title: Handwriting difficulties in primary school children: a search for underlying mechanisms.

text:
OBJECTIVE
This study investigated the contribution of perceptual-motor dysfunction and cognitive planning problems to the quality or speed of handwriting in children with handwriting problems (HWP).

METHOD
Twenty-nine children with HWP and 20 classroom peers attending regular schools (grade 2 and grade 3) were tested with regard to visual perception, visual-motor integration, fine motor coordination, and cognitive planning abilities.

RESULTS
The HWP group scored significantly lower on visual perception, visual-motor integration, fine motor coordination, and cognitive planning in comparison with classroom controls. Regression analyses showed that visual-motor integration was the only significant predictor for quality of handwriting in the HWP group, whereas fine motor coordination (i.e., unimanual dexterity) was the only significant predictor of quality of handwriting in the control group.

CONCLUSIONS
Results suggest that two different mechanisms underlie the quality of handwriting in children with and without handwriting problems. Poor quality of handwriting of children with HWP seems particularly related to a deficiency in visual-motor integration.


title: INFORMATION TECHNOLOGY AND ORGANIZATIONAL CHANGE : CAUSAL STRUCTURE IN THEORY AND

text: JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. This article concerns theories about why and how information technology affects organizational life. Good theory guides research, which, when applied, increases the likelihood that information technology will be employed with desirable consequences for users, organizations, and other interested parties. But what is a good theory? Theories are often evaluated in terms of their content-the specific concepts used and the human values served. This article examines theories in terms of their structures-theorists' assumptions about the nature and direction of causal influence. Three dimensions of causal structure are considered-causal agency, logical structure, and level of analysis. Causal agency refers to beliefs about the nature of causality: whether external forces cause change, whether people act purposefully to accomplish intended objectives, or whether changes emerge unpredictably from the interaction of people and events. Logical structure refers to the temporal aspect of theory-static versus dynamic-and to the logical relationships between the "causes" and the outcomes. Level of analysis refers to the entities about which the theory poses concepts and relationships-individuals, groups, organizations, and society. While there are many possible structures for good theory about the role of information technology in organizational change, only a few of these structures can be seen in current theorizing. Increased awareness of the options, open discussion of their advantages and disadvantages , and explicit characterization of future theoretical statements in terms of the dimensions and categories discussed here should, we believe, promote the development of better theory.


title: Mechanical design of a gravity-balancing wearable exoskeleton for the motion enhancement of human upper limb

text: Powered exoskeletons can provide motion enhancement for both healthy and physically challenged people. Upper limb exoskeletons are required to have multiple degrees-of-freedom and can still produce sufficient force to augment the upper limb motion. The design using serial mechanisms usually results in a complicated and bulky exoskeleton that prevents itself from being wearable. This paper presents a new exoskeleton design aimed to achieve compactness and wearability. We consider a shoulder exoskeleton that consists of a parallel spherical mechanism with two slider crank mechanisms. The actuators can be placed on a stationary platform and attached closely to human body. Thus a better inertia property can be obtained while maintaining lightweight. Through the use of a gravity-balancing mechanism, the required actuator power becomes smaller and with better efficiency. A static model is developed to analyze and optimize the exoskeleton. Through illustrations of a prototype, the exoskeleton is shown to be wearable and can provide adequate motion enhancement of a human's upper limb.


title: A Virtual Feedback Assistance System for Remote Operation of a 3DOF Micromanipulator in Micro-Nanorobotic Manipulation

text: Manipulation in micro or nanoscale with robotic manipulators under observation of electron microscopes is a widely used strategy for fabrication of nanodevices and nanoscale material property characterization. These types of manipulation systems can handle the relatively larger scale of objects. However, the complexity of manipulation increases highly for 3D manipulation. Since the manipulation system consists of multiple components including manipulator, microscope, and also some end-effector tools, a proper offline visualization of the system is necessary for operation. Therefore, we propose a web-based virtual interface between the user and the actual manipulator operated under digital microscope initially. It gives the operator 3D positional feedback from the virtual model by mapping data read during remote operation. The same interface is used for remote operation of the manipulator within the SEM chamber and a manipulation task is performed.


title: Dependence on computer games by adolescents.

text: As computer game playing is a popular activity among adolescents, a questionnaire study was undertaken with 387 adolescents (12-16 years of age) to establish their "dependence" using a scale adapted from the DSM-III-R criteria for pathological gambling. Analysis indicated that one in five adolescents were currently "dependent" upon computer games. Boys played significantly more regularly than girls and were more likely to be classified as "dependent." The earlier children began playing computer games it appeared the more likely they were to be playing at "dependent" levels. These and other results are discussed in relation to research on other gaming dependencies.


title: Ambiguities in Spacebornene Synthetic Aperture Radar Systems

text: Several aspects of range and azimuth (time delay and Doppler) ambiguities in spaceborne synthetic aperture radars (SARs) are examined. An accurate method to evaluate the ratio of the intensities of the ambiguities to that of the signal is described. This method has been applied to the nominal SAR system on SEASAT and the variations of this ratio as a function of orbital latitude and attitude control error are discussed. It is also shown that the detailed range migration-azimuth phase history of an ambiguity is different from that of a signal. The images of ambiguities are, therefore, dispersed. Several examples of such dispersed images observed by the SEASAT SAR are presented. These dispersions are eliminated when the processing parameters are adjusted appropriately. Finally, a method is described which uses a set of multiple pulse repetition frequencies to determine the absolute values of the Doppler centroid frequencies for SARs with high carrier frequencies and relatively poor attitude measurements.


title: Ship Surveillance With TerraSAR-X

text: Ship detection is an important application of global monitoring of environment and security. In order to overcome the limitations by other systems, surveillance with satellite synthetic aperture radar (SAR) is used because of its possibility to provide ship detection at high resolution over wide swaths and in all weather conditions. A new X-band radar onboard the TerraSAR-X (TS-X) satellite gives access to spatial resolution as fine as 1 m. In this paper, first results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (SatAIS) is presented. The AIS system is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS, as a space-based system, allows almost global coverage for monitoring of ships since not all ships operate their AIS and smaller ships are not equipped with AIS. The system is considered to be of cooperative nature. In this paper, the quality of TS-X images with respect to ship detection is evaluated, and a first assessment of its performance for ship detection is given. The velocity of a moving ship is estimated using complex TS-X data. As test cases, images were acquired over the North Sea, Baltic Sea, Atlantic Ocean, and Pacific Ocean in Stripmap mode with a resolution of 3 m at a coverage of 30 km 100 km. Simultaneous information on ship positions was available from TS-X and terrestrial as well as SatAIS. First results on the simultaneous superposition of SatAIS and high-resolution radar images are presented.


