title: English Conversational Telephone Speech Recognition by Humans and Machines

abstract: One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major speech recognition improvements on the representative Switchboard conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates? A recent paper by Microsoft suggests that we have already achieved human performance. In trying to verify this statement, we performed an independent set of human performance measurements on two conversational tasks and found that human performance may be considerably better than what was earlier reported, giving the community a significantly harder goal to achieve. We also report on our own efforts in this area, presenting a set of acoustic and language modeling techniques that lowered the word error rate of our own English conversational telephone LVCSR system to the level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation, which - at least at the writing of this paper - is a new performance milestone (albeit not at what we measure to be human performance!). On the acoustic side, we use a score fusion of three models: one LSTM with multiple feature inputs, a second LSTM trained with speaker-adversarial multi-task learning and a third residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. On the language modeling side, we use word and character LSTMs and convolutional WaveNet-style language models.


title: AnO(ND) difference algorithm and its variations

abstract: The problems of finding a longest common subsequence of two sequencesA andB and a shortest edit script for transformingA intoB have long been known to be dual problems. In this paper, they are shown to be equivalent to finding a shortest/longest path in an edit graph. Using this perspective, a simpleO(ND) time and space algorithm is developed whereN is the sum of the lengths ofA andB andD is the size of the minimum edit script forA andB. The algorithm performs well when differences are small (sequences are similar) and is consequently fast in typical applications. The algorithm is shown to haveO(N+D 2) expected-time performance under a basic stochastic model. A refinement of the algorithm requires onlyO(N) space, and the use of suffix trees leads to anO(N logN+D 2) time variation.


title: A user browsing model to predict search engine click data from past observations.

abstract: Search engine click logs provide an invaluable source of relevance information but this information is biased because we ignore which documents from the result list the users have actually seen before and after they clicked. Otherwise, we could estimate document relevance by simple counting. In this paper, we propose a set of assumptions on user browsing behavior that allows the estimation of the probability that a document is seen, thereby providing an unbiased estimate of document relevance. To train, test and compare our model to the best alternatives described in the Literature, we gather a large set of real data and proceed to an extensive cross-validation experiment. Our solution outperforms very significantly all previous models. As a side effect, we gain insight into the browsing behavior of users and we can compare it to the conclusions of an eye-tracking experiments by Joachims et al. [12]. In particular, our findings confirm that a user almost always see the document directly after a clicked document. They also explain why documents situated just after a very relevant document are clicked more often.


title: A BPMN-Based Design and Maintenance Framework for ETL Processes

abstract: Business Intelligence BI applications require the design, implementation, and maintenance of processes that extract, transform, and load suitable data for analysis. The development of these processes known as ETL is an inherently complex problem that is typically costly and time consuming. In a previous work, the authors have proposed a vendor-independent language for reducing the design complexity due to disparate ETL languages tailored to specific design tools with steep learning curves. Nevertheless, the designer still faces two major issues during the development of ETL processes: i how to implement the designed processes in an executable language, and ii how to maintain the implementation when the organization data infrastructure evolves. In this paper, the authors propose a model-driven framework that provides automatic code generation capability and ameliorate maintenance support of our ETL language. They present a set of model-to-text transformations able to produce code for different ETL commercial tools as well as model-to-model transformations that automatically update the ETL models with the aim of supporting the maintenance of the generated code according to data source evolution. A demonstration using an example is conducted as an initial validation to show that the framework covering modeling, code generation and maintenance could be used in practice.


title: An Iterative Algorithm for Ontology Mapping Capable of Using Training Data

abstract: We present a new iterative algorithm for ontology mapping where we combine standard string distance metrics with a structural similarity measure that is based on a vector representation. After all pairwise similarities between concepts have been calculated we apply well-known graph algorithms to obtain an optimal matching. Our algorithm is also capable of using existing mappings to a third ontology as training data to improve accuracy. we compare the performance of our algorithm with the performance of other alignment. algorithms and show that our algorithm can compete well against, the current state-of-the-art.


title: Subgroup analysis and other (mis)uses of baseline data in clinical trials

abstract: Summary Background Baseline data collected on each patient at randomisation in controlled clinical trials can be used to describe the population of patients, to assess comparability of treatment groups, to achieve balanced randomisation, to adjust treatment comparisons for prognostic factors, and to undertake subgroup analyses. We assessed the extent and quality of such practices in major clinical trial reports. Methods A sample of 50 consecutive clinical-trial reports was obtained from four major medical journals during July to September, 1997. We tabulated the detailed information on uses of baseline data by use of a standard form. Findings Most trials presented baseline comparability in a table. These tables were often unduly large, and about half the trials inappropriately used significance tests for baseline comparison. Methods of randomisation, including possible stratification, were often poorly described. There was little consistency over whether to use covariate adjustment and the criteria for selecting baseline factors for which to adjust were often unclear. Most trials emphasised the simple unadjusted results and covariate adjustment usually made negligible difference. Two-thirds of the reports presented subgroup findings, but mostly without appropriate statistical tests for interaction. Many reports put too much emphasis on subgroup analyses that commonly lacked statistical power. Interpretation Clinical trials need a predefined statistical analysis plan for uses of baseline data, especially covariate-adjusted analyses and subgroup analyses. Investigators and journals need to adopt improved standards of statistical reporting, and exercise caution when drawing conclusions from subgroup findings.


title: A MID dipole antenna in LDS technology

abstract: Molded interconnect devices (MID) allow the realization of electronic circuits on injection molded thermoplastics. MID antennas can be manufactured as part of device casings without the need for additional printed circuit boards or attachment of antennas printed on foil. Baluns, matching networks, amplifiers and connectors can be placed on the polymer in the vicinity of the antenna. A MID dipole antenna for 1 GHz is designed, manufactured and measured. A prototype of the antenna is built with laser direct structuring (LDS) on a Xantar LDS 3720 substrate. Measured return loss and calibrated gain patterns are compared to simulation results.


title: Warpage simulation and experiment for panel level fan-out package

abstract: Nowadays, fan-out package is regarded as one of the latest and most potential technologies due to lower cost, smaller and thinner form factor, better electrical performance, and better thermal performance. Compared with traditional flip chip ball grid array (FCBGA), fan-out technology eliminates many process steps such as substrate, wafer bumping, flip chip reflow, flux cleaning, underfill, etc. However, warpage induced by molding is a critical issue due to the mismatch of the coefficient of thermal expansion (CTE) among the constituent materials, which is needed to be solved for successful subsequent process of the fan-out package. In this study, warpage issue of panel level fan-out package induced by molding based on "Die Last" process is investigated. Warpage simulation and experiment are carried out by finite element method (FEM) and shadow moire, respectively. A novel warpage simulation method is presented for large area panel or wafer level simulation. In order to verify the accuracy and precision of the simulation method, a molded panel sample without dies is fabricated, and warpage experiment is carried out by shadow moire. Simulation and experimental result are compared with good agreement.


title: EPOCHS: a platform for agent-based electric power and communication simulation built from commercial off-the-shelf components

abstract: This paper reports on the development and subsequent use of the electric power and communication synchronizing simulator (EPOCHS), a distributed simulation environment. Existing electric power simulation tools accurately model power systems of the past, which were controlled as large regional power pools without significant communication elements. However, as power systems increasingly turn to protection and control systems that make use of computer networks, these simulators are less and less capable of predicting the likely behavior of the resulting power grids. Similarly, the tools used to evaluate new communication protocols and systems have been developed without attention to the roles they might play in power scenarios. EPOCHS integrates multiple research and commercial off-the-shelf systems to bridge the gap.


title: Efficient cellular fractionation improves RNA sequencing analysis of mature and nascent transcripts from human tissues

abstract: The starting material for RNA sequencing (RNA-seq) studies is usually total RNA or polyA+ RNA. Both forms of RNA represent heterogeneous pools of RNA molecules at different levels of maturation and processing. Such heterogeneity, in addition to the biases associated with polyA+ purification steps, may influence the analysis, sensitivity and the interpretation of RNA-seq data. We hypothesize that subcellular fractions of RNA may provide a more accurate picture of gene expression. We present results for sequencing of cytoplasmic and nuclear RNA after cellular fractionation of tissue samples. In comparison with conventional polyA+ RNA, the cytoplasmic RNA contains a significantly higher fraction of exonic sequence, providing increased sensitivity in expression analysis and splice junction detection, and in improved de novo assembly of RNA-seq data. Conversely, the nuclear fraction shows an enrichment of unprocessed RNA compared with total RNA-seq, making it suitable for analysis of nascent transcripts and RNA processing dynamics. Our results show that cellular fractionation is a more rapid and cost effective approach than conventional polyA+ enrichment when studying mature RNAs. Thus, RNA-seq of separated cytosolic and nuclear RNA can significantly improve the analysis of complex transcriptomes from mammalian tissues.


