant to the quality and safety of patient care. ::: ::: ::: RESULTS ::: Evidence from three main areas of research supports the relationship between teamwork and patient safety: (1) Studies investigating the factors contributing to critical incidents and adverse events have shown that teamwork plays an important role in the causation and prevention of adverse events. (2) Research focusing on healthcare providers' perceptions of teamwork demonstrated that (a) staff's perceptions of teamwork and attitudes toward safety-relevant team behavior were related to the quality and safety of patient care and (b) perceptions of teamwork and leadership style are associated with staff well-being, which may impact clinician' ability to provide safe patient care. (3) Observational studies on teamwork behaviors related to high clinical performance have identified patterns of communication, coordination, and leadership that support effective teamwork. ::: ::: ::: CONCLUSION ::: In recent years, research using diverse methodological approaches has led to significant progress in team research in healthcare. The challenge for future research is to further develop and validate instruments for team performance assessment and to develop sound theoretical models of team performance in dynamic medical domains integrating evidence from all three areas of team research identified in this review. This will help to improve team training efforts and aid the design of clinical work systems supporting effective teamwork and safe patient care.


title: Vision-based Pose Estimation for Augmented Reality : A Comparison Study

abstract: Augmented reality aims to enrich our real world by inserting 3D virtual objects. In order to accomplish this goal, it is important that virtual elements are rendered and aligned in the real scene in an accurate and visually acceptable way. The solution of this problem can be related to a pose estimation and 3D camera localization. This paper presents a survey on different approaches of 3D pose estimation in augmented reality and gives classification of key-points-based techniques. The study given in this paper may help both developers and researchers in the field of augmented reality.


title: Camera-based drowsiness reference for driver state classification under real driving conditions

abstract: Experts assume that accidents caused by drowsiness are significantly under-reported in police crash investigations (1–3%). They estimate that about 24–33% of the severe accidents are related to drowsiness. In order to develop warning systems that detect reduced vigilance based on the driving behavior, a reliable and accurate drowsiness reference is needed. Studies have shown that measures of the driver's eyes are capable to detect drowsiness under simulator or experiment conditions. In this study, the performance of the latest eye tracking based in-vehicle fatigue prediction measures are evaluated. These measures are assessed statistically and by a classification method based on a large dataset of 90 hours of real road drives. The results show that eye-tracking drowsiness detection works well for some drivers as long as the blinks detection works properly. Even with some proposed improvements, however, there are still problems with bad light conditions and for persons wearing glasses. As a summary, the camera based sleepiness measures provide a valuable contribution for a drowsiness reference, but are not reliable enough to be the only reference.


title: Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction

abstract: The locality preserving projections (LPP) algorithm is a recently developed linear dimensionality reduction algorithm that has been frequently used in face recognition and other applications. However, the projection matrix in LPP is not orthogonal, thus creating difficulties for both reconstruction and other applications. As the orthogonality property is desirable, orthogonal LPP (OLPP) has been proposed so that an orthogonal projection matrix can be obtained based on a step by step procedure; however, this makes the algorithm computationally more expensive. Therefore, in this paper, we propose a fast and orthogonal version of LPP, called FOLPP, which simultaneously minimizes the locality and maximizes the globality under the orthogonal constraint. As a result, the computation burden of the proposed algorithm can be effectively alleviated compared with the OLPP algorithm. Experimental results on two face recognition data sets and two hyperspectral data sets are presented to demonstrate the effectiveness of the proposed algorithm.


title: From Eliza to XiaoIce: challenges and opportunities with social chatbots

abstract: Conversational systems have come a long way since their inception in the 1960s. After decades of research and development, we have seen progress from Eliza and Parry in the 1960s and 1970s, to task-completion systems as in the Defense Advanced Research Projects Agency (DARPA) communicator program in the 2000s, to intelligent personal assistants such as Siri, in the 2010s, to today’s social chatbots like XiaoIce. Social chatbots’ appeal lies not only in their ability to respond to users’ diverse requests, but also in being able to establish an emotional connection with users. The latter is done by satisfying users’ need for communication, affection, as well as social belonging. To further the advancement and adoption of social chatbots, their design must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with a social chatbot; as such, we define the success metric for social chatbots as conversation-turns per session (CPS). Using XiaoIce as an illustrative example, we discuss key technologies in building social chatbots from core chat to visual awareness to skills. We also show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses. As we become the first generation of humans ever living with artificial intelligenc (AI), we have a responsibility to design social chatbots to be both useful and empathetic, so they will become ubiquitous and help society as a whole.


title: Approximate counting with a floating-point counter

abstract: Memory becomes a limiting factor in contemporary applications, such as analyses of the Webgraph and molecular sequences, when many objects need to be counted simultaneously. Robert Morris [Communications of the ACM, 21:840--842, 1978] proposed a probabilistic technique for approximate counting that is extremely space-efficient. The basic idea is to increment a counter containing the value $X$ with probability $2^{-X}$. As a result, the counter contains an approximation of $\lg n$ after $n$ probabilistic updates stored in $\lg\lg n$ bits. Here we revisit the original idea of Morris, and introduce a binary floating-point counter that uses a $d$-bit significand in conjunction with a binary exponent. The counter yields a simple formula for an unbiased estimation of $n$ with a standard deviation of about $0.6\cdot n2^{-d/2}$, and uses $d+\lg\lg n$ bits. We analyze the floating-point counter's performance in a general framework that applies to any probabilistic counter, and derive practical formulas to assess its accuracy.


