title: Power converters with compact winding SCIGs for HEVs, micro hydroelectric power stations and wind turbines

abstract: The article considers one variant for solving problem of development high-efficiency power converters for hybrid and electric vehicles, wind turbines and micro hydroelectric power stations. The results of designing high-efficiency power converters with power electronic converters and Squirrel-Cage Induction Generators (SCIGs), which have compact front parts due to specific winding, are given. These results include modeling processes in these converters and shows reducing electric losses and materials intensity of the new SCIGs. The theoretical justifications of the offered calculation method for these electric machines are considered. This method takes into account a non-uniform distribution of current density in the winding's conductors. The comparison results of the SCIGs are considered. Sample tests in the composition of micro hydroelectric power stations are reported.


title: Computer-aided breast cancer detection using mammograms: A review

abstract: Breast cancer is the second most common cancer in the world and more prevalent in the female population. Since the cause of the disease remains unknown, early detection and diagnosis is the optimal solution to prevent tumor progression and allow a successful medical intervention, save lives and reduce cost. Mammography is an x-ray of the breasts performed in the absence of symptoms. It can detect very small tumors, even before they are tangible or they manifest other symptoms. Conducted as part of a screening program, mammography is currently the recommended method for early detection of breast cancer in women 50 to 70 years. It can detect very small tumors that generally have not yet formed metastases, which increases the chances of survival and recovery. Mammographic screening has been shown to be effective in reducing breast cancer mortality rates: screening programs have reduced mortality rates by 30–70%. Mammograms are difficult to interpret, especially in the screening context. The sensitivity of screening mammography is affected by image quality and the radiologist's level of expertise. Computer-aided diagnosis (CAD) technology can improve the performance of radiologists, by increasing sensitivity to rates comparable to those obtained by double reading, in a cost-effective manner. This paper presents an overview of digital image processing and pattern analysis techniques to address several areas in CAD of breast cancer, including the four stages of CAD system: image preprocessing, image segmentation, features extraction and selection and image classification.


title: Neurocognitive Mechanisms of Synesthesia

abstract: nomenological, behavioral, and neuroimaging methods has begun to identify the cognitive and neural basis of synesthesia. Here, we review this literature with anemphasis on grapheme-color synesthesia, inwhich viewing letters and numbers induces the perception of colors. We discuss both the substantial progress that has been made inthe pastfifteen years and some open questions. In particular, we focus on debates in the fieldrelating to theneuralbasis ofsynesthesia, including the relationship between synesthesia and attention and the role of meaning in synesthetic colors. We propose that some, but probably not all, of these differences can be accounted for by differences in the synesthetes studied and discuss some methodological implications of these individual differences.


title: Internet Banking in Europe: A Comparative Analysis

abstract: A key strategic issue for banks is the implementation of internet banking. The ‘click and mortar’ model that complements classical branch banking with online facilities is competing with pure internet banks. The objective of this paper is to compare the performance of these two models across countries, so as to examine the role of differences in the banking system and technological progress. A fuzzy cluster analysis on the performance of banks in Finland, Spain, Italy and the UK shows that internet banks are hard to distinguish from banks that follow a click and mortar strategy; country borders are more important. We therefore explain bank performance by a group of selected bank features, country-specific economic and IT indicators over the period 1995-2004. We find that the strategy of banking groups to incorporate internet banks reflects some competitive edge that these banks have in their business models. Extensive technological innovation boosts internet banking.


title: Design and Implementation of Augmented Reality Environment for Complex Anatomy Training: Inguinal Canal Case Study

abstract: Adhering to contemporary requirements for reduction of cadaveric training of medical trainees we have developed a prototype augmented reality environment which investigates complex anatomical sections. A human 3D model has been implemented in order to facilitate educational tactics presented in a Virtual Reality (VR) environment. Opting for a sophisticated approach of interaction, the interface elements are based on simplified visual representation of real anatomical elements, and can be operated through haptic devices and surround auditory cues. This paper discusses the challenges involved in the development process of the augmented reality environment, and the HCI design, introduces the visual components of the interface and presents the outcome of a preliminary evaluation of the proposed VR training method on a group of twelve medical doctors. The paper concludes with a tentative plan of future work which aims to expand the context and interactivity of the system so as to enable the trainees to rehearse surgical methods in a simulated VR environment.


title: A Trustworthy, Responsible and Interpretable System to Handle Chit Chat in Conversational Bots

abstract: Most often, chat-bots are built to solve the purpose of a search engine or a human assistant: Their primary goal is to provide information to the user or help them complete a task. However, these chat-bots are incapable of responding to unscripted queries like "Hi, what's up", "What's your favourite food". Human evaluation judgments show that 4 humans come to a consensus on the intent of a given query which is from chat domain only 77% of the time, thus making it evident how non-trivial this task is. In our work, we show why it is difficult to break the chitchat space into clearly defined intents. We propose a system to handle this task in chat-bots, keeping in mind scalability, interpretability, appropriateness, trustworthiness, relevance and coverage. Our work introduces a pipeline for query understanding in chitchat using hierarchical intents as well as a way to use seq-seq auto-generation models in professional bots. We explore an interpretable model for chat domain detection and also show how various components such as adult/offensive classification, grammars/regex patterns, curated personality based responses, generic guided evasive responses and response generation models can be combined in a scalable way to solve this problem.


title: Series-Connected HV-IGBTs Using Active Voltage Balancing Control With Status Feedback Circuit

abstract: Transient voltage unbalance is the major problem that limits the application of series-connected IGBTs in high-voltage and high-power converters. Asynchronous gate delay causes series-connected IGBTs not to turn-on and turn-off at the same time resulting in severely unbalanced voltage sharing. An active voltage balancing control technique is proposed in this paper to solve the asynchronous gate delay problem. By sampling the feedback signal caused by unbalanced voltage sharing, the microcontroller generates a time delay for the gate driver to compensate the asynchronous gate delay. The most vital part of active voltage balancing control, the status feedback circuit, is also discussed in detail in this paper. The function of the status feedback circuit and the effect of active voltage balancing control are verified in a two series-connected HV-IGBTs platform in rated operation (5 kV bus voltage and 600 A load current).


title: Gender differences in emotional responses: A psychophysiological study

abstract: Abstract Gender differences in emotional responses have been investigated in two groups of students, 22 males and 21 females. Participants watched a set of sixty emotional standardized slides divided into pleasant, neutral and unpleasant, while Startle reflex, Evoked Potentials, Heart Rate, facial EMG and Skin Conductance were recorded. Startle reflex amplitude, an index modulated by amygdala and orbitofrontal cortex and sensitive to aversive emotional stimuli, was overall larger in women. In addition, startle emotion modulation was greater in women with respect to men. Slow Evoked Potentials (400–800 ms), a measure representing the cognitive component of the emotional response, revealed gender differences in the left prefrontal site, with women showing greater positivity to unpleasant compared with pleasant slides while men had greater positivity to pleasant vs. neutral slides. Women, compared with men, perceived all slides as less pleasant and reported greater arousal to unpleasant condition. Results are in line with known functional brain differences, at level of limbic and paralimbic structures, between men and women, and point to biologically grounded greater sensitivity and vulnerability of women to adverse/stressful events.


title: Modelling uncertainty in deep learning for camera relocalization

abstract: We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6 degrees accuracy for very large scale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset. We leverage the uncertainty measure to estimate metric relocalization error and to detect the presence or absence of the scene in the input image. We show that the model's uncertainty is caused by images being dissimilar to the training dataset in either pose or appearance.


title: Meta-learning for evolutionary parameter optimization of classifiers

abstract: The performance of most of the classification algorithms on a particular dataset is highly dependent on the learning parameters used for training them. Different approaches like grid search or genetic algorithms are frequently employed to find suitable parameter values for a given dataset. Grid search has the advantage of finding more accurate solutions in general at the cost of higher computation time. Genetic algorithms, on the other hand, are able to find good solutions in less time, but the accuracy of these solutions is usually lower than those of grid search. ::: ::: This paper uses ideas from meta-learning and case-based reasoning to provide good starting points to the genetic algorithm. The presented approach reaches the accuracy of grid search at a significantly lower computational cost. We performed extensive experiments for optimizing learning parameters of the Support Vector Machine (SVM) and the Random Forest classifiers on over 100 datasets from UCI and StatLib repositories. For the SVM classifier, grid search achieved an average accuracy of 81 % and took six hours for training, whereas the standard genetic algorithm obtained 74 % accuracy in close to one hour of training. Our method was able to achieve an average accuracy of 81 % in only about 45 minutes. Similar results were achieved for the Random Forest classifier. Besides a standard genetic algorithm, we also compared the presented method with three state-of-the-art optimization algorithms: Generating Set Search, Dividing Rectangles, and the Covariance Matrix Adaptation Evolution Strategy. Experimental results show that our method achieved the highest average accuracy for both classifiers. Our approach can be particularly useful when training classifiers on large datasets where grid search is not feasible.


