title: Simulation of CAN bus physical layer using SPICE

text: This paper deals with a computational simulation of high speed CAN bus physical layer using SPICE. At the beginning, the general electric parameters of the physical layer are described - these parameters are in compliance with the international standard ISO 11898-2. The next part of the paper describes a SPICE library, which contains all basic electronic parts of physical layer, including CAN transceivers, transmission lines, common mode chokes and ESD protections. The model of ESD generator is also included in the library. In the paper are shown the most important results from the illustrative simulation and the results are compared with the results of the experimental measurement.


title: Low-Power High Speed 1-bit Full Adder Circuit Design

text: In this paper we presented a new 13T full adder design based on hybrid --CMOS logic design style. Adders are one of the most basic building blocks in digital components present in the Arithmetic Logic Unit (ALU). The performance of an adder have a significant impact on the overall performance of a digital system. The new design is compared with some existing designs for power consumption, delay, PDP at various frequencies viz 10 MHz, 200 MHz and 1 GHz. the simulations are carried out on Cadence Virtuoso at 180nm CMOS technology and the simulation results are analyzed to verify the superiority of the proposed design over the existing designs. Maximum saving of power delay product is at low frequency by proposed circuit is 96.8% with respect to C-CMOS and significant improvement is observed at other frequencies also. The power consumption increases at a slow rate in comparison to other adders with increase in frequency.


title: 1 . 2 Named Entity Recognition Task

text: In this report, we explore various methods that are applied to solve NER. In section 1, we introduce the named entity problem. In section 2, various named entity recognition methods are discussed in three three broad categories of machine learning paradigm and explore few learning techniques in them. In the first part, we discuss various supervised techniques. Subsequently we move to semi-supervised and unsupervised techniques. In the end we discuss about the method from deep learning to solve NER.


title: Semantic description, discovery and integration for the Internet of Things

text: To share and publish the domain knowledge of IoT objects, the development of a semantic IoT model based directory system that manages meta-data and relationships of IoT objects is required. Many researches focus on static relationships between IoT objects. However, because complex relationships between various resources change with time in an IoT environment, an efficient method for updating the meta-data is required. Thus, we propose an IoT-DS as the IoT directory that supports semantic description, discovery, and integration of IoT objects. Firstly, we introduce a semantic IoT component model to establish a shared conceptualization. Secondly, we present general cases of relationships to efficiently interact between IoT-DS and IoT objects. Thirdly, we construct IoT-DS as a Web portal. Finally, we verify in our evaluation study that the query processing time and communication workload imposed by the proposed approach are reduced.


title: How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks

text: Maybe the single most important goal of representation learning is making subsequent learning faster. Surprisingly, this fact is not well reflected in the way embeddings are evaluated. In addition, recent practice in word embeddings points towards importance of learning specialized representations. We argue that focus of word representation evaluation should reflect those trends and shift towards evaluating what useful information is easily accessible. Specifically, we propose that evaluation should focus on data efficiency and simple supervised tasks, where the amount of available data is varied and scores of a supervised model are reported for each subset (as commonly done in transfer learning). In order to illustrate significance of such analysis, a comprehensive evaluation of selected word embeddings is presented. Proposed approach yields a more complete picture and brings new insight into performance characteristics, for instance information about word similarity or analogy tends to be non–linearly encoded in the embedding space, which questions the cosine–based, unsupervised, evaluation methods. All results and analysis scripts are available online.


title: Torque-Maximizing Design of Double-Stator, Axial-Flux, PM Machines Using Simple Non-Linear Magnetic Analysis

text: This paper presents a torque-maximizing design of a double-stator, axial-flux PM machine using a simple non-linear magnetic analysis. The proposed analysis consists of a geometric- flux-tube-based equivalent-magnetic-circuit model. The model can take into account not only the magnetic saturation but also the three-dimensional(3D) flux distribution. The proposed analysis has realized a dramatic reduction in the computation time compared to 3D-FEA, keeping reasonable analytical accuracy. At first, the validity of the proposed analysis is examined for the various design parameters. After verifying the accuracy of the torque computation of the proposed analysis through the comparisons with 3D-FEA, an 8-pole axial-flux PM machine with double-stator-single-rotor is optimally designed. The performance of the optimized motor are finally verified by 3D-FEA.


title: Fast and Accurate Closest Point Search on Triangulated Surfaces and its Application to Head Motion Estimation

text: The Iterative Closest Point (ICP) algorithm is widely used to register two roughly aligned surfaces. Its most expensive step is the search for the closest point. Many efficient variants have been proposed to speed up this step, however they do not guarantee that the closest point on a triangulated surface is found. Instead they might result in an approximation of the closest point. In this paper we present a method for the closest point search that is fast and exact. The method was implemented and used to evaluate the accuracy of head motion estimation using dense 3D data obtained from a stereo camera system. The results show that the accuracy of the estimated head motion is better than 1 mm for translational movements and better than 1 degree for rotations. Key-Words: Iterative Closest Point, 3D Registration, Stereo, Head Motion Estimation


title: Twitter vigilance: A multi-user platform for cross-domain Twitter data analytics, NLP and sentiment analysis

text: The growth and diffusion of online social media have been enormously increased in recent years, as well as the research and commercial interests toward these rising sources of information as a direct public expression of the communities. Moreover, the depth and the quality of data that can be harvested by monitoring and analysis tools have evolved significantly. In particular, Twitter has revealed to be one of the most widespread microblogging services for instantly publishing and sharing opinions, feedbacks, ratings etc., contributing in the development of the emerging role of users as sensors. However, due to the huge amount of data to be collected and analyzed and limitations on data access imposed by Twitter public APIs, more efficient requirements are needed for analytics tools, both in terms of data ingestion and processing, as well as for the computation of analysis metrics, to be provided for deeper statistic insights and further investigations. In this paper, the Twitter Vigilance platform is presented, realized by the DISIT Lab at University of Florence. Twitter Vigilance has been designed as a cross-domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users' influence network, Natural Language Processing and Sentiment Analysis of textual content. The proposed architecture has been validated against a dataset of about 270 million tweets showing a high efficiency in recovering Twitter data. For this reason it has been adopted by a number of researchers as a study platform for social media analysis, early warning, etc.


title: Stochastic Dynamics for Video Infilling.

text: In this paper, we introduce a stochastic generation framework (SDVI) to infill long intervals in video sequences. To enhance the temporal resolution, video interpolation aims to produce transitional frames for a short interval between every two frames. Video Infilling, however, aims to complete long intervals in a video sequence. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation to guarantee the spatial-temporal coherency among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear and varied sequences. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the referenced start frame to the end frame.


title: A Study of Parsing Process on Natural Language Processing in Bahasa Indonesia

text: Research on Natural Language Processing (NLP) in Indonesian is still limited and the results of available research that can be used for further research are also limited. In a series of natural language processing, the initial step is parsing the sentence in a particular language based on the grammar in order to help understanding the meaning of a sentence. This research aims to produce a simulation of Indonesian parser by adapting the process which was conducted by using Collins Algorithm. The three main stages are: 1) preprocessing to generate corpus and events files, 2) lexical analysis to convert the corpus into tokens, and 3) syntax analysis to build parse tree that requires file events to calculate the probability of the grammar by count the occurrence frequency on file events to determine the best sentence trees. An evaluation was performed to the parser using 30 simple sentences and the outcomes were able to generate a corpus file, file events, parse-tree and probability calculations. Nevertheless some sentences could not be parsed completely true because of the limitations of the Tree bank file in Indonesian. Some future works are to develop complete and valid Tree bank and Lexicon files.


