title: Applying semantic web techniques to poem analysis

abstract: Computing technology has been eyed in many fields. Creative Computing addresses the challenges of reconciling the objective precision of computer science with the subjective ambiguities of the arts and humanities. Poetry is a creative language which is full of imagination and beauty. The ambiguity of poetry increases the difficulty in interpretation and appreciation. Based on Semantic Web techniques, the research intends to comprehensively analyse poetic elements such as syntax, style, metaphor and genre. The creativity and innovation of poetry will also be considered. In terms of the analysis results, there will be an assessment to the poem. The method aims to help promote the interdisciplinary study of language and contribute to the analysis and appreciation of verbal art.


title: Enhancing the Working Memory of Stroke Patients Using tDCS

abstract: Jo JM, Kim Y-H, Ko M-H, Ohn SH, Joen B, Lee KH: Enhancing the working memory of stroke patients using tDCS. Am J Phys Med Rehabil 2009;88: 404‐409. Objectives: We investigated whether anodal transcranial direct current stimulation over the left dorsolateral prefrontal cortex affected the working memory performance of patients after a stroke. Design: Ten patients (mean age 47.7 yrs) with cognitive deficits after a first-ever stroke participated in this single-blind, crossover, and shamcontrolled experiment. Each patient was randomly assigned to undergo two transcranial direct current stimulation sessions: anodal dorsolateral prefrontal cortex and sham stimulation within 48 hrs of a washout period. All participants performed a two-back working memory task before and after the administration of the transcranial direct current stimulation. Accuracy (correction rate), recognition accuracy (correction rate-commission error rate), and response time were measured during each experiment. Results: Repeated-measures analysis of variance indicated a significant interaction effect of transcranial direct current stimulation type and time on the recognition accuracy. Post hoc analyses revealed a significant difference between prestimulation and poststimulation in the anodal stimulation group but not in the sham stimulation group. Regarding the accuracy, the paired t test indicated significant improvement only after anodal transcranial direct current stimulation without a significant interaction effect between the two transcranial direct current stimulation types. The response time was not significantly different in the anodal and sham stimulation groups. Conclusion: Our results demonstrated that anodal transcranial direct current stimulation over the left dorsolateral prefrontal cortex was associated with enhanced working memory performance as indexed by the recognition accuracy in patients after a stroke.


title: Web Modeling Language ( WebML ) : a modeling language for designing Web sites

abstract: Abstract Designing and maintaining Web applications is one of the major challenges for the software industry of the year 2000. In this paper we present Web Modeling Language (WebML), a notation for specifying complex Web sites at the conceptual level. WebML enables the high-level description of a Web site under distinct orthogonal dimensions: its data content (structural model), the pages that compose it (composition model), the topology of links between pages (navigation model), the layout and graphic requirements for page rendering (presentation model), and the customization features for one-to-one content delivery (personalization model). All the concepts of WebML are associated with a graphic notation and a textual XML syntax. WebML specifications are independent of both the client-side language used for delivering the application to users, and of the server-side platform used to bind data to pages, but they can be effectively used to produce a site implementation in a specific technological setting. WebML guarantees a model-driven approach to Web site development, which is a key factor for defining a novel generation of CASE tools for the construction of complex sites, supporting advanced features like multi-device access, personalization, and evolution management. The WebML language and its accompanying design method are fully implemented in a pre-competitive Web design tool suite, called ToriiSoft.


title: To eat or not to eat. A comparison of current and former animal product limiters

abstract: Abstract In this exploratory study, we compared current and former pescatarians, vegetarians and vegans on a number of variables including the motivations for their food choices. Participants were recruited via online message boards as well as through snowball sampling. Of the 247 participants, 196 were currently limiting animal products and 51 were former animal product limiters. Current limiters were more likely to have made a gradual rather than abrupt transition to animal product limitation and were more likely to have joined a vegetarian or vegan group than former limiters. Furthermore, current limiters indicated that their eating pattern was a part of their self identity. These findings shed light on the differences among current and former vegans and vegetarians and can inform individuals interested in promoting animal product limitation for health or ethical reasons.


title: CMOS implementation of efficient 16-Bit square root carry-select adder

abstract: A 16-bit Square Root Carry-Select Adder (SQRT CSA) is implemented and analyzed in this paper. The SQRT CSA is an architecture level modification to reduce area and power dissipation as compared to that of conventional CSA. Conventional CSA with Cin=1 block is replaced with binary-to-excess-1 converter (BEC) in the modified SQRT CSA structure. The architecture of 16-bit CSA is configured into five different stages with progressivley increasing data size. In order to realize 16-bit CSA, the basic building blocks, e.g., XOR gate, AND gate, 2:1 Mux, half adder (HA) and full adder (FA) are implemented using CMOS transmission gate (CMOS TG). The ripple carry adder (RCA) and binary-to-excess converter (BEC) of different bit sizes are also implemented. Transistor level schematics are drawn using Mentor Graphics Design Architect and simulations are carried out using Eldo with TSMC 0.35μm CMOS technology and supply voltage of 3.3 V.


title: Adversarial Semantic Scene Completion from a Single Depth Image

abstract: We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image. We improve the accuracy of the regressed semantic 3D maps by a novel architecture based on adversarial learning. In particular, we suggest using multiple adversarial loss terms that not only enforce realistic outputs with respect to the ground truth, but also an effective embedding of the internal features. This is done by correlating the latent features of the encoder working on partial 2.5D data with the latent features extracted from a variational 3D auto-encoder trained to reconstruct the complete semantic scene. In addition, differently from other approaches that operate entirely through 3D convolutions, at test time we retain the original 2.5D structure of the input during downsampling to improve the effectiveness of the internal representation of our model. We test our approach on the main benchmark datasets for semantic scene completion to qualitatively and quantitatively assess the effectiveness of our proposal.


title: A liveness detection method for face recognition based on optical flow field

abstract: It is a common spoof to use a photograph to fool face recognition algorithm. In light of differences in optical flow fields generated by movements of two-dimensional planes and three-dimensional objects, we proposed a new liveness detection method for face recognition. Under the assumption that the test region is a two-dimensional plane, we can obtain a reference field from the actual optical flow field data. Then the degree of differences between the two fields can be used to distinguish between a three-dimensional face and a two-dimensional photograph. Empirical study shows that the proposed approach is both feasible and effective.


title: Explosive activity of the South Italian volcanoes during the past 80,000 years as determined by marine tephrochronology

abstract: Abstract A chronological reconstruction of the explosive activity of the south Italian volcanoes over the past 80 ka is proposed from the geochemical study of marine tephra, interbedded in deep-sea sediment cores collected in the central Mediterranean Sea. Chronology is derived from detailed oxygen-isotope analyses, and from correlation of the marine tephra with their terrestrial equivalent, dated by the classical radiometric methods. The origin of the ash-layers was determined by major-element analyses. Products of Mount Etna, the island of Pantelleria, the Eolian islands and the Campanian area were studied. On the island of Ischia, two distinct major periods of activity were observed, related to the “Green Tuff Series” from 62 to 50 ka and to the “Citara-Ciglio-Serrara Series” from 42 to 25 ka. In the Campi Flegrei, the periods of major explosive activity occurred between about 40 and 25 ka, referred to as the “Campanian Ignimbrite Series”, and during the past 16 ka, known as the “Neapolitan Yellow tuff and Agnano Series”. Geochemical and chronological investigations of the marine tephra suggest that such larger terrestrial deposits, as the “Green Tuff” of Mount Epomeo on Ischia island, or the “Campanian Ignimbrite” in the Campi Flegrei result from several distinct eruptions. The “Green Tuff” of Mount Epomeo would have originated from at least 2 large eruptions, dated at 55.4 ± 2.2 and 60.3 ± 2.2 ka. The formation of the “Campanian Ignimbrite” would be the result of 5 distinct eruptions, the most important occurring at 36, 33.5 and 26.9 ka. Finally, the geographical shift in the activity, as is observed particularly by the decrease in the explosive activity in the past 15 ka on the island of Ischia, matching the increase in the Campi Flegrei, suggests modifications of the tectonic context of the Campanian area.


