title: The role of self-determination theory in explaining teachers' motivation to continue to use e-learning technology

abstract: Based on self-determination theory, this study proposes an extended information systems continuance theory in the context of teachers' utilization of e-learning technology in connection with on-site courses. In the proposed model teachers' extrinsic motivation (i.e. perceived usefulness), confirmation of pre-acceptance expectations and intrinsic motivation are predicted to be influenced by perceived autonomy, perceived competence and perceived relatedness. Even though information systems continuance theory has received quite extensive attention in prior research, this study is among the first to examine the effects of self-determination theory constructs in the context of teachers' utilization of e-learning technology. The results show that extension of IS-continuance theory with constructs that represent users' basic psychological needs and intrinsic motivation can be useful for predicting their e-learning continuance intentions.


DESIGN OF A MULTI-AGENT SYSTEM ARCHITECTURE FOR THE SCRUM METHODOLOGY


title: Semantic description of scholar-oriented social network cloud

abstract: Social networks have experienced exponential growth in membership and have become a hotspot for research in recent years. It helps users manage relations by establishing new relations and maintaining existing relations. On the basis of social networks' research, to encourage communication between scholars and facilitate management of academic information and academic relations, we built the Scholar-Oriented Social Network Cloud (SOSN) that is an academic information-centric social network. SOSN extracts academic information from various web sources on the Internet, and provides an application model for the social interaction and the semantic description of all related information. SOSN works as follows. Firstly, an academic search cloud is created that can provides academic search services according to users' web context. Then SOSN extracts public academic information from various literature databases and search engines on the web using the academic extracting services; secondly, SOSN devises an application model in the domain of scholar social network, which is used to represent the complex relations of entities in the Scholar-Oriented Social Network Cloud; thirdly, based on FOAF (Friend of a Friend), MarcOnt ontology, FRBR (Functional Requirements for Bibliographic Records) and SWAP (Scholarly Works Application Profile) academic semantic project, SOSN defines Scholar, AcademicWork, AcademicTeam ontology, etc. using RDF language according to semantic web technology and implements the semantic description of Scholar-Oriented Social Network Cloud.


title: A concept for context-aware computing in manufacturing: the white goods case

abstract: A key characteristic for industrial production in the Industry 4.0 paradigm is the connection of physical items like sensors, devices and enterprise assets, connected both to each other and to the Internet. In this Internet of Things environment, things will sense more data, become context-aware and provide added-value information to assist people take more relevant and valuable decisions. Context-aware information distribution may offer substantial value to manufacturing. It provides task-relevant information or services to users in a manufacturing shop-floor improving decision making through context-driven recommendations. This paper presents a context-aware information distribution system to support users in an industrial environment. The system aims at utilising data collected from sensors located at a shop-floor in order to increase the visibility of shop-floor processes by providing the right information, to the right people, at the right time. The system’s architecture and key elements have been developed for a pilot implementation in the white goods industry and are presented in this study.


Development of Crack Detection System with Unmanned Aerial Vehicles and Digital Image Processing


title: A Radiating Near-Field Patch Rectenna for Wireless Power Transfer to Medical Implants at 2.4 GHz

abstract: A radiating near-field method of recharging and activating medical implants using a 2.4-GHz rectifying patch antenna (rectenna) is designed and tested. Traditional near-field charging uses magnetically coupled coils, but these are highly sensitive to misalignments between the transmitter and receiver. In contrast, the proposed design employs the principles of wireless power transfer using radiating antennas. These antennas provide a misalignment-insensitive power delivery method, even when the receive antenna footprint is small (27.5 mm × 19.75 mm). A misalignment analysis is performed up to 15 cm, showing a maximum loss of 7.5 dB. As a proof-of-concept demonstration, a rectenna receiver was fabricated consisting of a patch antenna attached to a radio frequency (RF) rectifier. This integrated rectifier is a voltage quadrupling circuit that provides RF–DC rectification with efficiency of 40% at 0 dBm. For validation, a real-time actuation of a medical drug pump is demonstrated using only wirelessly transmitted power with no additional power storage elements.


title: The SAM algorithm for analogy-based story generation

abstract: Analogy-based Story Generation (ASG) is a relatively under-explored approach for story generation and computational narrative. In this paper, we present the SAM (Story Analogies through Mapping) algorithm as our attempt to expand the scope and complexity of stories generated by ASG. Comparing with existing work and our prior work, there are two main contributions of SAM: it employs 1) analogical reasoning both at the specific story content and general domain knowledge levels, and 2) temporal reasoning about the story (phase) structure in order to generate more complex stories. We illustrate SAM through a few example stories.


title: Cardiovascular outcome studies with incretin-based therapies: Comparison between DPP-4 inhibitors and GLP-1 receptor agonists

abstract: Abstract Dipeptidyl peptidase-4 inhibitors (DPP-4is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) represent two distinct classes of incretin-based therapies used for the treatment of type 2 diabetes. Non-inferiority versus placebo was shown in large prospective cardiovascular outcome trials in patients with high cardiovascular risk: SAVOR-TIMI 53 (saxagliptin), EXAMINE (alogliptin), and TECOS (sitagliptin); ELIXA (lixisenatide), LEADER (liraglutide) and SUSTAIN 6 (semaglutide). The promises raised by meta-analyses of phase 2–3 trials with DPP-4is were non confirmed as no cardiovascular protection could be evidenced. However, LEADER showed a significant reduction in major cardiovascular events, myocardial infarction, cardiovascular and all-cause mortality in patients treated by liraglutide compared to placebo. These positive results contrasted with the non-inferiority results with lixisenatide in ELIXA. They were partially confirmed with semaglutide in SUSTAIN 6 despite the absence of reduction in cardiovascular mortality. Hospitalisation for heart failure was not increased except with saxagliptin in SAVOR-TIMI 53. The reasons for different outcomes between trials remain largely unknown as well as the precise underlying mechanisms explaining the cardiovascular protection by liraglutide. The clinical relevance of results with DPP-4is and GLP-1RAs is discussed. Ongoing trials with linagliptin and several once-weekly GLP-1RAs should provide new insights into remaining fundamental questions.


title: The Role of Prior Model Calibration on Predictions with Ensemble Kalman Filter

abstract: This paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained.


title: Chinese Language Processing Based on Stroke Representation and Multidimensional Representation

abstract: With the development of deep learning and artificial intelligence, deep neural networks are increasingly being applied for natural language processing tasks. However, the majority of research on natural language processing focuses on alphabetic languages. Few studies have paid attention to the characteristics of ideographic languages, such as the Chinese language. In addition, the existing Chinese processing algorithms typically regard Chinese words or Chinese characters as the basic units while ignoring the information contained within the deeper architecture of Chinese characters. In the Chinese language, each Chinese character can be split into several components, or strokes. This means that strokes are the basic units of a Chinese character, in a manner similar to the letters of an English word. Inspired by the success of character-level neural networks, we delve deeper into Chinese writing at the stroke level for Chinese language processing. We extract the basic features of strokes by considering similar Chinese characters to learn a continuous representation of Chinese characters. Furthermore, word embeddings trained at different granularities are not exactly the same. In this paper, we propose an algorithm for combining different representations of Chinese words within a single neural network to obtain a better word representation. We develop a Chinese word representation service for several natural language processing tasks, and cloud computing is introduced to deal with preprocessing challenges and the training of basic representations from different dimensions.


title: Tail Sensitivity Analysis in Bayesian Networks

abstract: The paper presents an efficient method for simulating the tails of a target variable Z=h(X) which depends on a set of basic variables X=(X_1, ..., X_n). To this aim, variables X_i, i=1, ..., n are sequentially simulated in such a manner that Z=h(x_1, ..., x_i-1, X_i, ..., X_n) is guaranteed to be in the tail of Z. When this method is difficult to apply, an alternative method is proposed, which leads to a low rejection proportion of sample values, when compared with the Monte Carlo method. Both methods are shown to be very useful to perform a sensitivity analysis of Bayesian networks, when very large confidence intervals for the marginal/conditional probabilities are required, as in reliability or risk analysis. The methods are shown to behave best when all scores coincide. The required modifications for this to occur are discussed. The methods are illustrated with several examples and one example of application to a real case is used to illustrate the whole process.


