title: DiscLDA: Discriminative learning for dimensionality reduction and classification

abstract: Probabilistic topic models have become popular as methods for dimensionality reduction in collections of text documents or images. These models are usually treated as generative models and trained using maximum likelihood or Bayesian methods. In this paper, we discuss an alternative: a discriminative framework in which we assume that supervised side information is present, and in which we wish to take that side information into account in finding a reduced dimensionality representation. Specifically, we present DiscLDA, a discriminative variation on Latent Dirichlet Allocation (LDA) in which a class-dependent linear transformation is introduced on the topic mixture proportions. This parameter is estimated by maximizing the conditional likelihood. By using the transformed topic mixture proportions as a new representation of documents, we obtain a supervised dimensionality reduction algorithm that uncovers the latent structure in a document collection while preserving predictive power for the task of classification. We compare the predictive power of the latent structure of DiscLDA with unsupervised LDA on the 20 Newsgroups document classification task and show how our model can identify shared topics across classes as well as class-dependent topics.


title: A Review of Data Mining-Based Financial Fraud Detection Research

abstract: Nationwide, financial losses due to financial statement frauds (FSF) are mounting. The industry recognizes the problem and is just now starting to act. Although prevention is the best way to reduce frauds, fraudsters are adaptive and will usually find ways to circumvent such measures. Detecting fraud is essential once prevention mechanism has failed. Several data mining algorithms have been developed that allow one to extract relevant knowledge from a large amount of data like fraudulent financial statements to detect FSF. Detecting FSF is a new attempt; thus, several research questions have often being asked: (1) Can FSF be detected? How likely and how to do it? (2) What data features can be used to predict FSF? (3) What kinds of algorithm can be used to detect FSF? (4) How to measure the performance of the detection? And (5) How effective of these algorithms in terms of fraud detection? To help answer these questions, we conduct an extensive review on literatures. We present a generic framework to guide our analysis. Critical issues for FSF detection are identified and discussed. Finally, we share directions for future research.


title: Exploiting learning and scenario-based specification languages for the verification and validation of highly automated driving

abstract: We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue capturing requirements for controlling criticality for highly autonomous vehicles. We sketch underlying mathematical foundations which allow to derive formal confidence levels that vehicles tested by such a scenario catalogue will maintain the required control of criticality in real traffic matching the probability distributions of key parameters of data recorded in the reference data base employed for this process.


title: A sketch-based distance oracle for web-scale graphs

abstract: We study the fundamental problem of computing distances between nodes in large graphs such as the web graph and social networks. Our objective is to be able to answer distance queries between pairs of nodes in real time. Since the standard shortest path algorithms are expensive, our approach moves the time-consuming shortest-path computation offline, and at query time only looks up precomputed values and performs simple and fast computations on these precomputed values. More specifically, during the offline phase we compute and store a small "sketch" for each node in the graph, and at query-time we look up the sketches of the source and destination nodes and perform a simple computation using these two sketches to estimate the distance.


title: CityHeat: visualizing cellular automata-based traffic heat in Unity3D

abstract: Cellular Automata (CA) based simulation can be employed to understand traffic heat emission at micro scale level. Simulation space is divided into cubes of various sizes that encapsulate temperature within the cubes volume at different time stamps. Using this simulation result, we introduce CityHeat, a visualization tool to observe traffic heat distribution developed using Unity3D. Game engine is employed as a tool to reach interdisciplinary stakeholder groups that might not be coming from engineering background to understand the domain problem.


title: Demystifying Envelope Tracking: Use for High-Efficiency Power Amplifiers for 4G and Beyond

abstract: The efficiency of a radio-frequency (RF) power amplifier (PA) is generally defined as the ratio between the desired transmitted radio power and the total power from a power supply, which is rather low in average with modern high peak-to-average-powerratio (PAPR) signals, as illustrated in Figure 1(a). Consequently, a small improvement in the efficiency of the RF PA can have substantial benefits for a wireless system and cut the overall costs of system operations.


title: Age standardization of rates: a new WHO standard

abstract: Summary A recent WHO analysis has revealed the need for a new world standard population (see attached table). This has become particularly pertinent given the rapid and continued declines in age-specific mortality rates among the oldest old, and the increasing availability of epidemiological data for higher age groups. There is clearly no conceptual justification for choosing one standard over another, hence the choice is arbitrary. However, choosing a standard population with higher proportions in the younger age groups tends to weight events at these ages disproportionately. Similarly, choosing an older standard does the opposite. Hence, rather than selecting a standard to match the current age-structure of some population(s), the WHO adopted a standard based on the average age-structure of those populations to be compared (the world) over the likely period of time that a new standard will be used (some 25-30 years), using the latest UN assessment for 1998 (UN Population Division, 1998). From these estimates, an average world population age-structure was constructed for the period 2000-2025. The use of an average world population, as well as a time series of observations, removes the effects of historical events such as wars and famine on population age composition. The terminal age group in the new WHO standard population has been extended out to 100 years and over, rather than the 85 and over as is the current practice. The WHO World Standard population has fewer children and notably more adults aged 70 and above than the world standard. It is also notably younger than the European standard. It is important to note, however, that the age standardized death rates based on the new standard are not comparable to previous estimates that are based on some earlier standard(s). However, to facilitate comparative analyses, WHO will disseminate trend analyses of the “complete” historical mortality data using on the new WHO World Standard Population in future editions of the World Health Statistics Annual.


title: FIGR: Few-shot Image Generation with Reptile

abstract: Generative Adversarial Networks (GAN) boast impressive capacity to generate realistic images. However, like much of the field of deep learning, they require an inordinate amount of data to produce results, thereby limiting their usefulness in generating novelty. In the same vein, recent advances in meta-learning have opened the door to many few-shot learning applications. In the present work, we propose Few-shot Image Generation using Reptile (FIGR), a GAN meta-trained with Reptile. Our model successfully generates novel images on both MNIST and Omniglot with as little as 4 images from an unseen class. We further contribute FIGR-8, a new dataset for few-shot image generation, which contains 1,548,944 icons categorized in over 18,409 classes. Trained on FIGR-8, initial results show that our model can generalize to more advanced concepts (such as "bird" and "knife") from as few as 8 samples from a previously unseen class of images and as little as 10 training steps through those 8 images. This work demonstrates the potential of training a GAN for few-shot image generation and aims to set a new benchmark for future work in the domain.


title: Hierarchical Taxonomy Aware Network Embedding

abstract: Network embedding learns the low-dimensional representations for vertices, while preserving the inter-vertex similarity reflected by the network structure. The neighborhood structure of a vertex is usually closely related with an underlying hierarchical taxonomy---the vertices are associated with successively broader categories that can be organized hierarchically. The categories of different levels reflects similarity of different granularity. The hierarchy of the taxonomy therefore requires that the learned representations support multiple levels of granularity. Moreover, the hierarchical taxonomy enables the information to flow between vertices via their common categories, and thus provides an effective mechanism for alleviating data scarcity. However, incorporating the hierarchical taxonomy into network embedding poses a great challenge (since the taxonomy is generally unknown), and it is neglected by the existing approaches. In this paper, we propose NetHiex, a NETwork embedding model that captures the latent HIErarchical taXonomy. In our model, a vertex representation consists of multiple components that are associated with categories of different granularity. The representations of both the vertices and the categories are co-regularized. We employ the nested Chinese restaurant process to guide the search of the most plausible hierarchical taxonomy. The network structure is then recovered from the latent representations via a Bernoulli distribution. The whole model is unified within a nonparametric probabilistic framework. A scalable expectation-maximization algorithm is derived for optimization. Empirical results demonstrate that NetHiex achieves significant performance gain over the state-of-arts.


title: Java Idioms: Code Blocks and Control Flow

abstract: For example, polymorphism can in principle be used instead of switch statements [Fowler99]. This can significantly reduce the complexity of the using code because it encapsulates the selection of behavior. However, this comes at a price: for every different case a type needs to be defined. The effort and complexity associated with the creation of types makes this approach inapplicable for sufficiently small scopes: obviously, most if statements are not candidates for replacement with polymorphism.


title: Deep Survival Analysis

abstract: The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we investigate survival analysis in the context of EHR data. We introduce deep survival analysis, a hierarchical generative approach to survival analysis. It departs from previous approaches in two primary ways: (1) all observations, including covariates, are modeled jointly conditioned on a rich latent structure; and (2) the observations are aligned by their failure time, rather than by an arbitrary time zero as in traditional survival analysis. Further, it (3) scalably handles heterogeneous (continuous and discrete) data types that occur in the EHR. We validate deep survival analysis model by stratifying patients according to risk of developing coronary heart disease (CHD). Specifically, we study a dataset of 313,000 patients corresponding to 5.5 million months of observations. When compared to the clinically validated Framingham CHD risk score, deep survival analysis is significantly superior in stratifying patients according to their risk.


