title: Creating A Multi-track Classical Musical Performance Dataset for Multimodal Music Analysis: Challenges, Insights, and Applications

abstract: We introduce a dataset for facilitating audio-visual analysis of music performances. The dataset comprises 44 simple multi-instrument classical music pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground-truth annotation files including frame-level and note-level transcriptions. We describe our methodology for the creation of the dataset, particularly highlighting our approaches to address the challenges involved in maintaining synchronization and expressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset with existing widely used music audio datasets. We anticipate that the dataset will be useful for the development and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multimodal tasks. We benchmark two existing MIR tasks (multipitch analysis and score-informed source separation) on the dataset and compare them with other existing music audio datasets. In addition, we consider two novel multimodal MIR tasks (visually informed multipitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measurements and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.


title: Common overuse tendon problems: A review and recommendations for treatment.

abstract: There is a common misconception that symptomatic tendon injuries are inflammatory; because of this, these injuries often are mislabeled as "tendonitis."' Acute inflammatory tendinopathies exist, but most patients seen in primary care will have chronic symptoms suggesting a degenerative condition that should be labeled as "tendinosus" or "tendinopathy." Accurate diagnosis requires physicians to recognize the historical features, anatomy, and useful physical examination maneuvers for these common tendon problems. The natural history is gradually increasing load-related localized pain coinciding with increased activity. The most common overuse tendinopathies involve the rotator cuff, medial and lateral elbow epicondyles, patellar tendon, and Achilles tendon. Examination should include thorough inspection to assess for swelling, asymmetry, and erythema of involved tendons; range-of-motion testing; palpation for tenderness; and examination maneuvers that simulate tendon loading and reproduce pain. Plain radiography, ultrasonography, and magnetic resonance imaging can be helpful if the diagnosis remains unclear. Most patients with overuse tendinopathies (about 80 percent) fully recover within three to six months, and outpatient treatment should consist of relative rest of the affected area, icing, and eccentric strengthening exercises. Although topical and systemic nonsteroidal anti-inflammatory drugs are effective for acute pain relief, these cannot be recommended in favor of other analgesics. Injected corticosteroids also can relieve pain, but these drugs should be used with caution. Ultrasonography, shock wave therapy, orthotics, massage, and technique modification are treatment options, but few data exist to support their use at this time. Surgery is an effective treatment that should be reserved for patients who have failed conservative therapy.


title: Kinect depth inpainting in real time

abstract: RGBD cameras are used in many fields, such as monitoring, robot navigation and assistive solutions for visually impaired people. The advance in technology led to the development of cheap and easy to use cameras, such as Kinect, that gained a lot of popularity. One of the technologies that allow the capturing of depth maps, the D component in RGBD sequences, is structured light. The main drawback of this technology is the noise introduced during the acquisition process and the missing values for many pixels in the depth map. This paper presents several new depth inpainting algorithms and their results. The algorithms improve depth signal significantly and can be used as a pre-processing step for segmentation or reconstruction of RGBD images based on depth maps.


title: Cognitive vs. affective listening modes and judgments of music – An ERP study

abstract: The neural correlates of processing deviations from Western music rules are relatively well known. Less is known of the neural dynamics of top-down listening modes and affective liking judgments in relation with judgments of tonal correctness. In this study, subjects determined if tonal chord sequences sounded correct or incorrect, or if they liked them or not, while their electroencephalogram (EEG) was measured. The last chord of the sequences could be congruous with the previous context, ambiguous (unusual but still enjoyable) or harmonically inappropriate. The cognitive vs. affective listening modes were differentiated in the event-related potential (ERP) responses already before the ending chord, indicating different preparation for the judgment tasks. Furthermore, three neural events tagged the decision process preceding the behavioral responses. First, an early negativity, peaking at about 280 ms, was elicited by chord incorrectness and by disliking judgments only over the right hemisphere. Second, at about 500 ms from the end of the sequence a positive brain response was elicited by the negative answers of both tasks. Third, at about 1200 ms, a late positive potential (LPP) was elicited by the liking judgment task whereas a large negative brain response was elicited by the correctness judgment task, indexing that only at that late latency preceding the button press subjects decided how to judge the cadences. This is the first study to reveal the dissociation between neural processes occurring during affective vs. cognitive listening modes and judgments of music.


title: Unregulated Use of Laptops over Time in Large Lecture Classes

abstract: Students often have their own individual laptop computers in university classes, and researchers debate the potential benefits and drawbacks of laptop use. In the presented research, we used a combination of surveys and in-class observations to study how students use their laptops in an unmonitored and unrestricted class setting—a large lecture-based university class with nearly 3000 enrolled students. By analyzing computer use over the duration of long (165 minute) classes, we demonstrate how computer use changes over time. The observations and studentreports provided similar descriptions of laptop activities. Note taking was the most common use for the computers, followed by the use of social media web sites. Overall, the data show that students engaged in off-task computer activities for nearly two-thirds of the time. An analysis of the frequency of the various laptop activities over time showed that engagement in individual activities varied significantly over the duration of the class.


title: Computer science principles: analysis of a proposed advanced placement course

abstract: In this paper we analyze the CS Principles project, a proposed Advanced Placement course, by focusing on the second pilot that took place in 2011-2012. In a previous publication the first pilot of the course was explained, but not in a context related to relevant educational research and philosophy. In this paper we analyze the content and the pedagogical approaches used in the second pilot of the project. We include information about the third pilot being conducted in 2012-2013 and the portfolio exam that is part of that pilot. Both the second and third pilots provide evidence that the CS Principles course is succeeding in changing how computer science is taught and to whom it is taught.


title: Autonomous HVAC Control, A Reinforcement Learning Approach

abstract: Recent high profile developments of autonomous learning thermostats by companies such as Nest Labs and Honeywell have brought to the fore the possibility of ever greater numbers of intelligent devices permeating our homes and working environments into the future. However, the specific learning approaches and methodologies utilised by these devices have never been made public. In fact little information is known as to the specifics of how these devices operate and learn about their environments or the users who use them. This paper proposes a suitable learning architecture for such an intelligent thermostat in the hope that it will benefit further investigation by the research community. Our architecture comprises a number of different learning methods each of which contributes to create a complete autonomous thermostat capable of controlling a HVAC system. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. Our results show that the learning thermostat can achieve cost savings of 10% over a programmable thermostat, whilst maintaining high occupant comfort standards.


title: Unpacking Time Online: Connecting Internet and Massively Multiplayer Online Game Use With Psychosocial Well-Being

abstract: By unpacking different forms of Internet and massively multiplayer online game (MMO) use, the present study adopts a nuanced approach to examine the connections between online activities and psychosocial well-being. It combined self-reported survey data with unobtrusive behavioral data from server logs of a large virtual world, EverQuest II. Over 5,000 players were surveyed about how they use the Internet, their specific activities in the virtual world, and their psychosocial well-being. In-game communication networks were also constructed and analyzed.The results showed support for both time displacement and social augmentation effects for various activities. Whether Internet and MMO use were associated with negative or positive outcomes was largely dependent on the purposes, contexts, and individual characteristics of users. The results suggest that Internet use and game play have significant nuances and should not be considered as monolithic sources of effects.


title: Efficient KLMS and KRLS algorithms: A random fourier feature perspective

abstract: We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS (e.g., kernel trick), we map the data to a finite dimensional Euclidean space, using random features of the kernel's Fourier transform. The advantage is that, the inner product of the mapped data approximates the kernel function. The resulting"linear"algorithm does not require any form of sparsification, since, in contrast to all existing algorithms, the solution's size remains fixed and does not increase with the iteration steps. As a result, the obtained algorithms are computationally significantly more efficient compared to previously derived variants, while, at the same time, they converge at similar speeds and to similar error floors.


