title: TokDoc: a self-healing web application firewall

abstract: The growing amount of web-based attacks poses a severe threat to the security of web applications. Signature-based detection techniques increasingly fail to cope with the variety and complexity of novel attack instances. As a remedy, we introduce a protocol-aware reverse HTTP proxy TokDoc (the token doctor), which intercepts requests and decides on a per-token basis whether a token requires automatic "healing". In particular, we propose an intelligent mangling technique, which, based on the decision of previously trained anomaly detectors, replaces suspicious parts in requests by benign data the system has seen in the past. Evaluation of our system in terms of accuracy is performed on two real-world data sets and a large variety of recent attacks. In comparison to state-of-the-art anomaly detectors, TokDoc is not only capable of detecting most attacks, but also significantly outperforms the other methods in terms of false positives. Runtime measurements show that our implementation can be deployed as an inline intrusion prevention system.


title: Anomaly detection and motif discovery in symbolic representations of time series

abstract: The advent of the Big Data hype and the consistent recollection of event logs and real-time data from sensors, monitoring software and machine configuration has generated a huge amount of time-varying data in about every sector of the industry. Rule-based processing of such data has ceased to be relevant in many scenarios where anomaly detection and pattern mining have to be entirely accomplished by the machine. Since the early 2000s, the de-facto standard for representing time series has been the Symbolic Aggregate approXimation (SAX).In this document, we present a few algorithms using this representation for anomaly detection and motif discovery, also known as pattern mining, in such data. We propose a benchmark of anomaly detection algorithms using data from Cloud monitoring software.


title: Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features

abstract: In this paper we present a novel image analysis methodology for automatically distinguishing low and high grades of breast cancer from digitized histopathology. A set of over 3,400 image features, including textural and nuclear architecture based features, are extracted from a database of 48 breast biopsy tissue studies (30 cancerous and 18 benign images). Spectral clustering is used to reduce the dimensionality of the feature set. A support vector machine (SVM) classifier is used (1) to distinguish between cancerous and non-cancerous images, and (2) to distinguish between images containing low and high grades of cancer. Classification is repeated using different subsets of features to compare their performance. The system achieves a 95.8% accuracy in distinguishing cancer from non-cancer using texture-based characteristics (Gabor filter features), and 93.3% accuracy in distinguishing high from low grades of cancer using architectural features. In addition, we investigate the underlying manifold structure on which the different grades of breast cancer lie as revealed through spectral clustering. The manifold shows a smooth spatial transition from low to high grade breast cancer.


title: Cutaneous melanoma in women: anatomic distribution in relation to sun exposure and phenotype.

abstract: An analysis of the relationship between the anatomic site of cutaneous melanoma, sun exposure, and phenotype was conducted in 355 women with histologically confirmed superficial-spreading melanoma and in 935 control subjects. The most frequent site for superficial-spreading melanoma was the leg. However, when major sun-related and phenotype risk factors were examined by site, risk ratios were lowest for melanomas that occurred on the leg. A history of frequent sunburns during elementary or high school, increased number of self-assessed large nevi, and blond hair were more strongly associated with melanoma sites other than the leg. Tumors on the trunk were more likely than tumors at other sites to be associated with histological evidence of a preexisting nevus. Results of this work indicate that associations between melanoma phenotypic factors may differ by anatomic site.


title: On the limits of PCI auto configuration and reuse in 4G/5G ultra dense networks

abstract: Increased demand for higher user throughput has led to deployment of multi-layer networks commonly called heterogeneous networks (Hetnets). Therein, small cells are deployed alongside traditional macro cells, in many cases on the same spectrum. Such scenarios complicate the configuration of network parameters such as the Physical Cell Identity (PCI). A number of approaches have as such been proposed to automate the allocation of PCIs in such scenarios. These approaches seek to address the two conflicting objectives for PCI assignment in a hetnet scenario: 1) the need for optimal performance by avoiding conflicts, against 2) the requirement to separate the different layers and avoid any need to share knowledge among the layers. However, as the density of small cells increases evolving the Hetnets into what are called Ultra Dense Networks (UDN), these approaches reach their limits. In this paper, we study the performance of the current PCI allocation strategies in such UDN scenarios and evaluate their break down points. Our results show that these strategies do not adequately address PCI allocation for the UDN scenario. Specifically, we observe that PCI assignment in one layer requires knowledge of the assignments in the other layer, otherwise the consequence is a very high count of PCI confusions.


title: Future impact: Predicting scientific success

abstract: Daniel E. Acuna, Stefano Allesina and Konrad P. Kording present a formula to estimate the future h-index of life scientists.


title: Selection-fusion approach for classification of datasets with missing values

abstract: This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values.


title: Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

abstract: Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis (ICA) for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-continuous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine (SVM) is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.


title: An Approach to Predict Drive-by-Download Attacks by Vulnerability Evaluation and Opcode

abstract: Drive-by-download attacks exploit vulnerabilities in web browsers, and users are unnoticeably downloading malware which accesses to the compromised websites. A number of detection approaches and tools against such attacks have been proposed so far. Especially, it is becoming easy to specify vulnerabilities of attacks, because researchers well analyze the trend of various attacks. Unfortunately, in the previous schemes, vulnerability information has not been used in the detection/prediction approaches of drive-by-download attacks. In this paper, we propose a prediction approach of "malware downloading" during drive-by-download attacks (approach-I), which uses vulnerability information. Our experimental results show our approach-I achieves the prediction rate (accuracy) of 92%, FNR of 15% and FPR of 1.0% using Naive Bayes. Furthermore, we propose an enhanced approach (approach-II) which embeds Opcode analysis (dynamic analysis) into our approach-I (static approach). We implement our approach-I and II, and compare the three approaches (approach-I, II and Opcode approaches) using the same datasets in our experiment. As a result, our approach-II has the prediction rate of 92%, and improves FNR to 11% using Random Forest, compared with our approach-I.


title: Blockchain for the Internet of Things: A systematic literature review

abstract: In the Internet of Things (IoT) scenario, the block-chain and, in general, Peer-to-Peer approaches could play an important role in the development of decentralized and dataintensive applications running on billion of devices, preserving the privacy of the users. Our research goal is to understand whether the blockchain and Peer-to-Peer approaches can be employed to foster a decentralized and private-by-design IoT. As a first step in our research process, we conducted a Systematic Literature Review on the blockchain to gather knowledge on the current uses of this technology and to document its current degree of integrity, anonymity and adaptability. We found 18 use cases of blockchain in the literature. Four of these use cases are explicitly designed for IoT. We also found some use cases that are designed for a private-by-design data management. We also found several issues in the integrity, anonymity and adaptability. Regarding anonymity, we found that in the blockchain only pseudonymity is guaranteed. Regarding adaptability and integrity, we discovered that the integrity of the blockchain largely depends on the high difficulty of the Proof-of-Work and on the large number of honest miners, but at the same time a difficult Proof-of-Work limits the adaptability. We documented and categorized the current uses of the blockchain, and provided a few recommendations for future work to address the above-mentioned issues.


title: Analysis of Dynamic Brain Imaging Data

abstract: Modern imaging techniques for probing brain function, including functional magnetic resonance imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques for analysis and visualization of such imaging data to separate the signal from the noise and characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging, and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: "noise" characterization and suppression, and "signal" characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for nonstationarity in the data. Of particular note are 1) the development of a decomposition technique (space-frequency singular value decomposition) that is shown to be a useful means of characterizing the image data, and 2) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.


title: A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis

abstract: AbstractAn artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, d...


title: Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

abstract: Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.


title: Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks

abstract: Over the past decade, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is power learning machine learning algorithm in classification while extracting high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.


