title: Testing the interactivity model: communication processes, partner assessments, and the quality of collaborative work

abstract: A major consideration in designing and adopting new communication technologies is their impact on communication processes and outcomes. One way to understand this impact is according to the principle of interpersonal interactivity.Findings from two investigations are reported here that address how properties of task-related communication conducted with differing interfaces relate to perceptions of interaction partners and the outcomes of their collaborative work. Study 1 manipulated the interface affordances of mediation, contingency, and modality richness. Study 2 examined the affordance of mediation. Results show that interfaces that promote higher mutuality and involvement lead to more favorable perceptions of partners' credibility and attraction, and those perceptions are systematically related to higher-quality decisions and more influence. Discussion focuses on the relation between user perceptions, design features, and task outcomes in human-computer interaction and computer-mediated communication.


title: Boron Nitride Nanotubes: Recent Advances in Their Synthesis, Functionalization, and Applications

abstract: A comprehensive overview of current research progress on boron nitride nanotubes (BNNTs) is presented in this article. Particularly, recent advancements in controlled synthesis and large-scale production of BNNTs will first be summarized. While recent success in mass production of BNNTs has opened up new opportunities to implement the appealing properties in various applications, concerns about product purity and quality still remain. Secondly, we will summarize the progress in functionalization of BNNTs, which is the necessary step for their applications. Additionally, selected potential applications in structural composites and biomedicine will be highlighted.


title: Finer Grained Entity Typing with TypeNet

abstract: We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNet, a dataset of entity types consisting of over 1941 types organized in a hierarchy, obtained by manually annotating a mapping from 1081 Freebase types to WordNet. We also experiment with several models comparable to state-of-the-art systems and explore techniques to incorporate a structure loss on the hierarchy with the standard mention typing loss, as a first step towards future research on this dataset.


title: Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet

abstract: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy $c$ -means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.


A Survey On Privacy Preserving Data Mining Techniques


title: An undervoltage lockout of hysteretic threshold of zero temperature coefficients

abstract: This paper presents a method of merging the threshold voltage of zero temperature coefficients and comparative function into a single circuit. With application to undervoltage lockout circuit, it can reduce temperature sensitivity of threshold voltage and improve accuracy of threshold. Meanwhile, this undervoltage protection circuit realizes hysteretic threshold by a feedback control method so as to overcome disadvantage of poor interference rejection capability of single threshold. This undervoltage lockout circuit can be applied to DC-DC converter.


title: DVB-NGH: The Next Generation of Digital Broadcast Services to Handheld Devices

abstract: This paper reviews the main technical solutions adopted by the next-generation mobile broadcasting standard DVB-NGH, the handheld evolution of the second-generation digital terrestrial TV standard DVB-T2. The main new technical elements introduced with respect to DVB-T2 are: layered video coding with multiple physical layer pipes, time-frequency slicing, full support of an IP transport layer with a dedicated protocol stack, header compression mechanisms for both IP and MPEG-2 TS packets, new low-density parity check coding rates for the data path (down to 1/5), nonuniform constellations for 64 Quadrature Amplitude Modulation (QAM) and 256QAM, 4-D rotated constellations for Quadrature Phase Shift Keying (QPSK), improved time interleaving in terms of zapping time, end-to-end latency and memory consumption, improved physical layer signaling in terms of robustness, capacity and overhead, a novel distributed multiple input-single output transmit diversity scheme for single-frequency networks (SFNs), and efficient provisioning of local content in SFNs. All these technological solutions, together with the high performance of DVB-T2, make DVB-NGH a real next-generation mobile multimedia broadcasting technology. In fact, DVB-NGH can be regarded the first third-generation broadcasting system because it allows for the possibility of using multiple input-multiple output antenna schemes to overcome the Shannon limit of single antenna wireless communications. Furthermore, DVB-NGH also allows the deployment of an optional satellite component forming a hybrid terrestrial-satellite network topology to improve the coverage in rural areas where the installation of terrestrial networks could be uneconomical.


title: Lost in quantization: Improving particular object retrieval in large scale image databases

abstract: The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images are characterized using high-dimensional descriptors which are then mapped to ldquovisual wordsrdquo selected from a discrete vocabulary.This paper explores techniques to map each visual region to a weighted set of words, allowing the inclusion of features which were lost in the quantization stage of previous systems. The set of visual words is obtained by selecting words based on proximity in descriptor space. We describe how this representation may be incorporated into a standard tf-idf architecture, and how spatial verification is modified in the case of this soft-assignment. We evaluate our method on the standard Oxford Buildings dataset, and introduce a new dataset for evaluation. Our results exceed the current state of the art retrieval performance on these datasets, particularly on queries with poor initial recall where techniques like query expansion suffer. Overall we show that soft-assignment is always beneficial for retrieval with large vocabularies, at a cost of increased storage requirements for the index.


title: Design of a 1.5 kW high speed switched reluctance motor for electric supercharger with optimal performance assessment

abstract: In this paper, a design of the 3-phase, 6-stator-pole and 4-rotor-pole, 1.7 kW high speed switched reluctance motor for the supercharging application is presented. The design objectives include minimizing rotor inertial and air friction losses for rapid acceleration, reducing copper and core losses for sustained operation and heat reduction. The candidate design is then analyzed for optimal performance assessment in terms of output torque and efficiency.


A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair.


title: Properties and Benefits of Calibrated Classifiers

abstract: A calibrated classifier provides reliable estimates of the true probability that each test sample is a member of the class of interest. This is crucial in decision making tasks. Procedures for calibration have already been studied in weather forecasting, game theory, and more recently in machine learning, with the latter showing empirically that calibration of classifiers helps not only in decision making, but also improves classification accuracy. In this paper we extend the theoretical foundation of these empirical observations. We prove that (1) a well calibrated classifier provides bounds on the Bayes error (2) calibrating a classifier is guaranteed not to decrease classification accuracy, and (3) the procedure of calibration provides the threshold or thresholds on the decision rule that minimize the classification error. We also draw the parallels and differences between methods that use receiver operating characteristic (ROC) curves and calibration based procedures that are aimed at findig a threshold of minimum error. In particular, calibration leads to improved performance when multiple thresholds exist.


title: On the Traveling Salesman Problem with Simple Temporal Constraints

abstract: Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for some special cases.


title: Mirror, mirror on the wall, tell me, is the error small?

abstract: Do object part localization methods produce bilaterally symmetric results on mirror images? Surprisingly not, even though state of the art methods augment the training set with mirrored images. In this paper we take a closer look into this issue. We first introduce the concept of mirrorability as the ability of a model to produce symmetric results in mirrored images and introduce a corresponding measure, namely the mirror error that is defined as the difference between the detection result on an image and the mirror of the detection result on its mirror image. We evaluate the mirrorability of several state of the art algorithms in two of the most intensively studied problems, namely human pose estimation and face alignment. Our experiments lead to several interesting findings: 1) Most of state of the art methods struggle to preserve the mirror symmetry, despite the fact that they do have very similar overall performance on the original and mirror images; 2) the low mirrorability is not caused by training or testing sample bias - all algorithms are trained on both the original images and their mirrored versions; 3) the mirror error is strongly correlated to the localization/alignment error (with correlation coefficients around 0.7). Since the mirror error is calculated without knowledge of the ground truth, we show two interesting applications - in the first it is used to guide the selection of difficult samples and in the second to give feedback in a popular Cascaded Pose Regression method for face alignment.


title: Ultra-wide band vivaldi antenna array using low loss SIW power divider and GCPW wide band transition

abstract: A compact antipodal Vivaldi antenna array fed by a waveguide feed network is optimized to significantly reduce feed loss and achieve a wide band performance. It is implemented using SIW technology, and has eight antipodal Vivaldi antenna elements. The feed network utilizes a novel low-loss wide band GCPW transition to get a good input match and prevent the excitation of higher order modes. The array demonstrated a broadband performance from 8 to 12 GHz, a 12 dB gain, 13° BW, and −14dB SLL at the center frequency of 10 GHz and occupies only 10cm×5.7cm.


