title: An effective way of word-level language identification for code-mixed facebook comments using word-embedding via character-embedding

abstract: Individuals utilize online networking sites like Facebook and Twitter to express their interests, opinions or reviews. The users used English language as their medium for communication in earlier days. Despite the fact that content can be written in Unicode characters now, people find it easier to communicate by mixing two or more languages together or lean toward writing their native language in Roman script. These types of data are called code-mixed text. While processing such social-media data, recognizing the language of the text is an important task. In this work, we have developed a system for word-level language identification on code-mixed social media text. The work is accomplished for Tamil-English and Malayalam-English code-mixed Facebook comments. The methodology used for the system is a novel approach which is implemented based on features obtained from character-based embedding technique with the context information and uses a machine learning based classifier, Support Vector Machine for training and testing. An accuracy of 93% was obtained for Malayalam-English and 95% for Tamil-English code-mixed text.


title: Improved surface quality in 3D printing by optimizing the printing direction

abstract: We present a pipeline of algorithms that decomposes a given polygon model into parts such that each part can be 3D printed with high (outer) surface quality. For this we exploit the fact that most 3D printing technologies have an anisotropic resolution and hence the surface smoothness varies significantly with the orientation of the surface. Our pipeline starts by segmenting the input surface into patches such that their normals can be aligned perpendicularly to the printing direction. A 3D Voronoi diagram is computed such that the intersections of the Voronoi cells with the surface approximate these surface patches. The intersections of the Voronoi cells with the input model's volume then provide an initial decomposition. We further present an algorithm to compute an assembly order for the parts and generate connectors between them. A post processing step further optimizes the seams between segments to improve the visual quality. We run our pipeline on a wide range of 3D models and experimentally evaluate the obtained improvements in terms of numerical, visual, and haptic quality.


title: Performance analysis and comparison of ZF and MRT based downlink massive MIMO systems

abstract: In this paper the performances of zero-forcing (ZF) and maximum ratio transmission (MRT) are analyzed and compared in a downlink massive multiple-input multiple-output system. The system employs a large number of base station antennas serving multiple user terminals within the same cell. The achievable sum rate and the required downlink transmit power using each of the precoding schemes are derived, analyzed and compared under the same conditions and assumptions. Simulation results are found to coincide with the theoretical results, and show that ZF performs better than MRT under the same conditions.


title: The haptic hand: providing user interface feedback with the non-dominant hand in virtual environments

abstract: We present a user interface for virtual environments that utilizes the non-dominant hand to provide haptic feedback to the dominant hand while it interacts with widgets on a virtual control panel. We believe this technique improves on existing prop-based methods of providing haptic feedback. To gauge the interface's effectiveness, we performed a usability study. We do not present a formal comparison with prior techniques here. The goal of this study was to determine the feasibility of using the non-dominant hand for haptic feedback, and to obtain subjective data about usability. The results demonstrated that the interface allowed users to perform precision tasks. These results have convinced us that this technique has potential and warrants further development.


title: Machine Learning for Detecting Brute Force Attacks at the Network Level

abstract: The tremendous growth in computer network and Internet usage, combined with the growing number of attacks makes network security a topic of serious concern. One of the most prevalent network attacks that can threaten computers connected to the network is brute force attack. In this work we investigate the use of machine learners for detecting brute force attacks (on the SSH protocol) at the network level. We base our approach on applying machine learning algorithms on a newly generated dataset based upon network flow data collected at the network level. Applying detection at the network level makes the detection approach more scalable. It also provides protection for the hosts who do not have their own protection. The new dataset consists of real-world network data collected from a production network. We use four different classifiers to build brute force attack detection models. The use of different classifiers facilitates a relatively comprehensive study on the effectiveness of machine learners in the detection of brute force attack on the SSH protocol at the network level. Empirical results show that the machine learners were quite successful in detecting the brute force attacks with a high detection rate and low false alarms. We also investigate the effectiveness of using ports as features during the learning process. We provide a detailed analysis of how the models built can change as a result of including or excluding port features.


title: Face Recognition with Convolutional Neural Networks and subspace learning

abstract: Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods.


title: Online Learning in Limit Order Book Trade Execution

abstract: In this paper, we propose an online learning algorithm for optimal execution in the limit order book of a financial asset. Given a certain number of shares to sell and an allocated time window to complete the transaction, the proposed algorithm dynamically learns the optimal number of shares to sell via market orders at prespecified time slots within the allocated time interval. We model this problem as a Markov Decision Process (MDP), which is then solved by dynamic programming. First, we prove that the optimal policy has a specific form, which requires either selling no shares or the maximum allowed amount of shares at each time slot. Then, we consider the learning problem, in which the state transition probabilities are unknown and need to be learned on the fly. We propose a learning algorithm that exploits the form of the optimal policy when choosing the amount to trade. Interestingly, this algorithm achieves bounded regret with respect to the optimal policy computed based on the complete knowledge of the market dynamics. Our numerical results on several finance datasets show that the proposed algorithm performs significantly better than the traditional Q-learning algorithm by exploiting the structure of the problem.


title: Consensus formation on coevolving networks: groups' formation and structure

abstract: We study the effect of adaptivity on a social model of opinion dynamics and consensus formation. We analyze how the adaptivity of the network of contacts between agents to the underlying social dynamics affects the size and topological properties of groups and the convergence time to the stable final state. We find that, while on static networks these properties are determined by percolation phenomena, on adaptive networks the rewiring process leads to different behaviors: Adaptive rewiring fosters group formation by enhancing communication between agents of similar opinion, though it also makes possible the division of clusters. We show how the convergence time is determined by the characteristic time of link rearrangement. We finally investigate how the adaptivity yields nontrivial correlations between the internal topology and the size of the groups of agreeing agents.


Educational robotic platform based on arduino


Retarding senescence of cut leaves of Hosta plantaginea by growth regulators


title: Memory Controller Design Under Cloud Workloads

abstract: This work studies the behavior of state-of-the-art memory controller designs when executing scale-out workloads. It considers memory scheduling techniques, memory page management policies, the number of memory channels, and the address mapping scheme used. Experimental measurements demonstrate: 1) Several recently proposed memory scheduling policies are not a good match for these scale-out workloads. 2) The relatively simple First-Ready-First-Come- First-Served (FR-FCFS) policy performs consistently better, and 3) for most of the studied workloads, the even simpler First-Come-First-Served scheduling policy is within 1% of FRFCFS. 4) Increasing the number of memory channels offers negligible performance benefits, e.g., performance improves by 1.7% on average for 4-channels vs. 1-channel. 5) 77%- 90% of DRAM rows activations are accessed only once before closure. These observation can guide future development and optimization of memory controllers for scale-out workloads.


title: A Lattice-Based Access Control Model for Social Networks

abstract: Nowadays, social networks have been used widely. They help people communicate with family, friends or colleagues easily. However, they are lack of effective protection and monitoring about message transmission. In addition, the rapid updates of messages and user information also give difficulties to administrators. The existing security protection mechanisms in social networks could not protect users' private information effectively. In this paper, a lattice-based access control method is used to solve these problems. In our method, first, we will extract users' attributes to form the lattice element and construct the access control model based on lattice according the policies, then we define the partial relation to optimize the polices and append the policies on lattice element, at last, we do some experiments to verify it.


title: Detecting objective-C malware through memory forensics

abstract: Major advances in memory forensics in the past decade now allow investigators to efficiently detect and analyze many types of sophisticated kernel-level malware. With operating systems vendors now routinely enforcing driver signing and integrating strategies for protecting kernel data, such as Patch Guard, userland attacks are becoming more attractive to malware authors, as evidenced in the notorious Crisis malware. We therefore turn our attention to improving memory forensics techniques for analysis of malware in userland.In this paper, we focus on new methods for detecting userland malware written in Objective-C on Mac OS X. As the paper illustrates, Objective-C provides a rich set of APIs that malware can use to manipulate and steal application data and to perform other malicious activities. Our novel memory forensics techniques deeply examine the state of the Objective-C runtime inside of targeted processes, identifying a number of suspicious activities, from keystroke logging to pointer swizzling. We then examine our techniques against memory samples infected with malware found in targeted OS X attacks.


title: New Concept of Telemetry X-Band Circularly Polarized Antenna Payload for CubeSat

abstract: This letter presents a new concept of compact circularly polarized X-band [8–8.4 GHz] antenna for the 3U-CubeSat platforms. Despite the integration constraints on the top face of the CubeSat, the design aims at an isoflux radiation pattern. This antenna associates a driving patch antenna and 12 parasitic crossed dipoles, both to minimize the axial ratio in the opening angle θ = ±65° and to shape the radiation pattern. The patch excitation is carried out by a compact sequential-phase feed microstrip circuit. This antenna is manufactured and measured.