title: Airwriting: a wearable handwriting recognition system

abstract: We present a wearable input system which enables interaction through 3D handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. The handwriting gestures are captured wirelessly by motion sensors applying accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a support vector machine to identify those data segments which contain handwriting. The recognition stage uses hidden Markov models (HMMs) to generate a text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary. A statistical language model is used to enhance recognition performance and to restrict the search space. We show that continuous gesture recognition with inertial sensors is feasible for gesture vocabularies that are several orders of magnitude larger than traditional vocabularies for known systems. In a first experiment, we evaluate the spotting algorithm on a realistic data set including everyday activities. In a second experiment, we report the results from a nine-user experiment on handwritten sentence recognition. Finally, we evaluate the end-to-end system on a small but realistic data set.


title: Lexicon based sentiment analysis system for malayalam language

abstract: Sentiment Analysis is a natural language processing task that mines information from various text forms such as reviews, news, and blogs and classifies them by their polarity as positive, negative or neutral. Mining sentiments in Malayalam come with a lot of issues and challenges. As compared to English, Malayalam is a free order and morphologically rich language, which adds complexity while handling the user- generated content. Much of the research in Malayalam sentiment analysis has been done using different supervised learning techniques. Although the Supervised learning methods provide better accuracy compared to dictionary-based approach, supervised learning method cannot perform well without sufficient training examples. The accuracy of supervised learning method is directly related to the quality of training corpus created. In Dictionary based approach a sentiment lexicon is created from a pre-annotated seed list of words and its synonyms and antonyms obtained from WorldNet for the purpose of classifying the sentiment. Compared to supervised learning techniques dictionary based approach takes less processing time. But there is no sentiment lexicon readily available for the Malayalam language. So in order to perform sentiment analysis by using lexicon based approach, a sentiment lexicon should be created. In this work, a lexicon based document-level sentiment analysis system is proposed for Malayalam language. Dictionary based approach is used to develop the Malayalam sentiment lexicon. This is because, dictionary based method is typically more efficient than other approaches and include all the words. Besides, dictionary approach is not domain specific that means it is applicable to all domains. The proposed system gives an accuracy of 87.5% for sentence level classification and 90% for document-level classification.


title: Analysis and design of an omnidirectional platform for operation on non-ideal floors

abstract: An omnidirectional platform with an active offset split caster (ASOC) is described and its ability to operate on non-ideal floors is studied. It is shown that all of its driven wheels of the platform will remain in contact with an uneven floor at all times, a condition necessary to maintain good traction and dead-reckoning capabilities. It is shown that planning algorithms developed for an ideally flat floor perform adequately for a realistic uneven floor. Furthermore, it is shown that the ASOC design consumes less power than other conventional wheel omnidirectional designs and is more suitable for heavier loads. Analytical and experimental results are presented.


title: Survey and new directions for physics-based attack detection in control systems

abstract: Monitoring the “physics” of control systems to detect attacks is a growing area of research. In its basic form a security monitor creates time-series models of sensor readings for an industrial con ...


title: Management and Analysis of Big Graph Data: Current Systems and Open Challenges

abstract: Many big data applications in business and science require the management and analysis of huge amounts of graph data. Suitable systems to manage and to analyze such graph data should meet a number of challenging requirements including support for an expressive graph data model with heterogeneous vertices and edges, powerful query and graph mining capabilities, ease of use as well as high performance and scalability. In this chapter, we survey current system approaches for management and analysis of “big graph data”. We discuss graph database systems, distributed graph processing systems such as Google Pregel and its variations, and graph dataflow approaches based on Apache Spark and Flink. We further outline a recent research framework called Gradoop that is build on the so-called Extended Property Graph Data Model with dedicated support for analyzing not only single graphs but also collections of graphs. Finally, we discuss current and future research challenges.


title: Sexual differentiation of the human brain in relation to gender identity and sexual orientation.

abstract: It is believed that during the intrauterine period the fetal brain develops in the male direction through a direct action of testosterone on the developing nerve cells, or in the female direction through the absence of this hormone surge. According to this concept, our gender identity (the conviction of belonging to the male or female gender) and sexual orientation should be programmed into our brain structures when we are still in the womb. However, since sexual differentiation of the genitals takes place in the first two months of pregnancy and sexual differentiation of the brain starts in the second half of pregnancy, these two processes can be influenced independently, which may result in transsexuality. This also means that in the event of ambiguous sex at birth, the degree of masculinization of the genitals may not reflect the degree of masculinization of the brain. ::: ::: There is no proof that social environment after birth has an effect on gender identity or sexual orientation. Data on genetic and hormone independent influence on gender identity are presently divergent and do not provide convincing information about the underlying etiology. To what extent fetal programming may determine sexual orientation is also a matter of discussion. A number of studies show patterns of sex atypical cerebral dimorphism in homosexual subjects. Although the crucial question, namely how such complex functions as sexual orientation and identity are processed in the brain remains unanswered, emerging data point at a key role of specific neuronal circuits involving the hypothalamus.


title: Morphology and Ontogenesis of Psilotrichides hawaiiensis nov. gen., nov. spec. and Molecular Phylogeny of the Psilotrichidae (Ciliophora, Hypotrichia)

abstract: The Psilotrichidae are a family of middle-sized hypotrichs with unique morphological and ontogenetic features (e.g. the oral primordium develops in a deep pouch) that, however, did not provide a definite phylogenetic signal. Thus, we studied the 18S rRNA gene of Urospinula succisa (Müller 1786) Esteban et al., 2001 as well as the morphology and ontogenesis of Psilotrichides hawaiiensis, a new genus and species from an ephemeral swamp on Oahu Island, Hawaii. The molecular data classify the psilotrichids into the oxytrichids but without clear branching position. A brief revision, using the structure of the oral apparatus, the location of the contractile vacuole, and three ontogenetic features, showed four distinct genera: Psilotricha Stein, 1859; Urospinula Corliss, 1960; Hemiholosticha Gelei, 1954; and Psilotrichides nov. gen., which differs from the confamilials mainly by the obliquely oriented buccal cavity and the shape of the undulating membranes as well as by a distinct ridge along the right buccal margin. The pyriform species, P. hawaiiensis, is about 65 × 45 μm in size and is easily recognized by the table tennis racket-shaped appearance due to the elongated last cirrus of the left marginal row. Refined diagnoses are provided for the family Psilotrichidae Bütschli, 1889 and the genera contained.


title: Rotor Configurations for Improved Starting and Synchronous Performance of Line Start Permanent-Magnet Synchronous Motor

abstract: This paper presents two new rotor configurations for the line start permanent-magnet synchronous motors (PMSMs). The first configuration proposed here uses inset consequent magnet pole arrangement with double cage, and results into improved starting performance when compared to the other rotor configurations previously used. The second configuration proposed uses a combination of circumferentially and radially magnetized magnets (hybrid rotor), with induced magnet poles, which results into improved synchronous performance. Two-dimensional finite-element analysis has been employed in the analysis of transient and steady-state performances. The experimental prototypes for the second configuration are built and tested extensively. To demonstrate the effectiveness of the hybrid rotor configuration, an additional prototype is made that employs the circumferentially magnetized magnets (spoke magnet type rotor), and performance of the proposed rotor is benchmarked with this. Analysis, simulation, and experimental results indicate that the proposed rotor configuration leads to improved steady-state performance, by utilizing 10% less magnet volume than the benchmarked spoke rotor. The proposed configurations are also suitable for inverter-fed brushless dc, and PMSMs.


title: Wi-Fi offloading between LTE and WLAN with combined UE and BS information

abstract: With the increasing popularity of mobile devices, cellular networks are suffering the consequences of a mobile data traffic explosion. Operators and standard development organizations have adopted Heterogeneous Network (HetNet) solutions which offload traffic from cellular networks to other Radio Access Technologies to reduce the overloaded situation. To minimize service blocking ratios, HetNets need to be designed with an efficient approach to allocate heterogeneous channel resources. This work proposes a Combined UE and BS Information scheme (CUBI), which follows the standard architecture proposed by 3rd generation partnership project. We compare the impacts of using the User Equipment (UE) information only, and Base Stations (BS) information only to achieve network selection. We then propose a 2-round solution that benefits from both UE and BS information. The UE-information scheme is based on channel qualities of all available networks, and using it selects the network which provides the highest data rate. On the other hand, the BS-information scheme (BSI) is based on traffic loads of all UEs to allocate bandwidth, and using BSI will prevent BSs from overload situations. The simulation results demonstrate that CUBI can decrease service blocking ratios by 26 and 8.9 %, compared to schemes separately using US and BS information, respectively.