Pingmesh: A Large-Scale System for Data Center Network Latency Measurement and Analysis


title: Short-Range Wideband FMCW Radar for Millimetric Displacement Measurements

abstract: The frequency-modulated continuous-wave (FMCW) radar is an alternative to the pulse radar when the distance to the target is short. Typical FMCW radar implementations have a homodyne architecture transceiver which limits the performances for short-range applications: The beat frequency can be relatively small and placed in the frequency range affected by the specific homodyne issues (dc offset, self-mixing, and 1/f noise). In addition, one classical problem of an FMCW radar is that the voltage-controlled oscillator adds a certain degree of nonlinearity which can cause a dramatic resolution degradation for wideband sweeps. This paper proposes a short-range X-band FMCW radar platform which solves these two problems by using a heterodyne transceiver and a wideband nonlinearity correction algorithm based on high-order ambiguity functions and time resampling. The platform's displacement measurement capability was tested on range profiles and synthetic aperture radar images acquired for various targets. The displacements were computed from the interferometric phase, and the measurement errors were situated below 0.1 mm for metal bar targets placed at a few meters from the radar.


title: Antimicrobial and anti-Quorum Sensing activities of selected medicinal plants of Ethiopia: Implication for development of potent antimicrobial agents

abstract: Background ::: Traditional medicinal plants have been used as an alternative medicine in many parts of the world, including Ethiopia. There are many documented scientific reports on antimicrobial activities of the same. To our knowledge, however, there is no report on the anti-Quorum Sensing (Quorum Quenching, QQ) potential of traditional Ethiopian medicinal plants. As many of the opportunistic pathogenic bacteria depend on Quorum Sensing (QS) systems to coordinate their virulence expression, interference with QS could be a novel approach to control bacterial infections. Thus, the aim of this study was to evaluate selected medicinal plants from Ethiopia for their antimicrobial activities against bacterial and fungal pathogens; and to assess the interference of these plant extracts with QS of bacteria.


title: Delaunay simplices pruning based clustering

abstract: In this paper, we introduce a new clustering method using the Delaunay triangulation of a set of points as an input. The proposed method is based on pruning extra simplices of a triangulation according to a local heterogeneity measure, which we introduce here. This measure produces good clustering results as it yields to better inter-cluster simplices detection. The efficiency of the measure is evaluated on 2-D shape data set.


title: A fully associative, tagless DRAM cache

abstract: This paper introduces a tagless cache architecture for large in-package DRAM caches. The conventional die-stacked DRAM cache has both a TLB and a cache tag array, which are responsible for virtual-to-physical and physical-to-cache address translation, respectively. We propose to align the granularity of caching with OS page size and take a unified approach to address translation and cache tag management. To this end, we introduce cache-map TLB (cTLB), which stores virtual-to-cache, instead of virtual-to-physical, address mappings. At a TLB miss, the TLB miss handler allocates the requested block into the cache if it is not cached yet, and updates both the page table and cTLB with the virtual-to-cache address mapping. Assuming the availability of large in-package DRAM caches, this ensures that an access to the memory region within the TLB reach always hits in the cache with low hit latency since a TLB access immediately returns the exact location of the requested block in the cache, hence saving a tag-checking operation. The remaining cache space is used as victim cache for memory pages that are recently evicted from cTLB. By completely eliminating data structures for cache tag management, from either on-die SRAM or in-package DRAM, the proposed DRAM cache achieves best scalability and hit latency, while maintaining high hit rate of a fully associative cache. Our evaluation with 3D Through-Silicon Via (TSV)-based in-package DRAM demonstrates that the proposed cache improves the IPC and energy efficiency by 30.9% and 39.5%, respectively, compared to the baseline with no DRAM cache. These numbers translate to 4.3% and 23.8% improvements over an impractical SRAM-tag cache requiring megabytes of on-die SRAM storage, due to low hit latency and zero energy waste for cache tags.


title: Two decades of statistical language modeling: where do we go from here?

abstract: Statistical language models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them, point to a few promising directions, and argue for a Bayesian approach to integration of linguistic theories with data.


title: Confidence Based Dual Reinforcement Q-Routing: An adaptive online network routing algorithm

abstract: This paper describes and evaluates the Confidence-based Dual Reinforcement Q-Routing algorithm (CDRQ-Routing) for adaptive packet routing in communication networks. CDRQ-Routing is based on the Q-learning framework of Q-Routing. The main contribution of this work is the increased quantity and improved quality of exploration in CDRQ-Routing, which lead to faster adaptation and better routing policies learned as compared to Q-Routing, the state-of-the-art adaptive Bellman-Ford Routing, and the non-adaptive shortest path routing. Experiments over several network topologies have shown that at different loads, CDRQ-Routing learns superior policies significantly faster than Q-Routing. Moreover, CDRQ-Routing learns policies that sustain higher load levels than Q-Routing. Analysis shows that overhead due to exploration is insignificant as eqmpared to the improvements in CDRQ-Routing.


title: Comparison of the RADIUS and Diameter protocols

abstract: In this research, the differences between the diameter and the RADIUS protocols were studied on a practical example, and their behavior was tested in a real life situation. Diameter and the RADIUS test environment was created via the VitalAAA program which supports the RADIUS server and client, and the Diameter server and client. The reaction time for individual measurements was read from the log file. The following measurements were made: connection times for RADIUS and diameter protocols, client reaction time in case primary server is unavailable, transport reliability for diameter and RADIUS, and node search capability for diameter. The results are presented both tabular and graphically.


title: Immunology of asthma and chronic obstructive pulmonary disease

abstract: Asthma and chronic obstructive pulmonary disease (COPD) are both obstructive airway diseases that involve chronic inflammation of the respiratory tract, but the type of inflammation is markedly different between these diseases, with different patterns of inflammatory cells and mediators being involved. As described in this Review, these inflammatory profiles are largely determined by the involvement of different immune cells, which orchestrate the recruitment and activation of inflammatory cells that drive the distinct patterns of structural changes in these diseases. However, it is now becoming clear that the distinction between these diseases becomes blurred in patients with severe asthma, in asthmatic subjects who smoke and during acute exacerbations. This has important implications for the development of new therapies.


title: A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN)

abstract: Sales forecasting plays a key role for each business in this competitive environment. The forecasting of sales data in automobile industry has become a primary concern to predict the accuracy in future sales. This work addresses the problem of monthly sales forecasting in automobile industry (maruti car). The data set is based on monthly sales (past 5 year data from 2008 to 2012). Primarily, we used two forecasting methods namely Moving Average and Exponential smoothing to forecast the past data set and then we use these forecasted values as a input for ANFIS (Adaptive Neuro Fuzzy Inference System). Here, MA and ES forecasted values used as input variable for ANFIS to obtain the final accurate sales forecast. Finally we compare our model with two other forecasting models: ANN (Artificial Neural Network) and Linear Regression. Empirical results demonstrate that the ANFIS model gives better results out than other two models.


title: Automatic Discovery and Execution of Personal Applications from Shared IO Devices

abstract: Shared office information appliances such as copiers, scanners and meeting-room displays are increasingly being integrated into complex electronic document workflows. To support this new role these appliances are being designed with advanced features such as optical character recognition, networked storage, content-based routing and integration with back-end databases, yet availability of these features is often hampered by various factors. Here we present an architecture, code named Odessa, that moves all task-specific processing off the office appliance and on to a user's own device: PC / laptop, or smartphone. Key features of the architecture include a simple RESTful HTTP application interface and automatic service discovery.


title: 35-Gb/s VCSEL-Based optical link using 32-nm SOI CMOS circuits

abstract: A VCSEL-based multimode fiber optical link using 32nm SOI CMOS circuits is demonstrated. Record optical link power efficiencies of 1pJ/bit at 25Gb/s and 2.7pJ/bit at 35Gb/s are achieved.


title: A 2.8-to-5.8 GHz harmonic VCO in a 28 nm UTBB FD-SOI CMOS process

abstract: A 2.8-to-5.8GHz VCO designed in a 28nm UTBB FD-SOI CMOS process adopts a reconfigurable active core to save power at the lower oscillation frequencies, and to enable a trade-off between power consumption and phase noise at all frequencies. The UTBB FD-SOI CMOS process is instrumental to achieve a tuning range in excess of one octave at low power consumption, while the use of an 8-shaped tank coil yields a VCO that is highly insensitive to external magnetic fields. The VCO operates from 0.9V and has a figure-of-merit of 186–189 dBc/Hz, depending on the oscillation frequency and the configuration of the oscillator core. The active area of the VCO is 380 µm × 700 µm.


