title: A convex relaxation approach for computing minimal partitions

abstract: In this work we propose a convex relaxation approach for computing minimal partitions. Our approach is based on rewriting the minimal partition problem (also known as Potts model) in terms of a primal dual Total Variation functional. We show that the Potts prior can be incorporated by means of convex constraints on the dual variables. For minimization we propose an efficient primal dual projected gradient algorithm which also allows a fast implementation on parallel hardware. Although our approach does not guarantee to find global minimizers of the Potts model we can give a tight bound on the energy between the computed solution and the true minimizer. Furthermore we show that our relaxation approach dominates recently proposed relaxations. As a consequence, our approach allows to compute solutions closer to the true minimizer. For many practical problems we even find the global minimizer. We demonstrate the excellent performance of our approach on several multi-label image segmentation and stereo problems.


title: Demand-Side Management for the Smart Grid

abstract: Efficient Demand Response or Demand-Side Management (DSM) has long been one of the key aspirations of the Energy Sector. In Smart Grids, there could be many opportunities for semi-automated DSM, assuming proper coordination (i.e. de-synchronization) mechanisms are in place. In this paper, we present the results of a simulation study that was carried out to outline the potential benefits and sketch the key ICT requirements for DSM in Smart Grids. Our preliminary findings indicate that a simple, centralized reactive pricing scheme could be implemented with an acceptable communication overhead, but also suggest high computational needs.


title: Secure automotive gateway — Secure communication for future cars

abstract: The main focus of the paper is to secure the onboard communication of automobiles. The current trend in the automotive domain is to incorporate technologies known from the consumer segment (e.g., WLAN, Ethernet) into the car. This makes it easier for an attacker to attack the on-board networks of the car, even without having physical access. To detect attacks against the automotive networks such as CAN and FlexRay, we introduce the concept of the so called “security gateway” which is part of the automotive architecture and is located on transition points, where different networks connect with each other. A language was created to specify the correct application behavior and to configure the security gateway. Using this representation of the application behavior the security gateway not only detects failures caused by an attacker but also detects failures caused by malfunctions.


title: Steganography- A Data Hiding Technique

abstract: Steganography is the art of hiding information and an effort to conceal the existence of the embedded information. It serves as a better way of securing message than cryptography which only conceals the content of the message not the existence of the message. Original message is being hidden within a carrier such that the changes so occurred in the carrier are not observable. In this paper we will discuss how digital images can be used as a carrier to hide messages. This paper also analyses the performance of some of the steganography tools. Steganography is a useful tool that allows covert transmission of information over an over the communications channel. Combining secret image with the carrier image gives the hidden image. The hidden image is difficult to detect without retrieval. This paper will take an in-depth look at this technology by introducing the reader to various concepts of Steganography, a brief history of Steganography and a look at some of the Steganographic technique.


title: A Noise Reduction and Linearity Improvement Technique for a Differential Cascode LNA

abstract: A typical common source cascode low-noise amplifier (CS-LNA) can be treated as a CS-CG two stage amplifier. In the published literature, an inductor is added at the drain of the main transistor to reduce the noise contribution of the cascode transistors. In this work, an inductor connected at the gate of the cascode transistor and capacitive cross-coupling are strategically combined to reduce the noise and the nonlinearity influences of the cascode transistors in a differential cascode CS-LNA. It uses a smaller noise reduction inductor compared with the conventional inductor based technique. It can reduce the noise, improve the linearity and also increase the voltage gain of the LNA. The proposed technique is theoretically formulated. Furthermore, as a proof of concept, a 2.2 GHz inductively degenerated CS-LNA was fabricated using TSMC 0.35 mum CMOS technology. The resulting LNA achieves 1.92 dB noise figure, 8.4 dB power gain, better than 13 dB S11, more than 30 dB isolation (S12), and -2.55 dBm IIP3, with the core fully differential LNA consuming 9 mA from a 1.8 V power supply.


title: Psychological Thought psyct.psychopen.eu | 2193-7281 Effect of Prayer and “OM ” Meditation in Enhancing Galvanic Skin Response

abstract: The research was conducted with the purpose to study the effect of prayer and meditation on galvanic skin response (GSR). It was hypothesized that there was a significant positive effect of prayer and meditation (Om chanting) on galvanic skin response (GSR). The sample consisted of 20 normal, healthy female participants through purposive sampling. The age group of the sample was 18 to 24 years (Mean= 18.7, SD= 1.55). Gender was female and minimum education was graduation. The daily practice time of prayer and meditation session was 30 minutes for one month. Pre- Post data were recorded before and after intervention of prayer and meditation session by using single group pre-post research design. Recordings of galvanic skin response (GSR) were made on a computerized polygraph (Model Physiopac, PP 4, Medicaid Systems, Chandigarh, India) test. The results revealed a significant increase in GSR values as an effect of prayer and meditation which suggested the psychophysiological relaxation. Practicing prayer and meditation increases the galvanic skin response and hence decreases the stress level of the individual. Language: English


title: Remote Laboratories for Education and Research Purposes in Automatic Control Systems

abstract: This paper describes the experiences using remote laboratories for education and research in the field of Control Engineering. The use of remote laboratories for education in subjects of control is increasingly becoming a resorted method by the universities in order to offer a flexible service in schedules with greater and better operation of available resources. Nevertheless, for research activities, remote laboratories are not widely used. The aim of this contribution is thereby to apply the experience of remote laboratories in research applications in order to share complex equipments among different researchers. Some experiments are carried out to demonstrate the effectiveness of using remote laboratories in research experiments related to robotic system. The results of the implementation of remote experimentations to control a 3-DOF parallel robot by using Distance Laboratory System (SLD) are exposed. The performance of the system is evaluated by the possibilities and functionality of the proposed remote laboratory platform.


title: Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial

abstract: Background: For children with attention deficit/hyperactivity disorder (ADHD), a reduction of inattention, impulsivity and hyperactivity by neurofeedback (NF) has been reported in several studies. But so far, unspecific training effects have not been adequately controlled for and/or studies do not provide sufficient statistical power. To overcome these methodological shortcomings we evaluated the clinical efficacy of neurofeedback in children with ADHD in a multisite randomised controlled study using a computerised attention skills training as a control condition. Methods: 102 children with ADHD, aged 8 to 12 years, participated in the study. Children performed either 36 sessions of NF training or a computerised attention skills training within two blocks of about four weeks each (randomised group assignment). The combined NF treatment consisted of one block of theta/beta training and one block of slow cortical potential (SCP) training. Pre-training, intermediate and post-training assessment encompassed several behaviour rating scales (e.g., the German ADHD rating scale, FBB-HKS) completed by parents and teachers. Evaluation (‘placebo’) scales were applied to control for parental expectations and satisfaction with the treatment. Results: For parent and teacher ratings, improvements in the NF group were superior to those of the control group. For the parent-rated FBB-HKS total score (primary outcome measure), the effect size was .60. Comparable effects were obtained for the two NF protocols (theta/beta training, SCP training). Parental attitude towards the treatment did not differ between NF and control group. Conclusions: Superiority of the combined NF training indicates clinical efficacy of NF in children with ADHD. Future studies should further address the specificity of effects and how to optimise the benefit of NF as treatment module for ADHD. Keywords: Neurofeedback, attention deficit/hyperactivity disorder (ADHD), slow cortical potentials (SCPs), theta/beta training, randomised controlled trial (RCT), EEG.


title: CASIA@V2: A MLN-based Question Answering System over Linked Data

abstract: We present a question answering system (CASIA@V2) over Linked Data (DBpedia), which translates natural language questions into structured queries automatically. Existing systems usually adopt a pipeline framework, which con- tains four major steps: 1) Decomposing the question and detecting candidate phrases; 2) mapping the detected phrases into semantic items of Linked Data; 3) grouping the mapped semantic items into semantic triples; and 4) generat- ing the rightful SPARQL query. We present a jointly learning framework using Markov Logic Network(MLN) for phrase detection, phrases mapping to seman- tic items and semantic items grouping. We formulate the knowledge for resolving the ambiguities in three steps of QALD as first-order logic clauses in a MLN. We evaluate our approach on QALD-4 test dataset and achieve an F-measure score of 0.36, an average precision of 0.32 and an average recall of 0.40 over 50 questions.


