title: Non-negative matrix factorization under heavy noise

abstract: The Noisy Non-negative Matrix factorization (NMF) is: given a data matrix A (d × n), find non-negative matrices B, C (d × k, k × n respy.) so that A = BC + N, where N is a noise matrix. Existing polynomial time algorithms with proven error guarantees require each column N.,j to have l1 norm much smaller than ||(BC).,j||1, which could be very restrictive. In important applications of NMF such as Topic Modeling as well as theoretical noise models (eg. Gaussian with high σ), almost every column of N.j violates this condition. We introduce the heavy noise model which only requires the average noise over large subsets of columns to be small. We initiate a study of Noisy NMF under the heavy noise model. We show that our noise model subsumes noise models of theoretical and practical interest (for eg. Gaussian noise of maximum possible σ). We then devise an algorithm TSVDNMF which under certain assumptions on B,C, solves the problem under heavy noise. Our error guarantees match those of previous algorithms. Our running time of O((n + d)2k) is substantially better than the O(n3d) for the previous best. Our assumption on B is weaker than the "Separability" assumption made by all previous results. We provide empirical justification for our assumptions on C. We also provide the first proof of identifiability (uniqueness of B) for noisy NMF which is not based on separability and does not use hard to check geometric conditions. Our algorithm outperforms earlier polynomial time algorithms both in time and error, particularly in the presence of high noise.


title: Identifying Emergent Dynamical Structures in Network Models

abstract: The identification of emergent structures in dynamical systems is a major challenge in complex systems science. In particular, the formation of intermediate-level dynamical structures is of particular interest for what concerns biological as well as artificial network models. In this work, we present a new technique aimed at identifying clusters of nodes in a network that behave in a coherent and coordinated way and that loosely interact with the remainder of the system. This method is based on an extension of a measure introduced for detecting clusters in biological neural networks. Even if our results are still preliminary, we have evidence for showing that our approach is able to identify these “emerging things” in some artificial network models and that it is way more powerful than usual measures based on statistical correlation. This method will make it possible to identify mesolevel dynamical structures in network models in general, from biological to social networks.


title: Supply chain scheduling to minimize holding costs with outsourcing

abstract: This paper addresses a scheduling problem in a flexible supply chain, in which the jobs can be either processed in house, or outsourced to a third-party supplier. The goal is to minimize the sum of holding and delivery costs. This problem is proved to be strongly $\mathcal{NP}$ -hard. Consider two special cases, in which the jobs have identical processing times. For the problem with limited outsourcing budgets, a $\mathcal{NP}$ -hardness proof, a pseudo-polynomial algorithm and a fully polynomial time approximation scheme are presented. For the problem with unlimited outsourcing budgets, the problem is shown to be equivalent to the shortest path problem, and therefore it is in class $\mathcal{P}$ . This shortest-path-problem solution approach is further shown to be applicable to a similar but more applicable problem, in which the number of deliveries is upper bounded. Copyright Springer Science+Business Media New York 2014


title: Predictive phase locked loop for sensorless control of PMSG based variable-speed wind turbines

abstract: In this work, a new predictive phase-locked loop (PLL) for encoderless control of a permanent-magnet synchronous generator (PMSG) in a variable-speed wind energy conversion system (WECS) is presented. The idea of the predictive PLL is derived from the direct-model predictive control (DMPC) principle. The predictive PLL uses a limited (discretized) number of rotor-angles for predicting/estimating the back-electromotive-force (BEMF) of the PMSG. subsequently, that predicted angle, which optimizes a pre-defined quality function, is chosen to become the best rotor-angle/position. Accordingly, the fixed gain proportional integral (FGPI) regulator that is normally used in PLLs is eliminated. The performance of the predictive PLL is validated experimentally and compared with that of the traditional one under various operating scenarios and under variations of the PMSG parameters.


Cold Hard E-Cash: Friends and Vendors in the Venmo Digital Payments System.


title: Dealing with betrayal in close relationships: Does commitment promote forgiveness

abstract: This work complements existing research regarding the forgiveness process by highlighting the role of commitment in motivating forgiveness. On the basis of an interdependence–theoretic analysis, the authors suggest that (a) victims’ self-oriented reactions to betrayal are antithetical to forgiveness, favoring impulses such as grudge and vengeance, and (b) forgiveness rests on prorelationship motivation, one cause of which is strong commitment. A priming experiment, a cross-sectional survey study, and an interaction record study revealed evidence of associations (or causal effects) of commitment with forgiveness. The commitment–forgiveness association appearred to rest on intent to persist rather than long-term orientation or psychological attachment. In addition, the commitment–forgiveness association was mediated by cognitive interpretations of betrayal incidents; evidence for mediation by emotional reactions was inconsistent. Although close partners often treat one another in a positive and considerate manner, negative interactions are nearly inevitable. Such interactions result from a variety of causes, including incompatible preferences, external sources of stress, and extrarelationship temptation. We suggest that the violation of a relationshiprelevant norm— or betrayal of one’s partner— constitutes one of the more serious threats to a relationship, and we propose that the resolution of betrayal incidents is not easy. Indeed, forgiveness of betrayal arguably is one of the more difficult tasks in an ongoing relationship. The film The War of the Rosesdarkly and humorously illustrates the complexities of betrayal and forgiveness: The marriage of Oliver and Barbara Rose is marked by repeated betrayal. Oliver belittles Barbara’s career as a chef. Barbara fails to support Oliver during a frightening health crisis. Each humiliates the other, delivering impossible-to-forget attacks on the other’s tastes and habits. Cumulatively, such incidents reduce their once-loving marriage to a sequence of vicious, increasingly deadly battles. During their marital Armageddon, the two find themselves entangled in a chandelier suspended above a hallway. The mechanism supporting the chandelier gives way, and— embraced in the arms of the chandelier—the two crash to the unyielding terazzo floor 30 feet below. With his dying breath, Oliver reaches out to touch Barbara’s shoulder, offering amends and seeking forgiveness. Barbara’s hand slowly rises to meet Oliver’s (perhaps, one imagines, to reciprocate Oliver’s act), and with her dying breath, Barbara flings Oliver’s hand away from her. Again, forgiveness of betrayal arguably is one of the more difficult tasks in an ongoing relationship.


title: Challenges in real-time virtualization and predictable cloud computing

abstract: Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.


title: Lock Your Robot: A Review of Locking Devices in Robotics

abstract: Locking devices are widely used in robotics, for instance to lock springs and joints or to reconfigure robots. This review article classifies the locking devices currently described in the literature and performs a comparative study. Designers can therefore better determine which locking device best matches the needs of their application. The locking devices are divided into three main categories based on different locking principles: 1) mechanical locking, 2) friction-based locking, and 3) singularity locking. Different locking devices in each category can be passive or active. Based on an extensive literature survey, this article summarizes the findings by comparing different locking devices on a set of properties of an ideal locking device.


title: Analysis of Unintentional Insider Threats Deriving from Social Engineering Exploits

abstract: Organizations often suffer harm from individuals who bear no malice against them but whose actions unintentionally expose the organizations to risk-the unintentional insider threat (UIT). In this paper we examine UIT cases that derive from social engineering exploits. We report on our efforts to collect and analyze data from UIT social engineering incidents to identify possible behavioral and technical patterns and to inform future research and development of UIT mitigation strategies.


title: Distributed medical image retrieval techniques: A review

abstract: Medical image retrieval is a core process of illness diagnosis and treatment procedure in healthcare area today. The new medical image scanner produces a large number of images with high resolution in 2D and 3D. The main concern in this paper is to clear out that medical image retrieval is one of the hot areas even after long time of its existence, because of the implementation of new technologies such as cloud. The review is based on the platform that used in the medical image retrieval, cloud, distributed system, GRID and peer-to-peer. After covering a number of researches in this area, it is pointed out that cloud computing as a platform is the direction point of researchers in medical image retrieval. Additionally, enhancing the response time of the medical image retrieval is the main issue that has been motivating researchers to provide new techniques and algorithms.


title: Survey on Feature Selection for Data Reduction

abstract: The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, which deals with the reduction of attributes from the original data without affecting the main information content. Based on the training data used for different applications of knowledge discovery, FS technique falls into supervised, unsupervised. In this paper an extensive survey on supervised FS technique describing the different searching approach, methods and application areas with an outline of a comparative study is covered.