title: The influence of Mozart’s music on brain activity in the process of learning

abstract: Abstract Objective The study investigated the influence Mozart's music has on brain activity in the process of learning. A second objective was to test Rauscher et al.'s (1993) priming explanation of the Mozart effect. Methods In Experiment 1 individuals were first trained in how to solve spatial rotation tasks, and then solved similar tasks. Fifty-six students were divided into 4 groups: a control one – CG who prior to and after training relaxed, and three experimental groups: MM – who prior to and after training listened to music; MS – who prior to training listened to music and subsequently relaxed; and SM – who prior to training relaxed and afterward listened to music. The music used was the first movement of Mozart's sonata (K. 448). In Experiment 2, thirty-six respondents were divided into three groups: CG, MM (same procedure as in Experiment 1), and BM – who prior to and after training listened to Brahms' Hungarian dance No. 5. In both experiments the EEG data collected during problem solving were analyzed using the methods of event-related desynchronization/synchronization (ERD/ERS) and approximated entropy (ApEn). Results In the first experiment the respondents of the MM, MS, and SM groups showed a better task-performance than did the respondents of the CG group. Individuals of the MM group displayed less complex EEG patterns and more α band synchronization than did respondents of the other three groups. In Experiment 2 individuals who listened to Mozart showed a better task performance than did the respondents of the CG and BM groups. They displayed less complex EEG patterns and more lower-1 α and γ band synchronization than did the respondents of the BM group. Conclusions Mozart's music, by activating task-relevant brain areas, enhances the learning of spatio-temporal rotation tasks. Significance The results support Rauscher et al.'s (1993) priming explanation of the Mozart effect.


title: Liver proteomic response to hypertriglyceridemia in human-apolipoprotein C-III transgenic mice at cellular and mitochondrial compartment levels

abstract: BackgroundHypertriglyceridemia (HTG) is defined as a triglyceride (TG) plasma level exceeding 150 mg/dl and is tightly associated with atherosclerosis, metabolic syndrome, obesity, diabetes and acute pancreatitis. The present study was undertaken to investigate the mitochondrial, sub-mitochondrial and cellular proteomic impact of hypertriglyceridemia in the hepatocytes of hypertriglyceridemic transgenic mice (overexpressing the human apolipoproteinC-III).MethodsQuantitative proteomics (2D-DIGE) analysis was carried out on both “low-expressor” (LE) and “high-expressor” (HE) mice, respectively exhibiting moderate and severe HTG, to characterize the effect of the TG plasma level on the proteomic response.ResultsThe mitoproteome analysis has revealed a large-scale phenomenon in transgenic mice, i.e. a general down-regulation of matricial proteins and up-regulation of inner membrane proteins. These data also demonstrate that the magnitude of proteomic changes strongly depends on the TG plasma level. Our different analyses indicate that, in HE mice, the capacity of several metabolic pathways is altered to promote the availability of acetyl-CoA, glycerol-3-phosphate, ATP and NADPH for TG de novo biosynthesis. The up-regulation of several cytosolic ROS detoxifying enzymes has also been observed, suggesting that the cytoplasm of HTG mice is subjected to oxidative stress. Moreover, our results suggest that iron over-accumulation takes place in the cytosol of HE mice hepatocytes and may contribute to enhance oxidative stress and to promote cellular proliferation.ConclusionsThese results indicate that the metabolic response to HTG in human apolipoprotein C-III overexpressing mice may support a high TG production rate and that the cytosol of hepatocytes is subjected to an important oxidative stress, probably as a result of FFA over-accumulation, iron overload and enhanced activity of some ROS-producing catabolic enzymes.


title: A heuristic checklist for an accessible smartphone interface design

abstract: Smartphone technology has evolved into a multi-functional device with advanced capabilities, but this mobile technology remains inaccessible to many individuals with visual impairments or upper extremity disabilities. This paper provides a heuristic checklist for accessible smartphone interface design, developed through reviewing existing design standards and guidelines and validating these guidelines with user involvement. Specifically, a set of preliminary user requirements (59 items) was extracted from existing standards, guidelines, and user requirements regarding mobile handheld device accessibility. Subsequently, the requirement set was filtered using a participatory method and then integrated to create an operational version of design guidelines. These guidelines were then used in a heuristic evaluation and usability testing on high-fidelity prototypes produced by a commercial manufacturer. A heuristic checklist for designing accessible smartphones was formed, which may also be applicable to other touchscreen handheld devices (e.g., printer screen) in terms of accessibility features. The initial set of 59 user requirements was re-organized into 44 statements in six general categories: mechanical controls, display, speech and general operation controls, audio feedback controls, touch-operated controls, and others. Using results from both qualitative and quantitative methods provides support, though with some limitations, for this accessibility checklist. This checklist is intended as a practical design support tool for use in early design phases of handheld products. A number of challenges and limitations are discussed as well.


title: COMPARATIVE ANALYSIS THE EFFECT OF FUSION ALGORITHM ON HIGH RESOLUTION IMAGES

abstract: Images fusion has been widely applied to imaging sensors for the purpose of resolution merge, image ::: integration, and multi sensor data fusion. In image fusion, a low spatial resolution multispectral image is ::: fused with a higher resolution panchromatic image to produce an image with higher spectral and spatial ::: resolution. In this paper, we investigate the existing fusion methods based on visual ,spectral analyse and ::: classification acuracy. The accuracy of classification result is assessed by means of the support vector ::: machine based on radial basis function kernel. Comparing the performance of various fusion methods, it is shown that the Gram-Schmidt (GS) method yields the best accuracy, followed by the principal component analysis (PCA). The producer’s and user’s accuracies of the GS method are 91.8% and 91.1%, respectively, followed by 90.8% and 90.0% of the PCA method.


title: Technology innovation and knowledge management in the high-tech industry

abstract: This study presents a comprehensive and integrated discussion of the various facets of technology innovation and Knowledge Management (KM) for high-tech firms. KM represents a key contemporary trend and is a key management discipline in decision making, strategy development, information systems and technology management. KM provides a broad perspective, an efficient method of problem-solving, sharing managerial knowledge and leveraging technological knowledge. This work focuses mainly on influences that might shape the future of knowledge-based technological innovation in the knowledge economy, particularly, the influence of knowledge activities. Accordingly, it is important to provide some brief perspectives on the development of technological innovation and KM.


title: Total Quality Management in Public Sector Higher Education Institutions

abstract: This study attempts to measure the extent to which Total Quality Management (TQM) is being implemented in Public Sector Higher Education Institutions of Pakistan. The study following a quantitative approach is based on data collected through a questionnaire. The Cronbach’s Alpha for this instrument was above 0.80 for almost all dimensions of TQM considered in the study. After meeting its pre-requisites (Kaiser-Meyer-Olkin (KMO) and Bartlett's test of Sphericity), the statistical tool of Exploratory Factor Analysis (EFA) was used to meet the main objective of this research. This study identifies areas of improvement as far as Total Quality in Public Sector Higher Education Institutions (HEI’s) is concerned. Main conclusion of the study is that TQM is being adopted in higher education institution but at a slower pace. Whereas, areas of improvement include Leadership, Vision Ownership, Evaluation Standardization, Process and Continuous Improvement, Employee Training and Student Focus. Decision and policy makers in Higher Education Institutions can use the findings of this study and opt for affirmative actions in order to bridge the gaps in TQM implementation.


title: Measuring Distributional Similarity in Context

abstract: The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.


title: On surveillance for safety critical events: In-vehicle video networks for predictive driver assistance systems

abstract: A distributed camera-sensor system for driver assistance and situational awareness.Systematic, comparative evaluation of cues for prediction of safety critical events.Real-time prediction of overtaking and braking maneuvers.Detailed temporal analysis of the utility of various cues for maneuver prediction.Early prediction (1-2s) before the maneuver is shown on real-world data. We study techniques for monitoring and understanding real-world human activities, in particular of drivers, from distributed vision sensors. Real-time and early prediction of maneuvers is emphasized, specifically overtake and brake events. Study this particular domain is motivated by the fact that early knowledge of driver behavior, in concert with the dynamics of the vehicle and surrounding agents, can help to recognize dangerous situations. Furthermore, it can assist in developing effective warning and driver assistance systems. Multiple perspectives and modalities are captured and fused in order to achieve a comprehensive representation of the scene. Temporal activities are learned from a multi-camera head pose estimation module, hand and foot tracking, ego-vehicle parameters, lane and road geometry analysis, and surround vehicle trajectories. The system is evaluated on a challenging dataset of naturalistic driving in real-world settings.