title: Research on Symbol Recognition for Mathematical Expressions

abstract: The recognition of mathematical symbols is an important step in the mathematical expression recognition system. It consists of two stages: symbol segmentation and symbol recognition. Aiming at the fact that the commercial OCR systems cannot recognize expression symbols correctly because of the differences between mathematical symbols and ordinary characters, we do lots of research work on the recognition of printed expressions. An approach that contains feedback mechanism is proposed for symbol segmentation and recognition. Firstly, projective features and connected components labeling method are used to segment symbols in expressions. Then the peripheral features and directional line element features are extracted from symbols. Finally, a coarse-to-fine classification strategy is employed to recognize symbols with these features. The experiments show that these methods can obtain satisfactory recognition accuracy with a high speed.


title: Saccade target selection and object recognition: Evidence for a common attentional mechanism

abstract: Abstract The spatial interaction of visual attention and saccadic eye movements was investigated in a dual-task paradigm that required a target-directed saccade in combination with a letter discrimination task. Subjects had to saccade to locations within horizontal letter strings left and right of a central fixation cross. The performance in discriminating between the symbols “E” and “∃”, presented tachistoscopically before the saccade within the surrounding distractors was taken as a measure of visual attention. The data show that visual discrimination is best when discrimination stimulus and saccade target refer to the same object; discrimination at neighboring items is close to chance level. Also, it is not possible, in spite of prior knowledge of discrimination target position, to direct attention to the discrimination target while saccading to a spatially close saccade target. The data strongly argue for an obligatory and selective coupling of saccade programming and visual attention to one common target object. The results favor a model in which a single attentional mechanism selects objects for perceptual processing and recognition, and also provides the information necessary for motor action.


title: Abstractive text summarization using LSTM-CNN based deep learning

abstract: Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. It is very difficult and time consuming for human beings to manually summarize large documents of text. In this paper, we propose an LSTM-CNN based ATS framework (ATSDL) that can construct new sentences by exploring more fine-grained fragments than sentences, namely, semantic phrases. Different from existing abstraction based approaches, ATSDL is composed of two main stages, the first of which extracts phrases from source sentences and the second generates text summaries using deep learning. Experimental results on the datasets CNN and DailyMail show that our ATSDL framework outperforms the state-of-the-art models in terms of both semantics and syntactic structure, and achieves competitive results on manual linguistic quality evaluation.


title: Applications of the conjugate gradient method for implicit feedback collaborative filtering

abstract: The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.


title: Authorship Attribution Using the Chaos Game Representation

abstract: The Chaos Game Representation, a method for creating images from nucleotide sequences, is modified to make images from chunks of text documents. Machine learning methods are then applied to train classifiers based on authorship. Experiments are conducted on several benchmark data sets in English, including the widely used Federalist Papers, and one in Portuguese. Validation results for the trained classifiers are competitive with the best methods in prior literature. The methodology is also successfully applied for text categorization with encouraging results. One classifier method is moreover seen to hold promise for the task of digital fingerprinting.


title: Garment perception and its folding using a dual-arm robot

abstract: The work addresses the problem of clothing perception and manipulation by a two armed industrial robot aiming at a real-time automated folding of a piece of garment spread out on a flat surface. A complete solution combining vision sensing, garment segmentation and understanding, planning of the manipulation and its real execution on a robot is proposed. A new polygonal model of a garment is introduced. Fitting the model into a segmented garment contour is used to detect garment landmark points. It is shown how folded variants of the unfolded model can be derived automatically. Universality and usefulness of the model is demonstrated by its favorable performance within the whole folding procedure which is applicable to a variety of garments categories (towel, pants, shirt, etc.) and evaluated experimentally using the two armed robot. The principal novelty with respect to the state of the art is in the new garment polygonal model and its manipulation planning algorithm which leads to the speed up by two orders of magnitude.


title: A Micropower Low-Noise Neural Recording Front-End Circuit for Epileptic Seizure Detection

abstract: This paper describes a micropower low-noise neural front-end circuit capable of recording epileptic fast ripples (FR). The front-end circuit consisting of a preamplifier followed by a 6th-order bandpass filter is designed for signal sensing in a future epileptic deep brain stimulator. A current-splitting technique is combined with an output-branch current scaling technique in a folded-cascode amplifier structure to improve the noise and power tradeoff in the preamplifier. In measurements, the preamplifier exhibits 39.4 dB DC gain, 0.36 Hz to 1.3 kHz of -3 dB bandwidth, and 3.07 μVrms total input-referred noise while consuming 2.4 μW from a 2.8 V power supply provided by an on-chip regulator circuit. A noise efficiency factor (NEF) of 3.09 is achieved with minimal power consumption and is one of the lowest published to date. The 6th-order follow-the-leader feedback elliptic bandpass filter passes FR signals and provides -110 dB/decade attenuation to out-of-band frequency components. In measurements, the entire front-end circuit achieves a mid-band gain of 38.5 dB, a bandwidth from 250 to 486 Hz, and a total input-referred noise of 2.48 μVrms while consuming 4.5 μW from the 2.8 V power supply. The front-end NEF achieved is 7.6. To the authors' knowledge, the proposed epileptic seizure- detection system is the first to achieve the FR-recording functionality. The chip is fabricated in a standard 0.6 μm CMOS process. Die size is 0.45 mm2.


Value Maximization, Stakeholder Theory, and the Corporate Objective Function


title: A Light CNN for Deep Face Representation With Noisy Labels

abstract: Convolution neural network (CNN) has significantly pushed forward the development of face recognition and analysis techniques. Current CNN models tend to be deeper and larger to better fit large amounts of training data. When training data are from internet, their labels are often ambiguous and inaccurate. This paper presents a light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels. First, we introduce the concept of maxout activation into each convolutional layer of CNN, which results in a Max-Feature-Map (MFM). Different from Rectified Linear Unit that suppresses a neuron by a threshold (or bias), MFM suppresses a neuron by a competitive relationship. MFM can not only separate noisy signals and informative signals but also plays a role of feature selection. Second, a network of five convolution layers and four Network in Network (NIN) layers are implemented to reduce the number of parameters and improve performance. Lastly, a semantic bootstrapping method is accordingly designed to make the prediction of the models be better consistent with noisy labels. Experimental results show that the proposed framework can utilize large-scale noisy data to learn a light model in terms of both computational cost and storage space. The learnt single model with a 256-D representation achieves state-of-the-art results on five face benchmarks without fine-tuning. The light CNN model is released on this https URL


title: Prediction based bandwidth reservation

abstract: A goal in soft real-time systems is to maintain quality of service in the presence of variations in computation times while efficiently utilizing the system. Several feedback-based scheduling algorithms have been proposed for this application. This paper presents a prediction-based bandwidth reservation policy with the same goal. A proof is presented to show stability, of the job queue. Simulation results are also presented to show the effectiveness of the policy


title: Age and motives for volunteering: Testing hypotheses derived from socioemotional selectivity theory

abstract: Following a meta-analysis of the relations between age and volunteer motives (career, understanding, enhancement, protective, making friends, social, and values), the authors tested hypotheses derived from socioemotional selectivity theory regarding the effects of age on these volunteer motives. The Volunteer Functions Inventory was completed by 523 volunteers from 2 affiliates of the International Habitat for Humanity. Multiple regression analyses revealed, as predicted, that as age increases, career and understanding volunteer motivation decrease and social volunteer motivation increases. Contrary to expectations, age did not contribute to the prediction of enhancement, protective, and values volunteer motivations and the relation between age and making friends volunteer motivation was nonlinear. The results were discussed in the context of age-differential and age-similarity perspectives on volunteer motivation.


title: Implementation of Modbus RTU and Modbus TCP communication using Siemens S7-1200 PLC for batch process

abstract: Batch process is widely applied in pharmaceutical, paint, food and beverage industries etc. In Batch process specific amount of input feed is passing through desired set of equipment for a specified amount of time to get a definite amount of valuable output product. In batch process, temperature of batch reactor is very important parameter. This temperature loop is mostly controlled through PID controller, but for precise control and plant optimization of batch process are performed by some advanced control strategies like fuzzy, MPC (Model Predictive Control), neural etc. In industries these advanced control schemes implemented through DCS (Distributed Control System), PLCs (Programmable Logic Controller). For communication with dif- ferent make controllers user can use various communication protocol such as Modbus TCP, Modbus RTU, Ethernet, Profibus, Profinet etc. In this experiment Modbus RTU, Modbus TCP communication protocol are used for communication and control of batch process from remote controllers.


