title: The academic social network

abstract: By means of their academic publications, authors form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors select co-authors and reference papers written by other authors. Thanks to various efforts (such as Microsoft Academic Search and DBLP), the data necessary for analyzing the academic social network is becoming more available on the Internet. What type of information and queries would be useful for users to discover, beyond the search queries already available from services such as Google Scholar? In this paper, we explore this question by defining a variety of ranking metrics on different entities--authors, publication venues, and institutions. We go beyond traditional metrics such as paper counts, citations, and h-index. Specifically, we define metrics such as influence, connections, and exposure for authors. An author gains influence by receiving more citations, but also citations from influential authors. An author increases his or her connections by co-authoring with other authors, and especially from other authors with high connections. An author receives exposure by publishing in selective venues where publications have received high citations in the past, and the selectivity of these venues also depends on the influence of the authors who publish there. We discuss the computation aspects of these metrics, and the similarity between different metrics. With additional information of author-institution relationships, we are able to study institution rankings based on the corresponding authors' rankings for each type of metric as well as different domains. We are prepared to demonstrate these ideas with a web site ( http://pubstat.org ) built from millions of publications and authors.


title: Adaptive diversification of recommendation results via latent factor portfolio

abstract: This paper studies result diversification in collaborative filtering. We argue that the diversification level in a recommendation list should be adapted to the target users' individual situations and needs. Different users may have different ranges of interests -- the preference of a highly focused user might include only few topics, whereas that of the user with broad interests may encompass a wide range of topics. Thus, the recommended items should be diversified according to the interest range of the target user. Such an adaptation is also required due to the fact that the uncertainty of the estimated user preference model may vary significantly between users. To reduce the risk of the recommendation, we should take the difference of the uncertainty into account as well. In this paper, we study the adaptive diversification problem theoretically. We start with commonly used latent factor models and reformulate them using the mean-variance analysis from the portfolio theory in text retrieval. The resulting Latent Factor Portfolio (LFP) model captures the user's interest range and the uncertainty of the user preference by employing the variance of the learned user latent factors. It is shown that the correlations between items (and thus the item diversity) can be obtained by using the correlations between latent factors (topical diversity), which in return significantly reduce the computation load. Our mathematical derivation also reveals that diversification is necessary, not only for risk-averse system behavior (non-adpative), but also for the target users' individual situations (adaptive), which are represented by the distribution and the variance of the latent user factors. Our experiments confirm the theoretical insights and show that LFP succeeds in improving latent factor models by adaptively introducing recommendation diversity to fit the individual user's needs.


title: A CMOS High-Voltage Transmitter IC for Ultrasound Medical Imaging Applications

abstract: A high-voltage (HV) transmitter integrated circuit for ultrasound medical imaging applications is implemented using 0.18-μm bipolar/CMOS/DMOS technology. The proposed HV transmitter achieves high integration by only employing standard CMOS transistors in a stacked configuration with dynamic gate biasing circuit while successfully driving the capacitive micromachined ultrasound transducer device immersed in an oil environment without breakdown reliability issues. The HV transmitter including the output driver and the voltage level shifters generates over 10-Vp-p pulses at 1.25-MHz frequency and occupies only 0.022 mm2 of core die area.


title: A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

abstract: Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.


title: Incentive-based modeling and inference of attacker intent, objectives, and strategies

abstract: Although the ability to model and infer Attacker Intent, Objectives and Strategies (AIOS) may dramatically advance the literature of risk assessment, harm prediction, and predictive or proactive cyber defense, existing AIOS inference techniques are ad hoc and system or application specific. In this paper, we present a general incentive-based method to model AIOS and a game theoretic approach to infer AIOS. On one hand, we found that the concept of incentives can unify a large variety of attacker intents; the concept of utilities can integrate incentives and costs in such a way that attacker objectives can be practically modeled. On the other hand, we developed a game theoretic AIOS formalization which can capture the inherent inter-dependency between AIOS and defender objectives and strategies in such a way that AIOS can be automatically inferred. Finally, we use a specific case study to show how AIOS can be inferred in real world attack-defense scenarios.


title: Global inference for sentence compression: An integer linear programming approach

abstract: Sentence compression holds promise for many applications ranging from summarization to subtitle generation. Our work views sentence compression as an optimization problem and uses integer linear programming (ILP) to infer globally optimal compressions in the presence of linguistically motivated constraints. We show how previous formulations of sentence compression can be recast as ILPs and extend these models with novel global constraints. Experimental results on written and spoken texts demonstrate improvements over state-of-the-art models.


title: Synthetic biology and molecular genetics in non-conventional yeasts: Current tools and future advances

abstract: Coupling the tools of synthetic biology with traditional molecular genetic techniques can enable the rapid prototyping and optimization of yeast strains. While the era of yeast synthetic biology began in the well-characterized model organism Saccharomyces cerevisiae, it is swiftly expanding to include non-conventional yeast production systems such as Hansenula polymorpha, Kluyveromyces lactis, Pichia pastoris, and Yarrowia lipolytica. These yeasts already have roles in the manufacture of vaccines, therapeutic proteins, food additives, and biorenewable chemicals, but recent synthetic biology advances have the potential to greatly expand and diversify their impact on biotechnology. In this review, we summarize the development of synthetic biological tools (including promoters and terminators) and enabling molecular genetics approaches that have been applied in these four promising alternative biomanufacturing platforms. An emphasis is placed on synthetic parts and genome editing tools. Finally, we discuss examples of synthetic tools developed in other organisms that can be adapted or optimized for these hosts in the near future.


title: Bidirectional Expansion For Keyword Search on Graph Databases

abstract: Relational, XML and HTML data can be represented as graphs with entities as nodes and relationships as edges. Text is associated with nodes and possibly edges. Keyword search on such graphs has received much attention lately. A central problem in this scenario is to efficiently extract from the data graph a small number of the "best" answer trees. A Backward Expanding search, starting at nodes matching keywords and working up toward confluent roots, is commonly used for predominantly text-driven queries. But it can perform poorly if some keywords match many nodes, or some node has very large degree.In this paper we propose a new search algorithm, Bidirectional Search, which improves on Backward Expanding search by allowing forward search from potential roots towards leaves. To exploit this flexibility, we devise a novel search frontier prioritization technique based on spreading activation. We present a performance study on real data, establishing that Bidirectional Search significantly outperforms Backward Expanding search.


title: Towards affective camera control in games

abstract: Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we propose inclusion of camera control as a vital component of affective adaptive interaction in games. We investigate the impact of camera viewpoints on psychophysiology of players through preference surveys collected from a test game. Data is collected from players of a 3D prey/predator game in which player experience is directly linked to camera settings. Computational models of discrete affective states of fun, challenge, boredom, frustration, excitement, anxiety and relaxation are built on biosignal (heart rate, blood volume pulse and skin conductance) features to predict the pairwise self-reported emotional preferences of the players. For this purpose, automatic feature selection and neuro-evolutionary preference learning are combined providing highly accurate affective models. The performance of the artificial neural network models on unseen data reveals accuracies of above 80% for the majority of discrete affective states examined. The generality of the obtained models is tested in different test-bed game environments and the use of the generated models for creating adaptive affect-driven camera control in games is discussed.


title: Shape and Appearance Context Modeling

abstract: In this work we develop appearance models for computing the similarity between image regions containing deformable objects of a given class in realtime. We introduce the concept of shape and appearance context. The main idea is to model the spatial distribution of the appearance relative to each of the object parts. Estimating the model entails computing occurrence matrices. We introduce a generalization of the integral image and integral histogram frameworks, and prove that it can be used to dramatically speed up occurrence computation. We demonstrate the ability of this framework to recognize an individual walking across a network of cameras. Finally, we show that the proposed approach outperforms several other methods.


title: A study of evaluation metrics and datasets for video captioning

abstract: With the fast growing interest in deep learning, various applications and machine learning tasks are emerged in recent years. Video captioning is especially gaining a lot of attention from both computer vision and natural language processing fields. Generating captions is usually performed by jointly learning of different types of data modalities that share common themes in the video. Learning with the joining representations of different modalities is very challenging due to the inherent heterogeneity resided in the mixed information of visual scenes, speech dialogs, music and sounds, and etc. Consequently, it is hard to evaluate the quality of video captioning results. In this paper, we introduce well-known metrics and datasets for evaluation of video captioning. We compare the the existing metrics and datasets to derive a new research proposal for the evaluation of video descriptions.