title: Improving tracking accuracy using information of dissimilar sensors

abstract: Making use of information acquired from a sensor network to improve the accuracy of target tracking is one of the most important issues in sensor network research. This paper demonstrates this philosophy using a distributed dissimilar sensor fusion scenario, where a tracker was established and maintained by a surface radar sensor and the distributed sensor fusion is performed whenever target measurement from an angle-only sensor is available to the radar sensor. The target state information is extracted from the angle-only sensor measurement so that the distributed track fusion at radar sensor can be performed. The extended Kalman filters (EKF) have been used to implement all tracking functions due to the nonlinearity between target state and the associated sensor observations. The scenario is conveniently implemented using advanced radar tracking system (ARTS) toolbox in Matlab Simulink environment. Our simulation results have shown the improvement of the (racking accuracy bv applying distributed track fusion. The convenience of using ARTS toolbox for complex algorithm implementation and testing are also clear from the context.


title: The architecture development for the interoperability between BIM and GIS

abstract: The purpose of this study is to propose a scalable architecture that can be supported via the BIM (Building Information Modeling) on a GIS (Geographic Information System) platform for information interoperability between various heterogeneous systems such as BIM, GIS, and FM (Facility Management). This platform requires the acquisition of information from various data sources such as IFC (Industry Foundation Classes) and DBMS (Database Management System) if a use case for facility management, energy management, and design evaluation needs to be implemented, followed by a transformation of the information into appropriate information that can represent the perspective suitable for the use case. IFC may be considered a method for information interoperability, but it has a limitation in representing information in the perspectives of the use cases. Unlike the support of information interoperability based on the existing IFC, we would like to approach the problem of GIS- and BIM-based information interoperability by separating the problem in terms of geometry and property information. The geometry information is transformed into a simplified surface model from the IFC geometry information for that visualization of a number of objects represented in the GIS. The information required for the use case perspective is extracted and transformed from the property information by utilizing ETL (Extraction, Transform, and Load). ETL is a technology that extracts, transforms, and loads information from a variety of data sources and has been used for OLAP (Online Analytical Processing) function implementation via data mining in the management engineering arena to represent perspective-oriented information. In this study, we have applied ETL from the perspective of BIM. For this purpose, we have reviewed the related study trends and derived a general use case of BIM on a GIS platform. Further, component architecture is designed to implement the use case as well as a Star Schema to represent information according to the perspective for the development of a data warehouse. On the basis of these, BGP architecture is proposed for implementing the ETL concept using BIM on a GIS platform. Furthermore, a use case for the facility management of Korea Institute of Construction Technology is implemented as a prototype to show the usefulness of the proposed architecture. Thus, in this study, we demonstrate that information required from the perspective of project stakeholders can be interoperated effectively.


title: There are differences in cerebral activation between females in distinct menstrual phases during viewing of erotic stimuli: a fMRI study

abstract: There is evidence that men experience more sexual arousal than women but also that women in mid-luteal phase experience more sexual arousal than women outside this phase. Recently, a few functional brain imaging studies have tackled the issue of gender differences as pertaining to reactions to erotica. The question of whether or not gender differences in reactions to erotica are maintained with women in different phases has not yet been answered from a functional brain imaging perspective. In order to examine this issue, functional MRI was performed in 22 male and 22 female volunteers. Subjects viewed erotic film excerpts alternating with emotionally neutral excerpts in a standard block-design paradigm. Arousal to erotic stimuli was evaluated using standard rating scales after scanning. Two-sample t-test with uncorrected P<0.001 values for a priori determined region of interests involved in processing of erotic stimuli and with corrected P<0.05 revealed gender differences: Comparing women in mid-luteal phase and during their menses, superior activation was revealed for women in mid-luteal phase in the anterior cingulate, left insula, and orbitofrontal cortex. A superior activation for men was found in the left thalamus, the bilateral amygdala, the anterior cingulate, the bilateral orbitofrontal, bilateral parahippocampal, and insular regions, which were maintained at a corrected P in the amygdala, the insula, and thalamus. There were no areas of significant superior activation for women neither in mid-luteal phase nor during their menses. Our results indicate that there are differences between women in the two cycle times in cerebral activity during viewing of erotic stimuli. Furthermore, gender differences with women in mid-luteal phases are similar to those in females outside the mid-luteal phase.


title: Composite Quantization

abstract: This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several ($M$M) elements, each of which is selected from a different dictionary, to accurately approximate a $D$D-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to $O(M)$O(M) from $O(D)$O(D) through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, inner-product similarity search, and query compression for mobile search.


title: Coalitional Bargaining with Agent Type Uncertainty

abstract: Coalition formation is a problem of great interest in AI, allowing groups of autonomous, individually rational agents to form stable teams. Automating the negotiations underlying coalition formation is, naturally, of special concern. However, research to date in both AI and economics has largely ignored the potential presence of uncertainty in coalitional bargaining. We present a model of discounted coalitional bargaining where agents are uncertain about the types (or capabilities) of potential partners, and hence the value of a coalition. We cast the problem as a Bayesian game in extensive form, and describe its Perfect Bayesian Equilibria as the solutions to a polynomial program. We then present a heuristic algorithm using iterative coalition formation to approximate the optimal solution, and evaluate its performance.


title: The Noncoding RNA Revolution—Trashing Old Rules to Forge New Ones

abstract: Noncoding RNAs (ncRNAs) accomplish a remarkable variety of biological functions. They regulate gene expression at the levels of transcription, RNA processing, and translation. They protect genomes from foreign nucleic acids. They can guide DNA synthesis or genome rearrangement. For ribozymes and riboswitches, the RNA structure itself provides the biological function, but most ncRNAs operate as RNA-protein complexes, including ribosomes, snRNPs, snoRNPs, telomerase, microRNAs, and long ncRNAs. Many, though not all, ncRNAs exploit the power of base pairing to selectively bind and act on other nucleic acids. Here, we describe the pathway of ncRNA research, where every established "rule" seems destined to be overturned.


title: A Gradient-Based Boosting Algorithm for Regression Problems

abstract: In adaptive boosting, several weak learners trained sequentially are combined to boost the overall algorithm performance. Recently adaptive boosting methods for classification problems have been derived as gradient descent algorithms. This formulation justifies key elements and parameters in the methods, all chosen to optimize a single common objective function. We propose an analogous formulation for adaptive boosting of regression problems, utilizing a novel objective function that leads to a simple boosting algorithm. We prove that this method reduces training error, and compare its performance to other regression methods.


title: 94 GHz Dual-Reflector Antenna With Reflectarray Subreflector

abstract: The design, construction and measured performance is described of an offset parabolic reflector antenna which employs a reflectarray subreflector to tilt the focused beam from the boresight direction at 94 GHz. An analysis technique based on the method of moments (MoM) is used to design the dual-reflector antenna. Numerical simulations were employed to demonstrate that the high gain pattern of the antenna can be tilted to a predetermined angle by introducing a progressive phase shift across the aperture of the reflectarray. Experimental validation of the approach was made by constructing a 28 times 28 element patch reflectarray which was designed to deflect the beam 5deg from the boresight direction in the azimuth plane. The array was printed on a 115 mum thick metal backed quartz wafer and the radiation patterns of the dual reflector antenna were measured from 92.6-95.5 GHz. The experimental results are used to validate the analysis technique by comparing the radiation patterns and the reduction in the peak gain due to beam deflection from the boresight direction. Moreover the results demonstrate that this design concept can be developed further to create an electronically scanned dual reflector antenna by using a tunable reflectarray subreflector.


title: On the Semantics and Pragmatics of Linguistic Feedback

abstract: This paper is an exploration in the semantics and pragmatics of linguistic feedback, i.e., linguistic mechanisms which enable the participants in spoken interaction to exchange information about basic communicative functions, such as contact, perception, understanding, and attitudinal reactions to the communicated content. Special attention is given to the type of reaction conveyed by feedback utterances, the communicative status of the information conveyed (i. e., the level of awareness and intentionality of the communicating sender), and the context sensitivity of feedback expressions. With regard to context sensitivity, which is one of the most characteristic features of feedback expressions, the discussion focuses on the way in which the type of speech act (mood), the factual polarity and the information status of the preceding utterance influence the interpretation of feedback utterances. The different content dimensions are exemplified by data from recorded dialogues and by data given through linguistic intuition. Finally, two different ways of formalizing the analysis are examined, one using attribute-value matrices and one based on the theory of situation semantics.