title: Trust-Based Personalized Service Recommendation: A Network Perspective

abstract: Recent years have witnessed a growing trend of Web services on the Internet. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services (e.g., functional and non-functional properties) but largely ignore users’ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation (TSR) to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.


title: Tangible authoring of 3 D virtual scenes in dynamic augmented reality environment

abstract: This paper proposes tangible interfaces and interactions for authoring 3D virtual and immersive scenes easily and intuitively in tangible augmented reality (AR) environment. It provides tangible interfaces for manipulating virtual objects in a natural and intuitive manner and supports adaptive and accurate vision-based tracking in AR environments. In particular, RFID is used to directly integrate physical objects with virtual objects and to systematically support the tangible query of the relation between physical objects and virtual ones, which can provide more intuitive tangibility and a new way of virtual object manipulation. Moreover, the proposed approach offers an easy and intuitive switching mechanism between tangible environment and virtual environment. This paper also proposes a context-adaptive marker tracking method which can remove an inconsistent problem while embedding virtual objects into physical ones in tangible AR environments. The context-adaptive tracking method not only adjusts the locations of invisible markers by interpolating the locations of existing reference markers and those of previous ones, but also removes a jumping effect of movable virtual objects when their references are changed from one marker to another. Several case studies for generating tangible virtual scenes and comparison with previous work are given to show the effectiveness and novelty of the proposed approach.


title: Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time

abstract: As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.


title: An alternative text representation to TF-IDF and Bag-of-Words

abstract: In text mining, information retrieval, and machine learning, text documents are commonly represented through variants of sparse Bag of Words (sBoW) vectors (e.g. TF-IDF). Although simple and intuitive, sBoW style representations suffer from their inherent over-sparsity and fail to capture word-level synonymy and polysemy. Especially when labeled data is limited (e.g. in document classification), or the text documents are short (e.g. emails or abstracts), many features are rarely observed within the training corpus. This leads to overfitting and reduced generalization accuracy. In this paper we propose Dense Cohort of Terms (dCoT), an unsupervised algorithm to learn improved sBoW document features. dCoT explicitly models absent words by removing and reconstructing random sub-sets of words in the unlabeled corpus. With this approach, dCoT learns to reconstruct frequent words from co-occurring infrequent words and maps the high dimensional sparse sBoW vectors into a low-dimensional dense representation. We show that the feature removal can be marginalized out and that the reconstruction can be solved for in closed-form. We demonstrate empirically, on several benchmark datasets, that dCoT features significantly improve the classification accuracy across several document classification tasks.


title: The Author-Topic Model for Authors and Documents

abstract: We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors. We apply the model to a collection of 1,700 NIPS conference papers and 160,000 CiteSeer abstracts. Exact inference is intractable for these datasets and we use Gibbs sampling to estimate the topic and author distributions. We compare the performance with two other generative models for documents, which are special cases of the author-topic model: LDA (a topic model) and a simple author model in which each author is associated with a distribution over words rather than a distribution over topics. We show topics recovered by the author-topic model, and demonstrate applications to computing similarity between authors and entropy of author output.


title: A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization

abstract: Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the nondominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is evaluated on a number of well-known benchmark problems having 3–15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test functions considered in this paper, and it is compared favorably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.


title: Semi-supervised maximum mutual information training of deep neural network acoustic models.

abstract: Maximum Mutual Information (MMI) is a popular discriminative criterion that has been used in supervised training of acoustic models for automatic speech recognition. However, standard discriminative training is very sensitive to the accuracy of the transcription and hence its implementation in a semisupervised setting requires extensive filtering of data. We will show that if the supervision transcripts are not known, the natural analogue of MMI is to minimize the conditional entropy of the lattice of possible transcripts of the data. This is equivalent to the weighted average of MMI criterion over different reference transcripts, taking those reference transcripts and their weighting from the lattice itself. In this paper we describe experiments where we applied this method to the semi-supervised training of Deep Neural Network acoustic models. In our experimental setup, the proposed method gives up to 0.5% absolute WER improvement over a DNN trained with sMBR only on the transcribed part of the data. This is 37% of the improvement that we would get from doing sMBR training if we had the transcripts for the untranscribed part of the data.


title: Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data

abstract: The development of audio event recognition models requires labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the large amount of multimedia data on the web. In particular, if the audio content analysis must itself be performed on web audio, it is important to train the recognizers themselves from such data. Training from these web data, however, poses several challenges, the most important being the availability of labels : labels, if any, that may be obtained for the data are generally {\em weak}, and not of the kind conventionally required for training detectors or classifiers. We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data. We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data. The proposed method can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained with {\em strongly labeled} web data by a considerable margin.


title: Cultivating mindfulness: effects on well‐being

abstract: There has been great interest in determining if mindfulness can be cultivated and if this cultivation leads to well-being. The current study offers preliminary evidence that at least one aspect of mindfulness, measured by the Mindful Attention and Awareness Scale (MAAS; K. W. Brown & R. M. Ryan, 2003), can be cultivated and does mediate positive outcomes. Further, adherence to the practices taught during the meditation-based interventions predicted positive outcomes. College undergraduates were randomly allocated between training in two distinct meditation-based interventions, Mindfulness Based Stress Reduction (MBSR; J. Kabat-Zinn, 1990; n=15) and E. Easwaran's (1978/1991) Eight Point Program (EPP; n=14), or a waitlist control (n=15). Pretest, posttest, and 8-week follow-up data were gathered on self-report outcome measures. Compared to controls, participants in both treatment groups (n=29) demonstrated increases in mindfulness at 8-week follow-up. Further, increases in mindfulness mediated reductions in perceived stress and rumination. These results suggest that distinct meditation-based practices can increase mindfulness as measured by the MAAS, which may partly mediate benefits. Implications and future directions are discussed. © 2008 Wiley Periodicals, Inc. J Clin Psychol 64: 1–23, 2008.


title: AWEsome: An open-source test platform for airborne wind energy systems

abstract: In this paper we present AWEsome (Airborne Wind Energy Standardized Open-source Model Environment), a test platform for airborne wind energy systems that consists of low-cost hardware and is entirely based on open-source software. It can hence be used without the need of large financial investments, in particular by research groups and startups to acquire first experiences in their flight operations, to test novel control strategies or technical designs, or for usage in public relations. Our system consists of a modified off-the-shelf model aircraft that is controlled by the pixhawk autopilot hardware and the ardupilot software for fixed wing aircraft. The aircraft is attached to the ground by a tether. We have implemented new flight modes for the autonomous tethered flight of the aircraft along periodic patterns. We present the principal functionality of our algorithms. We report on first successful tests of these modes in real flights.


title: Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

abstract: The application of machine learning for the detection of malicious network traffic has been well researched over the past several decades; it is particularly appealing when the traffic is encrypted because traditional pattern-matching approaches cannot be used. Unfortunately, the promise of machine learning has been slow to materialize in the network security domain. In this paper, we highlight two primary reasons why this is the case: inaccurate ground truth and a highly non-stationary data distribution. To demonstrate and understand the effect that these pitfalls have on popular machine learning algorithms, we design and carry out experiments that show how six common algorithms perform when confronted with real network data. With our experimental results, we identify the situations in which certain classes of algorithms underperform on the task of encrypted malware traffic classification. We offer concrete recommendations for practitioners given the real-world constraints outlined. From an algorithmic perspective, we find that the random forest ensemble method outperformed competing methods. More importantly, feature engineering was decisive; we found that iterating on the initial feature set, and including features suggested by domain experts, had a much greater impact on the performance of the classification system. For example, linear regression using the more expressive feature set easily outperformed the random forest method using a standard network traffic representation on all criteria considered. Our analysis is based on millions of TLS encrypted sessions collected over 12 months from a commercial malware sandbox and two geographically distinct, large enterprise networks.