A Priority Based Job Scheduling Algorithm in Cloud Computing


title: A Forecasting Model of Financial Distress for Listed Companies

abstract: On the basis of factor-analyzing, we provide a discriminating model of financial distress for the listed companies. Our model includes four factors: marginal ratio?ratio of other accounts receivable to total assets?ratio of short-term liabilities to total assets and ownership concentration coefficient. Ownership concentration coefficient is a direct expression of corporate governance and is also the dominant difference from other forecasting models for financial distress. The accurate rates of our model are 84.52% for-1-year data and 95.45% for the new-added "ST" companies of 2000.


title: The additional value of hyperspectral data for smart farming

abstract: The spectral information contained in hyperspectral data allows for a more detailed crop and soil parameter retrieval, in turn making possible to derive precise information about plant health (nutrient deficiencies, water stress, crop diseases) but also about soil status (from soil moisture to humus content). Thus, smart farming, that is farming practice that wants to account for the whole ecosystem on the field and react precisely to any challenges, can be supported by hyperspectral data with exactly the spatial information it needs.


title: Text detection in scene images using stroke width and nearest-neighbor constraints

abstract: Text in scene images can provide very useful as well as vital information and hence, its detection and recognition is an important task. We propose an adaptive edge-based connected-component method for text-detection in natural scene images. The approach is based on three reasonable assumptions - (i) characters of a particular word are locally aligned in a certain direction (ii) each character is of uniform color ( iii) stroke width is almost constant for most of the characters in a particular word. We apply color quantization and use the luminance to obtain the intensity values. An improved edge-detection technique that performs adaptive thresholding is used to capture all possible text components with some non-text components initially. Then, we remove obvious non-text component based on a few heuristics. Further, we classify those components as text for which we successfully obtain two consecutive nearest-neighbors that are aligned in a direction and satisfy certain constraints based on size and inter component distance. Finally, we estimate stroke width and foreground color for each component and those having a fairly uniform value of the same are classified as text. Results on ICDAR 2003 Robust Reading Competition data show that the method is competitive for text-detection. The main advantage of our method is that it is robust to font-size, degraded intensities and complex backgrounds. Also, the use of stroke width and color in this manner for text detection is novel to the best of the authorpsilas knowledge.


title: MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

abstract: Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.


title: Double Precision Hybrid-Mode Floating-Point FPGA CORDIC Co-processor

abstract: FPGA chips have become a promising option for accelerating scientific applications, which involve many floating-point transcendental functions, such as sin, log, exp, sqrt and etc. In this paper, we present a 64-bit ANSI/IEEE floating-point CORDIC co-processor on FPGA, providing all known CORDIC functions. And there is no 64-bit CORDIC implementation on FPGA known to us. We propose a hybrid-mode CORDIC algorithm, combining hybrid rotation angle methods with argument reduction algorithm to reduce hardware area usage and meanwhile keep unlimited convergence domain for any floating-point inputs of the functions. Our hybrid-mode CORDIC co-processor is organized into three phases, argument reduction, CORDIC calculation and normalization with 69 pipeline stages for FPGA implementation. The synthesis results show the clock frequency can reach 173 MHz on Xilinx Virtex5 FPGA. Comparing to general-purpose microprocessor in three scientific program kernels, the CORDIC co-processor can achieve a maximum speedup of 49.3 times, 28.7 times in average.


title: Discovering, assessing, and mitigating data bias in social media

abstract: Abstract Social media has generated a wealth of data. Billions of people tweet, sharing, post, and discuss everyday. Due to this increased activity, social media platforms provide new opportunities for research about human behavior, information diffusion, and influence propagation at a scale that is otherwise impossible. Social media data is a new treasure trove for data mining and predictive analytics. Since social media data differs from conventional data, it is imperative to study its unique characteristics. This work investigates data collection bias associated with social media. In particular, we propose computational methods to assess if there is bias due to the way a social media site makes its data available, to detect bias from data samples without access to the full data, and to mitigate bias by designing data collection strategies that maximize coverage to minimize bias. We also present a new kind of data bias stemming from API attacks with both algorithms, data, and validation results. This work demonstrates how some characteristics of social media data can be extensively studied and verified and how corresponding intervention mechanisms can be designed to overcome negative effects. The methods and findings of this work could be helpful in studying different characteristics of social media data.


title: Decoupled coverage anti-aliasing

abstract: State-of-the-art methods for geometric anti-aliasing in real-time rendering are based on Multi-Sample Anti-Aliasing (MSAA), which samples visibility more than shading to reduce the number of expensive shading calculations. However, for high-quality results the number of visibility samples needs to be large (e.g., 64 samples/pixel), which requires significant memory because visibility samples are usually 24-bit depth values. In this paper, we present Decoupled Coverage Anti-Aliasing (DCAA), which improves upon MSAA by further decoupling coverage from visibility for high-quality geometric anti-aliasing. Our work is based on the previously-explored idea that all fragments at a pixel can be consolidated into a small set of visible surfaces. Although in the past this was only used to reduce the memory footprint of the G-Buffer for deferred shading with MSAA, we leverage this idea to represent each consolidated surface with a 64-bit binary mask for coverage and a single decoupled depth value, thus significantly reducing the overhead for high-quality anti-aliasing. To do this, we introduce new surface merging heuristics and resolve mechanisms to manage the decoupled depth and coverage samples. Our prototype implementation runs in real-time on current graphics hardware, and results in a significant reduction in geometric aliasing with less memory overhead than 8×MSAA for several complex scenes.


title: The psychological profile and affective response of women diagnosed with unexplained infertility undergoing in vitro fertilization

abstract: It has been hypothesized that unexplained infertility may be related to specific personality and coping styles. We studied two groups of women with explained infertility (EIF, n = 63) and unexplained infertility (UIF, n = 42) undergoing an in vitro fertilization (IVF) cycle. Women completed personality and coping style questionnaires prior to the onset of the cycle, and state depression and anxiety scales before and at two additional time points during the cycle. Almost no in-between group differences were found at any of the measured time points in regards to the Minnesota Multiphasic Personality Inventory-2 validity and clinical scales, Illness Cognitions and Life Orientation Test, or for the situational measures. The few differences found suggest a more adaptive, better coping, and functioning defensive system in women with EIF. In conclusion, we did not find any clinically significant personality differences or differences in depression or anxiety levels between women with EIF and UIF during an IVF cycle. Minor differences found are probably a reaction to the ambiguous medical situation with its uncertain prognosis, amplifying certain traits which are not specific to one psychological structure but rather to the common experience shared by the group. The results of this study do not support the possibility that personality traits are involved in the pathophysiology of unexplained infertility.


title: A Large Image Database for Color Constancy Research

abstract: We present a study on various statistics relevant to research on color constancy. Many of these analyses could not have been done before simply because a large database for color constancy was not available. Our image database consists of approximately 11,000 images in which the RGB color of the ambient illuminant in each scene is measured. To build such a large database we used a novel set-up consisting of a digital video camera with a neutral gray sphere attached to the camera so that the sphere always appears in the field of view. Using a gray sphere instead of the standard gray card facilitates measurement of the variation in illumination as a function of incident angle. The study focuses on the analysis of the distribution of various illuminants in the natural scenes and the correlation between the rg-chromaticity of colors recorded by the camera and the rg-chromaticity of the ambient illuminant. We also investigate the possibility of improving the performance of the naive Gray World algorithm by considering a sequence of consecutive frames instead of a single image. The set of images is publicly available and can also be used as a database for testing color constancy algorithms.


title: IMSI-catch me if you can: IMSI-catcher-catchers

abstract: IMSI Catchers are used in mobile networks to identify and eavesdrop on phones. When, the number of vendors increased and prices dropped, the device became available to much larger audiences. Self-made devices based on open source software are available for about US$ 1,500. In this paper, we identify and describe multiple methods of detecting artifacts in the mobile network produced by such devices. We present two independent novel implementations of an IMSI Catcher Catcher (ICC) to detect this threat against everyone's privacy. The first one employs a network of stationary (sICC) measurement units installed in a geographical area and constantly scanning all frequency bands for cell announcements and fingerprinting the cell network parameters. These rooftop-mounted devices can cover large areas. The second implementation is an app for standard consumer grade mobile phones (mICC), without the need to root or jailbreak them. Its core principle is based upon geographical network topology correlation, facilitating the ubiquitous built-in GPS receiver in today's phones and a network cell capabilities fingerprinting technique. The latter works for the vicinity of the phone by first learning the cell landscape and than matching it against the learned data. We implemented and evaluated both solutions for digital self-defense and deployed several of the stationary units for a long term field-test. Finally, we describe how to detect recently published denial of service attacks.


