title: IOD-CNN: Integrating object detection networks for event recognition

abstract: Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network. We present a novel unified deep CNN architecture which integrates architecturally different, yet semantically-related object detection networks to enhance the performance of the event recognition task. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition, rigid object detection and non-rigid object detection.


title: Supporting compressed-sparse activations and weights on SIMD-like accelerator for sparse convolutional neural networks

abstract: Sparsity is widely observed in convolutional neural networks by zeroing a large portion of both activations and weights without impairing the result. By keeping the data in a compressed-sparse format, the energy consumption could be considerably cut down due to less memory traffic. However, the wide SIMD-like MAC engine adopted in many CNN accelerators can not support the compressed input due to the data misalignment. In this work, a novel Dual Indexing Module (DIM) is proposed to efficiently handle the alignment issue where activations and weights are both kept in compressed-sparse format. The DIM is implemented in a representative SIMD-like CNN accelerator, and able to exploit both compressed-sparse activations and weights. The synthesis results with 40nm technology have shown that DIM can enhance up to 46% of energy consumption and 55.4% Energy-Delay-Product (EDP).


title: Architectures and algorithms for user customization of CNNs

abstract: In this paper we present a convolutional neural network architecture that supports user customization through incremental transfer learning. The architecture consists of a large basic inference engine and a small augmenting engine. After training the basic inference engine and augmenting engine on a large general dataset, the basic inference engine is fixed. For user customization, only the augmenting engine is re-trained on-device using a small user specific dataset provided by the user. To accelerate the training of the augmenting engine we map this to a coarsegrained reconfigurable array processor. The complete network architecture is evaluated using the Caffe framework, and a C-code equivalent network is implemented and tested on a CGRA processor. Experiments with NIST '19 and our user-specific datasets show an increase in accuracy of the system from 76.3% to 93.2% after user customization. Mapping this code to a CGRA gives us a speed up of 45x and a 49- and 3-fold reduced energy consumption over an ARMv7 processor and a 3-way VLIW processor, respectively, showing the potential of CGRAs as DNN processors.


title: Ghera: A Repository of Android App Vulnerability Benchmarks

abstract: Security of mobile apps affects the security of their users. This has fueled the development of techniques to automatically detect vulnerabilities in mobile apps and help developers secure their apps; specifically, in the context of Android platform due to openness and ubiquitousness of the platform. Despite a slew of research efforts in this space, there is no comprehensive repository of up-to-date and lean benchmarks that contain most of the known Android app vulnerabilities and, consequently, can be used to rigorously evaluate both existing and new vulnerability detection techniques and help developers learn about Android app vulnerabilities. In this paper, we describe Ghera, an open source repository of benchmarks that capture 25 known vulnerabilities in Android apps (as pairs of exploited/benign and exploiting/malicious apps). We also present desirable characteristics of vulnerability benchmarks and repositories that we uncovered while creating Ghera.


title: Incremental Learning of 3D-DCT Compact Representations for Robust Visual Tracking

abstract: Visual tracking usually requires an object appearance model that is robust to changing illumination, pose, and other factors encountered in video. Many recent trackers utilize appearance samples in previous frames to form the bases upon which the object appearance model is built. This approach has the following limitations: 1) The bases are data driven, so they can be easily corrupted, and 2) it is difficult to robustly update the bases in challenging situations. In this paper, we construct an appearance model using the 3D discrete cosine transform (3D-DCT). The 3D-DCT is based on a set of cosine basis functions which are determined by the dimensions of the 3D signal and thus independent of the input video data. In addition, the 3D-DCT can generate a compact energy spectrum whose high-frequency coefficients are sparse if the appearance samples are similar. By discarding these high-frequency coefficients, we simultaneously obtain a compact 3D-DCT-based object representation and a signal reconstruction-based similarity measure (reflecting the information loss from signal reconstruction). To efficiently update the object representation, we propose an incremental 3D-DCT algorithm which decomposes the 3D-DCT into successive operations of the 2D discrete cosine transform (2D-DCT) and 1D discrete cosine transform (1D-DCT) on the input video data. As a result, the incremental 3D-DCT algorithm only needs to compute the 2D-DCT for newly added frames as well as the 1D-DCT along the third dimension, which significantly reduces the computational complexity. Based on this incremental 3D-DCT algorithm, we design a discriminative criterion to evaluate the likelihood of a test sample belonging to the foreground object. We then embed the discriminative criterion into a particle filtering framework for object state inference over time. Experimental results demonstrate the effectiveness and robustness of the proposed tracker.


title: www.elsevier.com/locate/neubiorev Review The somatic marker hypothesis: A critical evaluation

abstract: The somatic marker hypothesis (SMH; [Damasio, A. R., Tranel, D., Damasio, H., 1991. Somatic markers and the guidance of behaviour: ::: theory and preliminary testing. In Levin, H.S., Eisenberg, H.M., Benton, A.L. (Eds.), Frontal Lobe Function and Dysfunction. Oxford ::: University Press, New York, pp. 217–229]) proposes that emotion-based biasing signals arising from the body are integrated in higher brain ::: regions, in particular the ventromedial prefrontal cortex (VMPFC), to regulate decision-making in situations of complexity. Evidence for the ::: SMH is largely based on performance on the Iowa Gambling Task (IGT; [Bechara, A., Tranel, D., Damasio, H., Damasio, A.R., 1996. Failure ::: to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex 6 (2), 215–225]), linking ::: anticipatory skin conductance responses (SCRs) to successful performance on a decision-making paradigm in healthy participants. These ::: ‘marker’ signals were absent in patients with VMPFC lesions and were associated with poorer IGT performance. The current article reviews ::: the IGT findings, arguing that their interpretation is undermined by the cognitive penetrability of the reward/punishment schedule, ambiguity ::: surrounding interpretation of the psychophysiological data, and a shortage of causal evidence linking peripheral feedback to IGT ::: performance. Further, there are other well-specified and parsimonious explanations that can equally well account for the IGT data. Next, ::: lesion, neuroimaging, and psychopharmacology data evaluating the proposed neural substrate underpinning the SMH are reviewed. Finally, ::: conceptual reservations about the novelty, parsimony and specification of the SMH are raised. It is concluded that while presenting an elegant ::: theory of how emotion influences decision-making, the SMH requires additional empirical support to remain tenable.


Visual Hallucination For Computational Creation


title: Traffic-Sign Detection and Classification in the Wild

abstract: Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. We call this benchmark Tsinghua-Tencent 100K. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify trafficsigns. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available1.


title: The Uncanny Valley: Effect of Realism on the Impression of Artificial Human Faces

abstract: Roboticists believe that people will have an unpleasant impression of a humanoid robot that has an almost, but not perfectly, realistic human appearance. This is called the uncanny valley, and is not limited to robots, but is also applicable to any type of human-like object, such as dolls, masks, facial caricatures, avatars in virtual reality, and characters in computer graphics movies. The present study investigated the uncanny valley by measuring observers' impressions of facial images whose degree of realism was manipulated by morphing between artificial and real human faces. Facial images yielded the most unpleasant impressions when they were highly realistic, supporting the hypothesis of the uncanny valley. However, the uncanny valley was confirmed only when morphed faces had abnormal features such as bizarre eyes. These results suggest that to have an almost perfectly realistic human appearance is a necessary but not a sufficient condition for the uncanny valley. The uncanny valley emerges only when there is also an abnormal feature.


Graph Priors for Deep Neural Networks


title: Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies

abstract: Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%-63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with "overprediction" of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation.


title: Combining Static and Dynamic Features for Multivariate Sequence Classification

abstract: Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data. In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data. We explore how the extracted information can be combined with the static features in order to improve the classification performance. We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.