title: Spine Loading Characteristics of Patients With Low Back Pain Compared With Asymptomatic Individuals

abstract: Study Design. Patients with low back pain and asymptomatic individuals were evaluated while performing controlled and free-dynamic lifting tasks in a laboratory setting. Objective. To evaluate how low back pain influences spine loading during lifting tasks. Summary of Background Data. An important, yet unresolved, issue associated with low back pain is whether patients with low back pain experience spine loading that differs from that of individuals who are asymptomatic for low back pain. This is important to understand because excessive spine loading is suspected of accelerating disc degeneration in those whose spines are damaged already. Methods. In this study, 22 patients with low back pain and 22 asymptomatic individuals performed controlled and free-dynamic exertions. Trunk muscle activity, trunk kinematics, and trunk kinetics were used to evaluate threedimensional spine loading using an electromyographyassisted model in conjunction with a new electromyographic calibration procedure. Results. Patients with low back pain experienced 26% greater spine compression and 75% greater lateral shear (normalized to moment) than the asymptomatic group during the controlled exertions. The increased spine loading resulted from muscle coactivation. When permitted to move freely, the patients with low back pain compensated kinematically in an attempt to minimize external moment exposure. Increased muscle coactivation and greater body mass resulted in significantly increased absolute spine loading for the patients with low back pain, especially when lifting from low vertical heights. Conclusions. The findings suggest a significant mechanical spine loading cost is associated with low back pain resulting from trunk muscle coactivation. This loading is further exacerbated by the increases in body weight that often accompany low back pain. Patient weight control and proper workplace design can minimize the additional spine loading associated with low back pain. [Key words: electromyography, lifting, low back pain, musculoskeletal, spinal loads] Spine 2001;26:2566 –2574


title: Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

abstract: In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according to the error between the estimated motor speed and the speed given by the reference model. The speed of the vector-controlled IM is estimated using the MRASCC rotor speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor speed given by the proposed speed estimator. Simulation results are verified by experimental tests over a wide range of motor speed and drive parameter changes.


title: Discrete Event Simulation and Virtual Reality Use in Industry: New Opportunities and Future Trends

abstract: This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets.


title: One-step and Two-step Classification for Abusive Language Detection on Twitter

abstract: Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.


Digital Entrepreneurship of Born Digital and Grown Digital Firms: Comparing the Effectuation Process of Yihaodian and Suning


title: Best practices for eye tracking of television and video user experiences

abstract: Eye tracking is a usability tool that employs a device that measures on-screen eye fixations and movements to determine how users visually interact with an interface. In this paper, we present best practices for planning and moderating eye tracking sessions as well as offer details regarding the technical configuration used during our studies. We found that eye tracking is pivotal in determining complex usability issues that would be difficult to uncover with traditional observational and think-aloud techniques.


title: LiDAR Point Clouds to 3-D Urban Models$:$ A Review

abstract: Three-dimensional (3-D) urban models are an integral part of numerous applications, such as urban planning and performance simulation, mapping and visualization, emergency response training and entertainment, among others. We consolidate various algorithms proposed for reconstructing 3-D models of urban objects from point clouds. Urban models addressed in this review include buildings, vegetation, utilities such as roads or power lines and free-form architectures such as curved buildings or statues, all of which are ubiquitous in a typical urban scenario. While urban modeling, building reconstruction, in particular, clearly demand specific traits in the models, such as regularity, symmetry, and repetition; most of the traditional and state-of-the-art 3-D reconstruction algorithms are designed to address very generic objects of arbitrary shapes and topology. The recent efforts in the urban reconstruction arena, however, strive to accommodate the various pressing needs of urban modeling. Strategically, urban modeling research nowadays focuses on the usage of specialized priors, such as global regularity, Manhattan-geometry or symmetry to aid the reconstruction, or efficient adaptation of existing reconstruction techniques to the urban modeling pipeline. Aimed at an in-depth exploration of further possibilities, we review the existing urban reconstruction algorithms, prevalent in computer graphics, computer vision and photogrammetry disciplines, evaluate their performance in the architectural modeling context, and discuss the adaptability of generic mesh reconstruction techniques to the urban modeling pipeline. In the end, we suggest a few directions of research that may be adopted to close in the technology gaps.


title: Breast cancer diagnosis using least square support vector machine

abstract: The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. In this paper, breast cancer diagnosis was conducted using least square support vector machine (LS-SVM) classifier algorithm. The robustness of the LS-SVM is examined using classification accuracy, analysis of sensitivity and specificity, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 98.53% and it is very promising compared to the previously reported classification techniques. Consequently, by LS-SVM, the obtained results show that the used method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.


title: CoReach: Cooperative Gestures for Data Manipulation on Wall-sized Displays

abstract: Multi-touch wall-sized displays afford collaborative exploration of large datasets and re-organization of digital content. However, standard touch interactions, such as dragging to move content, do not scale well to large surfaces and were not designed to support collaboration, such as passing an object around. This paper introduces CoReach, a set of collaborative gestures that combine input from multiple users in order to manipulate content, facilitate data exchange and support communication. We conducted an observational study to inform the design of CoReach, and a controlled study showing that it reduced physical fatigue and facilitated collaboration when compared with traditional multi-touch gestures. A final study assessed the value of also allowing input through a handheld tablet to manipulate content from a distance.


title: A Novice Guide towards Human Motion Analysis and Understanding

abstract: Human motion analysis and understanding has been, and is still, the focus of attention of many disciplines which is considered an obvious indicator of the wide and massive importance of the subject. The purpose of this article is to shed some light on this very important subject, so it can be a good insight for a novice computer vision researcher in this field by providing him/her with a wealth of knowledge about the subject covering many directions. There are two main contributions of this article. The first one investigates various aspects of some disciplines (e.g., arts, philosophy, psychology, and neuroscience) that are interested in the subject and review some of their contributions stressing on those that can be useful for computer vision researchers. Moreover, many examples are illustrated to indicate the benefits of integrating concepts and results among different disciplines. The second contribution is concerned with the subject from the computer vision aspect where we discuss the following issues. First, we explore many demanding and promising applications to reveal the wide and massive importance of the field. Second, we list various types of sensors that may be used for acquiring various data. Third, we review different taxonomies used for classifying motions. Fourth, we review various processes involved in motion analysis. Fifth, we exhibit how different surveys are structured. Sixth, we examine many of the most cited and recent reviews in the field that have been published during the past two decades to reveal various approaches used for implementing different stages of the problem and refer to various algorithms and their suitability for different situations. Moreover, we provide a long list of public datasets and discuss briefly some examples of these datasets. Finally, we provide a general discussion of the subject from the aspect of computer vision.


title: The new face of violence tendency: Cyber bullying perpetrators and their victims

abstract: The present study examines the role of violence tendecy on cyberbullying perpetrations and their victims. Turkish adolescents N?=?286, 167 girls (58,4%) and 119 boys (41,6%) aged 15-18 years mean age of 17.6 years (df:0.78) were recruited from different high school types 101 (35.4%) of the participants were students in vocational high school, 88 (30.7%) anatolian high school and 97 (33.9%) public high school. Participants completed Revised Cyber Bullying Inventory (RCBI, Topcu & Erdur-Baker, 2010) and Violence Tendency Scale (VTS, Haskan & Yildirim, 2012). Collected data were analyzed Pearson correlation coefficients and simple regression. Pearson correlations revealed that violence tendency was positively related to cyberbullying perpetration (r?=?0.344, p?<?0.01). Also, violence tendency was positively related to cyber victimization (r?=?0.242, p?<?0.01). Also simple regressions revealed that violence tendency was significant individual predictors both cyberbullying perpetration and cyber victimization. There was a positive correlation between cyberbullies and violence tendency.There was a positive correlation between cyber victims and violence tendency.Cyberbullies and victims were significantly predicted by violence tendency.


title: Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs

abstract: Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two entities connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering.


title: A Hyperspectral Target Detection Framework With Subtraction Pixel Pair Features

abstract: In recent years, due to its strong nonlinear mapping and research capacities, the convolutional neural network (CNN) has been widely used in the field of hyperspectral image (HSI) processing. Recently, pixel pair features (PPFs) and spatial PPFs (SPPFs) for HSI classification have served as the new tools for feature extraction. In this paper, on top of PPF, improved subtraction pixel pair features (subtraction-PPFs) are applied for HSI target detection. Unlike original PPF and SPPF, the subtraction-PPF considers target classes to afford the CNN, a target detection function. Using subtraction-PPF, a sufficiently large number of samples are obtained to ensure the excellent performance of the multilayer CNN. For a testing pixel, the input of the trained CNN is the spectral difference between the central pixel and its adjacent pixels. When a test pixel belongs to the target, the output score will be close to the target label. To verify the effectiveness of the proposed method, aircrafts and vehicles are used as targets of interest, while another 27 objects are chosen as background classes (e.g., vegetation and runways). Our experimental results on four images indicate that the proposed detector outperforms classic hyperspectral target detection algorithms.


