Unsupervised Word Alignment by Agreement Under ITG Constraint


title: A flow optimization approach for the rebalancing of mobility on demand systems

abstract: In this paper, we propose an optimization policy for increasing the utilization level of mobility on demand systems (MOD). The system is modeled through a closed queueing network, on which a suitable parameter identification phase must be executed. We define a measure of imbalance of the system based on customer demand, then we use such measure to cast the problem of the redistribution of vehicles in a flow optimization fashion. Leveraging the model characteristics, we are able to account for a wide range of features of MOD systems, enabling the design of efficient and effective redistribution strategies. Our strategy is validated on datasets provided by the “ToBike” bike sharing system, located in the city of Turin, Italy.


title: Fast-SCNN: Fast Semantic Segmentation Network

abstract: The encoder-decoder framework is state-of-the-art for offline semantic image segmentation. Since the rise in autonomous systems, real-time computation is increasingly desirable. In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation on embedded devices with low memory. Building on existing two-branch methods for fast segmentation, we introduce our `learning to downsample' module which computes low-level features for multiple resolution branches simultaneously. Our network combines spatial detail at high resolution with deep features extracted at lower resolution, yielding an accuracy of 68.0% mean intersection over union at 123.5 frames per second on Cityscapes. We also show that large scale pre-training is unnecessary. We thoroughly validate our metric in experiments with ImageNet pre-training and the coarse labeled data of Cityscapes. Finally, we show even faster computation with competitive results on subsampled inputs, without any network modifications.


A Density-Based Approach for Clustering Spatial Database


title: How Students Learn using MOOCs: An Eye-tracking Insight

abstract: We present the results of an eye-tracking study on a Massive Open Online Course (MOOC) lecture showing the relation between gaze variables and students’ performance and learning strategy. 40 students watched a MOOC lecture while their eye-movements were being recorded. We present a method to define stimuli-based gaze variables that can be defined for any kind of stimulus. The advantage of using stimuli-based gaze variables is that the relation of the gaze indicators with performance measures and behavioral measures can be interpreted differently for different kinds of stimulus. MOOCs are very diverse in nature; having such a variable description enables the researchers to have a common measure for the different kind of stimulus present in different MOOCs. The long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide the students with gaze-aware feedback to improve the overall learning process.


Persuasive Technology and Moral Responsibility Toward an ethical framework for persuasive technologies Paper for Persuasive 06


Clean-Label Backdoor Attacks


title: Big data for transport and logistics: A review

abstract: To face the worldwide competition and meet the recent requirements in the new information technologies era, digitalization and adoption of new information techniques have become a must for all transport and logistics companies and organizations to improve their activities. This digital transformation of transport and logistics sectors is giving birth to huge and increasingly growing sets of voluminous data with high velocity and varied data sources, also known as Big Data. With new manipulation and management infrastructures, as well as more real-time analysis and techniques, these enormous datasets can be efficiently harvested to carry out valuable operational improvements and create new business values in the transport and logistics domains. This paper provides a review of the Big Data in the transport and logistic fields, discusses the current research challenges and identifies some of the promising directions for future research.


title: Friend recommendation techniques in social network

abstract: Online social network has obtained a significant increase in recent years. Making friends is a way of creating social relationships with others in online social network to be in contact with their friends in the real world and to have access to the information they are interested in. Therefore friend recommendation is becoming a very important aspect and attracting extensive attention in visual communities and social network. Various available friend recommendation techniques apply social network configuration, user profile information and/or user interactions for this purpose. In this paper, few friend recommendation techniques with their advantages and disadvantages are discussed along with a proposed method.


title: MMSE Based Block Diagonalization for Cognitive Radio MIMO Broadcast Channels

abstract: In this paper, we present a linear precoder design for cognitive radio (CR) multi-user multiple-input multiple-output (MU-MIMO) downlink systems where unlicensed secondary users (SUs) can simultaneously utilize the same spectrum used by a licensed primary user (PU). Although a zero-forcing block diagonalization (ZF-BD) precoder is extended to the CR network with the PU, a transmit power boost problem occurs. Therefore, we propose a regularized BD precoder method based on the minimum mean-squared error (MMSE) criteria subject to the interference power constraint under a predetermined threshold for the PU. As a result, the proposed CR-MMSE-BD scheme improves the signal-to-interference-plus-noise ratio at each SU's receiver, compared to the ZF-BD based method. The simulation results demonstrate that the proposed algorithm outperforms the ZF based technique for CR MU-MIMO downlink systems.


Investigation Of Lagrangian Heuristics For Set Covering Problems


title: Analysis of a Fall-Back Transverse-Flux Permanent-Magnet Generator

abstract: The conventional transverse-flux permanent-magnet generator (TFPMG), preferred for wind power applications, has a few limitations, e.g., the flux distribution in a U-shaped stator core is uneven, and its inactive permanent magnet (PM) poles lead to high magnetic flux leakage. In this paper, a new fall-back transverse-flux permanent-magnet generator (FB-TFPMG), suitable for direct-drive applications, is proposed to reduce the magnetic flux leakage between the stator cores and the PMs. In the proposed configuration, half the number of PMs (i.e., only active PMs) are employed with the elliptical-shaped stator core and toroidal-shaped coil. This leads to immediate benefit of lower mass and better flux utilization. The performance of the new topology is analyzed, under no-load and loaded conditions, using a 3-D finite-element tool, and the results are presented in comparison with the conventional TFPMG. The results show improved outcome of the proposed topology in comparison to the conventional TFPMG.


title: Parallel Patterns for Window-Based Stateful Operators on Data Streams: An Algorithmic Skeleton Approach

abstract: The topic of Data Stream Processing is a recent and highly active research area dealing with the in-memory, tuple-by-tuple analysis of streaming data. Continuous queries typically consume huge volumes of data received at a great velocity. Solutions that persistently store all the input tuples and then perform off-line computation are impractical. Rather, queries must be executed continuously as data cross the streams. The goal of this paper is to present parallel patterns for window-based stateful operators, which are the most representative class of stateful data stream operators. Parallel patterns are presented "a la" Algorithmic Skeleton, by explaining the rationale of each pattern, the preconditions to safely apply it, and the outcome in terms of throughput, latency and memory consumption. The patterns have been implemented in the $$\mathtt {FastFlow}$$FastFlow framework targeting off-the-shelf multicores. To the best of our knowledge this is the first time that a similar effort to merge the Data Stream Processing domain and the field of Structured Parallelism has been made.


title: Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games

abstract: Turn-based strategy games are interesting testbeds for developing artificial players because their rules present developers with several challenges. Currently, Monte-Carlo tree search variants are often utilized to address these challenges. However, we consider it worthwhile introducing minimax search variants with pruning techniques because a turn-based strategy is in some points similar to the games of chess and Shogi, in which minimax variants are known to be effective. Thus, we introduced three forward-pruning techniques to enable us to apply alpha beta search (as a minimax search variant) to turn-based strategy games. This type of search involves fixing unit action orders, generating unit actions selectively, and limiting the number of moving units in a search. We applied our proposed pruning methods by implementing an alpha beta-based artificial player in the Turn-based strategy Academic Package (TUBSTAP) open platform of our institute. This player competed against first- and second-rank players in the TUBSTAP AI competition in 2016. Our proposed player won against the other players in five different maps with an average winning ratio exceeding 70%.


title: Self-Tuning Fuzzy PID Control of a Four-Mecanum Wheel Omni-directional Mobile Platform

abstract: In this paper we restrict our attention to self-tuning control of a four-Mecanum mobile robot and present detailed kinematics and controller architecture of a robot. Since the Mecanum wheels worked based on slipping, therefore, its wheels may slip due to various floor conditions. During such a slip condition, Mecanum wheeled robot moves in unexpected path. In order to reduce this error, four incremental encoders are utilized. Then the fuzzy PID controller is described. This proposed controller can be implemented in microcontroller easily. Finally, the merit of the controller is investigated experimentally. Results show that the Self-Tuning Fuzzy PID Control and encoder sensors provide a reasonable structure for Mecanum wheel robot.


FrameIT Reloaded: Serious Math Games from Modular Math Ontologies


title: Adverse reactions to dermal fillers: A review of European experiences

abstract: BACKGROUND ::: In Europe, numerous dermal fillers have been utilized for the past decade. A lot of drawbacks have been reported and sometimes, severe complications occurred. ::: ::: ::: OBJECTIVE ::: Our purpose is to report the clinical aspects of the adverse reactions following injections of some of the dermal fillers. Histological aspects of complications are also described. ::: ::: ::: RESULTS ::: Adverse reactions secondary to biodegradable products are usually time limited, but with the non-biodegradable products, we have observed severe, persistent, and recurrent complications. Histological examinations, in cases of non-biodegradable products, may show the presence and persistence of the filler. ::: ::: ::: CONCLUSION ::: For the moment, there is no ideal dermal filler. All fillers can lead to adverse events and we need to inform patients fully before injecting. Clinical studies with long-term follow-up before launching a new product on the market are recommended. We believe that in Europe, at present, the CE mark is not a guarantee of safety of dermal fillers.


