title: Comparison of caloric and sinusoidal tests in the vestibulotoxic cat.

abstract: In order to evaluate the relative sensitivity of caloric and sinusoidal angular acceleration tests, thermal and rotational stimuli were administered to cats rendered vestibulotoxic with streptomycin sulfate. Three groups of six cats each, receiving daily subcutaneous injections of saline, 100 mg/kg streptomycin or 200 mg/kg streptomycin, were administered optokinetic, caloric and sinusoidal stimuli. Vestibular responses were evaluated by changes in the total slow phase displacement of the eyes and frequency of nystagmic beats. The results demonstrated that the saline treated animals had a decrease in response consistent with habituation; whereas, experimental animals showed a marked, rapid abolition of eye movement. The decrease in response was greater for the larger dose of streptomycin. Caloric or sinusoidal acceleration tests were equally sensitive in monitoring the degree of nystagmic depression. The data suggested that sinusoidal acceleration tests could be easily applied to the clinical vestibular assessment of ototoxicity.


title: 3D strip meander delay line structure for multilayer LTCC-based SiP applications

abstract: Recently, the timing control of high-frequency signals is strongly demanded due to the high integration density in three-dimensional (3D) LTCC-based SiP applications. Therefore, to control the skew or timing delay, new 3D delay lines will be proposed. For frailty of the signal via, we adopt the concept of coaxial line and proposed an advanced signal via structure with quasi coaxial ground (QCOX-GND) vias. We will show the simulated results using EM and circuit simulator.


title: Analysis of universal adversarial perturbations

abstract: Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.


title: Live Virtual Machine Migration Techniques in Cloud Computing: A Survey

abstract: Cloud computing is a service where storage and computing resources accessed on subscription basis. Cloud computing is powered by virtualization technology .Live migration is the process of moving a running virtual machine or an application between different physical machines without disconnecting the client , memory, network connectivity and storage of the virtual machine are transferred from the original host machine to the destination. This capability is being increasingly utilized in today’s enterprise environments to provide efficient online system maintenance, reconfiguration, load balancing and fault tolerance. This paper presents a detailed survey on Live Migration of Virtual machines (VM) in cloud computing.


title: Simplified Modal Expansion to Analyze Frequency-Selective Surfaces: An Equivalent Circuit Approach

abstract: This communication extends a previously reported analytical circuit that models the plane wave scattering by 2-D arrays of planar patches/apertures embedded in a layered media. In this extension, it is assumed that the current/electric field spatial profile in the patch/aperture region can directly be taken from the eigenfields of a waveguide whose cross section matches the scatterer boundaries. This methodology exploits all the advantages of the circuit modeling and gives place to a very efficient computational tool for many practical frequency-selective surfaces (FSSs). The circuit model is expected to work up to frequencies where the isolated scatterers have their third resonance. Thus, it allows us to provide unified equivalent circuits to deal with conical incidence and with certain symmetric cases involving two or three patches/apertures in the unit cell.


title: A theorem on polygon cutting with applications

abstract: Let P be a simple polygon with N vertices, each being assigned a weight ∈ {0,1}, and let C, the weight of P, be the added weight of all vertices. We prove that it is possible, in O(N) time, to find two vertices a,b in P, such that the segment ab lies entirely inside the polygon P and partitions it into two polygons, each with a weight not exceeding 2C/3. This computation assumes that all the vertices have been sorted along some axis, which can be done in O(Nlog N) time. We use this result to derive a number of efficient divide-and-conquer algorithms for: 1. Triangulating an N-gon in O(Nlog N) time. 2. Decomposing an N-gon into (few) convex pieces in O(Nlog N) time. 3. Given an O(Nlog N) preprocessing, computing the shortest distance between two arbitrary points inside an N-gon (i.e., the internal distance), in O(N) time. 4. Computing the longest internal path in an N-gon in O(N2) time. In all cases, the algorithms achieve significant improvements over previously known methods, either by displaying better performance or by gaining in simplicity. In particular, the best algorithms for Problems 2,3,4, known so far, performed respectively in O(N2), O(N2), and O(N4) time.


title: Post training 4-bit quantization of convolutional networks for rapid-deployment

abstract: Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources. Neural network quantization has significant benefits in reducing the amount of intermediate results, but it often requires the full datasets and time-consuming fine tuning to recover the accuracy lost after quantization. This paper introduces the first practical 4-bit post training quantization approach: it does not involve training the quantized model (fine-tuning), nor it requires the availability of the full dataset. We target the quantization of both activations and weights and suggest three complementary methods for minimizing quantization error at the tensor level, two of whom obtain a closed-form analytical solution. Combining these methods, our approach achieves accuracy that is just a few percents less the state-of-the-art baseline across a wide range of convolutional models. The source code to replicate all experiments is available on GitHub: \url{https://github.com/submission2019/cnn-quantization}.


title: Design of Koch Fractal Circularly Polarized Antenna for Handheld UHF RFID Reader Applications

abstract: In this study, a novel design of circularly polarized antenna based on Koch fractal geometry is presented and experimentally investigated. Circular polarization and size reduction are achieved by placing two asymmetric Koch fractal geometries on x- and y-planes of the single-probe-feed square radiator. Further, to tune resonant frequency around the 911-MHz, four arrow-shaped slots are inserted in diagonal axes of the square radiator. It is perceived that compact size of the antenna can be achieved by increasing the overall size of the arrow-shaped slots and indentation angles of Koch fractal geometry. The antenna is fabricated on FR4 substrate with a size of $54 \times 54 \times 1.6\,\text{mm}^3$ ( $0.162 \lambda_{0}\times 0.162 \lambda_{0}\times 0.0048 \lambda_{0}$ at 900 MHz) and tested to validate the proposed design. The 3-dB axial-ratio bandwidth and impedance bandwidth of the proposed antenna design are found to be 8 MHz (907.0–915 MHz) and 37.0 MHz (891.0–928.0 MHz), respectively. The measured results confirm that the proposed design covers US radio-frequency identification (RFID) band (902–928 MHz) and can be used for RFID short-range reading applications.


title: Comparison of complex network analysis software: Citespace, SCI2 and Gephi

abstract: Big Data Analysis (BDA) has attracted considerable interest and curiosity from scientists of various fields recently. As big size and complexity of big data, it is pivotal to uncover hidden patterns, bursts of activity, correlations and laws of it. Complex network analysis could be effective method for this purpose, because of its powerful data organization and visualization ability. Besides the general complex software (Gephi), some bibliometrics software (Citespace and SCI2) could also be used for this analysis, due to their powerful data process and visualization functions. This paper presents a comparison of Citespace, SCI2 and Gephi from the following three aspects: (1) Data preprocessing. Citespace is time-consuming and laborious on merging synonyms. SCI2 is efficient and suitable for massive data. Both of them could remove duplicate records. However, Gephi lacks these. (2) Network extraction. Citespace pays more attention on temporal analysis, but SCI2 and Gephi pay less attention on it. Besides, Citespace and SCI2 could provide pruning algorithms highlight the main structure of social network, but Gephi lacks this. (3) Visualization. In Citespace, co-occurrence network could present time, frequency and betweenness centrality simultaneously; cluster view could label the clusters with phrases. However, SCI2 and Gephi provide more various layout algorithms to present network. Besides, they have a better edit-ability than Citespace on generated network.


title: Simple Encrypted Arithmetic Library - SEAL v2.1

abstract: Traditional encryption schemes, both symmetric and asymmetric, were not designed to respect the algebraic structure of the plaintext and ciphertext spaces. Many schemes, such as Elgamal (resp. e.g. Paillier), are multiplicatively homomorphic (resp. additively homomorphic), so that one can perform certain limited types of computations directly on the encrypted data and have them pass through the encryption to the underlying plaintext data, without requiring access to any secret key(s). The restriction to a one particular type of operation is very strong, however, and instead a much more powerful fully homomorphic encryption scheme, that respects two algebraic operations between the plaintext and ciphertext spaces, would be needed for most applications. The first such encryption scheme was presented by Craig Gentry in his famous work [14], and since then researchers have introduced a number of new and more efficient fully homomorphic encryption schemes. Despite the promising theoretical power of homomorphic encryption, the practical side still remains somewhat underdeveloped. Recently new implementations, new data encoding techniques, and new applications have started to improve the situation, but much remains to be done. In 2015 we released the Simple Encrypted Arithmetic Library SEAL with the goal of providing a well engineered and documented homomorphic encryption library, with no external dependencies, that would be easy to use both by experts and by non-experts with little or no cryptographic background. The library is available at http://sealcrypto.codeplex.com, and is licensed under the MSR License Agreement. Recently a large number of major changes were implemented in SEAL, and the new version was released as SEAL v2.0. In this document we describe in detail this new release, and hope to provide a practical guide to using homomorphic encryption for a wide audience. The reader is also advised to go over the code examples that come with the library, and to read through the detailed comments. For users of previous versions of SEAL we hope to provide clear instructions for how to port old code to use SEAL v2.0. An introductory paper to an older version of SEAL was given in [10], which the user new to SEAL v2.0 may also find helpful as large parts of the API have remained unchanged.