title: A Single-Stage AC/DC LLC Resonant Converter

abstract: A single-stage ac/dc LLC resonant converter is proposed to draw a sinusoidal line current, for achieving power factor correction (PFC). The proposed ac/dc converter composes of a boost-PFC semi-stage and a LLC-SRC semi-stage. The boost-PFC semi-stage, working in discontinuous conduction mode (DCM) functions as a power factor corrector, and the LLC-SRC semi-stage operates above the resonance to achieve zero-voltage switching (ZVS) operation. The proposed converter has the feature of variable-frequency control, which provides a constant output voltage, high power factor, high efficiency, and low component count. Finally, the experimental results are provided to verify the performance as expected.


title: Address obfuscation: an efficient approach to combat a broad range of memory error exploits

abstract: Attacks which exploit memory programming errors (such as buffer overflows) are one of today's most serious security threats. These attacks require an attacker to have an in-depth understanding of the internal details of a victim program, including the locations of critical data and/or code. Program obfuscation is a general technique for securing programs by making it difficult for attackers to acquire such a detailed understanding. This paper develops a systematic study of a particular kind of obfuscation called address obfuscation that randomizes the location of victim program data and code. We discuss different implementation strategies to randomize the absolute locations of data and code, as well as relative distances between data locations. We then present our implementation that transforms object files and executables at link-time and load-time. It requires no changes to the OS kernel or compilers, and can be applied to individual applications without affecting the rest of the system. It can be implemented with low runtime overheads. Address obfuscation can reduce the probability of successful attacks to be as low as a small fraction of a percent for most memory-error related attacks. Moreover, the randomization ensures that an attack that succeeds against one victim will likely not succeed against another victim, or even for a second time against the same victim. Each failed attempt will typically crash the victim program, thereby making it easy to detect attack attempts. These aspects make it particularly effective against large-scale attacks such as Code Red, since each infection attempt requires significantly more resources, thereby slowing down the propagation rate of such attacks.


title: Effective Spell Checking Methods Using Clustering Algorithms

abstract: This paper presents a novel approach to spell checking using dictionary clustering. The main goal is to reduce the number of times distances have to be calculated when finding target words for misspellings. The method is unsupervised and combines the application of anomalous pattern initialization and partition around medoids (PAM). To evaluate the method, we used an English misspelling list compiled using real examples extracted from the Birkbeck spelling error corpus.


A Partitioning Algorithm for Markov Decision Processes with Applications to Market Microstructure


title: Simulating Search Engines

abstract: A simulation methodology is proposed to evaluate the performance of large-scale Web search engines hosted by datacenters. The salient features of the methodology are the use of models of parallel computing to overcome the complexities associated with the simulation of hardware and system software details; a circulating tokens approach to represent sequences of operations that compete for search engine resources; benchmark programs to measure the cost of relevant operations; and simulations driven by real user traces to consider the dynamics of user behavior. An experimental evaluation of the methodology, which ranges from clusters of processors to single multithreaded processors, shows that it can generate respective simulation programs capable of predicting performance in a precise and efficient manner.


title: A Brief Survey on Image Segmentation Methods

abstract: Term segmentation is about splitting the whole image into segments. In case of image analysis, image processing one of the crucial steps is segmentation of image. Segmentation of image concern about dividing entire image in sub parts that may be similar or dissimilar with respect to features. Output of image segmentation has consequence on analysis of image, further processing of image. Analysis of image comprises depiction of object and object representation, measurement of feature. Therefore characterization, area of interest’s visualization in the image, description have crucial job in segmentation of image. This survey explains some methods of image segmentation.


title: Co-Existence Study Between Analog TV (PAL-I) and LTE in Digital Dividend Band: South African Case Study

abstract: Due to spectrum demand for mobile communication services, many studies has considered the impact of coexistence of two services in the same band. This paper presents a coexistence study between analog television (ATV) frequency using PAL-I systems and Long Term Evolution (LTE) in the digital dividend frequency band using South Africa as a case study. Four different scenarios between ATV and LTE are presented and simulated in SEAMCAT. The ATV as the primary user of the band while DTV is a secondary user. The results obtained from the simulation shows that the ATV and LTE can coexist.


title: Latent Space Optimal Transport for Generative Models

abstract: Variational Auto-Encoders enforce their learned intermediate latent-space data distribution to be a simple distribution, such as an isotropic Gaussian. However, this causes the posterior collapse problem and loses manifold structure which can be important for datasets such as facial images. A GAN can transform a simple distribution to a latent-space data distribution and thus preserve the manifold structure, but optimizing a GAN involves solving a Min-Max optimization problem, which is difficult and not well understood so far. Therefore, we propose a GAN-like method to transform a simple distribution to a data distribution in the latent space by solving only a minimization problem. This minimization problem comes from training a discriminator between a simple distribution and a latent-space data distribution. Then, we can explicitly formulate an Optimal Transport (OT) problem that computes the desired mapping between the two distributions. This means that we can transform a distribution without solving the difficult Min-Max optimization problem. Experimental results on an eight-Gaussian dataset show that the proposed OT can handle multi-cluster distributions. Results on the MNIST and the CelebA datasets validate the effectiveness of the proposed method.


title: Boosting cross-age face verification via generative age normalization

abstract: Despite the tremendous progress in face verification performance as a result of Deep Learning, the sensitivity to human age variations remains an Achilles' heel of the majority of the contemporary face verification software. A promising solution to this problem consists in synthetic aging/rejuvenation of the input face images to some predefined age categories prior to face verification. We recently proposed [3] Age-cGAN aging/rejuvenation method based on generative adversarial neural networks allowing to synthesize more plausible and realistic faces than alternative non-generative methods. However, in this work, we show that Age-cGAN cannot be directly used for improving face verification due to its slightly imperfect preservation of the original identities in aged/rejuvenated faces. We therefore propose Local Manifold Adaptation (LMA) approach which resolves the stated issue of Age-cGAN resulting in the novel Age-cGAN+LMA aging/rejuvenation method. Based on Age-cGAN+LMA, we design an age normalization algorithm which boosts the accuracy of an off-the-shelf face verification software in the cross-age evaluation scenario.


title: Quantitative and Qualitative Evaluation of Performance and Robustness of Image Stitching Algorithms

abstract: Many different image stitching algorithms, and mechanisms to assess their quality have been proposed by different research groups in the past decade. However, a comparison across different stitching algorithms and evaluation mechanisms has not been performed before. Our objective is to recognize the best algorithm for panoramic image stitching. We measure the robustness of different algorithms by means of assessing image quality of a set of panoramas. For the evaluation itself a varied set of assessment criteria are used, and the evaluation is performed over a large range of images captured using differing cameras. In an ideal stitching algorithm, the resulting stitched image should be without visible seams and other noticeable anomalies. An objective evaluation for image quality should give results corresponding to a similar evaluation by the Human Visual System. Our conclusion is that the choice of stitching algorithm is scenario dependent, with run-time and accuracy being the primary considerations.


title: Botnet: Survey and Case Study

abstract: Botnet is a hybrid of previous threats integrated with a command and control system and hundreds of millions of computers are infected. Although botnets are widespread development, the research and solutions for botnets are not mature. In this paper, we present an overview of research on botnets. We discuss in detail the botnet and related research including infection mechanism, botnet malicious behavior, command and control models, communication protocols, botnet detection, and botnet defense. We also present a simple case study of IRC-based SpyBot.


title: Effective explanations of recommendations: user-centered design

abstract: This paper characterizes general properties of useful, or Effective, explanations of recommendations. It describes a methodology based on focus groups, in which we elicit what helps moviegoers decide whether or not they would like a movie. Our results highlight the importance of personalizing explanations to the individual user, as well as considering the source of recommendations, user mood, the effects of group viewing, and the effect of explanations on user expectations.