title: Hybrid control for large swarms of aquatic drones

abstract: Maritime tasks, such as surveillance and patrolling, aquaculture inspection, and wildlife monitoring, typically require large operational crews and expensive equipment. Only recently have unmanned vehicles started to be used for such missions. These vehicles, however, tend to be expensive and have limited coverage, which prevents large-scale deployment. In this paper, we propose a scalable robotics system based on swarms of small and inexpensive aquatic drones. We take advantage of bio-inspired artificial evolution techniques in order to synthesize scalable and robust collective behaviors for the drones. The behaviors are then combined hierarchically with preprogrammed control in an engineeredcentric approach, allowing the overall behavior for a particular mission to be quickly configured and tested in simulation before the aquatic drones are deployed. We demonstrate the scalability of our hybrid approach by successfully deploying up to 1,000 simulated drones to patrol a 20 km long strip for 24 hours.


title: Efficient Spam Email Filtering using Adaptive Ontology

abstract: Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of spam emails. As spammers always try to find a way to evade existing filters, new filters need to be developed to catch spam. Ontologies allow for machine-understandable semantics of data. It is important to share information with each other for more effective spam filtering. Thus, it is necessary to build ontology and a framework for efficient email filtering. Using ontology that is specially designed to filter spam, bunch of unsolicited bulk email could be filtered out on the system. This paper proposes to find an efficient spam email filtering method using adaptive ontology


title: Reactive Game Engine Programming for STEM Outreach

abstract: In this paper we present our experiences using a novel programming style, reactive programming, to deliver a summer camp for students in grades 8 through 12. This software uses a declarative programming approach to allow students without a background in computing to explore a wide variety of subject material within a 3D virtual environment, including computer science, mathematics, physics, and art. This work is based on PyFRP, a reactive programming library written in Python. We describe our camp experience and provide examples of how this style of programming supports a wide variety of educational activities.


A New Kind of Science


title: FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

abstract: We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on this http URL


title: SDN Enabled Dual Cluster Head Selection and Adaptive Clustering in 5G-VANET

abstract: Nowadays, self-driving vehicles which would shoulder the burden of driving and set free human on board are gradually becoming a reality. Consequently, the supporting of growing in-vehicle data traffic will be challenging in future 5G and vehicular networks, due to the high mobility nature of vehicles and the densified irregular distribution on road especially during rush time. Therefore in this paper, a Software-Defined Networking (SDN) enabled integrated 5G-VANET architecture is proposed to improve heterogeneous network (HetNet) management and aggregate vehicle traffic through IEEE 802.11p; a novel vehicle clustering method and dual cluster head design are then introduced to reduce signaling overhead and enhance the overall communication quality in 5G-VANET HetNet under the coordination of SDN. It is also proved by simulation that the proposed design reduced 5G users' blocking probability to the operators services with a back-up cluster head (CH) in each cluster, and also realized adaptive clustering without excessive SDN's processing delay.


Improvement of distant-talking speaker identification using bottleneck features of DNN.


title: Genetic diversity and population structure of Chinese Lentinula edodes revealed by InDel and SSR markers

abstract: Genetic diversity and population structure of 88 Chinese Lentinula edodes strains belonging to four geographic populations were inferred from 68 Insertion-Deletion (InDel) and two simple sequence repeat (SSR) markers. The overall values of Shannon’s information index and gene diversity were 0.836 and 0.435, respectively, demonstrating a high genetic diversity in Chinese L. edodes strains. Among the four geographic populations, the Central China population displayed a lower genetic diversity. Multiple analyses resolved two unambiguous genetic groups that corresponded to two regions from which the samples were collected—one was a high-altitude region (region 1) and the other was a low-altitude region (region 2). Results from analysis of molecular variance suggested that the majority of genetic variation was contained within populations (74.8 %). Although there was a strong genetic differentiation between populations (FST = 0.252), the variability of ITS sequences from representative strains of the two regions (<3 %) could not support the existence of cryptic species. Pairwise FST values and Nei’s genetic distances showed that there were relatively lower genetic differentiations and genetic distances between populations from the same region. Geographic distribution could play a vital role in the formation of the observed population structure. Mycelium growth rate and precocity of L. edodes strains displayed significant differences between the two regions. Strains from region 2 grew faster and fructified earlier, which could be a result of adaptation to local environmental factors. To the best of our knowledge, this was the first study on the genetic structure and differentiation between populations, as well as the relationship between genetic structure and phenotypic traits in L. edodes.


title: A feasibility study of crowdsourcing and google street view to determine sidewalk accessibility

abstract: We explore the feasibility of using crowd workers from Amazon Mechanical Turk to identify and rank sidewalk accessibility issues from a manually curated database of 100 Google Street View images. We examine the effect of three different interactive labeling interfaces (Point, Rectangle, and Outline) on task accuracy and duration. We close the paper by discussing limitations and opportunities for future work.


title: Adaptive image compression in wireless sensor networks

abstract: The main obstacle to transmit images in wireless sensor networks is the lack of an appropriate strategy for processing the large volume of data such as images. The high rate packets errors because of what numbers very high packets carrying the data of the captured images and the need for retransmission in case of errors, and more, the energy reserve and band bandwidth is insufficient to accomplish these tasks. This paper presents new effective technique called “Background subtraction” to compress, process and transmit the images in a wireless sensor network. The practical results show the effectiveness of this approach to make the image compression in the networks of wireless sensors achievable, reliable and efficient in terms of energy and the minimization of amount of image data.


title: The Road to Fully Integrated DC–DC Conversion via the Switched-Capacitor Approach

abstract: This paper provides a perspective on progress toward realization of efficient, fully integrated dc-dc conversion and regulation functionality in CMOS platforms. In providing a comparative assessment between the inductor-based and switched-capacitor approaches, the presentation reviews the salient features in effectiveness in utilization of switch technology and in use and implementation of passives. The analytical conclusions point toward the strong advantages of the switched-capacitor (SC) approach with respect to both switch utilization and much higher energy densities of capacitors versus inductors. The analysis is substantiated with a review of recently developed and published integrated dc-dc converters of both the inductor-based and SC types.


title: A brain-computer interface for extended reality interfaces

abstract: Extended reality (XR) technologies, such as augmented reality (AR) and virtual reality (VR), remain limited in their interaction modalities. Prevailing interaction methods such as hand gestures and voice recognition prove awkward in XR environments, even when performing common tasks (e.g., object selection, menu navigation, and others). In contrast, an ideal interaction method would robustly and naturally translate a user's intention into both 2D and 3D environmental controls. A direct brain-computer interface (BCI) system is ideally situated to accomplish this. Neurable's technology provides a solution to maximize XR's potential, affording users real-time mental selection via dry electroencephalography (EEG).


title: What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation

abstract: The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographical influence, social correlations and textual content indications. For example, user's visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload photos associating with locations. Photos not only reflect users' interests but also provide informative descriptions about locations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recommendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommendation (VPOI), which incorporates visual contents for POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.


title: 1800A/3.3kV IGBT Module using Advanced Trench HiGT Structure and Module Design Optimization

abstract: 1800A/3.3kV IGBT module with the highest current rating was developed. Advanced Trench HiGT structure was used to achieve low loss characteristics. Module electrical and thermal characteristic were optimized in order to reduce thermal resistance and parasitic inductance. The current ratings of new IGBT module can be increased by 20% from the conventional product type.


title: Methods to identify degree of deception & represent non-deceptive information in concurrent engineering

abstract: This paper is a continuation of a previous work regarding deception detection in concurrent engineering. In earlier work, we studied the deception detection techniques, analyzed the deceptive information in concurrent engineering and identified the concepts to represent the deception. Earlier work revealed that ‘Sharing of Reliable and complete information’ plays a significant role in implementing concurrent engineering. It is essential to have a framework and methods which enable deception detection in product development that provides an indication on the reliability of the information. This paper is concerned with identifying the degree of deception and a model to represent non-deceptive information. The model has been tested with the case study using the information pertaining to Process Data Model and Product Data Model of the Design of Stapler and found successful.


title: Potential Bacillus probiotics enhance bacterial numbers, water quality and growth during early development of white shrimp (Litopenaeus vannamei).