title: Battery powered BION FES network

abstract: The Alfred Mann Foundation is completing development of a coordinated network of BION/spl reg/ microstimulator/sensor (hereinafter implant) that has broad stimulating, sensing and communication capabilities. The network consists of a master control unit (MCU) in communication with a group of BION implants. Each implant is powered by a custom lithium-ion rechargeable 10 mW-hr battery. The charging, discharging, safety, stimulating, sensing, and communication circuits are designed to be highly efficient to minimize energy use and maximize battery life and time between charges. The stimulator can be programmed to deliver pulses in any value in the following range: 5 /spl mu/A to 20 mA in 3.3% constant current steps, 7 /spl mu/s to 2000 /spl mu/s in 7 /spl mu/s pulse width steps, and 1 to 4000 Hz in frequency. The preamp voltage sensor covers the range 10 /spl mu/V to 1.0 V with bandpass filtering and several forms of data analysis. The implant also contains sensors that can read out pressure, temperature, DC magnetic field, and distance (via a low frequency magnetic field) up to 20 cm between any two BION implants. The MCU contains a microprocessor, user interface, two-way communication system, and a rechargeable battery. The MCU can command and interrogate in excess of 800 BlON implants every 10 ms, i.e., 100 times a second.


title: Blood-brain barrier disruption induced by diagnostic ultrasound combined with microbubbles in mice

abstract: // Bingxia Zhao 1, 2, * , Yihan Chen 1, 2, * , Jinfeng Liu 1, 2 , Li Zhang 1, 2 , Jing Wang 1, 2 , Yali Yang 1, 2 , Qing Lv 1, 2 and Mingxing Xie 1, 2 1 Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China 2 Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China * These authors contributed equally to this work Correspondence to: Mingxing Xie, email: xiemx@hust.edu.cn Keywords: blood-brain barrier; diagnostic ultrasound; microbubble; drug delivery Received: September 07, 2017 Accepted: December 04, 2017 Published: December 21, 2017 ABSTRACT Objective: To investigate the effects of the microbubble (MB) dose, mechanism index (MI) and sonication duration on blood-brain barrier (BBB) disruption induced by diagnostic ultrasound combined with MBs as well as to investigate the potential molecular mechanism. Results: The extent of BBB disruption increased with MB dose, MI and sonication duration. A relatively larger extent of BBB disruption associated with minimal tissue damage was achieved by an appropriate MB dose and ultrasound exposure parameters with diagnostic ultrasound. Decreased expression of ZO-1, occludin and claudin-5 were correlated with disruption of the BBB, as confirmed by paracellular passage of the tracer lanthanum nitrate into the brain parenchyma after BBB disruption. Conclusions: These findings indicated that this technique is a promising tool for promoting brain delivery of diagnostic and therapeutic agents in the diagnosis and treatment of brain diseases. Methods: The extent of BBB disruption was qualitatively assessed by Evans blue (EB) staining and quantitatively analyzed by an EB extravasation measurement. A histological examination was performed to evaluate tissue damage. Expression of tight junction (TJ) related proteins ZO-1, occludin and claudin-5 was determined by western blotting analysis and immunohistofluorescence. Transmission electron microscopy was performed to observe ultrastructure changes of TJs after BBB disruption.


title: Illuminating the Effects of Dynamic Lighting on Student Learning

abstract: Light is universally understood as essential to the human condition. Yet light quality varies substantially in nature and in controlled environments leading to questions of which artificial light characteristics facilitate maximum learning. Recent research has examined lighting variables of color temperature, and illumination for affecting sleep, mood, focus, motivation, concentration, and work and school performance. This has resulted in artificial light systems intended to support human beings in their actualization through dynamic lighting technology allowing for different lighting conditions per task. A total of 84 third graders were exposed to either focus (6000K-100fc average maintained) or normal lighting. Focus lighting led to a higher percentage increase in oral reading fluency performance (36%) than did control lighting (17%). No lighting effects were found for motivation or concentration, possibly attributable to the younger age level of respondents as compared with European studies. These findings illuminate the need for further research on artificial light and learning.


title: Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales

abstract: Nature provides a wide range of benefits to people. There is increasing consensus about the importance of incorporating these “ecosystem services” into resource management decisions, but quantifying the levels and values of these services has proven difficult. We use a spatially explicit modeling tool, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), to predict changes in ecosystem services, biodiversity conservation, and commodity production levels. We apply InVEST to stakeholder-defined scenarios of land-use/land-cover change in the Willamette Basin, Oregon. We found that scenarios that received high scores for a variety of ecosystem services also had high scores for biodiversity, suggesting there is little tradeoff between biodiversity conservation and ecosystem services. Scenarios involving more development had higher commodity production values, but lower levels of biodiversity conservation and ecosystem services. However, including payments for carbon sequestration alleviates this tradeoff. Quantifying ecosystem services in a spatially explicit manner, and analyzing tradeoffs between them, can help to make natural resource decisions more effective, efficient, and defensible.


title: Convolutional neural network bottleneck features for bi-directional generalized variable parameter HMMs

abstract: Recently, convolutional neural networks (CNNs) have been applied successfully to acoustic modelling in speech recognition. As the bottleneck features from CNNs contain inherently discriminative and rich context information, the standard approach is to augment the conventional acoustic features with the CNN bottleneck features in a tandem framework. To better capture the highly complex relationship between them, a novel bidirectional generalized variable parameter HMM (GVP-HMM) based approach is proposed in this paper. In this approach, the trajectories of continuous acoustic features space HMM parameters, as well as the model space linear transforms against CNN bottleneck features are modelled by polynomial functions. The optimal GVP-HMM model structure for each direction, which is determined by the locally varying polynomial parameters and degrees, can be automatically learnt using model selection techniques. The proposed bi-directional GVP-HMM based approach gave a word error rate of 12.22% on the Aurora 4 task. In particular, a significant error rate reduction of 18.09% relative was obtained over the baseline tandem HMM system using CNN bottleneck features on the secondary microphone channel condition.


title: Towards Exascale Scientific Metadata Management

abstract: Author(s): Blanas, Spyros; Byna, Surendra | Abstract: Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions.


title: Beautification of Design Sketches Using Trainable Stroke Clustering and Curve Fitting

abstract: We propose a new sketch parsing and beautification method that converts digitally created design sketches into beautified line drawings. Our system uses a trainable, sequential bottom-up and top-down stroke clustering method that learns how to parse input pen strokes into groups of strokes each representing a single curve, followed by point-cloud ordering that facilitates curve fitting and smoothing. This approach enables greater conceptual freedom during visual ideation activities by allowing designers to develop their sketches using multiple, casually drawn strokes without requiring them to indicate the separation between different stroke groups. With the proposed method, raw sketches are seamlessly converted into vectorized geometric models, thus, facilitating downstream assessment and editing activities.


title: Loss Functions for Top-k Error: Analysis and Insights

abstract: In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase in the number of classes comes along with increased ambiguity between the class labels, raising the question if top-1 error is the right performance measure. In this paper, we provide an extensive comparison and evaluation of established multiclass methods comparing their top-k performance both from a practical as well as from a theoretical perspective. Moreover, we introduce novel top-k loss functions as modifications of the softmax and the multiclass SVM losses and provide efficient optimization schemes for them. In the experiments, we compare on various datasets all of the proposed and established methods for top-k error optimization. An interesting insight of this paper is that the softmax loss yields competitive top-k performance for all k simultaneously. For a specific top-k error, our new topk losses lead typically to further improvements while being faster to train than the softmax.


title: Modern Recommender Systems: from Computing Matrices to Thinking with Neurons

abstract: Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: from matrix factorization methods and multi-armed bandits to deep neural networks. In this tutorial, we will cover recent algorithmic advances in recommender systems, highlight their capabilities, and their impact. We will give many examples of industrial-scale recommender systems that define the future of the recommender systems area. We will discuss related evaluation issues, and outline future research directions. The ultimate goal of the tutorial is to encourage the application of novel recommendation approaches to solve problems that go beyond user consumption and to further promote research in the intersection of recommender systems and databases.