title: Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

abstract: Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an important role in interpreting the scene. A natural modeling framework for capturing such effects is structured prediction, which optimizes over complex labels, while modeling within-label interactions. However, it is unclear what principles should guide the design of a structured prediction model that utilizes the power of deep learning components. Here we propose a design principle for such architectures that follows from a natural requirement of permutation invariance. We prove a necessary and sufficient characterization for architectures that follow this invariance, and discuss its implication on model design. Finally, we show that the resulting model achieves new state of the art results on the Visual Genome scene graph labeling benchmark, outperforming all recent approaches.


title: Suppressing the azimuth ambiguities in synthetic aperture radar images

abstract: A method for removing the azimuth ambiguities from synthetic aperture radar (SAR) images is proposed. The basic idea is to generate a two-dimensional reference function for SAR processing which provides, in addition to the matched filtering for the unaliased part of the received signal, the deconvolution of the azimuth ambiguities. This approach corresponds to an ideal filter concept, where an ideal impulse response function is obtained even in the presence of several phase and amplitude errors. Modeling the sampled azimuth signal shows that the absolute phase value of the ambiguities cannot easily be determined due to their undersampling. The concept of the ideal filter is then extended to accommodate the undefined phase of the ambiguities and also the fading of the azimuth signal. Raw data from the E-SAR system have been used to verify the improvement in image quality obtained by the new method. It has a substantial advantage in enabling the pulse-repetition frequency (PRF) constraints in the SAR system design to be relaxed and also for improving SAR image quality and interpretation. >


title: Better Word Representations with Recursive Neural Networks for Morphology

abstract: Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way.


title: Multiscale Shear-Lag Analysis of Stiffness Enhancement in Polymer–Graphene Nanocomposites

abstract: Graphene and other two-dimensional (2D) materials are of emerging interest as functional fillers in polymer–matrix composites. In this study, we present a multiscale atomistic-to-continuum approach for modeling interfacial stress transfer in graphene–high-density polyethylene (HDPE) nanocomposites. Via detailed characterization of atomic-level stress profiles in submicron graphene fillers, we develop a modified shear-lag model for short fillers. A key feature of our approach lies in the correct accounting of stress concentration at the ends of fillers that exhibits a power-law dependence on filler (“flaw”) size, determined explicitly from atomistic simulations, without any ad hoc modeling assumptions. In addition to two parameters that quantify the end stress concentration, only one additional shear-lag parameter is required to quantify the atomic-level stress profiles in graphene fillers. This three-parameter model is found to be reliable for fillers with dimensions as small as ∼10 nm. Our model predicts...


title: A Case for Abductive Reasoning over Ontologies

abstract: We argue for the usefulness of abductive reasoning in the context of ontologies. We discuss several applicaton scenarios in which various forms of abduction would be useful, introduce corresponding abductive reasoning tasks, give examples, and begin to develop the formal apparatus needed to employ abductive inference in expressive description


Word 2 vec : What and Why


CONTRIBUTION Approximation Capabilities of Muitilayer Feedforward Networks


title: Maternal expectations of postpartum social support: validation of the postpartum social support questionnaire during pregnancy

abstract: Thirteen percent of women experience postpartum depression. Prenatal screening for anticipated postpartum social support, a postpartum depression risk factor, may allow for early intervention. We sought to validate use of a modified version of the Postpartum Social Support Questionnaire (PSSQ) in pregnant women at increased risk for postpartum depression. Factor analysis using orthogonal varimax rotation was used. The modified PSSQ, administered during pregnancy, yields similar loading patterns as observed in postpartum administration of the original PSSQ.


title: Vector quantile regression beyond correct specification

abstract: This paper studies vector quantile regression (VQR), which is a way to model the dependence of a random vector of interest with respect to a vector of explanatory variables so to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is formulated as an optimal transport problem subject to an additional mean-independence condition. This paper provides a new set of results on VQR beyond the case with correct specification which had been the focus of previous work. First, we show that even under misspecification, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Second, we provide a detailed comparison with the classical approach of Koenker and Bassett in the case when the dependent variable is univariate and we show that in that case, VQR is equivalent to classical quantile regression with an additional monotonicity constraint.


title: Don't take my folders away!: organizing personal information to get ghings done

abstract: A study explores the way people organize information in support of projects ("teach a course", "plan a wedding", etc.). The folder structures to organize project information - especially electronic documents and other files - frequently resembled a "divide and conquer" problem decomposition with subfolders corresponding to major components (subprojects) of the project. Folders were clearly more than simply a means to one end: Organizing for later retrieval. Folders were information in their own right - representing, for example, a person's evolving understanding of a project and its components. Unfortunately, folders are often "overloaded" with information. For example, folders sometimes included leading characters to force an ordering ("aa", "zz"). And folder hierarchies frequently reflected a tension between organizing information for current use vs. repeated re-use.


title: Evolutionary k-means for distributed data sets

abstract: One of the challenges for clustering resides in dealing with data distributed in separated repositories, because most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential data mining algorithms for being simple, scalable and easily modifiable to a variety of contexts and application domains. Although distributed versions of k-means have been proposed, the algorithm is still sensitive to the selection of the initial cluster prototypes and requires the number of clusters to be specified in advance. In this paper, we propose the use of evolutionary algorithms to overcome the k-means limitations and, at the same time, to deal with distributed data. Two different distribution approaches are adopted: the first obtains a final model identical to the centralized version of the clustering algorithm; the second generates and selects clusters for each distributed data subset and combines them afterwards. The algorithms are compared experimentally from two perspectives: the theoretical one, through asymptotic complexity analyses; and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests. The obtained results indicate which variant is more adequate for each application scenario.


title: Various arbitration algorithm for on-chip(AMBA) shared bus multi-processor SoC

abstract: On-chip communication architectures play an important role in determining the overall performance of System-on- Chip (SoC) designs. Communication architectures should be flexible so as to offer high performance over a wide range of traffic characteristics. In state-of-the-art multi-processor systems-on-chip (MPSoC), interconnect of processing elements has a major impact on the system's overall average -case and worst-case performance. In shared SoC bus systems, arbiters are usually adopted to solve bus contentions with various kinds of arbitration algorithms. In shared-memory MPSoCs buses are still the prevalent means of on-chip communication for small to medium size chip-multi-processors (CMPs). Still, bus arbitration schemes employed in current architectures either deliver good average-case performance ( maximize bus utilization) or enable tight bounding of worst-case-execution time. This paper presents a shared bus arbitration approach allowing high bus utilization while guaranteeing a fixed bandwidth per time frame to each master. Thus it provides high-performance to both real time and anytime applications or even a mixture of both.


title: Fabric defect detection based on open source computer vision library OpenCV

abstract: A method for fabric defect detection based on OpenCV with rich computer vision and image processing algorithms and functions is presented. Firstly, OpenCV image processing functions implement fabric image preprocessing. We use morphological opening and closing operations to segment image because of their blur defects. Secondly, “seed filling” algorithm is applied to connect broke lines to keep defect edge smoothing. Finally, the edge detection function is to complete accurate positioning defects. Experimental results under Borland C++ Builder 6.0 show that OpenCV based fabric defect detection methods are simple, high code integration, accurate defects positioning, which can be applied to develop real-time fabric defect detection system.


title: Partial Transfer Learning with Selective Adversarial Networks

abstract: Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains. Existing domain adversarial networks assume fully shared label space across domains. In the presence of big data, there is strong motivation of transferring both classification and representation models from existing big domains to unknown small domains. This paper introduces partial transfer learning, which relaxes the shared label space assumption to that the target label space is only a subspace of the source label space. Previous methods typically match the whole source domain to the target domain, which are prone to negative transfer for the partial transfer problem. We present Selective Adversarial Network (SAN), which simultaneously circumvents negative transfer by selecting out the outlier source classes and promotes positive transfer by maximally matching the data distributions in the shared label space. Experiments demonstrate that our models exceed state-of-the-art results for partial transfer learning tasks on several benchmark datasets.


