title: Efficient Hardware Implementation For Fingerprint Image Enhancement Using Anisotropic Gaussian Filter

abstract: A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter. To further improve the performance of the filter, the input image is homogenized by a local image normalization. In the proposed structure, for a middle-range reconfigurable FPGA, both parallel compute-intensive and real-time demands were achieved. We manage to efficiently speed up the image-processing time and improve the resource utilization of the FPGA. Test results show an improved speed for its hardware architecture while maintaining reasonable enhancement benchmarks.


title: Cantor meets scott: semantic foundations for probabilistic networks

abstract: ProbNetKAT is a probabilistic extension of NetKAT with a denotational semantics based on Markov kernels. The language is expressive enough to generate continuous distributions, which raises the question of how to compute effectively in the language. This paper gives an new characterization of ProbNetKAT’s semantics using domain theory, which provides the foundation needed to build a practical implementation. We show how to use the semantics to approximate the behavior of arbitrary ProbNetKAT programs using distributions with finite support. We develop a prototype implementation and show how to use it to solve a variety of problems including characterizing the expected congestion induced by different routing schemes and reasoning probabilistically about reachability in a network.


title: Craniofacial Superimposition in Forensic Identification using Genetic Algorithms

abstract: Craniofacial superimposition is a process that aims to identify a person by overlaying a photograph and a model of the skull. This process is usually carried out manually by forensic anthropologists; thus being very time consuming and presenting several difficulties in finding a good fit between the 3D model of the skull and the 2D photo of the face. In this paper we present a fast and automatic procedure to tackle the superimposition problem. The proposed method is based on real-coded genetic algorithms. Synthetic data are used to validate the method. Results on a real case from our Physical Anthropology lab of the University of Granada are also presented.


title: Scaling measurements of the effect of surface slant on perceived lightness

abstract: The light reflected from an object depends on its reflectance, the illumination, and the pose of the object within the scene. An observer is called lightness constant if the perceived reflectance (lightness) of achromatic objects stays the same despite variation in object-extrinsic factors such as illumination and pose. Here, we used a dissimilarity scaling task to measure lightness constancy as the intensity of the illuminant and the slant of test surfaces were varied. Across two experiments, we had observers rate the dissimilarity of flat grayscale test stimulus pairs. The test stimuli were real illuminated surfaces, not computer simulations. Each test stimulus was seen in its own illuminated chamber, with the two chambers viewed side by side. We varied test surface reflectance, chamber illumination intensity, and the slant of the test in relation to the single light source in each chamber. Data were analyzed using nonmetric multidimensional scaling. The data were well-described by a one-dimensional perceptual representation. This representation was consistent across observers, revealed partial lightness constancy with respect to a change in illumination intensity, and no lightness constancy with respect to changes in surface slant. An additional experiment using a matching procedure and the same stimulus set, however, revealed moderate constancy with respect to changes in surface slant. The difference in results between the two methods is interesting, but not understood.


title: Probabilistic neural network playing and learning Tic-Tac-Toe

abstract: A probabilistic neural network is applied as a tool to approximate the statistical evaluation function for a simple version of the game Tic-Tac-Toe. We solve the problem by a sequential estimation of the underlying discrete distribution mixture of product components.


title: Part-of-Speech Tagging and Partial Parsing

abstract: The initial impetus for the current popularity of statistical methods in computational linguistics was provided in large part by the papers on part-of-speech tagging by Church (1988), DeRose (1988), and Garside (1987b). In contradiction to common wisdom, these taggers showed that it was indeed possible to carve part-of-speech disambiguation out of the apparently monolithic problem of natural language understanding, and solve it with impressive accuracy.


title: Class vs. Student in a Bayesian Network Student Model

abstract: For decades, intelligent tutoring systems researchers have been developing various methods of student modeling. Most of the models, including two of the most popular approaches: Knowledge Tracing model and Performance Factor Analysis, all have similar assumption: the information needed to model the student is the student’s performance. However, there are other sources of information that are not utilized, such as the performance on other students in same class. This paper extends the Student-Skill extension of Knowledge Tracing, to take into account the class information, and learns four parameters: prior knowledge, learn, guess and slip for each class of students enrolled in the system. The paper then compares the accuracy using the four parameters for each class versus the four parameters for each student to find out which parameter set works better in predicting student performance. The result shows that modeling at coarser grain sizes can actually result in higher predictive accuracy, and data about classmates’ performance is results in a higher predictive accuracy on unseen test data.