Large-scale Use of Sabin Type 2 Attenuated Poliovirus Vaccine in Singapore During a Type 1 Poliomyelitis Epidemic


title: Representations of language in a model of visually grounded speech signal

abstract: We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.


title: The role of sleep in declarative memory consolidation: passive, permissive, active or none?

abstract: Those inclined to relish in scientific controversy will not be disappointed by the literature on the effects of sleep on memory. Opinions abound. Yet refinements in the experimental study of these complex processes of sleep and memory are bringing this fascinating relationship into sharper focus. A longstanding position contends that sleep passively protects memories by temporarily sheltering them from interference, thus providing precious little benefit for memory. But recent evidence is unmasking a more substantial and long-lasting benefit of sleep for declarative memories. Although the precise causal mechanisms within sleep that result in memory consolidation remain elusive, recent evidence leads us to conclude that unique neurobiological processes within sleep actively enhance declarative memories.


title: A Hidden-picture Puzzles Generator

abstract: A hidden-picture puzzle contains objects hidden in a background image, in such a way that each object fits closely into a local region of the background. Our system converts image of the background and objects into line drawing, and then finds places in which to hide transformed versions of the objects using rotation-invariant shape context matching. During the hiding process, each object is subjected to a slight deformation to enhance its similarity to the background. The results were assessed by a panel of puzzle-solvers.


title: A simple genetic algorithm for music generation by means of algorithmic information theory

abstract: Recent large scale experiments have shown that the normalized information distance, an algorithmic information measure, is among the best similarity metrics for melody classification. This paper proposes the use of this distance as a fitness function which may be used by genetic algorithms to automatically generate music in a given pre-defined style. The minimization of this distance of the generated music to a set of musical guides makes it possible to obtain computer-generated music which recalls the style of a certain human author. The recombination operator plays an important role in this problem and thus several variations are tested to fine tune the genetic algorithm for this application. The superiority of the relative pitch envelope over other music parameters, such as the lengths of the notes, brought us to develop a simplified algorithm that nevertheless obtains interesting results.


title: Personalized Grade Prediction: A Data Mining Approach

abstract: To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 undergraduate students who have taken an introductory digital signal processing over the past 7 years. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.


title: Robust vision based lane tracking using multiple cues and particle filtering

abstract: One of the more startling effects of road related accidents is the economic and social burden they cause. Between 750,000 and 880,000 people died globally in road related accidents in 1999 alone, with an estimated cost of US$518 billion. One way of combating this problem is to develop Intelligent Vehicles that are self-aware and act to increase the safety of the transportation system. This paper presents the development and application of a novel multiple-cue visual lane tracking system for research into Intelligent Vehicles (IV). Particle filtering and cue fusion technologies form the basis of the lane tracking system which robustly handles several of the problems faced by previous lane tracking systems such as shadows on the road, unreliable lane markings, dramatic lighting changes and discontinuous changes in road characteristics and types. Experimental results of the lane tracking system running at 15 Hz will be discussed, focusing on the particle filter and cue fusion technology used.


title: Efficient indexing for large scale visual search

abstract: With the popularity of “bag of visual terms” representations of images, many text indexing techniques have been applied in large-scale image retrieval systems. However, due to a fundamental difference between an image query (e.g. 1500 visual terms) and a text query (e.g. 3–5 terms), the usages of some text indexing techniques, e.g. inverted list, are misleading. In this work, we develop a novel indexing technique for this problem. The basic idea is to decompose a document-like representation of an image into two components, one for dimension reduction and the other for residual information preservation. The computing of similarity of two images can be transferred to measuring similarities of their components. The decomposition has two major merits: 1) these components have good properties which enable them to be efficiently indexed and retrieved; 2) The decomposition has better generalization ability than other dimension reduction algorithms. The decomposition can be achieved by either a graphical model or a matrix factorization approach. Theoretic analysis and extensive experiments over a 2.3 million image database show that this framework is scalable to index large scale image database to support fast and accurate visual search.


title: A survey of IoT cloud providers

abstract: Cloud Computing enables flexible resource provisions that has become hugely popular for many businesses to take advantage of responding quickly to new demands from customers. Many cloud providers follow recent trends in cloud application development, and offer federation capabilities at the platform level, thus creating Platform-as-a-Service solutions. There is a growing number of providers offering IoT-specific services, since cloud computing has the potential to satisfy IoT needs such as hiding data generation, processing and visualization tasks. While cloud provider offerings have been widely studied, IoT-specific features are still unexplored due to their recent appearance. In this paper we investigate such basic properties of IoT cloud providers by presenting a survey and a classification of them, which are the main contributions of this work. This work can be useful for application developers planning to exploit IoT capabilities of PaaS cloud providers. We also present the needs for an IoT device simulator based on the results of this survey, which can further ease IoT cloud system development in the future.


title: Big data from smart grids

abstract: This paper gives a general introduction to “Big Data” in general and to Big Data in smart grids in particular. Large amounts of data (Big Data) contains a lots of information, however developing the analytics to extract such information is a big challenge due to some of the particular characteristics of Big Data. This paper investigates some existing analytic algorithms, especially deep learning algorithms, as tools for handling Big Data. The paper also explains the potential for deep learning application in smart grids.


title: Balun and power divider based on multilayer ring resonators (MRR)

abstract: This paper presents novel super compact microwave power dividers and balun (balanced-to-unbalanced) circuits. The proposed devices are based on multilayer ring resonators (MRR) structure. These new microwave devices are highly compact and flexible in design that can operate within various desirable bandwidths from narrow-band to ultrawideband (UWB), hence performing as bandpass filters simultaneously along with their own functions. It is also possible to have arbitrary power divisions. By this technique, a balun can be simply converted to a power diver and vice versa. Sample circuits are designed and the scattering characteristics are provided using electromagnetic simulation software. The dimensions of the devices are 2.3 mm χ 2.3 mm χ 1.5 mm.


Compounding medications in a rural setting: an interprofessional perspective


title: Real-Time EEG-Based Happiness Detection System

abstract: We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness.


title: Applying an ontology approach to IT service management for business-IT integration

abstract: Best practice frameworks focused on the integration of business and IT, such as ITIL, help organizations create and share effective service management. However, guidelines and models are commonly specified using natural language or graphical representations, both lacking the computational semantics needed to enable their automated validation, simulation or execution. This paper presents an ontology approach, which can help service providers add semantics to their service management process models and detect semantic ambiguities, uncertainties and contradictions. The proposed ontology draws its knowledge from good practice guidance for ITSM, enabling the current business gap that exists in many IT service providers to be overcome. To do so, service management processes are formalized in terms of an ontology defined using OWL combined with SWRL and SQWRL, the latter two being used to specify constraints and infer new knowledge. Our ontology provides support for executable service models with computational semantics. SWRL rules associated with the ontology can be categorized into three groups: (1) Model consistency; (2) SLA breaches; and (3) Proactive actions. Such rules allow us to better manage actual service management processes which are delivered in line with business needs. Also, the resulting specifications can be shared, reused and interchanged by automated means using e-business frameworks such as ebXML.