title: Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering

abstract: Motivation: Imaging mass spectrometry (IMS) is one of the few measurement technology s of biochemistry which, given a thin sample, is able to reveal its spatial chemical composition in the full molecular range. IMS produces a hyperspectral image, where for each pixel a high-dimensional mass spectrum is measured. Currently, the technology is mature enough and one of the major problems preventing its spreading is the under-development of computational methods for mining huge IMS datasets. This article proposes a novel approach for spatial segmentation of an IMS dataset, which is constructed considering the important issue of pixel-to-pixel variability. ::: ::: Methods: We segment pixels by clustering their mass spectra. Importantly, we incorporate spatial relations between pixels into clustering, so that pixels are clustered together with their neighbors. We propose two methods. One is non-adaptive, where pixel neighborhoods are selected in the same manner for all pixels. The second one respects the structure observable in the data. For a pixel, its neighborhood is defined taking into account similarity of its spectrum to the spectra of adjacent pixels. Both methods have the linear complexity and require linear memory space (in the number of spectra). ::: ::: Results: The proposed segmentation methods are evaluated on two IMS datasets: a rat brain section and a section of a neuroendocrine tumor. They discover anatomical structure, discriminate the tumor region and highlight functionally similar regions. Moreover, our methods provide segmentation maps of similar or better quality if compared to the other state-of-the-art methods, but outperform them in runtime and/or required memory. ::: ::: Contact: theodore@math.uni-bremen.de


title: The Benefit of Generating Errors During Learning

abstract: Testing has been found to be a powerful learning tool, but educators might be reluctant to make full use of its benefits for fear that any errors made would be harmful to learning. We asked whether testing could be beneficial to memory even during novel learning, when nearly all responses were errors, and where errors were unlikely to be related to either cues or targets. In 4 experiments, participants learned definitions for unfamiliar English words, or translations for foreign vocabulary, by generating a response and being given corrective feedback, by reading the word and its definition or translation, or by selecting from a choice of definitions or translations followed by feedback. In a final test of all words, generating errors followed by feedback led to significantly better memory for the correct definition or translation than either reading or making incorrect choices, suggesting that the benefits of generation are not restricted to correctly generated items. Even when information to be learned is novel, errorful generation may play a powerful role in potentiating encoding of corrective feedback. Experiments 2A, 2B, and 3 revealed, via metacognitive judgments of learning, that participants are strikingly unaware of this benefit, judging errorful generation to be a less effective encoding method than reading or incorrect choosing, when in fact it was better. Predictions reflected participants' subjective experience during learning. If subjective difficulty leads to more effort at encoding, this could at least partly explain the errorful generation advantage. (PsycINFO Database Record


title: An efficient leaf recognition algorithm for plant classification using support vector machine

abstract: Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machine learning approach for the classification purpose. This proposed approach consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase involves a typical image processing steps such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the common DMF from five fundamental features. The main contribution of this approach is the Support Vector Machine (SVM) classification for efficient leaf recognition. 12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the SVM. Classifier tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach produces very high accuracy and takes very less execution time.


Pattern analysis in stock markets optimized by genetic algorithms using modified SAX


title: Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control

abstract: In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system design choices. We decompose the end-to-end system into a vision module and a closed-loop controller module. The two modules use target object segmentation as their common interface. The vision module extracts information from the robot end-effector camera, in the form of a binary segmentation mask of the target. We train it to achieve effective domain transfer by composing real background images with simulated images of the target. The controller module takes as input the binary segmentation mask, and thus is agnostic to visual discrepancies between simulated and real environments. We train our closed-loop controller in simulation using imitation learning and show it is robust with respect to discrepancies between the dynamic model of the simulated and real robot: when combined with eye-in-hand observations, we achieve a 90% success rate in grasping a tiny sphere with a real robot. The controller can generalize to unseen scenarios where the target is moving and even learns to recover from failures.


title: A dynamic decoupling control algorithm for Halbach array permanent magnet spherical motor based on computed torque method

abstract: A Halbach array permanent magnet spherical motor (HPMSM) can provide 3 degrees-of-freedom motion in a single joint, simplify the mechanical structure greatly, improve the positioning precision and response speed. However, a HPMSM is a multivariable, nonlinear and strong coupling system with serious inter-axis nonlinear coupling. The dynamic model of a HPMSM is described in this paper, and a control algorithm based on computed torque method is proposed to realize the dynamic decoupling control of the HPMSM. Simulations results indicate that this algorithm can make the system track continues trajectory ideally and eliminate the influences of inter-axis nonlinear coupling effectively to achieve a good control performance.


title: Standard cell library based layout characterization and power analysis for 10nm gate-all-around (GAA) transistors

abstract: Gate-all-around (GAA) nanowire transistor is promising for continuing scaling down the feature size of transistors beyond sub-10nm because it provides the gate with better controllability over the channel by wrapping around. In this paper, the device model for 10nm gate length conventional GAA (C-GAA) and junctionless GAA (JL-GAA) are extracted based on the TCAD simulation. The layout design of GAA transistors are characterized for different sizing methods. Liberty-formatted standard cell libraries are constructed by appropriately sizing pull-up and pull-down networks of each logic cell. Based on the library, power densities of 10nm technology node C-GAA and JL-GAA are analyzed under benchmark circuits in comparing with 7nm FinFET technology. Experimental results show that the vertical C-GAA transistor can achieve 28% area reduction and the horizontal C-GAA transistor can reduce 29% power consumption comparing with other C-GAA geometries. The power density of JL-GAA circuits can reach above the limit of air cooling and thermal management techniques are needed for JL-GAA circuits.


title: Classification of cotton leaf spot diseases using image processing edge detection techniques

abstract: This Proposed Work exposes, a advance computing technology that has been developed to help the farmer to take superior decision about many aspects of crop development process. Suitable evaluation and diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian regions. In this work we express new technological strategies using mobile captured symptoms of cotton leaf spot images and categorize the diseases using HPCCDD Proposed Algorithm. The classifier is being trained to achieve intelligent farming, including early Identification of diseases in the groves, selective fungicide application, etc. This proposed work is based on Image RGB feature ranging techniques used to identify the diseases (using Ranging values) in which, the captured images are processed for enhancement first. Then color image segmentation is carried out to get target regions (disease spots). Next Homogenize techniques like Sobel and Canny filter are used to Identify the edges, these extracted edge features are used in classification to identify the disease spots. Finally, pest recommendation is given to the farmers to ensure their crop and reduce the yeildloss.


title: An Examination of Whether and How Racial and Gender Biases Influence Customer Satisfaction

abstract: We examined whether and how various biases may influence customers’ satisfaction evaluations and produce discriminatory judgments for minority and female service employees. We argue that customer satisfaction evaluations are biased because they are anonymous judgments by untrained raters that usually lack an evaluation standard. Laboratory and field samples provide disturbing evidence generally confirming our arguments and suggesting that the presence of nonwhite and women service employees may produce lower aggregated customer satisfaction evaluations that may ultimately hurt individuals and organizations financially.


title: Automatic generation of artistic chinese calligraphy

abstract: Chinese calligraphy is among the finest and most important of all Chinese art forms and an inseparable part of Chinese history. Its delicate aesthetic effects are generally considered to be unique among all calligraphic arts. Its subtle power is integral to traditional Chinese painting. A novel intelligent system uses a constraint-based analogous-reasoning process to automatically generate original Chinese calligraphy that meets visually aesthetic requirements. We propose an intelligent system that can automatically create novel, aesthetically appealing Chinese calligraphy from a few training examples of existing calligraphic styles. To demonstrate the proposed methodology's feasibility, we have implemented a prototype system that automatically generates new Chinese calligraphic art from a small training set.


title: A Composite Boyer-Moore Algorithm for the String Matching Problem

abstract: The string matching problem has found wide application in computer science, molecular biology, genetic engineering, semantics and many other fields. In this paper, we give analysis to several classical algorithms, KMP, BM and their improvements. Then, by compositing the main method of the BM algorithm, we propose a new algorithm — the Composite BM algorithm (CBM). Differing from the BM algorithm that only uses current matching information, the CBM algorithm tries to take full advantage of the historical matching information. In this way, CBM accelerates the forward speed of the pattern during the matching, and hence the whole string matching process is speeded up effectively. Finally, for binary matching, we made random test to BM and CBM, the result shown the efficiency of CBM is higher than of BM.