title: Frontal object perception using radar and mono-vision

abstract: In this paper, we detail a complete software architecture of a key task that an intelligent vehicle has to deal with: frontal object perception. This task is solved by processing raw data of a radar and a mono-camera to detect and track moving objects. Data sets obtained from highways, country roads and urban areas were used to test the proposed method. Several experiments were conducted to show that the proposed method obtains a better environment representation, i.e., reduces the false alarms and missed detections from individual sensor evidence.


title: Password Mistyping in Two-Factor-Authenticated Key Exchange

abstract: We study the problem of Key Exchange (KE), where authentication is two-factor and based on both electronically stored long keys and human-supplied credentials (passwords or biometrics). The latter credential has low entropy and may be adversarilymistyped. Our main contribution is the first formal treatment of mistyping in this setting. ::: ::: Ensuring security in presence of mistyping is subtle. We show mistyping-related limitations of previous KE definitions and constructions (of Boyen et al. [6,7,10] and Kolesnikov and Rackoff [16]). ::: ::: We concentrate on the practical two-factor authenticated KE setting where serversexchange keys with clients, who use short passwords (memorized) and long cryptographic keys (stored on a card). Our work is thus a natural generalization of Halevi-Krawczyk [15] and Kolesnikov-Rackoff [16]. We discuss the challenges that arise due to mistyping. We propose the first KE definitions in this setting, and formally discuss their guarantees. We present efficient KE protocols and prove their security.


title: Mechanized semantics for the Clight subset of the C language

abstract: This article presents the formal semantics of a large subset of the C language called Clight. Clight includes pointer arithmetic, struct and union types, C loops and structured switch statements. Clight is the source language of the CompCert verified compiler. The formal semantics of Clight is a big-step operational semantics that observes both terminating and diverging executions and produces traces of input/output events. The formal semantics of Clight is mechanized using the Coq proof assistant. In addition to the semantics of Clight, this article describes its integration in the CompCert verified compiler and several ways by which the semantics was validated.


Using Ontologies to Quantify Attack Surfaces


title: Review in terahertz spectral analysis

abstract: Nowadays terahertz spectroscopy is a well-established technique and recent progresses in technology demonstrated that this new technique is useful for both fundamental research and industrial applications. Varieties of applications such as imaging, non destructive testing, quality control are about to be transferred to industry supported by permanent improvements from basic research. Since chemometrics is today routinely applied to IR spectroscopy, we discuss in this paper the advantages of using chemometrics in the framework of terahertz spectroscopy. Different analytical procedures are illustrates. We conclude that advanced data processing is the key point to validate routine terahertz spectroscopy as a new reliable analytical technique.


title: STT-MRAM-Based PUF Architecture Exploiting Magnetic Tunnel Junction Fabrication-Induced Variability

abstract: Physically Unclonable Functions (PUFs) are emerging cryptographic primitives used to implement low-cost device authentication and secure secret key generation. Weak PUFs (i.e., devices able to generate a single signature or to deal with a limited number of challenges) are widely discussed in literature. One of the most investigated solutions today is based on SRAMs. However, the rapid development of low-power, high-density, high-performance SoCs has pushed the embedded memories to their limits and opened the field to the development of emerging memory technologies. The Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) has emerged as a promising choice for embedded memories due to its reduced read/write latency and high CMOS integration capability. In this article, we propose an innovative PUF design based on STT-MRAM memory. We exploit the high variability affecting the electrical resistance of the Magnetic Tunnel Junction (MTJ) device in anti-parallel magnetization. We will demonstrate that the proposed solution is robust, unclonable, and unpredictable.


title: A complete KALDI recipe for building Arabic speech recognition systems

abstract: In this paper we present a recipe and language resources for training and testing Arabic speech recognition systems using the KALDI toolkit. We built a prototype broadcast news system using 200 hours GALE data that is publicly available through LDC. We describe in detail the decisions made in building the system: using the MADA toolkit for text normalization and vowelization; why we use 36 phonemes; how we generate pronunciations; how we build the language model. We report results using state-of-the-art modeling and decoding techniques. The scripts are released through KALDI and resources are made available on QCRI's language resources web portal. This is the first effort to share reproducible sizable training and testing results on MSA system.