PHANTOM: A Scalable BlockDAG Protocol


title: Smart Health Monitoring Systems: An Overview of Design and Modeling

text: Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.


title: Aerial-guided navigation of a ground robot among movable obstacles

text: We demonstrate the fully autonomous collaboration of an aerial and a ground robot in a mock-up disaster scenario. Within this collaboration, we make use of the individual capabilities and strengths of both robots. The aerial robot first maps an area of interest, then it computes the fastest mission for the ground robot to reach a spotted victim and deliver a first-aid kit. Such a mission includes driving and removing obstacles in the way while being constantly monitored and commanded by the aerial robot. Our mission-planning algorithm distinguishes between movable and fixed obstacles and considers both the time for driving and removing obstacles. The entire mission is executed without any human interaction once the aerial robot is launched and requires a minimal amount of communication between the robots. We describe both the hardware and software of our system and detail our mission-planning algorithm. We present exhaustive results of both simulation and real experiments. Our system was successfully demonstrated more than 20 times at a trade fair.


title: Beat them or ban them: the characteristics and social functions of anger and contempt.

text: This article reports 3 studies in which the authors examined (a) the distinctive characteristics of anger and contempt responses and (b) the interpersonal causes and effects of both emotions. In the 1st study, the authors examined the distinction between the 2 emotions; in the 2nd study, the authors tested whether contempt could be predicted from previous anger incidents with the same person; and in the 3rd study, the authors examined the effects of type of relationship on anger and contempt reactions. The results of the 3 studies show that anger and contempt often occur together but that there are clear distinctions between the 2 emotions: Anger is characterized more by short-term attack responses but long-term reconciliation, whereas contempt is characterized by rejection and social exclusion of the other person, both in the short-term and in the long-term. The authors also found that contempt may develop out of previously experienced anger and that a lack of intimacy with and perceived control over the behavior of the other person, as well as negative dispositional attributions about the other person, predicted the emergence of contempt.


title: FPMR: MapReduce Framework on FPGA A Case Study of RankBoost Acceleration

text: Machine learning and data mining are gaining increasing attentions of the computing society. FPGA provides a highly parallel, low power, and flexible hardware platform for this domain, while the difficulty of programming FPGA greatly limits its prevalence. MapReduce is a parallel programming framework that could easily utilize inherent parallelism in algorithms. In this paper, we describe FPMR, a MapReduce framework on FPGA, which provides programming abstraction, hardware architecture, and basic building blocks to developers. An on-chip processor scheduler is implemented to maximize the utilization of computation resources and achieve better load balancing. An efficient data access scheme is carefully designed to maximize data reuse and throughput. Meanwhile, the FPMR framework hides the task control, synchronization, and communication away from designers so that more attention can be paid to the application itself. A case study of RankBoost acceleration based on FPMR demonstrates that FPMR efficiently helps with the development productivity; and the speedup is 31.8x versus CPU-based implementation. This performance is comparable to a fully manually designed version, which achieves 33.5x speedup. Two other applications: SVM, PageRank are also discussed to show the generalization of the framework.


title: Area-efficient high-speed hybrid 1-bit full adder circuit using modified XNOR gate

text: A hybrid 1-bit full adder design is presented here using modified 3T-XNOR gate to improve the area and speed performance. The design is implemented for 1-bit full adder and then is scaled to 32-bit adder. Combination of CMOS and transmission gate logic is used to enhance the performance in terms of area, delay and power. The performance of the proposed design is evaluated through the simulation analysis in 90-nm technology with 1.2v supply voltage. The effect of scaling on the overall performance is also analyzed through the performance evaluation of 1-bit and 32-bit adder. The performance of proposed design is also compared with conventional design to verify the effectiveness in terms of area, power, delay.


title: Evaluating Knowledge Representation and Reasoning Capabilites of Ontology Specification Languages

text: The interchange of ontologies across the World Wide Web (WWW) and the cooperation among heterogeneous agents placed on it is the main reason for the development of a new set of ontology specification languages, based on new web standards such as XML or RDF. These languages (SHOE, XOL, RDF, OIL, etc) aim to represent the knowledge contained in an ontology in a simple and human-readable way, as well as allow for the interchange of ontologies across the web. In this paper, we establish a common framework to compare the expressiveness of “ traditional” ontology languages (Ontolingua, OKBC, OCML, FLogic, LOOM) and “web-based” ontology languages. As a result of this study, we conclude that different needs in KR and reasoning may exist in the building of an ontology-based application, and these needs must be evaluated in order to choose the most suitable ontology language(s).


title: The SWAL-QOL Outcomes Tool for Oropharyngeal Dysphagiain Adults: II. Item Reduction and Preliminary Scaling

text: The SWAL-QOL outcomes tool was constructed for use in clinical research for patients with oropharyngeal dysphagia. The SWAL-QOL was constructed a priori to enable preliminary psychometric analyses of items and scales before its final validation. This article describes data analysis from a pretest of the SWAL-QOL. We evaluated the different domains of the SWAL-QOL for respondent burden, data quality, item variability, item convergent validity, internal consistency reliability as measured by Cronbach's alpha, and range and skewness of scale scores upon aggregation and floor and ceiling effects. The item reduction techniques outlined reduced the SWAL-QOL from 185 to 93 items. The pretest of the SWAL-QOL afforded us the opportunity to select items for the ongoing validation study which optimally met our a priori psychometric criteria of high data quality, normal item distributions, and robust evidence of item convergent validity.


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

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


title: A Low-Profile Broadband 32-Slot Continuous Transverse Stub Array for Backhaul Applications in $E$ -Band

text: A high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched ( $S_{11} < -13.6$  dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band.


title: News Article Teaser Tweets and How to Generate Them

text: We define the task of teaser generation and provide an evaluation benchmark and baseline systems for it. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read the news item. Teasers are one of the main vehicles for transmitting news to social media users. We compile a novel dataset of teasers by systematically accumulating tweets and selecting ones that conform to the teaser definition. We compare a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al. (2017)’s seq2seq with pointer network.


title: Markov Chain Monte Carlo Maximum Likelihood

text: Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood estimation is explained, and its performance is compared with maximum pseudo likelihood estimation.


title: Modeling and power conditioning for thermoelectric generation

text: In this paper, the principle and basic structure of the thermoelectric module is introduced. The steady- state and dynamic behaviors of a single TE module are characterized. An electric model of TE modules is developed and can be embedded in the simulation software for circuit analysis and design. The issues associated with the application of the TEG models is analyzed and pointed out. Power electronic technologies provide solutions for thermoelectric generation with features such as load interfacing, maximum power point tracking, power conditioning and failed module bypassing. A maximum power point tracking algorithm is developed and implemented with a DC-DC converter and low cost microcontroller. Experimental results demonstrated that the power electronic circuit can extract the maximum electrical power from the thermoelectric modules and feed electric loads regardless of the thermoelectric module's heat flux and load impedance or conditions.


title: Identifying Analogies Across Domains

text: Identifying analogies across domains without supervision is an important task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching sample from the other domain. In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques. We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function. The tasks can be iteratively solved, and as the alignment is improved, the unsupervised translation function reaches quality comparable to full supervision.


title: Photovoltaic based water pumping system

text: In this paper a stand-alone Photovoltaic (PV) systems is presented for water pumping. Solar PV water pumping systems are used for irrigation and drinking water. PV based pumping systems without battery can provide a cost-effective use of solar energy. For the purpose of improving efficiency of the system perturb and observe (P&O) algorithm based Maximum Power Point Tracker (MPPT) is connected to this system. The aim of this paper is to show how to achieve an effective photovoltaic pumping system without battery storage. Results are presented based on different cases of irrigation pumping application and availability of solar irradiance. Simulation results using MATLAB/SIMULINK show that the performance of the controllers both in transient as well as in steady state is quite satisfactory.