title: A two-stage approach to blind spatially-varying motion deblurring

abstract: Many blind motion deblur methods model the motion blur as a spatially invariant convolution process. However, motion blur caused by the camera movement in 3D space during shutter time often leads to spatially varying blurring effect over the image. In this paper, we proposed an efficient two-stage approach to remove spatially-varying motion blurring from a single photo. There are three main components in our approach: (i) a minimization method of estimating region-wise blur kernels by using both image information and correlations among neighboring kernels, (ii) an interpolation scheme of constructing pixel-wise blur matrix from region-wise blur kernels, and (iii) a non-blind deblurring method robust to kernel errors. The experiments showed that the proposed method outperformed the existing software based approaches on tested real images.


title: Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation

abstract: The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains. Alleviating the domain shift problem, especially in the challenging setting where no labeled data are available for the target domain, is paramount for having visual recognition systems working in the wild. As the problem stems from a shift among distributions, intuitively one should try to align them. In the literature, this has resulted in a stream of works attempting to align the feature representations learned from the source and target domains. Here we take a different route. Rather than introducing regularization terms aiming to promote the alignment of the two representations, we act at the distribution level through the introduction of \emph{DomaIn Alignment Layers} (\DIAL), able to match the observed source and target data distributions to a reference one. Thorough experiments on three different public benchmarks we confirm the power of our approach.


title: Effects of Different Combinations of Strength, Power, and Plyometric Training on the Physical Performance of Elite Young Soccer Players

abstract: The combination of strength (ST) and plyometric training (PT) has been shown to be effective for improving sport-specific performance. However, there is no consensus about the most effective way to combine these methods in the same training session to produce greater improvements in neuromuscular performance of soccer players. Thus, the purpose of this study was to compare the effects of different combinations of ST and PT sequences on strength, jump, speed, and agility capacities of elite young soccer players. Twenty-seven soccer players (age: 18.9 ± 0.6 years) participated in an 8-week resistance training program and were divided into 3 groups: complex training (CP) (ST before PT), traditional training (TD) (PT before ST), and contrast training (CT) (ST and PT performed alternately, set by set). The experimental design took place during the competitive period of the season. The ST composed of half-squat exercises performed at 60-80% of 1 repetition maximum (1RM); the PT composed of drop jump exercises executed in a range from 30 to 45 cm. After the experimental period, the maximum dynamic strength (half-squat 1RM) and vertical jump ability (countermovement jump height) increased similarly and significantly in the CP, TD, and CT (48.6, 46.3, and 53% and 13, 14.2, and 14.7%, respectively). Importantly, whereas the TD group presented a significant decrease in sprinting speed in 10 (7%) and 20 m (6%), the other groups did not show this response. Furthermore, no significant alterations were observed in agility performance in any experimental group. In conclusion, in young soccer players, different combinations and sequences of ST and PT sets result in similar performance improvements in muscle strength and jump ability. However, it is suggested that the use of the CP and CT methods is more indicated to maintain/maximize the sprint performance of these athletes.


title: Latent Tree Models for Hierarchical Topic Detection

abstract: We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a document generation process and use word variables instead of token variables. They use a tree structure to model the relationships between topics and words, which is conducive to the discovery of meaningful topics and topic hierarchies.


title: A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients

abstract: We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures.


title: The Hardness of Approximation of Euclidean k-means

abstract: The Euclidean $k$-means problem is a classical problem that has been extensively studied in the theoretical computer science, machine learning and the computational geometry communities. In this problem, we are given a set of $n$ points in Euclidean space $R^d$, and the goal is to choose $k$ centers in $R^d$ so that the sum of squared distances of each point to its nearest center is minimized. The best approximation algorithms for this problem include a polynomial time constant factor approximation for general $k$ and a $(1+\epsilon)$-approximation which runs in time $poly(n) 2^{O(k/\epsilon)}$. At the other extreme, the only known computational complexity result for this problem is NP-hardness [ADHP'09]. The main difficulty in obtaining hardness results stems from the Euclidean nature of the problem, and the fact that any point in $R^d$ can be a potential center. This gap in understanding left open the intriguing possibility that the problem might admit a PTAS for all $k,d$. In this paper we provide the first hardness of approximation for the Euclidean $k$-means problem. Concretely, we show that there exists a constant $\epsilon>0$ such that it is NP-hard to approximate the $k$-means objective to within a factor of $(1+\epsilon)$. We show this via an efficient reduction from the vertex cover problem on triangle-free graphs: given a triangle-free graph, the goal is to choose the fewest number of vertices which are incident on all the edges. Additionally, we give a proof that the current best hardness results for vertex cover can be carried over to triangle-free graphs. To show this we transform $G$, a known hard vertex cover instance, by taking a graph product with a suitably chosen graph $H$, and showing that the size of the (normalized) maximum independent set is almost exactly preserved in the product graph using a spectral analysis, which might be of independent interest.


title: Classification of sleep apnea using cross wavelet transform

abstract: In this paper, a novel approach for classifying sleep apneas using cross wavelet transform has been proposed. This is the first time that cross wavelet transform has ever been applied to sleep apnea type classification. The developed method takes the airflow and thoracic effort signals, as an in-put, which are then transformed to time-frequency and phase plane in order to extract the information of correlation between the two signals during different apnea condition. As the cross-wavelet returns large number of coefficients, which may be difficult to handle in some automated detection system, therefore dimension reduction was necessary. In the work, kernel principal component analysis (KPCA) based dimension reduction technique has been applied, and four Eigen values from each of the cross-wavelet amplitude and phase coefficients found to be effective for detection of apnea into three categories i.e., obstructive, central and mixed. The proposed system has been tested on the recordings obtained from 23 subjects. The average classification rate obtained using simple threshold technique was 85% ± 0.78%, and the values for each class were 85.2% (obstructive), 86.4% (central) and 83.6% (mixed). The results show that cross-wavelet is useful in order to distinguish the apneas, as it looks into the phase and amplitude coherence between the two signals.


title: Modified rectifier circuit for high efficiency and low power RF energy harvester

abstract: In this work, a passive rectifier circuit is presented, which is operating at 868 MHz. It allows energy harvesting from low power RF waves with a high efficiency. It consists of a novel multiplier circuit design and high quality components to reduce parasitic effects, losses and reaches a low startup voltage. Using lower capacitor rises up the switching speed of the whole circuit. An inductor L serves to store energy in a magnetic field during the negative cycle wave and returns it during the positive one. A low pass filter is arranged in cascade with the rectifier circuit to reduce ripple at high frequencies and to get a stable DC signal. A 50 kΩ load is added at the output to measure the output power and to visualize the behavior of the whole circuit. Simulation results show an outstanding potential of this RF-DC converter witch has a relative high sensitivity beginning with −40 dBm.


title: Yield Point Spectra for Seismic Design and Rehabilitation

abstract: Abstract A new spectral representation of seismic demand is described for use in the seismic design of new structures and in the evaluation and rehabilitation of existing structures. Yield Point Spectra (YPS) retain the intuitive appeal of the Capacity Spectrum Method (Freeman 1978) and join the Nonlinear Static Procedures of FEMA 273/274 (1997) and ATC 40 (1996) for use in estimating displacement demands. YPS also may be used to establish admissible combinations of strength and stiffness for the design of new structures to limit system ductility and drift to arbitrary values. Graphical procedures allow admissible design regions to be established to satisfy multiple performance objectives. YPS computed for 15 ground motions classified as Short Duration, Long Duration, or as containing near‐fault Forward Directivity pulses are presented for bilinear and stiffness‐degrading hysteretic models.


title: Geometrical model fitting for interferometric data: GEM-FIND

abstract: We developed the tool GEM-FIND that allows to constrain the morphology and brightness distribution of objects. The software fits geometrical models to spectrally dispersed interferometric visibility measurements in the N-band using the Levenberg-Marquardt minimization method. Each geometrical model describes the brightness distribution of the object in the Fourier space using a set of wavelength-independent and/or wavelength-dependent parameters. In this contribution we numerically analyze the stability of our nonlinear fitting approach by applying it to sets of synthetic visibilities with statistically applied errors, answering the following questions: How stable is the parameter determination with respect to (i) the number of uv-points, (ii) the distribution of points in the uv-plane, (iii) the noise level of the observations?