title: Separating Style and Content with Bilinear Models

abstract: Perceptual systems routinely separate “content” from “style,” classifying familiar words spoken in an unfamiliar accent, identifying a font or handwriting style across letters, or recognizing a familiar face or object seen under unfamiliar viewing conditions. Yet a general and tractable computational model of this ability to untangle the underlying factors of perceptual observations remains elusive (Hofstadter, 1985). Existing factor models (Mardia, Kent, & Bibby, 1979; Hinton & Zemel, 1994; Ghahramani, 1995; Bell & Sejnowski, 1995; Hinton, Dayan, Frey, & Neal, 1995; Dayan, Hinton, Neal, & Zemel, 1995; Hinton & Ghahramani, 1997) are either insufficiently rich to capture the complex interactions of perceptually meaningful factors such as phoneme and speaker accent or letter and font, or do not allow efficient learning algorithms. We present a general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nonetheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization. We report promising results on three different tasks in three different perceptual domains: spoken vowel classification with a benchmark multi-speaker database, extrapolation of fonts to unseen letters, and translation of faces to novel illuminants.


title: A HAMR Media Technology Roadmap to an Areal Density of 4 Tb/in$^2$

abstract: This paper discusses heat-assisted magnetic recording (HAMR) media requirements and challenges for areal densities (AD) beyond 1 Tb/in2. Based on recent roadmap discussions the focus is primarily on granular chemically ordered L10 FePtX-Y-perpendicular media with reduced average grain size down to 〈D〉 = 3-5 nm relative to current CoCrPt based perpendicular magnetic recording (PMR) media with average grain size 〈D〉 = 7-9 nm. In HAMR media the combination of thermal conductivity and Curie temperature TC determines the required laser power during recording. Key challenges are sigma variations of D and TC which need to be reduced to σD/D ~ 10-15% and σTC/TC ~ 2%. In addition AD is limited by switching field distribution (SFD) and thermal spot size. The key goal going forward is to optimize heads, media, head-media-spacing (HMS) and read-back channel technologies to extend AD to 4 Tb/in2 and beyond.


title: The Biocultural is a Pluripotent Concept for Theory: A Response to Six Provocations

abstract: Abstract:This essay expounds on five themes raised by the symposium participants' engagement with the notion that humans are biocultural creatures. These themes concern the excitement and richness of non-binary concept development, the reconsideration of what constitutes knowledge, the possibilities and challenges of collaborative work, the methodological questions posed by cross-or trans-disciplinary research, and the ways that the multiscalar spatial and temporal logics accompanying the concept of the bio-cultural transform the domains, objects, and agents of politics.


title: The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games

abstract: Purpose – The focus in this study is a model that predicts continuance intention of online multi-player games. In this integrated model, the social cognitive theory (SCT) lays out the foundation of two types of pre-use (pre-play) expectations, the flow theory captures the affective feeling with the game as a moderator for the effect from the two pre-use expectations, and subjective norm together with its associated antecedents cover a wide spectrum of social influences. Design/methodology/approach – A questionnaire was designed and pre-tested before distributing to target respondents. The reliability and validity of the instrument both met the commonly accepted guidelines. The integrated model was assessed first by examining its measurement model and then the structural model. Findings – The integration of cognitive, affective and social influence in this model explains a larger amount of variance compared to the competing models and existing studies. Originality/value – Unlike a popular trend that studie...


title: Question Answering on Linked Data: Challenges and Future Directions

abstract: Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While, recently, datasets underlying QA systems have been promoted from unstructured datasets to structured datasets with semantically highly enriched metadata, question answering systems are still facing serious challenges and are therefore not meeting users' expectations. This paper provides an exhaustive insight of challenges known so far for building QA systems, with a special focus on employing structured data (i.e. knowledge graphs).It thus helps researchers to easily spot gaps to fill with their future research agendas.


Dynamic Filter Networks for Predicting Unobserved Views


title: Securing user identity and transactions symbiotically: IoT meets blockchain

abstract: Swarms of embedded devices provide new challenges for privacy and security. We propose Permissioned Blockchains as an effective way to secure and manage these systems of systems. A long view of blockchain technology yields several requirements absent in extant blockchain implementations. Our approach to Permissioned Blockchains meets the fundamental requirements for longevity, agility, and incremental adoption. Distributed Identity Management is an inherent feature of our Permissioned Blockchain and provides for resilient user and device identity and attribute management.


title: Application of the essential work of fracture (EWF) concept for polymers, related blends and composites: A review

abstract: The essential work of fracture (EWF) concept has become very popular to characterize the plane stress toughness of ductile polymers and related systems. The widespread use of the EWF is due to the simple specimens’ preparation, easy testing and simple data reduction procedure. Though the EWF method is usually used for mode-I type loading, it has been successfully adopted for mode-II and mode-III type deformations, too. Moreover, attempts have also been made to deduce plane strain toughness values from EWF tests. This paper critically reviews the application of the EWF to polymers, polymer blends and composites. The literature survey covers all major aspects of testing and related data reduction methods, and lists the EWF results achieved on different polymer systems. The latter are classified according to their synthesis/production and modifications. Special attention was paid to disclose the correlations between EWF and other fracture mechanics parameters, and to trace the EWF response to molecular and morphological parameters of the tested polymers.


title: A handle bar metaphor for virtual object manipulation with mid-air interaction

abstract: Commercial 3D scene acquisition systems such as the Microsoft Kinect sensor can reduce the cost barrier of realizing mid-air interaction. However, since it can only sense hand position but not hand orientation robustly, current mid-air interaction methods for 3D virtual object manipulation often require contextual and mode switching to perform translation, rotation, and scaling, thus preventing natural continuous gestural interactions. A novel handle bar metaphor is proposed as an effective visual control metaphor between the user's hand gestures and the corresponding virtual object manipulation operations. It mimics a familiar situation of handling objects that are skewered with a bimanual handle bar. The use of relative 3D motion of the two hands to design the mid-air interaction allows us to provide precise controllability despite the Kinect sensor's low image resolution. A comprehensive repertoire of 3D manipulation operations is proposed to manipulate single objects, perform fast constrained rotation, and pack/align multiple objects along a line. Three user studies were devised to demonstrate the efficacy and intuitiveness of the proposed interaction techniques on different virtual manipulation scenarios.


title: Application of Fuzzy Logic to Cognitive Radio Systems

abstract: SUMMARY This paper reviews applications of fuzzy logic to telecommunications and proposes a novel fuzzy combining scheme for cooperative spectrum sensing in cognitive radio systems. A summary of previous applications of fuzzy logic to telecommunications is given outlining also potential applications of fuzzy logic in future cognitive radio systems. In complex and dynamic operational environments, future cognitive radio systems will need sophisticated decision making and environment awareness techniques that are capable of handling multidimensional, conflicting and usually non-predictable decision making problems where optimal solutions can not be necessarily found. The results indicate that fuzzy logic can be used in cooperative spectrum sensing to provide additional flexibility to


title: A multivariate regression approach to association analysis of a quantitative trait network

abstract: Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to address this issue in a principled way. Our approach explicitly represents the dependency structure among the quantitative traits as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical method, and borrow information across correlated phenotypes to discover the genetic markers that perturbe a subset of correlated triats jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis, and other sparse regression methods such as the ridge regression and the lasso that do not use any structural information in the traits. Our results show that there is a significant advantage in detecting the true causal SNPs when we incorporate the correlation pattern in traits using our proposed methods.


title: CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

abstract: We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selection cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge of the properties of individual object classes, by solving a sequence of Constrained Parametric Min-Cut problems (CPMC) on a regular image grid. In a subsequent step, we learn to rank the corresponding segments by training a continuous model to predict how likely they are to exhibit real-world regularities (expressed as putative overlap with ground truth) based on their mid-level region properties, then diversify the estimated overlap score using maximum marginal relevance measures. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC 2009 and 2010 data sets. In our companion papers [1], [2], we show that the algorithm can be used, successfully, in a segmentation-based visual object category recognition pipeline. This architecture ranked first in the VOC2009 and VOC2010 image segmentation and labeling challenges.


title: Exploring the knowledge landscape: four emerging views of knowledge

abstract: Purpose – The concept of “knowledge” is presented in diverse and sometimes even controversial ways in the knowledge management (KM) literature. The aim of this paper is to identify the emerging views of knowledge and to develop a framework to illustrate the interrelationships of the different knowledge types.Design/methodology/approach – This paper is a literature review to explore how “knowledge” as a central concept is presented and understood in a selected range of KM publications (1990‐2004).Findings – The exploration of the knowledge landscape showed that “knowledge” is viewed in four emerging and complementary ways. The ontological, epistemological, commodity, and community views of knowledge are discussed in this paper. The findings show that KM is still a young discipline and therefore it is natural to have different, sometimes even contradicting views of “knowledge” side by side in the literature.Practical implications – These emerging views of knowledge could be seen as opportunities for researc...