title: The effect of mobile retailing effect on consumption experiences: a dynamic perspective

abstract: The emerging retail culture is characterized by the extensive use of mobile technologies, high connectivity, ubiquitous computing and contactless technologies, which enable consumers to experience shopping differently. In fact, innovative mobile technologies provide new tools (apps) which are able to separate the moment of purchase from the moment of effective consumption, by allowing consumers to make purchases by mobile phone and collect them at home or at a store (a pick-up boutique or collection point), in addition to the traditional in-store service (purchase in the store and collect/consume in the store). The aim of this paper is to understand the extent to which mobile technologies have an impact on consumer behaviour, with emphasis on the drivers motivating consumers to adopt the consumer experience of mobile shopping. To achieve this goal we used a qualitative approach involving 29 consumers in the Italian market, where mobile shopping is still at an early stage. The findings shed a light on the extent to which consumers are moving from e-channels to mobile channels and take into account the effect of these technological innovations in retail settings from a cognitive standpoint, where studies are limited. The implications for researchers and practitioners are then discussed, with emphasis on retailers need to develop new mobile service competences, and integrate and synthetize physical retail settings with mobile opportunities and functionalities.


title: Early identification and interventions for dyslexia: a contemporary view

abstract: This paper reviews current proposals concerning the definition of dyslexia and contrasts it with reading comprehension impairment. We then discuss methods for early identification and review evidence that teacher assessments and ratings may be valid screening tools. Finally, we argue that interventions should be theoretically motivated and evidence based. We conclude that early identification of children at risk of dyslexia followed by the implementation of intervention is a realistic aim for practitioners and policy-makers.


title: Keystroke analysis of free text

abstract: Keystroke dynamics can be useful to ascertain personal identity even after an authentication phase has been passed, provided that we are able to deal with the typing rhythms of free text, chosen and entered by users without any specific constraint. In this paper we present a method to compare typing samples of free text that can be used to verify personal identity. We have tested our technique with a wide set of experiments on 205 individuals, obtaining a False Alarm Rate of less than 5p and an Impostor Pass Rate of less than 0.005p. Different trade-offs are, however, possible. Our approach can rely on what is typed by people because of their normal job, and a few lines of text, even collected in different working sessions, are sufficient to reach a high level of accuracy, which improves proportionally to the amount of available information: As a consequence, we argue that our method can be useful in computer security as a complementary or alternative way to user authentication and as an aid to intrusion detection.


title: Study on interior permanent magnet synchronous motors for hybrid electric vehicle traction drive application considering permanent magnet type and temperature

abstract: Recently, interior permanent magnet synchronous motors (Interior-PMSMs) have become known as a good candidate for hybrid electric vehicle (HEV) traction drive application due to their unique merits. However, the dynamic and steady-state behaviors of these motors are quite dependent on the permanent magnet (PM) type, conguration, and volume in rotor structures. This paper uses a novel structure of Interior-PMSMs for traction applications with fragmental buried rotor magnets in order to achieve low torque ripple, iron losses, and cogging torque. In this paper, rst, the eect of the PM type on a d-q equivalent circuit model is examined. Next, the design and simulation of an Interior-PMSM for HEV traction drive application, in order to extract the output values of the motor and sensitivity analysis of the PM type, are done using a 3-dimensional nite element method model. We then present the back electromotive force, power factor, cogging torque, ux density, torque per ampere diagram, PM volume, and constant power speed ratio value behavior of the designed Interior-PMSM with dierent PMs in the rotor structure, and we discuss the eect of temperature variation on these output parameters. This study can help designers in the design approach of such motors.


title: Music genre classification using explicit semantic analysis

abstract: Music genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem in the musical information retrieval community, has been gaining more attention with advances in the development of the digital music industry. Most current genre classification methods tend to be based on the extraction of short-time features in combination with high-level audio features to perform genre classification. However, the representation of short-time features, using time windows, in a semantic space has received little attention. This paper proposes a vector space model of mel-frequency cepstral coefficients (MFCCs) that can, in turn, be used by a supervised learning schema for music genre classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a semantic space model is proposed to represent music samples. The effectiveness of this representation of audio samples is then demonstrated in music genre classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering. Our preliminary results suggest that the proposed method is comparable to genre classification methods that use low-level audio features.


title: Vision-based lane detection and tracking for driver assistance systems: A survey

abstract: Lane detection and tracking has been an active research area in the past twenty years mainly for the driver assistance application. Due to the large variations of traffic scenes and illumination conditions, this problem causes the usage of diverse approaches and sensing modalities. In this paper, we review the vision-based lane detection and tracking methods complemented with other sensor information when necessary. Approaches that adopt conventional computer vision techniques are reviewed and compared according to the separate functional modules in a generic framework. The recently developed machine learning especially deep learning based methods in the limited literature are analysed and also discussed, demonstrating high potential in the current and future challenging lane perception scenarios. While impressive achievements have been demonstrated under limited scenarios, new ideas and approaches are still desired such that the next generation robust and efficient system can be built in the service of autonomous vehicles.


title: Who Changed You? Obfuscator Identification for Android

abstract: Android developers commonly use app obfuscation to secure their apps and intellectual property. Although obfuscation provides protection, it presents an obstacle for a number of legitimate program analyses such as detection of app cloning and repackaging, malware detection, identification of third-party libraries, provenance analysis for digital forensics, and reverse engineering for test generation and performance analysis. If the obfuscator used to create an app can be identified, and if some details of the obfuscation process can be inferred, subsequent analyses can exploit this knowledge. Thus, it is desirable to be able to automatically analyze a given app and determine (1) whether it was obfuscated, (2) which obfuscator was used, and (3) how the obfuscator was configured. We have developed novel techniques to identify the obfuscator of an Android app for several widely-used obfuscation tools and for a number of their configuration options. We define the obfuscator identification problem and propose a solution based on machine learning. To the best of our knowledge, this is the first work to formulate and solve this problem. We identify a feature vector that represents the characteristics of the obfuscated code. We then implement a tool that extracts this feature vector from Dalvik bytecode and uses it to identify the obfuscator provenance information. We evaluate the proposed approach on real-world Android apps obfuscated with different obfuscators, under several configurations. Our experiments indicate that the approach identifies the obfuscator with about 97% accuracy and recognizes the configuration with more than 90% accuracy.


title: Tools to improve documentation of smoking status. Continuous quality improvement and electronic medical records.

abstract: BACKGROUND ::: Despite the deleterious effects of smoking on the nation's health and evidence that smoking cessation advice by family practice physicians is cost-effective, self-sustaining office systems to identify smokers in primary care clinics have been difficult to establish. We worked on a continuous quality improvement project group, aided by an electronic medical record, to design a system to document and periodically update smoking status in a consistent place in the medical record. ::: ::: ::: INTERVENTION ::: Using the continuous quality improvement plan-do-study-act cycle, a 7-member group worked with nursing staff to define roles, routines and responsibilities for medical assistants to screen for and document 1 of 4 categories of smoking status in the major problem list of the electronic medical record for at least 80% of patient appointments. Screening rate was tracked monthly by means of the electronic medical record and feedback was given to staff. ::: ::: ::: RESULTS ::: The screening rate rose from 18.4% to 80.3% within 2 weeks after the system was implemented and was maintained for 19 months. An additional benefit was an increased rate of smoking cessation counseling documented by providers, from a baseline rate of 17.1% to 48.3%. ::: ::: ::: CONCLUSIONS ::: A continuous quality improvement group process aided by an electronic medical record is useful to develop a self-sustaining office system to screen, document, and periodically update smoking status in a consistent place in the medical record. Although screening for and documenting smoking status are only the first step toward helping patients stop smoking, it is an important one.


title: Seismic facies recognition based on prestack data using deep convolutional autoencoder

abstract: Prestack seismic data carries much useful information that can help us find more complex atypical reservoirs. Therefore, we are increasingly inclined to use prestack seismic data for seis- mic facies recognition. However, due to the inclusion of ex- cessive redundancy, effective feature extraction from prestack seismic data becomes critical. In this paper, we consider seis- mic facies recognition based on prestack data as an image clus- tering problem in computer vision (CV) by thinking of each prestack seismic gather as a picture. We propose a convo- lutional autoencoder (CAE) network for deep feature learn- ing from prestack seismic data, which is more effective than principal component analysis (PCA) in redundancy removing and valid information extraction. Then, using conventional classification or clustering techniques (e.g. K-means or self- organizing maps) on the extracted features, we can achieve seismic facies recognition. We applied our method to the prestack data from physical model and LZB region. The re- sult shows that our approach is superior to the conventionals.


