title: Research articles suggestion using topic modelling

abstract: Searching research articles effectively is significantly important to researchers. Currently, researchers use search engines like Google Scholar and search by keywords. The typical search result includes a lot of articles which match the keywords exactly; however, they are on many different topics. It is very time and effort consuming to go through the articles manually and select desirable ones. We propose a method to search articles by topics. Our method trains on a large set of articles and analyzes every article in it to generate its distribution over topics. The method recommends articles to the researcher by analyzing the input given by her/him and comparing with the topic distribution of all articles in the training set. The articles with most similar distributions are recommended to the researcher. In this way, articles are recommended by matching by topics rather than keywords. Our experimental results are analyzed using Mean average precision (MAP) and Normalized Discounted Cumulative Gain (NDCG). The obtained results demonstrate that our method successfully extracts the topics beneath the words of an article and recommend closely related ones.


title: A Survey and Taxonomy of Graph Sampling

abstract: Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. It has a wide spectrum of applications, e.g. survey hidden population in sociology [54], visualize social graph [29], scale down Internet AS graph [27], graph sparsification [8], etc. In some scenarios, the whole graph is known and the purpose of sampling is to obtain a smaller graph. In other scenarios, the graph is unknown and sampling is regarded as a way to explore the graph. Commonly used techniques are Vertex Sampling, Edge Sampling and Traversal Based Sampling. We provide a taxonomy of different graph sampling objectives and graph sampling approaches. The relations between these approaches are formally argued and a general framework to bridge theoretical analysis and practical implementation is provided. Although being smaller in size, sampled graphs may be similar to original graphs in some way. We are particularly interested in what graph properties are preserved given a sampling procedure. If some properties are preserved, we can estimate them on the sampled graphs, which gives a way to construct efficient estimators. If one algorithm relies on the perserved properties, we can expect that it gives similar output on original and sampled graphs. This leads to a systematic way to accelerate a class of graph algorithms. In this survey, we discuss both classical text-book type properties and some advanced properties. The landscape is tabularized and we see a lot of missing works in this field. Some theoretical studies are collected in this survey and simple extensions are made. Most previous numerical evaluation works come in an ad hoc fashion, i.e. evaluate different type of graphs, different set of properties, and different sampling algorithms. A systematical and neutral evaluation is needed to shed light on further graph sampling studies.


End-to-End Text-Independent Speaker Verification with Triplet Loss on Short Utterances


title: Automatic recognition of physiological parameters in the human voice: Heart rate and skin conductance

abstract: We show that high pulse/low pulse, heart rate and skin conductance recognition can reach good accuracies using classification on a large group of 4k audio features extracted from sustained vowels and breathing periods. A database containing audio, heart rate and skin conductance recordings from 19 subjects is established for evaluation of audio-based bio-signal recognition. On this database in speaker-dependent testing, heart rate and skin conductance can be determined with a correlation coefficient of .861/.960 and mean absolute error of 8.1 BPM/88.2 μMhO for regression based on sustained vowels recorded from a room microphone. Using the same set-up, a high pulse/low pulse classification can reach an unweighted accuracy of 82.7%. The results are largely independent from microphone type and the two bio-signals can be determined from breathing periods as well. Performance does, however, degrade in speaker-independent setting.


title: RDDShare: Reusing Results of Spark RDD

abstract: In recent years, Spark has become a hotspot for big data processing. For a single user, Spark provides the cache method to share the results between the jobs in a single application. When accessed concurrently by multi users, there may exist same computation among the submitted applications, however, Spark does not provide a method to share computing results between applications. In traditional databases, one way to optimize the performance of queries is to cache part or all of the results of a query to share with other requests. Based on this, we propose RDDShare system based on Spark SQL to manage the cache and reuse the results. Finally, the results of simulate experiments show that RDDShare system can significant optimize the query performance of Spark SQL.


title: Where do you know what you know? The representation of semantic knowledge in the human brain

abstract: Mr M, a patient with semantic dementia - a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory - was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route. Then, pointing at the sheep in the field, he asked her "What are those things?" Prior to the onset of symptoms in his late 40s, this man had normal semantic memory. What has gone wrong in his brain to produce this dramatic and selective erosion of conceptual knowledge?


title: Fast and automatic video object segmentation and tracking for content-based applications

abstract: The new video-coding standard MPEG-4 enables content-based functionality, as well as high coding efficiency, by taking into account shape information of moving objects. A novel algorithm for segmentation of moving objects in video sequences and extraction of video object planes (VOPs) is proposed . For the case of multiple video objects in a scene, the extraction of a specific single video object (VO) based on connected components analysis and smoothness of VO displacement in successive frames is also discussed. Our algorithm begins with a robust double-edge map derived from the difference between two successive frames. After removing edge points which belong to the previous frame, the remaining edge map, moving edge (ME), is used to extract the VOP. The proposed algorithm is evaluated on an indoor sequence captured by a low-end camera as well as MPEG-4 test sequences and produces promising results.


title: Static detection of security vulnerabilities in scripting languages

abstract: We present a static analysis algorithm for detecting security vulnerabilities in PHP, a popular server-side scripting language for building web applications. Our analysis employs a novel three-tier architecture to capture information at decreasing levels of granularity at the intrablock, intraprocedural, and interprocedural level. This architecture enables us to handle dynamic features of scripting languages that have not been adequately addressed by previous techniques. ::: ::: We demonstrate the effectiveness of our approach on six popular open source PHP code bases, finding 105 previously unknown security vulnerabilities, most of which we believe are remotely exploitable.


title: Using Retweets to Shape our Online Persona: A Topic Modeling Approach

abstract: Online social networking technologies have given rise to new social behaviors, with the like on Facebook being a prominent example. We study on a specific type of social behavior: the ability to reiterate a friend’s activity, that is, to redistribute an exact copy of content that he or she has posted online (e.g., words, videos, or pictures). Such reiteration tools enable the user to leverage someone else’s self-expression to enhance his or her own image as reflected by the online platform. We focus on the effects of such reiteration tools on the way users present themselves online. Specifically, using data from Twitter we ask: (1) Do users utilize retweets to expand the breadth of topics they discuss, by adding topics not discussed in their self-tweets, or do they use it to enhance their self-produced persona, adding further content relating to the topics they already discuss in their self-tweets? (2) Do users utilize the retweet option to change the distribution of the topics they discuss in their self-tweets? (3) We focus on users' heterogeneity by asking whether the behaviors we analyze differ between expert users and non-expert users.We analyzed data taken from Twitter over a period of 3 weeks in 2015, with regard to 2,435 non-expert core users (defined as users who tweet regularly and are likely to use the platform for personal rather than professional purposes), 415 expert users and the users whom they followed. We find that users rarely add new topics to their profile when retweeting; instead, they enrich their persona by deepening the discussion of topics that they address in their self-tweets. Furthermore, we find that a user's retweets do remarkably little to alter the distribution of topics she discusses in her self-tweets. Finally, we find that both non-expert and expert users tend to add few new topics via retweets, and that this tendency is stronger among expert users. Specifically, on average, experts retweet less often and add fewer topics per tweet, indicating that they rely more on their own words when engaging in impression management.


title: COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems

abstract: A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers.


title: ADL recognition based on the combination of RFID and accelerometer sensing

abstract: The manual assessment of activities of daily living (ADLs) is a fundamental problem in elderly care. The use of miniature sensors placed in the environment or worn by a person has great potential in effective and unobtrusive long term monitoring and recognition of ADLs. This paper presents an effective and unobtrusive activity recognition system based on the combination of the data from two different types of sensors: RFID tag readers and accelerometers. We evaluate our algorithms on non-scripted datasets of 10 housekeeping activities performed by 12 subjects. The experimental results show that recognition accuracy can be significantly improved by fusing the two different types of sensors. We analyze different acceleration features and algorithms, and based on tag detections we suggest the best tagspsila placements and the key objects to be tagged for each activity.


title: A Planar Electronically Steerable Patch Array Using Tunable PRI/NRI Phase Shifters

abstract: This paper presents a planar electronically steerable series-fed patch array for 2.4-GHz industrial, scientific, and medical band applications. The proposed steerable array uses 0deg tunable positive/negative-refractive-index (PRI/NRI) phase shifters to center its radiation about the broadside direction and allow scanning in both directions off the broadside. Using the PRI/NRI phase shifters also minimizes the squinting of the main beam across the operating bandwidth. The tunable PRI/NRI phase shifters employ 0.13-mum CMOS tunable active inductors, as well as varactors in order to extend their phase tuning range and maintain a low return loss across the entire phase tuning range. The feed network of the proposed array uses lambda/4 impedance transformers. This allows using identical interstage phase shifters, which share the same control voltages to tune all stages. Furthermore, using the impedance transformers in combination with the CMOS-based constant-impedance PRI/NRI phase shifters guarantees a low return loss for the antenna array across its entire scan angle range. The antenna array was fabricated, and is capable of continuously steering its main beam from -27deg to +22deg off the broadside direction with a gain of 8.4 dBi at 2.4 GHz. This is achieved by changing the varactors' control voltage from 3.5 to 15 V. Across the entire scan angle range, the array return loss is less than -10 dB across a bandwidth of 70 MHz, and the relative sidelobe level is always less than -10 dB. Furthermore, the proposed design achieves very low beam squinting of 1.3deg/100 MHz at broadside and a 1-dB compression point of 4.5 dBm.