title: Filtering of Azimuth Ambiguity in Stripmap Synthetic Aperture Radar Images

text: Due to the specific characteristics of the SAR system, peculiar artifacts can appear on SAR images. In particular, finite pulse repetition frequency (PRF) and nonideal antenna pattern give rise to azimuth ambiguity, with the possible presence of “ghosts” on the image. They are due to the replica of strong targets located outside of the antenna main beam, superposed onto low intensity areas of the imaged scene. In this paper, we propose a method for the filtering of azimuth ambiguities on stripmap SAR images, that we name “asymmetric mapping and selective filtering” (AM&SF) method. Our framework is based on the theory of selective filtering and on a two-step procedure. In the first step, two asymmetric filters are used to suppress ambiguities due to each sidelobe of the antenna pattern, and the ratios between the original and filtered images are used to produce two maps of the ambiguity-affected areas (one for each sidelobe). In the second step, these maps are used to produce a final image in which only the areas affected by the ambiguities are replaced by their filtered (via the proper of the two filters) versions. The proposed method can be employed in situations in which similar approaches fail, and it has a smaller computational burden. The framework is positively tested on TerraSAR-X and COSMO/SkyMed SAR images of different marine scenes.


title: Adaptive removal of azimuth ambiguities in SAR images

text: We introduce an innovative algorithm that is capable of suppression of strong azimuth ambiguities in single-look complex (SLC) synthetic aperture radar images, to be used both for detected products and for interferometric surveys. The algorithm exploits a band-pass filter to select that portion of the azimuth spectrum that is less influenced by aliasing, the one that corresponds to the nulls in the replicated azimuth antenna pattern. The selectivity of this filter adapts locally depending on the estimated ambiguity intensity: the filter is more selective in the presence of strong ambiguities and becomes all-pass in absence of ambiguities. The design of such filter frames in the Wiener approach with two different normalization options, depending on the use of the SLC image, either for getting multilook averaged detected products or for interferometric applications. Preliminary results achieved by processing ENVISAT Advanced Synthetic Aperture Radar sensor data are provided.


title: Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

text: Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network’s reconstruction error obtainable for a journal entry and regularized by the entry’s individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries, show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.


title: Predicting the fix time of bugs

text: Two important questions concerning the coordination of development effort are which bugs to fix first and how long it takes to fix them. In this paper we investigate empirically the relationships between bug report attributes and the time to fix. The objective is to compute prediction models that can be used to recommend whether a new bug should and will be fixed fast or will take more time for resolution. We examine in detail if attributes of a bug report can be used to build such a recommender system. We use decision tree analysis to compute and 10-fold cross validation to test prediction models. We explore prediction models in a series of empirical studies with bug report data of six systems of the three open source projects Eclipse, Mozilla, and Gnome. Results show that our models perform significantly better than random classification. For example, fast fixed Eclipse Platform bugs were classified correctly with a precision of 0.654 and a recall of 0.692. We also show that the inclusion of postsubmission bug report data of up to one month can further improve prediction models.


title: Analysis on DNA based Cryptography to Secure Data Transmission

text: The biological research in the field of information technology paves the exploitation of storing capabilities, parallelism and also in conservative cryptography which enhances the security features for data transmission. DNA is the gene information which encodes information of all living beings. Though the DNA computing has its application in the field of huge information storage, massive parallel processing, low energy consumption which have been proposed and proved by the researchers and soon the molecular computer can replace the existing silicon computer and it exploits the world smallest computer. The combination of DNA molecules can be interpreted as a result to give a solution to a specific problem. The DNA strands can be replicated 500 times per second with greater accuracy. It can also be used in the field of cryptography based upon the vast parallelism which is used to break the existing cryptographic approach. This paper analysis an existing approach to the DNA computing method and DNA based cryptographic approach which provides the clear idea and limitations of existing research works.


title: Indoor climate, psychosocial work environment and symptoms in open-plan offices.

text: To study the indoor climate, the psychosocial work environment and occupants' symptoms in offices a cross-sectional questionnaire survey was made in 11 naturally and 11 mechanically ventilated office buildings. Nine of the buildings had mainly cellular offices; five of the buildings had mainly open-plan offices, whereas eight buildings had a mixture of cellular, multi-person and open-plan offices. A total of 2301 occupants, corresponding to a response rate of 72%, completed a retrospective questionnaire. The questionnaire comprised questions concerning environmental perceptions, mucous membrane irritation, skin irritation, central nervous system (CNS) symptoms and psychosocial factors. Occupants in open-plan offices are more likely to perceive thermal discomfort, poor air quality and noise and they more frequently complain about CNS and mucous membrane symptoms than occupants in multi-person and cellular offices. The association between psychosocial factors and office size was weak. Open-plan offices may not be suited for all job types. PRACTICAL IMPLICATION: Open-plan offices may be a risk factor for adverse environmental perceptions and symptoms.


title: Natural Language Interface to Database-An Introduction

text: Information plays an important role in our lives .it is required numerous applications. One of the major sources of information is databases. Databases and database technology are growing rapidly and are having major impact on the growing use of computers. Computers are used in accessing of information from database. Almost all IT applications are storing, retrieving organizing, accessing analyzing, and information from databases. using the internet. Retrieving information from database requires knowledge of database languages like Structured Query Language (SQL) However, not everybody is able to write SQL queries as they may not be aware of the syntax of SQL structure of the database,. This has lead to the developing such a system where non-expert users compose their questions in their natural language and obtain the results in the form of database. So instead of working with the SQL one can query relational databases in their natural language. This new idea is provoked to develop new type of processing system called natural language interface to database Natural language interface to database enhances the users performance and is a flexible approach querying in database. This paper is an introduction to natural language interfaces to databases (NLIDBS). Covers a brief overview of the NLIDB its components its advantages disadvantages, approaches and techniques .


title: Active Learning for Convolutional Neural Networks: A Core-Set Approach

text: Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (i.e. active learning). Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, i.e. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.


title: Brain mechanisms of pain affect and pain modulation

text: Recent animal studies reveal ascending nociceptive and descending modulatory pathways that may contribute to the affective-motivational aspects of pain and play a critical role in the modulation of pain. In humans, a reliable pattern of cerebral activity occurs during the subjective experience of pain. Activity within the anterior cingulate cortex and possibly in other classical limbic structures, appears to be closely related to the subjective experience of pain unpleasantness and may reflect the regulation of endogenous mechanisms of pain modulation.


title: Crime Data Analysis and Prediction of Perpetrator Identity Using Machine Learning Approach

text: Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. The aim of this model is to increase the efficiency of crime investigation systems. This model detects crime patterns from inferences collected from the crime scene and predicts the description of the perpetrator who is likely suspected to commit the crime. This work has two major aspects: Crime Analysis and Prediction of perpetrator identity. The Crime Analysis phase identifies the number of unsolved crimes, and analyses the influence of various factors like year, month, and weapon on the unsolved crimes. The prediction phase estimates the description of the perpetrators like, their age, sex and relationship with the victim. These predictions are done based on the evidences collected from the crime scene. The system predicts the description of the perpetrator using algorithms like, Multilinear Regression, K- Neighbors Classifier and Neural Networks. It was trained and tested using San Francisco Homicide dataset (1981–2014) and implemented using python.


title: Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

text: Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-theart approaches focus on selecting onebest sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.