title: Quality Management in Systems Development: An Organizational System Perspective

text: We identify top management leadership, a sophisticated management infrastructure, process management efficacy, and stakeholder participation as important elements of a quality-oriented organizational system for software development. A model interrelating these constructs and quality performance is proposed. Data collected through a national survey of IS executives in Fortune 1000 companies and government agencies was used to Robert Zmud was the accepting senior editor for this paper. test the model using a Partial Least Squares analysis methodology. Our results suggest that software quality goals are best attained when top management creates a management infrastructure that promotes improvements in process design and encourages stakeholders to evolve the design of the development processes. Our results also suggest that all elements of the organizational system need to be developed in order to attain quality goals and that piecemeal adoption of select quality management practices are unlikely to be effective. Implications of this research for IS theory and practice are discussed.


title: Experiences with Selecting Search Engines Using Metasearch

text: Search engines are among the most useful and high-profile resources on the Internet. The problem of finding information on the Internet has been replaced with the problem of knowing where search engines are, what they are designed to retrieve, and how to use them. This article describes and evaluates SavvySearch, a metasearch engine designed to intelligently select and interface with multiple remote search engines. The primary metasearch issue examined is the importance of carefully selecting and ranking remote search engines for user queries. We studied the efficacy of SavvySearch's incrementally acquired metaindex approach to selecting search engines by analyzing the effect of time and experience on performance. We also compared the metaindex approach to the simpler categorical approach and showed how much experience is required to surpass the simple scheme.


title: Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

text: We introduce a method to train Binarized Neural Networks (BNNs) neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. To validate the effectiveness of BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line.


title: An Accurate Multi-Row Panorama Generation Using Multi-Point Joint Stitching

text: Most of the existing panorama generation tools require the input images to be captured along one direction, and yield a narrow strip panorama. To generate a large viewing field panorama, this paper proposes a multi-row panorama generation (MRPG) method. For a pan/tilt camera whose scanning path covers a wide range of horizontal and vertical views, the image frames in different views correspond to different coordinate benchmarks and different projections. And the image frame should not only be aligned with the continuous frames in timeline but also be aligned with other frames in spatial neighborhood even with long time intervals. For these problems, MRPG first designs an optimal scanning path to cover the large viewing field, and chooses the center frame as the reference frame to start to stitch. The stitching order of multi-frame is arranged in first-column and second-row to ensure a small alignment error. Moreover, MRPG proposes a multi-point joint stitching method to eliminate the seams and correct the distortions, which makes the current frame accurately integrated into the panoramic canvas from all directions. Experimental results show that MRPG can generate a more accurate panorama than other state-of-the-art image stitching methods, and give a better visual effect for a large viewing field panorama.


title: Privacy-Preserving Image Denoising From External Cloud Databases

text: Along with the rapid advancement of digital image processing technology, image denoising remains a fundamental task, which aims to recover the original image from its noisy observation. With the explosive growth of images on the Internet, one recent trend is to seek high quality similar patches at cloud image databases and harness rich redundancy therein for promising denoising performance. Despite the well-understood benefits, such a cloud-based denoising paradigm would undesirably raise security and privacy issues, especially for privacy-sensitive image data sets. In this paper, we initiate the first endeavor toward privacy-preserving image denoising from external cloud databases. Our design enables the cloud hosting encrypted databases to provide secure query-based image denoising services. Considering that image denoising intrinsically demands high quality similar image patches, our design builds upon recent advancements on secure similarity search, Yao’s garbled circuits, and image denoising operations, where each is used at a different phase of the design for the best performance. We formally analyze the security strengths. Extensive experiments over real-world data sets demonstrate that our design achieves the denoising quality close to the optimal performance in plaintext.


title: Induction and synchronous reluctance motors comparison

text: The aim of this paper is to investigate and compare the performances of two induction motors and two transverse laminated synchronous reluctance motors (induction motor nameplate data: 2.2 and 4 kW, 380 V, 50 Hz, 4 pole). Each induction motor is compared with a synchronous reluctance motor, that has the same stator lamination and winding, but, obviously, different rotor. The analysis has been based both on an analytical and experimental approach. The results have shown that the synchronous reluctance motor, compared to the induction motor, is capable of around a 10% to 15% larger rated torque for a given frame size. The direct comparison of the performances of the motor types has been made both for constant load and constant dissipated power conditions.


title: The learning and use of traversability affordance using range images on a mobile robot

text: We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room tilled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.


title: The role of tutoring in problem solving.

text: THIS PAPER is concerned with the nature of the tutorial process; the means whereby an adult or "expert" helps somebody who is less adult or less expert. Though its aim is general, it is expressed in terms of a particular task: a tutor seeks to teach children aged 3, 4 and 5 yr to build a particular three-dimensional structure that requires a degree of skill that is initially beyond them. It is the usual type of tutoring situation in which one member "knows the answer" and the other does not, rather like a "practical" in which only the instructor "knows how". The changing interaction of tutor and children provide our data. A great deal of early problem solving by the developing child is of this order. Although from the earliest months of life he is a "natural" problem solver in his own right (e.g. Bruner, 1973) it is often the ease that his efforts are assisted and fostered by others who are more skilful than he is (Kaye, 1970). Whether he is learning the procedures that constitute the skills of attending, communicating, manipulating objects, locomoting, or, indeed, a more effective problem solving procedure itself, there are usually others in attendance who help him on his way. Tutorial interactions are, in short, a crucial feature of infancy and childhood. Our species, moreover, appears to be the only one in which any "intentional" tutoring goes on (Bruner, 1972; Hinde, 1971). For although it is true that many of the higher primate species learn by observation of their elders (Hamburg, 1968; van Lawick-Goodall, 1968), there is no evidence that those elders do anything to instruct their charges in the performance of the skill in question. What distinguishes man as a species is not only his capacity for learning, but for teaching as well. It is the main aim of this paper to examine some of the major implications of this interactive, instructional relationship between the developing child and his elders for the study of skill acquisition and problem solving. The acquisition of skill in the human child can be fruitfully conceived as a hierarchical program in which component skills are combined into "higher skills" by appropriate orchestration to meet new, more complex task requirements (Bruner, 1973). The process is analogous to problem solving in which mastery of "lower order" or constituent problems in a sine qua non for success with a larger jjroblcm, each level influencing the other—as with reading where the deciphering of words makes possible the deciphering of sentences, and sentences then aid in the deciphering of particular words (F. Smith, 1971). Given persistent intention in the young learner, given a "lexicon" of constituent skills, the crucial task is often one of com-


title: Increasing fruit and vegetable intake by changing environments, policy and pricing: restaurant-based research, strategies, and recommendations.

text:
BACKGROUND
Restaurants are among the most important and promising venues for environmental, policy, and pricing initiatives to increase fruit and vegetable (F&V) intake. This article reviews restaurant-based environmental, policy and pricing strategies for increasing intake of fruits and vegetables and identifies promising strategies, research needs, and innovative opportunities for the future.

METHODS
The strategies, examples, and research reported here were identified through an extensive search of published journal articles, government documents, the internet, and inquiries to leaders in the field. Recommendations were expanded based on discussion by participants in the CDC/ACS-sponsored Fruit and Vegetable, Environment Policy and Pricing Workshop held in September of 2002.

RESULTS
Six separate types of restaurant-based interventions were identified: increased availability, increased access, reduced prices and coupons, catering policies, point-of-purchase (POP) information, and promotion and communication. Combination approaches have also been implemented. Evaluation data on these interventions show some significant impact on healthful diets, particularly with point-of-purchase information. However, most published reports emphasize low-fat eating, and there is a need to translate and evaluate interventions focused on increasing fruit and vegetable intake.