title: Dial One for Scam: A Large-Scale Analysis of Technical Support Scams

abstract: In technical support scams, cybercriminals attempt to convince users that their machines are infected with malware and are in need of their technical support. In this process, the victims are asked to provide scammers with remote access to their machines, who will then "diagnose the problem", before offering their support services which typically cost hundreds of dollars. Despite their conceptual simplicity, technical support scams are responsible for yearly losses of tens of millions of dollars from everyday users of the web. In this paper, we report on the first systematic study of technical support scams and the call centers hidden behind them. We identify malvertising as a major culprit for exposing users to technical support scams and use it to build an automated system capable of discovering, on a weekly basis, hundreds of phone numbers and domains operated by scammers. By allowing our system to run for more than 8 months we collect a large corpus of technical support scams and use it to provide insights on their prevalence, the abused infrastructure, the illicit profits, and the current evasion attempts of scammers. Finally, by setting up a controlled, IRB-approved, experiment where we interact with 60 different scammers, we experience first-hand their social engineering tactics, while collecting detailed statistics of the entire process. We explain how our findings can be used by law-enforcing agencies and propose technical and educational countermeasures for helping users avoid being victimized by technical support scams.


title: Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems!

abstract: The rise of “Big Data” analytics over unstructured text has led to renewed interest in information extraction (IE). We surveyed the landscape of IE technologies and identified a major disconnect between industry and academia: while rule-based IE dominates the commercial world, it is widely regarded as dead-end technology by the academia. We believe the disconnect stems from the way in which the two communities measure the benefits and costs of IE, as well as academia’s perception that rulebased IE is devoid of research challenges. We make a case for the importance of rule-based IE to industry practitioners. We then lay out a research agenda in advancing the state-of-theart in rule-based IE systems which we believe has the potential to bridge the gap between academic research and industry practice.


Chapter 1 Piconet interconnection strategies in IEEE 802 . 15 . 3 networks


title: A systematic review of research on open source software in commercial software product development

abstract: Background: The popularity of the open source software development in the last decade, has brought about an increased interest from the industry on how to use open source components, participate in the open source community, build business models around this type of software development, and learn more about open source development methodologies. Aim: The aim of this study is to review research carried out on usage of open source components and development methodologies by the industry, as well as companies’ participation in the open source community. Method: Systematic review through searches in library databases and manual identification of articles from the open source conference. Results: 19 articles were identified. Conclusions: The articles could be divided into four categories: open source as part of component based software engineering, business models with open source in commercial organization, company participation in open source development communities, and usage of open source processes within a company. (Less)


title: A methodology for personalized competency-based learning in undergraduate courses

abstract: In this paper, we describe a methodology for integrating personalized competency-based learning in a group of correlated courses in typical undergraduate education settings. The personalization is provided both in terms of content and learning pace. The material is organized in hierarchical modules based on the competencies desired to be achieved, containing both presentation materials and various type of tests. The methodology was integrated in several database and programming courses and its preliminary evaluation shows significant improvement in student grades.


title: ONDUX: on-demand unsupervised learning for information extraction

abstract: Information extraction by text segmentation (IETS) applies to cases in which data values of interest are organized in implicit semi-structured records available in textual sources (e.g. postal addresses, bibliographic information, ads). It is an important practical problem that has been frequently addressed in the recent literature. In this paper we introduce ONDUX (On Demand Unsupervised Information Extraction), a new unsupervised probabilistic approach for IETS. As other unsupervised IETS approaches, ONDUX relies on information available on pre-existing data to associate segments in the input string with attributes of a given domain. Unlike other approaches, we rely on very effective matching strategies instead of explicit learning strategies. The effectiveness of this matching strategy is also exploited to disambiguate the extraction of certain attributes through a reinforcement step that explores sequencing and positioning of attribute values directly learned on-demand from test data, with no previous human-driven training, a feature unique to ONDUX. This assigns to ONDUX a high degree of flexibility and results in superior effectiveness, as demonstrated by the experimental evaluation we report with textual sources from different domains, in which ONDUX is compared with a state-of-art IETS approach.


title: Ontology of Blockchain Technologies. Principles of Identification and Classification

abstract: A comparative study across the most widely known blockchain technologies is conducted with a bottom-up approach. Blockchains are disentangled into building blocks. Each building block is then hierarchically classified in main and subcomponents. Then, alternative layouts for the subcomponents are identified and compared between them. Finally, a taxonomy tree summarises the study and provides a navigation tool across different blockchain architectural configurations.


title: WEARPG: game design implications for movement-based play in table-top role-playing games with arm-worn devices

abstract: Combining the physical and the digital is one of the most trending topics in game research in HCI. Augmenting the table-top role-playing games (TTRPG) by adding electronic devices is a growing research area, yet the introduction of new play styles is still open for exploration. We believe that integration of wearable devices and movement-based play, which are also prominent research areas for gaming, hold potential for increasing the TTRPG experience since these are observed to increase the connectedness of players to imaginary world of games by previous studies. However, such augmentation will also bring changes in game design and these changes were not investigated thorougly in previous studies. To understand how game design of such games may change due to the usage of wearables and movement-based play, we conducted a participatory design workshop with (1) 25 participants, (2) designed a new game system according to our findings and (3) evaluated it with iterative tests with 15 participants in TTRPG sessions. Our study resulted in 8 player-centered game design implications for the use of arm-worn devices and movment-based play in narrative based long-term games like TTRPG. The implications present clues about forming the narrative, regulating the rules and the functional use of the device in game mechanics.


title: Anomaly Detection for Discrete Sequences: A Survey

abstract: This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve. These problem formulations are: 1) identifying anomalous sequences with respect to a database of normal sequences; 2) identifying an anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence whose frequency of occurrence is anomalous. We show how each of these problem formulations is characteristically distinct from each other and discuss their relevance in various application domains. We review techniques from many disparate and disconnected application domains that address each of these formulations. Within each problem formulation, we group techniques into categories based on the nature of the underlying algorithm. For each category, we provide a basic anomaly detection technique, and show how the existing techniques are variants of the basic technique. This approach shows how different techniques within a category are related or different from each other. Our categorization reveals new variants and combinations that have not been investigated before for anomaly detection. We also provide a discussion of relative strengths and weaknesses of different techniques. We show how techniques developed for one problem formulation can be adapted to solve a different formulation, thereby providing several novel adaptations to solve the different problem formulations. We also highlight the applicability of the techniques that handle discrete sequences to other related areas such as online anomaly detection and time series anomaly detection.


title: Tunable optical properties of multilayers black phosphorus thin films

abstract: Black phosphorus thin films might offer attractive alternatives to narrow gap compound semiconductors for optoelectronics across mid- to near-infrared frequencies. In this work, we calculate the optical conductivity tensor of multilayer black phosphorus thin films using the Kubo formula within an effective low-energy Hamiltonian. The optical absorption spectra of multilayer black phosphorus are shown to vary sensitively with thickness, doping, and light polarization. In conjunction with experimental spectra obtained from infrared absorption spectroscopy, we also discuss the role of interband coupling and disorder on the observed anisotropic absorption spectra.


title: Evolution and Game Theory

abstract: Research in noncooperative game theory has focused attention on two questions: Should we expect equilibrium play? If so, which of the multiple equilibria that arise in many games should we expect? This paper summarizes recent approaches to these questions that have been based on evolutionary models.


title: A haze removal module for mutlispectral satellite imagery

abstract: Ever-present spatial varying haze contamination in satellite scenes limits applications, especially urban remote sensing using visible and near infrared bands of low temporal resolution multispectral satellite imageries. We introduce a haze removal module coded in IDL language (used with ENVI software) which is a three-step framework comprised of haze detection, haze perfection and haze removal. The framework is based on several previous researches, loose and open, so that users can pick any method contained in each step or develop and use their own methods. Some TM and QuickBird images are present for visual assessment. Shortcoming of this module is too much human intervention on parameter determination and further search should aim at automation and more new methods in each step.


title: Detecting Student Engagement: Human Versus Machine

abstract: Engagement is complex and multifaceted, but crucial to learning. Computerized learning environments can provide a superior learning experience for students by automatically detecting student engagement (and, thus also disengagement) and adapting to it. This paper describes results from several previous studies that utilized facial features to automatically detect student engagement, and proposes new methods to expand and improve results. Videos of students will be annotated by third-party observers as mind wandering (disengaged) or not mind wandering (engaged). Automatic detectors will also be trained to classify the same videos based on students' facial features, and compared to the machine predictions. These detectors will then be improved by engineering features to capture facial expressions noted by observers and more heavily weighting training instances that were exceptionally-well classified by observers. Finally, implications of previous results and proposed work are discussed.


title: Miniaturized Substrate Integrated Waveguide (SIW) Power Dividers

abstract: In this letter, compact substrate integrated waveguide (SIW) power dividers are presented. Both equal and unequal power divisions are considered. A quarter-wavelength long wedge shape SIW structure is used for the power division. Direct coaxial feed is used for the input port and SIW-to-microstrip transitions are used for the output ports. Four-way equal, unequal and an eight-way equal division power dividers are presented. The four-way and the eight-way power dividers provide −10 dB input matching bandwidth of 39.3% and 13%, respectively, at the design frequency $\text {f}_{0}= 2.4$ GHz. The main advantage of the power dividers is their compact sizes. Including the microstrip to SIW transitions, size is reduced by at least 46% compared to other reported miniaturized SIW power dividers.