title: Mindfulness-Based Mind Fitness Training: A Case Study of a High-Stress Predeployment Military Cohort.

abstract: Abstract Current military deployments have resulted in many psychological and physical health issues and created interest in protective measures to mitigate effects of prolonged and repetitive stress. Mindfulness training has been successfully used for stress reduction in other contexts. The following case report presents a detachment of U.S. Marines who received Mindfulness-Based Mind Fitness Training (MMFT) prior to deployment. Self-report measures of mindfulness, perceptions of stress, predictors of compliance with mindfulness practice, and time spent engaging in practice were indexed. More time spent engaging in practice corresponded with greater self-reported mindfulness; increases in mindfulness were associated with decreases in perceived stress.


title: Modification of a CTAB DNA extraction protocol for plants containing high polysaccharide and polyphenol components

abstract: A relatively quick, inexpensive and consistent protocol for extraction of DNA from expanded leaf material containing large quantities of polyphenols, tannins and polysaccharides is described. Mature strawberry leaves, which contain high levels of these secondary components, were used as a study group. The method involves a modified CTAB extraction, employing high salt concentrations to remove polysaccharides, the use of polyvinyl pyrrolidone (PVP) to remove polyphenols, an extended RNase treatment and a phenol-chloroform extraction. Average yields range from 20 to 84 μg/g mature leaf tissue for both wild and cultivated octoploid and diploidFragaria species. Results from 60 plants were examined, and were consistently amplifiable in the RAPD reaction with as little as 0.5 ng DNA per 25-μL reaction. Presently, this is the first procedure for the isolation of DNA from mature strawberry leaf tissue that produces consistent results for a variety of different species, both octoploid and diploid, and is both stable and PCR amplifiable before and after extended storage. This procedure may prove useful for other difficult species in the family Rosaceae.


title: International Journal of Innovations in Engineering and Technology A Survey of Recommender Systems: Approaches and Limitations

abstract: Recommendation as a social process plays an important role in many applications as WWW has created the universe as a global village, with an explosive growth of enormous information. The paper presents an overview of the field of recommender systems along with the description of various approaches that are being used for generating recommendations. Recommendation techniques can be classified in to three major categories: Collaborative Filtering, Content Based and Hybrid Recommendations. The paper elaborates these approaches and discusses their limitations by describing the major problems suffered by recommendation methods. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for recommendation techniques, which can be served as a roadmap for research and practice in this area.


title: Analyzing students’ performance using multi-criteria classification

abstract: Education is a key factor for achieving long-term economic progress. During the last decades, higher standards in education have become easier to attain due to the availability of knowledge and resources worldwide. With the emergence of new technology enhanced by using data mining it has become easier to dig into data and extract useful knowledge from data. In this research, we use data analytic techniques applied to real case studies to predict students’ performance using their past academic experience. We introduce a new hybrid classification technique which utilize decision tree and fuzzy multi-criteria classification. The technique is used to predict students’ performance based on several criteria such as age, school, address, family size, evaluation in previous grades, and activities. To check the accuracy of the model, our proposed method is compared with other well-known classifiers. This study on existing student data showed that this method is a promising classification tool.


title: A 0.3–6GHz broadband noise cancelling low noise amplifier

abstract: A broadband noise cancelling low noise amplifier (LNA) composed of a CS stage, a single-to-differential (S-to-D) converter, a voltage combiner, a negative feedback network, and a couple of differential-to-single buffers is proposed for multi-standard radio application in this paper. Different from conventional broadband feedback LNA, the proposed LNA shows improved performance parameters including gain, noise figure (NF), 1dB compression point (P1dB), and input-referred third-order intercept point (IIP3), Especially for wideband LNA design. The LNA is fabricated in TSMC 0.13-gm process. From 0.3 to 6GHz, the simulated results show a high-gain of 20 dB, a superior low noise figure of 1.6dB at 3GHz, input power at 1dB compression point (P1dB) of −19.2dBm at 2GHz, it consume 13.2mA from a 1.2V supply and occupies an area of 0.19 mm2.


title: Measuring Thinking Skills Through Classroom Assessment

abstract: The classroom assessment procedures o f 36 teachers in grades 2 to 12 were studied in depth to determine the extent to which they measure students” higher order thinking skills in mathematics, science, social studies, and language arts. A wide variety o f assessment documents were analyzed, teachers were observed asking oral questions in their classrooms, and each teacher was interviewed. The results revealed that paper-and-pencil assessment documents were dominated by recall questions across all grade levels. However, inference was assessed also, especially in mathematics. Oral questions tended to tap recall too, with analysis and inference reflected to some extent. Across grades, subjects, and forms o f assessment, comparison and evaluation questions were rare. Although these teachers had been trained to teach thinking skills to some extent, they were less often trained to assess such skills. Those who were trained tended to ask a higher proportion o f thinking skills questions than those who were not. The training implications o f the results are discussed.


title: Paper-based analytical devices for clinical diagnosis: recent advances in the fabrication techniques and sensing mechanisms.

abstract: INTRODUCTION ::: There is a significant interest in developing inexpensive portable biosensing platforms for various applications including disease diagnostics, environmental monitoring, food safety, and water testing at the point-of-care (POC) settings. Current diagnostic assays available in the developed world require sophisticated laboratory infrastructure and expensive reagents. Hence, they are not suitable for resource-constrained settings with limited financial resources, basic health infrastructure, and few trained technicians. Cellulose and flexible transparency paper-based analytical devices have demonstrated enormous potential for developing robust, inexpensive and portable devices for disease diagnostics. These devices offer promising solutions to disease management in resource-constrained settings where the vast majority of the population cannot afford expensive and highly sophisticated treatment options. Areas covered: In this review, the authors describe currently developed cellulose and flexible transparency paper-based microfluidic devices, device fabrication techniques, and sensing technologies that are integrated with these devices. The authors also discuss the limitations and challenges associated with these devices and their potential in clinical settings. Expert commentary: In recent years, cellulose and flexible transparency paper-based microfluidic devices have demonstrated the potential to become future healthcare options despite a few limitations such as low sensitivity and reproducibility.


title: That's Not My Question: Learning to Weight Unmatched Terms in CQA Vertical Search

abstract: A fundamental task in Information Retrieval (IR) is term weighting. Early IR theory considered both the presence or absence of all terms in the lexicon for ranking and needed to weight them all. Yet, as the size of lexicons grew and models became too complex, common weighting models preferred to aggregate only the weights of the query terms that are matched in candidate documents. Thus, unmatched term contribution in these models is only considered indirectly, such as in probability smoothing with corpus distribution, or in weight normalization by document length. In this work we propose a novel term weighting model that directly assesses the weights of unmatched terms, and show its benefits. Specifically, we propose a Learning To Rank framework, in which features corresponding to matched terms are also "mirrored" in similar features that account only for unmatched terms. The relative importance of each feature is learned via a click-through query log. As a test case, we consider vertical search in Community-based Question Answering(CQA) sites from Web queries. Queries that result in viewing CQA content often contain fine grained information needs and benefit more from unmatched term weighting. We assess our model both via manual evaluation and via automatic evaluation over a clickthrough log. Our results show consistent improvement in retrieval when unmatched information is taken into account. This holds both when only identical terms are considered matched, and when related terms are matched via distributional similarity.


title: Tool Demonstration: FSolidM for Designing Secure Ethereum Smart Contracts

abstract: Blockchain-based distributed computing platforms enable the trusted execution of computation—defined in the form of smart contracts—without trusted agents. Smart contracts are envisioned to have a variety of applications, ranging from financial to IoT asset tracking. Unfortunately, the development of smart contracts has proven to be extremely error prone. In practice, contracts are riddled with security vulnerabilities comprising a critical issue since bugs are by design non-fixable and contracts may handle financial assets of significant value. To facilitate the development of secure smart contracts, we have created the FSolidM framework, which allows developers to define contracts as finite state machines (FSMs) with rigorous and clear semantics. FSolidM provides an easy-to-use graphical editor for specifying FSMs, a code generator for creating Ethereum smart contracts, and a set of plugins that developers may add to their FSMs to enhance security and functionality.


