title: Temporal Form in Interaction Design

abstract: IntroductionInteraction design is distinguished from most other design disciplines through its temporal form. Temporal form is the computational structure that enables and demands a temporal expression in the resulting design. When programming computers we create a temporal form that then comes to expression through an output of actuators and other materials. Indeed, it is these material manifestations of temporal forms that enable our interactions with computational things, as digital computations in themselves are inaccessible. Thus, temporal form gives interaction design a kinship with temporal arts like music, dance, and film. While several interaction design researchers have already articulated this aspect, we have still to grasp the details of what it entails (cf. Hallnas & Redstrom, 2001; Hallnas & Redstrom, 2006; Maze & Redstrom, 2005; Vallgarda, 2014). How do we experience temporal form in computational things? What significance does the temporal form hold for the overall experience? What is the relation between temporal form and the other form-elements in interaction design? What relations can we find between the expressions of computational things and our experience of them when it comes to temporal forms?Temporal form is what enables poetry. In music, temporal form is the composition of tones, pauses, and timbre, arranged into harmonies and rhythms. In movies, it is the composition of actions and backgrounds moving stories forward. In poetry, it is the composition of meanings and rhythms. Temporal form holds functional as well as aesthetic power in the composition of the overall design--just as physical form does. The traditional view on temporal form in relation to computational things has been that of speed. It has been a matter of removing delays from hardware and software to enable instantaneous representation on graphical displays; it has been about speeding up computations to achieve faster results, etc. Certainly, the execution speed in a computational thing is important and will affect what we do with it, but there are more aspects of temporal form to be explored than speed. Material and tangible computing have changed what forms of interaction we design for and the materials we use. They have entailed that the contexts in which we use computational things have multiplied and that not all of those contexts are suited for speed as the only temporal expression. Indeed, it has become crucial to pay attention to the underexplored notions of timbre and pauses as well as the relations between materials and state changes. From what can we design our rhythms and harmonies?Vallgarda (2014) has proposed to see temporal form as part of a trinity of forms that would constitute the form-giving concerns in the practice of interaction design, with the other forms being physical form and interaction gestalt. We have long understood physical form and its relation to materials. In industrial design schools, for instance, students are taught how any physical form can be broken down into cubes and cylinders, and that every other form comes out of the transitions between them (cf. Itten, 1975; Ching, 2010). We know about materials and their influence on form and expression (cf. Manzini, 1989), and we know about style of physical objects (cf. Semper, 2004). We also have an understanding of the interaction gestalt where concepts like affordances (Gibson, 1986) or signifiers (Norman, 2011) help us understand how physical form is linked to the interaction gestalt. Additionally, Dourish (2001) has succeeded in establishing all interaction as social as well as embodied. Indeed, what we lack most is an understanding of temporal form's role in all this.In the first part of this paper we underline the importance of working explicitly with temporal form in the design of computational things. We give a nuanced account of what temporal form is in interaction design. We then look at related work synthesizing what we already know of the temporal concerns in interaction design and human-computer interaction (HCI). …


title: Effect of age and social connection on perceived anxiety over radiation exposure among decontamination workers in Fukushima Prefecture, Japan

abstract: To reveal the effect of age and other factors on perceived anxiety over radiation exposure among decontamination workers in Fukushima Prefecture, Japan.A survey questionnaire was sent to 1505 workers, with questions regarding age, presence of a written employment contract, previous residence, radiation passbook ownership, presence of close persons for consultation, knowledge of how to access public assistance, and a four-point scale of radiation-related anxiety (1= "Very much," 2= "Somewhat," 3= "A little bit," and 4= "None" ). The relationships between the degree of anxiety and variables were analyzed using the chi-square test and residual analysis.In all, 512 participants responded to the questionnaire. The mean age of participants was 46.2 years (SD: 13.1, range: 18-77). Of them, 50, 233, 168, and 61 workers chose "Very much," "Somewhat," "A little bit," and "None," respectively, on the anxiety scale. Chi-square test showed that participants aged 61 years and over had higher degrees of anxiety (p<0.001). Ordinal logistic regression showed that the degree of anxiety increased if they did not have a written contract (p=0.042) or persons to consult (p=0.034) and if they routinely checked the dose rate (p=0.046).Decontamination workers who do not have a written contract or who are in socially isolated situations have greater anxiety over radiation exposure. Thus, it is important to both create supportive human relationships for consultation and enhance labor management in individual companies.


title: Detecting Isodistance Hotspots on Spatial Networks: A Summary of Results

abstract: Spatial hotspot detection aims to find regions of interest with statistically significant high concentration of activities. In recent years, it has presented significant value in many critical application domains such as epidemiology, criminology and transportation engineering. However, existing spatial hotspot detection approaches focus on either on Euclidean space or are unable to find the entire set of hotspots. In this paper, we first formulate the problem of Network Isodistance Hotspot Detection (NIHD) as finding all sub-networks whose nodes and edges are reachable from a activity center and have significantly high concentration of activities. Then, we propose a novel algorithm based on network partitioning and pruning (NPP) which overcomes the computational challenges due to the high costs from candidate enumeration and statistical significance test based on randomization. Theoretical and experimental analysis show that NPP substantially improves the scalability over the baseline approach while keeping the results correct and complete. Moreover, case studies on real crime datasets show that NPP detects hotspots with higher accuracy and is able to reveal the hotspots that are missed by existing approaches.


title: A compact millimeter-wave slot antenna array for 5G standards

abstract: A millimeter wave (mm-wave) two-dimensional slot antenna array, operating at 28 GHz, is designed and presented in this paper. The antenna array consists of 8 elements in a 2×4 configuration. A 1-to-4 power divider is feeding the 2×4 slot antenna array in both schemes; parallel and series. The bandwidth achieved for the integrated system was almost 5% with a center frequency at 28.2 GHz. The occupied size was 29.9×28.7×0.13 mm3, making it easier to fit within handheld devices for the upcoming 5G cellular standards.


title: HOG feature based SVM classification of glaucomatous fundus image with extraction of blood vessels

abstract: Glaucoma at the later stages causes eye blindness, it is essential for early detection to minimize the risks and warn patients who might eventually lose their vision. The study gives an assessment on Glaucoma detection using the Histogram of Oriented Gradients (HOG) Feature extraction along with SVM classification of retinal fundus image with the extraction of blood vessels using Gabor filter. Retinal fundus images are reduced in their dimension. Then the blood vessels are extracted by using Gabor filter, morphological operations and thresholding. This improves the accuracy. Textural features within the images are used for accurate and efficient glaucoma classification. The image is then given to a HOG feature descriptor. The feature sets (statistical properties) are then extracted from the HOG Features that are used for Support Vector Machine classification of glaucomatous images.


Universal mechanisms and cultural diversity: Replacing the blank slate with a coloring book


title: Electricity Price Forecasting in Ontario Electricity Market Using Wavelet Transform in Artificial Neural Network Based Model

abstract: Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.


title: Wideband Periodic Endfire Antenna With Bowtie Dipoles

abstract: A wideband periodic antenna with bowtie dipoles, fed by a microstrip line, is proposed. A wideband transition from a microstrip line to a parallel strip line (PSL) is utilized to achieve a good differential-fed mechanism. The measurements show that the antenna presents an impedance bandwidth of 51.4% for SWR les 2, an average gain of 4 dBi and stable unidirectional radiation patterns. Its total size is 1.2 times 0.7 lambda2 (lambda is the free-space wavelength at the center frequency). The studies of four critical parameters are performed for practical applications.


title: A survey on brain tumor detection using image processing techniques

abstract: Biomedical Image Processing is a growing and demanding field. It comprises of many different types of imaging methods likes CT scans, X-Ray and MRI. These techniques allow us to identify even the smallest abnormalities in the human body. The primary goal of medical imaging is to extract meaningful and accurate information from these images with the least error possible. Out of the various types of medical imaging processes available to us, MRI is the most reliable and safe. It does not involve exposing the body to any sorts of harmful radiation. This MRI can then be processed, and the tumor can be segmented. Tumor Segmentation includes the use of several different techniques. The whole process of detecting brain tumor from an MRI can be classified into four different categories: Pre-Processing, Segmentation, Optimization and Feature Extraction. This survey involves reviewing the research by other professionals and compiling it into one paper.


title: Anatomy of an online misinformation network

abstract: Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.