title: Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey

abstract: Growing environmental awareness coupled with stricter governmental regulations has fueled the need for integrating sustainability into supply chain and logistics activities. Accordingly, recent studies in the literature have emphasized the significance of environmentally concerned logistics operations (ECLO). Research in the broad area of ECLO encompasses a wide range of topics including sustainable supply chain, green supply chain, closed-loop supply chain, low-carbon logistics, and waste management. In this paper, a comprehensive content analysis and area review is presented. Over 800 papers published between 1994 and 2017 in peer-reviewed journals, proceedings, and book chapters are utilized. These papers are analyzed in consecutive stages after being reviewed under a structural dimension process that addresses the fields of environmentally concerned logistics operations. Following the state-of-the-art review, a detailed analysis of ECLO research with a special emphasis on fuzzy applications is provided. The findings clearly indicate that the fuzzy multi-criteria decision making technique is a frequently used hybrid method, whereas fuzzy sets theory and other fuzzy hybrid techniques identify a gap in the related literature. This paper provides further critical analysis and other research suggestions in order to clarify these gaps and offer additional research perspectives. This information may provide extensive data that will enable future researchers to fill these gaps within this field.


title: An FPGA-optimized architecture of horn and schunck optical flow algorithm for real-time applications

abstract: Optical flow estimation of image sequences is one of the key elements for motion detection. However, processing the optical flow in real-time is still an open task due to its computationally expensive nature. In this paper we present an FPGA-optimized architecture for optical flow estimation based on the algorithm of Horn and Schunck. While existing FPGA-realizations are only partly real-time capable, on a Stratix IV our architecture enables the computation of the optical flow for each pixel of a frame with 640 × 512 pixels at a framerate of 30 fps in iterative and up to 4k resolution (4,096 × 2,304 pixels) at a framerate of 20 fps in full-pipelined form.


title: Improving “bag-of-keypoints” image categorisation

abstract: In this paper we propose two distinct enhancements to the basic “bag-of-keypoints” image categorisation scheme proposed in [4]. In this approach images are represented as a variable sized set of local image features (keypoints). Thus, we require machine learning tools which can operate on sets of vectors. In [4] this is achieved by representing the set as a histogram over bins found by k-means. We show how this approach can be improved and generalised using Gaussian Mixture Models (GMMs). Alternatively, the set of keypoints can be represented directly as a probability density function, over which a kernel can be defined. This approach is shown to give state of the art categorisation performance.


title: A Stacked Sub-Word Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

abstract: The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efficiency and effectiveness. Our solution is a two step process. First, one word-based segmenter, one character-based segmenter and one local character classifier are trained to produce coarse segmentation and POS information. Second, the outputs of the three predictors are merged into sub-word sequences, which are further bracketed and labeled with POS tags by a fine-grained sub-word tagger. The coarse-to-fine search scheme is efficient, while in the sub-word tagging step rich contextual features can be approximately derived. Evaluation on the Penn Chinese Tree-bank shows that our model yields improvements over the best system reported in the literature.


title: Protective Effect of Withania somnifera and Cardiospermum halicacabum Extracts Against Collagenolytic Degradation of Collagen

abstract: The irreversible destruction of extracellular matrix (ECM) such as cartilage, tendon, and bone that comprise synovial joints is the hallmark of both rheumatoid arthritis and osteoarthritis by over-expression of matrix metalloproteinase (MMP)-collagenases. We report herein the detailed study on the inhibitory effects of Withania somnifera extract (WSE) and Cardiospermum halicacabum extract (CHE) on Clostridium histolyticum collagenase (ChC) activity against the degradation of the ECM component of bovine Achilles tendon type I collagen by hydroxyproline assay method. Interaction of WSE and CHE with ChC exhibited 71% and 88% inhibition, respectively, to the collagenolytic activity of ChC against collagen degradation, and the inhibition was found to be concentration-dependent. The inhibition kinetics of ChC by both the extracts has been deduced from the extent of hydrolysis of N-[3-(2-furyl) acryloyl]-Leu-Gly-Pro-Ala. Both WSE and CHE are provided competitive and mixed type inhibition on ChC activity, respectively. Circular dichroism studies of ChC on treatment with WSE and CHE revealed changes in the secondary structure of collagenase. These results suggest that the WSE and CHE facilitated collagen stabilization through collagenase inhibition.


title: A Factorized Quaternion Approach to Determine the Arm Motions Using Triaxial Accelerometers With Anatomical and Sensor Constraints

abstract: In this paper, we present a new factorized quaternion approach for determining the arm limbs' orientation using triaxial accelerometers with consideration of anatomical and sensor constraints. Typical use of the quaternion method determines the angle and axis of rotation represented by a single angle-axis quaternion. Different from the conventional approach, we propose using the factorized quaternion approach for the determination of arm motions. This approach allows the implementation of anatomical arm constraints which match the range of motion of the human arm and also reduces the ambiguity in solutions. In addition, the singularities arising from the use of triaxial accelerometers can be detected and resolved for a transient state. Measurement of the upper arm motion is demonstrated along a vertical plane and extended along a tilted plane for the forearm. Experiments have been conducted using a wireless sensor network equipped with triaxial accelerometers attached to the arm. The results have been benchmarked with a commercial inertial measurement unit to validate the feasibility and advantages of this new approach. Comparable performance in terms of accuracy has been obtained at a much reduced cost and power consumption.


title: Supervised Transformer Network for Efficient Face Detection

abstract: Large pose variations remain to be a challenge that confronts real-word face detection. We propose a new cascaded Convolutional Neural Network, dubbed the name Supervised Transformer Network, to address this challenge. The first stage is a multi-task Region Proposal Network (RPN), which simultaneously predicts candidate face regions along with associated facial landmarks. The candidate regions are then warped by mapping the detected facial landmarks to their canonical positions to better normalize the face patterns. The second stage, which is a RCNN, then verifies if the warped candidate regions are valid faces or not. We conduct end-to-end learning of the cascaded network, including optimizing the canonical positions of the facial landmarks. This supervised learning of the transformations automatically selects the best scale to differentiate face/non-face patterns. By combining feature maps from both stages of the network, we achieve state-of-the-art detection accuracies on several public benchmarks. For real-time performance, we run the cascaded network only on regions of interests produced from a boosting cascade face detector. Our detector runs at 30 FPS on a single CPU core for a VGA-resolution image.