title: Efficient detection of missing object using Zigbee technology

abstract: In today's era, whether the client left a wallet in a taxicab or lost something at home, losing things is a genuine annoyance. What is more awful is that it feels like an issue that we shouldn't need to manage in the time of smart devices. The process of detecting the misplaced object manually is also time consuming. There are some most serious issues like size, range and area precision. To overcome these issues we use a technique to detect the misplaced objects based on Zigbee technology. Zigbee communication setup is used for detecting the misplaced objects. Zigbee is connected to mobile through OTG (On-The-Go) cable and one more Zigbee is connected to object. In case if any object is missing, with the help of Zigbee communication the object is detected automatically. The Zigbee is used to detect the objects in the indoor as well as in the outdoor environment. Thus the user can detect the missing object easily, the Zigbee and a phone app work together to do this.


title: Query Expansion Based on Crowd Knowledge for Code Search

abstract: As code search is a frequent developer activity in software development practices, improving the performance of code search is a critical task. In the text retrieval based search techniques employed in the code search, the term mismatch problem is a critical language issue for retrieval effectiveness. By reformulating the queries, query expansion provides effective ways to solve the term mismatch problem. In this paper, we propose Query Expansion based on Crowd Knowledge (QECK), a novel technique to improve the performance of code search algorithms. QECK identifies software-specific expansion words from the high quality pseudo relevance feedback question and answer pairs on Stack Overflow to automatically generate the expansion queries. Furthermore, we incorporate QECK in the classic Rocchio's model, and propose QECK based code search method QECKRocchio . We conduct three experiments to evaluate our QECK technique and investigate QECKRocchio in a large-scale corpus containing real-world code snippets and a question and answer pair collection. The results show that QECK improves the performance of three code search algorithms by up to 64 percent in Precision, and 35 percent in NDCG. Meanwhile, compared with the state-of-the-art query expansion method, the improvement of QECK Rocchio is 22 percent in Precision, and 16 percent in NDCG.


title: Regional Level Influenza Study with Geo-Tagged Twitter Data

abstract: The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.


title: Performance of a Protected Wireless Sensor Network in a Fire. Analysis of Fire Spread and Data Transmission

abstract: The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m2.


title: A novel method for horizontal eye line detection under various environments

abstract: The eye line is dened to be a horizontal line passing the two eyes of a human face. It can be used to help locate the true positions of eyes and face. In this paper, we propose a method to extract the horizontal eye line of a face under various environments. Based on the facts that the eye color is very di®erent from skin color and the gray level variance of an eye is high, some eye-like regions arerst located. Then the horizontal eye line is extracted based on the located eye-like regions and some geometric properties in a face. Experimental results are given to show that the proposed method is robust under a wide range of lighting conditions, di®erent poses and races. The detection rate for HHI face database is 95.63%. For Champion face database, the detection rate is 94.27%.


title: A seamless three-dimensional carbon nanotube graphene hybrid material.

abstract: Graphene and single-walled carbon nanotubes are carbon materials that exhibit excellent electrical conductivities and large specific surface areas. Theoretical work suggested that a covalently bonded graphene/single-walled carbon nanotube hybrid material would extend those properties to three dimensions, and be useful in energy storage and nanoelectronic technologies. Here we disclose a method to bond graphene and single-walled carbon nanotubes seamlessly during the growth stage. The hybrid material exhibits a surface area >2,000 m(2) g(-1) with ohmic contact from the vertically aligned single-walled carbon nanotubes to the graphene. Using aberration-corrected scanning transmission electron microscopy, we observed the covalent transformation of sp(2) carbon between the planar graphene and the single-walled carbon nanotubes at the atomic resolution level. These findings provide a new benchmark for understanding the three-dimensional graphene/single-walled carbon nanotube-conjoined materials.


title: Physiological, energetic and behavioural correlates of successful fishway passage of adult sockeye salmonOncorhynchus nerkain the Seton River, British Columbia

abstract: Electromyogram (EMG) radio telemetry was used in conjunction with physiological biopsy to relate prior physiological condition and subsequent swimming energetics and behaviours to passage success of 13 wild adult sockeye salmon Oncorhynchus nerka at a vertical-slot fishway on the Seton River, British Columbia. At the time of capture, plasma lactate, glucose and cortisol levels indicated that fish were not exhibiting unusually high levels of physiological stress. Very few differences existed between successful and unsuccessful fish in body size, initial plasma physiology and energy state and mean swim speed and energy use during passage. Generally, fish did not employ burst swimming during successful or failed attempts at passage, indicating that failure was probably not related to metabolic acidosis. Plasma Na(+) concentration was significantly lower in unsuccessful fish (P < 0.05), which is suggestive of a depressed ionic state or a possible stress component, although values in all fish were within an expected range for migrant adult O. nerka. Nevertheless, six of 13 fish failed to reascend the fishway and remained in the tailrace of the dam for more than a day on average before moving downstream and away from the dam. During this time, fish were observed actively seeking a means of passage, suggesting that there may have been other, undetermined causes of passage failure.


title: Font Recognition Based on Global Texture Analysis

abstract: We describe a novel texture analysis-based approach toward font recognition. Existing methods are typically based on local typographical features that often require connected components analysis. In our method, we take the document as an image containing some specific textures and regard font recognition as texture identification. The method is content-independent and involves no detailed local feature analysis. Experiments are carried out by using 14000 samples of 24 frequently used Chinese fonts (six typefaces combined with four styles), as well as 32 frequently used English fonts (eight typefaces combined with four styles). An average recognition rate of 99.1 percent is achieved. Experimental results are also included on the robustness of the method against image degradation (e.g., pepper and salt noise) and on the comparison with existing methods.


title: Supervised clustering - algorithms and benefits

abstract: This work centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified and has the goal of identifying class-uniform clusters that have high probability densities. Four representative-based algorithms for supervised clustering are introduced: a greedy algorithm with random restart, named SRIDHCR, that seeks for solutions by inserting and removing single objects from the current solution, SPAM (a variation of the clustering algorithm PAM), an evolutionary computing algorithm named SCEC, and a fast medoid-based top-down splitting algorithm, named TDS. The four algorithms were evaluated using a benchmark consisting of four UCI machine learning data sets. In general, it seems that "greedy" algorithms, such as SPAM, SRIDHCR, and TDS, do not perform particularly well for supervised clustering and seem to terminate prematurely too often. We also briefly describe the applications of supervised clustering.


title: The Long-Term Sequelae of Child and Adolescent Abuse: A Longitudinal Community Study.

abstract: Abstract The purpose of the present study was to examine the relationship between childhood and adolescent physical and sexual abuse before the age of 18 and psychosocial functioning in mid-adolescence (age 15) and early adulthood (age 21) in a representative community sample of young adults. Subjects were 375 participants in an ongoing 17-year longitudinal study. At age 21, nearly 11% reported physical or sexual abuse before age 18. Psychiatric disorders based on DSM-III-R criteria were assessed utilizing the NIMH Diagnostic Interview Schedule, Revised Version (DIS-III-R). Approximately 80% of the abused young adults met DSM-III-R criteria for at least one psychiatric disorder at age 21. Compared to their nonabused counterparts, abused subjects demonstrated significant impairments in functioning both at ages 15 and at 21, including more depressive symptomatology, anxiety, psychiatric disorders, emotional-behavioral problems, suicidal ideation, and suicide attempts. While abused individuals were functioning significantly more poorly overall at ages 15 and 21 than their nonabused peers, gender differences and distinct patterns of impaired functioning emerged. These deficits underscore the need for early intervention and prevention strategies to forestall or minimize the serious consequences of child abuse.