Unfulfilled Promise: The Dimensions and Characteristics of Philadelphia's Dropout Crisis, 2000-2005.


title: Revisions to the JDL data fusion model

abstract: The Data Fusion Model maintained by the Joint Directors of Laboratories (JDL) Data Fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise the expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi- sensor/multi-source systems. Data fusion involves combining information - in the broadest sense - to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people, it can be useful to include consideration of informational and perceptual states in addition to the physical state. Developing cost-effective multi-source information systems requires a method for specifying data fusion processing and control functions, interfaces, and associate databases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology: current developments do not lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data FUsion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data Fusion Tree Architecture model for system description, design and development.


title: A Survey of Neuromorphic Computing and Neural Networks in Hardware

abstract: Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.


title: High-performance software rasterization on GPUs

abstract: In this paper, we implement an efficient, completely software-based graphics pipeline on a GPU. Unlike previous approaches, we obey ordering constraints imposed by current graphics APIs, guarantee hole-free rasterization, and support multisample antialiasing. Our goal is to examine the performance implications of not exploiting the fixed-function graphics pipeline, and to discern which additional hardware support would benefit software-based graphics the most. We present significant improvements over previous work in terms of scalability, performance, and capabilities. Our pipeline is malleable and easy to extend, and we demonstrate that in a wide variety of test cases its performance is within a factor of 2--8x compared to the hardware graphics pipeline on a top of the line GPU. Our implementation is open sourced and available at http://code.google.com/p/cudaraster/


title: Watch Your Step: Learning Graph Embeddings Through Attention

abstract: Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random walk hyper-parameters with trainable parameters that we automatically learn via backpropagation. In particular, we learn a novel attention model on the power series of the transition matrix, which guides the random walk to optimize an upstream objective. Unlike previous approaches to attention models, the method that we propose utilizes attention parameters exclusively on the data (e.g. on the random walk), and not used by the model for inference. We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art on a comprehensive suite of real world datasets including social, collaboration, and biological networks. Adding attention to random walks can reduce the error by 20% to 45% on datasets we attempted. Further, our learned attention parameters are different for every graph, and our automatically-found values agree with the optimal choice of hyper-parameter if we manually tune existing methods.


LATENT EMBEDDING OPTIMIZATION


WHY DO PEOPLE REJECT TECHNOLOGIES – A LITERATURE-BASED DISCUSSION OF THE PHENOMENA “RESISTANCE TO CHANGE ” IN INFORMATION SYSTEMS


title: Second language teacher education

abstract: Preface Part I. Issues and Approaches in Teacher Educaton: 1. The dilemma of teacher education in second language teaching Jack C. Richards 2. Beyond prescription: the student teacher as investigator Jerry G. Gebhard, Sergio Gaitan and Robert Oprandy 3. Activities and procedures for teacher preparation Rod Ellis Questions and tasks Part II. Investigating Teachers and Learners in the Classroom: 4. Teacher observation in second language teacher education Richard R. Day 5. Action research in the language classroom David Nunan 6. Understanding classroom role relationships Tony Wright Questions and tasks Part III. The Practicum: 7. Intervening in practice teaching Donald Freeman 8. Interaction in a teaching practicum Jerry G. Gebhard 9. A professional development focus for the language teaching practicum Martha C. Pennington Part IV. Supervision: 10. Models of supervision: choices Jerry G. Gebhard 11. Clinical supervision of language teaching: the supervisor as trainer and educator Stephen Gaies and Roger Bowers 12. 'Let's see': contrasting conversations about teaching John F. Fanselow Questions and tasks Part V. Self-Observation in Teacher Development: 13. Teacher development through reflective teaching Leo Bartlett 14. The use of diary studies in teacher education programs Kathleen M. Bailey 15. An ongoing dialogue: learning logs for teacher preparation Patricia A. Porter, Lynn M. Goldstein, Judith Leatherman and Susan Conrad Questions and tasks Part VI. Case Studies: 16. A blueprint for a teacher development program Dale L. Lange 17. Developing teachers' language resources Robert Keith Johnson 18. An ethnographic approach to inservice preparation: the Hungary file Fraida Dubin and Rita Wong 19. Observing classroom behaviours and learning outcomes in different second language programs Nina Spada Questions and tasks References Index.


title: Performance Analysis of Authentication Protocols in Vehicular Ad Hoc Networks (VANET)

abstract: Traditionally traffic safety was addressed by traffic awareness and passive safety measures like solid chassis, seat belts, air bags etc. With the recent breakthroughs in the domain of mobile ad hoc networks, the concept of vehicular ad hoc networks (VANET) was realised. Safety messaging is the most important aspect of VANETs, where the passive safety (accident readiness) in vehicles was reinforced with the idea of active safety (accident prevention). In safety messaging vehicles will message each other over wireless media, updating each other on traffic conditions and hazards. Security is an important aspect of safety messaging, that aims to prevent participants spreading wrong information in the network that are likely to cause mishaps. Equally important is the fact that secure communication protocols should satisfy the communication constraints of VANETs. VANETs are delay intolerant. Features like high speeds, large network size, constant mobility etc. induce certain limitations in the way messaging can be carried out in VANETs. This thesis studies the impact of total message size on VANET messaging system performance, and conducts an analysis of secure communication protocols to measure how they perform in a VANET messaging system.


title: Efficacy and safety of ultrasound‐guided high intensity focused ultrasound ablation of symptomatic uterine fibroids in Black women: a preliminary study

abstract: OBJECTIVE ::: To evaluate the therapeutic effect and safety of ultrasound-guided high-intensity focused ultrasound (USgHIFU) treatment on symptomatic uterine fibroids in Black women. ::: ::: ::: DESIGN ::: A feasibility study. ::: ::: ::: SETTING ::: Gynaecological department in a teaching hospital in South Africa. ::: ::: ::: POPULATION ::: Premenopausal women with uterus fibroids. ::: ::: ::: METHODS ::: Twenty-six patients with 53 fibroids who underwent USgHIFU treatment were enrolled. The USgHIFU treatment information was recorded, including treatment time, sonication time and total energy. Adverse events were also observed and recorded during and after treatment. ::: ::: ::: MAIN OUTCOME MEASURES ::: Safety and efficacy of USgHIFU for the treatment of uterine fibroids in Black women. ::: ::: ::: RESULTS ::: The median volume of fibroids was 52.7 (interquartile range, 18.6-177.4) cm3 . According to USgHIFU treatment plan, total energy of 298.6 ± 169.3 kJ (range, 76.0-889.2) within treatment time of 90.3 ± 43.3 minutes (range, 14.0-208.0), in which sonication time of 774.0 ± 432.9 seconds (range, 190.0-2224.0) was used to ablate fibroids. The average ablation rate was 80.6 ± 9.7% (range, 46.5-94.5%). During the procedure, 69.2% of the patients reported lower abdominal pain, 57.7% sciatic/buttock pain, 38.5% burning skin, and 34.6% transient leg pain. No severe complications were observed. ::: ::: ::: CONCLUSIONS ::: USgHIFU is feasible and safe to use to treat symptomatic uterine fibroids in Black women. ::: ::: ::: TWEETABLE ABSTRACT ::: Multiple uterine fibroids are more frequently detected in Black women. USgHIFU is feasible and safe for the treatment of uterine fibroids in Black women.


title: Rorschach object relations of adolescents who committed homicide.

abstract: We investigated the object relations of adolescents who committed homicide. A clinical sample of 55 adolescents who committed homicide did not differ from a comparison group of nonviolent delinquents on Rorschach measures of object differentiation, mutuality of autonomy, and aggressive content. However, the subgroup of adolescents who committed homicides in the context of another crime (e.g., robbery or burglary) did manifest significantly lower object relations (poorer object differentiation and more victim responses) than the subgroup of adolescents whose homicides were committed in the context of an interpersonal conflict or dispute with the victim. These findings support the need for differentiated classification of violent individuals as urged by Megargee (1970) over 20 years ago.


title: Quantized Massive Gauge Fields and Anomalous Angle-resolved Photoemission Spectra in High-Tc Cuprates

abstract: We have argued the anomalous spectra of angle-resolved photoemission of high-Tc cuprates, using quantized massive collective-gauge fields, from the field-theoretical point of view. The relation between the bosonic function of electric origin and the quantized massive collective-gauge fields has been discussed.


title: Event-Driven Order Rescheduling Model for Just-In-Sequence Deliveries to a Mixed-Model Assembly Line

abstract: Today’s buyer markets and lean supply chains require build-to-order assembly systems with just-in-sequence (JIS) deliveries. Simultaneously production systems have become prone to supply disturbances (i.e. events) that endanger the synchronized delivery of all JIS components to the assembly line. To uphold production sequence stability, rescheduling is frequently required. Current methods, however, make assumptions that are often insufficiently aligned with real-world problems and focus on production issues while neglecting the implications of today’s tight integration of supply chain with production processes. To this end, this contribution derives a general model of a mixed model assembly line. It then proposes and evaluates an event-driven rescheduling model for JIS deliveries. The results indicate that rework due to missing JIS components can be avoided without compromising performance.


Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction


title: A user study of the expandable grid applied to P3P privacy policy visualization

abstract: Displaying website privacy policies to consumers in ways they understand is an important part of gaining consumers' trust and informed consent, yet most website privacy policies today are presented in confusing, legalistic natural language. Moreover, because website privacy policy presentations vary from website to website, policies are difficult to compare and it is difficult for consumers to determine which websites offer the best privacy protections. The Platform for Privacy Preferences P3P) addresses part of the problem with natural language policies by providing a formal, machine-readable language for expressing privacy policies in a manner that is standardized across websites. To address remaining problems, an automated tool must be developed to read P3P policies and display them to users in a comprehensible way. To this end, we have developed a P3P policy presentation tool based on the Expandable Grid, a visualization technique for displaying policies in an interactive matrix. In prior work, the Expandable Grid has been shown to work well for displaying file permissions policies, so it appears to hold promise for presenting online privacy policies as well. To evaluate our Expandable Grid interface, we conducted two user studies, an online study with 520 participants and a laboratory study with 12 participants. The studies compared participants' comprehension of privacy policies presented with the Grid interface with their comprehension of the same policies presented in natural language. To our surprise, comprehension of policies was, for the most part, no better with the Grid interface than with natural language. We describe why the Grid interface did not perform well in our study and discuss implications for when and how the Expandable Grid concept can be usefully applied.


title: Player Profiling with Fallout 3

abstract: In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player's behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants' personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create personality profiles.


title: An Introduction to ULDBs and the Trio System

abstract: We introduce ULDBs: relational databases that add uncertainty and lineage of the data as first-class concepts. The ULDB model underlies the Trio system under development at Stanford. We describe the ULDB model, then present TriQL, our SQL-based query language for ULDBs. TriQL's semantics over ULDBs is defined both formally and operationally, and TriQL extends SQL with constructs for querying lineage and confidence values. We also briefly describe our initial prototype Trio implementation, which encodes ULDBs in conventional relations and automatically translates TriQL queries into SQL commands over the encoding. We conclude with research directions for ULDBs and the Trio system.


title: Are you TED talk material? comparing prosody in professors and TED speakers.

abstract: TED talks are the pinnacle of public speaking. They combine compelling content with flawless delivery, and their popularity is attested by the millions of views they attract. In this work, we compare the prosodic voice characteristics of TED speakers and university professors. Our aim is to identify the characteristics that separate TED speakers from other public speakers. Based on a simple set of features derived from pitch and energy, we train a discriminative classifier to predict whether a 5 minute audio sample is from a TED talk or a university lecture. We are able to achieve < 10% equal error rate. We then investigate which features are most discriminative, and discuss conflating factors that might contribute to those features.


title: Conformal Prediction Using Decision Trees

abstract: Conformal prediction is a relatively new framework in which the predictive models output sets of predictions with a bound on the error rate, i.e., in a classification context, the probability of excluding the correct class label is lower than a predefined significance level. An investigation of the use of decision trees within the conformal prediction framework is presented, with the overall purpose to determine the effect of different algorithmic choices, including split criterion, pruning scheme and way to calculate the probability estimates. Since the error rate is bounded by the framework, the most important property of conformal predictors is efficiency, which concerns minimizing the number of elements in the output prediction sets. Results from one of the largest empirical investigations to date within the conformal prediction framework are presented, showing that in order to optimize efficiency, the decision trees should be induced using no pruning and with smoothed probability estimates. The choice of split criterion to use for the actual induction of the trees did not turn out to have any major impact on the efficiency. Finally, the experimentation also showed that when using decision trees, standard inductive conformal prediction was as efficient as the recently suggested method cross-conformal prediction. This is an encouraging results since cross-conformal prediction uses several decision trees, thus sacrificing the interpretability of a single decision tree.


title: Using Visual Text Mining to Support the Study Selection Activity in Systematic Literature Reviews

abstract: Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually. Objective: We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques. Method: We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness. Results: Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included. Conclusions: Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR.


title: A 2.45/5.8 GHz folded monopole antenna for WLAN applications

abstract: A probe-fed printed folded monopole antenna for WLAN applications is presented. The antenna operates at 2.45 GHz and 5.8 GHz, is compact and has omni-directional characteristics in both bands. A parametric study of key geometrical parameters is reported.


title: Aqueous enzyme assisted oil extraction from oilseeds and emulsion de-emulsifying methods: A review

abstract: Regulatory, safety, and environmental issues have prompted the development of aqueous enzymatic extraction (AEE) for extracting components from oil-bearing materials. The emulsion resulting from AEE requires de-emulsification to separate the oil; when enzymes are used for this purpose, the method is known as aqueous enzymatic emulsion de-emulsification (AEED). In general, enzyme assisted oil extraction is known to yield oil having highly favourable characteristics. This review covers technological aspects of enzyme assisted oil extraction, and explores the quality characteristics of the oils obtained, focussing particularly on recent efforts undertaken to improve process economics by recovering and re-using enzymes.


title: Samba: a smartphone-based robot system for energy-efficient aquatic environment monitoring

abstract: Monitoring aquatic environment is of great interest to the ecosystem, marine life, and human health. This paper presents the design and implementation of Samba -- an aquatic surveillance robot that integrates an off-the-shelf Android smartphone and a robotic fish to monitor harmful aquatic processes such as oil spill and harmful algal blooms. Using the built-in camera of on-board smartphone, Samba can detect spatially dispersed aquatic processes in dynamic and complex environments. To reduce the excessive false alarms caused by the non-water area (e.g., trees on the shore), Samba segments the captured images and performs target detection in the identified water area only. However, a major challenge in the design of Samba is the high energy consumption resulted from the continuous image segmentation. We propose a novel approach that leverages the power-efficient inertial sensors on smartphone to assist the image processing. In particular, based on the learned mapping models between inertial and visual features, Samba uses real-time inertial sensor readings to estimate the visual features that guide the image segmentation, significantly reducing energy consumption and computation overhead. Samba also features a set of lightweight and robust computer vision algorithms, which detect harmful aquatic processes based on their distinctive color features. Lastly, Samba employs a feedback-based rotation control algorithm to adapt to spatiotemporal evolution of the target aquatic process. We have implemented a Samba prototype and evaluated it through extensive field experiments, lab experiments, and trace-driven simulations. The results show that Samba can achieve 94% detection rate, 5% false alarm rate, and a lifetime up to nearly two months.


title: Topic oriented community detection through social objects and link analysis in social networks

abstract: Community detection is an important issue in social network analysis. Most existing methods detect communities through analyzing the linkage of the network. The drawback is that each community identified by those methods can only reflect the strength of connections, but it cannot reflect the semantics such as the interesting topics shared by people. To address this problem, we propose a topic oriented community detection approach which combines both social objects clustering and link analysis. We first use a subspace clustering algorithm to group all the social objects into topics. Then we divide the members that are involved in those social objects into topical clusters, each corresponding to a distinct topic. In order to differentiate the strength of connections, we perform a link analysis on each topical cluster to detect the topical communities. Experiments on real data sets have shown that our approach was able to identify more meaningful communities. The quantitative evaluation indicated that our approach can achieve a better performance when the topics are at least as important as the links to the analysis.