Localization of Fatty Acyl and Double Bond Positions in Phosphatidylcholines Using a Dual Stage CID Fragmentation Coupled with Ion Mobility Mass Spectrometry


title: Selection of optimal supplier in supply chain management strategy with analytic network process and choquet integral

abstract: Selection of appropriate suppliers in supply chain management strategy (SCMS) is a challenging issue because it requires battery of evaluation criteria/attributes, which are characterized with complexity, elusiveness, and uncertainty in nature. This paper proposes a novel hierarchical evaluation framework to assist the expert group to select the optimal supplier in SCMS. The rationales for the evaluation framework are based upon (i) multi-criteria decision making (MCDM) analysis that can select the most appropriate alternative from a finite set of alternatives with reference to multiple conflicting criteria, (ii) analytic network process (ANP) technique that can simultaneously take into account the relationships of feedback and dependence of criteria, and (iii) choquet integral-a non-additive fuzzy integral that can eliminate the interactivity of expert subjective judgment problems. A case PCB manufacturing firm is studied and the results indicated that the proposed evaluation framework is simple and reasonable to identify the primary criteria influencing the SCMS, and it is effective to determine the optimal supplier even with the interactive and interdependent criteria/attributes. This hierarchical evaluation framework provides a complete picture in SCMS contexts to both researchers and practitioners.


title: Design, kinematics, and control of a soft spatial fluidic elastomer manipulator

abstract: This paper presents a robotic manipulation system capable of autonomously positioning a multi-segment soft fluidic elastomer robot in three dimensions. Specifically, we present an extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable. To understand the deformation of a single arm segment, we develop and experimentally validate a static deformation model. Then, to kinematically model the multi-segment manipulator, we use a piece-wise constant curvature assumption consistent with more traditional continuum manipulators. In addition, we define a complete fabrication process for this new manipulator and use this process to make multiple functional prototypes. In order to power the robot's spatial actuation, a high capacity fluidic drive cylinder array is implemented, providing continuously variable, closed-circuit gas delivery. Next, using real-time data from a vision system, we develop a processing and control algorithm that generates realizable kinematic curvature trajectories and controls the manipulator's configuration along these trajectories. Lastly, we experimentally demonstrate new capabilities offered by this soft fluidic elastomer manipulation system such as entering and advancing through confined three-dimensional environments as well as conforming to goal shape-configurations within a sagittal plane under closed-loop control.


title: Miniaturized 122 GHz ISM band FMCW radar with micrometer accuracy

abstract: In this paper, a miniaturized 122 GHz ISM band FMCW radar is used to achieve micrometer accuracy. The radar consists of a SiGe single chip radar sensor and LCP off-chip antennas. The antennas are integrated in a QFN package. To increase the gain of the radar, an additional lens is used. A combined frequency and phase evaluation algorithm provides micrometer accuracy. The influence of the lens phase center on the beat frequency phase and hence, the overall accuracy is shown. Furthermore, accuracy limitations of the radar system over larger measurement distances are investigated. Accuracies of 200 µm and 2 µm are achieved over a distance of 1.9 m and 5 mm, respectively.


title: Audio feature reduction and analysis for automatic music genre classification

abstract: Multimedia database retrieval is growing at a fast rate thereby subsequent increase in the popularity of online retrieval system. The large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, there is a need of robust automatic music genre classification method for organizing these music data into different classes according to the certain viable information. There are two fundamental components to be considered for genre classification namely audio feature extraction and classifier design. In this paper, diverse audio features set have been proposed to characterize the music contents precisely. The feature sets belong to four different groups, i.e. dynamic, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as representatives, including up to the 4 th order central moments of each feature, and covariance components. Ultimately, significant numbers of representative attributes are controlled by MRMR algorithm. The algorithm calculates the score level of all feature attributes and orders them. The high score feature attributes are only considered for genre classification. Moreover, we can visualize that which audio features and which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficients (MFCCs) have higher scored level than other feature attributes. Furthermore, MRMR does not transform the feature value like as principal component analysis (PCA). Besides these, the comparison has been made based on classification accuracy between two-dimensionality reduction methodologies using support vector machine (SVM). The classification accuracy of MRMR feature reduction set outperforms than PCA. The overall classification is also higher than other existing state-of-the-art of frame base methods.


title: Xen and the art of repeated research

abstract: Xen is an x86 virtual machine monitor produced by the University of Cambridge Computer Laboratory and released under the GNU General Public License. Performance results comparing XenoLinux (Linux running in a Xen virtual machine) to native Linux as well as to other virtualization tools such as User Mode Linux (UML) were recently published in the paper "Xen and the Art of Virtualization" at the Symposium on Operating Systems Principles (October 2003). In this study, we repeat this performance analysis of Xen. We also extend the analysis in several ways, including comparing XenoLinux on x86 to an IBM zServer. We use this study as an example of repeated research. We argue that this model of research, which is enabled by open source software, is an important step in transferring the results of computer science research into production environments.