Challenges in Secure Software Evolution - The Role of Software Architecture


title: Computational intelligence and tower defence games

abstract: The aim of this paper is to introduce the use of Tower Defence (TD) games in Computational Intelligence (CI) research. We show how TD games can provide an important test-bed for the often under-represented casual games research area. Additionally, the use of CI in the TD games has the potential to create a more interesting, interactive and ongoing game experience for casual gamers. We present a definition of the current state and development of TD games, and include a classification of TD game components. We then describe some potential ways CI can be used to augment the TD experience. Finally, a prototype TD game based on experience-driven procedural content generation is presented.


title: Using X3D for medical training simulations

abstract: Nowadays medical training simulators play an important role in education and further training of surgeons. With Virtual Reality based training systems it is possible to simulate a surgery under realistic conditions. Input data for the visualization of anatomic structures is tomographic image data. For the visualization of medical datasets direct volume rendering is the method of choice. In this paper we introduce a system based on the new X3D extension proposal of the Medical Working Group for a volume rendering component, with some extensions for controlling the speed vs. quality trade-off. For a convincing and instructive training simulation, our implementation delivers high performance combined with high quality visualization of medical datasets. Furthermore, it integrates haptic force-feedback devices to assure realistic interactions.


title: One-stage partial vertebrectomy, titanium mesh implantation and pedicle screw fixation in the treatment of thoracolumbar burst fractures through a posterior approach

abstract: OBJECTIVE: To analyze the clinical results of a partial vertebrectomy with titanium mesh implantation and pedicle screw fixation using a posterior approach to reconstruct the spine in the treatment of thoracolumbar burst fractures. METHOD: From January 2006 to August 2008, 20 patients with severe thoracolumbar fractures were treated.For vertebral bodies associated with one injured intervertebral disk, subtotal vertebrectomy surgery and single-segment fusion were performed. For vertebral bodies with two injured adjacent intervertebral disks, partial vertebrectomy surgery and two-segment fusion were performed. RESULTS: All 20 patients were followed up for 12 to 24 months (average of 18 months). There were no complications such as wound infections, hemopneumothorax or abdominal infections in any of the patients. The neurological status of all of the patients was improved by at least one American Spinal Injury Association grade by the last follow-up. The anterior vertebral body height was an average of 50.77% before surgery, 88.51% after surgery and 87.86% at the last follow up; the sagittal Cobb angle was improved, on average, from 26.15° to 5.39° and was 5.90° at the last follow up. The percentage of spinal stenosis was improved, on average, from 26.07% to 4.93%° and was 6.15% at the last follow up. There were significant differences in the anterior vertebral body height pre- and post-surgery and in the sagittal Cobb angle and the percentage of spinal stenosis (p<0) in all patients. CONCLUSIONS: This surgical procedure is simple and can accomplish decompression, reduction, fixation and fusion of the spine in one stage. This approach could be widely used in orthopedics.


title: Distribution Distance Minimization for Unsupervised User Identity Linkage

abstract: Nowadays, it is common for one natural person to join multiple social networks to enjoy different services. Linking identical users across different social networks, also known as the User Identity Linkage (UIL), is an important problem of great research challenges and practical value. Most existing UIL models are supervised or semi-supervised and a considerable number of manually matched user identity pairs are required, which is costly in terms of labor and time. In addition, existing methods generally rely heavily on some discriminative common user attributes, and thus are hard to be generalized. Motivated by the isomorphism across social networks, in this paper we consider all the users in a social network as a whole and perform UIL from the user space distribution level. The insight is that we convert the unsupervised UIL problem to the learning of a projection function to minimize the distance between the distributions of user identities in two social networks. We propose to use the earth mover's distance (EMD) as the measure of distribution closeness, and propose two models UUIL$_gan $ and UUIL$_omt $ to efficiently learn the distribution projection function. Empirically, we evaluate the proposed models over multiple social network datasets, and the results demonstrate that our proposal significantly outperforms state-of-the-art methods.


title: Tag, you can see it!: using tags for access control in photo sharing

abstract: Users often have rich and complex photo-sharing preferences, but properly configuring access control can be difficult and time-consuming. In an 18-participant laboratory study, we explore whether the keywords and captions with which users tag their photos can be used to help users more intuitively create and maintain access-control policies. We find that (a) tags created for organizational purposes can be repurposed to create efficient and reasonably accurate access-control rules; (b) users tagging with access control in mind develop coherent strategies that lead to significantly more accurate rules than those associated with organizational tags alone; and (c) participants can understand and actively engage with the concept of tag-based access control.


title: A Mechatronic System for Robot-Mediated Hand Telerehabilitation

abstract: This paper presents a novel mechatronics master–slave setup for hand telerehabilitation. The system consists of a sensorized glove acting as a remote master and a powered hand exoskeleton acting as a slave. The proposed architecture presents three main innovative solutions. First, it provides the therapist with an intuitive interface (a sensorized wearable glove) for conducting the rehabilitation exercises. Second, the patient can benefit from a robot-aided physical rehabilitation in which the slave hand robotic exoskeleton can provide an effective treatment outside the clinical environment without the physical presence of the therapist. Third, the mechatronics setup is integrated with a sensorized object, which allows for the execution of manipulation exercises and the recording of patient's improvements. In this paper, we also present the results of the experimental characterization carried out to verify the system usability of the proposed architecture with healthy volunteers.


title: Dynamic Coloring of Graphs

abstract: Dynamics is an inherent feature of many real life systems so it is natural to define and investigate the properties of models that reflect their dynamic nature. Dynamic graph colorings can be naturally applied in system modeling, e.g. for scheduling threads of parallel programs, time sharing in wireless networks, session scheduling in high-speed LAN's, channel assignment in WDM optical networks as well as traffic scheduling. In the dynamic setting of the problem, a graph we color is not given in advance and new vertices together with adjacent edges are revealed one after another at algorithm's input during the coloring process. Moreover, independently of the algorithm, some vertices may lose their colors and the algorithm may be asked to color them again. We formally define a dynamic graph coloring problem, the dynamic chromatic number and prove various bounds on its value. We also analyze the effectiveness of the dynamic coloring algorithm Dynamic-Fit for selected classes of graphs. In particular, we deal with trees, products of graphs and classes of graphs for which Dynamic-Fit is competitive. Motivated by applications, we state the problem of dynamic coloring with discoloring constraints for which the performance of the dynamic algorithm Time-Fit is analyzed and give a characterization of graphs k-critical for Time-Fit. Since for any fixed k > 0 the number of such graphs is finite, it is possible to decide in polynomial time whether Time-Fit will always color a given graph with at most k colors.


title: A Customer Churn Analysis Model in E-business Environment

abstract: Today, more and more companies in e-business have acknowledged that their business strategies should focus on identifying those customers who are likely to churn as markets become increasingly saturated. In this paper a new method was put forward to analyze and predict customer churn behavior based on data mining techniques, such as decision tree, clustering, neural network, etc. By collecting a large amount of customer churn questionnaire data, this paper established an e-business customer churn prediction and analysis model, analyzed the related factors which influence customer retention of e-business enterprises, and proposed many corresponding loss control measures.


title: The evolving roles and controversies of radiotherapy in the treatment of glioblastoma

abstract: Numerous randomised controlled trials have demonstrated the benefit of radiation therapy in patients with newly diagnosed glioblastoma and it has been the cornerstone of treatment for decades. The aims of this review are to (1) Briefly outline the historical studies which resulted in radiation being the current standard of care as used in the Stupp et al. trial (2) Discuss the evolving role of radiation therapy in the management of elderly patients (3) Review the current evidence and ongoing studies of radiation use in the recurrent/salvage setting and (4) Discuss the continuing controversies of volume delineation in the planning of radiation delivery.