title: Characteristics of Impulsive Suicide Attempts and Attempters

abstract: Suicide attempts often are impulsive, yet little is known about the characteristics of impulsive suicide. We examined impulsive suicide attempts within a population-based, case-control study of nearly lethal suicide attempts among people 13-34 years of age. Attempts were considered impulsive if the respondent reported spending less than 5 minutes between the decision to attempt suicide and the actual attempt. Among the 153 case-subjects, 24% attempted impulsively. Impulsive attempts were more likely among those who had been in a physical fight and less likely among those who were depressed. Relative to control subjects, male sex, fighting, and hopelessness distinguished impulsive cases but depression did not. Our findings suggest that inadequate control of aggressive impulses might be a greater indicator of risk for impulsive suicide attempts than depression.


title: Monitoring of respiratory and heart rates using a fibre-optic sensor

abstract: A new method, which uses a fibre-optic probe to monitor respiratory and heart rates simultaneously, has been developed and evaluated. The results provide evidence that it is possible to monitor respiratory and heart rates using the reflection mode of photoplethysmography (PPG). The fibre-optic probe makes it possible to monitor from different sites on the patient, and the method is convenient to use. In addition, the probe is X-ray transparent, insensitive to electromagnetic interference (e.g. during MRI investigation) and may be made very light and small. Therefore the method is suitable for the observation of both adults and neonates, in hospitals as well as in other environments.


title: Super-Resolution Image Reconstruction:

abstract: A new approach toward increasing spatial resolution is required to overcome the limitations of the sensors and optics manufacturing technology. One promising approach is to use signal processing techniques to obtain an high-resolution (HR) image (or sequence) from observed multiple low-resolution (LR) images. Such a resolution enhancement approach has been one of the most active research areas, and it is called super resolution (SR) (or HR) image reconstruction or simply resolution enhancement. In this article, we use the term "SR image reconstruction" to refer to a signal processing approach toward resolution enhancement because the term "super" in "super resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of LR imaging systems. The major advantage of the signal processing approach is that it may cost less and the existing LR imaging systems can be still utilized. The SR image reconstruction is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications. The goal of this article is to introduce the concept of SR algorithms to readers who are unfamiliar with this area and to provide a review for experts. To this purpose, we present the technical review of various existing SR methodologies which are often employed. Before presenting the review of existing SR algorithms, we first model the LR image acquisition process.


title: Polyhedral patterns

abstract: We study the design and optimization of polyhedral patterns, which are patterns of planar polygonal faces on freeform surfaces. Working with polyhedral patterns is desirable in architectural geometry and industrial design. However, the classical tiling patterns on the plane must take on various shapes in order to faithfully and feasibly approximate curved surfaces. We define and analyze the deformations these tiles must undertake to account for curvature, and discover the symmetries that remain invariant under such deformations. We propose a novel method to regularize polyhedral patterns while maintaining these symmetries into a plethora of aesthetic and feasible patterns.


title: Measuring developer contribution from software repository data

abstract: Apart from source code, software infrastructures supporting agile and distributed software projects contain traces of developer activity that does not directly affect the product itself but is important for the development process. We propose a model that, by combining traditional contribution metrics with data mined from software repositories, can deliver accurate developer contribution measurements. The model creates clusters of similar projects to extract weights that are then applied to the actions a developer performed on project assets to extract a combined measurement of the developer's contribution. We are currently implementing the model in the context of a software quality monitoring system while we are also validating its components by means of questionnaires.


title: Re-inventing PTPTN study loan with blockchain and smart contracts

abstract: The issue of default payments from borrowers of the National Higher Education Fund Corporation (PTPTN) is worrisome. Many borrowers fail to pay their loans and claims that the PTPTN has poor management and filing system. This study proposed a prototype for managing study loan repayment utilizing blockchain and smart contracts. Borrowers have full access toward their accounts and ledgers while corporation filing and management system get automatically up-to-date with the assistance of smart contracts.


title: Novel Centrally Loaded Resonators and Their Applications to Bandpass Filters

abstract: This paper presents several novel centrally loaded resonators and their applications to various types of bandpass filters. Based on the theoretical analysis, it is found that the even- mode resonance of the proposed resonators can be conveniently controlled without affecting the responses at the odd-mode resonant frequencies. Benefiting from this feature, the resonator can be utilized to design not only harmonic-suppressed bandpass filters and dual-band bandpass filters but also tunable bandpass filters. The tunable filter offers a fixed passband and a tunable one. The center frequency of the upper passband can be electrically tuned, whereas the performance of the lower one is maintained constant. To demonstrate these applications, four filters using the proposed resonators are implemented. The experiments verify the theoretical predictions and simulations.


title: Application of Virtual, Augmented, and Mixed Reality to Urology

abstract: Recent developments in virtual, augmented, and mixed reality have introduced a considerable number of new devices into the consumer market. This momentum is also affecting the medical and health care sector. Although many of the theoretical and practical foundations of virtual reality (VR) were already researched and experienced in the 1980s, the vastly improved features of displays, sensors, interactivity, and computing power currently available in devices offer a new field of applications to the medical sector and also to urology in particular. The purpose of this review article is to review the extent to which VR technology has already influenced certain aspects of medicine, the applications that are currently in use in urology, and the future development trends that could be expected.


title: The development of strategic management in the non-profit context: Intellectual capital in social service non-profit organizations

abstract: Very little systematic research has examined the applicability of strategic management concepts including SWOT (strengths, weaknesses, opportunities and threats) analysis, industrial organization, resource-based view and core competency, knowledge-based view, Balanced Scorecard and intellectual capital (IC) through the lens of strategic management development in the non-profit context. This paper aims to examine the above concepts in the light of the unique non-profit environment and determine which one is most applicable to social service non-profit organizations (SSNPOs) in the knowledge economy. Based on a review of the development of strategic management with a focus on the above concepts within the non-profit context, this paper argues that the IC concept is more effective compared with the other concepts in the social service non-profit sector. The paper is considered as a starting point and serves as a milestone in applying IC as a strategic management conceptual framework in the social service non-profit sector. It helps to build a nascent body of literature suggesting that IC can be used as a competent strategic management conceptual framework in the social service non-profit sector. A better understanding of the strategic management development in the non-profit context also helps non-profit leaders to appreciate that IC is the most appropriate strategic management concept in SSNPOs. The increased awareness of the IC concept in SSNPOs, as a result of this paper, will probably generate further research from both academic scholars and non-profit practitioners.


title: Learning a Neural Semantic Parser from User Feedback

abstract: We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its full expressivity, bypassing any intermediate meaning representations. These models are immediately deployed online to solicit feedback from real users to flag incorrect queries. Finally, the popularity of SQL facilitates gathering annotations for incorrect predictions using the crowd, which is directly used to improve our models. This complete feedback loop, without intermediate representations or database specific engineering, opens up new ways of building high quality semantic parsers. Experiments suggest that this approach can be deployed quickly for any new target domain, as we show by learning a semantic parser for an online academic database from scratch.


Dialog State Tracking Using Long Short-term Memory Neural Networks


title: BotCop: An Online Botnet Traffic Classifier

abstract: A botnet is a network of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. As one the most serious security threats to the Internet, a botnet cannot only be implemented with existing network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also can be constructed by unknown or creative applications, thus making the botnet detection a challenging problem. In this paper, we propose a new online botnet traffic classification system, called BotCop, in which the network traffic are fully classified into different application communities by using payload signatures and a novel decision tree model, and then on each obtained application community, the temporal-frequent characteristic of flows is studied and analyzed to differentiate the malicious communication traffic created by bots from normal traffic generated by human beings. We evaluate our approach with about 30 million flows collected over one day on a large-scale WiFi ISP network and results show that the proposed approach successfully detects an IRC botnet from about 30 million flows with a high detection rate and a low false alarm rate.


title: A Descriptive Content Analysis of Trust-Building Measures in B2B Electronic Marketplaces

abstract: Because business-to-business (B2B) electronic marketplaces (e-marketplaces) facilitate transactions between buyers and sellers, they strive to foster a trustworthy trading environment with a variety of trust-building measures. However, little research has been undertaken to explore trust-building measures used in B2B e-marketplaces, or to determine to what extent these measures are applied in B2B e-marketplaces and how they are applied. Based on reviews of the scholarly, trade, and professional literature on trust in electronic commerce, we identified 11 trustbuilding measures used to create trust in B2B e-marketplaces. Zucker’s trust production theory [1986] was applied to understand how these trust-building measures will enhance participants’ trust in buyers and sellers in B2B e-marketplaces or in B2B e-marketplace providers. A descriptive content analysis of 100 B2B e-marketplaces was conducted to survey the current usage of the 11 trust-building measures. Many of the trust-building measures were found to be widely used in the B2B e-marketplaces. However, although they were proven to be effective in building trust-related beliefs in online business environments, several institutional-based trustbuilding measures, such as escrow services, insurance and third-party assurance seals, are not widely used in B2B e-marketplaces.