Waveform Inspired Models and the Harmonic Balance Emulator


title: Who Owns the Media

abstract: The authors examine patterns of media ownership in 97 countries around the world. They find that almost universally the largest media firms are controlled by the government or by private families. Government ownership is more pervasive in broadcasting than in the printed media. Government ownership is generally associated with less press freedom, fewer political and economic rights, inferior governance, and, most conspicuously, inferior social outcomes in education and health. The adverse effects of government ownership on political and economic freedom are stronger for newspapers than for television. The adverse effects of government ownership of the media do not appear to be restricted solely to instances of government monopoly. The authors present a range of evidence on the adverse consequences of state ownership of the media. State ownership of the media is often argued to be justified on behalf of the social needs of the disadvantaged. But if their findings are correct, increasing private ownership of the media--through privatization or by encouraging the entry of privately owned media--can advance a variety of political and economic goals, especially those of meeting the social needs of the poor.


title: Design and Implementation of a CUDA-Compatible GPU-based Core for Gapped BLAST Algorithm

abstract: Abstract This paper presents the first ever reported implementation of the Gapped Basic Local Alignment Search Tool (Gapped BLAST) for biological sequence alignment, with the Two-Hit method, on CUDA (compute unified device architecture)-compatible Graphic Processing Units (GPUs). The latter have recently emerged as relatively low cost and easy to program high performance platforms for general purpose computing. Our Gapped BLAST implementation on an NVIDIA Geforce 8800 GTX GPU is up to 2.7x quicker than the most optimized CPU-based implementation, namely NCBI BLAST, running on a Pentium4 3.4 GHz desktop computer with 2GB RAM.


title: Compact hybrid-integrated 100-Gbit/s TOSA for 40-km transmission

abstract: We developed a compact hybrid-integrated 100 Gbit/s TOSA using an EADFB laser array with a spot-size converter and a silica-based AWG multiplexer. Error-free operation for a 40-km transmission was demonstrated at an operating temperature of 55 °C.


title: Watch, Attend and Parse: An End-to-end Neural Network Based Approach to Handwritten Mathematical Expression Recognition

abstract: Abstract Machine recognition of a handwritten mathematical expression (HME) is challenging due to the ambiguities of handwritten symbols and the two-dimensional structure of mathematical expressions. Inspired by recent work in deep learning, we present Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format. Inherently unlike traditional methods, our proposed model avoids problems that stem from symbol segmentation, and it does not require a predefined expression grammar. Meanwhile, the problems of symbol recognition and structural analysis are handled, respectively, using a watcher and a parser. We employ a convolutional neural network encoder that takes HME images as input as the watcher and employ a recurrent neural network decoder equipped with an attention mechanism as the parser to generate LaTeX sequences. Moreover, the correspondence between the input expressions and the output LaTeX sequences is learned automatically by the attention mechanism. We validate the proposed approach on a benchmark published by the CROHME international competition. Using the official training dataset, WAP significantly outperformed the state-of-the-art method with an expression recognition accuracy of 46.55% on CROHME 2014 and 44.55% on CROHME 2016.


title: Conditional Text Paraphrasing: A Survey and Taxonomy

abstract: This work introduces a survey for the Text Para-phrasing task. The survey covers the different types of tasks around text paraphrasing and mentions the techniques and models that are regularly used when approaching towards it, alongside the datasets that are used while training and evaluating the models. Text paraphrasing has an effective impact when it is used in other applications, so, the paper mentions some text paraphrasing applications. Also, this work proposes a new taxonomy that it is called Conditional Text Paraphrasing. To the best of our knowledge, this is the first work that shows varieties and sub-problems of the original text paraphrasing task. The target of this taxonomy is to expand the definition of the text paraphrasing by adding some conditional constraints as features that either control the paraphrase generation or discrimination. This expanded definition opens in mind a new domain for research in Natural Language Processing (NLP) and Machine Learning. Finally, some useful applications for the conditional text paraphrasing are represented.


title: "How May I Help You?": Modeling Twitter Customer ServiceConversations Using Fine-Grained Dialogue Acts

abstract: Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.


title: Text line segmentation in handwritten document using a production system

abstract: We present in this paper a digitization project of cultural heritage manuscripts and we discuss the underlying problems, particularly those relative to document analysis. Considering the drawbacks of traditional methods for text line extraction in handwritten documents, we propose to adopt a new approach for handwritten page segmentation, based on a traditional problem solving framework used in artificial intelligence.


title: Predicting Smartphone Battery Life based on Comprehensive and Real-time Usage Data

abstract: Smartphones and smartphone apps have undergone an explosive growth in the past decade. However, smartphone battery technology hasn't been able to keep pace with the rapid growth of the capacity and the functionality of smartphones and apps. As a result, battery has always been a bottleneck of a user's daily experience of smartphones. An accurate estimation of the remaining battery life could tremendously help the user to schedule their activities and use their smartphones more efficiently. Existing studies on battery life prediction have been primitive due to the lack of real-world smartphone usage data at scale. This paper presents a novel method that uses the state-of-the-art machine learning models for battery life prediction, based on comprehensive and real-time usage traces collected from smartphones. The proposed method is the first that identifies and addresses the severe data missing problem in this context, using a principled statistical metric called the concordance index. The method is evaluated using a dataset collected from 51 users for 21 months, which covers comprehensive and fine-grained smartphone usage traces including system status, sensor indicators, system events, and app status. We find that the remaining battery life of a smartphone can be accurately predicted based on how the user uses the device at the real-time, in the current session, and in history. The machine learning models successfully identify predictive features for battery life and their applicable scenarios.


title: A perceptual account of symbolic reasoning

abstract: People can be taught to manipulate symbols according to formal mathematical and logical rules. Cognitive scientists have traditionally viewed this capacity—the capacity for symbolic reasoning—as grounded in the ability to internally represent numbers, logical relationships, and mathematical rules in an abstract, amodal fashion. We present an alternative view, portraying symbolic reasoning as a special kind of embodied reasoning in which arithmetic and logical formulae, externally represented as notations, serve as targets for powerful perceptual and sensorimotor systems. Although symbolic reasoning often conforms to abstract mathematical principles, it is typically implemented by perceptual and sensorimotor engagement with concrete environmental structures.


title: Could Global Software Development Benefit from Agile Methods?

abstract: At first glance, agile methods and global software development might seem incompatible. Agile methods stress continuous face-to-face communication, whereas communication has been reported as the biggest problem of global software development. One challenge to solve is how to apply agile practices in settings where continuous face-to-face interaction is missing. However, agile methods have been successfully used in distributed projects, indicating that they could benefit global software development. This paper discusses potential benefits and challenges of adopting agile methods in global software development. The literature on real industrial case studies reporting on experiences of using agile methods in distributed projects is still scarce. Therefore we suggest further research on the topic. We present our plans for research in companies using agile methods in their distributed projects. We also intend to test the use of agile principles in globally distributed student projects developing software for industrial clients.


title: Too painful to think about: chemsex and trauma

abstract: Purpose ::: ::: ::: ::: ::: Whilst chemsex is a relatively new phenomenon, trauma is not. Freud borrowed the word from physical medicine, where it was used to describe tissue damage, and applied it, for the first time, as a metaphor to a psychological process by which the protective functioning of the mind can too be pierced and wounded by events. The chemsex environment hosts a myriad of potentially traumatising scenarios and experiences, though perhaps disguised as exhilaration or excitement. The paper aims to discuss these issues. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The paper is a practitioner’s experience. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: These experiences piled on top of childhood experiences of being “less than” for being gay, can be responsible for widespread undiagnosed post-traumatic stress disorder (PTSD) among those who engage in chemsex. This paper explores this possibility and offers solutions. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Compounded trauma and PTSD symptoms amongst MSM who engage in chemsex has to date, not been researched.


title: Data mining application to detect financial fraud in Indonesia's public companies

abstract: Association of Certified Fraud Examiners explains that there are 3 types of occupational fraud: financial statement fraud, asset misappropriation and corruption. Among these three, financial statement fraud caused the biggest losses, which amounted to $ 1,000,000 in 2014. Financial statement has important role as an indicator of the success of a company, also for depicting the overall condition of the company, deciding company's stock price, and determining whether the company could be granted a loan or not. Given its important role, many cases of fraud occur. Audit activities are conducted to minimize losses, but the number of available auditors is limited, and the time required for traditional audit is quite long. Therefore, an effective model of financial fraud detection is needed to help auditors in analyzing financial statements. Data mining algorithms, support vector machine (SVM) and artificial neural network (ANN), were applied in this study. The results of this study give insight to the auditor that significant indicators in detecting financial fraud are profitability and efficiency. Feature selection improves SVM algorithm accuracy to 88.37%. ANN produces the highest accuracy, 90.97%, for data without feature selection.