title: Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

abstract: The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier across these groups. Constraints of this form are susceptible to intentional or inadvertent "fairness gerrymandering", in which a classifier appears to be fair on each individual group, but badly violates the fairness constraint on one or more structured subgroups defined over the protected attributes. We propose instead to demand statistical notions of fairness across exponentially (or infinitely) many subgroups, defined by a structured class of functions over the protected attributes. This interpolates between statistical definitions of fairness and recently proposed individual notions of fairness, but raises several computational challenges. It is no longer clear how to audit a fixed classifier to see if it satisfies such a strong definition of fairness. We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses. ::: We then derive two algorithms that provably converge to the best fair classifier, given access to oracles which can solve the agnostic learning problem. The algorithms are based on a formulation of subgroup fairness as a two-player zero-sum game between a Learner and an Auditor. Our first algorithm provably converges in a polynomial number of steps. Our second algorithm enjoys only provably asymptotic convergence, but has the merit of simplicity and faster per-step computation. We implement the simpler algorithm using linear regression as a heuristic oracle, and show that we can effectively both audit and learn fair classifiers on real datasets.


title: Suicide by plastic bag suffocation combined with the mixture of citric acid and baking soda in an adolescent

abstract: We describe a case of suicidal asphyxiation using a plastic bag combined with carbon dioxide (CO2) gas. A 20-year-old male, whose head was covered with a plastic bag, was found dead in his room. In the plastic bag, there were two glass-made cups containing liquid-like substance. Through crime scene investigation by police staffs, a bottle of citric acid and a box of baking soda were also discovered in his room. The forensic autopsy revealed that there were neither lesions nor injuries in all of the organs. Moreover, any drugs and poisons could not be detected in blood samples. Based on autopsy findings and crime scene investigation, the cause of death was diagnosed as acute asphyxia due to CO2 intoxication by the mixture of citric acid with baking soda in the plastic bag. To the best of our knowledge, there are no medical literatures describing plastic bag suffocation combined with CO2 gas generated from citric acid and baking soda, which has been widely distributed as suicidal means through websites. This case report promotes forensic pathologists and medical coroners to emphasize that the Internet has a crucial role on a source of suicidal information or a promoter of suicide all over the world.


title: Hand gesture recognition for Indian Sign Language

abstract: In this paper, we introduce a hand gesture recognition system to recognize the alphabets of Indian Sign Language. In our proposed system there are 4 modules: real time hand tracking, hand segmentation, feature extraction and gesture recognition. Camshift method and Hue, Saturation, Intensity (HSV) color model are used for hand tracking and segmentation. For gesture recognition, Genetic Algorithm is used. We propose an easy-to-use and inexpensive approach to recognize single handed as well as double handed gestures accurately. This system can definitely help millions of deaf people to communicate with other normal people.


title: A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems

abstract: The evaluation of recommender systems is key to the successful application of recommender systems in practice. However, recommender-systems evaluation has received too little attention in the recommender-system community, in particular in the community of research-paper recommender systems. In this paper, we examine and discuss the appropriateness of different evaluation methods, i.e. offline evaluations, online evaluations, and user studies, in the context of research-paper recommender systems. We implemented different content-based filtering approaches in the research-paper recommender system of Docear. The approaches differed by the features to utilize (terms or citations), by user model size, whether stop-words were removed, and several other factors. The evaluations show that results from offline evaluations sometimes contradict results from online evaluations and user studies. We discuss potential reasons for the non-predictive power of offline evaluations, and discuss whether results of offline evaluations might have some inherent value. In the latter case, results of offline evaluations were worth to be published, even if they contradict results of user studies and online evaluations. However, although offline evaluations theoretically might have some inherent value, we conclude that in practice, offline evaluations are probably not suitable to evaluate recommender systems, particularly in the domain of research paper recommendations. We further analyze and discuss the appropriateness of several online evaluation metrics such as click-through rate, link-through rate, and cite-through rate.


title: Ontology-based decision making on uncontrolled intersections and narrow roads

abstract: Many Advanced Driver Assistance Systems (ADAS) have been developed to improve car safety. However, it is still a challenging problem to make autonomous vehicles to drive safely on urban streets such as uncontrolled intersections (without traffic lights) and narrow roads. In this paper, we introduce a decision making system that can assist autonomous vehicles at uncontrolled intersections and narrow roads. We constructed a machine understandable ontology-based Knowledge Base, which contains maps and traffic regulations. The system makes decisions in comply with traffic regulations such as Right-Of-Way rules when it receives a collision warning signal. The decisions are sent to a path planning system to change the route or stop to avoid collisions.


title: Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures

abstract: Contemporary mate preferences can provide important clues to human reproductive history. Little is known about which characteristics people value in potential mates. Five predictions were made about sex differences in human mate preferences based on evolutionary conceptions of parental investment, sexual selection, human reproductive capacity, and sexual asymmetries regarding certainty of paternity versus maternity. The predictions centered on how each sex valued earning capacity, ambition— industriousness, youth, physical attractiveness, and chastity. Predictions were tested in data from 37 samples drawn from 33 countries located on six continents and five islands (total N = 10,047). For 27 countries, demographic data on actual age at marriage provided a validity check on questionnaire data. Females were found to value cues to resource acquisition in potential mates more highly than males. Characteristics signaling reproductive capacity were valued more by males than by females. These sex differences may reflect different evolutionary selection pressures on human males and females; they provide powerful cross-cultural evidence of current sex differences in reproductive strategies. Discussion focuses on proximate mechanisms underlying mate preferences, consequences for human intrasexual competition, and the limitations of this study.


Possibility / Ability ” M odals in Malaysian English Textbooks : A Corpus-Based Analysis


title: FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge

abstract: The field of Automatic Facial Expression Analysis has grown rapidly in recent years. However, despite progress in new approaches as well as benchmarking efforts, most evaluations still focus on either posed expressions, near-frontal recordings, or both. This makes it hard to tell how existing expression recognition approaches perform under conditions where faces appear in a wide range of poses (or camera views), displaying ecologically valid expressions. The main obstacle for assessing this is the availability of suitable data, and the challenge proposed here addresses this limitation. The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views. In this paper we present the third challenge in automatic recognition of facial expressions, to be held in conjunction with the 12th IEEE conference on Face and Gesture Recognition, May 2017, in Washington, United States. Two sub-challenges are defined: the detection of AU occurrence, and the estimation of AU intensity. In this work we outline the evaluation protocol, the data used, and the results of a baseline method for both sub-challenges.


title: Composing tree graphical models with persistent homology features for clustering mixed-type data

abstract: Clustering data with both continuous and discrete attributes is a challenging task. Existing methods often lack a principled probabilistic formulation. In this paper, we propose a clustering method based on a tree-structured graphical model to describe the generation process of mixed-type data. Our tree-structured model factorizes into a product of pairwise interactions, and thus localizes the interaction between feature variables of different types. To provide a robust clustering method based on the tree-model, we adopt a topographical view and compute peaks of the density function and their attractive basins for clustering. Furthermore, we leverage the theory from topology data analysis to adaptively merge trivial peaks into large ones in order to achieve meaningful clusterings. Our method outperforms state-of-the-art methods on mixed-type data.


title: Key Developments in Human Pose Estimation for Kinect

abstract: The last few years have seen a surge in the development of natural user interfaces. These interfaces do not require devices such as keyboards and mice that have been the dominant modes of interaction over the last few decades. An important milestone in the progress of natural user interfaces was the recent launch of Kinect with its unique ability to reliably estimate the pose of the human user in real time. Human pose estimation has been the subject of much research in Computer Vision, but only recently with the introduction of depth cameras and algorithmic advances has pose estimation made it out of the lab and into the living room. In this chapter we briefly summarize the work on human pose estimation for Kinect that has been undertaken at Microsoft Research Cambridge, and discuss some of the remaining open challenges. Due to the summary nature of this chapter, we limit our description to the key insights and refer the reader to the original publications for the technical details.