title: Polarization properties of volume hologram

abstract: The diffraction efficiency of volume hologram is calculated by using the character matrix of multilayer medium films. The polarization characteristic of volume holographic grating is discussed in detail, while the diffraction properties are analyzed respectively for p-polarized light and s-polarized light by computer simulation. We have found that the diffraction efficiency of reflection hologram can be highly dependent on the polarization of incident light.


title: Data-Driven Response Generation in Social Media

abstract: We present a data-driven approach to generating responses to Twitter status posts, based on phrase-based Statistical Machine Translation. We find that mapping conversational stimuli onto responses is more difficult than translating between languages, due to the wider range of possible responses, the larger fraction of unaligned words/phrases, and the presence of large phrase pairs whose alignment cannot be further decomposed. After addressing these challenges, we compare approaches based on SMT and Information Retrieval in a human evaluation. We show that SMT outperforms IR on this task, and its output is preferred over actual human responses in 15% of cases. As far as we are aware, this is the first work to investigate the use of phrase-based SMT to directly translate a linguistic stimulus into an appropriate response.


title: Compact tri-band MIMO antenna with high port isolation for WLAN and WiMAX applications

abstract: This paper presents a Tri-band multiple input multiple output (MIMO) antennae for Wi-Fi/WiMAX applications. The antenna consists of two radiating elements placed symmetrically. The overall dimensions of the antenna are 47 ×36 × 1.6 mm3. The separation distance between the two elements is 3mm (0.024λ o ). The Co-Planar Waveguide (CPW) technique is used as a feeding network. The Isolation factor is improved by inserting a neutralization line between the ground planes of the two antennas. The proposed MIMO antenna is implemented and tested. The measured results are in reasonable agreement with the simulated ones.


title: Compact E-Cash

abstract: This paper presents efficient off-line anonymous e-cash schemes where a user can withdraw a wallet containing 2 coins each of which she can spend unlinkably. Our first result is a scheme, secure under the strong RSA and the y-DDHI assumptions, where the complexity of the withdrawal and spend operations is O( + k) and the user's wallet can be stored using O( + k) bits, where k is a security parameter. The best previously known schemes require at least one of these complexities to be O(2 . k). In fact, compared to previous e-cash schemes, our whole wallet of 2 coins has about the same size as one coin in these schemes. Our scheme also offers exculpability of users, that is, the bank can prove to third parties that a user has double-spent. We then extend our scheme to our second result, the first e-cash scheme that provides traceable coins without a trusted third party. That is, once a user has double spent one of the 2 coins in her wallet, all her spendings of these coins can be traced. However, the price for this is that the complexity of the spending and of the withdrawal protocols becomes O( . k) and O( . k+ k 2 ) bits, respectively, and wallets take O( . k) bits of storage. All our schemes are secure in the random oracle model.


title: Feature sensitive surface extraction from volume data

abstract: The representation of geometric objects based on volumetric data structures has advantages in many geometry processing applications that require, e.g., fast surface interrogation or boolean operations such as intersection and union. However, surface based algorithms like shape optimization (fairing) or freeform modeling often need a topological manifold representation where neighborhood information within the surface is explicitly available. Consequently, it is necessary to find effective conversion algorithms to generate explicit surface descriptions for the geometry which is implicitly defined by a volumetric data set. Since volume data is usually sampled on a regular grid with a given step width, we often observe severe alias artifacts at sharp features on the extracted surfaces. In this paper we present a new technique for surface extraction that performs feature sensitive sampling and thus reduces these alias effects while keeping the simple algorithmic structure of the standard Marching Cubes algorithm. We demonstrate the effectiveness of the new technique with a number of application examples ranging from CSG modeling and simulation to surface reconstruction and remeshing of polygonal models.


title: Graspeo: a social media platform for knowledge management in NGOs

abstract: Timely access to critical information is crucial for any organization operating in situations of emergency. Deploying an adequate information system tailored to specific organizational needs and matched to the organizational structure is essential. To understand the knowledge management needs of Medecins Sans Frontieres, one of the leading NGOs in the humanitarian and medical fields, we conducted in total 145 hours of in-depth interviews. This paper presents three identified key requirements for an effective knowledge management system specifically designed for large distributed NGOs, like MSF. Additionally, we introduce a novel social media called Graspeo designed and built from ground up to fulfill these requirements.


title: Deviation-based and similarity-based contextual SLIM recommendation algorithms

abstract: Context-aware recommender systems (CARS) have been demonstrated to be able to enhance recommendations by adapting users' preferences to different contextual situations. In recent years, several CARS algorithms have been developed to incorporated into the recommender systems. For example, differential context modeling (DCM) was modified based on traditional neighborhood collaborative filtering (NBCF), context-aware matrix factorization (CAMF) coupled contextual dependency with the matrix factorization technique (MF), and tensor factorization directly models contexts as additional dimensions in the multi-dimensional space, etc. CAMF works well but it is difficult to interpret the latent features in the algorithm. DCM is good for explanation but it may only work well on data sets with dense contextual ratings. Recently, we successfully incorporate contexts into Sparse LInear Method (SLIM) and develop contextual SLIM (CSLIM) recommendation algorithms which take advantages of both NBCF and MF. CSLIM are demonstrated as more effective and promising context-aware recommenders. In this work, we provide the introduction on the framework of the CSLIM algorithms, present the current state of the research, and discuss our ongoing future work to develop and improve our CSLIM models for context-aware recommendations.


title: Techno-stress: a prospective psychophysiological study of the impact of a controlled stress-reduction program in advanced telecommunication systems design work.

abstract: There is a void of studies concerning occupational health aspects from working with the most advanced forms of information technologies techniques such as are found in some of the world-renowned telecommunication systems development laboratories. However, many of these techniques will later be applied in the regular office environment. We wanted to identify some of the major stressors perceived by advanced telecommunication systems design employees and develop a valid and reliable instrument by which to monitor such stressors. We were also interested in assessing the impact of a controlled prospective stress-reduction program on perceived mental stress and specific psychophysiological parameters. A total of 116 employees were recruited. Sixty-one were offered to participate in one of three stress-reduction training programs (intervention group). The additional 50 functioned as a reference group. After a detailed baseline assessment, including a comprehensive questionnaire and psychophysiological measurements, new assessments were made at the end of the formal training program (+ 3 months) and after an additional 5-month period. Results reveal a significant improvement in the intervention group with regard to circulating levels of the stress-sensitive hormone prolactin as well as an attenuation in mental strain. Cardiovascular risk indicators were also improved. Circulating thrombocytes decreased in the intervention group. Type of stress-reduction programs chosen and intensity of participation did not significantly impact results. Coping style was not affected and no beneficial effects were observed with regard to the psychological characteristics of the work, eg intellectual discretion and control over work processes. The survey instrument is now being used in the continuous improvement of work processes and strategic leadership of occupational health issues. The results suggest that prior psychophysiological stress research, based on low- and medium-skill, rather repetitive kinds of jobs, may not be completely applicable to that of skilled professionals. The results indicate that cutting-edge telecommunication systems design employees suffer from significant amounts of mental strain. In addition, there are limited but measurable benefits from a stress-reduction program. More attention should be directed as to how to design healthy and productive work environments in workplaces using advanced information technologies.


title: Application of Arabic language processing in language learning

abstract: Natural Language Processing (NLP) provides several solutions that can refine the language learning. Nowadays, Arabic Language Processing (ALP) has become the focus of many researchers and developers. Further, the amount of ALP tools and applications has grown significantly over the last years. The integration of these tools will result in positive outcomes in interactive programs for learning the Arabic language. The purpose of this work is to base on effective tools for providing assistance in language learning system. This paper highlights how ALP tools can be used to enhance the process of language education. Further, it describes some of ALP tools to present propositions that could guide current and future practitioners in the field of Arabic language learning. Finally, we shed the light on an ongoing project of developing an effective interactive system for Arabic learning.


