title: A Framework for Developing Serious Games to meet Learner Needs

abstract: This paper will communicate preliminary findings from applied research exploring how to ensure that serious games are cost effective and engaging components of future training solutions. The applied research is part of a multimillion pound program for the Department of Trade and Industry, and involves a partnership between UK industry and academia to determine how bespoke serious games should be used to best satisfy learning needs in a range of contexts. The main objective of this project is to produce a minimum of three serious games prototypes for clients from different sectors (e.g., military, medical and business) each prototype addressing a learning need or learning outcome that helps solve a priority business problem or fulfill a specific training need. This paper will describe a development process that aims to encompass learner specifics and targeted learning outcomes in order to ensure that the serious game is successful. A framework for describing game-based learning scenarios is introduced, and an approach to the analysis that effectively profiles the learner within the learner group with respect to game-based learning is outlined. The proposed solution also takes account of relevant findings from serious games research on particular learner groups that might support the selection and specification of a game. A case study on infection control will be used to show how this approach to the analysis is being applied for a healthcare issue.


An Active Suspension System for a Planetary Rover


title: Design of two-degree-of-freedom tracking controller for constrained descriptor systems

abstract: This paper proposes a design of tracking controller using full-information method for constrained mechanical systems, which belong to a quasi-causal class of descriptor systems. First, to solve this problem, the original descriptor system is decomposed to the reliable stair-case form of matrix pencils. Secondly, the tracking control and H/sub /spl infin// full-information approach are solved under the explicit existence conditions of similar forms as those in the cases of state-space systems. Thus, the tracking problem is addressed as a model following method and full-information control is considered for augmented system with a nominal control constraint. Finally, to demonstrate the effectiveness of the proposed method, simulation results are presented for the numerical model of SICE-DD Arm.


title: Ideals, varieties, and algorithms: an introduction to computational algebraic geometry and commutative algebra

abstract: This text covers topics in algebraic geometry and commutative algebra with a strong perspective toward practical and computational aspects. The first four chapters form the core of the book. A comprehensive chart in the Preface illustrates a variety of ways to proceed with the material once these chapters are covered. In addition to the fundamentals of algebraic geometrythe elimination theorem, the extension theorem, the closure theorem and the Nullstellensatzthis new edition incorporates several substantial changes, all of which are listed in the Preface. The largest revision incorporates a new Chapter (ten), which presents some of the essentials of progress made over the last decades in computing Grbner bases. The book also includes current computer algebra material in Appendix C and updated independent projects (Appendix D).The book may serve as a first or second course in undergraduate abstract algebra and with some supplementation perhaps, for beginning graduate level courses in algebraic geometry or computational algebra. Prerequisites for the reader include linear algebra and a proof-oriented course.It is assumed that the reader has access to a computer algebra system. Appendix C describes features of Maple, Mathematica and Sage, as well as other systems that are most relevant to the text. Pseudocode is used in the text; Appendix B carefully describes the pseudocode used.From the reviews of previous editions:The book gives an introduction to Buchbergers algorithm with applications to syzygies, Hilbert polynomials, primary decompositions. There is an introduction to classical algebraic geometry with applications to the ideal membership problem, solving polynomial equations and elimination theory. The book is well-written. The reviewer is sure that it will be an excellent guide to introduce further undergraduates in the algorithmic aspect of commutative algebra and algebraic geometry. Peter Schenzel, zbMATH, 2007I consider the book to be wonderful. ... The exposition is very clear, there are many helpful pictures and there are a great many instructive exercises, some quite challenging ... offers the heart and soul of modern commutative and algebraic geometry. The American Mathematical Monthly


title: Dynamic Routing Between Capsules

abstract: A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.


title: Deep learning for healthcare decision making with EMRs

abstract: Computer aid technology is widely applied in decision-making and outcome assessment of healthcare delivery, in which modeling knowledge and expert experience is technically important. However, the conventional rule-based models are incapable of capturing the underlying knowledge because they are incapable of simulating the complexity of human brains and highly rely on feature representation of problem domains. Thus we attempt to apply a deep model to overcome this weakness. The deep model can simulate the thinking procedure of human and combine feature representation and learning in a unified model. A modified version of convolutional deep belief networks is used as an effective training method for large-scale data sets. Then it is tested by two instances: a dataset on hypertension retrieved from a HIS system, and a dataset on Chinese medical diagnosis and treatment prescription from a manual converted electronic medical record (EMR) database. The experimental results indicate that the proposed deep model is able to reveal previously unknown concepts and performs much better than the conventional shallow models.


title: A Dynamic Capability Perspective to Understanding the Typology of Big Data Capabilities. Evidence from a Ghanaian Health Insurance Firm

abstract: Big data has become a key competitive advantage tool for firms. Big data is disrupting several markets by causing firm to rethink and change their traditional way of handling and using data within and outside. This makes big data possess dynamic capabilities. Given that only few predominantly big technology firms have been able to fully adopt big data to obtain benefits. This research grounded in the theoretical lens of dynamic capabilities examines the constituents or typology of big data capabilities that are needed by firms to adopt big data. The research proposes a hierarchical model for the typology of big data capabilities which is made up technological capabilities, human skills capabilities and organisational capabilities as the core capabilities of big data capabilities. Beyond the second order capabilities, first-order capabilities and resource base or zero-order capabilities are proposed. An investigation of a health insurance firm implementing big data in Ghana is used to evaluate the suggested model and identify challenges in the development of the big data capabilities as experienced by the health insurance firm.


title: Structured prediction models for RNN based sequence labeling in clinical text

abstract: Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies for structured prediction in order to improve the exact phrase detection of various medical entities.


Polymer Nanocomposites—A Comparison between Carbon Nanotubes, Graphene, and Clay as Nanofillers


title: An analysis of accelerator coupling in heterogeneous architectures

abstract: Existing research on accelerators has emphasized the performance and energy efficiency improvements they can provide, devoting little attention to practical issues such as accelerator invocation and interaction with other on-chip components (e.g. cores, caches). In this paper we present a quantitative study that considers these aspects by implementing seven high-throughput accelerators following three design models: tight coupling behind a CPU, loose out-of-core coupling with Direct Memory Access (DMA) to the LLC, and loose out-of-core coupling with DMA to DRAM. A salient conclusion of our study is that working sets of non-trivial size are best served by loosely-coupled accelerators that integrate private memory blocks tailored to their needs.


title: Analysis of small infrared target features and learning-based false detection removal for infrared search and track

abstract: An infrared search and track system is an important research goal for military applications. Although there has been much research into small infrared target detection methods, we cannot apply them in real field situations due to the high false alarm rate caused by clutter. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. In our study, eight target features were extracted and analyzed statistically. Learning-based classifiers, such as SVM and Adaboost, have been incorporated and then compared to conventional classifiers using real infrared images. In addition, the generalization capability has also been inspected for various types of infrared clutter.


title: Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India

abstract: Purpose – The purpose of this paper is to accomplish two objectives – to test the functional relationship between adoption readiness (AR), perceived risk (PR) and usage intention for mobile payments in India and to investigate the stability of proposed structural relationships across different customer groups. Design/methodology/approach – The literature concerning major attributes of technology acceptance were systematically reviewed to develop construct of AR. Post that a comprehensive model consisting of AR, personal innovativeness and PR was put together. The model was then empirically tested using structural equation modelling. Findings – On appraising the proposed model, five out of six hypotheses were fully supported while one hypothesis was partially supported. Test of invariance showed significant variance among users and non-users. Research limitations/implications – The results of the study may vary with national context, service offerings, regulatory framework and other customer personal varia...


title: A Fair Adaptive Data Rate Algorithm for LoRaWAN

abstract: LoRaWAN exhibits several characteristics that can lead to an unfair distribution of the Data Extracted Rate (DER) among nodes. Firstly, the capture effect leads to a strong signal suppressing a weaker signal at the gateway and secondly, the spreading codes used are not perfectly orthogonal, causing packet loss if an interfering signal is strong enough. In these conditions, nodes experiencing higher attenuation are less likely to see their packets received correctly. We develop FADR, a Fair Adaptive Data Rate algorithm for LoRaWAN that exploits the different Spreading Factors (SFs) and Transmission Powers (TPs) settings available in LoRa to achieve a fair Data Extraction Rate among all nodes while at the same time avoiding excessively high TPs. Simulations show that FADR, in highly congested cells, achieves 300% higher fairness than the minimum airtime allocation approach and 22% higher fairness than Brechts approach, while consuming almost 22% lower energy.


title: The importance of usability in product choice: a mobile phone case study.

abstract: Usability has become established as an important aspect of product design. This paper describes an investigation that was carried out to identify the importance of usability in product choice related to other product attributes. Interviews were initially carried out to identify possible attributes that contribute to product choice. Experiments were then undertaken using the methods of active information search, structured preference elicitation, ranking and interviews in order to find out what attributes were important to people when choosing an example product - mobile phones. It was found that usability is indeed important in product choice but perhaps not as much as users themselves believe. Other attributes that were found to be more important were features, aesthetics and cost. The process of product choice was found to be complex and it may be the case that people have come to expect usability in their products. Further research is needed to understand more fully the changing role of usability in product choice and to further improve the quality of the user-product relationship.