title: HIV/AIDS-related knowledge and behaviour of FET college students: Implications for sexual health promotion

abstract: The impact of the HIV/AIDS pandemic is mostly felt by adolescents as half of all new HIV/AIDS infections have occurred in people aged 15-24 years. Statistics show that campaigns implemented by the South African government have failed to bring about positive behavioural change among young people. The aim of this study was to determine the HIV/AIDS-related knowledge among students at a college and the association between knowledge, self-efficacy, self-concept in sexual practices. This study was conducted at a Further Education and Training College in Cape Town. Data were collected using self-administered questionnaires consisting of five sections including demographic information; sexual practices; knowledge of HIV/AIDS, levels of self-efficacy; and self-concept. Fifty four percent of the participants indicated no condom use when having sex, either by themselves or by a partner; 43% indicated that they had more than 2 sexual partners in the 12 months prior to the study. The odds that a person with higher HIV/AIDS knowledge will use a condom were 1.047 times greater than someone with less HIV/AIDS knowledge. The odd’s ratio for self-efficacy indicates a positive relationship with the number of partners of an individual. The findings of the present study suggest adequate/high HIV/AIDS knowledge among the study sample. The study further highlights that for males, there is a greater likelihood that lower self-efficacy would predict more sexual partners in comparison to females. The results further suggest that although governmental organisations’ efforts improved knowledge of HIV/AIDS, programmes avidly promoting self-efficacy for males should be implemented. Keywords: HIV/AIDS-related knowledge, self-efficacy, self-concept, college students.


title: Leveraging Joint Interactions for Credibility Analysis in News Communities

abstract: Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of"citizen journalists"in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.


title: Game theory for cyber security

abstract: While there are significant advances in information technology and infrastructure which offer new opportunities, cyberspace is still far from completely secured. In many cases, the employed security solutions are ad hoc and lack a quantitative decision framework. While they are effective in solving the particular problems they are designed for, they generally fail to respond well in a dynamically changing scenario. To this end, we propose a holistic security approach in this paper. We find that game theory provides huge potential to place such an approach on a solid analytical setting. We consider the interaction between the attacks and the defense mechanisms as a game played between the attacker and the defender (system administrator). In particular, we propose a game theory inspired defense architecture in which a game model acts as the brain. We focus on one of our recently proposed game models, namely imperfect information stochastic game. Although this game model seems to be promising, it also faces new challenges which warrant future attention. We discuss our current ideas on extending this model to address such challenges.


title: Cross-Media Hashing with Neural Networks

abstract: Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional hamming space, has attracted intensive attention in recent years. This is motivated by the facts a) the multi-modal data is widespread, e.g., the web images on Flickr are associated with tags, and b) hashing is an effective technique towards large-scale high-dimensional data processing, which is exactly the situation of cross-media retrieval. Inspired by recent advances in deep learning, we propose a cross-media hashing approach based on multi-modal neural networks. By restricting in the learning objective a) the hash codes for relevant cross-media data being similar, and b) the hash codes being discriminative for predicting the class labels, the learned Hamming space is expected to well capture the cross-media semantic relationships and to be semantically discriminative. The experiments on two real-world data sets show that our approach achieves superior cross-media retrieval performance compared with the state-of-the-art methods.


title: uMine: a Blockchain based on Human Miners.

abstract: Blockchain technology like Bitcoin is a rapidly growing field of research which has found a wide array of applications. However, the power consumption of the mining process in the Bitcoin blockchain alone is estimated to be at least as high as the electricity consumption of Ireland which constitutes a serious liability to the widespread adoption of blockchain technology. We propose a novel instantiation of a proof of human-work which is a cryptographic proof that an amount of human work has been exercised, and show its use in the mining process of a blockchain. Next to our instantiation there is only one other instantiation known which relies on indistinguishability obfuscation, a cryptographic primitive whose existence is only conjectured. In contrast, our construction is based on the cryptographic principle of multiparty computation (which we use in a black box manner) and thus is the first known feasible proof of human-work scheme. Our blockchain mining algorithm called uMine, can be regarded as an alternative energy-efficient approach to mining.


Edge-based Object Tracking Candidate


title: Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects

abstract: Smart homes equipped with ambient intelligence technology constitute a promising direction to enable the growing number of elderly to continue to live in their own home as long as possible. However, this calls for technological solutions that suit their specific needs and capabilities. The Sweet-Home project aims at developing a new user friendly technology for home automation based on voice command. This paper reports a user evaluation assessing the acceptance and fear of this new technology. Eight healthy persons between 71 and 88 years old, 7 relatives (child, grandchild or friend) and 3 professional carers participated in a user evaluation. During about 45 min, the persons were questioned in co-discovery in the Domus smart home alternating between interview and wizard of Oz periods followed by a debriefing. The experience aimed at testing four important aspects of the project: voice command, communication with the outside world, domotics system interrupting a person's activity, and electronic agenda. Voice interface appeared to have a great potential to ease daily living for elderly and frail persons and would be better accepted than more intrusive solutions. By considering still healthy and independent elderly people in the user evaluation, an interesting finding that came up is their overall acceptance provided the system does not drive them to a lazy lifestyle by taking control of everything. This particular fear must be addressed for the development of smart homes that support daily living by giving them more ability to control rather than putting them away from the daily routine.


REDD : A Public Data Set for Energy Disaggregation Research


title: Processing theta-joins using MapReduce

abstract: Joins are essential for many data analysis tasks, but are not supported directly by the MapReduce paradigm. While there has been progress on equi-joins, implementation of join algorithms in MapReduce in general is not sufficiently understood. We study the problem of how to map arbitrary join conditions to Map and Reduce functions, i.e., a parallel infrastructure that controls data flow based on key-equality only. Our proposed join model simplifies creation of and reasoning about joins in MapReduce. Using this model, we derive a surprisingly simple randomized algorithm, called 1-Bucket-Theta, for implementing arbitrary joins (theta-joins) in a single MapReduce job. This algorithm only requires minimal statistics (input cardinality) and we provide evidence that for a variety of join problems, it is either close to optimal or the best possible option. For some of the problems where 1-Bucket-Theta is not the best choice, we show how to achieve better performance by exploiting additional input statistics. All algorithms can be made 'memory-aware', and they do not require any modifications to the MapReduce environment. Experiments show the effectiveness of our approach.


title: CRYSTAL: Inducing a Conceptual Dictionary

abstract: One of the central knowledge sources of an information extraction (IE) system IS a dictionary of linguistic patterns that can be used to identify references to relevant information in a text Automatic creation of conceptual dictionaries is important for portability and scalability of an IE system This paper describes CRYSTAL, a system which automatically induces a dictionary of "concept-node definitions" sufficient to identify relevant information from a training corpus Each of these concept-node definitions is generalized as far as possible without producing errors, so that a minimum number of dictionary entries cover the positive training instances Because it tests the accuracy of each proposed definition, CRYSTAL can often surpass human intuitions in creating reliable extraction rules.


title: Structural basis of haem-iron acquisition by fungal pathogens

abstract: Pathogenic microorganisms must cope with extremely low free-iron concentrations in the host's tissues. Some fungal pathogens rely on secreted haemophores that belong to the Common in Fungal Extracellular Membrane (CFEM) protein family, to extract haem from haemoglobin and to transfer it to the cell's interior, where it can serve as a source of iron. Here we report the first three-dimensional structure of a CFEM protein, the haemophore Csa2 secreted by Candida albicans. The CFEM domain adopts a novel helical-basket fold that consists of six α-helices, and is uniquely stabilized by four disulfide bonds formed by its eight signature cysteines. The planar haem molecule is bound between a flat hydrophobic platform located on top of the helical basket and a peripheral N-terminal ‘handle’ extension. Exceptionally, an aspartic residue serves as the CFEM axial ligand, and so confers coordination of Fe3+ haem, but not of Fe2+ haem. Histidine substitution mutants of this conserved Asp acquired Fe2+ haem binding and retained the capacity to extract haem from haemoglobin. However, His-substituted CFEM proteins were not functional in vivo and showed disturbed haem exchange in vitro, which suggests a role for the oxidation-state-specific Asp coordination in haem acquisition by CFEM proteins. The first crystal structure of a fungal secreted haemophore—Csa2 from Candida albicans—provides insight into how pathogenic fungi extract haem from haemoglobin and transfer it to the cytoplasm, where it serves as a source of iron.