title: A Mixed Methods Approach to Technology Acceptance Research

abstract: The aim of this paper is to discuss the significance and potentials of a mixed methods approach in technology acceptance research. After critically reviewing the dominance of the quantitative survey method in TAM-based research, this paper reports a mixed methods study of user acceptance of emergency alert technology in order to illustrate the benefits of combining qualitative and quantitative techniques in a single study. The main conclusion is that a mixed methods approach provides opportunities to move beyond the vague conceptualizations of “usefulness” and “ease of use” and to advance our understanding of user acceptance of technology in context.


title: Incredible high performance SAW resonator on novel multi-layerd substrate

abstract: High Q and low TCF are elemental characteristics for SAW resonator, in order to realize high performance filters and duplexers coping with new LTE frequency bands. Authors focus on the acoustic energy confinement in SAW propagation surface and electrical characteristics have been investigated theoretically and experimentally. A new multi-layered structure substrate have realized incredible high performances such as both three times Q and one fifth TCF compared with conventional structure. Band25 duplexer using the new structure has been developed successfully with low loss and good temperature stability.


title: An arrayed-range-gate data acquisition for spatial distribution analysis of myocardial tissue vibration from stenosis in coronary Doppler vibrometry

abstract: Coronary Doppler vibrometry (CDV) is a non-invasive diagnosis to detect vibrations from stenosis in coronary artery using ultrasound. Although recent CDV shows considerable sensitivity and specificity in diagnosing coronary artery disease, there still remains a serious problem that normal subjects can also have high-frequency components in vibration. We developed new data acquisition and analysis algorithms that focus on the spatial distribution of high-frequency vibration in myocardial tissue. Our purpose is to observe the location-dependent variation of vibration in myocardial tissue. We recruited total 33 subjects in a hospital clinical study; four are known as patients who have coronary artery stenosis (over 50 % occlusion in coronary angiography) and the other 29 subjects are normal. We use 12 range gates in a view of echocardiography. Six range gates are located near stenosis (left anterior descending (LAD)) and the other six are located at normal tissue (or right coronary artery (RCA)). We define the segment vibration index (SVI) as the proportion of high-frequency component intensity (400 Hz ∼ 800 Hz) to total intensity (100 Hz ∼ 800Hz) in early diastole period. In addition, we define the myocardial tissue vibration distribution index (MVDI) as the ratio of the standard deviation of SVI in stenosis (or LAD) segments to that of SVI in normal (or RCA) segments. Thus, it is expected that myocardial tissue vibrations caused by stenosis can give a spatial distribution of vibration intensity, i.e., a large MDVI. Our proposed MDVI algorithm effectively classifies normal subjects and patients with sensitivity of 75% and specificity of 83%. This result shows that the patients with coronary artery disease tend to have higher MDVI value than normal subjects.


title: An eigenvalue search method using the Orr–Sommerfeld equation for shear flow

abstract: A physically-based computational technique was investigated which is intended to estimate an initial guess for complex values of the wavenumber of a disturbance leading to the solution of the fourth-order Orr-Sommerfeld (O-S) equation. The complex wavenumbers, or eigenvalues, were associated with the stability characteristics of a semi-infinite shear flow represented by a hyperbolic-tangent function. This study was devoted to the examination of unstable flow assuming a spatially growing disturbance and is predicated on the fact that flow instability is correlated with elevated levels of perturbation kinetic energy per unit mass. A MATLAB computer program was developed such that the computational domain was selected to be in quadrant IV, where the real part of the wavenumber is positive and the imaginary part is negative to establish the conditions for unstable flow. For a given Reynolds number and disturbance wave speed, the perturbation kinetic energy per unit mass was computed at various node points in the selected subdomain of the complex plane. The initial guess for the complex wavenumber to start the solution process was assumed to be associated with the highest calculated perturbation kinetic energy per unit mass. Once the initial guess had been approximated, it was used to obtain the solution to the O-S equation by performing a Runge-Kutta integration scheme that computationally marched from the far field region in the shear layer down to the lower solid boundary. Results compared favorably with the stability characteristics obtained from an earlier study for semi-infinite Blasius flow over a flat boundary.


title: Effect of Methanol Addition on the Resistivity and Morphology of PEDOT:PSS Layers on Top of Carbon Nanotubes for Use as Flexible Electrodes

abstract: Overcoating carbon nanotube (CNT) films on flexible poly(ethylene terephthalate) (PET) foils with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) layers reduces the surface roughness, which is interesting for use in organic electronics. Adding methanol to the PEDOT:PSS aqueous solution used for spin coating of the PEDOT:PSS layer improves the wetting behavior of the CNT/PET surface. Samples with different volume fractions of methanol (0, 33, 50, 67, and 75 vol %) are compared with respect to the transmission, horizontal, and vertical resistivity. With grazing-incidence small-angle X-ray scattering, the film morphologies are probed, which is challenging because of the substrate flexibility. At 50 vol %, methanol optimum conditions are achieved with the resistivity close to that of the bare CNT/PET substrates because of the best contact between the PEDOT:PSS film and CNT surface. At lower methanol ratios, the PEDOT:PSS films cannot adapt the CNT morphology, and at higher methanol ratios,...


Design And Application Guide For High Speed MOSFET Gate Drive Circuits By


title: A Lightweight Causal Message Logging Protocol to Lower Fault Tolerance Overhead

abstract: Rollback recovery is a trustworthy and key approach to fault tolerance in high performance computing and to parallel program debugging. In various rollback recovery protocols, causal message logging shows some desirable characteristics, but its high piggybacking overhead obstructs its applications, especially in large-scale distributed systems. Its high overhead arises from its conservation in the assumption on program execution model. This paper identifies the influence of non-deterministic message delivery on the correct outcome of a process, and then gives a scheme to relax the constraints from the piecewise deterministic execution model. Subsequently, a lightweight implementation of causal message logging is proposed to decrease the overhead of piggybacking and rolling forward. The experimental results of 3 NAS NPB2.3 benchmarks show that the proposed scheme achieves a significant improvement in the overhead reduction.


title: A Siamese Deep Forest

abstract: A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. It can be also regarded as an alternative to the well-known Siamese neural networks. The SDF uses a modified training set consisting of concatenated pairs of vectors. Moreover, it defines the class distributions in the deep forest as the weighted sum of the tree class probabilities such that the weights are determined in order to reduce distances between similar pairs and to increase them between dissimilar points. We show that the weights can be obtained by solving a quadratic optimization problem. The SDF aims to prevent overfitting which takes place in neural networks when only limited training data are available. The numerical experiments illustrate the proposed distance metric method.


title: Needs, affect, and interactive products - Facets of user experience

abstract: Subsumed under the umbrella of User Experience (UX), practitioners and academics of Human-Computer Interaction look for ways to broaden their understanding of what constitutes ''pleasurable experiences'' with technology. The present study considered the fulfilment of universal psychological needs, such as competence, relatedness, popularity, stimulation, meaning, security, or autonomy, to be the major source of positive experience with interactive technologies. To explore this, we collected over 500 positive experiences with interactive products (e.g., mobile phones, computers). As expected, we found a clear relationship between need fulfilment and positive affect, with stimulation, relatedness, competence and popularity being especially salient needs. Experiences could be further categorized by the primary need they fulfil, with apparent qualitative differences among some of the categories in terms of the emotions involved. Need fulfilment was clearly linked to hedonic quality perceptions, but not as strongly to pragmatic quality (i.e., perceived usability), which supports the notion of hedonic quality as ''motivator'' and pragmatic quality as ''hygiene factor.'' Whether hedonic quality ratings reflected need fulfilment depended on the belief that the product was responsible for the experience (i.e., attribution).


title: Does Information Network Affect Technology Diffusion? A Study on the Spread of Banana and Guava Cultivation Among Farmers of Nadia District, West Bengal, India

abstract: Adoption research for many years has considered individual farmer as the basis of analysis, whereas, the effect of networks governing a farmer's decision-making has received limited attention. Moreover, the spread of technology over different generation of adopters has not been addressed adequately. Hence, farmers' position within the agricultural information networks and their adoption decision, may be studied to formulate some lower order propositions regarding the diffusion of agricultural innovations within social networks. The present study was conducted at Nadia District of West Bengal, India to study the spread of banana (Musa paradisiacal L.) and guava (Psidium guajava L.) cultivation among the farmers of selected villages. Case study method and focused group discussion was used to track this spread over different generations of adopters. Sociometric technique was employed to find out the network score of farmers in the agricultural information network. The fractional ranking of network scores of farmers was then compared with their relative earliness in the spread of banana and guava cultivation. It was found that both in the spread of banana and guava cultivation, most of the farmers who had higher network scores were earlier adopters of banana and guava cultivation practices and vice versa. This indicates the possible relations between farmers' adoption-decision regarding new crops and their position in social networks.