title: MATHEMATICAL MODELING AND SIMULATION OF AN ELECTRIC VEHICLE

text: As electric vehicles become promising alternatives for sustainable and cleaner energy emissions in transportation, the modeling and simulation of electric vehicles has attracted increasing attention from researchers. This paper presents a simulation model of a full electric vehicle on the Matlab-Simulink platform to examine power flow during motoring and regeneration. The drive train components consist of a motor, a battery, a motor controller and a battery controller; modeled according to their mathematical equations. All simulation results are plotted and discussed. The torque and speed conditions during motoring and regeneration were used to determine the energy flow, and performance of the drive. This study forms the foundation for further research and development.


title: The child sexual abuse accommodation syndrome.

text: Child victims of sexual abuse face secondary trauma in the crisis of discovery. Their attempts to reconcile their private experiences with the realities of the outer world are assaulted by the disbelief, blame and rejection they experience from adults. The normal coping behavior of the child contradicts the entrenched beliefs and expectations typically held by adults, stigmatizing the child with charges of lying, manipulating or imagining from parents, courts and clinicians. Such abandonment by the very adults most crucial to the child's protection and recovery drives the child deeper into self-blame, self-hate, alienation and revictimization. In contrast, the advocacy of an empathic clinician within a supportive treatment network can provide vital credibility and endorsement for the child. Evaluation of the responses of normal children to sexual assault provides clear evidence that societal definitions of "normal" victim behavior are inappropriate and procrustean, serving adults as mythic insulators against the child's pain. Within this climate of prejudice, the sequential survival options available to the victim further alienate the child from any hope of outside credibility or acceptance. Ironically, the child's inevitable choice of the "wrong" options reinforces and perpetuates the prejudicial myths. The most typical reactions of children are classified in this paper as the child sexual abuse accommodation syndrome. The syndrome is composed of five categories, of which two define basic childhood vulnerability and three are sequentially contingent on sexual assault: (1) secrecy, (2) helplessness, (3) entrapment and accommodation, (4) delayed, unconvincing disclosure, and (5) retraction. The accommodation syndrome is proposed as a simple and logical model for use by clinicians to improve understanding and acceptance of the child's position in the complex and controversial dynamics of sexual victimization. Application of the syndrome tends to challenge entrenched myths and prejudice, providing credibility and advocacy for the child within the home, the courts, and throughout the treatment process. The paper also provides discussion of the child's coping strategies as analogs for subsequent behavioral and psychological problems, including implications for specific modalities of treatment.


title: All-hex meshing using closed-form induced polycube

text: The polycube-based hexahedralization methods are robust to generate all-hex meshes without internal singularities. They avoid the difficulty to control the global singularity structure for a valid hexahedralization in frame-field based methods. To thoroughly utilize this advantage, we propose to use a frame field without internal singularities to guide the polycube construction. Theoretically, our method extends the vector fields associated with the polycube from exact forms to closed forms, which are curl free everywhere but may be not globally integrable. The closed forms give additional degrees of freedom to deal with the topological structure of high-genus models, and also provide better initial axis alignment for subsequent polycube generation. We demonstrate the advantages of our method on various models, ranging from genus-zero models to high-genus ones, and from single-boundary models to multiple-boundary ones.


title: A Wideband Magnetoelectric Dipole Antenna With Microstrip Line Aperture-Coupled Excitation

text: In this communication, a novel wideband magnetoelectric (ME) dipole antenna is proposed and investigated. A microstrip line aperture-coupled feeding structure is used to excite the antenna. This simple feeding structure can significantly enhance the impedance bandwidth. An antenna prototype is fabricated and exemplified to validate the proposed concept. Measured results show that the proposed ME dipole antenna with microstrip line aperture-coupled excitation achieves an impedance bandwidth of 85% for VSWR < 2 from 2.5 to 6.2 GHz. Further, a measured maximum boresight gain up to 8 dBi is obtained and a 1 dB gain bandwidth is about 67% from 2.5 to 5 GHz. The antenna radiation patterns perform well over the 1 dB gain bandwidth range. A low cross-polarization level is obtained over the operating band.


title: A Spatio-temporal Role-Based Access Control Model

text: With the growing advancement of pervasive computing technologies, we are moving towards an era where spatio-temporal information will be necessary for access control. The use of such information can be used for enhancing the security of an application, and it can also be exploited to launch attacks. For critical applications, a formal model for spatio-temporal-based access control is needed that increases the security of the application and ensures that the location information cannot be exploited to cause harm. In this paper, we propose a spatio-temporal access control model, based on the Role-Based Access Control (RBAC) model, that is suitable for pervasive computing applications. We show the association of each component of RBAC with spatio-temporal information. We formalize the model by enumerating the constraints. This model can be used for applications where spatial and temporal information of a subject and an object must be taken into account before granting or denying access.


title: Functional maps: a flexible representation of maps between shapes

text: We present a novel representation of maps between pairs of shapes that allows for efficient inference and manipulation. Key to our approach is a generalization of the notion of map that puts in correspondence real-valued functions rather than points on the shapes. By choosing a multi-scale basis for the function space on each shape, such as the eigenfunctions of its Laplace-Beltrami operator, we obtain a representation of a map that is very compact, yet fully suitable for global inference. Perhaps more remarkably, most natural constraints on a map, such as descriptor preservation, landmark correspondences, part preservation and operator commutativity become linear in this formulation. Moreover, the representation naturally supports certain algebraic operations such as map sum, difference and composition, and enables a number of applications, such as function or annotation transfer without establishing point-to-point correspondences. We exploit these properties to devise an efficient shape matching method, at the core of which is a single linear solve. The new method achieves state-of-the-art results on an isometric shape matching benchmark. We also show how this representation can be used to improve the quality of maps produced by existing shape matching methods, and illustrate its usefulness in segmentation transfer and joint analysis of shape collections.


title: Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

text: Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.


title: Numba: a LLVM-based Python JIT compiler

text: Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].


title: Modelling Railway Interlocking Tables Using Coloured Petri Nets

text: Interlocking tables are the functional specification defining the routes, on which the passage of the train is allowed. Associated with the route, the states and actions of all related signalling equipment are also specified. This paper formally models the interlocking tables using Coloured Petri Nets (CPN). The CPN model comprises two parts: Signaling Layout and Interlocking Control. The Signaling Layout part is used to simulate the passage of the train. It stores geographic information of the signalling layout in tokens. The Interlocking Control part models actions of the controller according to the functions specified in the interlocking tables. The arc inscriptions in the model represent the content of the interlocking tables. Following our modelling approach we can reuse the same CPN net structure to model any new or modified interlocking system regardless of its size. Experimental results are presented to provide increased confidence in the model correctness.


title: Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs

text: Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database. Article Type Communicated by Submitted Revised Regular Paper U. Brandes September 2005 January 2007 D. Conte et al., Maximum Common Subgraph, JGAA, 11(1) 99–143 (2007) 100


title: Probabilistic Subgraph Matching Based on Convex Relaxation

text: We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. For the resulting quadratic integer program, a mathematically tight relaxation is derived by exploiting the degrees of freedom of the embedding space of positive semidefinite matrices. We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all close-to-optimal combinatorial matchings within the original solution space. As a result, the approach can even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.