3 D Indoor Exploration with a Computationally Constrained MAV


Printed Chinese Character Recognition


title: THE MUTUALITY OF AUTONOMY SCALE: AN IMPLICIT MEASURE OF OBJECT RELATIONS FOR THE RORSCHACH INKBLOT METHOD

abstract: The mental internalization of critical experiences with important early childhood caregivers forms the foundation of how each individual perceives, reacts to, and organizes his or her life with people. The quality and character of these internal object relationships impact the quality of current functioning, ranging from relative health and happiness to debilitating experiences of suspiciousness, fear of intrusion, and psychosis. Psychoanalysts generally place great importance on the theoretical and clinical significance of object relations and the accurate assessment of these phenomena. The Mutuality of Autonomy scale for the Rorschach (MOA: Urist, 1977) is one implicit measure of object relations that has broad empirical and clinical support. This brief article provides an introduction to the scale’s development, scoring, and applications.


title: A fault-tolerant framework for QoS-aware web service composition via case-based reasoning

abstract: Service-oriented systems are performed in a highly dynamic and heterogeneous environment to achieve a user's business requirements. In this paper, we introduce a case-based reasoning approach to improve the system's fault-tolerant ability meeting the end-to-end Quality-of-Service QoS constraints. We consider and quantify the fault information and QoS information together in a case. In addition, one case also includes one solution to cope with the corresponding fault. When a new fault occurs, its failure symptoms are extracted and matched against the case base to look for the most similar case. Furthermore, solution templates are presented considering the business process structure features. We also develop an influential region-based algorithm to minimise the handle cost. Based on our proposed method, a fault-tolerant framework is implemented. Comprehensive experiments are conducted employing a real QoS dataset and the experimental results show that our approach improves the fault-tolerant capability encouragingly.


title: Control of twin rotor MIMO system using PID controller with derivative filter coefficient

abstract: This paper presents a proportional integral derivative (PID) controller with a derivative filter coefficient to control a twin rotor multiple input multiple output system (TRMS), which is a nonlinear system with two degrees of freedom and cross couplings. The mathematical modeling of TRMS is done using MATLAB/Simulink. The simulation results are compared with the results of conventional PID controller. The results of proposed PID controller with derivative filter shows better transient and steady state response as compared to conventional PID controller.


title: 2006, “Solar winds driven by nonlinear low-frequency Alfvén waves from the photosphere: Parametric study for fast/slow winds and disappearance of solar winds

abstract: [1] We investigate how properties of the corona and solar wind in open coronal holes depend on properties of magnetic fields and their footpoint motions at the surface. We perform one-dimensional magnetohydrodynamical (MHD) simulations for the heating and the acceleration in coronal holes by low-frequency Alfven waves from the photosphere to 0.3 or 0.1 AU. We impose low-frequency (≲0.05 Hz) transverse fluctuations of the field lines at the photosphere with various amplitude, spectrum, and polarization in the open flux tubes with different photospheric field strength, Br,0, and superradial expansion of the cross section, fmax. We find that transonic solar winds are universal consequences. The atmosphere is also stably heated up to ≳106 K by the dissipation of the Alfven waves through compressive-wave generation and wave reflection in the cases of the sufficient wave input with photospheric amplitude, 〈dv⊥,0〉 ≳ 0.7 km s−1. The density, and accordingly the mass flux, of solar winds show a quite sensitive dependence on 〈dv⊥,0〉 because of an unstable aspect of the heating by the nonlinear Alfven waves. A case with 〈dv⊥,0〉 = 0.4 km s−1 gives ≃50 times smaller mass flux than the fiducial case for the fast wind with 〈dv⊥,0〉 = 0.7 km s−1; solar wind virtually disappears only if 〈dv⊥,0〉 becomes ≃1/2. We also find that the solar wind speed has a positive correlation with Br,0/fmax, which is consistent with recent observations by Kojima et al. On the basis of these findings, we show that both fast and slow solar winds can be explained by the single process, the dissipation of the low-frequency Alfven waves, with different sets of 〈dv⊥,0〉 and Br,0/fmax. Our simulations naturally explain the observed (1) anticorrelation of the solar wind speed and the coronal temperature and (2) larger amplitude of Alfvenic fluctuations in the fast wind. In Appendix A, we also explain our implementation of the outgoing boundary condition of the MHD waves with some numerical tests.


title: Cloud-Based Image Coding for Mobile Devices—Toward Thousands to One Compression

abstract: Current image coding schemes make it hard to utilize external images for compression even if highly correlated images can be found in the cloud. To solve this problem, we propose a method of cloud-based image coding that is different from current image coding even on the ground. It no longer compresses images pixel by pixel and instead tries to describe images and reconstruct them from a large-scale image database via the descriptions. First, we describe an input image based on its down-sampled version and local feature descriptors. The descriptors are used to retrieve highly correlated images in the cloud and identify corresponding patches. The down-sampled image serves as a target to stitch retrieved image patches together. Second, the down-sampled image is compressed using current image coding. The feature vectors of local descriptors are predicted by the corresponding vectors extracted in the decoded down-sampled image. The predicted residual vectors are compressed by transform, quantization, and entropy coding. The experimental results show that the visual quality of reconstructed images is significantly better than that of intra-frame coding in HEVC and JPEG at thousands to one compression .


title: Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

abstract: State-of-the-art pedestrian detection models have achieved great success in many benchmarks. However, these models require lots of annotation information and the labeling process usually takes much time and efforts. In this paper, we propose a method to generate labeled pedestrian data and adapt them to support the training of pedestrian detectors. The proposed framework is built on the Generative Adversarial Network (GAN) with multiple discriminators, trying to synthesize realistic pedestrians and learn the background context simultaneously. To handle the pedestrians of different sizes, we adopt the Spatial Pyramid Pooling (SPP) layer in the discriminator. We conduct experiments on two benchmarks. The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details. To quantitatively evaluate our approach, we add the generated samples into training data of the baseline pedestrian detectors and show the synthetic images are able to improve the detectors' performance.


title: The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

abstract: Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024x1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.


title: Optimisation of switched reluctance motors for hybrid electric vehicles

abstract: A switched reluctance motor (SRM) is well suited to the electric propulsion of a hybrid electric vehicle (HEV), due to its simple and rugged construction, low cost, and ability to operate over a wide speed range at constant power. This paper presents the design optimisation of SRMs for HEV applications, by using a combination of 2D electromagnetic finite element (FE) analysis, experience from previous designs, 3D correction factors, simple lumped-parameter thermal models, and computer search techniques. A prototype SRM was built and fully tested. The experimental results validate the design methodology. Based on the optimal lamination profile of the prototype, five motors were constructed for two HEVs: two for a parallel HEV and three for a series HEV. The two cars have demonstrated excellent performance with the optimised SRMs.


title: Practical byzantine fault tolerance and proactive recovery

abstract: Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2p faster to 24p slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.


THE DIFFERENCE BETWEEN THE CLIQUE NUMBERS OF A GRAPH


title: Findings of the Third Shared Task on Multimodal Machine Translation

abstract: We present the results from the third shared task on multimodal machine translation. In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech. The image can be used in addition to (or instead of) the source sentence. This year the task was extended with a third target language (Czech) and a new test set. In addition, a variant of this task was introduced with its own test set where the source sentence is given in multiple languages: English, French and German, and participating systems are required to generate a translation in Czech. Seven teams submitted 45 different systems to the two variants of the task. Compared to last year, the performance of the multimodal submissions improved, but text-only systems remain competitive.


title: Prejunctional Inhibitory Effects of Isoprostanes on Dopaminergic Neurotransmission in Bovine Retinae, In vitro

abstract: We investigated the effect of isoprostanes (IsoPs) on potassium (K+)-depolarization-evoked release of [3H]dopamine from isolated bovine retinae. Isolated retinae were preloaded with [3H]dopamine and then prepared for studies of [3H]dopamine release using the superfusion method. 8-iso(15R)PGF 2alpha, 8-isoPGE2, 8-isoPGE1 and 8-isoPGF 2alpha attenuated [3H]dopamine release from isolated bovine retinae. At a concentration of 1 microM, the rank order of activity displayed by IsoP agonists was: 8-iso(15R)PGF 2alpha > 8-isoPGE2 > 8-isoPGE1 > 8-isoPGF 2alpha. Inhibition of cyclooxygenase (COX) with flurbiprofen reversed the effects caused by 8-isoPGE2 (10 nM and 10 microM), 8-iso(15R)PGF 2alpha (1 microM) and 8-isoPGE1 (1 microM). Although the EP1/EP2 antagonist, AH 6809 (10 microM) had no significant effect on K+-induced [3H]dopamine release, it blocked the inhibitory effect of both 8-isoPGE1 (10 microM) and 8-isoPGE2 (10 microM). In conclusion, IsoPs attenuate K+-induced [3H]dopamine release in isolated bovine retinae, presumably via an indirect action on COX pathway leading to the production of prostanoids, which in turn, activates EP receptors.