title: Your Mouse Reveals Your Next Activity: Towards Predicting User Intention from Mouse Interaction

abstract: This paper presents an investigation into user intention prediction in two common web-based tasks: crowdsourcing annotation and web search, based on human-mouse interaction information. User experience is gaining importance within the research area of human-centered computing, and is particularly useful for complex, multi-step tasks. To enhance user experience, the computer should be intelligent enough to be able to predict the user intention. For instance, an intelligent agent might be able to anticipate when the user is about to press a button, and helpfully enlarge or highlight it in advance. In this paper, we propose two prediction models on user intention: a classical model that considers only historical mouse activity sequence, and a multimodal model that utilizes mouse interaction signals as well as features extracted from mouse trajectory and clicking events. We evaluate our models and find that they achieve reasonable accuracy. Our preliminary results indicate that we can dynamically learn a multimodal model that can effectively predict a user’s next activity from historical activity sequence and mouse interaction signals.


title: Kronos: the design and implementation of an event ordering service

abstract: This paper proposes a new approach to determining the order of interdependent operations in a distributed system. The key idea behind our approach is to factor the task of tracking happens-before relationships out of components that comprise the system, and to centralize them in a separate event ordering service. This not only simplifies implementation of individual components by freeing them from having to propagate dependence information, but also enables dependence relationships to be maintained across multiple independent systems. A novel API enables the system to detect and take advantage of concurrency whenever possible by maintaining fine-grained information and binding events to a time order as late as possible. We demonstrate the benefits of this approach through several example applications, including a transactional key-value store, and an online graph store. Experiments show that our event ordering service scales well and has low overhead in practice.


title: Dither for Smoothing Relay Feedback Systems: An Averaging Approach

abstract: Dither signals provide an effective way to compensate for nonlinearities in control systems. The seminal works by Zames and Shneydor, and more recently, by Mossaheb, present rigorous tools for systematic design of dithered systems. Their results rely, however, on a Lipschitz assumption relating to nonlinearity, and thus, do not cover important applications with discontinuities. This paper presents initial results on how to analyze and design dither in nonsmooth systems. In particular, it is shown that a dithered relay feedback system can be approximated by a smoothed system. Guidelines are given for tuning the amplitude and the period time of the dither signal, in order to stabilize the nonsmooth system.


title: Motivators for Implementing Environmental Management in Thermal Power Plants: An Empirical Study in India

abstract: 2 Abstract: Environmental management system (EMS) consists of collection of internal policies, assessments, plans and implementation actions affecting the entire organizational unit and its relationships with the natural environment. There are several issues related with EM. However, literature suggest that most of the researchers have dealt with issues like Stake holder's Pressure, Barriers, mechanisms for EM implementation etc. In addition to the above, Motivators for Implementing EMS is also identified as an important issue. EM approach is valuable to cost reduction, efficient utilization of resources, greater motivation of employees and better compliance to social obligations. Motivators behind the adoption of EMS may be large in number, but few critical motivators are: Concern for environment, Image and Marketing benefits, pressure from customers, legal compliance and pressure from other stakeholders. Thermal power plants are pollution prone industries. Impact of power plants on environment has been greatly emphasized in the literature. Therefore, this study aims to assess perception of Motivators for Implementing EMS in Indian thermal power plants. As thermal power plants in India are either private sector plants or public sector plants. Therefore, it is interesting to explore important motivators separately for two sectors. The study revealed that the most important motivator for private sector power plants is cost saving whereas public sectors plants are more motivated because of their concern for environment.


title: TMCoI-SIOT: A trust management system based on communities of interest for the social Internet of Things

abstract: We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.


title: A Computational Dynamic Trust Model for User Authorization

abstract: Development of authorization mechanisms for secure information access by a large community of users in an open environment is an important problem in the ever-growing Internet world. In this paper we propose a computational dynamic trust model for user authorization, rooted in findings from social science. Unlike most existing computational trust models, this model distinguishes trusting belief in integrity from that in competence in different contexts and accounts for subjectivity in the evaluation of a particular trustee by different trusters. Simulation studies were conducted to compare the performance of the proposed integrity belief model with other trust models from the literature for different user behavior patterns. Experiments show that the proposed model achieves higher performance than other models especially in predicting the behavior of unstable users.


title: SUPR-Q: a comprehensive measure of the quality of the website user experience

abstract: A three part study conducted over five years involving 4,000 user responses to experiences with over 100 websites was analyzed to generate an eight-item questionnaire of website quality---the Standardized User Experience Percentile Rank Questionnaire (SUPR-Q). The SUPR-Q contains four factors: usability, trust, appearance, and loyalty. The factor structure was replicated across three studies with data collected both during usability tests and retrospectively in surveys. There was evidence of convergent validity with existing questionnaires, including the System Usability Scale (SUS). The overall average score was shown to have high internal consistency reliability (a = .86). An initial distribution of scores across the websites generated a database used to produce percentile ranks and make scores more meaningful to researchers and practitioners. The questionnaire can be used to generate reliable scores in benchmarking websites, and the normed scores can be used to understand how well a website scores relative to others in the database.


title: Accurate and Novel Recommendations: An Algorithm Based on Popularity Forecasting

abstract: Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders.


title: Convolutional Oriented Boundaries

abstract: We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and MS-COCO, showing that COB provides state-of-the-art contours, region hierarchies, and object proposals in all datasets.


title: 1 Introduction Inferring learning and attitudes from a Bayesian Network of log file data

abstract: A student's goals and attitudes while interacting with a tutor are typically unseen and unknowable. However their outward behavior (e.g. problem-solving time, mistakes and help requests) is easily recorded and can reflect hidden affect status. This research evaluates the accuracy of a Bayesian Network to infer a student's hidden attitude toward learning, amount learned and perception of the system from log-data. The long term goal is to develop tutors that self-improve their student models and their teaching, dynamically can adapt pedagogical decisions about hints and help improve student's affective, intellectual and learning situation based on inferences about their goals and attitude.


title: Understanding heart rate sharing: towards unpacking physiosocial space

abstract: Advances in biosensing make it possible to include heart rate monitoring in applications and several studies have suggested that heart rate communication has potential for improving social connectedness. However, it is not known how people understand heart rate feedback, or what issues need to be taken into account when designing technologies including heart rate feedback. To explore this, we created a heart rate communication probe that was used in two qualitative in-lab studies and a two-week field trial in participants' homes. Results show that heart rate feedback is a strong connectedness cue that affects the interaction in various ways, depending on a number of interrelated factors. In particular, we found two distinct categories of effects: heart rate as information and heart rate as connection. We propose two mechanisms that could explain these observations and draw out the implications they have for future use of heartbeat communication to support social connectedness or other aspects of social interaction.


title: Lattice-Boltzmann Simulations of Particle-Fluid Suspensions

abstract: This paper reviews applications of the lattice-Boltzmann method to simulations of particle-fluid suspensions. We first summarize the available simulation methods for colloidal suspensions together with some of the important applications of these methods, and then describe results from lattice-gas and lattice-Boltzmann simulations in more detail. The remainder of the paper is an update of previously published work,(69, 70) taking into account recent research by ourselves and other groups. We describe a lattice-Boltzmann model that can take proper account of density fluctuations in the fluid, which may be important in describing the short-time dynamics of colloidal particles. We then derive macro-dynamical equations for a collision operator with separate shear and bulk viscosities, via the usual multi-time-scale expansion. A careful examination of the second-order equations shows that inclusion of an external force, such as a pressure gradient, requires terms that depend on the eigenvalues of the collision operator. Alternatively, the momentum density must be redefined to include a contribution from the external force. Next, we summarize recent innovations and give a few numerical examples to illustrate critical issues. Finally, we derive the equations for a lattice-Boltzmann model that includes transverse and longitudinal fluctuations in momentum. The model leads to a discrete version of the Green–Kubo relations for the shear and bulk viscosity, which agree with the viscosities obtained from the macro-dynamical analysis. We believe that inclusion of longitudinal fluctuations will improve the equipartition of energy in lattice-Boltzmann simulations of colloidal suspensions.