title: Clustering by passing messages between data points.

text: Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.


title: A Comparison of String Metrics for Matching Names and Records

text: We describe an open-source Java toolkit of methods for matching names and records. We summarize results obtained from using various string distance metrics on the task of matching entity names: these metrics include distance functions proposed by several different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. We then describe an extension to the toolkit which allows records to be compared. We discuss some issues involved in performing a similar comparison for record-matching techniques, and finally present results for some baseline record-matching algorithms which are based on string comparisons between fields.


title: Development and validation of blue ray, an optical modem for the MEDUSA class AUVs

text: Wireless optical communications are emerging as a viable solution for high-speed data transmission over short ranges in the ocean, complementing mainstream acoustic communication systems that operate over much longer ranges, but at lower data rates. The current drive to develop cooperative autonomous vehicular systems to carry out surveying and other complex missions in the ocean critically depends on the existence of such communication links to share sensory and coordination information. This paper presents a compact, low power consumption, cost-efficient, and lightweight optical modem designed for fast data transmission between MEDUSA underwater vehicles at ranges on the order of 10 m. The transmitter uses LEDs for ON-OFF keying, while the receiver front-end adopts a transimpedance architecture with a photodiode detector. This simplistic transmission and reception technique reduces the overall design and hardware complexities compared to, e.g., laser diodes and photomultiplier tubes. The LEDs are arranged in a circular array, with a photodiode at the centre to enable half-duplex operation. These modems are designed to achieve data rates of 20-200 kb/s over short-range, line-of-sight, configurations. A transparent casing is customised to fit the Medusa vehicles, with proper alignment for inter-vehicle communication when they move in formation.


title: Agnostic Estimation of Mean and Covariance

text: We consider the problem of estimating the mean and covariance of a distribution from i.i.d. samples in the presence of a fraction of malicious noise. This is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when a fraction of data is adversarially corrupted, agnostically learning mixtures, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.


title: Robotics-logistics: Challenges for automation of logistic processes

text: Robotics-logistics can be understood as the field of activities in which applications of industrial robot-technologies are offered and demanded in order to ensure the optimization of internal material flows. According to a survey conducted by BIBA in 2007, the area of robotics-logistics has great need for modernization. Robotics-Logistics activities can be classified in several scenarios as unloading / loading of goods and palletizing / depalletizing of goods. Possible scenarios for research and development activities within theses fields are given.


title: Clothing identification via deep learning: forensic applications

text: Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals. An item such as clothing is a visual attribute because it can usually be used to describe people. The method proposed in this article aims to identify people based on the visual information derived from their attire. Deep learning is used to train the computer to classify images based on clothing content. We first demonstrate clothing classification using a large scale dataset, where the proposed model performs relatively poorly. Then, we use clothing classification on a dataset containing popular logos and famous brand images. The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%. Finally, we evaluate clothing classification using footage from surveillance cameras. The system performs well on this dataset, labelling 70% of the test images correctly.


title: Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion

text: Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).


title: Weight-averaged consistency targets improve semi-supervised deep learning results

text: The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when learning large datasets. To overcome this problem, we propose Mean Teacher, a method that averages model weights instead of label predictions. As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than Temporal Ensembling. Without changing the network architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels. We also show that a good network architecture is crucial to performance. Combining Mean Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with 4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels from 35.24% to 9.11%.


title: Ensembles of nested dichotomies for multi-class problems

text: Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.


title: A Case Study on Korean Wave: Focused on K-POP Concert by Korean Idol Group in Paris, June 2011

text: The study dealt with Korean Wave focusing on K-POP and analyzed its success factors, the changes in Korean Wave and the future directions for development. Also the study has compared the results of the idol groups’ performance, held by SM Entertainment in June 2011 in Paris, and the perspective of Korean and French media. Key reasons were examined to analyze what led K-POP to play a crucial part in spreading the Korean Wave: The expansion of the age of its takers ranging from teens to females in their 20s; the fusion of a variety of cultural elements including oriental dance and occidental pop; the systematic system of idol training; marketing activities based on social media, etc. For the expansion of Korean Wave including K-POP and its successful positioning in the world market, there are several suggestions to make which are inventing the differentiated contents and highly appealing stories, approaching the local customers while considering local features, operating co-marketing with other cultural products.


title: An acute bout of self-myofascial release increases range of motion without a subsequent decrease in muscle activation or force.

text: Foam rolling is thought to improve muscular function, performance, overuse, and joint range of motion (ROM); however, there is no empirical evidence demonstrating this. Thus, the objective of the study was to determine the effect of self-myofascial release (SMR) via foam roller application on knee extensor force and activation and knee joint ROM. Eleven healthy male (height 178.9 ± 3.5 cm, mass 86.3 ± 7.4 kg, age 22.3 ± 3.8 years) subjects who were physically active participated. Subjects' quadriceps maximum voluntary contraction force, evoked force and activation, and knee joint ROM were measured before, 2 minutes, and 10 minutes after 2 conditions: (a) 2, 1-minute trials of SMR of the quadriceps via a foam roller and (b) no SMR (Control). A 2-way analysis of variance (condition × time) with repeated measures was performed on all dependent variables recorded in the precondition and postcondition tests. There were no significant differences between conditions for any of the neuromuscular dependent variables. However, after foam rolling, subjects' ROM significantly (p < 0.001) increased by 10° and 8° at 2 and 10 minutes, respectively. There was a significant (p < 0.01) negative correlation between subjects' force and ROM before foam rolling, which no longer existed after foam rolling. In conclusion, an acute bout of SMR of the quadriceps was an effective treatment to acutely enhance knee joint ROM without a concomitant deficit in muscle performance.


title: SATIRE: a software architecture for smart AtTIRE

text: Personal instrumentation and monitoring services that collect and archive the physical activities of a user have recently been introduced for various medical, personal, safety, and entertainment purposes. A general software architecture is needed to support different categories of such monitoring services. This paper presents a software architecture, implementation, and preliminary evaluation of SATIRE, a wearable personal monitoring service transparently embedded in user garments. SATIRE records the owner's activity and location for subsequent automated uploading and archiving. The personal archive can later be searched for particular events to answer questions regarding past and present user activity, location, and behavior patterns. A short feasibility and usage study of a prototype based on MicaZ motes provides a proof of concept for the SATIRE architecture.


title: CDN brokering

text: Content distribution networks (CDNs) increase the capacity of individual Web sites and attempt to deliver content from caches that are located “closer” to end-users than the origin servers that provide the content. CDN brokering provides CDNs a way to interoperate by allowing one CDN to intelligently redirect clients dynamically to other CDNs. This paper describes the goals, architecture, and performance of a CDN brokerage system. Our system has been deployed on the Internet on a provisional basis, and our architectural ideas have helped advance the evolution of Internet standards for interoperating CDNs.


title: Editing Description Logic Ontologies with the Protégé OWL Plugin

text: The growing interest in the Semantic Web and the Web Ontology Language (OWL) will reveal the potential of Description Logics in industrial projects. The rich semantics of OWL provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes. However, before OWL can unfold its full potential, user-friendly tools with a scalable architecture are required. We present the OWL Plugin, an extension of the Protégé ontology development environment, which can be used to define classes and properties, to edit logical class expressions, to invoke reasoners, and to link ontologies into the Semantic Web. We analyze some of the challenges for developers of Description Logic editors, and discuss some of our user interface design decisions.