title: Collaborative Ranking between Supervised and Unsupervised Approaches for Keyphrase Extraction

abstract: Automatic keyphrase extraction methods have generally taken either supervised or unsupervised approaches. Supervised methods extract keyphrases by using a training document set, thus acquiring knowledge from a global collection of texts. Conversely, unsupervised methods extract keyphrases by determining their relevance in a single-document context, without prior learning. We present a hybrid keyphrase extraction method for short articles, HybridRank, which leverages the benefits of both approaches. Our system implements modified versions of the TextRank (Mihalcea and Tarau, 2004)—unsupervised—and KEA (Witten et al., 1999)—supervised—methods, and applies a merging algorithm to produce an overall list of keyphrases. We have tested HybridRank on more than 900 abstracts belonging to a wide variety of subjects, and show its superior effectiveness. We conclude that knowledge collaboration between supervised and unsupervised methods can produce higher-quality keyphrases than applying these methods individually.


International Journal of Electrical and Computer Engineering 2:8 2007 Application of Extreme Learning Machine Method for Time Series Analysis


title: The neuroscience of grasping

abstract: People have always been fascinated by the exquisite precision and flexibility of the human hand. When hand meets object, we confront the overlapping worlds of sensorimotor and cognitive functions. We reach for objects, grasp and lift them, manipulate them and use them to act on other objects. This review examines one of these actions — grasping. Recent research in behavioural neuroscience, neuroimaging and electrophysiology has the potential to reveal where in the brain the process of grasping is organized, but has yet to address several questions about the sensorimotor transformations that relate to the control of the hands.


title: Fractionating impulsivity: neuropsychiatric implications

abstract: Impulsivity comes in various forms, with some forms considered more or less advantageous than others. Dalley and Robbins review the different types of impulsivity and their underlying neural mechanisms, and comment on the applicability of measures of impulsivity in research into psychiatric disorders.


title: A Mosaic Approach to Touchless Fingerprint Image with Multiple Views

abstract: Touch-based fingerprint technology causes distortions to the fingerprint features due to contact between finger and sensor device. Touch-less fingerprint technique is introduced in an effort to solve this problem by avoiding contact between the finger and the surface of the sensor. However, single contact-less images of the finger leads to less captured features and less overlap between the different views of the fingerprint. In this paper, a new touchless approach for fingerprints based on multiple views images is proposed. Three fingerprint images are captured from the left, center and right side of finger using mobile camera. These three images are combined together using the mosaic method in order to construct a large usable area and increase the overlap area. The proposed method has been compared with other proposed touchless methods. Our touchless mosaic method has offered better performance and achieves more fingerprint features compare to single view touchless method. The proposed method has been evaluated using our touchless database that consists of 480 fingerprint images.


title: An analysis of power consumption in a smartphone

abstract: Mobile consumer-electronics devices, especially phones, are powered from batteries which are limited in size and therefore capacity. This implies that managing energy well is paramount in such devices. ::: ::: Good energy management requires a good understanding of where and how the energy is used. To this end we present a detailed analysis of the power consumption of a recent mobile phone, the Openmoko Neo Freerunner. We measure not only overall system power, but the exact breakdown of power consumption by the device's main hardware components. We present this power breakdown for micro-benchmarks as well as for a number of realistic usage scenarios. These results are validated by overall power measurements of two other devices: the HTC Dream and Google Nexus One. ::: ::: We develop a power model of the Freerunner device and analyse the energy usage and battery lifetime under a number of usage patterns. We discuss the significance of the power drawn by various components, and identify the most promising areas to focus on for further improvements of power management. We also analyse the energy impact of dynamic voltage and frequency scaling of the device's application processor.


title: Learning from imbalanced data: open challenges and future directions

abstract: Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.


title: Toward a Middle-range Theory of Resilience

abstract: The ability to transform disaster into a growth experience and move forward defines the concept of resilience.Nursing is concerned with individuals in this process of moving through adversity and with its own contribution to that process. However, when one examines the literature on resilience for i


title: DISCOVERY AND INTEGRATION OF JOB MARKET SERVICE USING SEMANTIC WEB SERVICE APPROACH

abstract: Nowadays, there have been many websites applying information services of job vacancy. However, they ::: still have many weaknesses and have not been able to provide relevant information for the job seekers yet. ::: One of the weaknesses is that there have not been any information sharing with other job fair websites, ::: therefore the information of the job being offered is still very limited. The presence of semantic web ::: service technology makes any web services possible to be integrated dynamically. It is now possible to do ::: the process of discovery, composition, invocation, and monitoring of web service automatically. This paper ::: is using the technology of semantic web service to integrate several services on job fair information in the computer network. The conducted research was focused on the service discovery to support the searching and selecting of the relevant and reliable web service of job market. The method applied on the service discovery is combining the algorithm of semantic matching and simple additive weighting. The result of the testing is that the relevant and the best web service is found out based on the calculation of the similarity of the functional parameter and the service quality.


title: High-performance web crawling

abstract: High-performance web crawlers are an important component of many web services. For example, search services use web crawlers to populate their indices, comparison shopping engines use them to collect product and pricing information from online vendors, and the Internet Archive uses them to record a history of the Internet. The design of a high-performance crawler poses many challenges, both technical and social, primarily due to the large scale of the web. The web crawler must be able to download pages at a very high rate, yet it must not overwhelm any particular web server. Moreover, it must maintain data structures far too large to fit in main memory, yet it must be able to access and update them efficiently. This chapter describes our experience building and operating such a high-performance crawler.