D 3 : Data-Driven Documents


title: Yeast Cultures in Ruminant Nutrition

abstract: DENEV, S. A., Tz. PEEVA, P. RADULOVA, P. STANCHEVA, G. STAYKOVA, G. BEEV, P. TODOROVA and S. TCHOBANOVA, 2007. Yeast cultures in ruminant nutrition. Bulg. J. Agric. Sci. 13: 357-374 Interest in the use of fungal direct-fed microbials in ruminant nutrition is considerable. The ban of antibiotic growth promoters in feed for production of animal foods has increased interest in evaluating the effect of yeast cultures (YC) on the gastrointestinal ecosystem, rumen microbial populations and function. The effects of specific YC preparations on the rumen environment and performance of ruminants have been well documented, and has generated considerable scientific interest over the last two decades. The precise mode of action by which YC, which are mostly derived from Saccharomyces cerevisiae, improve livestock performance has attracted the attention of a number of researchers in the world. It is clear from these research efforts that YC supplements can beneficially modify microbial activities, fermentative and digestive functions in the rumen. The research has demonstrated that viable YC preparations can stimulate specific groups of beneficial bacteria in the rumen, and has provided mechanistic models that can explain their effects on animal performance. The effects of YC on animal productivity are strain-dependant. So, all YC preparations are not equivalent in efficiency. This aspect opens a new field of research for new strains, each being more specialized in its use. The goal of many of these research activities has been to define the application and production strategies that can optimize animal responses to YC supplements. Continuous research with live YC supplements has clearly established scientifically-proven strategies for modifying and optimizing microbial activities in the gastrointestinal ecosystem and techniques for improving performance and health of ruminants. This article reviews the current status of the use of live yeast cultures in ruminant nutrition.


Android based Automated Irrigation System using Raspberry Pi


title: Ontological Constructs to Create Money Laundering Schemes

abstract: There is an increasing tendency in the money laundering sector to utilize electronic commerce and web services. Misuse of web services and electronic money transfers occurs at many points in complex trading schemes. We provide ontological components that can be combined to construct some of these money laundering schemes. These constructs can be helpful for investigators, in order to decompose suspected financial schemes and recognize financial misuses.


title: SEAL: A Domain-Specific Language for Novice Wireless Sensor Network Programmers

abstract: A lot of the prospective wireless sensor network users are novice programmers. Their experience in general-purpose programming languages is either limited or completely nonexistent. There are both financial and scientific incentives to empower these users and allow them to write sensor network applications on their own, rather than having to rely on a qualified computer science professional. We present SEAL, a sensor network programming language designed for novice programmers. SEAL manages to avoid computer science concepts that are hard to grasp for novices, while remaining suitable for typical sensor network application scenarios. The language is extensible in application-specific way, has easy-to-learn syntax and allows to implement common sensor network tasks by writing compact, readable code. It is also shown to have high run-time efficiency.


title: Atmospheric propagation of partially coherent radiation*

abstract: An extended, Rayleigh-Sommerfeld integral method is used to derive expressions for the mutual coherence function and radiation intensity derived from a planar, partially coherent source propagating through the atmosphere. The derived results reduce to previous results for (i) coherent radiation propagation in the atmosphere and (ii) the relations relating the far-field intensity angular distribution and the source coherence for a partially coherent source in vacuo. A mathematical description of the predicted results in terms of the vacuum distribution and scattering functions (related to the Fourier-transformed two-source mutual coherence function) is permitted by this development. Analytical results are calculated for a homogeneous atmosphere and a source coherence that simulates a laser-illuminated rough surface. The effective far-field range is determined by the source size, wavelength, and source coherence length. The phase of the calculated mutual coherence function is determined by the field-point separation for off-axial propagation directions. Numerical results for the amplitude and phase coherence lengths are calculated and illustrated as a function of the source size, source coherence length, propagation angle, range, and refractive-index structure constant.


title: Pyroptosis: host cell death and inflammation

abstract: Microorganism- and host-derived signals can stimulate formation of a multiprotein complex called the inflammasome, which activates the cysteine protease caspase 1. In turn, caspase 1 triggers an inflammatory programmed cell death pathway known as pyroptosis. Numerous pathogens have evolved a mechanism to subvert pyroptosis and persist within infected cells.


title: RFID tag antenna based temperature sensing in the frequency domain

abstract: The efficiency of cold supply chain operations can be improved with pervasive temperature sensing. In this paper, we investigate the design of a low-cost, single-use RFID based temperature threshold sensor that is capable of relating the violation of a temperature threshold to a shift in the optimal operating frequency at which the tag antenna is well matched to the tag IC. This shift is detectable by commercial UHF RFID readers operating in the 902–928 MHz frequency band. We will illustrate how state change information is preserved using a nonelectric memory mechanism that works even in the absence of reader transmitted power. We demonstrate that the sensor works reliably for a read distance of over 3 m and in noisy environments.


title: Circular Fourier-HOG features for rotation invariant object detection in biomedical images

abstract: In this paper we present a new system for generic rotation invariant 2D object detection based on circular Fourier HOG features. Our system combines the advantages of a dense voting scheme as it is used in the Holomorphic Filter framework with features based on local orientation statistics. Experiments on two different biological datasets show superior detection performance over four state-of-the-art reference approaches.


title: Person Re-identification by Deep Learning Attribute-Complementary Information

abstract: Automatic person re-identification (re-id) across camera boundaries is a challenging problem. Approaches have to be robust against many factors which influence the visual appearance of a person but are not relevant to the person's identity. Examples for such factors are pose, camera angles, and lighting conditions. Person attributes are a semantic high level information which is invariant across many such influences and contain information which is often highly relevant to a person's identity. In this work we develop a re-id approach which leverages the information contained in automatically detected attributes. We train an attribute classifier on separate data and include its responses into the training process of our person re-id model which is based on convolutional neural networks (CNNs). This allows us to learn a person representation which contains information complementary to that contained within the attributes. Our approach is able to identify attributes which perform most reliably for re-id and focus on them accordingly. We demonstrate the performance improvement gained through use of the attribute information on multiple large-scale datasets and report insights into which attributes are most relevant for person re-id.


Electronic word of mouth effects on consumers ’ brand attitudes , brand image and purchase intention : an empirical study in Egypt


title: Human Uncertainty and Ranking Error -- The Secret of Successful Evaluation in Predictive Data Mining

abstract: One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or explicit knowledge, either by observing user interactions, or by asking users directly. But these sources of information are always subject to the volatility of human decisions, making utilised data uncertain to a particular extent. In this contribution, we elaborate on the impact of this human uncertainty when it comes to comparative assessments of different data mining approaches. In particular, we reveal two problems: (1) biasing effects on various metrics of model-based prediction and (2) the propagation of uncertainty and its thus induced error probabilities for algorithm rankings. For this purpose, we introduce a probabilistic view and prove the existence of those problems mathematically, as well as provide possible solution strategies. We exemplify our theory mainly in the context of recommender systems along with the metric RMSE as a prominent example of precision quality measures.


title: Abnormal Time Experiences in Major Depression: An Empirical Qualitative Study

abstract: Background: Phenomenological psychopathology, through theoretical and idiographic studies, conceptualizes major depressive disorder (MDD) as a disorder of time experience. Investigations on abnormal time experience (ATE) in MDD adopting methodologies requested by the standards of empirical sciences are still lacking. Our study aimed to provide a qualitative analysis, on an empirical ground and on a large scale, of narratives of temporal experiences of persons affected by MDD. Methods: We interviewed 550 consecutive patients affected by affective and schizophrenic disorders. Clinical files were analysed by means of consensual qualitative research. Results: Out of 100 MDD patients, 96 reported at least 1 ATE. The principal categories of ATE are vital retardation - the experience of a stagnation of endogenous vital processes (37 patients), the experience of present and future dominated by the past (29 patients), and the experience of the slackening of the flow oftime (25 patients). A comparison with ATE in schizophrenia patients showed that in MDD, unlike in schizophrenia, there is no disarticulation of time experience (disorder of temporal synthesis) but rather a disorder of conation or inhibition of becoming. Limitations: The interview style was not meant to make a quantitative assessment (“false negatives” cannot be excluded). Conclusions: Our findings confirm the relevance of distinctive features of ATE in MDD, support the hypothesis of an intrinsic disordered temporal structure in depressive symptoms, and may have direct implications in clinical practice, especially in relation to differential diagnosis, setting the boundaries between “true” and milder forms of depression, and neurobiological research.