title: A method to identify VR-based set-up to foster elderly in design evaluation

abstract: User involvement during design process is an important issue in inclusive design ID. To this purpose, in the last years, virtual reality VR technologies have been introduced to construct virtual prototypes, in order to allow test with users even at the end of the conceptual design phase. The present paper aims to propose a method to support the selection of VR interaction technologies, which can be suited by end users accordingly to their physical and cognitive abilities. The proposed method is applied in order to compare the main VR interaction technologies with the objective to identify an optimal low-cost VR set-up to involve elderly people in evaluation of an innovative concept of kitchen environment which implement smart grid and home automation technologies.


title: Mindful Self-Compassion Strategies for Survivors of Intimate Partner Abuse

abstract: Intimate partner abuse is a significant public health issue that is associated with a number of negative emotional responses (such as self-blame and shame), as well as mental health outcomes (such as depression, anxiety, PTSD, and suicidality). Although not commonly utilized with survivors of intimate partner abuse (IPA), current research indicates that mindful self-compassion (MSC), a concept embodied by the principles of self-kindness, common humanity, and mindfulness, can improve emotional responses and mental health outcomes for individuals who have experienced trauma. We lay out the research and potential benefits of using MSC as a healing technique for those who have experienced IPA. Intervention strategies to assist survivors in applying MSC are offered as tools for practitioners in working with survivors. Recommendations are made to guide future research in this area.


title: XbarGen: A memristor based boolean logic synthesis tool

abstract: The shrinking process of CMOS technology is reaching its physical limits, thus impacting on several aspects, such as performances, power consumption and many others. Alternative solutions are under investigation in order to overcome CMOS limitations. Among them, the memristor is one of the promising technologies. Several works have been proposed so far, describing how to implement boolean logic functions employing memristors in a crossbar architecture. In this paper, we propose a tool able to automatically map any boolean function to a memristor based crossbar implementation. The proposed tool helps to perform a design space exploration to identify the best implementation w.r.t. performances and area overhead.


title: Application classification using packet size distribution and port association

abstract: Traffic classification is an essential part in common network management applications such as intrusion detection and network monitoring. Identifying traffic by looking at port numbers is only suitable to well-known applications, while signature-based classification is not applicable to encrypted messages. Our preliminary observation shows that each application has distinct packet size distribution (PSD) of the connections. Therefore, it is feasible to classify traffic by analyzing the variances of packet sizes of the connections without analyzing packet payload. In this work, each connection is first transformed into a point in a multi-dimensional space according to its PSD. Then it is compared with the representative points of pre-defined applications and recognized as the application having a minimum distance. Once a connection is identified as a specific application, port association is used to accelerate the classification by combining it with the other connections of the same session because applications usually use consecutive ports during a session. Using the proposed techniques, packet size distribution and port association, a high accuracy rate, 96% on average, and low false positive and false negative rates, 4-5%, are achieved. Our proposed method not only works well for encrypted traffic but also can be easily incorporated with a signature-based method to provide better accuracy.


title: A modular planar robotic manipulandum with end-point torque control

abstract: Robotic manipulanda are extensively used in investigation of the motor control of human arm movements. They permit the application of translational forces to the arm based on its state and can be used to probe issues ranging from mechanisms of neural control to biomechanics. However, most current designs are optimized for studying either motor learning or stiffness. Even fewer include end-point torque control which is important for the simulation of objects and the study of tool use. Here we describe a modular, general purpose, two-dimensional planar manipulandum (vBOT) primarily optimized for dynamic learning paradigms. It employs a carbon fibre arm arranged as a parallelogram which is driven by motors via timing pulleys. The design minimizes the intrinsic dynamics of the manipulandum without active compensation. A novel variant of the design (WristBOT) can apply torques at the handle using an add-on cable drive mechanism. In a second variant (StiffBOT) a more rigid arm can be substituted and zero backlash belts can be used, making the StiffBOT more suitable for the study of stiffness. The three variants can be used with custom built display rigs, mounting, and air tables. We investigated the performance of the vBOT and its variants in terms of effective end-point mass, viscosity and stiffness. Finally we present an object manipulation task using the WristBOT. This demonstrates that subjects can perceive the orientation of the principal axis of an object based on haptic feedback arising from its rotational dynamics.


title: Low cost ZVS PFC driver for power LEDs

abstract: This paper presents a low cost converter for driving power light emitting diodes (LEDs). The converter is a series-resonant half-bridge with reduced dc-link capacitor which increases the conduction angle of the bridge rectifier and the power factor. It also allows the elimination of electrolytic capacitors, making the converter suitable for long life applications. Due to the resonant operation, there is no need for a current sensor to stabilize the average current through the LED string and it operates with zero-voltage-switching clamped-voltage (ZVS-CV). The simplification of the control design, and of the converter, reduces the component count and the cost. Analytical results are verified by laboratory experiment.


title: Accounting academics’ teaching self-efficacy and ethics integration in accounting courses: A Malaysian study

abstract: Purpose - The purpose of this paper is to examine the extent to which ethics education is incorporated in the curriculum by accounting academics (EXTENT) and its relationship with the following four factors: accounting academics’ attitudes towards ethics education (ATTDE); head of department support (HODS); peer support (PEERS); and accounting academics’ ethics teaching self-efficacy (ETSE). Design/methodology/approach - The study utilises data from a questionnaire survey of 117 accounting academics in Malaysia and engages path analysis to test various hypothesised relationships. Findings - The results indicate that ATTDE, HODS and PEERS have a significant and positive impact on accounting academics’ ETSE. The findings also suggest that ETSE and PEERS have a direct and positive impact on EXTENT. Overall, ETSE is found to be a significant mediating variable in the relationship between ATTDE, HODS, PEERS and EXTENT. Research limitations/implications - The relatively small sample of 117 Malaysian accounting academics and the limited number of factors studied as drivers of ETSE, which limits generalisability of the results. Practical implications - This paper is particularly useful for informing heads of departments and the regulatory and professional bodies of resourcing and fostering a work environment that supports peer support and interactions as well as knowledge resources that facilitate individual accounting academics’ to integrate ethics content in their courses or units. Originality/value - The study is guided by Bandura’s (1977, 1997) self-efficacy theory and adapts Tschannen-Moran and Hoy’s (2001) teacher efficacy construct in understanding how accounting academic’s belief in one’s ability to complete tasks and achieve goals affects the level of integration of ethics in their courses.


title: A Ka-band power amplifier based on the traveling-wave power-dividing/combining slotted-waveguide circuit

abstract: An eight-device Ka-band solid-state power amplifier has been designed and fabricated using a traveling-wave power-dividing/combining technique. The low-profile slotted-waveguide structure employed in this design provides not only a high power-combining efficiency over a wide bandwidth, but also efficient heat sinking for the active devices. The measured maximum small-signal gain of the eight-device power amplifier is 19.4 dB at 34 GHz with a 3-dB bandwidth of 3.2 GHz (f/sub L/=31.8 GHz, f/sub H/=35 GHz). The measured maximum output power at 1-dB compression (P/sub out/ at 1 dB) from the power amplifier is 33 dBm (/spl sim/2 W) at 32.2 GHz, with a power-combining efficiency of 80%. Furthermore, performance degradation of this power amplifier due to device failures has also been simulated and measured.


title: Adaptive estimation for periodic signal enhancement and tracking

abstract: A fast algorithm for enhancement and tracking of periodic signals with fixed or slowly varying central frequency in additive noise is proposed. The algorithm is based on results from discounted lea ...


title: Object Counting with Small Datasets of Large Images

abstract: We explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. To reduce overfitting when training on full resolution samples, we propose to use global sum pooling (GSP) instead of global average pooling (GAP) or fully connected (FC) layers at the backend of a convolutional neural network. Although computationally equivalent to GAP, we show via detailed experimentation that GSP allows convolutional networks to learn the counting task as a simple linear mapping problem generalized over the input shape and the number of objects present. We evaluate our approach on four different aerial image datasets - three car counting datasets (CARPK, PUCPR+, and COWC) and one new challenging dataset for wheat spike counting. Our GSP approach achieves state-of-the-art performance on all four datasets and GSP models trained with smaller-sized image patches localize objects better than their GAP counterparts.