title: Crypto-tutor: An educational tool for learning modern cryptography

abstract: This paper presents Crypto-Tutor, a tool for learning modern cryptographic algorithms. Our tool offers an interactive way to learn about the uses and structure of several popular cryptographic algorithms, as well as the underlying mathematical concepts which guarantee the security of these algorithms. Crypto-Tutor also offers performance benchmarking of the algorithms for several datasets, as well as an interactive environment where the algorithms can be executed for custom input and configuration. This tool was used in the classroom, as part of two undergraduate courses in the Faculty of Technical Sciences, University of Novi Sad. Here we also present our experiences with using Crypto-Tutor in the classroom, and formally evaluate the usefulness of this tool.


title: The digital agenda of virtual currencies: Can BitCoin become a global currency?

abstract: AbstractThis paper identifies and analyzes BitCoin features which may facilitate BitCoin to become a global currency, as well as characteristics which may impede the use of BitCoin as a medium of exchange, a unit of account and a store of value, and compares BitCoin with standard currencies with respect to the main functions of money. Among all analyzed BitCoin features, the extreme price volatility stands out most clearly compared to standard currencies. In order to understand the reasons for such extreme price volatility, we attempt to identify drivers of BitCoin price formation and estimate their importance econometrically. We apply time-series analytical mechanisms to daily data for the 2009–2014 period. Our estimation results suggest that BitCoin attractiveness indicators are the strongest drivers of BitCoin price followed by market forces. In contrast, macro-financial developments do not determine BitCoin price in the long-run. Our findings suggest that as long as BitCoin price will be mainly driven by speculative investments, BitCoin will not be able to compete with standard currencies.


title: Real Robots Don ’ t Drive Straight

abstract: Over last fifteen years, robot technology has become popularinclassroomsacrossourwholeeducationalsystem. BothengineeringandAIeducatorshavedeveloped ways to integrate robots into their teaching. Engineering educators are primarily concerned with engineering science (e.g., feedback control) and process (e.g., design skills). AI educators have different goals—namely, AI educators want students to learn AI concepts. Both agree that students are enthusiastic about working with robots, and in both cases, the pedagogical challenge is to develop robotics technology and provide classroom assignments that highlight key ideas in the respective field. Mobile robots are particularly intriguing because of their dual nature as both deterministic machines and unpredictable entities. This paper explores challenges for both engineering and AI educators as robot toolkits evolve.


title: An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition

abstract: Video-based traffic sign detection, tracking, and recognition is one of the important components for the intelligent transport systems. Extensive research has shown that pretty good performance can be obtained on public data sets by various state-of-the-art approaches, especially the deep learning methods. However, deep learning methods require extensive computing resources. In addition, these approaches mostly concentrate on single image detection and recognition task, which is not applicable in real-world applications. Different from previous research, we introduce a unified incremental computational framework for traffic sign detection, tracking, and recognition task using the mono-camera mounted on a moving vehicle under non-stationary environments. The main contributions of this paper are threefold: 1) to enhance detection performance by utilizing the contextual information, this paper innovatively utilizes the spatial distribution prior of the traffic signs; 2) to improve the tracking performance and localization accuracy under non-stationary environments, a new efficient incremental framework containing off-line detector, online detector, and motion model predictor together is designed for traffic sign detection and tracking simultaneously; and 3) to get a more stable classification output, a scale-based intra-frame fusion method is proposed. We evaluate our method on two public data sets and the performance has shown that the proposed system can obtain results comparable with the deep learning method with less computing resource in a near-real-time manner.


Identifying Factors of E-Government Acceptance – A Literature Review


title: Seeing the Big Picture: Deep Embedding with Contextual Evidences

abstract: In the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improving retrieval performance. Conventionally, local cues of a keypoint are employed. However, such strategy does not consider the contextual evidences of a keypoint, a problem which would lead to the prevalence of false matches. To address this problem, this paper defines "true match" as a pair of keypoints which are similar on three levels, i.e., local, regional, and global. Then, a principled probabilistic framework is established, which is capable of implicitly integrating discriminative cues from all these feature levels. ::: Specifically, the Convolutional Neural Network (CNN) is employed to extract features from regional and global patches, leading to the so-called "Deep Embedding" framework. CNN has been shown to produce excellent performance on a dozen computer vision tasks such as image classification and detection, but few works have been done on BoW based image retrieval. In this paper, firstly we show that proper pre-processing techniques are necessary for effective usage of CNN feature. Then, in the attempt to fit it into our model, a novel indexing structure called "Deep Indexing" is introduced, which dramatically reduces memory usage. ::: Extensive experiments on three benchmark datasets demonstrate that, the proposed Deep Embedding method greatly promotes the retrieval accuracy when CNN feature is integrated. We show that our method is efficient in terms of both memory and time cost, and compares favorably with the state-of-the-art methods.


title: Information theory and coding problems in genetics

abstract: The aim of this paper is to describe a new class of problems and some new results in coding theory arising from the analysis of the composition and functionality of the genetic code. The major goal of the proposed work is to initiate research on investigating possible connections between the regulatory network of gene interactions (RNGI) and the proofreading (error-control) mechanism of the processes of the central dogma of genetics. New results include establishing a direct relationship between Boolean network (BN) models of RNGI and Gallager's LDPC decoding algorithms. The proposed research topics and described results are expected to have a two-fold impact on coding theory and genetics research. Firstly, they may provide a different setting in which to analyze standard LDPC decoding algorithms, by using dynamical systems and Boolean function theory. Secondly, they may be of use in establishing deeper relationships between the DNA proofreading mechanism, RNGI, and their joint influence on the development and possible treatment of genetic diseases like cancer.


title: Rhinoplasty in the Middle Eastern nose.

abstract: In this article, the main characteristics of Middle Eastern patients seeking rhinoplasty are reported. Besides the preoperative preparation, surgical techniques that can be used to obtain good results are discussed. The pitfalls of profile alignment, tip procedures and skin have been noted as well.


title: A Chatbot for Psychiatric Counseling in Mental Healthcare Service Based on Emotional Dialogue Analysis and Sentence Generation

abstract: There are early studies to attempt users for psychiatric counseling with chatbot. They lead to changes in drinking habit based on intervention approach via chat bot. The application does not consider the user's psychiatric status through the conversations, continuous user monitoring, and ethical judgment in the intervention. We contend that more accurate and continuous emotion recognition gives better satisfaction to users who need mental health care. In addition, appropriate clinical psychological response based on ethical responses is as well. We suggest a conversational service for psychiatric counseling that is adapted methodologies to understand counseling contents based on of high-level natural language understanding (NLU), and emotion recognition based on multi-modal approach. The methodologies enable continuous observation of emotional changes sensitively. In addition, the case-based counseling response model that combines ethical judgment model provides a suitable response to clinical psychiatric counseling.


title: Performance Evaluation of Non-tunneling Edge-Overlay Model on 40GbE Environment

abstract: Network virtualization is an essential technology for cloud datacenters that provide multi-tenancy services. Cutting-edge SDN-enabled datacenters have introduced an edge-overlay (distributed tunneling) model to construct virtual tenant networks. The edge-overlay model generally uses L2-in-L3 tunneling protocols, and VXLAN is a representative of such protocols. However, the tunneling-based edge-overlay model has some performance and compatibility problems. We have proposed yet another edge-overlay model that does not rely on L2-in-L3 tunneling protocols so far. Our model utilizes two methods, OpenFlow-based address translation and host-based VLAN. The former method replaces VMs' MAC addresses to physical servers' ones, which prevents unnecessary IP fragmentation as well as unnecessary MAC address learning by physical switches. The later method breaks a limitation of the number of VLAN-based virtual tenant networks (4094) by allocating entire VLAN ID space to each physical server and by mapping VLAN ID to VM with OpenFlow controller support. Our previous study has shown that performance of the proposed model is superior to existing other models on 1GbE environment. However, the upper bound of our proposed method, performance behavior with large packet size, and effect of hardware-assisted technology like segmentation offload have not been evaluated. In this paper, we evaluate performance effects of these aspects on high-bandwidth (40GbE) environment. The results show that the performance of VM-to-VM communication with the proposed model is close to that of physical communication and exceeds 10Gbps throughput with large TCP segment.