title: Spaces of well-being among young adults with physical disabilities transitioning from pediatric to adult healthcare

abstract: Abstract Background Youth with disabilities are at risk of poor health outcomes as they transition to adult healthcare. Although space and place play an important role in accessing healthcare little is known about the spatial aspects of youth's transition from pediatric to adult healthcare. Objective To understand the spaces of well-being as youth with physical disabilities transition from pediatric to adult healthcare. Methods This study draws on a qualitative design involving 63 in-depth interviews with young adults (n = 22), parents (n = 17), and clinicians (n = 24) involved in preparing young adults for transition. All participants were recruited from a pediatric rehabilitation hospital within a metropolitan area of Ontario, Canada. Data were analyzed using an inductive content analysis approach that was informed by the spaces of well-being framework. Results The results highlight that within the ‘spaces of capability’ those with more disability-related complications and/or those using a mobility device encountered challenges in their transition to adult care. The ‘spaces of security’ influencing youth's well-being during their transition included: temporary (in)security while they were away at college, and health (in)security. Most of the focus on youth's transition included ‘integrative spaces’, which can enhance or hinder their well-being. Such spaces included: spatial (dis)connections (distance to access care), embeddedness (family and community), physical access, and distance. Meanwhile, therapeutic spaces involved having spaces that youth were satisfied with and enhanced their well-being as they transitioned to adult care. Conclusions In applying the spaces of well-being framework, the findings showed that youth had varied experiences regarding spaces of capability, security, integrative, and therapeutic spaces.


title: Advances and Applications of Single-Cell Sequencing Technologies

abstract: Single-cell sequencing (SCS) has emerged as a powerful new set of technologies for studying rare cells and delineating complex populations. Over the past 5 years, SCS methods for DNA and RNA have had a broad impact on many diverse fields of biology, including microbiology, neurobiology, development, tissue mosaicism, immunology, and cancer research. In this review, we will discuss SCS technologies and applications, as well as translational applications in the clinic.


Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks


title: A (2004). A decade of system justification theory: Accumulated evidence of conscious and unconscious bolstering of the status quo

abstract: Most theories in social and political psychology stress self-interest, intergroup conflict, ethnocentrism, homophily, ingroup bias, outgroup antipathy, dominance, and resistance. System justification theory is influenced by these perspectives—including social identity and social dominance theories—but it departs from them in several respects. Advocates of system justification theory argue that (a) there is a general ideological motive to justify the existing social order, (b) this motive is at least partially responsible for the internalization of inferiority among members of disadvantaged groups, (c) it is observed most readily at an implicit, nonconscious level of awareness and (d) paradoxically, it is sometimes strongest among those who are most harmed by the status quo. This article reviews and integrates 10 years of research on 20 hypotheses derived from a system justification perspective, focusing on the phenomenon of implicit outgroup favoritism among members of disadvantaged groups (including African Americans, the elderly, and gays/lesbians) and its relation to political ideology (especially liberalism-conservatism).


title: Here, there, everywhere: Correlated online behaviors can lead to overestimates of the effects of advertising

abstract: Measuring the causal effects of online advertising (adfx) on user behavior is important to the health of the WWW publishing industry. In this paper, using three controlled experiments, we show that observational data frequently lead to incorrect estimates of adfx. The reason, which we label "activity bias," comes from the surprising amount of time-based correlation between the myriad activities that users undertake online. In Experiment 1, users who are exposed to an ad on a given day are much more likely to engage in brand-relevant search queries as compared to their recent history for reasons that had nothing do with the advertisement. In Experiment 2, we show that activity bias occurs for page views across diverse websites. In Experiment 3, we track account sign-ups at a competitor's (of the advertiser) website and find that many more people sign-up on the day they saw an advertisement than on other days, but that the true "competitive effect" was minimal. In all three experiments, exposure to a campaign signals doing "more of everything" in given period of time, making it difficult to find a suitable "matched control" using prior behavior. In such cases, the "match" is fundamentally different from the exposed group, and we show how and why observational methods lead to a massive overestimate of adfx in such circumstances.


title: IoT Security: An End-to-End View and Case Study

abstract: In this paper, we present an end-to-end view of IoT security and privacy and a case study. Our contribution is three-fold. First, we present our end-to-end view of an IoT system and this view can guide risk assessment and design of an IoT system. We identify 10 basic IoT functionalities that are related to security and privacy. Based on this view, we systematically present security and privacy requirements in terms of IoT system, software, networking and big data analytics in the cloud. Second, using the end-to-end view of IoT security and privacy, we present a vulnerability analysis of the Edimax IP camera system. We are the first to exploit this system and have identified various attacks that can fully control all the cameras from the manufacturer. Our real-world experiments demonstrate the effectiveness of the discovered attacks and raise the alarms again for the IoT manufacturers. Third, such vulnerabilities found in the exploit of Edimax cameras and our previous exploit of Edimax smartplugs can lead to another wave of Mirai attacks, which can be either botnets or worm attacks. To systematically understand the damage of the Mirai malware, we model propagation of the Mirai and use the simulations to validate the modeling. The work in this paper raises the alarm again for the IoT device manufacturers to better secure their products in order to prevent malware attacks like Mirai.


title: Using Wavefront Tracing for the Visualization and Optimization of Progressive Lenses

abstract: Progressive addition lenses are a relatively new approach to compensate for defects of the human visual system. While traditional spectacles use rotationally symmetric lenses, progressive lenses require the specification of free-form surfaces. This poses difficult problems for the optimal design and its visual evaluation. ::: ::: This paper presents two new techniques for the visualization of optical systems and the optimization of progressive lenses. Both are based on the same wavefront tracing approach to accurately evaluate the refraction properties of complex optical systems. ::: ::: We use the results of wavefront tracing for continuously re-focusing the eye during rendering. Together with distribution ray tracing, this yields high-quality images that accurately simulate the visual quality of an optical system. The design of progressive lenses is difficult due to the trade-off between the desired properties of the lens and unavoidable optical errors, such as astigmatism and distortions. We use wavefront tracing to derive an accurate error functional describing the desired properties and the optical error across a lens. Minimizing this error yields optimal free-form lens surfaces. ::: ::: While the basic approach is much more general, in this paper, we describe its application to the particular problem of designing and evaluating progressive lenses and demonstrate the benefits of the new approach with several example images.


title: Vaccine hesitancy, vaccine refusal and the anti-vaccine movement: influence, impact and implications

abstract: Despite being recognized as one of the most successful public health measures, vaccination is perceived as unsafe and unnecessary by a growing number of parents. Anti-vaccination movements have been implicated in lowered vaccine acceptance rates and in the increase in vaccine-preventable disease outbreaks and epidemics. In this review, we will look at determinants of parental decision-making about vaccination and provide an overview of the history of anti-vaccination movements and its clinical impact.


jModelTest 2: more models, new heuristics and parallel computing


title: CTC Network with Statistical Language Modeling for Action Sequence Recognition in Videos

abstract: We propose a method for recognizing an action sequence in which several actions are concatenated and their boundaries are not given. The proposed method combines Connectionist Temporal Classification (CTC) and a statistical language model. CTC can learn the nature of each element action given no boundary information in an end-to-end manner. The statistical language model can learn the relationship between actions. We evaluate our method on the Breakfast dataset. When we use the trigram as the language model, its accuracy rate is 43.4%, which is better than the state-of-the-art ECTC method by 6.7 percentage points.


title: Dynamic RPL for multi-hop routing in IoT applications

abstract: The Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard routing protocol for the Internet of Things (IoT). This paper investigates the use of RPL in dynamic networks and presents an enhanced RPL for different applications with dynamic mobility and diverse network requirements. This implementation of RPL is designed with a new dynamic Objective-Function (D-OF) to improve the Packet Delivery Ratio (PDR), end-to-end delay and energy consumption while maintaining low packet overhead and loop-avoidance. We propose a controlled reverse-trickle timer based on received signal strength identification (RSSI) readings to maintain high responsiveness with minimum overhead and consult the objective function when a movement or an inconsistency is detected to help nodes make an informed decision. Simulations are done using Cooja with random waypoint mobility scenario for healthcare applications considering multi-hop routing. The results show that the proposed dynamic RPL (D-RPL) adapts to the nodes mobility and has a higher PDR, slightly lower end-to-end delay and reasonable energy consumption compared to related existing protocols.


title: Homeologous proteins synthesis controlled by homeologous chromosomes in wheat

abstract: Two homeologous proteins have been isolated from the endosperm of common wheat (genomes ABD). Synthesis of these two proteins is controlled by the homeologous chromosomes 7B and 7D respectively. However, Aegilops speltoides, the more generally accepted B genome donor, does not synthesize the 7B protein.


title: Replication of hepatitis B virus in a hepatocellular carcinoma.