title: Online Processing Algorithms for Influence Maximization

abstract: Influence maximization is a classic and extensively studied problem with important applications in viral marketing. Existing algorithms for influence maximization, however, mostly focus on offline processing, in the sense that they do not provide any output to the user until the final answer is derived, and that the user is not allowed to terminate the algorithm early to trade the quality of solution for efficiency. Such lack of interactiveness and flexibility leads to poor user experience, especially when the algorithm incurs long running time. To address the above problem, this paper studies algorithms for online processing of influence maximization (OPIM), where the user can pause the algorithm at any time and ask for a solution (to the influence maximization problem) and its approximation guarantee, and can resume the algorithm to let it improve the quality of solution by giving it more time to run. (This interactive paradigm is similar in spirit to online query processing in database systems.) We show that the only existing algorithm for OPIM is vastly ineffective in practice, and that adopting existing influence maximization methods for OPIM yields unsatisfactory results. Motivated by this, we propose a new algorithm for OPIM with both superior empirical effectiveness and strong theoretical guarantees, and we show that it can also be extended to handle conventional influence maximization. Extensive experiments on real data demonstrate that our solutions outperform the state of the art for both OPIM and conventional influence maximization.


title: MRI-based assessment of hip joint translations

abstract: To better understand movement limitations and, to some extent, the pathogenesis of osteoarthritis, it is important to quantitatively measure femoroacetabular translations to assess if any joint subluxation occurs. In this paper, we aim at measuring hip joint displacements from magnetic resonance images (MRI) based on a surface registration technique. Because this measurement is related to the location of the hip joint center (HJC), we investigate and compare different HJC estimation approaches based on patient-specific 3D bone models. We estimate the HJC based on a simulated circumduction while minimizing inter-articular distance changes. Measurements of femoroacetabular translations during low amplitude abductions (80 samples) and extreme flexions (60 samples) in female professional dancers, which is a population potentially exposed to femoroactebaluar impingements, do not show any significant subluxation.


title: Do We Really Understand Tacit Knowledge?

abstract: This paper advances the claim that tacit knowledge has been greatly misunderstood in management studies. Nonaka and Takeuchi’s widely adopted interpretation of tacit knowledge as knowledge awaiting “translation” or “conversion” into explicit knowledge is erroneous: contrary to Polanyi’s argument, it ignores the essential ineffability of tacit knowledge. In the paper I show why the idea of focussing on a set of tacitly known particulars and “converting” them into explicit knowledge is unsustainable. However, the ineffability of tacit knowledge does not mean that we cannot discuss the skilled performances in which we are involved. We can discuss them provided we stop insisting on “converting” tacit knowledge and, instead, start recursively drawing our attention to how we draw each other’s attention to things. Instructive forms of talk help us re-orientate ourselves to how we relate to others and the world around us, thus enabling us to talk and act differently. Following Wittgenstein and Shotter, I argue that we can command a clearer view of our skilled performances if we “re-mind” ourselves of how we do things, so that distinctions, which we had previously not noticed, and features, which had previously escaped our attention, may be brought forward. We cannot operationalise tacit knowledge but we can find new ways of talking, fresh forms of interacting and novel ways of distinguishing and connecting. Tacit knowledge cannot be “captured”, “translated”, or “converted” but only displayed and manifested, in what we do. New knowledge comes about not when the tacit becomes explicit, but when our skilled performance is punctuated in new ways through social interaction. Presented to Knowledge Economy and Society Seminar, LSE Department of Information Systems, 14 June 2002


title: Human reconsolidation does not always occur when a memory is retrieved: The relevance of the reminder structure

abstract: Abstract Memory reconsolidation is defined as a process in which the retrieval of a previously consolidated memory returns to a labile state which is then subject to stabilization. The reminder is the event that begins with the presentation of the learned cue and triggers the labilization-reconsolidation process. Since the early formulation of the hypothesis, several controversial items have arisen concerning the conditions that define reconsolidation. It is herein proposed that two diagnostic features characterize reconsolidation, namely: the labilization of the reactivated memory and the specificity of the reminder structure. To study this proposal, subjects received two different training sessions on verbal material on Day 1 and Day 2, respectively. Finally, they were tested for the first and second acquired memories on Day 3. It is demonstrated that the human declarative memory fulfills the two requirements that define the process. First, the reactivated memory is impaired by a new learning only when it is given closely after the reminder, revealing that the memory is labilized. Second, the omission of at least one of the reminder’s components prevents labilization. Therefore, results show that the new learning fails to produce an amnesic effect on the target memory either when the reminder omits the learned cue or includes the beginning of the reinforcement.


title: A Bayesian Nonparametric Approach for Multi-label Classification

abstract: Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for prediction. Traditional classification methods have attempted to reveal these correlations in different ways. However, existing methods demand expensive computation complexity for finding such correlation structures. Furthermore, these approaches can not identify the suitable number of label-feature correlation patterns. In this paper, we propose a Bayesian nonparametric (BNP) framework for multi-label classification that can automatically learn and exploit the unknown number of multi-label correlation. We utilize the recent techniques in stochastic inference to derive the cheap (but efficient) posterior inference algorithm for the model. In addition, our model can naturally exploit the useful information from missing label samples. Furthermore, we extend the model to update parameters in an online fashion that highlights the flexibility of our model against the existing approaches. We compare our method with the state-of-the-art multi-label classification algorithms on real-world datasets using both complete and missing label settings. Our model achieves better classification accuracy while our running time is consistently much faster than the baselines in an order of magnitude.


title: In Search of an Internet of Things Service Architecture: REST or WS-*? A Developers ’ Perspective

abstract: Current trends inspired from the development of the Web 2.0 advocate designing smart things (e.g., wireless sensors nodes or home appliances) as service platforms. Interoperable services are mainly achieved using two different approaches: WS-* and RESTful Web services. These approaches have previously been compared with respect to performance and features, but no work has been done to elicit the developers’ preferences and programming experiences. We conducted a study in which 69 novice developers learned both technologies and implemented mobile phone applications that retrieve sensor data, both through a RESTful and through a WS-* service architecture. The results complement the available technological decision framework when building Internet of Things applications. The results suggest that developers find REST easier to learn than WS-* and consider it more suitable for programming smart things. However, for applications with advanced security and Quality of Service requirements, WS-* Web services are perceived to be better suited.


title: Conceptual analysis of fire fighting robots' control systems

abstract: This paper is dedicated to the overview of the firefighting robots' control systems. The main goal of this paper is to show the variety of different firefighting robots and to analyze their advantages and imperfections.


title: Prospective products and benefits of the Green AGH Campus Project: Providing scaled-down future smart grid experience

abstract: Smart grid stakeholders have partially opposing goals. New tools and utilities are needed to reach them. The paper discusses prospective products and benefits of the Green AGH Campus Project. The project provides energy management infrastructure being a test bed for future smart grid solutions. It is based on three main components: an Advanced Distribution Management System, a Power Data Warehouse and a Simulation System. Coupled together, they are capable of providing advices regarding either optimization of grid operations, or its design and topology. The optimizations are multicriteria and multivariant balancing stakeholders' needs.


Foreign object detection (FOD) using multi-class classifier with single camera vs. distance map with stereo configuration


title: Business process rearrangement and renaming

abstract: Purpose – Process orientation, which involves managing organizations based on horizontal end-to-end processes, has been shown to increase the quality of products/services, decrease costs, and make business functions faster and more reliable. However, current process orientation methods are radical and destructive, leading to failure in most cases. The purpose of this paper is to present a non-destructive method of implementation. Design/methodology/approach – Supporting by literature review and two case studies, this paper presents a new process orientation methodology named as Business Process Rearrangement and Renaming (BPR2). Findings – The existing process management methodologies mostly aim to provide a comprehensive view on all of the main activities involved in process management and strategic alignment, with insignificant focus on mitigating the risk of failure in their redesign stage. In contrast, every step in the methodology presented in this paper, including the design phase, aim to reduce the...


title: Resorcinol peels as a possible self-treatment of painful nodules in hidradenitis suppurativa

abstract: Summary ::: Background. Hidradenitis suppurativa (HS) is a chronic, inflammatory skin disease characterized by abscess formation localized to apocrine sweat gland-bearing skin. The most important factor in patients’ overall assessment of disease severity is pain. The duration of abscesses takes days to weeks and are always painful. ::: ::: Aim. To assess the efficacy of self-treatment with topical 15% resorcinol in an open study. ::: ::: Methods. The case notes of 12 women with stage 1 or 2 HS treated with topical resorcinol and followed up for at least 1 year were reviewed. The patients rated the efficacy of treatment on global maximum pain of nodules and abscesses on a visual analogue scale (VAS) and by self-report of the mean duration (days) of a painful lesion. ::: ::: Results. All patients experienced a significant decrease in pain as assessed by VAS and reported a reduction in mean duration of the painful abscesses. ::: ::: Conclusions. Topical treatment with 15% resorcinol reduced pain from painful nodules in all patients with HS. Further trials are warranted to confirm these results.