Psychomotor Vigilance Performance : Neurocognitive Assay Sensitive to Sleep Loss


title: Socially trusted collaborative edge computing in ultra dense networks

abstract: Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.


title: A low-power 433MHz transmitter for battery-less Tire Pressure Monitoring System

abstract: A low-power 433MHz transmitter is designed using 0.18um CMOS process for battery-less TPMS. It is mainly composed of a PLL frequency synthesizer and a PA, and the transmitter is achieved in a fully integrated chip except for choke and output match inductor of PA. Based on post-simulation, locked time and phase noise of the PLL are respectively about 60us and −105dBm/Hz@300kHz, the power consumption of transmitter is 25mW when output power of PA is 7dBm. The area of the whole transmitter is 1mm × 1mm, including ESD pads. The sample chip is under fabrication1.


title: Eye tracking assisted extraction of attentionally important objects from videos

abstract: Visual attention is a crucial indicator of the relative importance of objects in visual scenes to human viewers. In this paper, we propose an algorithm to extract objects which attract visual attention from videos. As human attention is naturally biased towards high level semantic objects in visual scenes, this information can be valuable to extract salient objects. The proposed algorithm extracts dominant visual tracks using eye tracking data from multiple subjects on a video sequence by a combination of mean-shift clustering and Hungarian algorithm. These visual tracks guide a generic object search algorithm to get candidate object locations and extents in every frame. Further, we propose a novel multiple object extraction algorithm by constructing a spatio-temporal mixed graph over object candidates. Bounding box based object extraction inference is performed using binary linear integer programming on a cost function defined over the graph. Finally, the object boundaries are refined using grabcut segmentation. The proposed technique outperforms state-of-the-art video segmentation using eye tracking prior and obtains favorable object extraction over algorithms which do not utilize eye tracking data.


title: Interactive Learning from Policy-Dependent Human Feedback

abstract: This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teaching and is independent from the learner's current policy. We present empirical results that show this assumption to be false— whether human trainers give a positive or negative feedback for a decision is influenced by the learner's current policy. Based on this insight, we introduce Convergent Actor-Critic by Humans (COACH), an algorithm for learning from policy-dependent feedback that converges to a local optimum. Finally, we demonstrate that COACH can successfully learn multiple behaviors on a physical robot.


title: Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation

abstract: HIGHLIGHTSAn efficient 11‐layers deep, multi‐scale, 3D CNN architecture.A novel training strategy that significantly boosts performance.The first employment of a 3D fully connected CRF for post‐processing.State‐of‐the‐art performance on three challenging lesion segmentation tasks.New insights into the automatically learned intermediate representations. ABSTRACT We propose a dual pathway, 11‐layers deep, three‐dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in‐depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post‐processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi‐channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state‐of‐the‐art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.


title: Powering E-Learning In the New Millennium: An Overview of E-Learning and Enabling Technology

abstract: In today's new economy characterized by industrial change, globalization, increased intensive competition, knowledge sharing and transfer, and information technology revolution, traditional classroom education or training does not always satisfy all the needs of the new world of lifelong learning. Learning is shifting from instructor-centered to learner-centered, and is undertaken anywhere, from classrooms to homes and offices. E-Learning, referring to learning via the Internet, provides people with a flexible and personalized way to learn. It offers learning-on-demand opportunities and reduces learning cost. This paper describes the demands for e-Learning and related research, and presents a variety of enabling technologies that can facilitate the design and implementation of e-Learning systems. Armed with the advanced information and communication technologies, e-Learning is having a far-reaching impact on learning in the new millennium.


title: The development of a survey to examine knowledge about and attitudes toward concussion in high-school students

abstract: The development of a new measure of concussion knowledge and attitudes that is more comprehensive and psychometrically sound than previous measures is described. A group of high-school students (N = 529) completed the measure. The measure demonstrated fair to satisfactory test–retest reliability (knowledge items, r = .67; attitude items, r = .79). Exploratory factor analysis of the attitude items revealed a four-factor solution (eigenvalues ranged from 1.07–3.35) that displayed adequate internal consistency (Cohen's alpha range = .59–.72). Cluster analysis of the knowledge items resulted in a three-cluster solution distributed according to their level of difficulty. The potential uses for the measure are described.


title: DISEASE PREDICTING SYSTEM USING DATA MINING TECHNIQUES

abstract: The successful application of data mining in highly visible fields like e-business, commerce and trade has led to its application in other industries. The medical environment is still information rich but knowledge weak. There is a wealth of data possible within the medical systems. However, there is a lack of powerful analysis tools to identify hidden relationships and trends in data. Heart disease is a term that assigns to a large number of heath care conditions related to heart. These medical conditions describe the unexpected health conditions that directly control the heart and all its parts. Medical data mining techniques like association rule mining, classification, clustering is implemented to analyze the different kinds of heart based problems. Classification is an important problem in data mining. Given a database contain collection of records, each with a single class label, a classifier performs a brief and clear definition for each class that can be used to classify successive records . A number of popular classifiers construct decision trees to generate class models. The data classification is based on MAFIA algorithms which result in accuracy, the data is estimated using entropy based cross validations and partition techniques and the results are compared. C4.5 algorithm is used as the training algorithm to show rank of heart attack with the decision tree. The heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database. Keywords—Data mining; MAFIA (Maximal Frequent Itemset Algorithm); C4.5 Algorithm; K-means clustering


title: Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances

abstract: Summary Microalgae represent an exceptionally diverse but highly specialized group of micro-organisms adapted to various ecological habitats. Many microalgae have the ability to produce substantial amounts (e.g. 20–50% dry cell weight) of triacylglycerols (TAG) as a storage lipid under photo-oxidative stress or other adverse environmental conditions. Fatty acids, the building blocks for TAGs and all other cellular lipids, are synthesized in the chloroplast using a single set of enzymes, of which acetyl CoA carboxylase (ACCase) is key in regulating fatty acid synthesis rates. However, the expression of genes involved in fatty acid synthesis is poorly understood in microalgae. Synthesis and sequestration of TAG into cytosolic lipid bodies appear to be a protective mechanism by which algal cells cope with stress conditions, but little is known about regulation of TAG formation at the molecular and cellular level. While the concept of using microalgae as an alternative and renewable source of lipid-rich biomass feedstock for biofuels has been explored over the past few decades, a scalable, commercially viable system has yet to emerge. Today, the production of algal oil is primarily confined to high-value specialty oils with nutritional value, rather than commodity oils for biofuel. This review provides a brief summary of the current knowledge on oleaginous algae and their fatty acid and TAG biosynthesis, algal model systems and genomic approaches to a better understanding of TAG production, and a historical perspective and path forward for microalgae-based biofuel research and commercialization.


title: Patient Monitoring System Using GSM Technology

abstract: In this fast pace of life, it is difficult for people to be constantly available for their near ones who might need them while they are suffering from a disease or physical disorder. So also constant monitoring of the patient’s body parameters such as temperature, pulse rate, sugar level etc. becomes difficult. Hence to remove human error and to lessen the burden of monitoring patient’s health from doctor’s head, this paper presents the methodology for monitoring patients remotely using GSM network and Very Large Scale Integration (VLSI) technology. Patient monitoring systems measure physiological characteristics either continuously or at regular intervals of time.


