title: Implementing tablet PCs in schools: Students' attitudes and opinions

abstract: The study explored students' attitude toward tablet PCs.Qualitative and quantitative data collection tools were used in the research.Students have a positive attitude towards tablet PCs. In this study, the attitudes, expectations and views of 206 students in four high schools within the scope of the FATIH project in Turkey were assessed regarding tablet PC technology after six months of a pilot plan that included the distribution of tablet PCs to students. The research questions of this study are whether there is a meaningful difference between tablet PC use by male and female students and the effect of computer and Internet by students on attitudes toward tablet PC use. Qualitative and quantitative data collection tools were used in the research. The Computer Attitude Measure for Young students (CAMYS) developed by Teo and Noyes (2008) was used in evaluating the students' attitudes toward the tablet PC usage. Interviews were conducted with eight teachers at pilot schools concerning the integration of tablet PCs into their classes; the positive and negative dimensions of tablet PCs were analyzed. The findings indicate that students have a positive attitude toward tablet PCs. There was not a meaningful difference between the attitudes of male and female students toward tablet PCs. The length of computer and Internet by the students did not affect their attitudes toward tablet PCs. The ways that teachers used tablet PCs in classes, the positive and negative aspects of tablet PC usage and the students' expectations of tablet PCs were discussed in the study.


title: Information metrics (iMetrics): a research specialty with a socio-cognitive identity?

abstract: "Bibliometrics", "scientometrics", "informetrics", and "webometrics" can all be considered as manifestations of a single research area with similar objectives and methods, which we call "information metrics" or iMetrics. This study explores the cognitive and social distinctness of iMetrics with respect to the general information science (IS), focusing on a core of researchers, shared vocabulary and literature/knowledge base. Our analysis investigates the similarities and differences between four document sets. The document sets are drawn from three core journals for iMetrics research (Scientometrics, Journal of the American Society for Information Science and Technology, and Journal of Informetrics). We split JASIST into document sets containing iMetrics and general IS articles. The volume of publications in this representation of the specialty has increased rapidly during the last decade. A core of researchers that predominantly focus on iMetrics topics can thus be identified. This core group has developed a shared vocabulary as exhibited in high similarity of title words and one that shares a knowledge base. The research front of this field moves faster than the research front of information science in general, bringing it closer to Price's dream.


title: Towards knowledge-driven annotation

abstract: While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.


title: Audio Steganography using Echo Hiding in Wavelet Domain with Pseudorandom Sequence

abstract: paper deals with the encoding of message bits in a cover audio carrier signal. It applies the basic concepts of audio steganography in transform domain to achieve higher efficiency in data transmission, while preserving the secrecy of the information being transmitted. The proposed model in this paper deals with the application of echo hiding of binary message bits in the carrier signal, in the transform coefficients obtained by applying 2D- discrete Haar Wavelet Transform on the cover signal. Moreover, this algorithm applies pseudorandom sequence to encode the data, which gives the method more efficiency and prevents unauthorized decoding at any moment. The application of echo hiding technique makes the stego signal more immune to noise and disturbances, during transmission through the channel network. The performance of this method is analyzed on the basis of the output SNR and PSNR values calculated for several test cases, that has been discussed later in the paper.


title: Evaluation of Production Serverless Computing Environments

abstract: Serverless computing provides a small runtime container to execute lines of codes without infrastructure management which is similar to Platform as a Service (PaaS) but a functional level. Amazon started the event-driven compute named Lambda functions in 2014 with a 25 concurrent limitation, but it now supports at least a thousand of concurrent invocation to process event messages generated by resources like databases, storage and system logs. Other providers, i.e., Google, Microsoft, and IBM offer a dynamic scaling manager to handle parallel requests of stateless functions in which additional containers are provisioning on new compute nodes for distribution. However, while functions are often developed for microservices and lightweight workload, they are associated with distributed data processing using the concurrent invocations. We claim that the current serverless computing environments can support dynamic applications in parallel when a partitioned task is executable on a small function instance. We present results of throughput, network bandwidth, a file I/O and compute performance regarding the concurrent invocations. We deployed a series of functions for distributed data processing to address the elasticity and then demonstrated the differences between serverless computing and virtual machines for cost efficiency and resource utilization.


Detecting Similarities in Mobility Patterns.


title: Evaluation of bacterial and archaeal diversity in the rumen of Xiangxi yellow cattle (Bos taurus) fed Miscanthus sinensis or common mixed feedstuff

abstract: Miscanthus sinensis is a potential biofuel that is distributed widely in China, but with difficulties for decomposition and utilization due to the complexity of its fibrous cell walls. To detect whether M. sinensis could increase the population of rumen fibrolitic microbes, two16S rRNA gene libraries were constructed using ruminal samples from Xiangxi yellow cattle fed with either common mixed feedstuff (group C) or M. sinensis (group M), and the diversity of ruminal bacteria and archaea in the rumens of cattle of both groups was identified. Based on the comparative analysis of these two groups, the microbial composition in group C/M was found to be: Bacteroidetes (16.33 %/28.15 %), Firmicutes (68.88 %/60.92 %), Proteobacteria (10.71 %/3.78 %), Planctomycetes (0/0.84 %), Lentisphaerae (0/0.42 %), Spirochaetes (1.02 %/0) in the Bacteria domain and Thermoplasmata (13.09 %/46.67 %), Methanomicrobia (57.14 %/12.22 %) and uncultured archaea (29.76 %/41.11 %) in the Archaea domain, respectively. Moreover, through phylogenetic analysis, we also detected the increase of Bacteroidetes and the decrease of Methanomicrobia in group M. These results indicated that feeding cattle with M. sinensis will change the microbial composition in the rumen; the increased bacteroidetes may be responsible for digesting M. sinensis, which will benefit us in further screening for potentially valuable bio-enzymes.


title: Security Threats in ZigBee-Enabled Systems: Vulnerability Evaluation, Practical Experiments, Countermeasures, and Lessons Learned

abstract: In this paper, two practical attacks against ZigBee security are proposed and the latter one is also carried out in our laboratory environment. The attack scenarios are based on utilizing several vulnerabilities found from the main security components of ZigBee technology. The first attack is based on sabotaging the ZigBee End-Device by sending a special signal that makes it wake-up constantly until the battery runs out. The second attack is based on exploiting the key exchange process in ZigBee when using the Standard Security level defined by the ZigBee specification: we also demonstrate with experimental figures that attacks against ZigBee-enabled devices become practical by using our attack scenario. In addition, countermeasures that render the proposed attacks impractical, although not totally eliminating their potential danger, are devised. Moreover, some new ideas that will be used in our future research work are proposed.


title: Optical Character Recognition Based Speech Synthesis System Using LabVIEW

abstract: Knowledge extraction by just listening to sounds is a distinctive property. Speech signal is more effective means ofcommunication than text because blind and visually impaired persons can also respond to sounds. This paper aims todevelop a cost effective, and user friendly optical character recognition (OCR) based speech synthesis system. TheOCR based speech synthesis system has been developed using Laboratory virtual instruments engineeringworkbench (LabVIEW) 7.1.


title: Big Neural Networks Waste Capacity

abstract: This article exposes the failure of some big neural networks to leverage added capacity to reduce underfitting. Past research suggest diminishing returns when increasing the size of neural networks. Our experiments on ImageNet LSVRC-2010 show that this may be due to the fact there are highly diminishing returns for capacity in terms of training error, leading to underfitting. This suggests that the optimization method - first order gradient descent - fails at this regime. Directly attacking this problem, either through the optimization method or the choices of parametrization, may allow to improve the generalization error on large datasets, for which a large capacity is required.


title: Competitive and Professional Gaming: Discussing Potential Benefits of Scientific Study

abstract: Competitive and professional gaming, in which video game players participate in tournaments, offers numerous opportunities for much-needed research and enhanced knowledge about digital technology. To date, competitive gaming has received scant scientific attention, even though it may foster the development of adaptive skills or, in other instances, carry adverse consequences. Examining the lifestyles and successes of competitive gamers can increase our knowledge of gaming and yield useful information about extreme or excessive involvement in digital technology. Studying the most successful competitive gamers, who appear to develop exceptional skills, could be highly beneficial. Suggestions for research are provided.