Proxy Cryptography Revisited


title: Reachability analysis of nonlinear systems using conservative polynomialization and non-convex sets

abstract: A new technique for computing the reachable set of hybrid systems with nonlinear continuous dynamics is presented. Previous work showed that abstracting the nonlinear continuous dynamics to linear differential inclusions results in a scalable approach for reachability analysis. However, when the abstraction becomes inaccurate, linearization techniques require splitting of reachable sets, resulting in an exponential growth of required linearizations. In this work, the nonlinearity of the dynamics is more accurately abstracted to polynomial difference inclusions. As a consequence, it is no longer guaranteed that reachable sets of consecutive time steps are mapped to convex sets as typically used in previous works. Thus, a non-convex set representation is developed in order to better capture the nonlinear dynamics, requiring no or much less splitting. The new approach has polynomial complexity with respect to the number of continuous state variables when splitting can be avoided and is thus promising when a linearization technique requires splitting for the same problem. The benefits are presented by numerical examples.


title: Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

abstract: Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.


title: Local information statistics of LBP and HOG for pedestrian detection

abstract: We present several methods of pedestrian detection in intensity images using different local statistical measures applied to two classes of features extensively used in pedestrian detection: uniform local binary patterns - LBP and a modified version of histogram of oriented gradients - HOG. Our work extracts local binary patterns and magnitude and orientation of the gradient image. Then we divide the image into blocks. Within each block we extract different statistics like: histogram (weighted by the gradient magnitude in the case of HOG), information, entropy and energy of the local binary code. We use Adaboost for training four classifiers and we analyze the classification error of each method on the Daimler benchmark pedestrian dataset.


title: A Secure Sum Protocol and Its Application to Privacy-preserving Multi-party Analytics

abstract: Many enterprises are transitioning towards data-driven business processes. There are numerous situations where multiple parties would like to share data towards a common goal if it were possible to simultaneously protect the privacy and security of the individuals and organizations described in the data. Existing solutions for multi-party analytics that follow the so called Data Lake paradigm have parties transfer their raw data to a trusted third-party (i.e., mediator), which then performs the desired analysis on the global data, and shares the results with the parties. However, such a solution does not fit many applications such as Healthcare, Finance, and the Internet-of-Things, where privacy is a strong concern. Motivated by the increasing demands for data privacy, we study the problem of privacy-preserving multi-party data analytics, where the goal is to enable analytics on multi-party data without compromising the data privacy of each individual party. In this paper, we first propose a secure sum protocol with strong security guarantees. The proposed secure sum protocol is resistant to collusion attacks even with N-2 parties colluding, where N denotes the total number of collaborating parties. We then use this protocol to propose two secure gradient descent algorithms, one for horizontally partitioned data, and the other for vertically partitioned data. The proposed framework is generic and applies to a wide class of machine learning problems. We demonstrate our solution for two popular use-cases, regression and classification, and evaluate the performance of the proposed solution in terms of the obtained model accuracy, latency and communication cost. In addition, we perform a scalability analysis to evaluate the performance of the proposed solution as the data size and the number of parties increase.


title: Representing Model Differences by Delta Operations

abstract: Software models are subject to subsequent changes. Models evolve over time to meet changes resulting in several versions. The differences between subsequent model versions are represented in differences documents usually referred to as Modeling Deltas. The model differences are used in establishing several further services and operations of version control, collaborative modeling and history analysis tools for software models. Therefore, finding an adequate difference representation approach is essential for applicability, re-usability and analysis of the model differences. This paper introduces the Delta Operation Language (DOL), a meta-model independent and operation-based concept to model difference representations. The proposed approach provides several operative DOL-services for utilising the DOL-based differences. The application areas of DOL include Model Versioning, Model History Analysis and Collaborative Modeling. The approach is applied to UML activity diagrams as a running example.


Optical rotatory dispersion measurements in the third cholesteric blue phase


title: Integrated Metal-Frame Antenna for Smartwatch Wearable Device

abstract: A design concept of integrating the loop antenna into the front metal frame of a smartwatch Bluetooth device is introduced. The antenna was constructed from a 0.3-mm-thick, rectangular metal frame with a dimension of $5\;{\text{mm}} \times 40\;{\text{mm}} \times 50\;{\text{mm}}$ , which encircled the system circuit board of the watch. A separation distance of 2 mm between the loop and the system ground plane was also set to alleviate the strong coupling between the antenna and the ground. With the signal feeding fixed in the middle of either one of the frame’s longer edges and followed by shorting the loop in a proper location, the antenna can easily cover the 2.4-GHz band (2400–2485 MHz) for Bluetooth operation. The design and the results thereof are described and discussed in the article.


title: Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding

abstract: Neural network based approaches for sentence relation modeling automatically generate hidden matching features from raw sentence pairs. However, the quality of matching feature representation may not be satisfied due to complex semantic relations such as entailment or contradiction. To address this challenge, we propose a new deep neural network architecture that jointly leverage pre-trained word embedding and auxiliary character embedding to learn sentence meanings. The two kinds of word sequence representations as inputs into multi-layer bidirectional LSTM to learn enhanced sentence representation. After that, we construct matching features followed by another temporal CNN to learn high-level hidden matching feature representations. Experimental results demonstrate that our approach consistently outperforms the existing methods on standard evaluation datasets.


title: Impact of gut microbiota on diabetes mellitus.

abstract: Various functions of the gut are regulated by sophisticated interactions among its functional elements, including the gut microbiota. These microorganisms play a crucial role in gastrointestinal mucosa permeability. They control the fermentation and absorption of dietary polysaccharides to produce short-chain fatty acids, which may explain their importance in the regulation of fat accumulation and the subsequent development of obesity-related diseases, suggesting that they are a crucial mediator of obesity and its consequences. In addition, gut bacteria play a crucial role in the host immune system, modulation of inflammatory processes, extraction of energy from the host diet and alterations of human gene expression. Dietary modulation of the human colonic microbiota has been shown to confer a number of health benefits to the host. Simple therapeutic strategies targeted at attenuating the progression of chronic low-grade inflammation and insulin resistance are urgently required to prevent or slow the development of diabetes in susceptible individuals. The main objective of this review is to address the pathogenic association between gut microbiota and diabetes, and to explore any novel related therapeutic targets. New insights into the role of the gut microbiota in diabetes could lead to the development of integrated strategies using probiotics to prevent and treat these metabolic disorders.


title: Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.

abstract: A noise power spectral density (PSD) estimation is an indispensable component of speech spectral enhancement systems. In this paper we present a noise PSD tracking algorithm, which employs a noise presence probability estimate delivered by a deep neural network (DNN). The algorithm provides a causal noise PSD estimate and can thus be used in speech enhancement systems for communication purposes. An extensive performance comparison has been carried out with ten causal state-of-the-art noise tracking algorithms taken from the literature and categorized acc. to applied techniques. The experiments showed that the proposed DNN-based noise PSD tracker outperforms all competing methods with respect to all tested performance measures, which include the noise tracking performance and the performance of a speech enhancement system employing the noise tracking component.


title: The continuing evolution of integration in manufacturing automation

abstract: Since the wide adoption of Internet technologies in manufacturing automation, integration has become one of the most advertised trends in the field. The concept of integration in automation is by no means new. It was first formulated in the mid 1970s with the computer-integrated manufacturing (CIM) idea. Its aim was to integrate several already existing islands of computer-assisted manufacturing subsystems to a comprehensive and transparent, enterprise-wide information processing system. This article sketches the roots as well as the historical evolution of the integration idea and describes the pieces it takes to complete the picture of integration with its horizontal, vertical, and temporal aspects. Emphasis is put on building blocks like networks and their interconnections, middleware technologies, and application software frameworks. Standardization issues are addressed as well as future challenges in the field.


title: Developing a multi-network urbanization model: A case study of urban growth in Denver, Colorado

abstract: Urbanization is an important issue concerning diverse scientific and policy communities. Computational models quantifying locations and quantities of urban growth offer numerous environmental and socioeconomic benefits. Traditional urban growth models are based on a single-algorithm fitting procedure and thus restricted on their ability to capture spatial heterogeneity. Accordingly, a GIS-based modeling framework titled multi-network urbanization (MuNU) model is developed that integrates multiple neural networks. The MuNU model enables a filtering approach where input data patterns are automatically reallocated into appropriate neural networks with targeted accuracies. We hypothesize that observations classified by individual neural networks share greater homogeneity, and thus modeling accuracy will increase with the integration of multiple targeted algorithms. Land use and land cover data sets of two time snapshots (1977 and 1997) covering the Denver Metropolitan Area are used for model training and validation. Compared to a single-step algorithm-either a stepwise logistic regression or a single neural network-several improvements are evident in the visual output of the MuNU model. Statistical validations further quantify the superiority of the MuNU model and support our hypothesis of effective incorporation of spatial heterogeneity.