title: Shrinking the Tube: A New Support Vector Regression Algorithm

abstract: A new algorithm for Support Vector regression is described. For a priori chosen ν, it automatically adjusts a flexible tube of minimal radius to the data such that at most a fraction ν of the data points lie outside. Moreover, it is shown how to use parametric tube shapes with non-constant radius. The algorithm is analysed theoretically and experimentally.


title: Creating persuasive technologies: an eight-step design process

abstract: This paper outlines eight steps to follow as best practices in the early stages of persuasive technology design. The eight-step process, drawn from demonstrated successes in industry practice, begins with defining the persuasion goal to match a target audience with an appropriate technology channel. Subsequent steps include imitating successful examples of persuasive design, performing rapid trials, measuring behavioral outcomes, and building on small successes.


title: alpha1-Adrenergic stimulation of FGF-2 promoter in cardiac myocytes and in adult transgenic mouse hearts.

abstract: Fibroblast growth factor (FGF-2), a mitogenic, angiogenic, and cardioprotective agent, is reported to be released from the postnatal heart by a mechanism of transient remodeling of the sarcolemma during contraction. This release can be increased with adrenergic stimulation. RNA blotting was used to assess whether FGF-2 synthesis in neonatal rat cardiomyocytes might also be regulated by adrenergic stimulation. FGF-2 RNA levels were increased after treatment with norepinephrine for 6 h or with the alpha-adrenergic agonist phenylephrine for 48 h. To assess an effect on transcription, neonatal rat cardiomyocytes were transfected with a hybrid rat FGF-2 promoter/luciferase gene (-1058FGFp.luc) and treated with norepinephrine or phenylephrine for 6 or 48 h, respectively. FGF-2 promoter activity was increased two- to sevenfold in an alpha1-specific manner. Putative phenylephrine-responsive elements (PEREs) were identified at positions -780 and -761 relative to a major transcription initiation site. However, deletion analysis of -1058FGFp.luc showed that the phenylephrine response was independent of the putative PEREs, cell contraction, and Ca2+ influx. In transgenic mice expressing -1058FGFp.luc, a significant three- to sevenfold stimulation of FGF-2 promoter activity was detected in the hearts of two independent lines 6 h after intraperitoneal administration of phenylephrine (50 mg/kg). This increase was still apparent at 24 h but was not detected at 48 h posttreatment. Analysis of FGF-2 mRNA in normal mouse hearts revealed accumulation of the 6.1-kb transcript at 24 h. Control of local FGF-2 synthesis at the transcriptional level through adrenergic stimulation may be important in the response to injury as well as in the maintenance of a healthy myocardium.


title: Wireless spectrum prediction model based on time series analysis method

abstract: Cognitive Radio (CR) is considered to be a solution for mitigating the conflict between limited spectrum resources and increasing demand of various applications. For the purpose of guaranteeing primary users, we propose a spectrum prediction model using the spectrum historic utilization information with time series analysis. We conduct experiment on wide TV bands of China from 603.25 MHz to 843.25 MHz, the results show that this model can accurately predict status of channels and find vacant spectrum for cognitive users. We take channel 2 whose center frequency locates in 615.25 MHz for example, average forecasting error ratio is 1.01% with a minimum of 0.79%.


title: Design of low-power analog drivers based on slew-rate enhancement circuits for CMOS low-dropout regulators

abstract: Low-power analog driver based on a single-stage amplifier with an embedded current-detection slew-rate enhancement (SRE) circuit is presented. By developing a systematic way to design both the response time and optimal sizing of driving transistors in the SRE circuit, the SRE circuit can be controlled to turn on or turn off properly. In addition, the analog driver only dissipates low static power and its transient responses are significantly improved without transient overshoot when driving large capacitive loads. Implemented in a 0.6-/spl mu/m CMOS process, a current-mirror amplifier with the current-detection SRE circuit has achieved over 43 times improvement in both slew rate and 1% settling time when driving a 470-pF load capacitor. When the proposed analog driver is employed in a 50-mA CMOS low-dropout regulator (LDO), the resultant load transient response of the LDO has 2-fold improvement for the maximum load-current change, while the total quiescent current is only increased by less than 3%.


title: ABACUS: Mining Arbitrary Shaped Clusters from Large Datasets based on Backbone Identification

abstract: A wide variety of clustering algorithms exist that cater to applications based on certain special characteristics of the data. Our focus is on methods that capture arbitrary shaped clusters in data, the so called spatial clustering algorithms. With the growing size of spatial datasets from diverse sources, the need for scalable algorithms is paramount. We propose a shape-based clustering algorithm, ABACUS, that scales to large datasets. ABACUS is based on the idea of identifying the intrinsic structure for each cluster, which we also refer to as the backbone of that cluster. The backbone comprises of a much smaller set of points, thus giving this method the desired ability to scale to larger datasets. ABACUS operates in two stages. In the first stage, we identify the backbone of each cluster via an iterative process made up of globbing (or point merging) and point movement operations. The backbone enables easy identification of the true clusters in a subsequent stage. Experiments on a range of real (images from geospatial satellites, etc.) and synthetic datasets demonstrate the efficiency and effectiveness of our approach. In particular, ABACUS is over an order of magnitude faster than existing shape-based clustering methods, yet it provides a comparable or better clustering quality.


title: KNET: A General Framework for Learning Word Embedding Using Morphological Knowledge

abstract: Neural network techniques are widely applied to obtain high-quality distributed representations of words (i.e., word embeddings) to address text mining, information retrieval, and natural language processing tasks. Most recent efforts have proposed several efficient methods to learn word embeddings from context such that they can encode both semantic and syntactic relationships between words. However, it is quite challenging to handle unseen or rare words with insufficient context. Inspired by the study on the word recognition process in cognitive psychology, in this article, we propose to take advantage of seemingly less obvious but essentially important morphological knowledge to address these challenges. In particular, we introduce a novel neural network architecture called KNET that leverages both words’ contextual information and morphological knowledge to learn word embeddings. Meanwhile, this new learning architecture is also able to benefit from noisy knowledge and balance between contextual information and morphological knowledge. Experiments on an analogical reasoning task and a word similarity task both demonstrate that the proposed KNET framework can greatly enhance the effectiveness of word embeddings.


title: Integrated non-inverting buck-boost DC-DC converter with average-current-mode control

abstract: With the increasing use of electrical portable devices, an efficient power management solution is needed to extend the battery life. Generally, basic switching regulators, such as buck and boost converters, are not capable of using the entire battery output voltage range. In this work, a non-inverting buck-boost dc-dc converter which can use the full-range output voltage (i.e., 2.5–4.7 V) of a Li-ion battery is proposed. Moreover, average-current-mode control is adopted to improve noise immunity. The die area of this chip is 1.9×1.7mm2, and it was fabricated by using TSMC 0.35µm 2P4M 5V mixed-signal polycide process. The input range of the converter is 2.5–5 V, and its output is set to 3.3V. It can supply up to 300 mA load current.


title: CityMomentum: an online approach for crowd behavior prediction at a citywide level

abstract: Human movements are difficult to predict, especially, when we consider rare behaviors that deviate from normal daily routines. By tracing the behavior of a person over a long period, we can model their daily routines and predict periodical behaviors, whereas rare behaviors, such as participating in the New Year's Eve countdown, can hardly be predicted readily and thus they have usually been treated as outliers of the daily routines in most existing studies. However, for scenarios such as emergency management or intelligent traffic regulation, we are more interested in rare behaviors than daily routines. Using human mobility Big Data, the rare behavior of each individual in a social crowd is no longer rare and thus it may be predicted when we analyze the crowd behavior at a citywide level. Therefore in this study, instead of predicting movement based on daily routines, we make short-term predictions based on the recent movement observations. We propose a novel model called CityMomentum as a predicting-by-clustering framework for sampling future movement using a mixture of multiple random Markov chains, each of which is a Naive Movement Predictive model trained with the movements of the subjects that belong to each cluster. We apply our approach to a big mobile phone GPS log dataset and predict the short-term future movements, especially during the Comiket 80 and New Year's Eve celebration. We evaluate our prediction by a Earth Mover Distance (EMD) based metric, and show our approach accurately predicts the crowd behavior during the rare crowd events, which makes an early crowd event warning and regulation possible in the emergent situations.