title: Adlerian Psychology and Music Therapy: The Harmony of Sound and Matter and Community Feeling

abstract: Music is a language of its own, immanent, with no words, yet precise, based on mathematical ratios and harmonics. Music links people together, yet resonates emotionally with each person individually. Half a century ago Dreikurs wrote, “In music therapy, individual deficiencies, symptoms, and maladjustments are deprived of their stigma” (1953). The principles of musical harmony and resonance described here find their correspondence in the standing waves that make up the electron shells of atoms and molecules that constitute matter. This human resonance in matter provides a theoretical basis for Adler’s concept of Gemeinshaftsgefuhl , “a cosmic feeling, a reflection of the coherence of everything cosmic, which lives in us and which gives us the ability to empathize with things which lie outside our physical body,” (1953) and describes the great scientists and artists in their moments of originality. Modern music therapy is examined in this article and is shown to be a most useful adjunct to conventional psychotherapy as Dreikurs recommended. Adlerian Individual Psychology can provide a natural foundation for the practice of music therapy.


title: Exploring the Geographical Relations Between Social Media and Flood Phenomena to Improve Situational Awareness A Study About the River Elbe Flood in June 2013

abstract: Recent research has shown that social media platforms like twitter can provide relevant information to improve situation awareness during emergencies. Previous work is mostly concentrated on the classification and analysis of tweets utilizing crowdsourcing or machine learning techniques. However, managing the high volume and velocity of social media messages still remains challenging. In order to enhance information extraction from social media, this chapter presents a new approach that relies upon the geographical relations between twitter data and floodphenomena.Ourapproachusesspecificgeographicalfeatureslikehydrological data and digital elevation models to prioritize crisis-relevant twitter messages. We apply this approach to examine the River Elbe Flood in Germany in June 2013. The results show that our approach based on geographical relations can enhance informationextractionfromvolunteeredgeographicinformation,thusbeingvaluable for both crisis response and preventive flood monitoring.


title: Search on the Semantic Web

abstract: To help human users and software agents find relevant knowledge on the Semantic Web, the Swoogle search engine discovers, indexes, and analyzes the ontologies and facts that are encoded in Semantic Web documents.


title: Shape calibration for a nominally linear equispaced array

abstract: The author considers a thin flexible line array of equispaced hydrophones that is towed through the sea, and develops a procedure that allows testing of the straightness of the array. The motion of the towing ship, the currents of the ocean and other forces induce deformations on the array and affect the performance of spatial processing of the data developed under the assumption that the array is straight. When the ship is maneuvering, the processing is generally turned off for long periods of time, an extremely penalizing situation that can be overcome by applying the procedure described to determine the maximum size of admissible sub-arrays on which the standard processing can be pursued. >


title: SUDOKU Colorings of the Hexagonal Bipyramid Fractal

abstract: The hexagonal bipyramid fractal is a fractal in three dimensional space, which has fractal dimension two and which has six square projections. We consider its 2nd level approximation model, which is composed of 81 hexagonal bipyramid pieces. When this object is looked at from each of the 12 directions with square appearances, the pieces form a 9×9 grid of squares which is just the grid of the SUDOKU puzzle. In this paper, we consider colorings of the 81 pieces with 9 colors so that it has a SUDOKU solution pattern in each of the 12 appearances, that is, each row, each column, and each of the nine 3×3 blocks contains all the 9 colors in each of the 12 appearances. We show that there are 140 solutions modulo change of colors, and, if we identify isomorphic ones, we have 30 solutions. We also show that SUDOKU coloring solutions exist for every level 2n approximation models (n *** 1).


title: SoK: Privacy Technologies for Smart Grids – A Survey of Options.

abstract: We present the problems associated with protecting privacy in smart grids and survey currently proposed solutions. First we discuss use-cases, the policy environment and the literature on attacks based on monitoring the electricity supply. Then we review the most commonly deployed policy tools, and dive into a detailed survey of privacy technologies for aggregation of readings, billing for consumption and even hiding the consumption from physical adversaries. Keywords-Smart Grid; Smart meter; Privacy Technology;


title: Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

abstract: We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field.


title: DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet

abstract: This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is based on rule templates, instantiating at run-time a set of ABox OWL 2 RL/RDF Jena rules dedicated to a particular TBox that is handled by Pellet. The goal of DLEJena is to handle efficiently, through instantiated rules, the OWL 2 RL ontologies under direct semantics, where classes and properties cannot be at the same time individuals. The TBox semantics are treated by Pellet, reusing in that way efficient and sophisticated TBox DL reasoning algorithms. The experimental evaluation shows that DLEJena achieves more scalable ABox reasoning than the direct implementation of the OWL 2 RL/RDF rule set in the Jena's production rule engine, which is the main target of the system. DLEJena can be also used as a generic framework for applying an arbitrary number of entailments beyond the OWL 2 RL profile.


title: A supervisory hierarchical control approach for text to 2D scene generation

abstract: Photo-realistic image generation conditioned on text is an important problem and would have a wide range of applications. The gap between the high level abstract semantic information and the detailed low level image elements and operations on them hampers utilization of text-to-image systems. Many research works have been proposed to bridge the gap. However, most existing approaches do not allow operations on objects during the image synthesis. To generate photo-realistic images with more flexibility, in this work, we propose a two level hierarchical control system framework for image synthesis. The high level system acts as task planner and cope with abstract concepts extracted from input text. While the lower level system plays the role of action planner, which takes command from task planner and generates an action sequence for lower level implementation. The proposed approach allows control of the location and dimension of each object appeared in the text input, which helps to generate more complex scene. In addition, the proposed method allows to synthesize images with higher resolution. Experimental results have been provided to validate the proposed method.


title: Behavioral anomaly detection of malware on home routers

abstract: The Internet of Things (IoT) introduced new targets and attack vectors for malicious actors who infect insecure devices with malware in order to form large botnets that can launch distributed denial of service (DDoS) attacks. These botnets comprise various infected devices such as Internet-connected cameras and home routers. This paper focuses on the unsolved problem of creating robust malware detection to secure home routers. This research compares the effectiveness of three different approaches to behavioral malware detection on home endpoint routers through the observation of kernel-level system calls on these routers: i) principal component analysis (PCA), ii) one-class support vector machines, and iii) a naive anomaly detector based on unseen n-grams.


title: Local stakeholders and local legitimacy: MNEs' social strategies in emerging economies

abstract: Despite the ever-increasing footprint of multinational enterprises (MNEs) in the world's emerging economies, the role of corporate social responsibility in MNEs' emerging economy operations has only recently started to attract the interest of international management scholars. We add to this growing stream of research by investigating how MNEs' social conduct in their emerging economy subsidiaries relates to their relationships with certain local stakeholders in the respective countries. Using stakeholder theory and the concept of local legitimacy, we argue that employees and the local governments are among the forces that drive firms to push for high standards in their working conditions and to engage in developing the local community. Further, we hypothesize that such efforts positively affect the performance of the subsidiary's employees and its relationship with local authorities. To test our hypotheses, we conduct a large-scale, cross-industry analysis using data from 213 MNE subsidiaries in Asia, Eastern Europe, and Latin America. Our results show that MNEs' local mid-level employees drive corporate social efforts and also positively react to the corporate social engagement of their employer. In contrast, local governments do not have a significant influence on MNEs' social strategies. Their support of the firm may increase, however, as a result of the firm's active development of the local community.


title: Expert System for Ice Hockey Game Prediction: Data Mining with Human Judgment

abstract: This paper describes an expert system to predict National Hockey League (NHL) game outcome. A new method based on both data and judgments is used to estimate the hockey game performance. There are many facts and judgments that could influence an outcome. We employed the support vector machine to determine the importance of these factors before we incorporate them into the prediction system. Our system combines data and judgments and used them to predict the win–lose outcome of all the 89 post-season games before they took place. The accuracy of our prediction with the combined factors was 77.5%. This is to date the best accuracy reported of hockey games prediction.


title: Learning parameterized motor skills on a humanoid robot

abstract: We demonstrate a sample-efficient method for constructing reusable parameterized skills that can solve families of related motor tasks. Our method uses learned policies to analyze the policy space topology and learn a set of regression models which, given a novel task, appropriately parameterizes an underlying low-level controller. By identifying the disjoint charts that compose the policy manifold, the method can separately model the qualitatively different sub-skills required for solving distinct classes of tasks. Such sub-skills are useful because they can be treated as new discrete, specialized actions by higher-level planning processes. We also propose a method for reusing seemingly unsuccessful policies as additional, valid training samples for synthesizing the skill, thus accelerating learning. We evaluate our method on a humanoid iCub robot tasked with learning to accurately throw plastic balls at parameterized target locations.


title: Directing Gaze in 3 D Models with Stylized Focus

abstract: We present an interactive system for placing emphasis in stylized renderings of 3D models. The artist chooses a camera position, an area of interest, and a rendering style for the scene. The system then automatically renders the scene with emphasis in the area of interest, an effect we call "stylized focus." Stylized focus draws the viewer's gaze to the emphasized area, through local variations in shading effects such as color saturation and contrast as well as line qualities such as texture and density. We introduce a novel algorithm for local control of line density that exhibits a degree of temporal coherence suitable for animation. Animating the area of emphasis produces an effect we call the "stylized focus pull." Finally, an eye-tracking experiment verifies that the emphasis does indeed draw the viewer's gaze to the area of interest.