abstract: Abstract Hepatitis B virus transcripts and DNA from paired samples of neoplastic and nonneoplastic liver tissue of HBsAg seropositive patients were analyzed. The data obtained support the view that transcription of integrated DNA is frequent, both in neoplastic as well as in nonneoplastic liver tissue. In the case of one patient, integrated and free forms of hepatitis B virus DNA were detected in the tumor. Complete cycles of viral replication in this tumor were suggested by the following markers: (i) DNA and RNA intermediates expected to occur during replication of the viral genome, (ii) HBcAg and HBsAg, (iii) core and Dane particles. Viral DNA cloned from tumor tissue was proven to be replication competent in a transient replication assay. Five independent clones of viral DNA were established and found to be closely related at the nucleotide level. A preX open reading frame and a stop codon within preC were common features. In tissue surrounding the tumor, a nonreplicative state of virus infection prevailed, characterized by free viral DNA exclusively of the covalently closed, circular form. The replication of the viral DNA appeared to be blocked at the level of transcription.


title: Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification

abstract: The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.


title: Serious games for movement therapy after stroke

abstract: This paper details the development and testing of a serious-game based movement therapy aimed at encouraging stroke patients with upper limb motor disorders to practice physical exercises. The system contains a series of Virtual Reality (VR) games. A framework for VR movement therapy is described which consists of a number of serious games designed to encourage patients' physical activity in highly motivating, virtual environments where various factors such as size and gravity can be scaled to adapt to individual patient's abilities and in-game performance. Another goal of this study is to determine whether the provision of serious games based interventions improves motor outcome after stroke. A pilot study with 8 participants who have a first hemispheric stroke shows improvements on impairment measurement and functional measurement shortly after completion of the intervention and 6 weeks after the intervention. Despite its limitations the findings of this study support the effectiveness of serious games in the treatment of participants with hemiplegia. The study also raises awareness of the benefits of using serious games in movement therapy after stroke.


title: Deep learning of the tissue-regulated splicing code

abstract: Motivation: Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS. Methods: Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters. Results: We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented. Contact: frey@psi.toronto.edu Supplementary information: Supplementary data are available at Bioinformatics online.


title: A Systematic Identification and Analysis of Scientists on Twitter

abstract: Metrics derived from Twitter and other social media—often referred to as altmetrics—are increasingly used to estimate the broader social impacts of scholarship. Such efforts, however, may produce highly misleading results, as the entities that participate in conversations about science on these platforms are largely unknown. For instance, if altmetric activities are generated mainly by scientists, does it really capture broader social impacts of science? Here we present a systematic approach to identifying and analyzing scientists on Twitter. Our method can identify scientists across many disciplines, without relying on external bibliographic data, and be easily adapted to identify other stakeholder groups in science. We investigate the demographics, sharing behaviors, and interconnectivity of the identified scientists. We find that Twitter has been employed by scholars across the disciplinary spectrum, with an over-representation of social and computer and information scientists; under-representation of mathematical, physical, and life scientists; and a better representation of women compared to scholarly publishing. Analysis of the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a small fraction of shared URLs are science-related. We find an assortative mixing with respect to disciplines in the networks between scientists, suggesting the maintenance of disciplinary walls in social media. Our work contributes to the literature both methodologically and conceptually—we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists, thus providing foundational information for the construction and use of indicators on the basis of social media metrics.


title: Code bubbles: a working set-based interface for code understanding and maintenance

abstract: Developers spend significant time reading and navigating code fragments spread across multiple locations. The file-based nature of contemporary IDEs makes it prohibitively difficult to create and maintain a simultaneous view of such fragments. We propose a novel user interface metaphor for code understanding based on collections of lightweight, editable fragments called bubbles, which form concurrently visible working sets. We present the results of a qualitative usability evaluation, and the results of a quantitative study which indicates Code Bubbles significantly improved code understanding time, while reducing navigation interactions over a widely-used IDE, for two controlled tasks.


title: Versatile rigid-fluid coupling for incompressible SPH

abstract: We propose a momentum-conserving two-way coupling method of SPH fluids and arbitrary rigid objects based on hydrodynamic forces. Our approach samples the surface of rigid bodies with boundary particles that interact with the fluid, preventing deficiency issues and both spatial and temporal discontinuities. The problem of inhomogeneous boundary sampling is addressed by considering the relative contribution of a boundary particle to a physical quantity. This facilitates not only the initialization process but also allows the simulation of multiple dynamic objects. Thin structures consisting of only one layer or one line of boundary particles, and also non-manifold geometries can be handled without any additional treatment. We have integrated our approach into WCSPH and PCISPH, and demonstrate its stability and flexibility with several scenarios including multiphase flow.


title: Vectorization for SIMD architectures with alignment constraints

abstract: When vectorizing for SIMD architectures that are commonly employed by today's multimedia extensions, one of the new challenges that arise is the handling of memory alignment. Prior research has focused primarily on vectorizing loops where all memory references are properly aligned. An important aspect of this problem, namely, how to vectorize misaligned memory references, still remains unaddressed.This paper presents a compilation scheme that systematically vectorizes loops in the presence of misaligned memory references. The core of our technique is to automatically reorganize data in registers to satisfy the alignment requirement imposed by the hardware. To reduce the data reorganization overhead, we propose several techniques to minimize the number of data reorganization operations generated. During the code generation, our algorithm also exploits temporal reuse when aligning references that access contiguous memory across loop iterations. Our code generation scheme guarantees to never load the same data associated with a single static access twice. Experimental results indicate near peak speedup factors, e.g., 3.71 for 4 data per vector and 6.06 for 8 data per vector, respectively, for a set of loops where 75% or more of the static memory references are misaligned.


title: Music emotion recognition: the role of individuality

abstract: It has been realized in the music emotion recognition (MER) community that personal difference, or individuality, has significant impact on the success of an MER system in practice. However, no previous work has explicitly taken individuality into consideration in an MER system. In this paper, the group-wise MER approach (GWMER) and personalized MER approach (PMER) are proposed to study the role of individuality. GWMER evaluates the importance of each individual factor such as sex, personality, and music experience, whereas PMER evaluates whether the prediction accuracy for a user is significantly improved if the MER system is personalized for the user. Experimental results demonstrate the effect of personalization and suggest the need for a better representation of individuality and for better prediction accuracy.


title: Transformation of SBVR business design to UML models

abstract: This paper presents a methodology for transforming business designs written in OMG's standard Semantics of Business Vocabulary and Rules (SBVR) framework, into a set of UML models. It involves the transformation of business vocabulary and rules written in SBVR's "Structured English" into a set of UML diagrams, which includes Activity Diagram(AD), Sequence Diagram(SD), and Class Diagram(CD). This transformation works by detecting the distinction between rules which will participate in the construction of Activity Diagram and rules which do not. These rules are imperative in nature. The work in the paper also includes the detection of activities embedded implicitly in those rules and establishment of sequence between those activities. These activities incur some action. We also detect their owner and refer to them as the doer of the action. This plays a very important role in the development of Class Diagrams


title: Association of Bronchiolitis Clinical Pathway Adherence With Length of Stay and Costs

abstract: OBJECTIVES: To examine the associations between the level of adherence to bronchiolitis clinical pathway recommendations, health care use, and costs. METHODS: We conducted a retrospective cohort study of 267 patients ≤24 months old diagnosed with bronchiolitis from 12/2009 to 7/2012. Clinical pathway adherence was assessed by using a standardized scoring system (0–100) for 18 quality measures obtained by medical record review. Level of adherence was categorized into low, middle, and high tertiles. Generalized linear models were used to examine relationships between adherence tertile and (1) length of stay (LOS) and (2) costs. Logistic regression was used to examine the associations between adherence tertile and probability of inpatient admission and 7-day readmissions. RESULTS: Mean adherence scores were: ED, 78.8 (SD, 18.1; n = 264), inpatient, 95.0 (SD, 6.3; n = 216), and combined ED/inpatient, 89.1 (SD, 8.1; n = 213). LOS was significantly shorter for cases in the highest versus the lowest adherence tertile (ED, 90 vs 140 minutes, adjusted difference, –51 [95% confidence interval (CI), –73 to –29; P P P P P CONCLUSIONS: High adherence to bronchiolitis clinical pathway recommendations across care settings was associated with shorter LOS and lower cost.