Mispronunciation Detection Leveraging Maximum Performance Criterion Training of Acoustic Models and Decision Functions


title: Learning to Map Vehicles into Bird's Eye View

abstract: Awareness of the road scene is an essential component for both autonomous vehicles and Advances Driver Assistance Systems and is gaining importance both for the academia and car companies. This paper presents a way to learn a semantic-aware transformation which maps detections from a dashboard camera view onto a broader bird’s eye occupancy map of the scene. To this end, a huge synthetic dataset featuring 1M couples of frames, taken from both car dashboard and bird’s eye view, has been collected and automatically annotated. A deep-network is then trained to warp detections from the first to the second view. We demonstrate the effectiveness of our model against several baselines and observe that is able to generalize on real-world data despite having been trained solely on synthetic ones.


title: Towards an open framework for adaptive, agent-supported e-learning

abstract: Refer to: Van Rosmalen, P., Brouns, F., Tattersall, C.,Vogten, H. Van Bruggen, J, Sloep, P., & Koper, E.J.R. (in press). Towards an Open Framework for Adaptive, Agent-supported e-learning. International Journal of Continuing Engineering Education.


Bootstrapped Self Training for Knowledge Base Population


title: Design pattern detection based on the graph theory

abstract: Design patterns are strategies for solving commonly occurring problems within a given context in software design. In the process of re-engineering, detection of design pattern instances from source codes can play a major role in understanding large and complex software systems. However, detecting design pattern instances is not always a straightforward task. In this paper, based on the graph theory, a new design pattern detection method is presented. The proposed detection process is subdivided into two sequential phases. In the first phase, we concern both the semantics and the syntax of the structural signature of patterns. To do so, the system under study and the patterns asked to be detected, are transformed into semantic graphs. Now, the initial problem is converted into the problem of finding matches in the system graph for the pattern graph. To reduce the exploration space, based on a predetermined set of criteria, the system graph is broken into the possible subsystem graphs. After applying a semantic matching algorithm and obtaining the candidate instances, by analyzing the behavioral signature of the patterns, in the second phase, final matches will be obtained. The performance of the suggested technique is evaluated on three open source systems regarding precision and recall metrics. The results demonstrate the high efficiency and accuracy of the proposed method.


title: Constant-Deposit Multiparty Lotteries on Bitcoin

abstract: An active research trend is to exploit the consensus mechanism of cryptocurrencies to secure the execution of distributed applications. In particular, some recent works have proposed fair lotteries which work on Bitcoin. These protocols, however, require a deposit from each player which grows quadratically with the number of players. We propose a fair lottery on Bitcoin which only requires a constant deposit.


title: Graph Classification using Structural Attention

abstract: Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attention-based graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of "informative" nodes. Experimental results on multiple real-world datasets show that the proposed method is competitive against various well-known methods in graph classification even though our method is limited to only a portion of the graph.


title: Bayesian Convolutional Neural Networks.

abstract: We introduce Bayesian Convolutional Neural Networks (BayesCNNs), a variant of Convolutional Neural Networks (CNNs) which is built upon Bayes by Backprop. We demonstrate how this novel reliable variational inference method can serve as a fundamental construct for various network architectures. On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100, and STL-10), our proposed variational inference method achieves performances equivalent to frequentist inference in identical architectures, while a measurement for uncertainties and a regularisation are incorporated naturally. In the past, Bayes by Backprop has been successfully implemented in feedforward and recurrent neural networks, but not in convolutional ones. This work symbolises the extension of Bayesian neural networks which encompasses all three aforementioned types of network architectures now.


Evaluating Generative Models for Text Generation


title: Distinguishing between gaming and gambling activities in addiction research

abstract: Background and AimsGambling and gaming activities have become increasingly recognised as sharing many common features at a structural and aesthetic level. Both have also been implicated as contributing to harm through excessive involvement. Despite this, relatively little attention has been given to the fundamental characteristics that differentiate these two classes of activity, especially in situations where the boundaries between them may be particularly hard to distinguish. This is evident, for example, in digital games that incorporate free and paid virtual currencies or items, as well as the capacity for wagering. Such overlaps create problems for regulatory classifications, screening, diagnosis and treatment. Is the problem related to the gambling or gaming content?MethodsIn this paper, we review the principal sources of overlap between the activity classes in terms of several dimensions: interactivity, monetisation, betting and wagering, types of outcomes, structural fidelity, context and centrali...


title: Attacking the Elliptic Curve Discrete Logarithm Problem

abstract: Definition 1.1. Given a finite abelian group G written multiplicatively and elements b and g in G, the discrete logarithm problem (DLP) consists of finding an integer n such that bn = g, if such an n exists. The difficulty involved in computing the discrete logarithm varies with the choice of G. For example, in the additive group of integers modulo n, (Z/nZ)+, the problem can be solved efficiently. Given b and g such that 0 ≤ b, g ≤ n − 1 and kb ≡ g mod n for some integer k, then the Extended Euclidean Algorithm can be used to quickly compute a such that ba ≡ 1 mod n, so that akb ≡ k mod n. See [3] for a more detailed explanation. For the purposes of cryptography, a group in which the discrete logoarithm is difficult to compute is desirable. In such a context, the discrete logarithm becomes a trapdoor, or one-way, function, since exponentiation (the inverse operation) can always be performed efficiently through repeated squaring. There are two widely used groups in public key cryptosystems based on the discrete logarithm problem, the multiplicative group of integers (Z/pZ)∗ and the elliptic curve group E(Z/nZ). The first popular public key cryptosystems were based on the DLP in (Z/nZ)∗. Systems based on E(Z/nZ) have drawn substantial interest more recently due to the DLP being harder in that context. In this paper, we focus on the DLP in E(Z/nZ).


title: WiCatch: A Wi-Fi Based Hand Gesture Recognition System

abstract: In recent years, a large number of researchers are endeavoring to develop wireless sensing and related applications as Wi-Fi devices become ubiquitous. As a significant research branch, gesture recognition has become one of the research hotspots. In this paper, we propose WiCatch, a novel device free gesture recognition system which utilizes the channel state information to recognize the motion of hands. First of all, with the aim of catching the weak signals reflected from hands, a novel data fusion-based interference elimination algorithm is proposed to diminish the interference caused by signals reflected from stationary objects and the direct signal from transmitter to receiver. Second, the system catches the signals reflected from moving hands and rebuilds the motion locus of the gesture by constructing the virtual antenna array based on signal samples in time domain. Finally, we adopt support vector machines to complete the classification. The extensive experimental results demonstrate that the WiCatch can achieves a recognition accuracy over 0.96. Furthermore, the WiCatch can be applied to two-hand gesture recognition and reach a recognition accuracy of 0.95.


title: Algorithmic Aspects of Vertex Elimination on Graphs

abstract: We consider a graph-theoretic elimination process which is related to performing Gaussian elimination on sparse symmetric positive definite systems of linear equations. We give a new linear-time algorithm to calculate the fill-in produced by any elimination ordering, and we give two new related algorithms for finding orderings with special properties. One algorithm, based on breadth-first search, finds a perfect elimination ordering, if any exists, in $O(n + e)$ time, if the problem graph has n vertices and e edges. An extension of this algorithm finds a minimal (but not necessarily minimum) ordering in $O(ne)$ time. We conjecture that the problem of finding a minimum ordering is NP-complete


title: Comet whole-core solution to a stylized 3-dimensional pressurized water reactor benchmark problem with UO{sub 2}and MOX fuel

abstract: A stylized pressurized water reactor (PWR) benchmark problem with UO{sub 2} and MOX fuel was used to test the accuracy and efficiency of the coarse mesh radiation transport (COMET) code. The benchmark problem contains 125 fuel assemblies and 44,000 fuel pins. The COMET code was used to compute the core eigenvalue and assembly and pin power distributions for three core configurations. In these calculations, a set of tensor products of orthogonal polynomials were used to expand the neutron angular phase space distribution on the interfaces between coarse meshes. The COMET calculations were compared with the Monte Carlo code MCNP reference solutions using a recently published an 8-group material cross section library. The comparison showed both the core eigenvalues and assembly and pin power distributions predicated by COMET agree very well with the MCNP reference solution if the orders of the angular flux expansion in the two spatial variables and the polar and azimuth angles on the mesh boundaries are 4, 4, 2 and 2. The mean and maximum differences in the pin fission density distribution ranged from 0.28%-0.44% and 3.0%-5.5%, all within 3-sigma uncertainty of the MCNP solution. These comparisons indicate that COMET can achieve accuracy comparable to Monte Carlo.more » It was also found that COMET's computational speed is 450 times faster than MCNP. (authors)« less