title: Effects of White Noise and Holding on Pain Perception in Newborns

abstract: Abstract This experimental study on newborns was conducted to compare the effects of various atraumatic care procedures during an infant's crying response to pain. Included in this study were 120 newborns chosen from among healthy infants admitted to the Obstetrics Department of Canakkale State Hospital between April 2010 and June 2010. The patients were divided into three physically homogeneous groups. Infants in group 1 were held on the mothers' laps, infants in group 2 were held on the mother's laps and listened to white noise, and infants in group 3 lay in their cribs and listened to white noise while undergoing a painful procedure. Data collection included the Neonatal Infant Pain Scale, which was used to evaluate the behavioral responses to pain during a heel prick blood draw and a newborn information sheet developed by the researcher. Changes in cardiac and respiratory rates recorded during the invasive procedure were statistically significant among the three groups ( p According to the results, white noise is an effective nonpharmacologic method to control pain, reduce crying time, and positively effect vital signs. Therefore, it is recommended that the use of white noise be practiced on newborns when they undergo painful procedures.


title: Adaptive Rendering Based on Weighted Local Regression

abstract: Monte Carlo ray tracing is considered one of the most effective techniques for rendering photo-realistic imagery, but requires a large number of ray samples to produce converged or even visually pleasing images. We develop a novel image-plane adaptive sampling and reconstruction method based on local regression theory. A novel local space estimation process is proposed for employing the local regression, by robustly addressing noisy high-dimensional features. Given the local regression on estimated local space, we provide a novel two-step optimization process for selecting bandwidths of features locally in a data-driven way. Local weighted regression is then applied using the computed bandwidths to produce a smooth image reconstruction with well-preserved details. We derive an error analysis to guide our adaptive sampling process at the local space. We demonstrate that our method produces more accurate and visually pleasing results over the state-of-the-art techniques across a wide range of rendering effects. Our method also allows users to employ an arbitrary set of features, including noisy features, and robustly computes a subset of them by ignoring noisy features and decorrelating them for higher quality.


title: DEVELOPMENT OF A BIOINSPIRED APPROACH FOR THE DESIGN OF KINEMATIC CHAINS

abstract: In recent years, bioinspired lightweight design has become a high priority in technology. An important source is the musculoskeletal system, which achieves a light construction by the interplay of different effects, which have the aim of minimizing bending stresses. In this paper the potentials for technology and the challenges for a transfer are discussed using kinematic chains as an example. An iterative approach is presented, which represents a solution to integrate the simultaneous optimization process of the musculoskeletal system into the technical product development process.


title: NFC mobile credit card: The next frontier of mobile payment?

abstract: With the advancement of mobile devices and the emergence of Near Field Communication (NFC) technology, payment today is a mere wave-of-the-phone. However, the adoption of mobile credit card (MCC) is still not widespread despite its potential as documented. Premised on this, the study extends the Technology Acceptance Model (TAM) with four additional constructs. The moderating effect of gender was also examined. Data collected from 156 respondents were analyzed using Structural Equation Modeling (SEM) and multi group analysis. Cohen's f-square statistic for effect size is 0.815. The results revealed that only finance-related risks and the moderating effect of gender are the non-significant factors in this study. The research provides useful theoretical and managerial implications for mobile phone manufacturers, merchants, bank decision makers, software developers, governments and private practitioners when devising their marketing campaigns and business strategies. The study also extends the applicability of TAM in the area of MCC from the perspective of an emerging market.


Notes on the syntax of the adjective in modern english


An Incentive Mechanism for Decongesting the Roads: A Pilot Program in Bangalore


title: A 3.45–4.22 GHz PLL frequency synthesizer with constant loop bandwidth for WLAN applications

abstract: A fully integrated phase-locked loop (PLL) frequency synthesizer for WLAN applications is designed in a 0.13-μm CMOS process. In order to make the loop bandwidth constant across the whole operating frequency range, a switched varactor array based LC-VCO is used to achieve a low KVCO variation, the charge pump current is programmed proportional to division ratio, and the variation on LPF resistance is cancelled by a matched reference resistance in a band-gap. By combination of these techniques, a constant loop bandwidth is achieved over the entire output frequency range. The proposed PLL attains less than ± 4% variation in loop bandwidth over an operating frequency range from 3.45 to 4.22 GHz. The chip consumes 21.1 mW of power while occupying an area of 0.88 mm2.


title: Accessing Accurate Documents by Mining Auxiliary Document Information

abstract: Earlier techniques of text mining included algorithms like k-means, Naive Bayes, SVM which classify and cluster the text document for mining relevant information about the documents. The need for improving the mining techniques has us searching for techniques using the available algorithms. This paper proposes one technique which uses the auxiliary information that is present inside the text documents to improve the mining. This auxiliary information can be a description to the content. This information can be either useful or completely useless for mining. The user should assess the worth of the auxiliary information before considering this technique for text mining. In this paper, a combination of classical clustering algorithms is used to mine the datasets. The algorithm runs in two stages which carry out mining at different levels of abstraction. The clustered documents would then be classified based on the necessary groups. The proposed technique is aimed at improved results of document clustering.


title: BLUE: A bipedal robot with variable stiffness and damping

abstract: Exploiting variable impedance for dynamic tasks such as walking is both challenging and topical - research progress in this area impacts not only autonomous, bipedal mobility but also prosthetics and exoskeletons. In this work, we present the design, construction and preliminary testing of a planar bipedal robot with joints capable of physically varying both their stiffness and damping independently - the first of its kind. A wide variety of candidate variable stiffness and damping actuator designs are investigated. Informed by human biophysics and locomotion studies, we design an appropriate (heterogenous) impedance modulation mechanism that fits the necessary torque and stiffness range and rate requirements at each joint while ensuring the right form factor. In addition to hip, knee and ankle, the constructed robot is also equipped with a three part compliant foot modelled on human morphology. We describe in detail the hardware construction and the communication and control interfaces. We also present a full physics based dynamic simulation which matches the hardware closely. Finally, we test impedance modulation response characteristics and a basic walking gait realised through a simple movement controller, both in simulation and on the real hardware.


title: Deep Learning for Object Saliency Detection and Image Segmentation

abstract: In this paper, we propose several novel deep learning methods for object saliency detection based on the powerful convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify an input image based on the pixel-wise gradients to reduce a cost function measuring the class-specific objectness of the image. The pixel-wise gradients can be efficiently computed using the back-propagation algorithm. The discrepancy between the modified image and the original one may be used as a saliency map for the image. Moreover, we have further proposed several new training methods to learn saliency-specific convolutional nets for object saliency detection, in order to leverage the available pixel-wise segmentation information. Our methods are extremely computationally efficient (processing 20-40 images per second in one GPU). In this work, we use the computed saliency maps for image segmentation. Experimental results on two benchmark tasks, namely Microsoft COCO and Pascal VOC 2012, have shown that our proposed methods can generate high-quality salience maps, clearly outperforming many existing methods. In particular, our approaches excel in handling many difficult images, which contain complex background, highly-variable salient objects, multiple objects, and/or very small salient objects.


title: Classifying Purchase Decision Based on User Clickstream in E-Commerce Using Web Usage Mining

abstract: The advance of internet usage in Indonesia gives positive impact on the development of e-commerce in Indonesia where 63.5% of internet users have made online transactions. Along with e-commerce B2C growth in Indonesia, firm needs for an effective promotional strategy to understand the preferences and potential purchases for each consumer to increase return on investment (ROI). This empirical study investigated purchase decision of e-commerce users using Web Usage Mining framework. The high combination of purchasing product categories by users of e-commerce website required a multi-label classification technique that could classify those pair of purchase decision. Label Powerset method with Support Vector Machine (SVM) algorithm was applied to classify e-commerce users purchase decisions using general and detailed information features. Feature selection using Information retained 60 from 90 features. The proposed feature selection with Information Gain and parameter selection using Grid Search proved that they had an ability to enhance performance to classify purchase decision of e-commerce user. Radial Basis Function (RBF) as the most effective kernel presented an accuracy of 75.6%, with slightly difference of 2.2% with classification without using feature selection.


title: Computing Neck-Shaft Angle of Femur for X-Ray Fracture Detection

abstract: Worldwide, 30% – 40% of women and 13% of men suffer from osteoporotic fractures of the bone, particularly the older people. Doctors in the hospitals need to manually inspect a large number of x-ray images to identify the fracture cases. Automated detection of fractures in x-ray images can help to lower the workload of doctors by screening out the easy cases, leaving a small number of difficult cases and the second confirmation to the doctors to examine more closely. To our best knowledge, such a system does not exist as yet. This paper describes a method of measuring the neck-shaft angle of the femur, which is one of the main diagnostic rules that doctors use to determine whether a fracture is present at the femur. Experimental tests performed on test images confirm that the method is accurate in measuring neck-shaft angle and detecting certain types of femur fractures.