title: Quantifying Political Polarity Based on Bipartite Opinion Networks

abstract: Political inclinations of individuals (liberal vs. conservative) largely shape their opinions on several issues such as abortion, gun control, nuclear power, etc. These opinions are openly exerted inonline forums, news sites, the parliament, and so on. In this paper, we address the problem of quantifying political polarity of individuals and of political issues for classification and ranking. We use signed bipartite networks to represent the opinions of individuals on issues, and formulate the problem as a node classification task. We propose a linear algorithm that exploits network effects to learn both the polarity labels as well as the rankings of people and issues in a completely unsupervised manner. Through extensive experiments we demonstrate that our proposed method provides an effective, fast, and easy-to-implement solution, while outperforming three existing baseline algorithms adapted to signed networks, on real political forum and US Congress datasets.Experiments on a wide variety of synthetic graphs with varying polarity and degree distributions of the nodes further demonstrate the robustness of our approach.


title: Metric visual-inertial navigation system using single optical flow feature

abstract: This paper introduces a state estimation framework that allows estimating the attitude, full metric speed and the orthogonal metric distance of an IMU-camera system with respect to a plane. The filter relies only on a single optical flow feature as well as gyroscope and accelerometer measurements. The underlying assumption is that the observed visual feature lies on a static plane. The orientation of the observed plane is not required to be known a priori and is also estimated at runtime. The estimation framework fuses visual and inertial measurements in an Unscented Kalman Filter (UKF). The theoretical limitations of the UKF are investigated using a nonlinear observability analysis based on Lie-derivatives. Simulations using realistic sensor noise values successfully demonstrate the performance of the filter as well as validate the findings of the observability analysis. It is shown that the state estimate is converging correctly, even in presence of substantial initial state errors. To the authors' knowledge, this paper documents for the first time the estimation of the heading and metric distance to a wall with no range sensor, relying solely on optical flow as the only exteroceptive sensing modality.


Data-Driven De-Anonymization in Bitcoin


title: Can Natural Language Processing Become Natural Language Coaching?

abstract: How we teach and learn is undergoing a revolution, due to changes in technology and connectivity. Education may be one of the best application areas for advanced NLP techniques, and NLP researchers have much to contribute to this problem, especially in the areas of learning to write, mastery learning, and peer learning. In this paper I consider what happens when we convert natural language processors into natural language coaches. 1 Why Should You Care, NLP Researcher? There is a revolution in learning underway. Students are taking Massive Open Online Courses as well as online tutorials and paid online courses. Technology and connectivity makes it possible for students to learn from anywhere in the world, at any time, to fit their schedules. And in today’s knowledge-based economy, going to school only in one’s early years is no longer enough; in future most people are going to need continuous, lifelong education. Students are changing too — they expect to interact with information and technology. Fortunately, pedagogical research shows significant benefits of active learning over passive methods. The modern view of teaching means students work actively in class, talk with peers, and are coached more than graded by their instructors. In this new world of education, there is a great need for NLP research to step in and help. I hope in this paper to excite colleagues about the possibilities and suggest a few new ways of looking at them. I do not attempt to cover the field of language and learning comprehensively, nor do I claim there is no work in the field. In fact there is quite a bit, such as a recent special issue on language learning resources (Sharoff et al., 2014), the long running ACL workshops on Building Educational Applications using NLP (Tetreault et al., 2015), and a recent shared task competition on grammatical error detection for second language learners (Ng et al., 2014). But I hope I am casting a few interesting thoughts in this direction for those colleagues who are not focused on this particular topic.


title: Security Vulnerabilities of Internet of Things: A Case Study of the Smart Plug System

abstract: With the rapid development of the Internet of Things, more and more small devices are connected into the Internet for monitoring and control purposes. One such type of devices, smart plugs, have been extensively deployed worldwide in millions of homes for home automation. These smart plugs, however, would pose serious security problems if their vulnerabilities were not carefully investigated. Indeed, we discovered that some popular smart home plugs have severe security vulnerabilities which could be fixed but unfortunately are left open. In this paper, we case study a smart plug system of a known brand by exploiting its communication protocols and successfully launching four attacks: 1) device scanning attack; 2) brute force attack; 3) spoofing attack; and 4) firmware attack. Our real-world experimental results show that we can obtain the authentication credentials from the users by performing these attacks. We also present guidelines for securing smart plugs.


title: Dental Abnormalities in the Deciduous and Permanent Dentitions of Individuals with Cleft Lip and Palate

abstract: ANORMALITATES DENTAL IN LE DENTITION PRIMARI E PERMANENTE DE INDIVIDUOS CON FiSSURA DE LABIOS E/o DE PALATO.—Le dentitiones primari e permanente de 39 subjectos con fissura del labios e/o del palato esseva studiate a base de material in le collection de modulos serial del Centro pro le Studio de Palatos Fisse al Universitate Illinois. Le objectivo esseva determinar le natura, le frequentia, e le distribution de anormalitates morphologic del coronas. Plus anormalitates dental occurreva in fissuras palata que in altere typos. Ex un gruppo total de 1716 unitates dental, 189 esseva anormal (11 pro cento). In casos de fissura palatal, le dentes mandibular esseva afficite non minus frequentemente que le dentes maxillar.


title: How and why people Twitter: the role that micro-blogging plays in informal communication at work

abstract: Micro-blogs, a relatively new phenomenon, provide a new communication channel for people to broadcast information that they likely would not share otherwise using existing channels (e.g., email, phone, IM, or weblogs). Micro-blogging has become popu-lar quite quickly, raising its potential for serving as a new informal communication medium at work, providing a variety of impacts on collaborative work (e.g., enhancing information sharing, building common ground, and sustaining a feeling of connectedness among colleagues). This exploratory research project is aimed at gaining an in-depth understanding of how and why people use Twitter - a popular micro-blogging tool - and exploring micro-blog's poten-tial impacts on informal communication at work.


title: Unmanned Aerial Vehicle Localization Based on Monocular Vision and Online Mosaicking

abstract: This paper presents a new approach for vision-based UAV localization, using mosaics as environment representations. Inter-image motions are used to estimate the motion of the UAV. Online mosaicking is applied to reduce the impact of the accumulative errors in UAV position estimation. A new method to build an stochastic mosaic given the image-to-image homographies is detailed. The mosaic consists of a network of inter-image relations, and is used to create a consistent view of the environment of the UAV and hence, to detect the drift in position estimation by using the mosaic as a resource. The technique could be called simultaneous localization and mosaicking. This technique is specially suitable for monitoring and surveillance tasks in which the UAV will repeatedly cover the same area. The paper also shows experimental results with real UAVs where the benefits of the proposed method are evident.


title: Efficient screen-space approach to high-quality multiscale ambient occlusion

abstract: We present a new screen-space ambient occlusion (SSAO) algorithm that improves on the state-of-the-art SSAO methods in both performance and quality. Our method computes ambient occlusion (AO) values at multiple image resolutions and combines them to obtain the final, high-resolution AO value for each image pixel. It produces high-quality AO that includes both high-frequency shadows due to nearby, occluding geometry and low-frequency shadows due to distant geometry. Our approach only needs to use very small sampling kernels at every resolution, thereby achieving high performance without resorting to random sampling. As a consequence, our results do not suffer from noise and excessive blur, which are common of other SSAO methods. Therefore, our method also avoids the expensive, final blur pass commonly used in other SSAO methods. The use of multiple resolutions also helps reduce errors that are caused by SSAO’s inherent lack of visibility checking. Temporal incoherence caused by using coarse resolutions is solved with an optional temporal filtering pass. Our method produces results that are closer to ray-traced solutions than those of any existing SSAO methods, while running at similar or higher frame rates than the fastest ones.


title: Empathy in narcissistic personality disorder: From clinical and empirical perspectives.