title: Breakdown Enhancement and Current Collapse Suppression by High-Resistivity GaN Cap Layer in Normally-Off AlGaN/GaN HEMTs

abstract: In this letter, a device structure of high-resistivity-cap-layer HEMT (HRCL-HEMT) is developed for normally-off p-GaN gate HEMT toward high breakdown voltage and low current collapse. It demonstrates that the breakdown capability and current collapse of the device were effectively improved due to the introduction of a thick HR-GaN cap layer. The fabricated HRCL-HEMT exhibits a high breakdown voltage of 1020 V at ${I}_{\text {DS}} = 10~\mu \text{A}$ /mm with the substrate grounded. Meanwhile, the dynamic ${R}_{\text {on}}$ is only 2.4 times the static ${R}_{\text {on}}$ after off-state ${V}_{\text {DS}}$ stress of 1000 V with the substrate grounded (the OFF to ON switching time interval is set to $200~\mu \text{s}$ ).


title: Cellulose chemical markers in transformer oil insulation Part 1: Temperature correction factors

abstract: The concentrations of cellulose chemical markers, in oil, are influenced by various parameters due to the partition between the oil and the cellulose insulation. One major parameter is the oil temperature which is a function of the transformer load, ambient temperature and the type of cooling. To accurately follow the chemical markers concentration trends during all the transformer life, it is crucial to normalize the concentrations at a specific temperature. In this paper, we propose equations for the normalization of methanol, ethanol and 2-furfural at 20 °C. The proposed equations have been validated on some real power transformers.


title: Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

abstract: Deep neural networks (DNNs) provide excellent performance across a wide range of classification tasks, but their training requires high computational resources and is often outsourced to third parties. Recent work has shown that outsourced training introduces the risk that a malicious trainer will return a backdoored DNN that behaves normally on most inputs but causes targeted misclassifications or degrades the accuracy of the network when a trigger known only to the attacker is present. In this paper, we provide the first effective defenses against backdoor attacks on DNNs. We implement three backdoor attacks from prior work and use them to investigate two promising defenses, pruning and fine-tuning. We show that neither, by itself, is sufficient to defend against sophisticated attackers. We then evaluate fine-pruning, a combination of pruning and fine-tuning, and show that it successfully weakens or even eliminates the backdoors, i.e., in some cases reducing the attack success rate to 0% with only a 0.4% drop in accuracy for clean (non-triggering) inputs. Our work provides the first step toward defenses against backdoor attacks in deep neural networks.


title: A DDoS Attack Detection and Mitigation With Software-Defined Internet of Things Framework

abstract: With the spread of Internet of Things’ (IoT) applications, security has become extremely important. A recent distributed denial-of-service (DDoS) attack revealed the ubiquity of vulnerabilities in IoT, and many IoT devices unwittingly contributed to the DDoS attack. The emerging software-defined anything (SDx) paradigm provides a way to safely manage IoT devices. In this paper, we first present a general framework for software-defined Internet of Things (SD-IoT) based on the SDx paradigm. The proposed framework consists of a controller pool containing SD-IoT controllers, SD-IoT switches integrated with an IoT gateway, and IoT devices. We then propose an algorithm for detecting and mitigating DDoS attacks using the proposed SD-IoT framework, and in the proposed algorithm, the cosine similarity of the vectors of the packet-in message rate at boundary SD-IoT switch ports is used to determine whether DDoS attacks occur in the IoT. Finally, experimental results show that the proposed algorithm has good performance, and the proposed framework adapts to strengthen the security of the IoT with heterogeneous and vulnerable devices.


title: Multiband rectangular dielectric resonator antenna for 5G applications

abstract: This paper introduces the design and simulation of multiband dielectric resonator antenna (DRA) for the fifth generation (5G) of cellular networks. The radiating element is a unique rectangular shaped DRA element with a distribution feeding technique based on micro-strip feed line coupled via an elliptical slot. The feed position and configuration has been deliberately designed for optimized matching. The structure is approximately 40mm×48mm in size, conceived on Arlan dielectric substrate. The antenna has been optimized for the 10.5–17 GHz frequency bands. Simulated return loss, radiation pattern and gain over the frequencies of interest have been presented. The proposed antenna structure is a good candidate for future 5G cellular applications.


title: Cutaneous haptic feedback to ensure the stability of robotic teleoperation systems

abstract: Cutaneous haptic feedback can be used to enhance the performance of robotic teleoperation systems while guaranteeing their safety. Delivering ungrounded cutaneous cues to the human operator conveys in fact information about the forces exerted at the slave side and does not affect the stability of the control loop. In this work we analyze the feasibility, effectiveness, and implications of providing solely cutaneous feedback in robotic teleoperation. We carried out two peg-in-hole experiments, both in a virtual environment and in a real teleoperated environment. Two novel 3-degree-of-freedom fingertip cutaneous displays deliver a suitable amount of cutaneous feedback at the thumb and index fingers. Results assessed the feasibility and effectiveness of the proposed approach. Cutaneous feedback was outperformed by full haptic feedback provided by grounded haptic interfaces, but it outperformed conditions providing no force feedback at all. Moreover, cutaneous feedback always kept the system stable, even in the presence of destabilizing factors such as communication delays and hard contacts.


title: NaturalLI: Natural Logic Inference for Common Sense Reasoning

abstract: Common-sense reasoning is important for AI applications, both in NLP and many vision and robotics tasks. We propose NaturalLI: a Natural Logic inference system for inferring common sense facts ‐ for instance, that cats have tails or tomatoes are round ‐ from a very large database of known facts. In addition to being able to provide strictly valid derivations, the system is also able to produce derivations which are only likely valid, accompanied by an associated confidence. We both show that our system is able to capture strict Natural Logic inferences on the FraCaS test suite, and demonstrate its ability to predict common sense facts with 49% recall and 91% precision.


title: Missing data imputataion for traffic flow based on weighted local least squres

abstract: Data missing is the issue that is encounter in Intelligent Transportation System (ITS). The missing problem gives rise to many difficulties to further utilization. To overcome these difficulties, various methods have been proposed for the missing data imputation. A imputation method based on weighted local least squares for the missing data imputation of traffic flow is proposed in the paper. The real-time flow data recorded by SCOOT system in Beijing is applied to test the performance of the method and experiments results shown that the raised method has better imputing capabilities than BPCA imputation method.


title: Distance transformations in digital images

abstract: A distance transformation converts a binary digital image, consisting of feature and non-feature pixels, into an image where all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. Computing these distances is in principle a global operation. However, global operations are prohibitively costly. Therefore algorithms that consider only small neighborhoods, but still give a reasonable approximation of the Euclidean distance, are necessary. In the first part of this paper optimal distance transformations are developed. Local neighborhoods of sizes up to 7×7 pixels are used. First real-valued distance transformations are considered, and then the best integer approximations of them are computed. A new distance transformation is presented, that is easily computed and has a maximal error of about 2%. In the second part of the paper six different distance transformations, both old and new, are used for a few different applications. These applications show both that the choice of distance transformation is important, and that any of the six transformations may be the right choice.


title: A 60-GHz Wideband Circularly Polarized Aperture-Coupled Magneto-Electric Dipole Antenna Array

abstract: A novel circularly polarized (CP) aperture-coupled magneto-electric (ME) dipole antenna is proposed. The CP ME-dipole antenna fed by a transverse slot etched on the broad wall of a section of shorted-end substrate integrated waveguide (SIW) is convenient to integrate into substrates. An impedance bandwidth of wider than 28.8%, a wide 3-dB axial ratio (AR) bandwidth of 25.9%, and gain of $7.7 \pm 1.4\,{\text{dBic}}$ over the operating band are achieved. Additionally, since the CP radiation is generated by the combination of two orthogonal ME-dipole modes, the antenna element has stable unidirectional radiation patterns that are almost identical in two principle planes throughout the operating band, which is desirable to array applications. By employing the proposed CP ME-dipole as radiating elements, an $8 \times 8$ high-gain wideband planar antenna array is proposed for 60-GHz millimeter-wave applications. A fabrication procedure of using conductive adhesive films to bond all print circuit board (PCB) layers together is successfully implemented to realize the array design with a three-layered geometry, which has advantages of low costs and possibility of large-scale manufacture. The measured impedance bandwidth of the fabricated prototype is 18.2% for $\vert{\rm S}_{11}\vert . Because of the wide AR bandwidth of the new antenna element, a wide AR bandwidth of 16.5% can be achieved by this array without the use of sequential feed. Gain up to 26.1 dBic and good radiation efficiency of around 70% are also obtained due to the use of a full-corporate SIW feed network with low insertion loss at millimeter-wave frequencies.