title: Occupational-related chemical ocular injuries: an analysis of 82 patients

abstract: Objectives. To investigate the characteristics of occupational chemical eye injuries. Methods . Medical records of patients, who were registered to hospital officially as occupational chemical eye injuries between January 2010 and December 2013, were reviewed. The age, gender, injured eye, chemical agent, nature of the chemical, ocular findings, emerging complications and the information whether the patients knew the chemical agent causing the injury was recorded. Results. One hundred one eyes of 82 patients (2 women, 80 men) were included in the study. The mean age of the patients was 32.9±8.6 years (range: 19-59 years). Injury was bilateral in 19 patients. Chemical agents were not known by 53% of the patients. The most known agents were caustic agents (23%). Sulfuric acid (9%) and calcium hydroxide (4%) were other known chemical agents. The most common injury was superficial punctate epitheliopathy. Ten eyes of 9 patients had corneal edema. This injury was caused by acidic agents in 5 and alkaline agents in 3 patients. Corneoscleral perforation accompanied by chemical injury in one patient with car battery explosion. Conclusions . Most of the workers, who had eye injuries with chemicals, do not know the nature of chemical agent which caused the injury. Ocular morbidities may be decreased with the education of the workers about chemicals, working environment and protective measures.


title: Behavior recognition based on machine learning algorithms for a wireless canine machine interface

abstract: Training and handling working dogs is a costly process and requires specialized skills and techniques. Less subjective and lower-cost training techniques would not only improve our partnership with these dogs but also enable us to benefit from their skills more efficiently. To facilitate this, we are developing a canine body-area-network (cBAN) to combine sensing technologies and computational modeling to provide handlers with a more accurate interpretation for dog training. As the first step of this, we used inertial measurement units (IMU) to remotely detect the behavioral activity of canines. Decision tree classifiers and Hidden Markov Models were used to detect static postures (sitting, standing, lying down, standing on two legs and eating off the ground) and dynamic activities (walking, climbing stairs and walking down a ramp) based on the heuristic features of the accelerometer and gyroscope data provided by the wireless sensing system deployed on a canine vest. Data was collected from 6 Labrador Retrievers and a Kai Ken. The analysis of IMU location and orientation helped to achieve high classification accuracies for static and dynamic activity recognition.


Online News Media Bias Analysis using an LDA-NLP Approach


title: Sparse Gaussian Processes for Multi-task Learning

abstract: Multi-task learning models using Gaussian processes (GP) have been recently developed and successfully applied in various applications. The main difficulty with this approach is the computational cost of inference using the union of examples from all tasks. The paper investigates this problem for the grouped mixed-effect GP model where each individual response is given by a fixed-effect, taken from one of a set of unknown groups, plus a random individual effect function that captures variations among individuals. Such models have been widely used in previous work but no sparse solutions have been developed. The paper presents the first sparse solution for such problems, showing how the sparse approximation can be obtained by maximizing a variational lower bound on the marginal likelihood, generalizing ideas from single-task Gaussian processes to handle the mixed-effect model as well as grouping. Experiments using artificial and real data validate the approach showing that it can recover the performance of inference with the full sample, that it outperforms baseline methods, and that it outperforms state of the art sparse solutions for other multi-task GP formulations.


Applied Text Generation


title: Image de-fencing

abstract: We introduce a novel image segmentation algorithm that uses translational symmetry as the primary foreground/background separation cue. We investigate the process of identifying and analyzing image regions that present approximate translational symmetry for the purpose of image forground/background separation. In conjunction with texture-based inpainting, understanding the different see-through layers allows us to perform powerful image manipulations such as recovering a mesh-occluded background (as much as 53% occluded area) to achieve the effect of image and photo de-fencing. Our algorithm consists of three distinct phases- (1) automatically finding the skeleton structure of a potential frontal layer (fence) in the form of a deformed lattice, (2) separating foreground/background layers using appearance regularity, and (3) occluded foreground inpainting to reveal a complete, non-occluded image. Each of these three tasks presents its own special computational challenges that are not encountered in previous, general image de-layering or texture inpainting applications.


title: Online Cross-Modal Scene Retrieval by Binary Representation and Semantic Graph

abstract: In recent years, cross-modal scene retrieval has attracted more attention. However, most existing approaches neglect the semantic relationship between objects in a scene together with the embedded spatial layouts. Moreover, these methods mostly apply the batch learning strategy, which is not suitable for processing streaming data. To address the aforementioned problems, we propose a new framework for online cross-modal scene retrieval based on binary representations and semantic graph. Specially, we adopt the cross-modal hashing based on the quantization loss of different modalities. By introducing the semantic graph, we are able to extract wealthy semantics and measure their correlation across different modalities. Further more, we propose a two-step optimization procedure based on stochastic gradient descent for online update. Experimental results on four datasets show the superiority of our approach over the state-of-the-art.


title: Modeling Guidelines and a Benchmark for Power System Simulation Studies of Three-Phase Single-Stage Photovoltaic Systems

abstract: This paper presents modeling guidelines and a benchmark system for power system simulation studies of grid-connected, three-phase, single-stage Photovoltaic (PV) sys- tems that employ a voltage-sourced converter (VSC) as the power processor. The objective of this work is to introduce the main com- ponents, operation/protection modes, and control layers/schemes of medium- and high-power PV systems, to assist power engi- neers in developing circuit-based simulation models for impact assessment studies, analysis, and identification of potential issues with respect to the grid integration of PV systems. Parameter selection, control tuning, and design guidelines are also briefly dis- cussed. The usefulness of the benchmark system is demonstrated through a fairly comprehensive set of test cases, conducted in the PSCAD/EMTDC software environment. However, the models and techniques presented in this paper are independent of any specific circuit simulation software package. Also, they may not fully conform to the methods exercised by all manufacturers, due to the proprietary nature of the industry.


title: Generating Linear Orders Of Text-Based Events

abstract: Events described in textual narratives do not always occur in neat, chronological order but occur, for example, during or overlapping each other or as simultaneous events. Summarizations of narratives, however, benefit from a simpler, linear ordering of events. This paper describes an approach for modeling events in text as event intervals and for generating linear orders of event intervals, useful for the summarization of events or as the basis for question answering systems. Linear orders are derived through reducing the set of thirteen possible event interval relations to a set of only before or equal relations. The mapping of event interval relations into before/after sequences requires the support of additional constraints in order to preserve the original semantics of the events presented in the text and to derive plausible orders of events.


title: Putting flesh on the Duality of Structure: the case of IT Consumerization

abstract: The use of consumer IT at work, known as "IT Consumerization", is changing the dynamic between employees and IT departments. Employees are empowered and now have greater freedom but also responsibility as to what technologies to use at work and how. At the same time, organizational factors such as rules on technology use still exert considerable influence on employees' actions. Drawing on Structuration Theory, we frame this interaction between organization and employee as one of structure and agency. In the process, we pursue an explorative approach and rely on qualitative data from interviews with public-sector employees. We identify four organizational structures that influence people's behavior with respect to IT Consumerization: policies, equipment, tasks and authority. By spotlighting the mutual influence of these organizational structures and Consumerization-related behavior, we show Giddens's duality of structure in action and demonstrate its relevance to the study of IT Consumerization.


title: An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, & CART Ensembles

abstract: Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and Bio Stat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.