Co-attention CNNs for Unsupervised Object Co-segmentation


title: Sentiment Analysis of Citations Using Word2vec

abstract: Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short texts. In this work, I conducted empirical research with the question: how well does word2vec work on the sentiment analysis of citations? The proposed method constructed sentence vectors (sent2vec) by averaging the word embeddings, which were learned from Anthology Collections (ACL-Embeddings). I also investigated polarity-specific word embeddings (PS-Embeddings) for classifying positive and negative citations. The sentence vectors formed a feature space, to which the examined citation sentence was mapped to. Those features were input into classifiers (support vector machines) for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on a set of annotated citations. The results showed that word embeddings are effective on classifying positive and negative citations. However, hand-crafted features performed better for the overall classification.


title: The antecedents of purchase and re-purchase intentions of online auction consumers

abstract: This research explores some factors affecting purchase and repurchase intentions towards online auctions. An experiment, in the context of PChome & eBay and Yahoo Auction online auctions websites, was conducted to simulate an environment manipulating branding, seller evaluation, volume of information, and price ranges to influence purchase intentions, reflecting some aspects of utilitarian and hedonic approaches towards purchase and repurchase intentions. Furthermore, mediation strategies, trust, and media richness were also introduced in the study to examine their influence on repurchase intentions. 120 participants validated the experimental design, where all variables influencing purchase intentions were found to be significant in varying degrees, and where mediator strategies and trust were found to significantly influence repurchase intentions. These results reinforce notions that online auctions are complex processes, especially to induce purchase and repurchase intentions, and that both buyers and sellers have complex utilitarian and hedonic approaches to develop purchase and repurchase intentions. Further theoretical and managerial implications are detailed in this research. This research examines the factors influencing purchase and repurchase intentions.The study describes some of the complexities and uncertainties in online auctions.Pchome & eBay and Yahoo online auction websites were the basis for the experiment.Product, price, and seller information represented website-based factors.Mediation strategies, media richness, and trust represented seller behavior factors.


title: Dense Volume-to-Volume Vascular Boundary Detection

abstract: In this work, we tackle the important problem of dense 3D volume labeling in medical imaging. We start by introducing HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED). Next, we develop a novel 3D-Convolutional Neural Network (CNN) architecture, I2I-3D, that predicts boundary location in volumetric data. Our fine-to-fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label training and prediction (2) precise voxel-level prediction to capture fine scale structures prevalent in medical data and (3) directed multi-scale, multi-level feature learning. We evaluate our approaches on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. We show that our deep learning approaches out-perform the current state-of-the-art in 3D vascular boundary detection (structured forests 3D), by a large margin, as well as HED applied to slices. Prediction takes about one minute on a typical \(512\,\times \,512\,\times \,512\) volume, when using GPU.


title: Motif: Supporting Novice Creativity through Expert Patterns

abstract: Creating personal narratives helps people build meaning around their experiences. However, novices lack the knowledge and experience to create stories with strong narrative structure. Current storytelling tools often structure novice work through templates, enforcing a linear creative process that asks novices for materials they may not have. In this paper, we propose scaffolding creative work using storytelling patterns extracted from stories created by experts. Patterns are modular sets of related camera shots that expert videographers commonly use to achieve a specific narrative function. After identifying a set of patterns from high-quality storytelling videos, we created Motif, a mobile video storytelling application that allows users to construct video stories by combining these patterns. By making existing solutions used by experts available to novices, we encourage capturing shots with story structure and narrative goals in mind. In a controlled study where we asked participants to create travel video stories, videos created with patterns conveyed stronger narrative structure and were considered higher quality by expert evaluators than videos created without patterns.


title: Fast Zero-Shot Image Tagging

abstract: The well-known word analogy experiments show that the recent word vectors capture fine-grained linguistic regularities in words by linear vector offsets, but it is unclear how well the simple vector offsets can encode visual regularities over words. We study a particular image-word relevance relation in this paper. Our results show that the word vectors of relevant tags for a given image rank ahead of the irrelevant tags, along a principal direction in the word vector space. Inspired by this observation, we propose to solve image tagging by estimating the principal direction for an image. Particularly, we exploit linear mappings and nonlinear deep neural networks to approximate the principal direction from an input image. We arrive at a quite versatile tagging model. It runs fast given a test image, in constant time w.r.t. the training set size. It not only gives superior performance for the conventional tagging task on the NUSWIDE dataset, but also outperforms competitive baselines on annotating images with previously unseen tags.


title: Modeling wrinkles on smooth surfaces for footwear design

abstract: We describe two new shape operators that superimpose wrinkles on top of a smooth NURBS surface. Previous research studying wrinkles focused mostly on cloth modeling or in animations, which are driven more by visual realism, but allow large elastic deformations. Our operators generate wrinkle-shaped deformations in a region of a smooth surface along a given boundary based on a few basic parametric inputs such as wrinkle magnitude and extent (these terms will be defined in the paper). The essential geometric transformation to map the smooth surface to a wrinkled one will be defined purely in terms of the geometry of the surface and the input parameters. Our model is based on two surface properties: geodesic offsets and surface energy. Practical implementation of the operators is discussed, and examples presented. Finally, the motivation for the operators will be given through their application in the computer-aided design and manufacture of footwear.


title: Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

abstract: DR-submodular continuous functions are important objectives with wide real-world applications spanning MAP inference in determinantal point processes (DPPs), and mean-field inference for probabilistic submodular models, amongst others. DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time. ::: In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints. We start by investigating geometric properties that underlie such objectives, e.g., a strong relation between (approximately) stationary points and global optimum is proved. These properties are then used to devise two optimization algorithms with provable guarantees. Concretely, we first devise a "two-phase" algorithm with $1/4$ approximation guarantee. This algorithm allows the use of existing methods for finding (approximately) stationary points as a subroutine, thus, harnessing recent progress in non-convex optimization. Then we present a non-monotone Frank-Wolfe variant with $1/e$ approximation guarantee and sublinear convergence rate. Finally, we extend our approach to a broader class of generalized DR-submodular continuous functions, which captures a wider spectrum of applications. Our theoretical findings are validated on synthetic and real-world problem instances.


title: Secondary trauma symptoms in clinicians: A critical review of the construct, specificity, and implications for trauma-focused treatment

abstract: Abstract Advocates of the concept of secondary traumatization propose that clinicians who provide trauma-focused treatment may be particularly at risk for experiencing secondary trauma symptoms. This specific symptom presentation purportedly develops following exposure to the traumatic experiences described by their clients. Consequently, these professionals have advocated for increases in resources devoted to the prevention and treatment of secondary trauma symptoms (e.g., enhanced clinician training, increase in availability of treatment options for affected trauma workers, etc.). A review of empirical literature examining prevalence and specificity of secondary trauma symptoms in trauma clinicians is provided. Findings are mixed and often indicate that trauma clinicians are not frequently experiencing “clinically significant” levels of symptoms and that these symptoms may not be uniquely associated with trauma-focused treatment. Finally, it is argued that additional clarification and research on the criterion, course, and associated impairment are needed. Recommendations for future research are provided.


title: Synthesis of Submicrometer-Sized CdS Hollow Spheres in Aqueous Solutions of a Triblock Copolymer

abstract: Submicrometer-sized hollow CdS spheres with a wall thickness of about 20 nm have been synthesized in aqueous solutions of cadmium acetate (Cd(OAc)2) and the triblock copolymer EO20PO70EO20 (P123) at room temperature through the slow release of S2- ions from thioacetamide (TAA). X-ray powder diffraction (XRD), transmission electron microscopy (TEM), high-resolution TEM (HRTEM), thermogravimetric analysis (TGA), UV−vis absorption, and photoluminescence (PL) spectroscopy have been used for the characterization of the obtained hollow CdS spheres. It has been revealed that a minimum concentration of the polymer P123 was required for the formation of hollow CdS spheres and a double sonication condition was essential for the formation of pure hollow spheres. TEM and dynamic light scattering (DLS) studies have suggested that Cd(OAc)2 could induce P123 to form large complex micelles in the solution but Cd(NO3)2 could not, resulting in the formation of hollow CdS spheres in the acetate system and loose CdS aggregat...


title: A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition

abstract: This paper presents a new framework for human action recognition from a 3D skeleton sequence. Previous studies do not fully utilize the temporal relationships between video segments in a human action. Some studies successfully used very deep Convolutional Neural Network (CNN) models but often suffer from the data insufficiency problem. In this study, we first segment a skeleton sequence into distinct temporal segments in order to exploit the correlations between them. The temporal and spatial features of a skeleton sequence are then extracted simultaneously by utilizing a fine-to-coarse (F2C) CNN architecture optimized for human skeleton sequences. We evaluate our proposed method on NTU RGB+D and SBU Kinect Interaction dataset. It achieves 79.6% and 84.6% of accuracies on NTU RGB+D with cross-object and cross-view protocol, respectively, which are almost identical with the state-of-the-art performance. In addition, our method significantly improves the accuracy of the actions in two-person interactions.


title: Human Rights Event Detection from Heterogeneous Social Media Graphs

abstract: Abstract Human rights organizations are increasingly monitoring social media for identification, verification, and documentation of human rights violations. Since manual extraction of events from the massive amount of online social network data is difficult and time-consuming, we propose an approach for automated, large-scale discovery and analysis of human rights–related events. We apply our recently developed Non-Parametric Heterogeneous Graph Scan (NPHGS), which models social media data such as Twitter as a heterogeneous network (with multiple different node types, features, and relationships) and detects emerging patterns in the network, to identify and characterize human rights events. NPHGS efficiently maximizes a nonparametric scan statistic (an aggregate measure of anomalousness) over connected subgraphs of the heterogeneous network to identify the most anomalous network clusters. It summarizes each event with information such as type of event, geographical locations, time, and participants, and p...