abstract: Epidemics of epizootics and occurrence of multiresistant antibiotics of pathogenic bacteria in aquaculture have put forward a development of effective probiotics for the sustainable culture. This study examined the effectiveness of forms of mixed Bacillus probiotics (probiotic A and probiotic B) and mode of probiotic administration on growth, bacterial numbers and water quality during rearing of white shrimp (Litopenaeus vannamei) in two separated experiments: (1) larval stages and (2) postlarval (PL) stages. Forms of Bacillus probiotics and modes of probiotic administration did not affect growth and survival of larval to PL shrimp. The compositions of Bacillus species in probiotic A and probiotic B did not affect growth and survival of larvae. However, postlarvae treated with probiotic B exhibited higher (P<0.05) growth than probiotic A and controls, indicating Bacillus probiotic composition affects the growth of PL shrimp. Total heterotrophic bacteria and Bacillus numbers in larval and PL shrimp or culture water of the treated groups were higher (P<0.05) than in controls. Levels of pH, ammonia and nitrite of the treated shrimp were significantly decreased, compared to the controls. Microencapsulated Bacillus probiotic was effective for rearing of PL L. vannamei. This investigation showed that administration of mixed Bacillus probiotics significantly improved growth and survival of PL shrimp, increased beneficial bacteria in shrimp and culture water and enhanced water quality for the levels of pH, ammonia and nitrite of culture water.


title: A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

abstract: We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both a molecular data-based, and a mitosis counting-based tumor proliferation score. The framework integrates three modules, each fine-tuned to maximize the overall performance: An image processing component for handling whole slide images, a deep learning based mitosis detection network, and a proliferation scores prediction module. We have achieved 0.567 quadratic weighted Cohen’s kappa in mitosis counting-based score prediction and 0.652 F1-score in mitosis detection. On Spearman’s correlation coefficient, which evaluates predictive accuracy on the molecular data based score, the system obtained 0.6171. Our approach won first place in all of the three tasks in Tumor Proliferation Assessment Challenge 2016 which is MICCAI grand challenge.


title: Illuminating search spaces by mapping elites

abstract: Many fields use search algorithms, which automatically explore a search space to find high-performing solutions: chemists search through the space of molecules to discover new drugs; engineers search for stronger, cheaper, safer designs, scientists search for models that best explain data, etc. The goal of search algorithms has traditionally been to return the single highest-performing solution in a search space. Here we describe a new, fundamentally different type of algorithm that is more useful because it provides a holistic view of how high-performing solutions are distributed throughout a search space. It creates a map of high-performing solutions at each point in a space defined by dimensions of variation that a user gets to choose. This Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm illuminates search spaces, allowing researchers to understand how interesting attributes of solutions combine to affect performance, either positively or, equally of interest, negatively. For example, a drug company may wish to understand how performance changes as the size of molecules and their cost-to-produce vary. MAP-Elites produces a large diversity of high-performing, yet qualitatively different solutions, which can be more helpful than a single, high-performing solution. Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms. We demonstrate the benefits of this new algorithm in three different problem domains ranging from producing modular neural networks to designing simulated and real soft robots. Because MAP- Elites (1) illuminates the relationship between performance and dimensions of interest in solutions, (2) returns a set of high-performing, yet diverse solutions, and (3) improves finding a single, best solution, it will advance science and engineering.


title: Automated Demand-driven Resource Scaling in Relational Database-as-a-Service

abstract: Relational Database-as-a-Service (DaaS) platforms today support the abstraction of a resource container that guarantees a fixed amount of resources. Tenants are responsible for selecting a container size suitable for their workloads, which they can change to leverage the cloud's elasticity. However, automating this task is daunting for most tenants since estimating resource demands for arbitrary SQL workloads in an RDBMS is complex and challenging. In addition, workloads and resource requirements can vary significantly within minutes to hours, and container sizes vary by orders of magnitude both in the amount of resources as well as monetary cost. We present a solution to enable a DaaS to auto-scale container sizes on behalf of its tenants. Approaches to auto-scale stateless services, such as web servers, that rely on historical resource utilization as the primary signal, often perform poorly for stateful database servers which are significantly more complex. Our solution derives a set of robust signals from database engine telemetry and combines them to significantly improve accuracy of demand estimation for database workloads resulting in more accurate scaling decisions. Our solution raises the abstraction by allowing tenants to reason about monetary budget and query latency rather than resources. We prototyped our approach in Microsoft Azure SQL Database and ran extensive experiments using workloads with realistic time-varying resource demand patterns obtained from production traces. Compared to an approach that uses only resource utilization to estimate demand, our approach results in 1.5x to 3x lower monetary costs while achieving comparable query latencies.


title: Whole-body aerial manipulation by transformable multirotor with two-dimensional multilinks

abstract: In this paper, we introduce the achievement of the aerial manipulation by using the whole body of a transformable aerial robot, instead of attaching an additional manipulator. The aerial robot in our work is composed by two-dimensional multilinks which enable a stable aerial transformation and can be employed as an entire gripper. We propose a planning method to find the optimized grasping form for the multilinks while they are on the air, which is based on the original planar enveloping algorithm, along with the optimization of the internal force and joint torque for the force-closure. We then propose the aerial approach and grasp motion strategy, which is devoted to the determination of the form and position of the aerial robot to approach and grasp effectively the object from the air. Finally we present the experimental results of the aerial manipulation which involves grasping, carrying and dropping different types of object. These results validate the performance of aerial grasping based on our proposed whole-body grasp planning and motion control method.


title: Optimizing the Multiclass F-Measure via Biconcave Programming

abstract: The F-measure and its variants are performance measures of choice for evaluating classification and retrieval tasks in the presence of severe class imbalance. It is thus highly desirable to be able to directly optimize these performance measures on large-scale data. Recent advances have shown that this is possible in the simple binary classification setting. However, scant progress exists in multiclass settings with a large number of classes where, in addition, class-imbalance is much more severe. The lack of progress is especially conspicuous for the macro-averaged F-measure, which is the widely preferred F-measure variant in multiclass settings due to its equal emphasis on rare classes. Known methods of optimization scale poorly for macro F-measure, often requiring run times that are exponential in the number of classes. We develop BEAM-F, the first efficient method for directly optimizing the macro F-measure in multiclass settings. The challenge here is the intractability of optimizing a sum of fractional-linear functions over the space of confusion matrices. We overcome this difficulty by formulating the problem as a biconcave maximization program and solve it using an efficient alternating maximization approach that involves a Frank-Wolfe based iterative solver. Our approach offers guaranteed convergence to a stationary point and experiments show that, for a range synthetic data sets and real-world applications, our method offers superior performance on problems exhibiting large class imbalance.


title: RAPter: rebuilding man-made scenes with regular arrangements of planes

abstract: With the proliferation of acquisition devices, gathering massive volumes of 3D data is now easy. Processing such large masses of pointclouds, however, remains a challenge. This is particularly a problem for raw scans with missing data, noise, and varying sampling density. In this work, we present a simple, scalable, yet powerful data reconstruction algorithm. We focus on reconstruction of man-made scenes as regular arrangements of planes (RAP), thereby selecting both local plane-based approximations along with their global inter-plane relations. We propose a novel selection formulation to directly balance between data fitting and the simplicity of the resulting arrangement of extracted planes. The main technical contribution is a formulation that allows less-dominant orientations to still retain their internal regularity, and not become overwhelmed and regularized by the dominant scene orientations. We evaluate our approach on a variety of complex 2D and 3D pointclouds, and demonstrate the advantages over existing alternative methods.


Co-Reference Resolution for the Indonesian Language Using Association Rules


Acaricidal properties of extracts of some medicinal and culinary plants against Tetranychus urticae Koch.


title: Monitoring the water quality of the gongchoncheon river over a one year period in south Korea

abstract: Water is one of the most important substances required by humans and other living things; therefore there are many international regulations and laws in place to prevent pollution. The purpose of this study was to assess the water quality and analyze the concentration variables over a one year period. Environmental sampling was conducted for 3 months at 4 sites in the Gongchoncheon river for one year in Incheon, Korea. Variables such as dissolved oxygen (DO), pH, temperature, and electrical conductivity (EC were measured using the YSI 556 water quality meter The biochemical oxygen demand (BOD), chemica oxygen demand (COD), and suspended solids (SSs were manually analyzed according to the standard method. The concentrations of most of the variables were over the water standard I. The time history concentration patterns, except in site A, were increased from summer to winter. In addition, the levels of the variables at site D were higher than those of the other sites. Based on the correlation analysis results, the BOD and SSs were positively correlated, but DO and temperature were negatively correlated. Overall, this research suggests that some caution is required in managing the water quality downriver, based on the season.


title: On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

abstract: This chapter reproduces the English translation by B. Seckler of the paper by Vapnik and Chervonenkis in which they gave proofs for the innovative results they had obtained in a draft form in July 1966 and announced in 1968 in their note in Soviet Mathematics Doklady. The paper was first published in Russian as Вапник В. Н. and Червоненкис А. Я. О равномерноЙ сходимости частот появления событиЙ к их вероятностям. Теория вероятностеЙ и ее применения 16(2), 264–279 (1971).


title: Objective Classes for Micro-Facial Expression Recognition

abstract: Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.


title: Programming by optimization

abstract: Avoid premature commitment, seek design alternatives, and automatically generate performance-optimized software.


Indicators for Measuring the Global Performance and Value of Innovations*


title: Slice-timing effects and their correction in functional MRI

abstract: Exact timing is essential for functional MRI data analysis. Datasets are commonly measured using repeated 2D imaging methods, resulting in a temporal offset between slices. To compensate for this timing difference, slice-timing correction (i.e. temporal data interpolation) has been used as an fMRI pre-processing step for more than fifteen years. However, there has been an ongoing debate about the effectiveness and applicability of this method. This paper presents the first elaborated analysis of the impact of the slice-timing effect on simulated data for different fMRI paradigms and measurement parameters, taking into account data noise and smoothing effects. Here we show, depending on repetition time and paradigm design, slice-timing effects can significantly impair fMRI results and slice-timing correction methods can successfully compensate for these effects and therefore increase the robustness of the data analysis. In addition, our results from simulated data were supported by empirical in vivo datasets. Our findings suggest that slice-timing correction should be included in the fMRI pre-processing pipeline.


title: Printing teddy bears: a technique for 3D printing of soft interactive objects

abstract: This paper considers the design, construction, and example use of a new type of 3D printer which fabricates three-dimensional objects from soft fibers (wool and wool blend yarn). This printer allows the substantial advantages of additive manufacturing techniques (including rapid turn-around prototyping of physical objects and support for high levels of customization and configuration) to be employed with a new class of material. This material is a form of loose felt formed when fibers from an incoming feed of yarn are entangled with the fibers in layers below it. The resulting objects recreate the geometric forms specified in the solid models which specify them, but are soft and flexible -- somewhat reminiscent in character to hand knitted materials. This extends 3D printing from typically hard and precise forms into a new set of forms which embody a different aesthetic of soft and imprecise objects, and provides a new capability for researchers to explore the use of this class of materials in interactive devices.


title: Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European

abstract: Ancient genomic sequences have started to reveal the origin and the demographic impact of farmers from the Neolithic period spreading into Europe. The adoption of farming, stock breeding and sedentary societies during the Neolithic may have resulted in adaptive changes in genes associated with immunity and diet. However, the limited data available from earlier hunter-gatherers preclude an understanding of the selective processes associated with this crucial transition to agriculture in recent human evolution. Here we sequence an approximately 7,000-year-old Mesolithic skeleton discovered at the La Brana-Arintero site in Leon, Spain, to retrieve a complete pre-agricultural European human genome. Analysis of this genome in the context of other ancient samples suggests the existence of a common ancient genomic signature across western and central Eurasia from the Upper Paleolithic to the Mesolithic. The La Brana individual carries ancestral alleles in several skin pigmentation genes, suggesting that the light skin of modern Europeans was not yet ubiquitous in Mesolithic times. Moreover, we provide evidence that a significant number of derived, putatively adaptive variants associated with pathogen resistance in modern Europeans were already present in this hunter-gatherer.


title: HoloClean: Holistic Data Repairs with Probabilistic Inference

abstract: We introduce HoloClean, a framework for holistic data repairing driven by probabilistic inference. HoloClean unifies qualitative data repairing, which relies on integrity constraints or external data sources, with quantitative data repairing methods, which leverage statistical properties of the input data. Given an inconsistent dataset as input, HoloClean automatically generates a probabilistic program that performs data repairing. Inspired by recent theoretical advances in probabilistic inference, we introduce a series of optimizations which ensure that inference over HoloClean's probabilistic model scales to instances with millions of tuples. We show that HoloClean finds data repairs with an average precision of ∼ 90% and an average recall of above ∼ 76% across a diverse array of datasets exhibiting different types of errors. This yields an average F1 improvement of more than 2× against state-of-the-art methods.


title: for the Expansion of Death and Mourning

abstract: Online identities survive the deaths of those they represent, leaving friends and families to struggle with the appropriate ways to incorporate these identities into the practices of grief and mourning, raising important questions. How are practices of online memorialization connected to conventional rituals of grief and mourning? What is the role of online digital identity postmortem? How do trajectories of death and dying incorporate both online and offline concerns? Based on our qualitative study of death and mourning online, we identify the way that social networking sites enable expansion—temporally, spatially, and socially—of public mourning. Rather than looking at online practices as disruptions of traditional practices of grief and memorialization, we examine them as new sites in which public mourning takes place.


title: Location management for mobile commerce applications in wireless Internet environment

abstract: With recent advances in devices, middleware, applications and networking infrastructure, the wireless Internet is becoming a reality. We believe that some of the major drivers of the wireless Internet will be emerging mobile applications such as mobile commerce. Although many of these are futuristic, some applications including user-and location-specific mobile advertising, location-based services, and mobile financial services are beginning to be commercialized. Mobile commerce applications present several interesting and complex challenges including location management of products, services, devices, and people. Further, these applications have fairly diverse requirements from the underlying wireless infrastructure in terms of location accuracy, response time, multicast support, transaction frequency and duration, and dependability. Therefore, research is necessary to address these important and complex challenges. In this article, we present an integrated location management architecture to support the diverse location requirements of m-commerce applications. The proposed architecture is capable of supporting a range of location accuracies, wider network coverage, wireless multicast, and infrastructure dependability for m-commerce applications. The proposed architecture can also support several emerging mobile applications. Additionally, several interesting research problems and directions in location management for wireless Internet applications are presented and discussed.


title: Axial-Flux PM Machines With Variable Air Gap

abstract: Laminated soft magnetic steel is very often used to manufacture the stator cores of axial-flux PM machines. However, as the magnetic flux typically has main components parallel to the lamination plane, different magnetic flux density levels may occur over the radial direction: High flux densities near the saturation level are found at the inner radius, while the laminations at the outer radius are used inefficiently. To obtain a leveled magnetic flux density, this paper introduces a radially varying air gap: At the inner radius, the air gap is increased, while at the outer radius, the air gap remains unchanged. This results in equal flux densities in the different lamination layers. As the total flux in the stator cores is decreased due to the variable air gap, the permanent-magnet thickness should be increased to compensate for this. The effect of a variable air gap is tested for both a low-grade non-oriented and a high-grade grain-oriented material. For both materials, the redistribution of the magnetic flux due to the variable air gap results in a significant decrease of the iron losses. In the presented prototype machine, the iron losses are reduced up to 8% by introducing a variable air gap. Finally, a prototype machine is constructed using an efficient manufacturing procedure to construct the laminated magnetic stator cores with variable air gap.


title: Integrating the Microsoft Kinect With Simulink: Real-Time Object Tracking Example

abstract: The introduction of low-cost, relatively high-resolution 3-D sensing systems, like the Microsoft Kinect, has considerable potential in autonomous system applications. One of the impediments to wider Kinect application development is that the available Kinect drivers define C language interfaces. To help fully realize the Kinect's potential, the aims of this paper are 1) to develop a “VU-Kinect ” block which provides easy access to the sensor's camera and depth image streams, and which enables them to be incorporated seamlessly within a higher level, Simulink-based, image-processing and real-time control strategy; 2) to address implementation issues associated with the Kinect, such as sensor calibration; and 3) to show the utility of both the VU-Kinect block and the Kinect itself through a simple 3-D object tracking example.


Confusion Graph: Detecting Confusion Communities in Large Scale Image Classification


title: Fingerprint quality and validity analysis

abstract: Discusses methods to estimate the quality as well as validity of a fingerprint image. Orientation certainty is used to certify the localized texture pattern of the fingerprint images while ridge to valley structure is analyzed to detect invalid images. Global uniformity and continuity ensures that the image is valid as a whole. 150 images with various qualities are evaluated using the proposed algorithm and quality benchmark we defined. A monotonic relationship is found indicating that the proposed algorithm is feasible in detecting low quality as well as invalid fingerprint images.


title: Caffeine acts through neuronal adenosine A2Areceptors to prevent mood and memory dysfunction triggered by chronic stress

abstract: Abstract ::: The consumption of caffeine (an adenosine receptor antagonist) correlates inversely with depression and memory deterioration, and adenosine A2A receptor (A2AR) antagonists emerge as candidate therapeutic targets because they control aberrant synaptic plasticity and afford neuroprotection. Therefore we tested the ability of A2AR to control the behavioral, electrophysiological, and neurochemical modifications caused by chronic unpredictable stress (CUS), which alters hippocampal circuits, dampens mood and memory performance, and enhances susceptibility to depression. CUS for 3 wk in adult mice induced anxiogenic and helpless-like behavior and decreased memory performance. These behavioral changes were accompanied by synaptic alterations, typified by a decrease in synaptic plasticity and a reduced density of synaptic proteins (synaptosomal-associated protein 25, syntaxin, and vesicular glutamate transporter type 1), together with an increased density of A2AR in glutamatergic terminals in the hippocampus. Except for anxiety, for which results were mixed, CUS-induced behavioral and synaptic alterations were prevented by (i) caffeine (1 g/L in the drinking water, starting 3 wk before and continued throughout CUS); (ii) the selective A2AR antagonist KW6002 (3 mg/kg, p.o.); (iii) global A2AR deletion; and (iv) selective A2AR deletion in forebrain neurons. Notably, A2AR blockade was not only prophylactic but also therapeutically efficacious, because a 3-wk treatment with the A2AR antagonist SCH58261 (0.1 mg/kg, i.p.) reversed the mood and synaptic dysfunction caused by CUS. These results herald a key role for synaptic A2AR in the control of chronic stress-induced modifications and suggest A2AR as candidate targets to alleviate the consequences of chronic stress on brain function.


title: A 34.5GHz planar magic-T based on coupling slot and substrate integrated waveguide

abstract: In this paper, a ka-band magic-T using substrate integrated waveguide (SIW) and coupling slot is proposed and developed for microwave applications on the basis of the substrate integrated waveguide (SIW) technology. Due to the metal ground between the input and output ports the planar magic-T consists of a 180 degree phase-reversal junction and an H-plane SIW T-junction. Theoretical analyses and the optimized wideband magic-T structure is demonstrated and discussed with the models of the magic-T. The magic-T is simulated with a 3D FEM electromagnetic simulation software and the results are presented. The frequency bandwidth is 20.3%, when the simulated VSWR of the port of the magic-T is less than 1.5 from 31GHz to 38GHz, and the simulated amplitude imbalance is less than ±0.22dB, and the simulated phase imbalance is less than ±0.8°, and the simulated isolation between the sum port and the difference port is higher than 43dB.


title: Open-Domain Neural Conversational Agents: The Step Towards Artificial General Intelligence

abstract: Development of conversational agents started half century ago and since then it has transformed into a technology that is accessible in various aspects in everyday life. This paper presents a survey current state-of-the-art in the open domain neural conversational agent research and future research directions towards Artificial General Intelligence (AGI) creation. In order to create a conversational agent which is able to pass the Turing Test, numerous research efforts are focused on open-domain dialogue system. This paper will present latest research in domain of Neural Network reasoning and logical association, sentiment analysis and real-time learning approaches applied to open domain neural conversational agents. As an effort to provide future research directions, current cuttingedge approaches applied to open domain neural conversational agents, current cutting-edge approaches in rationale generation and the state-of-the-art research directions in alternative training methods will be discussed in this paper.


title: Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks

abstract: To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images.


title: Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning

abstract: This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG_TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21% and 45.21%, respectively.


title: Sketching in circuits: designing and building electronics on paper

abstract: The field of new methods and techniques for building electronics is quickly growing - from research in new materials for circuit building, to modular toolkits, and more recently to untoolkits, which aim to incorporate more off-the-shelf parts. However, the standard mediums for circuit design and construction remain the breadboard, protoboard, and printed circuit board (PCB). As an alternative, we introduce a method in which circuits are hand-made on ordinary paper substrates, connected with conductive foil tape and off-the-shelf circuit components with the aim of supporting the durability, scalability, and accessibility needs of novice and expert circuit builders alike. We also used electrified notebooks to investigate how the circuit design and build process would be affected by the constraints and affordances of the bound book. Our ideas and techniques were evaluated through a series of workshops, through which we found our methods supported a wide variety of approaches and results - both technical and expressive - to electronics design and construction.


title: Soft robotic glove with integrated sEMG sensing for disabled people with hand paralysis

abstract: Stroke affects about 15 million people worldwide annually, and 80% of stroke survivors are left with hand paralysis. A wearable soft robotic glove that aims to restore basic hand function was designed to improve quality of life for people with hand paralysis. This soft robotic glove utilizes a cable system and an under-actuated mechanism to assist patients to grasp objects or open hand. The glove part of this device only weights 50g, and can generate a maximum 35N grasp force, which is capable of handling most daily life activities. Objects with various shapes can be grasped without active control due to the usage of under-actuated mechanism. Myoelectric signals are used as control source of the glove through integrated surface electromyography electrodes (sEMG). Besides, a proportional control method is adopted to improve the performance of our device. We also combine sEMG control and Mirror Therapy together in order to generate an effective method for rehabilitation training.


title: An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications

abstract: Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems have been designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning methods in detecting the new threat scenarios of command and data injection. Similar to network intrusion detection systems in the cyber security domain, the command and control communications in a critical infrastructure setting are monitored, and vetted against examples of benign and malicious command traffic, in order to identify potential attack events. Multiple learning methods are evaluated using a dataset of Remote Terminal Unit communications, which included both normal operations and instances of command and data injection attack scenarios.


title: Noise in the animal shelter environment : building design and the effects of daily noise exposure

abstract: Sound levels in animal shelters regularly exceed 100 dB. Noise is a physical stressor on animals that can lead to behavioral, physiological, and anatomical responses. There are currently no policies regulating noise levels in dog kennels. The objective of this study was to evaluate the noise levels dogs are exposed to in an animal shelter on a continuous basis and to determine the need, if any, for noise regulations. Noise levels at a newly constructed animal shelter were measured using a noise dosimeter in all indoor dog-holding areas. These holding areas included large dog adoptable, large dog stray, small dog adoptable, small dog stray, and front intake. The noise level was highest in the large adoptable area. Sound from the large adoptable area affected some of the noise measurements for the other rooms. Peak noise levels regularly exceeded the measuring capability of the dosimeter (118.9 dBA). Often, in new facility design, there is little attention paid to noise abatement, despite the evidence that ...


title: A Real Linear and Parallel Multiple Longest Common Subsequences (MLCS) Algorithm

abstract: Information in various applications is often expressed as character sequences over a finite alphabet (e.g., DNA or protein sequences). In Big Data era, the lengths and sizes of these sequences are growing explosively, leading to grand challenges for the classical NP-hard problem, namely searching for the Multiple Longest Common Subsequences (MLCS) from multiple sequences. In this paper, we first unveil the fact that the state-of-the-art MLCS algorithms are unable to be applied to long and large-scale sequences alignments. To overcome their defects and tackle the longer and large-scale or even big sequences alignments, based on the proposed novel problem-solving model and various strategies, e.g., parallel topological sorting, optimal calculating, reuse of intermediate results, subsection calculation and serialization, etc., we present a novel parallel MLCS algorithm. Exhaustive experiments on the datasets of both synthetic and real-world biological sequences demonstrate that both the time and space of the proposed algorithm are only linear in the number of dominants from aligned sequences, and the proposed algorithm significantly outperforms the state-of-the-art MLCS algorithms, being applicable to longer and large-scale sequences alignments.


title: IP Lookup using Two-level Indexing and B-Trees.

abstract: Networks are expanding very fast and the number of clients is increasing dramatically, this causes the router forwarding table to become very large and present more demand on faster router operations. In this paper, we address the problem of packet forwarding in the routers aiming to increase the speed of address lookup and minimize the memory required for storing the forwarding table. We propose a new algorithm that makes use of two-level indexing and Btrees. We test the approach and compare it to other famous IP lookup approaches. The preliminary simulations show 20% less memory requirements and the lookup speed scaling linearly with increasing table size.


title: Determinants of accepting wireless mobile data services in China

abstract: Data services via wireless networks and mobile devices have experienced rapid growth worldwide. We investigated the factors influencing adoption of wireless mobile data services (WMDS) in China and tested our model for explaining adoption intentions there. We argued that individuals form their intention to adopt WMDS under the influence of wireless mobile technology, the social environment, personal innovativeness of IT, trust awareness, and the facilitating conditions. We examined the simultaneous effects of these five influences on beliefs in the context of wireless Internet data services via mobile phones. Survey data were collected from 1432 participants in several metro cities across China. The findings suggest that WMDS adoption intention in China is determined by consumers' perceived usefulness and perceived ease of use of WMDS. Theoretical and practical implications are included in our paper.


title: COnGRATS: Realistic simulation of traffic sequences for autonomous driving

abstract: For evaluating or training different kinds of vision algorithms, a large amount of precise and reliable data is needed. In this paper we present a system to create extended synthetic sequences of traffic environment scenarios, associated with several types of ground truth data. By integrating vehicle dynamics in a configuration tool, and by using path-tracing in an external rendering engine to render the scenes, a system is created that allows ongoing and flexible creation of highly realistic traffic images. For all images, ground truth data is provided for depth, optical flow, surface normals and semantic scene labeling. Sequences that are produced with this system are more varied and closer to natural images than other synthetic datasets before.


title: Efficient and secure template blinding for biometric authentication

abstract: Fingerprints as biometric authenticators are rapidly increasing in popularity, with fingerprint scanners available on many modern smartphones and laptops. Because these authenticators are non-revocable, special care must be taken to prevent leakage of the representative feature information of a user's fingerprint. While secure multiparty computation protocols have been designed to maintain fingerprint privacy during authentication, they do not protect the data stored on the authentication server. In this work, we develop a technique for blinding the stored biometric template such that the authentication server never observes biometric information in the clear, and cannot accidentally leak this information in the event of a breach. We show how our blinding technique can be combined with the privacy-preserving GSHADE protocol to privately compare biometric feature vectors using a variety of distance metrics with negligible overhead in computation time. We then construct a complete privacy-preserving remote fingerprint authentication system based on the Euclidean Distance metric, and show that a user can authenticate using privacy-preserving techniques in as little as 1.5 seconds. This work provides a template for designing low-cost blinding techniques for biometric authentication systems, and shows a practical use-case for secure multiparty computation protocols in remote authentication systems.


title: A Sequence Labelling Approach to Quote Attribution

abstract: Quote extraction and attribution is the task of automatically extracting quotes from text and attributing each quote to its correct speaker. The present state-of-the-art system uses gold standard information from previous decisions in its features, which, when removed, results in a large drop in performance. We treat the problem as a sequence labelling task, which allows us to incorporate sequence features without using gold standard information. We present results on two new corpora and an augmented version of a third, achieving a new state-of-the-art for systems using only realistic features.


title: Understanding the transcriptome through RNA structure

abstract: The ways in which an RNA molecule folds to form structures can be crucial to its function, and so methods for studying RNA structures make an important contribution to understanding transcriptomes. Experimental approaches in this field are being advanced by coupling them to high-throughput sequencing.


title: Intrusion Detection Using Sequences of System Calls

abstract: A method is introduced for detecting intrusions at the level of privileged processes. Evidence is given that short sequences of system calls executed by running processes are a good discriminator between normal and abnormal operating characteristics of several common UNIX programs. Normal behavior is collected in two waysc Synthetically, by exercising as many normal modes of usage of a program as possible, and in a live user environment by tracing the actual execution of the program. In the former case several types of intrusive behavior were studieds in the latter case, results were analyzed for false positives.


title: Attachment style

abstract: Attachment theory, developed by Bowlby to explain human bonding, has profound implications for conducting and adapting psychotherapy. We summarize the prevailing definitions and measures of attachment style. We review the results of three meta-analyses examining the association between attachment anxiety, avoidance, and security and psychotherapy outcome. Fourteen studies were synthesized, which included 19 separate therapy cohorts with a combined sample size of 1,467. Attachment anxiety showed a d of -.46 with posttherapy outcome, while attachment security showed a d of.37 association with outcome. Attachment avoidance was uncorrelated with outcome. The age and gender composition of the samples moderated the relation between attachment security and outcome: samples with a higher proportion of female clients and a higher mean age showed a smaller relation between security and outcome. We discuss the practice implications of these findings and related research on the link between attachment and the therapy relationship.


title: Beard infantile hemangioma and subglottic involvement: are median pattern and telangiectatic aspect the clue?

abstract: BACKGROUND ::: Identification of patient at risk of subglottic infantile hemangioma (IH) is challenging because subglottic IH can grow fast and cause airway obstruction with a fatal course. ::: ::: ::: OBJECTIVE ::: To refine the cutaneous IH pattern at risk of subglottic IH. ::: ::: ::: METHODS ::: Prospective and retrospective review of patients with cutaneous IH involving the beard area. IHs were classified in the bilateral pattern group (BH) or in the unilateral pattern group (UH). Infantile hemangioma topography, subtype (telangiectatic or tuberous), ear, nose and throat (ENT) manifestations and subglottic involvement were recorded. ::: ::: ::: RESULTS ::: Thirty-one patients (21 BH and 10 UH) were included during a 20-year span. Nineteen patients (16 BH and 3 UH) had subglottic hemangioma. BH and UH group overlap on the median pattern (tongue, gum, lips, chin and neck). Median pattern, particularly the neck area and telangiectatic subtype of IH were significantly associated with subglottic involvement. ::: ::: ::: CONCLUSION ::: Patients presenting with telangiectatic beard IH localized on the median area need early ENT exploration. They should be treated before respiratory symptoms occur.


title: Emotion recognition from speech signal using epoch parameters

abstract: This paper proposes epoch parameters extracted from LP (Linear Prediction) residual and zero frequency filtered speech signal for recognising the emotions present in speech. Instant of glottal closure within pitch period of LP residual is known as an 'epoch'. The significant excitation of vocal tract usually takes place at the instant of glottal closure. In this paper the epoch parameters namely strength of epoch, instantaneous frequency, sharpness of epochs, slope of strength of epochs are used as features for classification of emotions. These features are extracted from the glottal closure region of LP residual. For analysing emotion recognition, using the proposed epoch parameters, actor recorded Telugu database (IITKGP-Simulated Emotion Speech Corpus) and Berlin emotional database are used. In the study we have considered six emotions namely anger, disgust, fear, happy, neutral and sadness. Gaussian mixture models and support vector machines are used for developing the models. Average emotion recognition of 61% and 58% is observed respectively for the above models.


title: Lessons Learned in Applying Formal Concept Analysis to Reverse Engineering

abstract: A key difficulty in the maintenance and evolution of complex software systems is to recognize and understand the implicit dependencies that define contracts that must be respected by changes to the software. Formal Concept Analysis is a well-established technique for identifying groups of elements with common sets of properties. We have successfully applied FCA to complex software systems in order to automatically discover a variety of different kinds of implicit, recurring sets of dependencies amongst design artifacts. In this paper we describe our approach, outline three case studies, and draw various lessons from our experiences. In particular, we discuss how our approach is applied iteratively in order to draw the maximum benefit offered by FCA.


title: PATHOLOGICAL BRAIN DETECTION IN MAGNETIC RESONANCE IMAGING SCANNING BY WAVELET ENTROPY AND HYBRIDIZATION OF BIOGEOGRAPHY-BASED OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION

abstract: Background) We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.


title: Optimal Inference in Crowdsourced Classification via Belief Propagation

abstract: Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that Belief Propagation (BP) exactly matches this lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that BP is close to optimal for all regimes considered and improves upon competing state-of-the-art algorithms.


title: Service-Oriented Architectures for a Flexible and Safe Use of Unmanned Aerial Vehicles

abstract: Unmanned Aerial Vehicles (UAVs) are becoming widely used for supporting multiple heterogeneous missions, requiring continuous evolution and adaptation of the implemented services. This scenario calls for the definition of architectural abstractions that support the integration of service-variants, in particular the notion of SOA (Service-Oriented Architecture) for services composition, to fulfill application-specified performance and dependability requirements. In this paper, we discuss the problems of using SOA (a non-deterministic approach) in UAVs (a real-time based embedded system) and the safety issues that such environment may raise. We then put forward a framework to tackle the identified challenges and propose potential solutions considering the opportunities and threats of using SOA in UAVs.


title: Disparity Estimation on Stereo Mammograms

abstract: We consider the problem of depth estimation on digital stereo mammograms. Being able to elucidate 3D information from stereo mammograms is an important precursor to conducting 3D digital analysis of data from this promising new modality. The problem is generally much harder than the classic stereo matching problem on visible light images of the natural world, since nearly all of the 3D structural information of interest exists as complex network of multilayered, heavily occluded curvilinear structures. Toward addressing this difficult problem, we formulate a new stereo model that minimizes a global energy functional to densely estimate disparity on stereo mammogram images, by introducing a new singularity index as a constraint to obtain better estimates of disparity along critical curvilinear structures. Curvilinear structures, such as vasculature and spicules, are particularly salient structures in the breast, and being able to accurately position them in 3D is a valuable goal. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model.


title: Automatic Game Design via Mechanic Generation

abstract: Game designs often center on the game mechanics-- rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics--what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements--controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.


title: Smart home services as the next mainstream of the ICT industry: determinants of the adoption of smart home services

abstract: This study investigated the core motivations for adopting smart home services and explored the approaches and processes through which the motivations were incorporated with the original technology acceptance model (TAM) and the acceptance of the services. To achieve this purpose, an Internet survey was conducted in South Korea. The data (N = 799) from the survey were analyzed using structural equation modeling and confirmatory factor analysis. The results suggested that the perceived compatibility, connectedness, control, system reliability, and enjoyment of smart home services were positively related to the users' intention to use the services, whereas there was a negative association between the perceived cost and usage intention. The structural results also provided evidence of the validity of the original TAM. Although smart home services have attracted users' interest in the housing context, only a few studies have examined how the users' intention to use the services is motivated. The present study represents an initial step to explore the process of adopting smart home services with potential future research areas.


title: A gate driver circuit design for SiC MOSFET based three level NPC inverter with reduced high frequency switching noise

abstract: Superior characteristics of silicon carbide (SiC) MOSFETs such as higher breakdown voltage, higher band-gap energy, lower switching losses, lower on-state resistance turns out to be the best alternative to silicon (Si) MOSFETs and Si-IGBTs for high power density converters. However, its low threshold voltage and lower transconductance value increases difficulties in the driver circuit design due to high frequency noise and EMI problems. Therefore, appropriate selection of driver circuit parameters is necessary to avoid high frequency switching losses, noise and EMI effect which further affects the performance of power converters and decreases its efficiency. In this paper, a simple gate driver circuit design for SiC MOSFET based three level diode clamped inverter is presented. Step by step selection procedure of driver circuit components is explained. A 10kVA, three phase, three level inverter is developed and performance of proposed driver circuit in eliminating high frequency noise, rise in the gate-source voltage spikes and electromagnetic interference (EMI) effect is experimentally evaluated.


title: IMpixel 65nm BSI 320MHz demodulated TOF Image sensor with 3μm global shutter pixels and analog binning

abstract: The quest for accurate, high-resolution, low-power-consumption, and small-footprint 3D depth cameras has driven a rapid improvement in Continuous-Wave (CW) Time-of-Flight (ToF) technology. Commercially available 3D image acquisition techniques include Stereo Vision, Structured Light, and ToF. CW ToF imaging systems offer excellent mechanical robustness, no baseline requirement, high effective depth image resolution, low computational cost, and simultaneous IR ambient light invariant intensity capture (Active Brightness). In a CW ToF camera, light from an amplitude modulated light source is backscattered by objects in the camera's field of view, and the phase delay of the amplitude envelope is measured between the emitted and reflected light. This phase difference is translated into a distance value for each pixel in the imaging array.


title: The maternal brain and its plasticity in humans

abstract: Abstract This article is part of a Special Issue “Parental Care”. Early mother–infant relationships play important roles in infants' optimal development. New mothers undergo neurobiological changes that support developing mother–infant relationships regardless of great individual differences in those relationships. In this article, we review the neural plasticity in human mothers' brains based on functional magnetic resonance imaging (fMRI) studies. First, we review the neural circuits that are involved in establishing and maintaining mother–infant relationships. Second, we discuss early postpartum factors (e.g., birth and feeding methods, hormones, and parental sensitivity) that are associated with individual differences in maternal brain neuroplasticity. Third, we discuss abnormal changes in the maternal brain related to psychopathology (i.e., postpartum depression, posttraumatic stress disorder, substance abuse) and potential brain remodeling associated with interventions. Last, we highlight potentially important future research directions to better understand normative changes in the maternal brain and risks for abnormal changes that may disrupt early mother–infant relationships.


title: Recognition of Handwritten Devanagari Script Using Soft Computing

abstract: Development of a Character recognition system for Devnagri is difficult because (i) there are about 350 basic, modified (“matra”) and compound character shapes in the script and (ii) the characters in a words are topologically connected. Here focus is on the recognition of offline handwritten Hindi characters that can be used in common applications like bank cheques, commercial forms, government records, bill processing systems, Postcode Recognition, Signature Verification, passport readers, offline document recognition generated by the expanding technological society. Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Challenges in handwritten characters recognition lie in the variation and distortion of offline handwritten Hindi characters since different people may use different style of handwriting, and direction to draw the same shape of any Hindi character. This overview describes the nature of handwritten language, how it is translated into electronic data, and the basic concepts behind written language recognition algorithms. Handwritten Hindi character are imprecise in nature as their corners are not always sharp, lines are not perfectly straight, and curves are not necessarily smooth, unlikely the printed character. Furthermore, Hindi character can be drawn in different sizes and orientation in contrast to handwriting which is often assumed to be written on a baseline in an upright position. Therefore, a robust offline Hindi handwritten recognition system has to account for all of these factors. An approach using Artificial Neural Network is considered for recognition of Handwritten Hindi Character Recognition.


Alleviating depressitn tnly tt bectme prtblematic mtbile phtne users: Can face-tt- face ctmmunicatitn be the antidtte?


Thermo-Mechanical Simulation of Wire Bonding Joints in Power Modules


title: The 7 th International Conference on Ambient Systems , Networks and Technologies ( ANT 2016 ) DENCLUE-IM : A New Approach for Big Data Clustering

abstract: Abstract Every day, a large volume of data is generated by multiple sources, social networks, mobile devices, etc. This variety of data sources produce an heterogeneous data, which are engendered in high frequency. One of the techniques allowing to a better use and exploit this kind of complex data is clustering. Finding a compromise between performance and speed response time present a major challenge to classify this monstrous data. For this purpose, we propose an efficient algorithm which is an improved version of DENCLUE, called DENCLUE-IM. The idea behind is to speed calculation by avoiding the crucial step in DENCLUE which is the Hill Climbing step. Experimental results using large datasets proves the efficiency of our proposed algorithm.


title: LEARNING AND ATTITUDE CHANGE AT BRITISH NUCLEAR FUEL'S SELLAFIELD VISITORS CENTRE

abstract: Abstract Knowledge of and attitude towards nuclear power were measured in a before/after evaluation study of Sellafield Visitors Centre. The attitude scale was broken down into its component factors using a Principal Components Analysis with orthogonal rotation. Five factors were extracted and these were interpreted as: (a) economic and technical; (b) health and safety; (c) emotional; (d) environmental; (e) passive acceptance. These factors were then analysed in ANOVAs to assess changes due to the Visitor Centre by gender, age and previous education. A verbal knowledge questionnaire was also administered before/after and analysed with similar ANOVAs but with the attitudinal factors included as covariates. It was found that knowledge increased significantly across all categories of visitor. In attitude measures, the environmental and the economic and technical factors changed positively but although health and safety changed positively for men—it changed negatively for women. The emotional factor changed negatively for the younger visitors and did not change positively for any of the demographic groupings. The knowledge questionnaire, together with a pictorial knowledge test, were compared with a behavioural mapping measure to explore the properties of displays that contribute most to learning.


title: Joint Top-K Spatial Keyword Query Processing

abstract: Web users and content are increasingly being geopositioned, and increased focus is being given to serving local content in response to web queries. This development calls for spatial keyword queries that take into account both the locations and textual descriptions of content. We study the efficient, joint processing of multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also occurs when multiple queries are used to obfuscate a user's true query. We propose a novel algorithm and index structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed solution is efficient on real data sets. We also offer analytical studies on synthetic data sets to demonstrate the efficiency of the proposed solution.


title: Learning Gated Bayesian Networks for Algorithmic Trading

abstract: Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algo ...


title: Performance Enhancement of Boost Converter Based on PID Controller Plus Linear-to-Nonlinear Translator

abstract: Here, A PID controller plus a novel linear-to-nonlinear translator is proposed and applied to the boost converter to improve the operating performance of the boost converter under large transient variations in load all over the quiescent dc input voltage range. The proposed translator is inserted between the PID controller and the main power stage such that the PID controller considers the behavior of the boost converter to be quasi-linear, thereby suppressing the effect of nonlinear behavior of the boost converter on the PID controller. Besides, variations in dc gain of the control-to-output transfer function with the translator are smaller than those without the translator, thus making system control easy to attain. Also, one-comparator counter-based sampling without any A-D converter is presented herein to realize the proposed control strategy. As mentioned above, the proposed control strategy can make the boost converter stably operated under large load transient responses all over the quiescent dc input voltage range.


title: The colonial context of Filipino American immigrants’ psychological experiences.

abstract: Because of the long colonial history of Filipinos and the highly Americanized climate of postcolonial Philippines, many scholars from various disciplines have speculated that colonialism and its legacies may play major roles in Filipino emigration to the United States. However, there are no known empirical studies in psychology that specifically investigate whether colonialism and its effects have influenced the psychological experiences of Filipino American immigrants prior to their arrival in the United States. Further, there is no existing empirical study that specifically investigates the extent to which colonialism and its legacies continue to influence Filipino American immigrants’ mental health. Thus, using interviews (N 6) and surveys (N 219) with Filipino American immigrants, two studies found that colonialism and its consequences are important factors to consider when conceptualizing the psychological experiences of Filipino American immigrants. Specifically, the findings suggest that (a) Filipino American immigrants experienced ethnic and cultural denigration in the Philippines prior to their U.S. arrival, (b) ethnic and cultural denigration in the Philippines and in the United States may lead to the development of colonial mentality (CM), and (c) that CM may have negative mental health consequences among Filipino American immigrants. The two studies’ findings suggest that the Filipino American immigration experience cannot be completely captured by the voluntary immigrant narrative, as they provide empirical support to the notion that the Filipino American immigration experience needs to be understood in the context of colonialism and its most insidious psychological legacy— CM.


title: Inter-strand circulating current analysis of end-region transposed coil of power generator using 3D multi-layer FEM

abstract: Stator coils of large rotating machines are divided into strands which are transposed to reduce the inter-strand circulating current loss. To evaluate the effect of transposition, circulating current analysis is required but full 3D analysis is difficult because of the complicated configuration of the transposed coils. In this paper, magnetic field analysis model using 3D multi-layer finite element method is proposed to calculate the inter-strand circulating current in the stator coil with transposition in the coil end region especially with non-transposed region. Then circulating currents are analyzed considering the magnetic flux in the end region and some transposition patterns are compared.


title: Establishing an Organization's Master Data Management Function: A Stepwise Approach

abstract: Master data management (MDM) provides an access to the consistent views of an organization's master data. Yet the establishment of MDM function, i.e. a department that attempts to ensure master data consistency is not an easy task as several stakeholders have different interests and expectations, among many other reasons. This paper is based on a year-long MDM project, from which we identify the process for establishing MDM functions in an organization, and what are the different steps and interdependencies that should be taken into account. These steps and their dependencies, and other incidental issues help organizations when establishing MDM function, and complement the scarce MDM literature.


title: Mapping WirelessHART into PROFINET and PROFIBUS fieldbusses

abstract: WirelessHART is a new protocol for Wireless Sensor Networks (WSN). In most usage scenarious, such a WSN has to be integrated into existing wired infrastructures to build an automation process. Based on the examples PROFIBUS and PROFINET, this paper presents some design decisions needed for this integration and their advantages and disadvantages.


title: Bidirectional recurrent neural networks

abstract: In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported.


title: LDAvis: A method for visualizing and interpreting topics

abstract: We present LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3. Our visualization provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model.


title: Watertight surface reconstruction of caves from 3D laser data

abstract: The generation of accurate, watertight, three-dimensional models of environments are often crucial for the purposes of scientific study and infrastructure management. Most commonly, such models are acquired by using range sensors producing point clouds, and further processing steps are required for the construction of a surface model. We used a mobile lidar to map several kilometers of a natural cave system in order to obtain 3D volumetric models for use in scientific research studying the local palaeo-climatic record. For unstructured and GPS-denied environments, such as cave systems, the process of acquiring a complete map is difficult and further complicated by limited mobility within the cave. During the mapping process, many unwanted measurements occur due to occlusions from moving objects such as other people present in the cave. Most common point cloud surface reconstruction techniques are not designed to deal these occlusions; i.e., they require manual cleanup of the data set or are not capable of generating watertight surfaces. The large scale of the environments introduces the additional challenge of dealing with memory limitations. We propose a new volume-based approach to reconstruct a watertight surface from range measurements of enclosed environments without limitation on the scale of the collected data. Our approach carves all unoccupied voxels from the sensor to a triangulated and rasterized surface between successive scans, which is intended to fill in the missing data between the scan rays. The surface is then constructed from the isosurface between unoccupied and unknown cells. By decomposing the space, we are able to handle large-scale data without exceeding the memory limitation of a standard PC, at the cost of some additional computation time. The algorithm has been evaluated across several datasets within a variety of environments and observed to build more complete volumetric models than a simple space carving approach. We have mapped several kilometers of cave networks and, with the described method, produced watertight reconstructions suitable for further scientific analysis.


title: Fecal Microbiota and Metabolome of Children with Autism and Pervasive Developmental Disorder Not Otherwise Specified

abstract: This study aimed at investigating the fecal microbiota and metabolome of children with Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) and autism (AD) in comparison to healthy children (HC). Bacterial tag-encoded FLX-titanium amplicon pyrosequencing (bTEFAP) of the 16S rDNA and 16S rRNA analyses were carried out to determine total bacteria (16S rDNA) and metabolically active bacteria (16S rRNA), respectively. The main bacterial phyla (Firmicutes, Bacteroidetes, Fusobacteria and Verrucomicrobia) significantly (P<0.05) changed among the three groups of children. As estimated by rarefaction, Chao and Shannon diversity index, the highest microbial diversity was found in AD children. Based on 16S-rRNA and culture-dependent data, Faecalibacterium and Ruminococcus were present at the highest level in fecal samples of PDD-NOS and HC children. Caloramator, Sarcina and Clostridium genera were the highest in AD children. Compared to HC, the composition of Lachnospiraceae family also differed in PDD-NOS and, especially, AD children. Except for Eubacterium siraeum, the lowest level of Eubacteriaceae was found on fecal samples of AD children. The level of Bacteroidetes genera and some Alistipes and Akkermansia species were almost the highest in PDD-NOS or AD children as well as almost all the identified Sutterellaceae and Enterobacteriaceae were the highest in AD. Compared to HC children, Bifidobacterium species decreased in AD. As shown by Canonical Discriminant Analysis of Principal Coordinates, the levels of free amino acids and volatile organic compounds of fecal samples were markedly affected in PDD-NOS and, especially, AD children. If the gut microbiota differences among AD and PDD-NOS and HC children are one of the concomitant causes or the consequence of autism, they may have implications regarding specific diagnostic test, and/or for treatment and prevention.


title: The Behavioral Consequences of Terrorism: A Meta-Analysis

abstract: Effective postterrorism public health interventions require the recognition that behavioral consequences are, in fact, the intent of terrorists. The authors searched published and unpublished post-1980 studies that documented population-level behavioral and psychological consequences of terrorist incidents, focusing on posttraumatic stress disorder (PTSD). Results were tabulated, and random effects models were used to calculate overall effect sizes. The analysis indicates that in the year following terrorist incidents, the prevalence of PTSD in directly affected populations varies between 12% and 16%. The review also shows that this prevalence can be expected to decline 25% over the course of that year. These prevalence estimates mask great variability, depending on who is being studied, who is conducting the study, and where the event occurred. Higher rates of disease are reported for survivors and rescue workers, and higher overall rates are also reported from studies conducted in Western Europe compared with studies conducted in North America. Prior psychiatric diagnoses are strongly associated with subsequent PTSD and may be a useful triage factor, particularly when considered together with factors such as female gender and direct exposure to events as either a survivor or rescuer. The review indicates that these associations are consistent across study types and environments and represent important variables to consider when developing triage, outreach, and treatment programs.


title: Predicting crowd behavior with big public data

abstract: With public information becoming widely accessible and shared on today's web, greater insights are possible into crowd actions by citizens and non-state actors such as large protests and cyber activism. We present efforts to predict the occurrence, specific timeframe, and location of such actions before they occur based on public data collected from over 300,000 open content web sources in 7 languages, from all over the world, ranging from mainstream news to government publications to blogs and social media. Using natural language processing, event information is extracted from content such as type of event, what entities are involved and in what role, sentiment and tone, and the occurrence time range of the event discussed. Statements made on Twitter about a future date from the time of posting prove particularly indicative. We consider in particular the case of the 2013 Egyptian coup d'etat. The study validates and quantifies the common intuition that data on social media (beyond mainstream news sources) are able to predict major events.