title: The roles of basic psychological needs, self-compassion, and self-efficacy in the development of mastery goals among medical students

abstract: AbstractAim: Competency-based medical education aims to foster mastery goals in learners. We examined medical students’ mastery approach (beneficial) and mastery avoidance (maladaptive) goals and their associations with students’ basic psychological needs, self-compassion, and self-efficacy.Methods: This was a cross-sectional study employing an online questionnaire. Two hundred medical students in all four years of the medical program completed the questionnaire, containing measures of mastery goals, basic psychological needs (autonomy, competence, relatedness), self-compassion, and self-efficacy. Regression analyses were performed.Results: Of the three basic psychological needs, the need for competence was significant in explaining both types of mastery goals. Self-efficacy and self-compassion were significant in explaining mastery approach and mastery avoidance goals, respectively.Conclusions: Creating learning environments that are supportive of students’ need for competence, raising students’ awarenes...


title: Solvent-controlled growth of inorganic perovskite films in dry environment for efficient and stable solar cells

abstract: Inorganic halide perovskites such as cesium lead halide are promising due to their excellent thermal stability. Cesium lead iodide (CsPbI3) has a bandgap of 1.73 eV and is very suitable for making efficient tandem solar cells, either with low-bandgap perovskite or silicon. However, the phase instability of CsPbI3 is hindering the further optimization of device performance. Here, we show that high quality and stable α-phase CsPbI3 film is obtained via solvent-controlled growth of the precursor film in a dry environment. A 15.7% power conversion efficiency of CsPbI3 solar cells is achieved, which is the highest efficiency reported for inorganic perovskite solar cells up to now. And more importantly, the devices can tolerate continuous light soaking for more than 500 h without efficiency drop.


title: Emotional intelligence, Machiavellianism and emotional manipulation: Does EI have a dark side?

abstract: Abstract Associations of Machiavellianism (Mach) with self-report and performance emotional intelligence (EI) and with personality were examined. The possible existence of an emotional manipulation capability, not covered within current EI measures, was also examined by constructing an emotional manipulation scale. Mach was found to be negatively correlated with self-report and performance EI, and also with Agreeableness and Conscientiousness. Emotional manipulation was positively correlated with Mach but unrelated to EI. Thus high Machs endorse emotionally-manipulative behaviour, although the extent to which they are successful in this behaviour, given the negative Mach/EI association, remains to be established.


title: Lifetime-Aware Scheduling and Power Control for M2M Communications in LTE Networks

abstract: In this paper the scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in LTE networks. To enable efficient scheduling for a massive number of machine-type subscribers, a novel distributed scheme is proposed to let machine nodes form local clusters and communicate with the base-station through the cluster-heads. Then, uplink scheduling and power control in LTE networks are introduced and lifetime-aware solutions are investigated to be used for the communication between cluster-heads and the base-station. Beside the exact solutions, low-complexity suboptimal solutions are presented in this work which can achieve near optimal performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended using the proposed protocols.


title: Exploring the structure of a real-time, arbitrary neural artistic stylization network

abstract: In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.


title: USMART: An Unsupervised Semantic Mining Activity Recognition Technique

abstract: Recognising high-level human activities from low-level sensor data is a crucial driver for pervasive systems that wish to provide seamless and distraction-free support for users engaged in normal activities. Research in this area has grown alongside advances in sensing and communications, and experiments have yielded sensor traces coupled with ground truth annotations about the underlying environmental conditions and user actions. Traditional machine learning has had some success in recognising human activities; but the need for large volumes of annotated data and the danger of overfitting to specific conditions represent challenges in connection with the building of models applicable to a wide range of users, activities, and environments. We present USMART, a novel unsupervised technique that combines data- and knowledge-driven techniques. USMART uses a general ontology model to represent domain knowledge that can be reused across different environments and users, and we augment a range of learning techniques with ontological semantics to facilitate the unsupervised discovery of patterns in how each user performs daily activities. We evaluate our approach against four real-world third-party datasets featuring different user populations and sensor configurations, and we find that USMART achieves up to 97.5p accuracy in recognising daily activities.


title: GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

abstract: Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the `Frechet Inception Distance'' (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.


title: Research on Neural Network PID Control Algorithm for a Quadrotor

abstract: Quad rotor is a kind of popular unmanned aerial vehicle which obtains prime advantages in simple structure, vertically taking off and landing and hovering ability, hence it possesses wide application prospects in reconnaissance and rescue, geological exploration and video surveillance. However, attitude and position control of the quad rotor are challenging tasks because it is an under-actuated system with strong nonlinear, coupling and model uncertainty characteristics. In this paper, the dynamics model and the state space function of the micro-quad rotor are firstly established. Then, a cascade control scheme is proposed to decouple the control system and a multivariate RBF(Radial Basis Function) neural network control PID algorithm is proposed to realize robust control of the quad rotor. This algorithm is not only characterized by simple structure and easy implementation, but also capable of self-adaption and online learning. Simulation results show that the proposed control algorithm performs well in tracking and under disturbances and model uncertainties.


WebGL Earth


title: Evaluating role mining algorithms

abstract: While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.


title: 2006. Statistical Machine Translation of German Compound Words

abstract: German compound words pose special problems to statistical machine translation systems: the occurence of each of the components in the training data is not sufficient for successful translation. Even if the compound itself has been seen during training, the system may not be capable of translating it properly into two or more words. If German is the target language, the system might generate only separated components or may not be capable of choosing the correct compound. In this work, we investigate and compare different strategies for the treatment of German compound words in statistical machine translation systems. For translation from German, we compare linguistic-based and corpus-based compound splitting. For translation into German, we investigate splitting and rejoining German compounds, as well as joining English potential components. Additionaly, we investigate word alignments enhanced with knowledge about the splitting points of German compounds. The translation quality is consistently improved by all methods for both translation directions.


title: A Bayesian Perspective of Statistical Machine Learning for Big Data

abstract: Statistical Machine Learning (SML) refers to a body of algorithms and methods by which computers are allowed to discover important features of input data sets which are often very large in size. The very task of feature discovery from data is essentially the meaning of the keyword `learning' in SML. Theoretical justifications for the effectiveness of the SML algorithms are underpinned by sound principles from different disciplines, such as Computer Science and Statistics. The theoretical underpinnings particularly justified by statistical inference methods are together termed as statistical learning theory. ::: This paper provides a review of SML from a Bayesian decision theoretic point of view -- where we argue that many SML techniques are closely connected to making inference by using the so called Bayesian paradigm. We discuss many important SML techniques such as supervised and unsupervised learning, deep learning, online learning and Gaussian processes especially in the context of very large data sets where these are often employed. We present a dictionary which maps the key concepts of SML from Computer Science and Statistics. We illustrate the SML techniques with three moderately large data sets where we also discuss many practical implementation issues. Thus the review is especially targeted at statisticians and computer scientists who are aspiring to understand and apply SML for moderately large to big data sets.


title: Recent Advance On Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) has received wide attention in the machine learning field because it can generate real-like data by estimating real data probability distribution. GANs has been successfully applied to many fields such as computer vision, pattern recognition, natural language processing and so on. By now many kinds of extended models of GANs have been proposed and investigated by different researchers from different viewpoints. Although there are a few review papers on the extended models of GANs in the literature, some remarkable extensions of GANs published in the recent years are not included in these surveys. This paper attempts to provide the potential readers with a recent advance on GANs by surveying its twelve representative variants. Furthermore, we also present the lineage of the extended models of GANs. This paper can provide researchers engaged in related works with very valuable help.


title: Factors Influencing Continuance Usage of Mobile Wallets in Indonesia

abstract: This study identifies the factors that influence the continuance usage of mobile wallets and examines continuance usage of mobile wallets that have been registered with Bank Indonesia and issued by non-banks. The respondents were the Indonesian population that have used one of the mobile wallets, such as DOKU, Dompetku, Telkomsel Wallet (T-Cash), UNIK, GO-PAY, XL Tunal, or TrueMoney. The data were then analyzed with a structural equation modeling (SEM). The results of the data processing show that the factors influencing continuance usage of mobile wallets are trust, flow, and satisfaction. Furthermore, quality factors consist of system quality, service quality, and information quality, which affect trust and flow directly. Satisfaction is directly influenced by trust and indirectly by the previously mentioned quality factors. Thus, mobile wallet service providers must continue to improve the system quality, service quality, and information quality of their m-wallet applications. The trust factor also influences flow and satisfaction; thus, directly improving the quality can improve the user experience and user satisfaction.