title: A waterfall model for knowledge management and experience management

abstract: This paper examines experience and knowledge, experience management (EM) and knowledge management (KM), and their interrelationships. It then proposes waterfall models for both EM and KM. The models characterize EM and KM as the integration of experience processing and corresponding management, that of knowledge processing and corresponding management respectively. The proposed approach facilitates research and development of KM, EM, and hybrid intelligent systems.


title: Differential Effects of Non-REM and REM Sleep on Memory Consolidation?

abstract: Sleep benefits memory consolidation. Previous theoretical accounts have proposed a differential role of slow-wave sleep (SWS), rapid-eye-movement (REM) sleep, and stage N2 sleep for different types of memories. For example the dual process hypothesis proposes that SWS is beneficial for declarative memories, whereas REM sleep is important for consolidation of non-declarative, procedural and emotional memories. In fact, numerous recent studies do provide further support for the crucial role of SWS (or non-REM sleep) in declarative memory consolidation. However, recent evidence for the benefit of REM sleep for non-declarative memories is rather scarce. In contrast, several recent studies have related consolidation of procedural memories (and some also emotional memories) to SWS (or non-REM sleep)-dependent consolidation processes. We will review this recent evidence, and propose future research questions to advance our understanding of the role of different sleep stages for memory consolidation.


title: Echocardiographic Assessment of Preload Responsiveness in Critically Ill Patients

abstract: Fluid challenges are considered the cornerstone of resuscitation in critically ill patients. However, clinical studies have demonstrated that only about 50% of hemodynamically unstable patients are volume responsive. Furthermore, increasing evidence suggests that excess fluid resuscitation is associated with increased mortality. It therefore becomes vital to assess a patient's fluid responsiveness prior to embarking on fluid loading. Static pressure (CVP, PAOP) and echocardiographic (IVC diameter, LVEDA) parameters fails to predict volume responsiveness. However, a number of dynamic echocardiographic parameters which are based on changes in vena-caval dimensions or cardiac function induce by positive pressure ventilation or passive leg raising appear to be highly predictive of volume responsiveness.


title: Studying engagement and performance with learning technology in an African classroom

abstract: In this paper, we study the engagement and performance of students in a classroom using a system the Cognitive Learning Companion (CLC). CLC is designed to keep track of the relationship between the student, content interaction and learning progression. It also provides evidence-based engagement-oriented actionable insights to teachers by assessing information from a sensor-rich instrumented learning environment in order to infer a learner's cognitive and affective states. Data captured from the instrumented environment is aggregated and analyzed to create interlinked insights helping teachers identify how students engage with learning content and view their performance records on selected assignments. We conducted a 1 month pilot with 27 learners in a primary school in Nairobi, Kenya during their maths and science instructional periods. We present our primary analysis of content-level interactions and engagement at the individual student and classroom level.


title: Prosthetic gingival reconstruction in fixed partial restorations. Part 3: laboratory procedures and maintenance.

abstract: Part 1 of the present series presented a rationale for including prosthetic gingiva in the planning of a fixed restoration to ensure an esthetic result for patients with severe horizontal and vertical ridge deficiencies. The second part focused on the diagnostic and treatment planning aspects of the use of artificial gingiva. This third and final installment in the series focuses on the laboratory and clinical procedures involved in fabricating a prosthesis with artificial gingiva and provides information on proper maintenance of these restorations.


Detection and Classification of Dental Caries in X-ray Images Using Deep Neural Networks


title: Exploring compassion: A meta-analysis of the association between self-compassion and psychopathology

abstract: Abstract Compassion has emerged as an important construct in studies of mental health and psychological therapy. Although an increasing number of studies have explored relationships between compassion and different facets of psychopathology there has as yet been no systematic review or synthesis of the empirical literature. We conducted a systematic search of the literature on compassion and mental health. We identified 20 samples from 14 eligible studies. All studies used the Neff Self Compassion Scale (Neff, 2003b). We employed meta-analysis to explore associations between self-compassion and psychopathology using random effects analyses of Fisher's Z correcting for attenuation arising from scale reliability. We found a large effect size for the relationship between compassion and psychopathology of r = − 0.54 (95% CI = − 0.57 to − 0.51; Z = − 34.02; p


title: Advances in the prevention of Alzheimer's Disease

abstract: Background ::: Definitions and diagnostic criteria for all medical conditions are regularly subjected to reviews and revisions as knowledge advances. In the field of Alzheimer's disease (AD) research, it has taken almost three decades for diagnostic nomenclature to undergo major re-examination. The shift towards presymptomatic and pre-dementia stages of AD has brought prevention and treatment trials much closer to each other than before. ::: ::: Methods ::: Here we discuss: (i) the impact of diagnostic reliability on the possibilities for developing preventive strategies for AD; (ii) the scientific evidence to support moving from observation to action; (iii) ongoing intervention studies; and (iv) the methodological issues and prospects for balancing strategies for high-risk individuals with those for broad population-based prevention. ::: ::: Results ::: The associations between neuropathology and cognition are still not entirely clear. In addition, the risk factors for AD dementia and the neuropathological hallmarks of AD may not necessarily be the same. Cognitive impairment has a clearer clinical significance and should therefore remain the main focus of prevention. Risk/protective factors for dementia/AD need to be studied from a life-course perspective. New approaches in prevention trials include enrichment strategies based on genetic risk factors or beta-amyloid biomarkers (at least four ongoing pharmacological trials), and multidomain interventions simultaneously targeting various vascular and lifestyle-related risk factors (at least three ongoing trials). Experience from prevention programmes in other chronic diseases can provide additional methodological improvements. ::: ::: Conclusions ::: Building infrastructures for international collaborations is necessary for managing the worldwide public health problem of AD and dementia. The International Database on Aging and Dementia (IDAD) and the European Dementia Prevention Initiative (EDPI) are examples of ongoing international efforts aiming to improve the methodology of preventive studies and provide the basis for larger intervention trials.