title: Critical soft-tissue dimensions with dental implants and treatment concepts

abstract: Dental implants have proven to be a successful treatment option in fully and partially edentulous patients, rendering long-term functional and esthetic outcomes. Various factors are crucial for predictable long-term peri-implant tissue stability, including the biologic width; the papilla height and the mucosal soft-tissue level; the amounts of soft-tissue volume and keratinized tissue; and the biotype of the mucosa. The biotype of the mucosa is congenitally set, whereas many other parameters can, to some extent, be influenced by the treatment itself. Clinically, the choice of the dental implant and the position in a vertical and horizontal direction can substantially influence the establishment of the biologic width and subsequently the location of the buccal mucosa and the papilla height. Current treatment concepts predominantly focus on providing optimized peri-implant soft-tissue conditions before the start of the prosthetic phase and insertion of the final reconstruction. These include refined surgical techniques and the use of materials from autogenous and xenogenic origins to augment soft-tissue volume and keratinized tissue around dental implants, thereby mimicking the appearance of natural teeth.


title: Clouds and stars: efficient real-time procedural sky rendering using 3D hardware

abstract: Real-time virtual reality applications, including games, increasingly use outdoor environments. A common task in an earth-type environment is to render a sky that is realistic both in terms of imagery and physics. Programmable graphics hardware offers the opportunity to procedurally generate and render a highly realistic sky at a minimal cost. We propose an integrated set of efficient algorithms that run in graphics hardware for interactive sky rendering that is fully parameterized for real-time control. Features of our method include multi-layered dynamic clouds and stars that individually flicker at varying intensity and rate.


title: Single-trial dynamics of motor cortex and their applications to brain-machine interfaces

abstract: Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs.


title: Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data

abstract: We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label (ML) classification problem, which takes as input time-series data and outputs a set of templates, while capturing the dependencies between selected templates. We show that this method generates output closer to the feedback that lecturers actually generated, achieving 3.5% higher accuracy and 15% higher F-score than multiple simple classifiers that keep a history of selected templates. Furthermore, we compare a ML classifier with a Reinforcement Learning (RL) approach in simulation and using ratings from real student users. We show that the different methods have different benefits, with ML being more accurate for predicting what was seen in the training data, whereas RL is more exploratory and slightly preferred by the students.


Early word learning and conceptual development: Everything had a name, and each name gave birth to a new thought: Handbooks of Developmental Psychology


title: Finding Bursty Topics from Microblogs

abstract: Microblogs such as Twitter reflect the general public's reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneously captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) posts published by the same user are more likely to have the same topic. The former helps find event-driven posts while the latter helps identify and filter out "personal" posts. Our experiments on a large Twitter dataset show that there are more meaningful and unique bursty topics in the top-ranked results returned by our model than an LDA baseline and two degenerate variations of our model. We also show some case studies that demonstrate the importance of considering both the temporal information and users' personal interests for bursty topic detection from microblogs.


Neural Semantic Parsing with Type Constraints for Semi-Structured Tables.


title: Advanced battery management system using MATLAB/Simulink

abstract: A battery management system (BMS) is a system that manages a rechargeable battery (cell or battery pack), by protecting the battery to operate beyond its safe limits and monitoring its state of charge (SoC) & state of health (SoH). BMS has been the essential integral part of hybrid electrical vehicles (HEVs) & electrical vehicles (EVs). BMS provides safety to the system and user with run time monitoring of battery for any critical hazarder conditions. In the present work, design & simulation of BMS for EVs is presented. The entire model of BMS & all other functional blocks of BMS are implemented in Simulink toolbox of MATLAB R2012a. The BMS presented in this research paper includes Neural Network Controller (NNC), Fuzzy Logic Controller (FLC) & Statistical Model. The battery parameters required to design and simulate the BMS are extracted from the experimental results and incorporated in the model. The Neuro-Fuzzy approach is used to model the electrochemical behavior of the Lead-acid battery (selected for case study) then used to estimate the SoC. The Statistical model is used to address battery's SoH. Battery cycle test results have been used for initial model design, Neural Network training and later; it is transferred to the design & simulation of BMS using Simulink. The simulation results are validated by experimental results and MATLAB/Simulink simulation. This model provides more than 97% accuracy in SoC and reasonably accurate SoH.


title: Using confidence intervals in within-subject designs

abstract: We argue that to best comprehend many data sets, plotting judiciously selected sample statistics with associated confidence intervals can usefully supplement, or even replace, standard hypothesis-testing procedures. We note that most social science statistics textbooks limit discussion of confidence intervals to their use in between-subject designs. Our central purpose in this article is to describe how to compute an analogous confidence interval that can be used in within-subject designs. This confidence interval rests on the reasoning that because between-subject variance typically plays no role in statistical analyses of within-subject designs, it can legitimately be ignored; hence, an appropriate confidence interval can be based on the standard within-subject error term—that is, on the variability due to the subject × condition interaction. Computation of such a confidence interval is simple and is embodied in Equation 2 on p. 482 of this article. This confidence interval has two useful properties. First, it is based on the same error term as is the corresponding analysis of variance, and hence leads to comparable conclusions. Second, it is related by a known factor (√2) to a confidence interval of the difference between sample means; accordingly, it can be used to infer the faith one can put in some pattern of sample means as a reflection of the underlying pattern of population means. These two properties correspond to analogous properties of the more widely used between-subject confidence interval.


Supervised Learning for Autonomous Driving


title: Teachers' Instructional Perspectives And Use Of Educational Software

abstract: Abstract It has been argued that technology will promote the use of constructivist approaches to teaching and learning advocated by the current reform movement. Yet computer technology, in and of itself, does not embody a single pedagogical orientation. Different types of software can be used to address different educational goals. This article examines relationships between teachers’ instructional perspectives and their use of technology in instruction. Findings indicate that views about effective computer-based pedagogy are related to the types of software teachers report using with their students. Addressing these perspectives about effective instruction is necessary if computers are to reach their educational potential.


LOOKING FOR INTELLIGENT RECONFIGURABLE SIMULATION


title: Protective effects of Annona muricata linn. (Annonaceae) leaf aqueous extract on serum lipid profiles and oxidative stress in hepatocytes of streptozotocin-treated diabetic rats

abstract: Extracts from various morphological parts of Annona muricata Linn. (Annonaceae) are widely used medicinally in many parts of the world for the management, control and/or treatment of a plethora of human ailments, including diabetes mellitus (DM). The present study was undertaken to investigate the possible protective effects of A. muricata leaf aqueous extract (AME) in rat experimental paradigms of DM. The animals used were broadly divided into four (A, B, C and D) experimental groups. Group A rats served as ‘control’ animals and received distilled water in quantities equivalent to the administered volumes of AME and reference drugs' solutions intraperitoneally. Diabetes mellitus was induced in Groups B and C rats by intraperitoneal injections of streptozotocin (STZ, 70 mg kg−1). Group C rats were additionally treated with AME (100 mg kg−1 day−1, p.o.) as from day 3 post STZ injection, for four consecutive weeks. Group D rats received AME (100 mg kg−1 day−1 p.o.) only for four weeks. Post-euthanization, hepatic tissues were excised and processed biochemically for antioxidant enzymes and lipid profiles, such as catalase (CAT), reactive oxygen species (ROS), glutathione (GSH), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), thiobarbituric acid reactive substances (TBARS), triglycerides (TG), total cholesterol (TC), high density lipoprotein (HDL) and low density lipoprotein (LDL), respectively. Treatment of Groups B and C rats with STZ (70 mg kg−1 i. p.) resulted in hyperglycaemia, hypoinsulinaemia, and increased TBARS, ROS, TC, TG and LDL levels. STZ treatment also significantly decreased (p<0.05) CAT, GSH, SOD, GSH-Px activities, and HDL levels. AME-treated Groups C and D rats showed significant decrease (p<0.05) in elevated blood glucose, ROS, TBARS, TC, TG and LDL. Furthermore, AME treatment significantly increased (p<0.05) antioxidant enzymes' activities, as well as serum insulin levels. The findings of this laboratory animal study suggest that A. muricata extract has a protective, beneficial effect on hepatic tissues subjected to STZ-induced oxidative stress, possibly by decreasing lipid peroxidation and indirectly enhancing production of insulin and endogenous antioxidants.


title: Transferring Knowledge from a RNN to a DNN

abstract: Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent Neural Network (RNN) models have been shown to outperform DNNs counterparts. However, state-of-the-art DNN and RNN models tend to be impractical to deploy on embedded systems with limited computational capacity. Traditionally, the approach for embedded platforms is to either train a small DNN directly, or to train a small DNN that learns the output distribution of a large DNN. In this paper, we utilize a state-of-the-art RNN to transfer knowledge to small DNN. We use the RNN model to generate soft alignments and minimize the Kullback-Leibler divergence against the small DNN. The small DNN trained on the soft RNN alignments achieved a 3.93 WER on the Wall Street Journal (WSJ) eval92 task compared to a baseline 4.54 WER or more than 13% relative improvement.