title: Smart traffic light control using fuzzy logic and wireless sensor network

abstract: Inadequate space and funds for the construction of new roads and the steady increase in number of vehicles has prompted researchers to investigate other solutions to traffic congestion. One area gaining interest is the use of intelligent systems to make traffic routing decisions with the potential to enhance vehicle traffic management. This paper presents the design and implementation of a smart traffic light (STL) using fuzzy logic and wireless sensor network (WSN). Designed for an isolated four way roundabout; the STL incorporates a WSN to collect traffic data in real time. This data is aggregated and then fed into a fuzzy logic controller (FLC) engine in form of two inputs — traffic quantity (TQ) and waiting time (WT) for each lane. Based on the inputs, the FLC then computes an output priority degree (PD) that determines order of green light assignment. Using the PD, a smart algorithm then assigns green light to the lane with highest PD value. The cycle continues until all lanes get green light. To analyze the performance of the STL, we designed a simulation software using java to virtually represent the functions of the WSN and FLC. The results from simulations indicate that the system can effectively manage traffic at a roundabout.


title: Neural Language Modeling by Jointly Learning Syntax and Lexicon

abstract: We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information. On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation. In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model. In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network. Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.


title: Drawing Disorders in Alzheimer’s Disease and Other Forms of Dementia

abstract: Drawing is a multicomponential process that can be impaired by many kinds of brain lesions. Drawing disorders are very common in Alzheimer's disease and other forms of dementia, and can provide clinical information for the distinction of the different dementing diseases. In our review we started from an overview of the neural and cognitive bases of drawing, and from a recollection of the drawing tasks more frequently used for assessing individuals with dementia. Then, we analyzed drawing disorders in dementia, paying special attention to those observed in Alzheimer's disease, from the prodromal stages of the amnesic mild cognitive impairment to the stages of full-blown dementia, both in the sporadic forms with late onset in the entorhino-hippocampal structures and in those with early onset in the posterior neocortical structures. We reviewed the drawing features that could differentiate Alzheimer's disease from vascular dementia and from the most frequent forms of degenerative dementia, namely frontotemporal dementia and Lewy body disease. Finally, we examined some peculiar aspects of drawing disorders in dementia, such as perseverations, rotations, and closing-in. We argue that a careful analysis of drawing errors helps to differentiate the different forms of dementia more than overall accuracy in drawing.


title: Extended Range ZVS Active-Clamped Current-Fed Full-Bridge Isolated DC/DC Converter for Fuel Cell Applications: Analysis, Design, and Experimental Results

abstract: This paper presents analysis and design of zero-voltage switching (ZVS) active-clamped current-fed full-bridge isolated dc/dc converter for fuel cell applications. The designed converter maintains ZVS of all switches from full load down to very light load condition over wide input voltage variation. Detailed operation, analysis, design, simulation, and experimental results for the proposed design are presented. The additional auxiliary active clamping circuit absorbs the turn-off voltage spike limiting the peak voltage across the devices allowing the selection and use of low-voltage devices with low on-state resistance. In addition, it also assists in achieving ZVS of semiconductor devices. The converter utilizes the energy stored in the transformer leakage inductance aided by its magnetizing inductance to maintain ZVS. ZVS range depends upon the design, in particular the ratio of leakage and magnetizing inductances of the transformer. Rectifier diodes operate with zero-current switching. An experimental converter prototype rated at 500 W has been designed, built, and tested in the laboratory to verify the analysis, design, and performance for wide variations in input voltage and load.


title: Internet of Things applications: A systematic review

abstract: Abstract Internet of Things (IoT) is considered as an ecosystem that contains smart objects equipped with sensors, networking and processing technologies integrating and working together to provide an environment in which smart services are taken to the end users. The IoT is leading numerous benefits into the human life through the environment wherein smart services are provided to utilize every activity anywhere and anytime. All these facilities and services are conveyed through the diverse applications which are performed in the IoT environment. The most important utilities that are achieved by the IoT applications are monitoring and consequently immediate decision making for efficient management. In this paper, we intend to survey in divers IoT application domains to comprehend the different approaches in IoT applications which have been recently presented based on the Systematic Literature Review (SLR) method. The aim of this paper is to categorize analytically and statistically, and analyze the current research techniques on IoT applications approaches published from 2011 to 2018. A technical taxonomy is presented for the IoT applications approaches according to the content of current studies that are selected with SLR process in this study including health care, environmental monitoring, smart city, commercial, industrial and general aspects in IoT applications. IoT applications are compared with each other according to some technical features such as Quality of Service (QoS), proposed case study and evaluation environments. The achievements and disadvantages of each study is discussed as well as presenting some hints for addressing their weaknesses and highlighting the future research challenges and open issues in IoT applications.


title: Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval

abstract: Given a query image of an object, our objective is to retrieve all instances of that object in a large (1M+) image database. We adopt the bag-of-visual-words architecture which has proven successful in achieving high precision at low recall. Unfortunately, feature detection and quantization are noisy processes and this can result in variation in the particular visual words that appear in different images of the same object, leading to missed results. In the text retrieval literature a standard method for improving performance is query expansion. A number of the highly ranked documents from the original query are reissued as a new query. In this way, additional relevant terms can be added to the query. This is a form of blind rele- vance feedback and it can fail if 'outlier' (false positive) documents are included in the reissued query. In this paper we bring query expansion into the visual domain via two novel contributions. Firstly, strong spatial constraints between the query image and each result allow us to accurately verify each return, suppressing the false positives which typically ruin text-based query expansion. Secondly, the verified images can be used to learn a latent feature model to enable the controlled construction of expanded queries. We illustrate these ideas on the 5000 annotated image Oxford building database together with more than 1M Flickr images. We show that the precision is substantially boosted, achieving total recall in many cases.


title: Iterative joint extraction of entities, relationships and coreferences from text sources

abstract: Machine understanding of textual documents has been challenging since the early computer era. Since the information extraction research field emerged it has inferred multiple natural language processing tasks, such as named entities recognition, relationships extraction and coreference resolution. Even though for the purpose of the end-to-end information extraction all of the three tasks are crucial, existing work has been focusing merely on one specific task at the time or at best on their connection in a pipeline. In this paper we introduce a novel iterative and joint information extraction system that interconnects all the three tasks together using iterative feature functions which use the advantage of the intermediate extractions. Furthermore, we introduce a special transformation of data into skip-mention sequences to enable the extraction of relations and coreferences using fast first-order graphical models. Additionally, the system uses an ontology as its knowledge source, as a list of inferred extraction rules, and as a data schema of extracted results. Experimental results show that the accuracy of extractions improves after each iteration. In particular, our model obtained a 15% error reduction on named entity recognition over individual models.


title: Chinese input with keyboard and eye-tracking: an anatomical study

abstract: Chinese input presents unique challenges to the field of human computer interaction. This study provides an anatomical analysis of today's standard Chinese input process, which is based on pinyin, a phonetic spelling system in Roman characters. Through a combination of human performance modeling and experimentation, our study decomposed the Chinese input process into sub-tasks and found that choice reaction time and numeric keying, two component resulted from the large number of homophones in Chinese, were the major usability bottlenecks. Choice reaction alone took 36% of the total input time in our experiment. Numeric keying for multiple candidates selection tends to take the user's attention away from the computer visual screen. We designed and implemented the EASE (Eye Assisted Selection and Entry) system to help maintaining complete touch-typing experience without diverting visual (spacebar) and implicit eye-tracking to replace the numeric keystrokes. Our experiment showed that such a system could indeed work, even with today's imperfecteye-tracking technology.


Binocular Goggle Augmented Imaging and Navigation System provides real-time fluorescence image guidance for tumor resection and sentinel lymph node mapping


title: A Comparison of Tactile, Visual, and Auditory Warnings for Rear-End Collision Prevention in Simulated Driving

abstract: OBJECTIVE ::: This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities as a function of warning timing in a driving simulator. ::: ::: ::: BACKGROUND ::: The proliferation of in-vehicle information and entertainment systems threatens driver attention and may increase the risk of rear-end collisions. Collision warning systems have been shown to improve inattentive and/or distracted driver response time (RT) in rear-end collision situations. However, most previous rear-end collision warning research has not directly compared auditory, visual, and tactile warnings. ::: ::: ::: METHOD ::: Sixteen participants in a fixed-base driving simulator experienced four warning conditions: no warning, visual, auditory, and tactile. The warnings activated when the time-to-collision (TTC) reached a critical threshold of 3.0 or 5.0 s. Driver RT was captured from a warning below critical threshold to brake initiation. ::: ::: ::: RESULTS ::: Drivers with a tactile warning had the shortest mean RT. Drivers with a tactile warning had significantly shorter RT than drivers without a warning and had a significant advantage over drivers with visual warnings. ::: ::: ::: CONCLUSION ::: Tactile warnings show promise as effective rear-end collision warnings. ::: ::: ::: APPLICATION ::: The results of this study can be applied to the future design and evaluation of automotive warnings designed to reduce rear-end collisions.