Virtual Analog Modeling of Nonlinear Musical Circuits


title: Sequence and analysis of rice chromosome 4

abstract: Rice is the principal food for over half of the population of the world. With its genome size of 430 megabase pairs (Mb), the cultivated rice species Oryza sativa is a model plant for genome research. Here we report the sequence analysis of chromosome 4 of O. sativa, one of the first two rice chromosomes to be sequenced completely. The finished sequence spans 34.6 Mb and represents 97.3% of the chromosome. In addition, we report the longest known sequence for a plant centromere, a completely sequenced contig of 1.16 Mb corresponding to the centromeric region of chromosome 4. We predict 4,658 protein coding genes and 70 transfer RNA genes. A total of 1,681 predicted genes match available unique rice expressed sequence tags. Transposable elements have a pronounced bias towards the euchromatic regions, indicating a close correlation of their distributions to genes along the chromosome. Comparative genome analysis between cultivated rice subspecies shows that there is an overall syntenic relationship between the chromosomes and divergence at the level of single-nucleotide polymorphisms and insertions and deletions. By contrast, there is little conservation in gene order between rice and Arabidopsis.


title: Neurological and spinal manifestations of the Ehlers–Danlos syndromes

abstract: The Ehlers–Danlos syndromes (EDS) are a heterogeneous group of heritable connective tissue disorders characterized by joint hypermobility, skin extensibility, and tissue fragility. This communication briefly reports upon the neurological manifestations that arise including the weakness of the ligaments of the craniocervical junction and spine, early disc degeneration, and the weakness of the epineurium and perineurium surrounding peripheral nerves. Entrapment, deformation, and biophysical deformative stresses exerted upon the nervous system may alter gene expression, neuronal function and phenotypic expression. This report also discusses increased prevalence of migraine, idiopathic intracranial hypertension, Tarlov cysts, tethered cord syndrome, and dystonia, where associations with EDS have been anecdotally reported, but where epidemiological evidence is not yet available. Chiari Malformation Type I (CMI) has been reported to be a comorbid condition to EDS, and may be complicated by craniocervical instability or basilar invagination. Motor delay, headache, and quadriparesis have been attributed to ligamentous laxity and instability at the atlanto-occipital and atlantoaxial joints, which may complicate all forms of EDS. Discopathy and early degenerative spondylotic disease manifest by spinal segmental instability and kyphosis, rendering EDS patients prone to mechanical pain, and myelopathy. Musculoskeletal pain starts early, is chronic and debilitating, and the neuromuscular disease of EDS manifests symptomatically with weakness, myalgia, easy fatigability, limited walking, reduction of vibration sense, and mild impairment of mobility and daily activities. Consensus criteria and clinical practice guidelines, based upon stronger epidemiological and pathophysiological evidence, are needed to refine diagnosis and treatment of the various neurological and spinal manifestations of EDS. © 2017 Wiley Periodicals, Inc.


title: Towards Good Practices for Deep 3D Hand Pose Estimation

abstract: 3D hand pose estimation from single depth image is an important and challenging problem for human-computer interaction. Recently deep convolutional networks (ConvNet) with sophisticated design have been employed to address it, but the improvement over traditional random forest based methods is not so apparent. To exploit the good practice and promote the performance for hand pose estimation, we propose a tree-structured Region Ensemble Network (REN) for directly 3D coordinate regression. It first partitions the last convolution outputs of ConvNet into several grid regions. The results from separate fully-connected (FC) regressors on each regions are then integrated by another FC layer to perform the estimation. By exploitation of several training strategies including data augmentation and smooth $L_1$ loss, proposed REN can significantly improve the performance of ConvNet to localize hand joints. The experimental results demonstrate that our approach achieves the best performance among state-of-the-art algorithms on three public hand pose datasets. We also experiment our methods on fingertip detection and human pose datasets and obtain state-of-the-art accuracy.


title: Learning from Chunk-based Feedback in Neural Machine Translation

abstract: We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation. We propose a simple and effective way of utilizing such feedback in NMT training. We demonstrate how the common machine translation problem of domain mismatch between training and deployment can be reduced solely based on chunk-level user feedback. We conduct a series of simulation experiments to test the effectiveness of the proposed method. Our results show that chunk-level feedback outperforms sentence based feedback by up to 2.61% BLEU absolute.


title: Inherent structural characteristics approach for solving loss allocation problems

abstract: The solution to the well-known loss allocation problems in interconnected power networks has usually been through power-flow analysis. This power-flow based approach is computationally intensive and the results may not converge for large practical power systems. This paper, therefore, explores an alternative approach to solving this problem using the Inherent Structural Characteristics Theory (ISCT) [6]. The total transmission loss within power network based on both the ISCT and power-flow-based approach is determined, and then used to allocate losses to loads in the network. The results obtained from the two approaches are compared. The ISCT approach, apart from being faster, allocates the total loss within the network to loads in a fairer, equitable and transparent manner. Matlab 2013a is employed as a simulation tool. The sparsity inherent in power networks is explored to reduce the computational intensity and computer memory required. This new approach is tested on a standard IEEE 14-bus system and a practical system of the Nigerian 40-bus network. The results obtained from the simulations show the ISCT approach to be superior.


title: Skin-like pressure and strain sensors based on transparent elastic films of carbon nanotubes.

abstract: Transparent, elastic conductors are essential components of electronic and optoelectronic devices that facilitate human interaction and biofeedback, such as interactive electronics, implantable medical devices and robotic systems with human-like sensing capabilities. The availability of conducting thin films with these properties could lead to the development of skin-like sensors that stretch reversibly, sense pressure (not just touch), bend into hairpin turns, integrate with collapsible, stretchable and mechanically robust displays and solar cells, and also wrap around non-planar and biological surfaces such as skin and organs, without wrinkling. We report transparent, conducting spray-deposited films of single-walled carbon nanotubes that can be rendered stretchable by applying strain along each axis, and then releasing this strain. This process produces spring-like structures in the nanotubes that accommodate strains of up to 150% and demonstrate conductivities as high as 2,200 S cm(-1) in the stretched state. We also use the nanotube films as electrodes in arrays of transparent, stretchable capacitors, which behave as pressure and strain sensors.


title: RNN Fisher Vectors for Action Recognition and Image Annotation

abstract: Recurrent Neural Networks (RNNs) have had considerable success in classifying and predicting sequences. We demonstrate that RNNs can be effectively used in order to encode sequences and provide effective representations. The methodology we use is based on Fisher Vectors, where the RNNs are the generative probabilistic models and the partial derivatives are computed using backpropagation. State of the art results are obtained in two central but distant tasks, which both rely on sequences: video action recognition and image annotation. We also show a surprising transfer learning result from the task of image annotation to the task of video action recognition.


title: Stochastic gradient boosted distributed decision trees

abstract: Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate models. However, most implementations today are computationally expensive and require all training data to be in main memory. As training data becomes ever larger, there is motivation for us to parallelize the GBDT algorithm. Parallelizing decision tree training is intuitive and various approaches have been explored in existing literature. Stochastic boosting on the other hand is inherently a sequential process and have not been applied to distributed decision trees. In this work, we present two different distributed methods that generates exact stochastic GBDT models, the first is a MapReduce implementation and the second utilizes MPI on the Hadoop grid environment.


title: Predictive-Corrective Networks for Action Detection

abstract: While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing. Architectures and optimization techniques used for video are largely based off those for static images, potentially underutilizing rich video information. In this work, we rethink both the underlying network architecture and the stochastic learning paradigm for temporal data. To do so, we draw inspiration from classic theory on linear dynamic systems for modeling time series. By extending such models to include nonlinear mappings, we derive a series of novel recurrent neural networks that sequentially make top-down predictions about the future and then correct those predictions with bottom-up observations. Predictive-corrective networks have a number of desirable properties: (1) they can adaptively focus computation on surprising frames where predictions require large corrections, (2) they simplify learning in that only residual-like corrective terms need to be learned over time and (3) they naturally decorrelate an input data stream in a hierarchical fashion, producing a more reliable signal for learning at each layer of a network. We provide an extensive analysis of our lightweight and interpretable framework, and demonstrate that our model is competitive with the two-stream network on three challenging datasets without the need for computationally expensive optical flow.


title: Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?

abstract: Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical structures of standard sharp images to predict corresponding depth maps. However, cameras can also produce images with defocus blur depending on the depth of the objects and camera settings. Hence, these features may represent an important hint for learning to predict depth. In this paper, we propose a full system for single-image depth prediction in the wild using depth-from-defocus and neural networks. We carry out thorough experiments to test deep convolutional networks on real and simulated defocused images using a realistic model of blur variation with respect to depth. We also investigate the influence of blur on depth prediction observing model uncertainty with a Bayesian neural network approach. From these studies, we show that out-of-focus blur greatly improves the depth-prediction network performances. Furthermore, we transfer the ability learned on a synthetic, indoor dataset to real, indoor and outdoor images. For this purpose, we present a new dataset containing real all-focus and defocused images from a Digital Single-Lens Reflex (DSLR) camera, paired with ground truth depth maps obtained with an active 3D sensor for indoor scenes. The proposed approach is successfully validated on both this new dataset and standard ones as NYUv2 or Depth-in-the-Wild. Code and new datasets are available at this https URL


title: A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems

abstract: Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback in the learning process. Experiments are carried out on two TREC datasets and outline the effectiveness of our approach.


title: A delineating procedure to retrieve relevant publication data in research areas: the case of nanocellulose

abstract: Advances concerning publication-level classification system have been demonstrated striking results by dealing properly with emergent, complex and interdisciplinary research areas, such as nanotechnology and nanocellulose. However, less attention has been paid to propose a delineating method to retrieve relevant research areas on specific subjects. This study aims at proposing a procedure to delineate research areas addressed in case nanocellulose. We investigate how a bibliometric analysis could provide interesting insights into research about this sustainable nanomaterial. The research topics clustered by a Publication-level Classification System were used. The procedure involves an iterative process, which includes developing and cleaning a set of core publication regarding the subject and an analysis of clusters they are associated with. Nanocellulose was selected as the subject of study, but the methodology may be applied to any other research area or topic. A discussion about each step of the procedure is provided. The proposed delineation procedure enables us to retrieve relevant publications from research areas involving nanocellulose. Seventeen research topics were mapped and associated with current research challenges on nanocellulose.