title: A Novel Ultrawideband (UWB) Printed Antenna With a Dual Complementary Characteristic

abstract: A novel design concept for a versatile multi-functional coplanar waveguide (CPW)-fed ultrawideband (UWB) printed antenna is described in this letter. The antenna also exhibits a dual complementary characteristic of frequency notched response and narrowband response invoked by different combinative loading of a pair of split ring resonators (SRRs) and metallic shunt strips on the CPW feed line. The antenna restores its UWB response when the SRRs’ split gaps are shorted. The simulated impedance and radiation characteristics are validated with measured results obtained using fabricated prototypes to establish the proof of concept.


title: Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation

abstract: As more industries integrate machine learning into socially sensitive decision processes like hiring, loan-approval, and parole-granting, we are at risk of perpetuating historical and contemporary socioeconomic disparities. This is a critical problem because on the one hand, organizations who use but do not understand the discriminatory potential of such systems will facilitate the widening of social disparities under the assumption that algorithms are categorically objective. On the other hand, the responsible use of machine learning can help us measure, understand, and mitigate the implicit historical biases in socially sensitive data by expressing implicit decision-making mental models in terms of explicit statistical models. In this paper we specify, implement, and evaluate a "fairness-aware" machine learning interface called themis-ml, which is intended for use by individual data scientists and engineers, academic research teams, or larger product teams who use machine learning in production systems.


title: Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor

abstract: Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verification. LNPS has interesting properties, such as scale invariance and rotation invariance after linear combination, and shows promising results in online signature verification. Experiments on the publicly available SVC-2004 dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).


title: Learning Action Maps of Large Environments via First-Person Vision

abstract: When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes where people have behaved, as well as novel scenes where no behaviors are observed. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe human activities, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our proposed mathematical framework allows for the prediction of Action Maps in new environments. Additionally, we offer a preliminary glance of the applicability of Action Maps by demonstrating a proof-of concept application in which they are used in concert with activity detections to perform localization.


title: A survey of testing as a service in cloud computing

abstract: Cloud computing simply referred as cloud is a pay per use technology. To be simple a cloud computing by using resource allocation algorithms will allocate resource to the user as per his/her request and the user will pay only for what they use similar to electricity. There are several available services in cloud like SaaS, IaaS, PaaS, TaaS etc. We are going to concentrate on TaaS (Testing as a Service). So what is cloud testing? Cloud testing is a type of software testing in which cloud computing is used to generate real world user traffic for web based applications. In this paper we would mentioning the types, steps and forms of Cloud Testing. We would then consider two types of testing namely Security Testing and Performance Testing. In this paper we would also be also concentrating on Data Integrity, Data confidentiality and Data availability since they form the core of the Security testing and Performance testing.


A Theoretical Framework for Structured Prediction using Factor Graph Complexity ∗


The elusive weapons effect: demand awareness, evaluation apprehension, and slightly sophisticated subjects.


title: Fuzzy clustering of time series data using dynamic time warping distance

abstract: Abstract Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the data becomes necessary and subsequently it exhibits a significant impact on the results of clustering. This selection should be based upon the nature of time series and the application itself. When grouping time series based on their shape information is of interest (shape-based clustering), using a Dynamic Time Warping (DTW) distance is a desirable choice. Using stretching or compressing segments of temporal data, DTW determines an optimal match between any two time series. In this way, time series exhibiting similar patterns occurring at different time periods, are considered as being similar. Although DTW is a suitable choice for comparing data with respect to their shape information, calculating the average of a collection of time series (which is required in clustering methods) based on this distance becomes a challenging problem. As the result, employing clustering techniques like K-Means and Fuzzy C-Means (where the cluster centers – prototypes are calculated through averaging the data) along with the DTW distance is a challenging task and may produce unsatisfactory results. In this study, three alternatives for fuzzy clustering of time series using DTW distance are proposed. In the first method, a DTW-based averaging technique proposed in the literature, has been applied to the Fuzzy C-Means clustering. The second method considers a Fuzzy C-Medoids clustering, while the third alternative comes as a hybrid technique, which exploits the advantages of both the Fuzzy C-Means and Fuzzy C-Medoids when clustering time series. Experimental studies are reported over a set of time series coming from the UCR time series database.


title: Evolutionary reinforcement learning in FX order book and order flow analysis

abstract: As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.


title: Antibiotics and Bacterial Resistance in the 21st Century

abstract: Dangerous, antibiotic resistant bacteria have been observed with increasing frequency over the past several decades. In this review the factors that have been linked to this phenomenon are addressed. Profiles of bacterial species that are deemed to be particularly concerning at the present time are illustrated. Factors including economic impact, intrinsic and acquired drug resistance, morbidity and mortality rates, and means of infection are taken into account. Synchronously with the waxing of bacterial resistance there has been waning antibiotic development. The approaches that scientists are employing in the pursuit of new antibacterial agents are briefly described. The standings of established antibiotic classes as well as potentially emerging classes are assessed with an emphasis on molecules that have been clinically approved or are in advanced stages of development. Historical perspectives, mechanisms of action and resistance, spectrum of activity, and preeminent members of each class are discussed.


title: Atomically Trading with Roger: Gambling on the Success of a Hardfork

abstract: We present atomic trade protocols for Bitcoin and Ethereum that can bind two parties to swap coins in the event that two blockchains emerge from a single “pre-fork” blockchain. This work is motivated by a bet between two members of the Bitcoin community, Loaded and Roger Ver, to trade 60,000 bitcoins in the event that Bitcoin Unlimited’s planned hardfork occurs and the blockchain splits into two distinct forks. Additionally we study several ways to provide replay protection in the event of hardfork alongside a novel mechanism called migration inputs. We provide a detailed survey and history of previous softforks and hardforks in Ethereum and Bitcoin.


title: Design of a low cost cavity backed wideband bow-tie antenna for ground penetrating radar systems

abstract: The ground penetrating radar (GPR) designs demand antennas with ultra wide bandwidths (UWB). Thus the conventional antennas such as dipoles cannot be used effectively. The GPR antennas are expensive. In this paper, the feasibility of building a low cost ultra-wide band bow-tie antenna for GPR using widely available low cost materials such as FR-4 PCB substrates, low cost TV baluns and scrap iron is explored. The objective was to develop the antenna to radiate in the range from 200 MHz to 600 MHz whilst restricting the antenna lobes to the direction of the ground. A cavity backed design is considered to obtain these requirements. The antenna was modeled using Ansoft HFSS (presently Ansys HFSS) software package which is widely used in current industrial domain. The simulations were carried out for different antenna configurations by changing its parameters according to linear programming techniques. Further, electromagnetic interferences (EMI) have been successfully reduced by integrating a metal cavity which acts as a reflector to the antenna. Parameters related to the cavity were tweaked in order to obtain the optimum frequency response. Considering the simulation results the most satisfactory antenna configuration was obtained and implemented.


title: Limitations of Voltage-Oriented PI Current Control of Grid-Connected PWM Rectifiers With $LCL$ Filters

abstract: Voltage-oriented PI control of three-phase grid-connected pulsewidth-modulation rectifiers with LCL filters is addressed. LCL filters require resonance damping. Active resonance damping is state of the art to face the problem, but it is still under investigation because of the manifold solutions. It is often realized using many sensors and/or complex control algorithms. In contrast, pure PI control requires only one set of current sensors, and its implementation and design are rather simple and well known from the L filter control. PI control has already been shown to be a suitable solution also for LCL filters, but there are limitations. These are investigated in this paper. System stability is analyzed with respect to different ratios of LCL filter resonance and control frequencies. The latter are important parameters for system design and control. Both line and converter current control are analyzed. For a certain range of frequency ratios, the voltage-oriented PI control gives stable performance without additional feedback, but for ratios outside this range, stable operation is impossible. Experimental tests validate the theoretical results. In addition, an experimentally determined LCL filter transfer function is shown in this paper, which shows a lower resonance peak as expected from commonly used filter models.


title: High efficiency, high switching speed, AlGaAs/GaAs P-HEMT DC---DC converter for integrated power amplifier modules

abstract: This paper presents a high efficiency, high switching frequency DC---DC buck converter in AlGaAs/GaAs technology, targeting integrated power amplifier modules for wireless communications. The switch mode, inductor load DC---DC converter adopts an interleaved structure with negatively coupled inductors. Analysis of the effect of negative coupling on the steady state and transient response of the converter is given. The coupling factor is selected to achieve a maximum power efficiency under a given duty cycle with a minimum penalty on the current ripple performance. The DC---DC converter is implemented in 0.5 μm GaAs p-HEMT process and occupies 2 × 2.1 mm2 without the output network. An 8.7 nH filter inductor is implemented in 65 μm thick top copper metal layer, and flip chip bonded to the DC---DC converter board. The integrated inductor achieves a quality factor of 26 at 150 MHz. The proposed converter converts 4.5 V input to 3.3 V output for 1 A load current under 150 MHz switching frequency with a measured power efficiency of 84%, which is one of the highest efficiencies reported to date for similar current/voltage ratings.