title: Development and Validation of the Goal Content for Exercise Questionnaire

abstract: Self-determination theory (SDT; Deci & Ryan, 2000) proposes that intrinsic, relative to extrinsic, goal content is a critical predictor of the quality of an individual’s behavior and psychological well-being. Through three studies, we developed and psychometrically tested a measure of intrinsic and extrinsic goal content in the exercise context: the Goal Content for Exercise Questionnaire (GCEQ). In adults, exploratory (N = 354; Study 1) and confrmatory factor analyses (N = 312; Study 2) supported a 20-item solution consisting of 5 lower order factors (i.e., social affliation, health management, skill development, image and social recognition) that could be subsumed within a 2-factor higher order structure (i.e., intrinsic and extrinsic). Evidence for external validity, temporal stability, gender invariance, and internal consistency of the GCEQ was found. An independent sample (N = 475; Study 3) provided further support for the lower order structure of the GCEQ and some support for the higher order struct...


title: ST-CTC: a spanning tree-based competitive and truly complete coverage algorithm for mobile robots

abstract: In this paper, we propose a new robot coverage algorithm using approximate cellular decomposition. While in most coverage algorithms using approximate cellular decomposition, only a resolution-complete coverage is achievable, the proposed algorithm achieves complete coverage in true sense by covering even partially occupied cells, with minimal (or no) overlapping/retracing of the path. The algorithm is implemented at graph level in Matlab. Further, the performance of the proposed algorithm is compared with that of STC and Competitive-STC, two representative approximate cellular decomposition based coverage algorithms reported in the literature.


title: High bandwidth 0.35μm CMOS transimpedance amplifier

abstract: A transimpedance amplifier (TIA) has been designed in a 0.35 μm digital CMOS technology for Gigabit Ethernet. It is based on the structure proposed by Mengxiong Li [1]. This paper presents an amplifier which exploits the regulated cascode (RGC) configuration as the input stage with an integrated optical receiver which consists of an integrated photodetector, thus achieving as large effective input transconductance as that of Si Bipolar or GaAs MESFET. The RGC input configuration isolates the input parasitic capacitance including photodiode capacitance from the bandwidth determination better than common-gate TIA. A series inductive peaking is used for enhancing the bandwidth. The proposed TIA has transimpedance gain of 51.56 dBΩ, and 3-dB bandwidth of 6.57 GHz with two inductor between the RGC and source follower for 0.1 pF photodiode capacitance. The proposed TIA has an input courant noise level of about 21.57 pA/Hz0.5 and it consumes DC power of 16 mW from 3.3 V supply voltage.


title: Anatomy of Mandibular Vital Structures. Part II: Mandibular Incisive Canal, Mental Foramen and Associated Neurovascular Bundles in Relation with Dental Implantology

abstract: Objectives: The purpose of the present study was to review the literature of how to identify the mental foramen, mandibular incisive canal and associated neurovascular bundles during implant surgery and how to detect and avoid the damage of these vital structures during implant therapy. Material and Methods: Literature was selected through a search of PubMed, Embase and Cochrane electronic databases. The keywords used for search were mandibular incisive canal, mental foramen, mental nerve, anterior mental loop. The search was restricted to English language articles, published from 1979 to November 2009. Additionally, a manual search in the major anatomy, dental implant, and periodontal journals and books was performed. Results: In total, 47 literature sources were obtained and reviewed. The morphology and variations of the mandibular incisive canal, mental foramen and associated neurovascular bundles were presented as two entities. It suggested that clinicians should carefully assess these vital structures to avoid nerve/artery damage. Conclusions: The mandibular incisive canal, mental foramen and associated neurovascular bundles exist in different locations and possess many variations. Individual, gender, age, race, assessing technique used and degree of edentulous alveolar bone atrophy largely influence these variations. It suggests that the clinicians should carefully identify these anatomical landmarks, by analyzing all influencing factors, prior to their implant surgical operation.


title: IOT base smart home appliances by using Cloud Intelligent Tetris Switch

abstract: To enhance the convenience of life, Internet of things today is a famous research topic. However, different home appliances provide different functions and services. Hence, in this research, the IOT base Smart Home Appliances by using Cloud Intelligent Tetris Switch is proposed which including the Cloud Intelligent Tetris Switch, Cloud Home as a Service (HaaS) Server, and IOT based Appliances. The Cloud Intelligent Tetris Switch is proposed to achieve the power control and local data exchanging. In addition, the dynamic extendable module is embedded. The IOT based Appliances provide the service of identification. Similar to the EPC network, the corresponding home appliance description data with RFID unique number can be obtained from the Internet and manufacture. The Cloud Home as a Service (HaaS) Server is proposed to provide the user interface for client users, storage all the information or data corresponding to the specific house, and query the function information of individual home appliance.


title: CCNet: Criss-Cross Attention for Semantic Segmentation

abstract: Full-image dependencies provide useful contextual information to benefit visual understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for obtaining such contextual information in a more effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module in CCNet harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies from all pixels. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block in computing full-image dependencies. 3) The state-of-the-art performance. We conduct extensive experiments on popular semantic segmentation benchmarks including Cityscapes, ADE20K, and instance segmentation benchmark COCO. In particular, our CCNet achieves the mIoU score of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results. The source code is available at https://github.com/speedinghzl/CCNet.


title: LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLES

abstract: License Plate Recognition (LPR) has been intensively studied in many countries. Due to the different types of number plates being used, the requirements of an automatic number plate recognition system are different for each country. In general, objective of any LPR system is to localize and recognize potential license plate region(s) of the vehicle images captured from handheld device/digital camera/mobile phone camera. The proposed method for License Plate Recognition (LPR) system works in three modules: localization of license plate, segmentation of the characters and recognition of the characters from the license plate. Localization of the license plate is done using morphological operations, horizontal & vertical edge processing. Character segmentation is carried out using connected component labeling. Character recognition is done by using Neural Network classifier. The method is tested on 100 samples of Indian vehicle images. The system achieves 86% accuracy in localization, 81% accuracy in segmentation and 80% accuracy in character recognition.


title: FMEDA-Based Fault Injection and Data Analysis in Compliance with ISO-26262

abstract: With the growing demand on automotive electronics for the advanced driver assistance systems and autonomous driving, the functional safety becomes one of the most important issues in the hardware development. Thus, the safety standard for automotive E/E system, ISO-26262, becomes state-of-the-art guideline to ensure that the required safety level can be achieved. In this study, we base on ISO-26262 to develop a FMEDA-based fault injection and data analysis framework. The main contribution of this study is to effectively reduce the effort for generating FMEDA report which is used to evaluate hardware's safety level based on ISO-26262 standard.


title: Inverse molecular design using machine learning: Generative models for matter engineering

abstract: The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.


title: PuDianNao: A Polyvalent Machine Learning Accelerator

abstract: Machine Learning (ML) techniques are pervasive tools in various emerging commercial applications, but have to be accommodated by powerful computer systems to process very large data. Although general-purpose CPUs and GPUs have provided straightforward solutions, their energy-efficiencies are limited due to their excessive supports for flexibility. Hardware accelerators may achieve better energy-efficiencies, but each accelerator often accommodates only a single ML technique (family). According to the famous No-Free-Lunch theorem in the ML domain, however, an ML technique performs well on a dataset may perform poorly on another dataset, which implies that such accelerator may sometimes lead to poor learning accuracy. Even if regardless of the learning accuracy, such accelerator can still become inapplicable simply because the concrete ML task is altered, or the user chooses another ML technique. In this study, we present an ML accelerator called PuDianNao, which accommodates seven representative ML techniques, including k-means, k-nearest neighbors, naive bayes, support vector machine, linear regression, classification tree, and deep neural network. Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, PuDianNao can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm^2, and consumes 596 mW only. Compared with the NVIDIA K20M GPU (28nm process), PuDianNao (65nm process) is 1.20x faster, and can reduce the energy by 128.41x.