title: Survey the Inverse Property of Quantum Gates for Concurrent Error Detection

abstract: Reversible logic gates are extensively known to be compatible with future computing technologies which approximately dissipate zero heat. Concurrent Error Detection (CED) is very noticeable and widely utilized to enhance system dependability. All the inputs can be reproduced at the outputs, by utilizing the inverse property of reversible logic. Hence, by comparing the original inputs with the reproduced inputs, the errors in reversible circuits can be detected. The focus of this paper is on introducing inverse property of reversible logic gates. Also, some well-known reversible logic gates with their inverse characteristics are described. Results have been confirmed by the truth table. Finally, the proposed structures significantly show the sample circuits design for Concurrent Error Detection.


title: Doubly Attentive Transformer Machine Translation

abstract: In this paper a doubly attentive transformer machine translation model (DATNMT) is presented in which a doubly-attentive transformer decoder normally joins spatial visual features obtained via pretrained convolutional neural networks, conquering any gap between image captioning and translation. In this framework, the transformer decoder figures out how to take care of source-language words and parts of an image freely by methods for two separate attention components in an Enhanced Multi-Head Attention Layer of doubly attentive transformer, as it generates words in the target language. We find that the proposed model can effectively exploit not just the scarce multimodal machine translation data, but also large general-domain text-only machine translation corpora, or image-text image captioning corpora. The experimental results show that the proposed doubly-attentive transformer-decoder performs better than a single-decoder transformer model, and gives the state-of-the-art results in the English-German multimodal machine translation task.


title: Speaker Recognition from Raw Waveform with SincNet

abstract: Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal.This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application.Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms.


title: Category-level 6D Object Pose Recovery in Depth Images

abstract: Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks. In this study, we address category-level full 6D object pose estimation in the context of depth modality, introducing a novel part-based architecture that can tackle the above-mentioned challenges. Our architecture particularly adapts the distribution shifts arising from shape discrepancies, and naturally removes the variations of texture, illumination, pose, etc., so we call it as"Intrinsic Structure Adaptor (ISA)". We engineer ISA based on the followings: i)"Semantically Selected Centers (SSC)"are proposed in order to define the"6D pose"at the level of categories. ii) 3D skeleton structures, which we derive as shape-invariant features, are used to represent the parts extracted from the instances of given categories, and privileged one-class learning is employed based on these parts. iii) Graph matching is performed during training in such a way that the adaptation/generalization capability of the proposed architecture is improved across unseen instances. Experiments validate the promising performance of the proposed architecture on both synthetic and real datasets.


title: What Do I Know About It Versus How Do I Feel About It? A Theoretical Analysis of Response Processes in Evaluative Conditioning

abstract: Evaluative conditioning (EC) is defined as the change in the evaluation of a conditioned stimulus (CS) due to its pairing with a valenced unconditioned stimulus (US). Because EC involves the acquisition of evaluative responses, previous EC research has paid relatively little attention to the processes involved in the expression of evaluative responses. Drawing on research on response processes underlying evaluative judgments, we argue that EC effects on evaluative judgments can be mediated by the use of (1) recollective memory for events involving the CS or (2) one's spontaneous affective reaction toward the CS. Because the two proximal outcomes of CS-US pairings can have independent effects on evaluative judgments as a distal outcome, influences on expression-related processes can lead to inaccurate conclusions when they are attributed to acquisition-related processes. Our analysis suggests that a deeper understanding of EC requires a broader theoretical approach that includes both acquisition-related an...


title: Psychometric Evaluation of the Problem Gambling Severity Index-Chinese Version (PGSI-C)

abstract: The purpose of this study was to examine the psychometric properties of the Chinese 9-item Problem Gambling Severity Index (PGSI) derived from the 31-item Canadian Problem Gambling Index (CPGI) originally developed by Ferris and Wynne (2001). Exploratory factor analysis (EFA; n = 386; Group A data) and confirmatory factor analysis (CFA; n = 387; Group B data) on the Chinese student and community data (Mean age = 25.36 years) showed that a unifactorial model fitted the data with good reliability score (Cronbach’s alpha = 0.77). The concurrent validity of the PGSI-C was good in terms of the Chinese data matching the expected correlation between PGSI-C and other variables or scales such as SOGS, gambling frequency, gambling urge, gambling cognitions, depression, anxiety, and stress. The scale also reported good discriminant and predictive validity. In sum, the PGSI-C has good psychometric properties and can be used among Chinese communities to identify at-risk problem gamblers. Implications and suggestions for future research are discussed.


title: Association of sleep patterns with psychological positive health and health complaints in children and adolescents

abstract: Purpose ::: Psychological positive health and health complaints have long been ignored scientifically. Sleep plays a critical role in children and adolescents development. We aimed at studying the association of sleep duration and quality with psychological positive health and health complaints in children and adolescents from southern Spain.


title: A Functional Data Analysis Approach to Traffic Volume Forecasting

abstract: Traffic volume forecasts are used by many transportation analysis and management systems to better characterize and react to fluctuating traffic patterns. Most current forecasting methods do not take advantage of the underlying functional characteristics of the time series to make predictions. This paper presents a methodology that uses functional principal components analysis to create high-quality online traffic volume forecasts. The methodology is validated with a data set of 1755 days of 15 min aggregated traffic volume time series. Compared with 365 randomly selected days, the functional forecasts are found to outperform traditional seasonal autoregressive integrated moving average-based methods in both count deviation and root mean squared error. In addition, through the functional data analysis approach the full exploitation of the continuous nature of the data can be achieved.


title: Fast and Robust Segmentation of White Blood Cell Images by Self-supervised Learning

abstract: Abstract A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets.


title: Heterogeneous Domain Adaptation Using Manifold Alignment

abstract: We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications.