title: Whole body emotion expressions for KOBIAN humanoid robot — preliminary experiments with different Emotional patterns —

abstract: Personal robots and robot technology (RT)-based assistive devices are expected to play a major role in our elderly-dominated society, with an active participation to joint works and community life with humans, as partner and as friends for us. In particular, these robots are expected to be fundamental for helping and assisting elderly and disabled people during their activities of daily living (ADLs). To achieve this result, personal robots should be also capable of human-like emotion expressions. To this purpose we developed a new whole body emotion expressing bipedal humanoid robot, named KOBIAN, which is also capable to express human-like emotions. In this paper we presented three different evaluations of the emotional expressiveness of KOBIAN. In particular In particular, we presented the analysis of the roles of the face, the body, and their combination in emotional expressions. We also compared Emotional patterns created by a Photographer and a Cartoonist with the ones created by us. Overall, although the experimental results are not as good as we were expecting, we confirmed the robot can clearly express its emotions, and that very high recognition ratios are possible.


title: Neural networks and differential dynamic programming for reinforcement learning problems

abstract: We explore a model-based approach to reinforcement learning where partially or totally unknown dynamics are learned and explicit planning is performed. We learn dynamics with neural networks, and plan behaviors with differential dynamic programming (DDP). In order to handle complicated dynamics, such as manipulating liquids (pouring), we consider temporally decomposed dynamics. We start from our recent work [1] where we used locally weighted regression (LWR) to model dynamics. The major contribution of this paper is making use of deep learning in the form of neural networks with stochastic DDP, and showing the advantages of neural networks over LWR. For this purpose, we extend neural networks for: (1) modeling prediction error and output noise, (2) computing an output probability distribution for a given input distribution, and (3) computing gradients of output expectation with respect to an input. Since neural networks have nonlinear activation functions, these extensions were not easy. We provide an analytic solution for these extensions using some simplifying assumptions. We verified this method in pouring simulation experiments. The learning performance with neural networks was better than that of LWR. The amount of spilled materials was reduced. We also present early results of robot experiments using a PR2. Accompanying video: https://youtu.be/aM3hE1J5W98


title: Learning Hybrid Representations to Retrieve Semantically Equivalent Questions

abstract: Retrieving similar questions in online QA (2) BOW-CNN is more robust than the pure CNN for long texts.


title: A 30-mA CMOS low dropout regulator for WiMAX analog front ends with 50 dB PSRR at 10 M Hz

abstract: While the application space for WiMAX has seen fast growth in recent years, on-chip digital circuits and high frequency switching converters have resulted in power supply noise with high frequency spectral content, which jeopardizes the reliability of sensitive WiMAX analog front ends. As a cost-effective solution, this paper presents a low dropout (LDO) regulator design, which achieves high PSRR at 10 MHz. The LDO is implemented and verified in a CMOS 0.25-µm process. With a 3-V input power supply, it provides a 2.8-V regulated output with a PSRR of 76 dB at 1 kHz and 50 dB at 10 MHz. The target maximum load current is 30 mA for this application.


title: Recursive distributed representations

abstract: Abstract A longstanding difficulty for connectionist modeling has been how to represent variable-sized recursive data structures, such as trees and lists, in fixed-width patterns. This paper presents a connectionist architecture which automatically develops compact distributed representations for such compositional structures, as well as efficient accessing mechanisms for them. Patterns which stand for the internal nodes of fixed-valence trees are devised through the recursive use of backpropagation on three-layer auto-associative encoder networks. The resulting representations are novel, in that they combine apparently immiscible aspects of features, pointers, and symbol structures. They form a bridge between the data structures necessary for high-level cognitive tasks and the associative, pattern recognition machinery provided by neural networks.


title: Enabling packet fan-out in the libpcap library for parallel traffic processing

abstract: The large availability of multi-gigabit network cards for commodity PCs requires network applications to potentially cope with high volumes of traffic. However, computation intensive operations may not catch up with high traffic rates and need to be run in parallel over multiple processing cores. As of today, the vast majority of network applications are still based on the use of the pcap library interface which, unfortunately, does not provide a native multi-core support, even though the underlying capture technologies do. This paper introduces a novel version of the pcap library for the Linux operating-system that allows application level parallelism. The new library natively supports fanout operations for both multi-threaded and multi-process applications, by means of extended API as well as by a declarative grammar configuration suitable for legacy applications. In addition, the library can transparently run on top of the standard Linux socket and other accelerated capture engines. Performance evaluation has been carried out on a multi-core architecture in pure capture tests and in more realistic use cases involving monitoring applications such as Tstat and Bro, with standard Linux socket and the PFQ accelerated engine.


title: A Survey of Wearable Devices and Challenges

abstract: As smartphone penetration saturates, we are witnessing a new trend in personal mobile devices—wearable mobile devices or simply wearables as it is often called. Wearables come in many different forms and flavors targeting different accessories and clothing that people wear. Although small in size, they are often expected to continuously sense, collect, and upload various physiological data to improve quality of life. These requirements put significant demand on improving communication security and reducing power consumption of the system, fueling new research in these areas. In this paper, we first provide a comprehensive survey and classification of commercially available wearables and research prototypes. We then examine the communication security issues facing the popular wearables followed by a survey of solutions studied in the literature. We also categorize and explain the techniques for improving the power efficiency of wearables. Next, we survey the research literature in wearable computing. We conclude with future directions in wearable market and research.


title: Truthful and Near-Optimal Mechanism Design via Linear Programming

abstract: We give a general technique to obtain approximation mechanisms that are truthful in expectation. We show that for packing domains, any α-approximation algorithm that also bounds the integrality gap of the LP relaxation of the problem by α can be used to construct an α-approximation mechanism that is truthful in expectation. This immediately yields a variety of new and significantly improved results for various problem domains and furthermore, yields truthful (in expectation) mechanisms with guarantees that match the best-known approximation guarantees when truthfulness is not required. In particular, we obtain the first truthful mechanisms with approximation guarantees for a variety of multiparameter domains. We obtain truthful (in expectation) mechanisms achieving approximation guarantees of O(√m) for combinatorial auctions (CAs), (1 + e) for multiunit CAs with B = Ω(log m) copies of each item, and 2 for multiparameter knapsack problems (multi-unit auctions). Our construction is based on considering an LP relaxation of the problem and using the classic VCG mechanism to obtain a truthful mechanism in this fractional domain. We argue that the (fractional) optimal solution scaled down by α, where α is the integrality gap of the problem, can be represented as a convex combination of integer solutions, and by viewing this convex combination as specifying a probability distribution over integer solutions, we get a randomized, truthful in expectation mechanism. Our construction can be seen as a way of exploiting VCG in a computational tractable way even when the underlying social-welfare maximization problem is NP-hard.


title: CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows

abstract: We present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless, it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of its data model, the CaosDB Server and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it.


title: The appropriation and repurposing of social technologies in higher education

abstract: This paper presents some of the findings from a recent project that conducted a virtual ethnographic study of three formal courses in higher education that use 'Web 2.0' or social technologies for learning and teaching. It describes the pedagogies adopted within these courses, and goes on to explore some key themes emerging from the research and relating to the pedagogical use of weblogs and wikis in particular. These themes relate primarily to the academy's tendency to constrain and contain the possibly more radical effects of these new spaces. Despite this, the findings present a range of student and tutor perspectives which show that these technologies have significant potential as new collaborative, volatile and challenging environments for formal learning.


title: Electric field-driven building blocks for introducing multiple gradients to hydrogels

abstract: Gradient biomaterials are considered as preferable matrices for tissue engineering due to better simulation of native tissues. The introduction of gradient cues usually needs special equipment and complex process but is only effective to limited biomaterials. Incorporation of multiple gradients in the hydrogels remains challenges. Here, beta-sheet rich silk nanofibers (BSNF) were used as building blocks to introduce multiple gradients into different hydrogel systems through the joint action of crosslinking and electric field. The blocks migrated to the anode along the electric field and gradually stagnated due to the solution-hydrogel transition of the systems, finally achieving gradient distribution of the blocks in the formed hydrogels. The gradient distribution of the blocks could be tuned easily through changing different factors such as solution viscosity, which resulted in highly tunable gradient of mechanical cues. The blocks were also aligned under the electric field, endowing orientation gradient simultaneously. Different cargos could be loaded on the blocks and form gradient cues through the same crosslinking-electric field strategy. The building blocks could be introduced to various hydrogels such as Gelatin and NIPAM, indicating the universality. Complex niches with multiple gradient cues could be achieved through the strategy. Silk-based hydrogels with suitable mechanical gradients were fabricated to control the osteogenesis and chondrogenesis. Chondrogenic-osteogenic gradient transition was obtained, which stimulated the ectopic osteochondral tissue regeneration in vivo. The versatility and highly controllability of the strategy as well as multifunction of the building blocks reveal the applicability in complex tissue engineering and various interfacial tissues.