title: An Axial-Flux Permanent-Magnet Synchronous Generator for a Direct-Coupled Wind-Turbine System

abstract: This paper presents a unique axial-flux permanent-magnet synchronous generator (AFPMSG), which is suitable for both vertical-axis and horizontal-axis wind turbine generation systems. An outer-rotor design facilitates direct coupling of the generator to the wind turbine, while a coreless armature eliminates the magnetic pull between the stationary and moving parts. The design and construction features of the AFPMSG are reviewed. The flux-density distribution is studied, with the aid of a finite element software package, in order to predict the generated e.m.f. waveform. The performance equations of the AFPMSG are derived, and the condition for maximum efficiency is deduced, for both constant-speed and variable-speed operations. The experimental results, in general, confirm the theory developed


title: Semantic model for distributed augmented reality services

abstract: In this paper, a semantic model for distributed Augmented Reality services is presented. The services are built with the use of a novel Semantic Augmented Reality Ontology (SARO). SARO is based on semantic web standards (RDF, RDFS and OWL 2) and constitutes one of the main elements of the Contextual Augmented Reality Environments (CARE) approach. The model is used to build ubiquitous dynamic Augmented Reality presentations based on semantically described AR services in a contextual manner. In the paper, a visual design tool - the CARE Modeler - is also presented. The tool conforms to the SARO ontology and helps developers to model contextual Augmented Reality experiences. Finally, example presentations based on an AR service designed with the use of the tool are presented.


title: A Survey on Security and Privacy Issues of Bitcoin

abstract: Bitcoin is a popular cryptocurrency that records all transactions in a distributed append-only public ledger called blockchain . The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus protocol, which is run by the network nodes called miners . In exchange for the incentive, the miners are expected to maintain the blockchain honestly. Since its launch in 2009, Bitcoin economy has grown at an enormous rate, and it is now worth about 150 billions of dollars. This exponential growth in the market value of bitcoins motivate adversaries to exploit weaknesses for profit, and researchers to discover new vulnerabilities in the system, propose countermeasures, and predict upcoming trends. In this paper, we present a systematic survey that covers the security and privacy aspects of Bitcoin. We start by giving an overview of the Bitcoin system and its major components along with their functionality and interactions within the system. We review the existing vulnerabilities in Bitcoin and its major underlying technologies such as blockchain and PoW-based consensus protocol. These vulnerabilities lead to the execution of various security threats to the standard functionality of Bitcoin. We then investigate the feasibility and robustness of the state-of-the-art security solutions. Additionally, we discuss the current anonymity considerations in Bitcoin and the privacy-related threats to Bitcoin users along with the analysis of the existing privacy-preserving solutions. Finally, we summarize the critical open challenges, and we suggest directions for future research towards provisioning stringent security and privacy solutions for Bitcoin.


title: 61, Part 2, pp. 381^400 Inference in generalized additive mixed models by using smoothing splines

abstract: Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of models allows flexible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects. We estimate nonparametric functions by using smoothing splines and jointly estimate smoothing parameters and variance components by using marginal quasi-likelihood. Because numerical integration is often required by maximizing the objective functions, double penalized quasi-likelihood is proposed to make approximate inference. Frequentist and Bayesian inferences are compared. A key feature of the method proposed is that it allows us to make systematic inference on all model components within a unified parametric mixed model framework and can be easily implemented by fitting a working generalized linear mixed model by using existing statistical software. A bias correction procedure is also proposed to improve the performance of double penalized quasi-likelihood for sparse data. We illustrate the method with an application to infectious disease data and we evaluate its performance through simulation.


title: Disclosure, Stress, and Support During Gender Transition on Facebook

abstract: Social computing technologies, such as social networking sites (SNSs), often privilege people who fit within expected, static categories. Thus, users embarking on major identity changes, such as gender transition, often encounter stress when using SNSs to interact with their online social networks. To address this problem and reflect on the design of SNSs and other social computing systems, we present the results of a comprehensive online survey of transgender and gender non-conforming SNS users. Our findings indicate that although Facebook can be a stressful place for gender transition due to difficulties of transition disclosure, support from one's Facebook network can help to mitigate some of this stress. We examine Facebook both as a site of stress and as a site of support. Better understanding the relationships between stress, disclosure, and support on SNSs for these particular users can inform technology design that will benefit people who struggle with navigating a wide range of major identity changes online.