title: CUTE: constrained and unconstrained testing environment

abstract: The purpose of this article is to discuss the scope and functionality of a versatile environment for testing small- and large-scale nonlinear optimization algorithms. Although many of these facilities were originally produced by the authors in conjunction with the software package LANCELOT, we believe that they will be useful in their own right and should be available to researchers for their development of optimization software. The tools can be obtained by anonymous ftp from a number of sources and may, in many cases, be installed automatically. The scope of a major collection of test problems written in the standard input format (SIF) used by the LANCELOT software package is described. Recognizing that most software was not written with the SIF in mind, we provide tools to assist in building an interface between this input format and other optimization packages. These tools provide a link between the SIF and a number of existing packages, including MINOS and OSL. Additionally, as each problem includes a specific classification that is designed to be useful in identifying particular classes of problems, facilities are provided to build and manage a database of this information. There is a Unix and C shell bias to many of the descriptions in the article, since, for the sake of simplicity, we do not illustrate everything in its fullest generality. We trust that the majority of potential users are sufficiently familiar with Unix that these examples will not lead to undue confusion.


title: A GaN power amplifier for 100 VDC bus in GPS L-band

abstract: An innovative approach in RF GaN technology suitable for Global Positioning System (GPS) L-band transmitters is presented. The solution operates directly from the 100 VDC voltage bus without the need for a DC-DC step-down converter to typical 28 V or 50 V. It is based on a mature GaN HEMT process which is radiation tolerant and reliable for space applications. The prototype single stage amplifier reported in this paper operates at 100 V bias and achieves 100 W CW output power with 19 dB gain and high efficiency > 60% at the L1 frequency band of 1575 ± 50 MHz. Preliminary results at L2 and L5 GPS bands are also reported. The demo amplifier consists of a single die with 12 mm gate periphery in a hermetic package. Higher power can easily be attained by utilizing multiple chips in one package. The proposed technology reduces SWaP figure of merit and simplifies the design of transmitters for GPS satellites.


title: Non-photorealistic volume rendering using stippling techniques

abstract: Simulating hand-drawn illustration techniques can succinctly express information in a manner that is communicative and informative. We present a framework for an interactive direct volume illustration system that simulates traditional stipple drawing. By combining the principles of artistic and scientific illustration, we explore several feature enhancement techniques to create effective, interactive visualizations of scientific and medical datasets. We also introduce a rendering mechanism that generates appropriate point lists at all resolutions during an automatic preprocess, and modifies rendering styles through different combinations of these feature enhancements. The new system is an effective way to interactively preview large, complex volume datasets in a concise, meaningful, and illustrative manner. Volume stippling is effective for many applications and provides a quick and efficient method to investigate volume models.


title: Convolutional Neural Network Based Semantic Tagging with Entity Embeddings

abstract: Unsupervised word embeddings provide rich linguistic and conceptual information about words. However, they may provide weak information about domain specific semantic relations for certain tasks such as semantic parsing of natural language queries, where such information about words or phrases can be valuable. To encode the prior knowledge about the semantic word relations, we extended the neural network based lexical word embedding objective function by incorporating the information about relationship between entities that we extract from knowledge bases [1]. In this paper, we focus on the semantic tagging of conversational utterances as our end task and we investigate two different ways of using these embeddings: as additional features to a linear sequence learning method, Conditional Random Fields (CRF), and as initial embeddings to a convolutional neural networks based CRF model (CNN-CRF) with shared feature layers and globally normalized sequence modeling components. While we obtain an average of 2% improvement in F-score compared to the previous baselines when the enriched embeddings are used as additional features for CRF models, we obtain slightly more gains when the embeddings are used as initial word representations for the CNN-based CRF models.


title: Evolutionary Clustering via Message Passing

abstract: We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.


title: CDK4 regulates cancer stemness and is a novel therapeutic target for triple-negative breast cancer

abstract: Triple negative breast cancers exhibit very aggressive features and poor patient outcomes. These tumors are enriched in cancer stem cells and exhibit resistance to most treatments and chemotherapy. In this study, we found the cyclin-dependent kinase (CDK4) to act as a cancer stem cell regulator and novel prognostic marker in triple negative breast cancers. We found CDK4 to be highly expressed in these tumors and its expression to correlate with poor overall and relapse free survival outcomes, high tumor grade and poor prognostic features of triple negative breast cancer patients. Moreover, we found that blocking CDK4 expression or kinase activity, using a pharmacological inhibitor prevented breast cancer stem cell self-renewal. Interestingly, suppression of CDK4 expression or kinase activity reversed the basal-B TNBC mesenchymal phenotype to an epithelial- and luminal-like phenotype which correlates with better clinical prognosis. Finally, blocking CDK4 activity efficiently eliminated both normal and chemotherapy-resistant cancer cells in triple negative breast cancers, highlighting CDK4 as a promising novel therapeutic target for these aggressive breast tumors.


title: The Bitcoin Backbone Protocol with Chains of Variable Difficulty

abstract: Bitcoin’s innovative and distributedly maintained blockchain data structure hinges on the adequate degree of difficulty of so-called “proofs of work,” which miners have to produce in order for transactions to be inserted. Importantly, these proofs of work have to be hard enough so that miners have an opportunity to unify their views in the presence of an adversary who interferes but has bounded computational power, but easy enough to be solvable regularly and enable the miners to make progress. As such, as the miners’ population evolves over time, so should the difficulty of these proofs. Bitcoin provides this adjustment mechanism, with empirical evidence of a constant block generation rate against such population changes.


title: Silicon nanowire solar cells

abstract: Silicon nanowire-based solar cells on metal foil are described. The key benefits of such devices are discussed, followed by optical reflectance, current-voltage, and external quantum efficiency data for a cell design employing a thin amorphous silicon layer deposited on the nanowire array to form the p-n junction. A promising current density of ∼1.6mA∕cm2 for 1.8cm2 cells was obtained, and a broad external quantum efficiency was measured with a maximum value of ∼12% at 690nm. The optical reflectance of the silicon nanowire solar cells is reduced by one to two orders of magnitude compared to planar cells.


title: Strong higher order mutation-based test data generation

abstract: This paper introduces SHOM, a mutation-based test data generation approach that combines Dynamic Symbolic Execution and Search Based Software Testing. SHOM targets strong mutation adequacy and is capable of killing both first and higher order mutants. We report the results of an empirical study using 17 programs, including production industrial code from ABB and Daimler and open source code as well as previously studied subjects. SHOM achieved higher strong mutation adequacy than two recent mutation-based test data generation approaches, killing between 8% and 38% of those mutants left unkilled by the best performing previous approach.


title: Segmentation of 3D objects from MRI volume data using constrained elastic deformations of flexible Fourier surface models

abstract: This paper describes a new model-based segmentation technique combining desirable properties of physical models (snakes, [2]), shape representation by Fourier parametrization (Fourier snakes, [12]), and modelling of natural shape variability (eigenmodes, [7, 10]). Flexible shape models are represented by a parameter vector describing the mean contour and by a set of eigenmodes of the parameters characterizing the shape variation with respect to a small set of stable landmarks (AC-PC in our application) and explaining the remaining variability among a series of images with the model flexibility. Although straightforward, the extension to 3-D is severely impeded by finding a proper surface parametrization for arbitrary objects with spherical topology. We apply a newly developed surface parametrization [16, 17] which achieves a uniform mapping between object surface and parameter space. The 3D model building and Fourier-snake procedure are demonstrated by segmenting deep structures of the human brain from MR volume data.


title: Model predictive hovering-translation control of an unmanned Tri-TiltRotor

abstract: The experimental translational hovering control of a Tri-TiltRotor Unmanned Aerial Vehicle is the subject of this paper. This novel UAV is developed to possess the capability to perform autonomous conversion between the Vertical Take-Off and Landing, and the Fixed-Wing flight modes. Via this design's implemented features however, the capability for additional control authority on the UAV's longitudinal translational motion arises: The rotor-tilting servos are utilized in performing thrust vectoring of the main rotors, thus exploiting their fast response characteristics in directly providing translation-controlling forces. The system's hovering translation is handled by a Model Predictive Control scheme, following the aforementioned actuation principles. While performing experimental studies of the overall controlled system's efficiency, the advantageous effects of this novel control authority are clearly noted. Additionally, in this article the considerations and requirements for operational autonomy-related on-board-only state estimation are addressed.