Evaluating Technology Deployment at the State Level: Methods, Results, and Insights from the Georgia Manufacturing Extension Alliance


title: Detection & Prevention of Gray Hole Attack in Mobile Ad-Hoc Network using AODV Routing Protocol

abstract: this paper, we describe the basic idea related with the implementation of AODV protocol & impact of gray hole attack on adhoc network. Information exchange in a network of mobile and wireless nodes without any infrastructure support such networks are called as adhoc networks. A Mobile adhoc network is mobile, multihop wireless network which is capable of autonomous operation. A Gray hole is a node that selectively drops and forwards data packets after advertises itself as having the shortest path to the destination node in response to a route request message. Our mechanism helps to protect the network by detecting and reacting to malicious activities of any node. The results enable us to minimize the attacks on integrated MANET-Internet communication efficiently. Simulation will be carried out by using network simulator tool so as to address the problem of detection & prevention of gray hole attack in mobile ad-hoc network.


title: Text classification and named entities for new event detection

abstract: New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.


title: How Capacity Limits of Attention Influence Information Visualization Effectiveness

abstract: In this paper, we explore how the capacity limits of attention influence the effectiveness of information visualizations. We conducted a series of experiments to test how visual feature type (color vs. motion), layout, and variety of visual elements impacted user performance. The experiments tested users' abilities to (1) determine if a specified target is on the screen, (2) detect an odd-ball, deviant target, different from the other visible objects, and (3) gain a qualitative overview by judging the number of unique categories on the screen. Our results show that the severe capacity limits of attention strongly modulate the effectiveness of information visualizations, particularly the ability to detect unexpected information. Keeping in mind these capacity limits, we conclude with a set of design guidelines which depend on a visualization's intended use.


title: Sign Language and the Brain: A Review

abstract: How are signed languages processed by the brain? This review briefly outlines some basic principles of brain structure and function and the methodological principles and techniques that have been used to investigate this question. We then summarize a number of different studies exploring brain activity associated with sign language processing especially as compared to speech processing. We focus on lateralization: is signed language lateralized to the left hemisphere (LH) of native signers, just as spoken language is lateralized to the LH of native speakers, or could sign processing involve the right hemisphere to a greater extent than speech processing? Experiments that have addressed this question are described, and some problems in obtaining a clear answer are outlined.


title: Imaging vector fields using line integral convolution

abstract: Imaging vector fields has applications in science, art, image processing and special effects. An effective new approach is to use linear and curvilinear filtering techniques to locally blur textures along a vector field. This approach builds on several previous texture generation and filtering techniques[8, 9, 11, 14, 15, 17, 23]. It is, however, unique because it is local, one-dimensional and independent of any predefined geometry or texture. The technique is general and capable of imaging arbitrary two- and three-dimensional vector fields. The local one-dimensional nature of the algorithm lends itself to highly parallel and efficient implementations. Furthermore, the curvilinear filter is capable of rendering detail on very intricate vector fields. Combining this technique with other rendering and image processing techniques — like periodic motion filtering — results in richly informative and striking images. The technique can also produce novel special effects.


title: OSLC Resource Shape: A language for defining constraints on Linked Data

abstract: IBM has for several years been employing a read/write usage of Linked Data as an architectural style for integrating a suite of applications. (1) We are encouraged by the work done by the W3C Linked Data Platform Working Group which is chartered to produce a W3C Recommendation for HTTP-based (RESTful) application integration patterns using read/write Linked Data . The Linked Data Platform Recommendation will provide the industry with a solid foundation to build on. Yet, more work will need to be done to address in a standard way the needs of enterprise solutions that use Linked Data as an application integration platform. One such need is a type definition language that can be used to communicate and validate constraints on RDF data. This paper explains the need for such a language, why standards like RDFS and OWL are not suitable answers and, finally, introduces OSLC Resource Shapes as a proposed solution.


title: Context-aware Natural Language Generation with Recurrent Neural Networks

abstract: This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic representation and then decode the semantic representation into text sequences with recurrent neural networks. During decoding, the context information are attended through a gating mechanism, addressing the problem of long-range dependency caused by lengthy sequences. We evaluate the effectiveness of the proposed approaches on user review data, in which rich contexts are available and two informative contexts, sentiments and products, are selected for evaluation. Experiments show that the fake reviews generated by our approaches are very natural. Results of fake review detection with human judges show that more than 50\% of the fake reviews are misclassified as the real reviews, and more than 90\% are misclassified by existing state-of-the-art fake review detection algorithm.


title: THE DEBUGGABLE INTERPRETER DESIGN PATTERN

abstract: The use of Interpreter and Visitor design patterns has been widely adopted to implement programming language interpreters due to their expressive and simple design. However, no general approach to conceive a debugger is commonly adopted. This paper presents the debuggable interpreter design pattern as a general approach to extend a language interpreter with debugging facilities such as step-over and step-into. Moreover, it enables multiple debuggers coexisting and extends the Interpreter and Visitor design patterns with a few hooks and a debugging service. SmallJS, an interpreter for Javascript-like language, serves as an illustration.


title: Orthogonal learning particle swarm optimization

abstract: This paper proposes an orthogonal learning particle swarm optimization (OLPSO) by designing an orthogonal learning (OL) strategy through the orthogonal experimental design (OED) method. The OL strategy takes the dimensions of the problem as the orthogonal experimental factors. The levels of each dimension (factor) are the two choices of the personal best position and the neighborhood's best position. By orthogonally combining the two learning exemplars, the useful information can be discovered, preserved and utilized to construct an efficient exemplar to guide the particle to fly in a more promising direction towards the global optimum. The effectiveness and efficiency of the OL strategy is demonstrated on a set of benchmark functions by comparing the PSOs with and without OL strategy. The OL strategy improves the PSO algorithm in terms of higher quality solution and faster convergence speed.


title: RoboProf and an introductory computer programming course

abstract: RoboProf is an online teaching system. It is based on WWW technology and can easily incorporate WWW-compatible media such as graphics, audio and video. It is structured as a self-paced course book: RoboProf presents the student with information on a closely-defined topic and then marks a set of exercises covering that material. When the students results are satisfactory, a new topic is introduced.The idea behind RoboProf is to increase motivation by borrowing ideas from certain games. These ideas include providing a challenge, giving quick feedback, making progress visible and encouraging experimentation.RoboProf was used to teach an introductory computer programming course. An introductory computer programming course must cover two main areas, the computer model (syntax and semantics of a programming language) and program design. In this paper I show how RoboProf can be effectively used to help teach the syntax and semantics of a programming language.


title: Full-space spectrum-sharing strategy for TDD/FDD massive MIMO cognitive radio systems

abstract: This paper presents a new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems, where two CR base stations (CBS) are employed at the adjacent sides of each cell to provide a full-space coverage for the whole cell. Thanks to the high spatial resolution of massive antennas, CRs are distinguished by their angular information and their uplink/downlink channels are also represented with reduced parameter dimensions by the proposed two-dimensional spatial basis expansion model (2D-SBEM). To improve the spectral efficiency and the scheduling probability of CRs, a greedy CR scheduling algorithm is designed for the dual CBS system. As the proposed strategy is mainly based on angular information and since the angle reciprocity holds for two frequency carriers with moderate distance, it can be applied for both TDD and FDD systems.