title: Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages

abstract: In this paper, we propose a novel and elegant solution to "Multi-Source Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way multilingual corpus without modifying the Neural Machine Translation (NMT) architecture or training procedure. We simply concatenate the source sentences to form a single long multi-source input sentence while keeping the target side sentence as it is and train an NMT system using this preprocessed corpus. We evaluate our method in resource poor as well as resource rich settings and show its effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using 5 source languages) by comparing against existing methods for MSNMT. We also provide some insights on how the NMT system leverages multilingual information in such a scenario by visualizing attention.


title: Stewardship and Usefulness: Policy Principles for Information-based Transparency

abstract: This paper is a conceptual and empirical exploratio n of the tensions inherent in the drive to increase openness and transparency in government by means of information access and dissemination. The idea that democratic governments should be open, accessible, and transparent to the governed is receiving renewed em phasis through the combination of government reform efforts and the emergence of adva nced technology tools for information access. Although these initiatives are young, they already exhibit daunting complexity, with significant management, technology, and policy chal lenges. A variety of traditional and emerging information policy frameworks offer guidan ce, while diverse research perspectives highlight both challenges to and opportunities for promoting information-based transparency. Early experience with Data.gov, a central component of the US Open Government Initiative, suggests that two fundamental information policy pr inciples, stewardship and usefulness, can help guide and evaluate efforts to achieve informat ion-based transparency.


title: Okula Yönelik Tutum Ölçeği’nin Geliştirilmesi: Güvenirlik ve Geçerlik Çalışması Development of an Attitude Scale Towards School: A Study on Reliability and Validity

abstract: The purpose of this study is to develop a scale to determine the attitudes of high school students towards school. A total of 495 students attending 9th-12th grades participated in this research. The research data is obtained from the 5 point Likert type scale with 43 items. The results of the EFA indicated that the scale has one factor with three components factorial structure including 20 items. It has been found that Cronbach alpha for all scale, first, second and third components are 0,91; 0,87; 0,81 and 0,79 respectively. The results of CFA indicated that factor structure fit with the data. It is concluded that the scale is a reliable and a valid tool for measuring the attitudes of the students towards school.


title: Voronoi-based k nearest neighbor search for spatial network databases

abstract: A frequent type of query in spatial networks (e.g., road networks) is to find the K nearest neighbors (KNN) of a given query object. With these networks, the distances between objects depend on their network connectivity and it is computationally expensive to compute the distances (e.g., shortest paths) between objects. In this paper, we propose a novel approach to efficiently and accurately evaluate KNN queries in spatial network databases using first order Voronoi diagram. This approach is based on partitioning a large network to small Voronoi regions, and then pre-computing distances both within and across the regions. By localizing the precomputation within the regions, we save on both storage and computation and by performing across-the-network computation for only the border points of the neighboring regions, we avoid global pre-computation between every node-pair. Our empirical experiments with several real-world data sets show that our proposed solution outperforms approaches that are based on on-line distance computation by up to one order of magnitude, and provides a factor of four improvement in the selectivity of the filter step as compared to the index-based approaches.


title: Gender differences in online and offline self‐disclosure in pre‐adolescence and adolescence

abstract: Although there is developmental research on the prevalence of offline self-disclosure in pre-adolescence and adolescence, it is still unknown (a) how boys’ and girls’online self-disclosure develops in this period and (b) how online and offline self-disclosure interact with each other. We formulated three hypotheses to explain the possible interaction between online and offline self-disclosure: the displacement, the rich-get-richer, and the rehearsal hypothesis. We surveyed 690 pre-adolescents and adolescents (10-17 years) at three time points with half-year intervals in between. We found significant gender differences in the developmental trajectories of self-disclosure. For girls, both online and offline self-disclosure increased sharply during pre- (10-11 years) and early adolescence (12-13 years), and then stabilized in middle and late adolescence. For boys, the same trajectory was found although the increase in self-disclosure started 2 years later. We found most support for the rehearsal hypothesis: Both boys and girls seemed to use online self-disclosure to rehearse offline self-disclosure skills. This particularly held for boys in early adolescence who typically have difficulty disclosing themselves offline.


title: Dynamic service composition towards database virtualization for efficient data management

abstract: Automated data centric technology of Cloud computing facilitate the end users through the service module, named SaaS. Where, these group of end users are either be skilled or unskilled. Recently, the most intellectual decision is to retrieve the requested data from the enormous flooded data storage through the service based cloud architecture by any type of cloud users through the remarkably efficient way using the methodologies like DBaaS, multi-tenancy, database integration. Among them, multi-tenancy and database integration can be applicable in the SaaS service model through the tightly coupled nature of service composition. But, this static service composition suffers from implementation complexity, cost factor, flexibility and scalability for further database adaptability and efficient data availability. Here, the proposed Dynamic Service Composition (abbreviated as DSC) methodology is sophisticated enough to retrieve different types of data from the multiple heterogeneous cloud databases after connectivity setup with new databases at runtime and on-demand basis. This dynamic database connectivity through the loosely coupled service composition is able to supply the requested data within a revolutionary computational speed. This methodology is able to overcome the challenges introduced by static service composition. DSC can govern multiple cloud databases through the flexible services connectivity without any information about their position in the cloud. This concept can be termed as database virtualization. Overall, the proposed DSC mechanism can monitor heterogeneous cloud databases and is responsible for significant growth over computational power for efficient data availability within a remarkable lower cost in a flexible and scalable way.


Bilingual Minds


title: QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases

abstract: The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.


title: Simplified Stochastic Feedforward Neural Networks

abstract: It has been believed that stochastic feedforward neural networks (SFNNs) have several advantages beyond deterministic deep neural networks (DNNs): they have more expressive power allowing multi-modal mappings and regularize better due to their stochastic nature. However, training large-scale SFNN is notoriously harder. In this paper, we aim at developing efficient training methods for SFNN, in particular using known architectures and pre-trained parameters of DNN. To this end, we propose a new intermediate stochastic model, called Simplified-SFNN, which can be built upon any baseline DNNand approximates certain SFNN by simplifying its upper latent units above stochastic ones. The main novelty of our approach is in establishing the connection between three models, i.e., DNN->Simplified-SFNN->SFNN, which naturally leads to an efficient training procedure of the stochastic models utilizing pre-trained parameters of DNN. Using several popular DNNs, we show how they can be effectively transferred to the corresponding stochastic models for both multi-modal and classification tasks on MNIST, TFD, CASIA, CIFAR-10, CIFAR-100 and SVHN datasets. In particular, we train a stochastic model of 28 layers and 36 million parameters, where training such a large-scale stochastic network is significantly challenging without using Simplified-SFNN


title: Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation

abstract: Driver-assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively considered. It is therefore important to take a critical look at key aspects of these systems, one of which is lane-position tracking. It is for these driver-assistance objectives that motivate the development of the novel "video-based lane estimation and tracking" (VioLET) system. The system is designed using steerable filters for robust and accurate lane-marking detection. Steerable filters provide an efficient method for detecting circular-reflector markings, solid-line markings, and segmented-line markings under varying lighting and road conditions. They help in providing robustness to complex shadowing, lighting changes from overpasses and tunnels, and road-surface variations. They are efficient for lane-marking extraction because by computing only three separable convolutions, we can extract a wide variety of lane markings. Curvature detection is made more robust by incorporating both visual cues (lane markings and lane texture) and vehicle-state information. The experiment design and evaluation of the VioLET system is shown using multiple quantitative metrics over a wide variety of test conditions on a large test path using a unique instrumented vehicle. A justification for the choice of metrics based on a previous study with human-factors applications as well as extensive ground-truth testing from different times of day, road conditions, weather, and driving scenarios is also presented. In order to design the VioLET system, an up-to-date and comprehensive analysis of the current state of the art in lane-detection research was first performed. In doing so, a comparison of a wide variety of methods, pointing out the similarities and differences between methods as well as when and where various methods are most useful, is presented


title: A point-based MDP for robust single-lane autonomous driving behavior under uncertainties

abstract: In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle decision making is modeled as a Markov Decision Process (MDP), then extended to a QMDP framework. Based on MDP/QMDP, three kinds of uncertainties are taken into account: sensor noise, perception constraints and surrounding vehicles' behavior. In simulation, the QMDP-based reasoning framework makes the autonomous vehicle perform with differing levels of conservativeness corresponding to different perception confidence levels. Road tests also indicate that the proposed algorithm helps the vehicle in avoiding potentially unsafe situations under these uncertainties. In general, the results indicate that the proposed QMDP-based algorithm makes autonomous driving more robust to limited sensing ability and occasional sensor failures.