title: Geodesic Polar Coordinates on Polygonal Meshes

abstract: Geodesic Polar Coordinates (GPCs) on a smooth surface S are local surface coordinates that relates a surface point to a planar parameter point by the length and direction of a corresponding geodesic curve on S . They are intrinsic to the surface and represent a natural local parameterization with useful properties. We present a simple and efficient algorithm to approximate GPCs on both triangle and general polygonal meshes. Our approach, named DGPC, is based on extending an existing algorithm for computing geodesic distance. We compare our approach with previous methods, both with respect to efficiency, accuracy and visual qualities when used for local mesh texturing. As a further application we show how the resulting coordinates can be used for vector space methods like local remeshing at interactive frame-rates even for large meshes. © 2012 Wiley Periodicals, Inc.


title: Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing

abstract: For the cloud computing, task scheduling problems are of paramount importance. It becomes more challenging when takes into account energy consumption, traditional make span criteria and users QoS as objectives. This paper considers independent tasks scheduling in cloud computing as a bi-objective minimization problem with make span and energy consumption as the scheduling criteria. We use Dynamic Voltage Scaling (DVS) to minimize energy consumption and propose two algorithms. These two algorithms use the methods of unify and double fitness to define the fitness function and select individuals. They adopt the genetic algorithm to parallel find the reasonable scheduling scheme. The simulation results demonstrate the two algorithms can efficiently find the right compromise between make span and energy consumption.


title: Intel SGX Explained.

abstract: Intel’s Software Guard Extensions (SGX) is a set of extensions to the Intel architecture that aims to provide integrity and confidentiality guarantees to securitysensitive computation performed on a computer where all the privileged software (kernel, hypervisor, etc) is potentially malicious. This paper analyzes Intel SGX, based on the 3 papers [14, 78, 137] that introduced it, on the Intel Software Developer’s Manual [100] (which supersedes the SGX manuals [94, 98]), on an ISCA 2015 tutorial [102], and on two patents [108, 136]. We use the papers, reference manuals, and tutorial as primary data sources, and only draw on the patents to fill in missing information. This paper’s contributions are a summary of the Intel-specific architectural and micro-architectural details needed to understand SGX, a detailed and structured presentation of the publicly available information on SGX, a series of intelligent guesses about some important but undocumented aspects of SGX, and an analysis of SGX’s security properties.


title: The relationship between BOLD and neural activity arises from temporally sparse events

abstract: Abstract Resting state functional magnetic resonance (rs-fMRI) imaging offers insights into how different brain regions are connected into functional networks. It was recently shown that networks that are almost identical to the ones created from conventional correlation analysis can be obtained from a subset of high-amplitude data, suggesting that the functional networks may be driven by instantaneous co-activations of multiple brain regions rather than ongoing oscillatory processes. The rs-fMRI studies, however, rely on the blood oxygen level dependent (BOLD) signal, which is only indirectly sensitive to neural activity through neurovascular coupling. To provide more direct evidence that the neuronal co-activation events produce the time-varying network patterns seen in rs-fMRI studies, we examined the simultaneous rs-fMRI and local field potential (LFP) recordings in rats performed in our lab over the past several years. We developed complementary analysis methods that focus on either the temporal or spatial domain, and found evidence that the interaction between LFP and BOLD may be driven by instantaneous co-activation events as well. BOLD maps triggered on high-amplitude LFP events resemble co-activation patterns created from rs-fMRI data alone, though the co-activation time points are defined differently in the two cases. Moreover, only LFP events that fall into the highest or lowest thirds of the amplitude distribution result in a BOLD signal that can be distinguished from noise. These findings provide evidence of an electrophysiological basis for the time-varying co-activation patterns observed in previous studies.


title: SiC JFET in Contrast to High Speed Si IGBT in Matrix Converter Topology

abstract: The retrograde efforts in the field of silicon carbide (SiC) technology led to outstanding device parameters of high voltage unipolar devices. Overcoming silicon (Si) unipolar limits in SiC, currently the vertical JFET is the most serious active switch which seems to outperform all the established technology. With exemplaryly 48 Wan'3 the matrix converter is predicted to reach highest power densities in future using SiC devices [1][2]. But on the silicon side, 1200 V field-stop IGBTs with planar gate structure enabling operating frequencies in hard switching applications up to 100 kHz, too [3]. In this paper the conduction and switching behaviour of both transistor types is analyzed with respect to the matrix converter demands. The combination of measured data and a particular simulation gives results in terms of power losses and efficiency.


title: PSCAN: A Parallel Structural Clustering Algorithm for Big Networks in MapReduce

abstract: Big data such as complex networks with over millions of vertices and edges is infeasible to process using conventional computation. MapReduce is a programming model that empowers us to analyze big data in a cluster of computers. In this paper we propose a Parallel Structural Clustering Algorithm for big Networks (PSCAN) in MapReduce for the detection of clusters or community structures in big networks such as Twitter. PSCAN is based on the structural clustering algorithm of SCAN, which not only finds cluster accurately, but also identifies vertices playing special roles such as hubs and outliers. An empirical evaluation of PSCAN using both real and synthetic networks demonstrated an outstanding performance in terms of accuracy and running time. We analyzed a Twitter network with over 40 million users and 1.4 billion follower/following relationships by using PSCAN on a Hadoop cluster with 15 computers. The result shows that PSCAN successfully detected interesting communities of people who share common interests.


title: Effect of Host and Guest Structures on Hydrogen Bonding Association Influence on Stoichiometry and Equilibrium Constants

abstract: The study of the electrochemical reduction of sevcral quinones, Q, in the presence of alcohols. n-ROH, and diols, I,n-K(OH)2, with different chemical structures is reported. From the stoichiometry and the formation constants of the different association complexes, obtained from voltammetric results, mole-fraction distribution and average-number-of-ligand diagrams were constructed to analyze structural effects on hydrogen bonding association between the quinone dianion species and the alcohols. It was demonstrated that the association process can be determined by the quinone basicity, steric effects, and by the a-hydrogens in the quinone nucleus. The quinone basicity determines the strength of the association process when no u-hydrogens are present. Meanwhile, the strength of this interaction is intensified when the number of a-hydrogens is increased in the quinone nucleus. The passage from a situation of an association sterically controlled to a situation controlled by the basicity of the quinone can be modulated with the structure and the amount of the hydrogen-bonding donor present in the medium. Molecular recognition is well recognized to play a crucial role in molecular redox switches and shuttles involving bimolecular or supramolecular systems. They present two forms, whose interconversion can bc modulated by redox reactions, as in the case of some molecular switch systems. The effect of the several structure factors on the redox behavior and physical properties of redox -active organic molecules has been studied by several authors.'-' The conformational and physical properties in such molecular recognition systems result in different hydrogen bond interactions which depend on the redox states of the ~om~onents.~-~ Due to their strength and directionality, hydrogen bonds are being used in redox-mediated molecular recognition ~~stems.~.~ They are ubiquitous in biological systems, providing essential recognition, structural, and control ele ments needed to coordinate and run the complex molecular machinery required for life.'0,"


In God We Trust. All Others Must Bring Data. - W. Edwards Deming. Using Word Embeddings to Recognize Idioms


title: Automatic deception detection: methods for finding fake news

abstract: This research surveys the current state-of-the-art technologies that are instrumental in the adoption and development of fake news detection. "Fake news detection" is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres. ::: ::: The paper provides a typology of several varieties of veracity assessment methods emerging from two major categories -- linguistic cue approaches (with machine learning), and network analysis approaches. We see promise in an innovative hybrid approach that combines linguistic cue and machine learning, with network-based behavioral data. Although designing a fake news detector is not a straightforward problem, we propose operational guidelines for a feasible fake news detecting system.


title: Genome shuffling: a method to improve biotechnological processes

abstract: Genome shuffling is widely used for increasing the production of metabolites by bacterial strains, improving substrate uptake as well as enhancing strain tolerance. This technique combines the advantage of multiparental crossing allowed by DNA shuffling together with the recombination of entire genomes normally associated with conventional breeding, or through protoplast fusion that increases the recombination process. The method of genome shuffling was first presented by Stemmer and co-workers in 2002 when it was used to improve the production of tylosin by Streptomyces fradiae. Nowadays, it is used in many experiments for increasing the production of metabolites by bacterial strains, improving substrate uptake, and enhancing strain tolerance. Genome shuffling is a major milestone in the strain-improvement technology and metabolic engineering.