title: Inductorless 1–10.5 GHz wideband LNA for multistandard applications

abstract: This article presents the design of a fully integrated inductorless LNA for wireless applications including WLAN, Bluetooth and UWB. The circuit was fabricated in 65nm CMOS technology and operates at a supply voltage of 1.2 V. The two-stage design is comprised of a current reuse shunt feedback input stage followed by a differential pair, incorporating an active inductor load to compensate the gain roll-off. The circuit exhibits a peak gain of 16.5 dB, while the 3-dB bandwidth as well as the input and output matching of better than −10 dB range from 1–10.5 GHz. The noise figure is kept below 5 dB within this frequency range, offering a minimum noise figure of 3.9 dB. The linearity in terms of P 1dB, out and oIP3 offers nearly constant behavior with −5 dBm and 3 dBm respectively. The active area takes up only 0.021 mm2.


title: FrontFace: facilitating communication between HMD users and outsiders using front-facing-screen HMDs

abstract: A head-mounted display (HMD) immerses users in a virtual world, but separates them from outsiders in the real world. We present FrontFace, which is a novel HMD that combines an eye-tracker with a front-facing screen, to lower the communication barrier between HMD users and outsiders. The front-facing screen reveals user attention (e.g., the users eye motions) and user presence in the virtual or real world by displaying the scene in the virtual world or a skin background respectively, enabling eye-contact interactions between the HMD user and the outsiders. FrontFace has the following benefits. Firstly, it communicates the presence of the HMD user to outsiders; secondly, it reveals the player's visual attention by introducing the HMD users originally occluded eye motions, enabling outsiders to make sense of the HMD user's reaction in the virtual world or the real world. Three interactive techniques for the outsiders to initiate communication to HMD users are proposed: they are tap-trigger, hand-gesture trigger, and voice-trigger interactions. A small focus group provided feedback.


title: Reassessing media violence effects using a risk and resilience approach to understanding aggression

abstract: Public discourse about the effect of violent media on aggression has become contentious. We propose that violent media effects can best be understood within a risk and resilience framework that considers multiple factors that facilitate or inhibit aggression. In a prospective study, 430 third through fourth grade children, their peers, and their teachers were surveyed twice during the school year, about 6 months apart. Six risk/protective factors for aggression were measured: media violence exposure (TV, movies, video games), physical victimization, participant sex, hostile attribution bias, parental monitoring, and prior aggression. Each Time 1 risk factor (including media violence exposure) was associated with an increased risk of physical aggression at Time 2, whereas protective factors were associated with a decreased risk. There was also a Gestalt-type effect, where the combination of risk factors was a better predictor of aggression than the sum of their individual parts. The results offer strong support for a risk and resilience framework for aggression. Results also suggest that the effects of media violence exposure may be underestimated by standard data analysis procedures. Exposure to media violence acts similarly to other risk factors for aggression and therefore deserves neither special acclaim nor dismissal as a risk factor.


title: A 3us wake-up time nonvolatile processor based on ferroelectric flip-flops

abstract: Nonvolatile processors offer a number of desirable properties including instant on/off, zero standby power and resilience to power failures. This paper presents a fabricated nonvolatile processor based on ferroelectric flip-flops. These flipflops are used in a distributed fashion and are able to maintain system states without any power supply indefinitely. An efficient controller is employed to achieve parallel reads and writes to the flip-flops. A reconfigurable voltage detection system is designed for automatic system backup during power failures. Measurement results show that this nonvolatile processor can operate continuously even under power failures occurring at 20 KHz. It can backup system states within 7μs and restore them within 3 μs. Such capabilities will provide a new level of support to chip-level fine-grained power management and energy harvesting applications.


title: The Mapping of Applications to Multiple Bus and Banyan Interconnected Multiprocessor Systems: A Case Study

abstract: We study the mapping of a robot elbow manipulator application, to two different classes of multiprocessor systems the multiple bus and Banyan interconnected systems. A comparative performance analysis of the two systems is performed. The application is partitioned into communicating computational modules and three different partitions of it are approximated. Fast heuristic algorithms are used to produce assignments of modules to processors. A number of performance measures are also employed to evaluate the matching of application/architecture pairs.


title: Efficient computation of Gaussian process regression for large spatial data sets by patching local Gaussian processes

abstract: This paper develops an efficient computational method for solving a Gaussian process (GP) regression for large spatial data sets using a collection of suitably defined local GP regressions. The conventional local GP approach first partitions a domain into multiple non-overlapping local regions, and then fits an independent GP regression for each local region using the training data belonging to the region. Two key issues with the local GP are (1) the prediction around the boundary of a local region is not as accurate as the prediction at interior of the local region, and (2) two local GP regressions for two neighboring local regions produce different predictions at the boundary of the two regions, creating undesirable discontinuity in the prediction. We address these issues by constraining the predictions of local GP regressions sharing a common boundary to satisfy the same boundary constraints, which in turn are estimated by the data. The boundary constrained local GP regressions are solved by a finite element method. Our approach shows competitive performance when compared with several state-of-the-art methods using two synthetic data sets and three real data sets.


title: A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling

abstract: Because of image sampling, traditional measures of pixel dissimilarity can assign a large value to two corresponding pixels in a stereo pair, even in the absence of noise and other degrading effects. We propose a measure of dissimilarity that is provably insensitive to sampling because it uses the linearly interpolated intensity functions surrounding the pixels. Experiments on real images show that our measure alleviates the problem of sampling with little additional computational overhead.


title: Minority report in fraud detection: classification of skewed data

abstract: This paper proposes an innovative fraud detection method, built upon existing fraud detection research and Minority Report, to deal with the data mining problem of skewed data distributions. This method uses backpropagation (BP), together with naive Bayesian (NB) and C4.5 algorithms, on data partitions derived from minority oversampling with replacement. Its originality lies in the use of a single meta-classifier (stacking) to choose the best base classifiers, and then combine these base classifiers' predictions (bagging) to improve cost savings (stacking-bagging). Results from a publicly available automobile insurance fraud detection data set demonstrate that stacking-bagging performs slightly better than the best performing bagged algorithm, C4.5, and its best classifier, C4.5 (2), in terms of cost savings. Stacking-bagging also outperforms the common technique used in industry (BP without both sampling and partitioning). Subsequently, this paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversampling, and SMOTEing without partitioning (sampling approach). Results show that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions. The most interesting find is confirming that the combination of classifiers to produce the best cost savings has its contributions from all three algorithms.


title: Errata: Distant Supervision for Relation Extraction with Matrix Completion

abstract: The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (entity pairs) in a sparse matrix that concatenates training and testing textual features with training labels. Our algorithmic framework is based on the assumption that the rank of item-byfeature and item-by-label joint matrix is low. We apply two optimization models to recover the underlying low-rank matrix leveraging the sparsity of feature-label matrix. The matrix completion problem is then solved by the fixed point continuation (FPC) algorithm, which can find the global optimum. Experiments on two widely used datasets with different dimensions of textual features demonstrate that our low-rank matrix completion approach significantly outperforms the baseline and the state-of-the-art methods.


Community Detection in Large Scale Big Data Networks


title: Conflict-Free Replicated Data Types

abstract: Replicating data under Eventual Consistency (EC) allows any replica to accept updates without remote synchronisation. This ensures performance and scalability in large-scale distributed systems (e.g., clouds). However, published EC approaches are ad-hoc and error-prone. Under a formal Strong Eventual Consistency (SEC) model, we study sufficient conditions for convergence. A data type that satisfies these conditions is called a Conflict-free Replicated Data Type (CRDT). Replicas of any CRDT are guaranteed to converge in a self-stabilising manner, despite any number of failures. This paper formalises two popular approaches (state- and operation-based) and their relevant sufficient conditions. We study a number of useful CRDTs, such as sets with clean semantics, supporting both add and remove operations, and consider in depth the more complex Graph data type. CRDT types can be composed to develop large-scale distributed applications, and have interesting theoretical properties.


title: Parameterized Shape Grammar for Generating n-fold Islamic Geometric Motifs

abstract: The complex formation of Islamic geometric Patterns (IGP) is one of the distinctive features in Islamic art and architecture. Many have attempted to reproduce these patterns in digital form, using various pattern generation techniques. Shape grammars are an effective pattern generation method, providing good aesthetic results. In this paper we present a novel approach in generating 3D IGP using an extended shape grammar: Parametrized Shape Grammar (PSG). PSG allows a user to generate both original and novel forms of Islamic geometric motifs (a repeated unit of a pattern). PSG is generalized to generate n-fold Islamic geometric motifs in a 3D environment, practically implemented as a 3D modeling tool within Autodesk Maya. The parametrization within each grammar rule is the key to generating numerous original and novel Islamic geometric motifs.