title: Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention

abstract: Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the state-of-the-art performance in a Japanese sentiment classification task.


title: Sparse Flow: Sparse Matching for Small to Large Displacement Optical Flow

abstract: Despite recent advances, the extraction of optical flow with large displacements is still challenging for state-of the-art methods. The approaches that are the most successful at handling large displacements blend sparse correspondences from a matching algorithm with an optimization that refines the optical flow. We follow the scheme of Deep-Flow [33]. We first extract sparse pixel correspondences by means of a matching procedure and then apply a variational approach to obtain a refined optical flow. In our approach, coined 'Sparse Flow', the novelty lies in the matching. This uses an efficient sparse decomposition of a pixel's surrounding patch as a linear sum of those found around candidate corresponding pixels. As matching pixel the one dominating the decomposition is chosen. The pixel pairs matching in both directions, i.e. in a forward-backward fashion, are used as guiding points in the variational approach. Sparse-Flow is competitive on standard optical flow benchmarks with large displacements, while showing excellent performance for small and medium displacements. Moreover, it is fast in comparison to methods with a similar performance.


title: Domain analysis in information science Eleven approaches traditional as well as innovative

abstract: What kind of knowledge is needed by information specialists working in a specific subject field like medicine, sociology or music? What approaches have been used in information science to produce kinds of domain‐specific knowledge? This article presents 11 approaches to domain analysis. Together these approaches make a unique competence for information specialists. The approaches are: producing literature guides and subject gateways; producing special classifications and thesauri; research on indexing and retrieving specialities; empirical user studies; bibliometrical studies; historical studies; document and genre studies; epistemological and critical studies; terminological studies, LSP (languages for special purposes), discourse studies; studies of structures and institutions in scientific communication; and domain analysis in professional cognition and artificial intelligence. Specific examples and selective reviews of literature are provided, and the strengths and drawbacks of each of these approaches are discussed.


title: Non-invasive Wet Electrocochleography

abstract: To detect electrocochleographic (ECochG) potentials generated by the cochlea in response to auditory stimuli, either transtympanic or tympanic/extratympanic electrodes are currently used. The first are invasive, while the second are arranged in contact or very close to the tympanic membrane (TM). To avoid the discomfort and the risks inherent to the application of such conventional electrodes, this Letter presents an alternative technique. A conducting liquid is inserted into the ear canal to act as a distributed electrical interface between the TM and an external electrode. Thus, ECochG potentials are detected without any direct contact between the solid electrode and the sensitive TM. This technique was tested on ten volunteers with single-click auditory stimuli. Results showed its efficacy to noninvasively detect useful ECochG responses, with accurate morphology and significant amplitude. The technique is comfortable, sedation/anesthesia-free, inherently safe, and easy to perform. It also favors improvements of contact stability, reductions of contact impedance, and relative tolerance to misplacements. As a contraindication, the liquid prevents use on subjects affected by tympanic perforation. All these features encourage further investigations on this technique as a possible additional tool for the ECochG practice.


title: Expression: A dyadic conversation aid using Google Glass for people who are blind or visually impaired

abstract: This paper presents ‘Expression’ — an integrated assistive solution using Google Glass. The key function of the system is to enable the user in perceiving social signals during a natural dyadic conversation. The design and implementation of the system addressed a number of technical and research challenges — video acquisition and communication over Wi-Fi, efficient detection and tracking of faces, overheating of Google Glass, robust detection of facial features and modeling behavioral expressions, and feedback system for perceiving social signals. Performance evaluation was conducted to ensure the completeness and generalizability of models. Furthermore, usability studies were performed with ten (10) subjects (six visually impaired and four blind-folded) to illustrate the utility of the ‘Expression’. Subjective evaluation of Expression was performed using a five (5) point Likert Scale and was found to be excellent (4.383).


title: Joint Entity Linking with Deep Reinforcement Learning

abstract: Entity linking is the task of aligning mentions to corresponding entities in a given knowledge base. Previous studies have highlighted the necessity for entity linking systems to capture the global coherence. However, there are two common weaknesses in previous global models. First, most of them calculate the pairwise scores between all candidate entities and select the most relevant group of entities as the final result. In this process, the consistency among wrong entities as well as that among right ones are involved, which may introduce noise data and increase the model complexity. Second, the cues of previously disambiguated entities, which could contribute to the disambiguation of the subsequent mentions, are usually ignored by previous models. To address these problems, we convert the global linking into a sequence decision problem and propose a reinforcement learning model which makes decisions from a global perspective. Our model makes full use of the previous referred entities and explores the long-term influence of current selection on subsequent decisions. We conduct experiments on different types of datasets, the results show that our model outperforms state-of-the-art systems and has better generalization performance.


title: A Web of Hate: Tackling Hateful Speech in Online Social Spaces

abstract: Online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches.


title: IEEE SIGNAL PROCESSING MAGAZINE 1 Melody Extraction from Polyphonic Music Signals: Approaches, Applications and Challenges

abstract: Melody extraction algorithms aim to produce a sequence of frequency values corresponding to the pitch of the dominant melody from a musical recording. Over the past decade, melody extraction has emerged as an active research topic, comprising a large variety of proposed algorithms spanning a wide range of techniques. This article provides an overview of these techniques, the applications for which melody extraction is useful, and the challenges that remain. We start with a discussion of ?melody? from both musical and signal processing perspectives and provide a case study that interprets the output of a melody extraction algorithm for specific excerpts. We then provide a comprehensive comparative analysis of melody extraction algorithms based on the results of an international evaluation campaign. We discuss issues of algorithm design, evaluation, and applications that build upon melody extraction. Finally, we discuss some of the remaining challenges in melody extraction research in terms of algorithmic performance, development, and evaluation methodology.


title: Learned Step Size Quantization

abstract: Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.


Autocompletion of Code from Keywords


title: InvisiSpec: Making Speculative Execution Invisible in the Cache Hierarchy

abstract: Hardware speculation offers a major surface for micro-architectural covert and side channel attacks. Unfortunately, defending against speculative execution attacks is challenging. The reason is that speculations destined to be squashed execute incorrect instructions, outside the scope of what programmers and compilers reason about. Further, any change to micro-architectural state made by speculative execution can leak information. In this paper, we propose InvisiSpec, a novel strategy to defend against hardware speculation attacks in multiprocessors by making speculation invisible in the data cache hierarchy. InvisiSpec blocks micro-architectural covert and side channels through the multiprocessor data cache hierarchy due to speculative loads. In InvisiSpec, unsafe speculative loads read data into a speculative buffer, without modifying the cache hierarchy. When the loads become safe, InvisiSpec makes them visible to the rest of the system. InvisiSpec identifies loads that might have violated memory consistency and, at this time, forces them to perform a validation step. We propose two InvisiSpec designs: one to defend against Spectre-like attacks and another to defend against futuristic attacks, where any speculative load may pose a threat. Our simulations with 23 SPEC and 10 PARSEC workloads show that InvisiSpec is effective. Under TSO, using fences to defend against Spectre attacks slows down execution by 74% relative to a conventional, insecure processor; InvisiSpec reduces the execution slowdown to only 21%. Using fences to defend against futuristic attacks slows down execution by 208%; InvisiSpec reduces the slowdown to 72%.


title: Computer- vs. paper-based tasks: Are they equivalent?