abstract: Narcissistic personality disorder (NPD) is associated with an assortment of characteristics that undermine interpersonal functioning. A lack of empathy is often cited as the primary distinguishing feature of NPD. However, clinical presentations of NPD suggest that empathy is not simply deficient in these individuals, but dysfunctional and subject to a diverse set of motivational and situational factors. Consistent with this presentation, research illustrates that empathy is multidimensional, involving 2 distinct emotional and cognitive processes associated with a capacity to respectively understand and respond to others’ mental and affective states. The goal of this practice review is to bridge the gap between our psychobiological understanding of empathy and its clinical manifestations in NPD. We present 3 case studies highlighting the variability in empathic functioning in people with NPD. Additionally, we summarize the literature on empathy and NPD, which largely associates this disorder with deficient emotional empathy, and dysfunctional rather than deficient cognitive empathy. Because this research is limited, we also present empathy-based findings for related syndromes (borderline and psychopathy). Given the complexity of narcissism and empathy, we propose that multiple relationships can exist between these constructs. Ultimately, by recognizing the multifaceted relationship between empathy and narcissism, and moving away from an all or nothing belief that those with NPD simply lack empathy, therapists may better understand narcissistic patients’ behavior and motivational structure.


title: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.

abstract: Accelerometry offers a practical and low cost method of objectively monitoring human movements, and has particular applicability to the monitoring of free-living subjects. Accelerometers have been used to monitor a range of different movements, including gait, sit-to-stand transfers, postural sway and falls. They have also been used to measure physical activity levels and to identify and classify movements performed by subjects. This paper reviews the use of accelerometer-based systems in each of these areas. The scope and applicability of such systems in unsupervised monitoring of human movement are considered. The different systems and monitoring techniques can be integrated to provide a more comprehensive system that is suitable for measuring a range of different parameters in an unsupervised monitoring context with free-living subjects. An integrated approach is described in which a single, waist-mounted accelerometry system is used to monitor a range of different parameters of human movement in an unsupervised setting.


title: Visual Discovery and Analysis

abstract: We have developed a flexible software environment called ADVIZOR for visual information discovery. ADVIZOR complements existing assumptive-based analyses by providing a discovery-based approach. ADVIZOR consists of five parts: a rich set of flexible visual components, strategies for arranging the components for particular analyses, an in-memory data pool, data manipulation components, and container applications. Working together, ADVIZOR's architecture provides a powerful production platform for creating innovative visual query and analysis applications.


title: Field-split parallel architecture for high performance multi-match packet classification using FPGAs

abstract: Multi-match packet classification is a critical function in network intrusion detection systems (NIDS), where all matching rules for a packet need to be reported. Most of the previous work is based on ternary content addressable memories (TCAMs) which are expensive and are not scalable with respect to clock rate, power consumption, and circuit area. This paper studies the characteristics of real-life Snort NIDS rule sets, and proposes a novel SRAM-based architecture. The proposed architecture is called field-split parallel bit vector (FSBV) where some header fields of a packet are further split into bit-level subfields. Unlike previous multi-match packet classification algorithms which suffer from memory explosion, the memory requirement of FSBV is linear in the number of rules. FPGA technology is exploited to provide high throughput and to support dynamic updates. Implementation results show that our architecture can store on a single Xilinx Virtex-5 FPGA the full set of packet header rules extracted from the latest Snort NIDS and sustains 100 Gbps throughput for minimum size (40 bytes) packets. The design achieves 1.25× improvement in throughput while the power consumption is approximately one fourth that of the state-of-the-art solutions.


title: AES Encryption Implementation on CUDA GPU and Its Analysis

abstract: GPU has a good performance ratio and exhibits the capability for applications with high level of parallelism despite its inexpensive price. The support of integer and logical instructions on the latest generation of GPU makes us to implement cipher algorithms easier with the same instructions. However the decisions such as parallel processing granularity or memory allocation place imposed heavy burden on programmers. For this reason this paper shows the results of several experiments to study relation between memory allocation style of AES parameters and granularity as the parallelism exploited from AES encoding process using CUDA with NVIDIA Geforce GTX285. The result of experiments cleared up that the 16Byte/thread granularity had the highest performance and it achieved approximately 35Gbps throughput. Moreover, implementation with overlapping between processing and data transfer brought up 22.5Gbps throughput including data transfer time. Also, it cleared up that it is important to decide granularity and memory allocation to effective processing in AES encryption on GPU.


title: Multi-Adversarial Domain Adaptation

abstract: Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators. The adaptation can be achieved by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets.


title: A Novel PMU Fog Based Early Anomaly Detection for an Efficient Wide Area PMU Network

abstract: Based on phasor measurement units (PMUs), a synchronphasor system is widely recognized as a promising smart grid measurement system. It is able to provide high-frequency, high-accuracy phasor measurements sampling for Wide Area Monitoring and Control (WAMC) applications. However, the high sampling frequency of measurement data under strict latency constraints introduces new challenges for real time communication. It would be very helpful if the collected data can be prioritized according to its importance such that the existing quality of service (QoS) mechanisms in the communication networks can be leveraged. To achieve this goal, certain anomaly detection functions should be conducted by the PMUs. Inspired by the recent emerging edge-fog-cloud computing hierarchical architecture, which allows computing tasks to be conducted at the network edge, a novel PMU fog is proposed in this paper. Two anomaly detection approaches, Singular Spectrum Analysis (SSA) and K-Nearest Neighbors (KNN), are evaluated in the PMU fog using the IEEE 16-machine 68-bus system. The simulation experiments based on Riverbed Modeler demonstrate that the proposed PMU fog can effectively reduce the data flow end-to-end (ETE) delay without sacrificing data completeness.


title: Structure from motion using dense CNN features with keypoint relocalization

abstract: Structure from Motion (SfM) using imagery that involves extreme appearance changes is yet a challenging task due to a loss of feature repeatability. Using feature correspondences obtained by matching densely extracted convolutional neural network (CNN) features significantly improves the SfM reconstruction capability. However, the reconstruction accuracy is limited by the spatial resolution of the extracted CNN features which is not even pixel-level accuracy in the existing approach. Providing dense feature matches with precise keypoint positions is not trivial because of memory limitation and computational burden of dense features. To achieve accurate SfM reconstruction with highly repeatable dense features, we propose an SfM pipeline that uses dense CNN features with relocalization of keypoint position that can efficiently and accurately provide pixel-level feature correspondences. Then, we demonstrate on the Aachen Day-Night dataset that the proposed SfM using dense CNN features with the keypoint relocalization outperforms a state-of-the-art SfM (COLMAP using RootSIFT) by a large margin.


title: Teaching Reinforcement Learning with Mario: An Argument and Case Study

abstract: Integrating games into the computer science curriculum has been gaining acceptance in recent years, particularly when used to improve student engagement in introductory courses. This paper argues that games can also be useful in upper level courses, such as general artificial intelligence and machine learning. We provide a case study of using a Mario game in a machine learning class to provide one successful data point where both content-specific and general learning outcomes were successfully achieved.


title: Product attachment and replacement: implications for sustainable design

abstract: This research investigated a number of main areas of attachment in order to determine how consumer-product relationships are formed and to identify whether these feelings influence replacement decisions. Primary research comprised of interviews with consumers to discuss the topic area in relation to three possessions selected for their attachment qualities. The research highlighted how attachment is determined by multiple themes, many of which are circumstantial to consumers' experiences and therefore difficult for designers to control. Findings showed that memories were the most prominent theme of participants' attachment, closely followed by pleasure and usability. Enjoyment and pleasure were found to be the primary reason for attachment to newly purchased items, whereas nostalgia was highest for older possessions. Appearance and reliability were found to have considerable influence on participants' attitudes towards replacement.


title: Fascial Components of the Myofascial Pain Syndrome

abstract: Myofascial pain syndrome (MPS) is described as the muscle, sensory, motor, and autonomic nervous system symptoms caused by stimulation of myofascial trigger points (MTP). The participation of fascia in this syndrome has often been neglected. Several manual and physical approaches have been proposed to improve myofascial function after traumatic injuries, but the processes that induce pathological modifications of myofascial tissue after trauma remain unclear. Alterations in collagen fiber composition, in fibroblasts or in extracellular matrix composition have been postulated. We summarize here recent developments in the biology of fascia, and in particular, its associated hyaluronan (HA)-rich matrix that address the issue of MPS.


title: BRIDE - A toolchain for framework-independent development of industrial service robot applications

abstract: Software integration is still a challenging and time consuming task and therefore a major part of the development of industrial and domestic service robot applications. The presented toolchain BRIDE is able to streamline this process by the separation of user roles and the separation of developer concerns of software components to ensure a frame-work independent implementation. The impact of the BRIDE toolchain in the development process is demonstrated in a case study on the SyncMM mobile manipulation control framework.