title: Efficient hand eye calibration method for a delta robot pick-and-place system

abstract: Delta Robot is a kind of parallel robot manipulator specifically designed for the pick-and-place task. Most of such system requires vision to detect the randomly placed workpieces. Therefore, hand eye calibration is a necessary process when installing new product line or modifying the existing robotic configuration. Because the vision and robot do not share the same workspace, and the workpiece move continuously on the conveyor, it is difficult to perform the hand eye calibration efficiently and directly. In this paper, we propose a systematic framework to efficiently calibrate the vision, conveyor and the robot. Beyond two commonly used chess board based calibration board, no external equipments are needed. A virtual coordinate frame which shares the same origin with the vision coordinate frame is imposed on the conveyor. Based on this assumption, two independent experiments can be performed to gather data sets which can be used to optimize the coordinate transformation matrices. Linear Mean Square(LMS) algorithm is used to find the optimal parameters. A self feed high speed pick and place experimental platform is built and the proposed algorithm is implemented. The experimental results show the effectiveness and efficiency. All the system can be calibrated within 10 minutes.


title: A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification

abstract: Convolutional Neural Networks (CNNs) have demonstrated their superiority in image classification, and evolutionary computation (EC) methods have recently been surging to automatically design the architectures of CNNs to save the tedious work of manually designing CNNs. In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN. There are three new ideas in the proposed DECNN method. Firstly, an existing effective encoding scheme is refined to cater for variable-length CNN architectures; Secondly, the new mutation and crossover operators are developed for variable-length DE to optimise the hyperparameters of CNNs; Finally, the new second crossover is introduced to evolve the depth of the CNN architectures. The proposed algorithm is tested on six widely-used benchmark datasets and the results are compared to 12 state-of-the-art methods, which shows the proposed method is vigorously competitive to the state-of-the-art algorithms. Furthermore, the proposed method is also compared with a method using particle swarm optimisation with a similar encoding strategy named IPPSO, and the proposed DECNN outperforms IPPSO in terms of the accuracy.


title: Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage

abstract: Data sharing is an important functionality in cloud storage. In this paper, we show how to securely, efficiently, and flexibly share data with others in cloud storage. We describe new public-key cryptosystems that produce constant-size ciphertexts such that efficient delegation of decryption rights for any set of ciphertexts are possible. The novelty is that one can aggregate any set of secret keys and make them as compact as a single key, but encompassing the power of all the keys being aggregated. In other words, the secret key holder can release a constant-size aggregate key for flexible choices of ciphertext set in cloud storage, but the other encrypted files outside the set remain confidential. This compact aggregate key can be conveniently sent to others or be stored in a smart card with very limited secure storage. We provide formal security analysis of our schemes in the standard model. We also describe other application of our schemes. In particular, our schemes give the first public-key patient-controlled encryption for flexible hierarchy, which was yet to be known.


title: Relationships between Handwriting Performance and Organizational Abilities among Children with and without Dysgraphia: A Preliminary Study.

abstract: Abstract Organizational ability constitutes one executive function (EF) component essential for common everyday performance. The study aim was to explore the relationship between handwriting performance and organizational ability in school-aged children. Participants were 58 males, aged 7–8 years, 30 with dysgraphia and 28 with proficient handwriting. Group allocation was based on children's scores in the Handwriting Proficiency Screening Questionnaire (HPSQ). They performed the Hebrew Handwriting Evaluation (HHE), and their parents completed the Questionnaire for Assessing Students’ Organizational Abilities-for Parents (QASOA-P). Significant differences were found between the groups for handwriting performance (HHE) and organizational abilities (QASOA-P). Significant correlations were found in the dysgraphic group between handwriting spatial arrangement and the QASOA-P mean score. Linear regression indicated that the QASOA-P mean score explained 42% of variance of handwriting proficiency (HPSQ). Based on one discriminant function, 81% of all participants were correctly classified into groups. Study results strongly recommend assessing organizational difficulties in children referred for therapy due to handwriting deficiency.


title: The emerging roles of exosomes in the modulation of immune responses in cancer

abstract: Exosomes are promising tools for improving cancer care, but conversely may also contribute to tumor progression. Here, we highlight recently discovered roles of exosomes in modulating immune responses in cancer, with emphasis on exosomal surface proteins and on RNA and DNA content. We also discuss how exosomes could be exploited as biomarkers and delivery vehicles in cancer therapy.


title: The Interpersonal Effects of Anger and Happiness in Negotiations

abstract: Three experiments investigated the interpersonal effects of anger and happiness in negotiations. In the course of a computer-mediated negotiation, participants received information about the emotional state (anger, happiness, or none) of their opponent. Consistent with a strategic-choice perspective, Experiment 1 showed that participants conceded more to an angry opponent than to a happy one. Experiment 2 showed that this effect was caused by tracking--participants used the emotion information to infer the other's limit, and they adjusted their demands accordingly. However, this effect was absent when the other made large concessions. Experiment 3 examined the interplay between experienced and communicated emotion and showed that angry communications (unlike happy ones) induced fear and thereby mitigated the effect of the opponent's experienced emotion. These results suggest that negotiators are especially influenced by their opponent's emotions when they are motivated and able to consider them.


title: Adaptive resource allocation for wildlife protection against illegal poachers

abstract: Illegal poaching is an international problem that leads to the extinction of species and the destruction of ecosystems. As evidenced by dangerously dwindling populations of endangered species, existing anti-poaching mechanisms are insufficient. This paper introduces the Protection Assistant for Wildlife Security (PAWS) application - a joint deployment effort done with researchers at Uganda's Queen Elizabeth National Park (QENP) with the goal of improving wildlife ranger patrols. While previous works have deployed applications with a game-theoretic approach (specifically Stackelberg Games) for counter-terrorism, wildlife crime is an important domain that promotes a wide range of new deployments. Additionally, this domain presents new research challenges and opportunities related to learning behavioral models from collected poaching data. In addressing these challenges, our first contribution is a behavioral model extension that captures the heterogeneity of poachers' decision making processes. Second, we provide a novel framework, PAWS-Learn, that incrementally improves the behavioral model of the poacher population with more data. Third, we develop a new algorithm, PAWS-Adapt, that adaptively improves the resource allocation strategy against the learned model of poachers. Fourth, we demonstrate PAWS's potential effectiveness when applied to patrols in QENP, where PAWS will be deployed.


title: A Novel QoE-Centric SDN-Based Multipath Routing Approach for Multimedia Services over 5G Networks

abstract: The explosion of enhanced applications such as live video streaming, video gaming and Virtual Reality calls for efforts to optimize transport protocols to manage the increasing amount of data traffic on future 5G networks. Through bandwidth aggregation over multiple paths, the Multi-Path Transmission Control Protocol (MPTCP) can enhance the performance of network applications. MPTCP can split a large multimedia flow into subflows and apply a congestion control mechanism on each subflow. Segment Routing (SR), a promising source routing approach, has emerged to provide advanced packet forwarding over 5G networks. In this paper, we explore the utilization of MPTCP and SR in SDN-based networks to improve network resources utilization and end- user's QoE for delivering multimedia services over 5G networks. We propose a novel QoE-aware, SDN- based MPTCP/SR approach for service delivery. In order to demonstrate the feasibility of our approach, we implemented an intelligent QoE- centric Multipath Routing Algorithm (QoMRA) on an SDN source routing platform using Mininet and POX controller. We carried out experiments on Dynamic Adaptive video Steaming over HTTP (DASH) applications over various network conditions. The preliminary results show that, our QoE-aware SDN- based MPTCP/SR scheme performs better compared to the conventional TCP approach in terms of throughput, link utilization and the end-user's QoE.


title: A new combination approach based on improved evidence distance

abstract: AbstractDempster-Shafer evidence theory is a powerful tool in information fusion.When the evidence are highly conﬂicting, the counter-intuitive results willbe presented. To adress this open issue, a new method based on evidencedistance of Jousselme and Hausdorﬀ distance is proposed. Weight of eachevidence can be computed, preprocess the original evidence to generate anew evidence. The Dempster’s combination rule is used to combine the newevidence. Comparing with the existing methods, the new proposed methodis eﬃcient. Keywords: Evidence theory, Conﬂict evicence, Evidence distance,Combination rule, Target recognition Corresponding author at: School of Computer and Information Science, SouthwestUniversity, Chongqing 400715, China. Tel.: +86 023 68254555; Fax: +86 023 68254555.Emailaddress: ydeng@swu.edu.cn, prof.deng@hotmail.com(Yong Deng)This work is partially supported by National Natural Science Foundation of China(Grant No. 61174022), National High Technology Research and Development Programof China(863 Program) (No. 2013AA013801), Chongqing Natural Science Foundation(Grant No. CSCT, 2010BA2003), the Chenxing Scholarship Youth Found of ShanghaiJiao Tong University (Grant No.T241460612).PreprintsubmittedtoJournalofSystemsEngineeringandElectronics May7,2014


Quick Layered Response ( QLR ) Codes


title: Papilla proportions in the maxillary anterior dentition.