title: Internet of Things: Applications and Challenges in Technology and Standardization

abstract: The phrase Internet of Things (IoT) heralds a vision of the future Internet where connecting physical things, from banknotes to bicycles, through a network will let them take an active part in the Internet, exchanging information about themselves and their surroundings. This will give immediate access to information about the physical world and the objects in it--leading to innovative services and increase in efficiency and productivity. This paper studies the state-of-the-art of IoT and presents the key technological drivers, potential applications, challenges and future research areas in the domain of IoT. IoT definitions from different perspective in academic and industry communities are also discussed and compared. Finally some major issues of future research in IoT are identified and discussed briefly.


title: Sources of semantic similarity

abstract: Sources of Semantic Similarity Simon De Deyne (simon.dedeyne@psy.kuleuven.be) a Yves Peirsman (yves.peirsman@arts.kuleuven.be) b Gert Storms (gert.storms@psy.kuleuven.be) a University of Leuven, Belgium Department of Psychology, Tiensestraat 102, B-3000 Leuven, Belgium b QLVL, Department of Linguistics, Blijde-Inkomststraat PO Box 3308, B-3000 Leuven, Belgium a Abstract Similarity is the key notion underlying many contemporary theories about the representation of meaning through words or concepts. However, these representations are strongly col- ored by the kind of information captured by various semantic measures. In this paper we present a systematic comparison of human similarity judgments and calculated similarity coef- ficients from different sources of semantic similarity based on concept features, word associations, word co-occurrence and expert knowledge. We show that these measures capture our semantic representations to a large extent, but also model dif- ferent aspects of our semantic knowledge, depending on (a) the semantic domain and (b) the range of similarity comparisons. Keywords: Concepts; similarity; distributional semantics, word associations, WordNet. Introduction Semantic knowledge is a multifaceted concept. About ba- nanas, we know that they are a type of fruit just like pineap- ples, that they are typically eaten by monkeys, and that peo- ple can slip on their skin. These various types of information, and much more, are part of our knowledge about the con- cept banana. Yet, studies of semantic knowledge often make use of only one source of information, like the free associa- tions to a word, or its distribution in a large collection of texts (Landauer & Dumais, 1997; Burgess, Livesay, & Lund, 1998; Steyvers, Shiffrin, & Nelson, 2004). At the same time, it is unlikely that these different sources of information all corre- late with the same types of semantic knowledge. In this paper, we will therefore focus on just one type of semantic knowl- edge instantiated by human judgments of semantic similarity and investigate what sources of information are best able to model these judgments. We explore four sources of semantic information. The first is that of concept features, like and for the concept robin (McRae, Cree, Seidenberg, & Mc- Norgan, 2005). The second is that of free associations, like the words foot and shoelace for the cue word shoe (Nelson, McEvoy, & Dennis, 2000). The third source is that of lex- ical co-occurrence in text, where we determine what words typically appear in the context of a target word (Sahlgren, 2006). The fourth and final source of semantic information is the Dutch EuroWordNet, a lexical ontology which contains taxonomical expert information about Dutch nouns (Vossen, 1998). It tells us, for instance, that eagle IS - A , which in turn IS - A . It has been suggested before (Maki & Buchanan, 2008) that these different sources of information tap into different types of human semantic knowledge. Still, it remains unclear what precise knowledge they capture. Our first goal is therefore to focus on semantic similarity in particular and measure to what extent our several sources of information are able to mir- ror people’s similarity judgments. A second aim of this paper is to provide more insight into the performance of these tech- niques on intra-category rather than domain or inter-category similarity judgments. While earlier studies have mostly fo- cused on inter-category judgments (e.g., Miller & Charles, 1991), far less is known about our ability to model intra- category judgments. These last should not only be harder to model; they can also give us more clues as to where the prob- lems of the particular approaches may emerge — for instance in relation to specific categories of natural kinds or artifacts. Our third contribution lies in the fact that we make use of a controlled collection of experimental data and corpus mate- rial. Since many earlier individual studies that focused on one particular model made use of different materials, their results were difficult to compare. Our approach ensures that the re- sults of the several models are perfectly comparable through- out. Semantic Models Types of Information Features The first type of information we will study is that of concept features. Features cover the perceptual, physical, functional, etc. characteristics of a particular en- tity. They have proved their usefulness in the prediction of a large variety of semantic phenomena such as response times in a speeded categorization task (Storms, De Boeck, & Ruts, 2000), typicality (Hampton, 1997), similarity judg- ments (Markman & Gentner, 1993) and concept coherence (Sloman, Love, & Ahn, 1998). We used the feature sets pre- sented in De Deyne et al. (2008). These sets contained fea- tures for a total of 420 concepts. Each concept belonged to one of 15 different categories. The categories organized by domain are shown in Table 2. The number of exemplars in each category varied from 20 to 33. A first type of feature set consisted of 15 exemplar × feature matrices, one for each cat- egory. These matrices were constructed using a two-phased approach. First, a large group of participants generated fea- tures for each exemplar. Next, these features were tallied and


Security and Privacy in Sensor Networks


title: Solving University Scheduling Problem with a Memetic Algorithm

abstract: Scheduling problem is one of the Non-deterministic Polynomial (NP) problems. This means that using a normal algorithm to solve NP problems is so time-consuming a process (it may take months or even years with available equipment), and thus such an algorithm is regarded as an impracticable way of dealing with NP problems. The method of Memetic Algorithm presented in this paper is different from other available algorithms. In this algorithm the problem of a university class Scheduling is solved through applying a new chromosome structure, modifying the normal genetic methods and adding a local search, which is claimed to considerably improve the solution. We included the teacher, class and course information with their maximal constraints in the proposed algorithm, and it produced an optimized scheduling table for a weekly program of the university after creating the initial population of chromosomes and running genetic operators. The results of the study show a high efficiency for the proposed algorithm compared with other algorithms considering maximum Constraints. DOI: http://dx.doi.org/10.11591/ij-ai.v1i2.512 Full Text: PDF


title: Privacy Protection for Preventing Data Over-Collection in Smart City

abstract: In smart city, all kinds of users’ data are stored in electronic devices to make everything intelligent. A smartphone is the most widely used electronic device and it is the pivot of all smart systems. However, current smartphones are not competent to manage users’ sensitive data, and they are facing the privacy leakage caused by data over-collection. Data over-collection, which means smartphones apps collect users’ data more than its original function while within the permission scope, is rapidly becoming one of the most serious potential security hazards in smart city. In this paper, we study the current state of data over-collection and study some most frequent data over-collected cases. We present a mobile-cloud framework, which is an active approach to eradicate the data over-collection. By putting all users’ data into a cloud, the security of users’ data can be greatly improved. We have done extensive experiments and the experimental results have demonstrated the effectiveness of our approach.


title: Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution

abstract: Recently, various deep-neural-network (DNN)-based approaches have been proposed for single-image super-resolution (SISR). Despite their promising results on major structure regions such as edges and lines, they still suffer from limited performance on texture regions that consist of very complex and fine patterns. This is because, during the acquisition of a low-resolution (LR) image via down-sampling, these regions lose most of the high frequency information necessary to represent the texture details. In this paper, we present a novel texture enhancement framework for SISR to effectively improve the spatial resolution in the texture regions as well as edges and lines. We call our method, high-resolution (HR) style transfer algorithm. Our framework consists of three steps: (i) generate an initial HR image from an interpolated LR image via an SISR algorithm, (ii) generate an HR style image from the initial HR image via down-scaling and tiling, and (iii) combine the HR style image with the initial HR image via a customized style transfer algorithm. Here, the HR style image is obtained by down-scaling the initial HR image and then repetitively tiling it into an image of the same size as the HR image. This down-scaling and tiling process comes from the idea that texture regions are often composed of small regions that similar in appearance albeit sometimes different in scale. This process creates an HR style image that is rich in details, which can be used to restore high-frequency texture details back into the initial HR image via the style transfer algorithm. Experimental results on a number of texture datasets show that our proposed HR style transfer algorithm provides more visually pleasing results compared with competitive methods.


title: Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods

abstract: The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.