title: Vegetation succession in reclaimed coal-mining slopes in a Mediterranean-dry environment

abstract: Abstract Mining reclamation results obtained in the Teruel coalfield (Mediterranean-dry Spain) during the last 30 years have been quite limited. In order to improve restoration operations we conducted a study to analyse the trajectories of ecological succession and identify the main driving forces that control vegetation dynamic in reclaimed artificial slopes. A total of 87 slopes of different ages and restoration treatments were classified and characterized after recording different variables related to topography, restoration techniques, vegetation, local disturbances and soil erosion. Successional trends were inferred from gradient analysis as well as the factors, mechanisms and processes implied. We found a wide variety of plant communities and successional trajectories. Initial conditions (soil quality and revegetation treatments) as well as the environmental scenario (climatic conditions and vicinity of preserved propagule sources) were the main driving forces directing vegetation succession. Soil erosion triggered by external run-on coming from surrounding structures was also identified as a key factor determining the evolution of vegetation in these dry environments. Other local disturbances (grazing and fungal pests) can favour vegetation transition in communities dominated by highly competitive non-native sown species to more diverse shrub communities. Some practical considerations for future reclamation projects are suggested.


title: Better Protection of SS7 Networks with Machine Learning

abstract: Deregulation and migration to IP have made SS7 vulnerable to serious attacks such as location tracking of subscribers, interception of calls and SMS, fraud, and denial of services. Unfortunately, current protection measures such as firewalls, filters, and blacklists are not able to provide adequate protection of SS7. In this paper, a method for detection of SS7 attacks using machine learning is proposed. The paper clarifies the vulnerabilities of SS7 networks and explains how machine learning techniques can help improve SS7 security. A proof- of- concept SS7 protection system using machine learning is also described thoroughly.


title: A Miniaturized Millimeter-Wave Standing-Wave Filtering Switch With High P1dB

abstract: This paper presents the design considerations of a silicon-based transmit/receive (T/R) switch for microwave and millimeter-wave frequencies. Techniques for minimizing switch size and loss while increasing linearity are discussed. With the dedicated idea of the standing-wave concept, a combo design of a bandpass filter and switch, called the filtering switch, is introduced for the front-end system to achieve better performance in terms of low insertion loss, compact size, and good linearity simultaneously. Based on the proposed switchable artificial resonator, a standing-wave single-pole single-throw (SPST) and a single-pole double-throw (SPDT) filtering switches are designed and implemented for demonstration. Fabricated with a 0.18- $\mu{\hbox {m}}$ SiGe BiCMOS process, the standing-wave SPST exhibits only 1.9-dB insertion loss and 18-dB isolation with a compact chip area of only 0.027 ${\hbox {mm}}^{2}$ at 50 GHz. The SPDT T/R switch exhibits 3.2-dB insertion loss, good stopband rejection, and 20-dB isolation with only 0.0675 ${\hbox {mm}}^{2}$ active chip area at 60 GHz. With the proposed standing-wave topology, a good linearity with the input 1-dB compression point (P1dB) of 21 dBm is obtained for both switches.


title: Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches

abstract: BackgroundWe investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.MethodologyWe used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models – BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.ConclusionsPubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts.


The art of deep learning ( applied to NLP )


title: Reproductive biology of the greyfin croaker Pennahia anea in the northern South China Sea

abstract: The greyfin croaker Pennahia anea is one of the most common croakers currently on retail sale in Hong Kong, but there are no regional studies on its biology or fishery. The reproductive biology of the species, based on 464 individuals obtained from local wet markets, was studied over 16 months (January 2008–April 2009) using gonadosomatic index (GSI) and gonad histology. Sizes used in this study ranged from 8.0 to 19.0 cm in standard length (SL). Both the larger and smaller size classes were missing from samples, implying that they are infrequently caught in the fishery. Based on GSI data, the approximate minimum sizes for male and female maturation were 12 cm SL. The size at 50% maturity for females was 14.3 cm SL, while all males in the samples were mature. Both GSI and gonad histology suggest that spawning activity occurred from March–April to June, with a peak in May. Since large croakers are declining in the local and regional fisheries, small species such as P. anea are becoming important, although they are mostly taken as bycatch. In view of unmanaged fishing pressure, and given the decline in large croakers and sizes of P. anea presently caught, proper management of the species is suggested.


title: Community Discovery via Metagraph Factorization

abstract: This work aims at discovering community structure in rich media social networks through analysis of time-varying, multirelational data. Community structure represents the latent social context of user actions. It has important applications such as search and recommendation. The problem is particularly useful in the enterprise domain, where extracting emergent community structure on enterprise social media can help in forming new collaborative teams, in expertise discovery, and in the long term reorganization of enterprises based on collaboration patterns. There are several unique challenges: (a) In social media, the context of user actions is constantly changing and coevolving; hence the social context contains time-evolving multidimensional relations. (b) The social context is determined by the available system features and is unique in each social media platform; hence the analysis of such data needs to flexibly incorporate various system features. In this article we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from dynamic, multidimensional social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multirelational and multidimensional social data; (2) an efficient multirelational factorization method for community extraction on a given metagraph; (3) an online method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from an enterprise and the public Digg social media Web site suggest that our technique is scalable and is able to extract meaningful communities from social media contexts. We illustrate the usefulness of our framework through two prediction tasks: (1) in the enterprise dataset, the task is to predict users’ future interests on tag usage, and (2) in the Digg dataset, the task is to predict users’ future interests in voting and commenting on Digg stories. Our prediction significantly outperforms baseline methods (including aspect model and tensor analysis), indicating the promising direction of using metagraphs for handling time-varying social relational contexts.


title: Packet Classification Using Tuple Space Search

abstract: Multidimensional packet classification has attracted considerable research interests in the past few years due to the increasing demand on policy based packet forwarding and security services. These network services typically involve determining the action to take on packets according to a set of rules. As the number of rules increases, time for determining the best matched rule for an incoming IP packet will increase and subsequently incur long processing delay. To address this issue, in this paper we propose a two-dimensional packet classification algorithm which focuses on reducing time for classification while keeping reasonable memory requirement in practice. Our approach extends the tuple space framework and then allows performing binary search on the tuple space. To our knowledge, the proposed scheme is the first binary search scheme on two-dimensional tuples. With the proposed scheme, given a filter set with n two-dimensional filters, it requires only O(log(w)) hash operations to determine the best matched filter, where w is the maximum prefix length of filters. The proposed scheme achieves fast packet classification, and according to our experimental results, it does not require huge memory space. This makes it useful for network applications that require high speed packet classification.


title: Statistical limits of spiked tensor models

abstract: We study the statistical limits of both detecting and estimating a rank-one deformation of a symmetric random Gaussian tensor. We establish upper and lower bounds on the critical signal-to-noise ratio, under a variety of priors for the planted vector: (i) a uniformly sampled unit vector, (ii) i.i.d. $\pm 1$ entries, and (iii) a sparse vector where a constant fraction $\rho$ of entries are i.i.d. $\pm 1$ and the rest are zero. For each of these cases, our upper and lower bounds match up to a $1+o(1)$ factor as the order $d$ of the tensor becomes large. For sparse signals (iii), our bounds are also asymptotically tight in the sparse limit $\rho \to 0$ for any fixed $d$ (including the $d=2$ case of sparse PCA). Our upper bounds for (i) demonstrate a phenomenon reminiscent of the work of Baik, Ben Arous and Peche: an `eigenvalue' of a perturbed tensor emerges from the bulk at a strictly lower signal-to-noise ratio than when the perturbation itself exceeds the bulk; we quantify the size of this effect. We also provide some general results for larger classes of priors. In particular, the large $d$ asymptotics of the threshold location differs between problems with discrete priors versus continuous priors. Finally, for priors (i) and (ii) we carry out the replica prediction from statistical physics, which is conjectured to give the exact information-theoretic threshold for any fixed $d$. ::: Of independent interest, we introduce a new improvement to the second moment method for contiguity, on which our lower bounds are based. Our technique conditions away from rare `bad' events that depend on interactions between the signal and noise. This enables us to close $\sqrt{2}$-factor gaps present in several previous works.


title: A Statistical Model For Domain-Independent Text Segmentation

abstract: We propose a statistical method that finds the maximum-probability segmentation of a given text. This method does not require training data because it estimates probabilities from the given text. Therefore, it can be applied to any text in any domain. An experiment showed that the method is more accurate than or at least as accurate as a state-of-the-art text segmentation system.