A 3 DTV Approach Using Depth-Image-Based Rendering ( DIBR )


title: Human Brain Mapping 15:1–25(2001) � Nonparametric Permutation Tests For Functional Neuroimaging:

abstract: Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7–22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.


title: Fractional Free-Volume dependence of Oxygen in Beta- Irradiated Polystyrene Probed by Positron life time

abstract: Positron Annihilation Lifetime (PAL) technique has been employed to investigate the effect of low doses of -irradiation (in air and vacuum) on the type of polymers namely: polystyrene (PS). A conventional fast-slow coincidence system was used for measuring the positron lifetime spectra. The time resolution of the system was 422±20 ps (the full width at half maximum (FWHM)) at 22Na energy window. The lifetime spectra were resolved into three components by using PFPOSFIT program. The irradiation performed in air (in the presence of oxygen) and under vacuum with β--rays by using (90Sr/90Y) source at room temperature for ten different total dose, ranging from (0.3-12.3) Gy . The o-Ps lifetime parameters were used to determine the microstructure properties, namely: free volume hole size (Vh) and free volume hole fraction (Fh). The parameters were measured as a function of -irradiation dose. The results of -irradiation show that, the influence of -irradiation on o-Ps lifetime, hence the Vh and Fh values for samples irradiated in air are less than their values for samples irradiated under vacuum, due to the effect of peroxy and hydroperoxide radicals formed in the presence of oxygen. The results show that cross-linking process is the predominant process as a result of -irradiation PS.


title: Enhancing Light Extraction in Top-Emitting Organic Light-Emitting Devices Using Molded Transparent Polymer Microlens Arrays

abstract: Abstract The light extraction efficiency in organic light-emitting devices (OLEDs) is enhanced by up to 2.6 times when a close-packed, hemispherical transparent polymer microlens array (MLA) is molded on the light-emitting surface of a top-emitting device. The microlens array helps to extract the waveguided optical emission in the organic layers and the transparent top electrode, and can be manufactured in large area with low cost.


title: Cloud computing for Internet of Things & sensing based applications

abstract: Internet of Things (IoT) is a concept that envisions all objects around us as part of internet. IoT coverage is very wide and include variety of objects like smart phones, tablets, digital cameras, sensors, etc. Once all these devices are connected with each other, they enable more and more smart processes and services that support our basic needs, economies, environment and health. Such enormous number of devices connected to internet provides many kinds of services and produce huge amount of data and information. Cloud computing is a model for on-demand access to a shared pool of configurable resources (e.g. compute, networks, servers, storage, applications, services, and software) that can be easily provisioned as Infrastructure (IaaS), software and applications (SaaS). Cloud based platforms help to connect to the things (IaaS) around us so that we can access anything at any time and any place in a user friendly manner using customized portals and in built applications (SaaS). Hence, cloud acts as a front end to access Internet of Things. Applications that interact with devices like sensors have special requirements of massive storage to storage big data, huge computation power to enable the real time processing of the data, and high speed network to stream audio or video. In this paper, we describe how Internet of Things and Cloud computing can work together can address the Big Data issues. We also illustrate about Sensing as a service on cloud using few applications like Augmented Reality, Agriculture and Environment monitoring. Finally, we also propose a prototype model for providing sensing as a service on cloud.


title: Dynamic Adaptation on Non-Stationary Visual Domains

abstract: Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results.


title: Image Segmentation Techniques Overview

abstract: The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.


title: Robust Multilingual Named Entity Recognition with Shallow Semi-Supervised Features

abstract: We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.


title: Unifying color and texture transfer for predictive appearance manipulation

abstract: Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from "sunny" to "overcast". However, such methods have a hard time creating new image content, such as leaves on a bare tree. Texture transfer, on the other hand, can synthesize such content but tends to destroy image structure. We propose the first algorithm that unifies color and texture transfer, outperforming both by leveraging their respective strengths. A key novelty in our approach resides in teasing apart appearance changes that can be modeled simply as changes in color versus those that require new image content to be generated. Our method starts with an analysis phase which evaluates the success of color transfer by comparing the exemplar with the source. This analysis then drives a selective, iterative texture transfer algorithm that simultaneously predicts the success of color transfer on the target and synthesizes new content where needed. We demonstrate our unified algorithm by transferring large temporal changes between photographs, such as change of season -- e.g., leaves on bare trees or piles of snow on a street -- and flooding.


title: Hybrid Transformer-Based Tunable Differential Duplexer in a 90-nm CMOS Process

abstract: Practical implementation of monolithic hybrid transformer duplexers have been limited by the poor common-mode isolation. In this paper, a differential hybrid transformer duplexer that covers 3GPP bands I, II, III, and IX between 1.7 and 2.2 GHz is introduced. It achieves a differential to differential isolation of 60 dB in the transmit (TX) band and 40 dB in the receive (RX) band, and a differential to common-mode isolation of 60 dB in both bands. The duplexer with a cascaded low-noise amplifier (LNA) achieves a noise figure of 5.6 dB in the RX path and an insertion loss of 3.7 dB in the TX path. The duplexer and LNA are implemented in a 90-nm CMOS process, consume 20 mA, and occupy an active area of 0.6 mm2.


title: Improving refactoring speed by 10X

abstract: Refactoring engines are standard tools in today's Integrated Development Environments (IDEs). They allow programmers to perform one refactoring at a time, but programmers need more. Most design patterns in the Gang-of-Four text can be written as a refactoring script -- a programmatic sequence of refactorings. In this paper, we present R3, a new Java refactoring engine that supports refactoring scripts. It builds a main-memory, non-persistent database to encode Java entity declarations (e.g., packages, classes, methods), their containment relationships, and language features such as inheritance and modifiers. Unlike classical refactoring engines that modify Abstract Syntax Trees (ASTs), R3 refactorings modify only the database; refactored code is produced only when pretty-printing ASTs that reference database changes. R3 performs comparable precondition checks to those of the Eclipse Java Development Tools (JDT) but R3's codebase is about half the size of the JDT refactoring engine and runs an order of magnitude faster. Further, a user study shows that R3 improved the success rate of retrofitting design patterns by 25% up to 50%.


title: Navigating blind people with walking impairments using a smart walker

abstract: Navigation in complex and unknown environments is a major challenge for elderly blind people. Unfortunately, conventional navigation aids such as white canes and guide dogs provide only limited assistance to blind people with walking impairments as they can hardly be combined with a walker, required for walking assistance. Additionally, such navigation aids are constrained to the local vicinity only. We believe that technologies developed in the field of robotics have the potential to assist blind people with walking disabilities in complex navigation tasks as they can provide information about obstacles and reason on both global and local aspects of the environment. The contribution of this article is a smart walker that navigates blind users safely by leveraging recent developments in robotics. Our walker can support the user in two ways, namely by providing information about the vicinity to avoid obstacles and by guiding the user to reach the designated target location. It includes vibro-tactile user interfaces and a controller that takes into account human motion behavior obtained from a user study. In extensive qualitative and quantitative experiments that also involved blind and age-matched participants we demonstrate that our smart walker safely navigates users with limited vision.


title: Hypocretin/Orexin Neurons Contribute to Hippocampus-Dependent Social Memory and Synaptic Plasticity in Mice

abstract: Hypocretin/orexin (Hcrt)-producing neurons in the lateral hypothalamus project throughout the brain, including to the hippocampus, where Hcrt receptors are widely expressed. Hcrt neurons activate these targets to orchestrate global arousal state, wake-sleep architecture, energy homeostasis, stress adaptation, and reward behaviors. Recently, Hcrt has been implicated in cognitive functions and social interaction. In the present study, we tested the hypothesis that Hcrt neurons are critical to social interaction, particularly social memory, using neurobehavioral assessment and electrophysiological approaches. The validated “two-enclosure homecage test” devices and procedure were used to test sociability, preference for social novelty (social novelty), and recognition memory. A conventional direct contact social test was conducted to corroborate the findings. We found that adult orexin/ataxin-3-transgenic (AT) mice, in which Hcrt neurons degenerate by 3 months of age, displayed normal sociability and social novelty with respect to their wild-type littermates. However, AT mice displayed deficits in long-term social memory. Nasal administration of exogenous Hcrt-1 restored social memory to an extent in AT mice. Hippocampal slices taken from AT mice exhibited decreases in degree of paired-pulse facilitation and magnitude of long-term potentiation, despite displaying normal basal synaptic neurotransmission in the CA1 area compared to wild-type hippocampal slices. AT hippocampi had lower levels of phosphorylated cAMP response element-binding protein (pCREB), an activity-dependent transcription factor important for synaptic plasticity and long-term memory storage. Our studies demonstrate that Hcrt neurons play an important role in the consolidation of social recognition memory, at least in part through enhancements of hippocampal synaptic plasticity and cAMP response element-binding protein phosphorylation.