title: Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval

abstract: Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research.


title: Forecasting Cyber Attacks with Imbalanced Data Sets and Different Time Granularities

abstract: If cyber incidents are predicted a reasonable amount of time before they occur, defensive actions to prevent their destructive effects could be planned. Unfortunately, most of the time we do not have enough observables of the malicious activities before they are already under way. Therefore, this work suggests to use unconventional signals extracted from various data sources with different time granularities to predict cyber incidents for target entities. A Bayesian network is used to predict cyber attacks where the unconventional signals are used as indicative random variables. This work also develops a novel minority class over sampling technique to improve cyber attack prediction on imbalanced data sets. The results show that depending on the selected time granularity, the unconventional signals are able to predict cyber attacks for the anonimyzed target organization even though the signals are not explicitly related to that organization. Furthermore, the minority over sampling approach developed achieves better performance compared to the existing filtering techniques in the literature.


title: Automatic Labeling of Topic Models Using Text Summaries

abstract: Labeling topics learned by topic models is a challenging problem. Previous studies have used words, phrases and images to label topics. In this paper, we propose to use text summaries for topic labeling. Several sentences are extracted from the most related documents to form the summary for each topic. In order to obtain summaries with both high relevance, coverage and discrimination for all the topics, we propose an algorithm based on submodular optimization. Both automatic and manual analysis have been conducted on two real document collections, and we find 1) the summaries extracted by our proposed algorithm are superior over the summaries extracted by existing popular summarization methods; 2) the use of summaries as labels has obvious advantages over the use of words and phrases.


title: QARLA: A Framework For The Evaluation Of Text Summarization Systems

abstract: This paper presents a probabilistic framework, QARLA, for the evaluation of text summarisation systems. The input of the framework is a set of manual (reference) summaries, a set of baseline (automatic) summaries and a set of similarity metrics between summaries. It provides i) a measure to evaluate the quality of any set of similarity metrics, ii) a measure to evaluate the quality of a summary using an optimal set of similarity metrics, and iii) a measure to evaluate whether the set of baseline summaries is reliable or may produce biased results.Compared to previous approaches, our framework is able to combine different metrics and evaluate the quality of a set of metrics without any a-priori weighting of their relative importance. We provide quantitative evidence about the effectiveness of the approach to improve the automatic evaluation of text summarisation systems by combining several similarity metrics.


title: A Unified Artificial Neural Network Architecture for Active Power Filters

abstract: In this paper, an efficient and reliable neural active power filter (APF) to estimate and compensate for harmonic distortions from an AC line is proposed. The proposed filter is completely based on Adaline neural networks which are organized in different independent blocks. We introduce a neural method based on Adalines for the online extraction of the voltage components to recover a balanced and equilibrated voltage system, and three different methods for harmonic filtering. These three methods efficiently separate the fundamental harmonic from the distortion harmonics of the measured currents. According to either the Instantaneous Power Theory or to the Fourier series analysis of the currents, each of these methods are based on a specific decomposition. The original decomposition of the currents or of the powers then allows defining the architecture and the inputs of Adaline neural networks. Different learning schemes are then used to control the inverter to inject elaborated reference currents in the power system. Results obtained by simulation and their real-time validation in experiments are presented to compare the compensation methods. By their learning capabilities, artificial neural networks are able to take into account time-varying parameters, and thus appreciably improve the performance of traditional compensating methods. The effectiveness of the algorithms is demonstrated in their application to harmonics compensation in power systems


title: Visual-inertial direct SLAM

abstract: The so-called direct visual SLAM methods have shown a great potential in estimating a semidense or fully dense reconstruction of the scene, in contrast to the sparse reconstructions of the traditional feature-based algorithms. In this paper, we propose for the first time a direct, tightly-coupled formulation for the combination of visual and inertial data. Our algorithm runs in real-time on a standard CPU. The processing is split in three threads. The first thread runs at frame rate and estimates the camera motion by a joint non-linear optimization from visual and inertial data given a semidense map. The second one creates a semidense map of high-gradient areas only for camera tracking purposes. Finally, the third thread estimates a fully dense reconstruction of the scene at a lower frame rate. We have evaluated our algorithm in several real sequences with ground truth trajectory data, showing a state-of-the-art performance.


title: The Influence of Soft Tissue Thickness on Crestal Bone Changes Around Implants: A 1-Year Prospective Controlled Clinical Trial

abstract: Purpose: The aim of this clinical trial was to evaluate the influence of gingival tissue thickness on crestal bone loss around dental implants after a 1-year follow-up. Materials and Methods: Forty-six implants (23 test and 23 control) were placed in 19 patients. The test implants were placed about 2 mm supracrestally, whereas the control implants were positioned at the bone level. Before implant placement, the tissue thickness at implant sites was measured with a periodontal probe. After healing, metal-ceramic cement-retained prostheses were constructed. According to tissue thickness, the test implants were divided into A (thin) and B (thick) groups. Intraoral radiographs were performed and crestal bone changes were measured at implant placement and after 1 year. Results: Mean bone loss around the test implants in group A (thin mucosa) was 1.61 ± 0.24 mm (SE; range, 0.9 to 3.3 mm) on the mesial and 1.28 ± 0.167 mm (range, 0.8 to 2.1 mm) on the distal. Mean bone loss in test group B (thick mucosa) implants was 0.26 ± 0.08 mm (range, 0.2 to 0.9 mm) on the mesial aspect and 0.09 ± 0.05 mm (range, 0.2 to 0.6 mm) on the distal aspect. Mean bone loss around control implants was 1.8 ± 0.164 mm (range, 0.6 to 4.0 mm) and 1.87 ± 0.166 mm (range, 0.0 to 4.1 mm) on the mesial and distal aspects, respectively. Analysis of variance revealed a significant difference in terms of bone loss between test A (thin) and B (thick) groups on both the mesial and the distal. Conclusion: Initial gingival tissue thickness at the crest may be considered as a significant influence on marginal bone stability around implants. If the tissue thickness is 2.0 mm or less, crestal bone loss up to 1.45 mm may occur, despite a supracrestal position of the implant-abutment interface. INT J ORAL MAXILLOFAC IMPLANTS 2009;24:712–719


title: Survey on Evaluation of Student's Performance in Educational Data Mining

abstract: Educational information mining is rising field that spotlights on breaking down educational information to create models for enhancing learning encounters and enhancing institutional viability. Expanding enthusiasm for information mining and educational frameworks, make educational information mining as another developing exploration group. Educational Data Mining intends to remove the concealed learning from expansive Educational databases with the utilization of procedures and apparatuses. Educational Data Mining grows new techniques to find information from Educational database and it is utilized for basic decision making in Educational framework. The knowledge is hidden among the Educational informational Sets and it is extractable through data mining techniques. It is essential to think about and dissect Educational information particularly understudies execution. Educational Data Mining (EDM) is the field of study relates about mining Educational information to discover intriguing examples and learning in Educational associations. This investigation is similarly worried about this subject, particularly, the understudies execution. This study investigates numerous components theoretically expected to influence student's performance in higher education, and finds a subjective model which best classifies and predicts the student's performance in light of related individual and phenomenal elements.


title: Stakeholders in Explainable AI

abstract: There is general consensus that it is important for artificial ::: intelligence (AI) and machine learning systems to be explainable ::: and/or interpretable. However, there is no general ::: consensus over what is meant by ‘explainable’ and ‘interpretable’. ::: In this paper, we argue that this lack of consensus ::: is due to there being several distinct stakeholder communities. ::: We note that, while the concerns of the individual ::: communities are broadly compatible, they are not identical, ::: which gives rise to different intents and requirements for explainability/ ::: interpretability. We use the software engineering ::: distinction between validation and verification, and the epistemological ::: distinctions between knowns/unknowns, to tease ::: apart the concerns of the stakeholder communities and highlight ::: the areas where their foci overlap or diverge. It is not ::: the purpose of the authors of this paper to ‘take sides’ — we ::: count ourselves as members, to varying degrees, of multiple ::: communities — but rather to help disambiguate what stakeholders ::: mean when they ask ‘Why?’ of an AI.