Internet of Things Approach for Automation of the Complex Industrial Systems


title: Finding compact structural motifs

abstract: Protein structure motif detection is one of the fundamental problems in Structural Bioinformatics. Compared with sequence motifs, structural motifs are more sensitive in detecting the evolutionary relationships among proteins. A variety of algorithms have been proposed to attack this problem. However, they are either heuristic without theoretical performance guarantee, or inefficient for employing an exhaustive search strategy. Here, we study a reasonably restricted version of this problem: the compact structural motif problem. In this paper, we prove that this restricted version is still NP-hard, and we present a polynomialtime approximation scheme to solve it. To the best of our knowledge, this is the first approximation algorithm with a guarantee ratio for the protein structural motif problem.


title: Miniaturized Double Side Cooling Packaging for High Power 3 Phase SiC Inverter Module with Junction Temperature over 220°C

abstract: In this paper, authors developed miniaturizeddouble side cooling packaging for SiC (silicon carbide) highpower inverter module using new material solutions towithstand high temperature condition over 220oC. Instead ofconventional thick wire bonding on the device, the flip chipbonding for high power source and gate interconnections aredeveloped. For the drain interconnection, copper clips areattached using high temperature endurable interconnectionmaterials. By utilizing these flip-chip structures, the powermodule with double side cooling design was enabled effectivelyfor the heat dissipation induced by high power switchingoperation. Through the thermal modeling and characterization, the power module's package thermal dissipation performancewas found to be enhanced by 2 times compared with theconventional single side cooling type power module. To copewith the increased maximum junction temperature limit tooperate wide band gap power devices the novel packagingmaterial and its process which can endure up to 220 oC weredeveloped also. Bi and Ag based high temperature solder wasapplied for the flip-chip bumping for SiC device on the DBC(direct bonded copper). The copper clip was applied on thedevice backside as drain interconnection to DBC also. Hightemperature endurable EMC (epoxy molding compound) andTIM (thermal interface material) were also evaluated. Thepackaging process was optimized and developed along with themodeling. Through the reliability assessment, we could showthe potential applicability of double side cooling power modulewith flip-chip and clip bonding design and it would be usefulfor the SiC and GaN based high power module and highjunction and environmental temperature endurableapplications for the near future.


title: Representation Learning by Reconstructing Neighborhoods

abstract: Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we propose a novel unsupervised representation learning framework called neighbor-encoder, in which domain knowledge can be easily incorporated into the learning process without modifying the general encoder-decoder architecture of the classic autoencoder.In contrast to autoencoder, which reconstructs the input data itself, neighbor-encoder reconstructs the input data's neighbors. As the proposed representation learning problem is essentially a neighbor reconstruction problem, domain knowledge can be easily incorporated in the form of an appropriate definition of similarity between objects. Based on that observation, our framework can leverage any off-the-shelf similarity search algorithms or side information to find the neighbor of an input object. Applications of other algorithms (e.g., association rule mining) in our framework are also possible, given that the appropriate definition of neighbor can vary in different contexts. We have demonstrated the effectiveness of our framework in many diverse domains, including images, text, and time series, and for various data mining tasks including classification, clustering, and visualization. Experimental results show that neighbor-encoder not only outperforms autoencoder in most of the scenarios we consider, but also achieves the state-of-the-art performance on text document clustering.


title: Body Structure Aware Deep Crowd Counting

abstract: Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This paper aims to take a broader view to address crowd counting from the perspective of semantic modeling. In essence, crowd counting is a task of pedestrian semantic analysis involving three key factors: pedestrians, heads, and their context structure. The information of different body parts is an important cue to help us judge whether there exists a person at a certain position. Existing methods usually perform crowd counting from the perspective of directly modeling the visual properties of either the whole body or the heads only, without explicitly capturing the composite body-part semantic structure information that is crucial for crowd counting. In our approach, we first formulate the key factors of crowd counting as semantic scene models. Then, we convert the crowd counting problem into a multi-task learning problem, such that the semantic scene models are turned into different sub-tasks. Finally, the deep convolutional neural networks are used to learn the sub-tasks in a unified scheme. Our approach encodes the semantic nature of crowd counting and provides a novel solution in terms of pedestrian semantic analysis. In experiments, our approach outperforms the state-of-the-art methods on four benchmark crowd counting data sets. The semantic structure information is demonstrated to be an effective cue in scene of crowd counting.


title: Virtual AoA and AoD estimation for sparse millimeter wave MIMO channels

abstract: In this paper, we investigate an angle of arrival (AoA) and angle of departure (AoD) estimation algorithm for sparse millimeter wave multiple-input multiple-output (MIMO) channels. The analytical channel model whose use we advocate here is the beam space (or virtual) MIMO channel representation. By leveraging the beam space MIMO concept, we characterize probabilistic channel priors under an analog precoding and combining constraints. This investigation motivates Bayesian inference approaches to virtual AoA and AoD estimation. We divide the estimation task into downlink sounding for AoA estimation and uplink sounding for AoD estimation. A belief propagation (BP)-type algorithm is adopted, leading to computationally efficient approximate message passing (AMP) and approximate log-likelihood ratio testing (ALLRT) algorithms. Numerical results demonstrate that the proposed algorithm outperforms the conventional AMP in terms of the AoA and AoD estimation accuracy for the sparse millimeter wave MIMO channel.


title: Recognition of handwritten Roman Numerals using Tesseract open source OCR engine

abstract: The objective of the paper is to recognize handwritten samples of Roman numerals using Tesseract open source Optical Character Recognition (OCR) engine. Tesseract is trained with data samples of different persons to generate one user-independent language model, representing the handwritten Roman digit-set. The system is trained with 1226 digit samples collected form the different users. The performance is tested on two different datasets, one consisting of samples collected from the known users (those who prepared the training data samples) and the other consisting of handwritten data samples of unknown users. The overall recognition accuracy is obtained as 92.1% and 86.59% on these test datasets respectively.


title: Detection of Schizophrenia in brain MR images based on segmented ventricle region and deep belief networks

abstract: Schizophrenia (SZ) is a brain disorder that affects manifold cognitive domains which include language, memory, attention and executive functions. Magnetic resonance imaging is used to capture structural abnormalities in brain regions. Many studies have indicated brain region volume changes in Schizophrenia patients. In this work, an attempt has been made to analyze the schizophrenic subjects based on ventricle region using deep belief networks (DBNs). The effectiveness of the proposed method is evaluated on center of biomedical research excellence database. Initially, the ventricle region from the normal and SZ images is segmented using multiplicative intrinsic component optimization method. The DBN using different learning algorithms such as stochastic gradient descent (SGD), adaptive gradient (Adagrad) and root-mean-square propagation (RMSProp) is used to train the considered region. The effect of number of layers and the learning algorithm used to discriminate the normal and SZ subjects in DBN is analyzed. Then, the DBN model is evaluated on test set using accuracy, precision, sensitivity and specificity measures. The classification performance of the proposed system is analyzed using receiver operating characteristic curve. Further, the performance of DBN based on segmented ventricle is compared with region of interest (ROI) image that consists of ventricle along with other tissues. Here threefold validations are carried out for the same set of images. Results show that DBN with RMSProp learning with two hidden layers gives better performance compared to other learning methods such as SGD and Adagrad. In addition, DBN on segmented ventricle region gives least error compared to ROI image. DBN with segmented ventricle provides better classification accuracy of 90%. The proposed method achieves high area under the curve (0.899) for the segmented ventricle image, which clearly demonstrates its effectiveness. Thus, the DBN with RMSProp learning-based classification of segmented ventricle could be used as a supplement in the investigation of Schizophrenia.


title: HEp-2 Cell Classification Using Shape Index Histograms With Donut-Shaped Spatial Pooling

abstract: We present a new method for automatic classification of indirect immunoflourescence images of HEp-2 cells into different staining pattern classes. Our method is based on a new texture measure called shape index histograms that captures second-order image structure at multiple scales. Moreover, we introduce a spatial decomposition scheme which is radially symmetric and suitable for cell images. The spatial decomposition is performed using donut-shaped pooling regions of varying sizes when gathering histogram contributions. We evaluate our method using both the ICIP 2013 and the ICPR 2012 competition datasets. Our results show that shape index histograms are superior to other popular texture descriptors for HEp-2 cell classification. Moreover, when comparing to other automated systems for HEp-2 cell classification we show that shape index histograms are very competitive; especially considering the relatively low complexity of the method.


title: Power cycling with high temperature swing of discrete components based on different technologies

abstract: We present the results of power cycling tests of transfer molded, DCB and copper based power components at temperature swings up to 155 K. The results of the DCB based components are statistically analysed with Weibull distribution. An Analysis of the components after the tests provides an overview on the failure mechanisms. The results of the DCB based, transfer molded components exceed the expectations based on extrapolation of LESIT results.


title: ExploitMeter: Combining Fuzzing with Machine Learning for Automated Evaluation of Software Exploitability

abstract: Exploitable software vulnerabilities pose severe threats to its information security and privacy. Although a great amount of efforts have been dedicated to improving software security, research on quantifying software exploitability is still in its infancy. In this work, we propose ExploitMeter, a fuzzing-based framework of quantifying software exploitability that facilitates decision-making for software assurance and cyber insurance. Designed to be dynamic, efficient and rigorous, ExploitMeter integrates machine learning-based prediction and dynamic fuzzing tests in a Bayesian manner. Using 100 Linux applications, we conduct extensive experiments to evaluate the performance of ExploitMeter in a dynamic environment.