title: Highly automated driving, secondary task performance, and driver state.

abstract: OBJECTIVE ::: A driving simulator study compared the effect of changes in workload on performance in manual and highly automated driving. Changes in driver state were also observed by examining variations in blink patterns. ::: ::: ::: BACKGROUND ::: With the addition of a greater number of advanced driver assistance systems in vehicles, the driver's role is likely to alter in the future from an operator in manual driving to a supervisor of highly automated cars. Understanding the implications of such advancements on drivers and road safety is important. ::: ::: ::: METHOD ::: A total of 50 participants were recruited for this study and drove the simulator in both manual and highly automated mode. As well as comparing the effect of adjustments in driving-related workload on performance, the effect of a secondary Twenty Questions Task was also investigated. ::: ::: ::: RESULTS ::: In the absence of the secondary task, drivers' response to critical incidents was similar in manual and highly automated driving conditions. The worst performance was observed when drivers were required to regain control of driving in the automated mode while distracted by the secondary task. Blink frequency patterns were more consistent for manual than automated driving but were generally suppressed during conditions of high workload. ::: ::: ::: CONCLUSION ::: Highly automated driving did not have a deleterious effect on driver performance, when attention was not diverted to the distracting secondary task. ::: ::: ::: APPLICATION ::: As the number of systems implemented in cars increases, an understanding of the implications of such automation on drivers' situation awareness, workload, and ability to remain engaged with the driving task is important.


title: Information security awareness and behavior: a theory-based literature review

abstract: Purpose – This paper aims to provide an overview of theories used in the field of employees’ information systems (IS) security behavior over the past decade. Research gaps and implications for future research are worked out by analyzing and synthesizing existing literature. Design/methodology/approach – This paper presents the results of a literature review comprising 113 publications. The literature review was designed to identify applied theories and to understand the cognitive determinants in the research field. A meta-model that explains employees’ IS security behavior is introduced by assembling the core constructs of the used theories. Findings – The paper identified 54 used theories, but four behavioral theories were primarily used: Theory of Planned Behavior (TPB), General Deterrence Theory (GDT), Protection Motivation Theory (PMT) and Technology Acceptance Model (TAM). By synthesizing results of empirically tested research models, a survey of factors proven to have a significant influence on empl...


One-DOF Rigid Foldable Structures from Space Curves


title: Improving speaker verification using MFCC order

abstract: This paper presents a text-dependent speaker verification using Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machine (SVM). Mel-Frequency Cepstral Coefficients technique has been used to extract the characteristic from the recorded voice spoken by the user and SVM is used to classify the all models of the speakers and impostors. A Malay spoken digit database is utilized for the training and testing. The aim of this paper is to improve the performance of SVM by selecting the best order of Mel-Frequency Cepstral Coefficients. Five types of Mel-Frequency Cepstral Coefficients order (5, 10, 15, 20, 25) have been tested and classified using SVM. It is shown that 20th and 25th order of MFCC achieved the best total success rate (TSR) and Equal Error Rate (EER).


title: Ghost Riders: Sybil Attacks on Crowdsourced Mobile Mapping Services

abstract: Real-time crowdsourced maps, such as Waze provide timely updates on traffic, congestion, accidents, and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. To defend against Sybil devices, we propose a new approach based on co-location edges , authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and how they can be used to dramatically reduce the impact of the attacks. We have informed Waze/Google team of our research findings. Currently, we are in active collaboration with Waze team to improve the security and privacy of their system.


title: Classifying Bugs with Interpolants

abstract: We present an approach to the classification of error messages in the context of static checking in the style of ESC/Java. The idea is to compute a semantics-based signature for each error message and then group together error messages with the same signature. The approach aims at exploiting modern verification techniques based on, e.g., Craig interpolation in order to generate small but significant signatures. We have implemented the approach and applied it to three benchmark sets (from Apache Ant, Apache Cassandra, and our own tool). Our experiments indicate an interesting practical potential. More than half of the considered error messages (for procedures with more than just one error message) can be grouped together with another error message.


title: A new multi-agent approach for lane detection and tracking

abstract: This paper proposes an unconventional approach for multi-lane detection and tracking based on a reactive multi-agent system. Most of the algorithms use camera information with a two-step process to detect road marking (1) extraction of road marking features, (2) lane estimation and tracking, performed by studying the extracted point distribution. However, our proposed method is based on a confidence map instead of lane marking features, and a multi-agent model instead of geometric fitting. This approach takes better account of the specific features of road markings, and more precisely, parts defined by clothoids. The method has been tested on a real-world dataset of images in real condition and evaluated with a sequence of more than 2500 synthetic images provided by the SiVIC platform. First results are very promising, with more than 98% for ego lane detection and 97% for multi-lane detection with 4% of false alarm. Furthermore, this approach gives us new opportunities to improve lane detection which would be difficult to implement in a more conventional approach.


title: Tarantula: a vector extension to the alpha architecture

abstract: Tarantula is an aggressive floating point machine targeted at technical, scientific and bioinformatics workloads, originally planned as a follow-on candidate to the EV8 processor [6, 5]. Tarantula adds to the EV8 core a vector unit capable of 32 double-precision flops per cycle. The vector unit fetches data directly from a 16 MByte second level cache with a peak bandwidth of sixty four 64-bit values per cycle. The whole chip is backed by a memory controller capable of delivering over 64 GBytes/s of raw band- width. Tarantula extends the Alpha ISA with new vector instructions that operate on new architectural state. Salient features of the architecture and implementation are: (1) it fully integrates into a virtual-memory cache-coherent system without changes to its coherency protocol, (2) provides high bandwidth for non-unit stride memory accesses, (3) supports gather/scatter instructions efficiently, (4) fully integrates with the EV8 core with a narrow, streamlined interface, rather than acting as a co-processor, (5) can achieve a peak of 104 operations per cycle, and (6) achieves excellent "real-computation" per transistor and per watt ratios. Our detailed simulations show that Tarantula achieves an average speedup of 5X over EV8, out of a peak speedup in terms of flops of 8X. Furthermore, performance on gather/scatter intensive benchmarks such as Radix Sort is also remarkable: a speedup of almost 3X over EV8 and 15 sustained operations per cycle. Several benchmarks exceed 20 operations per cycle.


title: On the Importance of Spatial Perception for the Design of Adaptive User Interfaces

abstract: Automatically changing interfaces, such as AdaptiveUser Interfaces, are employed in state of the art softwareproducts, applications for mobile devices and web sites. Whileprevious work has reported that some adaptive approaches touser interfaces achieve a low user satisfaction score, it has alsobeen shown that they obtain a higher user performance scorewhen compared with their static equivalent. This phenomenonhas not been further investigated by previous research. Themotivation of this paper is to further investigate this issueand consequently propose design principles for automaticallychanging interfaces. This is achieved by a fourfold contribution:a literature review of the current state of the art researchof interface adaptation methods, the identification of the importanceof spatial properties of interface elements (such asposition, orientation and size) based on the review, a secondliterature review of human capabilities and limits that affectthe perception and interpretation of spatial changes and lastlydesign principles for Adaptive User Interfaces are proposedthat are derived based on the two literature reviews.


title: Multi-channel Integration And Its Implications For Retail Web Sites

abstract: The demise of electronic-only retailers has led to the domination of electronic retailing by multichannel retailers. Many of the latter have recognized that multi-channel integration can improve their customer acquisition and retention capabilities. To realize these benefits, however, retailers need to provide a consistent and superior shopping experience across channels. This implies that the purpose of Web sites should no longer be solely to induce consumers to purchase products online. Instead, Web sites should facilitate and support consumers in their interaction with retailers throughout their purchase and consumption activities, regardless of in which channel the actual transaction takes place. Web sites thus become IT-based self-service instruments for consumers. Focusing on the retailing of tangible products, this paper elaborates on how multi-channel integration can lead to benefits for consumers and retailers. Specifically, the paper elucidates the new requirements and opportunities that arise for Web sites as integrated parts of retailers’ channel portfolios.


title: HiEve: A Corpus for Extracting Event Hierarchies from News Stories

abstract: In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent‐subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter-annotator agreement.


title: Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of ConVisIT

abstract: Since the mid-2000s, there has been exponential growth of asynchronous online conversations, thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. A promising solution to this problem is topic modeling, since it may help the user to understand quickly what was discussed in a long conversation and to explore the comments of interest. However, the results of topic modeling can be noisy, and they may not match the user’s current information needs. To address this problem, we propose a novel topic modeling system for asynchronous conversations that revises the model on the fly on the basis of users’ feedback. We then integrate this system with interactive visualization techniques to support the user in exploring long conversations, as well as in revising the topic model when the current results are not adequate to fulfill the user’s information needs. Finally, we report on an evaluation with real users that compared the resulting system with both a traditional interface and an interactive visual interface that does not support human-in-the-loop topic modeling. Both the quantitative results and the subjective feedback from the participants illustrate the potential benefits of our interactive topic modeling approach for exploring conversations, relative to its counterparts.