title: Aurora: a data stream management system

abstract: The Aurora system [1] is an experimental data stream management system with a fully functional prototype. It includes both a graphical development environment, and a runtime system. We propose to demonstrate the Aurora system with its development environment and runtime system, with several example monitoring applications developed in consultation with defense, financial, and natural science communities. We will also demonstrate the effect of various system alternatives on various workloads. For example, we will show how different scheduling algorithms affect tuple latency and internal queue lengths. We will use some of our visualization tools to accomplish this. Data Stream Management Aurora is a data stream management system for monitoring applications. Streams are continuous data feeds from such sources as sensors, satellites and stock feeds. Monitoring applications track the data from numerous streams, filtering them for signs of abnormal activity and processing them for purposes of aggregation, reduction and correlation. The management requirements for monitoring applications differ profoundly from those satisfied by a traditional DBMS: o A traditional DBMS assumes a passive model where most data processing results from humans issuing transactions and queries. Data stream management requires a more active approach, monitoring data feeds from unpredictable external sources (e.g., sensors) and alerting humans when abnormal activity is detected. o A traditional DBMS manages data that is currently in its tables. Data stream management often requires processing data that is bounded by some finite window of values, and not over an unbounded past. o A traditional DBMS provides exact answers to exact queries, and is blind to real-time deadlines. Data stream management often must respond to real-time deadlines (e.g., military applications monitoring positions of enemy platforms) and therefore must often provide reasonable approximations to queries. o A traditional query processor optimizes all queries in the same way (typically focusing on response time). A stream data manager benefits from application specific optimization criteria (QoS). o A traditional DBMS assumes pull-based queries to be the norm. Push-based data processing is the norm for a data stream management system. A Brief Summary of Aurora Aurora has been designed to deal with very large numbers of data streams. Users build queries out of a small set of operators (a.k.a. boxes). The current implementation provides a user interface for tapping into pre-existing inputs and network flows and for wiring boxes together to produces answers at the outputs. While it is certainly possible to accept input as declarative queries, we feel that for a very large number of such queries, the process of common sub-expression elimination is too difficult. An example of an Aurora network is given in Screen Shot 1. A simple stream is a potentially infinite sequence of tuples that all have the same stream ID. An arc carries multiple simple streams. This is important so that simple streams can be added and deleted from the system without having to modify the basic network. A query, then, is a sub-network that ends at a single output and includes an arbitrary number of inputs. Boxes can connect to multiple downstream boxes. All such path splits carry identical tuples. Multiple streams can be merged since some box types accept more than one input (e.g., Join, Union). We do not allow any cycles in an operator network. Each output is supplied with a Quality of Service (QoS) specification. Currently, QoS is captured by three functions (1) a latency graph, (2) a value-based graph, and (3) a loss-tolerance graph. The latency graph indicates how utility drops as an answer is delayed. The value-based graph shows which values of the output space are most important. The loss-tolerance graph is a simple way to describe how averse the application is to approximate answers. Tuples arrive at the input and are queued for processing. A scheduler selects a box with waiting tuples and executes that box on one or more of the input tuples. The output tuples of a box are queued at the input of the next box in sequence. In this way, tuples make their way from the inputs to the outputs. If the system is overloaded, QoS is adversely affected. In this case, we invoke a load shedder to strategically eliminate Aurora supports persistent storage in two different ways. First, when box queues consume more storage than available RAM, the system will spill tuples that are less likely to be needed soon to secondary storage. Second, ad hoc queries can be connected to (and disconnected from) any arc for which a connection point has been defined. A connection point stores a historical portion of a stream that has flowed on the arc. For example, one could define a connection point as the last hour’s worth of data that has been seen on a given arc. Any ad hoc query that connects to a connection point has access to the full stored history as well as any additional data that flows past while the query is connected.


title: Parameterized Dataflow Modeling for DSP Systems

abstract: Dataflow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of dataflow, termed synchronous dataflow (SDF), that offers strong compile-time predictability properties, but has limited expressive power, has been studied extensively in the DSP context. Many extensions to synchronous dataflow have been proposed to increase its expressivity while maintaining its compile-time predictability properties as much as possible. We proposed a parameterized dataflow framework that can be applied as a meta-modeling technique to significantly improve the expressive power of any dataflow model that possesses a well-defined concept of a graph iteration, Indeed, the parameterized dataflow framework is compatible with many of the existing dataflow models for DSP including SDF, cyclo-static dataflow, scalable synchronous dataflow, and Boolean dataflow. In this paper, we develop precise, formal semantics for parameterized synchronous dataflow (PSDF)-the application of our parameterized modeling framework to SDF-that allows data-dependent, dynamic DSP systems to be modeled in a natural and intuitive fashion. Through our development of PSDF, we demonstrate that desirable properties of a DSP modeling environment such as dynamic reconfigurability and design reuse emerge as inherent characteristics of our parameterized framework. An example of a speech compression application is used to illustrate the efficacy of the PSDF approach and its amenability to efficient software synthesis techniques. In addition, we illustrate the generality of our parameterized framework by discussing its application to cyclo-static dataflow, which is a popular alternative to the SDF model.


title: Microlearning based mobile application for preparation to CCNA examination

abstract: In the modern society, education is increasingly changing standards according to people needs, forcing young people to know more. We have many methodologies of teaching, but the most effective is using instruments, that well known to audience. Therefore, it will be productive to use technologies in learning and education processes to increase interest of students towards the different subjects. This paper presents a newly designed application for interactive evaluation of students based on Android mobile platform that can help teacher and students themselves to control learning process and keep in track students' improvements. Application allows the user implement and answer quizzes, and extract and publish statistics.


title: Provenance management in curated databases

abstract: Curated databases in bioinformatics and other disciplines are the result of a great deal of manual annotation, correction and transfer of data from other sources. Provenance information concerning the creation, attribution, or version history of such data is crucial for assessing its integrity and scientific value. General purpose database systems provide little support for tracking provenance, especially when data moves among databases. This paper investigates general-purpose techniques for recording provenance for data that is copied among databases. We describe an approach in which we track the user's actions while browsing source databases and copying data into a curated database, in order to record the user's actions in a convenient, queryable form. We present an implementation of this technique and use it to evaluate the feasibility of database support for provenance management. Our experiments show that although the overhead of a naive approach is fairly high, it can be decreased to an acceptable level using simple optimizations.


title: ID-Match: A Hybrid Computer Vision and RFID System for Recognizing Individuals in Groups

abstract: Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.


title: Concept-Based Information Retrieval Using Explicit Semantic Analysis

abstract: Information retrieval systems traditionally rely on textual keywords to index and retrieve documents. Keyword-based retrieval may return inaccurate and incomplete results when different keywords are used to describe the same concept in the documents and in the queries. Furthermore, the relationship between these related keywords may be semantic rather than syntactic, and capturing it thus requires access to comprehensive human world knowledge. Concept-based retrieval methods have attempted to tackle these difficulties by using manually built thesauri, by relying on term cooccurrence data, or by extracting latent word relationships and concepts from a corpus. In this article we introduce a new concept-based retrieval approach based on Explicit Semantic Analysis (ESA), a recently proposed method that augments keyword-based text representation with concept-based features, automatically extracted from massive human knowledge repositories such as Wikipedia. Our approach generates new text features automatically, and we have found that high-quality feature selection becomes crucial in this setting to make the retrieval more focused. However, due to the lack of labeled data, traditional feature selection methods cannot be used, hence we propose new methods that use self-generated labeled training data. The resulting system is evaluated on several TREC datasets, showing superior performance over previous state-of-the-art results.


title: Regret: A reputation model for gregarious societies

abstract: Reputation has been studied in many fields but few formal models have been defined up to now. In this paper we propose a new model for reputation that takes into account the social dimension of agents and a hierarchical ontology structure. This model is a natural extension of other models currently being used in the area of electronic commerce. We show how the model relates to these other systems and provide initial experimental results about the benefits of using a social view on the modeling of reputation.


title: Open issues in the sentiment analysis of Arabic social media: A case study

abstract: With the rapid increase in the volume of Arabic opinionated posts on different microblogging mediums comes an increasing demand for Arabic sentiment analysis tools. Yet, research in the area of Arabic sentiment analysis is progressing at a very slow pace compared to that being carried out in English and other languages. This paper highlights the major problems and open research issues that face sentiment analysis of Arabic social media. The paper also presents a case study the goal of which is to investigate the possibility of determining the semantic orientation of Arabic Egyptian tweets and comments given limited Arabic resources. One of the outcomes of the presented study, is an Egyptian dialect sentiment lexicon.