title: Utilization of open government data: A systematic literature review of types, conditions, effects and users

abstract: This paper presents a comprehensive overview of the literature on the types, effects, conditions and user of Open Government Data (OGD). The review analyses 101 academic studies about OGD which discuss at least one of the four factors of OGD utilization: the different types of utilization, the effects of utilization, the key conditions, and the different users. Our analysis shows that the majority of studies focus on the OGD provisions while assuming, but not empirically testing, various forms of utilization. The paper synthesizes the hypothesized relations in a multi-dimensional framework of OGD utilization. Based on the framework we suggest four future directions for research: 1) investigate the link between type of utilization and type of users (e.g. journalists, citizens) 2) investigate the link between type of user and type of effect (e.g. societal, economic and good governance benefits) 3) investigate the conditions that moderate OGD effects (e.g. policy, data quality) and 4) establishing a causal link between utilization and OGD outcomes.


title: What Grades and Achievement Tests Measure

abstract: Intelligence quotient (IQ), grades, and scores on achievement tests are widely used as measures of cognition, but the correlations among them are far from perfect. This paper uses a variety of datasets to show that personality and IQ predict grades and scores on achievement tests. Personality is relatively more important in predicting grades than scores on achievement tests. IQ is relatively more important in predicting scores on achievement tests. Personality is generally more predictive than IQ on a variety of important life outcomes. Both grades and achievement tests are substantially better predictors of important life outcomes than IQ. The reason is that both capture personality traits that have independent predictive power beyond that of IQ.


title: Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology

abstract: Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper describes the main features of BLE, explores its potential applications, and investigates the impact of various critical parameters on its performance. BLE represents a trade-off between energy consumption, latency, piconet size, and throughput that mainly depends on parameters such as connInterval and connSlaveLatency. According to theoretical results, the lifetime of a BLE device powered by a coin cell battery ranges between 2.0 days and 14.1 years. The number of simultaneous slaves per master ranges between 2 and 5,917. The minimum latency for a master to obtain a sensor reading is 676 μs, although simulation results show that, under high bit error rate, average latency increases by up to three orders of magnitude. The paper provides experimental results that complement the theoretical and simulation findings, and indicates implementation constraints that may reduce BLE performance.


title: Compact Rep-Rate GW Pulsed Generator Based on Forming Line With Built-In High-Coupling Transformer

abstract: In this paper, a compact rep-rate GW pulsed power generator is developed. First, its three key subsystems are theoretically analyzed, engineering designed, and experimentally investigated, respectively. The emphases are put on these four problems: the theoretical analysis of the voltage distribution across the conical secondary windings of the high-coupling transformer, the investigation of the high energy storage density dielectric used in the pulse forming line, the choice of the gas flow velocity of the gas blowing system, and theoretical analysis of the passive stability of the pulsed power generator operated in rep-rate mode. Second, the developed pulsed power generator is described in detail. It has a 0.2-m diameter, a 1.0-m length, and a 20- Ω wave impedance. Across a 100- Ω resistive dummy load, it can steadily operate at a 300-kV output voltage in 50-Hz rep-rate and 250 kV in 150 Hz without gas blowing system. The average power is ~ 1 kW. Finally, the pulsed power generator is applied to drive a relativistic backward-wave oscillator, generating a high-power microwave with peak output power of 200 MW and duration (full-width at half-maximum) of 5 ns in 150-Hz rep-rate. These efforts set a good foundation for the development of a compact rep-rate pulsed power generator and show a promising application for the future.


title: Beyond the pretty penny: the Economic Impact of Cybercrime

abstract: Over the past decade, considerable research effort has been devoted to articulating and measuring the various ways through which cyber crime impacts overall society. The large volume of literature on the topic contains few attempts to produce estimates of the financial impact of specific cyber incidents and little agreement on how to derive such estimates. An important substrata of this literature focuses on placing a monetary value on the costs of cyber crime but little is known about the long-term economic impact to society. In this article, we first assess the shortcomings of existing cost estimates and focus on the relevant issues pertinent to the feasibility of deriving valid and useful estimates beyond cost-benefit analyses. Following a mixed top-down/bottom-up methodology, we propose a theoretical framework to systematically identify the short and long-term impacts of cyber crime both at the agent and societal level. This framework serves as the foundation to assess the economic consequences of cyber crime beyond monetary costs by focusing on the impact on economic growth.


title: The Inclusion of Orbicularis Oculi Muscle in the SMAS Flap in Asian Facelift: Anatomical Consideration of Orbicularis Muscle and Zygomaticus Major Muscle

abstract: BACKGROUND ::: In the malar region, the SMAS flap can be thin and tear easily, making it difficult to securely fix it. And the surgical anatomy of the region may be unclear and confusing. The authors performed an anatomical study on the location of the lateral margin of the orbicularis oculi muscle (OOM) and the origin of the zygomaticus major muscle (ZMM) when using a high-SMAS facelift with finger-assisted spaces dissection technique, which included elevation of the SMAS flap with OOM. ::: ::: ::: METHODS ::: One hundred twenty-one Asian patients underwent this facelift procedure. Of those, the distances between the posterior margin of tragus and the lateral margin of the OOM and the center of the origin of the ZMM were measured in 20 patients. ::: ::: ::: RESULTS ::: The mean age of the initial 121 patients was 50.9 years. In all cases, improvement was seen in soft tissue sagging of the midface and lower face. There was no functional impairment of the OOM. In 20 patients of them, the lateral margin of the OOM and the center of the origin of the ZMM were located at mean distances of 50.6 (range 48-53 mm) and 61.0 mm (range 60-65 mm) from the posterior margin of the tragus. ::: ::: ::: CONCLUSIONS ::: The SMAS flap with the OOM is sufficiently strong enough so that it can maintain the pulling force and also helps to securely fix it. The authors hope that these anatomical findings would be useful when performing it and aid in the understanding of the relationship between the muscles in the malar area. ::: ::: ::: LEVEL OF EVIDENCE IV ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: HICO: A Benchmark for Recognizing Human-Object Interactions in Images

abstract: We introduce a new benchmark "Humans Interacting with Common Objects" (HICO) for recognizing human-object interactions (HOI). We demonstrate the key features of HICO: a diverse set of interactions with common object categories, a list of well-defined, sense-based HOI categories, and an exhaustive labeling of co-occurring interactions with an object category in each image. We perform an in-depth analysis of representative current approaches and show that DNNs enjoy a significant edge. In addition, we show that semantic knowledge can significantly improve HOI recognition, especially for uncommon categories.


title: Compact Global Descriptors for Visual Search

abstract: The first step in an image retrieval pipeline consists of comparing global descriptors from a large database to find a short list of candidate matching images. The more compact the global descriptor, the faster the descriptors can be compared for matching. State-of-the-art global descriptors based on Fisher Vectors are represented with tens of thousands of floating point numbers. While there is significant work on compression of local descriptors, there is relatively little work on compression of high dimensional Fisher Vectors. We study the problem of global descriptor compression in the context of image retrieval, focusing on extremely compact binary representations: 64-1024 bits. Motivated by the remarkable success of deep neural networks in recent literature, we propose a compression scheme based on deeply stacked Restricted Boltzmann Machines (SRBM), which learn lower dimensional non-linear subspaces on which the data lie. We provide a thorough evaluation of several state-of-the-art compression schemes based on PCA, Locality Sensitive Hashing, Product Quantization and greedy bit selection, and show that the proposed compression scheme outperforms all existing schemes.


title: Circle hairs: Clinical, trichoscopic and histopathologic findings

abstract: Circle hairs (CH) represent a body hair growth disorder characterized by asymptomatic presence of hairs with typical circular or spiraliform arrangement, not associated with follicular or inflammatory abnormalities. Although this condition is rarely reported, it is probably underestimated, as a medical consultation for CH only is rare in practice. Trichoscopic and histopathological findings of CH have never been reported and this article will present and discuss six cases along with literature review.


title: Integrating depth and color cues for dense multi-resolution scene mapping using RGB-D cameras

abstract: The mapping of environments is a prerequisite for many navigation and manipulation tasks. We propose a novel method for acquiring 3D maps of indoor scenes from a freely moving RGB-D camera. Our approach integrates color and depth cues seamlessly in a multi-resolution map representation. We consider measurement noise characteristics and exploit dense image neighborhood to rapidly extract maps from RGB-D images. An efficient ICP variant allows maps to be registered in real-time at VGA resolution on a CPU. For simultaneous localization and mapping, we extract key views and optimize the trajectory in a probabilistic framework. Finally, we propose an efficient randomized loop-closure technique that is designed for on-line operation. We benchmark our method on a publicly available RGB-D dataset and compare it with a state-of-the-art approach that uses sparse image features.