Deep Learning for abnormality detection in Chest X-Ray images


title: Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical text

abstract: The class imbalance problem is a key factor that affects the performance of many classification tasks when using machine learning methods. This mainly refers to the problem where the number of samples in certain classes is much greater than in others. Such imbalance considerably affects the performance of classifiers in which the majority class or classes are often favored, thus resulting in high-precision/low-recall classifiers. Named entity recognition in free text suffers from this problem to a large extent because in any given free text, many samples do not belong to a specific entity. Furthermore, the data used in this specific type of classification is in sequenced mode and is different than that used in other common classification tasks such as image classification, spam detection, and text classification in which no semantic or syntactic relation exists between samples. In this study, we propose an undersampling approach for sequenced data that preserves existing correlations between sequenced samples that comprise sentences and thus improve the performance of classifiers. We call this method balanced undersampling (BUS). Considering the recent increased interest in the use of NER in the chemical and biomedical domains, the proposed method is developed and tested on four recent state-of-the-art corpora in these domains, including BioCreative IV ChemDNER, Bio-entity Recognition Challenge of JNLPBA (JNLPBA), SemEval2013 DDI DrugBank, and SemEval2013 DDI Medline datasets. The performance of the proposed method is evaluated against two other common undersampling methods: random undersampling and stop-word filtering. Our method is shown to outperform both methods with respect to F-score for all datasets used.


title: Random Access Preamble Design and Detection for 3GPP Narrowband IoT Systems

abstract: Narrowband internet of things (NB-IoT) is an emerging cellular technology that will provide improved coverage for massive number of low-throughput low-cost devices with low device power consumption in delay-tolerant applications. A new single tone signal with frequency hopping has been designed for NB-IoT physical random access channel (NPRACH). In this letter we describe this new NPRACH design and explain in detail the design rationale. We further propose possible receiver algorithms for NPRACH detection and time-of-arrival estimation. Simulation results on NPRACH performance including detection rate, false alarm rate, and time-of-arrival estimation accuracy are presented to shed light on the overall potential of NB-IoT systems.


title: Do Automatically Generated Unit Tests Find Real Faults? An Empirical Study of Effectiveness and Challenges (T)

abstract: Rather than tediously writing unit tests manually, tools can be used to generate them automatically --- sometimes even resulting in higher code coverage than manual testing. But how good are these tests at actually finding faults? To answer this question, we applied three state-of-the-art unit test generation tools for Java (Randoop, EvoSuite, and Agitar) to the 357 real faults in the Defects4J dataset and investigated how well the generated test suites perform at detecting these faults. Although the automatically generated test suites detected 55.7% of the faults overall, only 19.9% of all the individual test suites detected a fault. By studying the effectiveness and problems of the individual tools and the tests they generate, we derive insights to support the development of automated unit test generators that achieve a higher fault detection rate. These insights include 1) improving the obtained code coverage so that faulty statements are executed in the first instance, 2) improving the propagation of faulty program states to an observable output, coupled with the generation of more sensitive assertions, and 3) improving the simulation of the execution environment to detect faults that are dependent on external factors such as date and time.


title: Chromosome 10q26 deletion syndrome: Two new cases and a review of the literature

abstract: The current study presents the cases of two unrelated patients with similar clinical features, including craniofacial anomalies, developmental delay/intellectual disability and cardiac malformations, that are consistent with chromosome 10q26 deletion syndrome. High‑resolution single‑nucleotide polymorphism analysis revealed that 10q26 terminal deletions were present in these two patients. The locations and sizes of the 10q26 deletions in these two patients were compared with the locations and sizes of 10q26 deletions in 30 patients recorded in the DECIPHER database and 18 patients characterized in previous studies through chromosomal microarray analysis. The clinical features and locations of the 10q26 deletions of these patients were reviewed in an attempt to map or refine a critical region (CR) for phenotypes. Additionally, the association between previously suggested CRs and phenotypic variability was discussed. The current study emphasize that a distal 10q26 terminal deletion with a breakpoint at ~130 Mb may contribute to the common clinical features of 10q26 deletion syndrome.


title: Diagnostic management of chronic obstructive pulmonary disease.

abstract: Detection of early chronic obstructive pulmonary disease (COPD) in patients presenting with respiratory symptoms is recommended; however, diagnosing COPD is difficult because a single gold standard is not available. The aim of this article is to review and interpret the existing evidence, theories and consensus on the individual parts of the diagnostic work-up for COPD. Relevant articles are discussed under the subheadings: history taking, physical examination, spirometry and additional lung function assessment. Wheezing, cough, phlegm and breathlessness on exertion are suggestive signs for COPD. The diagnostic value of the physical examination is limited, except for auscultated pulmonary wheezing or reduced breath sounds, increasing the probability of COPD. Spirometric airflow obstruction after bronchodilation, defined as a lowered ratio of the forced volume in one second to the forced vital capacity (FEV1/FVC ratio), is a prerequisite, but can only confirm COPD in combination with suggestive symptoms. Different thresholds are being recommended to define low FEV1/FVC, including a fixed threshold, and one varying with gender and age; however, the way physicians interpret these thresholds in their assessment is not well known. Body plethysmography allows a more complete assessment of pulmonary function, providing results on the total lung capacity and the residual volume and is indicated when conventional spirometry results are inconclusive. Chest radiography has no diagnostic value for COPD but is useful to exclude alternative diagnoses such as heart failure or lung cancer. Extensive history taking is of key importance in diagnosing COPD.


title: Research on high-frequency induction heating load-matched based on LLC resonant circuit

abstract: Modern high-frequency induction heating technology puts forward higher requirements for load matching of the induction heating power supply. LLC resonant load circuit can replace the traditional induction heating load matching transformer, and increase power density. This paper analyzes the load matching characteristics of LLC load resonant circuit in high frequency induction heating power supply, and gives the parameter selection method of matching components. At last, combining with phase-shifting pulse-width modulation (PWM) control method, the phase-shifting induction heating experiment platform based on LLC load resonant circuit is established. Experimental results show that the performance of LLC circuit in high frequency induction heating power supply load matching is excellent.


title: Hierarchical Clustering via Spreading Metrics

abstract: We study the cost function for hierarchical clusterings introduced by [arXiv:1510.05043] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [arXiv:1510.05043] that a top-down algorithm returns a hierarchical clustering of cost at most $O\left(\alpha_n \log n\right)$ times the cost of the optimal hierarchical clustering, where $\alpha_n$ is the approximation ratio of the Sparsest Cut subroutine used. Thus using the best known approximation algorithm for Sparsest Cut due to Arora-Rao-Vazirani, the top down algorithm returns a hierarchical clustering of cost at most $O\left(\log^{3/2} n\right)$ times the cost of the optimal solution. We improve this by giving an $O(\log{n})$-approximation algorithm for this problem. Our main technical ingredients are a combinatorial characterization of ultrametrics induced by this cost function, deriving an Integer Linear Programming (ILP) formulation for this family of ultrametrics, and showing how to iteratively round an LP relaxation of this formulation by using the idea of \emph{sphere growing} which has been extensively used in the context of graph partitioning. We also prove that our algorithm returns an $O(\log{n})$-approximate hierarchical clustering for a generalization of this cost function also studied in [arXiv:1510.05043]. Experiments show that the hierarchies found by using the ILP formulation as well as our rounding algorithm often have better projections into flat clusters than the standard linkage based algorithms. We also give constant factor inapproximability results for this problem.


title: A Model of User Preference Learning for Content-Based Recommender Systems

abstract: This paper focuses to a formal model of user preference learning for content-based recommender systems. First, some fundamental and special requirements to user preference learning are identified and proposed. Three learning tasks are introduced as the exact, the order preserving and the iterative user preference learning tasks. The first two tasks concern the situation where we have the user's rating available for a large part of objects. The third task does not require any prior knowledge about the user's ratings (i.e. the user's rating history). Local and global preferences are distinguished in the presented model. Methods for learning these preferences are discussed. Finally, experiments and future work will be described.


title: External characteristics of the boost-converter with Cockroft-Walton voltage multiplier

abstract: This paper presents the method and results of calculating the output voltage of the boost-converter with Cockroft-Walton voltage multiplier according to the values of the capacitors and the load. The combination of boost-converter with voltage multiplier provides a very high voltage gain, which can be adjusted by changing the duty cycle of the boost converter. It is shown that the output voltage of the converter is a complex function of the switching frequency, duty cycle, number and value of the capacitors and load resistance. The proposed method for calculating the external characteristic is based on the analysis of the energy losses in the capacitors of the multiplier. The formulas and graphs for estimating the output voltage as a function of the converter and load parameters are obtained. Experimental results confirmed the theoretical expectations.


title: Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals

abstract: We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets.