Causes of Delinquency


title: NB-IoT deployment study for low power wide area cellular IoT

abstract: In 3GPP, a narrowband system based on Long Term Evolution (LTE) has been introduced to support the Internet of Things. This system, named Narrowband Internet of Things (NB-IoT), provides low-cost devices, high coverage (20 dB improvement over LTE/GPRS), long device battery life (more than 10 years), and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. NB-IoT can be deployed in three different operation modes — (1) stand-alone as a dedicated carrier, (2) in-band within the occupied bandwidth of a wideband LTE carrier, and (3) within the guard-band of an existing LTE carrier. In this paper, we undertake a deployment study of NB-IoT using existing LTE infrastructure. We consider the case when only a fraction of the existing LTE cell sites support NB-IoT (so called partial deployment of NB-IoT). In this case, NB-IoT devices cannot attach to the best cell if that cell does not support NB-IoT. As a result, the path loss can be very high. In addition, they also suffer from high interference from non-NB-IoT cells. We examine potential techniques to compensate for the high path-loss and high interference and provide analysis to indicate when partial deployment of NB-IoT is feasible. We also examine interference issues in asynchronous deployments and study performance.


title: Query Optimization in Database Systems

abstract: Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.


A Survey on Mobile Banking Applications and the Adopted Models


title: Incomplete and Inaccurate Vocal Imitation after Knockdown of FoxP2 in Songbird Basal Ganglia Nucleus Area X

abstract: The gene encoding the forkhead box transcription factor, FOXP2, is essential for developing the full articulatory power of human language. Mutations of FOXP2 cause developmental verbal dyspraxia (DVD), a speech and language disorder that compromises the fluent production of words and the correct use and comprehension of grammar. FOXP2 patients have structural and functional abnormalities in the striatum of the basal ganglia, which also express high levels of FOXP2. Since human speech and learned vocalizations in songbirds bear behavioral and neural parallels, songbirds provide a genuine model for investigating the basic principles of speech and its pathologies. In zebra finch Area X, a basal ganglia structure necessary for song learning, FoxP2 expression increases during the time when song learning occurs. Here, we used lentivirus-mediated RNA interference (RNAi) to reduce FoxP2 levels in Area X during song development. Knockdown of FoxP2 resulted in an incomplete and inaccurate imitation of tutor song. Inaccurate vocal imitation was already evident early during song ontogeny and persisted into adulthood. The acoustic structure and the duration of adult song syllables were abnormally variable, similar to word production in children with DVD. Our findings provide the first example of a functional gene analysis in songbirds and suggest that normal auditory-guided vocal motor learning requires FoxP2.


The Impact of Promotional Tools on Consumer Buying Behavior in Retail Market


title: Improving Citation Polarity Classification with Product Reviews

abstract: Recent work classifying citations in scientific literature has shown that it is possible to improve classification results with extensive feature engineering. While this result confirms that citation classification is feasible, there are two drawbacks to this approach: (i) it requires a large annotated corpus for supervised classification, which in the case of scientific literature is quite expensive; and (ii) feature engineering that is too specific to one area of scientific literature may not be portable to other domains, even within scientific literature. In this paper we address these two drawbacks. First, we frame citation classification as a domain adaptation task and leverage the abundant labeled data available in other domains. Then, to avoid over-engineering specific citation features for a particular scientific domain, we explore a deep learning neural network approach that has shown to generalize well across domains using unigram and bigram features. We achieve better citation classification results with this cross-domain approach than using in-domain classification.


title: An efficient solution to the five-point relative pose problem

abstract: An efficient algorithmic solution to the classical five-point relative pose problem is presented. The problem is to find the possible solutions for relative camera pose between two calibrated views given five corresponding points. The algorithm consists of computing the coefficients of a tenth degree polynomial in closed form and, subsequently, finding its roots. It is the first algorithm well-suited for numerical implementation that also corresponds to the inherent complexity of the problem. We investigate the numerical precision of the algorithm. We also study its performance under noise in minimal as well as overdetermined cases. The performance is compared to that of the well-known 8 and 7-point methods and a 6-point scheme. The algorithm is used in a robust hypothesize-and-test framework to estimate structure and motion in real-time with low delay. The real-time system uses solely visual input and has been demonstrated at major conferences.


title: Push-push oscillator design based on a substrate integrated waveguide (SIW) resonator

abstract: A substrate integrated waveguide resonator (SIW) is used to design a push-push oscillator. The SIW resonator presents a resonant mode at approximately half the output signal frequency. Due to the symmetry of the circuit architecture both an in-phase and an out of phase mode may exist, with the out of phase mode being the desired one, as the fundamental frequency components cancel at the output node while the second harmonic components combine. Harmonic balance analysis is used together with continuation methods in order to trace the solution curves of the two modes versus a selected circuit parameter. This provides a design methodology as it allows one to identify parameter ranges where the in-phase mode does not exist. The results are demonstrated on a 29.5 GHz oscillator.


title: Spectral methods for network community detection and graph partitioning

abstract: We consider three distinct and well-studied problems concerning network structure: community detection by modularity maximization, community detection by statistical inference, and normalized-cut graph partitioning. Each of these problems can be tackled using spectral algorithms that make use of the eigenvectors of matrix representations of the network. We show that with certain choices of the free parameters appearing in these spectral algorithms the algorithms for all three problems are, in fact, identical, and hence that, at least within the spectral approximations used here, there is no difference between the modularity- and inference-based community detection methods, or between either and graph partitioning.


title: Particle Swarm Optimization and Differential Evolution Algorithms : Technical Analysis , Applications and Hybridization Perspectives

abstract: Since the beginning of the nineteenth century, a significant evolution in optimization theory has been noticed. Classical linear programming and traditional non-linear optimization techniques such as Lagrange’s Multiplier, Bellman’s principle and Pontyagrin’s principle were prevalent until this century. Unfortunately, these derivative based optimization techniques can no longer be used to determine the optima on rough non-linear surfaces. One solution to this problem has already been put forward by the evolutionary algorithms research community. Genetic algorithm (GA), enunciated by Holland, is one such popular algorithm. This chapter provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE). The algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. The chapter explores several schemes for controlling the convergence behaviors of PSO and DE by a judicious selection of their parameters. Special emphasis is given on the hybridizations of PSO and DE algorithms with other soft computing tools. The article finally discusses the mutual synergy of PSO with DE leading to a more powerful global search algorithm and its practical applications.


title: Disease Ontology: a backbone for disease semantic integration

abstract: The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.


title: Simulating Action Dynamics with Neural Process Networks

abstract: Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics. Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers. The model updates the states of the entities by executing learned action operators. Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.


title: Authcoin: Validation and Authentication in Decentralized Networks

abstract: Authcoin is an alternative approach to the commonly used public key infrastructures such as central authorities and the PGP web of trust. It combines a challenge response-based validation and authentication process for domains, certificates, email accounts and public keys with the advantages of a block chain-based storage system. As a result, Authcoin does not suffer from the downsides of existing solutions and is much more resilient to sybil attacks.


Data compression via textual substitution


title: RGBD Datasets: Past, Present and Future

abstract: Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. ::: Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.


title: Scalable Gaussian process regression using deep neural networks

abstract: We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pretrain the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales well with the size of the training set due to the avoidance of kernel matrix inversion. Moreover, we present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.


title: A comparative study of the use of visual communicative signals in interactions between dogs (Canis familiaris) and humans and cats (Felis catus) and humans

abstract: Dogs’ (Canis familiaris) and cats’ (Felis catus) interspecific communicative behavior toward humans was investigated. In Experiment 1, the ability of dogs and cats to use human pointing gestures in an object-choice task was compared using 4 types of pointing cues differing in distance between the signaled object and the end of the fingertip and in visibility duration of the given signal. Using these gestures, both dogs and cats were able to find the hidden food; there was no significant difference in their performance. In Experiment 2, the hidden food was made inaccessible to the subjects to determine whether they could indicate the place of the hidden food to a naive owner. Cats lacked some components of attention-getting behavior compared with dogs. The results suggest that individual familiarization with pointing gestures ensures high-level performance in the presence of such gestures; however, species-specific differences could cause differences in signaling toward the human.