A compositional distributional model of meaning


title: Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs

abstract: Many problems in engineering and scientific computing require the solution of a large number of small systems of linear equations. Due to their high processing power, Graphics Processing Units became an attractive target for this class of problems, and routines based on the LU and the QR factorization have been provided by NVIDIA in the cuBLAS library. This work addresses the situation where the systems of equations are symmetric positive definite. The paper describes the implementation and tuning of the kernels for the Cholesky factorization and the forward and backward substitution. Targeted workloads involve the solution of thousands of linear systems of the same size, where the focus is on matrix dimensions from 5 by 5 to 100 by 100. Due to the lack of a cuBLAS Cholesky factorization, execution rates of cuBLAS LU and cuBLAS QR are used for comparison against the proposed Cholesky factorization in this work. Execution rates of forward and backward substitution routines are compared to equivalent cuBLAS routines. Comparisons against optimized multicore implementations are also presented. Superior performance is reached in all cases.


title: Game Taxonomies: A High Level Framework for Game Analysis and Design

abstract: An output apparatus to which a plurality of detachable character pattern generators (font cartridges) can be attached comprises: a first character generator which is provided in the output apparatus main body and includes character pattern information constructed by a storing method other than an unprocessed dot matrix; a second character generator which is detachable from the output apparatus main body and includes character pattern information constructed by a storing method other than the unprocessed dot matrix; a socket to attach the second character generator to the apparatus main body; a discriminating circuit to decide whether the second character generator has keen attached to the socket or not; a controller for preferentially converting the characters included in the second character generator into the character patterns in the unprocessed dot matrix form that the characters included in the first character generator when the second character generator has been set to the socket and for storing the converted character patterns. When the font cartridge is set into the socket, the font of the font cartridge is developed into the font cache memory and if not, the font provided in the apparatus is developed into the font cache memory. Thus, the memory can be effectively used and the output throughput of the apparatus is improved.


title: The Impact of Enhancing Students ’ Social and Emotional Learning : A Meta-Analysis of School-Based Universal Interventions

abstract: This article presents findings from a meta-analysis of 213 school-based, universal social and emotional learning (SEL) programs involving 270,034 kindergarten through high school students. Compared to controls, SEL participants demonstrated significantly improved social and emotional skills, attitudes, behavior, and academic performance that reflected an 11-percentile-point gain in achievement. School teaching staff successfully conducted SEL programs. The use of 4 recommended practices for developing skills and the presence of implementation problems moderated program outcomes. The findings add to the growing empirical evidence regarding the positive impact of SEL programs. Policy makers, educators, and the public can contribute to healthy development of children by supporting the incorporation of evidence-based SEL programming into standard educational practice.


title: CAMP: Content-Agnostic Malware Protection

abstract: --In spite of recent advances, the world wide web remains an important vector for malware installation. Approaches to evaluating potentially malicious code before execution in a browser, such as blacklisting or content-based detection are hindered by an attacker’s ability to easily change hosting domains or mutate malware binaries. On the other hand, whitelistbased approaches are challenged by the large, dynamic, and heterogeneous space of benign binaries that they must track. In practice these approaches continue to provide value for popular binaries at either extreme of maliciousness (e.g., the current large outbreak of malware, the benign binaries shipped with an OS), but bridging the gap between whitelist and blacklist detection for web malware remains a significant challenge. This paper presents CAMP, a content-agnostic malware protection system based on binary reputation that is designed to address these shortcomings. CAMP is built into the browser and determines the reputation of most downloads locally, relying on server-side reputation data only when a local decision cannot be made. This paper gives a detailed overview of CAMP and its architecture and provides an evaluation of the system through a six-month deployment in which 200 million users of Google Chrome requested between eight to ten million reputation requests a day. Our evaluation shows that CAMP exhibits accuracy close to 99% relative to proprietary VM-based dynamic analysis, is able to process requests in less than 130 ms on average, and was able to detect approximately five million intentional malware downloads per month that were not detected by existing solutions.


title: MaltParser: A Data-Driven Parser-Generator for Dependency Parsing

abstract: We introduce MaltParser, a data-driven parser generator for dependency parsing. Given a treebank in dependency format, MaltParser can be used to induce a parser for the language of the treebank. Ma ...


title: Bored mondays and focused afternoons: the rhythm of attention and online activity in the workplace

abstract: While distractions using digital media have received attention in HCI, understanding engagement in workplace activities has been little explored. We logged digital activity and continually probed perspectives of 32 information workers for five days in situ to understand how attentional states change with context. We present a framework of how engagement and challenge in work relate to focus, boredom, and rote work. Overall, we find more focused attention than boredom in the workplace. Focus peaks mid-afternoon while boredom is highest in early afternoon. People are happiest doing rote work and most stressed doing focused work. On Mondays people are most bored but also most focused. Online activities are associated with different attentional states, showing different patterns at beginning and end of day, and before and after a mid-day break. Our study shows how rhythms of attentional states are associated with context and time, even in a dynamic workplace environment.


title: Developing a Computer Science Concept Inventory for Introductory Programming

abstract: A Concept Inventory (CI) is a set of multiple choice questions used to reveal student's misconceptions related to some topic. Each available choice (besides the correct choice) is a distractor that is carefully developed to address a specific misunderstanding, a student wrong thought. In computer science introductory programming courses, the development of CIs is still beginning, with many topics requiring further study and analysis. We identify, through analysis of open-ended exams and instructor interviews, introductory programming course misconceptions related to function parameter use and scope, variables, recursion, iteration, structures, pointers and boolean expressions. We categorize these misconceptions and define high-quality distractors founded in words used by students in their responses to exam questions. We discuss the difficulty of assessing introductory programming misconceptions independent of the syntax of a language and we present a detailed discussion of two pilot CIs related to parameters: an open-ended question (to help identify new misunderstandings) and a multiple choice question with suggested distractors that we identified.


title: Haptic skin stretch on a steering wheel for displaying preview information in autonomous cars

abstract: Lateral skin stretch is a promising technology for haptic display of information between an autonomous or semi-autonomous car and a driver. We present the design of a steering wheel with an embedded lateral skin stretch display and report on the results of tests (N=10) conducted in a driving vehicle in suburban traffic. Results are generally consistent with previous results utilizing skin stretch in stationary applications, but a slightly higher, and particularly a faster rate of stretch application is preferred for accurate detection of direction and approximate magnitude.


title: Reconfigurable Magnetic Resonance-Coupled Wireless Power Transfer System

abstract: This paper presents a method for a reconfigurable magnetic resonance-coupled wireless power transfer (R-MRC-WPT) system in order to achieve higher transmission efficiency under various transmission distance and/or misalignment conditions. Higher efficiency, longer transmission distance, and larger misalignment tolerance can be achieved with the presented R-MRC-WPT system when compared to the conventional four-coil MRC-WPT (C-MRC-WPT) system. The reconfigurability in the R-MRC-WPT system is achieved by adaptively switching between different sizes of drive loops and load loops. All drive loops are in the same plane and all load loops are also in the same plane; this method does not require mechanical movements of the drive loop and load loop and does not result in the system volume increase. Theoretical basis of the method for the R-MRC-WPT system is derived based on a circuit model and an analytical model. Results from a proof-of-concept experimental prototype, with transmitter and receiver coil diameter of 60 cm each, show that the transmission efficiency of the R-MRC-WPT system is higher than the transmission efficiency of the C-MRC-WPT system and the capacitor tuning system for all distances up to 200 cm (∼3.3 times the coil diameter) and for all lateral misalignment values within 60 cm (one coil diameter).


title: Four-dimensional computed tomographic imaging in the wrist: proof of feasibility in a cadaveric model

abstract: OBJECTIVE ::: High-resolution real-time three-dimensional (3D) imaging of the moving wrist may provide novel insights into the pathophysiology of joint instability. The purpose of this work was to assess the feasibility of using retrospectively gated spiral computed tomography (CT) to perform four-dimensional (4D) imaging of the moving wrist joint. ::: ::: ::: MATERIALS AND METHODS ::: A cadaver forearm from below the elbow was mounted on a motion simulator which performed radioulnar deviation of the wrist at 30 cycles per minute. An electronic trigger from the simulator provided the "electrocardiogram" (ECG) signal required for gated reconstructions. Four-dimensional and 3D images were compared by a blinded observer for image quality and presence of artifacts. ::: ::: ::: RESULTS ::: Image quality of 4D images was found to be excellent at the extremes of radial and ulnar deviation (end-motion phases). Some artifacts were seen in mid-motion phases. ::: ::: ::: CONCLUSION ::: 4D CT musculoskeletal imaging is feasible. Four-dimensional CT may allow clinicians to assess functional (dynamic) instabilities of the wrist joint.