title: PCB substrate integrated waveguide-filter using via fences at millimeter-wave

abstract: In this paper, the implementation and the embedding method of the existing air-filled waveguide-filters at millimeter-wave on general Printed Circuit Board(PCB) substrate are introduced by systematically inserting the vias inside waveguide and mathematically manipulating the simple equations obtained from the classical circular-post waveguide the wave propagation. Because the mass production on PCB is possible without fabricating a large-scaled metal waveguide of WR-22 as input/output ports at millimeter-wave, th Bandpass-Filter(BPF) design procedure. Side walls and poles inside the waveguide are realized by placing two series array of via and tuning the via diameters. The each length of x, y, and z axes is reduced in proportion to root square of employed substrate dielectric constant and, especially, the length of z-axis can be more reduced due to the characteristics of e manufacturing cost can be reduced considerably. Finally, when using multi-layer process like low temperature cofired ceramic(LTCC) for small-sized module, it is one of advantages to use only one layer for the filter fabrication. To evaluate the validity of this novel technique, order-3 Chebyshev BPF centered at 40 GHz-band with a 2.5% Fractional Bandwidth(FBW) was used. The employed substrate has relative dielectric constant of 2.2 and thickness of 10 mils of Rogers RT/Duroid 5880. According to design and measurement results, a good performance of insertion loss of 2 dB and return loss of -30 dB is achieved at full input/output ports.


Semi-supervised semantic role labeling


title: A distribution-based clustering algorithm for mining in large spatial databases

abstract: The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper, we introduce the new clustering algorithm DBCLASD (Distribution-Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS (Clustering Large Applications based on RANdomized Search), discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) requiring two input parameters, which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.


title: Monitoring of Civil Infrastructures by Interferometric Radar: A Review

abstract: Ground-based radar interferometry is an increasingly popular technique for monitoring civil infrastructures. Many research groups, professionals, and companies have tested it in different operative scenarios, so it is time for a first systematic survey of the case studies reported in the literature. This review is addressed especially to the engineers and scientists interested to consider the applicability of the technique to their practice, so it is focused on the issues of the practical cases rather than on theory and principles, which are now well consolidated.


title: An overview of polyurethane foams in higher specification foam mattresses.

abstract: Soft polyurethane foams exist in thousands of grades and constitute essential components of hospital mattresses. For pressure ulcer prevention, the ability of foams to control the immersion and envelopment of patients is essential. Higher specification foam mattresses (i.e., foam mattresses that relieve pressure via optimum patient immersion and envelopment while enabling patient position changes) are claimed to be more effective for preventing pressure ulcers than standard mattresses. Foam grade evaluations should include resiliency, density, hardness, indentation force/load deflection, progressive hardness, tensile strength, and elongation along with essential criteria for higher specification foam mattresses. Patient-specific requirements may include optimal control of patient immersion and envelopment. Mattress cover characteristics should include breathability, impermeability to fluids, and fire safety and not affect mattress function. Additional determinations such as hardness are assessed according to the guidelines of the American Society for Testing and Materials and the International Organization for Standardization. At this time, no single foam grade provides an optimal combination of the above key requirements, but the literature suggests a combination of at least 2 foams may create an optimal higher specification foam mattress for pressure ulcer prevention. Future research and the development of product specification accuracy standards are needed to help clinicians make evidence-based decisions about mattress use.


title: Occluded Imaging with Time-of-Flight Sensors

abstract: We explore the question of whether phase-based time-of-flight (TOF) range cameras can be used for looking around corners and through scattering diffusers. By connecting TOF measurements with theory from array signal processing, we conclude that performance depends on two primary factors: camera modulation frequency and the width of the specular lobe (“shininess”) of the wall. For purely Lambertian walls, commodity TOF sensors achieve resolution on the order of meters between targets. For seemingly diffuse walls, such as posterboard, the resolution is drastically reduced, to the order of 10cm. In particular, we find that the relationship between reflectance and resolution is nonlinear—a slight amount of shininess can lead to a dramatic improvement in resolution. Since many realistic scenes exhibit a slight amount of shininess, we believe that off-the-shelf TOF cameras can look around corners.


OTDR DISTRIBUTED SENSING OF LIQUID HYDROCARBONS USING POLYMER-CLAD OPTICAL FIBERS


title: Sentence Ordering using Recurrent Neural Networks

abstract: Modeling the structure of coherent texts is a task of great importance in NLP. The task of organizing a given set of sentences into a coherent order has been ::: commonly used to build and evaluate models that understand such structure. In this work we propose an end-to-end neural approach based on the recently proposed ::: set to sequence mapping framework to address the sentence ordering problem. Our model achieves state-of-the-art performance in the order discrimination task ::: on two datasets widely used in the literature. We also consider a new interesting task of ordering abstracts from conference papers and research proposals and ::: demonstrate strong performance against recent methods. Visualizing the sentence representations learned by the model shows that the model has captured high ::: level logical structure in these paragraphs. The model also learns rich semantic sentence representations by learning to order texts, performing comparably to ::: recent unsupervised representation learning methods in the sentence similarity and paraphrase detection tasks.


title: Toward an integrated knowledge discovery and data mining process model

abstract: The knowledge discovery and data mining (KDDM) process models describe the various phases (e.g. business understanding, data understanding, data preparation, modeling, evaluation and deployment) of the KDDM process. They act as a roadmap for implementation of the KDDM process by presenting a list of tasks for executing the various phases. The checklist approach of describing the tasks is not adequately supported by appropriate tools, which specify ‘how’ the particular task can be implemented. This may result in tasks not being implemented. Another disadvantage is that the long checklist does not capture or leverage the dependencies that exist among the various tasks of the same and different phases. This not only makes the process cumbersome to implement, but also hinders possibilities for semi-automation of certain tasks. Given that each task in the process model serves an important goal and even affects the execution of related tasks due to the dependencies, these limitations are likely to negatively affect the efficiency and effectiveness of KDDM projects. This paper proposes an improved KDDM process model that overcomes these shortcomings by prescribing tools for supporting each task as well as identifying and leveraging dependencies among tasks for semi-automation of tasks, wherever possible.


title: Kinematics of a 3-DOF parallel manipulator with an R-P-S joint structure

abstract: A new solution of the inverse kinematics task for a 3-DOF parallel manipulator with a R-P-S joint structure is obtained for a given position of end-effector in the form of simple position equations. Based on this the number of the inverse kinematics task solutions was investigated, in general, equal to four. We identify the size of the manipulator feasible area and simple relationships are found between the position and orientation of the platform. We prove a new theorem stating that, while the end-effector traces a circular horizontal path with its centre at the vertical z-axis, the norm of the joint coordinates vector remains constant.


title: Meditation therapies for attention-deficit/hyperactivity disorder (ADHD).

abstract: BACKGROUND ::: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common developmental disorders experienced in childhood and can persist into adulthood. The disorder has early onset and is characterized by a combination of overactive, poorly modulated behavior with marked inattention. In the long term it can impair academic performance, vocational success and social-emotional development. Meditation is increasingly used for psychological conditions and could be used as a tool for attentional training in the ADHD population. ::: ::: ::: OBJECTIVES ::: To assess the effectiveness of meditation therapies as a treatment for ADHD. ::: ::: ::: SEARCH STRATEGY ::: Our extensive search included: CENTRAL, MEDLINE, EMBASE, CINAHL, ERIC, PsycINFO, C2-SPECTR, dissertation abstracts, LILACS, Virtual Health Library (VHL) in BIREME, Complementary and Alternative Medicine specific databases, HSTAT, Informit, JST, Thai Psychiatric databases and ISI Proceedings, plus grey literature and trial registries from inception to January 2010. ::: ::: ::: SELECTION CRITERIA ::: Randomized controlled trials that investigated the efficacy of meditation therapy in children or adults diagnosed with ADHD. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two authors extracted data independently using a pre-designed data extraction form. We contacted study authors for additional information required. We analyzed data using mean difference (MD) to calculate the treatment effect. The results are presented in tables, figures and narrative form. ::: ::: ::: MAIN RESULTS ::: Four studies, including 83 participants, are included in this review. Two studies used mantra meditation while the other two used yoga compared with drugs, relaxation training, non-specific exercises and standard treatment control. Design limitations caused high risk of bias across the studies. Only one out of four studies provided data appropriate for analysis. For this study there was no statistically significant difference between the meditation therapy group and the drug therapy group on the teacher rating ADHD scale (MD -2.72, 95% CI -8.49 to 3.05, 15 patients). Likewise, there was no statistically significant difference between the meditation therapy group and the standard therapy group on the teacher rating ADHD scale (MD -0.52, 95% CI -5.88 to 4.84, 17 patients). There was also no statistically significant difference between the meditation therapy group and the standard therapy group in the distraction test (MD -8.34, 95% CI -107.05 to 90.37, 17 patients). ::: ::: ::: AUTHORS' CONCLUSIONS ::: As a result of the limited number of included studies, the small sample sizes and the high risk of bias, we are unable to draw any conclusions regarding the effectiveness of meditation therapy for ADHD. The adverse effects of meditation have not been reported. More trials are needed.