title: Clinical and radiological pictures of two newborn babies with manifestations of chondrodysplasia punctata and review of available literature

abstract: BACKGROUND ::: Chondrodysplasia punctata (CDP) is a rare, heterogeneous congenital skeletal dysplasia, characterized by punctate or dot-like calcium deposits in cartilage observed on neonatal radiograms. A number of inborn metabolic diseases are associated with CDP, including peroxisomal and cholesterol biosynthesis dysfunction and other inborn errors of metabolism such as: mucolipidosis type II, mucopolysacharidosis type III, GM1 gangliosidosis. CDP is also related to disruption of vitamin K-dependent metabolism, causing secondary effects on the embryo, as well as fetal alcohol syndrome (FAS), chromosomal abnormalities that include trisomies 18 and 21, Turner syndrome. ::: ::: ::: CASE REPORT ::: This article presents clinical data and diagnostic imaging findings of two newborn babies with chondrodysplasia punctata. Children presented with skeletal and cartilage anomalies, dysmorphic facial feature, muscles tone abnormalities, skin changes and breathing difficulties. One of the patients demonstrated critical stenosis of spinal canal with anterior subluxation of C1 vertebra relative to C2. The aim of this article is to present cases and briefly describe current knowledge on etiopathogenesis as well as radiological and clinical symptoms of diseases coexisting with CDP. ::: ::: ::: CONCLUSIONS ::: Radiological diagnostic imaging allows for visualization of punctate focal mineralization in bone epiphyses during neonatal age and infancy. Determining the etiology of chondrodysplasia punctata requires performing various basic as well as additional examinations, including genetic studies.


title: DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique

abstract: A dataset exhibits the class imbalance problem when a target class has a very small number of instances relative to other classes. A trivial classifier typically fails to detect a minority class due to its extremely low incidence rate. In this paper, a new over-sampling technique called DBSMOTE is proposed. Our technique relies on a density-based notion of clusters and is designed to over-sample an arbitrarily shaped cluster discovered by DBSCAN. DBSMOTE generates synthetic instances along a shortest path from each positive instance to a pseudo-centroid of a minority-class cluster. Consequently, these synthetic instances are dense near this centroid and are sparse far from this centroid. Our experimental results show that DBSMOTE improves precision, F-value, and AUC more effectively than SMOTE, Borderline-SMOTE, and Safe-Level-SMOTE for imbalanced datasets.


title: A journey from normative to behavioral operations in supply chain management

abstract: Systematic interdisciplinary literature review on behavioral operations.Latent Semantic Analysis applied for review and knowledge extraction methodology.Text analysis and mining to combine statistical methods and expert judgment.Knowledge extracted in the form of key latent factors.Text mining approach aids taxonomy of research on power influences in supply chain. This study aims to systematically review the cross disciplinary literature covering the time period from 1934 to January 2013 on behavioral operations in supply chain in order to identify and define the taxonomy of the research on power influences in supply chain. A list of noted journals and search results from Science Direct and Web of Knowledge, IEEE Xplore, and INFORMS (approximately 11,000 journal articles) is used to prepare content collection. Latent Semantic Analysis (LSA) is applied as the review and knowledge extraction methodology. Using the text analysis and mining method we can combine statistical methods and expert human judgment to extract knowledge in the form of key latent factors. The LSA based analysis gives the study a scientific grounding which helps to overcome the subjectivity of collective opinion about the trends. This approach allows proposing taxonomy of the research on power influences in supply chain. The adopted systems approach is used to find research gaps in each class of taxonomy. An emerging trend is noticed in the research of behavioral operations in supply chain. Understanding such a scholarly structure and future trends will assist researchers to assimilate the divergent developments of this multidisciplinary research in one place. This review will be beneficial for practitioners as they consider behavioral aspects in decision making. We have also studied articles related to supply chain published in Expert Systems with Applications (ESWA) journal. We have speculated what an ESWA-related community would like to see in future publications. This will encourage researchers to explore the recommended areas and publish to these outlets.


title: Artificial Neural Network for Diffraction Based Overlay Measurement

abstract: Diffraction-based overlay (DBO) accuracy is critical to the intelligent nanolithography process control for producing advanced semiconductor fabrication nodes. Optical gratings located on various layers are commonly used as the targets for the detection of the overlay displacement offset in DBO measurement. The asymmetry in intensity between the 1st and -1st beams diffracted by the targets is used for the prediction of grating displacement offset. This paper describes the effect of grating targets with sidewall angles (SWAs) on asymmetry in intensity and proposes an artificial neural network (ANN) method for enhancing the accuracy of grating displacement offset prediction. Grating targets with a 1:3 line-to-pitch ratio and SWA profiles varying from 86° to 90° were employed in this paper. The asymmetry in the intensity of the designed targets was computed for incident beams with transverse-electric and transverse-magnetic polarization at visible wavelength. An ANN feed-forward model was developed for the displacement offset prediction. The ANN, the conventional linear model, and the regression models were evaluated using diffraction data calculated by a numerical electromagnetic solver. The mean square error and the mean of the residual indicated that using the ANN model and incident beams at wavelengths of 600, 650, and 750 nm is substantially more effective for prediction than the conventional linear model is.


title: Engineering Gene Circuits for Mammalian Cell–Based Applications

abstract: Synthetic gene switches are basic building blocks for the construction of complex gene circuits that transform mammalian cells into useful cell-based machines for next-generation biotechnological and biomedical applications. Ligand-responsive gene switches are cellular sensors that are able to process specific signals to generate gene product responses. Their involvement in complex gene circuits results in sophisticated circuit topologies that are reminiscent of electronics and that are capable of providing engineered cells with the ability to memorize events, oscillate protein production, and perform complex information-processing tasks. Microencapsulated mammalian cells that are engineered with closed-loop gene networks can be implanted into mice to sense disease-related input signals and to process this information to produce a custom, fine-tuned therapeutic response that rebalances animal metabolism. Progress in gene circuit design, in combination with recent breakthroughs in genome engineering, may result in tailored engineered mammalian cells with great potential for future cell-based therapies.


title: Near light correction for image relighting and 3D shape recovery

abstract: In this paper, we propose a near-light illumination model for image relighting and 3D shape recovery. Classic methods such as used by the popular RTI software from Cultural Heritage Imaging assume that lighting is infinitely far away from the scene. However, this constraint is impossible to achieve in practice: light sources cannot be too far away from the scene due to space and power constraints. This causes non-uniform illumination artifacts due to variations in the distance between the light source and points in the scene. We correct this effect to provide much more uniformly lit images that yield more appealing image for relighting applications. Furthermore, we use our near-light model for more accurate photometric stereo calculations of surface normals, eliminating the “potato-chip” shaped surface reconstruction error that results from violating the far-light assumption. We verify our model with both free-form capture using hand-held flash as the illumination source, and capture using LED lights mounted on a dome shaped surface.


title: A novel approach to fingerprint image quality

abstract: We present a novel measure of fingerprint image quality, which can be used to estimate fingerprint match performance. This means presenting the matcher with good quality fingerprint images will result in high matcher performance, and vice versa, the matcher will perform poorly for poor quality fingerprints. We discuss the implementation of our fingerprint image quality metric and we present the results of testing it on 280 different combinations of fingerprint image data and fingerprint matcher systems. We found that the metric predicts matcher performance for all systems and datasets. Our definition of quality can be applied to other biometric modalities and upon proper feature extraction can be used to assess quality of any mode of biometric samples.


title: 3D object manipulation in a single photograph using stock 3D models

abstract: Photo-editing software restricts the control of objects in a photograph to the 2D image plane. We present a method that enables users to perform the full range of 3D manipulations, including scaling, rotation, translation, and nonrigid deformations, to an object in a photograph. As 3D manipulations often reveal parts of the object that are hidden in the original photograph, our approach uses publicly available 3D models to guide the completion of the geometry and appearance of the revealed areas of the object. The completion process leverages the structure and symmetry in the stock 3D model to factor out the effects of illumination, and to complete the appearance of the object. We demonstrate our system by producing object manipulations that would be impossible in traditional 2D photo-editing programs, such as turning a car over, making a paper-crane flap its wings, or manipulating airplanes in a historical photograph to change its story.


title: Various views on spatial prepositions

abstract: In this article, principles involving the intrinsic, deictic, and extrinsic use of spatial prepositions are examined from linguistic, psychological, and AI approaches. First, I define some important terms. Second, those prepositions which permit intrinsic, deictic, and extrinsic use are specified. Third, I examine how the frame of reference is determined for all three cases. Fourth, I look at ambiguities in the use of prepositions and how they can be resolved. Finally, I introduce the natural language dialog system CITYTOUR, which can cope with the intrinsic, deictic, and extrinsic use of spatial prepositions, and compare it with the approaches dealt with in the previous sections as well as to some other AI systems.


title: SIF: Enforcing confidentiality and integrity in Web applications

abstract: SIF (Servlet Information Flow) is a novel software framework for building high-assurance web applications, using language-based information-flow control to enforce security. Explicit, end-to-end confidentiality and integrity policies can be given either as compile-time program annotations, or as run-time user requirements. Compile-time and run-time checking efficiently enforce these policies. Information flow analysis is known to be useful against SQL injection and cross-site scripting, but SIF prevents inappropriate use of information more generally: the flow of confidential information to clients is controlled, as is the flow of low-integrity information from clients. Expressive policies allow users and application providers to protect information from one another. ::: ::: SIF moves trust out of the web application, and into the framework and compiler. This provides application deployers with stronger security assurance. ::: ::: Language-based information flow promises cheap, strong information security. But until now, it could not effectively enforce information security in highly dynamic applications. To build SIF, we developed new language features that make it possible to write realistic web applications. Increased assurance is obtained with modest enforcement overhead.


title: How Can Automatic Feedback Help Students Construct Automata?

abstract: In computer-aided education, the goal of automatic feedback is to provide a meaningful explanation of students' mistakes. We focus on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern. Natural choices for feedback are binary feedback (correct/wrong) and a counterexample of a string that is processed incorrectly. Such feedback is easy to compute but might not provide the student enough help. Our first contribution is a novel way to automatically compute alternative conceptual hints. Our second contribution is a rigorous evaluation of feedback with 377 students. We find that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time. However, both strategies have particular strengths and weaknesses. Since our feedback is completely automatic, it can be deployed at scale and integrated into existing massive open online courses.