abstract: Two hundred forty interdental papilla sites in 20 healthy patients were investigated. Interdental papilla heights of maxillary anterior teeth were measured from the gingival zenith, along with clinical crown lengths. Percentages of papilla height to crown length were computed and defined as papilla proportion, mesial papilla proportion (MPP), and distal papilla proportion (DPP). Mean interdental papilla heights of maxillary anterior teeth were 4 mm mesially and 4.1 mm distally. Mean MPP was 42% (n = 120), and mean DPP was 43% (n = 120). No significant differences were found between MPP and DPP for maxillary incisors (P >/= .5). Canines demonstrated a trend toward increased distal papilla heights. Papilla proportions were approximately 40% for all tooth groups. A more apical position of distal papilla heights from anterior to posterior teeth, mentioned in the literature, was not confirmed by the present data.


title: Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale

abstract: Suicide is a leading cause of death in the United States. One of the major challenges to suicide prevention is that those who may be most at risk cannot be relied upon to report their conditions to clinicians. This paper takes an initial step toward the automatic detection of suicidal risk factors through social media activity, with no reliance on self-reporting. We consider the performance of annotators with various degrees of expertise in suicide prevention at annotating microblog data for the purpose of training text-based models for detecting suicide risk behaviors. Consistent with crowdsourcing literature, we found that novice-novice annotator pairs underperform expert annotators and outperform automatic lexical analysis tools, such as Linguistic Inquiry and Word Count.


title: Robust algorithm for early detection of Alzheimer's disease using multiple feature extractions

abstract: An algorithm is proposed for early detection of Alzheimer's disease and is focused on detecting the condition that would lead to Alzheimer's disease in future. Alzheimer's disease is a prevalent case now and it mostly affects the elderly people. The disease condition makes a person lose his memory and have trouble in doing his day-to-day activities, and progressively the condition leads to death. No treatment is available to completely cure the disease but it would be beneficial if the disease is detected earlier as the necessary aid can be provided. The proposed algorithm uses Alzheimer's Disease (AD), Cognitively Normal (CN), Mild Cognitive Impairment non-convertible (MCInc) and Mild Cognitive Impairment convertible (MCIc) data from the ADNI (Alzheimer's Disease Neuroimaging Initiative) and it involves the process of pre-processing, feature extraction and classification. MCIc leads to Alzheimer's disease at a later stage. A combination of the Discrete Wavelet Transform (DWT) and the Principal Component Analysis (PCA) is used for the feature extraction and further subjected to classification using the Support Vector Machine (SVM). The multiple feature extractions with both the DWT and the PCA together give a better accuracy during classification compared to other algorithms.


title: A tale of two sieves

abstract: I t is the best of times for the game of factoring large numbers into their prime factors. In 1970 it was barely possible to factor “hard” 20-digit numbers. In 1980, in the heyday of the Brillhart-Morrison continued fraction factoring algorithm, factoring of 50-digit numbers was becoming commonplace. In 1990 my own quadratic sieve factoring algorithm had doubled the length of the numbers that could be factored, the record having 116 digits. By 1994 the quadratic sieve had factored the famous 129-digit RSA challenge number that had been estimated in Martin Gardner’s 1976 Scientific American column to be safe for 40 quadrillion years (though other estimates around then were more modest). But the quadratic sieve is no longer the champion. It was replaced by Pollard’s number field sieve in the spring of 1996, when that method successfully split a 130-digit RSA challenge number in about 15% of the time the quadratic sieve would have taken. In this article we shall briefly meet these factorization algorithms—these two sieves—and some of the many people who helped to develop them. In the middle part of this century, computational issues seemed to be out of fashion. In most books the problem of factoring big numbers was largely ignored, since it was considered trivial. After all, it was doable in principle, so what else was there to discuss? A few researchers ignored the fashions of the time and continued to try to find fast ways to factor. To these few it was a basic and fundamental problem, one that should not be shunted to the side. But times change. In the last few decades we have seen the advent of accessible and fast computing power, and we have seen the rise of cryptographic systems that base their security on our supposed inability to factor quickly (and on other number theoretic problems). Today there are many people interested in factoring, recognizing it not only as a benchmark for the security of cryptographic systems, but for computing itself. In 1984 the Association for Computing Machinery presented a plaque to the Institute for Electrical and Electronics Engineers (IEEE) on the occasion of the IEEE centennial. It was inscribed with the prime factorization of the number 2251 − 1 that was completed that year with the quadratic sieve. The president of the ACM made the following remarks:


title: A 3-Gb/s Single-LED OFDM-Based Wireless VLC Link Using a Gallium Nitride $\mu{\rm LED}$

abstract: This letter presents a visible light communication (VLC) system based on a single 50- μm gallium nitride light emitting diode (LED). A device of this size exhibits a 3-dB modulation bandwidth of at least 60 MHz - significantly higher than commercially available white lighting LEDs. Orthogonal frequency division multiplexing is employed as a modulation scheme. This enables the limited modulation bandwidth of the device to be fully used. Pre- and postequalization techniques, as well as adaptive data loading, are successfully applied to achieve a demonstration of wireless communication at speeds exceeding 3 Gb/s. To date, this is the fastest wireless VLC system using a single LED.


PRELIMINARY DESIGN AND SIMULATION OF A LIQUID PROPELLANT ROCKET ENGINE


title: The perceptions of an EMBA experience in China: an exploratory study

abstract: Purpose – The purpose of this paper is to report on perceptions of an Executive MBA (EMBA) experience to the development of Chinese managers’ careers.Design/methodology/approach – This qualitative research drew on in‐depth interviews with a sample of alumni from two business schools in China. Narrative analysis focused on career competency development.Findings – Chinese managers perceived the career outcomes of undertaking the EMBA and reported “knowing‐whom” benefits such as sharing information, building reputation and exchanging acquired knowledge, “knowing‐how” namely as a source of learning and “knowing‐why” namely work‐life balance.Research limitations/implications – The research presents a small sample and so cannot be applicable to the wider population. It is exploratory in nature and intended to encourage debate over the perceived importance of career competencies accrued from EMBA studies in an Eastern context.Originality/value – The paper adds to existing research on career competency developmen...


title: Reconfigurable manufacturing systems: Key to future manufacturing

abstract: Presented in this article is a review of manufacturing techniques and introduction of reconfigurable manufacturing systems; a new paradigm in manufacturing which is designed for rapid adjustment of production capacity and functionality, in response to new market conditions. A definition of reconfigurable manufacturing systems is outlined and an overview of available manufacturing techniques, their key drivers and enablers, and their impacts, achievements and limitations is presented. A historical review of manufacturing from the point-of-view of the major developments in the market, technology and sciences issues affecting manufacturing is provided. The new requirements for manufacturing are discussed and characteristics of reconfigurable manufacturing systems and their key role in future manufacturing are explained. The paper is concluded with a brief review of specific technologies and research issues related to RMSs.


title: The application of differential privacy to health data

abstract: Differential privacy has gained a lot of attention in recent years as a general model for the protection of personal information when used and disclosed for secondary purposes. It has also been proposed as an appropriate model for health data. In this paper we review the current literature on differential privacy and highlight important general limitations to the model and the proposed mechanisms. We then examine some practical challenges to the application of differential privacy to health data. The review concludes by identifying areas that researchers and practitioners in this area need to address to increase the adoption of differential privacy for health data.


A Review on Software Requirements Validation and Consistency Management


title: Collaborative Web-Based Tools for Multi-layer Text Annotation

abstract: Effectively managing the collaboration of many annotators is a crucial ingredient for the success of larger annotation projects. For collaboration, web-based tools offer a low-entry way gathering annotations from distributed contributors. While the management structure of annotation tools is more or less stable across projects, the kind of annotations vary widely between projects. The challenge for web-based tools for multi-layer text annotation is to combine ease of use and availability through the web with maximal flexibility regarding the types and layers of annotations. In this chapter, we outline requirements for web-based annotation tools in detail and review a variety of tools in respect to these requirements. Further, we discuss two web-based multi-layer annotation tools in detail: GATE Teamware and WebAnno. While differing in some aspects, both tools largely fulfill the requirements for today’s web-based annotation tools. Finally, we point out further directions, such as increased schema flexibility and tighter integration of automation for annotation suggestions.


title: VisualBackProp for learning using privileged information with CNNs

abstract: In many machine learning applications, from medical diagnostics to autonomous driving, the availability of prior knowledge can be used to improve the predictive performance of learning algorithms and incorporate `physical,' `domain knowledge,' or `common sense' concepts into training of machine learning systems as well as verify constraints/properties of the systems. We explore the learning using privileged information paradigm and show how to incorporate the privileged information, such as segmentation mask available along with the classification label of each example, into the training stage of convolutional neural networks. This is done by augmenting the CNN model with an architectural component that effectively focuses model's attention on the desired region of the input image during the training process and that is transparent to the network's label prediction mechanism at testing. This component effectively corresponds to the visualization strategy for identifying the parts of the input, often referred to as visualization mask, that most contribute to the prediction, yet uses this strategy in reverse to the classical setting in order to enforce the desired visualization mask instead. We verify our proposed algorithms through exhaustive experiments on benchmark ImageNet and PASCAL VOC data sets and achieve improvements in the performance of $2.4\%$ and $2.7\%$ over standard single-supervision model training. Finally, we confirm the effectiveness of our approach on skin lesion classification problem.