title: Predicting future traffic using Hidden Markov Models

abstract: Network traffic volume estimation and prediction is an important research topic that attracts persistent attention from the networking community and the machine learning community. Although there has been extensive work on estimating or predicting the traffic matrix using time series models, low rank matrix decomposition et. al, to the best of our knowledge, there is few work investigating the problem whether we are able to estimate and predict the traffic volume based on some statistics of the traffic which are much less costly to collect, for example, the flow counts. In this paper, we propose to model the relationship between the traffic volume and simple statistics about flows using a Hidden Markov Model based on which we can avoid direct measurement of the traffic volume but instead we estimate and predict the hidden traffic volume based on those simple flow statistics which are collected by some sketch techniques. We demonstrate the feasibility and effectiveness of our proposed method using some semi-simulation and real data experimental results.


title: Data-Oriented Programming: On the Expressiveness of Non-control Data Attacks

abstract: As control-flow hijacking defenses gain adoption, it is important to understand the remaining capabilities of adversaries via memory exploits. Non-control data exploits are used to mount information leakage attacks or privilege escalation attacks program memory. Compared to control-flow hijacking attacks, such non-control data exploits have limited expressiveness, however, the question is: what is the real expressive power of non-control data attacks? In this paper we show that such attacks are Turing-complete. We present a systematic technique called data-oriented programming (DOP) to construct expressive non-control data exploits for arbitrary x86 programs. In the experimental evaluation using 9 programs, we identified 7518 data-oriented x86 gadgets and 5052 gadget dispatchers, which are the building blocks for DOP. 8 out of 9 real-world programs have gadgets to simulate arbitrary computations and 2 of them are confirmed to be able to build Turing-complete attacks. We build 3 end-to-end attacks to bypass randomization defenses without leaking addresses, to run a network bot which takes commands from the attacker, and to alter the memory permissions. All the attacks work in the presence of ASLR and DEP, demonstrating how the expressiveness offered by DOP significantly empowers the attacker.


title: Question answering in TREC

abstract: Traditional text retrieval systems return a ranked list of documents in response to a user's request. While a ranked list of documents can be an appropriate response for the user, frequently it is not. Usually it would be better for the system to provide the answer itself instead of requiring the user to search for the answer in a set of documents. The Text REtrieval Conference (TREC) is sponsoring a question answering "track" to foster research on the problem of retrieving answers rather than document lists.TREC is a workshop series sponsored by the National Institute of Standards and Technology and the U.S. Department of Defense [7]. The purpose of the conference series is to encourage research on text retrieval for realistic applications by providing large test collections, uniform scoring procedures, and a forum for organizations interested in comparing results. The conference has focused primarily on the traditional IR problem of retrieving a ranked list of documents in response to a statement of information need, but has also included other tasks, called tracks, that focus on new areas or particularly difficult aspects of information retrieval. A question answering track was introduced in TREC-8 1999. The track has generated wide-spread interest in the QA problem [2, 3, 4], and has documented significant improvements in question answering system effectiveness in its two-year history.This paper provides a brief summary of the findings of the TREC question answering track to date and discusses the future directions of the track. The paper is extracted from a fuller description of the track given in "The TREC Question Answering Track" [8]. Complete details about the TREC question answering track can be found in the TREC proceedings.


Development of a scale to measure process changes in psychotherapy


title: Adaptive Weight Allocation-Based Subpixel Rendering Algorithm

abstract: In this letter, a new approach is presented for adaptive weight allocation-based subpixel rendering in organic light-emitting diode displays. Subpixel rendering is used to enhance the apparent resolution without changing a pixel structure. Existing methods have blurring and color fringing artifact after subpixel rendering. The proposed method, on the other hand, dynamically controls weights of current and neighboring pixels based on color difference. Thus, it preserves the image quality, while increasing the resolution. In experiments, the proposed subpixel rendering improved luminance sharpness by up to 0.043, when compared with the benchmark methods. For chrominance blending, the peak signal-to-noise ratio of the proposed method was up to 6.192 dB higher than those of benchmark methods.


title: Human-level intelligence or animal-like abilities?

abstract: The vision systems of the eagle and the snake outperform everything that we can make in the laboratory, but snakes and eagles cannot build an eyeglass or a telescope or a microscope. (Judea Pearl)


title: Comparative study of Proportional Integral and Backstepping controller for Buck converter

abstract: This paper describes the comparative study of Proportional Integral (PI) and Backstepping controller for Buck converter with R-load and DC motor. Backstepping approach is an efficient control design procedure for both regulation and tracking problems. This approach is based upon a systematic procedure which guarantees global regulation and tracking. The proposed control scheme is to stabilize the output (voltage or speed) and tracking error to converge zero asymptotically. Buck converter system is simulated in MATLAB, using state reconstruction techniques. Simulation results of buck converter with R-load and PMDC motor reveals that, settling time of Backstepping controller is less than PI controller


title: A full X-band high-efficiency 12-watt GaAs MMIC power amplifier with harmonic tuning

abstract: In this paper, the design and measurements of a 8-12GHz high-efficiency MMIC high power amplifier (HPA) implemented in a 0.25μm GaAS pHEMT process is described. The 3-stage amplifier has demonstrated from 37% to 54% power-added efficiency (PAE) with 12W of output power and up to 27dB of small signal gain range from 8-12GHz. In particular, over the frequency band of 9-11 GHz, the circuit achieved above 45% PAE. The key to this design is determining and matching the optimum source and load impedance for PAE at the first two harmonics in output stage.


title: Effectiveness of Art Therapy With Adult Clients in 2018—What Progress Has Been Made?

abstract: In the year 2000, an important art therapy literature review addressed an essential question – does art therapy work? It discussed 17 articles dealing with the issue of the effectiveness of art therapy. Two decades later, this research field has extended its scope and is flourishing. Several current reviews of research work have described the broad range of methods implemented today, which includes qualitative and quantitative studies; other reviews have focused on art therapy with specific populations, or by age group. The aim of this systematic literature review is to contribute to the ongoing discussion in the field by exploring the latest studies dealing with the effectiveness of art therapy with a broad scope of adult clients. We conducted a comprehensive search in four databases and review of every quantitative article that has addressed outcome measures in the art therapy field from 2000 to 2017. This paper presents the latest 27 studies in the field that examine the effectiveness of art therapy with adult clients and divides them into seven clinical categories: cancer patients, clients coping with a variety of medical conditions, mental health clients, clients coping with trauma, prison inmates, the elderly and clients who have not been diagnosed with specific issues but face ongoing daily challenges. It underscores the potential effects of art therapy on these seven clinical populations, and recommends the necessary expansions for future research in the field, to enable art therapy research to take further strides forward.


title: Smart Irrigation Using Internet of Things

abstract: Automation in agriculture system is very important these days. This paper proposes an automated system for irrigating the fields. ESP-8266 WIFI module chip is used to connect the system to the internet. Various types of sensors are used to check the content of moisture in the soil, and the water is supplied to the soil through the motor pump. IOT is used to inform the farmers of the supply of water to the soil through an android application. Every time water is given to the soil, the farmer will get to know about that.


title: Imaging of soft tissue abnormalities about the hip.

abstract: Soft tissue abnormalities about the hip represent a common clinical problem. Although the signs and symptoms of some of these abnormalities are clinically evident, other entities are frequently overlooked. This article provides an overview and discusses the role of major imaging modalities, especially MR imaging, the primary modality for evaluation of many soft-tissue abnormalities. An introduction to fundamental imaging anatomy and functional roles of soft tissue structures about the hip is provided, recognizing their importance in making the correct diagnosis. Intra-articular and extra-articular soft tissue abnormalities reviewed systematically according to their mechanism of injury and anatomic or functional compartments.


title: Experimental testbed for 5G cognitive radio access in 4G LTE cellular systems

abstract: Cognitive radio technology addresses the limited availability of wireless spectrum and inefficiency of spectrum usage. Cognitive Radio (CR) devices sense their environment, detect spatially unused spectrum and opportunistically access available spectrum without creating harmful interference to the incumbents. In cellular systems with licensed spectrum, the efficient utilization of the spectrum as well as the protection of primary users is equally important, which imposes opportunities and challenges for the application of CR. This paper introduces an experimental framework for 5G cognitive radio access in current 4G LTE cellular systems. It can be used to study CR concepts in different scenarios, such as 4G to 5G system migrations, machine-type communications, device-to-device communications, and load balancing. Using our framework, selected measurement results are presented that compare Long Term Evolution (LTE) Orthogonal Frequency Division Multiplex (OFDM) with a candidate 5G waveform called Generalized Frequency Division Multiplexing (GFDM) and quantify the benefits of GFDM in CR scenarios.