Formal Ontology and Information Systems


title: An investigation of spectral restoration algorithms for deep neural networks based noise robust speech recognition.

abstract: Deep Neural Networks (DNNs) are becoming widely accepted in automatic speech recognition (ASR) systems. The deep structured nonlinear processing greatly improves the model’s generalization capability, but the performance under adverse environments is still unsatisfactory. In the literature, there have been many techniques successfully developed to improve Gaussian mixture models’ robustness. Investigating the effectiveness of these techniques for the DNN is an important step to thoroughly understand its superiority, pinpoint its limitations and most importantly to further improve it towards the ultimate human-level robustness. In this paper, we investigate the effectiveness of speech enhancement using spectral restoration algorithms for DNNs. Four approaches are evaluated, namely minimum mean-square error spectral estimator (MMSE), maximum likelihood spectral amplitude estimator (MLSA), maximum a posteriori spectral amplitude estimator (MAPA), and generalized maximum a posteriori spectral amplitude algorithm (GMAPA). The preliminary experimental results on the Aurora 2 speech database show that with multi-condition training data the DNN itself is capable of learning robust representations. However, if only clean data is available, the MLSA algorithm is the best spectral restoration training method for DNNs.


title: The Liminal Magic Circle: Boundaries, Frames, and Participation in Pervasive Mobile Games Retrieved April 12, 2007 from http://wi. hexagram. ca/1_1_html/1_1_harvey. html

abstract: While the emphasis in traditional game theory has been on the rule formations and zone demarcations that distinguish games from play, mobile games tend to deliberately and thoughtfully blur the lines, not just between games and play but between a game and an experience, as well as between places of play (the “magic circle”) and places of everyday life. Mobile games often create moments of liminality as they are driven by the idea of playing with and within everyday spaces, technologies, and objects. In this brief paper, I will discuss these boundaries and the liminality of mobile games to date, and focus on one particularly difficult ethical area- that of player and non-player participation. The concepts of play and game are complexly intertwined. According to Johan Huizinga (1950), games can be found on a continuum between the two poles of ludus and paidia. Ludus (frequently understood as a pure game) is characterized by the corseting of exuberance with deliberate and arbitrary conventions that require the player to demonstrate effort, skill, patience, or intellect. Chess is often upheld as a pure ludus game. Paidia (or pure play), on the other hand, is characterized by a shared, ritualistic principle that is found in gaiety and improvisation, as is the case with children’s games, with less rigorous boundaries and rules guiding moments and places of play. Confusion often arises when discussing the play of games. For the purposes of this essay, I am discussing games in the sense of ludus and play as a verb to describe people who are taking part in games. When I refer to the magic circle, I am describing the place where a particular game takes place, with


title: DisturbLabel: Regularizing CNN on the Loss Layer

abstract: During a long period of time we are combating overfitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration. Although it seems weird to intentionally generate incorrect training labels, we show that DisturbLabel prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets. To the best of our knowledge, DisturbLabel serves as the first work which adds noises on the loss layer. Meanwhile, DisturbLabel cooperates well with Dropout to provide complementary regularization functions. Experiments demonstrate competitive recognition results on several popular image recognition datasets.


title: Low-Power Far-Field Wireless Powering for Wireless Sensors

abstract: This paper discusses far-field wireless powering for low-power wireless sensors, with applications to sensing in environments where it is difficult or impossible to change batteries and where the exact position of the sensors might not be known. With expected radio-frequency (RF) power densities in the 20-200- μW/cm2 range, and desired small sensor overall size, low-power nondirective wireless powering is appropriate for sensors that transmit data at low duty cycles. The sensor platform is powered through an antenna which receives incident electromagnetic waves in the gigahertz frequency range, couples the energy to a rectifier circuit which charges a storage device (e.g., thin-film battery) through an efficient power management circuit, and the entire platform, including sensors and a low-power wireless transmitter, and is controlled through a low-power microcontroller. For low incident power density levels, codesign of the RF powering and the power management circuits is required for optimal performance. Results for hybrid and monolithic implementations of the power management circuitry are presented with integrated antenna rectifiers operating in the 1.96-GHz cellular and in 2.4-GHz industrial-scientific-medical (ISM) bands.


Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database


title: Dedoop: Efficient Deduplication with Hadoop

abstract: We demonstrate a powerful and easy-to-use tool called Dedoop ( De duplication with Ha doop ) for MapReduce-based entity resolution (ER) of large datasets. Dedoop supports a browser-based specification of complex ER workflows including blocking and matching steps as well as the optional use of machine learning for the automatic generation of match classifiers. Specified workflows are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. To achieve high performance Dedoop supports several advanced load balancing strategies.


title: A New App for At-Home Cognitive Training: Description and Pilot Testing on Patients with Multiple Sclerosis.

abstract: BACKGROUND ::: Cognitive impairment is common in people with neurological diseases and severely affects their social and professional life. It has been shown that intensive and personalized cognitive rehabilitation (CR), based on working memory exercises, leads to improved cognitive status of healthy and cognitive-impaired subjects. New technologies would help to promote accessible, at-home, and self-managed CR interventions. ::: ::: ::: OBJECTIVE ::: The aim of this paper is to describe the design of Cognitive Training Kit (COGNI-TRAcK), an app for mobile devices, to self-administer an at-home, intensive, and personalized CR intervention based on working memory exercises, and test its disposability-to-use (usability, motivation to use, compliance to treatment) on cognitive-impaired patients with multiple sclerosis (MS). ::: ::: ::: METHODS ::: COGNI-TRAcK includes user-friendly interfaces for personal data input and management and for CR intervention configurations. Inner routines automatically implement adaptive working load algorithms and allow data processing and analysis. A dedicated team developed COGNI-TRAcK with C# programming language, by using the platform Xamarin Studio 4.0.10 for Android (API level 15 and following). Three exercises based on working memory are now available. To assess the disposability-to-use of the system, patients with MS were selected as likely users due to the young age of disease onset. Cognitive-impaired patients with MS (N=16) with a mean age of 49.06 years (SD 9.10) and a mean score of 3.75 (SD 1.92) on the Expanded Disability Status Scale (EDSS) were submitted to an 8-week at-home intervention administered by the app. The intervention consisted of 5 daily scheduled 30-minute sessions per week. Disposability-to-use of COGNI-TRAcK was investigated by means of a questionnaire administered to patients at the end of the training. ::: ::: ::: RESULTS ::: The adherence to the treatment was 84% (33.4/40). Of the patients with MS, 94% (15/16) understood the instructions given, 100% (16/16) felt independent to use COGNI-TRAcK at home, 75% (12/16) found the exercises interesting, and 81% (13/16) found the exercises useful and were motivated to use the app again. Moreover, during the exercises, patients with MS were highly motivated to perform well (mean score 3.19/4, SE 0.16), experienced rather low levels of stress (mean score 2.19/4, SE 0.26), were not bored (mean score 1.81/4, SE 0.30), and felt amusement (mean score 2.25/4, SE 0.23). ::: ::: ::: CONCLUSIONS ::: As COGNI-TRAcK is highly usable, motivating, and well-accepted by patients with MS, its effectiveness can now be investigated. To improve COGNI-TRAcK, new releases should contain more working memory exercises, have enhanced perceived amusement, and promote Internet communication procedures for data transfer and fostering remote control of the intervention.