Mapping the Paraphrase Database to WordNet


title: Declarative Probabilistic Programming with Datalog

abstract: Probabilistic programming languages are used for developing statistical models. They typically consist of two components: a specification of a stochastic process (the prior) and a specification of observations that restrict the probability space to a conditional subspace (the posterior). Use cases of such formalisms include the development of algorithms in machine learning and artificial intelligence. In this article, we establish a probabilistic-programming extension of Datalog that, on the one hand, allows for defining a rich family of statistical models, and on the other hand retains the fundamental properties of declarativity. Our proposed extension provides mechanisms to include common numerical probability functions; in particular, conclusions of rules may contain values drawn from such functions. The semantics of a program is a probability distribution over the possible outcomes of the input database with respect to the program. Observations are naturally incorporated by means of integrity constraints over the extensional and intensional relations. The resulting semantics is robust under different chases and invariant to rewritings that preserve logical equivalence.


title: Surveillance Video Processing Using Compressive Sensing

abstract: A compressive sensing method combined with decomposition of a matrix formed with image frames of a surveillance video into low rank and sparse matrices is proposed to segment the background and extract moving objects in a surveillance video. The video is acquired by compressive measurements, and the measurements are used to reconstruct the video by a low rank and sparse decomposition of matrix. The low rank component represents the background, and the sparse component is used to identify moving objects in the surveillance video. The decomposition is performed by an augmented Lagrangian alternating direction method. Experiments are carried out to demonstrate that moving objects can be reliably extracted with a small amount of measurements.


title: Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions

abstract: We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, inter-person variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.


title: Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests

abstract: Mass detection from mammograms plays a crucial role as a pre- processing stage for mass segmentation and classification. The detection of masses from mammograms is considered to be a challenging problem due to their large variation in shape, size, boundary and texture and also because of their low signal to noise ratio compared to the surrounding breast tissue. In this paper, we present a novel approach for detecting masses in mammograms using a cascade of deep learning and random forest classifiers. The first stage classifier consists of a multi-scale deep belief network that selects suspicious regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis are then processed by a two-level cascade of random forest classifiers that use morphological and texture features extracted from regions selected along the cascade. Finally, regions that survive the cascade of random forest classifiers are combined using connected component analysis to produce state-of-the-art results. We also show that the proposed cascade of deep learning and random forest classifiers are effective in the reduction of false positive regions, while maintaining a high true positive detection rate. We tested our mass detection system on two publicly available datasets: DDSM-BCRP and INbreast. The final mass detection produced by our approach achieves the best results on these publicly available datasets with a true positive rate of 0.96 ± 0.03 at 1.2 false positive per image on INbreast and true positive rate of 0.75 at 4.8 false positive per image on DDSM-BCRP.


title: Real-time Traffic Sign Recognition system with deep convolutional neural network

abstract: TSR (Traffic Sign Recognition), a part of ADAS (Advanced Drive Assistance System), helps driver (or car) to recognize traffic signs ahead with using front camera. According to EURO NCAP rating policy, car should be able to warn the driver when the car's speed is above the set speed threshold. It is thought that various types of traffic sign should be recognized to get more detailed information of road. In this paper, 6 types of traffic sign images are trained by LeNet-5 convolutional neural network architecture. In the detection phase, light-weight color-based segmentation algorithm and Hough transform algorithm are applied to extract candidate regions of traffic signs. The recognition system nearly achieves real-time performance. On-line recognition test is performed on the KAIST campus road, and the result shows all 16 traffic signs are recognized successfully through the driving. The recognition system is implanted into autonomous vehicle ‘Eurecar’. Different types of traffic signs are trained consistently and development of clustering algorithm is considered as a future work for robust recognition system.


title: 3D Shape Matching via Two Layer Coding

abstract: View-based 3D shape retrieval is a popular branch in 3D shape analysis owing to the high discriminative property of 2D views. However, many previous works do not scale up to large 3D shape databases. We propose a two layer coding (TLC) framework to conduct shape matching much more efficiently. The first layer coding is applied to pairs of views represented as depth images. The spatial relationship of each view pair is captured with so-called eigen-angle, which is the planar angle between the two views measured at the center of the 3D shape. Prior to the second layer coding, the view pairs are divided into subsets according to their eigen-angles. Consequently, view pairs that differ significantly in their eigen-angles are encoded with different codewords, which implies that spatial arrangement of views is preserved in the second layer coding. The final feature vector of a 3D shape is the concatenation of all the encoded features from different subsets, which is used for efficient indexing directly. TLC is not limited to encode the local features from 2D views, but can be also applied to encoding 3D features. Exhaustive experimental results confirm that TLC achieves state-of-the-art performance in both retrieval accuracy and efficiency.


title: Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets

abstract: Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract specific material boundaries and convey subtle surface properties. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to interactively render with multi-dimensional transfer functions. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.


title: Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks

abstract: While neural machine translation (NMT) models provide improved translation quality in an elegant, end-to-end framework, it is less clear what they learn about language. Recent work has started evaluating the quality of vector representations learned by NMT models on morphological and syntactic tasks. In this paper, we investigate the representations learned at different layers of NMT encoders. We train NMT systems on parallel data and use the trained models to extract features for training a classifier on two tasks: part-of-speech and semantic tagging. We then measure the performance of the classifier as a proxy to the quality of the original NMT model for the given task. Our quantitative analysis yields interesting insights regarding representation learning in NMT models. For instance, we find that higher layers are better at learning semantics while lower layers tend to be better for part-of-speech tagging. We also observe little effect of the target language on source-side representations, especially with higher quality NMT models.


title: Emotion Classification of Chinese Microblog Text via Fusion of BoW and eVector Feature Representations

abstract: Sentiment Analysis has been a hot research topic in recent years. Emotion classification is more detailed sentiment analysis which cares about more than the polarity of sentiment. In this paper, we present our system of emotion analysis for the Sina Weibo texts on both the document and sentence level, which detects whether a text is sentimental and further decides which emotion classes it conveys. The emotions of focus are seven basic emotion classes: anger, disgust, fear, happiness, like, sadness and surprise. Our baseline system uses supervised machine learning classifier (support vector machine, SVM) based on bag-of-words (BoW) features. In a contrast system, we propose a novel approach to construct an emotion lexicon and to generate a new feature representation of text which is named emotion vector eVector. Our experimental results show that both systems can classify emotion significantly better than random guess. Fusion of both systems obtains additional gain which indicates that they capture certain complementary information.


Data Clustering: Algorithms and Applications


Theory of mind in children with cerebral palsy: The impact of limited expressive linguistic abilities


title: Fast and Robust Vanishing Point Detection for Unstructured Road Following

abstract: Vision-based unstructured road following is a challenging task due to the nature of the scene. This paper describes a novel algorithm to improve the accuracy and robustness of vanishing point estimation with very low computational cost. The novelties of this paper are three aspects: 1) We use joint activities of four Gabor filters and confidence measure for speeding up the process of texture orientation estimation. 2) Misidentification chances and computational complexity of the algorithm are reduced by using a particle filter. It limits vanishing point search range and reduces the number of pixels to be voted. The algorithm combines the peakedness measure of vote accumulator space with the displacements of moving average of observations to regulate the distribution of vanishing point candidates. 3) Attributed to the design of a noise-insensitive observation model, the proposed system still has high detection accuracy even when less than 60 sparsely distributed vanishing point candidates are used for voting as the influence introduced by the stochastic measurement noise of vote function and the sparsity of the vanishing point candidates is reduced. The method has been implemented and tested over 20 000 video frames. Experimental results demonstrate that the algorithm achieves better performance than some state-of-the-art texture-based vanishing point detection methods in terms of detection accuracy and speed.


title: Riemannian Metric Learning for Symmetric Positive Definite Matrices

abstract: Over the past few years, symmetric positive definite (SPD) matrices have been receiving considerable attention from computer vision community. Though various distance measures have been proposed in the past for comparing SPD matrices, the two most widely-used measures are affine-invariant distance and log-Euclidean distance. This is because these two measures are true geodesic distances induced by Riemannian geometry. In this work, we focus on the log-Euclidean Riemannian geometry and propose a data-driven approach for learning Riemannian metrics/geodesic distances for SPD matrices. We show that the geodesic distance learned using the proposed approach performs better than various existing distance measures when evaluated on face matching and clustering tasks.