Fashion Retail Master Data Model and Business Development


title: Fertility treatment and child intelligence, attention, and executive functions in 5-year-old singletons: a cohort study

abstract: OBJECTIVE ::: To assess the association of fertility treatment and subfertility with offspring intelligence, attention, and executive functions in 5-year-old singletons. ::: ::: ::: DESIGN ::: Follow-up study. ::: ::: ::: SETTING ::: Denmark 2003-2008. ::: ::: ::: POPULATION ::: A cohort of 1782 children sampled from the Danish National Birth Cohort. ::: ::: ::: METHODS ::: The children were tested with a neuropsychological battery at age five. In addition to tests of intelligence, attention and executive functions, the follow up included extensive information on important covariates. The analyses were conducted using multiple linear regression and adjusted for parental educational level, maternal intelligence, age, parity, body mass index, smoking in pregnancy, alcohol consumption in pregnancy and child gender, child age, and examiner. ::: ::: ::: MAIN OUTCOME MEASURES ::: Wechsler Preschool and Primary Scale of Intelligence-Revised, the Test of Everyday Attention for Children at Five, and the Behavior Rating Inventory of Executive Functions scores. ::: ::: ::: RESULTS ::: A consistent pattern of nonsignificantly lower scores were only observed for intelligence and executive functions in children born after fertility treatment or by subfertile parents when the results were unadjusted for maternal intelligence and parental educational level. When adjusted for these and other covariates, there were no significant mean differences in intelligence (mean difference -2.8, 95% CI -7.8, 2.2), overall attention (-0.1, 95% CI -0.6, 0.3), or parent-rated executive functions (-0.1, 95% CI -3.0, 2.9) between children born after spontaneous conception and children born to parents conceiving after fertility treatment. Similarly, there were no significant mean differences in intelligence (mean difference 0.6, 95% CI -2.2, 3.4), overall attention (0.1, 95% CI -0.2, 0.4), or parent-rated executive functions (1.0, 95% CI -1.8, 3.7) between children born after spontaneous conception and children born to subfertile parents waiting more than 12 months before conceiving naturally. ::: ::: ::: CONCLUSIONS ::: This study suggests that parental subfertility and fertility treatment are unrelated to offspring intelligence, attention and executive functions.


title: Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays

abstract: We investigate the task of assessing sentence-level prompt relevance in learner essays. Various systems using word overlap, neural embeddings and neural compositional models are evaluated on two datasets of learner writing. We propose a new method for sentence-level similarity calculation, which learns to adjust the weights of pre-trained word embeddings for a specific task, achieving substantially higher accuracy compared to other relevant baselines.


title: A Novel MPPT Control of photovoltaic system using FLC algorithm

abstract: This paper proposes a novel maximum power point tracking (MPPT) using a fuzzy logic control (FLC) algorithm for robust in environment changing. The power available at the output of photovoltaic (PV) cell keeps changing with radiation and temperature because solar cell exhibits a nonlinear current-voltage characteristic. So its maximum power point (MPP) of PV cells varies with radiation and temperature. MPPT methods are used in PV systems to make full utilization of PV array output power which depends on radiation and temperature. Conventional MPPT controller such as constant voltage (CV), perturbation and observation (PO) and incremental conductance (IC) are researched. But these controllers have the problem that is failure to MPP with environment changing. The proposed FLC controller is based the fuzzy control algorithm and able to robust control with environment changing. Also the PV systems applied FLC controller is modeled by PSIM and the response characteristics of FLC method according to the environment variation is analyzed through comparison with performance of conventional methods. The validity of this controller is proved through response results.


title: Camera array calibration for light field acquisition

abstract: Light field cameras are becoming popular in computer vision and graphics, with many research and commercial applications already having been proposed. Various types of cameras have been developed with the camera array being one of the ways of acquiring a 4D light field image using multiple cameras. Camera calibration is essential, since each application requires the correct projection and ray geometry of the light field. The calibrated parameters are used in the light field image rectified from the images captured by multiple cameras. Various camera calibration approaches have been proposed for a single camera, multiple cameras, and a moving camera. However, although these approaches can be applied to calibrating camera arrays, they are not effective in terms of accuracy and computational cost. Moreover, less attention has been paid to camera calibration of a light field camera. In this paper, we propose a calibration method for a camera array and a rectification method for generating a light field image from the captured images. We propose a two-step algorithm consisting of closed form initialization and nonlinear refinement, which extends Zhang's well-known method to the camera array. More importantly, we introduce a rigid camera constraint whereby the array of cameras is rigidly aligned in the camera array and utilize this constraint in our calibration. Using this constraint, we obtained much faster and more accurate calibration results in the experiments.


title: Online shopping post-payment dissonance: Dissonance reduction strategy using online consumer social experiences

abstract: This study applied the concept of online consumer social experiences (OCSEs) to reduce online shopping post-payment dissonance (i.e., dissonance occurring between online payment and product receipt). Two types of OCSEs were developed: indirect social experiences (IDSEs) and virtual social experiences (VSEs). Two studies were conducted, in which 447 college students were enrolled. Study 1 compared the effects of OCSEs and non-OCSEs when online shopping post-payment dissonance occurred. The results indicate that providing consumers affected by online shopping post-payment dissonance with OCSEs reduces dissonance and produces higher satisfaction, higher repurchase intention, and lower complaint intention than when no OCSEs are provided. In addition, consumers interpersonal trust (IPT) and susceptibility to interpersonal informational influence (SIII) moderated the positive effects of OCSEs. Study 2 compared the effects of IDSEs and VSEs when online shopping post-payment dissonance occurred. The results suggest that the effects of IDSEs and VSEs on satisfaction, repurchase intention, and complaint intention are moderated by consumers computing need for control (CNC) and computer-mediated communication apprehension (CMCA). The consumers with high CNC and low CMCA preferred VSEs, whereas the consumers with low CNC and high CMCA preferred IDSEs. The effects of VSEs and IDSEs on consumers with high CNC and CMCA and those with low CNC and CMCA were not significantly different.


title: A broadband highly linear 3.5-6GHz CMOS VCO for multi-standard wireless transceivers

abstract: A broadband highly linear Voltage controlled oscillator (VCO) for multi-standard wireless transceivers application is presented in this paper. Highly linear VCO gain (Kvco) is exhibited in the VCO which can realize a linear Kvco, better phase noise performance and more stable oscillation in same time. A 6-bit switched capacitor array is employed to extend the tuning range, and also 6-bit switched current source to guarantee robust start-up condition and maintain the amplitude of different frequency to be approximately the same. Noise filter technology is adopted to further improve the phase noise. The simulated results show the VCO output covers frequency band from 3.5GHz to 6GHz. And the phase noise is −124.6dBc/Hz at 1MHz offset from a 3.5GHz carrier and −116dBc/Hz at 1MHz offset from a 6GHz carrier. The VCO is fabricated in 0.13µm CMOS process while consuming 7.8mA from a 1.2V supply. The total area of layout is 0.75×0.68 mm2.


title: The Kernel Trick for Distances

abstract: A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as norm-based distances in Hilbert spaces. It turns out that common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. ::: ::: As well as providing a useful new insight into how these algorithms work, the present work can form the basis for conceiving new algorithms.


title: Using deep learning for digital soil mapping

abstract: Abstract. Digital soil mapping (DSM) has been widely used as a cost-effective ::: method for generating soil maps. However, current DSM data representation ::: rarely incorporates contextual information of the landscape. DSM models are ::: usually calibrated using point observations intersected with spatially ::: corresponding point covariates. Here, we demonstrate the use of the ::: convolutional neural network (CNN) model that incorporates contextual information ::: surrounding an observation to significantly improve the prediction accuracy ::: over conventional DSM models. We describe a CNN model that takes inputs as images of covariates and explores spatial ::: contextual information by finding non-linear local spatial relationships of ::: neighbouring pixels. Unique features of the proposed model include input ::: represented as a 3-D stack of images, data augmentation to reduce overfitting, ::: and the simultaneous prediction of multiple outputs. Using a soil mapping example ::: in Chile, the CNN model was trained to simultaneously predict soil organic ::: carbon at multiples depths across the country. The results showed that, in ::: this study, the CNN model reduced the error by 30 % compared with ::: conventional techniques that only used point information of covariates. In ::: the example of country-wide mapping at 100 m resolution, the neighbourhood ::: size from 3 to 9 pixels is more effective than at a point location and larger ::: neighbourhood sizes. In addition, the CNN model produces less prediction ::: uncertainty and it is able to predict soil carbon at deeper soil layers more ::: accurately. Because the CNN model takes the covariate represented as images, it ::: offers a simple and effective framework for future DSM models.