title: Statistical Measure of a Population’s Propensity to Engage in Post-purchase Online

abstract: The emergence of online communities has enabled firms to monitor consumer-generated online word-of-mouth (WOM) in real-time by mining publicly available information from the Internet. A prerequisite for harnessing this new ability is the development of appropriate WOM metrics and the identification of relationships between such metrics and consumer behavior. Along these lines this paper introduces a metric of a purchasing population's propensity to rate a product online. Using data from a popular movie website we find that our metric exhibits several relationships that have been previously found to exist between aspects of a product and consumers' propensity to engage in offline WOM about it. Our study, thus, provides positive evidence for the validity of our metric as a proxy of a population's propensity to engage in post-purchase online WOM. Our results also suggest that the antecedents of offline and online WOM exhibit important similarities.


title: TUBE: time-dependent pricing for mobile data

abstract: The two largest U.S. wireless ISPs have recently moved towards usage-based pricing to better manage the growing demand on their networks. Yet usage-based pricing still requires ISPs to over-provision capacity for demand at peak times of the day. Time-dependent pricing (TDP) addresses this problem by considering when a user consumes data, in addition to how much is used. We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE. TUBE creates a price-based feedback control loop between an ISP and its end users. On the ISP side, it computes TDP prices so as to balance the cost of congestion during peak periods with that of offering lower prices in less congested periods. On mobile devices, it provides a graphical user interface that allows users to respond to the offered prices either by themselves or using an "autopilot" mode. We conducted a pilot TUBE trial with 50 iPhone or iPad 3G data users, who were charged according to our TDP algorithms. Our results show that TDP benefits both operators and customers, flattening the temporal fluctuation of demand while allowing users to save money by choosing the time and volume of their usage.


title: Large-scale video retrieval via semantic classification

abstract: Motivated by Google's great success on text document retrieval and recent progresses of semantic video understanding, researchers begin to build new generation of video retrieval systems that are able to support semantic sensitive video retrieval via keywords. Unfortunately, these systems are not able to provide satisfactory results for the masses because of several inter-related challenging problems. We have proposed novel algorithms to resolve some of these problems. Firstly, the salient object based semantic classification algorithm is proposed to extract semantic concepts of video clips. Secondly, the video visualization based interactive retrieval framework is proposed to help users input semantic and visual queries efficiently and effectively. Finally, the concept-oriented skimming algorithm is proposed to help users efficiently check search results.


title: Dynamic simulation of pneumatic muscle actuator in Matlab/Simulink environment

abstract: The pneumatic actuator with artificial muscles in antagonistic connection was designed and realized as result of several research projects solved at the authors' workplace. Three simulation models of this actuator using different muscle models were created in Matlab/Simulink environment for obtaining of some actuator dynamic characteristics. The position of the actuator arm can be obtained from subsystem of gear nonlinearity in simulation models. In the paper there are presented two approaches to the modeling of gear mechanism: by measured and approximated static characteristic of the actuator and by mathematical description of gear using equation of motion for rotation movement. The dynamic responses of the actuator arm position obtained by simulation of the designed models are also presented in the paper.


title: Assessment Construction and Factorial Validation of a Short Form of the Self-Compassion Scale

abstract: The objective of the present study was to construct and validate a short-form version of the Self-Compassion Scale (SCS). Two Dutch samples were used to construct and cross-validate the factorial structure of a 12-item Self-Compassion Scale–Short Form (SCS–SF). The SCS-SF was then validated in a third, English sample. The SCS–SF demonstrated adequate internal consistency (Cronbach's alpha ≥ 0.86 in all samples) and a near-perfect correlation with the long form SCS (r ≥ 0.97 all samples). Confirmatory factor analysis on the SCS–SF supported the same six-factor structure as found in the long form, as well as a single higher-order factor of self-compassion. The SCS–SF thus represents a reliable and valid alternative to the long-form SCS, especially when looking at overall self-compassion scores. Copyright © 2010 John Wiley & Sons, Ltd. ::: ::: Key Practitioner Message: ::: ::: • The 12-item Self-Compassion Scale–Short Form (SCS–SF) in Dutch and English offers an economical alternative to the long Self-Compassion Scale (SCS) to measure self-compassion. Although the original long form of the SCS is reduced to half, the SCS–SF is reliable and has the same factorial structure as the original scale.


title: Optimization of silver paste printed passive UHF RFID tags

abstract: Passive UHF (Ultra High Frequency) RFID (Radio Frequency Identification) is a promising technology for products tracking in logistics or routing packages in supply chain. Usually, the UHF RFID antenna is made by etched copper, or aluminum. These etching process introduce chemical wastes during the etching process. Recently, printing silver paste becomes popular due to its environmental friendly manufacturing process. However, there are two drawbacks on printed silver paste RFID antenna. One is low conductivity compared to metal and the other is the high material cost. In this paper, we exam the effects of printing thickness on the performance of UHF RFID tags and summarize the optimized thickness for different read range requirements. A commercial conductive silver paste is printed on PET film as RFID tag antennas. Both finite element simulations and experiments are conducted to evaluate the effects of printed thickness. The simulation results presents that the 10 um thick RFID antenna exhibits relatively good radiation efficiency. The results indicate that 10 um thick RFID tag antenna presents the minimum turn-on power of 18 dBm to 22 dBm which is enough for most applications. The simulation and measurement results present that thinner printed silver paste RFID tag antenna is a potential solution for low cost UHF RFID tags. The cost of these tags could be lower than current etched Al/Cu RFID tags because of material saving.


title: Specifying Distributed Software Architectures

abstract: There is a real need for clear and sound design specifications of distributed systems at the architectural level. This is the level of the design which deals with the high-level organisation of computational elements and the interactions between those elements. The paper presents the Darwin notation for specifying this high-level organisation. Darwin is in essence a declarative binding language which can be used to define hierarchic compositions of interconnected components. Distribution is dealt with orthogonally to system structuring. The language supports the specification of both static structures and dynamic structures which may evolve during execution. The central abstractions managed by Darwin are components and services. Services are the means by which components interact.


title: Feedback-Guided Anomaly Discovery via Online Optimization

abstract: Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. This can be exceedingly difficult and time consuming when most high-ranking anomalies are false positives and not interesting from an application perspective. In this paper, we study how to reduce the analyst's effort by incorporating their feedback about whether the anomalies they investigate are of interest or not. In particular, the feedback will be used to adjust the anomaly ranking after every analyst interaction, ideally moving anomalies of interest closer to the top. Our main contribution is to formulate this problem within the framework of online convex optimization, which yields an efficient and extremely simple approach to incorporating feedback compared to the prior state-of-the-art. We instantiate this approach for the powerful class of tree-based anomaly detectors and conduct experiments on a range of benchmark datasets. The results demonstrate the utility of incorporating feedback and advantages of our approach over the state-of-the-art. In addition, we present results on a significant cybersecurity application where the goal is to detect red-team attacks in real system audit data. We show that our approach for incorporating feedback is able to significantly reduce the time required to identify malicious system entities across multiple attacks on multiple operating systems.


title: Usage pattern, physical integrity & insecticidal efficacy of long-lasting insecticidal nets in Odisha State, India

abstract: Background & objectives: One of the major strategies being pursued for malaria control by the National Vector Borne Disease Control Programme is the distribution of long lasting insecticidal nets (LLINs) in endemic areas since 2009. Information on durability of insecticidal efficacy and physical integrity of LLINs and community usage at different time intervals of their use is essential to plan net replacements to maintain universal coverage for an effective and sustainable malaria control strategy. Therefore, a study was undertaken to assess these parameters in two malaria endemic districts of the Odisha State. Methods: A total of 309 households were selected in 15 villages of two community health centres (CHCs) (Borigumma and Laxmipur) from Koraput district and one (Khairput) from Malkangiri district. Data on net usage were collected during March to July 2014 using semi-structured questionnaires. PermaNet 2.0 were sampled from all households, replaced with new ones, and bioassays were carried out to determine the insecticidal efficacy of LLINs after four and half, four and two years of field use following the standard procedure of World Health Organization. Results: LLIN use rate varied from 57.9 to 90.2 per cent in the study CHCs. The annual washing rate per net in Borigumma, Khairput and Laxmipur was 6.6, 3.2 and 4.8, respectively. The LLINs used two years in the field caused 100 per cent mortality and four to four and half years caused below 80 per cent mortality, except one net. Interpretation & conclusions: Nearly 20 per cent of the people were out of net coverage and hence the Programme to ensure 100 per cent coverage. The community should adequately be educated so as to increase the net use rate and avoid incorrect washing practices.