title: Energy Harvesting From Hybrid Indoor Ambient Light and Thermal Energy Sources for Enhanced Performance of Wireless Sensor Nodes

abstract: In this paper, a hybrid of indoor ambient light and thermal energy harvesting scheme that uses only one power management circuit to condition the combined output power harvested from both energy sources is proposed to extend the lifetime of the wireless sensor node. By avoiding the use of individual power management circuits for multiple energy sources, the number of components used in the hybrid energy harvesting (HEH) system is reduced and the system form factor, cost and power losses are thus reduced. An efficient microcontroller-based ultra low power management circuit with fixed voltage reference based maximum power point tracking is implemented with closed-loop voltage feedback control to ensure near maximum power transfer from the two energy sources to its connected electronic load over a wide range of operating conditions. From the experimental test results obtained, an average electrical power of 621 μW is harvested by the optimized HEH system at an average indoor solar irradiance of 1010 lux and a thermal gradient of 10 K, which is almost triple of that can be obtained with conventional single-source thermal energy harvesting method.


title: Online anomalous vehicle detection at the edge using multidimensional SSA

abstract: We are witnessing the giant leap of Smart Cities and the prosperity of Internet of Things (loTs), but the anomalous vehicle surveillance issues in Intelligent Transportation Systems (ITS) are still challenging, especially when more computing tasks are migrated from Cloud center to the network edge. Different research studies attempted to tackle this problem, however, existing approaches are either requiring large training data sets or presenting detection unreliability without prior knowledge. In this paper, we propose to identify anomalous vehicles on roads in real-time using multidimensional Singular Spectrum Analysis (mSSA). Inspired by the excellent performance of the SSA algorithms in change point detection in time series, we adopted it to catch the differences in the dimension of characteristics of vehicles on roads. The multiple factors of vehicles' behavior are mapped into multiple channels in the mSSA framework. Instead of pre-training or defining normal motion patterns of vehicles, the anomaly detection is formatted as an outlier identifying problem. Using two vehicle trajectory data sets, the feasibility and effectiveness of our approach are verified. Comparing to other proposed methods like clustering, the experimental results show that our approach is more reliable and robust.


title: Coin flipping by telephone a protocol for solving impossible problems

abstract: Alice and Bob want to flip a coin by telephone. (They have just divorced, live in different cities, want to decide who gets the car.) Bob would not like to tell Alice HEADS and hear Alice (at the other end of the line) say "Here goes . . . I'm flipping the coin. . . . You lost!"Coin-flipping in the SPECIAL way done here has a serious purpose. Indeed, it should prove an INDISPENSABLE TOOL of the protocol designer. Whenever a protocol requires one of two adversaries, say Alice, to pick a sequence of bits at random, and whenever it serves Alice's interests best NOT to pick her sequence of bits at random, then coin-flipping (Bob flipping coins to Alice) as defined here achieves the desired goal:1. It GUARANTEES to Bob that Alice will pick her sequence of bits at random. Her bit is 1 if Bob flips heads to her, O otherwise.2. It GUARANTEES to Alice that Bob will not know WHAT sequence of bits he flipped to her.Coin-flipping has already proved useful in solving a number of problems once thought impossible: mental poker, certified mail, and exchange of secrets. It will certainly prove a useful tool in solving other problems as well.


A Dataset for Joint Noun-Noun Compound Bracketing and Interpretation


title: A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions

abstract: A photovoltaic (PV) generation system (PGS) is becoming increasingly important as renewable energy sources due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness. For large PGS, the probability for partially shaded condition (PSC) to occur is also high. Under PSC, the P-V curve of PGS exhibits multiple peaks, which reduces the effectiveness of conventional maximum power point tracking (MPPT) methods. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The standard version of PSO is modified to meet the practical consideration of PGS operating under PSC. The problem formulation, design procedure, and parameter setting method which takes the hardware limitation into account are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent, and high tracking efficiency. To validate the correctness of the proposed method, simulation, and experimental results of a 500-W PGS will also be provided to demonstrate the effectiveness of the proposed technique.


title: Combining sketch and tone for pencil drawing production

abstract: We propose a new system to produce pencil drawing from natural images. The results contain various natural strokes and patterns, and are structurally representative. They are accomplished by novelly combining the tone and stroke structures, which complement each other in generating visually constrained results. Prior knowledge on pencil drawing is also incorporated, making the two basic functions robust against noise, strong texture, and significant illumination variation. In light of edge, shadow, and shading information conveyance, our pencil drawing system establishes a style that artists use to understand visual data and draw them. Meanwhile, it lets the results contain rich and well-ordered lines to vividly express the original scene.


title: Does Agile work? — A quantitative analysis of agile project success

abstract: Abstract The Agile project management methodology has been widely used in recent years as a means to counter the dangers of traditional, front-end planning methods that often lead to downstream development pathologies. Although numerous authors have pointed to the advantages of Agile, with its emphasis on individuals and interactions over processes, customer collaboration over contracts and formal negotiations, and responsiveness over rigid planning, there are, to date, very few large-scale, empirical studies to support the contention that Agile methods can improve the likelihood of project success. Developed originally for software development, it is still predominantly an IT phenomenon. But due to its success it has now spread to non-IT projects. Using a data sample of 1002 projects across multiple industries and countries, we tested the effect of Agile use in organizations on two dimensions of project success: efficiency and overall stakeholder satisfaction against organizational goals. We further examined the moderating effects of variables such as perceived quality of the vision/goals of the project, project complexity, and project team experience. Our findings suggest that Agile methods do have a positive impact on both dimensions of project success. Further, the quality of the vision/goals is a marginally significant moderator of this effect. Implications of these findings and directions for future research are discussed.


title: An intelligent SMS-based remote Water Metering System

abstract: The Integrated Prepaid Water Meter System is a technology for prepaid billing of water along with sufficient monitoring of the water meter readings automatically from a remote place without any human intervention. This system promises fast and accurate billing of water as well as prevents any misuse of it. In this paper, a technique having adequate security support, for prepaid billing of water using Short Message Service (SMS) has been illustrated. Existing Global System for Mobile communications (GSM) networks have been used for sending and receiving SMS. A prototype of the system has been designed and developed for system exploration and experiment.


title: Rapid octree construction from image sequences

abstract: Abstract The construction of a three-dimensional object model from a set of images taken from different viewpoints is an important problem in computer vision. One of the simplest ways to do this is to use the silhouettes of the object (the binary classification of images into object and background) to construct a bounding volume for the object. To efficiently represent this volume, we use an octree, which represents the object as a tree of recursively subdivided cubes. We develop a new algorithm for computing the octree bounding volume from multiple silhouettes and apply it to an object rotating on a turntable in front of a stationary camera. The algorithm performs a limited amount of processing for each viewpoint and incrementally builds the volumetric model. The resulting algorithm requires less total computation than previous algorithms, runs in close to real-time, and builds a model whose resolution improves over time.


title: An evaluation of Naive Bayesian anti-spam filtering

abstract: It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail ("spam"). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter's performance, issues that had not been previously explored. After introducing appropriate cost-sensitive evaluation measures, we reach the conclusion that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.


title: 3D-printed microfluidic devices.

abstract: Microfluidics is a flourishing field, enabling a wide range of biochemical and clinical applications such as cancer screening, micro-physiological system engineering, high-throughput drug testing, and point-of-care diagnostics. However, fabrication of microfluidic devices is often complicated, time consuming, and requires expensive equipment and sophisticated cleanroom facilities. Three-dimensional (3D) printing presents a promising alternative to traditional techniques such as lithography and PDMS-glass bonding, not only by enabling rapid design iterations in the development stage, but also by reducing the costs associated with institutional infrastructure, equipment installation, maintenance, and physical space. With the recent advancements in 3D printing technologies, highly complex microfluidic devices can be fabricated via single-step, rapid, and cost-effective protocols, making microfluidics more accessible to users. In this review, we discuss a broad range of approaches for the application of 3D printing technology to fabrication of micro-scale lab-on-a-chip devices.


title: Multivariable Feedback Control - Analysis and Design (Skogestad, S. and Postlewaite, I.; 2005) [book review]

abstract: This edition is a revised and expanded version of the first edition, which was published in 1996. It is aimed at graduate students and practicing engineers who have a basic knowledge of classical control design and state-space control theory for linear systems. A basic knowledge of matrix theory and linear algebra is required to appreciate and digest the material offered. There are 13 chapters which cover such topics as linear systems, uncertainty modeling, controller design, model reduction, linear matrix inequalities, and applied control design for three case studies. The book proves a well-balanced, effective, and efficient treatment of robust multivariable control.