title: A Deep Learning Paradigm for Detection of Harmful Algal Blooms

abstract: Effective and cost-efficient monitoring is indispensable for ensuring environmental sustainability. Cyanobacterial Harmful Algal Blooms (CyanoHABs) are a major water quality and public health issue in inland water bodies. The recent popularity of online social media (OSM) platforms coupled with advances in cloud computing and data analytics has given rise to citizen science-based approaches to environmental monitoring. These approaches involve the lay community in the acquisition, collection and transmission of relevant data in the form of tweets, images, voice recordings and videos typically acquired using low-cost mobile devices such as smartphones or tablet computers. While cost effective, citizen science-based approaches are highly susceptible to noise, inaccuracies and missing data. In this paper we address the problem of automated detection of harmful algal blooms (HABs) via analysis of image data of inland water bodies. These image data are acquired using a variety of smartphones and communicated via popular OSM platforms such as Facebook, Twitter and Instagram. To account for the wide variations in imaging parameters and ambient environmental parameters we propose a deep learning approach to image feature extraction and classification for the purpose of HAB detection. The current system is a first step in the design of an automated early detection, warning and rapid response system that can be adopted to mitigate the detrimental effects of CyanoHAB contamination of inland water bodies.


title: Polaris : Lymba ’ s Semantic Parser

abstract: Semantic representation of text is key to text understanding and reasoning. In this paper, we present Polaris, Lymba’s semantic parser. Polaris is a supervised semantic parser that given text extracts semantic relations. It extracts relations from a wide variety of lexico-syntactic patterns, including verb-argument structures, noun compounds and others. The output can be provided in several formats: XML, RDF triples, logic forms or plain text, facilitating interoperability with other tools. Polaris is implemented using eight separate modules. Each module is explained and a detailed example of processing using a sample sentence is provided. Overall results using a benchmark are discussed. Per module performance, including errors made and pruned by each module are also analyzed.


title: Online steering: coordination and control of body center of mass, head and body reorientation

abstract: Steering is an integral component of adaptive locomotor behavior. Along with reorientation of gaze and body in the direction of intended travel, body center of mass must be controlled in the mediolateral plane. In this study we examine how these subtasks are sequenced when steering is planned early or initiated under time constraints. Whole body kinematics were monitored as individuals were required to change their direction of travel by varying amounts when visually cued either at the beginning of the walk or one stride before. The analyses focused on the transition stride from one travel direction to another. Timing of changes (with respect to first right foot contact) in trunk roll angle, head and trunk yaw angle, and right foot displacement in the mediolateral plane were analyzed. The magnitude of these measures along with right and left foot placement at the beginning and right foot placement at the end of the transition stride were also analyzed. The results show the CNS uses two mechanisms, foot placement and trunk roll motion (piking action about the hip joint in the frontal plane), to move the center of mass towards the new direction of travel in the transition stride, preferring to use the first option when planning can be done early. Control of body center of mass precedes all other changes and is followed by initiation of head reorientation. Only then is the rest of the body reorientation initiated.


title: Another Diversity-Promoting Objective Function for Neural Dialogue Generation

abstract: Although generation-based dialogue systems have been widely researched, the response generations by most existing systems have very low diversities. The most likely reason for this problem is Maximum Likelihood Estimation (MLE) with Softmax Cross-Entropy (SCE) loss. MLE trains models to generate the most frequent responses from enormous generation candidates, although in actual dialogues there are various responses based on the context. In this paper, we propose a new objective function called Inverse Token Frequency (ITF) loss, which individually scales smaller loss for frequent token classes and larger loss for rare token classes. This function encourages the model to generate rare tokens rather than frequent tokens. It does not complicate the model and its training is stable because we only replace the objective function. On the OpenSubtitles dialogue dataset, our loss model establishes a state-of-the-art DIST-1 of 7.56, which is the unigram diversity score, while maintaining a good BLEU-1 score. On a Japanese Twitter replies dataset, our loss model achieves a DIST-1 score comparable to the ground truth.


title: CONDITIONING OF TWO‐RESPONSE PATTERNS OF KEY PECKING IN PIGEONS1

abstract: On discrete trials, two response keys were made available to hungry pigeons and food reinforcement depended on the order in which the required two key pecks occurred. In different phases, only one of the four possible two-peck sequences (left-left, left-right, right-left, and right-right) produced food reinforcement. In each case, the pigeons learned to perform the correct two-peck sequence more often than the incorrect sequences. Furthermore, the course of differentiation mastery indicated that both reinforcement history and response-reinforcer contiguity influenced performance. These results reveal that response patterns comprising two instances of the same response left-left and right-right) or instances of two different responses (left-right and right-left) may function as operants, thereby extending the generality of conditioning principles from discrete responses to structured sequences of behavior. These and other results are discussed in terms of contiguity-based and memory-based models of learning.


title: Comparative pharmacology of antipsychotics possessing combined dopamine D2 and serotonin 5-HT1A receptor properties

abstract: Rationale ::: There is increasing interest in antipsychotics intended to manage positive symptoms via D2 receptor blockade and improve negative symptoms and cognitive deficits via 5-HT1A activation. Such a strategy reduces side-effects such as the extrapyramidal syndrome (EPS), weight gain, and autonomic disturbance liability.


title: TDOA based direct positioning maximum likelihood estimator and the cramer-rao bound

abstract: The maximum likelihood estimator (MLE) and its performance for the localization of a stationary emitter using a network of spatially separated passive stationary sensors is presented. The conventional approach for localization using multiple sensors is to first estimate the time differences of arrival (TDOAs) independently between pairs of sensors and then find the location of the emitter using the intersection point of the hyperbolas defined by these TDOAs. It has recently been shown that this two-step approach is suboptimal and an alternate direct position determination (DPD) approach has been proposed. In the work presented here we take the DPD approach to derive the MLE and show that the MLE outperforms the conventional two-step approach.We analyze the two commonly occurring cases of signal waveform unknown and signal waveform known with unknown transmission time. This paper covers a wide variety of transmitted signals such as narrowband or wideband, lowpass or bandpass, etc. Sampling of the received signals has a quantization-like effect on the location estimate and so a continuous time model is used instead.We derive the Fisher information matrix (FIM) and show that the proposed MLE attains the Cramer-Rao lower bound (CRLB) for high signal-to-noise ratios (SNRs).


title: Moral Outrage Mediates the Dampening Effect of System Justification on Support for Redistributive Social Policies

abstract: To understand how and why people tolerate ongoing social and economic inequality, we conducted two studies investigating the hypothesis that system justification is associated with reduced emotional distress and a lack of support for helping the disadvantaged. In Study 1, we found that the endorsement of a system-justifying ideology was negatively associated with moral outrage, existential guilt, and support for helping the disadvantaged. In Study 2, the induction of a system-justification mind-set through exposure to “rags-to-riches” narratives decreased moral outrage, negative affect, and therefore intentions to help the disadvantaged. In both studies, moral outrage (outward-focused distress) was found to mediate the dampening effect of system justification on support for redistribution, whereas existential guilt (Study 1) or negative affect in general (Study 2; inward-focused distress) did not. Thus, system-justifying ideology appears to undercut the redistribution of social and economic resources by a...


title: The prospects of biolubricants as alternatives in automotive applications

abstract: Abstract Lubricants perform as anti-friction media. They facilitate smooth operations, maintain reliable machine functions, and reduce the risks of frequent failures. At present, the increasing prices of crude oil, the depletion of crude oil reserves in the world, and global concern in protecting the environment from pollution have renewed interest in developing and using environment-friendly lubricants derived from alternative sources. A biolubricant is renewable lubricants that is biodegradable, non-toxic, and emits net zero greenhouse gas. This study presents the potential of a biolubricant based on vegetable oil as an alternative lubricant according to studies published in highly rated journals in scientific indices. In the first part of this paper, the source, properties, as well as advantages and disadvantages of the biolubricant are discussed. The second part describes the potential of vegetable oil-based biolubricants as alternative lubricants for automobile applications. The final part describes the world biolubricant market and its future prospects. Biolubricants are potential alternative lubricants because of their low toxicity, good lubricating properties, high viscosity index, high ignition temperature, increased equipment service life, high load-carrying abilities, good anti-wear characteristic, excellent coefficient of friction, natural multi-grade properties, low evaporation rates, and low emissions into the atmosphere.


title: Authorship Analysis of the Zeus Botnet Source Code

abstract: Authorship analysis has been used successfully to analyse the provenance of source code files in previous studies. The source code for Zeus, one of the most damaging and effective botnets to date, was leaked in 2011. In this research, we analyse the source code from the lens of authorship clustering, aiming to estimate how many people wrote this malware, and what their roles are. The research provides insight into the structure the went into creating Zeus and its evolution over time. The work has potential to be used to link the malware with other malware written by the same authors, helping investigations, classification, deterrence and detection.