title: Detecting Online Hate Speech Using Context Aware Models

abstract: In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.


title: Flexible intrinsic evaluation of hierarchical clustering for TDT

abstract: The Topic Detection and Tracking (TDT) evaluation program has included a "cluster detection" task since its inception in 1996. Systems were required to process a stream of broadcast news stories and partition them into non-overlapping clusters. A system's effectiveness was measured by comparing the generated clusters to "truth" clusters created by human annotators. Starting in 2003, TDT is moving to a more realistic model that permits overlapping clusters (stories may be on more than one topic) and encourages the creation of a hierarchy to structure the relationships between clusters (topics). We explore a range of possible evaluation models for this modified TDT clustering task to understand the best approach for mapping between the human-generated "truth" clusters and a much richer hierarchical structure. We demonstrate that some obvious evaluation techniques fail for degenerate cases. For a few others we attempt to develop an intuitive sense of what the evaluation numbers mean. We settle on some approaches that incorporate a strong balance between cluster errors (misses and false alarms) and the distance it takes to travel between stories within the hierarchy.


title: Series elastic actuation of an elbow rehabilitation exoskeleton with axis misalignment adaptation

abstract: Powered exoskeletons can facilitate rehabilitation of patients with upper limb disabilities. Designs using rotary motors usually result in bulky exoskeletons to reduce the problem of moving inertia. This paper presents a new linearly actuated elbow exoskeleton that consists of a slider crank mechanism and a linear motor. The linear motor is placed beside the upper arm and closer to shoulder joint. Thus better inertia properties can be achieved while lightweight and compactness are maintained. A passive joint is introduced to compensate for the exoskeleton-elbow misalignment and intersubject size variation. A linear series elastic actuator (SEA) is proposed to obtain accurate force and impedance control at the exoskeleton-elbow interface. Bidirectional actuation between exoskeleton and forearm is verified, which is required for various rehabilitation processes. We expect this exoskeleton can provide a means of robot-aided elbow rehabilitation.


title: A Ten Step Process for Forensic Readiness

abstract: A forensic investigation of digital evidence is commonly employed as a post-event response to a serious information security incident. In fact, there are many circumstances where an organisation may benefit from an ability to gather and preserve digital evidence before an incident occurs. Forensic readiness is defined as the ability of an organisation to maximise its potential to use digital evidence whilst minimising the costs of an investigation. The costs and benefits of such an approach are outlined. Preparation to use digital evidence may involve enhanced system and staff monitoring, technical, physical and procedural means to secure data to evidential standards of admissibility, processes and procedures to ensure that staff recognise the importance and legal sensitivities of evidence, and appropriate legal advice and interfacing with law enforcement. This paper proposes a ten step process for an organisation to implement forensic readiness.


title: Who are Like-minded: Mining User Interest Similarity in Online Social Networks

abstract: In this paper, we mine and learn to predict how similar a pair of users' interests towards videos are, based on demographic (age, gender and location) and social (friendship, interaction and group membership) information of these users. We use the video access patterns of active users as ground truth (a form of benchmark). We adopt tag-based user profiling to establish this ground truth, and justify why it is used instead of video-based methods, or many latent topic models such as LDA and Collaborative Filtering approaches. We then show the effectiveness of the different demographic and social features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and treebased models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation. Our study is based on a rich dataset from Tencent, a popular service provider of social networks, video services, and various other services in China.


title: Assessing Trauma Care Capabilities of the Health Centers in Northern Ghana

abstract: Introduction ::: Traffic-related injury is a major and increasing cause of global mortality, especially in low- and middle-income countries (LMICs). However, trauma systems, personnel, resources, and infrastructure are frequently insufficient to meet the needs of the population in this at-risk population in LMICs. In addition, these resources are not uniformly distributed, coordinated, nor well described within most countries. Trauma care resources have not previously been characterized in the Northern Region of Ghana.


title: CMAR: accurate and efficient classification based on multiple class-association rules

abstract: Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only a single high-confidence rule. The authors propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large databases efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted /spl chi//sup 2/ analysis using multiple strong association rules. Our extensive experiments on 26 databases from the UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.


When to use splay trees


title: Pulse width time walk compensation method for a pulsed time-of-flight laser rangefinder

abstract: A new walk compensation method for a pulsed time-of-flight rangefinder is suggested. The receiver channel operates without gain control using leading edge timing discrimination principle. The generated walk error is compensated for by measuring the pulse length and knowing the relation between the walk error and pulse length. The walk compensation is possible also at the range where the signal is clipped and where the compensation method by amplitude measurement is impossible. Based on the simulations walk error can be compensated within the dynamic range of 1:30 000.


Securing mobile devices: malware mitigation methods


title: Maturity Models for Systems Thinking

abstract: Recent decades have seen a rapid increase in the complexity of goods, products, and services that society has come to demand. This has necessitated a corresponding growth in the requirements demanded of organizational systems and the people who work in them. The competence a person requires to be effective in working in such systems has become an area of increased interest to scholars and practitioners in many disciplines. How can we assess the degree to which a person is executing the competencies required to do good systems work? Several industries now utilize maturity models in the attempt to evaluate and cultivate people’s ability to effectively execute complex tasks. This paper will examine current thought regarding the value and pitfalls of maturity models. It will identify principles and exemplars that could guide the development of a Maturity Model of Systems Thinking Competence (MMSTC) for the varied roles people inhabit in systems contexts.


Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpedia


title: Simulation of future climate scenarios with a weather generator

abstract: Abstract Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961–2000) and forecasting future (2081–2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.


title: On the links between argumentation-based reasoning and nonmonotonic reasoning

abstract: In this paper we investigate the links between instantiated argumentation systems and the axioms for non-monotonic reasoning described in [15] with the aim of characterising the nature of argument based reasoning. In doing so, we consider two possible interpretations of the consequence relation, and describe which axioms are met by \({{\textsc {aspic}}^\mathbf {+}}\) under each of these interpretations. We then consider the links between these axioms and the rationality postulates. Our results indicate that argument based reasoning as characterised by \({{\textsc {aspic}}^\mathbf {+}}\) is—according to the axioms of [15]—non-cumulative and non-monotonic, and therefore weaker than the weakest non-monotonic reasoning systems considered in [15]. This weakness underpins \({{\textsc {aspic}}^\mathbf {+}}\)’s success in modelling other reasoning systems. We conclude by considering the relationship between \({{\textsc {aspic}}^\mathbf {+}}\) and other weak logical systems.


Handwritten Chinese / Japanese Character Recognition


title: Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

abstract: Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this paper, we demonstrate a simple method to bridge this"reality gap."By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system. Our approach is demonstrated on an object pushing task using a robotic arm. Despite being trained exclusively in simulation, our policies are able to maintain a similar level of performance when deployed on a real robot, reliably moving an object to a desired location from random initial configurations. We explore the impact of various design decisions and show that the resulting policies are robust to significant calibration error.


title: Reflection, abstraction and theorizing in design and development research

abstract: Design theories have been proposed as means to capture abstract knowledge about the design and development of information technology (IT) and information systems (IS) artifacts. There is now an increasingly accepted and used body of knowledge on the processes of design research and the components of design theories. Explicit guidance is still sparse, however, as to how to extract design theories during design science research. In this paper, we focus on reflection and abstraction in design science research as distinct activities leading to design theory. We suggest an abstraction framework that recognizes different modes of causal analysis related to the discrete decisions made by designers and developers as well as to the artifact in use: creative (mental) causes, active causes, and passive causes. The first recognizes the creativity of the human mind, the second deliberate interventions and their consequences, and the third is built upon the notion of affordances that describe the potential uses of an artifact depending on its use context. We argue that these modes of causal analysis can be used to abstract from specific design processes in order to identify key components of design theory.