title: A Deep Learning-Based Formula Detection Method for PDF Documents

abstract: In practice, PDF files may be generated by different tools and their character information quality could be different. As a result, the approaches to detecting formulae from PDF documents usually have much different performance on different PDF files. To address this problem, in this paper we combine and refine the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model to detect formulae according to both their character and vision features. Based on the characteristic of PDF documents, we propose a series of strategies to train and optimize deep networks, such as the implicit class down-sampling strategy which can reduce the unbalancedness between formulae and other page elements (e.g., text paragraphs, tables, figures, etc.). The region proposal method is also redesigned to generate moderate formula candidates through combining the bottom-up and top-down layout analysis. The experimental results show that the combination of CNN and RNN can increase the robustness of our proposed detection method. Furthermore, the proposed method outperforms the existing formula detection methods on both a ground-truth dataset and a larger self-built dataset, which would be released and available for research purposes.


title: Mindfulness meditation, emotion, and cognitive control: Experienced meditators show distinct brain and behavior responses to emotional provocations

abstract: MINDFULNESS MEDITATION, EMOTION, AND COGNITIVE CONTROL: EXPERIENCED MEDITATORS SHOW DISTINCT BRAIN AND BEHAVIOR RESPONSES TO EMOTIONAL PROVOCATIONS Deidre Lynn Reis 2008 Mindfulness meditation (MM) is a process of developing non-judgmental awareness of internal thoughts and feelings. MM is associated with improved cognition and affect in brain and behavior outcomes, but little is known regarding the nature or extent of these relationships. This dissertation is the first investigation into how MM may be related to both affect and cognitive control broadly, with the more narrow aim of characterizing the association between MM expertise (MME) and responses to specific types of affective provocations in behavior and brain outcomes. Three specific goals were to 1) determine if MME is associated with differences in relatively automatic processing of emotionally evocative stimuli, 2) examine whether MME predicts greater activity in brain regions supporting emotion and cognitive control in response to an affective provocation in a social context, and 3) assess whether MME is associated with distinct patterns of brain activity while viewing aversive images. In a between-groups, crosssectional design, participants were expert meditators (1000 or more hours of practice, completion of at least 1 extended retreat, and regular practice at least 3 times per week) and control subjects matched for age, gender, and education. In study 1 (n = 36 meditators, 45 controls), I predicted and found that meditators showed better performance on an attentional blink task in which negatively valenced distractor images typically impair performance. In study 2 (n = 18 meditators, 12 controls), I predicted and found that MME was related to greater activity in the insula and anterior cingulate brain regions that subserve emotion and cognitive control during emotional provocation in a social decision-making task. Finally, in study 3 (n = 18 meditators, 12 controls) I predicted that meditators would show greater neural activation in regions associated with emotion regulation when viewing aversive images; however, this hypothesis was not supported. Results from these cross-sectional studies indicate that experience with MM is associated with distinct reactions to emotional provocations in attention and social decision-making tasks, and have implications for understanding the relationship between MM and emotion regulation.


title: Cyclodextrins in drug delivery: An updated review

abstract: The purpose of this review is to discuss and summarize some of the interesting findings and applications of cyclodextrins (CDs) and their derivatives in different areas of drug delivery, particularly in protein and peptide drug delivery and gene delivery. The article highlights important CD applications in the design of various novel delivery systems like liposomes, microspheres, microcapsules, and nanoparticles. In addition to their well-known effects on drug solubility and dissolution, bioavailability, safety, and stability, their use as excipients in drug formulation are also discussed in this article. The article also focuses on various factors influencing inclusion complex formation because an understanding of the same is necessary for proper handling of these versatile materials. Some important considerations in selecting CDs in drug formulation such as their commercial availability, regulatory status, and patent status are also summarized. CDs, because of their continuing ability to find several novel applications in drug delivery, are expected to solve many problems associated with the delivery of different novel drugs through different delivery routes.


title: Quantum inspired method of feature fusion based on von Neumann entropy

abstract: One mission of feature fusion is to obtain a complete yet concise presentation of all existing feature data by detecting and fusing the duplicate feature data. In contrast to the already developed feature fusion methods which have shown their limitations, this paper applies the theories of quantum information to feature fusion. Further, a novel and effective step-wise quantum inspired feature fusion method, which detects the duplicate feature data based on maximum von Neumann mutual information and fuses the duplicate feature data using the operations on quantum state, is developed. This same idea is also used for feature dimensionality reduction, and the corresponding models are investigated. For comparison, another quantum inspired feature fusion method based on average quantum phase is presented here. The experimental results show that the quantum inspired feature fusion method based on von Neumann entropy gives better results on completeness and conciseness than the method based on average quantum phase.


title: Development of a Small Legged Wall Climbing Robot with Passive Suction Cups

abstract: In this paper, we develop a small hexapod robot with passive suction cups and realize vertical wall climbing. The passive adhesion mechanisms make the robotic system small and simple, since it requires neither energy nor an additional actuator to stay on the vertical wall. Above all, the passive suction cup is suited to vertical wall climbing because it is strongly attached to a wall with a smooth surface and is simply detached from it by pulling the edge of the cup. These characteristics are intensified according to the decreasing robotic scale. In this study, we developed a small wall climbing robot 12 cm long and 34 g in weight and driven by single degree-of-freedom linkage legs based on the scale effect. Here, the linkage mechanism optimized by a genetic algorithm provided the effective trajectory of the legs and the attachment-detachment cycle. As a result, the robot traveled on the ground at a velocity of 2.7 cm/s and climbed on the vertical wall at a velocity of 2.2 cm/s.


The Influence of Perceived Belonging on Massively Multiplayer Online Role-Playing Games


title: Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals.

abstract: OBJECTIVE ::: This paper aims to report on the accuracy of estimating sleep stages using a wrist-worn device that measures movement using a 3D accelerometer and an optical pulse photoplethysmograph (PPG). ::: ::: ::: APPROACH ::: Overnight recordings were obtained from 60 adult participants wearing these devices on their left and right wrist, simultaneously with a Type III home sleep testing device (Embletta MPR) which included EEG channels for sleep staging. The 60 participants were self-reported normal sleepers (36 M: 24 F, age = 34 ± 10, BMI = 28 ± 6). The Embletta recordings were scored for sleep stages using AASM guidelines and were used to develop and validate an automated sleep stage estimation algorithm, which labeled sleep stages as one of Wake, Light (N1 or N2), Deep (N3) and REM (REM). Features were extracted from the accelerometer and PPG sensors, which reflected movement, breathing and heart rate variability. ::: ::: ::: MAIN RESULTS ::: Based on leave-one-out validation, the overall per-epoch accuracy of the automated algorithm was 69%, with a Cohen's kappa of 0.52 ± 0.14. There was no observable bias to under- or over-estimate wake, light, or deep sleep durations. REM sleep duration was slightly over-estimated by the system. The most common misclassifications were light/REM and light/wake mislabeling. ::: ::: ::: SIGNIFICANCE ::: The results indicate that a reasonable degree of sleep staging accuracy can be achieved using a wrist-worn device, which may be of utility in longitudinal studies of sleep habits.


FT‐IR Spectroscopic Study of Uracil Derivatives and Their Hydrogen‐ Bonded Complexes with Model Proton Donors. Part 5. Complexes of Uracils with Hydrogen Chloride in Argon Matrices.


title: A Context-Aware Method for Authentically Simulating Outdoors Shadows for Mobile Augmented Reality

abstract: Visual coherence between virtual and real objects is a major issue in creating convincing augmented reality (AR) applications. To achieve this seamless integration, actual light conditions must be determined in real time to ensure that virtual objects are correctly illuminated and cast consistent shadows. In this paper, we propose a novel method to estimate daylight illumination and use this information in outdoor AR applications to render virtual objects with coherent shadows. The illumination parameters are acquired in real time from context-aware live sensor data. The method works under unprepared natural conditions. We also present a novel and rapid implementation of a state-of-the-art skylight model, from which the illumination parameters are derived. The Sun's position is calculated based on the user location and time of day, with the relative rotational differences estimated from a gyroscope, compass and accelerometer. The results illustrated that our method can generate visually credible AR scenes with consistent shadows rendered from recovered illumination.


title: Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach

abstract: Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.


title: Efficacy, safety, and patient satisfaction of a monophasic cohesive polydensified matrix versus a biphasic nonanimal stabilized hyaluronic acid filler after single injection in nasolabial folds.