title: An Application of the Concept of the Therapeutic Alliance To Sadomasochistic Pathology

abstract: This paper traces the history of the therapeutic alliance concept, examining how it has been used and misused, at times elevated to a central position and at others rejected altogether. The loss of this concept created a vacuum in classical psychoanalysis that has been filled by rival theories. The continuing usefulness of looking at the treatment process through the lens of the therapeutic alliance, particularly in relation to the manifold difficulties of working with sadomasochistic pathology, is suggested. To this end, revisions of the theory of the therapeutic alliance are suggested to address some of the difficulties that have arisen in conceptualizing this aspect of the therapeutic relationship, and to provide an integrated dynamic model for working with patients at each phase of treatment. This revised model acknowledges the complexity of the domain and encompasses the multiple tasks, functions, partners, and treatment phases involved. The utility of the revised theory is illustrated in application...


title: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation

abstract: Image segmentation for detection of vessel walls is necessary for quantitative assessment of vessel diseases by intravascular ultrasound. A new segmentation method based on gradient vector flow (GVF) snake model is proposed in this paper. The main characteristics of the proposed method include two aspects: one is that nonlinear filtering is performed on GVF field to reduce the critical points, change the morphological structure of the parallel curves and extend the capture range; the other is that balloon snake is combined with the model. Thus, the improved GVF and balloon snake can be automatically initialized and overcome the problem caused by local energy minima. Results of 20 in vivo cases validated the accuracy and stability of the segmentation method for intravascular ultrasound images.


title: Applying Memory Forensics to Rootkit Detection

abstract: Volatile memory dump and its analysis is an essential part of digital forensics. Among a number of various software and hardware approaches for memory dumping there are authors who point out that some of these approaches are not resilient to various anti-forensic techniques, and others that require a reboot or are highly platform dependent. New resilient tools have certain disadvantages such as low speed or vulnerability to rootkits which directly manipulate kernel structures e.g. page tables. A new memory forensic system - Malware Analysis System for Hidden Knotty Anomalies (MASHKA) is described in this paper. It is resilient to popular anti-forensic techniques. The system can be used for doing a wide range of memory forensics tasks. This paper describes how to apply the system for research and detection of kernel mode rootkits and also presents analysis of the most popular anti-rootkit tools.


title: Unilateral Calcification of the Caudate and Putamen: Association with Underlying Developmental Venous Anomaly

abstract: SUMMARY: Stenosis of a DVA may result in chronic venous ischemia. We present 6 patients (3 men, 3 women; age range, 30–79 years; mean age, 53 years) with unilateral calcification of the caudate and putamen on noncontrast CT. This calcification typically spared the anterior limb of the internal capsule. No patient presented with symptoms referable to the basal ganglia or had an underlying metabolic disorder or other process associated with calcium deposition. All patients subsequently underwent gadolinium-enhanced MR imaging and/or CTA or conventional angiography demonstrating the presence of an adjacent DVA. We hypothesize that chronic venous ischemia in the drainage territory of the DVA causes the abnormal mineralization. Greater recognition of this entity will prevent misinterpretation of this finding as acute hemorrhage and will prevent unnecessary and sometimes invasive evaluation in such patients. Furthermore, this entity should be considered in the differential diagnosis of unilateral basal ganglia hyperattenuation.


Dog aggression survey in Slovakia – Characteristics and risk factors


title: An Empirical Study of Client-Side JavaScript Bugs

abstract: Context: Client-side JavaScript is widely used in web applications to improve user-interactivity and minimize client-server communications. Unfortunately, web applications are prone to JavaScript faults. While prior studies have demonstrated the prevalence of these faults, no attempts have been made to determine their root causes and consequences. Objective: The goal of our study is to understand the root causes and impact of JavaScript faults and how the results can impact JavaScript programmers, testers and tool developers. Method: We perform an empirical study of 317 bug reports from 12 bug repositories. The bug reports are thoroughly examined to classify and extract information about the fault's cause (the error) and consequence (the failure and impact). Result: The majority (65%) of JavaScript faults are DOM-related, meaning they are caused by faulty interactions of the JavaScript code with the Document Object Model (DOM). Further, 80% of the highest impact JavaScript faults are DOM-related. Finally, most JavaScript faults originate from programmer mistakes committed in the JavaScript code itself, as opposed to other web application components such as the server-side or HTML code. Conclusion: Given the prevalence of DOM-related faults, JavaScript programmers need development tools that can help them reason about the DOM. Also, testers should prioritize detection of DOM-related faults as most high impact faults belong to this category. Finally, developers can use the error patterns we found to design more powerful static analysis tools for JavaScript.


title: NAO-mark vs QR-code Recognition by NAO Robot Vision

abstract: Nowadays, the research on robot on-map localization while using landmarks is more intensively dealing with visual code recognition. One of the most popular landmarks of this type is the QR-code. This paper is devoted to the experimental evaluation of vision-based on-map localization procedures that apply QR-codes or NAO marks, as implemented in service robot control systems. In particular, the NAO humanoid robot is our test-bed platform, while the use of robotic systems for hazard detection is the motivation of this study. Especially, the robot can be a useful aid for elderly people affected by dementia and cognitive disorientation. The detection of the door opening is assumed to be important to ensure safety in the home environment. Thus, the paper focus on door opening detection while using QR-codes.


Detecting shadow for direct radiation using CityGML models for photovoltaic potentiality analysis


Can the Rights of Man be Founded


title: 3.6 W/mm high power density W-band InAlGaN/GaN HEMT MMIC power amplifier

abstract: We demonstrated a W-band high-power-density MMIC power amplifier with 80 nm InAlGaN/GaN HEMTs. The MMIC consists of two-stage cascade units, each of which has two transistors with the same gate periphery for a high gain and low-loss matching circuit. The MMIC achieved a maximum output power of 1.15 W and maximum PAE of 12.3 % at 86 GHz under CW operation. Its power density reached 3.6 W/mm, representing the highest performance of the W-band GaN HEMT MMIC power amplifier.


title: From t-Closeness-Like Privacy to Postrandomization via Information Theory

abstract: t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.


title: A Transformation System for Developing Recursive Programs

abstract: A system of rules for transforming programs is described, with the programs in the form of recursion equations. An initially very simple, lucid, and hopefully correct program is transformed into a more efficient one by altering the recursion structure. Illustrative examples of program transformations are given, and a tentative implementation is described. Alternative structures for programs are shown, and a possible initial phase for an automatic or semiautomatic program-manipulation system is indicated.


title: Performance Evaluation of a U-Shaped Less-Rare-Earth Hybrid Permanent Magnet Assisted Synchronous Reluctance Motor

abstract: In order to solve the rare-earth crisis emerged in recent years, a new permanent magnet assisted synchronous reluctance motor with less rare-earth materials is proposed in this paper. The novelty of the motor is the less rare-earth PMs and more ferrite PMs are artfully inset in its rotor with U shapes. Furthermore, an improved rotor structure with sectorial flux barriers is designed for less magnetic flux leakage. By using finite-element analysis method, the electromagnetic performances are evaluated, including flux density distribution, back-EMF and harmonic frequency analysis. In particular, the torque production characteristics are investigated in details. Finally, the analysis results indicate that the proposed motor can offer a desirable torque capability with relatively less rare earth materials.


title: Unified Point-of-Interest Recommendation with Temporal Interval Assessment

abstract: Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, in this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the WWO system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the WWO system by setting this time period long enough. Specifically, to quantify users' sequential preferences, we consider the distributions of the temporal intervals between dependent POIs in the historical check-in sequences. Then, to estimate the distributions with only sparse observations, we develop the low-rank graph construction model, which identifies a set of bi-weighted graph bases so as to learn the static user preferences and the dynamic sequential preferences in a coherent way. Finally, we evaluate the proposed approach using real-world data sets from several location-based social networks (LBSNs). The experimental results show that our method outperforms the state-of-the-art approaches for POI recommendation in terms of various metrics, such as F-measure and NDCG, with a significant margin.