title: CGLX: A Scalable, High-Performance Visualization Framework for Networked Display Environments

abstract: The Cross Platform Cluster Graphics Library (CGLX) is a flexible and transparent OpenGL-based graphics framework for distributed, high-performance visualization systems. CGLX allows OpenGL based applications to utilize massively scalable visualization clusters such as multiprojector or high-resolution tiled display environments and to maximize the achievable performance and resolution. The framework features a programming interface for hardware-accelerated rendering of OpenGL applications on visualization clusters, mimicking a GLUT-like (OpenGL-Utility-Toolkit) interface to enable smooth translation of single-node applications to distributed parallel rendering applications. CGLX provides a unified, scalable, distributed OpenGL context to the user by intercepting and manipulating certain OpenGL directives. CGLX's interception mechanism, in combination with the core functionality for users to register callbacks, enables this framework to manage a visualization grid without additional implementation requirements to the user. Although CGLX grants access to its core engine, allowing users to change its default behavior, general development can occur in the context of a standalone desktop. The framework provides an easy-to-use graphical user interface (GUI) and tools to test, setup, and configure a visualization cluster. This paper describes CGLX's architecture, tools, and systems components. We present performance and scalability tests with different types of applications, and we compare the results with a Chromium-based approach.


Self-supervised Spatiotemporal Feature Learning by Video Geometric Transformations.


title: Film Critics: Influencers or Predictors?:

abstract: Critics and their reviews pervade many industries and are particularly important in the entertainment industry. Few marketing scholars, however, have considered the relationship between the market ...


title: Airborne Magnetic Anomaly Navigation

abstract: Robust aerial navigation without GPS is a challenging problem. Alternative navigation systems for airborne use are often limited by where and when they can operate. For example, terrain following systems cannot operate over oceans, and star-tracker and computer-vision systems depend on weather and daylight. Navigation using the Earth's magnetic anomaly field, which is globally available at all times, shows promise to overcome many of these limitations. We designed an airborne navigation system that utilizes only passive instruments—an optically pumped scalar magnetometer, an inertial navigation system (INS), a barometer, and a magnetic anomaly map. The navigation system uses a particle filter to correct errors in an INS as well as estimate measurement-corrupting temporal variations by comparing actual magnetometer measurements to expected measurements from the magnetic anomaly map. A complete measurement equation is presented that describes the nontrivial relationship between the real-time magnetometer measurements and the magnetic anomaly map. We validated the navigation system with real data from a flight test over Louisa, VA. Horizontal DRMS errors of 13 m were achieved over an hour long flight at the map altitude of 1000-ft MSL using a magnetic anomaly map created 3 years ago. This result should be considered as a “best case” result when using the presented methods, since the flight test was conducted in ideal conditions. The demonstrated accuracy of the navigation system shows orders of magnitude improvement over previously published magnetic navigation experimental results, indicating that the magnetic anomaly navigation is a promising addition to current global positioning system alternative and backup systems.


title: Curriculum learning

abstract: Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them "curriculum learning". In the context of recent research studying the difficulty of training in the presence of non-convex training criteria (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. The experiments show that significant improvements in generalization can be achieved. We hypothesize that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of non-convex criteria, on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions).


title: S-match: an algorithm and an implementation of semantic matching

abstract: We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.


title: Mid-air pan-and-zoom on wall-sized displays

abstract: Very-high-resolution wall-sized displays offer new opportunities for interacting with large data sets. While pointing on this type of display has been studied extensively, higher-level, more complex tasks such as pan-zoom navigation have received little attention. It thus remains unclear which techniques are best suited to perform multiscale navigation in these environments. Building upon empirical data gathered from studies of pan-and-zoom on desktop computers and studies of remote pointing, we identified three key factors for the design of mid-air pan-and-zoom techniques: uni- vs. bimanual interaction, linear vs. circular movements, and level of guidance to accomplish the gestures in mid-air. After an extensive phase of iterative design and pilot testing, we ran a controlled experiment aimed at better understanding the influence of these factors on task performance. Significant effects were obtained for all three factors: bimanual interaction, linear gestures and a high level of guidance resulted in significantly improved performance. Moreover, the interaction effects among some of the dimensions suggest possible combinations for more complex, real-world tasks.


title: A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications

abstract: The Internet of Things (IoT) is attracting considerable attention from the universities, industries, citizens and governments for applications, such as healthcare, environmental monitoring and smart buildings. IoT enables network connectivity between smart devices at all times, everywhere, and about everything. In this context, Wireless Sensor Networks (WSNs) play an important role in increasing the ubiquity of networks with smart devices that are low-cost and easy to deploy. However, sensor nodes are restricted in terms of energy, processing and memory. Additionally, low-power radios are very sensitive to noise, interference and multipath distortions. In this context, this article proposes a routing protocol based on Routing by Energy and Link quality (REL) for IoT applications. To increase reliability and energy-efficiency, REL selects routes on the basis of a proposed end-to-end link quality estimator mechanism, residual energy and hop count. Furthermore, REL proposes an event-driven mechanism to provide load balancing and avoid the premature energy depletion of nodes/networks. Performance evaluations were carried out using simulation and testbed experiments to show the impact and benefits of REL in small and large-scale networks. The results show that REL increases the network lifetime and services availability, as well as the quality of service of IoT applications. It also provides an even distribution of scarce network resources and reduces the packet loss rate, compared with the performance of well-known protocols.


title: Optimal power generation in microgrid system using firefly algorithm

abstract: This paper proposes Firefly Algorithm (FA) for minimizing generation cost of a microgrid system. The microgrid system consists of renewable and conventional generating units have been considered as a case study in order to validate the performance of the proposed FA. This microgrid test system consists of seven generating units including two diesel generators, two wind turbines and three fuel-cell plants. The proposed FA is implemented to determine the optimal power output of each generator for 24 hours at minimum cost by using MAT LAB software. Moreover, the power generated by each generating unit must be satisfied the power balance and generator constraints. The performance of proposed FA have been investigated in terms of effect of population size, optimal solution, convergence characteristic and robustness. The results obtained by the proposed FA is also compared with several results of optimization methods. It found that FA outperformed other methods for cost minimization in considered microgrid test system.


title: A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection

abstract: This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a switch for noise reduction in the signal processing pipelines of hearing devices, enabling noise estimation or classification to be conducted in noise-only parts of noisy speech signals. The developed smartphone app is compared with a previously developed voice activity detection app as well as with two highly cited voice activity detection algorithms. The experimental results indicate that the developed app using convolutional neural network outperforms the previously developed smartphone app.


title: Vlogging: A survey of videoblogging technology on the web

abstract: In recent years, blogging has become an exploding passion among Internet communities. By combining the grassroots blogging with the richness of expression available in video, videoblogs (vlogs for short) will be a powerful new media adjunct to our existing televised news sources. Vlogs have gained much attention worldwide, especially with Google's acquisition of YouTube. This article presents a comprehensive survey of videoblogging (vlogging for short) as a new technological trend. We first summarize the technological challenges for vlogging as four key issues that need to be answered. Along with their respective possibilities, we give a review of the currently available techniques and tools supporting vlogging, and envision emerging technological directions for future vlogging. Several multimedia technologies are introduced to empower vlogging technology with better scalability, interactivity, searchability, and accessability, and to potentially reduce the legal, economic, and moral risks of vlogging applications. We also make an in-depth investigation of various vlog mining topics from a research perspective and present several incentive applications such as user-targeted video advertising and collective intelligence gaming. We believe that vlogging and its applications will bring new opportunities and drives to the research in related fields.


title: Using large-scale perturbations in gene network reconstruction

abstract: BackgroundRecent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics.ResultsIt was found that a relatively small number of perturbations significantly improve inference accuracy, particularly for low-order inputs of one or two genes. The perturbations themselves should alter the expression level of approximately 50–60% of the genes in the network.ConclusionsTime-series obtained from perturbations are a common form of expression data. This study illustrates how gene networks can be significantly reconstructed from such time-series while requiring only a relatively small number of calibrated perturbations, even for large networks, thus reducing experimental costs.


title: Refining Raw Sentence Representations for Textual Entailment Recognition via Attention

abstract: In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refined representations of both sentences into a single vector to be used for classification. With this model we obtained test accuracies of 72.057% and 72.055% in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM). When using an ensemble both accuracies increased to 72.247% and 72.827% respectively.