abstract: BACKGROUND ::: Intradermal injection of hyaluronic acid (HA) is currently the criterion standard to reduce the appearance of nasolabial folds (NLF). ::: ::: ::: OBJECTIVE ::: Effects of a monophasic HA filler using cohesive polydensified matrix (CPM) technology were compared with those of nonanimal stabilized HA (NASHA). ::: ::: ::: MATERIALS AND METHODS ::: In a double-blind, half-side comparison, 20 subjects (ages 35-65, mean 52 ± 5.6) with symmetric NLF grade 3 to 4 were randomized to contralateral treatment with a monophasic polydensified filler (CPM) and a biphasic HA filler (NASHA). Efficacy was assessed at baseline and after 2, 24, and 48 weeks using a wrinkle severity rating scale (WSRS) for NLF, subject questionnaire, and biophysical in vivo methods. ::: ::: ::: RESULTS ::: All subjects showed significant improvements with both fillers up to day 365. Subject questionnaires confirmed significantly less injection pain for the CPMHA, significantly greater patient satisfaction after 2 weeks with both fillers, and after 24 and 48 weeks significantly greater improvement with the CPMHA compared to baseline. WSRS and skin surface topography parameters improved significantly up to 48 weeks with both fillers. ::: ::: ::: CONCLUSION ::: A single intradermal injection of a monophasic CPMHA and a biphasic NASHA filler showed significant improvements in WSRS and measured wrinkle depth up to 48 weeks for both fillers and significant differences in injection comfort and patient satisfaction in favor of CPMHA.


title: Precise beam angle control in the S-UHE, SEN's single-wafer ultra-high energy ion implanter

abstract: In order to fabricate highly sensitive image sensors (IS), ultra-high energetic ion beams such as 5MeV of boron are required. In order to address the requirement as well as more aggressive requirements of leading-edge IS, SEN has developed the S-UHE, an ultra-high energy single-wafer ion implanter. One of the most important features in the S-UHE is a precise beam angle control system to obtain stable implant depth of ion species against angle-sensitive channeling effects. It is very important for the precise control both to design a sophisticated beam line and to measure beam angles accurately. In this report, measuring techniques of the beam angle and the results are presented.


title: Saliency Prediction in the Deep Learning Era: An Empirical Investigation

abstract: Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans and discuss remaining issues that need to be addressed to build the next generation of more powerful saliency models. Some specific questions that are addressed include: in what ways current models fail, how to remedy them, what can be learned from cognitive studies of attention, how explicit saliency judgments relate to fixations, how to conduct fair model comparison, and what are the emerging applications of saliency models.


title: Agnostic system identification for monte carlo planning

abstract: While model-based reinforcement learning is often studied under the assumption that a fully accurate model is contained within the model class, this is rarely true in practice. When the model class may be fundamentally limited, it can be difficult to obtain theoretical guarantees. Under some conditions the DAgger algorithm promises a policy nearly as good as the plan obtained from the most accurate model in the class, but only if the planning algorithm is near-optimal, which is also rarely the case in complex problems. This paper explores the interaction between DAgger and Monte Carlo planning, specifically showing that DAgger may perform poorly when coupled with a sub-optimal planner. A novel variation of DAgger specifically for use with Monte Carlo planning is derived and is shown to behave far better in some cases where DAgger fails.


title: Shining new light on the brain's “bilingual signature”: A functional Near Infrared Spectroscopy investigation of semantic processing

abstract: Decades of research have shown that, from an early age, proficient bilinguals can speak each of their two languages separately (similar to monolinguals) or rapidly switch between them (dissimilar to monolinguals). Thus we ask do monolingual and bilingual brains process language similarly or dissimilarly, and is this affected by the language context? Using an innovative brain imaging technology, functional Near Infrared Spectroscopy (fNIRS), we investigated how adult bilinguals process semantic information, both in speech and in print, in a monolingual language context (one language at a time) or in a bilingual language context (two languages in rapid alternation). While undergoing fNIRS recording, ten early-exposed, highly-proficient Spanish-English bilinguals completed a Semantic Judgment task in monolingual and bilingual contexts, and were compared to ten English monolingual controls. Two hypotheses were tested: the Signature Hypothesis predicts that early, highly proficient bilinguals will recruit neural tissue to process language differently from monolinguals across all language contexts. The Switching Hypothesis predicts that bilinguals will recruit neural tissue to process language similarly to monolinguals, when using one language at a time. Supporting the Signature Hypothesis, in the monolingual context, bilinguals and monolinguals showed differences in both hemispheres in the recruitment of DLPFC (BA 46/9) and IFC (BA 47/11), but similar recruitment of Broca’s area (BA 44/45). In particular, in the monolingual context, bilinguals showed greater signal intensity in channels maximally overlaying DLPFC and IFC regions as compared to monolinguals. In the bilingual context, bilinguals demonstrated a more robust recruitment of right DLPFC and right IFC. These findings reveal how extensive early bilingual exposure modifies language organization in the brain—thus imparting a possible “bilingual signature.” They further shed fascinating new light on how the bilingual brain may reveal the biological extent of the neural architecture underlying all human language and the language processing potential not fully recruited in the monolingual brain.


title: Changes in use and perception of facebook

abstract: As social computing systems persist over time, the user experiences and interactions they support may change. One type of social computing system, Social Network Sites (SNSs), are becoming more popular across broad segments of Internet users. Facebook, in particular, has very broad participation amongst college attendees, and has been growing in other populations as well. This paper looks at how use of Facebook has changed over time, as indicated by three consecutive years of survey data and interviews with a subset of survey respondents. Reported uses of the site remain relatively constant over time, but the perceived audience for user profiles and attitudes about the site show differences over the study period.


Aggression and Violent Behavior Bullying and the peer group : A review


title: SCMon: Leveraging segment routing to improve network monitoring

abstract: To guarantee correct operation of their networks, operators have to promptly detect and diagnose data-plane issues, like broken interface cards or link failures. Networks are becoming more complex, with a growing number of Equal Cost MultiPath (ECMP) and link bundles. Hence, some data-plane problems (e.g. silent packet dropping at one router) can hardly be detected with control-plane protocols or simple monitoring tools like ping or traceroute. In this paper, we propose a new technique, called SCMon, that enables continuous monitoring of the data-plane, in order to track the health of all routers and links. SCMon leverages the recently proposed Segment Routing (SR) architecture to monitor the entire network with a single box (and no additional monitoring protocol). In particular, SCMon uses SR to (i) force monitoring probes to travel over cycles; and (ii) test parallel links and bundles at a per-link granularity. We present original algorithms to compute cycles that cover all network links with a limited number of SR segments. Further, we prototype and evaluate SCMon both with simulations and Linux-based emulations. Our experiments show that SCMon quickly detects and precisely pinpoints data-plane problems, with a limited overhead.


title: Botanical compounds and their regulation of nuclear receptor action: The case of traditional Chinese medicine

abstract: Abstract Nuclear receptors (NRs) are major pharmacological targets that allow an access to the mechanisms controlling gene regulation. As such, some NRs were identified as biological targets of active compounds contained in herbal remedies found in traditional medicines. We aim here to review this expanding literature by focusing on the informative articles regarding the mechanisms of action of traditional Chinese medicines (TCMs). We exemplified well-characterized TCM action mediated by NR such as steroid receptors (ER, GR, AR), metabolic receptors (PPAR, LXR, FXR, PXR, CAR) and RXR. We also provided, when possible, examples from other traditional medicines. From these, we draw a parallel between TCMs and phytoestrogens or endocrine disrupting chemicals also acting via NR. We define common principle of action and highlight the potential and limits of those compounds. TCMs, by finely tuning physiological reactions in positive and negative manners, could act, in a subtle but efficient way, on NR sensors and their transcriptional network.


The evolution of connectivity: Pruning neural networks using genetic algorithms


title: Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures

abstract: In recent years, Brain-Computer Interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats.


title: Modular sensor system (MSS) for urban air pollution monitoring

abstract: Air pollution attracts extensive attention globally due to its critical impacts on human life. Monitoring systems providing real-time micro-level pollution information have been developed to provide authorities with data to mitigate these impacts. However, current systems are usually application-specific with fixed hardware and software configurations. They are inconvenient in maintenance, infeasible in reconfiguration, and unexpandable in sensing capabilities. This paper proposes a novel Modular Sensor System (MSS), which aims at tackling these issues by adopting the proposed Universal Sensor Interface (USI) and modular design in a sensor node. A compact MSS senor node with expandable plug-and-play sensor modules and multiple Wireless Sensor Networks (WSNs) compatibility is implemented and evaluated. Results indicate that MSS sensor node can be deployed in different scenarios while dynamically adapting to reconfigurations and monitoring air pollution at low concentration levels with high energy efficiency. We anticipate that MSS is able to relax the efforts on system maintenance, adaptation, and evolution in real-life large-scale deployment situation.


title: A Dataset of Online Handwritten Assamese Characters

abstract: This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.