Functions of Limited Variation in an Infinite Number of Dimension


title: Graph Database Applications and Concepts with Neo4j

abstract: Graph databases (GDB) are now a viable alternative to Relational Database Systems (RDBMS). Chemistry, biology, semantic web, social networking and recommendation engines are all examples of applications that can be represented in a much more natural form. Comparisons will be drawn between relational database systems (Oracle, MySQL) and graph databases (Neo4J) focusing on aspects such as data structures, data model features and query facilities. Additionally, several of the inherent and contemporary limitations of current offerings comparing and contrasting graph vs. relational database implementations will be explored.


title: Risk-based thinking of ISO 9001:2015 — The new methods, approaches and tools of risk management

abstract: This article discusses aspects of the new thinking founded on a risk-based ISO 9001:2015. It is noted that risk-based thinking is an effective tool for creating, auditing and improving quality management systems. Shows several practical examples of the implementation of the risk management process for the phase “Plan” PDCA-cycle, proposed a new model of the ISM, which contains all the basic entity for performance audits (criteria, object, audit observation) and allows to generate the level of security assessment. Additionally an approach is presented to determine the characteristics of technological devices for the healthcare industry.


title: Analyzing appearance and contour based methods for object categorization

abstract: Object recognition has reached a level where we can identify a large number of previously seen and known objects. However, the more challenging and important task of categorizing previously unseen objects remains largely unsolved. Traditionally, contour and shape based methods are regarded most adequate for handling the generalization requirements needed for this task. Appearance based methods, on the other hand, have been successful in object identification and detection scenarios. Today little work is done to systematically compare existing methods and characterize their relative capabilities for categorizing objects. In order to compare different methods we present a new database specifically tailored to the task of object categorization. It contains high-resolution color images of 80 objects from 8 different categories, for a total of 3280 images. It is used to analyze the performance of several appearance and contour based methods. The best categorization result is obtained by an appropriate combination of different methods.


title: Dual sliding mode contouring control with high accuracy contour error estimation for five-axis CNC machine tools

abstract: Abstract Five-axis CNC machine tools are widely used in the manufacturing of the complex parts, and the contour error is the key indicator for the accuracy of the final product. Contour error, by definition, is a geometrical quantity solely dependent on the current actual tool pose and the reference trajectory. Contouring control is the main method to reduce or eliminate it. In this paper, based on the geometrical information of the reference trajectory, a high accuracy contour error estimation method for five-axis machine tools is proposed. Then, to avoid the time-consuming calculation of the inverse and derivative of the Jacobian matrix, to reduce tracking and contour errors simultaneously as well as suppress the chattering in the control signal, a dual sliding mode contouring control method is proposed. The dual sliding surface is selected for each drive separately, which integrates the traditional PD-type tracking sliding surface, a PD-type contouring sliding surface consisting of the axis component of the contour errors, and the input signal together. Experiments are conducted on a tilting-rotary-table type five-axis CNC machine tool. The results demonstrate that the contour errors estimated using the proposed method are very close to the true ones, and compared with the traditional sliding mode controller, the proposed method can reduce the contour error and the chattering behavior effectively.


title: Head-worn displays: a review

abstract: Head-worn display design is inherently an interdisciplinary subject fusing optical engineering, optical materials, optical coatings, electronics, manufacturing techniques, user interface design, computer science, human perception, and physiology for assessing these displays. This paper summarizes the state-of-the-art in head-worn display design (HWD) and development. This review is focused on the optical engineering aspects, divided into different sections to explore principles and applications. Building on the guiding fundamentals of optical design and engineering, the principles section includes a summary of microdisplay or laser sources, the Lagrange invariant for understanding the trade-offs in optical design of HWDs, modes of image presentation (i.e., monocular, biocular, and stereo) and operational modes such as optical and video see-through. A brief summary of the human visual system pertinent to the design of HWDs is provided. Two optical design forms, namely, pupil forming and non-pupil forming are discussed. We summarize the results from previous design work using aspheric, diffractive, or holographic elements to achieve compact and lightweight systems. The applications section is organized in terms of field of view requirements and presents a reasonable collection of past designs


title: Zozzle: Low-overhead Mostly Static JavaScript Malware Detection

abstract: JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. From the standpoint of the attacker, the attraction is that these drive-by attacks can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods tend to incur. In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that can be deployed in the browser. Our experience also suggests that ZOZZLE may be used as a lightweight filter for a more costly detection technique or for standalone offline


title: Parallel labeling of massive XML data with MapReduce

abstract: The volume of XML data has become enormous and still grows very quickly as many data have been typed in XML by virtue of its simplicity and extensibility. While a tree labeling algorithm has a crucial role in XML query processing, conventional algorithms are all sequential so that they fail to label a large volume of XML data in a timely manner. To address this issue, we devise parallel tree labeling algorithms for massive XML data. Specifically, we focus on how to efficiently label a single large XML file in parallel. We first propose parallel versions of two prominent tree labeling schemes based on the MapReduce framework. We then present techniques for runtime workload balancing and data repartition to solve performance issues caused by data skewness and MapReduce's inherited limitation. Through extensive experiments with synthetic and real-world datasets on 15 nodes, we show that our parallel labeling algorithms are up to 17 times faster than conventional algorithms, providing strong durability against data skewness.


title: Representing layered monads

abstract: There has already been considerable research on constructing modular, monad-based specifications of computational effects (state, exceptions, nondeterminism, etc.) in programming languages. We present a simple framework in this tradition, based on a Church-style effect-typing system for an ML-like language. The semantics of this language is formally defined by a series of monadic translations, each one expanding away a layer of effects. Such a layered specification is easy to reason about, but its direct implementation (whether by parameterized interpretation or by actual translation) is often prohibitively inefficient.By exploiting deeper semantic properties of monads, however, it is also possible to derive a vastly more efficient implementation: we show that each layer of effects can be uniformly simulated by continuation-passing, and further that multiple such layers can themselves be simulated by a standard semantics for call/cc and mutable state. Thus, even multi-effect programs can be executed in Scheme or SML/NJ at full native speed, generalizing an earlier single-effect result. As an example, we show how a simple resumption-based semantics of concurrency allows us to directly simulate a shared-state program across all possible dynamic interleavings of execution threads.