title: A Low Voltage Four-Quadrant Analog Multiplier Using Triode-MOSFETs

abstract: A low voltage four-quadrant analog multiplier circuit is presented. Based on triode region operation of MOSFET, the multiplier circuit can be simply realized by connecting a pair of PMOS with three flipped voltage follower circuits. As a result, the proposed circuit is compact and can be operated under low voltage supply. Circuit simulation using SPICE with 0.35 micron CMOS model parameter shows that under 1.5 V single supply the proposed multiplier provides linear range of more than 50% of voltage supply (measured from THD less than 1%) and small signal bandwidth is higher than 95 MHz while the entire circuit consumes 46.4 muW of electrical power


Realtime procedural terrain generation - realtime synthesis of eroded fractal terrain for use in computer games


title: Learning deep features for image emotion classification

abstract: Images can both express and affect people's emotions. It is intriguing and important to understand what emotions are conveyed and how they are implied by the visual content of images. Inspired by the recent success of deep convolutional neural networks (CNN) in visual recognition, we explore two simple, yet effective deep learning-based methods for image emotion analysis. The first method uses off-the-shelf CNN features directly for classification. For the second method, we fine-tune a CNN that is pre-trained on a large dataset, i.e. ImageNet, on our target dataset first. Then we extract features using the fine-tuned CNN at different location at multiple levels to capture both the global and local information. The features at different location are aggregated using the Fisher Vector for each level and concatenated to form a compact representation. From our experimental results, both the deep learning-based methods outperforms traditional methods based on generic image descriptors and hand-crafted features.


title: Shape google: Geometric words and expressions for invariant shape retrieval

abstract: The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words” and treat them using text search approaches following the “bag of features” paradigm. In this article, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words,” we construct compact and informative shape descriptors by means of the “bag of features” approach. We also show that considering pairs of “geometric words” (“geometric expressions”) allows creating spatially sensitive bags of features with better discriminative power. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark.


title: 4G Mobile Communication Systems: Key Technology and Evolution

abstract: With the worldwide third-generation mobile communication system gradually implemented, the future development of mobile communications has become a hot topic and evolution of the problem. This paper introduces the fourth generation mobile communication system and its performance and network structure and OFDM, software defined radio, smart antennas, IPv6 and other key technologies, and analyzes the relationship between 4G mobile communication system for mobile communications and 3G, and the evolution of communication systems do Prospect.


title: Generalized time warping for multi-modal alignment of human motion

abstract: Temporal alignment of human motion has been a topic of recent interest due to its applications in animation, telerehabilitation and activity recognition among others. This paper presents generalized time warping (GTW), an extension of dynamic time warping (DTW) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GTW solves three major drawbacks of existing approaches based on DTW: (1) GTW provides a feature weighting layer to adapt different modalities (e.g., video and motion capture data), (2) GTW extends DTW by allowing a more flexible time warping as combination of monotonic functions, (3) unlike DTW that typically incurs in quadratic cost, GTW has linear complexity. Experimental results demonstrate that GTW can efficiently solve the multi-modal temporal alignment problem and outperforms state-of-the-art DTW methods for temporal alignment of time series within the same modality.


title: Reducing the harm of opioid overdose with the safe use of naloxone : a pharmacologic review.

abstract: Introduction: Opioid overdose fatality has increased threefold since 1999. As a result, prescription drug overdose surpassed motor vehicle collision as the leading cause of unintentional injury-related death in the USA. Naloxone, an opioid antagonist that has been available for decades, can safely reverse opioid overdose if used promptly and correctly. However, clinicians often overestimate the dose of naloxone needed to achieve the desired clinical outcome, precipitating acute opioid withdrawal syndrome (OWS).Areas covered: This article provides a comprehensive review of naloxone’s pharmacologic properties and its clinical application to promote the safe use of naloxone in acute management of opioid intoxication and to mitigate the risk of precipitated OWS. Available clinical data on opioid-receptor kinetics that influence the reversal of opioid agonism by naloxone are discussed. Additionally, the legal and social barriers to take home naloxone programs are addressed.Expert opinion: Naloxone is an intrin...


title: A Simple Homotopy Algorithm for Compressive Sensing

abstract: In this paper, we consider the problem of recovering the s largest elements of an arbitrary vector from noisy measurements. Inspired by previous work, we develop an homotopy algorithm which solves the l1-regularized least square problem for a sequence of decreasing values of the regularization parameter. Compared to the previous method, our algorithm is more efficient in the sense it only updates the solution once for each intermediate problem, and more practical in the sense it has a simple stopping criterion by checking the sparsity of the intermediate solution. Theoretical analysis reveals that our method enjoys a linear convergence rate in reducing the recovery error. Furthermore, our guarantee for recovering the top s elements of the target vector is tighter than previous results, and that for recovering the target vector itself matches the state of the art in compressive sensing.


title: High Network Utilization Load Balancing Scheme for Data Centers

abstract: We propose a high network utilization CONGestion- Aware load balancing approach, Multi-hop CONGA. It aims at improving the performance of handling traffic asymmetry, which happens occasionally in data centers. It leverages regular Clos topology and overlays for virtualization. In this paper, we schedule at the unit of flowlet, which is the burst of packets belonging to a flow, and make routing decisions on the first packet of each flowlet, based on the current global network situation, which is obtained by using a feedback mechanism. Moreover, in order to further balance loads, Multi-hop CONGA provides an alternative of a two- hop routing path by leveraging relative leisure links to relieve local traffic pressure, when all direct one- hop paths are heavily-loaded. We evaluate Multi-hop CONGA with extensive simulations, and compare it with several popular methods under different traffic conditions. Results demonstrate feasibility and efficiency of Multi-hop CONGA.


title: Smart grid sensor data collection, communication, and networking: a tutorial

abstract: The smart grid is an innovative energy network that will improve the conventional electrical grid network to be more reliable, cooperative, responsive, and economical. Within the context of the new capabilities, advanced data sensing, communication, and networking technology will play a significant role in shaping the future of the smart grid. The smart grid will require a flexible and efficient framework to ensure the collection of timely and accurate information from various locations in power grid to provide continuous and reliable operation. This article presents a tutorial on the sensor data collection, communications, and networking issues for the smart grid. First, the applications of data sensing in the smart grid are reviewed. Then, the requirements for data sensing and collection, the corresponding sensors and actuators, and the communication and networking architecture are discussed. The communication technologies and the data communication network architecture and protocols for the smart grid are described. Next, different emerging techniques for data sensing, communications, and sensor data networking are reviewed. The issues related to security of data sensing and communications in the smart grid are then discussed. To this end, the standardization activities and use cases related to data sensing and communications in the smart grid are summarized. Finally, several open issues and challenges are outlined. Copyright © 2012 John Wiley & Sons, Ltd.


title: A 13-bit 200MS/s PIPELINE ADC in 0.13µm CMOS

abstract: This paper presents a 0.13μm SHA-less pipeline ADC with LMS calibration technique. The nonlinearity of the first three stages is calibrated with blind LMS algorithm. Opamps and switches are carefully considered and co-designed with the calibration system. Around 7LSB closed-loop nonlinearity of MDAC is achieved. Simulation shows the SNDR of the proposed ADC at 200MS/s sampling rate is 78dB with 3.13MHz input and 75dB with 83.13MHz input.


title: WikiMirs: a mathematical information retrieval system for wikipedia

abstract: Mathematical formulae in structural formats such as MathML and LaTeX are becoming increasingly available. Moreover, repositories and websites, including ArXiv and Wikipedia, and growing numbers of digital libraries use these structural formats to present mathematical formulae. This presents an important new and challenging area of research, namely Mathematical Information Retrieval (MIR). In this paper, we propose WikiMirs, a tool to facilitate mathematical formula retrieval in Wikipedia. WikiMirs is aimed at searching for similar mathematical formulae based upon both textual and spatial similarities, using a new indexing and matching model developed for layout structures. A hierarchical generalization technique is proposed to generate sub-trees from presentation trees of mathematical formulae, and similarity is calculated based upon matching at different levels of these trees. Experimental results show that WikiMirs can efficiently support sub-structure matching and similarity matching of mathematical formulae. Moreover, WikiMirs obtains both higher accuracy and better ranked results over Wikipedia in comparison to Wikipedia Search and Egomath. We conclude that WikiMirs provides a new, alternative, and hopefully better service for users to search mathematical expressions within Wikipedia.


title: Enhancing Detection Through Linguistic Indexing And Topic Expansion

abstract: Natural language processing techniques may hold a tremendous potential for overcoming the inadequacies of purely quantitative methods of text information retrieval. Under the Tipster contracts in phases I through III, GE group has set out to explore this potential through development and evaluation of new text processing techniques. This work resulted in some significant advances and in a better understanding on how NLP may benefit IR. Tipster research has laid a critical groundwork for future work.In this paper we summarize GE work on document detection in Tipster Phase III. Our summarization research is described in a separate paper appearing in this volume.