title: Using Crowdsourcing to Improve Profanity Detection

abstract: Profanity detection is often thought to be an easy task. However, past work has shown that current, list-based systems are performing poorly. They fail to adapt to evolving profane slang, identify profane terms that have been disguised or only partially censored (e.g., @ss, f$#%) or intentionally or unintentionally misspelled (e.g., biatch, shiiiit). For these reasons, they are easy to circumvent and have very poor recall. Secondly, they are a one-size fits all solution – making assumptions that the definition, use and perceptions of profane or inappropriate holds across all contexts. In this article, we present work that attempts to move beyond list-based profanity detection systems by identifying the context in which profanity occurs. The proposed system uses a set of comments from a social news site labeled by Amazon Mechanical Turk workers for the presence of profanity. This system far surpasses the performance of list-based profanity detection techniques. The use of crowdsourcing in this task suggests an opportunity to build profanity detection systems tailored to sites and communities.


title: Integration of firm's resource and capability to implement enterprise CRM: A case study of a retail bank in Korea

abstract: This chronological in-depth case study describes that, although the integration of functional resources and capabilities including information resource, analytical capability, and operational capability might influence retention process, those are not sufficient to bring the desired performances of acquiring and expanding customer relationships. Find-Equity Bank (a pseudonym) in this case validates that employee satisfaction must be a prerequisite of successful relationship acquisition and expansion processes, both which require employees to have more customer orientation relatively. Thus, this study emphasizes the significance of tripartite resource and capability consisting of technology, process, and people for the successful implementation of an enterprise-wide CRM strategy.


title: Meme-tracking and the dynamics of the news cycle

abstract: Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events. We develop a framework for tracking short, distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis. As our principal domain of study, we show how such a meme-tracking approach can provide a coherent representation of the news cycle - the daily rhythms in the news media that have long been the subject of qualitative interpretation but have never been captured accurately enough to permit actual quantitative analysis. We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle. In particular, we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively, with divergent behavior around the overall peak and a "heartbeat"-like pattern in the handoff between news and blogs. We also develop and analyze a mathematical model for the kinds of temporal variation that the system exhibits.


title: Research on cloud storage architecture and key technologies

abstract: This paper proposes a general architecture of cloud storage system, analyzes the functions of the components, and discusses the key technologies, etc. Cloud storage is a novel storage service mode which the service providers supply storage capacities and data storage services through the Internet to the clients; meanwhile, the clients needn't know the details and lowered structures and mechanisms. The proposed architecture of cloud storage is layered and cooperative, and the discussed key technologies involve deployment, storage virtualization, data organization, migration, security, etc. The operation mechanism including ecology chain, game theory, ant colony optimization, data life cycle management, maintenance and update, convergence and evolution mechanisms are analyzed too. So an overall and new viewpoint to cloud storage system is illustrated.


title: Low-cost 60 GHz 3D printed lens fed by a planar source with WR15 transition integrated on FR4 PCB

abstract: We present in this paper a cost-effective, high-gain V-band lens antenna system. The lens is illuminated by a planar antenna integrated on standard FR4 PCB along with a built-in connector using a fin-line microstrip-to-waveguide transition (enabling WR15 waveguide connection). The lens has an elliptical dome-type design destined to be manufactured in ABS plastic using 3D printing. The proposed antenna solution achieves high gain (> 28 dBi) in V band (57–66 GHz) and targets low-cost backhaul links that will be required in 5G small cells.


title: Handwritten Character Recognition Based on Zoning Using Euler Number for English Alphabets and Numerals

abstract: Handwritten Character Recognition has been a challenging research domain due to its diverse applicable environment. Handwriting has always been and will possibly continue to be a means of communication. There is a need to convert these handwritten documents into an editable format which can be achieved by Handwritten Character Recognition systems. This considerably reduces the storage space required. In this paper focus is on offline handwritten English alphabets and numerals. Feature extraction is performed using zoning method together with the concept of euler number. This increases accuracy and speed of recognition as the search space can be reduced by dividing the character set into three groups.


title: Power allocation strategies for Non-Orthogonal Multiple Access

abstract: Non-Orthogonal Multiple Access (NOMA) is considered as a promising downlink Multiple Access (MA) scheme for future radio access. In this paper two power allocation strategies for NOMA are proposed. The first strategy is based on channel state information experienced by NOMA users. The other strategy is based on pre-defined QoS per NOMA user. In this paper we develop mathematical models for the proposed strategies. Also we clarify the potential gains of NOMA using proposed power allocation strategies over Orthogonal Multiple Access (OMA). Simulation results showed that NOMA performance using the proposed strategies achieves superior performance compared to that for OMA.


title: Learning analytics beyond the LMS: the connected learning analytics toolkit

abstract: We present a Connected Learning Analytics (CLA) toolkit, which enables data to be extracted from social media and imported into a Learning Record Store (LRS), as defined by the new xAPI standard. A number of implementation issues are discussed, and a mapping that will enable the consistent storage and then analysis of xAPI verb/object/activity statements across different social media and online environments is introduced. A set of example learning activities are proposed, each facilitated by the Learning Analytics beyond the LMS that the toolkit enables.


title: Setting up a Mechanism for Predicting Automobile Customer Defection at SAHAM Insurance (Cameroon)

abstract: As markets become more competitive, companies have realized the need to manage the loss of customers (Churn) especially in terms of its prediction. To achieve this, in datamining framework, the main challenge is the selection of variables and the technique adapted to the studied context. This article examines the case of SAHAM insurance and uses ANOVA, chi-square test and Pearson correlations table for variable selection. To make an objective decision on selection of a technique among others, the multi criteria decision aid method PROMETHEE-GAIA has been used. With the aim to improve the initial model, which results was mitigated; the data set has been separated in two groups: individual customers and corporations. Then, with computation of the new one, we observe that, in general, performance is better on the group of individual customers than on previous global model and on corporations.


title: Social motivations of live-streaming viewer engagement on Twitch

abstract: Abstract Little is known about the motivations underlying viewer engagement in the rapidly growing live-streaming multimedia phenomenon. This study trialled an eight-factor socio-motivational model, based on Uses and Gratifications Theory, to explain four aspects of live-stream viewer engagement. Cross-sectional data was collected through an international, online self-report survey of Twitch users ( N = 2227). Multiple and ordinal linear regression analyses identified six motivations which helped to explain live-stream engagement: social interaction, sense of community, meeting new people, entertainment, information seeking, and a lack of external support in real life. Compared to mass media, viewer motivations to engage in live-stream entertainment appear to have a stronger social and community basis. Furthermore, live-stream viewers who preferred smaller channels (


title: Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection

abstract: During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. Although pitch features have been commonly used to recognize emotions, it is not clear what aspects of the pitch contour are the most emotionally salient. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages. The results show that the recognition accuracy of the system is over 77% just with the pitch features (baseline 50%). When compared to conventional classification schemes, the proposed approach performs better in terms of both accuracy and robustness.


title: The identification of deviance and its impact on retention in a multiplayer game

abstract: Deviant behavior in online social systems is a difficult problem to address. Consequences of deviance include driving off users and tarnishing the system's public image. We present an examination of these concepts in a popular online game, League of Legends. Using a large collection of game records and player-given feedback, we develop a metric, toxicity index, to identify deviant players. We then look at the effects of interacting with deviant players, including effects on retention. We find that toxic players have several significant predictive patterns, such as playing in more competitive game modes and playing with friends. We also show that toxic players drive away new players, but that experienced players are more resilient to deviant behavior. Based on our findings, we suggest methods to better identify and counteract the negative effects of deviance.