Intelligent Prediction of Heart Disease Using Risk Factors Based on Data Mining Techniques


title: Query-Free Clothing Retrieval via Implicit Relevance Feedback

abstract: Image-based clothing retrieval is receiving increasing interest with the growth of online shopping. In practice, users may often have a desired piece of clothing in mind (e.g., either having seen it before on the street or requiring certain specific clothing attributes) but may be unable to supply an image as a query. We model this problem as a new type of image retrieval task in which the target image resides only in the user's mind (called "mental image retrieval" hereafter). Because of the absence of an explicit query image, we propose to solve this problem through relevance feedback. Specifically, a new Bayesian formulation is proposed that simultaneously models the retrieval target and its high-level representation in the mind of the user (called the "user metric" hereafter) as posterior distributions of pre-fetched shop images and heterogeneous features extracted from multiple clothing attributes, respectively. Requiring only clicks as user feedback, the proposed algorithm is able to account for the variability in human decision-making. Experiments with real users demonstrate the effectiveness of the proposed algorithm.


title: Publicly Private and Privately Public: Social Networking on YouTube

abstract: YouTube is a public video-sharing website where people can experience varying degrees of engagement with videos, ranging from casual viewing to sharing videos in order to maintain social relationships. Based on a one-year ethnographic project, this article analyzes how YouTube participants developed and maintained social networks by manipulating physical and interpretive access to their videos. The analysis reveals how circulating and sharing videos reflects different social relationships among youth. It also identifies varying degrees of "publicness" in video sharing. Some participants exhibited "publicly private" behavior, in which video makers' identities were revealed, but content was relatively private because it was not widely accessed. In contrast, "privately public" behavior involved sharing widely accessible content with many viewers, while limiting access to detailed information about video producers' identities.


title: [ESOPHAGEAL STENOSIS IN CHILDREN: MEDICAL TREATMENT EXPERIENCE]

abstract: We present a retrospective evaluation of our experience in the period that goes from January 1992 to December 1998, clinical records of 58 patients ages from 2 months old to 15 years, male and female, who were treated at the GI service of Instituto de Salud del Ni o, were reviewed. All of them had esophageal stenosis and were included in the Esophageal Dilatation Program with Savary-Gilliard bougies. The causes of Esophageal Stenosis in the study were: Caustic agents 37.9%, gastro esophageal reflux (GER) 20.7%, surgery sequela 19.0%, related to esophageal esclerotherapy 12.1%, foreign body itself or maneuvers to retrieve them 8.6%, stomach adenocarcinoma invading the esophagus. Patients were classified in three groups: Group 1: stenosis due to caustic agents Group 2: stenosis due to GER Group 3: The remaining causes mentioned above., In each the following was calculated: the arithmetical media, the range of dilatations sessions and the total number of them. The higher figures took place in group 1. So we conclude that the number of sessions is directly related with the cause of the stenosis, requiring more number of dilatations to get a better response. Finally the response to treatment is evaluated considering a good response in 72.4%. A mild response in 15.5% and therapeutic failure in 12.1% of patients. The rate of complications was 10.3 per patient and 1.1 in relation to the total number of dilatation sessions. The main complications were: gastric perforation, duodenal perforation, pneumomediastinum, esophago-tracheal fistulae andi pseudodiverticulum formation, with resolution as seen in posterior controls. These complications occured after the proceeding took place. The treatment was installed according to each case. The patients with duodenal perforation died. We conclude that esophageal dilatations in infants with esophageal stenosis, of different ethiology, are secure and efficient.


title: A purchasing decision: Selecting a supplier for a construction company

abstract: Supplier selection is one of the most crucial activities performed by organizations because of its strategic importance. Supplier selection is a multi-objective problem involving both quantitative and qualitative criteria. Over the years a number of quantitative approaches have been tried. Although the Analytic Hierarchy Process (AHP) has previously been used in supplier selection problems, one major weakness of the application-oriented AHP literature is that it tends to focus on the mechanics of AHP instead of on the theoretical and practical implications associated with finding a solution. Though it is one of the most extensively used Multiple Criteria Decision Analysis methodologies, our literature search indicated that most studies found the best solution and stopped there, ignoring sensitivity analysis. Performing sensitivity analysis is very important for practical decision making, sometimes even as important as finding the best solution. In this paper for the first time a comprehensive application of AHP for a real-world case is presented along with sensitivity analysis in choosing the best suppliers for a Turkish construction company. As a result of this study the company decided to allocate the order quantities between the two top suppliers.


title: Collaborative and Adversarial Network for Unsupervised Domain Adaptation

abstract: In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain-adversarial training of neural networks. We add several domain classifiers on multiple CNN feature extraction blocks1, in which each domain classifier is connected to the hidden representations from one block and one loss function is defined based on the hidden presentation and the domain labels (e.g., source and target). We design a new loss function by integrating the losses from all blocks in order to learn domain informative representations from lower blocks through collaborative learning and learn domain uninformative representations from higher blocks through adversarial learning. We further extend our CAN method as Incremental CAN (iCAN), in which we iteratively select a set of pseudo-labelled target samples based on the image classifier and the last domain classifier from the previous training epoch and re-train our CAN model by using the enlarged training set. Comprehensive experiments on two benchmark datasets Office and ImageCLEF-DA clearly demonstrate the effectiveness of our newly proposed approaches CAN and iCAN for unsupervised domain adaptation.


title: Large-Scale Collective Entity Matching

abstract: There have been several recent advancements in Machine Learning community on the Entity Matching (EM) problem. However, their lack of scalability has prevented them from being applied in practical settings on large real-life datasets. Towards this end, we propose a principled framework to scale any generic EM algorithm. Our technique consists of running multiple instances of the EM algorithm on small neighborhoods of the data and passing messages across neighborhoods to construct a global solution. We prove formal properties of our framework and experimentally demonstrate the effectiveness of our approach in scaling EM algorithms.


title: Optimization of the Wind Turbine Layout and Transmission System Planning for a Large-Scale Offshore WindFarm by AI Technology

abstract: The interest in the utilization of offshore wind power is increasing significantly worldwide. A typical offshore windfarm may have hundreds of generators, which is outspread in the range of several to tens of kilometers. Therefore, there are many feasible schemes for the wind turbine location and internal line connection in a wind farm. The planner must search for an optimal one from these feasible schemes, usually with a maximum wind power output and the lowest installation and operation cost. This paper proposes a novel procedure to determine the optimization wind turbine location and line connection topology by using artificial intelligence techniques: The genetic algorithm is utilized in the optimal layouts for the offshore wind farm, and the ant colony system algorithm is utilized to find the optimal line connection topology. Furthermore, the wake effect, real cable parameters, and wind speed series are also considered in this research. The concepts and methods proposed in this study could help establish more economical and efficient offshore wind farms in the world.


Unsupervised Deep Auditory Model Using Stack of Convolutional RBMs for Speech Recognition


title: Class E resonant inverter optimized design for high frequency (MHz) operation using eGaN HEMTs

abstract: The Class-E resonant converter can achieve zero-voltage switching and zero-dv/dt switching when the switch is turned on in optimum mode. This paper presents a design procedure for Class E converter operating at high switching frequency (tens of MHz), considering the tradeoff between device voltage stress and current stress. The operation of a class E inverter is presented, and analytical steady-state equations for the Class E inverter are given valid at any switch-on duty cycle for operation with highly loaded quality factor. The optimized design procedure is described in detail. To verify the proposed approach, a 10MHz switching frequency, 20W output power GaN Class-E inverter design example is given, and its Pspice simulation results are presented. Additionally, the analytical loss model of the Class-E inverter is derived.


title: Fetus in fetu: two case reports and literature review

abstract: Fetus in fetu is a rare congenital anomaly and is defined as a monozygotic twin incorporated into the abdomen of its sibling during development. Fetus in fetu is often overlooked in the differential diagnosis of an abdominal mass. Unlike teratomas, fetus in fetu is a benign disorder. We describe the clinical characteristics of two patients, a thirty-months old boy who was found to have abdominal distension and a neonate who was diagnosed antenatally with abdominal mass. Computed tomography scan revealed the mass in which the contents favor a fetus in fetu rather than a teratoma. Surgical removal revealed that the anencephalic fetus have limb buds situated relative to a palpable vertebral column, supporting the diagnosis of fetus in fetu. In the present report, presentation, diagnosis, pathology, management, and recent literature are also reviewed. Fetus in fetu is a rare entity that typically presents in infancy and early childhood. It should be differentiated from a teratoma because of the teratoma’s malignant potential. Preoperative diagnosis is based on radiologic findings. The treatment of fetus in fetu is operative to relieve obstruction, prevent further compression and possible complications. Complete excision allows confirmation of the diagnosis and lowers the risk of recurrence.


title: Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

abstract: Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i.e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation- and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed system, the world’s largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current semiconductor fabrication.


title: Studying the Dark Triad of Personality through Twitter Behavior

abstract: Research into the darker traits of human nature is growing in interest especially in the context of increased social media usage. This allows users to express themselves to a wider online audience. We study the extent to which the standard model of dark personality -- the dark triad -- consisting of narcissism, psychopathy and Machiavellianism, is related to observable Twitter behavior such as platform usage, posted text and profile image choice. Our results show that we can map various behaviors to psychological theory and study new aspects related to social media usage. Finally, we build a machine learning algorithm that predicts the dark triad of personality in out-of-sample users with reliable accuracy.