title: Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

text: We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based representation, and inserts objects iteratively into the scene by predicting their category, location, orientation and size with separate neural network modules. Our pipeline naturally supports automatic completion of partial scenes, as well as synthesis of complete scenes. Our method is significantly faster than the previous image-based method and generates result that outperforms state-of-the-art generative scene models in terms of faithfulness to training data and perceived visual quality.


title: When Is It Too Early for Single Sport Specialization?

text: Over the past 15 years, there has been an increase in youth sports participation with a concomitant increase in early year-round training in a single sport. Many factors contribute to the desire of parents and coaches to encourage early single sport specialization, including the desire to give the young athlete an edge in competition, pursuit of scholarships, and potential professional status, and the ability to label a young athlete as elite at an early age. Despite these perceived advantages, some data suggest that early sport specialization does not lead to a competitive advantage over athletes who participate in multiple sports. Although the data are limited, there is some evidence that early sport specialization may put the young athlete at risk for overuse injuries. The focus of this review is to highlight the evidence regarding early sport specialization and risk for injury; discuss the risk factors for overuse injury in high-risk sports including ice hockey, swimming, gymnastics, and baseball; and discuss future potential research that would help define the risk of injury for young athletes who participate in early sport specialization.


title: Coordinated Power Control of Variable-Speed Diesel Generators and Lithium-Battery on a Hybrid Electric Boat

text: This paper presents coordinated power management strategies for a hybrid electric boat (HEB) based on dynamic load's power distribution approach using diesel generators and batteries. Two variable-speed diesel generator sets connecting to dc-bus via fully controlled rectifiers serve as the main power supply source; one lithium-battery pack linked to dc-bus through a bidirectional DC/DC converter serves as an auxiliary power supply source. The power requirement of the thrusters and other onboard equipment is represented by a load power profile. The main contribution of this paper is focused on the load power sharing of the HEB between the two variable-speed diesel generators and the lithium-battery pack, through the fluctuations extracted from load power and their assignation to the batteries. Another important issue is that when the total load demand is low, one of the diesel generator set will be switched off to improve the diesel-fuel efficiency. The output powers of the diesel generators and the batteries are controlled based on the power distribution strategy via the converters controlling. The performances of the proposed hybrid power system control are evaluated through simulations in MATALB/Simulink, and through reduced-scale experimental tests carried out for a specific driving cycle of the HEB.


title: Multiple description-DASH: Pragmatic video streaming maximizing end-users' quality of experience

text: Over the past few years, adaptive bitrate streaming protocols, such as DASH, have risen to enhance the End-Users' experience towards video consumption over the Internet. Current methods perform a smoother playback (i.e., fewer re-buffering states) trading off with quality fluctuations due to the client-server link state. The proposed work goes a step further and introduces an innovative lightweight streaming solution by taking advantage of bandwidth aggregation over multiple paths using multiple content sources at the same time. This pragmatic evolving approach outperforms the QoE delivered by current DASH-based or P2P-based solutions and leverages the scope of action made available to the media delivery chain actors. The proposed solution has been implemented and is compared to a DASH-based approach used in most existing VoD use-cases through a first set of experiments. Results demonstrate the strong advantages in terms of quality delivered at the End-User's side and buffer occupancy. Discussion on the necessary trade-offs is also tackled.


title: Optimizing power-accuracy trade-off in approximate adders

text: Approximate circuit design has gained significance in recent years targeting applications like media processing where full accuracy is not required. In this paper, we propose an approximate adder in which the approximate part of the sum is obtained by finding a single optimal level that minimises the mean error distance. Therefore hardware needed for the approximate part computation can be removed, which effectively results in very low power consumption. We compare the proposed adder with various approximate adders in the literature in terms of power and accuracy metrics. The power savings of our adder is shown to be 17% to 55% more than power savings of the existing approximate adders over a significant range of accuracy values. Further, in an image addition application, this adder is shown to provide the best trade-off between PSNR and power.


title: Substrate Integrated Waveguide (SIW) Rotman Lens and Its Ka-Band Multibeam Array Antenna Applications

text: A new type of substrate integrated waveguide (SIW) multibeam antenna is proposed for mobile satellite communications using beam switching and diversity techniques. It employs an SIW Rotman lens as the beamforming network. The prototype of a single multibeam antenna is implemented at 28.5 GHz with seven input ports and an antenna array constructed by nine SIW linear slot arrays, which can generate a corresponding number of beams along one dimension. Several such antennas are grouped in two different ways to cover a 2-D solid angle with multiple beams. Experiment results show that the 2-D solid angle around (-40deg, 40deg) X (-35deg, 35deg) or (-25deg, 25deg) x (-35deg, 35deg) are covered with 20 or 25 beams with 5 -dB beamwidth, respectively. It is demonstrated that this type of printed multibeam antenna is a good choice for communication applications where mobility and high gain are simultaneously required.


title: From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction

text: Human automation interaction (HAI) systems have thus far failed to live up to expectations mainly because human users do not always interact with the automation appropriately. Trust in automation (TiA) has been considered a central influence on the way a human user interacts with an automation; if TiA is too high there will be overuse, if TiA is too low there will be disuse. However, even though extensive research into TiA has identified specific HAI behaviors, or trust outcomes, a unique mapping between trust states and trust outcomes has yet to be clearly identified. Interaction behaviors have been intensely studied in the domain of HAI and TiA and this has led to a reframing of the issues of problems with HAI in terms of reliance and compliance. We find the behaviorally defined terms reliance and compliance to be useful in their functionality for application in real-world situations. However, we note that once an inappropriate interaction behavior has occurred it is too late to mitigate it. We therefore take a step back and look at the interaction decision that precedes the behavior. We note that the decision neuroscience community has revealed that decisions are fairly stereotyped processes accompanied by measurable psychophysiological correlates. Two literatures were therefore reviewed. TiA literature was extensively reviewed in order to understand the relationship between TiA and trust outcomes, as well as to identify gaps in current knowledge. We note that an interaction decision precedes an interaction behavior and believe that we can leverage knowledge of the psychophysiological correlates of decisions to improve joint system performance. As we believe that understanding the interaction decision will be critical to the eventual mitigation of inappropriate interaction behavior, we reviewed the decision making literature and provide a synopsis of the state of the art understanding of the decision process from a decision neuroscience perspective. We forward hypotheses based on this understanding that could shape a research path toward the ability to mitigate interaction behavior in the real world.