title: TTL based MaxProp routing protocol

abstract: Routing messages in sparse and intermittently connected networks is one of the major challenging issues in Delay Tolerant Networking as an end-to-end path between the source and the destination is absent most of the time. To deal with this problem a wide range of routing protocols have emerged over time. One such protocol is MaxProp — which prioritizes both the schedule of packets to be transmitted (based on their hop count) and the schedule of packets to be dropped (based on their delivery likelihood) by splitting the buffer into two portions. In this paper, we propose a Time-To-Live (TTL)-based routing scheme that prioritizes the schedule of packets to be transmitted not only based on their hop count but also based on their TTL values. In our scheme, messages with high TTL values are taken into special consideration which saves them from being dropped earlier. A series of simulation has been carried out and the results show that our TTL based routing scheme significantly improves the number of delivered messages reducing the overhead ratio and delay, consuming the same amount of network resources as MaxProp.


title: Updates on Sorting of Fully Homomorphic Encrypted Data

abstract: In this paper, we show implementation results of various algorithms that sort data encrypted with Fully Ho-momorphic Encryption scheme based on Integers. We analyze the complexities of sorting algorithms over encrypted data by considering Bubble Sort, Insertion Sort, Bitonic Sort and Odd-Even Merge sort. Our complexity analysis together with implementation results show that Odd-Even Merge Sort has better performance than the other sorting techniques. We observe that complexity of sorting in homomorphic domain will always have worst case complexity independent of the nature of input. In addition, we show that combining different sorting algorithms to sort encrypted data does not give any performance gain when compared to the application of sorting algorithms individually.


title: Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

abstract: Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)–(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.


title: Cavity Resonators Do the Trick: A Packaged Substrate Integrated Waveguide, Dual-Band Filter

abstract: The 2015 Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium in Phoenix, Arizona, played host to a total of 16 student design competitions. One of these, sponsored jointly by the MTT-12 Technical Committee on Microwave and Millimeter-Wave Packaging and Manufacturing and the MTT-8 Technical Committee on Filters and Passive Components, was based on the design of a substrate integrated waveguide (SIW) dual-band filter.


title: ANS-Based Index Compression

abstract: Techniques for effectively representing the postings lists associated with inverted indexes have been studied for many years. Here we combine the recently developed "asymmetric numeral systems" (ANS) approach to entropy coding and a range of previous index compression methods, including VByte, Simple, and Packed. The ANS mechanism allows each of them to provide markedly improved compression effectiveness, at the cost of slower decoding rates. Using the 426 GB GOV2 collection, we show that the combination of blocking and ANS-based entropy-coding against a set of 16 magnitude-based probability models yields compression effectiveness superior to most previous mechanisms, while still providing reasonable decoding speed.


Combining Graph Degeneracy and Submodularity for Unsupervised


title: Sexual assault and posttraumatic stress disorder: A review of the biological, psychological and sociological factors and treatments

abstract: Sexual assault occurs with alarming frequency in Canada. The prevalence of Posttraumatic Stress Disorder (PTSD) in assault survivors is drastically higher than the national prevalence of the disorder, which is a strong indication that the current therapies for sexual-assault-related PTSD are in need of improvement. Increasing knowledge and understanding of the pathologies associated with rape trauma in biological, psychological and sociological domains will help to develop more effective treatments for survivors. A dysregulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis is observed in survivors of sexual assault and this may be a fundamental cause of the structural and functional abnormalities contributing to PTSD symptoms. Pharmacotherapies are available to treat PTSD; however, they are often inadequate or unwanted by the survivor. Psychological health is compromised following interpersonal trauma and many psychological therapies are available, but with varying efficacy. A person's cognitions have a dramatic effect on the onset, severity, and progress of PTSD following sexual assault. Sociological impacts of assault influence the development of PTSD through victim-blaming attitudes and the perpetuation of rape myths. Perceived positive regard and early social support is shown to be important to successful recovery. Education is vital in rape prevention and to foster a supportive environment for survivors. The biological, psychological and sociological impacts and treatments should not remain mutually exclusive. A better appreciation of the biopsychosocial repercussions of sexual assault will aid in developing a more holistic and individualized therapy to help alleviate the physical and emotional pain following the trauma of rape.


title: A recursive session token protocol for use in computer forensics and TCP traceback

abstract: We introduce a new protocol designed to assist in the forensic investigation of malicious network-based activity, specifically addressing the stepping-stone scenario in which an attacker uses a chain of connections through many hosts to hide his or her identity. Our protocol, the Session TOken Protocol (STOP), enhances the Identification Protocol (ident) infrastructure by sending recursive requests to previous hosts on the connection chain. The protocol has been designed to protect user's privacy by returning a token that is a hash of connection information; a system administrator can later decide whether to release the information relating to the token depending on the circumstances of the request.


title: A New Compact Microstrip-Fed Dual-Band Coplanar Antenna for WLAN Applications

abstract: A novel compact microstrip fed dual-band coplanar antenna for wireless local area network is presented. The antenna comprises of a rectangular center strip and two lateral strips printed on a dielectric substrate and excited using a 50 Omega microstrip transmission line. The antenna generates two separate resonant modes to cover 2.4/5.2/5.8 GHz WLAN bands. Lower resonant mode of the antenna has an impedance bandwidth (2:1 VSWR) of 330 MHz (2190-2520 MHz), which easily covers the required bandwidth of the 2.4 GHz WLAN, and the upper resonant mode has a bandwidth of 1.23 GHz (4849-6070 MHz), covering 5.2/5.8 GHz WLAN bands. The proposed antenna occupy an area of 217 mm2 when printed on FR4 substrate (epsivr=4.7). A rigorous experimental study has been conducted to confirm the characteristics of the antenna. Design equations for the proposed antenna are also developed


title: Minimum PCB footprint point-of-load DC-DC converter realized with Switched-Capacitor architecture

abstract: This work reports on the design and test of a CMOS-based Switched Capacitor (SC) DC-DC conversion integrated circuit (IC) for the point-of-load (POL) application conventionally addressed with the buck converter. A 12V-to-1.5V converter is fabricated in a 0.18µm technology, with an active die area of 3mm2. There is a significant reduction in printed circuit board (PCB) footprint, passive component height and cost when compared to surveyed buck converters. The converter achieves 93% peak efficiency and an efficiency of 80% over an output current range of 7mA to 1A. This work demonstrates the vast potential of SC converters for wide-range voltage conversion in deep-submicron CMOS technologies, when cost and efficiency are of critical importance.


title: Use of cone beam computed tomography in implant dentistry: current concepts, indications and limitations for clinical practice and research

abstract: Diagnostic radiology is an essential component of treatment planning in the field of implant dentistry. This narrative review will present current concepts for the use of cone beam computed tomography imaging, before and after implant placement, in daily clinical practice and research. Guidelines for the selection of three-dimensional imaging will be discussed, and limitations will be highlighted. Current concepts of radiation dose optimization, including novel imaging modalities using low-dose protocols, will be presented. For preoperative cross-sectional imaging, data are still not available which demonstrate that cone beam computed tomography results in fewer intraoperative complications such as nerve damage or bleeding incidents, or that implants inserted using preoperative cone beam computed tomography data sets for planning purposes will exhibit higher survival or success rates. The use of cone beam computed tomography following the insertion of dental implants should be restricted to specific postoperative complications, such as damage of neurovascular structures or postoperative infections in relation to the maxillary sinus. Regarding peri-implantitis, the diagnosis and severity of the disease should be evaluated primarily based on clinical parameters and on radiological findings based on periapical radiographs (two dimensional). The use of cone beam computed tomography scans in clinical research might not yield any evident beneficial effect for the patient included. As many of the cone beam computed tomography scans performed for research have no direct therapeutic consequence, dose optimization measures should be implemented by using appropriate exposure parameters and by reducing the field of view to the actual region of interest.