title: SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach

abstract: The SimpleQuestions dataset is one of the most commonly used benchmarks for studying single-relation factoid questions. In this paper, we present new evidence that this benchmark can be nearly solved by standard methods. First we show that ambiguity in the data bounds performance on this benchmark at 83.4%; there are often multiple answers that cannot be disambiguated from the linguistic signal alone. Second we introduce a baseline that sets a new state-of-the-art performance level at 78.1% accuracy, despite using standard methods. Finally, we report an empirical analysis showing that the upperbound is loose; roughly a third of the remaining errors are also not resolvable from the linguistic signal. Together, these results suggest that the SimpleQuestions dataset is nearly solved.


title: Decoding with Large-Scale Neural Language Models Improves Translation

abstract: We explore the application of neural language models to machine translation. We develop a new model that combines the neural probabilistic language model of Bengio et al., rectified linear units, and noise-contrastive estimation, and we incorporate it into a machine translation system both by reranking k-best lists and by direct integration into the decoder. Our large-scale, large-vocabulary experiments across four language pairs show that our neural language model improves translation quality by up to 1.1 Bleu.


title: To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

abstract: Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. ::: In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way bypassing the need for an explicit simulation. ::: We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli.


title: A Systematic Review of Information Security Frameworks in the Internet of Things (IoT)

abstract: By 2020, it is estimated that the number of connected devices is expected to grow exponentially to 50 billion. Internet of things has gained extensive attention, the deployment of sensors, actuators are increasing at a rapid pace around the world. There is tremendous scope for more streamlined living through an increase of smart services, but this coincides with an increase in security and privacy concerns. There is a need to perform a systematic review of Information security governance frameworks in the Internet of things (IoT). Objective – The aim of this paper to evaluate systematic review of information security management frameworks which are related to the Internet of things (IoT). It will also discuss different information security frameworks that cover IoT models and deployments across different verticals. These frameworks are classified according to the area of the framework, the security executives and senior management of any enterprise that plans to start using smart services needs to define a clear governance strategy concerning the security of their assets, this system review will help them to make a better decision for their investment for secure IoT deployments. Method – A set of standard criteria has been established to analyze which security framework will be the best fit among these classified security structures in particularly for Internet of Things (IoT). The first step to evaluate security framework by using standard criteria methodology is to identify resources, the security framework for IoT is selected to be assessed according to CCS. The second step is to develop a set of Security Targets (ST). The ST is the set of criteria to apply for the target of evaluation (TOE). The third step is data extraction, fourth step data synthesis, and final step is to write-up study as a report. Conclusion– After reviewing four information security risk frameworks, this study makes some suggestions related to information security risk governance in Internet of Things (IoT). The organizations that have decided to move to smart devices have to define the benefits and risks and deployment processes to manage security risk. The information security risk policies should comply with an organization's IT policies and standards to protect the confidentiality, integrity and availability of information security. The study observes some of the main processes that are needed to manage security risks. Moreover, the paper also drew attention on some suggestions that may assist companies which are associated with the information security framework in Internet of things (IoT).


title: Ultra-Dense Networks: A Survey

abstract: The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by today’s customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.


title: Lighting-Invariant Visual Odometry using Lidar Intensity Imagery and Pose Interpolation

abstract: Recent studies have demonstrated that images constructed from lidar reflectance information exhibit superior robustness to lighting changes in outdoor environments in comparison to traditional passive stereo camera imagery. Moreover, for visual navigation methods originally developed using stereo vision, such as visual odometry (VO) and visual teach and repeat (VT&R), scanning lidar can serve as a direct replacement for the passive sensor. This results in systems that retain the efficiency of the sparse, appearance-based techniques while overcoming the dependence on adequate/consistent lighting conditions required by traditional cameras. However, due to the scanning nature of the lidar and assumptions made in previous implementations, data acquired during continuous vehicle motion suffer from geometric motion distortion and can subsequently result in poor metric VO estimates, even over short distances (e.g., 5–10 m). This paper revisits the measurement timing assumption made in previous systems, and proposes a frame-to-frame VO estimation framework based on a novel pose interpolation scheme that explicitly accounts for the exact acquisition time of each feature measurement. In this paper, we present the promising preliminary results of our new method using data generated from a lidar simulator and experimental data collected from a planetary analogue environment with a real scanning laser rangefinder.


title: Design and Efficient FPGA Implementation of Ghash Core for AES-GCM

abstract: AES-GCM(Advanced Encryption Standard with Galois Counter Mode) is an encryption authentication algorithm, which includes two main components: an AES engine and Ghash module. Because of the computation feedback in Ghash operation, the Ghash module limits the performance of the whole AES-GCM system. In this study, an efficient architecture of Ghash is presented. The architecture uses an optimized bit-parallel multiplier. In addition, based on this multiplier, pipelined method is adopted to achieve higher clock rate and throughput. We also introduce a redundant register method, which is never mentioned before, for solving the big fan- out problem derived from the bit-parallel multiplier. In the end, the performance of proposed design is evaluated on Xilinx virtex4 FPGA platform. The experimental results show that our Ghash core has less clock delay and can easily achieve higher throughput, which is up to 40Gbps.


title: Holophrasm: a neural Automated Theorem Prover for higher-order logic

abstract: I propose a system for Automated Theorem Proving in higher order logic using deep learning and eschewing hand-constructed features. Holophrasm exploits the formalism of the Metamath language and explores partial proof trees using a neural-network-augmented bandit algorithm and a sequence-to-sequence model for action enumeration. The system proves 14% of its test theorems from Metamath's set.mm module.


title: Adsorption of fluoride to UiO-66-NH2 in water: Stability, kinetic, isotherm and thermodynamic studies.

abstract: To provide safe drinking water, fluoride in water must be removed and adsorption processes appear to be the most widely used method. Metal organic frameworks (MOFs) represent a new class of adsorbents that have been used in various adsorption applications. To study the adsorption mechanism of fluoride to MOFs in water and obtain related adsorption parameters, we synthesized a zirconium-based MOF with a primary amine group on its ligand, named UiO-66-NH2. The kinetics, adsorption isotherm and thermodynamics of fluoride adsorption to UiO-66-NH2 were investigated. The crystalline structure of UiO-66-NH2 remained intact and the local structure of zirconium in UiO-66-NH2 did not change significantly after being exposed to fluoride. The kinetics of the fluoride adsorption in UiO-66-NH2 could be well represented by the pseudo second order rate law. The enthalpy of the adsorption indicates that the F(-) adsorption to UiO-66-NH2 was classified as a physical adsorption. However, the comparison between the adsorption capacities of UiO-66-NH2 and UiO-66 suggests that the fluoride adsorption to UiO-66-NH2 might primarily involve a strong interaction between F(-) and the metal site. The fluoride adsorption capacity of UiO-66-NH2 was found to decrease when pH>7. While the presence of chloride/bromide ions did not noticeably change the adsorption capacity of UiO-66-NH2, the ionic surfactants slightly affected the adsorption capacity of UiO-66-NH2. These findings provide insights to further optimize the adsorption process for removal of fluoride using zirconium-based MOFs.


title: Permission-Based Android Malware Detection

abstract: Mobile devices have become popular in our lives since they offer almost the same functionality as personal computers. Among them, Android-based mobile devices had appeared lately and, they were now an ideal target for attackers. Android-based smartphone users can get free applications from Android Application Market. But, these applications were not certified by legitimate organizations and they may contain malware applications that can steal privacy information for users. In this paper, a framework that can detect android malware applications is propos ed to help organizing Android Market. The proposed framework intends to develop a machine learning-based malware detection system on Android to detect malware applications and to enhance security and privacy of smartphone users. This system monitors various permissionbased features and events obtained from the android applications, and analyses these features by using machine learning classifiers to classify whether the application is goodware or malware.


title: Opinion Fraud Detection in Online Reviews by Network Effects

abstract: User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.


title: Electronic Customer Relationship Management (eCRM): Opportunities and Challenges in a Digital World

abstract: Electronic customer relationship management (eCRM) is seen to arise from the consolidation of traditional CRM with the e-business applications marketplace and has created a flurry of activity among companies. eCRM is the proverbial double-edged sword, presenting both opportunities and challenges for companies considering its adoption and implementation. This paper explores the marketing opportunities eCRM creates for companies such as enhanced customer interactions and relationships as well as personalisation options, all of which are potential sources of competitive advantage. It also explores the challenges confronting companies implementing eCRM such as managing an on-line channel, data integration issues and information technology (IT) architecture challenges. Directions for future research are also suggested.


An Empirical Analysis of Cyber Security Incidents at a Large Organization