title: Predicting Students’ Performance using Modified ID3 Algorithm

text: The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every decision making point. Several other algorithms like J48 and Naive Bayes classification algorithm are also applied on the dataset. The WEKA tool was used for the analysis of J48 and Naive Bayes algorithms. The results are compared and presented. The dataset used in our study is taken from the School of Computing Sciences and Engineering (SCSE), VIT University. Keyworddata mining, educational data mining (EDM), decision tree, gain ratio, weighted ID3


title: Improving read performance of STT-MRAM based main memories through Smash Read and Flexible Read

text: Spin Transfer Torque Magnetoresistive RAM (STT-MRAM) has been recently deemed as one promising main memory alternative for high-end mobile processors. With process technology scaling, the amplitude of write current approaches that of read current in deep sub-micrometer STT-MRAM arrays. As a result, read disturbance errors (RDEs) emerge. Both high current restore required (HCRR) reads and low current long latency (LCLL) reads can guarantee read reliability and utterly remove RDEs. However, both of them degrade system performance, because of extra restores or a longer read latency. And neither of them always achieves the better performance when running a wide variety of applications. In this paper, we present two architectural techniques to boost read performance for STT-MRAM based main memories in the presence of RDEs. We first propose Smash Read (S-RD) to shorten the latency of HCRR reads by injecting a larger read current. We further introduce Flexible Read (F-RD) to dynamically adopt different types of read schemes, S-RD and LCLL, to maximize main memory system performance. On average, our techniques improve system performance by 9~13% and reduces total energy by 4~8% over all existing read schemes including HCRR and LCLL.


title: Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

text: This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version whose weights are constrained to be either powers of two or zero. Unlike existing methods which are struggled in noticeable accuracy loss, our INQ has the potential to resolve this issue, as benefiting from two innovations. On one hand, we introduce three interdependent operations, namely weight partition, group-wise quantization and re-training. A wellproven measure is employed to divide the weights in each layer of a pre-trained CNN model into two disjoint groups. The weights in the first group are responsible to form a low-precision base, thus they are quantized by a variable-length encoding method. The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained. On the other hand, these three operations are repeated on the latest re-trained group in an iterative manner until all the weights are converted into low-precision ones, acting as an incremental network quantization and accuracy enhancement procedure. Extensive experiments on the ImageNet classification task using almost all known deep CNN architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the efficacy of the proposed method. Specifically, at 5-bit quantization (a variable-length encoding: 1 bit for representing zero value, and the remaining 4 bits represent at most 16 different values for the powers of two) , our models have improved accuracy than the 32-bit floating-point references. Taking ResNet-18 as an example, we further show that our quantized models with 4-bit, 3-bit and 2-bit ternary weights have improved or very similar accuracy against its 32-bit floating-point baseline. Besides, impressive results with the combination of network pruning and INQ are also reported. We believe that our method sheds new insights on how to make deep CNNs to be applicable on mobile or embedded devices. The code is available at https://github.com/Zhouaojun/IncrementalNetwork-Quantization.


title: MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images

text: We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows us to deal with variable intensity cell boundaries and highly variable cell size in the mouse pancreatic tissue by adding extra convolutional layers which bypass the max-pooling operation. The results show that our method outperforms state-of-the-art deep learning based approaches for segmentation.


title: Machine Learning for Performance Prediction in Mobile Cellular Networks

text: In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.


title: Algorithmic Authority: the Ethics, Politics, and Economics of Algorithms that Interpret, Decide, and Manage

text: This panel will explore algorithmic authority as it manifests and plays out across multiple domains. Algorithmic authority refers to the power of algorithms to manage human action and influence what information is accessible to users. Algorithms increasingly have the ability to affect everyday life, work practices, and economic systems through automated decision-making and interpretation of "big data". Cases of algorithmic authority include algorithmically curating news and social media feeds, evaluating job performance, matching dates, and hiring and firing employees. This panel will bring together researchers of quantified self, healthcare, digital labor, social media, and the sharing economy to deepen the emerging discourses on the ethics, politics, and economics of algorithmic authority in multiple domains.


title: Joint pose estimation and action recognition in image graphs

text: Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.


title: Function points as a universal software metric

text: Function point metrics are the most accurate and effective metrics yet developed for software sizing and also for studying software productivity, quality, costs, risks, and economic value. Unlike the older "lines of code" metric function points can be used to study requirements, design, and in fact all software activities from development through maintenance. In the future function point metrics can easily become a universal metric used for all software applications and for all software contracts in all countries. The government of Brazil already requires function points for all software contracts, and South Korea and Italy may soon follow. However, there are some logistical problems with function point metrics that need to be understood and overcome in order for function point metrics to become the primary metric for software economic analysis. Manual function point counting is too slow and costly to be used on large software projects above 10,000 function points in size. Also, application size is not constant but grows at about 2% per calendar month during development and 8% or more per calendar year for as long as software is in active use. This paper discusses a method of high-speed function point counting that can size any application in less than two minutes, and which can predict application growth during development and for five years after release. This new method is based on pattern matching and is covered by U.S. utility patent application and hence is patent pending.


title: Security and Privacy Challenges in Smart Cities

text: The construction of smart cities will bring about a higher quality of life to the masses through digital interconnectivity, leading to increased efficiency and accessibility in cities. Smart cities must ensure individual privacy and security in order to ensure that its citizens will participate. If citizens are reluctant to participate, the core advantages of a smart city will dissolve. This article will identify and offer possible solutions to five smart city challenges, in hopes of anticipating destabilizing and costly disruptions. The challenges include privacy preservation with high dimensional data, securing a network with a large attack surface, establishing trustworthy data sharing practices, properly utilizing artificial intelligence, and mitigating failures cascading through the smart network. Finally, further research directions are provided to encourage further exploration of smart city challenges before their construction.


title: SRT 除算に基づくスケーラブル剰余乗算回路 A Scalable Modular Multiplication Hardware Based on High-radix SRT Division Algorithm

text: べき剰余演算は RSA暗号 [1]を始めとする公開鍵暗 号の基本演算である.このべき剰余演算は剰余乗算に分 解できる.演算のビット数が非常に大きく,処理時間が 長いことから,高速化のために多くの回路構成法が研究 されている.剰余乗算アルゴリズム [2]の中で,回路化 に有効なものとして,乗/除算をインタリーブする SRT 除算に基づく方法 [3]と,モンゴメリ乗算に基づく方法 [4]がある.後者は幾つかの前処理と後処理が必要にな るが,法の倍数の選択が容易になる.そのため,基本的 な基数 2の構成同士の比較では前者に比べて 2倍程度高 速であるとされる [5].このことから,近年は,モンゴ メリ乗算に基づく回路構成の研究が多い. また,近年は,モンゴメリ乗算に基づくスケーラブル な回路構成に関する研究が多い [6, 7, 8].これらは,ワー ド単位に処理することで,以前のものに比べてバス幅を 小さくして,より小さな回路量で実現できる.スマート カードなどの小型の装置では,従来の構成法では回路が 比較的大きいため,このような回路規模の小さい構成が 多少速度は劣るが有効となる. 本論文では,モンゴメリ乗算ではなく,高基数 SRT除 算 [9]に基づくスケーラブルな回路構成法を提案し,こ の構成の面積/速度の性能がモンゴメリ乗算に基づく構 成に匹敵することを示す.また,法の倍数選択には,高


title: Depth-Dependent Halos: Illustrative Rendering of Dense Line Data

text: We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.