title: HVC: A Hybrid Cloud Computing Framework in Vehicular Environments

abstract: As increasingly more applications are deployed in vehicles, how to provide the demanded computational capability becomes crucial. This paper proposes the hybrid vehicular cloud (HVC) framework for cloud computing on the road to increase the computational capability of vehicles by using resources from the centralized cloud, RSU, and neighboring vehicles. The proposed scheme can be used as a general framework for all types of applications. It is able to satisfy various types of job requirements, such as real time or host requirements, and it is adaptive to the dynamic vehicular environment. Specifically, this paper defines the procedures to support the autonomous organization of a vehicular cloud. An online scheduling algorithm is proposed to solve the job assignment problem with the objective of processing more jobs and reducing cellular network usage. Extensive simulations are conducted, and the results show the superiority of the proposed scheme over competing schemes in typical urban scenarios.


title: SIMA: Python software for analysis of dynamic fluorescence imaging data

abstract: Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic fluorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to fluorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at http://www.losonczylab.org/sima/.


title: Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

abstract: As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects from a group of images. On one hand, traditional co-saliency detection approaches rely heavily on human knowledge for designing hand-crafted metrics to possibly reflect the faithful properties of the co-salient regions. Such strategies, however, always suffer from poor generalization capability to flexibly adapt various scenarios in real applications. On the other hand, most current methods pursue co-saliency detection in unsupervised fashions. This, however, tends to weaken their performance in real complex scenarios because they are lack of robust learning mechanism to make full use of the weak labels of each image. To alleviate these two problems, this paper proposes a new SP-MIL framework for co-saliency detection, which integrates both multiple instance learning (MIL) and self-paced learning (SPL) into a unified learning framework. Specifically, for the first problem, we formulate the co-saliency detection problem as a MIL paradigm to learn the discriminative classifiers to detect the co-saliency object in the “instance-level”. The formulated MIL component facilitates our method capable of automatically producing the proper metrics to measure the intra-image contrast and the inter-image consistency for detecting co-saliency in a purely self-learning way. For the second problem, the embedded SPL paradigm is able to alleviate the data ambiguity under the weak supervision of co-saliency detection and guide a robust learning manner in complex scenarios. Experiments on benchmark datasets together with multiple extended computer vision applications demonstrate the superiority of the proposed framework beyond the state-of-the-arts.


Power saving and energy optimization techniques for Wireless Sensor Networks


title: Marine animal classification using UMSLI in HBOI optical test facility

abstract: Environmental monitoring is a critical aspect of marine renewable energy project success. A new system called Unobtrusive Multistatic Serial LiDAR Imager (UMSLI) has been prepared to capture and classify marine life interaction with electrical generation equipment. We present both hardware and software innovations of the UMSLI system. Underwater marine animal imagery has been captured for the first time using red laser diode serial LiDAR, which has advantages over conventional optical cameras in many areas. Moreover, given the scarcity of existing underwater LiDAR data, a shape matching based classification algorithm is proposed which requires few training data. On top of applying shape descriptors, the algorithm also adopts information theoretical learning based affine shape registration, improving point correspondences found by shape descriptors as well as the final similarity measure. Within Florida Atlantic University's Harbor Branch Oceanographic Institute optical test facility, experimental LiDAR data are collected through the front end of the UMSLI prototype, on which the classification algorithm is validated.


title: Ecological Sanitation--a way to solve global sanitation problems?

abstract: Today about 2.4 billion people in rural and urban areas do not have access to adequate sanitation services. Within 20 years, it is expected that an additional 2 billion will live in towns and cities, mainly in developing countries, demanding sanitation. Still over 90% of sewage in developing countries is discharged untreated, polluting rivers, lakes and coastal areas. Conventional sanitation concepts, based on flush toilets, a water wasting technology, are neither an ecological nor economical solution in both industrialized and developing countries. The water-based sewage systems were designed and built on the premises that human excreta are a waste; suitable only for disposal and that the environment is capable of assimilating this waste. A sanitation system that provides Ecological Sanitation (EcoSan) is a cycle--a sustainable, closed-loop system, which closes the gap between sanitation and agriculture. The EcoSan approach is resource minded and represents a holistic concept towards ecologically and economically sound sanitation. The underlying aim is to close (local) nutrient and water cycles with as less expenditure on material and energy as possible to contribute to a sustainable development. Human excreta are treated as a resource and are usually processed on-site and then treated off-site. The nutrients contained in excreta are then recycled by using them, e.g., in agriculture. EcoSan is a systemic approach and an attitude; single technologies are only means to an end and may range from near-natural wastewater treatment techniques to compost toilets, simple household installations to complex, mainly decentralised systems. These technologies are not ecological per se but only in relation to the observed environment. They are picked from the whole range of available conventional, modern and traditional technical options, combining them to EcoSan systems. The paper presents an introduction to EcoSan principles and concepts including re-use aspects (available nutrients and occurring risks), and case studies of EcoSan concepts in both industrialized and developing countries.


title: Exploring Segment Representations for Neural Segmentation Models

abstract: Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. Our model represents a segment both by composing the input units and embedding the entire segment. We thoroughly study different composition functions and different segment embeddings. We conduct extensive experiments on two typical segmentation tasks: named entity recognition (NER) and Chinese word segmentation (CWS). Experimental results show that our neural semi-CRF model benefits from representing the entire segment and achieves the stateof-the-art performance on CWS benchmark dataset and competitive results on the CoNLL03 dataset.


title: Two-factor user authentication in wireless sensor networks

abstract: Wireless sensor networks (WSN) are typically deployed in an unattended environment, where the legitimate users can login to the network and access data as and when demanded. Consequently, user authentication is a primary concern in this resource-constrained environment before accessing data from the sensor/gateway nodes. In this letter, we present a two-factor user authentication protocol for WSN, which provides strong authentication, session key establishment, and achieves efficiency.


title: A Comparative Performance Investigation of Single-Stage Dimmable Electronic Ballasts for Electrodeless Fluorescent Lamp Applications

abstract: This paper presents a comparative investigation, design and development issues about six different single-stage topologies, applied as electronic ballasts, for electrodeless fluorescent lamps. The ballast is intended to feed a 100 W lamp at 250 kHz, with dimming feature. The proposed topologies use different converters for the power factor correction (PFC) stage: Buck-Boost, SEPIC, Zeta, Cuk, Flyback, and Boost. All of them operate in discontinuous conduction mode. A resonant half-bridge inverter is used as lamp power control (PC) stage. The integration of both stages (PFC and PC) is proposed in this paper. In this way, it is possible to reduce the number of active switches, as well as simplify the required driving and control circuitry. Experimental results demonstrate the feasibility of the proposed dimming technique that achieved around 56% lamp power dimming, from 100 W down to 44 W. The implemented topologies attained high average power factor (>0.98), and low line current average total harmonic distortion (<;15.3%). A comparison among all the proposed topologies in terms of power factor, harmonic distortion, efficiency and number of components is presented. Finally, a comparison between an integrated and a nonintegrated SEPIC half-bridge converter is also performed.


title: Towards Release Strategy Optimization for Apps in Google Play

abstract: In the appstore-centric ecosystem, app developers have an urgent requirement to optimize their release strategy to maximize the success opportunity of their apps. To address this problem, we introduce an approach to assisting developers to select the proper release opportunity based on the purpose of the update and current condition of the app. Before that, we propose the interval of an update to its previous update to characterize release patterns, and find significance of the release opportunity through empirical analysis. We mined the update-history data of 17,820 apps from 33 categories in Google Play, over a period of 105 days. With 41,028 releases identified from these apps, we reveal important characteristics of update intervals and how these factors can influence update effects. We suggest developers to synthetically consider app ranking, rating trend, and what to update in addition to the opportunity before releasing an app version. We propose a Multinomial Naive Bayes model to help decide an optimal release opportunity to gain better user adoption.