abstract: In 1992, Dillon published his critical review of the empirical literature on reading from paper vs. screen. However, the debate concerning the equivalence of computer- and paper-based tasks continues, especially with the growing interest in online assessment. The current paper reviews the literature over the last 15 years and contrasts the results of these more recent studies with Dillon's findings. It is concluded that total equivalence is not possible to achieve, although developments in computer technology, more sophisticated comparative measures and more positive user attitudes have resulted in a continuing move towards achieving this goal. Many paper-based tasks used for assessment or evaluation have been transferred directly onto computers with little regard for any implications. This paper considers equivalence issues between the media by reviewing performance measures. While equivalence seems impossible, the importance of any differences appears specific to the task and required outcomes.


title: Deep Memory Networks for Attitude Identification

abstract: We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.


title: From Lifestyle Vlogs to Everyday Interactions

abstract: A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data. Most past efforts have gathered this data explicitly: starting with a laundry list of action labels, and then querying search engines for videos tagged with each label. In this work, we do the reverse and search implicitly: we start with a large collection of interaction-rich video data and then annotate and analyze it. We use Internet Lifestyle Vlogs as the source of surprisingly large and diverse interaction data. We show that by collecting the data first, we are able to achieve greater scale and far greater diversity in terms of actions and actors. Additionally, our data exposes biases built into common explicitly gathered data. We make sense of our data by analyzing the central component of interaction -- hands. We benchmark two tasks: identifying semantic object contact at the video level and non-semantic contact state at the frame level. We additionally demonstrate future prediction of hands.


title: Computational Thinking as a Computer Science Education Framework and the Related Effects on Gender Equity

abstract: I have recently completed my third year of study in the Educational Psychology and Educational Technology doctoral program at Michigan State University. I have successfully completed all of my coursework and comprehensive/qualifying exams. I will be proposing my dissertation study in the Fall semester of 2016 and expect to defend my dissertation in the Fall semester of 2017. My prior research has been focused on issues related to computational thinking, creativity, and computer science education. I am currently developing my literature review and honing my core research questions. I hope to subsequently develop my research methods and measures more fully, with plans to begin fieldwork in Fall of 2016.


title: Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams

abstract: Smart grids equipped with bi-directional communication flow are expected to provide more sophisticated consumption monitoring and energy trading. However, the issues related to the security and privacy of consumption and trading data present serious challenges. In this paper we address the problem of providing transaction security in decentralized smart grid energy trading without reliance on trusted third parties. We have implemented a proof-of-concept for decentralized energy trading system using blockchain technology, multi-signatures, and anonymous encrypted messaging streams, enabling peers to anonymously negotiate energy prices and securely perform trading transactions. We conducted case studies to perform security analysis and performance evaluation within the context of the elicited security and privacy requirements.


title: Clustering Large Datasets with Apriori-based Algorithm and Concurrent Processing

abstract:  Abstract—This paper presents the integrated data mining processing technique to find appropriate initial centroids in data clustering process by k-means algorithm. The processes include data cleansing, preprocessing, and finding features relation with Apriori algorithm to get appropriate features. Our clustering process compares different initial selection schemes: static selection and random selection. The calculation of SSE (Sum of Square Error) uses parallel calculation for better computational performance. We propose the Pre-KMA model that represents the processes for finding appropriate initial clustering centroids and selecting the most relevant features from large datasets. The clustering evaluation results of SSE, loop of clustering, and time of processing confirm that with the Pre-KMA model we can get better clustering result with k-means clustering methodology. The experimental result shows that calculated SSE and processing time are decreased.


title: The Blockchain-Based Digital Content Distribution System

abstract: The blockchain-based digital content distribution system was developed. Decentralized and pear-to-pear authentication mechanism can be considered as the ideal rights management mechanism. The blockchain has the potential to realize this ideal content distribution system. This is the successful model of the Superdistribution concept which was announced almost 30 years ago. The proposed system was demonstrated and got a lot of feedback for the future practical system.


title: Role of video games in improving health-related outcomes: a systematic review.

abstract: CONTEXT ::: Video games represent a multibillion-dollar industry in the U.S. Although video gaming has been associated with many negative health consequences, it also may be useful for therapeutic purposes. The goal of this study was to determine whether video games may be useful in improving health outcomes. ::: ::: ::: EVIDENCE ACQUISITION ::: Literature searches were performed in February 2010 in six databases: the Center on Media and Child Health Database of Research, MEDLINE, CINAHL, PsycINFO, EMBASE, and the Cochrane Central Register of Controlled Trials. Reference lists were hand-searched to identify additional studies. Only RCTs that tested the effect of video games on a positive, clinically relevant health consequence were included. Study selection criteria were strictly defined and applied by two researchers working independently. Study background information (e.g., location, funding source); sample data (e.g., number of study participants, demographics); intervention and control details; outcomes data; and quality measures were abstracted independently by two researchers. ::: ::: ::: EVIDENCE SYNTHESIS ::: Of 1452 articles retrieved using the current search strategy, 38 met all criteria for inclusion. Eligible studies used video games to provide physical therapy, psychological therapy, improved disease self-management, health education, distraction from discomfort, increased physical activity, and skills training for clinicians. Among the 38 studies, a total of 195 health outcomes were examined. Video games improved 69% of psychological therapy outcomes, 59% of physical therapy outcomes, 50% of physical activity outcomes, 46% of clinician skills outcomes, 42% of health education outcomes, 42% of pain distraction outcomes, and 37% of disease self-management outcomes. Study quality was generally poor; for example, two thirds (66%) of studies had follow-up periods of <12 weeks, and only 11% of studies blinded researchers. ::: ::: ::: CONCLUSIONS ::: There is potential promise for video games to improve health outcomes, particularly in the areas of psychological therapy and physical therapy. RCTs with appropriate rigor will help build evidence in this emerging area.


title: Quantifying randomness in real networks

abstract: Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.


title: BulaPay: a novel web service based third-party payment system for e-commerce

abstract: The availability of social media and 4G Mobile Internet services boosts e-commerce markets. The online payment systems are an integral part of e-commerce. It becomes the increasing need for online shopping and trading to use secure and low cost third-party payment systems. In this paper, we present an online purchase system of BulaPay that is capable of integrating common e-commerce frameworks and shopping cart systems. Using a set of Web services and simple HTML as its interfaces, BulaPay supports complete payment transactions for a business process in a worldwide way. BulaPay not only enables consumers and shop owners to carry out their daily business on the Internet, but also provides them secure, flexible, reliable, and efficient services. This paper presents an overview of the BulaPay system. The comparisons of our system to the existing third party payment systems are given. We also report the results of the end user evaluation on BulaPay.


title: Multiple Valued Logic Algebra for the Synthesis of Digital Circuits

abstract: The synthesis and simplification of digital circuits are performed in the well known two level logic switching algebra. By increasing the representation domain to B levels it is possible to design Multiple-Valued Logic (MV Logic)digital circuits. This work proposes an algebra based on a universal set of gates which carry out operators to allow synthesis and simplification of MV Logic digital circuits. This paper addresses: the generated algebra; the algebraic form of the function to be synthesized based on the canonical form of the Sum Of Extended Product terms; the duality; and circuit simplification procedures. Combinatorial and sequential circuits are synthesized to demonstrate the correctness of the algebra. The proposed algebra allows designing any MV Logic digital circuit taking advantage of the knowledge coming from the binary circuits by extending it to the MV Logic digital circuit synthesis.


title: Author Identification Using Deep Learning

abstract: Authorship identification is the task of identifying the author of a given text from a set of suspects. The main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In this paper, deep learning is used for feature extraction of documents represented using variable size character n-grams. We apply A Stacked Denoising Auto-Encoder (SDAE) for extracting document features with different settings, and then a support vector machine classifier is used for classification. The results show that the proposed system outperforms its counterparts.