title: Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development

abstract: In this article, the authors describe how they used a hybrid process of inductive and deductive thematic analysis to interpret raw data in a doctoral study on the role of performance feedback in the self-assessment of nursing practice. The methodological approach integrated data-driven codes with theory-driven ones based on the tenets of social phenomenology. The authors present a detailed exemplar of the staged process of data coding and identification of themes. This process demonstrates how analysis of the raw data from interview transcripts and organizational documents progressed toward the identification of overarching themes that captured the phenomenon of performance feedback as described by participants in the study.


title: A qualitative insight into informal childcare and childhood obesity in children aged 0–5 years in the UK

abstract: Previous studies in various countries have found that informal childcare (provided by relatives, friends etc.) was associated with an increased risk of obesity in children aged 0–5 years. However, no qualitative research has been done to explore possible reasons for such a relationship and potential interventions to tackle it. We conducted a qualitative study with both parents and informal carers to explore their 1) experiences in receiving or giving informal childcare for British children aged 0–5 years; 2) perceived explanations of the relationship between informal childcare and childhood obesity and 3) preferred intervention ideas and delivery strategies for preventing obesity among those children under informal care. Four in-depth focus groups with a total of 14 participants (7 parents, 7 informal caregivers) were conducted in Birmingham and Edinburgh (1 parent group and 1 informal caregiver group in each city). Data were audio recorded, transcribed verbatim and analysed using a thematic approach. The significance of informal care to parents, carers, and society was recognised (theme one). Informal carers were identified to have practical and emotional support roles for the parents (theme two). Informal care was perceived to contribute to childhood obesity in four ways (theme three): cross-generation conflict preventing adoption of healthy practices; the trade-off for parents between receiving childcare and maintaining control; reduced energy capacity of carers; and increased snacking. Potential intervention ideas and delivery strategies (theme four) were identified. Examples of identified ideas included providing carers with up-to-date weaning advice, and suggestions of healthy snacks and ways to increase physical activity level in informal care. The suggestion of utilising existing primary care platforms (e.g. health visitor check-ups) to reach and deliver low-cost information based interventions, to all children aged 0–5 years who receive informal care, was highlighted. This exploratory qualitative study provided novel insights into potential explanations for the evidenced link between informal care and childhood obesity in children aged 0–5 years, despite a small size and limited participants in each focus group. Our findings support the idea of and inform the development towards an information based and low-cost intervention delivered through existing primary care platforms.


title: Fractional-Slot Concentrated-Windings Synchronous Permanent Magnet Machines: Opportunities and Challenges

abstract: Fractional-slot concentrated-winding (FSCW) synchronous permanent magnet (PM) machines have been gaining interest over the last few years. This is mainly due to the several advantages that this type of windings provides. These include high-power density, high efficiency, short end turns, high slot fill factor particularly when coupled with segmented stator structures, low cogging torque, flux-weakening capability, and fault tolerance. This paper is going to provide a thorough analysis of FSCW synchronous PM machines in terms of opportunities and challenges. This paper will cover the theory and design of FSCW synchronous PM machines, achieving high-power density, flux-weakening capability, comparison of single- versus double-layer windings, fault-tolerance rotor losses, parasitic effects, comparison of interior versus surface PM machines, and various types of machines. This paper will also provide a summary of the commercial applications that involve FSCW synchronous PM machines.


title: Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification

abstract: This paper presents a new speaker verification system architecture based on Joint Factor Analysis (JFA) as feature extractor. In this modeling, the JFA is used to define a new low-dimensional space named the total variability factor space, instead of both channel and speaker variability spaces for the classical JFA. The main contribution in this approach, is the use of the cosine kernel in the new total factor space to design two different systems: the first system is Support Vector Machines based, and the second one uses directly this kernel as a decision score. This last scoring method makes the process faster and less computation complex compared to others classical methods. We tested several intersession compensation methods in total factors, and we found that the combination of Linear Discriminate Analysis and Within Class Covariance Normalization achieved the best performance. We achieved a remarkable results using fast scoring method based only on cosine kernel especially for male trials, we yield an EER of 1.12% and MinDCF of 0.0094 on the English trials of the NIST 2008 SRE dataset. Index Terms: Total variability space, cosine kernel, fast scoring, support vector machines.