title: PENS Syndrome: A New Neurocutaneous Phenotype

abstract: BACKGROUND ::: The central nervous system originates ontogenetically from the ectoderm and therefore numerous neurocutaneous syndromes exist. ::: ::: ::: OBJECTIVE ::: We describe a novel neurocutaneous syndrome with characteristic epidermal papules and plaques associated with mild psychomotor retardation and epilepsy with a good prognosis. ::: ::: ::: METHODS ::: We investigated history, clinical features, laboratory parameters including karyotype, histopathological and immunohistological examination, electron microscopy, HPV infection, EEG and neurologic imaging in patients with an unusual symptom complex. ::: ::: ::: RESULTS ::: Six children are reported with characteristic keratotic papules and plaques of 'gem-like' appearance, randomly distributed on the trunk and the proximal part of the limbs. Four patients were unrelated and two were siblings. The cutaneous lesions appeared congenitally or developed soon after birth. The skin lesions were rounded or of an irregularly polycyclic shape, ranging from 0.5 to 1.5 cm in diameter. A few to eighteen lesions were present. The lesional surface was rough and of white or yellowish color. Histopathological examination showed acanthokeratosis, but immunostaining for keratins and melanocytes, electron microscopy and laboratory results were within normal range. During the natural course of the disease the lesions were stable and asymptomatic. All but one of our six patients developed neurologic signs and symptoms during the first year of life, in the form of epilepsy and psychomotor delay. Five children had low intellectual performance together with EEG abnormalities and some had MRI changes of the brain. One patient had marked language impairment. The central nervous system involvement had a peculiar benign and self-limiting course and total normalization occurred in one case. ::: ::: ::: LIMITATIONS ::: We present only a small series of patients. ::: ::: ::: CONCLUSION ::: Cutaneous lesions showing identical clinical and microscopical features, but without any extracutaneous abnormalities, have recently been described by Torrelo et al. [J Am Acad Dermatol 2011;64:888-892] under the designation 'papular epidermal nevus with "skyline" basal cell layer (PENS)'. An association of such nevi with neurologic abnormalities had so far only been published in the 'Atlas of Genodermatoses' edited by Caputo and Tadini. We believe that this unique and otherwise undescribed symptom complex is a new neurocutaneous phenotype that may be called PENS syndrome. Whether this new syndrome can be categorized as a distinct phenotype within the group of epidermal nevus syndromes remains speculative and has to be clarified by further investigations.


Address Oblivious Code Reuse: On the Effectiveness of Leakage-Resilient Diversity


LineLens : Automatic Data Extraction from Line Charts


title: Conceptual Impact-Based Recommender System for CiteSeerx

abstract: CiteSeer is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This system recommends documents based on an automaticallyconstructed user profile. Unlike traditional content-based recommender systems, the documents and the user profile are represented as concepts vectors rather than keyword vectors and papers are recommended based on conceptual matches rather than keyword matches between the profile and the documents. Although the current system provides recommendations that are on-topic, they are not necessarily high quality papers. In this work, we introduce the Conceptual Impact-Based Recommender (CIBR), a hybrid recommender system that extends the existing conceptual recommender system in CiteSeer by including an explicit quality factor as part of the recommendation criteria. To measure quality, our system considers the impact factor of each paper’s authors as measured by the authors’ h-index. Experiments to evaluate the effectiveness of our hybrid system show that the CIBR system recommends more relevant papers as compared to the conceptual recommender system.


title: Finding top-k similar graphs in graph databases

abstract: Querying similar graphs in graph databases has been widely studied in graph query processing in recent years. Existing works mainly focus on subgraph similarity search and supergraph similarity search. In this paper, we study the problem of finding top-k graphs in a graph database that are most similar to a query graph. This problem has many applications, such as image retrieval and chemical compound structure search. Regarding the similarity measure, feature based and kernel based similarity measures have been used in the literature. But such measures are rough and may lose the connectivity information among substructures. In this paper, we introduce a new similarity measure based on the maximum common subgraph (MCS) of two graphs. We show that this measure can better capture the common and different structures of two graphs. Since computing the MCS of two graphs is NP-hard, we propose an algorithm to answer the top-k graph similarity query using two distance lower bounds with different computational costs, in order to reduce the number of MCS computations. We further introduce an indexing technique, which can better make use of the triangle property of similarities among graphs in the database to get tighter lower bounds. Three different indexing methods are proposed with different tradeoffs between pruning power and construction cost. We conducted extensive performance studies on large real datasets to evaluate the performance of our approaches.


title: A Computational Approach to Style in American Poetry

abstract: We develop a quantitative method to assess the style of American poems and to visualize a collection of poems in relation to one another. Qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. These features are used to discover comprehensive stylistic information from a poem's multi-layered latent structure, and to compute distances between poems in this space. Visualizations provide ready access to the analytical components. We demonstrate our method on several collections of poetry, showing that it better delineates poetry style than the traditional word-occurrence features that are used in typical text analysis algorithms. Our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.


title: MultiE: Multi-Task Embedding for Knowledge Base Completion

abstract: Completing knowledge bases (KBs) with missing facts is of great importance, since most existing KBs are far from complete. To this end, many knowledge base completion (KBC) methods have been proposed. However, most existing methods embed each relation into a vector separately, while ignoring the correlations among different relations. Actually, in large-scale KBs, there always exist some relations that are semantically related, and we believe this can help to facilitate the knowledge sharing when learning the embedding of related relations simultaneously. Along this line, we propose a novel KBC model by Multi -Task E mbedding, named MultiE. In this model, semantically related relations are first clustered into the same group, and then learning the embedding of each relation can leverage the knowledge among different relations. Moreover, we propose a three-layer network to predict the missing values of incomplete knowledge triples. Finally, experiments on three popular benchmarks FB15k, FB15k-237 and WN18 are conducted to demonstrate the effectiveness of MultiE against some state-of-the-art baseline competitors.


title: Standards-Based DevOps Automation and Integration Using TOSCA

abstract: DevOps is an emerging paradigm to tightly integrate developers with operations personnel. This is required to enable fast and frequent releases in the sense of continuously delivering software. Users and customers of today's Web applications and mobile apps running in the Cloud expect fast feedback to problems and feature requests. Thus, it is a critical competitive advantage to be able to respond quickly. Beside cultural and organizational changes that are necessary to implement DevOps in practice, tooling is required to implement end-to-end automation of deployment processes. Automation is the key to efficient collaboration and tight integration between development and operations. The DevOps community is constantly pushing new approaches, tools, and open-source artifacts to implement such automated processes. However, as all these proprietary and heterogeneous DevOps automation approaches differ from each other, it is hard to integrate and combine them to deploy applications in the Cloud. In this paper we present a systematic classification of DevOps artifacts and show how different kinds of artifacts can be transformed toward TOSCA, an emerging standard in this field. This enables the seamless and interoperable orchestration of arbitrary artifacts to model and deploy application topologies. We validate the presented approach by a prototype implementation, show its practical feasibility by a detailed case study, and evaluate its performance.


title: Scaling Web-Based Acquisition Of Entailment Relations

abstract: Paraphrase recognition is a critical step for natural language interpretation. Accordingly, many NLP applications would benefit from high coverage knowledge bases of paraphrases. However, the scalability of state-of-the-art paraphrase acquisition approaches is still limited. We present a fully unsupervised learning algorithm for Web-based extraction of entailment relations, an extended model of paraphrases. We focus on increased scalability and generality with respect to prior work, eventually aiming at a full scale knowledge base. Our current implementation of the algorithm takes as its input a verb lexicon and for each verb searches the Web for related syntactic entailment templates. Experiments show promising results with respect to the ultimate goal, achieving much better scalability than prior Web-based methods.


title: A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for Video-Based Salient Object Detection

abstract: Image-based salient object detection (SOD) has been extensively studied in the past decades. However, video-based SOD is much less explored since there lack large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos (64 minutes). In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects that free-view all videos. From the user data, we find salient objects in video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for video-based salient object detection. Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at pixel, superpixel and object levels. With these saliency cues, stacked autoencoders are unsupervisedly constructed which automatically infer a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. Experimental results show that the proposed unsupervised approach outperforms 30 state-of-the-art models on the proposed dataset, including 19 image-based&classic (unsupervised or non-deep learning), 6 image-based&deep learning, and 5 video-based&unsupervised. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.