CONCLUSIONS
Several models for changing environments, policy and pricing to increase fruit and vegetable availability, access, attractiveness and consumption in restaurants have been tested and found to have some promise. There is a need to evaluate fruit and vegetable-specific strategies; to obtain data from industry; to disseminate promising programs; and to enhance public-private partnerships and collaboration to expand on current knowledge.


title: Many regression algorithms, one unified model: A review

text: Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since the seminal work of Rosenblatt (1958). The aims of this paper are to provide an overview of many regression algorithms, and to demonstrate how the function representation whose parameters they regress fall into two classes: a weighted sum of basis functions, or a mixture of linear models. Furthermore, we show that the former is a special case of the latter. Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms from first principles and visualizations of their inner workings allow this article to be used as a tutorial for those new to regression.


title: User Models for Adaptive Hypermedia and Adaptive Educational Systems

text: One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user


title: Multimedia Question Answering

text: By surveying recent research in multimedia question answering, this article explores QA's evolution from text to multimedia and identifies the challenges in the field.


title: Sexual Orientation and Gender Identity Data Collection in Clinical Settings and in Electronic Health Records: A Key to Ending LGBT Health Disparities.

text: The Institute of Medicine's (IOM's) 2011 report on the health of LGBT people pointed out that there are limited health data on these populations and that we need more research. It also described what we do know about LGBT health disparities, including lower rates of cervical cancer screening among lesbians, and mental health issues related to minority stress. Patient disclosure of LGBT identity enables provider-patient conversations about risk factors and can help us reduce and better understand disparities. It is essential to the success of Healthy People 2020's goal of eliminating LGBT health disparities. This is why the IOM's report recommended data collection in clinical settings and on electronic health records (EHRs). The Center for Medicare and Medicaid Services and the Office of the National Coordinator of Health Information Technology rejected including sexual orientation and gender identity (SOGI) questions in meaningful use guidelines for EHRs in 2012 but are considering this issue again in 2013. There is overwhelming community support for the routine collection of SOGI data in clinical settings, as evidenced by comments jointly submitted by 145 leading LGBT and HIV/AIDS organizations in January 2013. Gathering SOGI data in EHRs is supported by the 2011 IOM's report on LGBT health, Healthy People 2020, the Affordable Care Act, and the Joint Commission. Data collection has long been central to the quality assurance process. Preventive health care from providers knowledgeable of their patients' SOGI can lead to improved access, quality of care, and outcomes. Medical and nursing schools should expand their attention to LGBT health issues so that all clinicians can appropriately care for LGBT patients.


title: Collision-free object movement using vector fields

text: The topics of computer animation and object collision detection and resolution have become increasingly desirable in a wide range of applications including autonomous vehicle navigation, robotics, and the movie industry. However, the techniques currently available are typically difficult to use or are restricted to a particular domain. This paper presents a technique for providing automatic animation and collision avoidance of arbitrary objects in a computer graphics system. The underlying construct used in this process is a surrounding volume octree vector field. The system automatically generates these structures around objects in the scene. By judicious creation and use of these vector fields, objects in the scene move and interact, but do not collide. The manner in which these vector fields are created is given. Two applications cloud movement over terrain and autonomous aircraft navigation are presented which show typical usage of this technique.


title: : Android Deobfuscation via “ Big Code ”

text: This work presents a new approach for deobfuscating Android APKs based on probabilistic learning of large code bases (termed “Big Code”). The key idea is to learn a probabilistic model over thousands of non-obfuscated Android applications and to use this probabilistic model to deobfuscate new, unseen Android APKs. The concrete focus of the paper is on reversing layout obfuscation, a popular transformation which renames key program elements such as classes, packages and methods, thus making it difficult to understand what the program does. Concretely, the paper: (i) phrases the layout deobfuscation problem of Android APKs as structured prediction in a probabilistic graphical model, (ii) instantiates this model with a rich set of features and constraints that capture the Android setting, ensuring both semantic equivalence and high prediction accuracy, and (iii) shows how to leverage powerful inference and learning algorithms to achieve overall precision and scalability of the probabilistic predictions. We implemented our approach in a tool called DeGuard and used it to: (i) reverse the layout obfuscation performed by the popular ProGuard system on benign, open-source applications, (ii) predict third-party libraries imported by benign APKs (also obfuscated by ProGuard), and (iii) rename obfuscated program elements of Android malware. The experimental results indicate that DeGuard is practically effective: it recovers 79.1% of the program element names obfuscated with ProGuard, it predicts third-party libraries with accuracy of 91.3%, and it reveals string decoders and classes that handle sensitive data in Android malware.


title: Detection of Slang Words in e-Data using semi-Supervised Learning

text: The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.


title: Thermally Stable Enhancement-Mode GaN Metal-Isolator-Semiconductor High-Electron-Mobility Transistor With Partially Recessed Fluorine-Implanted Barrier

text: Al2O3/AlGaN/GaN enhancement-mode metalisolator-semiconductor high-electron-mobility transistor (MIS-HEMT) featuring a partially recessed (Al) GaN barrier was realized by a fluorine plasma implantation/etch technique. By properly adjusting the RF power driving the fluorine plasma, the fluorine plasma is able to produce two desirable results: 1) a well-controlled slow dry etching for gate recess and 2) implanting fluorine ions into the AlGaN barrier. The fluorine ions become negatively charged in the barrier layer and induce a positive shift in the threshold voltage. The proposed MIS-HEMT exhibits a threshold voltage (VTH) of +0.6 V at a drain current of 10 μA/mm, a maximum drive current of 730 mA/mm, an ON-resistance of 7.07 Ω · mm, and an OFF-state breakdown voltage of 703 V at an OFF-state drain leakage current of 1 μA/mm. From room temperature to 200 °C, the device exhibits a small negative shift of VTH (~0.5 V) that is attributed to the high-quality dielectric/F-implanted-(Al) GaN interface and the partially recessed barrier.


title: Inference and Design in Kuba and Zillij Art with Shape Grammars

text: We present a simple method for structural inference in African Kuba cloth, and Moorish zillij mosaics. Our work is based on Stiny and Gips’ formulation of “Shape Grammars”. It provides art scholars and geometers with a simple yet powerful medium to perform analysis of existing art and draw inspiration for new designs. The analysis involves studying an artwork and capturing its structure as simple shape grammar rules. We then show how interesting families of artworks could be generated using simple variations in their corresponding grammars.


title: Deep Learning for Fixed Model Reuse

text: Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and videos. There are several studies have the sense of model reuse, by trying to reuse pre-trained deep networks architectures or deep model features to train a new deep model. They, however, neglect the fact that there are many other fixed models or features available. In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse). FMR utilizes the learning power of deep models to implicitly grab the useful discriminative information from fixed model/features that have been widely used in general tasks. We firstly arrange the convolution layers of a deep network and the provided fixed model/features in parallel, fully connecting to the output layer nodes. Then, the dependencies between the output layer nodes and the fixed model/features are knockdown such that only the raw feature inputs are needed when the model is being used for testing, though the helpful information in the fixed model/features have already been incorporated into the model. On one hand, by the FMR scheme, the required amount of training data can be significantly reduced because of the reuse of fixed model/features. On the other hand, the fixed model/features are not explicitly used in testing, and thus, the scheme can be quite useful in applications where the fixed model/features are protected by patents or commercial secrets. Experiments on five real-world datasets validate the effectiveness of FMR compared with state-of-the-art deep methods.


title: A mixture model for preferences data analysis

text: A mixture model for preferences data, which adequately represents the composite nature of the elicitation mechanism in ranking processes, is proposed. Both probabilistic features of the mixture distribution and inferential and computational issues arising from the maximum likelihood parameters estimation are addressed. Moreover, empirical evidence from different data sets confirming the goodness of fit of the proposed model to many real preferences data is shown. © 2004 Elsevier B.V. All rights reserved.