title: Texture Based Image Retrieval Using Framelet Transform–Gray Level Co-occurrence Matrix(GLCM)

abstract: This paper presents a novel content based image retrieval (CBIR) system based on Framelet Transform combined with gray level co-occurrence matrix (GLCM).The proposed method is shift invariant which captured edge information more accurately than conventional transform domain methods as well as able to handle images of arbitrary size. Current system uses texture as a visual content for feature extraction. First Texture features are obtained by computing the energy, standard deviation and mean on each sub band of the Framelet transform decomposed image .Then a new method as a combination of the Framelet transform-Gray level co-occurrence matrix (GLCM) is applied. The results of the proposed methods are compared with conventional methods. We have done the comparison of results of these two methods for image retrieval. Euclidean distance, Canberra distance, city black distance is used as similarity measure in the proposed CBIR system.


title: Biomechanical considerations in the design of lower limb exoskeletons

abstract: This paper presents an analysis of the human biomechanical considerations related to the development of lower limb exoskeletons. Factors such as kinematic alignment and compatibility, joint range of motion, maximum torque, and joint bandwidth are discussed in the framework of a review of the design specifications for exoskeleton prototypes discussed in the literature. From this analysis, we discuss major gaps in the research related to the topic and how those might be filled.


title: Architectural Design and the Brain: Effects of Ceiling Height and Perceived Enclosure on Beauty Judgments and Approach-avoidance Decisions

abstract: Abstract We examined the effects of ceiling height and perceived enclosure—defined as perceived visual and locomotive permeability—on aesthetic judgments and approach-avoidance decisions in architectural design. Furthermore, to gain traction on the mechanisms driving the observed effects, we employed functional magnetic resonance imaging (fMRI) to explore their neural correlates. Rooms with higher ceilings were more likely to be judged as beautiful, and activated structures involved in visuospatial exploration and attention in the dorsal stream. Open rooms were more likely to be judged as beautiful, and activated structures underlying perceived visual motion. Additionally, enclosed rooms were more likely to elicit exit decisions and activated the anterior midcingulate cortex (aMCC)—the region within the cingulate gyrus with direct projections from the amygdala. This suggests that a reduction in perceived visual and locomotive permeability characteristic of enclosed spaces might elicit an emotional reaction that accompanies exit decisions.


title: A topological coverage algorithm for mobile robots

abstract: In applications such as vacuum cleaning, painting, demining and foraging, a mobile robot must cover an unknown surface. The efficiency and completeness of coverage is improved via the construction of a map of covered regions while the robot covers the surface. Existing methods generally use grid maps, which are susceptible to odometry error and may require considerable memory and computation. This paper proposes a topological map and presents a coverage algorithm in which natural landmarks are added as nodes in a partial map. The completeness of the algorithm is argued. Simulation tests show over 99% of the surface is covered; 85% for real (Khepera) robot tests. The path length is about 10% worse than optimal in simulation tests, and about 20% worse than optimal for the real robot, which are within theoretical upper bounds for approximates solutions to traveling salesman based coverage problems. The proposed algorithm generates shorter paths and covers a wider variety of environments than topological coverage based on Morse decompositions.


title: Design in FPGA Implementation Of Quadrature Phase Shift Keying ( QPSK ) Digital Modulator

abstract: The objective of this project deals with the desi gn & implementation of a verilog design of QPSK Digital Modulator based on the FPGA Quartus-II simulator. Digital modulation is less complex, more secure and more efficient in long distance transmission. The noise detection and correction in digital is more efficie nt than analog counterpart. Digital modulation represents the tran sfer of the digital bit stream from the transmitter to the receiver via the analog channels. During the modulation proc ess the information signal modifies one or more car rier parameters, leading to shift keying techniques. In past we are using analog modulation in which power, delay and area are important parameters to be considered. Hen ce in order to reduce power, delay and area we are using the digital modulation techniques such as BASK, BPSK and BFSK. In this paper minimum number of blocks are necessary for achieving QPSK modulation and for full integration with the other functional part of the Alter Development and Education FPGA board. The input carrier signal and the bit stream are user controllabl e. Hardware implemented in an FPGA can be reconfigured by programming the logic elements and interconnections for specific applications.


title: Self-consistency: a fundamental concept in statistics

abstract: The term "self-consistency" was introduced in 1989 by Hastie and Stuetzle to describe the property that each point on a smooth curve or surface is the mean of all points that project orthogonally onto it. We generalize this concept to self-consistent random vectors: a random vector Y is self-consistent for X if 49(XIY) = Y almost surely. This al- lows us to construct a unified theoretical basis for principal components, principal curves and surfaces, principal points, principal variables, prin- cipal modes of variation and other statistical methods. We provide some general results on self-consistent random variables, give examples, show relationships between the various methods, discuss a related notion of self-consistent estimators and suggest directions for future research.


title: PV plant modeling for power system integration using PSCAD software

abstract: PV Plants connected to the medium voltage grid do not contribute to the grid stability. In order to prevent grid instability, directives (codes) for connecting PV plants to the medium voltage power grid have been released. The supply and control of the reactive power from the renewable generation plants are becoming important issues to be studied, because they can facilitate the integration of PV in power grids. In this paper, two new models of a 6.09 MW PV plant, used to analyze its grid integration according to the grid code, are presented. The first is a simplified model, without taking into account transformers and cables, while the second one is a more complex model which includes these components. The model was developed using PSCAD-EMTDC software. The final part of the paper presents the active-reactive power (P-Q) charts, calculated at the common coupling point (CCP), for different levels of solar radiation (0% to 100%). Based on these charts, it is determined the maximum output power level which can be generated by the plant, according to the current grid code.


title: A 40-Gb/s serial link transceiver in 28-nm CMOS technology

abstract: A 40 Gb/s serial link interface is presented that includes four lanes of transceiver optimized for chip-to-chip communication while compensating for 20 dB of channel loss. Transmit equalization consists of a 2-tap feed-forward equalizer (FFE) while receive equalization includes a 2-tap FFE using a transversal filter, a 3-stage continuous-time linear equalizer with active feedback, and discrete-time equalizers consisting of a 17-tap decision feedback equalizer (DFE) and a 3-tap sampled FFE. The receiver uses quarter-rate double integrate-and-hold sampling. The clock and data recovery (CDR) unit uses a split-path CDR/DFE design which facilitates wider bandwidth and lower jitter simultaneously. A phase detection scheme that filters out edges affected by residual inter-symbol interference allows recovering a low-jitter clock from a partially-equalized eye. A fractional-N PLL is implemented for frequency offset tracking. Combining these techniques, the digital CDR recovers a stable 10 GHz clock from an eye containing 0.8 UI p-p input jitter and achieves 1-10 MHz of tracking bandwidth. The transceiver achieves horizontal and vertical eye openings of 0.27 UI and 120 mV, respectively, at BER = 10 -9 . The quad SerDes is realized in 28 nm CMOS technology. Amortizing common blocks, it occupies 0.81 mm $^{2}$ per lane and achieves 23.2 mW/Gb/s power efficiency at 40 Gb/s.


title: Probe-to-Bone Test for Diagnosing Diabetic Foot Osteomyelitis: Reliable or relic?

abstract: OBJECTIVE —We sought to assess the accuracy of the probe-to-bone (PTB) test in diagnosing foot osteomyelitis in a cohort of diabetic patients with bone culture proven disease. RESEARCH DESIGN AND METHODS —In this 2-year longitudinal cohort study, we enrolled 1,666 consecutive diabetic individuals who underwent an initial standardized detailed foot assessment, followed by examinations at regular intervals. Patients were instructed to immediately come to the foot clinic if they developed a lower-extremity complication. For all patients with a lower-extremity wound, we compared the results of the PTB test with those of a culture of the affected bone. We called PTB positive if the bone or joint was palpable and defined osteomyelitis as a positive bone culture. RESULTS —Over a mean of 27.2 months of follow-up, 247 patients developed a foot wound and 151 developed 199 foot infections. Osteomyelitis was found in 30 patients: 12% of those with a foot wound and 20% in those with a foot infection. When all wounds were considered, the PTB test was highly sensitive (0.87) and specific (0.91); the positive predictive value was only 0.57, but the negative predictive value was 0.98. CONCLUSIONS —The PTB test, when used in a population of diabetic patients with a foot wound among whom the prevalence of osteomyelitis was 12%, had a relatively low positive predictive value, but a negative test may exclude the diagnosis.