Training and Evaluating Improved Dependency-Based Word Embeddings


title: A High-Voltage-Gain DC–DC Converter Based on Modified Dickson Charge Pump Voltage Multiplier

abstract: A high-voltage-gain dc–dc converter is introduced in this paper. The proposed converter resembles a two-phase interleaved boost converter on its input side while having a Dickson-charge-pump-based voltage multiplier (VM) on its output side. This converter offers continuous input current, which makes it more appealing for the integration of renewable sources like solar panels to a 400-V dc bus. Also, the proposed converter is capable of drawing power from either a single source or two independent sources. Furthermore, the VM used offers low voltage ratings for capacitors that potentially leads to size reduction. The converter design and component selection have been discussed in detail with supporting simulation results. A hardware prototype of the proposed converter with ${V}_{\rm{in}}= \rm{ 20}$ and ${V}_{\rm{out}}= \rm{ 400}$ V has been developed to validate the analytical results.


title: A Power-Aware Chopper-Stabilized Instrumentation Amplifier for Resistive Wheatstone Bridge Sensors

abstract: This paper presents a power-aware instrumentation amplifier (IA) based on differential difference amplifier (DDA) dedicated to a resistive Wheatstone bridge sensor system. The design incorporates a proposed current source replica (CSR) circuit that facilitates the pair matching layout for all critical pairs in DDA input stage. This is in contrast with the conventional design where the stated layout technique can only be applied to the input transistor pairs. Besides, a new analysis on common-mode signals and differential common-mode signal in chopper-stabilized DDA is given. It has shown that the differential common-mode signal is not modulated in chopping DDA. The relationship between dc offset and differential common-mode rejection ratio (CMRRd) is illustrated. Enhancing CMRRd will lead to reduction of dc offset and offset drift in DDA. Fabricated by a 0.18-μm CMOS process, the experimental results have shown that the maximum input-referred offset for the injection-nulling switch chopper DDA with CSR circuit is 3 μV, while the input-referred dc offset is 1.78 μV (mean + standard deviation) at a chopping frequency of 10 kHz from eight fabricated samples. For a closed-loop gain of 40.17 dB, its output ripple is reduced by three times with respect to itself without CSR. The IA powered with a high-power supply rejection regulator has been tested with a resistive Wheatstone bridge. The transducer achieves an input-referred noise of 3.76 μVrms for 2-kHz bandwidth.


title: A High-Power CMOS Switch Using A Novel Adaptive Voltage Swing Distribution Method in Multistack FETs

abstract: A high-power CMOS switch using a novel adaptive voltage swing distribution method in a multistack field-effect transistor (FET) scheme is proposed. The proposed adaptive voltage swing distribution method in multistack FETs is very effective in preventing unwanted channel formation with low control voltage supply in OFF-state FETs. This, in turn, increases power-handling capability when a large-signal voltage swing is applied. In the proposed CMOS switch, the behavior of the voltage swing in OFF-state multistack FETs shows a difference with respect to the level of input voltage swing. The characteristics of voltage swing distribution and leakage channel formation in the CMOS switch is fully analyzed with incorporation of the novel adaptive voltage swing distribution method into a three-stacked nMOS Rx switch in a standard 0.18-mum triple-well CMOS process. In addition, linearity of the proposed technique is verified through the measurement data of the single-pole double-throw switches that employ the proposed technique in the Rx switch. Two different types of configurations are implemented and characterized at the Rx switches, which consist of four-stacked nMOS devices, to demonstrate the method of minimizing voltage stress issues on one of the multistacked FETs. Layout consideration was also taken to prevent interference between leakage signals at the substrate. The measured performance of the proposed design shows an input 0.3-dB compression point of 33.5 dBm at 1.9 GHz. To the best of our knowledge, this is the highest power-handling capability of a CMOS switch in a standard CMOS process ever reported. The insertion losses of the Tx and Rx switches are 1.6 and 1.9 dB, respectively, at 1.9 GHz. The isolation of the Tx and Rx switches is around 20 and 30 dB, respectively, at 1.9 GHz.


title: Thirteen Ways to Look at the Correlation Coefficient

abstract: Abstract In 1885, Sir Francis Galton first defined the term “regression” and completed the theory of bivariate correlation. A decade later, Karl Pearson developed the index that we still use to measure correlation, Pearson's r. Our article is written in recognition of the 100th anniversary of Galton's first discussion of regression and correlation. We begin with a brief history. Then we present 13 different formulas, each of which represents a different computational and conceptual definition of r. Each formula suggests a different way of thinking about this index, from algebraic, geometric, and trigonometric settings. We show that Pearson's r (or simple functions of r) may variously be thought of as a special type of mean, a special type of variance, the ratio of two means, the ratio of two variances, the slope of a line, the cosine of an angle, and the tangent to an ellipse, and may be looked at from several other interesting perspectives.


title: Assessing Resilience: A Review of Measures across the Life Course

abstract: Through this systematic review the authors analyze scales used to measure resilience in individuals across the life course. The scales were obtained according to a priori inclusion criteria through searches using electronic databases, cited references, and requests to human services researchers currently engaged in research utilizing a resiliency theory framework. Eleven measurement tools meeting study inclusion criteria were located within the existing literature. Currently validated instruments measure specific populations and vary in length and format. The need for an analytical approach to measuring resilience is long overdue. This assessment is intended to aid social work practitioners working with populations that have faced adversity.


title: Eye metrics as an objective assessment of surgical skill.

abstract: OBJECTIVE ::: Currently, surgical skills assessment relies almost exclusively on subjective measures, which are susceptible to multiple biases. We investigate the use of eye metrics as an objective tool for assessment of surgical skill. ::: ::: ::: SUMMARY BACKGROUND DATA ::: Eye tracking has helped elucidate relationships between eye movements, visual attention, and insight, all of which are employed during complex task performance (Kowler and Martins, Science. 1982;215:997-999; Tanenhaus et al, Science. 1995;268:1632-1634; Thomas and Lleras, Psychon Bull Rev. 2007;14:663-668; Thomas and Lleras, Cognition. 2009;111:168-174; Schriver et al, Hum Factors. 2008;50:864-878; Kahneman, Attention and Effort. 1973). Discovery of associations between characteristic eye movements and degree of cognitive effort have also enhanced our appreciation of the learning process. ::: ::: ::: METHODS ::: Using linear discriminate analysis (LDA) and nonlinear neural network analyses (NNA) to classify surgeons into expert and nonexpert cohorts, we examine the relationship between complex eye and pupillary movements, collectively referred to as eye metrics, and surgical skill level. ::: ::: ::: RESULTS ::: Twenty-one surgeons participated in the simulated and live surgical environments. In the simulated surgical setting, LDA and NNA were able to correctly classify surgeons as expert or nonexpert with 91.9% and 92.9% accuracy, respectively. In the live operating room setting, LDA and NNA were able to correctly classify surgeons as expert or nonexpert with 81.0% and 90.7% accuracy, respectively. ::: ::: ::: CONCLUSIONS ::: We demonstrate, in simulated and live-operating environments, that eye metrics can reliably distinguish nonexpert from expert surgeons. As current medical educators rely on subjective measures of surgical skill, eye metrics may serve as the basis for objective assessment in surgical education and credentialing in the future. Further development of this potential educational tool is warranted to assess its ability to both reliably classify larger groups of surgeons and follow progression of surgical skill during postgraduate training.