title: Trapping effects in GaN and SiC microwave FETs

abstract: It is well known that trapping effects can limit the output power performance of microwave field-effect transistors (FETs). This is particularly true for the wide bandgap devices. In this paper we review the various trapping phenomena observed in SiC- and GaN-based FETs that contribute to compromised power performance. For both of these material systems, trapping effects associated with both the surface and with the layers underlying the active channel have been identified. The measurement techniques utilized to identify these traps and some of the steps taken to minimize their effects, such as modified buffer layer designs and surface passivation, are described. Since similar defect-related phenomena were addressed during the development of the GaAs technology, relevant GaAs work is briefly summarized.


title: Seven Ways to Improve Example-Based Single Image Super Resolution

abstract: In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning. We validate our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial improvements.The techniques are widely applicable and require no changes or only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method sets new state-of-the-art results outperforming A+ by up to 0.9dB on average PSNR whilst maintaining a low time complexity.


title: Exemplar learning for extremely efficient anomaly detection in real-valued time series

abstract: We investigate algorithms for efficiently detecting anomalies in real-valued one-dimensional time series. Past work has shown that a simple brute force algorithm that uses as an anomaly score the Euclidean distance between nearest neighbors of subsequences from a testing time series and a training time series is one of the most effective anomaly detectors. We investigate a very efficient implementation of this method and show that it is still too slow for most real world applications. Next, we present a new method based on summarizing the training time series with a small set of exemplars. The exemplars we use are feature vectors that capture both the high frequency and low frequency information in sets of similar subsequences of the time series. We show that this exemplar-based method is both much faster than the efficient brute force method as well as a prediction-based method and also handles a wider range of anomalies. We compare our algorithm across a large variety of publicly available time series and encourage others to do the same. Our exemplar-based algorithm is able to process time series in minutes that would take other methods days to process.


title: Do blind people move more confidently with the Tactile Radar?

abstract: BACKGROUND: Individuals lacking or having impaired vision face serious difficulties during autonomous locomotion. Sensory substitution devices can contribute to alleviate such difficulties, significantly (and measurably) reducing anxiety. OBJECTIVE: The present paper evaluates a device – the Tactile Radar (TR) – that can detect obstacles at a certain distance from the user and generate meaningful and unobtrusive tactile stimuli. METHODS: We evaluate the impact of its use on the degree of anxiety that autonomous locomotion usually trigger on people who are blind. RESULTS:Decreased anxiety aswell as increased sense of safety and independence was observed on the tested subjects, through subjective (semi-structured interviews) and objective assessments (STAI inventories). CONCLUSIONS: This device seems promising. More experimentation is needed to evaluate the capacity of the TR to enhance indoor localization and body placement with respect to walls and obstacles, as well as evaluation of the device in real life situations including outdoors. Last but not least, we need to consider ways of moving from a prototyping to a real production phase – of an affordable yet reliable device that can reach as soon as possible the interested population.


title: Distant Supervision for Relation Extraction with Hierarchical Attention and Entity Descriptions

abstract: Distant supervision for relation extraction is an effective method to find novel relational facts from plain text. However, distant supervision inevitably suffers from wrong label problem, and most existing methods generally focus on direct sentences containing entity pairs, but ignore massive information from background knowledge. To tackle these problems, we propose a novel hierarchical attention model to select valid instances and capture vital semantic information in them. Furthermore, we incorporate entity descriptions extracted from Wikipedia into the hierarchical attention model to provide supplementary background knowledge. The proposed architecture can not only combat the noise introduced by distant supervision, but also adequately extract latent and helpful background information. The experimental results on both Chinese and English datasets show that the proposed approach consistently achieves significant improvements on relation extraction as compared with strong baselines.


title: Internet of Things network cognition and traffic management system

abstract: In a world of ever increasing technological diversity and an advancing ‘Internet of Things’ (IoTs), business landscapes are changing. New conditions alter the way in which competence resources are regarded and how they need to be managed in order for organizations to sustain as successful actors in a knowledge economy. Information technology play an important role in this new setting, including network management systems for handling information concerning competence resources. This research focus on smart IoTs traffic management system, which is advertised by minimal cost, long scalability, great compatibility, easy to promote, to replace conventional traffic management system and the proposed approach can develop public road traffic enormously. The aim of this research proposed to develop an IoT public traffic adaptive detection system and proficient of supposing the travel time associated with each street sector based on the traffic information streamlined every 18 s, which sequentially finds the path with the minimal travel time in the traffic network by using a dynamic procedure.


title: PRIVACY ISSUES IN KNOWLEDGE DISCOVERY AND DATA MINING

abstract: Recent developments in information technology have enabled collection and processing of vast amounts of personal data, such as criminal records, shopping habits, credit and medical history, and driving records. This information is undoubtedly very useful in many areas, including medical research, law enforcement and national security. However, there is an increasing public concern about the individuals' privacy. Privacy is commonly seen as the right of individuals to control information about themselves. The appearance of technology for Knowledge Discovery and Data Mining (KDDM) has revitalized concern about the following general privacy issues: • secondary use of the personal information, • handling misinformation, and • granulated access to personal information. They demonstrate that existing privacy laws and policies are well behind the developments in technology, and no longer offer adequate protection. We also discuss new privacy threats posed KDDM, which includes massive data collection, data warehouses, statistical analysis and deductive learning techniques. KDDM uses vast amounts of data to generate hypotheses and discover general patterns. KDDM poses the following new challenges to privacy.


Fake News Detection Through Multi-Perspective Speaker Profiles


title: ATLAS: A Three-Layered Approach to Facade Parsing

abstract: We propose a novel approach for semantic segmentation of building facades. Our system consists of three distinct layers, representing different levels of abstraction in facade images: segments, objects and architectural elements. In the first layer, the facade is segmented into regions, each of which is assigned a probability distribution over semantic classes. We evaluate different state-of-the-art segmentation and classification strategies to obtain the initial probabilistic semantic labeling. In the second layer, we investigate the performance of different object detectors and show the benefit of using such detectors to improve our initial labeling. The generic approaches of the first two layers are then specialized for the task of facade labeling in the third layer. There, we incorporate additional meta-knowledge in the form of weak architectural principles, which enforces architectural plausibility and consistency on the final reconstruction. Rigorous tests performed on two existing datasets of building facades demonstrate that we outperform the current state of the art, even when using outputs from lower layers of the pipeline. Finally, we demonstrate how the output of the highest layer can be used to create a procedural building reconstruction.


title: Improving Face Detection Performance with 3D-Rendered Synthetic Data

abstract: In this paper, we provide a synthetic data generator methodology with fully controlled, multifaceted variations based on a new 3D face dataset (3DU-Face). We customized synthetic datasets to address specific types of variations (scale, pose, occlusion, blur, etc.), and systematically investigate the influence of different variations on face detection performances. We examine whether and how these factors contribute to better face detection performances. We validate our synthetic data augmentation for different face detectors (Faster RCNN, SSH and HR) on various face datasets (MAFA, UFDD and Wider Face).


title: Spectrographic Characteristics of Turkish /h/

abstract: /h/ is described differently by different researchers. While some ::: argue that /h/ is a glottal fricative, others argue that it is the voiceless ::: counterpart of the following vowel, yet others argue that /h/ is a glide or an ::: approximant. However, de- tailed acoustic studies focusing on /h/ are ::: very limited. This study aims to describe the spectrographic characteristics of ::: /h/ in Turkish. Test words consisted of 48 monosyllabic and disyllabic words ::: containing /h/ which was followed by eight Turkish vowels. Totally 1440 tokens ::: were analyzed. After segmentation, /h/ was classified based on its spectrographic ::: characteristics: 1) segment exhibiting formants, 2) segment exhibiting ::: frication (but no formants) with ::: energy in lower frequencies and 3) segment exhibiting almost no energy. In order to find out if there is a ::: significant difference among these three categories, Chi-square test ::: was applied. The spectrographic characteristics of /h/ in Turkish suggest that ::: it is more like the voiceless version of the surrounding vowels, significantly ::: when it is in syllable initial position and the preceding vowel when in ::: syllable final position.


title: Intrusive Load Monitoring

abstract: Non-intrusive load monitoring (NILM) is a procedure that is used to disaggregate the contributions of different appliances in a building. Various traditional techniques use supervised and unsupervised learning for disaggregation. Very few papers utilize the semi-supervised or active approaches. The two approaches are used in case of training data scarcity. The semi-supervised approach is prone to errors as training dataset is small, while the active leaning approach relies completely on the user to get correct labels. A technique that leverages the semi-supervised and active learning together is applied. This can reduce the number of samples required to the users, also can increase the accuracy of the semi-supervised algorithm while more reliable samples can be added. Many cases are studied and the results showed the robustness and reliability of the new combination. The proposed combined technique showed that the number of queries can be reduced to fifth without losing more than 4% of the accuracy. Also, by combining the semi-supervised and active techniques, the accuracy increased by 6% compared to the stand alone semi-supervised approach.


title: Realizing High Performance NFV Service Chains

abstract: Network functions (NFs) hold a key role in networks, offering in-network services, such as enhanced performance, policy enforcement, and security. Traditionally, NFs have been implemented in specia ...