title: A Recurrent Latent Variable Model for Sequential Data

text: In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder. We argue that through the use of high-level latent random variables, our variational RNN (VRNN) is able to learn to model the kind of variability observed in highly-structured sequential data (such as speech). We empirically evaluate the proposed model against related sequential models on five sequential datasets, four of speech and one of handwriting. Our results show the importance of the role that latent random variables can play in the RNN dynamic hidden state.


title: Least Squares Temporal Difference Learning and Galerkin ’ s Method

text: The problem of estimating the value function underlying a Markovian reward process is considered. As it is well known, the value function underlying a Markovian reward process satisfied a linear fixed point equation. One approach to learning the value function from finite data is to find a good approximation to the value function in a given (linear) subspace of the space of value functions. We review some of the issues that arise when following this approach, as well as some results that characterize the finite-sample performance of some of the algorithms. 1 Markovian Reward Processes Let X be a measurable space and consider a stochastic process (X0, R1, X1, R2, X2, . . .), where Xt ∈ X and Rt+1 ∈ R, t = 0, 1, 2, . . .. The process is called a Markovian Reward process if • (X0, X1, . . .) is a Markov process, and • for any t ≥ 0, given Xt, Xt+1 the distribution of Rt+1 is independent of the history of the process. Here, Xt is called the state of the system at time t, while Rt+1 is the reward associated to transitioning from Xt to Xt+1. We shall denote by P the Markovian kernel underlying the process: Thus, the distribution of (Xt+1, Rt+1) given Xt is given by P(·, ·|Xt), t = 0, 1, . . .. Fix the so-called discount factor 0 ≤ γ ≤ 1 and define the (total discounted) return associated to the process R = ∞ ∑


title: A Dual Path Deep Network for Single Image Super-Resolution Reconstruction

text: Super-resolution reconstruction based on deep learning has come a long way since the first proposed method in 2015. Numerous methods have been developed for this task using deep learning approaches. Among these methods, residual deep learning algorithms have shown better performance. Although all early proposed deep learning based super-resolution frameworks used bicubic upsampled versions of low resolution images as the main input, most of the current ones use the low resolution images directly by adding up-sampling layers to their networks. In this work, we propose a new method by using both low resolution and bicubic upsampled images as the inputs to our network. The final results confirm that decreasing the depth of the network in lower resolution space and adding the bicubic path lead to almost similar results to those of the deeper networks in terms of PSNR and SSIM, yet making the network computationally inexpensive and more efficient.


title: BLE Beacon Based Patient Tracking in Smart Care Facilities

text: Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.


title: Immediate effect of a slow pace breathing exercise Bhramari pranayama on blood pressure and heart rate.

text: The study was carried out to evaluate the immediate effect Bhramari pranayama, a slow breathing exercise for 5 minutes on heart rate and blood pressure. Heart rate and blood pressure of volunteers were recorded. The subject was directed to inhale slowly up to the maximum for about 5 seconds and then to exhale slowly up to the maximum for about 15 sec keeping two thumbs on two external auditory canal, index and middle finger together on two closed eyes and ring finger on the two sides of the nose. During exhalation the subject must chant the word "O-U-Mmmma" with a humming nasal sound mimicking the sound of a humming wasp, so that the laryngeal walls and the inner walls of the nostril mildly vibrate (Bhramari pranayama, respiratory rate 3/min). After 5 minutes of this exercise, the blood pressure and heart rate were recorded again. Both the systolic and diastolic blood pressure were found to be'decreased with a slight fall in heart rate. Fall of diastolic pressure and mean pressure were significant. The result indicated that slow pace Bhramari pranayama for 5 minutes, induced parasympathetic dominance on cardiovascular system.


title: Deep Neural Networks for Object Detection

text: Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.


title: Point-and-shoot memories: the influence of taking photos on memory for a museum tour.

text: Two studies examined whether photographing objects impacts what is remembered about them. Participants were led on a guided tour of an art museum and were directed to observe some objects and to photograph others. Results showed a photo-taking-impairment effect: If participants took a photo of each object as a whole, they remembered fewer objects and remembered fewer details about the objects and the objects' locations in the museum than if they instead only observed the objects and did not photograph them. However, when participants zoomed in to photograph a specific part of the object, their subsequent recognition and detail memory was not impaired, and, in fact, memory for features that were not zoomed in on was just as strong as memory for features that were zoomed in on. This finding highlights key differences between people's memory and the camera's "memory" and suggests that the additional attentional and cognitive processes engaged by this focused activity can eliminate the photo-taking-impairment effect.


title: A Fast and Accurate Dependency Parser using Neural Networks

text: Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a small number of dense features, it can work very fast, while achieving an about 2% improvement in unlabeled and labeled attachment scores on both English and Chinese datasets. Concretely, our parser is able to parse more than 1000 sentences per second at 92.2% unlabeled attachment score on the English Penn Treebank.


title: The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies

text: The Conference on Computational Natural Language Learning is accompanied every year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. This shared task not only unifies the shared tasks of the previous four years under a unique dependency-based formalism, but also extends them significantly: this year’s syntactic dependencies include more information such as named-entity boundaries; the semantic dependencies model roles of both verbal and nominal predicates. In this paper, we define the shared task and describe how the data sets were created. Furthermore, we report and analyze the results and describe the approaches of the participating systems.


title: The Stanford Typed Dependencies Representation

text: This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, we address the question of the suitability of the Stanford scheme for parser evaluation.


title: brat: a Web-based Tool for NLP-Assisted Text Annotation

text: We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15% decrease in total annotation time. BRAT is available under an opensource license from: http://brat.nlplab.org


title: Measuring Similarity between Semantic Business Process Models

text: A business process may be modeled in different ways by different modelers even when utilizing the same modeling language. An appropriate method for solving ambiguity issues in process models caused by the use of synonyms, homonyms or different abstraction levels for process element names is the use of ontologybased descriptions of process models. So-called semantic business process models promise to support business process interoperability and interconnectivity. But, for (semi-) automatic process interoperability and interconnectivity two problems need to be solved. How can similar terms for process element names be automatically discovered and how can semantic business process composition be facilitated. In this paper we will present solutions for these problems based upon an OWL DL-based description of Petri nets.


title: Development of knee power assist using backdrivable electro-hydrostatic actuator

text: Backdrivability is a keyword with rising importance, not only in humanoid robots, but also to wearable robots. Power assist robots, namely exoskeletons are expected to provide mobility and independence to elderly and people with disability. In such robots, without backdrivability, error between human and robot motion can be painful; sometimes dangerous. In this research, we apply a type of hydraulic actuator, specially designed to realize backdrivability, to exoskeletal robot to improve backdrivability. We present the basic methodology to apply such hydraulic actuator to wearable robots. We developed prototype of knee joint power assist exoskeleton and performed fundamental tests to verify the design method validity.


title: Feature Engineering for Knowledge Base Construction

text: Knowledge base construction (KBC) is the process of populating a knowledge base, i.e., a relational database together with inference rules, with information extracted from documents and structured sources. KBC blurs the distinction between two traditional database problems, information extraction and information integration. For the last several years, our group has been building knowledge bases with scientific collaborators. Using our approach, we have built knowledge bases that have comparable and sometimes better quality than those constructed by human volunteers. In contrast to these knowledge bases, which took experts a decade or more human years to construct, many of our projects are constructed by a single graduate student. Our approach to KBC is based on joint probabilistic inference and learning, but we do not see inference as either a panacea or a magic bullet: inference is a tool that allows us to be systematic in how we construct, debug, and improve the quality of such systems. In addition, inference allows us to construct these systems in a more loosely coupled way than traditional approaches. To support this idea, we have built the DeepDive system, which has the design goal of letting the user “think about features— not algorithms.” We think of DeepDive as declarative in that one specifies what they want but not how to get it. We describe our approach with a focus on feature engineering, which we argue is an understudied problem relative to its importance to end-to-end quality.