title: An Empirical Comparison of Statistical Term Association Graphs with DBpedia and ConceptNet for Query Expansion

abstract: Term graphs constructed from document collections as well as external resources, such as encyclopedias (DBpedia) and knowledge bases (ConceptNet), can be used as sources of semantically related terms for query expansion. Although these resources individually have been shown to be effective for IR, it is not known how their retrieval effectiveness compares with each other. In this work, we use standard TREC collections to perform systematic evaluation and empirical comparison of retrieval effectiveness of both types of term graphs for all and difficult queries. Our results indicate that of the term association graphs constructed automatically from document collection using information theoretic measures are more effective for Web collections, while the term graphs derived from DBpedia and ConceptNet are more effective for newswire collections.


title: JColibri: An Object-Oriented Framework for Building CBR Systems

abstract: We present an object-oriented framework in Java for building CBR systems that is an evolution of previous work on knowledge intensive CBR [8,9]. JColibri is a software artifact that promotes software reuse for building CBR systems, integrating the application of well proven Software Engineering techniques with a knowledge level description that separates the problem solving method, that defines the reasoning process, from the domain model, that describes the domain knowledge. Framework instantiation is supported by a graphical interface that guides the configuration of a particular CBR system, alleviating the steep learning curve typical for these type of systems.


title: Interpreting random forest models using a feature contribution method

abstract: Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.


title: Challenges of Marketing E-banking Services in a Developing Country: The Case of Ghana

abstract: Even though there is a plethora of studies investigating the challenges of adopting e-banking services, a search through the literature indicates that prior studies have investigated either user adoption challenges or the bank implementation challenges. This study integrated both perspectives to provide a broader conceptual framework for investigating challenges banks face in marketing e-banking services in developing country such as Ghana. The results from the mixed method study indicated that institutional–based challenges as well as user- based challenges affect the marketing of e-banking products in Ghana. The strategic implications of the findings for marketing e-banking services are discussed to guide managers to implement e-banking services in Ghana.


title: Power Modeling and Optimization for OLED Displays

abstract: Emerging organic light-emitting diode (OLED)-based displays obviate external lighting, and consume drastically different power when displaying different colors, due to their emissive nature. This creates a pressing need for OLED display power models for system energy management, optimization as well as energy-efficient GUI design, given the display content or even the graphical-user interface (GUI) code. In this work, we study this opportunity using commercial QVGA OLED displays and user studies. We first present a comprehensive treatment of power modeling of OLED displays, providing models that estimate power consumption based on pixel, image, and code, respectively. These models feature various tradeoffs between computation efficiency and accuracy so that they can be employed in different layers of a mobile system. We validate the proposed models using a commercial QVGA OLED module and a mobile device with a QVGA OLED display. Then, based on the models, we propose techniques that adapt GUIs based on existing mechanisms as well as arbitrarily under usability constraints. Our measurement and user studies show that more than 75 percent display power reduction can be achieved with user acceptance.


title: Nickel Sulfide Thin Films from Thio- and Dithiobiuret Precursors

abstract: The nickel(II) complexes of several 1,1,5,5-tetraalkyl-2-thiobiurets (R = methyl (1); methyl, ethyl (2); ethyl (3); isopropyl (4)) and 1,1,5,5-tetraalkyl-2,4-dithiobiurets (R = methyl (5); methyl, ethyl (6); ethyl (7)) have been synthesized. The single crystal X-ray structures of complexes (1), (3), (4), (6), and (7) have been determined. Thermogravimetric analysis shows all seven complexes decompose in a single step to one or another form of nickel sulfide. The complexes were used as single-source precursors for the deposition of nickel sulfide thin films by aerosol assisted chemical vapor deposition (AACVD) at temperatures between 320 and 480 °C. Complex (1) gave orthorhombic Ni7S6 at all temperatures with spherical tipped wirelike crystallites and plates. Complex (2) gave mixtures of hexagonal Ni17S18 and orthorhombic Ni7S6 with wires and plates. Complex (3) also led to a mixture of hexagonal Ni17S18 and orthorhombic Ni7S6 phases but with platelike crystallites. In contrast complex (4) gave orthorhombi...


title: 3D Collaboration Method over HoloLens™ and Skype™ End Points

abstract: This paper describes a new Augmented Reality (AR) system called HoloLens developed by Microsoft, and the interaction model for supporting collaboration in this space with other users. Whereas traditional AR collaboration is between two or more head-mounted displays (HMD) users, we describe collaboration between a single HMD user and others who join the space by hitching on the view of the HMD user. The remote companions participate remotely through Skype-enabled devices such as tablets or PC's. The interaction is novel in the use of a 3D space with digital objects where the interaction by remote parties can be achieved asynchronously and reflected back to the primary user. We describe additional collaboration scenarios possible with this arrangement.


title: Layrub: layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems

abstract: Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities of memory reuse on both intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that cannot be run on one single GPU.


title: Attention-over-Attention Neural Networks for Reading Comprehension

abstract: Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces "attended attention" for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attention-over-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children's Book Test datasets.


title: Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS)

abstract: One method of evaluating the degree of neurologic impairment in MS has been the combination of grades (0 = normal to 5 or 6 = maximal impairment) within 8 Functional Systems (FS) and an overall Disability Status Scale (DSS) that had steps from 0 (normal) to 10 (death due to MS). A new Expanded Disability Status Scale (EDSS) is presented, with each of the former steps (1,2,3 … 9) now divided into two (1.0, 1.5, 2.0 … 9.5). The lower portion is obligatorily defined by Functional System grades. The FS are Pyramidal, Cerebellar, Brain Stem, Sensory, Bowel & Bladder, Visual, Cerebral, and Other; the Sensory and Bowel & Bladder Systems have been revised. Patterns of FS and relations of FS by type and grade to the DSS are demonstrated.


title: Device and System Level Design Considerations for Analog-Non-Volatile-Memory Based Neuromorphic Architectures

abstract: This paper gives an overview of recent progress in the brain-inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requirements and design targets on multilevel states, device variability, programming energy, array-level connectivity, fan-in/fan-out, wire energy, and IR drop are presented. Wires are increasingly important in design decisions, especially for large systems; and cycle-to-cycle variations have large impact on learning performance.


title: Entity Linking for Tweets

abstract: We study the task of entity linking for tweets, which tries to associate each mention in a tweet with a knowledge base entry. Two main challenges of this task are the dearth of information in a single tweet and the rich entity mention variations. To address these challenges, we propose a collective inference method that simultaneously resolves a set of mentions. Particularly, our model integrates three kinds of similarities, i.e., mention-entry similarity, entry-entry similarity, and mention-mention similarity, to enrich the context for entity linking, and to address irregular mentions that are not covered by the entity-variation dictionary. We evaluate our method on a publicly available data set and demonstrate the effectiveness of our method.


title: Depth Estimation From a Light Field Image Pair With a Generative Model

abstract: In this paper, we propose a novel method to estimate the disparity maps from a light field image pair captured by a pair of light field cameras. Our method integrates two types of critical depth cues, which are separately inferred from the epipolar plane images and binocular stereo vision into a global solution. At the same time, in order to produce highly accurate disparity maps, we adopt a generative model, which can estimate a light field image only with the central subaperture view and corresponding hypothesized disparity map. The objective function of our method is formulated to minimize two energy terms/differences. One is the difference between the two types of previously extracted disparity maps and the target disparity maps, directly optimized in the gray-scale disparity space. The other indicates the difference between the estimated light field images and the input light field images, optimized in the RGB color space. Comprehensive experiments conducted on real and virtual scene light field image pairs demonstrate the effectiveness of our method.


title: Online multi-object tracking with efficient track drift and fragmentation handling

abstract: This paper addresses the problem of multi-object tracking in complex scenes by a single, static, uncalibrated camera. Tracking-by-detection is a widely used approach for multi-object tracking. Challenges still remain in complex scenes, however, when this approach has to deal with occlusions, unreliable detections (e.g., inaccurate position/size, false positives, or false negatives), and sudden object motion/appearance changes, among other issues. To handle these problems, this paper presents a novel online multi-object tracking method, which can be fully applied to real-time applications. First, an object tracking process based on frame-by-frame association with a novel affinity model and an appearance update that does not rely on online learning is proposed to effectively and rapidly assign detections to tracks. Second, a two-stage drift handling method with novel track confidence is proposed to correct drifting tracks caused by the abrupt motion change of objects under occlusion and prolonged inaccurate detections. In addition, a fragmentation handling method based on a track-to-track association is proposed to solve the problem in which an object trajectory is broken into several tracks due to long-term occlusions. Based on experimental results derived from challenging public data sets, the proposed method delivers an impressive performance compared with other state-of-the-art methods. Furthermore, additional performance analysis demonstrates the effect and usefulness of each component of the proposed method.