An Improved LEACH Protocol using Fuzzy Logic in Wireless Sensor Network


GENDER AND FORGIVENESS : A META – ANALYTIC


title: A novel anomaly detection scheme based on principal component classifier

abstract: Abstract : This paper proposes a novel scheme that uses robust principal component classifier in intrusion detection problems where the training data may be unsupervised. Assuming that anomalies can be treated as outliers, an intrusion predictive model is constructed from the major and minor principal components of the normal instances. A measure of the difference of an anomaly from the normal instance is the distance in the principal component space. The distance based on the major components that account for 50% of the total variation and the minor components whose eigenvalues less than 0.20 is shown to work well. The experiments with KDD Cup 1999 data demonstrate that the proposed method achieves 98.94% in recall and 97.89% in precision with the false alarm rate 0.92% and outperforms the nearest neighbor method, density-based local outliers (LOF) approach, and the outlier detection algorithm based on Canberra metric.


A Neural Network Approach to Topic Spotting


title: Correcting Radial Distortion by Circle Fitting

abstract: A powerful and popular approach for estimating radial lens distortion parameters is to use the fact that lines which are straight in the scene should be imaged as straight lines under the pinhole camera model. This paper revisits this problem using the division model to parameterise the lens distortion. This turns out to have significant advantages over the more conventional parameterisation, especially for a single parameter model. In particular, we demonstrate that the locus of distorted points from a straight line is a circular arc. This allows distortion estimation to be reformulated as circle-fitting for which many algorithms are available. We compare a number of suboptimal methods offering closed-form solutions with an optimal, iterative technique which minimises a cost function on the actual image plane as opposed to existing techniques which suffer from a bias due to the fact that they optimise a geometric cost function on the undistorted image plane.


title: How people extract information from graphs: Evidence from a sentence-graph verification paradigm

abstract: Graph comprehension is constrained by the goals of the cognitive system that processes the graph and by the context in which the graph appears. In this paper we report the results of a study using a sentence-graph verification paradigm. We recorded participants' reaction times to indicate whether the information contained in a simple bar graph matched a written description of the graph. Aside from the consistency of visual and verbal information, we manipulated whether the graph was ascending or descending, the relational term in the verbal description, and the labels of the bars of the graph. Our results showed that the biggest source of variance in people's reaction times is whether the order in which the referents appear in the graph is the same as the order in which they appear in the sentence. The implications of this finding for contemporary theories of graph comprehension are discussed.


title: Stack Overflow in Github: Any Snippets There?

abstract: When programmers look for how to achieve certain programming tasks, Stack Overflow is a popular destination in search engine results. Over the years, Stack Overflow has accumulated an impressive knowledge base of snippets of code that are amply documented. We are interested in studying how programmers use these snippets of code in their projects. Can we find Stack Overflow snippets in real projects? When snippets are used, is this copy literal or does it suffer adaptations? And are these adaptations specializations required by the idiosyncrasies of the target artifact, or are they motivated by specific requirements of the programmer? The large-scale study presented on this paper analyzes 909k non-fork Python projects hosted on Github, which contain 290M function definitions, and 1.9M Python snippets captured in Stack Overflow. Results are presented as quantitative analysis of block-level code cloning intra and inter Stack Overflow and GitHub, and as an analysis of programming behaviors through the qualitative analysis of our findings.


title: Computational models of long term plasticity and memory

abstract: Memory is often defined as the mental capacity of retaining information about facts, events, procedures and more generally about any type of previous experience. Memories are remembered as long as they influence our thoughts, feelings, and behavior at the present time. Memory is also one of the fundamental components of learning, our ability to acquire any type of knowledge or skills. In the brain it is not easy to identify the physical substrate of memory. Basically, any long-lasting alteration of a biochemical process can be considered a form of memory, although some of these alterations last only a few milliseconds, and most of them, if taken individually, cannot influence our behavior. However, if we want to understand memory, we need to keep in mind that memory is not a unitary phenomenon, and it certainly involves several distinct mechanisms that operate at different spatial and temporal levels. One of the goals of theoretical neuroscience is to try to understand how these processes are orchestrated to store memories rapidly and preserve them over a lifetime. Theorists have mostly focused on synaptic plasticity, as it is one of the most studied memory mechanisms in experimental neuroscience and it is known to be highly effective in training artificial neural networks to perform real world tasks. Some of the synaptic plasticity models are purely phenomenological, some others have been designed to solve computational problems. In this article I will review some of these models and I will try to identify computational principles that underlie memory storage and preservation.


title: Efficient Structured Multifrontal Factorization for General Large Sparse Matrices

abstract: Rank structures provide an opportunity to develop new efficient numerical methods for practical problems, when the off-diagonal blocks of certain dense intermediate matrices have small (numerical) ranks. In this work, we present a framework of structured direct factorizations for general sparse matrices, including discretized PDEs on general meshes, based on the multifrontal method and hierarchically semiseparable (HSS) matrices. We prove the idea of replacing certain complex structured operations by fast simple ones performed on compact reduced matrix forms. Such forms result from the hierarchical factorization of a tree-structured HSS matrix in a ULV-type scheme, so that the tree structure is reduced into a single node, the root of the original tree. This idea is shown to be very useful in the partial ULV factorization of an HSS matrix (for quickly computing Schur complements) as well as the solution stage. These techniques are then built into the multifrontal method for sparse factorizations after nest...


title: Dynamic virtual machine management via approximate Markov decision process

abstract: Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs' resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter category are heuristical and lack theoretical performance guarantees. In this work, we formulate dynamic VM management as a large-scale Markov Decision Process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, MadVM can be implemented in a distributed system, which should suit the needs of real data centers. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.


title: It Takes More than Moore to Answer Existence-Questions

abstract: Several recent discussions of metaphysics disavow existence-questions, claiming that they are metaphysically uninteresting because trivially settled in the affirmative by Moorean facts. This is often given as a reason to focus metaphysical debate instead on questions of grounding. I argue that the strategy employed to undermine existence-questions fails against its usual target: Quineanism. The Quinean can protest that the formulation given of their position is a straw man: properly understood, as a project of explication, Quinean metaphysics does not counsel us to choose between obvious ordinary-language claims and absurd revisionist claims, even if appeal to Moorean facts is permitted.


title: From Secularism to Jihad: Sayyid Qutb and the Foundations of Radical Islamism

abstract: Prologue Intellectual, Political and Socio-economic Transformation in Egypt, 1919-1952 Qutb's Childhood and the Emergence of a Poet and Critic, 1906-1938 Emergence of the Student of the Qur'an and Early Changes, 1939-1947 The Alienation of Sayyid Qutb, 1939-1947 In Search of Social Justice: The Emergence of An Independent Islamic Ideologue, 1947-1948 Qutb's Experiences and Impressions in America, 1948-1950 and His Return, 1950-1952 Emergence of Radical Islamists, 1952-1964 Martyrdom, Posthumous Impact and Global Jihad Epilogue Bibliography General Index


title: Electrochemical Biosensors - Sensor Principles and Architectures

abstract: Quantification of biological or biochemical processes are of utmost importancefor medical, biological and biotechnological applications. However, converting the biologicalinformation to an easily processed electronic signal is challenging due to the complexity ofconnecting an electronic device directly to a biological environment. Electrochemical biosensorsprovide an attractive means to analyze the content of a biological sample due to thedirect conversion of a biological event to an electronic signal. Over the past decades severalsensing concepts and related devices have been developed. In this review, the most commontraditional techniques, such as cyclic voltammetry, chronoamperometry, chronopotentiometry,impedance spectroscopy, and various field-effect transistor based methods are presented alongwith selected promising novel approaches, such as nanowire or magnetic nanoparticle-basedbiosensing. Additional measurement techniques, which have been shown useful in combinationwith electrochemical detection, are also summarized, such as the electrochemical versionsof surface plasmon resonance, optical waveguide lightmode spectroscopy, ellipsometry,quartz crystal microbalance, and scanning probe microscopy.The signal transduction and the general performance of electrochemical sensors are often determinedby the surface architectures that connect the sensing element to the biological sampleat the nanometer scale. The most common surface modification techniques, the various electrochemicaltransduction mechanisms, and the choice of the recognition receptor moleculesall influence the ultimate sensitivity of the sensor. New nanotechnology-based approaches,such as the use of engineered ion-channels in lipid bilayers, the encapsulation of enzymesinto vesicles, polymersomes, or polyelectrolyte capsules provide additional possibilities forsignal amplification.In particular, this review highlights the importance of the precise control over the delicateinterplay between surface nano-architectures, surface functionalization and the chosen sensortransducer principle, as well as the usefulness of complementary characterization tools tointerpret and to optimize the sensor response.