title: DeepKey: An EEG and Gait Based Dual-Authentication System

abstract: Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are increasingly facing the risk of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoders, or fingerprint films. In this regard, we design a multimodal biometric authentication system named DeepKey which uses both gait and Electroencephalography (EEG) signals to provide better protection against such risks. DeepKey consists of three key components: an Invalid ID Filter Model to block invalid subjects, a Gait Identification Model to recognize Gait IDs and an EEG Identification Model to recognize EEG IDs. In particular, the first two models employ a one-class SVM algorithm and a Recurrent Neural Network based deep learning model, respectively. The third model combines autoregressive coefficients, an RNN structure, and an SVM classifier. DeepKey is trained with a gait dataset of 160,000 samples and an EEG dataset of 108,000 samples. Experimental results show DeepKey outperforms a series of comparison methods and achieves an overall accuracy of 0.983 along with an overall false acceptance rate (FAR) of 0.0 and a false rejection rate (FRR) of 0.019.


title: Towards the design of a consistent image contrast enhancement evaluation measure

abstract: Contrast Enhancement Evaluation (CEE) is a very challenging problem. In this work, we provide a detailed performance analysis of CEE measures. The study was conducted on our newly developed database dedicated to psychophysical Contrast Enhancement (CE) quality evaluation. The database contains 30 original images and 180 enhanced images obtained using six different CE methods as a representative set of the most common approaches used in the literature. The CE methods were subjectively evaluated and ranked by 23 observers using a Pairwise Comparison (PC) protocol. The correlation analysis between the subjective preferences and objective evaluations of the enhanced images show that most of the existing CEE metrics are not well consistent with the human judgment of quality. We also present in this paper a thorough discussion on the available CEE metrics, their strengths, their weaknesses, and their inter-correlation. In addition to the individual metrics, we show that by fusing different metrics together, a significant increase in correlation performance can be achieved. This study reveals that there is a clear need to develop more robust CEE measures which are perceptually motivated and correlated well with the quality of enhancement a given image is subjected to. The new database introduced in this paper is expected to contribute substantially promoting such research area, which is of primary importance to diverse multimedia applications. A new database dedicated to contrast enhancement evaluation (CEE) is introduced.An extensive set of psychophysical subjective experiments are conducted.A comprehensive performance analysis of state-of-the-art CEE metrics is presented.A new measure based on the fusion of top ranked CEE metrics is proposed.A discussion on the challenges in evaluating contrast enhancement is provided.


title: A High-Speed Mesh of Tactile Sensors Fitting Arbitrary Surfaces

abstract: A tactile sensor is developed with the aim of covering a robot's entire structure, while reducing wiring requirement and ensuring high-speed response. The sensor detects the center point of load distribution on 2-D surfaces as well as the overall load. There are only four signal wires from the sensor. The sensor response time is nearly constant (within 1 ms) regardless of the number of detection elements, their placements or sensor areas. In this paper, the principles behind the operation of this sensor and the results of experiments using the sensor are described.


title: A cross-cultural comparison of the development of the social smile A longitudinal study of maternal and infant imitation in 6- and 12-week-old infants

abstract: a b s t r a c t Social smiling is universally regarded as being an infant's first facial expression of pleasure. Underlying co-constructivist emotion theories are the assumptions that the emergence of social smiling is bound to experiences of face-to-face interactions with caregivers and the impact of two developmental mechanisms - maternal and infant imitation. We analyzed mother-infant interactions from two different socio-cultural contexts and hypothesized that cross-cultural differences in face-to-face interactions determine the occurrence of both of these mechanisms and of the frequency of social smiling by 12-week-old infants. Twenty mother-infant dyads from a socio-cultural community with many face-to-face interactions (German families, Munster) were compared with 24 mother-infant dyads from a socio- cultural community with few such interactions (rural Nso families, Cameroon) when the infants were aged 6 and 12 weeks. When infants were 6 weeks old, mothers and their infants from both cultural communities smiled at each other for similar (albeit very short) amounts of time and used imitated each other's smiling similarly rarely. In contrast, when infants were 12 weeks old, mothers and their infants from Munster smiled at and imitated each other more often than did Nso mothers and their infants.


title: From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards

abstract: In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.


title: Spaceborne Reflector SAR Systems with Digital Beamforming

abstract: Spaceborne synthetic aperture radar (SAR) imaging enters an era where increasingly short revisit times, or large swath widths, respectively, and high spatial resolutions are requested. These requirements impose contradicting constraints on conventional SAR systems using analog beamforming technology. The development for future radar satellites is therefore towards digital beamforming (DBF) systems where the analogous receiver hardware is replaced by digital components. Concerning the SAR antenna the innovative concept of a parabolic mesh reflector in conjunction with a digital feed array is becoming a promising architecture for this new SAR system generation. These antennas, already a mature technique for communication satellites, have the potential to outperform planar array antennas in terms of gain at a moderate hardware effort. This article provides a hardware concept study based on a design in X-band. Focus is put on DBF algorithms adopted to the SAR case and important performance figures are derived.


title: Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities

abstract: This paper proposes a framework for recognizing complex human activities in videos. Our method describes human activities in a hierarchical discriminative model that operates at three semantic levels. At the lower level, body poses are encoded in a representative but discriminative pose dictionary. At the intermediate level, encoded poses span a space where simple human actions are composed. At the highest level, our model captures temporal and spatial compositions of actions into complex human activities. Our human activity classifier simultaneously models which body parts are relevant to the action of interest as well as their appearance and composition using a discriminative approach. By formulating model learning in a max-margin framework, our approach achieves powerful multi-class discrimination while providing useful annotations at the intermediate semantic level. We show how our hierarchical compositional model provides natural handling of occlusions. To evaluate the effectiveness of our proposed framework, we introduce a new dataset of composed human activities. We provide empirical evidence that our method achieves state-of-the-art activity classification performance on several benchmark datasets.


title: Smart distribution protection using current-only directional overcurrent relay

abstract: Overcurrent relays are widely used for power systems protection. Transmission side uses more directional type relays, while distribution systems, e.g., radial and ring-main subtransmission systems use non-directional types. The fault direction may be forward (between relay and grid), or reverse (between relay and source), the normal power flow being from source to the grid. Traditional directional overcurrent relays utilize the reference voltage phasor for estimating the direction of the fault. This requires measurement of both current and voltage using respective sensors. This makes the directional overcurrent relays more costly than the non-directional type. In this paper, a novel current-only directional detection possibility is highlighted along with theoretical and test signal analysis. Possible utilization of the current-only directional relay for intelligent directional protection in the distribution systems are described. Directional protection for distribution systems is a key focus area for enabling the smart grid.


title: Wiener filtering based speech enhancement with Weighted Denoising Auto-encoder and noise classification

abstract: Abstract A novel speech enhancement method based on Weighted Denoising Auto-encoder (WDA) and noise classification is proposed in this paper. A weighted reconstruction loss function is introduced into the conventional Denoising Auto-encoder (DA), and the relationship between the power spectra of clean speech and noisy observation is described by WDA model. First, the sub-band power spectrum of clean speech is estimated by WDA model from the noisy observation. Then, the a priori SNR is estimated by the a Posteriori SNR Controlled Recursive Averaging (PCRA) approach. Finally, the clean speech is obtained by Wiener filter in frequency domain. In addition, in order to make the proposed method suitable for various kinds of noise conditions, a Gaussian Mixture Model (GMM) based noise classification method is employed. And the corresponding WDA model is used in the enhancement process. From the test results under ITU-T G.160, it is shown that, in comparison with the reference method which is the Wiener filtering method with decision-directed approach for SNR estimation, the WDA-based speech enhancement methods could achieve better objective speech quality, no matter whether the noise conditions are included in the training set or not. And the similar amount of noise reduction and SNR improvement can be obtained with smaller distortion on speech level.