title: Physical tests for shoulder impingements and local lesions of bursa, tendon or labrum that may accompany impingement

abstract: BACKGROUND ::: Impingement is a common cause of shoulder pain. Impingement mechanisms may occur subacromially (under the coraco-acromial arch) or internally (within the shoulder joint), and a number of secondary pathologies may be associated. These include subacromial-subdeltoid bursitis (inflammation of the subacromial portion of the bursa, the subdeltoid portion, or both), tendinopathy or tears affecting the rotator cuff or the long head of biceps tendon, and glenoid labral damage. Accurate diagnosis based on physical tests would facilitate early optimisation of the clinical management approach. Most people with shoulder pain are diagnosed and managed in the primary care setting. ::: ::: ::: OBJECTIVES ::: To evaluate the diagnostic accuracy of physical tests for shoulder impingements (subacromial or internal) or local lesions of bursa, rotator cuff or labrum that may accompany impingement, in people whose symptoms and/or history suggest any of these disorders. ::: ::: ::: SEARCH METHODS ::: We searched electronic databases for primary studies in two stages. In the first stage, we searched MEDLINE, EMBASE, CINAHL, AMED and DARE (all from inception to November 2005). In the second stage, we searched MEDLINE, EMBASE and AMED (2005 to 15 February 2010). Searches were delimited to articles written in English. ::: ::: ::: SELECTION CRITERIA ::: We considered for inclusion diagnostic test accuracy studies that directly compared the accuracy of one or more physical index tests for shoulder impingement against a reference test in any clinical setting. We considered diagnostic test accuracy studies with cross-sectional or cohort designs (retrospective or prospective), case-control studies and randomised controlled trials. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two pairs of review authors independently performed study selection, assessed the study quality using QUADAS, and extracted data onto a purpose-designed form, noting patient characteristics (including care setting), study design, index tests and reference standard, and the diagnostic 2 x 2 table. We presented information on sensitivities and specificities with 95% confidence intervals (95% CI) for the index tests. Meta-analysis was not performed. ::: ::: ::: MAIN RESULTS ::: We included 33 studies involving 4002 shoulders in 3852 patients. Although 28 studies were prospective, study quality was still generally poor. Mainly reflecting the use of surgery as a reference test in most studies, all but two studies were judged as not meeting the criteria for having a representative spectrum of patients. However, even these two studies only partly recruited from primary care.The target conditions assessed in the 33 studies were grouped under five main categories: subacromial or internal impingement, rotator cuff tendinopathy or tears, long head of biceps tendinopathy or tears, glenoid labral lesions and multiple undifferentiated target conditions. The majority of studies used arthroscopic surgery as the reference standard. Eight studies utilised reference standards which were potentially applicable to primary care (local anaesthesia, one study; ultrasound, three studies) or the hospital outpatient setting (magnetic resonance imaging, four studies). One study used a variety of reference standards, some applicable to primary care or the hospital outpatient setting. In two of these studies the reference standard used was acceptable for identifying the target condition, but in six it was only partially so. The studies evaluated numerous standard, modified, or combination index tests and 14 novel index tests. There were 170 target condition/index test combinations, but only six instances of any index test being performed and interpreted similarly in two studies. Only two studies of a modified empty can test for full thickness tear of the rotator cuff, and two studies of a modified anterior slide test for type II superior labrum anterior to posterior (SLAP) lesions, were clinically homogenous. Due to the limited number of studies, meta-analyses were considered inappropriate. Sensitivity and specificity estimates from each study are presented on forest plots for the 170 target condition/index test combinations grouped according to target condition. ::: ::: ::: AUTHORS' CONCLUSIONS ::: There is insufficient evidence upon which to base selection of physical tests for shoulder impingements, and local lesions of bursa, tendon or labrum that may accompany impingement, in primary care. The large body of literature revealed extreme diversity in the performance and interpretation of tests, which hinders synthesis of the evidence and/or clinical applicability.


title: A New Individually Addressable Micro-LED Array for Photogenetic Neural Stimulation

abstract: Here, we demonstrate the use of a micro light emitting diode (LED) array as a powerful tool for complex spatiotemporal control of photosensitized neurons. The array can generate arbitrary, 2-D, excitation patterns with millisecond and micrometer resolution. In particular, we describe an active matrix control address system to allow simultaneous control of 256 individual micro LEDs. We present the system optically integrated into a microscope environment and patch clamp electrophysiology. The results show that the emitters have sufficient radiance at the required wavelength to stimulate neurons expressing channelrhodopsin-2 (ChR2).


title: Extracting 3D Layout From a Single Image Using Global Image Structures

abstract: Extracting the pixel-level 3D layout from a single image is important for different applications, such as object localization, image, and video categorization. Traditionally, the 3D layout is derived by solving a pixel-level classification problem. However, the image-level 3D structure can be very beneficial for extracting pixel-level 3D layout since it implies the way how pixels in the image are organized. In this paper, we propose an approach that first predicts the global image structure, and then we use the global structure for fine-grained pixel-level 3D layout extraction. In particular, image features are extracted based on multiple layout templates. We then learn a discriminative model for classifying the global layout at the image-level. Using latent variables, we implicitly model the sublevel semantics of the image, which enrich the expressiveness of our model. After the image-level structure is obtained, it is used as the prior knowledge to infer pixel-wise 3D layout. Experiments show that the results of our model outperform the state-of-the-art methods by 11.7% for 3D structure classification. Moreover, we show that employing the 3D structure prior information yields accurate 3D scene layout segmentation.


title: An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors

abstract: This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known (to a certain accuracy). This problem aggregates two existing problems: (i) nonnegative matrix factorization where all entries of M are given, and (ii) low-rank matrix completion where nonnegativity is not required. By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties. We propose to solve the non-convex constrained least-squares problem using an algorithm based on the classic alternating direction augmented Lagrangian method. Preliminary convergence properties of the algorithm and numerical simulation results are presented. Compared to a recent algorithm for nonnegative matrix factorization, the proposed algorithm produces factorizations of similar quality using only about half of the matrix entries. On tasks of recovering incomplete grayscale and hyperspectral images, the proposed algorithm yields overall better qualities than those produced by two recent matrix-completion algorithms that do not exploit nonnegativity.


title: Identification and Verification of Simple Claims about Statistical Properties

abstract: In this paper we study the identification and verification of simple claims about statistical properties, e.g. claims about the population or the inflation rate of a country. We show that this problem is similar to extracting numerical information from text and following recent work, instead of annotating data for each property of interest in order to learn supervised models, we develop a distantly supervised baseline approach using a knowledge base and raw text. In experiments on 16 statistical properties about countries from Freebase we show that our approach identifies simple statistical claims about properties with 60% precision, while it is able to verify these claims without requiring any explicit supervision for either tasks. Furthermore, we evaluate our approach as a statistical property extractor and we show it achieves 0.11 mean absolute percentage error.


title: Measuring Privacy and Utility in Privacy-Preserving Visualization

abstract: In previous work, we proposed a technique for preserving the privacy of quasi-identifiers in sensitive data when visualized using parallel coordinates. This paper builds on that work by introducing a number of metrics that can be used to assess both the level of privacy and the amount of utility that can be gained from the resulting visualizations. We also generalize our approach beyond parallel coordinates to scatter plots and other visualization techniques. Privacy preservation generally entails a trade-off between privacy and utility: the more the data is protected, the less useful the visualization. Using a visually-oriented approach, we can provide a higher amount of utility than directly applying data anonymization techniques used in data mining. To demonstrate this, we use the visual uncertainty framework for systematically defining metrics based on cluster artifacts and information theoretic principles. In a case study, we demonstrate the effectiveness of our technique as compared to standard data-based clustering in the context of privacy-preserving visualization.


title: Non-genetic transmission of memes by diffusion

abstract: In recent years, there has been an increase in research activities on Memetic Algorithm (MA). MA works with memes; a meme being defined as "the basic unit of cultural transmission, or imitation" [5]. In this respect, a Memetic Algorithm essentially refers to "an algorithm that mimics the mechanisms of cultural evolution". To date, there has been significant effort in bringing MA closer to the idea of cultural evolution. In this paper we assess MAs from the perspectives of "Universal Darwinism" and "Memetics". Subsequently, we propose a Diffusion Memetic Algorithm where the memetic material is transmitted by means of non-genetic transfer. Numerical studies are presented based on some of the commonly used synthetic problems in continuous optimization.