title: OpenSky: A swiss army knife for air traffic security research

text: The Automatic Dependent Surveillance - Broadcast (ADS-B) protocol is one of the key components of the next generation air transportation system. Since ADS-B will become mandatory by 2017 in the European airspace, it is crucial that aspects such as its security and privacy are promptly investigated by the research community. However, as expensive specialized equipment was previously necessary to collect real-world data on a large scale, such data has not been freely accessible until now. To enable researchers around the world to conduct experimental studies based on real air traffic data, we have created OpenSky, a participatory sensor network for air traffic research. In this paper, we describe the setup and capabilities of OpenSky, and detail some of the research into air traffic security that we have conducted using OpenSky.


title: Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

text: This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap.


title: Antibacterial drug discovery in the resistance era

text: The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.


title: Hunting ELFs : An investigation into Android malware detection

text: Hunting ELFs: An Investigation Into Android Malware Detection by Thomas Sheridan Atkinson Master of Science in Information Security Royal Holloway, University of London 2015/2016 Android has risen to become the de facto operating system for mobile technologies. Its huge success and ubiquity has made it a target for malware writers. It has been found that recently sampled Android malware apps make use of repackaged malicious ELF binaries. The research carried out by this project has found a previously unknown technique used by malicious ELF binaries to hide calls to external packed binaries. An original and robust method for detecting ELF binaries that make calls to a packed binary has been developed and implemented into a state of the art framework with an aim to improving how the framework detects malicious APKs. When trained and tested on 84,979 benign samples and 16,132 malware samples, an accuracy of 96.50% was maintained using XGBoost as the classifier algorithm. The accuracy achieved using the Extra Trees algorithm was improved by 0.01% to 95.79% and the accuracy achieved when using the Random Forest algorithm was increased by 0.05% to 95.34%.


title: A unified framework for generalized Linear Discriminant Analysis

text: Linear discriminant analysis (LDA) is one of the well- known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.


title: You can't always get what you want: educational attainment, agency, and choice.

text: Using educational attainment to indicate socioeconomic status, the authors examined models of agency and effects of choice among European American adults of different educational backgrounds in 3 studies. Whereas college-educated (BA) participants and their preferred cultural products (i.e., rock music lyrics) emphasized expressing uniqueness, controlling environments, and influencing others, less educated (HS) participants and their preferred cultural products (i.e., country music lyrics) emphasized maintaining integrity, adjusting selves, and resisting influence. Reflecting these models of agency, HS and BA participants differently responded to choice in dissonance and reactance paradigms: BA participants liked chosen objects more than unchosen objects, but choice did not affect HS participants' preferences. Results suggest that HS and BA models of agency qualitatively differ, despite overlap between HS and BA worlds.


title: Implicit Discourse Relation Classification via Multi-Task Neural Networks

text: Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.


title: Multi-level bootstrap analysis of stable clusters in resting-state fMRI

text: A variety of methods have been developed to identify brain networks with spontaneous, coherent activity in resting-state functional magnetic resonance imaging (fMRI). We propose here a generic statistical framework to quantify the stability of such resting-state networks (RSNs), which was implemented with k-means clustering. The core of the method consists in bootstrapping the available datasets to replicate the clustering process a large number of times and quantify the stable features across all replications. This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level of a group; (2) it provides a principled measure of RSN stability; and (3) the maximization of the stability measure can be used as a natural criterion to select the number of RSNs. A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects. At the group level, seven RSNs were identified which exhibited a good agreement with the previous findings from the literature. The comparison between the individual and group-level stability maps demonstrated the capacity of BASC to establish successful correspondences between these two levels of analysis and at the same time retain some interesting subject-specific characteristics, e.g. the specific involvement of subcortical regions in the visual and fronto-parietal networks for some subjects.


title: Haptic identification of objects using a modular soft robotic gripper

text: This work presents a soft hand capable of robustly grasping and identifying objects based on internal state measurements. A highly compliant hand allows for intrinsic robustness to grasping uncertainty, but the specific configuration of the hand and object is not known, leaving undetermined if a grasp was successful in picking up the right object. A soft finger was adapted and combined to form a three finger gripper that can easily be attached to existing robots, for example, to the wrist of the Baxter robot. Resistive bend sensors were added within each finger to provide a configuration estimate sufficient for distinguishing between a set of objects. With one data point from each finger, the object grasped by the gripper can be identified. A clustering algorithm to find the correspondence for each grasped object is presented for both enveloping grasps and pinch grasps. This hand is a first step towards robust proprioceptive soft grasping.


title: Onboard Reconfigurable Battery Charger for Electric Vehicles With Traction-to-Auxiliary Mode

text: This paper proposes a single-phase reconfigurable battery charger for an electric vehicle (EV) that operates in three different modes: grid-to-vehicle (G2V) mode, in which the traction batteries are charged from the power grid; vehicle-to-grid (V2G) mode, in which the traction batteries deliver part of the stored energy back to the power grid; and traction-to-auxiliary (T2A) mode, in which the auxiliary battery is charged from the traction batteries. When connected to the power grid, the battery charger works with a sinusoidal current in the ac side, for both G2V and V2G modes, and regulates the reactive power. When the EV is disconnected from the power grid, the control algorithms are modified, and the full-bridge ac-dc bidirectional converter works as a full-bridge isolated dc-dc converter that is used to charge the auxiliary battery of the EV, avoiding the use of an additional charger to accomplish this task. To assess the behavior of the proposed reconfigurable battery charger under different operation scenarios, a 3.6-kW laboratory prototype has been developed, and experimental results are presented.


title: AutoISES: Automatically Inferring Security Specification and Detecting Violations

text: The importance of software security cannot be overstated. In the past, researchers have applied program analysis techniques to automatically detect security vulnerabilities and verify security properties. However, such techniques have limited success in reality because they require manually provided code-level security specifications. Manually writing and generating these code-level security specifications are tedious and error-prone. Additionally, they seldom exist in production software. In this paper, we propose a novel method and tool, called AutoISES, which Automatically Infers Security Specifications by statically analyzing source code, and then directly use these specifications to automatically detect security violations. Our experiments with the Linux kernel and Xen demonstrated the effectiveness of this approach – AutoISES automatically generated 84 security specifications and detected 8 vulnerabilities in the Linux kernel and Xen, 7 of which have already been confirmed by the corresponding developers.


title: Cultural capital or relative risk aversion? Two mechanisms for educational inequality compared.

text: In this paper we empirically examined two explanatory mechanisms for educational inequality: cultural reproduction and relative risk aversion, using survey data taken from secondary school pupils in Amsterdam. Cultural reproduction theory seeks to explain class variations in schooling by cultural differences between social classes. Relative risk aversion theory argues that educational inequalities can be understood by between-class variation in the necessity of pursuing education at branching points in order to avoid downward mobility. We showed that class variations in early demonstrated ability are for a substantial part cultural: cultural capital - measured by parental involvement in highbrow culture - affected school performance at the primary and secondary level. However, relative risk aversion - operationalized by being concerned with downward mobility - strongly affects schooling ambitions, whereas cultural capital had no effect. Thus, we conclude that 'primary effects' of social origin on schooling outcomes are manifested through cultural capital and not through relative risk aversion (in addition to other potential sources of class variations such as genetics). Relative risk aversion, and not cultural capital, affects schooling ambitions, which is relevant for our understanding of secondary effects.