title: SIMULATION OF HOLLOW ELECTRON BEAM COLLIMATION IN THE FERMILAB TEVATRON COLLIDER

abstract: The concept of augmenting the conventional collimation system of high-energy storage rings with a hollow electron beam was successfully demonstrated in experiments at the Tevatron. A reliable numerical model is required for understanding particle dynamics in the presence of a hollow beam collimator. Several models were developed to describe imperfections of the electron beam profile and alignment. The features of the imperfections are estimated from electron beam profile measurements. Numerical simulations of halo removal rates are compared with experimental data taken at the Tevatron.


title: Bootstrap Learning of Heuristic Functions

abstract: search algorithms such as IDA* or heuristic-search planners. Our method aims to generate a strong heuristic from a given weak heuristic h 0 through bootstrapping. The "easy" problem instances that can be solved using h 0 provide training examples for a learning algorithm that produces a heuristic h 1 that is expected to be stronger than h 0 . If h 0 is too weak to solve any of the given instances we use a random walk technique to create a sequence of successively more difficult instances starting with ones that are solvable by h 0 . The bootstrap process is then repeated using h i in lieu of h i –1 until a sufficiently strong heuristic is produced. We test our method on the 15- and 24-sliding tile puzzles, the 17- and 24-pancake puzzles, and the 15- and 20-blocks world. In every case our method produces a heuristic that allows IDA* to solve randomly generated problem instances extremely quickly with solutions very close to optimal.


Automated Test Case Generation and Optimization : A Comparative Review


title: Temporal-Kernel Recurrent Neural Networks

abstract: Abstract A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using them to make a prediction. In this paper we consider the problem of training RNNs to predict sequences that exhibit significant long-term dependencies, focusing on a serial recall task where the RNN needs to remember a sequence of characters for a large number of steps before reconstructing it. We introduce the Temporal-Kernel Recurrent Neural Network (TKRNN), which is a variant of the RNN that can cope with long-term dependencies much more easily than a standard RNN, and show that the TKRNN develops short-term memory that successfully solves the serial recall task by representing the input string with a stable state of its hidden units.


title: Features for Masking-Based Monaural Speech Separation in Reverberant Conditions

abstract: Monaural speech separation is a fundamental problem in speech and signal processing. This problem can be approached from a supervised learning perspective by predicting an ideal time–frequency mask from features of noisy speech. In reverberant conditions at low signal-to-noise ratios (SNRs), accurate mask prediction is challenging and can benefit from effective features. In this paper, we investigate an extensive set of acoustic–phonetic features extracted in adverse conditions. Deep neural networks are used as the learning machine, and separation performance is evaluated using standard objective speech intelligibility metrics. Separation performance is systematically evaluated in both nonspeech and speech interference, in a variety of SNRs, reverberation times, and direct-to-reverberant energy ratios. Considerable performance improvement is observed by using contextual information, likely due to temporal effects of room reverberation. In addition, we construct feature combination sets using a sequential floating forward selection algorithm, and combined features outperform individual ones. We also find that optimal feature sets in anechoic conditions are different from those in reverberant conditions.


title: Auto/biography, personal testimony and epiphany moments: a case study in research-informed teaching

abstract: AbstractThis article examines an example of how academic research may inform how we teach. In this case study, we look at the use of auto/biography as a method in the sociological study of crime. Goodey’s (2000) use of a “biographical continuum? to identify “epiphanal moments? in the life course was adopted by one of the authors of this paper for his PhD research on the experiences of war veterans. This was later adapted for use with first-year criminology students as part of their preparation for an essay assignment on identity formation which required them to utilise their own auto/biographical accounts. Despite the well-documented tension between research and teaching in universities, it is argued that our teaching can be enhanced by the appropriate use of research methods and findings.


Linear Optimal Control Systems


title: Reasoning With Inconsistent Ontologies: Framework, Prototype, and Experiment

abstract: Classical logical inference engines assume the consistency of the ontologies they reason with. Conclusions drawn from an inconsistent ontology by classical inference may be completely meaningless. An inconsistency reasoner is one which is able to return meaningful answers to queries, given an inconsistent ontology. In this chapter, we propose a general framework for reasoning with inconsistent ontologies. We present the formal definitions of soundness, meaningfulness, local completeness, and maximality of an inconsistency reasoner. We propose and investigate a pre-processing algorithm, discuss the strategies of inconsistency reasoning based on pre-defined selection functions dealing with concept relevance. We have implemented a system called PION (Processing Inconsistent ONtologies) for reasoning with inconsistent ontologies. We discuss how the syntactic relevance can be used for PION. In this chapter, we also report the preliminary experiments with PION.


title: Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST)

abstract: CMR quantification of LV chamber volumes typically and manually defines the basal-most LV, which adds processing time and user-dependence. This study developed an LV segmentation method that is fully automated based on the spatiotemporal continuity of the LV (LV-FAST). An iteratively decreasing threshold region growing approach was used first from the midventricle to the apex, until the LV area and shape discontinued, and then from midventricle to the base, until less than 50% of the myocardium circumference was observable. Region growth was constrained by LV spatiotemporal continuity to improve robustness of apical and basal segmentations. The LV-FAST method was compared with manual tracing on cardiac cine MRI data of 45 consecutive patients. Of the 45 patients, LV-FAST and manual selection identified the same apical slices at both ED and ES and the same basal slices at both ED and ES in 38, 38, 38, and 41 cases, respectively, and their measurements agreed within -1.6 ± 8.7 mL, -1.4 ± 7.8 mL, and 1.0 ± 5.8% for EDV, ESV, and EF, respectively. LV-FAST allowed LV volume-time course quantitatively measured within 3 seconds on a standard desktop computer, which is fast and accurate for processing the cine volumetric cardiac MRI data, and enables LV filling course quantification over the cardiac cycle.


title: On Optimal Power Allocation for Downlink Non-Orthogonal Multiple Access Systems

abstract: Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.


title: Effects of Small Addition of Ti on Strength and Microstructure of a Cu-Ni-Si Alloy

abstract: The effect of addition of 0.04 or 0.2 mass pct Ti on the mechanical properties of a Cu-2.0 mass pct Ni-0.5 mass pct Si alloy has been investigated. The addition of 0.04 mass pct Ti enhances the strength of the Cu-Ni-Si alloy without reducing its electrical conductivity. This increase in strength is caused by the decrease in inter-precipitate spacing of δ-Ni2Si precipitates. The addition of trace Ti reduces the equilibrium concentration of Ni and Si atoms in the alloy bearing the δ precipitates, resulting in an increase in the volume fraction of δ precipitates and decrease in the inter-precipitate spacing. However, the addition of 0.2 mass pct Ti to the Cu-Ni-Si alloy decreases the strength of the alloy. The reduction in strength is attributed to the decrease in the volume fraction of δ precipitates caused by the reduction in Ni and Si atoms in the Cu matrix resulting from the formation of Ni16Si7Ti6 particles.


title: Free Riding in BitTorrent is Cheap

abstract: While it is well-known that BitTorrent is vulnerable to selfish behavior, this paper demonstrates that even entire files can be downloaded without reciprocating at all in BitTorrent. To this end, we present BitThief, a free riding client that never contributes any real data. First, we show that simple tricks suffice in order to achieve high download rates, even in the absence of seeders. We also illustrate how peers in a swarm react to various sophisticated attacks. Moreover, our analysis reveals that sharing communities—communities originally intended to offer downloads of good quality and to promote cooperation among peers—provide many incentives to cheat.


title: Implementation of 64-bit Kogge Stone Carry Select Adder with ZFC for efficient area

abstract: Carry Select Adder (CSLA) is one of the faster adder used in many data-processing processors to perform fast arithmetic functions. The speed of operation of such an adder is limited by carry propagation from input to output. This paper discusses about the implementation of linear Carry Select Adder with Kogge Stone Adderwith out ZFC. The Kogge Stone parallel approach will give option to generate fast carry for intermediate stages. From the structure of linear CSLA it is clear that there is scope for reducing the area in CSLA by using a first zero finding logic. 64-bit linear CSLA architecture is implemented with out using multiplexer which reduces area with slight increase in delay. Simulation and Synthesis are carried on Modelsim 6.3 and Xilinx ISE 12.2.