title: Multidisciplinary Treatment of Eating Disorders—Part 2 Primary Goals and Content of Treatment

abstract: This article describes the program content of a multidisciplinary, hospital-based program for severe eating disorders. Common goals for treatment of patients with eating disorders are discussed. Details about the specific therapeutic strategies used in individual and group therapy are provided. Also, information about dietary counseling and exercise prescriptionis described. This article is the second in a two-part article. The first article (Stewart & Williamson, 2004 [this issue]) described the structural organization and costs associated with this multidisciplinary treatment program.


title: AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video

abstract: Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted. We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset; AJILE includes automatically annotated poses of 7 upper body joints for four human subjects over 670 total hours (more than 72 million frames), along with the corresponding simultaneously acquired intracranial neural recordings. The size and scope of AJILE greatly exceeds all previous datasets with movements and electrocorticography (ECoG), making it possible to take a deep learning approach to movement prediction. We propose a multimodal model that combines deep convolutional neural networks (CNN) with long short-term memory (LSTM) blocks, leveraging both ECoG and video modalities. We demonstrate that our models are able to detect movements and predict future movements up to 800 msec before movement initiation. Further, our multimodal movement prediction models exhibit resilience to simulated ablation of input neural signals. We believe a multimodal approach to natural neural decoding that takes context into account is critical in advancing bioelectronic technologies and human neuroscience.


title: A Low-Power Gateable Vernier Ring Oscillator Time-to-Digital Converter for Biomedical Imaging Applications

abstract: In this paper, a high resolution, high precision and ultra-low power consumption time-to-digital converter (TDC) is presented. The proposed TDC is based on the gateable Vernier ring oscillator architecture. Fine resolution is achieved through two ring oscillators arranged in the Vernier configuration. This TDC employs a single-transition end-of-conversion detection circuit and turns off the ring oscillators whenever the conversion is completed to reduce power consumption. The prototype chip is fabricated in a standard 130 nm digital CMOS process and its area is only 0.03 ${\rm mm}^{2}$ . Using a 1.2 V supply, the TDC achieves a resolution of 7.3 ps, a single-shot precision of 1.0LSB, and an average power consumption of 1.2 mW. A root-mean-square integral nonlinearity (INL) of 1.2 LSB is obtained with the help of an INL look-up-table calibration. Compared to previously reported ring-oscillator based TDCs, the proposed design achieves the lowest power consumption to date.


title: Mechanical design of a gravity-balancing wearable exoskeleton for the motion enhancement of human upper limb

abstract: Powered exoskeletons can provide motion enhancement for both healthy and physically challenged people. Upper limb exoskeletons are required to have multiple degrees-of-freedom and can still produce sufficient force to augment the upper limb motion. The design using serial mechanisms usually results in a complicated and bulky exoskeleton that prevents itself from being wearable. This paper presents a new exoskeleton design aimed to achieve compactness and wearability. We consider a shoulder exoskeleton that consists of a parallel spherical mechanism with two slider crank mechanisms. The actuators can be placed on a stationary platform and attached closely to human body. Thus a better inertia property can be obtained while maintaining lightweight. Through the use of a gravity-balancing mechanism, the required actuator power becomes smaller and with better efficiency. A static model is developed to analyze and optimize the exoskeleton. Through illustrations of a prototype, the exoskeleton is shown to be wearable and can provide adequate motion enhancement of a human's upper limb.