The effects of dark chocolate on lipid profile, apo-lipoprotein A-1, apo-lipoprotein B and inflammation in type-2 diabetic patients: A randomized clinical trial


title: Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

abstract: Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.


title: Battery optimization in smartphones for remote health monitoring systems to enhance user adherence

abstract: Remote health monitoring (RHM) can help save the cost burden of unhealthy lifestyles. Of increased popularity is the use of smartphones to collect data, measure physical activity, and provide coaching and feedback to users. One challenge with this method is to improve adherence to prescribed medical regimens. In this paper we present a new battery optimization method that increases the battery lifetime of smartphones which monitor physical activity. We designed a system, WANDA-CVD, to test our battery optimization method. The focus of this report describes our in-lab pilot study and a study aimed at reducing cardiovascular disease (CVD) in young women, the Women's Heart Health study. Conclusively, our battery optimization technique improved battery lifetime by 300%. This method also increased participant adherence to the remote health monitoring system in the Women's Heart Health study by 53%.


title: Why People Continue to Use Social Networking Services: Developing a Comprehensive Model

abstract: Social networking (SNW) services such as Facebook and MySpace are growing exponentially. While many people are spending an increasing amount of their time on the services, others use them minimally or discontinue use after a short period of time. This research is asking the question: What are the salient factors influencing individuals to continue using and extending the range of SNW services after their initial acceptance? This research recognizes that long-term viability and the eventual success of these services depends on continued usage rather than initial acceptance, and usage continuance of SNW services at the individual level is fundamental to the survival of many social technology-empowered businesses and organizations. We look to the Expectation-Confirmation Model of information systems (IS) continuance and a series of social theories as the underlying theoretical foundations. We develop the Usage Continuance Model of SNW Services to investigate continued usage behavior and enduring impacts of SNW services. The model proposes that usage continuance behavior of SNW services is a joint function of individuals’ perceptions of (1) intrinsic flow experience of SNW services, expected instrumentality of SNW services in managing and improving informational and relational values, and social influence as the outgrowth of social capital, and (2) costs in informational risks and participative efforts of time and social exchanges. The joint function is moderated by individuals’ use history of SNW features. The proposed model and hypotheses offer a comprehensive framework for empirically extending the IS continuance research to the ever pervasive SNW context.


title: Synthesizing Filamentary Structured Images with GANs

abstract: This paper aims at synthesizing filamentary structured images such as retinal fundus images and neuronal images, as follows: Given a ground-truth, to generate multiple realistic looking phantoms. A ground-truth could be a binary segmentation map containing the filamentary structured morphology, while the synthesized output image is of the same size as the ground-truth and has similar visual appearance to what have been presented in the training set. Our approach is inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer. In particular, it is dedicated to our problem context with the following properties: Rather than large-scale dataset, it works well in the presence of as few as 10 training examples, which is common in medical image analysis; It is capable of synthesizing diverse images from the same ground-truth; Last and importantly, the synthetic images produced by our approach are demonstrated to be useful in boosting image analysis performance. Empirical examination over various benchmarks of fundus and neuronal images demonstrate the advantages of the proposed approach.


title: Convolutional neural networks applied to house numbers digit classification

abstract: We classify digits of real-world house numbers using convolutional neural networks (ConvNets). Con-vNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 95.10% accuracy on the SVHN dataset (48% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.


With Severe Phenotype Succinic Semialdehyde Dehydrogenase Deficiency in a Chinese Boy : A Novel ALDH 5 A 1 Mutation


title: IT resources, organizational capabilities, and value creation in public-sector organizations: a public-value management perspective

abstract: What value does information technology (IT) create in governments and how does it do so? While business value of IT has been extensively studied in the information systems field, this has not been the case for public value. This is in part due to a lack of theoretical bases for investigating IT value in the public sector. To address this issue, we present a conceptual model on the mechanism by which IT resources contribute to value creation in the public-sector organizations. We propose that the relationship between IT resources and organizational performance in governments is mediated by organizational capabilities and develop a theoretical model that delineates the paths from IT resources to organizational performance, drawing upon public-value management theory. This theory asserts that public managers, on behalf of the public, should actively strive to generate greater public value, as managers in the private sector seek to achieve greater private business value. On the basis of the review of public-value management literature, we suggest that the following five organizational capabilities mediate the relationship between IT resources and public value – public service delivery capability, public engagement capability, co-production capability, resource-building capability, and public-sector innovation capability. We argue that IT resources in public organizations can enable public managers to advance public-value frontiers by cultivating these five organizational capabilities and to overcome conflicts among competing values.


title: OCR performance prediction using cross-OCR alignment

abstract: Since 2006 the national library of France (BnF) has developed many mass digitization projects on its collections. The indexation of digital documents on Gallica (the digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition software (OCR). The modern technologies of OCR achieve good performances on modern documents produced with uniform layout and known fonts. However, for old documents, OCR results are of lower quality. The OCR quality assessment is a real challenge for the BnF. On the one hand, due to the sequential architecture of OCR treatments, the identification of OCR errors sources is intractable. On the other hand, besides the word confidence, no additional quality information is reported in OCR outputs. In this paper, we present a study on OCR performance estimation aiming to control the quality of word transcriptions achieved by OCR. This quality assessment process has to operate without any comparison with ground truthed data. In this respect, our methodology relies on cross alignment of the OCR results with those of a secondary OCR called reference OCR. This secondary OCR provides uncertain but useful information that will be used as uncertain groundtruth. OCR performance is estimated using support vector regression. This predictor uses some global features computed on the cross-alignment results. The experimentations reported show that our estimate describes more faithfully the quality of OCR outputs than average word confidence scores that are computed by OCR. The proposed methodology can be adapted easily to various corpora by tuning the system using a training dataset of documents that have similar properties to those to be treated.


title: Uncovering Crowdsourced Manipulation of Online Reviews

abstract: Online reviews are a cornerstone of consumer decision making. However, their authenticity and quality has proven hard to control, especially as polluters target these reviews toward promoting products or in degrading competitors. In a troubling direction, the widespread growth of crowdsourcing platforms like Mechanical Turk has created a large-scale, potentially difficult-to-detect workforce of malicious review writers. Hence, this paper tackles the challenge of uncovering crowdsourced manipulation of online reviews through a three-part effort: (i) First, we propose a novel sampling method for identifying products that have been targeted for manipulation and a seed set of deceptive reviewers who have been enlisted through crowdsourcing platforms. (ii) Second, we augment this base set of deceptive reviewers through a reviewer-reviewer graph clustering approach based on a Markov Random Field where we define individual potentials (of single reviewers) and pair potentials (between two reviewers). (iii) Finally, we embed the results of this probabilistic model into a classification framework for detecting crowd-manipulated reviews. We find that the proposed approach achieves up to 0.96 AUC, outperforming both traditional detection methods and a SimRank-based alternative clustering approach.


title: ResBinNet: Residual Binary Neural Network

abstract: Recent efforts on training light-weight binary neural networks offer promising execution/memory efficiency. This paper introduces ResBinNet, which is a composition of two interlinked methodologies aiming to address the slow convergence speed and limited accuracy of binary convolutional neural networks. The first method, called residual binarization, learns a multi-level binary representation for the features within a certain neural network layer. The second method, called temperature adjustment, gradually binarizes the weights of a particular layer. The two methods jointly learn a set of soft-binarized parameters that improve the convergence rate and accuracy of binary neural networks. We corroborate the applicability and scalability of ResBinNet by implementing a prototype hardware accelerator. The accelerator is reconfigurable in terms of the numerical precision of the binarized features, offering a trade-off between runtime and inference accuracy.


title: Data mining techniques and applications — A decade review

abstract: Data mining is also known as Knowledge Discovery in Database (KDD). It is also defined as the process which includes extracting the interesting, interpretable and useful information from the raw data. There are different sources that generate raw data in very large amount. This is the main reason the applications of data mining are increasing rapidly. This paper reviews data mining techniques and its applications such as educational data mining (EDM), finance, commerce, life sciences and medical etc. We group existing approaches to determine how the data mining can be used in different fields. Our categorization specifically focuses on the research that has been published over the period 2007–2017. With this categorization, we present an easy and concise view of different models adapted in the data mining.