title: An evolutionary computational theory of prefrontal executive function in decision-making

abstract: The prefrontal cortex subserves executive control and decision-making, that is, the coordination and selection of thoughts and actions in the service of adaptive behaviour. We present here a computational theory describing the evolution of the prefrontal cortex from rodents to humans as gradually adding new inferential Bayesian capabilities for dealing with a computationally intractable decision problem: exploring and learning new behavioural strategies versus exploiting and adjusting previously learned ones through reinforcement learning (RL). We provide a principled account identifying three inferential steps optimizing this arbitration through the emergence of (i) factual reactive inferences in paralimbic prefrontal regions in rodents; (ii) factual proactive inferences in lateral prefrontal regions in primates and (iii) counterfactual reactive and proactive inferences in human frontopolar regions. The theory clarifies the integration of model-free and model-based RL through the notion of strategy creation. The theory also shows that counterfactual inferences in humans yield to the notion of hypothesis testing, a critical reasoning ability for approximating optimal adaptive processes and presumably endowing humans with a qualitative evolutionary advantage in adaptive behaviour.


title: Babies and Brains: Habituation in Infant Cognition and Functional Neuroimaging

abstract: Many prominent studies of infant cognition over the past two decades have relied on the fact that infants habituate to repeated stimuli — i.e. that their looking times tend to decline upon repeated stimulus presentations. This phenomenon had been exploited to reveal a great deal about the minds of preverbal infants. Many prominent studies of the neural bases of adult cognition over the past decade have relied on the fact that brain regions habituate to repeated stimuli — i.e. that the hemodynamic responses observed in fMRI tend to decline upon repeated stimulus presentations. This phenomenon has been exploited to reveal a great deal about the neural mechanisms of perception and cognition. Similarities in the mechanics of these two forms of habituation suggest that it may be useful to relate them to each other. Here we outline this analogy, explore its nuances, and highlight some ways in which the study of habituation in functional neuroimaging could yield novel insights into the nature of habituation in infant cognition — and vice versa.


title: New Media for Teaching Applied Cryptography and Network Security

abstract: Considering that security education needs to train students to deal with security problems in real environments, we developed new media for teaching applied cryptography and network security. They permit students to gain real-life security experience from virtual laboratories on CD, DVD, or online. The application of those media is helpful to reduce investment and administration costs as well as effectively support security experiments which previously had to be done in conventional security laboratories.


A Context Based Dialog System with a Personality


title: The Adressa dataset for news recommendation

abstract: Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.


title: A Multi-scale Approach to Gesture Detection and Recognition

abstract: We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of characteristic motion impulses, or dynamic poses. Each modality is first processed separately in short spatio-temporal blocks, where discriminative data-specific features are either manually extracted or learned. Finally, we employ a Recurrent Neural Network for modeling large-scale temporal dependencies, data fusion and ultimately gesture classification. Our experiments on the 2013 Challenge on Multimodal Gesture Recognition dataset have demonstrated that using multiple modalities at several spatial and temporal scales leads to a significant increase in performance allowing the model to compensate for errors of individual classifiers as well as noise in the separate channels.


Acoustic Scene Recognition with Deep Neural Networks ( DCASE challenge 2016 )


Developing Characters Based on Local Wisdom of Bali in Teaching Physics in Senior High School


title: MATLAB based defect detection and classification of printed circuit board

abstract: A variety of ways has been established to detect defects found on printed circuit boards (PCB). In previous studies, defects are categories into seven groups with a minimum of one defect and up to a maximum of 4 defects in each group. Using Matlab image processing tools this research separates two of the existing groups containing two defects each into four new groups containing one defect each by processing synthetic images of bare through-hole single layer PCBs.


title: The dynamic microstructure of organizational commitment

abstract: In this study, we examine how the classic elements of affect, cognition, and behaviour combine to form within-person trajectories of organizational commitment. We test several key features of this ‘dynamic microstructure’ of commitment in an experience sampling setting (25 weekly measurements) among 72 organizational entrants and 23 exiters. We find support for the idea that within-person change in affect is slower than within-person change in cognition and behaviour. Wefind no support for the notion that affectwouldinfluencecognitionandbehaviour atthewithin-person level.In fact,wehave strong indications that the episodes of committing (organizational entry) and uncommitting(organizationalexit)aremainlycognitiondriven.Wediscusstheimplicationsofour study for tripartite attitude theory and commitment theory. Practitioner points


title: Efficient and effective realtime prediction of drive-by download attacks

abstract: Drive-by download attacks are common attack vector for compromising personal computers. While several alternatives to mitigate the threat have been proposed, approaches to realtime detection of drive-by download attacks has been predominantly limited to static and semi-dynamic analysis techniques. These techniques examine the original or deobfuscated JavaScript source code to assess the potential maliciousness of a webpage. However, static and semi-dynamic analysis techniques are vulnerable to commonly employed evasion techniques. Dynamic anomaly detection approaches are less susceptible to targeted evasion, but are used less often as a realtime solution on the individual systems because these techniques are typically resource intensive. This paper presents a novel approach to detect drive-by downloads in web browser environments using low resource dynamic analysis. By dynamically monitoring the bytecode stream generated by a web browser during rendering, the approach is able to detect previously unseen drive-by download attacks at runtime. The proposed method is effective, space efficient, and performs the analysis with low performance overhead, making the approach amenable to in-browser drive-by download detection on resource constrained devices, such as mobile phones.


title: Ultralow-voltage , minimum-energy CMOS

abstract: Energy efficiency has become a ubiquitous design requirement for digital circuits. Aggressive supply-voltage scaling has emerged as the most effective way to reduce energy use. In this work, we review circuit behavior at low voltages, specifically in the subthreshold (Vdd < Vth) regime, and suggest new strategies for energy-efficient design. We begin with a study at the device level, and we show that extreme sensitivity to the supply and threshold voltages complicates subthreshold design. The effects of this sensitivity can be minimized through simple device modifications and new device geometries. At the circuit level, we review the energy characteristics of subthreshold logic and SRAM circuits, and demonstrate that energy efficiency relies on the balance between dynamic and leakage energies, with process variability playing a key role in both energy efficiency and robustness. We continue the study of energy-efficient design by broadening our scope to the architectural level. We discuss the energy benefits of techniques such as multiple-threshold CMOS (MTCMOS) and adaptive body biasing (ABB), and we also consider the performance benefits of multiprocessor design at ultralow supply voltages.


title: Security for Future Software Defined Mobile Networks

abstract: 5G constitutes the next revolution in mobile communications. It is expected to deliver ultra-fast, ultra-reliable network access supporting a massive increase of data traffic and connected nodes. Different technologies are emerging to address the requirements of future mobile networks, such as Software Defined Networking (SDN), Network Function Virtualization (NFV) and cloud computing concepts. In this paper, we introduce the security challenges these new technologies are facing, inherent to the new telecommunication paradigm. We also present a multitier approach to secure Software Defined Mobile Network (SDMN) by tackling security at different levels to protect the network itself and its users. First, we secure the communication channels between network elements by leveraging Host Identity Protocol (HIP) and IPSec tunnelling. Then, we restrict the unwanted access to the mobile backhaul network with policy based communications. It also protects the backhaul devices from source address spoofing and Denial of Service (DoS) attacks. Finally, we leverage Software Defined Monitoring (SDM) and data collection to detect, prevent and react to security threats.


title: Expected Policy Gradients for Reinforcement Learning

abstract: We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadric critics and then extend it to an analytical method for the universal case, covering a broad class of actors and critics, including Gaussian, exponential families, and reparameterised policies with bounded support. For Gaussian policies, we show that it is optimal to explore using covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. EPG also provides a general framework for reasoning about policy gradient methods, which we use to establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we show that EPG outperforms existing approaches on six challenging domains involving the simulated control of physical systems.