The Collaborative Design of Technologies that Scaffold and Assess during Web-Based Science Inquiry.


title: A 0.8-mW 55-GHz Dual-Injection-Locked CMOS Frequency Divider

abstract: This paper presents the dual-injection-locking technique to enhance the locking range of resonator-based frequency dividers. By fully utilizing the voltage and current injection of the input signal, the divider locking range is extended significantly. The 0.8-mW dual-injection-locked frequency divider was developed in 90-nm digital CMOS technology. The total chip size is 0.77 mm times 0.5 mm. Without any varactor or inductor tuning, the input signal frequency coverage of the divider is from 35.7 to 54.9 GHz. Combined with the excellent locking range and sub-milliwatt power consumption, the figure-of-merit of this work surpasses those of the previous resonator-based dividers by more than one order.


title: Recommending academic papers via users' reading purposes

abstract: The past decades have witnessed the rapid development of academic research, which results in a growing number of scholarly papers. As a result, paper recommender systems have been proposed to help researchers find their interested papers. Most previous studies in paper recommendations mainly concentrate on paper-paper or user-paper similarities without taking users' reading purposes into account. It is common that different users may prefer to different aspects of a paper, e.g., the focused problem/task or the proposed solution. In this paper, we propose to satisfy user-specific reading purposes by recommending the most problem-related papers or solution-related papers to users separately. For a target paper, we use the paper citation graph to generate a set of potential relevant papers. Once getting the candidate set, we calculate the problem-based similarities and solution-based similarities between candidates and the target paper through a concept based topic model, respectively. We evaluate our models on a real academic paper dataset and our experiments show that our approach outperforms a traditional similarity based model and can provide highly relevant paper recommendations according to different reading purposes for researchers.


title: Key points in dermoscopic diagnosis of basal cell carcinoma and seborrheic keratosis in Japanese.

abstract: Basal cell carcinoma (BCC) and seborrheic keratosis (SK) are representative pigmented skin tumors, and they are differentiated as non-melanocytic lesions in the two-step dermoscopy algorithm proposed by the Consensus Net Meeting on Dermoscopy. Because most BCC in Japanese patients are pigmented clinically, dermoscopy plays an important role in their differential diagnosis. The dermoscopic criteria for BCC include the lack of a pigment network and the presence of at least one positive feature for BCC, such as large blue-gray ovoid nests, multiple blue-gray globules, leaf-like areas, spoke wheel areas, arborizing vessels and ulceration. Whereas various dermoscopic features are seen in SK, comedo-like openings, milia-like cysts, and fissures and ridges are especially important features. It is necessary for clinicians to consider the pathological conditions causing the dermoscopic features of BCC and SK. In addition, the sensitivity and specificity of each feature should be taken into consideration to ensure an accurate dermoscopic diagnosis.


title: Virtual Machine Introspection Based Malware Behavior Profiling and Family Grouping

abstract: The proliferation of malwares have been attributed to the alternations of a handful of original malware source codes. The malwares alternated from the same origin share some intrinsic behaviors and form a malware family. Expediently, identifying its malware family when a malware is first seen on the Internet can provide useful clues to mitigate the threat. In this paper, a malware profiler (VMP) is proposed to profile the execution behaviors of a malware by leveraging virtual machine introspection (VMI) technique. The VMP inserts plug-ins inside the virtual machine monitor (VMM) to record the invoked API calls with their input parameters and return values as the profile of malware. In this paper, a popular similarity measurement Jaccard distance and a phylogenetic tree construction method are adopted to discover malware families. The studies of malware profiles show the malwares from a malware family are very similar to each others and distinct from other malware families as well as benign software. This paper also examines VMP against existing anti-malware detection engines and some well-known malware grouping methods to compare the goodness in their malware family constructions. A peer voting approach is proposed and the results show VMP is better than almost all of the compared anti-malware engines, and compatible with the fine tuned text-mining approach and high order N-gram approaches. We also establish a malware profiling website based on VMP for malware research.


MayoNLP at the BioCreative VI PM Track : Entity-enhanced Hierarchical Attention Neural Networks for Mining Protein Interactions from Biomedical Text


title: Eye-tracking technology for the analysis of dynamic data

abstract: In this paper, known eye-tracking algorithms, which in most cases require the use of infrared light, as well as the presence of some specialized physical device (eye-tracker) are considered. This greatly limits the range of tasks that can be solved with eye-tracking technology. The aim of the work is to increase the effectiveness of the appliance of eye-tracking technology through the development of an intelligent software module based on the proposed by authors “grid” method for tracking the eye pupil in the analysis of dynamic data. Efficiency and expediency of the proposed intelligent software module is proved when solving the task of analyzing streaming video with a high frame rate, in particular an advertising video clip. In addition, this approach will increase safety for dependable computing, in particular, for solving the problem of rapid (critical) reaction of a car in case of consciousness loss and/or falling asleep during driving.


title: Incremental Active Learning for Optimal Generalization

abstract: The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models. The effectiveness of this method is shown through computer simulations. The other is the optimal sampling method in trigonometric polynomial models. This method precisely specifies the optimal sampling locations.


title: Speech based emotion classification framework for driver assistance system

abstract: Automated analysis of human affective behavior has attracted increasing attention in recent years. Driver's emotion often influences driving performance which can be improved if the car actively responds to the emotional state of the driver. It is important for an intelligent driver support system to accurately monitor the driver's state in an unobtrusive and robust manner. Ever changing environment while driving poses a serious challenge to existing techniques for speech emotion recognition. In this paper, we utilize contextual information of the outside environment as well as inside car user to improve the emotion recognition accuracy. In particular, a noise cancellation technique is used to suppress the noise adaptively based on the driving context and a gender based context information is analyzed for developing the classifier. Experimental analyses show promising results.


title: Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

abstract: Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.


title: Institutionally Distributed Deep Learning Networks

abstract: Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In such cases, sharing a deep learning model is a more attractive alternative. The best method of performing such a task is unclear, however. In this study, we simulate the dissemination of learning deep learning network models across four institutions using various heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in three independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance (testing accuracy = 77.3%) that was closest to that of centrally hosted patient data (testing accuracy = 78.7%). We also found that there is an improvement in the performance of cyclical weight transfer heuristic with high frequency of weight transfer.


Surface from scattered points - a brief survey of recent developments


title: Generating Representative Executions [Extended Abstract]

abstract: Analyzing the behaviour of a concurrent program is made difficult by the number of possible executions. This problem can be alleviated by applying the theory of Mazurkiewicz traces to focus only on the canonical representatives of the equivalence classes of the possible executions of the program. This paper presents a generic framework that allows to specify the possible behaviours of the execution environment, and generate all Foata-normal executions of a program, for that environment, by discarding abnormal executions during the generation phase. The key ingredient of Mazurkiewicz trace theory, the dependency relation, is used in the framework in two roles: first, as part of the specification of which executions are allowed at all, and then as part of the normality checking algorithm, which is used to discard the abnormal executions. The framework is instantiated to the relaxed memory models of the SPARC hierarchy.