What Analogies Reveal about Word Vectors and their Compositionality


title: DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

abstract: Deep learning applications are computation-intensive and often employ GPU as the underlying computing devices. Deep learning frameworks provide powerful programming interfaces, but the gap between source codes and practical GPU operations make it difficult to analyze the performance of deep learning applications. In this paper, through examing the features of GPU traces and deep learning applications, we use the suffix tree structure to extract the repeated patten in GPU traces. Performance analysis graphs can be generated from the preprocessed GPU traces. We further present \texttt{DeepProf}, a novel tool to automatically process GPU traces and generate performance analysis reports for deep learning applications. Empirical study verifies the effectiveness of \texttt{DeepProf} in performance analysis and diagnosis. We also find out some interesting properties of Tensorflow, which can be used to guide the deep learning system setup.


title: TM dual-mode pseudoelliptic filters using nonresonating modes

abstract: This paper presents a new class of very compact pseudoelliptic dual-mode filters. The basic idea consists of using a pair of resonant TM mn0 modes, while nonresonating modes are exploited so as to produce a direct coupling between the input and the output of the cavity. In this way, a single cavity can provide two reflection and two transmission zeros, thus leading to Nth order dual-mode filters having N transmission zeros. In addition, the proposed configuration yields very compact and geometrically simple structures that can be properly analyzed by a fullwave mode-matching technique, thus dramatically easing the design procedure. The analysis technique has been validated by comparison with commercial EM tools and by the measurement of a manufactured prototype. Several filter examples are proposed showing the design flexibility of the proposed filter class.


title: Posttraumatic Stress Disorder: A Social-Cognitive Perspective

abstract: This study offers a developmentally sensitive conceptual understanding of trauma by viewing posttraumatic stress disorder (PTSD) through the lens of social cognition. First, we justify our focus on social cognition by examining the literature on problematic interpersonal relationships associated with PTSD. Next, we link impaired social cognition to the developmental compromise of mentalizing capacity in attachment relationships. We then integrate the diverse research literature into a social-cognitive model of the development of PTSD. We finally conclude by suggesting directions for future research, as it might be shaped by trends in social-cognitive neuroscience.


Opportunity Makes the Thief Practical theory for crime prevention


title: Innovative Approach for IBS Vendor Selection Problem

abstract: Supply chain management in Industrialised Building System (IBS) construction management has significantly determined the successful of company and project performance. Due to the wide variety of criteria and vendor available, the vendor selection process for a specific project needs is becoming more difficult. The need of decision aid for vendor selection in other areas is widely discussed in previous research. However, study on vendor selection for IBS project is largely neglected. Decision Support System (DSS) is proposed for this purpose. Yet, most of the DSS models are impractical since they are complicated and difficult for a layman such as project managers to use. Research indicates that the rapid development of ICT has highly potential towards simple and effective DSS. Thus, this paper highlights the importance and research approach for vendor selection in IBS project management. The study is based on Design Science Research Methodology with combination of case studies. It is anticipates that this study will yield an effective value-for-money decision making platform to manage vendor selection process.


title: Anatomical variations of the saphenofemoral junction in patients with varicose veins.

abstract: Varicose veins are an extremely common medical condition (present in 5-30% of adults). Surgery remains the gold standard of care in patients with varicose veins, however several newer interventions have been recently introduced which need to be evaluated. During the standard surgery it is imperative to demonstrate and ligate the tributaries of the saphenofemoral junction in combination with stripping of the great saphenous vein. We studied 228 patients with varicose veins who underwent surgery during two years. The number and name of tributaries at the saphenofemoral junction, presence of external pudendal artery and its relationship to the saphenofemoral junction were recorded. The number of tributaries varied from 2 to 7 at the first five centimeters of the great saphenous vein. The average number of branches was 3.87 and the most common branch was the superficial external pudendal vein. The external pudendal artery was identified during all surgical exploration. It crossed anterior to saphenofemoral junction in 39.5% and posterior in 60.5%. Anatomical variations in venous branches at the saphenofemoral junction are varied (from 2 to 7), so it is recommended to explore the location of varicose veins precisely to ensure appropriate surgical technique.


title: Applying Principles of Robotics to Understand the Biomechanics, Neuromuscular Control and Clinical Rehabilitation of Human Digits

abstract: An apparatus for making hollow carbon microspheres starting with hollow microballoons made of a material which can by heating be degraded to a carbon structure having a shape similar to that of the original microballoon. The disclosure teaches specific batch treating steps and apparatus for optimizing the rate of production of the microspheres, while at the same time minimizing degradation, rupture and cracking of the resulting carbon product.


title: Robust observer-based fault diagnosis for an unmanned aerial vehicle

abstract: In this paper, a new robust fault detection and isolation (FDI) methodology for an unmanned aerial vehicle (UAV) is proposed. The fault diagnosis scheme is constructed based on observer-based techniques according to fault models corresponding to each component (actuator, sensor, and structure). The proposed fault diagnosis method takes advantage of the structural perturbation of the UAV model due to the icing (the main structural fault in aircraft), sensor, and actuator faults to reduce the error of observers that are used in the FDI module in addition to distinguishing among faults in different components. Moreover, the accuracy of the FDI module is increased by considering the structural perturbation of the UAV linear model due to wind disturbances which is the major environmental disturbance affecting an aircraft. Our envisaged FDI strategy is capable of diagnosing recurrent faults through properly designed residuals with different responses to different types of faults. Simulation results are provided to illustrate and demonstrate the effectiveness of our proposed FDI approach due to faults in sensors, actuators, and structural components of unmanned aerial vehicles.


title: Hummingbird: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education.

abstract: In this paper, we introduce the Hummingbird: the first completely open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a highly versatile, dual-rotor design and is engineered to be low-cost and easily extensible/modifiable. Our open-source release includes all of the design documents, software resources, and simulation tools needed to build and fly a high-performance tail-sitter for research and educational purposes. To the best of our knowledge, this is the first time that a comprehensive set of (free and open) resources for tail-sitters has been made available to the aerial robotics community. ::: The Hummingbird has been developed for precision flight with a high degree of control authority. Our design methodology included extensive testing and characterization of the aerodynamic properties of the vehicle. The platform incorporates many off-the-shelf components and 3D-printed parts, in order to keep the cost down. Nonetheless, the paper includes results from flight trials which demonstrate that the vehicle is capable of very stable hovering and accurate trajectory tracking. ::: Our hope is that the open-source Hummingbird reference design will be useful to both researchers and educators. In particular, the details in this paper and the available open-source materials should enable learners to gain an understanding of aerodynamics, flight control, state estimation, software design, and simulation, while experimenting with a unique aerial robot.


A methodology for cost-risk analysis in the statistical validation of simulation models


Kafka: a distributed messaging system for log processing. NetDB’11


title: MATISSE 2.0: A Large-Scale Multi-agent Simulation System for Agent-Based ITS

abstract: In this paper we present MATISSE 2.0, a microscopic multi-agent based simulation system for the specification and execution of simulation scenarios for Agent-based intelligent Transportation Systems (ATS). In MATISSE, each smart traffic element (e.g., vehicle, intersection control device) is modeled as a virtual agent which continuously senses its surroundings and communicates and collaborates with other agents. MATISSE incorporates traffic control strategies such as contraflow operations and dynamic traffic sign changes. Experimental results show the ability of MATISSE 2.0 to simulate traffic scenarios with thousands of agents on a single PC.


title: Visual interest and NPR: an evaluation and manifesto

abstract: Using eye tracking, we study the way viewers look at photos and image based NPR illustrations. Viewers examine the same number of locations in photos and in NPR images with uniformly high or low detail. In contrast, viewers are attracted to areas where detail is locally preserved in meaningfully abstracted images. This accords with the idea that artists carefully manipulate detail to control interest and understanding. It also validates the method of meaningful abstraction used in DeCarlo and Santella [2002]. Results also suggest eye tracking can be a useful tool for evaluation of NPR systems.