title: The Influence of Presidential Rhetoric on Public Perception of Drugs and the Government?s Handling of the ?War on Drugs? in the United States

abstract: 3 Introduction 4 Methodology 6 Methodology 1. Review of the Literature 6 Methodology 2. Analysis of the Rhetoric 7 Findings 8 Findings 1. Review of the Literature 9 Conclusion to Findings 1. Review of the Literature 14 Findings 2. Analysis of the Rhetoric 14 Conclusion to Findings 2. Analysis of the Rhetoric 25 Conclusion 26 References 29


title: Requirement on Antenna Cross-Polarization Isolation for the Operational Use of C-Band SAR Constellations in Maritime Surveillance

abstract: The issue of antenna cross-polarization isolation has been previously discussed for the design of fully polarimetric synthetic aperture radars (SARs). Dual-polarized antennas with cross-polarization isolation that is better than -30 dB are desirable for more convenient polarimetric data calibration since measurements of antenna crosstalk (magnitude and phase) variations with incidence angle are not required. For an antenna with significant polarization crosstalk, it is still possible to retrieve pure polarization measurements of HH, HV, VH, and VV provided that the four corresponding received voltages are measured. However, it is not possible to recover from cross-polarization contamination for single- or dual-polarization measurements. Therefore, it is important to set up a minimum requirement on dual-polarized antenna isolation so that single- and dual-polarization applications are not unduly affected. In this letter, the minimum requirement on cross-polarization antenna isolation is investigated for operational use of C-band SARs in maritime surveillance applications. Calibrated polarimetric RADARSAT-2 data are used to simulate single- and dual-polarization data with cross-polarization contamination for a dual-polarized antenna with cross-polarization isolation ranging from -20 to -35 dB. It is shown that the cross-polarization HV (or VH) channel can be significantly affected, particularly at steep incidence angles. As a result, key applications that require the use of pure HV, such as ship detection and wind-speed measurements, are significantly affected. A requirement for a minimum of -30-dB antenna isolation is established. Antennas with cross-polarization isolation better than -35 dB are desirable for reliable exploitation of HV data at steep incidence angles.


title: Turn-on Performance of Reverse Blocking IGBT (RB IGBT) and Optimization Using Advanced Gate Driver

abstract: Turn-on performance of a reverse blocking insulated gate bipolar transistor (RB IGBT) is discussed in this paper. The RB IGBT is a specially designed IGBT having ability to sustain blocking voltage of both the polarities. Such a switch shows superior conduction but much worst switching (turn- on) performances compared to a combination of an ordinary IGBT and blocking diode. Because of that, optimization of the switching performance is a key issue that makes the RB IGB not well accepted in the real applications. In this paper, the RB IGBT turn-on losses and reverse recovery current are analyzed for different gate driver techniques, and a new gate driver is proposed. Commonly used conventional gate drivers do not have capability for the switching dynamics optimization. In contrast to this, the new proposed gate driver provides robust and simple way to control and optimize the reverse recovery current and turn-on losses. The collector current slope and reverse recovery current are controlled by the means of the gate emitter voltage control in feedforward manner. In addition, the collector emitter voltage slope is controlled during the voltage falling phase by the means of inherent increase of the gate current. Therefore, the collector emitter voltage tail and the total turn- on losses are reduced, independently on the reverse recovery current. The proposed gate driver was experimentally verified and the results presented and discussed.


title: Learning Spectral Clustering

abstract: Spectral clustering refers to a class of techniques which rely on the eigen-structure of a similarity matrix to partition points into disjoint clusters with points in the same cluster having high similarity and points in different clusters having low similarity. In this paper, we derive a new cost function for spectral clustering based on a measure of error between a given partition and a solution of the spectral relaxation of a minimum normalized cut problem. Minimizing this cost function with respect to the partition leads to a new spectral clustering algorithm. Minimizing with respect to the similarity matrix leads to an algorithm for learning the similarity matrix. We develop a tractable approximation of our cost function that is based on the power method of computing eigenvectors.


title: Thinning of the Cerebral Cortex in Aging

abstract: 1MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA, 2Departments of Psychology, Anatomy and Neurobiology, Washington University, St Louis, MO, USA, 3Department of Radiology, Washington University, St Louis, MO, USA, 4Department of Neurology, Washington University, St Louis, MO, USA and 5Howard Hughes Medical Institute


title: The Mirror World: Preparing for Mixed-Reality Living

abstract: A new kind of smart space is emerging in which digital, physical, and social layers are strongly intertwined. These spaces extend the classic assistive functionality of ambient intelligence toward more proactive possibilities, where the smart environment not only monitors people as they perform tasks but also influences their plans and intentions. The authors explore this concept of the smart space as a mirror world, looking in particular at how it will affect our cognitive abilities and noting some of the research challenges that will need to be addressed. This department is part of a special issue on smart spaces.


Auto-marking: using computational linguistics to score short, free text responses. Paper presented at the 29th annual conference


title: Nepenthes Honeypots based Botnet Detection

abstract: the numbers of the botnet attacks are increasing day by day and the detection of botnet spreading in the network has become very challenging. Bots are having specific characteristics in comparison of normal malware as they are controlled by the remote master server and usually don't show their behavior like normal malware until they don't receive any command from their master server. Most of time bot malware are inactive, hence it is very difficult to detect. Further the detection or tracking of the network of theses bots requires an infrastructure that should be able to collect the data from a diverse range of data sources and correlate the data to bring the bigger picture in view.In this paper, we are sharing our experience of botnet detection in the private network as well as in public zone by deploying the nepenthes honeypots. The automated framework for malware collection using nepenthes and analysis using anti- virus scan are discussed. The experimental results of botnet detection by enabling nepenthes honeypots in network are shown. Also we saw that existing known bots in our network can be detected.


title: Behavioral and Physiological Consequences of Sleep Restriction

abstract: Adequate sleep is essential for general healthy functioning. This paper reviews recent research on the effects of chronic sleep restriction on neurobehavioral and physiological functioning and discusses implications for health and lifestyle. Restricting sleep below an individual's optimal time in bed (TIB) can cause a range of neurobehavioral deficits, including lapses of attention, slowed working memory, reduced cognitive throughput, depressed mood, and perseveration of thought. Neurobehavioral deficits accumulate across days of partial sleep loss to levels equivalent to those found after 1 to 3 nights of total sleep loss. Recent experiments reveal that following days of chronic restriction of sleep duration below 7 hours per night, significant daytime cognitive dysfunction accumulates to levels comparable to that found after severe acute total sleep deprivation. Additionally, individual variability in neurobehavioral responses to sleep restriction appears to be stable, suggesting a traitlike (possibly genetic) differential vulnerability or compensatory changes in the neurobiological systems involved in cognition. A causal role for reduced sleep duration in adverse health outcomes remains unclear, but laboratory studies of healthy adults subjected to sleep restriction have found adverse effects on endocrine functions, metabolic and inflammatory responses, suggesting that sleep restriction produces physiological consequences that may be unhealthy. ::: ::: Citation: ::: Banks S; Dinges DF. Behavioral and physiological consequences of sleep restriction. J Clin Sleep Med 2007;3(5):519-528.


title: Large-Scale Cover Song Identification Using Chord Profiles

abstract: This paper focuses on cover song identification among datasets potentially containing millions of songs. A compact representation of music contents plays an important role in large-scale analysis and retrieval. The proposed approach is based on high-level summarization of musical songs using chord profiles. Search is performed in two steps. In the first step, the Locality Sensitive Hashing (LHS) method is used to retrieve songs with similar chord profiles. On the resulting list of songs a second processing step is applied to progressively refine the ranking. Experiments conducted on both the Million Song Dataset (MSD) and a subset of the Second Hand Songs (SHS) dataset showed the effectiveness of the proposed solution, which provides state-of-the-art results.


title: Gray-scale skeletonization of small vessels in magnetic resonance angiography

abstract: Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. We present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determination of branching patterns of vascular trees. They are based on the ordered region growing (ORG) algorithm that represents the image as an acyclic graph, which can be reduced to a skeleton by specifying vessel endpoints or by a pruning process. Ambiguities in the vessel branching due to vessel overlap are effectively resolved by heuristic methods that incorporate a priori knowledge of bifurcation spacing. Vessel paths are detected at interactive speeds on a 500-MHz processor using vessel endpoints. These methods apply best to smaller vessels where the image intensity peaks at the center of the lumen which, for the abdominal MRA, includes vessels whose diameter is less than 1 cm.


title: Goal‐orientated cognitive rehabilitation for dementias associated with Parkinson's disease─A pilot randomised controlled trial

abstract: Health and Care Research Wales (formerly The National Institute for Social Care and Health Research), Grant/Award Number: RFPPB‐2042‐1020; Health and Care Research Wales,Grant/Award Number: RFPPB‐2042‐1020


Convolutional Neural Networks for Real-Time Beat Tracking: A Dancing Robot Application