title: Microstructure/property relationships in the slurry erosion of tungsten carbide-cobalt

abstract: Abstract The slurry erosion performance of ultrafine WC–Co composites sintered from powder produced through the spray conversion process has been evaluated and compared to that of conventional materials. Slurry erosion testing has been carried out using the jet impingement method and slurry composed of water and 500 μm diameter silica sand. The erosion resistance of WC–Co hard metals is seen to increase dramatically as the grain size decreases below about 1 μm, with the result that ultrafine WC–Co may be up to three times more erosion resistant than the closest conventional material. This increase in erosion resistance is considerably higher than the corresponding increase in hardness, which is at most 25%. Furthermore, these increases are not achieved at the expense of fracture toughness, which is maintained at a similar level to that of conventional materials. The results of microscopic examination indicate that this rapid increase in erosion resistance may be due to a transition from a predominantly brittle to a predominantly ductile mode of erosion. The transition results from a change in the relative dimensions of the microstructure and the erodent impact zone. This hypothesis is supported by the results of a qualitative investigation into the dependence of erosion rates on impact angle. These show that as the grain size of the material decreases, erosion occurs preferentially at lower impact angles.


title: 47,XXX females, sex chromosomes, and tooth crown structure

abstract: Enamel thickness of the maxillary permanent central incisors and canines in seven Finnish 47,XXX females, their first-degree male and female relatives, and control males and females from the general population were determined from radiographs. The results showed that enamel in teeth of 47,XXX females was clearly thicker than that of normal controls. On the other hand, the thickness of "dentin" (distance between mesial and distal dentinoenamel junctions) in 47,XXX females' teeth was about the same as that in normal control females, but clearly reduced as compared with that in control males. It is therefore obvious that in the triple-X chromosome complement the extra X chromosome is active in amelogenesis, whereas it has practically no influence on the growth of dentin. The calculations based on present and previous results in 45,X females and 47,XYY males indicate that the X chromosome increases metric enamel growth somewhat more effectively than the Y chromosome. Possibly, halfway states exist between active and repressed enamel genes on the X chromosome. The Y chromosome seems to promote dental growth in a holistic fashion.


title: Crash data collection: a Windows case study

abstract: Reliability is a rapidly growing concern in contemporary personal computer (PC) industry, both for computer users as well as product developers. To improve dependability, systems designers and programmers must consider failure and usage data for operating systems as well as applications. In this paper, we discuss our experience with crash and usage data collection for Windows machines. We analyze results based on crashes in the UC Berkeley EECS department.


title: Multi-Instance Learning with Any Hypothesis Class

abstract: In the supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function is the Boolean OR. The learner observes a sample of bags and the bag labels, but not the instance labels that determine the bag labels. The learner is then required to emit a classification rule for bags based on the sample. MIL has numerous applications, and many heuristic algorithms have been used successfully on this problem, each adapted to specific settings or applications. In this work we provide a unified theoretical analysis for MIL, which holds for any underlying hypothesis class, regardless of a specific application or problem domain. We show that the sample complexity of MIL is only poly-logarithmically dependent on the size of the bag, for any underlying hypothesis class. In addition, we introduce a new PAC-learning algorithm for MIL, which uses a regular supervised learning algorithm as an oracle. We prove that efficient PAC-learning for MIL can be generated from any efficient non-MIL supervised learning algorithm that handles one-sided error. The computational complexity of the resulting algorithm is only polynomially dependent on the bag size.


title: Profiting from arbitrage and odds biases of the European football gambling market

abstract: A gambling market is usually described as being inefficient if there are one or more betting strategies that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This paper examines the online European football gambling market based on 14 European football leagues over a period of seven years, from season 2005/06 to 2011/12 inclusive, and takes into consideration the odds provided by numerous bookmaking firms. Contrary to common misconceptions, we demonstrate that the accuracy of bookmakers' odds has not improved over this period. More importantly, our results question market efficiency by demonstrating high profitability on the basis of consistent odds biases and numerous arbitrage opportunities.


title: Reduced-order modeling of soft robots

abstract: We present a general strategy for the modeling and simulation-based control of soft robots. Although the presented methodology is completely general, we restrict ourselves to the analysis of a model robot made of hyperelastic materials and actuated by cables or tendons. To comply with the stringent real-time constraints imposed by control algorithms, a reduced-order modeling strategy is proposed that allows to minimize the amount of online CPU cost. Instead, an offline training procedure is proposed that allows to determine a sort of response surface that characterizes the response of the robot. Contrarily to existing strategies, the proposed methodology allows for a fully non-linear modeling of the soft material in a hyperelastic setting as well as a fully non-linear kinematic description of the movement without any restriction nor simplifying assumption. Examples of different configurations of the robot were analyzed that show the appeal of the method.