title: Safe & Efficient Gradual Typing for TypeScript

abstract: Current proposals for adding gradual typing to JavaScript, such as Closure, TypeScript and Dart, forgo soundness to deal with issues of scale, code reuse, and popular programming patterns. We show how to address these issues in practice while retaining soundness. We design and implement a new gradual type system, prototyped for expediency as a 'Safe' compilation mode for TypeScript. Our compiler achieves soundness by enforcing stricter static checks and embedding residual runtime checks in compiled code. It emits plain JavaScript that runs on stock virtual machines. Our main theorem is a simulation that ensures that the checks introduced by Safe TypeScript (1) catch any dynamic type error, and (2) do not alter the semantics of type-safe TypeScript code. Safe TypeScript is carefully designed to minimize the performance overhead of runtime checks. At its core, we rely on two new ideas: differential subtyping, a new form of coercive subtyping that computes the minimum amount of runtime type information that must be added to each object; and an erasure modality, which we use to safely and selectively erase type information. This allows us to scale our design to full-fledged TypeScript, including arrays, maps, classes, inheritance, overloading, and generic types. We validate the usability and performance of Safe TypeScript empirically by type-checking and compiling around 120,000 lines of existing TypeScript source code. Although runtime checks can be expensive, the end-to-end overhead is small for code bases that already have type annotations. For instance, we bootstrap the Safe TypeScript compiler (90,000 lines including the base TypeScript compiler): we measure a 15% runtime overhead for type safety, and also uncover programming errors as type safety violations. We conclude that, at least during development and testing, subjecting JavaScript/TypeScript programs to safe gradual typing adds significant value to source type annotations at a modest cost.


title: SPAIN: Security Patch Analysis for Binaries towards Understanding the Pain and Pills

abstract: Software vulnerability is one of the major threats to software security. Once discovered, vulnerabilities are often fixed by applying security patches. In that sense, security patches carry valuable information about vulnerabilities, which could be used to discover, understand and fix (similar) vulnerabilities. However, most existing patch analysis approaches work at the source code level, while binary-level patch analysis often heavily relies on a lot of human efforts and expertise. Even worse, some vulnerabilities may be secretly patched without applying CVE numbers, or only the patched binary programs are available while the patches are not publicly released. These practices greatly hinder patch analysis and vulnerability analysis. In this paper, we propose a scalable binary-level patch analysis framework, named SPAIN, which can automatically identify security patches and summarize patch patterns and their corresponding vulnerability patterns. Specifically, given the original and patched versions of a binary program, we locate the patched functions and identify the changed traces (i.e., a sequence of basic blocks) that may contain security or non-security patches. Then we identify security patches through a semantic analysis of these traces and summarize the patterns through a taint analysis on the patched functions. The summarized patterns can be used to search similar patches or vulnerabilities in binary programs. Our experimental results on several real-world projects have shown that: i) SPAIN identified security patches with high accuracy and high scalability; ii) SPAIN summarized 5 patch patterns and their corresponding vulnerability patterns for 5 vulnerability types; and iii) SPAIN discovered security patches that were not documented, and discovered 3 zero-day vulnerabilities.


title: Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN)

abstract: Image-based computer-aided diagnosis (CADx) algorithm by use of convolutional neural network (CNN) does not necessarily require an image-feature extractor. Therefore, image-based CADx is powerful compared with feature-based CADx that requires the image-feature extractor for differential diagnosis of lung abnormalities such as lung nodules and diffuse lung diseases. We have also developed an image-based computer-aided detection (CADe) algorithm by use of regions with CNN features (R-CNN) for detection of lung abnormalities. We evaluated the performance of image-based CADx by use of CNN and that of image-based CADe by use of R-CNN for various kinds of lung abnormalities such as lung nodules and diffuse lung diseases.


title: Verification of Generic Ubiquitous Middleware for Smart Home Using Coloured Petri Nets

abstract: Smart home is a relatively new technology, where we applied pervasive computing in all the aspects, so as to make our jobs or things that we normally do in-side the home in a very easier way. Originally, a smart home technology was used to control environmental systems such as lighting and heating; but recently the use of smart technology has been developed so that almost any electrical component within the home can be included in the system. Usually in pervasive computing, a middleware is developed to provide interaction between the user and device. In previous, a middleware is only suitable for specific Smart Home architecture, that can't be applicable to any other architecture but the Generic Ubiquitous Middleware is suitable for different Smart Home architecture. This paper proposes that any smart home can be built with single architecture and it is verified using a Coloured Petri Nets tool. We have given a verification model of various Smart home Environments.


title: Performance Tuning and Modeling for Big Data Applications in Docker Containers

abstract: Docker container is experiencing a rapid development with the support from industry and being widely used in large scale production cloud environment, due to the benefits of speedy launching time and tiny memory footprint. However the performance of big data applications (e.g., Spark) running in Docker containers is still not clear due to the complex parameter configuration and interference between neighbor containers. This paper investigates the impacts of docker configuration and resource interference on the performance of big data applications in a typical container environment. In particular, we first conduct a series of experiments to measure the performance impact by adjusting the docker configuration parameters, such as resource limits, and observe the Spark performance is not linear with increasing resource allocation for containers. Then, we evaluate the interference between multiple containers by controlling the resource competition and detect the performance interference phenomenon between multiple containers. Finally, we propose a performance prediction model based on the Support Vector Regression (SVR) to predict the application performance with different configurations and resource competition settings. Experimental results show the prediction error is less than 10% for all the four typical Spark applications.


title: A low-cost smart sensor for non intrusive load monitoring applications

abstract: Next generation Smart Cities have the potential to enhance the citizens quality of life and to reduce the overall energy expenditure. In particular, emerging smart metering technologies promise to greatly increase energy efficiency in the residential and industrial areas. In this context, new power metering methods such as Non-Intrusive Load Monitoring (NILM) can provide important real-time information about the contribution of any single appliance. In this paper, we present a complete hardware-software design that concentrates locally an efficient event-based supervised NILM algorithm for load disaggregation. This new kind of power analysis, which usually requires high computing capability, is executed real-time on a low-cost and easy-to-install smart meter ready for the Internet of Things (IoT).


title: 50 Years of Test (Un)fairness: Lessons for Machine Learning

abstract: Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.


title: Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy

abstract: In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11%) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97% accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter.


title: Septoplasty and turbinate surgery.

abstract: When a patient requesting rhinoplasty also complains of nasal obstruction, it is critical to accurately diagnose the cause. The author reviews diagnostic procedures, including anterior rhinoscopy, nasal endoscopy, and coronal-sinus computed tomography scan. He discusses technical aspects of septoplasty and turbinate surgery, which address common causes of nasal obstruction, emphasizing traditional and endoscopic septoplasty, septoplasty techniques to address the caudal septum, and a graduated stepwise approach to the inferior turbinates.


title: Zero-Shot Learning via Attribute Regression and Class Prototype Rectification

abstract: Zero-shot learning (ZSL) aims at classifying examples for unseen classes (with no training examples) given some other seen classes (with training examples). Most existing approaches exploit intermedia-level information (e.g., attributes) to transfer knowledge from seen classes to unseen classes. A common practice is to first learn projections from samples to attributes on seen classes via a regression method, and then apply such projections to unseen classes directly. However, it turns out that such a manner of learning strategy easily causes projection domain shift problem and hubness problem, which hinder the performance of ZSL task. In this paper, we also formulate ZSL as an attribute regression problem. However, different from general regression-based solutions, the proposed approach is novel in three aspects. First, a class prototype rectification method is proposed to connect the unseen classes to the seen classes. Here, a class prototype refers to a vector representation of a class, and it is also known as a class center, class signature, or class exemplar. Second, an alternating learning scheme is proposed for jointly performing attribute regression and rectifying the class prototypes. Finally, a new objective function which takes into consideration both the attribute regression accuracy and the class prototype discrimination is proposed. By introducing such a solution, domain shift problem and hubness problem can be mitigated. Experimental results on three public datasets (i.e., CUB200-2011, SUN Attribute, and aPaY) well demonstrate the effectiveness of our approach.