A precise ranking method for outlier detection


title: Crawler Detection: A Bayesian Approach

text: In this paper, we introduce a probabilistic modeling approach for addressing the problem of Web robot detection from Web-server access logs. More specifically, we construct a Bayesian network that classifies automatically access-log sessions as being crawler- or human-induced, by combining various pieces of evidence proven to characterize crawler and human behavior. Our approach uses machine learning techniques to determine the parameters of the probabilistic model. We apply our method to real Web-server logs and obtain results that demonstrate the robustness and effectiveness of probabilistic reasoning for crawler detection


title: Analytical Design Methodology for Litz-Wired High-Frequency Power Transformers

text: In the last quarter of a century, high-frequency (HF) transformer design has been one of the major concerns to power electronics designers in order to increase converter power densities and efficiencies. Conventional design methodologies are based on iterative processes and rules of thumb founded more on expertise than on theoretical developments. This paper presents an analytical design methodology for litz-wired HF power transformers that provides a deep insight into the transformer design problem making it a powerful tool for converter designers. The most suitable models for the calculation of core and winding losses and the transformer thermal resistance are first selected and then validated with a 5-kW 50-kHz commercial transformer for a photovoltaic application. Based on these models, the design methodology is finally proposed, reducing the design issue to directly solve a five-variable nonlinear optimization problem. The methodology is illustrated with a detailed design in terms of magnetic material, core geometry, and primary and secondary litz-wire sizing. The optimal design achieves a 46.5% power density increase and a higher efficiency of 99.70% when compared with the commercial one.


title: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

text: A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname “neocognitron”. After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consits of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of “S-cells”, which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of “C-cells” similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any “teacher” during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cell of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.


title: Photoshopping the selfie: Self photo editing and photo investment are associated with body dissatisfaction in adolescent girls.

text:
OBJECTIVE
Social media engagement by adolescent girls is high. Despite its appeal, there are potential negative consequences for body dissatisfaction and disordered eating from social media use. This study aimed to examine, in a cross-sectional design, the relationship between social media use in general, and social media activities related to taking "selfies" and sharing specifically, with overvaluation of shape and weight, body dissatisfaction, and dietary restraint.

METHOD
Participants were 101 grade seven girls (M(age)  = 13.1, SD = 0.3), who completed self-report questionnaires of social media use and body-related and eating concerns measures.

RESULTS
Results showed that girls who regularly shared self-images on social media, relative to those who did not, reported significantly higher overvaluation of shape and weight, body dissatisfaction, dietary restraint, and internalization of the thin ideal. In addition, among girls who shared photos of themselves on social media, higher engagement in manipulation of and investment in these photos, but not higher media exposure, were associated with greater body-related and eating concerns, including after accounting for media use and internalization of the thin ideal.

DISCUSSION
Although cross-sectional, these findings suggest the importance of social media activities for body-related and eating concerns as well as potential avenues for targeted social-media-based intervention.


title: Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning

text: We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch. (2) Performing cross fold training on a single set of ground truth data (line images and their transcriptions) with a single OCR engine (OCRopus) produces a committee whose members then vote for the best outcome by also taking the top-N alternatives and their intrinsic confidence values into account. (3) Following the principle of maximal disagreement we select additional training lines which the voters disagree most on, expecting them to offer the highest information gain for a subsequent training (active learning). Evaluations on six early printed books yielded the following results: On average the combination of pretraining and voting improved the character accuracy by 46% when training five folds starting from the same mixed model. This number rose to 53% when using different models for pretraining, underlining the importance of diverse voters. Incorporating active learning improved the obtained results by another 16% on average (evaluated on three of the six books). Overall, the proposed methods lead to an average error rate of 2.5% when training on only 60 lines. Using a substantial ground truth pool of 1,000 lines brought the error rate down even further to less than 1% on average.


title: Inflammation and atherosclerosis.

text: Atherosclerosis, formerly considered a bland lipid storage disease, actually involves an ongoing inflammatory response. Recent advances in basic science have established a fundamental role for inflammation in mediating all stages of this disease from initiation through progression and, ultimately, the thrombotic complications of atherosclerosis. These new findings provide important links between risk factors and the mechanisms of atherogenesis. Clinical studies have shown that this emerging biology of inflammation in atherosclerosis applies directly to human patients. Elevation in markers of inflammation predicts outcomes of patients with acute coronary syndromes, independently of myocardial damage. In addition, low-grade chronic inflammation, as indicated by levels of the inflammatory marker C-reactive protein, prospectively defines risk of atherosclerotic complications, thus adding to prognostic information provided by traditional risk factors. Moreover, certain treatments that reduce coronary risk also limit inflammation. In the case of lipid lowering with statins, this anti-inflammatory effect does not appear to correlate with reduction in low-density lipoprotein levels. These new insights into inflammation in atherosclerosis not only increase our understanding of this disease, but also have practical clinical applications in risk stratification and targeting of therapy for this scourge of growing worldwide importance.


title: A Perspective Analysis of Traffic Accident using Data Mining Techniques

text: Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of


title: Sleep viewed as a state of adaptive inactivity

text: Sleep is often viewed as a vulnerable state that is incompatible with behaviours that nourish and propagate species. This has led to the hypothesis that sleep has survived because it fulfills some universal, but as yet unknown, vital function. I propose that sleep is best understood as a variant of dormant states seen throughout the plant and animal kingdoms and that it is itself highly adaptive because it optimizes the timing and duration of behaviour. Current evidence indicates that ecological variables are the main determinants of sleep duration and intensity across species.


title: Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies

text: In this paper, we explore a large multimodal dataset of about 65k albums constructed from a combination of Amazon customer reviews, MusicBrainz metadata and AcousticBrainz audio descriptors. Review texts are further enriched with named entity disambiguation along with polarity information derived from an aspect-based sentiment analysis framework. This dataset constitutes the cornerstone of two main contributions: First, we perform experiments on music genre classification, exploring a variety of feature types, including semantic, sentimental and acoustic features. These experiments show that modeling semantic information contributes to outperforming strong bag-of-words baselines. Second, we provide a diachronic study of the criticism of music genres via a quantitative analysis of the polarity associated to musical aspects over time. Our analysis hints at a potential correlation between key cultural and geopolitical events and the language and evolving sentiments found in music reviews.


title: A Flexible Low-Power 130 nm CMOS Read-Out Circuit With Tunable Sensitivity for Commercial Robotic Resistive Pressure Sensors

text: This paper presents a flexible and low-power ReadOut Circuit (ROC) with tunable sensitivity, designed to interface a wide range of commercial resistive pressure sensors for robotic applications. The ROC provides contact detection, monitoring small capacitance variations at low pressure (<;100 mbar), and encodes pressure measurement on 8 bit, evaluating resistive variations. Two all-digital circuits implement the conversion of the input resistance and capacitance-to-frequency, exploiting an on-chip ring oscillator as timing reference. A 130 nm RFCMOS prototype (with an active area of 428 × 159 μm2) has a power consumption of 27.2 μW, for VDD 1 V. Using digital control inputs, the ROC allows a wide range tuning of measurement sensitivity (6.7-46.4 mbar/LSB) and adjustable acquisition time (226.6-461.7 μs and 648-890 μs, for contact detection and pressure evaluation, respectively). The read-out time of ~1 ms is compatible with human response after touch.


title: Improving search engines by query clustering

text: search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.


title: Which phoneme-to-viseme maps best improve visual-only computer lip-reading?