title: Legalbot: A Deep Learning-Based Conversational Agent in the Legal Domain

abstract: This paper presents a deep learning based dialogue system which has been trained to answer user queries posed as questions during a conversation. The proposed system, though generative, takes advantage of domain specific knowledge for generating valid answers. The evaluation analysis shows that the proposed system obtained a promising result.


title: Current theoretical models of generalized anxiety disorder (GAD): Conceptual review and treatment implications

abstract: Theoretical conceptualizations of generalized anxiety disorder (GAD) continue to undergo scrutiny and refinement. The current paper critiques five contemporary models of GAD: the Avoidance Model of Worry and GAD [Borkovec, T. D. (1994). The nature, functions, and origins of worry. In: G. Davey & F. Tallis (Eds.), Worrying: perspectives on theory assessment and treatment (pp. 5–33). Sussex, England: Wiley & Sons; Borkovec, T. D., Alcaine, O. M., & Behar, E. (2004). Avoidance theory of worry and generalized


title: Arbitrary Analog/RF Spatial Filtering for Digital MIMO Receiver Arrays

abstract: Traditional digital multiple-input multiple-output (MIMO) receivers that feature element-level digitization face high instantaneous dynamic range challenges in the analog/RF domain due to the absence of analog/RF spatial filtering. Existing analog/RF spatial notch filtering techniques are limited in their noise, linearity, and spatial filtering bandwidth performance. More importantly, only single spatial notches have been demonstrated, providing insufficient filtering in practical scenarios. We propose a frequency-translational arbitrary spatial filtering technique that features not only arbitrary spatial filtering response generation at baseband for the protection of the following analog-to-digital converters, but modulated baseband input impedance that can be translated by passive mixers to achieve arbitrary spatial filtering at RF as well. This technique allows the synthesis and independent steering of an arbitrary number of spatial notches, and the independent adjustment of notch depths. Current-mode operation leads to superior linearity performance and ultra-wideband rejection. A four-element 65-nm CMOS 0.1–3.1 GHz prototype MIMO receiver array shows arbitrary spatial response formation, more than 50-dB spatial rejection across all measured directions, and an ultra-wide 320-MHz 20-dB rejection bandwidth for a single-notch setting at 500-MHz local oscillator (LO) frequency. Formation of a single spatial notch only moderately degrades the equivalent single-element double-sideband noise figure from 2.1–3.7 dB to 3.4–5.8 dB. In the notch direction, +34 dBV in-band output-referred IP3 is measured, an improvement of 33 dB compared with outside-notch directions. A wireless demonstration shows the receiver array demodulating a weak spatial signal in the presence of two strong in-band spatial signals, verifying the arbitrary spatial filtering functionality.


title: Rethinking Education in the Age of Technology

abstract: All around us people are learning with the aid of new technologies: children are playing complex video games, workers are taking online courses to get an advanced degree, students are taking courses at commercial learning centers to prepare for tests, adults are consulting Wikipedia, etc. New technologies create learning opportunities that challenge traditional schools and colleges. These new learning niches enable people of all ages to pursue learning on their own terms. People around the world are taking their education out of school into homes, libraries, Internet cafes, and workplaces, where they can decide what they want to learn, when they want to learn, and how they want to learn.


title: Ch2R: A Chinese Chatter Robot for Online Shopping Guide

abstract: In this paper we present a conversational dialogue system, Ch2R (Chinese Chatter Robot) for online shopping guide, which allows users to inquire about information of mobile phone in Chinese. The purpose of this paper is to describe our development effort in terms of the underlying human language technologies (HLTs) as well as other system issues. We focus on a mixed-initiative conversation mechanism for interactive shopping guide combining initiative guiding and question understanding. We also present some evaluation on the system in mobile phone shopping guide domain. Evaluation results demonstrate the efficiency of our approach.


title: YASENN: Explaining Neural Networks via Partitioning Activation Sequences

abstract: We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propose a model-specific interpretation method, called YASENN. Our method inherits many advantages of model-agnostic distillation, such as an ability to focus on the particular input region and to express an explanation in terms of features different from those observed by a neural network. Moreover, examination of distillation error makes the method applicable to the problems with low tolerance to interpretation mistakes. Technically, YASENN distills the network with an ensemble of layer-wise gradient boosting decision trees and encodes the sequences of neuron activations with leaf indices. The finite number of unique codes induces a partitioning of the input space. Each partition may be described in a variety of ways, including examination of an interpretable model (e.g. a logistic regression or a decision tree) trained to discriminate between objects of those partitions. Our experiments provide an intuition behind the method and demonstrate revealed artifacts in neural network decision making.


title: StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning

abstract: Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then, a parameter sharing multi-agent gradient-descent Sarsa( $\lambda$ ) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action–value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small-scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large-scale scenarios, the curriculum transfer learning method is used to progressively train a group of units, and it shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.


title: An Automatic Approach for Accurate Edge Detection of Concrete Crack Utilizing 2D Geometric Features of Crack

abstract: The automatic edge detection of cracks on concrete structures plays an important role in the damage assessment process for cracked structures. In this paper, we proposed an automatic method for accurate edge detection of concrete cracks from real 2D images of concrete surfaces containing noisy and unintended objects. In the 2D image of a damaged concrete surface, cracks are usually observed as tree-like topology dark objects of which the branches are line-like and have local symmetry across their center axes. We utilize these two geometric properties of cracks to detect crack edges and discriminate them with edges of other unintended objects. The novel automatic crack edge detection is composed of two sequential stages. In the first stage, cracks are enhanced by a novel phase symmetry-based crack enhancement filter (PSCEF) based on their symmetric and line-like properties while non-crack objects are removed. Estimated crack center-lines are then obtained by thresholding the filtered images and applying morphological thinning algorithm to the binary image. In the second stage, the estimated center lines of the detected cracks are fitted by cubic splines and the pixel intensity profiles in the directions perpendicular to the splines are used to determine the edge points. The edge points are linked together to form the desired continuous crack edges. Various experiments of real concrete crack images are used to demonstrate the excellent performance of the proposed method.


title: Percutaneous osteosynthesis in the pelvis in cancer patients

abstract: PURPOSE ::: Screw fixation (osteosynthesis) can be performed percutaneously by interventional radiologists. We report our experience in cancer patients. ::: ::: ::: MATERIAL/METHODS ::: We retrospectively reviewed all cases of percutaneous osteosynthesis (PO) of the pelvic ring and proximal femur performed in our hospital. PO were performed for fracture palliation or for osteolytic metastases consolidation. Screws were inserted under CT- or cone-beam CT- guidance and general anaesthesia. Patients were followed-up with pelvic-CT and medical consultation at 1 month, then every 3 months. For fractures, the goal was pain palliation and for osteolytic metastases, pathologic fracture prevention. ::: ::: ::: RESULTS ::: Between February 2010 and August 2014, 64 cancer patients were treated with PO. Twenty-one patients had PO alone for 33 painful fractures (13 bone-insufficiency, 20 pathologic fractures). The pain was significantly improved at 1 month (VAS score = 20/100 vs. 80/100). In addition, 43 cancer patients were preventively consolidated using PO plus cementoplasty for 45 impending pathologic fractures (10 iliac crests, 35 proximal femurs). For the iliac crests, no fracture occurred (median-FU = 75 days). For the proximal femurs, 2 pathological fractures occurred (fracture rate = 5.7 %, median-FU = 205 days). ::: ::: ::: CONCLUSION ::: PO is a new tool in the therapeutic arsenal of interventional radiologists for bone pain management. ::: ::: ::: KEY POINTS ::: • Screw fixation (osteosynthesis) can be performed percutaneously by interventional radiologists. • CT- or CBCT-guidance results in high technical success rates for screw placement. • This minimally invasive technique avoids extensive surgical exposure in bone cancer patients. • Osteosynthesis provides pain relief for bone-insufficiency fractures and for pathologic fractures. • Osteosynthesis plus cementoplasty provide prophylactic consolidation of impending pathological fractures.


title: Photonic crystal directional couplers formed by InAlGaAs nano-rods.

abstract: This study demonstrates the use of photonic crystal directional couplers to separate light of wavelengths 1.31 and 1.55µm. The photonic crystal structure consists of InAlGaAs nano-rods arranged in square lattice. The coupling length of the light in the directional coupler at a wavelength of 1.31µm was designed to be four times greater than that at 1.55µm. This behavior helps in designing devices to split the two wavelengths. The devices are fabricated by e-beam lithography and conventional photolithography. The measurement results confirm that 1.31µm/1.55µm directional couplers can be realized in PC structures formed by nano-rods.