title: A Survey of Intelligent Car Parking System

abstract: The industrialization of the world, increase in population, slow paced city development and mismanagement of the available parking space has resulted in parking related problems. There is a dire need for a secure, intelligent, efficient and reliable system which can be used for searching the unoccupied parking facility, guidance towards the parking facility, negotiation of the parking fee, along with the proper management of the parking facility. Intelligent Parking Service is a part of Intelligent Transportation Systems (ITS). This paper reviews different Intelligent Parking Services used for parking guidance, parking facility management and gives an insight into the economic analysis of such projects. The discussed systems will be able to reduce the problems which are arising due to unavailability of a reliable, efficient and modern parking system, while the economic analysis technique will help in analyzing the projects' feasibility.


title: Pathological synchronization in Parkinson's disease: networks, models and treatments

abstract: Parkinson's disease is a common and disabling disorder of movement owing to dopaminergic denervation of the striatum. However, it is still unclear how this denervation perverts normal functioning to cause slowing of voluntary movements. Recent work using tissue slice preparations, animal models and in humans with Parkinson's disease has demonstrated abnormally synchronized oscillatory activity at multiple levels of the basal ganglia–cortical loop. This excessive synchronization correlates with motor deficit, and its suppression by dopaminergic therapies, ablative surgery or deep-brain stimulation might provide the basic mechanism whereby diverse therapeutic strategies ameliorate motor impairment in patients with Parkinson's disease. This review is part of the INMED/TINS special issue, Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com/).


title: TopicExplorer: exploring document collections with topic models

abstract: The demo presents a prototype --- called TopicExplorer --- that combines topic modeling, key word search and visualization techniques to explore a large collection of Wikipedia documents. Topics derived by Latent Dirichlet Allocation are presented by top words. In addition, topics are accompanied by image thumbnails extracted from related Wikipedia documents to aid sense making of derived topics during browsing. Topics are shown in a linear order such that similar topics are close. Topics are mapped to color using that order. The auto-completion of search terms suggests words together with their color coded topics, which allows to explore the relation between search terms and topics. Retrieved documents are shown with color coded topics as well. Relevant documents and topics found during browsing can be put onto a shortlist. The tool can recommend further documents with respect to the average topic mixture of the shortlist.


title: Genetic diversity and differentiation of fragmented reedbeds (Phragmites australis) in the United Kingdom

abstract: Reedbeds, which are dominated by the common reed Phragmites australis, provide one of the most important aquatic habitats in the United Kingdom, and have been identified as a priority habitat under the UK Biodiversity Action Plan. Ongoing conservation and management of reedbeds is necessary because past eradication and fragmentation have been extensive. However, there has been little consideration of the potential long-term effects that may arise if processes such as founder effects during restoration projects, or genetic bottlenecks following harvesting, are leading to reductions in genetic diversity. In this study, we used microsatellite data to compare the genetic diversity of 19 P. australis stands in the UK, and found that neither size, management, nor habitat disturbance were affecting genetic diversity. Mixed reproduction (clonal and sexual), possibly combined with gene flow across relatively small spatial scales, appears to be maintaining genetic diversity within most stands. However, most stands were highly genetically differentiated from one another, which implies low gene flow. Long-term genetic diversity in managed stands may therefore require periodic bolstering from other populations, combined with the simultaneous cultivation of multiple generations. In addition, low gene flow suggests that managers should actively introduce plants into all areas in which reedbeds are desired. In conclusion, our study shows that reedbed restoration and maintenance do not seem to be adversely affecting the population genetics of P. australis, but the importance of genetic diversity should be factored into long-term projects.


title: Tram and trolleybus net traction energy consumption comparison

abstract: Electric driven public transportation systems have shown to be a solution to traffic congestion and its related pollution level in cities. Additionally, when compared with the traditional internal combustion engine (ICE), the electric driven mode of transport is much more efficient especially when regenerative braking is used. This paper took a step farther and compared two most common electric public transportation systems in cities. Energy consumption per passenger of a trolleybus was compared to that of a tram, both using regenerative braking, travelling from point A to point B, assuming the same route. For the sake of energy consumption comparison an 11.95 km route with six (6) stations from Ubungo maziwa to Central railway station in Dar es Salaam, Tanzania was chosen. With their particular specifications such as effective mass and coefficient of friction, the two vehicles were simulated using MATLAB, in such a way their acceleration, braking deceleration, maximum speed, and travelling time are all the same, their speed profiles which were more or less the same are presented in this paper alongside their corresponding energy consumption.


title: A Semantic Engine for Internet of Things: Cloud, Mobile Devices and Gateways

abstract: Semantics is becoming a requirement in Internet of Things (IoT). Recent works underline future challenges for IoT: (1) integrate semantic web technologies, (2) provide interoperability, (3) interpret IoT data, (4) ease the development of IoT applications, and (5) fit to the different requirements of people and applications. In this paper, we design a semantic engine to meet these requirements and integrate it in different components available in IoT architectures. To the best of our knowledge, the main novelty of this paper is the semantic engine flexible enough for different IoT architectures where semantics is integrated in: (1) cloud, (2) devices such as smart phones or tablets, and (3) M2M gateways. As proof of concepts, we discuss deployment of the proposed semantic engine at cloud systems and mobile devices. Moreover, we show that such deployments are coherent with ETSI M2M and one M2M standardizations.


title: Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis

abstract: Despite increasing awareness of the need to trace the trajectory of innovation system research, so far little attention has been given to quantitative depiction of the evolution of this fast-moving research field. This paper uses CiteSpace to demonstrate visually intellectual structures and developments. The study uses citation analysis to detect and visualize disciplinary distributions, keyword co-word networks and journal cocitation networks, highly cited references, as well as highly cited authors to identify intellectual turning points, pivotal points and emerging trends, in innovation systems system research from 1975 to 2012.


title: A review of optical Braille recognition

abstract: The process to take a Braille document image and convert its content into its equivalent natural language characters, is called Optical Braille Recognition (OBR). It involves two main consecutive steps: Braille cell recognition and Braille cell transcription. Braille cell recognition contains few steps including: Image acquisition, Image De-skewing, Image pre-processing, dot recognition, cell recognition and segmentation. Image transcription aims to convert the segmented Braille cell, into its equivalent natural language characters. In this survey we aim to study the earlier works done by other researchers on both Braille cell recognition and transcription.


title: Cognitive, language, social and behavioural outcomes in adults with autism spectrum disorders: a systematic review of longitudinal follow-up studies in adulthood.

abstract: Abstract Background Although increasing numbers of children diagnosed with Autism Spectrum Disorders (ASD) are now entering adolescence and adulthood, there is limited research on outcomes post childhood. A systematic review of the existing literature was conducted. Method PsycINFO, PubMed, MedLine and CINAHL were systematically searched using keywords related to ASD and adolescent and adult outcomes. Studies of individuals diagnosed with ASD in childhood and followed up into adulthood were identified and reviewed. Only studies with samples sizes > 10, mean age at outcome > 16 years and at least one previous assessment in childhood ( Results Twenty-five studies meeting criteria were identified. Reported outcomes in adulthood were highly variable across studies. Although social functioning, cognitive ability and language skills remained relatively stable in some studies, others reported deterioration over time. Adaptive functioning tended to improve in most studies. Diagnosis of autism or ASD was generally stable, although severity of autism-related behavioural symptoms was often reported to improve. Childhood IQ and early language ability appeared to be the strongest predictors of later outcome, but few studies examined other early variables associated with adult functioning. Discussion Implications of the findings are discussed in relation to methodological challenges in longitudinal outcome research and future research directions.


title: A 22nm ±0.95V CMOS OTA-C front-end with 50/60 Hz notch for biomedical signal acquisition

abstract: This paper presents a 22 nm CMOS technology analog front-end (AFE) for biomedical applications. The circuit is designed for low power and small size implementations, especially for battery-powered implantable devices, and is capable of reading out biomedical signals in the range of 0.01 Hz to 300 Hz in frequency, while rejecting power-line frequency of 50/60Hz. It employs Operational Transconductance Amplifiers (OTAs) in an OTA-C structure to realize a notch filter. The OTA designed has a very low transconductance, which is programmable from 1.069 nA/V to 2.114 nA/V. The notch at power-line frequency (50/60 Hz) achieves an attenuation of 20 dB. The power consumption of the entire AFE was found to be 11.34 nW at ±0.95V supply.