text: A critical assumption of all current visual speech recognition systems is that there are visual speech units called visemes which can be mapped to units of acoustic speech, the phonemes. Despite there being a number of published maps it is infrequent to see the effectiveness of these tested, particularly on visual-only lip-reading (many works use audio-visual speech). Here we examine 120 mappings and consider if any are stable across talkers. We show a method for devising maps based on phoneme confusions from an automated lip-reading system, and we present new mappings that show improvements for individual talkers.


title: Design and Analysis of Stacked Power Amplifier in Series-Input and Series-Output Configuration

text: The stacked-device power-combining technique is a proven method to increase the output power and load impedance of a power amplifier (PA) simultaneously. The series-input configuration is physically realizable for multicell stacked device configuration in monolithic circuits. The series-input and series-output stack configuration is rigorously analyzed and proven to increase both the input impedance and output impedance simultaneously, easing the matching circuit designs in high PAs. The effects of asymmetry of the input feed and amplifier cells due to distributed effects and process variation on the performance of the stack amplifier are discussed. A four-cell HBT amplifier operating at 5-6 GHz is demonstrated to validate the circuit concept.


title: Semi-Supervised AUC Optimization Without Guessing Labels of Unlabeled Data

text: Semi-supervised learning, which aims to construct learners that automatically exploit the large amount of unlabeled data in addition to the limited labeled data, has been widely applied in many real-world applications. AUC is a well-known performance measure for a learner, and directly optimizing AUC may result in a better prediction performance. Thus, semi-supervised AUC optimization has drawn much attention. Existing semi-supervised AUC optimization methods exploit unlabeled data by explicitly or implicitly estimating the possible labels of the unlabeled data based on various distributional assumptions. However, these assumptions may be violated in many real-world applications, and estimating labels based on the violated assumption may lead to poor performance. In this paper, we argue that, in semi-supervised AUC optimization, it is unnecessary to guess the possible labels of the unlabeled data or prior probability based on any distributional assumptions. We analytically show that the AUC risk can be estimated unbiasedly by simply treating the unlabeled data as both positive and negative. Based on this finding, two semi-supervised AUC optimization methods named SAMULT and SAMPURA are proposed. Experimental results indicate that the proposed methods outperform the existing methods. Introduction In many real-world applications, collecting a large amount of unlabeled data is relatively easy, while obtaining the labels for the collected data is rather expensive since much human effort and expertise is required for labeling. Semisupervised learning (Chapelle, Schlkopf, and Zien 2006; Zhu et al. 2009), aiming to construct learners that automatically exploit the large amount of unlabeled data in addition to the limited labeled data in the purpose of improving the learning performance, has drawn significant attention. Many semi-supervised learning methods have been proposed. To effectively exploit the unlabeled data, almost all of these methods elaborate to link the labeled data and the unlabeled data based on certain distributional assumption (Chapelle, Schlkopf, and Zien 2006), such as the cluster assumption, the manifold assumption, etc., and construct the learner by explicitly or implicitly estimating the labels of unlabeled instances. Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. AUC (area under ROC curve) (Hanley and McNeil 1982), which measures the probability of a randomly drawn positive instance being ranked before a randomly drawn negative instance, is a widely-used performance measure for a learner, especially when the data distribution exhibits certain imbalance. Directly optimizing the AUC during the learning procedure usually lead to a better prediction performance. Many studies are elaborated to show that AUC can be effectively and efficiently optimized (Herschtal and Raskutti 2004; Gao and Zhou 2015; Gao et al. 2013; Ying, Wen, and Lyu 2016). Some efforts have been devoted to AUC optimization in semi-supervised settings (Amini, Truong, and Goutte 2008; Fujino and Ueda 2016). These methods rely on the aforementioned distributional assumptions. However, those assumptions may be violated in real-world applications and the learner may be biased when explicitly or implicitly estimating the labels based on the violated assumptions, resulting in poor performance or even performance degradation (Cozman and Cohen 2002). Sakai, Niu, and Sugiyama (2017) proposed an unbiased semisupervised AUC optimization method based on positiveunlabeled learning. However, such a method requires an accurate estimation of the prior probability to reweight the unlabeled data, which is usually difficult especially when the number of labeled data is extremely small. In this paper, we argue that in semi-supervised AUC optimization problem, it is unnecessary to guess the possible labels of the unlabeled data or prior probability based on any distributional assumptions. We theoretically show that the AUC risk can be estimated unbiasedly by treating the unlabeled data as both positive and negative without any distributional assumptions. Such a theoretical finding enables us to address semisupervised AUC optimization problem by simply treating the unlabeled data as both positive and negative data, without designing specific strategies to identify the possible labels of the unlabeled data. Based on this theoretical finding, we propose two novel semi-supervised AUC optimization methods: SAMULT, a straightforward unbiased method by regarding the unlabeled data as both positive and negative data, and SAMPURA, an ensemble method by random partitioning the unlabeled data into pseudo-positive and pseudonegative sets to train base classifiers. The experimental reThe Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)


title: Offloading in Heterogeneous Networks: Modeling, Analysis, and Design Insights

text: Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate.


title: Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks

text: The deployment of femtocells in a macrocell network is an economical and effective way to increase network capacity and coverage. Nevertheless, such deployment is challenging due to the presence of inter-tier and intra-tier interference, and the ad hoc operation of femtocells. Motivated by the flexible subchannel allocation capability of OFDMA, we investigate the effect of spectrum allocation in two-tier networks, where the macrocells employ closed access policy and the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different spectrum allocation and femtocell access policies. In particular, we consider joint subchannel allocation, in which the whole spectrum is shared by both tiers, as well as disjoint subchannel allocation, whereby disjoint sets of subchannels are assigned to both tiers. We formulate the throughput maximization problem subject to quality of service constraints in terms of success probabilities and per-tier minimum rates, and provide insights into the optimal spectrum allocation. Our results indicate that with closed access femtocells, the optimized joint and disjoint subchannel allocations provide the highest throughput among all schemes in sparse and dense femtocell networks, respectively. With open access femtocells, the optimized joint subchannel allocation provides the highest possible throughput for all femtocell densities.


title: Power control in two-tier femtocell networks

text: In a two tier cellular network - comprised of a central macrocell underlaid with shorter range femtocell hotspots - cross-tier interference limits overall capacity with universal frequency reuse. To quantify near-far effects with universal frequency reuse, this paper derives a fundamental relation providing the largest feasible cellular Signal-to-Interference-Plus-Noise Ratio (SINR), given any set of feasible femtocell SINRs. We provide a link budget analysis which enables simple and accurate performance insights in a two-tier network. A distributed utility- based SINR adaptation at femtocells is proposed in order to alleviate cross-tier interference at the macrocell from cochannel femtocells. The Foschini-Miljanic (FM) algorithm is a special case of the adaptation. Each femtocell maximizes their individual utility consisting of a SINR based reward less an incurred cost (interference to the macrocell). Numerical results show greater than 30% improvement in mean femtocell SINRs relative to FM. In the event that cross-tier interference prevents a cellular user from obtaining its SINR target, an algorithm is proposed that reduces transmission powers of the strongest femtocell interferers. The algorithm ensures that a cellular user achieves its SINR target even with 100 femtocells/cell-site (with typical cellular parameters) and requires a worst case SINR reduction of only 16% at femtocells. These results motivate design of power control schemes requiring minimal network overhead in two-tier networks with shared spectrum.