Benefits of mass customized products: moderating role of product involvement and fashion innovativeness


title: Attack Intention Recognition: A Review

abstract: Sensitive information faces critical risks when it is trans- mitted through computer networks. Existing protection systems are still limited in their capacities to ensure network information has sufficient confidentiality, integrity, and availability. The rapid development in network technologies has only helped increase network attacks and hide their malicious intent. This paper analyzes attack types and classifies them according to their intent. A causal network approach is used to recognize attackers' plans and predict their intentions. Attack intention is the ultimate attack goal which the attacker attempts to achieve by executing various methods or techniques, and recognizing it will help security administrators select an appropriate protection system.


title: Evaluation Of Pavement Surface Distress Using Digital Image Collection And Analysis

abstract: Local Authority road networks commonly include roads with different functional characteristics and a variety of construction types, which require maintenance solutions tailored to their needs. Given this background, on local road network, pavement management is founded on the experience of the agency engineers and is often constrained by low budgets and a variety of environmental and external requirements. This paper forms part of a research work that investigates the use of digital techniques for obtaining field data in order to increase safety and reduce labour cost requirements using a semi-automated distress collection and measurement system. More specifically, a definition of a distress detection procedure is presented which aims at producing a result complying more closely to the distress identification manuals and protocols. The process comprises the following two steps: - Automated pavement image collection. Images are collected using the high speed digital acquisition system of the Mobile Laboratory designed and implemented by the Department of Civil and Environmental Engineering of the University of Catania; - Distress Detection. By way of the Pavement Distress Analyser (PDA), a specialised software, images are adjusted to eliminate their optical distortion. Cracks, potholes and patching are automatically detected and subsequently classified by means of an operator assisted approach. An intense, experimental field survey has made it possible to establish that the procedure obtains more consistent distress measurements than a manual survey thus increasing its repeatability, reducing costs and increasing safety during the survey. Moreover, the pilot study made it possible to validate results coming from a survey carried out under normal traffic conditions, concluding that it is feasible to integrate the procedure into a roadway pavement management system.


title: PASQUAL: Parallel Techniques for Next Generation Genome Sequence Assembly

abstract: The study of genomes has been revolutionized by sequencing machines that output many short overlapping substrings (called reads). The task of sequence assembly in practice is to reconstruct long contiguous genome subsequences from the reads. With Next Generation Sequencing (NGS) technologies, assembly software needs to be more accurate, faster, and more memory-efficient due to the problem complexity and the size of the data sets. In this paper, we develop parallel algorithms and compressed data structures to address several computational challenges of NGS assembly. We demonstrate how commonly available multicore architectures can be efficiently utilized for sequence assembly. In all stages (indexing input strings, string graph construction and simplification, extraction of contiguous subsequences) of our software Pasqual, we use shared-memory parallelism to speed up the assembly process. In our experiments with data of up to 6.8 billion base pairs, we demonstrate that Pasqual generally delivers the best tradeoff between speed, memory consumption, and solution quality. On synthetic and real data sets Pasqual scales well on our test machine with 40 CPU cores with increasing number of threads. Given enough cores, Pasqual is fastest in our comparison.


title: CRYSTALS - Kyber: A CCA-Secure Module-Lattice-Based KEM

abstract: textabstractRapid advances in quantum computing, together with the announcement by the National Institute of Standards and Technology (NIST) to define new standards for digital-signature, encryption, and key-establishment protocols, have created significant interest in post-quantum cryptographic schemes. This paper introduces Kyber (part of CRYSTALS - Cryptographic Suite for Algebraic Lattices - a package submitted to NIST post-quantum standardization effort in November 2017), a portfolio of post-quantum cryptographic primitives built around a key-encapsulation mechanism (KEM), based on hardness assumptions over module lattices. Our KEM is most naturally seen as a successor to the NEWHOPE KEM (Usenix 2016). In particular, the key and ciphertext sizes of our new construction are about half the size, the KEM offers CCA instead of only passive security, the security is based on a more general (and flexible) lattice problem, and our optimized implementation results in essentially the same running time as the aforementioned scheme. We first introduce a CPA-secure public-key encryption scheme, apply a variant of the Fujisaki-Okamoto transform to create a CCA-secure KEM, and eventually construct, in a black-box manner, CCA-secure encryption, key exchange, and authenticated-key-exchange schemes. The security of our primitives is based on the hardness of Module-LWE in the classical and quantum random oracle models, and our concrete parameters conservatively target more than 128 bits of post-quantum security.


title: The distribution of references across texts: Some implications for citation analysis

abstract: In citation network analysis, complex behavior is reduced to a simple edge, namely, node A cites node B. The implicit assumption is that A is giving credit to, or acknowledging, B. It is also the case that the contributions of all citations are treated equally, even though some citations appear multiply in a text and others appear only once. In this study, we apply text-mining algorithms to a relatively large dataset (866 information science articles containing 32,496 bibliographic references) to demonstrate the differential contributions made by references. We (1) look at the placement of citations across the different sections of a journal article, and (2) identify highly cited works using two different counting methods (CountOne and CountX). We find that (1) the most highly cited works appear in the Introduction and Literature Review sections of citing papers, and (2) the citation rankings produced by CountOne and CountX differ. That is to say, counting the number of times a bibliographic reference is cited in a paper rather than treating all references the same no matter how many times they are invoked in the citing article reveals the differential contributions made by the cited works to the citing paper.


title: Demon: An Efficient Solution for on-Device MMU Virtualization in Mediated Pass-Through

abstract: Memory Management Units (MMUs) for on-device address translation are widely used in modern devices. However, conventional solutions for on-device MMU virtualization, such as shadow page table implemented in mediated pass-through, still suffer from high complexity and low performance. We present Demon, an efficient solution for on-DEvice MMU virtualizatiON in mediated pass-through. The key insight is that Demon takes advantage of IOMMU to construct a two-dimensional address translation and dynamically switches the 2nd-dimensional page table to a proper candidate when the device owner switches. In order to support fine-grained parallelism for the device with multiple engines, we put forward a hardware proposal that separates the address space of each engine and enables simultaneous device address remapping for multiple virtual machines (VMs). We implement Demon with a prototype named gDemon which virtualizes Intel GPU MMU. Nonetheless, Demon is not limited to this particular case. Evaluations show that gDemon provides up to 19.73x better performance in the media transcoding workloads and achieves performance improvement of up to 17.09% and 13.73% in the 2D benchmarks and 3D benchmarks, respectively, compared with gVirt. The current release of gDemon scales up to 6 VMs with moderate performance in our experiments. In addition, gDemon simplifies the implementation of GPU MMU virtualization with 37% code reduction.


title: How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)

abstract: Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the exact reasons for BatchNorm's effectiveness are still poorly understood. The popular belief is that this effectiveness stems from controlling the change of the layers' input distributions during training to reduce the so-called "internal covariate shift". In this work, we demonstrate that such distributional stability of layer inputs has little to do with the success of BatchNorm. Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. These findings bring us closer to a true understanding of our DNN training toolkit.


title: Gated XNOR Networks: Deep Neural Networks with Ternary Weights and Activations under a Unified Discretization Framework

abstract: There is a pressing need to build an architecture that could subsume these networks undera unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multistep neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. In this way, we build a unified framework that subsumes the binary or ternary networks as its special cases.More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to gated XNOR networks (or sparse binary networks) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore,the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms.


title: Yin-yang: concealing the deep embedding of DSLs

abstract: Deeply embedded domain-specific languages (EDSLs) intrinsically compromise programmer experience for improved program performance. Shallow EDSLs complement them by trading program performance for good programmer experience. We present Yin-Yang, a framework for DSL embedding that uses Scala macros to reliably translate shallow EDSL programs to the corresponding deep EDSL programs. The translation allows program prototyping and development in the user friendly shallow embedding, while the corresponding deep embedding is used where performance is important. The reliability of the translation completely conceals the deep em- bedding from the user. For the DSL author, Yin-Yang automatically generates the deep DSL embeddings from their shallow counterparts by reusing the core translation. This obviates the need for code duplication and leads to reliability by construction.