title: Class imbalance and the curse of minority hubs

abstract: Most machine learning tasks involve learning from high-dimensional data, which is often quite difficult to handle. Hubness is an aspect of the curse of dimensionality that was shown to be highly detrimental to k-nearest neighbor methods in high-dimensional feature spaces. Hubs, very frequent nearest neighbors, emerge as centers of influence within the data and often act as semantic singularities. This paper deals with evaluating the impact of hubness on learning under class imbalance with k-nearest neighbor methods. Our results suggest that, contrary to the common belief, minority class hubs might be responsible for most misclassification in many high-dimensional datasets. The standard approaches to learning under class imbalance usually clearly favor the instances of the minority class and are not well suited for handling such highly detrimental minority points. In our experiments, we have evaluated several state-of-the-art hubness-aware kNN classifiers that are based on learning from the neighbor occurrence models calculated from the training data. The experiments included learning under severe class imbalance, class overlap and mislabeling and the results suggest that the hubness-aware methods usually achieve promising results on the examined high-dimensional datasets. The improvements seem to be most pronounced when handling the difficult point types: borderline points, rare points and outliers. On most examined datasets, the hubness-aware approaches improve the classification precision of the minority classes and the recall of the majority class, which helps with reducing the negative impact of minority hubs. We argue that it might prove beneficial to combine the extensible hubness-aware voting frameworks with the existing class imbalanced kNN classifiers, in order to properly handle class imbalanced data in high-dimensional feature spaces.


title: Path Planning for Autonomous Underwater Vehicles

abstract: Efficient path-planning algorithms are a crucial issue for modern autonomous underwater vehicles. Classical path-planning algorithms in artificial intelligence are not designed to deal with wide continuous environments prone to currents. We present a novel Fast Marching (FM)-based approach to address the following issues. First, we develop an algorithm we call FM* to efficiently extract a 2-D continuous path from a discrete representation of the environment. Second, we take underwater currents into account thanks to an anisotropic extension of the original FM algorithm. Third, the vehicle turning radius is introduced as a constraint on the optimal path curvature for both isotropic and anisotropic media. Finally, a multiresolution method is introduced to speed up the overall path-planning process


title: ExtraSensory App: Data Collection In-the-Wild with Rich User Interface to Self-Report Behavior

abstract: We introduce a mobile app for collecting in-the-wild data, including sensor measurements and self-reported labels describing people's behavioral context (e.g., driving, eating, in class, shower). Labeled data is necessary for developing context-recognition systems that serve health monitoring, aging care, and more. Acquiring labels without observers is challenging and previous solutions compromised ecological validity, range of behaviors, or amount of data. Our user interface combines past and near-future self-reporting of combinations of relevant context-labels. We deployed the app on the personal smartphones of 60 users and analyzed quantitative data collected in-the-wild and qualitative user-experience reports. The interface's flexibility was important to gain frequent, detailed labels, support diverse behavioral situations, and engage different users: most preferred reporting their past behavior through a daily journal, but some preferred reporting what they're about to do. We integrated insights from this work back into the app, which we make available to researchers for conducting in-the-wild studies.


title: Hand Strength, Handwriting, and Functional Skills in Children With Autism

abstract: OBJECTIVE ::: To establish hand strength development trends in children with autism and to investigate correlations between grip and pinch strength, components of handwriting, and functional activities in children with and without autism. ::: ::: ::: METHOD ::: Fifty-one children were divided into two groups: typically developing children and children on the autism spectrum. Each child completed testing for pinch and grip strength, handwriting legibility, pencil control, and independence in functional activities. ::: ::: ::: RESULTS ::: The children with autism followed the same strength development trends as the typically developing children. Grip strength correlated with pencil control in both groups and with handwriting legibility in the typically developing children but not in the children with autism. Grip and pinch strength correlated with independence with functional activities in both groups. ::: ::: ::: CONCLUSION ::: This study provides evidence that grip and pinch strength are important components in developing pencil control, handwriting legibility, and independence with functional fine motor tasks.


title: Lévy flights, non-local search and simulated annealing

abstract: We solve a problem of non-convex stochastic optimisation with help of simulated annealing of Levy flights of a variable stability index. The search of the ground state of an unknown potential is non-local due to big jumps of the Levy flights process. The convergence to the ground state is fast due to a polynomial decrease rate of the temperature.


title: Quasiresonant Control With a Dynamic Frequency Selector and Constant Current Startup Technique for 92% Peak Efficiency and 85% Light-Load Efficiency Flyback Converter

abstract: The proposed quasiresonant control scheme can be widely used in a dc–dc flyback converter because it can achieve high efficiency with minimized external components. The proposed dynamic frequency selector improves conversion efficiency especially at light loads to meet the requirement of green power since the converter automatically switches to the discontinuous conduction mode for reducing the switching frequency and the switching power loss. Furthermore, low quiescent current can be guaranteed by the constant current startup circuit to further reduce power loss after the startup procedure. The test chip fabricated in VIS 0.5 μm 500 V UHV process occupies an active silicon area of 3.6 mm $^{2}$ . The peak efficiency can achieve 92% at load of 80 W and 85% efficiency at light load of 5 W.


title: The poverty of embodied cognition

abstract: In recent years, there has been rapidly growing interest in embodied cognition, a multifaceted theoretical proposition that (1) cognitive processes are influenced by the body, (2) cognition exists in the service of action, (3) cognition is situated in the environment, and (4) cognition may occur without internal representations. Many proponents view embodied cognition as the next great paradigm shift for cognitive science. In this article, we critically examine the core ideas from embodied cognition, taking a “thought exercise” approach. We first note that the basic principles from embodiment theory are either unacceptably vague (e.g., the premise that perception is influenced by the body) or they offer nothing new (e.g., cognition evolved to optimize survival, emotions affect cognition, perception–action couplings are important). We next suggest that, for the vast majority of classic findings in cognitive science, embodied cognition offers no scientifically valuable insight. In most cases, the theory has no logical connections to the phenomena, other than some trivially true ideas. Beyond classic laboratory findings, embodiment theory is also unable to adequately address the basic experiences of cognitive life.


title: BGP Routing Table: Trends and Challenges

abstract: BGP is currently the most important protocol for ensuring global connectivity over the Internet. This puts a great deal of responsibility on BGP and creates a number of challenges for it. Of primary concern is the impact that various currently deployed BGP-based techniques have on the scalability of the global routing table. While these techniques provide ISPs with additional traffic management services (e.g., balancing, multi-homing, etc), they have expanded the routing table size at a pace that exceeds the allocation rate and is increasing. In our study we present a two-level analysis of BGP announcements for the period 2003–2009. First, we correlate IP allocation data with globally announced prefixes and show how efficiently ISPs announce their allocated address space. Second, we correlate BGP announcement data to itself and show various internal factors that contribute to routing table growth. Finally, we document in which regions of the world routing announcements have originated during the period of this study, and we draw conclusions about the spread of global Internet connectivity.


title: The impact of parietal cells on Helicobacter pylori tropism and host pathology: an analysis using gnotobiotic normal and transgenic mice.

abstract: Helicobacter pylori infection of the human stomach is common and typically benign, although a subset of hosts develops severe pathology. Infection occurs in an organ with distinct microenvironments characterized by pronounced differences in the composition of acid-producing parietal cells. In this study, we examine determinants of bacterial tropism to various gastric niches by using germ-free normal and transgenic mice with an engineered parietal cell ablation. Mice were colonized for 8 weeks with a clinical isolate (Hp1) that expresses adhesins recognized by epithelial NeuAcalpha2,3Galbeta1,4 glycan receptors. In normal mice, Hp1 has tropism for a parietal cell-deficient niche where sialylated glycans are expressed by a narrow band of pit cells positioned at the boundary between the squamous epithelium (forestomach) and the proximal glandular epithelium. Lymphoid aggregates that develop in this niche, but not elsewhere in the stomach, were analyzed by GeneChip and quantitative RT-PCR studies of laser capture microdissected mucosa and yielded a series of biomarkers indicative of immune cell activation and maturation. Genetic ablation of parietal cells produced a new source of NeuAcalpha2,3Galbeta1,4 glycans in amplified gastric epithelial lineage progenitors, with accompanying expansion of Hp1 within the glandular epithelium. Lymphoid aggregates that develop in this formerly acid-protected epithelium have molecular features similar to those observed at the forestomach/glandular junction. These findings demonstrate the important roles played by parietal cells and glycan receptors in determining the positioning of H. pylori within the gastric ecosystem, and emphasize the need to consider the evolution of pathology within a given host in a niche-specific context.