title: VSA-CubeBot: A modular variable stiffness platform for multiple degrees of freedom robots

abstract: We propose a prototype of a Variable Stiffness Actuator (VSA) conceived with low cost as its first goal. This approach was scarcely covered in past literature. Many recent works introduced a large number of actuators with adjustable stiffness, optimized for a wide set of applications. They cover a broad range of design possibilities, but their availability is still limited to small quantities. This work presents the design and implementation of a modular servo-VSA multi-unit system, called VSA-CubeBot. It offers a customizable platform for the realization and test of variable stiffness robotic structures with many degrees of freedom. We present solutions relative to the variable stiffness mechanism, embedded electronics, mechanical and electrical interconnections. Characteristics, both theoretic and experimental, of the single actuator are reported and, finally, five units are interconnected to form a single arm, to give an example of the many possible applications of this modular VSA actuation unit.


title: A Survey of Methods for Detection of Stator-Related Faults in Induction Machines

abstract: As evidenced by industrial surveys, stator-related failures account for a large percentage of faults in induction machines. The objective of this paper is to provide a survey of existing techniques for detection of stator-related faults, which include stator winding turn faults, stator core faults, temperature monitoring and thermal protection, and stator winding insulation testing. The root causes of fault inception, available techniques for detection, and recommendations for further research are presented. Although the primary focus is online and sensorless methods that use machine voltages and currents to extract fault signatures, offline techniques such as partial discharge detection are also examined. Condition monitoring, fault diagnostics, insulation testing, interlaminar core faults, partial discharge (PD), temperature monitoring, turn faults.


title: Role of television in adolescent women's body dissatisfaction and drive for thinness.

abstract: Objective ::: Many authors have implicated the media's promotion of an unrealistically thin ideal for women as a major causal factor in the current high levels of body dissatisfaction and increasing incidence of eating disorders. The present study aimed to investigate the relationship between exposure to one medium, television, and body dissatisfaction and drive for thinness. Method: Questionnaires were administered to 94 adolescent women who reported how much and what television they had watched in the previous week. Body dissatisfaction and drive for thinness were also assessed. Results: Amount of television watched did not correlate with either body dissatisfaction or drive for thinness, but category of program did. Specifically, amount of time spent watching soaps, movies, and (negatively) sport predicted body dissatisfaction, and the watching of music videos predicted drive for thinness. Discussion: The results are consistent with sociocultural explanations for body dissatisfaction and for the emergence of eating disorders in young women. © 1996 by John Wiley & Sons, Inc.


title: Quadruple-Mode UWB Bandpass Filter With Improved Out-of-Band Rejection

abstract: A quadruple-mode ultra-wideband (UWB) bandpass filter with sharp out-of-band rejection is presented in this work. As a starting part of designing a quadruple-mode filter, a mode graph of the initial triple-mode resonator is studied to choose its proper dimensions. Based on these pre-determined dimensions of this triple-mode resonator, two short-circuited stubs are introduced in this resonator to generate two transmission zeros near the lower and upper cut-off frequencies, leading to a higher rejection skirt outside the desired passband. Moreover, as these two stubs are installed, the fourth resonant mode falls down and works together with the first three resonant modes to form a novel quadruple-mode UWB filter. Finally, a filter prototype is designed and fabricated to experimentally validate the attractive in-band and out-of-band performances as predicted in theory.


title: Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video

abstract: How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture *how* the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.


title: Borrowing from the Crowd: A Study of Recombination in Software Design Competitions

abstract: One form of crowdsourcing is the competition, which poses an open call for competing solutions. Commercial systems such as TopCoder have begun to explore the application of competitions to software development, but have important limitations diminishing the potential benefits drawn from the crowd. In particular, they employ a model of independent work that ignores the opportunity for designs to arise from the ideas of multiple designers. In this paper, we examine the potential for software design competitions to incorporate recombination, in which competing designers are given the designs of others and encouraged to use them to revise their own designs. To explore this, we conducted two software design competitions in which participants were asked to produce both an initial and a revised design, drawing on lessons learned from the crowd. We found that, in both competitions, all participants borrowed ideas and most improved the quality of their designs. Our findings demonstrate the potential benefits of recombination in software design and suggest several ways in which software design competitions can be improved.


title: SPARQL-Based Applications for RDF-Encoded Sensor Data

abstract: Complex event processing is currently more dominated by proprietary systems and vertical products than open technologies. In the future, however, internet-connected people and things moving between smart spaces in smart cities will create a huge volume of events in a multi-actor, multi-platform environment. End-user applications would benefit from the possibility for open access to all relevant sensors and data sources. The work on semantic sensor networks concerns such open technologies to discover and access sensors on the Web, to integrate heterogeneous sensor data, and to make it meaningful to applications. In this study we address the question of how a set of applications can efficiently access a shared set of sensors while avoiding redundant data acquisition that would lead to energy-efficiency problems. The Instans event processing platform, based on the Rete-algorithm, offers continuous execution of interconnected SPARQL queries and update rules. Rete enables sharing of sensor access and caching of intermediate results in a natural and high-performance manner. Our solution suggests that with incremental query evaluation, standard-based SPARQL and RDF can handle complex event processing tasks relevant to sensor networks, and reduce the redundant access from a set of applications to shared sensors.


title: Inter-destination multimedia synchronization: schemes, use cases and standardization

abstract: Traditionally, the media consumption model has been a passive and isolated activity. However, the advent of media streaming technologies, interactive social applications, and synchronous communications, as well as the convergence between these three developments, point to an evolution towards dynamic shared media experiences. In this new model, geographically distributed groups of consumers, independently of their location and the nature of their end-devices, can be immersed in a common virtual networked environment in which they can share multimedia services, interact and collaborate in real-time within the context of simultaneous media content consumption. In most of these multimedia services and applications, apart from the well-known intra and inter-stream synchronization techniques that are important inside the consumers’ playout devices, also the synchronization of the playout processes between several distributed receivers, known as multipoint, group or Inter-destination multimedia synchronization (IDMS), becomes essential. Due to the increasing popularity of social networking, this type of multimedia synchronization has gained in popularity in recent years. Although Social TV is perhaps the most prominent use case in which IDMS is useful, in this paper we present up to 19 use cases for IDMS, each one having its own synchronization requirements. Different approaches used in the (recent) past by researchers to achieve IDMS are described and compared. As further proof of the significance of IDMS nowadays, relevant organizations’ (such as ETSI TISPAN and IETF AVTCORE Group) efforts on IDMS standardization (in which authors have been and are participating actively), defining architectures and protocols, are summarized.


title: EM*: An EM Algorithm for Big Data

abstract: Existing data mining techniques, more particularly iterative learning algorithms, become overwhelmed with big data. While parallelism is an obvious and, usually, necessary strategy, we observe that both (1) continually revisiting data and (2) visiting all data are two of the most prominent problems especially for iterative, unsupervised algorithms like Expectation Maximization algorithm for clustering (EM-T). Our strategy is to embed EM-T into a non-linear hierarchical data structure(heap) that allows us to (1) separate data that needs to be revisited from data that does not and (2) narrow the iteration toward the data that is more difficult to cluster. We call this extended EM-T, EM*. We show our EM* algorithm outperform EM-T algorithm over large real world and synthetic data sets. We lastly conclude with some theoretic underpinnings that explain why EM* is successful.


title: Fuzzy methods for forensic data analysis

abstract: In this paper we describe a methodology and an automatic procedure for inferring accurate and easily understandable expert-system-like rules from forensic data. This methodology is based on the fuzzy set theory. The algorithms we used are described in detail, and were tested on forensic data sets. We also present in detail some examples, which are representative for the obtained results.


title: Enabling efficient OS paging for main-memory OLTP databases

abstract: Even though main memory is becoming large enough to fit most OLTP databases, it may not always be the best option. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). Therefore, it is more economical to store the coldest records on a fast secondary storage device such as a solid-state disk. However, main-memory DBMS have no knowledge of secondary storage, while traditional disk-based databases, designed for workloads where data resides on HDD, introduce too much overhead for the common case where the working set is memory resident. In this paper, we propose a simple and low-overhead technique that enables main-memory databases to efficiently migrate cold data to secondary storage by relying on the OS's virtual memory paging mechanism. We propose to log accesses at the tuple level, process the access traces offline to identify relevant access patterns, and then transparently re-organize the in-memory data structures to reduce paging I/O and improve hit rates. The hot/cold data separation is performed on demand and incrementally through careful memory management, without any change to the underlying data structures. We validate experimentally the data re-organization proposal and show that OS paging can be efficient: a TPC-C database can grow two orders of magnitude larger than the available memory size without a noticeable impact on performance.