title: Collection and Processing of Data from Wrist Wearable Devices in Heterogeneous and Multiple-User Scenarios

abstract: Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working on the analytic exploitation of this kind of data towards the support of learners and teachers in educational contexts. More precisely, sleep and stress indicators are defined to assist teachers and learners on the regulation of their activities. During this development, we have identified interoperability challenges related to the collection and processing of data from wearable devices. Different vendors adopt specific approaches about the way data can be collected from wearables into third-party systems. This hinders such developments as the one that we are carrying out. This paper contributes to identifying key interoperability issues in this kind of scenario and proposes guidelines to solve them. Taking into account these topics, this work is situated in the context of the standardization activities being carried out in the Internet of Things and Machine to Machine domains.


title: Automatic vulnerability mining with abstract interpretation and static taint trace

abstract: With the expansion of software scale, effective approaches for automatic vulnerability mining have been in badly needed. This paper presents a novel approach which can generate test cases of high pertinence and reachability. Unlike standard fuzzing techniques which explore the test space blindly, our approach utilizes abstract interpretation based on intervals to locate the Frail-Points of program which may cause buffer over-flow in some special conditions and the technique of static taint trace to build mappings between the Frail-Points and program inputs. Moreover, acquire path constraints of each Frail-Point through symbolic execution. Finally, combine information of mappings and path constraints to propose a policy for guiding test case generation.


ADLs and dynamic architecture changes


title: Bitcoin and Beyond: Exclusively Informational Monies

abstract: The famous new money Bitcoin is classified as a technical informational money (TIM). Besides introducing the idea of a TIM, a more extreme notion of informational money will be developed: exclusively informational money (EXIM). The informational coins (INCOs) of an EXIM can be in control of an agent but are not owned by any agent. INCOs of an EXIM cannot be stolen, but they can be lost, or thrown away. The difference between an EXIM and a TIM shows up when considering a user perspective on security matters. Security for an EXIM user is discussed in substantial detail, with the remarkable conclusion that computer security (security models, access control, user names, passwords, firewalls etc.) is not always essential for an EXIM, while the application of cryptography based information security is unavoidable for the use of an EXIM. Bitcoin seems to meet the criteria of an EXIM, but the assertion that "Bitcoin is an EXIM", might also be considered problematic. As a thought experiment we will contemplate Bitguilder, a hypothetical copy of Bitcoin that qualifies as an EXIM. A business ethics assessment of Bitcoin is made which reveals a number of worries. By combining Bitguilder with a so-called technical informational near-money (TINM) a dual money system, having two units with a fluctuating rate, may be obtained. It seems that a dual money can remedy some, but not all, of the ethical worries that arise when contemplating Bitcoin after hypothetically having become a dominant form of money. The contributions that Bitcoin's designers can potentially make to the evolution of EXIMs and TIMs is analyzed in terms of the update of the portfolio of money related natural kinds that comes with Bitcoin.


title: Estimating the Success of Unsupervised Image to Image Translation

abstract: While in supervised learning, the validation error is an unbiased estimator of the generalization (test) error and complexity-based generalization bounds are abundant, no such bounds exist for learning a mapping in an unsupervised way. As a result, when training GANs and specifically when using GANs for learning to map between domains in a completely unsupervised way, one is forced to select the hyperparameters and the stopping epoch by subjectively examining multiple options. We propose a novel bound for predicting the success of unsupervised cross domain mapping methods, which is motivated by the recently proposed Simplicity Principle. The bound can be applied both in expectation, for comparing hyperparameters and for selecting a stopping criterion, or per sample, in order to predict the success of a specific cross-domain translation. The utility of the bound is demonstrated in an extensive set of experiments employing multiple recent algorithms. Our code is available at this https URL .


title: Information, Noise, and Energy Dissipation: Laws, Limits, and Applications

abstract: This chapter addresses various subjects, including some open questions related to energy dissipation, information, and noise, that are relevant for nano- and molecular electronics. The object is to give a brief and coherent presentation of the results of a number of recent studies of ours.


title: Flexible Sensors for Chronic Wound Management

abstract: Chronic nonhealing wounds are a major source of morbidity and mortality in bed-ridden and diabetic patients. Monitoring of physical and chemical parameters important in wound healing and remodeling process can be of immense benefit for optimum management of such lesions. Low-cost flexible polymeric and paper-based substrates are attractive platforms for fabrication of such sensors. In this review, we discuss recent advances in flexible physiochemical sensors for chronic wound monitoring. After a brief introduction to wound healing process and commercial wound dressings, we describe various flexible biocompatible substrates that can be used as the base platform for integration of wound monitoring sensors. We will then discuss several fabrication methods that can be utilized to integrate physical and chemical sensors onto such substrates. Finally, we will present physical and chemical sensors developed for monitoring wound microenvironment and outline future development venues.


Aesthetic Computing


Listen to Your Heart : Stress Prediction Using Consumer Heart Rate Sensors


title: Object Modeling of Data and DataSets in the International Standard IEC 61850

abstract: In the past decade, new communication schemes have been designed and retrofitted into substations by utilities to integrate data from relays and intelligent electronic devices (IEDs). The idea of standardizing the language of communication between IEDs has evolved as the key for the advancement of connectivity and interoperability within a SA system. The most important outcome of this standardization process has been the IEC 61850 standard. IEC 61850 is an abstract application layer protocol aimed at providing interoperability between a variety of substation and feeder devices by describing how devices are to communicate in a substation as well as the related system requirements. In this paper, the authors describe the standard's application-view model and present the use of OO methodology and techniques for the modelling and implementation of the data and dataset application-view object models of the standard. The target audience for this paper includes anyone interested in getting an overview of the IEC 61850 international standard, and understanding how the object-oriented models proposed in the standard standardize the data found in IEDs and the communication of this date between substation devices.


title: A More Nuanced View of Reliability: Specificity in the Trait Hierarchy

abstract: Retest reliability is a better predictor of validity than is internal consistency. One explanation for this is item-specific variance, which distinguishes different nuances of a facet and contributes to retest reliability but not internal consistency. Specific variance at the facet level is temporally stable, consensually validated, and heritable; a consideration of the role of specific variance in personality measures leads to a distinction between traits as the intersection (∩) versus the union (∪) of their constituents. I discuss specific variance at the item level and its implications for scale development and argue that retest reliability outpredicts internal consistency because item-specific variance has been shown to be observable, and is probably heritable and stable. I consider some implications of these ideas for the use of single-item scales, the causal interpretation of traits, and the notion of scalar equivalence. Finally, I note that the sources of random error in scales are still poorly und...


title: UML-based service robot software development: a case study

abstract: The research field of Intelligent Service Robots, which has become more and more popular over the last years, covers a wide range of applications from climbing machines for cleaning large storefronts to robotic assistance for disabled or elderly people. When developing service robot software, it is a challenging problem to design the robot architecture by carefully considering user needs and requirements, implement robot application components based on the architecture, and integrate these components in a systematic and comprehensive way for maintainability and reusability. Furthermore, it becomes more difficult to communicate among development teams and with others when many engineers from different teams participate in developing the service robot. To solve these problems, we applied the COMET design method, which uses the industry-standard UML notation, to developing the software of an intelligent service robot for the elderly, called T-Rot, under development at Center for Intelligent Robotics (CIR). In this paper, we discuss our experiences with the project in which we successfully addressed these problems and developed the autonomous navigation system of the robot with the COMET/UML method.