title: Using flipped classroom approach to teach computer programming

abstract: Flipped classroom approach has been increasingly adopted in higher institutions. Although this approach has many advantages, there are also many challenges that should be considered. In this paper, we discuss the suitability of this approach to teach computer programming, and we report on our pilot experience of using this approach at Qatar University to teach one subject of computer programming course. It is found that students has positive attitude to this approach, it improves their learning. However, the main challenge was how to involve some of the students in online learning activities.


title: XLore: A Large-scale English-Chinese Bilingual Knowledge Graph

abstract: Current Wikipedia-based multilingual knowledge bases still suffer the following problems: (i) the scarcity of non-English knowledge, (ii) the noise in the semantic relations and (iii) the limited coverage of equivalent cross-lingual entities. In this demo, we present a large-scale bilingual knowledge graph named XLore, which has adequately solved the above problems.


title: On the Lcm-Sum Function

abstract: We consider a generalization of the lcm-sum function, and we give two kinds of asymptotic formulas for the sum of that function. Our results include a generalization


title: CauseInfer: Automatic and distributed performance diagnosis with hierarchical causality graph in large distributed systems

abstract: Modern applications especially cloud-based or cloud-centric applications always have many components running in the large distributed environment with complex interactions. They are vulnerable to suffer from performance or availability problems due to the highly dynamic runtime environment such as resource hogs, configuration changes and software bugs. In order to make efficient software maintenance and provide some hints to software bugs, we build a system named CauseInfer, a low cost and blackbox cause inference system without instrumenting the application source code. CauseInfer can automatically construct a two layered hierarchical causality graph and infer the causes of performance problems along the causal paths in the graph with a series of statistical methods. According to the experimental evaluation in the controlled environment, we find out CauseInfer can achieve an average 80% precision and 85% recall in a list of top two causes to identify the root causes, higher than several state-of-the-art methods and a good scalability to scale up in the distributed systems.


title: Focal loss dense detector for vehicle surveillance

abstract: Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.


title: Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network

abstract: Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch--Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups.


title: Contactless vital signs measurement for self-service healthcare kiosk in intelligent building

abstract: Contactless healthcare system is becoming a new trend to solve the existing healthcare system limitations. High flexibility and accessibility are the main advantages of contactless measurements. In this paper, we proposed a contactless healthcare kiosk for intelligent building application. The Remote Imaging Photoplethysmography (rIPPG) based framework to measure multiple human's vital signs, including pulse rate (PR), respiratory rate (RR), and systolic blood pressure (SBP) was developed to fulfill the performance requirement with a personal computer. Our experimental results show the Mean Absolute Error (MAE) of each measured parameters are 1.7 BPM, 0.41 BPM and 8.15 mmHg for PR, RR, and SBP respectively compared with clinical apparatus. These statistical numbers show the feasibility of using the contactless based system to measure numerous vital signs and also meet the requirements of standard healthcare device.


title: Relationship between Color and Emotion: A Study of College Students.

abstract: Ninety-eight college students were asked to indicate their emotional responses to five principle hues (i.e., red, yellow, green, blue, purple), five intermediate hues (i.e., yellow-red, green-yellow, blue-green, purple-blue, and red-purple), and three achromatic colors (white, gray, and black) and the reasons for their choices. The color stimuli were referenced from the Munsell Color System. The results revealed that the principle hues comprised the highest number of positive emotional responses, followed by the intermediate hues and the achromatic colors. The color green evoked mainly positive emotions such as relaxation and comfort because it reminded most of the respondents of nature. The color green-yellow had the lowest number of positive responses because it was associated with vomit and elicited the feelings of sickness and disgust. For the achromatic colors, white attained a large number of positive responses, followed by the colors black and gray. The reasons for the color-emotion associations are discussed and future research areas are suggested. Introduction Color is an inseparable part of our everyday lives and its presence is evident in everything that we perceive. It is widely recognized that colors have also a strong impact on our emotions and feelings (Hemphill, 1996; Lang, 1993; Mahnke, 1996). For instance, the color red has been associated with excitement, orange has been perceived as distressing and upsetting, purple as dignified and stately, yellow as cheerful, and blue has been associated with comfort and security (Ballast, 2002; Wexner, 1982). Moreover, some colors may be associated with several different emotions and some emotions are associated with more than one color (Linton, 1999, Saito, 1996). Red, symbolically known as a dominant and dynamic color, has an exciting and stimulating hue effect. It has both positive and negative impressions such as active, strong, passionate, warm, but on the other hand aggressive, bloody, raging and intense. Green has been found to have a retiring and relaxing effect. It too has both positive and negative impressions such as refreshment, quietness, naturalness, and conversely tiredness and guilt (Davey, 1998, Mahnke, 1996, Saito, 1996). The relationship between color and emotion is closely tied to color preferences. In particular, color preferences are associated with whether a color elicits positive or negative feelings. While particular colors have been found to be highly preferred regardless of age, racial group, or culture (Adams & Osgood, 1973, Eysenck, 1941), there is some evidence that color preference may be culturally-based. For example, Choungourian (1968) found that the colors red and blue were the most preferred colors among American subjects, but were less preferred in other cultures. In a comparision of Japanese and Korean subjects, Saito (1996) found unique color preference tendencies between the two countries, and also with respect to age, gender, and geographical region within the individual country. In an investigation of children's emotional associations with colors, Boyatzis and Varghese (1994) found that light colors (e.g., yellow, blue) are associated with positive emotions (e.g., happy, strong) and dark colors (e.g., black, gray) with negative emotions (e.g., sad, angry). In a study examining color-emotion associations among college students in Australia, Hemphill (1996) also found that bright colors elicited mainly positive emotional associations, while dark colors elicited negative emotional associations, confirming the results obtained by Boyatzis and Varghese (1994). However, Saito (1996) found that the color black elicited both negative and positive responses among Japanese subjects, and that black was often a preferred color among young people. Colors can also be described in temperature terms, such as "warm" or "cool"as related to the dominant wavelength of the color. The cool colors (e. …


title: Complementary perspectives on metaphor: Cognitive linguistics and relevance theory.

abstract: Contemporary theories of metaphor differ in many dimensions, including the discipline they originate from (e.g., linguistics, psychology, philosophy), and whether they are developed primarily within a cognitive or pragmatic theoretical framework. This article evaluates two directions of metaphor research within linguistics, cognitive linguistics and relevance theory, which both aim to capture essential aspects of the reason for metaphor, and how people ordinarily use and understand metaphor in daily life. We argue, contrary to most received opinion, that cognitive linguistics and relevance theory provide complementary perspectives on metaphor. Both theories offer important insights into the role of metaphor in cognition and language use, and suggest detailed hypotheses on metaphor understanding that surely are part of a comprehensive theory of metaphor.


title: On the Role of Information Retrieval and Information Extraction in Question Answering Systems

abstract: Question Answering, the process of extracting answers to natural language questions is profoundly different from Information Retrieval (IR) or Information Extraction (IE). IR systems allow us to locate relevant documents that relate to a query, but do not specify exactly where the answers are. In IR, the documents of interest are fetched by matching query keywords to the index of the document collection. By contrast, IE systems extract the information of interest provided the domain of extraction is well defined. In IE systems, the information of interest is in the form of slot fillers of some predefined templates.


title: Fast Screening Algorithm for Rotation and Scale Invariant Template Matching

abstract: This paper presents a generic pre-processor for expediting conventional template matching techniques. Instead of locating the best matched patch in the reference image to a query template via exhaustive search, the proposed algorithm rules out regions with no possible matches with minimum computational efforts. While working on simple patch features, such as mean, variance and gradient, the fast pre-screening is highly discriminative. Its computational efficiency is gained by using a novel octagonal-star-shaped template and the inclusion-exclusion principle to extract and compare patch features. Moreover, it can handle arbitrary rotation and scaling of reference images effectively. Extensive experiments demonstrate that the proposed algorithm greatly reduces the search space while never missing the best match.