title: Seed-driven Document Ranking for Systematic Reviews in Evidence-Based Medicine

abstract: Systematic review (SR) in evidence-based medicine is a literature review which provides a conclusion to a specific clinical question. To assure credible and reproducible conclusions, SRs are conducted by well-defined steps. One of the key steps, the screening step, is to identify relevant documents from a pool of candidate documents. Typically about 2000 candidate documents will be retrieved from databases using keyword queries for a SR. From which, about 20 relevant documents are manually identified by SR experts, based on detailed relevance conditions or eligibility criteria. Recent studies show that document ranking, or screening prioritization, is a promising way to improve the manual screening process. In this paper, we propose a seed-driven document ranking (SDR) model for effective screening, with the assumption that one relevant document is known, i.e., the seed document. Based on a detailed analysis of characteristics of relevant documents, SDR represents documents using bag of clinical terms, rather than the commonly used bag of words. More importantly, we propose a method to estimate the importance of the clinical terms based on their distribution in candidate documents. On benchmark dataset released by CLEF'17 eHealth Task 2, we show that the proposed SDR outperforms state-of-the-art solutions. Interestingly, we also observe that ranking based on word embedding representation of documents well complements SDR. The best ranking is achieved by combining the relevances estimated by SDR and by word embedding. Additionally, we report results of simulating the manual screening process with SDR.


title: TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks

abstract: In this paper we present a deep neural network topology that incorporates a simple to implement transformationinvariant pooling operator (TI-POOLING). This operator is able to efficiently handle prior knowledge on nuisance variations in the data, such as rotation or scale changes. Most current methods usually make use of dataset augmentation to address this issue, but this requires larger number of model parameters and more training data, and results in significantly increased training time and larger chance of under-or overfitting. The main reason for these drawbacks is that that the learned model needs to capture adequate features for all the possible transformations of the input. On the other hand, we formulate features in convolutional neural networks to be transformation-invariant. We achieve that using parallel siamese architectures for the considered transformation set and applying the TI-POOLING operator on their outputs before the fully-connected layers. We show that this topology internally finds the most optimal "canonical" instance of the input image for training and therefore limits the redundancy in learned features. This more efficient use of training data results in better performance on popular benchmark datasets with smaller number of parameters when comparing to standard convolutional neural networks with dataset augmentation and to other baselines.


Case Report : Contagious Ecthyma-Deviations in the Anatomical Appearance of Lesions in an Outbreak in Lambs in Israel


title: Factors associated with sudden death of individuals requiring restraint for excited delirium.

abstract: The purpose of this article is to identify and rank factors associated with sudden death of individuals requiring restraint for excited delirium. Eighteen cases of such deaths witnessed by emergency medical service (EMS) personnel are reported. The 18 cases reported were restrained with the wrists and ankles bound and attached behind the back. This restraint technique was also used for all 196 surviving excited delirium victims encountered during the study period. Unique to these data is a description of the initial cardiopulmonary arrest rhythm in 72% of the sudden death cases. Associated with all sudden death cases was struggle by the victim with forced restraint and cessation of struggling with labored or agonal breathing immediately before cardiopulmonary arrest. Also associated was stimulant drug use (78%), chronic disease (56%), and obesity (56%). The primary cardiac arrest rhythm of ventricular tachycardia was found in 1 of 13 victims with confirmed initial cardiac rhythms, with none found in ventricular fibrillation. Our findings indicate that unexpected sudden death when excited delirium victims are restrained in the out-of-hospital setting is not infrequent and can be associated with multiple predictable but usually uncontrollable factors.


title: A New General Topology for Cascaded Multilevel Inverters With Reduced Number of Components Based on Developed H-Bridge

abstract: In this paper, a new general cascaded multilevel inverter using developed H-bridges is proposed. The proposed topology requires a lesser number of dc voltage sources and power switches and consists of lower blocking voltage on switches, which results in decreased complexity and total cost of the inverter. These abilities obtained within comparing the proposed topology with the conventional topologies from aforementioned points of view. Moreover, a new algorithm to determine the magnitude of dc voltage sources is proposed. The performance and functional accuracy of the proposed topology using the new algorithm in generating all voltage levels for a 31-level inverter are confirmed by simulation and experimental results.


title: Initiation of joint attention and related visual attention processes in infants with autism spectrum disorder: Literature review

abstract: Autism spectrum disorder (ASD) represents a group of neurodevelopmental disabilities that can be difficult to identify before the age of 2 or 3 years, the age when the full range of behavio...


A Tele-Autonomous Heavy Duty Robotic Lawn Mower


title: Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media

abstract: The growing popularity of social media (e.g., Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsupervised tri-clustering framework, which analyzes both user-level and tweet-level sentiments through co-clustering of a tripartite graph. A compelling feature of the proposed framework is that the quality of sentiment clustering of tweets, users, and features can be mutually improved by joint clustering. We further investigate the evolution of user-level sentiments and latent feature vectors in an online framework and devise an efficient online algorithm to sequentially update the clustering of tweets, users and features with newly arrived data. The online framework not only provides better quality of both dynamic user-level and tweet-level sentiment analysis, but also improves the computational and storage efficiency. We verified the effectiveness and efficiency of the proposed approaches on the November 2012 California ballot Twitter data.


title: Current State and Prospect of Micro-Machining

abstract: Micro-machining is a delicate process that requires pinpoint accuracy. Researchers in academia and industry worldwide are striving to develop efficient fabrication techniques for features in ranges of a few micrometers and even nanometers. The current leading micro-machining processes are micro drilling, micro milling, and micro turning, micro EDM, micro USM, micro LBM, micro ECM and LIGA. In this paper, the latest advances of diverse micromachining technologies will be discussed. Challenges still exist in fields like innovation in workpiece and tool material, development of auxiliary tooling equipment together with processes combination, etc.


A performance comparison of open-source erasure coding libraries for storage applications


title: Risk factors of malnutrition among preschool children in Terengganu, Malaysia: a case control study

abstract: Childhood malnutrition is a multi-dimensional problem. An increase in household income is not sufficient to reduce childhood malnutrition if children are deprived of food security, education, access to water, sanitation and health services. The aim of this study is to identify the characteristics of malnourished children below five years of age and to ascertain the risk factors of childhood malnutrition in a state in Malaysia. A case control study was conducted in the maternal and child health clinics in five districts of Terengganu, Malaysia from April to August 2012. Case was a child with moderate to severe malnutrition with z-scores < −2SD from the median of WHO reference. Control was a child without malnutrition with z-scores between -2SD and +2SD and the age matched with case’s. Face to face interviews with the respective child’s mother and measurements of the respective child’s weight and height were carried out. Information on socio-economic characteristics, household food security status, child’s dietary intake, caregivers’ practices and resources were enquired. Univariate and multivariate logistic regression analyses were conducted. Crude odds ratio and adjusted odds ratio with 95% confidence interval were calculated. A total of 274 children with 137 cases and 137 controls were recruited. All respondents were Malays. Among the cases, a larger proportion of them was female and originated from low income families. After adjusting all confounders, childhood malnutrition was significantly associated with number of children (aOR: 5.86, 95% CI: 1.96, 17.55), child hunger (aOR: 16.38, 95% CI: 1.34,199.72), dietary energy intake (aOR: 0.99, 95% CI: 0.98, 0.99), protein intake (aOR: 1.06, 95% CI: 1.01, 1.12), vitamin A intake (aOR: 0.999, 95% CI: 0.997, 1.00), low birth weight (aOR: 6.83, 95% CI: 1.62, 28.89), frequent illness (aOR: 2.79, 95% CI: 1.06, 7.31), and history of worm infection (aOR: 3.48, 95% CI: 1.25, 9.70). Lower socio-economic status, household food insecurity, and poor child caring practices were associated with childhood malnutrition. Besides implementation of programmes focusing on poverty reduction, community based nutrition and hygiene education with extensive family planning and de-worming programmes should be intensified to improve both mother and children’s nutritional status.


title: A Framework for Comparing Models of Computation

abstract: We give a denotational framework (a "meta model") within which certain properties of models of computation can be compared. It describes concurrent processes in general terms as sets of possible behaviors. A process is determinate if, given the constraints imposed by the inputs, there are exactly one or exactly zero behaviors. Compositions of processes are processes with behaviors in the intersection of the behaviors of the component processes. The interaction between processes is through signals, which are collections of events. Each event is a value-tag pair, where the tags can come from a partially ordered or totally ordered set. Timed models are where the set of tags is totally ordered. Synchronous events share the same tag, and synchronous signals contain events with the same set of tags. Synchronous processes have only synchronous signals as behaviors. Strict causality (in timed tag systems) and continuity (in untimed tag systems) ensure determinacy under certain technical conditions. The framework is used to compare certain essential features of various models of computation, including Kahn process networks, dataflow, sequential processes, concurrent sequential processes with rendezvous, Petri nets, and discrete-event systems.