title: A Hybrid Malicious Code Detection Method based on Deep Learning

abstract: In this paper, we propose a hybrid malicious code detection scheme based on AutoEncoder and DBN (Deep Belief Networks). Firstly, we use the AutoEncoder deep learning method to reduce the dimensionality of data. This could convert complicated high-dimensional data into low dimensional codes with the nonlinear mapping, thereby reducing the dimensionality of data, extracting the main features of the data; then using DBN learning method to detect malicious code. DBN is composed of multilayer Restricted Boltzmann Machines (RBM, Restricted Boltzmann Machine) and a layer of BP neural network. Based on unsupervised training of every layer of RBM, we make the output vector of the last layer of RBM as the input vectors of BP neural network, then conduct supervised training to the BP neural network, finally achieve the optimal hybrid model by fine-tuning the entire network. After inputting testing samples into the hybrid model, the experimental results show that the detection accuracy getting by the hybrid detection method proposed in this paper is higher than that of single DBN. The proposed method reduces the time complexity and has better detection performance.


title: A programming language for implementing computational models

abstract: The Scientific Computation Language (SCL) was designed mainly for developing computational models in education and research. This paper presents the justification for such a language, its relevant features, and a case study of a computational model implemented with the SCL. Development of the SCL language is part of the OOPsim project, which has had partial NSF support (CPATH). One of the goals of this project is to develop tools and approaches for designing and implementing computational models, emphasizing multi-disciplinary teams in the development process. A computational model is a computer implementation of the solution to a (scientific) problem for which a mathematical representation has been formulated. Developing a computational model consists of applying Computer Science concepts, principles and methods. The language syntax is defined at a higher level of abstraction than C, and includes language statements for improving program readability, debugging, maintenance, and correctness. The language design was influenced by Ada, Pascal, Eiffel, Java, C, and C++. The keywords have been added to maintain full compatibility with C. The SCL language translator is an executable program that is implemented as a one-pass language processor that generates C source code. The generated code can be integrated conveniently with any C and/or C++ library, on Linux and Windows (and MacOS). The semantics of SCL is informally defined to be the same C semantics.


title: Automated partitioning design in parallel database systems

abstract: In recent years, Massively Parallel Processors (MPPs) have gained ground enabling vast amounts of data processing. In such environments, data is partitioned across multiple compute nodes, which results in dramatic performance improvements during parallel query execution. To evaluate certain relational operators in a query correctly, data sometimes needs to be re-partitioned (i.e., moved) across compute nodes. Since data movement operations are much more expensive than relational operations, it is crucial to design a suitable data partitioning strategy that minimizes the cost of such expensive data transfers. A good partitioning strategy strongly depends on how the parallel system would be used. In this paper we present a partitioning advisor that recommends the best partitioning design for an expected workload. Our tool recommends which tables should be replicated (i.e., copied into every compute node) and which ones should be distributed according to specific column(s) so that the cost of evaluating similar workloads is minimized. In contrast to previous work, our techniques are deeply integrated with the underlying parallel query optimizer, which results in more accurate recommendations in a shorter amount of time. Our experimental evaluation using a real MPP system, Microsoft SQL Server 2008 Parallel Data Warehouse, with both real and synthetic workloads shows the effectiveness of the proposed techniques and the importance of deep integration of the partitioning advisor with the underlying query optimizer.


title: Personality and Mate Preferences: Five Factors In Mate Selection and Marital Satisfaction

abstract: ABSTRACT Although personality characteristics figure prominently in what people want in a mate, little is known about precisely which personality characteristics are most important, whether men and women differ in their personality preferences, whether individual women or men differ in what they want, and whether individuals actually get what they want. To explore these issues, two parallel studies were conducted, one using a sample of dating couples (N= 118) and one using a sample of married couples (N= 216). The five-factor model, operationalized in adjectival form, was used to assess personality characteristics via three data sources—self-report, partner report, and independent interviewer reports. Participants evaluated on a parallel 40-item instrument their preferences for the ideal personality characteristics of their mates. Results were consistent across both studies. Women expressed a greater preference than men for a wide array of socially desirable personality traits. Individuals differed in which characteristics they desired, preferring mates who were similar to themselves and actually obtaining mates who embodied what they desired. Finally, the personality characteristics of one's partner significantly predicted marital and sexual dissatisfaction, most notably when the partner was lower on Agreeableness, Emotional Stability, and Intellect-Openness than desired.


title: StarSync: An extendable standard-cell mesochronous synchronizer

abstract: StarSync, a mesochronous synchronizer, enables low latency and full throughput crossing of clock domain boundaries having same frequency but different phases. Full back pressure is supported, where the receiver can start and stop accepting words without any data loss. Variable depth buffering is provided, supporting a wide range of short and long range communications and accommodating multi-cycle wire delays. Burst data can also be accommodated thanks to buffering. Dynamic phase shifting due to varying voltage and temperature are mitigated by increasing the separation between write and read pointers. The synchronizer is exposed to metastability risk only during reset. It is suitable for implementation using standard cell design and requires neither delay lines nor other full custom circuits. It is shown that a minimum of four buffer stages are required, to mitigate skew in reset synchronization, in contrast with previous proposals for three stages.


title: An FPGA Realization of a Deep Convolutional Neural Network Using a Threshold Neuron Pruning

abstract: For a pre-trained deep convolutional neural network (CNN) for an embedded system, a high-speed and a low power consumption are required. In the former of the CNN, it consists of convolutional layers, while in the latter, it consists of fully connection layers. In the convolutional layer, the multiply accumulation operation is a bottleneck, while the fully connection layer, the memory access is a bottleneck. In this paper, we propose a neuron pruning technique which eliminates almost part of the weight memory. In that case, the weight memory is realized by an on-chip memory on the FPGA. Thus, it achieves a high speed memory access. In this paper, we propose a sequential-input parallel-output fully connection layer circuit. The experimental results showed that, by the neuron pruning, as for the fully connected layer on the VGG-11 CNN, the number of neurons was reduced by 89.3% with keeping the 99% accuracy. We implemented the fully connected layers on the Digilent Inc. NetFPGA-1G-CML board. Comparison with the CPU (ARM Cortex A15 processor) and the GPU (Jetson TK1 Kepler), as for a delay time, the FPGA was 219.0 times faster than the CPU and 12.5 times faster than the GPU. Also, a performance per power efficiency was 125.28 times better than CPU and 17.88 times better than GPU.


title: Modeling network intrusion detection alerts for correlation

abstract: Signature-based network intrusion-detection systems (NIDSs) often report a massive number of simple alerts of low-level security-related events. Many of these alerts are logically involved in a single multi-stage intrusion incident and a security officer often wants to analyze the complete incident instead of each individual simple alert. This paper proposes a well-structured model that abstracts the logical relation between the alerts in order to support automatic correlation of those alerts involved in the same intrusion. The basic building block of the model is a logical formula called a capability. We use capability to abstract consistently and precisely all levels of accesses obtained by the attacker in each step of a multistage intrusion. We then derive inference rules to define logical relations between different capabilities. Based on the model and the inference rules, we have developed several novel alert correlation algorithms and implemented a prototype alert correlator. The experimental results of the correlator using several intrusion datasets demonstrate that the approach is effective in both alert fusion and alert correlation and has the ability to correlate alerts of complex multistage intrusions. In several instances, the alert correlator successfully correlated more than two thousand Snort alerts involved in massive scanning incidents. It also helped us find two multistage intrusions that were missed in auditing by the security officers.


title: Real-time tracking and pose estimation for industrial objects using geometric features

abstract: This paper presents a fast tracking algorithm capable of estimating the complete pose (6DOF) of an industrial object by using its circular-shape features. Since the algorithm is part of a real-time visual servoing system designed for assembly of automotive parts on-the-fly, the main constraints in the design of the algorithm were: speed and accuracy. That is: close to frame-rate performance, and error in pose estimation smaller than a few millimeters. The algorithm proposed uses only three model features, and yet it is very accurate and robust. For that reason both constraints were satisfied: the algorithm runs at 60 fps (30 fps for each stereo image) on a PIII-800 MHz computer, and the pose of the object is calculated within an uncertainty of 2.4 mm in translation and 1.5 degree in rotation.


title: Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications

abstract: The use of color in QR codes brings extra data capacity, but also inflicts tremendous challenges on the decoding process due to chromatic distortion-cross-channel color interference and illumination variation. Particularly, we further discover a new type of chromatic distortion in high-density color QR codes-cross-module color interference-caused by the high density which also makes the geometric distortion correction more challenging. To address these problems, we propose two approaches, LSVM-CMI and QDA-CMI, which jointly model these different types of chromatic distortion. Extended from SVM and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular objective function and learn a color classifier. Furthermore, a robust geometric transformation method and several pipeline refinements are proposed to boost the decoding performance for mobile applications. We put forth and implement a framework for high-capacity color QR codes equipped with our methods, called HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR code samples. The comparison with the baseline method [2] on CUHK-CQRC shows that HiQ at least outperforms [2] by 188% in decoding success rate and 60% in bit error rate. Our implementation of HiQ in iOS and Android also demonstrates the effectiveness of our framework in real-world applications.