title: PixelNN: Example-based Image Synthesis

abstract: We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem. (2) they are not interpretable, making it difficult to control the synthesized output. We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets. We design a simple pipeline that combines the best of both worlds: the first stage uses a convolutional neural network (CNN) to maps the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner. We demonstrate our approach for various input modalities, and for various domains ranging from human faces to cats-and-dogs to shoes and handbags.


title: GraphCT: Multithreaded Algorithms for Massive Graph Analysis

abstract: The digital world has given rise to massive quantities of data that include rich semantic and complex networks. A social graph, for example, containing hundreds of millions of actors and tens of billions of relationships is not uncommon. Analyzing these large data sets, even to answer simple analytic queries, often pushes the limits of algorithms and machine architectures. We present GraphCT, a scalable framework for graph analysis using parallel and multithreaded algorithms on shared memory platforms. Utilizing the unique characteristics of the Cray XMT, GraphCT enables fast network analysis at unprecedented scales on a variety of input data sets. On a synthetic power law graph with 2 billion vertices and 17 billion edges, we can find the connected components in 2 minutes. We can estimate the betweenness centrality of a similar graph with 537 million vertices and over 8 billion edges in under 1 hour. GraphCT is built for portability and performance.


Comparisons of centerline extraction methods for liver blood vessels in ImageJ and 3D slicer


title: “Andromaly”: a behavioral malware detection framework for android devices

abstract: This article presents Andromaly--a framework for detecting malware on Android mobile devices. The proposed framework realizes a Host-based Malware Detection System that continuously monitors various features and events obtained from the mobile device and then applies Machine Learning anomaly detectors to classify the collected data as normal (benign) or abnormal (malicious). Since no malicious applications are yet available for Android, we developed four malicious applications, and evaluated Andromaly's ability to detect new malware based on samples of known malware. We evaluated several combinations of anomaly detection algorithms, feature selection method and the number of top features in order to find the combination that yields the best performance in detecting new malware on Android. Empirical results suggest that the proposed framework is effective in detecting malware on mobile devices in general and on Android in particular.


Advances in Automated Knowledge Base Construction


title: A secure light weight scheme for user authentication and key agreement in multi-gateway based wireless sensor networks

abstract: Wireless sensor networks can be deployed in any attended or unattended environments like environmental monitoring, agriculture, military, health care etc., where the sensor nodes forward the sensing data to the gateway node. As the sensor node has very limited battery power and cannot be recharged after deployment, it is very important to design a secure, effective and light weight user authentication and key agreement protocol for accessing the sensed data through the gateway node over insecure networks. Most recently, Turkanovic et?al. proposed a light weight user authentication and key agreement protocol for accessing the services of the WSNs environment and claimed that the same protocol is efficient in terms of security and complexities than related existing protocols. In this paper, we have demonstrated several security weaknesses of the Turkanovic et?al. protocol. Additionally, we have also illustrated that the authentication phase of the Turkanovic et?al. is not efficient in terms of security parameters. In order to fix the above mentioned security pitfalls, we have primarily designed a novel architecture for the WSNs environment and basing upon which a proposed scheme has been presented for user authentication and key agreement scheme. The security validation of the proposed protocol has done by using BAN logic, which ensures that the protocol achieves mutual authentication and session key agreement property securely between the entities involved. Moreover, the proposed scheme has simulated using well popular AVISPA security tool, whose simulation results show that the protocol is SAFE under OFMC and CL-AtSe models. Besides, several security issues informally confirm that the proposed protocol is well protected in terms of relevant security attacks including the above mentioned security pitfalls. The proposed protocol not only resists the above mentioned security weaknesses, but also achieves complete security requirements including specially energy efficiency, user anonymity, mutual authentication and user-friendly password change phase. Performance comparison section ensures that the protocol is relatively efficient in terms of complexities. The security and performance analysis makes the system so efficient that the proposed protocol can be implemented in real-life application.


title: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

abstract: High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.


title: Tribeca: a system for managing large databases of network traffic

abstract: The engineers who analyze traffic on high bandwidth networks must filter and aggregate either recorded traces of network packets or live traffic from the network itself. These engineers perform operations similar to database queries, but cannot use conventional data managers because of performance concerns and a semantic mismatch between the analysis operations and the operations supported by commercial DBMSs. Traffic analysis does not require fast random access, transactional update, or relational joins. Rather, it needs fast sequential access to a stream of traffic records and the ability to filter, aggregate, define windows, demultiplex, and remultiplex the stream. ::: ::: Tribeca is an extensible, stream-oriented DBMS designed to support network traffic analysis. It combines ideas from temporal and sequence databases with an implementation optimized for databases stored on high speed ID-1 tapes or arriving in real time from the network. The paper describes Tribeca's query language, executor and optimizer as well as performance measurements of a prototype implementation.


title: Directly buried gas-insulated transmission lines (GIL)

abstract: Regarding the increasing interest in underground transmission systems, gas-insulated lines (GIL) represent an attractive alternative to cable solutions. Changes in the regulatory framework with respect to planned extensions of extra high voltage (EHV) systems have intensified the demand for underground transmission lines in general.


title: An application of zero-inflated Poisson regression for software fault prediction

abstract: Poisson regression model is widely used in software quality modeling. When the response variable of a data set includes a large number of zeros, Poisson regression model will underestimate the probability of zeros. A zero-inflated model changes the mean structure of the pure Poisson model. The predictive quality is therefore improved. In this paper, we examine a full-scale industrial software system and develop two models, Poisson regression and zero-inflated Poisson regression. To our knowledge, this is the first study that introduces the zero-inflated Poisson regression model in software reliability. Comparing the predictive qualities of the two competing models, we conclude that for this system, the zero-inflated Poisson regression model is more appropriate in theory and practice.


title: Colored Point Cloud Registration Revisited

abstract: We present an algorithm for aligning two colored point clouds. The key idea is to optimize a joint photometric and geometric objective that locks the alignment along both the normal direction and the tangent plane. We extend a photometric objective for aligning RGB-D images to point clouds, by locally parameterizing the point cloud with a virtual camera. Experiments demonstrate that our algorithm is more accurate and more robust than prior point cloud registration algorithms, including those that utilize color information. We use the presented algorithms to enhance a state-of-the-art scene reconstruction system. The precision of the resulting system is demonstrated on real-world scenes with accurate ground-truth models.


title: Laplacian mesh optimization

abstract: We introduce a framework for triangle shape optimization and feature preserving smoothing of triangular meshes that is guided by the vertex Laplacians, specifically, the uniformly weighted Laplacian and the discrete mean curvature normal. Vertices are relocated so that they approximate prescribed Laplacians and positions in a weighted least-squares sense; the resulting linear system leads to an efficient, non-iterative solution. We provide different weighting schemes and demonstrate the effectiveness of the framework on a number of detailed and highly irregular meshes; our technique successfully improves the quality of the triangulation while remaining faithful to the original surface geometry, and it is also capable of smoothing the surface while preserving geometric features.


title: An Ecological Model of the Impact of Sexual Assault on Women's Mental Health (Book Chapter)

abstract: This review examines the psychological impact of adult sexual assault through an ecological theoretical perspective to understand how factors at multiple levels of the social ecology contribute to post-assault sequelae. Using Bronfenbrenner's (1979, 1986, 1995) ecological theory of human development, we examine how individual-level factors (e.g., sociodemographics, biological/genetic factors), assault characteristics (e.g., victim-offender relationship, injury, alcohol use), microsystem factors (e.g., informal support from family and friends), meso/ exosystem factors (e.g., contact with the legal, medical, and mental health systems, and rape crisis centers), macrosystem factors (e.g., societal rape myth acceptance), and chronosystem factors (e.g., sexual revictimization and history of other victimizations) affect adult sexual assault survivors' mental health outcomes (e.g., post-traumatic stress disorder, depression, suicidality, and substance use). Self-blame is conceptualized as meta-construct that stem...