title: Improved lossless intra coding for H.264/MPEG-4 AVC

abstract: A new lossless intra coding method based on sample-by-sample differential pulse code modulation (DPCM) is presented as an enhancement of the H.264/MPEG-4 AVC standard. The H.264/AVC design includes a multidirectional spatial prediction method to reduce spatial redundancy by using neighboring samples as a prediction for the samples in a block of data to be encoded. In the new lossless intra coding method, the spatial prediction is performed based on samplewise DPCM instead of in the block-based manner used in the current H.264/AVC standard, while the block structure is retained for the residual difference entropy coding process. We show that the new method, based on samplewise DPCM, does not have a major complexity penalty, despite its apparent pipeline dependencies. Experiments show that the new lossless intra coding method reduces the bit rate by approximately 12% in comparison with the lossless intra coding method previously included in the H.264/AVC standard. As a result, the new method is currently being adopted into the H.264/AVC standard in a new enhancement project


abstract: Characterizing Motherese: On the Computational Structure of Child-Directed Language Heidi Waterfall (he32@cornell.edu) 1 Peter Brodsky (pb86@cornell.edu) Shimon Edelman (se37@cornell.edu) Department of Psychology, Cornell University Ithaca, NY 14853 USA Abstract partial self-repetitions — variation sets — when speak- ing to young children acquiring language (Furrow, Nel- son, & Benedict, 1979; Kavanaugh & Jirovsky, 1982; Kaye, 1980; Snow, 1972; Hoff-Ginsberg, 1985, 1986, 1990; K¨ untay & Slobin, 1996; Waterfall, 2006). We report a quantitative analysis of the cross-utterance coordination observed in child-directed language, where successive utterances often overlap in a manner that makes their constituent structure more prominent, and describe the application of a recently published unsuper- vised algorithm for grammar induction to the largest available corpus of such language, producing a gram- mar capable of accepting and generating novel well- formed sentences. We also introduce a new corpus-based method for assessing the precision and recall of an auto- matically acquired generative grammar without recourse to human judgment. The present work sets the stage for the eventual development of more powerful unsuper- vised algorithms for language acquisition, which would make use of the coordination structures present in nat- ural child-directed speech. Keywords: Language acquisition; grammar inference; computational linguistics. Variation sets Hoff-Ginsberg (1985) conducted one of the initial ex- aminations of the effect of maternal self-repetitions on children’s progress in language acquisition. She showed that alternations in maternal self-repetitions that con- formed to major constituent boundaries were related to growth in children’s verb use, while those repeti- tions that altered material within a phrasal constituent aided in noun-phrase growth. In a subsequent study, Hoff-Ginsberg (1986) found that the frequency of self- repetitions and expansions was positively correlated with child verb phrase development. Similarly, Hoff-Ginsberg (1990) confirmed that maternal self-repetitions and ex- pansions were positively correlated with the average number of verbs per utterance in child speech. Hoff-Ginsberg’s analyses, however, concentrated on the corpus as a whole and did not examine the contin- gent nature of clusters of such repetitions. K¨ untay and Slobin (1996) pioneered the research into variation sets, conducting the first longitudinal study specifically ana- lyzing the effect of local clusters of partial repetitions in child-directed speech on language development. Focus- ing on the acquisition of Turkish, they found that vari- ation sets made up approximately 20% of child-directed speech. The use of variation sets was positively associ- ated with children’s acquisition of specific verbs. In sum, variation sets seem to be ideal environments for learning lexical items and constituent structures. By holding most of the utterance constant, while altering it slightly (see Table 1 for an example), parents may allow children to discover lexical items, syntactic constituents, and their place in the syntax, vis-`a-vis comparison and contrast, as envisaged (in the context of the discovery of grammar by linguists) by Zellig Harris (1946). Waterfall (2006) conducted the first longitudinal study of variation sets in English. We briefly mention here some of her findings (Waterfall, 2007). The participants were twelve parent-child dyads (ages 14-30 months). The subjects were balanced for child gender, child birth or- Introduction Does child-directed speech — what Newport, Gleitman, and Gleitman (1977) called “Motherese” — possess spe- cial characteristics that make it easier to learn from? In this paper, we present two kinds of corpus-based ev- idence that should be useful in addressing this ques- tion. First, we report a quantitative analysis of the cross-utterance coordination observed in child-directed language, where successive utterances often overlap in a manner that makes their constituent structure more prominent. Second, we describe the application of a re- cently published unsupervised algorithm for grammar in- duction to the largest available corpus of child-directed language, and the performance of the resulting grammar in accepting and generating novel well-formed sentences. This work sets the stage for the development of more powerful unsupervised algorithms for language acquisi- tion, which would make use of the coordinated structures present in natural child-directed speech. Cross-utterance coordination in Motherese There is a great deal of evidence suggesting that par- ents produce structured dialogues when talking with very young children. Parents’ speech to young chil- dren is highly repetitive and often includes clusters of Also with the Department of Psychology, University of Chicago, Chicago, IL 60637 USA.


title: The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty

abstract: In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The dataset is heterogeneous and consists of a mixture of computer generated automatic processes and manual human tasks. The goal is to work out a decision model, which represents the underlying tasks that make up the loan application service, and to assess potential areas of improvement of the institution's internal processes. To this end we study the impact of incomplete event logs for the extraction and analysis of business processes. It is quite common that event logs are incomplete with several amounts of missing information (for instance, workers forget to register their tasks). Absence of data is translated into a drastic decrease of precision and compromises the decision models, leading to biased and unrepresentative results. We investigate how classical probabilistic models are affected by incomplete event logs and we explore quantum-like probabilistic inferences as an alternative mathematical model to classical probability. This work represents a first step towards systematic investigation of the impact of quantum interference in a real life large scale decision scenario. The results obtained in this study indicate that, under high levels of uncertainty, the quantum-like models generate quantum interference terms, which allow an additional non-linear parameterisation of the data. Experimental results attest the efficiency of the quantum-like Bayesian networks, since the application of interference terms is able to reduce the error percentage of inferences performed over quantum-like models when compared to inferences produced by classical models.


title: Context-Aware Sequence-to-Sequence Models for Conversational Systems

abstract: This work proposes a novel approach based on sequence-to-sequence (seq2seq) models for context-aware conversational systems. Exist- ing seq2seq models have been shown to be good for generating natural responses in a data-driven conversational system. However, they still lack mechanisms to incorporate previous conversation turns. We investigate RNN-based methods that efficiently integrate previous turns as a context for generating responses. Overall, our experimental results based on human judgment demonstrate the feasibility and effectiveness of the proposed approach.


title: Learning Safe Policies with Expert Guidance

abstract: We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consistent with its existing knowledge. We propose two methods to solve the resulting optimization: an exact ellipsoid-based method and a method in the spirit of the "follow-the-perturbed-leader" algorithm. Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. The trained agent safely avoids states with potential negative effects while imitating the behavior of the expert in the other states.


Harmonic Drive Design & Application : A Review


title: Deeply-scaled self-aligned-gate GaN DH-HEMTs with ultrahigh cutoff frequency

abstract: We report record DC and RF performance in deeply-scaled self-aligned gate (SAG) GaN-HEMTs operating in both depletion-mode (D-mode) and enhancement-mode (E-mode). Through aggressive lateral scaling of the gate length (L g ) and the source-drain distance (L sd ) using a novel self-aligned gate technology and engineering of a thin top barrier layer, 20-nm gate AlN/GaN/AlGaN double-heterojunction (DH) HEMTs operating in D-mode (and E-mode) exhibited record DC and RF characteristics with high yield and uniformity; R on = 0.29 (0.33) Ω·mm, I dmax = 2.7 (2.6) A/mm, a peak extrinsic g m = 1.04 (1.63) S/mm, threshold voltage uniformity σ (V th ) = 44 (63) mV over a 3-inch wafer area, and a simultaneous f T /f max = 310/364 (343/236) GHz. Delay time analysis clarified that an unique dependence of f T on V ds resulted from suppressed drain delay and enhanced electron velocity due to the lateral source-drain (S-D) scaling.


title: Using Frankencerts for Automated Adversarial Testing of Certificate Validation in SSL/TLS Implementations

abstract: Modern network security rests on the Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols. Distributed systems, mobile and desktop applications, embedded devices, and all of secure Web rely on SSL/TLS for protection against network attacks. This protection critically depends on whether SSL/TLS clients correctly validate X.509 certificates presented by servers during the SSL/TLS handshake protocol. We design, implement, and apply the first methodology for large-scale testing of certificate validation logic in SSL/TLS implementations. Our first ingredient is "frankencerts," synthetic certificates that are randomly mutated from parts of real certificates and thus include unusual combinations of extensions and constraints. Our second ingredient is differential testing: if one SSL/TLS implementation accepts a certificate while another rejects the same certificate, we use the discrepancy as an oracle for finding flaws in individual implementations. Differential testing with frankencerts uncovered 208 discrepancies between popular SSL/TLS implementations such as OpenSSL, NSS, CyaSSL, GnuTLS, PolarSSL, MatrixSSL, etc. Many of them are caused by serious security vulnerabilities. For example, any server with a valid X.509 version1 certificate can act as a rogue certificate authority and issue fake certificates for any domain, enabling man-in-the-middle attacks against MatrixSSL and GnuTLS. Several implementations also accept certificate authorities created by unauthorized issuers, as well as certificates not intended for server authentication. We also found serious vulnerabilities in how users are warned about certificate validation errors. When presented with an expired, self-signed certificate, NSS, Safari, and Chrome (on Linux) report that the certificate has expired - a low-risk, often ignored error - but not that the connection is insecure against a man-in-the-middle attack. These results demonstrate that automated adversarial testing with frankencerts is a powerful methodology for discovering security flaws in SSL/TLS implementations.