title: Polly-ACC Transparent compilation to heterogeneous hardware

abstract: Programming today's increasingly complex heterogeneous hardware is difficult, as it commonly requires the use of data-parallel languages, pragma annotations, specialized libraries, or DSL compilers. Adding explicit accelerator support into a larger code base is not only costly, but also introduces additional complexity that hinders long-term maintenance. We propose a new heterogeneous compiler that brings us closer to the dream of automatic accelerator mapping. Starting from a sequential compiler IR, we automatically generate a hybrid executable that - in combination with a new data management system - transparently offloads suitable code regions. Our approach is almost regression free for a wide range of applications while improving a range of compute kernels as well as two full SPEC CPU applications. We expect our work to reduce the initial cost of accelerator usage and to free developer time to investigate algorithmic changes.


title: NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints

abstract: Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover meaningful subsequences suitable for their own application domains, such as finding gene transcription sites from DNA sequences or discovering patterns for time series data classification. Due to the inherent complexity of sequence patterns, including the exponential candidate space with respect to pattern letters and gap constraints, to date, existing sequence pattern mining methods are either incomplete or do not support the Apriori property because the support ratio of a pattern may be greater than that of its subpatterns. Most importantly, patterns discovered by these methods are either too restrictive or too general and cannot represent underlying meaningful knowledge in the sequences. In this paper, we focus on a nonoverlapping sequence pattern mining task with gap constraints, where a nonoverlapping sequence pattern allows sequence letters to be flexibly and maximally utilized for pattern discovery. A new Apriori -based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed. NOSEP is a complete pattern mining algorithm, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence. Experimental results and comparisons on biology DNA sequences, time series data, and Gazelle datasets demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence patterns compared to other methods.


title: Genetic Algorithms as an Approach to Configuration and Topology Design

abstract: The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of cantilevered plate topologies, and we investigate methods for optimizing finely-discretized design domains. The genetic algorithm’s ability to find families of highly-fit designs is also examined. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.


title: Adaptive Compression of Stereoscopic Images

abstract: Nowadays the growing availability of stereo cameras for common applications is becoming a commodity. This paper addresses the problem of stereoscopic images data compression proposing an innovative algorithm for compressing Multi Picture Object coded stereopairs. By means of self organizing reconstruction algorithm based on image redundancy we are able to reduce the size of the enclosed JPEG images. The overall perceived (and measured) quality is managed by considering that a stereoscopic image represents the same scene acquired from two different perspectives. In particular we achieve some compression gain just encoding the two images with different quality factors. The reported results and test benchmarks show the robustness and efficiency of the proposed algorithm.


ASM: A code manipulation tool to implement adaptable systems


Gene Transfer : Conjugation Structure and Function of the F Factor and Mechanism of Conjugation


title: An Unsupervised Algorithm for Learning Blocking Schemes

abstract: A pair wise comparison of data objects is a requisite step in many data mining applications, but has quadratic complexity. In applications such as record linkage, blocking methods may be applied to reduce the cost. That is, the data is first partitioned into a set of blocks, and pair wise comparisons computed for pairs within each block. To date, blocking methods have required the blocking scheme be given, or the provision of training data enabling supervised learning algorithms to determine a blocking scheme. In either case, a domain expert is required. This paper develops an unsupervised method for learning a blocking scheme for tabular data sets. The method is divided into two phases. First, a weakly labeled training set is generated automatically in time linear in the number of records of the entire dataset. The second phase casts blocking key discovery as a Fisher feature selection problem. The approach is compared to a state-of-the-art supervised blocking key discovery algorithm on three real-world databases and achieves favorable results.


title: Lock, stock, and barrel: a comprehensive assessment of the determinants of terror

abstract: We assess the robustness of previous findings on the determinants of terrorism. Using extreme bound analysis, the three most comprehensive terrorism datasets, and focusing on the three most commonly analyzed aspects of terrorist activity, i.e., location, victim, and perpetrator, we re-assess the effect of 65 proposed correlates. Evaluating around 13.4 million regressions, we find 18 variables to be robustly associated with the number of incidents occurring in a given country-year, 15 variables with attacks against citizens from a particular country in a given year, and six variables with attacks perpetrated by citizens of a particular country in a given year.


title: Can e-learning replace classroom learning?

abstract: In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.


title: Fast prediction algorithm for multiview video coding

abstract: The H.264/multiview video coding (MVC) standard has been developed to enable efficient coding for three-dimensional and multiple viewpoint video sequences. The inter-view statistical dependencies are utilized and an inter-view prediction is employed to provide more efficient coding; however, this increases the overall encoding complexity. Motion homogeneity is exploited here to selectively enable inter-view prediction, and to reduce complexity in the motion estimation (ME) and the mode selection processes. This has been accomplished by defining situations that relate macro-blocks’ motion characteristics to the mode selection and the inter-view prediction processes. When comparing the proposed algorithm to the H.264/MVC reference software and other recent work, the experimental results demonstrate a significant reduction in ME time while maintaining similar rate-distortion performance.


title: Modeling documents with Generative Adversarial Networks

abstract: This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.


title: Neuroevolution-based Inverse Reinforcement Learning

abstract: The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration. The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches. At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations. These valuations may correspond to state value or state reward. This results in better correspondence to observed examples as opposed to using linear combinations. This work also extends existing work on Bayesian Non-Parametric Feature Construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance. The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space. A conclusive performance hierarchy between evaluated algorithms is presented.


HST-582J/6.555J/16.456J Biomedical Signal and Image Processing Chapter 15 -BLIND SOURCE SEPARATION: Principal & Independent Component Analysis


title: Speaker-sensitive dual memory networks for multi-turn slot tagging

abstract: In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neural architecture with Speaker-Sensitive Dual Memory Networks which encode utterances differently depending on the speaker. This addresses the different extents of information available to the system - the system knows only the surface form of user utterances while it has the exact semantics of system output. We performed experiments on real user data from Microsoft Cortana, a commercial personal assistant. The result showed a significant performance improvement over the state-of-the-art slot tagging models using contextual information.


title: Aesthetic response to color combinations: preference, harmony, and similarity

abstract: Previous studies of preference for and harmony of color combinations have produced confusing results. For example, some claim that harmony increases with hue similarity, whereas others claim that it decreases. We argue that such confusions are resolved by distinguishing among three types of judgments about color pairs: (1) preference for the pair as a whole, (2) harmony of the pair as a whole, and (3) preference for its figural color when viewed against its colored background. Empirical support for this distinction shows that pair preference and harmony both increase as hue similarity increases, but preference relies more strongly on component color preference and lightness contrast. Although pairs with highly contrastive hues are generally judged to be neither preferable nor harmonious, figural color preference ratings increase as hue contrast with the background increases. The present results thus refine and clarify some of the best-known and most contentious claims of color theorists.


Towards a Research Framework on Requirements


title: Social capital in virtual learning communities and distributed communities of practice

abstract: Abstract. Social capital has recently emerged as an important interdisciplinary research area. It is frequently used as a framework for understanding various social issues in temporal communities, neighbourhoods and groups. In particular, researchers in the social sciences and the humanities have used social capital to understand trust, shared understanding, reciprocal relationships, social network structures, common norms and cooperation, and the roles these entities play in various aspects of temporal communities. Despite proliferation of research in this area, little work has been done to extend this effort to technology-driven learning communities (also known as virtual learning communities). This paper surveys key interdisciplinary research areas in social capital. It also explores how the notions of social capital and trust can be extended to virtual communities, including virtual learning communities and distributed communities of practice. Research issues surrounding social capital and trust as they relate to technology-driven learning communities are identified.


title: Using Recurrent Neural Network for Learning Expressive Ontologies

abstract: Recently, Neural Networks have been proven extremely effective in many natural language processing tasks such as sentiment analysis, question answering, or machine translation. Aiming to exploit such advantages in the Ontology Learning process, in this technical report we present a detailed description of a Recurrent Neural Network based system to be used to pursue such goal.