title: A controlled study of agoraphobia and the independent effect of virtual reality exposure therapy

abstract: Objectives:Past controlled clinical trials centred on virtual reality exposure therapy (VRET) for agoraphobia mostly used multicomponent therapy with success. However, the present paper aimed to evaluate the independent effect of VRET for agoraphobia.Methods:A controlled study involving 18 agoraphobic participants assigned to two groups: VRET only and VRET with cognitive therapy. Nine specific virtual environments were developed using an affordable game level editor.Results:Questionnaires, behavioural tests and physiological measures indicated a positive effect of VRET. Correlations supported the predictive value of presence towards treatment outcome. The addition of cognitive therapy did not provide significant additional benefit.Conclusions:Overall, the isolated effects of VRET did not seem to be significantly less than the effects of VRET combined with cognitive therapy. Future research should explore the use of other components in addition to cognitive therapy and VRET for agoraphobia as well as its p...


title: DEVELOPMENT OF SURVEY OF TECHNOLOGICAL PEDAGOGICAL AND CONTENT KNOWLEDGE (TPACK)

abstract: The purpose of this study is to develop a survey of technological pedagogical and content knowledge (TPACK). The survey consists of seven subscales forming the TPACK model: 1) technology knowledge (TK), 2) pedagogy knowledge (PK), 3) content knowledge (CK), 4) technological pedagogical knowledge (TPK), 5) technological content knowledge (TCK), 6) pedagogical content knowledge (PCK), and 7) TPACK. This study is conducted in five phases: 1) item pool, 2) validity and reliability, 3) discriminant validity, 4) test-retest reliability, and 5) translation of the TPACK survey. To examine language equivalence, both Turkish and English versions of the TPACK survey are administered to preservice teachers studying English language education. It is determined the questionnaire meets the language equivalence. Results demonstrate the TPACK survey is a valid and reliable measure.


title: High-speed and low-power programmable frequency divider

abstract: This paper presents a novel 2/3 divider cell circuit design for a truly modular programmable frequency divider with high-speed, low-power, and high input-sensitivity features. In this paper, the proposed flip-flop based 2/3 divider cell adopts dynamic E-TSPC circuit that not only reduces power consumption, but also improves operation speed and input sensitivity. The whole design was implemented using the TSMC 0.18 μm 1P6M CMOS process. With an 8-stage 2/3 divider cell, the measurement results indicate that the proposed circuit operates up to 5.8GHz with the power-consumption less than 3.24mW.


title: Optimizing wireless charger placement for directional charging

abstract: Wireless Power Transfer (WPT) technology has witnessed huge development because of its convenience and reliability. This paper concerns the fundamental issue of wireless charger PLacement with Optimized charging uTility (PLOT), that is, given a fixed number of chargers and a set of points on the plane, determining the positions and orientations of chargers such that the overall expected charging utility for all points is maximized. To address PLOT, we propose a 1 − 1/e − e approximation algorithm. First, we present techniques to approximate the nonlinear charging power and the expected charging utility to make the problem almost linear. Second, we develop a Dominating Coverage Set extraction method to reduce the continuous search space of PLOT to a limited and discrete one without performance loss. Third, we prove that the reformulated problem is essentially maximizing a monotone submodular function subject to a matroid constraint, and propose a greedy algorithm to address this problem. We conduct both simulation and field experiments to validate our theoretical results, and the results show that our algorithm can outperform comparison algorithms by at least 46.3%.


title: The multikernel: a new OS architecture for scalable multicore systems

abstract: Commodity computer systems contain more and more processor cores and exhibit increasingly diverse architectural tradeoffs, including memory hierarchies, interconnects, instruction sets and variants, and IO configurations. Previous high-performance computing systems have scaled in specific cases, but the dynamic nature of modern client and server workloads, coupled with the impossibility of statically optimizing an OS for all workloads and hardware variants pose serious challenges for operating system structures. We argue that the challenge of future multicore hardware is best met by embracing the networked nature of the machine, rethinking OS architecture using ideas from distributed systems. We investigate a new OS structure, the multikernel, that treats the machine as a network of independent cores, assumes no inter-core sharing at the lowest level, and moves traditional OS functionality to a distributed system of processes that communicate via message-passing. We have implemented a multikernel OS to show that the approach is promising, and we describe how traditional scalability problems for operating systems (such as memory management) can be effectively recast using messages and can exploit insights from distributed systems and networking. An evaluation of our prototype on multicore systems shows that, even on present-day machines, the performance of a multikernel is comparable with a conventional OS, and can scale better to support future hardware.


title: Predictive Analytics On Public Data - The Case Of Stock Markets

abstract: This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor’s 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.


title: A 1μW voice activity detector using analog feature extraction and digital deep neural network

abstract: Voice user interfaces (UIs) are highly compelling for wearable and mobile devices. They have the advantage of using compact and ultra-low-power (ULP) input devices (e.g. passive microphones). Together with ULP signal acquisition and processing, voice UIs can give energy-harvesting acoustic sensor nodes and battery-operating devices the sought-after capability of natural interaction with humans. Voice activity detection (VAD), separating speech from background noise, is a key building block in such voice UIs, e.g. it can enable power gating of higher-level speech tasks such as speaker identification and speech recognition [1]. As an always-on block, the power consumption of VAD must be minimized and meanwhile maintain high classification accuracy. Motivated by high power efficiency of analog signal processing, a VAD system using analog feature extraction (AFE) and mixed-signal decision tree (DT) classifier was demonstrated in [2]. While it achieved a record of 6μW, the system requires machine-learning based calibration of the DT thresholds on a chip-to-chip basis due to ill-controlled AFE variation. Moreover, the 7-node DT may deliver inferior classification accuracy especially under low input SNR and difficult noise scenario, compared to more advanced classifiers like deep neural networks (DNNs) [1,3]. Although heavy computational load in conventional floating-point DNNs prevents their adoption in embedded systems, the binarized neural networks (BNNs) with binary weights and activations proposed in [4] may pave the way to ULP implementations. In this paper, we present a 1μW VAD system utilizing AFE and a digital BNN classifier with an event-encoding A/D interface. The whole AFE is 9.4x more power-efficient than the prior art [5] and 7.9x than the state-of-the-art digital filter bank [6], and the BNN consumes only 0.63μW. To avoid costly chip-wise training, a variation-aware python model of the AFE was created and the generated features were used for offline BNN training. Measurements show 84.4%/85.4% mean speech/non-speech hit rate with 1.88%/4.65% 1-σ standard deviation among 10 dies using the same weights for 10dB SNR speech with restaurant noise.


title: A $160~\mu {\rm W}$ 8-Channel Active Electrode System for EEG Monitoring

abstract: This paper presents an active electrode system for gel-free biopotential EEG signal acquisition. The system consists of front-end chopper amplifiers and a back-end common-mode feedback (CMFB) circuit. The front-end AC-coupled chopper amplifier employs input impedance boosting and digitally-assisted offset trimming. The former increases the input impedance of the active electrode to 2 GΩ at 1 Hz and the latter limits the chopping induced output ripple and residual offset to 2 mV and 20 mV, respectively. Thanks to chopper stabilization, the active electrode achieves 0.8 μVrms (0.5-100 Hz) input referred noise. The use of a back-end CMFB circuit further improves the CMRR of the active electrode readout to 82 dB at 50 Hz. Both front-end and back-end circuits are implemented in a 0.18 μm CMOS process and the total current consumption of an 8-channel readout system is 88 μA from 1.8 V supply. EEG measurements using the proposed active electrode system demonstrate its benefits compared to passive electrode systems, namely reduced sensitivity to cable motion artifacts and mains interference.


title: From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

abstract: Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomena (i.e. cascading process) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors getting infected by a cascade after this node getting infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and present a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.


title: Sound empirical evidence in software testing

abstract: Several promising techniques have been proposed to automate different tasks in software testing, such as test data generation for object-oriented software. However, reported studies in the literature only show the feasibility of the proposed techniques, because the choice of the employed artifacts in the case studies (e.g., software applications) is usually done in a non-systematic way. The chosen case study might be biased, and so it might not be a valid representative of the addressed type of software (e.g., internet applications and embedded systems). The common trend seems to be to accept this fact and get over it by simply discussing it in a threats to validity section. In this paper, we evaluate search-based software testing (in particular the EvoSuite tool) when applied to test data generation for open source projects. To achieve sound empirical results, we randomly selected 100 Java projects from SourceForge, which is the most popular open source repository (more than 300,000 projects with more than two million registered users). The resulting case study not only is very large (8,784 public classes for a total of 291,639 bytecode level branches), but more importantly it is statistically sound and representative for open source projects. Results show that while high coverage on commonly used types of classes is achievable, in practice environmental dependencies prohibit such high coverage, which clearly points out essential future research directions. To support this future research, our SF100 case study can serve as a much needed corpus of classes for test generation.