Akrinol, a new topical anti-infective agent.


Measuring presence: A literature-based approach to the development of a standardized paper-and-pencil instrument


title: A Survey On Unicast Routing Protocols For VANET

abstract: Vehicular Ad Hoc Network (VANET) is a subgroup of mobile ad hoc network (MANET). It is an emerging new technology to exchange information between vehicles to vehicles. VANETs are considered as one of the most noticeable technologies for improving the efficiency and safety of transportation systems. VANET mainly used to exchange traffic information between the vehicles and prevent accident. In VANETs the high mobility of the nodes is the major concern. This dynamic topology makes the route unstable and unreliable for exchange of information or messages among the vehicles in the ad hoc network. To improve the throughput and performance of the VANETs, routes between nodes must be reliable, less overhead and stable. It is a challenging task to design a routing protocols for VANETs which should support the intelligent transportation system (ITS) for enhancing the driver's safety, improving whole driver experience and regulating traffic. In this paper, the various challenges and issues of routing protocols of VANETs are discussed about its advantages and disadvantages in VANETs scenarios.


title: Overview of spatial channel models for antenna array communication systems

abstract: Spatial antenna diversity has been important in improving the radio link between wireless users. Historically, microscopic antenna diversity has been used to reduce the fading seen by a radio receiver, whereas macroscopic diversity provides multiple listening posts to ensure that mobile communication links remain intact over a wide geographic area. In later years, the concepts of spatial diversity have been expanded to build foundations for emerging technologies, such as smart (adaptive) antennas and position location systems. Smart antennas hold great promise for increasing the capacity of wireless communications because they radiate and receive energy only in the intended directions, thereby greatly reducing interference. To properly design, analyze, and implement smart antennas and to exploit spatial processing in emerging wireless systems, accurate radio channel models that incorporate spatial characteristics are necessary. In this tutorial, we review the key concepts in spatial channel modeling and present emerging approaches. We also review the research issues in developing and using spatial channel models for adaptive antennas.


title: Quantifying the informativeness for biomedical literature summarization: An itemset mining method

abstract: Abstract Objective Automatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our summarizer quantifies the informativeness of each sentence using the support values of itemsets appearing in the sentence. Methods To address the concept-level analysis of text, our method initially maps the original document to biomedical concepts using the Unified Medical Language System (UMLS). Then, it discovers the essential subtopics of the text using a data mining technique, namely itemset mining, and constructs the summarization model. The employed itemset mining algorithm extracts a set of frequent itemsets containing correlated and recurrent concepts of the input document. The summarizer selects the most related and informative sentences and generates the final summary. Results We evaluate the performance of our itemset-based summarizer using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, performing a set of experiments. We compare the proposed method with GraphSum, TexLexAn, SweSum, SUMMA, AutoSummarize, the term-based version of the itemset-based summarizer, and two baselines. The results show that the itemset-based summarizer performs better than the compared methods. The itemset-based summarizer achieves the best scores for all the assessed ROUGE metrics (R-1: 0.7583, R-2: 0.3381, R-W-1.2: 0.0934, and R-SU4: 0.3889). We also perform a set of preliminary experiments to specify the best value for the minimum support threshold used in the itemset mining algorithm. The results demonstrate that the value of this threshold directly affects the accuracy of the summarization model, such that a significant decrease can be observed in the performance of summarization due to assigning extreme thresholds. Conclusion Compared to the statistical, similarity, and word frequency methods, the proposed method demonstrates that the summarization model obtained from the concept extraction and itemset mining provides the summarizer with an effective metric for measuring the informative content of sentences. This can lead to an improvement in the performance of biomedical literature summarization.


A Survey on Knowledge Extraction Techniques for Semantic Web


Improving Hate Speech Detection with Deep Learning Ensembles


title: Equine-Assisted Occupational Therapy: Increasing Engagement for Children With Autism Spectrum Disorder

abstract: Engagement in meaningful activities is essential to development and is often reduced in children with autism spectrum disorder (ASD) who have limited engagement in activities or relationships. A multiple-baseline design was used with 7 children with ASD ages 4-8 yr to assess the effect of including a horse in occupational therapy intervention on task engagement. The children showed improvements in engagement. Including horses in occupational therapy sessions may be a valuable addition to conventional treatments to increase task engagement of children with ASD. Factors related to the environment, therapeutic strategies, and individual participation need to be considered in understanding why this intervention may be effective and developing a theoretical basis for implementation.