title: Design considerations for battery-powered electronics

abstract: In this paper, we consider the problem of maximizing the battery life (or duration of service) in battery-powered CMOS circuits. We first show that the battery efficiency (or utilization factor) decreases as the average discharge current from the battery increases. The implication is that the battery life is a super-linear function of the average discharge current. Next we show that even when the average discharge current remains the same, different discharge current profiles (distributions) may result in very different battery lifetimes. In particular, the maximum battery life is achieved when the variance of the discharge current distribution is minimized. Analytical derivations and experimental results underline the importance of the correct modeling of the battery-hardware system as a whole and provide a more accurate basis (i.e., the battery discharge times delay product) for comparing various low power optimization methodologies and techniques targeted toward battery-powered electronics. Finally, we calculate the optimal value of V/sub dd/ for a battery-powered VLSI circuit so as to minimize the product of the battery discharge times the circuit delay.


title: A new approach for self adaptive video game for rehabilitation - Experiences in the amblyopia treatment

abstract: Self adaptive video games are important for rehabilitation at home. Recent works have explored different techniques with satisfactory results but these have a poor use of game design concepts like Challenge and Conservative Handling of Failure. Dynamic Difficult Adjustment with Help (DDA-Help) approach is presented as a new point of view for self adaptive video games for rehabilitation. Procedural Content Generation (PCG) and automatic helpers are used to a different work on Conservative Handling of Failure and Challenge. An experience with amblyopic children showed the proposal effectiveness, increasing the visual acuity 2-3 level following the Snellen Vision Test and improving the performance curve during the game time.


title: Hybrid Metaheuristics for the Clustered Vehicle Routing Problem

abstract: The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This paper presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is based on an Iterated Local Search algorithm, in which only feasible solutions are explored and problem-specific local search moves are utilized. The second algorithm is a hybrid genetic search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed. Using this information, a sequence of clusters can be used as a solution representation and large neighborhoods can be efficiently explored, by means of bi-directional dynamic programming, sequence concatenation, and appropriate data structures. Extensive computational experiments are performed on benchmark instances from the literature, as well as new large scale instances. Recommendations on the choice of algorithm are provided, based on average cluster size.


title: Evaluating Search Systems Using Result Page Context

abstract: We introduce a method for evaluating the relevance of all visible components of a Web search results page, in the context of that results page. Contrary to Cranfield-style evaluation methods, our approach recognizes that a user's initial search interaction is with the result page produced by a search system, not the landing pages linked from it. Our key contribution is that the method allows us to investigate aspects of component relevance that are difficult or impossible to judge in isolation. Such contextual aspects include component-level information redundancy and cross-component coherence. We report on how the method complements traditional document relevance measurement and its support for comparative relevance assessment across multiple search engines. We also study possible issues with applying the method, including brand presentation effects, inter-judge agreement, and comparisons with document-based relevance judgments. Our findings show this is a useful method for evaluating the dominant user experience in interacting with search systems.


title: Process-Based Material Workflow Modeling in Inbound Logistics: Modeling Tools Evaluation

abstract: This paper evaluates the suitability of the workflow modeling tools through process-based materialworkflow perspective. A case example in inbound logistics system of “Resource Reception” workflow a ...


title: Analysis and Detection of Fake Views in Online Video Services

abstract: Online video-on-demand(VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect fake viewsq Can we detect them (and stop them) efficientlyq What is the extent of fake views with current VoD service providersq These are the questions we study in this article. We develop some algorithms and show that they are quite effective for this problem.


title: Calculation of unate cube set algebra using zero-suppressed BDDs

abstract: Many combinatorial problems in LSI design can be described with cube set expressions. We discuss unate cube set algebra based on zero-suppressed BDDs, a new type of BDDs adapted for cube set manipulation. We propose efficient algorithms for computing unate cube set operations including multiplication and division, followed by some practical applications of unate cube set calculation.


title: A framework for the measurement of software quality

abstract: Research in software metrics incorporated in a framework established for software quality measurement can potentially provide significant benefits to software quality assurance programs. The research described has been conducted by General Electric Company for the Air Force Systems Command Rome Air Development Center. The problems encountered defining software quality and the approach taken to establish a framework for the measurement of software quality are described in this paper.


title: A Brief Survey On Document Clustering Techniques Using MATLAB

abstract: Document clustering is a more specific technique for unsupervised document organization, it is generally considered to be a centralized process. Clustering methods can be used to automatically group the retrieved documents into a list of meaningful categories. This paper gives an overview of some of the mostly used document clustering techniques and introduces the matlab tool which provides us many functions that helps in the clustering of the documents. In particular we concentrate on the most commonly used clustering techniques Agglomerative hierarchical clustering and K-means that are commonly used for document clustering and related matlab functions available in the matlab toolbox.


title: Learning Markov logic network structure via hypergraph lifting

abstract: Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning MLN structure from a relational database involves learning the clauses and weights. The state-of-the-art MLN structure learners all involve some element of greedily generating candidate clauses, and are susceptible to local optima. To address this problem, we present an approach that directly utilizes the data in constructing candidates. A relational database can be viewed as a hypergraph with constants as nodes and relations as hyperedges. We find paths of true ground atoms in the hypergraph that are connected via their arguments. To make this tractable (there are exponentially many paths in the hypergraph), we lift the hypergraph by jointly clustering the constants to form higherlevel concepts, and find paths in it. We variabilize the ground atoms in each path, and use them to form clauses, which are evaluated using a pseudo-likelihood measure. In our experiments on three real-world datasets, we find that our algorithm outperforms the state-of-the-art approaches.


title: Online Bursty Event Detection from Microblog

abstract: Microblogs (e.g., Twitter and Weibo) have become a large social media platform for users to share contents, their interests and events with friends. A surge of the number of event related posts always reflects that some people's concern real-life events happened. In this paper, we propose an incremental temporal topic model for microblogs namely BEE (Bur sty Event Detection) to detect these bur sty events. BEE supports to detect these bur sty events from short text datasets through modeling the temporal information of events. And BEE employs processing the post streaming incrementally to track the topic of events drifting over time. Therefore, the latent semantic indices are preserved from one time period to the next. After BEE detects the event-driven posts and related events, the bur sty detection module can identify the bur sty patterns for each event and rank the events using the bur sty patterns. Our experiments on a large Weibo dataset show that our algorithm can outperform the baselines for detecting the meaningful bur sty events. Subsequently, we also show some case studies that indicate the effectiveness of the temporal factor for bur sty event detection and how well BEE can track the topic drifting of events.


title: Tracking and estimation of ego-vehicle's state for lateral localization

abstract: For urban driving, knowledge of ego-vehicle's position is a critical piece of information that enables ad- vanced driver-assistance systems or self-driving cars to execute safety-related, autonomous driving maneuvers. This is because, without knowing the current location, it is very hard to autonomously execute any driving maneuvers for the future. The existing solutions for localization rely on a combination of Global Navigation Satellite System (GNSS), an inertial mea- surement unit, and a digital map. However, on urban driving environments, due to poor satellite geometry and disruption of radio signal reception, their longitudinal and lateral errors are too significant to be used to guide an autonomous system. To enhance the existing system's localization capability, this work presents an effort of developing a vision-based lateral localization algorithm. The algorithm aims at reliably counting, with or without observations of lane-markings, the number road-lanes and identifying the index of the road-lane on the roadway that our vehicle happens to be driving on. Testings the proposed algorithms against inter-city and inter-state highway videos showed promising results in terms of counting the number of road-lanes and the indices of the current road-lanes. I. INTRODUCTION


title: The value of learning analytics to networked learning on a personal learning environment

abstract: Some might argue that the analytics tools at our disposal are currently mainly used for boring purposes, such as improving processes and making money. In this paper we will try to define learning analytics and their purpose for learning and education. We will ponder on the best possible fit of particular types of research methods and their analysis. Methodological concerns related to the analysis of Big Data collected on online networks as well as ethical and privacy concerns will also be highlighted and a case study of the use of learning analytics in a Massive Open Online Course explored.


title: Scalable and Robust Management of Dynamic Graph Data

abstract: Most real-world networks evolve over time. This evolution can be modeled as a series of graphs that represent a network at different points in time. Our G* system enables efficient storage and querying of these graph snapshots by taking advantage of the commonalities among them. We are extending G* for highly scalable and robust operation. This paper shows that the classic challenges of data distribution and replication are imbued with renewed significance given continuously generated graph snapshots. Our data distribution technique adjusts the set of worker servers for storing each graph snapshot in a manner optimized for popular queries. Our data replication approach maintains each snapshot replica on a different number of workers, making available the most efficient replica configurations for different types of queries.