title: Dynamic malware detection using registers values set analysis

abstract: The number of Malicious files increase every day because of existing open source malware and obfuscation techniques. It means that traditional signature-based techniques are not adequate for detecting new variant of malware. Researchers and anti malware companies recently focus on more advanced protection which needs influential pattern extraction techniques. In this paper, a novel method is proposed based on similarities of binaries behaviors. At first, Run-time behavior of the binary files are found and logged in a controlled environment tool which is developed in-house. The approach assumes that behavior of each binary can be represented by the values of memory contents in its run-time. That is, values stored in different registers while the malware is running in the controlled environment can be a distinguishing factor to set it apart from those of benign programs. Then, the register values for each Application Programming Interface (API) call are extracted before and after API is invoked. After that, we traced the distribution and changes of registers values throughout the executable file and created a vector for each of the values of EAX, EBX, EDX, EDI, ESI and EBP registers. With comparing the similarity measures between old and unseen malware vectors, we detected 98% of unseen samples and with 2.9% false positive.


title: Neural networks and the estimation of hands' strength in contract bridge

abstract: This paper is focused on a Double Dummy Bridge Problem (DDBP) which consists in answering the question of how many tricks are to be taken by a pair of players assuming perfect play of all four sides with all cards being revealed. Several experiments are also presented in a variant of DDBP in which the information about to whom of the two players in a given pair a particular card belongs to is hidden. In contrast to our previous works, which were devoted to no trump contracts, here we concentrate on suit contracts. Several interesting conclusions are drawn from comparison of weight patterns of networks trained on no trump contracts only vs. those trained exclusively on suit contracts. The ultimate goal of this research is to construct a computer program playing the game of contract bridge using neural networks and other CI techniques with the basic assumption of using zero-human-knowledge approach and to learn purely on examples.


title: Hypothermic oscillating liver perfusion stimulates ATP synthesis prior to transplantation.

abstract: Abstract Background. ATP and glycogen depletion often have been demonstrated during cold storage of the liver prior to transplantation. Suppression of events that lead to metabolic depression and to lipid peroxidation could contribute to improvement of liver preservation. A new method of liver preservation for transplantation is therefore suggested, an oscillating oxygenated hypothermic liver perfusion. Methods. Biochemical analysis of liver tissue samples and perfusate after 10 h of perfusion by the presented oscillating perfusion model were compared with results after continuous liver perfusion for 10 h as well as with data derived from cold-stored livers over a period of 10 h. Particular reference was made to nucleotide metabolites, glycogen content, lipid peroxidation, glutathione content, glycolytic metabolites, and enzyme release before and after preservation. Results. Glycogen depletion occurred to the same degree in hypothermic storage and machine perfusion (oscillating as well as continuous perfusion), but the energy charge was significantly increased after oxygenated perfusion, whereas cold storage resulted in a significant energy charge depletion. In addition, perfusion by an oscillating technique yielded superior energy charge loading compared to the continuous perfusion technique and diminished the other hand lipid peroxidation. Conclusions. Hypothermic oscillating oxygenated perfusion could be important for the improvement of the quality of energy-depleted organs prior to transplantation.


Analytics on video-based learning. A literature review


title: A Decade Bandwidth 90 W GaN HEMT Push-Pull Power Amplifier for VHF / UHF Applications

abstract: A decade bandwidth 90 W, GaN HEMT push-pull power amplifier has been demonstrated. The power amplifier exhibits 18 dB small-signal gain with 20-1100 MHz 3-dB bandwidth and obtains 82.2-107.5 W CW output power with 51.9-73.8 % drain efficiency and 15.2-16.3 dB power gain over the 100-1000 MHz band. The push-pull power amplifier occupies a 2 x 2 inch PCB area and uses a novel compact broadband low loss coaxial coiled 1:1 balun to combine two 45 W packaged broadband lossy matched GaN HEMT amplifiers matched to 25 U. The packaged amplifiers contain a GaN on SiC HEMT operating at 50 V drain voltage with integrated passive matching circuitry on GaAs substrate. These amplifiers are targeted for use in multi-band multi-standard communication systems and for instrumentation applications.


title: Efficient Implementation of Security Applications in OpenFlow Controller with FleXam

abstract: Current OpenFlow specifications provide limited access to packet-level information such as packet content, making it very inefficient, if not impossible, to deploy security and monitoring applications as controller applications. In this paper, we propose FleXam, a flexible sampling extension for OpenFlow designed to provide access to packet level information at the controller. Simplicity of FleXam makes it possible to implement it easily in OpenFlow switches and operate at line rate without requiring any additional memory. At the same time, its flexibility allows implementation of various monitoring and security applications in the controller, while maintaining balance between overhead and collected information details. FleXam realizes the advantages of both proactive and reactive routing schemes by providing a tunable trade-off between the visibility of individual flows, and the controller load. As an example, we demonstrate how FleXam can be used to implement a port scan detection application with an extremely low overhead.


title: Parallel Implementation of Decision Tree Learning Algorithms

abstract: In the fields of data mining and machine learning the amount of data available for building classifiers is growing very fast. Therefore, there is a great need for algorithms that are capable of building classifiers from very-large datasets and, simultaneously, being computationally efficient and scalable. One possible solution is to employ parallelism to reduce the amount of time spent in building classifiers from very-large datasets and keeping the classification accuracy. This work first overviews some strategies for implementing decision tree construction algorithms in parallel based on techniques such as task parallelism, data parallelism and hybrid parallelism. We then describe a new parallel implementation of the C4.5 decision tree construction algorithm. Even though the implementation of the algorithm is still in final development phase, we present some experimental results that can be used to predict the expected behavior of the algorithm.


title: Object-Oriented Shadow Detection and Removal From Urban High-Resolution Remote Sensing Images

abstract: In accordance with the characteristics of urban high-resolution color remote sensing images, we put forward an object-oriented shadow detection and removal method. In this method, shadow features are taken into consideration during image segmentation, and then, according to the statistical features of the images, suspected shadows are extracted. Furthermore, some dark objects which could be mistaken for shadows are ruled out according to object properties and spatial relationship between objects. For shadow removal, inner–outer outline profile line (IOOPL) matching is used. First, the IOOPLs are obtained with respect to the boundary lines of shadows. Shadow removal is then performed according to the homogeneous sections attained through IOOPL similarity matching. Experiments show that the new method can accurately detect shadows from urban high-resolution remote sensing images and can effectively restore shadows with a rate of over 85%.


title: Integrated transitions toward sustainability: The case of water and energy policies in Israel

abstract: Transition Management literature has examined how long-term transitions could be directed toward greater sustainability. However, it has mostly taken a sectoral approach which neglects the potential relationships between environmental changes and policy dynamics in different sectors. This paper examines parallel and interrelated dynamics in the Israeli water and energy sectors by combining insights from the literature on policy dynamics, transition management, co-evolution, and policy integration. The developed approach examines how sectoral transitions may be coupled and technological regimes may co-evolve. Israel has battled water, energy and other scarcities from its formation. Consecutive dry years, the loss of stream flows, salinization of the coastal aquifer, and severe pollution are problems facing water managers, while air pollution, imported fossil fuels and carbon emissions are salient energy issues. Water and energy sectors are both in transition because earlier policies have resulted in socially-induced scarcity, degradation of environmental assets and loss of adaptive capacity to respond to future challenges. Current approaches to water and energy scarcities have evolved around technological configurations which emphasize traditional supply side solutions such as seawater desalination and additional power plants. They may be difficult to change without explicit integrative transitions management.


title: Reevaluating Bloom’s Taxonomy: What Measurable Verbs Can and Cannot Say about Student Learning

abstract: Faculty and assessment professionals rely on Bloom’s taxonomy to guide them when they write measurable student learning outcomes and describe their goals for developing students’ thinking skills. Over the past ten years, assessment offices and teaching and learning centers have compiled lists of measurable verbs aligned with the six categories that comprise Bloom’s taxonomy. The author analyzed 30 compilations posted on web sites and evaluated how well these verbs aligned with categories in Bloom’s taxonomy. The author discusses the value of Bloom’s taxonomy as a heuristic for writing student learning outcomes and other factors faculty should consider when they articulate learning outcomes to describe levels of expertise attained by students who complete an associate’s, bachelor’s, or graduate degree.


title: Blended deformable models

abstract: We develop a new class of parameterized models based on the linear interpolation of two parameterized shapes using a blending function. Using a small number of additional parameters, blending extends the coverage of shape primitives. In particular, it offers the ability to construct shapes whose genus can change. Blended models are incorporated into a physics-based shape estimation framework which uses dynamic deformable models with global and local deformations. We present experiments involving the extraction of complex shapes, including an example of dynamic genus change. >


title: Performance Efficiency Assessment for Software Systems

abstract: Software quality is a complex term. Various researchers have various different views for defining it. One common point in all is that quality is required and is indispensable. It should not only be able to meet the customer requirements but should exceed it. Customers not only mean the external customers but the internal ones too. Performance efficiency characteristic is one of the vital software quality characteristics. If we improve the performance efficiency, then it will definitely have a positive effect on the software quality. In this paper, we have identified various sub-characteristics that can affect the performance efficiency of the software and proposed a performance efficiency model. We assessed the performance efficiency of the software systems by using one of the multi-criteria decision making (MCDM) methods, namely analytical hierarchy process (AHP). Results suggest that the proposed model is consistent and may be used for comparing the software system.