Revisiting Distant Supervision for Relation Extraction


title: High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models

abstract: Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference, while maintaining a measure of uncertainty in the model parameters. Stochastic gradient MCMC algorithms (SG-MCMC) are a family of diffusion-based sampling methods for large-scale Bayesian learning. In SG-MCMC, multi-variate stochastic gradient thermostats (mSGNHT) augment each parameter of interest, with a momentum and a thermostat variable to maintain stationary distributions as target posterior distributions. As the number of variables in a continuous-time diffusion increases, its numerical approximation error becomes a practical bottleneck, so better use of a numerical integrator is desirable. To this end, we propose use of an efficient symmetric splitting integrator in mSGNHT, instead of the traditional Euler integrator. We demonstrate that the proposed scheme is more accurate, robust, and converges faster. These properties are demonstrated to be desirable in Bayesian deep learning. Extensive experiments on two canonical models and their deep extensions demonstrate that the proposed scheme improves general Bayesian posterior sampling, particularly for deep models.


title: Enhancing attention through training

abstract: Attention can be improved by repetition of a specific task that involves an attention network (network training), or by exercise or meditation that changes the brain state (state training). We first review the concept of attention networks that link changes in orienting, alerting and executive control to brain networks. Network training through video games or computer exercises can improve aspects of attention. The extent of transfer beyond the trained task is a controversial issue. Mindfulness is a form of meditation that keeps attention focused on the current moment. Some forms of meditation have been shown to improve executive attention reduce stress and produce specific brain changes. Additional research is needed to understand the limits and mechanisms of these effects.


title: Biomaterials approaches to treating implant-associated osteomyelitis.

abstract: Orthopaedic devices are the most common surgical devices associated with implant-related infections and Staphylococcus aureus (S. aureus) is the most common causative pathogen in chronic bone infections (osteomyelitis). Treatment of these chronic bone infections often involves combinations of antibiotics given systemically and locally to the affected site via a biomaterial spacer. The gold standard biomaterial for local antibiotic delivery against osteomyelitis, poly(methyl methacrylate) (PMMA) bone cement, bears many limitations. Such shortcomings include limited antibiotic release, incompatibility with many antimicrobial agents, and the need for follow-up surgeries to remove the non-biodegradable cement before surgical reconstruction of the lost bone. Therefore, extensive research pursuits are targeting alternative, biodegradable materials to replace PMMA in osteomyelitis applications. Herein, we provide an overview of the primary clinical treatment strategies and emerging biodegradable materials that may be employed for management of implant-related osteomyelitis. We performed a systematic review of experimental biomaterials systems that have been evaluated for treating established S. aureus osteomyelitis in an animal model. Many experimental biomaterials were not decisively more efficacious for infection management than PMMA when delivering the same antibiotic. However, alternative biomaterials have reduced the number of follow-up surgeries, enhanced the antimicrobial efficacy by delivering agents that are incompatible with PMMA, and regenerated bone in an infected defect. Understanding the advantages, limitations, and potential for clinical translation of each biomaterial, along with the conditions under which it was evaluated (e.g. animal model), is critical for surgeons and researchers to navigate the plethora of options for local antibiotic delivery.


title: Design of communication interface and control system for intelligent humanoid robot

abstract: Currently, robot applications have been fast expanding to many areas such as entertainment, home task, security, medical care, etc. With an increasing robot demand, the development of highly reliable and low-cost robot control system has become a hot research field in recent years. However, most robot control system requires special interface design or suffers from complexity or high cost. This phenomenon poses a great difficulty for students to learn such a robot development knowledge from a traditional classroom. Accordingly, this paper aims to propose a simple but reliable robot communication interface and local-loop control system based on RS232 and 8051 microprocessor, suitable use as lecture material for various kinds of robot control. In this proposed scheme, the robot action commands stored in the database using C++ Builder can be transmitted from the command-transmission microprocessor (CTM) and then received by the individual authorized action-processing microprocessor (APM) via RS232. Every APM is responsible to its respective robot's joint so that there are up to tens of modules to be operated for a variety of robot actions simultaneously and independently. Real-time implementation results are presented to demonstrate the effectiveness of the proposed approach in terms of robust, simple, flexible, and efficient performance. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 454–467, 2012


title: Deep Convolutional Neural Networks for Chest Diseases Detection

abstract: Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.


title: Language‐specific tuning of visual cortex? Functional properties of the Visual Word Form Area

abstract: The first steps in the process of reading a printed word belong to the domain of visual object perception. They culminate in a representation of letter strings as an ordered set of abstract letter identities, a representation known as the Visual Word Form (VWF). Brain lesions in patients with pure alexia and functional imaging data suggest that the VWF is subtended by a restricted patch of left‐hemispheric fusiform cortex, which is reproducibly activated during reading. In order to determine whether the operation of this Visual Word Form Area (VWFA) depends exclusively on the visual features of stimuli, or is influenced by language‐dependent parameters, brain activations induced by words, consonant strings and chequerboards were compared in normal subjects using functional MRI (fMRI). Stimuli were presented in the left or right visual hemifield. The VWFA was identified in both a blocked‐design experiment and an event‐related experiment as a left‐hemispheric inferotemporal area showing a stronger activation to alphabetic strings than to chequerboards, and invariant for the spatial location of stimuli. In both experiments, stronger activations of the VWFA to words than to strings of consonants were observed. Considering that the VWFA is equally activated by real words and by readable pseudowords, this result demonstrates that the VWFA is initially plastic and becomes attuned to the orthographic regularities that constrain letter combination during the acquisition of literacy. Additionally, the use of split‐field stimulation shed some light on the cerebral bases of the classical right visual field (RVF) advantage in reading. A left occipital extrastriate area was found to be activated by RVF letter strings more than by chequerboards, while no symmetrical region was observed in the right hemisphere. Moreover, activations in the precuneus and the left thalamus were observed when subjects were reading RVF versus left visual field (LVF) words, and are likely to reflect the attentional component of the RVF advantage.