Experts ’ Views on Digital Parenting Strategies


title: Dermoscopic and reflectance confocal microscopy features of two cases of vulvar basal cell carcinoma

abstract: Basal cell carcinoma (BCC) is the most common malignant skin cancer. Its genital localization is rare, and the diagnosis in this site could be challenging. Here, we report two patients with vulvar BCC and describe their clinical, dermoscopic and in vivo and ex vivo reflectance confocal microscopic (RCM) features. Dermoscopy and RCM can be useful tools for helping the clinical diagnosis of vulvar BCC and for identifying the correct surgical margins.


title: Who Uses Bitcoin? An exploration of the Bitcoin community

abstract: Many cryptocurrencies have come into existence in recent years, with Bitcoin the most prominent among them. Although its short history has been volatile, the virtual currency maintains a core group of committed users. This paper presents an exploratory analysis of Bitcoin users. As a virtual currency and peer-to-peer payment system, Bitcoin may signal future challenges to state oversight and financial powers through its decentralized structure and offer of instantaneous transactions with relative anonymity. Very little is known about the users of Bitcoin, however. Utilizing publicly available survey data of Bitcoin users, this analysis explores the structure of the Bitcoin community in terms of wealth accumulation, optimism about the future of Bitcoin, and themes that attract users to the cryptocurrency. Results indicate that age, time of initial use, geographic location, mining status, engaging online discourse, and political orientation are all relevant factors that help explain various aspects of Bitcoin wealth, optimism, and attraction.


title: Pixel-to-Pixel Matching for Image Recognition Using Hungarian Graph Matching

abstract: A fundamental problem in image recognition is to evaluate the similarity of two images. This can be done by searching for the best pixel-to-pixel matching taking into account suitable constraints. In this paper, we present an extension of a zero-order matching model called the image distortion model that yields state-of-the-art classification results for different tasks. We include the constraint that in the matching process each pixel of both compared images must be matched at least once. The optimal matching under this constraint can be determined using the Hungarian algorithm. The additional constraint leads to more homogeneous displacement fields in the matching. The method reduces the error rate of a nearest neighbor classifier on the well known USPS handwritten digit recognition task from 2.4% to 2.2%.


title: Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

abstract: Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.


title: Low-power LVDS receiver for 1.3Gbps physical layer (PHY) interface

abstract: This paper presents the design of a low voltage differential signaling (LVDS) receiver for a 1.3 Gb/s physical layer (PRY) interface. The receiver supports a wide input common mode range of 0.05 V to 2.35 V and a minimum input differential signal of 100 mV as specified by the IEEE LVDS standard. The design is implemented in 0.13 /spl mu/m CMOS technology using both thick (3.3 V) and thin (1.2 V) gate oxide devices and the receiver consumes 11 mW of power. The receiver provides the interface between PHY and media access control (MAC) sub-layers.


title: Design of attitude and path tracking controllers for quad-rotor robots using reinforcement learning

abstract: There is a lot of interest in using quad-rotor helicopters as Miniature Aerial Vehicles (MAVs) due to their simple mechanical construction and straightforward propulsion system. However, since these vehicles are highly unstable nonlinear dynamical systems, a suitable control system is required for their attitude stabilization and navigation. This article presents a simulation environment for the design and evaluation of attitude stabilization and path tracking controllers for quad-rotor aerial robots using Reinforcement Learning (RL). Firstly, the nonlinear mathematical model for a commercial X3D-BL quad-rotor robot from Ascending Technologies is introduced. The attitude stabilization and path tracking controllers for the quad-rotor robot are formulated. It is shown how the parameters of the controllers can be adjusted using a RL algorithm called Learning Automata. Next, the proposed simulation topology is presented and its main features are discussed. It employs 2 host computers where one host executes the control loops and the reinforcement learning algorithm using MATLAB/SIMULINK. The other host runs the quad-rotor robot model using the X-Plane Flight Simulator. The two hosts communicate using UDP (User Datagram Protocol) over a standard Ethernet wired network. Finally, some simulation cases are presented and the controllers adjusted by the RL algorithm are evaluated.


title: Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

abstract: The current economic crisis fuels the financial social responsibility after an epoch of many excesses with damaging effects. This work tackles two emerging streams in the financial literature: the behavioral portfolio theory with mental accounting and the socially responsible investment (SRI). Promoting SRI is regarded by a lot of financial experts, policymakers and researchers from the field of economic and social sciences, as one of the potential solutions in order to avoid future crises. Therefore, new models for this investment approach are necessary. We try to support the class of investors that select their investments under a mental accounting framework and also they want to achieve a certain level of SR quality in their portfolios. In order to reconcile the two choice frames, avoiding unnecessary sacrifices in financial performance, we have designed a model based on goal programming that integrates the two cornerstones of the investor. Furthermore, we propose a fuzzy inference system to determine the amount of money allocated to each mental account as well as the confidence level assigned to each mental account. This tool is based on expert knowledge modeled by fuzzy if–then rules.


title: International Journal of Industrial Ergonomics 37 (2007) 445–460 A review of enterprise agility: Concepts, frameworks, and attributes

abstract: Existing knowledge about agile manufacturing (AM) and an agile workforce was reviewed in order to extend the concept of agility to the whole enterprise. The frameworks that describe other elements of the enterprise besides manufacturing, or contain attributes that could be applied to all enterprise structures were also included into the review. The review identified a large number of papers related to the AM. However, there was little empirical research done on the agile workforce and agile organization. This review identified the global characteristics of agility which can be applied to all aspects of enterprise: flexibility, responsiveness, speed, culture of change, integration and low complexity, high quality and customized products, and mobilization of core competencies. The need for further research in order to empirically establish and validate the attributes and indices of the agile workforce and agile enterprise has also been discussed.


title: Modeling topic hierarchies with the recursive chinese restaurant process

abstract: Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the topics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using heldout likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized toward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family.


title: Error Backprojection Algorithms for Non-Line-of-Sight Imaging

abstract: Recent advances in computer vision and inverse light transport theory have resulted in several non-line-of-sight imaging techniques. These techniques use photon time-of-flight information encoded in light after multiple, diffuse reflections to reconstruct a three-dimensional scene. In this paper, we propose and describe two iterative backprojection algorithms, the additive error backprojection (AEB) and multiplicative error backprojection (MEB), whose goal is to improve the reconstruction of the scene under investigation over non-iterative backprojection algorithms. We evaluate the proposed algorithms’ performance applied to simulated and real data (gathered from an experimental setup where the system needs to reconstruct an unknown scene). Results show that the proposed iterative algorithms are able to provide better reconstruction than the unfiltered, non-iterative backprojection algorithm for both simulated and physical scenes, but are more sensitive to errors in the light transport model.


title: Low-Temperature, Strong SiO2-SiO2 Covalent Wafer Bonding for III–V Compound Semiconductors-to-Silicon Photonic Integrated Circuits

abstract: We report a low-temperature process for covalent bonding of thermal SiO 2 to plasma-enhanced chemical vapor deposited (PECVD) SiO 2 for Si-compound semiconductor integration. A record-thin interfacial oxide layer of 60 nm demonstrates sufficient capability for gas byproduct diffusion and absorption, leading to a high surface energy of 2.65 J/m 2 after a 2-h 300°C anneal. O 2 plasma treatment and surface chemistry optimization in dilute hydrofluoric (HF) solution and NH 4 OH vapor efficiently suppress the small-size interfacial void density down to 2 voids/cm 2 , dramatically increasing the wafer-bonded device yield. Bonding-induced strain, as determined by x-ray diffraction measurements, is negligible. The demonstration of a 50 mm InP epitaxial layer transferred to a silicon-on-insulator (SOI) substrate shows the promise of the method for wafer-scale applications.


title: Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

abstract: Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories.


Stereoselective Cyclization of Highly Enantioenriched Allylsilanes with Aldehydes via Acetal Formation: New Asymmetric Access to Tetrahydropyrans and Piperidines.


Families in Former Times: Kinship, Household and Sexuality


title: The Relationship between Parenting Styles and Adolescents’ Social Anxiety in Migrant Families: A Study in Guangdong, China

abstract: Previous studies indicated that parenting styles were important influencing factors for the development of children’s well-being. It is known that mass migration to the cities in China will affect family relations. However, few studies focused on the relationship between parenting styles and adolescents’ mental health in migrant families. Thus, this study aimed to investigate how parenting styles could affect adolescent’s social anxiety in migrant families. A total number of 1,345 adolescents in migrant families from four non-government-funded junior middle schools in Guangdong province formed the research sample. Parenting styles were measured using short-form of the Egna Minnen Betraffande Uppfostran, and social anxiety was evaluated using Social Anxiety Subscale of Self-Consciousness Scale. The results showed that emotional warmth, overprotection and rejection were significantly more often perceived from mothers than from fathers. Significant group differences between high social anxiety group and low social anxiety group were found in both father’s rearing styles and mother’s rearing styles. Furthermore, in migrant families, paternal emotional warmth could decrease adolescents’ social anxiety, whereas maternal overprotection could increase it.


title: ADASYN: Adaptive synthetic sampling approach for imbalanced learning

abstract: This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics.