title: A Hybrid Genetic Algorithm and Tabu Search Approach for Post Enrolment Course Timetabling

abstract: The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.


title: Affective Neuroscience: Past, Present, and Future

abstract: The discipline of affective neuroscience is concerned with the underlying neural substrates of emotion and mood. This review presents an historical overview of the pioneering work in affective neuroscience of James and Lange, Cannon and Bard, and Hess, Papez, and MacLean before summarizing the current state of research on the brain regions identified by these seminal researchers. We also discuss the more recent strides made in the field of affective neuroscience. A final section considers different hypothetical organizations of affective neuroanatomy and highlights future directions for the discipline.


title: Via-free microstrip to slotline baluns using slotted microstrip cross-junction

abstract: This paper presents two novel microstrip-to-slotline baluns. Their design is based on the slotted microstrip cross-junction and its multi-mode equivalent circuit model, i.e., each slotted microstrip supports two modes that have even and odd symmetry. The first balun is a modified version of the conventional 90° via-less microstrip to slotline one with different microstrip and slotline impedances. The 3 dB bandwidth is 2.44 GHz and the minimum insertion loss is 0.5 dB at 2.4 GHz. The second balun is a via-less straight microstrip-to-slotline one that has 3 dB bandwidth of 2.29 GHz and minimum insertion loss of 0.46 dB at 2.4 GHz. Theoretical predictions have been confirmed by EM simulations and measurements.


title: A New Class of Single-Phase High-Frequency Isolated Z-Source AC–AC Converters With Reduced Passive Components

abstract: In this paper, a class of single-phase Z-source (ZS) ac–ac converters is proposed with high-frequency transformer (HFT) isolation. The proposed HFT isolated (HFTI) ZS ac–ac converters possess all the features of their nonisolated counterparts, such as providing wide range of buck-boost output voltage with reversing or maintaining the phase angle, suppressing the in-rush and harmonic currents, and improved reliability. In addition, the proposed converters incorporate HFT for electrical isolation and safety, and therefore can save an external bulky line frequency transformer, for applications such as dynamic voltage restorers, etc. The proposed HFTI ZS converters are obtained from conventional (nonisolated) ZS ac–ac converters by adding only one extra bidirectional switch, and replacing two inductors with an HFT, thus saving one magnetic core. The switching signals for buck and boost modes are presented with safe-commutation strategy to remove the switch voltage spikes. A quasi-ZS-based HFTI ac–ac is used to discuss the operation principle and circuit analysis of the proposed class of HFTI ZS ac–ac converters. Various ZS-based HFTI proposed ac–ac converters are also presented thereafter. Moreover, a laboratory prototype of the proposed converter is constructed and experiments are conducted to produce output voltage of 110 Vrms / 60 Hz, which verify the operation of the proposed converters.


title: Recommendation in Heterogeneous Information Networks Based on Generalized Random Walk Model and Bayesian Personalized Ranking

abstract: Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods.


title: J-REED: Joint Relation Extraction and Entity Disambiguation

abstract: Information extraction (IE) from text sources can either be performed as Model-based IE (i.e, by using a pre-specified domain of target entities and relations) or as Open IE (i.e., with no particular assumptions about the target domain). While Model-based IE has limited coverage, Open IE merely yields triples of surface phrases which are usually not disambiguated into a canonical set of entities and relations. This paper presents J-REED: a joint approach for entity disambiguation and relation extraction that is based on probabilistic graphical models. J-REED merges ideas from both Model-based and Open IE by mapping surface names to a background knowledge base, and by making surface relations as crisp as possible.


title: Translating Simple Legal Text to Formal Representations

abstract: Various logical representations and frameworks have been proposed for reasoning with legal information. These approaches assume that the legal text has already been translated to the desired formal representation. However, the approaches for translating legal text into formal representations have mostly focused on inferring facts from text or translating it to a single representation. In this work, we use the NL2KR system to translate legal text into a wide variety of formal representations. This will enable the use of existing logical reasoning approaches on legal text (English), thus allowing reasoning with text.


title: Morphological changes induced by class III chitin synthase gene silencing could enhance penicillin production of Penicillium chrysogenum

abstract: Chitin synthases catalyze the formation of β-(1,4)-glycosidic bonds between N-acetylglucosamine residues to form the unbranched polysaccharide chitin, which is the major component of cell walls in most filamentous fungi. Several studies have shown that chitin synthases are structurally and functionally divergent and play crucial roles in the growth and morphogenesis of the genus Aspergillus although little research on this topic has been done in Penicillium chrysogenum. We used BLAST to find the genes encoding chitin synthases in P. chrysogenum related to chitin synthase genes in Aspergillus nidulans. Three homologous sequences coding for a class III chitin synthase CHS4 and two hypothetical proteins in P. chrysogenum were found. The gene which product showed the highest identity and encoded the class III chitin synthase CHS4 was studied in detail. To investigate the role of CHS4 in P. chrysogenum morphogenesis, we developed an RNA interference system to silence the class III chitin synthase gene chs4. After transformation, mutants exhibited a slow growth rate and shorter and more branched hyphae, which were distinct from those of the original strain. The results also showed that the conidiation efficiency of all transformants was reduced sharply and indicated that chs4 is essential in conidia development. The morphologies of all transformants and the original strain in penicillin production were investigated by light microscopy, which showed that changes in chs4 expression led to a completely different morphology during fermentation and eventually caused distinct penicillin yields, especially in the transformants PcRNAi1-17 and PcRNAi2-1 where penicillin production rose by 27 % and 41 %, respectively.


Political Compass : A Data-driven Analysis of Online Newspapers regarding Political Orientation


title: Non-electrolyte permeability as a tool for studying membrane fluidity

abstract: Abstract 1. 1. The reflection coefficient for the permeation of thiourea through bilayers of phosphatidylcholine is a function of the fatty-acid composition of the lipid molecules. By means of these reflection coefficients an index for membrane fluidity has been given to each of those lipids, relative to that of egg phosphatidylcholine. 2. 2. The maximum number of water molecules that can copermeate with each molecule of solute by means of solute-solvent interaction is a function of the packing of the lipid molecules in the bilayer. This parameter has been used in this paper for characterizing the fluidity of cholesterol-containing membranes and for membranes with their lipids in the gel state.


title: Validity and reliability of the N-PASS assessment tool with acute pain

abstract: To establish evidence of clinical validity and reliability of the Neonatal Pain, Agitation, and Sedation Scale (N-PASS) in neonates with acute heelstick pain. Prospective psychometric evaluation, randomized crossover design. Two nurses administered the N-PASS simultaneously and independently during an actual and sham heelstick done in randomized order. One nurse also administered the Premature Infant Pain Profile (PIPP) concurrently with the N-PASS. Heelsticks were videotaped for repeat analysis. Construct (discriminate) validity was established through the Wilcoxon Signed-ranks test, comparing the distribution of the heelstick and sham N-PASS scores. The mean pain scores were 3.93 (2.30) and 0.81 (1.21) for the heelstick and sham procedures, respectively (Z=−6.429, P<0.0001). Convergent validity was demonstrated by correlation with the PIPP scores (Spearman rank correlation coefficient of 0.75 and 0.72 for raters 1 and 2, respectively). Inter-rater reliability was high, measured by intra-class coefficients; the ICC estimates (95% CI) of the pain scale were 0.86 (0.78, 0.92) and 0.93 (0.88, 0.96) for a single rating and average of two independent ratings, respectively (P<0.0001). Internal consistency, measured by Cronbach's alpha, was evident (0.84 to 0.89). Test–retest reliability was demonstrated by repeat scoring of videotaped heelsticks, measured by Spearman's rho correlation (0.874, P<0.0001). This research provides beginning evidence that the N-PASS is a valid and reliable tool for assessing acute heelstick pain in infants 0 to 30 days of age, 23 to 40 weeks gestation.


title: Coded computation for multicore setups

abstract: Consider a distributed computing setup consisting of a master node and n worker nodes, each equipped with p cores, and a function f (x) = g(f 1 (x), f 2 (x),…, fk(x)), where each f i can be computed independently of the rest. Assuming that the worker computational times have exponential tails, what is the minimum possible time for computing f? Can we use coding theory principles to speed up this distributed computation? In [1], it is shown that distributed computing of linear functions can be expedited by applying linear erasure codes. However, it is not clear if linear codes can speed up distributed computation of ‘nonlinear’ functions as well. To resolve this problem, we propose the use of sparse linear codes, exploiting the modern multicore processing architecture. We show that 1) our coding solution achieves the order optimal runtime, and 2) it is at least Θ(√log n) times faster than any uncoded schemes where the number of workers is n.


title: Study on the Interpretation of the Results in a Volleyball Game by Using a Specific Program of Statistics

abstract: Abstract The need for knowledge as accurate and complete for the realities of sport performance, scientific basis of strategic decisions, tactical and operative organisation require an integrated system of means and methods for the collection, processing, analysis, storage and transmission of information related to all facets of the sport to those interested. The effective preparation of the team shall be subject to the development and implementation of programmes with an appropriate content in relation to the desiderata contained, to make these new operational information, extrapolating them from theory in the practice of athletic training.


title: Generalized Design of Multi-User MIMO Precoding Matrices

abstract: In this paper we introduce a novel linear precoding technique. The approach used for the design of the precoding matrix is general and the resulting algorithm can address several optimization criteria with an arbitrary number of antennas at the user terminals. We have achieved this by designing the precoding matrices in two steps. In the first step we minimize the overlap of the row spaces spanned by the effective channel matrices of different users using a new cost function. In the next step, we optimize the system performance with respect to specific optimization criteria assuming a set of parallel single- user MIMO channels. By combining the closed form solution with Tomlinson-Harashima precoding we reach the maximum sum-rate capacity when the total number of antennas at the user terminals is less or equal to the number of antennas at the base station. By iterating the closed form solution with appropriate power loading we are able to extract the full diversity in the system and reach the maximum sum-rate capacity in case of high multi-user interference. Joint processing over a group of multi-user MIMO channels in different frequency and time slots yields maximum diversity regardless of the level of multi-user interference.


title: FUZZY REPRESENTATION AND REASONING IN GEOTECHNICAL SITE CHARACTERIZATION

abstract: Abstract Geotechnical site characterization is an important first step in all geotechnical engineering problems. It is important to have a model of the subsurface conditions when doing any geotechnical analysis. To create this profile, samples are taken in boreholes which are then used to infer a subsurface profile. This paper discusses a method that uses fuzzy sets to represent the data collected from samples in a borehole. Then, a fuzzy reasoning system is developed to infer the subsurface profile. The resulting fuzzy system creates a new approach to developing subsurface profiles.


title: A flexible tree climbing robot: Treebot - design and implementation

abstract: This paper proposed a novel tree climbing robot “Treebot” that has high maneuverability on an irregular tree environment and surpasses the state of the art tree climbing robots. Treebot's body is a novel continuum maneuver structure that has high degrees of freedom and superior extension ability. Treebot also equips with a pair of omni-directional tree grippers that enable Treebot to adhere on a wide variety of trees with a wide range of gripping curvature. By combining these two novel designs, Treebot is able to reach many places on trees including branches. Treebot can maneuver on a complex tree environment, but only five actuators are used in the mechanism. As a result, Treebot can keep in compact size and lightweight. Although Treebot weighs only 600 grams, it has payload capability of 1.75 kg which is nearly three times of its own weight. On top of that, the special design of the gripper permits zero energy consumption in static gripping. Numerous experiments have been conducted on real trees. Experimental results reveal that Treebot has excellent climbing performance on a wide variety of trees.


title: Folding Versus Charge: Understanding Selective Target Recognition by the Thrombin Aptamers

abstract: The use of nucleic acids as drugs represents a consistently growing approach. Different therapeutical strategies take advantage of the biological and biophysical properties of DNA and RNA to properly modulate activity of selected targets. A peculiar characteristic of these molecules is their structural flexibility which allows them to assume distinct foldings depending upon their sequence and/or environment. During the last twenty years this has led to the theoretical and experimental development of oligonucleotide aptamers, short sequences which can recognize a target with specificity and affinity comparable to antibodies. A leading example is represented by the Thrombin aptamer (15fTBA), a 15-mer DNA selected by its high affinity for the exosite I (fibrinogen binding site) of the coagulation factor. The very stable protein-DNA complex formation is the result of complementarities between the two macromolecules promoted by the aptamer sequence and folding as well as of electrostatic interactions generated by the charge balance at the binding site/s. Here, we investigated the relative role of these contributions and their involvement in defining the biological properties of the resulting complex. Thus we compared the Thrombin binding and inhibition properties of TBA to those of unrelated single stranded oligonucleotides. Additionally, the differences between the two protein exosites were assessed by using 29hTBA, a longer (29-mer) aptamer known to bind exosite II (heparin binding site). A subtle balance of aptamer folding and sequence is shown to cooperate with charge density for effective and selective recognition of exosite I or exosite II by TBAs.


Language, speech, tools and writing. A cultural imperative


title: 1 Simplifying Network Administration using

abstract: The management of network infrastructure in an enterprise is a complex and. daunting affair. In an era of increasing technical cornplexity, it is becoming difficult to find trained personnel who can manage the new features introduced into the various servers, routers, and switches. Policy-based network management provides a means by which the administration process can be simplified and largely automated. In this article we look at a general policy-based architecture that can be used to simplify several new technologies emerging in the context of IP networks. We explain how network administration can be simplified by defining two levels of policies, a business level and a technology level. We discuss how business-level policies are validated and transformed into technology-level policies, and present some algorithms that can be used to check for policy conflicts and unreachable policies. We then show how to apply this architecture to two areas: managing performance service level agreements, and supporting enterprise extranets using IPSec communication.


title: Photographic Image Synthesis with Cascaded Refinement Networks

abstract: We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches. The results are shown in the supplementary video at this https URL


title: Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO)

abstract: The 2-D maximum entropy method not only considers the distribution of the gray information, but also takes advantage of the spatial neighbor information with using the 2-D histogram of the image. As a global threshold method, it often gets ideal segmentation results even when the image's signal noise ratio (SNR) is low. However, its time-consuming computation is often an obstacle in real time application systems. In this paper, the image thresholding approach based on the index of entropy maximization of the 2-D grayscale histogram is proposed to deal with infrared image. The threshold vector (t,s), where t is a threshold for pixel intensity and s is another threshold for the local average intensity of pixels, is obtained through a new optimization algorithm, namely, the particle swarm optimization (PSO) algorithm. PSO algorithm is realized successfully in the process of solving the 2-D maximum entropy problem. The experiments of segmenting the infrared images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.


RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision


title: SEARNN: Training RNNs with Global-Local Losses

abstract: We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction. Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes. This allows us to validate the benefits of our approach on a machine translation task.


Who Benefits from Social Health Insurance in Developing Countries


title: Is compulsive internet use related to sensitivity to reward and punishment, and impulsivity?

abstract: Aim of the present study was to examine whether the personality correlates sensitivity to reward and to punishment, and impulsivity predict compulsive internet use (CIU). Furthermore, the predictive value of these personality correlates was compared to the predictive value of factors relating to psychosocial wellbeing. The results showed that particularly rash spontaneous impulsivity predicts CIU and that this personality factor is more important than psychosocial wellbeing factors. Sensitivity to reward, which is supposed to play a role in craving processes associated with substance abuse and eating disorders, could not be related to CIU. The data suggest that internet users who are characterized by an impulsive personality feature, are less able to control their use of the internet, which makes them more vulnerable to develop CIU.


title: Highly Parameterized K-means Clustering on FPGAs: Comparative Results with GPPs and GPUs

abstract: K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amount of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors or GPPs to process large datasets may take a long time, therefore many acceleration methods have been proposed in the literature to speed-up the processing of such large datasets. In this work, we propose a parameterized Field Programmable Gate Array (FPGA) implementation of the Kmeans algorithm and compare it with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and with GPPs. The proposed FPGA implementation has shown higher performance in terms of speed-up over previous FPGA GPU and GPP implementations, and is more energy efficient.


title: A compact two-dimensional phased array using grounded coplanar-Waveguides Butler matrices

abstract: A 2.45 GHz two-dimensional (2-D) 4×4 multi-beam phased array was designed, based on the Butler matrix beamforming technique. Sixteen beam states are produced by two stacks of 4-way Butler matrices, where one stack controls the vertical angle and the other one controls the horizontal angle. To achieve system compactness, the Butler matrix in grounded coplanar-waveguide (CPWG) configuration is newly designed, leading to 54% size reduction comparing with the microstrip line-based Butler matrix. The demonstration of beamforming capability of the integrated array is provided.


title: OntoNotes: The 90% Solution

abstract: OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of sentences including syntactic parse, predicate-argument structure, coreference, and word senses linked to an ontology, allowing a new generation of language understanding technologies to be developed with new functional capabilities.


title: A simple algorithm for designing an artificial intelligence based Tic Tac Toe game

abstract: This paper proposes a simple technique to develop a game of Tic-Tac-Toe as a two player game, where one player is the computer itself. The algorithm developed uses soft computing techniques and gameplay is programmed using Javascript, HTML and CSS. Algorithm developed helps computer to decide the most appropriate move so as to either win the game or to draw it in the least amount of time.


title: AN EXPERIMENTAL INVESTIGATION OF OUTFLOW OF LIQUIDS FROM SINGLE-OUTLET VESSELS

abstract: Abstract This paper considers the outflow of a liquid from a single outlet vessel, i.e. a vessel in which the outflowing liquid is displaced by another fluid which enters the vessel through the same opening. The simplest possible arrangement is investigated: a sealed axisymmetric cylindrical vessel with an outlet in its base, in which water is displaced by air. It is shown experimentally that the average liquid discharge velocity is independent of the liquid level in the vessel and the shape of the outlet for the range of outlets employed; it increases weakly with both the diameter of the vessel and the diameter of the outlet.


ESTIMATION OF DELAY PROPAGATION IN AVIATION SYSTEM USING BAYESIAN NETWORK


title: Robust dynamical network structure reconstruction

abstract: This paper addresses the problem of network reconstruction from data. Previous work identified necessary and sufficient conditions for network reconstruction of LTI systems, assuming perfect measurements (no noise) and perfect system identification. This paper assumes that the conditions for network reconstruction have been met but here we additionally take into account noise and unmodelled dynamics (including nonlinearities). In order to identify the network structure that generated the data, we compute the smallest distances between the measured data and the data that would have been generated by particular network structures. We conclude with biologically inspired network reconstruction examples which include noise and nonlinearities.


title: Importance of mind-muscle connection during progressive resistance training

abstract: PURPOSE ::: This study evaluates whether focusing on using specific muscles during bench press can selectively activate these muscles. ::: ::: ::: METHODS ::: Altogether 18 resistance-trained men participated. Subjects were familiarized with the procedure and performed one-maximum repetition (1RM) test during the first session. In the second session, 3 different bench press conditions were performed with intensities of 20, 40, 50, 60 and 80 % of the pre-determined 1RM: regular bench press, and bench press focusing on selectively using the pectoralis major and triceps brachii, respectively. Surface electromyography (EMG) signals were recorded for the triceps brachii and pectoralis major muscles. Subsequently, peak EMG of the filtered signals were normalized to maximum maximorum EMG of each muscle. ::: ::: ::: RESULTS ::: In both muscles, focusing on using the respective muscles increased muscle activity at relative loads between 20 and 60 %, but not at 80 % of 1RM. Overall, a threshold between 60 and 80 % rather than a linear decrease in selective activation with increasing intensity appeared to exist. The increased activity did not occur at the expense of decreased activity of the other muscle, e.g. when focusing on activating the triceps muscle the activity of the pectoralis muscle did not decrease. On the contrary, focusing on using the triceps muscle also increased pectoralis EMG at 50 and 60 % of 1RM. ::: ::: ::: CONCLUSION ::: Resistance-trained individuals can increase triceps brachii or pectarilis major muscle activity during the bench press when focusing on using the specific muscle at intensities up to 60 % of 1RM. A threshold between 60 and 80 % appeared to exist.


title: Citation pattern matching algorithms for citation-based plagiarism detection: greedy citation tiling, citation chunking and longest common citation sequence

abstract: Plagiarism Detection Systems have been developed to locate instances of plagiarism e.g. within scientific papers. Studies have shown that the existing approaches deliver reasonable results in identifying copy&paste plagiarism, but fail to detect more sophisticated forms such as paraphrased plagiarism, translation plagiarism or idea plagiarism. The authors of this paper demonstrated in recent studies that the detection rate can be significantly improved by not only relying on text analysis, but by additionally analyzing the citations of a document. Citations are valuable language independent markers that are similar to a fingerprint. In fact, our examinations of real world cases have shown that the order of citations in a document often remains similar even if the text has been strongly paraphrased or translated in order to disguise plagiarism. This paper introduces three algorithms and discusses their suitability for the purpose of citation-based plagiarism detection. Due to the numerous ways in which plagiarism can occur, these algorithms need to be versatile. They must be capable of detecting transpositions, scaling and combinations in a local and global form. The algorithms are coined Greedy Citation Tiling, Citation Chunking and Longest Common Citation Sequence. The evaluation showed that if these algorithms are combined, common forms of plagiarism can be detected reliably.


title: Sentiment analysis: what is the end user's requirement?

abstract: In this paper we address the Sentiment Analysis problem from the end user's perspective. An end user might desire an automated at-a-glance presentation of the main points made in a single review or how opinion changes time to time over multiple documents. To meet the requirement we propose a relatively generic opinion 5Ws structurization, further used for textual and visual summary and tracking. The 5W task seeks to extract the semantic constituents in a natural language sentence by distilling it into the answers to the 5W questions: Who, What, When, Where and Why. The visualization system facilitates users to generate sentiment tracking with textual summary and sentiment polarity wise graph based on any dimension or combination of dimensions as they want i.e. "Who" are the actors and "What" are their sentiment regarding any topic, changes in sentiment during "When" and "Where" and the reasons for change in sentiment as "Why".


title: Robots, Gender & Sensemaking: Sex Segregation’s Impact On Workers Making Sense Of a Mobile Autonomous Robot

abstract: This ethnographic study suggests that social structures such as the sex segregation of jobs may impact how different groups of workers make sense of robots in their work environment. Designers may need to take such structures into account if mobile autonomous robots are to operate successfully alongside humans in the workplace.


Soft Robotics for Chemists


title: Electrical Machines for High-Speed Applications: Design Considerations and Tradeoffs

abstract: The high-speed (HS) electric machines are gaining attention in several different applications, and the development of advanced materials, electronic components, and control algorithms is pushing ahead the technological speed limits. In the electromagnetic sizing of HS machines, the mechanical and thermal involvements are very tight to make it necessary to deal with all the different aspects at the same time. This paper aims to summarize and discuss the electrical and the mechanical aspects involved in the HS machine design, highlighting the main issues and the tradeoffs that the designer has to consider. Furthermore, the correlation between volume reduction and speed increase, based on commercial high-frequency rotor-stator units, is also presented.


title: Guidelines on eosinophilic esophagitis: evidence-based statements and recommendations for diagnosis and management in children and adults

abstract: IntroductionEosinophilic esophagitis (EoE) is one of the most prevalent esophageal diseases and the leading cause of dysphagia and food impaction in children and young adults. This underlines the importance of optimizing diagnosys and treatment of the condition, especially after the increasing amount of knowledge on EoE recently published. Therefore, the UEG, EAACI ESPGHAN, and EUREOS deemed it necessary to update the current guidelines regarding conceptual and epidemiological aspects, diagnosis, and treatment of EoE.MethodsGeneral methodology according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used in order to comply with current standards of evidence assessment in formulation of recommendations. An extensive literature search was conducted up to August 2015 and periodically updated. The working group consisted of gastroenterologists, allergists, pediatricians, otolaryngologists, path...


title: An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

abstract: Nowadays, a huge number of images are available. However, retrieving a required image for an ordinary user is a challenging task in computer vision systems. During the past two decades, many types of research have been introduced to improve the performance of the automatic annotation of images, which are traditionally focused on content-based image retrieval. Although, recent research demonstrates that there is a semantic gap between content-based image retrieval and image semantics understandable by humans. As a result, existing research in this area has caused to bridge the semantic gap between low-level image features and high-level semantics. The conventional method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, we propose a novel AIA model based on the deep learning feature extraction method. The proposed model has three phases, including a feature extractor, a tag generator, and an image annotator. First, the proposed model extracts automatically the high and low-level features based on dual-tree continues wavelet transform (DT-CWT), singular value decomposition, distribution of color ton, and the deep neural network. Moreover, the tag generator balances the dictionary of the annotated keywords by a new log-entropy auto-encoder (LEAE) and then describes these keywords by word embedding. Finally, the annotator works based on the long-short-term memory (LSTM) network in order to obtain the importance degree of specific features of the image. The experiments conducted on two benchmark datasets confirm that the superiority of the proposed model compared to the previous models in terms of performance criteria.


title: Channelized and Signal Detection Technique for Intercept Receiver

abstract: The radar signal intercept problem under complex signal environment has been a research hotspot in the field of electronic reconnaissance. A kind of radar signal intercept receiver which has 500 MHz instantaneous bandwidth is proposed to receive multiple simultaneous radar signals and to give phase difference information to signal sorting. The channelized efficient architecture of odd arrangement is adopted and the parameters such as carrier frequency, pulse width, phase difference and so on are measured. The channelized efficient architecture of odd arrangement and the method of phase difference extraction are verified correct through computer simulation.


SHREC ’ 16 Track : 3 D Sketch-Based 3 D Shape Retrieval


title: Child safety wearable device

abstract: This paper discusses the concept of a smart wearable device for little children. The major advantage of this wearable over other wearable is that it can be used in any cellphone and doesn't necessarily require an expensive smartphone and not a very tech savvy individual to operate. The purpose of this device is to help parents locate their children with ease. At the moment there are many wearables in the market which help track the daily activity of children and also help find the child using Wi-Fi and Bluetooth services present on the device. But Wi-Fi and Bluetooth appear to be an unreliable medium of communication between the parent and child. Therefore, the focus of this paper is to have an SMS text enabled communication medium between the child's wearable and the parent as the environment for GSM mobile communication is almost present everywhere. The parent can send a text with specific keywords such as “LOCATION” “TEMPERATURE” “UV” “SOS” “BUZZ”, etc., the wearable device will reply back with a text containing the real time accurate location of the child which upon tapping will provide directions to the child's location on google maps app and will also provide the surrounding temperature, UV radiation index so that the parents can keep track if the temperature or UV radiation is not suitable for the child. The prime motivation behind this paper is that we know how important technology is in our lives but it can sometimes can't be trusted, and we always need to have a secondary measure at hand. The secondary measure used in this project is the people present in the surrounding of the child who could instantly react for the child's safety till the parents arrive or they could contact the parents and help locate them. The secondary measure implemented was using a bright SOS Light and distress alarm buzzer present on the wearable device which when activated by the parents via SMS text should display the SOS signal brightly and sound an alarm which a bystander can easily spot as a sign of distress. Hence this paper aims at providing parents with a sense of security for their child in today's time.


title: Cloud-assisted humanoid robotics for affective interaction

abstract: In recent years, the humanoid robot is received great attention, and gradually develop to households and personal service field. The prominent progress of cloud computing, big data, and machine learning fields provides a strong support for the research of the robot. With affective interaction ability of robot has a broad market space and research value. In this paper, we propose a cloud-assisted humanoid robotics for affective interaction system architecture, and introduce the essential composition, design and implementation of related components. Finally, through an actual robot emotional interaction test platform, validating the feasibility and extendibility of proposed architecture.


title: I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system

abstract: User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications.


title: Doodle around the world: online scheduling behavior reflects cultural differences in time perception and group decision-making

abstract: Event scheduling is a group decision-making process in which social dynamics influence people's choices and the overall outcome. As a result, scheduling is not simply a matter of finding a mutually agreeable time, but a process that is shaped by social norms and values, which can highly vary between countries. To investigate the influence of national culture on people's scheduling behavior we analyzed more than 1.5 million Doodle date/time polls from 211 countries. We found strong correlations between characteristics of national culture and several behavioral phenomena, such as that poll participants from collectivist countries respond earlier, agree to fewer options but find more consensus than predominantly individualist societies. Our study provides empirical evidence of behavioral differences in group decision-making and time perception with implications for cross-cultural collaborative work.


Facial Expression Analysis


title: A wearable reflectance pulse oximeter for remote physiological monitoring.

abstract: To save life, casualty care requires that trauma injuries are accurately and expeditiously assessed in the field. This paper describes the initial bench testing of a wireless wearable pulse oximeter developed based on a small forehead mounted sensor. The battery operated device employs a lightweight optical reflectance sensor and incorporates an annular photodetector to reduce power consumption. The system also has short range wireless communication capabilities to transfer arterial oxygen saturation (SpO2), heart rate (HR), body acceleration, and posture information to a PDA. It has the potential for use in combat casualty care, such as for remote triage, and by first responders, such as firefighters. I. INTRODUCTION TEADY advances in noninvasive physiological sensing, hardware miniaturization, and wireless communication


Case records of the Massachusetts General Hospital. Case 30-2005. A 56-year-old man with fever and axillary lymphadenopathy.


title: A Beginner's Guide to Interferometric SAR Concepts and Signal Processing [AESS Tutorial IV]

abstract: Interferometric synthetic aperture radar (IFSAR, also abbreviated as InSAR) employs pairs of high resolution SAR images to generate high quality terrain elevation maps using phase interferometry methods. IFSAR provides an all-weather, day/night capability to generate measurements of terrain elevation on a dense grid of sample points with accuracies of ones of meters. Both spaceborne and airborne IFSAR systems are in use. In this paper we present a tutorial introduction to the concepts, techniques, and applications of IFSAR. After a brief introduction to digital elevation models (DEMs) and digital terrain elevation data (DTED), the fundamental IFSAR equation relating interferometric phase measurements to terrain elevation is derived from simple geometric considerations. The central section of the paper describes the major algorithmic steps required to form an IFSAR terrain map. Finally, variations of IFSAR for mapping terrain elevation or reflectivity changes are briefly described. A Web site at users.ece.gatech.edu/~mrichard/AESSJFSAR.htm provides access to color versions of many of the IFSAR images included in this paper.


Deep Learning for Fast and Accurate Fashion Item Detection


title: Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength

abstract: Indoor wireless systems often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors for location-based applications. As such, these applications could benefit greatly from NLOS identification and mitigation techniques. These techniques have been primarily investigated for ultra-wide band (UWB) systems, but little attention has been paid to WiFi systems, which are far more prevalent in practice. In this study, we address the NLOS identification and mitigation problems using multiple received signal strength (RSS) measurements from WiFi signals. Key to our approach is exploiting several statistical features of the RSS time series, which are shown to be particularly effective. We develop and compare two algorithms based on machine learning and a third based on hypothesis testing to separate LOS/NLOS measurements. Extensive experiments in various indoor environments show that our techniques can distinguish between LOS/NLOS conditions with an accuracy of around 95%. Furthermore, the presented techniques improve distance estimation accuracy by 60% as compared to state-of-the-art NLOS mitigation techniques. Finally, improvements in distance estimation accuracy of 50% are achieved even without environment-specific training data, demonstrating the practicality of our approach to real world implementations.


title: Behavioral/Systems/Cognitive A Hierarchy of Temporal Receptive Windows in Human Cortex

abstract: Real-world events unfold at different time scales and, therefore, cognitive and neuronal processes must likewise occur at different time scales. We present a novel procedure that identifies brain regions responsive to sensory information accumulated over different time scales. We measured functional magnetic resonance imaging activity while observers viewed silent films presented forward, backward, or piecewise-scrambled in time. Early visual areas (e.g., primary visual cortex and the motion-sensitive area MT+) exhibited high response reliability regardless of disruptions in temporal structure. In contrast, the reliability of responses in several higher brain areas, including the superior temporal sulcus (STS), precuneus, posterior lateral sulcus (LS), temporal parietal junction (TPJ), and frontal eye field (FEF), was affected by information accumulated over longer time scales. These regions showed highly reproducible responses for repeated forward, but not for backward or piecewise-scrambled presentations. Moreover, these regions exhibited marked differences in temporal characteristics, with LS, TPJ, and FEF responses depending on information accumulated over longer durations (approximately 36 s) than STS and precuneus (approximately 12 s). We conclude that, similar to the known cortical hierarchy of spatial receptive fields, there is a hierarchy of progressively longer temporal receptive windows in the human brain.


title: Multi-User Private Keyword Search for Cloud Computing

abstract: Working in various service models ranging from SaaS, PaaS, to IaaS, cloud computing is viewed by many as anew revolution in IT, and could reshape the paradigm of how IT industry works today. Storage services are a fundamental function offered by cloud computing. A typical use scenario of the cloud storage services is that an enterprise outsources its database to the cloud and authorizes multiple users to access the database. In such cases of database outsourcing, data encryption is a good approach enabling the data owner to retain its control over the outsourced data. {\em Searchable encryption} is a cryptographic primitive allowing for private keyword based search over the encrypted database. The above setting of enterprise outsourcing database to the cloud requires multi-user searchable encryption, whereas virtually all existing schemes consider the single-user setting. To bridge this gap, we are motivated to propose a practical multi-user searchable encryption scheme, which has a number of advantages over the known approaches. The associated model and security requirements are also formulated.


title: Terrain estimation for high-speed rough-terrain autonomous vehicle navigation

abstract: High-speed unmanned ground vehicles have important potential applications, including reconnaissance, material transport, and planetary exploration. During high-speed operation, it is important for a vehicle to sense changing terrain conditions, and modify its control strategies to ensure aggressive, yet safe, operation. In this paper, a framework for terrain characterization and identification is briefly described, composed of 1) vision-based classification of upcoming terrain, 2) terrain parameter identification via wheel-terrain interaction analysis, and 3) terrain classification based on auditory wheel-terrain contact signatures. The parameter identification algorithm is presented in detail. The algorithm derives from simplified forms of classical terramechanics equations. An on-line estimator is developed to allow rapid identification of critical terrain parameters. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for sand.


title: Electronic system with memristive synapses for pattern recognition

abstract: Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks. Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns. In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics. The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes and has been successfully adapted to a neural network system. The system learns and later recognizes, the human thought pattern corresponding to three vowels, i.e. /a /, /i / and /u/, using electroencephalography signals generated while a subject imagines speaking vowels. Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction.


title: SMPL: a skinned multi-person linear model

abstract: We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.


title: Towards a Molecular Understanding of the Biosynthesis of Amaryllidaceae Alkaloids in Support of Their Expanding Medical Use

abstract: The alkaloids characteristically produced by the subfamily Amaryllidoideae of the Amaryllidaceae, bulbous plant species that include well know genera such as Narcissus (daffodils) and Galanthus (snowdrops), are a source of new pharmaceutical compounds. Presently, only the Amaryllidaceae alkaloid galanthamine, an acetylcholinesterase inhibitor used to treat symptoms of Alzheimer’s disease, is produced commercially as a drug from cultivated plants. However, several Amaryllidaceae alkaloids have shown great promise as anti-cancer drugs, but their further clinical development is restricted by their limited commercial availability. Amaryllidaceae species have a long history of cultivation and breeding as ornamental bulbs, and phytochemical research has focussed on the diversity in alkaloid content and composition. In contrast to the available pharmacological and phytochemical data, ecological, physiological and molecular aspects of the Amaryllidaceae and their alkaloids are much less explored and the identity of the alkaloid biosynthetic genes is presently unknown. An improved molecular understanding of Amaryllidaceae alkaloid biosynthesis would greatly benefit the rational design of breeding programs to produce cultivars optimised for the production of pharmaceutical compounds and enable biotechnology based approaches.


title: Dimensions and Metrics for Evaluating Recommendation Systems

abstract: Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions.


title: Structural cell contribution to resistance of trees to uprooting

abstract: Key message ::: Structural cells, as a form of suspended pavement, increases the resistance of urban trees to uprooting failure by confining the roots from growing above ground surface.


title: The dark side of personality at work

abstract: Although there has been increasing interest in dark personality traits in the organizational sciences, these characteristics remain relatively understudied and somewhat misunderstood. The present manuscript aims to clarify some of the issues surrounding dark personality traits by discussing the history of dark personality traits, how they relate to normal personality traits, their relative importance as determinants of organizational outcomes, and measurement issues surrounding the assessment of these characteristics. We will then discuss potential future directions for research investigating the causes and consequences of these traits as well as providing guidance on the implementation of dark personality assessment in the workplace for selection and training.


title: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res

abstract: Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT–PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.


title: Raster-to-Vector: Revisiting Floorplan Transformation

abstract: This paper addresses the problem of converting a rasterized floorplan image into a vector-graphics representation. Unlike existing approaches that rely on a sequence of lowlevel image processing heuristics, we adopt a learning-based approach. A neural architecture first transforms a rasterized image to a set of junctions that represent low-level geometric and semantic information (e.g., wall corners or door end-points). Integer programming is then formulated to aggregate junctions into a set of simple primitives (e.g., wall lines, door lines, or icon boxes) to produce a vectorized floorplan, while ensuring a topologically and geometrically consistent result. Our algorithm significantly outperforms existing methods and achieves around 90% precision and recall, getting to the range of production-ready performance. The vector representation allows 3D model popup for better indoor scene visualization, direct model manipulation for architectural remodeling, and further computational applications such as data analysis. Our system is efficient: we have converted hundred thousand production-level floorplan images into the vector representation and generated 3D popup models.


Reconsidering Peter Newmark ‟ s Theory on Literal Translation


title: The reading game - Encouraging learners to become question-makers rather than question-takers by getting feedback, Making friends and having fun

abstract: The Reading Game is a question and answer game designed to engage learners in the content of their coursework. The class of student participants creates a collective learning space where every action serves to introduce, build, or clarify concepts from the curriculum. The quality of the multiple-choice questions and the contents of the quizzes are determined by the participants who receive points for their efforts in both asking and answering questions. Participants can comment on and rate questions deemed outstanding by their peers, which directly impacts the contents of review quizzes. Participants progress to the next level of the game using their accumulated points onto asking open questions to the teachers and their cohort. Writing good questions is the winning strategy of the game. The key claim in the Reading Game is that creating questions is one of the fundamental cognitive elements that guide our conscious reasoning.


title: Independent learning of internal models for kinematic and dynamic control of reaching

abstract: Psychophysical studies of reaching movements suggest that hand kinematics are learned from errors in extent and direction in an extrinsic coordinate system, whereas dynamics are learned from proprioceptive errors in an intrinsic coordinate system. We examined consolidation and interference to determine if these two forms of learning were independent. Learning and consolidation of two novel transformations, a rotated spatial reference frame and altered intersegmental dynamics, did not interfere with each other and consolidated in parallel. Thus separate kinematic and dynamic models were constructed simultaneously based on errors computed in different coordinate frames, and possibly, in different sensory modalities, using separate working-memory systems. These results suggest that computational approaches to motor learning should include two separate performance errors rather than one.


title: FM/rate integrating MEMS gyroscope using independently controlled CW/CCW mode oscillations on a single resonator

abstract: This paper reports a frequency modulated (FM)/rate integrating MEMS gyroscope where superposed clockwise (CW) and counter clockwise (CCW) modes are independently controlled on a single resonator. A frequency and quality factor mismatch compensation method by adjusting the phases and amplitudes of driving signal is proposed and experimentally demonstrated. The mismatch compensation method proposed here could drastically reduce the both angular rate and angle error induced by interference between two oscillation modes. The scale factors were measured as high as about 0.73 having a quite small temperature dependency.


title: Farmers' perceptions of climate change and agricultural adaptation strategies in rural Sahel.

abstract: Farmers in the Sahel have always been facing climatic variability at intra- and inter-annual and decadal time scales. While coping and adaptation strategies have traditionally included crop diversification, mobility, livelihood diversification, and migration, singling out climate as a direct driver of changes is not so simple. Using focus group interviews and a household survey, this study analyzes the perceptions of climate change and the strategies for coping and adaptation by sedentary farmers in the savanna zone of central Senegal. Households are aware of climate variability and identify wind and occasional excess rainfall as the most destructive climate factors. Households attribute poor livestock health, reduced crop yields and a range of other problems to climate factors, especially wind. However, when questions on land use and livelihood change are not asked directly in a climate context, households and groups assign economic, political, and social rather than climate factors as the main reasons for change. It is concluded that the communities studied have a high awareness of climate issues, but climatic narratives are likely to influence responses when questions mention climate. Change in land use and livelihood strategies is driven by adaptation to a range of factors of which climate appears not to be the most important. Implications for policy-making on agricultural and economic development will be to focus on providing flexible options rather than specific solutions to uncertain climate.


title: NL-OOPS: from natural language to object oriented requirements using the natural language processing system LOLITA

abstract: This paper describes NL-OOPS, a CASE tool that supports requirements analysis by generating object oriented models from natural language requirements documents. The full natural language analysis is obtained using as a core system the Natural Language Processing System LOLITA. The object oriented analysis module implements an algorithm for the extraction of the objects and their associations for use in creating object models.


THE EFFECTS OF DATA QUALITY ON MACHINE LEARNING ALGORITHMS


title: A whole-body control framework for humanoids operating in human environments

abstract: Tomorrow's humanoids will operate in human environments, where efficient manipulation and locomotion skills, and safe contact interactions are critical design factors. We report here our recent efforts into these issues, materialized into a whole-body control framework. This framework integrates task-oriented dynamic control and control prioritization allowing to control multiple task primitives while complying with physical and movement-related constraints. Prioritization establishes a hierarchy between control spaces, assigning top priority to constraint-handling tasks, while projecting operational tasks in the null space of the constraints, and controlling the posture within the residual redundancy. This hierarchy is directly integrated at the kinematic level, allowing the program to monitor behavior feasibility at runtime. In addition, prioritization allows us to characterize the dynamic behavior of the individual control primitives subject to the constraints, and to synthesize operational space controllers at multiple levels. To complete this framework, we have developed free-floating models of the humanoid and incorporate the associated dynamics and the effects of the resulting support contacts into the control hierarchy. As part of a long term collaboration with Honda, we are currently implementing this framework into the humanoid robot Asimo


title: Radio Frequency Identification: Applications and Security Issues

abstract: Radio Frequency Identification (RFID) is name given to technology that uses tags, readers and backend servers to form a system that has numerous applications in many areas, much discovered and rest are to be explored. Before implementing RFID system security issues must be considered carefully, taking not care of security issues could lead to severe consequences. This paper is overview of Introduction to RFID, RFID Fundamentals, basic structure of a RFID system, some of its numerous applications, security issues and their remedies.


title: Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China

abstract: Background ::: Online social networking use has been integrated into adolescents’ daily life and the intensity of online social networking use may have important consequences on adolescents’ well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS) and validate it among junior middle school students in China. ::: ::: ::: Methods ::: A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods. ::: ::: ::: Results ::: Two factors, Social Function Use Intensity (SFUI) and Entertainment Function Use Intensity (EFUI), were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach’s alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, p<0.001). As expected, the SNAIS and its subscale scores were correlated significantly with emotional connection to social networking, social networking addiction, Internet addiction, and characteristics related to social networking use. ::: ::: ::: Conclusions ::: The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population.


title: Human falling detection algorithm using back propagation neural network

abstract: A fall monitor system is necessary to reduce the rate of fall fatalities in elderly people. As an accelerometer has been smaller and inexpensive, it has been becoming widely used in motion detection fields. This paper proposes the falling detection algorithm based on back propagation neural network to detect the fall of elderly people. In the experiment, a tri-axial accelerometer was attached to waists of five healthy and young people. In order to evaluate the performance of the fall detection, five young people were asked to simulate four daily-life activities and four falls; walking, jumping, flopping on bed, rising from bed, front fall, back fall, left fall and right fall. The experimental results show that the proposed algorithm can potentially distinguish the falling activities from the other daily-life activities.


title: Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

abstract: The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.


title: Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction

abstract: Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we show how to adapt some of these techniques to create a novel chained convolutional architecture with next-step conditioning for improving performance on protein sequence prediction problems. We explore its value by demonstrating its ability to improve performance on eight-class secondary structure prediction. We first establish a state-of-the-art baseline by adapting recent advances in convolutional neural networks which were developed for vision tasks. This model achieves 70.0% per amino acid accuracy on the CB513 benchmark dataset without use of standard performance-boosting techniques such as ensembling or multitask learning. We then improve upon this state-of-the-art result using a novel chained prediction approach which frames the secondary structure prediction as a next-step prediction problem. This sequential model achieves 70.3% Q8 accuracy on CB513 with a single model; an ensemble of these models produces 71.4% Q8 accuracy on the same test set, improving upon the previous overall state of the art for the eight-class secondary structure problem. Our models are implemented using TensorFlow, an open-source machine learning software library available at TensorFlow.org; we aim to release the code for these experiments as part of the TensorFlow repository.


EFFORT, RACE GAPS AND AFFIRMATIVE ACTION: A STRUCTURAL POLICY ANALYSIS OF US COLLEGE ADMISSIONS


title: The modified nodal approach to network analysis

abstract: The nodal method has been widely used for formulating circuit equations in computer-aided network analysis and design programs. However, several limitations exist in this method including the inability to process voltage sources and current-dependent circuit elements in a simple and efficient manner. A modified nodal analysis (MNA) method is proposed here which retains the simplicity and other advantages of nodal analysis while removing its limitations. A simple and effective pivoting scheme is also given. Numerical examples are used to compare the MNA method with the tableau method. Favorable results are observed for the MNA method in terms of the dimension, number of nonzeros, and fill-ins for comparable circuit matrices.


title: Optimizing Ground Vehicle Tracking Using Unmanned Aerial Vehicle and Embedded Apriltag Design

abstract: We investigate a vision-based vehicle tracking method which allows an unmanned aeriel vehicle(UAV) to track and follow a ground vehicle in coordinated Air-Ground operations. The UAV detects a customized Apriltag attached on the ground vehicle and estimates its motion status. We use a novel embedded-tag design to allow the UAV detect and lock the ground vehicle in both high and low altitude. The dynamic range achieved is from 0.3 meters to 10 meters. The motion-status estimation is improved using an optimum tag selection (as observation source) algorithm and a modified particle filter algorithm. The proposed algorithm improves the motion estimate accuracy significantly by 20%.


title: Data compression using SVD and Fisher information for radar emitter location

abstract: This paper presents a method that achieves large compression ratios for radar pulse trains exploited in time-difference-of-arrival/frequency-difference-of-arrival (TDOA/FDOA) emitter location; this method exploits pulse-to-pulse redundancy to get a compression ratio much higher than possible by using standard methods. We show how to use (i) the singular value decomposition (SVD) to exploit redundancy between radar pulses, and (ii) a Fisher information-based distortion criterion to enable elimination of pulses that are negligible to the FDOA estimation task. To use the SVD it is necessary to first gate the pulses, place them in a matrix and then align the pulses to get a matrix that is close to rank one. Finally, we employ reasonable coding schemes for the information to be sent and assess the achievable compression level. With such SVD-based coding the compression ratio depends on the number of pulses intercepted and the number of samples taken per pulse; depending on the scenario the compression ratio can range from about 4 up to 100 or more. Fisher information-based removal of pulses is shown to improve the compression ratio beyond what is achievable using only the SVD approach while having negligible impact on the FDOA accuracy; it does, however, degrade the TDOA accuracy. However, we demonstrate that the SVD method includes an inherent de-noising effect (common in SVD-based signal processing) that provides an improvement in TDOA accuracy over the case of no compression processing; thus, the overall impact on TDOA/FDOA accuracy can be negligible while providing significant compression ratios for typical radar signals.


title: FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction

abstract: The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e.g., image-level, region-level, and pixel-level are diverging. Generally, network structures designed specifically for image classification are directly used as default backbone structure for other tasks including detection and segmentation, but there is seldom backbone structure designed under the consideration of unifying the advantages of networks designed for pixel-level or region-level predicting tasks, which may require very deep features with high resolution. Towards this goal, we design a fish-like network, called FishNet. In FishNet, the information of all resolutions is preserved and refined for the final task. Besides, we observe that existing works still cannot \emph{directly} propagate the gradient information from deep layers to shallow layers. Our design can better handle this problem. Extensive experiments have been conducted to demonstrate the remarkable performance of the FishNet. In particular, on ImageNet-1k, the accuracy of FishNet is able to surpass the performance of DenseNet and ResNet with fewer parameters. FishNet was applied as one of the modules in the winning entry of the COCO Detection 2018 challenge. The code is available at this https URL.


title: Go game formal revealing by Ising model

abstract: Go gaming is a struggle for territory control between rival, black and white, stones on a board. We model the Go dynamics in a game by means of the Ising model whose interaction coefficients reflect essential rules and tactics employed in Go to build long-term strategies. At any step of the game, the energy functional of the model provides the control degree (strength) of a player over the board. A close fit between predictions of the model with actual games is obtained.


title: MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

abstract: We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents' optimal polices, but more importantly, the observation and understanding of individual agent's behaviors and social phenomena emerging from the AI society, including communication languages, leaderships, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. In this demo, we present three environments designed on MAgent and show emerged collective intelligence by learning from scratch.


title: Pedestrian Attribute Classification in Surveillance: Database and Evaluation

abstract: Attributes are helpful to infer high-level semantic knowledge of pedestrians, thus improving the performance of pedestrian tracking, retrieval, re-identification, etc. However, current pedestrian databases are mainly for the pedestrian detection or tracking application, and semantic attribute annotations related to pedestrians are rarely provided. In this paper, we construct an Attributed Pedestrians in Surveillance (APiS) database with various scenes. The APiS 1.0 database includes 3661 images with 11 binary and 2 multi-class attribute annotations. Moreover, we develop an evaluation protocol for researchers to evaluate pedestrian attribute classification algorithms. With the APiS 1.0 database, we present two baseline methods, one for binary attribute classification and the other for multi-class attribute classification. For binary attribute classification, we train AdaBoost classifiers with color and texture features, while for multi-class attribute classification, we adopt a weighted K Nearest Neighbors (KNN) classifier with color features. Finally, we report and discuss the baseline performance on the APiS 1.0 database following the proposed evaluation protocol.


title: An investigation of byte n-gram features for malware classification

abstract: Malware classification using machine learning algorithms is a difficult task, in part due to the absence of strong natural features in raw executable binary files. Byte n-grams previously have been used as features, but little work has been done to explain their performance or to understand what concepts are actually being learned. In contrast to other work using n-gram features, in this work we use orders of magnitude more data, and we perform feature selection during model building using Elastic-Net regularized Logistic Regression. We compute a regularization path and analyze novel multi-byte identifiers. Through this process, we discover significant previously unreported issues with byte n-gram features that cause their benefits and practicality to be overestimated. Three primary issues emerged from our work. First, we discovered a flaw in how previous corpora were created that leads to an over-estimation of classification accuracy. Second, we discovered that most of the information contained in n-grams stem from string features that could be obtained in simpler ways. Finally, we demonstrate that n-gram features promote overfitting, even with linear models and extreme regularization.


title: Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements

abstract: In this paper, we investigate the capability of an axle box acceleration (ABA) system to evaluate the degradation at railway crossings. For this purpose, information from multiple sensors, namely, ABA signals, 3D rail profiles, Global Positioning System (GPS) and tachometer recordings, was collected from both nominal and degraded crossings. By proper correlation of the gathered data, an algorithm was proposed to distinguish the characteristic ABA related to the degradation and then to evaluate the health condition of crossings. The algorithm was then demonstrated on a crossing with an unknown degradation status, and its capability was verified via a 3D profile measurement. The results indicate that the ABA system is effective at monitoring two types of degradations. The first type is uneven deformation between the wing rail and crossing nose, corresponding to characteristic ABA frequencies of 230-350 and 460-650 Hz. The second type is local irregularity in the longitudinal slope of the crossing nose, corresponding to characteristic ABA frequencies of 460-650 Hz. The types and severity of the degradation can be evaluated by the spatial distribution and energy concentration of the characteristic frequencies of the ABA signals.


How perceived supervisor support effects workplace deviance? Mediating role of perceived organizational support


Distractor Generation for Multiple Choice Questions Using Learning to Rank


title: Category-specific object reconstruction from a single image

abstract: Object reconstruction from a single image - in the wild - is a problem where we can make progress and get meaningful results today. This is the main message of this paper, which introduces an automated pipeline with pixels as inputs and 3D surfaces of various rigid categories as outputs in images of realistic scenes. At the core of our approach are deformable 3D models that can be learned from 2D annotations available in existing object detection datasets, that can be driven by noisy automatic object segmentations and which we complement with a bottom-up module for recovering high-frequency shape details. We perform a comprehensive quantitative analysis and ablation study of our approach using the recently introduced PASCAL 3D+ dataset and show very encouraging automatic reconstructions on PASCAL VOC.


title: Beliefs About Emotional Residue: The Idea That Emotions Leave a Trace in the Physical Environment

abstract: Drawing upon the literatures on beliefs about magical contagion and property transmission, we examined people’s belief in a novel mechanism of human-to-human contagion, emotional residue. This is the lay belief that people’s emotions leave traces in the physical environment, which can later influence others or be sensed by others. Studies 1–4 demonstrated that Indians are more likely than Americans to endorse a lay theory of emotions as substances that move in and out of the body, and to claim that they can sense emotional residue. However, when the belief in emotional residue is measured implicitly, both Indians and American believe to a similar extent that emotional residue influences the moods and behaviors of those who come into contact with it (Studies 5–7). Both Indians and Americans also believe that closer relationships and a larger number of people yield more detectable residue (Study 8). Finally, Study 9 demonstrated that beliefs about emotional residue can influence people’s behaviors. Together, these finding suggest that emotional residue is likely to be an intuitive concept, one that people in different cultures acquire even without explicit instruction.


Understanding Spoken Language


title: Towards a trust aware cognitive radio architecture

abstract: Cognitive radio (CR) is a promising concept for improving the utilization of scarce radio spectrum resources. A reliable strategy for the detection of unused spectrum bands is essential to the design and practical implementation of CR systems. It is widely accepted that in a real-world environment, cooperative spectrum sensing involving many secondary users scattered in a wide geographical area can greatly improve sensing accuracy. However, some secondary users may misbehave, i.e. provide false sensing information, in an attempt to maximize their own utility gains. Such selfish behaviour, if unchecked, can severely impact the operation of the CR system. In this paper, we propose a novel trustaware hybrid spectrum sensing scheme which can detect misbehaving secondary users and filter out their reported spectrum sensing results from the decision making process. The robustness and efficiency of the proposed scheme are verified through extensive computer simulations.


title: Unsupervised Learning by Probabilistic Latent Semantic Analysis

abstract: This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed technique uses a generative latent class model to perform a probabilistic mixture decomposition. This results in a more principled approach with a solid foundation in statistical inference. More precisely, we propose to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice. Probabilistic Latent Semantic Analysis has many applications, most prominently in information retrieval, natural language processing, machine learning from text, and in related areas. The paper presents perplexity results for different types of text and linguistic data collections and discusses an application in automated document indexing. The experiments indicate substantial and consistent improvements of the probabilistic method over standard Latent Semantic Analysis.


title: Hybrid Excitation Topologies and Control Strategies of Stator Permanent Magnet Machines for DC Power System

abstract: The stator permanent magnet (PM) machines have simple and robust rotor structure as well as high torque density. The hybrid excitation topology can realize flux regulation and wide constant power operating capability of the stator PM machines when used in dc power systems. This paper compares and analyzes the electromagnetic performance of different hybrid excitation stator PM machines according to different combination modes of PMs, excitation winding, and iron flux bridge. Then, the control strategies for voltage regulation of dc power systems are discussed based on different critical control variables including the excitation current, the armature current, and the electromagnetic torque. Furthermore, an improved direct torque control (DTC) strategy is investigated to improve system performance. A parallel hybrid excitation flux-switching generator employing the improved DTC which shows excellent dynamic and steady-state performance has been achieved experimentally.


title: Criteria for project assignments in multiple-project environments

abstract: Project assignment, assigning a project to a project manager, is one of the organization's most strategically important decisions since it is a determinant of the project performance and the organizational performance. Despite its importance, project assignment is not well studied in the literature and it is performed intuitively in practice. By using concepts of case study research, this paper explores project assignment processes in their real-life contexts. Then, the project assignment criteria obtained from the case studies are validated by a panel of experts, following the concepts of the Delphi method. In this paper, some of the research results are presented. In particular, we propose an integrated set of project assignment criteria, including some already proposed in the literature, and others that are new. As part of the criteria, we place a special emphasis on an integrated set of project managers' competencies that is applicable to the current multiple-project environments. In future research, we will use the criteria to build a model for project assignments. The conceptual outline of the model is presented.


title: Improved non-restoring division algorithm with dual path calculation

abstract: This paper focuses on improving the performance of non-restoring division by reducing the delay. To improve its performance, two new approaches are proposed here. For the first proposed approach, a novel method to find a quotient bit for every iteration, which hides the total delay of the multiplexer with dual path calculation is presented. The second new method uses a modified Most Significant Carry (MSC) generator, which determines the sign of each remainder faster than a carry lookahead adder. This reduces the total delay.


title: Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses

abstract: The Kiva warehouse-management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.


title: Numerical prediction of stress evolution during blowtorch reflow of fused silica micro-shell resonators

abstract: Micro-blowtorching is a new, fast and reliable fabrication process to form 3D hollow fused silica shell structures such as high Q-factor birdbath resonators. The performance of the fabricated shell as a resonator can be significantly degraded due to the thermal residual stress during the early cooling phase after fabrication. A multi-step finite element analysis is developed for a qualitative prediction of residual stresses and deformation of the birdbath shells. First, a novel non-isothermal model is used to simulate the reflow and molding process. In the next step, the calculated temperature field is used to conduct a transient heat transfer analysis to mimic the cooling phase up to a designated temperature. Finally, the non-uniform temperature field from the heat transfer simulation is used to conduct a thermoelastic analysis to predict the residual stresses and deformation that would exist at ambient temperature in the birdbath shell structure. The predicted internal stresses can usefully be employed to optimize the coarse and fine annealing processes to reduce the stresses in a fabricated shell to a desired level.


title: Effect of lurasidone on neurocognitive performance in patients with schizophrenia: A short-term placebo- and active-controlled study followed by a 6-month double-blind extension

abstract: Abstract This double-blind study evaluated change in cognitive performance and functional capacity in lurasidone and quetiapine XR-treated schizophrenia patients over a 6-week, placebo-controlled study, followed by a 6-month, double-blind extension. Cognitive performance and functional capacity were assessed with the CogState computerized cognitive battery and the UPSA-B. Analyses were conducted for all subjects, as well as the subsample whose test scores met prespecified validity criteria. No statistically significant differences were found for change in the composite neurocognitive score for lurasidone (80 mg/day and 160 mg/day) groups, quetiapine XR and placebo in the full sample at week 6. For the evaluable sample ( N =267), lurasidone 160 mg was superior to both placebo and quetiapine on the neurocognitive composite, while lurasidone 80 mg, quetiapine XR, and placebo did not differ. UPSA-B scores were superior to placebo at 6 weeks for all treatments. In the double-blind extension study, analysis of the full sample showed significantly better cognitive performance in the lurasidone (40–160 mg) group compared to the quetiapine XR (200–800 mg) group at both 3 and 6 months. Cognitive and UPSA-B total scores were significantly correlated at baseline and for change over time. This is the first study to date where the investigational treatment was superior to placebo on both cognitive assessments and a functional coprimary measure at 6 weeks, as well as demonstrated superiority to an active comparator on cognitive assessments at 6 weeks and at 6 months of extension study treatment. These findings require replication, but are not due to practice effects, because of the placebo and active controls.


title: INTERPERSONAL RELATIONS AND GROUP PROCESSES Alone in the Crowd: The Structure and Spread of Loneliness in a Large Social Network

abstract: The discrepancy between an individual's loneliness and the number of connections in a social network is well documented, yet little is known about the placement of loneliness within, or the spread of loneliness through, social networks. We use network linkage data from the population-based Framingham Heart Study to trace the topography of loneliness in people's social networks and the path through which loneliness spreads through these networks. Results indicated that loneliness occurs in clusters, extends up to three degrees of separation, is disproportionately represented at the periphery of social networks, and spreads through a contagious process. The spread of loneliness was found to be stronger than the spread of perceived social connections, stronger for friends than family members, and stronger for women than for men. The results advance our understanding of the broad social forces that drive loneliness and suggest that efforts to reduce loneliness in our society may benefit by aggressively targeting the people in the periphery to help repair their social networks and to create a protective barrier against loneliness that can keep the whole network from unraveling.


title: Computational intelligence in intrusion detection system for snort log using hadoop

abstract: Today Cyber-attacks are increasing and the security technologies so far we used are in capable of handling the large data sets of data. Now a day the attacks motive has changed from simple hacking, damaging to large scale network and server system. In the presence of fresh and formerly unknown activities, intrusion detection rate is inaccurate and low. Many research works are focused in this area. In all the security infrastructures, Network Intrusion Detection Systems (NIDS) have grown a standard and in separable component. For this reason, a new model has been proposed based on Big Data for detecting unknown attacks. The core idea of this paper is to analyze the activity of the network users, and to classify whether the user is normal or anomaly. For implementation of this project, we have used the snort, the tools which are used to capture the online behavior of network users. After collecting the network behavior, the dataset was analyzed with hadoop framework using C4.5 classification. The performance of the project was compared with KDD Dataset. The proposed approach improves the IDS performance and its capability is to provide quick response to various types of network attacks.


ACCURACY ASSESSMENT METHOD FOR WETLAND OBJECT-BASED CLASSIFICATION


title: General Compensation Method for a Marchand Balun With an Arbitrary Connecting Segment Between the Balance Ports

abstract: For a Marchand balun, the output imbalance due to the inevitable physical separation between the balance ports is a problem. In this paper, a general compensation method to cope with this imbalance issue is proposed with rigorous analysis and design formulas. The compensation relies on two intentionally shortened coupling sections and a pair of short-circuited transmission lines as the terminations. The proposed method is able to deal with a long connecting segment between the balance ports as long as the coupling sections are tight enough at the desired frequencies. The theory and formulation are first treated using transient analysis with multiple reflections/couplings between the networks. Design graphs are summarized and three examples are fabricated, validated, and discussed to demonstrate the design flexibility the proposed method provides.