title: Entropy-Based Subword Mining with an Application to Word Embeddings

text: Recent literature has shown a wide variety of benefits to mapping traditional onehot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have longtail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropybased subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.


title: High throughput ultralong (20 cm) nanowire fabrication using a wafer-scale nanograting template.

text: Nanowires are being actively explored as promising nanostructured materials for high performance flexible electronics, biochemical sensors, photonic applications, solar cells, and secondary batteries. In particular, ultralong (centimeter-long) nanowires are highly attractive from the perspective of electronic performance, device throughput (or productivity), and the possibility of novel applications. However, most previous works on ultralong nanowires have issues related to limited length, productivity, difficult alignment, and deploying onto the planar substrate complying with well-matured device fabrication technologies. Here, we demonstrate a highly ordered ultralong (up to 20 cm) nanowire array, with a diameter of 50 nm (aspect ratio of up to 4,000,000:1), in an unprecedented large (8 in.) scale (2,000,000 strands on a wafer). We first devised a perfectly connected ultralong nanograting master template on the whole area of an 8 in. substrate using a top-down approach, with a density equivalent to that achieved with e-beam lithography (100 nm). Using this large-area, ultralong, high-density nanograting template, we developed a fast and effective method for fabricating up to 20 cm long nanowire arrays on a plastic substrate, composed of metal, dielectric, oxide, and ferroelectric materials. As a suggestion of practical application, a prototype of a large-area aluminum wire grid polarizer was demonstrated.


title: pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts

text: The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, ‘Evolving role of diabetes educators’, ‘Cancer risk assessment’ and ‘Dynamic concepts on disease and comorbidity’ to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.r-project.org/web/packages/pubmed.mineR .


title: Visualization Criticism - The Missing Link Between Information Visualization and Art

text: Classifications of visualization are often based on technical criteria, and leave out artistic ways of visualizing information. Understanding the differences between information visualization and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches. We propose a classification of several types of information visualization based on aesthetic criteria. The notions of artistic and pragmatic visualization are introduced, and their properties discussed. Finally, the idea of visualization criticism is proposed, and its rules are laid out. Visualization criticism bridges the gap between design, art, and technical/pragmatic information visualization. It guides the view away from implementation details and single mouse clicks to the meaning of a visualization.


title: Automating the Design of Graphical Presentations of Relational Information

text: The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codification of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.


title: Parallel Coordinates: A Tool for Visualizing Multi-dimensional Geometry

text: A methodology for visualizing analytic and synthetic geometry in RN is presented. It is based on a system of parallel coordinates which induces a non-projective mapping between N-Dimensional and 2-Dimensional sets. Hypersurfaces are represented by their planar images which have some geometrical properties analogous to the properties of the hypersurface that they represent. A point ← → line duality when N = 2 generalizes to lines and hyperplanes enabling the representation of polyhedra in RN. The representation of a class of convex and non-convex hypersurfaces is discussed together with an algorithm for constructing and displaying any interior point. The display shows some local properties of the hypersurface and provides information on the point's proximity to the boundary. Applications to Air Traffic Control, Robotics, Computer Vision, Computational Geometry, Statistics, Instrumentation and other areas are discussed.


title: Visualizing the non-visual: spatial analysis and interaction with information from text documents

text: The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.


title: Accelerating fish detection and recognition by sharing CNNs with objectness learning

text: Daily increasing underwater visual data makes automatic object detection and recognition a great demand and challenging task. In this paper, we adopt a region proposal network to accelerate underwater object detection and recognition from Faster R-CNN. This process implement detection acceleration by using convolutional networks to generate high-quality object candidates, and by sharing these networks with original detection networks. Applied to a real-world fish dataset, which includes 24,277 ImageCLEF fish images belonging to 12 classes, this automatic detection and recognition system has a nearly real-time frame rate of 9.8 ftps, while yielding 15.1% higher Mean Average Precision (mAP) than the Deformable Parts Model (DPM) baseline.


title: Image annotation via deep neural network

text: Multilabel image annotation is one of the most important open problems in computer vision field. Unlike existing works that usually use conventional visual features to annotate images, features based on deep learning have shown potential to achieve outstanding performance. In this work, we propose a multimodal deep learning framework, which aims to optimally integrate multiple deep neural networks pretrained with convolutional neural networks. In particular, the proposed framework explores a unified two-stage learning scheme that consists of (i) learning to fune-tune the parameters of deep neural network with respect to each individual modality, and (ii) learning to find the optimal combination of diverse modalities simultaneously in a coherent process. Experiments conducted on the NUS-WIDE dataset evaluate the performance of the proposed framework for multilabel image annotation, in which the encouraging results validate the effectiveness of the proposed algorithms.


title: Procrastination and cramming: how adept students ace the system.

text: Clear power differentials between teacher and pupil and the assignment of well-delineated tasks within specified time constraints characterize the academic system. Most university students are supremely adept at working within that structure. Many students who outwardly adapt to the system, however, engage in an intense and private ritual that comprises five aspects: calculated procrastination, preparatory anxiety, climactic cramming, nick-of-time deadline-making, and a secret, if often uncelebrated, victory. These adept students often find it difficult to admit others into their efficient program of academic survival. Although such behaviors are adaptive for school rhythms and expectations, these students may also try to impose them onto personal relationships, including those that are psychotherapeutic. The students' tendency to transfer their longstanding habits of procrastination and cramming to the workplace after graduation is less problematic. The argument is made that school rhythms and expectations shape the workplace, not vice versa. Methods of addressing the troublesome aspects of these work styles are suggested. Rather than attempting to change existing work patterns, the therapist can identify the underlying psychodynamic drama and its attendant manifestations that are becoming problematic.


title: An efficient FPGA overlay for portable custom instruction set extensions

text: Custom instruction set extensions can substantially boost performance of reconfigurable softcore CPUs. While this approach is commonly tailored to one specific FPGA system, we are presenting a fine-grained FPGA-like overlay architecture which can be implemented in the user logic of various FPGA families from different vendors. This allows the execution of a portable application consisting of a program binary and an overlay configuration in a completely heterogeneous environment. Furthermore, we are presenting different optimizations for dramatically reducing the implementation cost of the proposed overlay architecture. In particular, this includes the mapping of the overlay interconnection network directly into the switch fabric of the hosting FPGA. Our case study demonstrates an overhead reduction of an order of magnitude as compared to related approaches.


title: What a Nasty Day: Exploring Mood-Weather Relationship from Twitter

text: While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated. In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter. We analyze 2 years' twitter data collected by twitter API which amounts to 10% of all postings and try to reveal the correlations between multiple dimensional structure of human mood with meteorological effects. Some of our findings confirm existing hypotheses, while others contradict them. We are hopeful that our approach, along with the new data source, can shed on the long-going debates on weather-mood correlation.