title: Component Model of Addiction Treatment: A Pragmatic Transdiagnostic Treatment Model of Behavioral and Substance Addictions

text: Behavioral addictions such as gambling, video games, sex, and shopping share many clinical features with substance use addictions including etiology, course, and neurobiology. Yet, the treatment of behavioral and substance use addictions tends to be separated. However, we argue that a more effective and efficient treatment approach is to conceptualize behavioral and substance use addictions as different expressions of a common underlying disorder and, in treatment, to address the underlying mechanisms common to both. To this end, the article presents a developing transdiagnostic treatment model of addictions that targets underlying similarities between behavioral and substance use addictions, called the component model of addiction treatment (CMAT). The CMAT is transdiagnostic in that it can be used in the treatment of both behavioral and substance use addictions. It is pragmatic in that it targets component vulnerabilities, which are enduring, yet malleable, individual psychological, cognitive, and neurobiological characteristics that are common to all addictive disorders and have been demonstrated to be modifiable. A working model of CMAT is presented, including proposed component vulnerabilities: lack of motivation, urgency, maladaptive expectancies, deficits in self-control, deficits in social support, and compulsivity, as well as their potential intervention possibilities. Future directions and potential implications of the CMAT are discussed.


title: Collaborative data mining for clinical trial analytics

text: This paper proposes a collaborative data mining technique to provide multi-level analysis from clinical trials data. Clinical trials for clinical research and drug development generate large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this paper, we propose a novel method using master data management (MDM) for analyzing clinical trial data, scattered across multiple databases, through collaborative data mining. Our aim is to validate findings by collaboratively utilizing multiple data mining techniques such as classification, clustering, and association rule mining. We complement our results with the help of interactive visualizations. The paper also demonstrates use of data stratification for identifying disparities between various subgroups of clinical trial participants. Overall, our approach aims at extracting useful knowledge from clinical trial data in order to improve design of clinical trials by gaining confidence in the outcomes using multi-level analysis. We provide experimental results in drug abuse clinical trial data.


title: Human-Like Rewards to Train a Reinforcement Learning Controller for Planar Arm Movement

text: High-level spinal cord injury (SCI) in humans causes paralysis below the neck. Functional electrical stimulation (FES) technology applies electrical current to nerves and muscles to restore movement, and controllers for upper extremity FES neuroprostheses calculate stimulation patterns to produce desired arm movement. However, currently available FES controllers have yet to restore natural movements. Reinforcement learning (RL) is a reward-driven control technique; it can employ user-generated rewards, and human preferences can be used in training. To test this concept with FES, we conducted simulation experiments using computer-generated “pseudo-human” rewards. Rewards with varying properties were used with an actor-critic RL controller for a planar two-degree-of-freedom biomechanical human arm model performing reaching movements. Results demonstrate that sparse, delayed pseudo-human rewards permit stable and effective RL controller learning. The frequency of reward is proportional to learning success, and human-scale sparse rewards permit greater learning than exclusively automated rewards. Diversity of training task sets did not affect learning. Long-term stability of trained controllers was observed. Using human-generated rewards to train RL controllers for upper-extremity FES systems may be useful. Our findings represent progress toward achieving human-machine teaming in control of upper-extremity FES systems for more natural arm movements based on human user preferences and RL algorithm learning capabilities.


title: Fixed-Point Factorized Networks

text: In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision. However, DNN-based methods are both computational-intensive and resource-consuming, which hinders the application of these methods on embedded systems like smart phones. To alleviate this problem, we introduce a novel Fixed-point Factorized Networks (FFN) for pretrained models to reduce the computational complexity as well as the storage requirement of networks. The resulting networks have only weights of -1, 0 and 1, which significantly eliminates the most resource-consuming multiply-accumulate operations (MACs). Extensive experiments on large-scale ImageNet classification task show the proposed FFN only requires one-thousandth of multiply operations with comparable accuracy.


title: The Method of 2/3 Sampled Sub-Pixel Rendering for AMOLED Display

text: Due to the capability of the AMOLED (Active matrix organic light emitting diode) display can provide superior color saturation, flexible utilization, slimmer screen profile and more impressive image quality, it has received increasing demand for application in electronic devices. The organic light emitting diode (OLED) is current driven device so that the characteristics of thin field transistor (TFT) influence panel uniformity. Multiple TFT of pixel circuitry has been designed to compensate the Vth (threshold voltage) variations of array process and other driving issues. The fine metal mask (FMM) used in the OLED evaporation is an accurate mask having physical margin limitations in the high PPI (pixel per inch) AMOLED application. This paper details the utilization of a 2/3 sampled sub-pixel rendering (SPR) method to liberate more 50% layout area than the RGB stripe configuration. The proposed algorithm utilizes edge detection and one-side color diffusion to improve display performance as well as RGB stripe compatibility. A 4.65” HD (720 ×1280) AMOLED panel with a 317 PPI retina display was implemented to realize the development of our SPR technology.


title: Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features for High-Dimensional Classification

text: Feature selection and discretisation have shown their effectiveness for data preprocessing especially for high-dimensional data with many irrelevant features. While feature selection selects only relevant features, feature discretisation finds a discrete representation of data that contains enough information but ignoring some minor fluctuation. These techniques are usually applied in two stages, discretisation and then selection since many feature selection methods work only on discrete features. Most commonly used discretisation methods are univariate in which each feature is discretised independently; therefore, the feature selection stage may not work efficiently since information showing feature interaction is not considered in the discretisation process. In this study, we propose a new method called PSO-DFS using bare-bone particle swarm optimisation (BBPSO) for discretisation and feature selection in a single stage. The results on ten high-dimensional datasets show that PSO-DFS obtains a substantial dimensionality reduction for all datasets. The classification performance is significantly improved or at least maintained on nine out of ten datasets by using the transformed “small” data obtained from PSO-DFS. Compared to applying the two-stage approach which uses PSO for feature selection on the discretised data, PSO-DFS achieves better performance on six datasets, and similar performance on three datasets with a much smaller number of features selected.


title: A novel high-gain quasi-Yagi antenna with a parabolic reflector

text: The simplicity and intuitive design of traditional planar printed quasi-Yagi antennas has led to its widespread popularity for its good directivity. In this paper, a novel quasi-Yagi antenna with a single director and a concave parabolic reflector, operating in S-band, is proposed. The impedance characteristic and radiation characteristic are simulated with CST-Microwave Studio, and the antenna is fabricated and measured. The measured results indicate that the antenna which can operate at 2.28-2.63GHz can achieve an average gain of 6.5dBi within the operating frequency range, especially a highest gain of 7.5dBi at 2.5GHz. The proposed antenna can be widely used in WLAN/TD-LTE/BD1 and so on.


title: SRP-PHAT methods of locating simultaneous multiple talkers using a frame of microphone array data

text: Two new methods for locating multiple sound sources using a single segment of data from a large-aperture microphone array are presented. Both methods employ the proven-robust steered response power using the phase transform (SRP-PHAT) as a functional. To cluster the data points into highly probable regions containing global peaks, the first method fits a Gaussian mixture model (GMM), whereas the second one sequentially finds the points with highest SRP-PHAT values that most likely represent different clusters. Then the low-cost global optimization method, stochastic region contraction (SRC), is applied to each cluster to find the global peaks. We test the two methods using real data from five simultaneous talkers in a room with high noise and reverberation. Results are presented and discussed.


title: Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation

text: Several theoretical analyses of the dynamics of particle swarms have been offered in the literature over the last decade. Virtually all rely on substantial simplifications, often including the assumption that the particles are deterministic. This has prevented the exact characterization of the sampling distribution of the particle swarm optimizer (PSO). In this paper we introduce a novel method that allows us to exactly determine all the characteristics of a PSO sampling distribution and explain how it changes over any number of generations, in the presence stochasticity. The only assumption we make is stagnation, i.e., we study the sampling distribution produced by particles in search for a better personal best. We apply the analysis to the PSO with inertia weight, but the analysis is also valid for the PSO with constriction and other forms of PSO.


title: Hybrid decision tree

text: In this paper, a hybrid learning approach named HDT is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary hybrid decision tree according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named FANNC that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.