title: Real-Time Billboard Substitution in a Video Stream

abstract: We present a system that accepts as input a continuous stream of TV broadcast images from sports events. The system automatically detects a predetermined billboard in the scene, and replaces it with a user defined pattern, with no cooperation from the billboards or camera operators. The replacement is performed seamlessly so that a viewer should not be able to detect the substitution. This allows the targeting of advertising to the appropriate audience, which is especially useful for international events.


title: The effects of morphology and fitness on catastrophic interference

abstract: Catastrophic interference occurs when an agent improves in one training instance but becomes worse in other instances. Many methods intended to combat interference have been reported in the literat...


title: Learning Globally Optimized Object Detector via Policy Gradient

abstract: In this paper, we propose a simple yet effective method to learn globally optimized detector for object detection, which is a simple modification to the standard cross-entropy gradient inspired by the REINFORCE algorithm. In our approach, the cross-entropy gradient is adaptively adjusted according to overall mean Average Precision (mAP) of the current state for each detection candidate, which leads to more effective gradient and global optimization of detection results, and brings no computational overhead. Benefiting from more precise gradients produced by the global optimization method, our framework significantly improves state-of-the-art object detectors. Furthermore, since our method is based on scores and bounding boxes without modification on the architecture of object detector, it can be easily applied to off-the-shelf modern object detection frameworks.


title: Arithmetic circuit design with memristor based high fan-out logic gates

abstract: The application of memristors in logic operations have recently gained importance and are being investigated. The work presented in this paper describes a new low power technique of using memristors to perform arithmetic operations like addition and multiplication. It focuses on reducing the hardware costs and associated design complexity. The idea of “logic implication” is combined with an analog technique of using current mirrors to perform logic operations. By using current mirrors, the problem of limited fan-out of logic implication can be overcome, enabling more parallel operations to run together. The delay for even an XOR operation can be reduced to 1 cycle, compared to the 5 cycles taken by logic implication. The design of a full adder, ripple carry adder and array multiplier are described. Metrics of area and latency are compared to previous works.


A focused crawler for Dark Web forums


title: Proteomics of Pseudomonas aeruginosa Australian epidemic strain 1 (AES-1) cultured under conditions mimicking the cystic fibrosis lung reveals increased iron acquisition via the siderophore pyochelin.

abstract: Pseudomonas aeruginosa is an opportunistic pathogen that is the major cause of morbidity and mortality in patients with cystic fibrosis (CF). While most CF patients are thought to acquire P. aeruginosa from the environment, person-to-person transmissible strains have been identified in CF clinics worldwide, and the molecular basis for transmissibility remains poorly understood. We undertook a complementary proteomics approach to characterize protein profiles from a transmissible, acute isolate of the Australian epidemic strain 1 (AES-1R), the virulent burns/wound isolate PA14, and the poorly virulent, laboratory-associated strain PAO1 when grown in an artificial medium that mimics the CF lung environment compared to growth in standard laboratory medium. Proteins elevated in abundance in AES-1R included those involved in methionine and S-adenosylmethionine biosynthesis and in the synthesis of phenazines. Proteomic data were validated by measuring culture supernatant levels of the virulence factor pyocyanin...


title: Transaction aggregation as a strategy for credit card fraud detection

abstract: The problem of preprocessing transaction data for supervised fraud classification is considered. It is impractical to present an entire series of transactions to a fraud detection system, partly because of the very high dimensionality of such data but also because of the heterogeneity of the transactions. Hence, a framework for transaction aggregation is considered and its effectiveness is evaluated against transaction-level detection, using a variety of classification methods and a realistic cost-based performance measure. These methods are applied in two case studies using real data. Transaction aggregation is found to be advantageous in many but not all circumstances. Also, the length of the aggregation period has a large impact upon performance. Aggregation seems particularly effective when a random forest is used for classification. Moreover, random forests were found to perform better than other classification methods, including SVMs, logistic regression and KNN. Aggregation also has the advantage of not requiring precisely labeled data and may be more robust to the effects of population drift.


title: Universal, Unsupervised, Uncovered Sentiment Analysis

abstract: Abstract We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/ .


title: Complex Systems, Artificial Intelligence and Theoretical Psychology

abstract: The main finding of complex systems research is that there can be a disconnect between the local behavior of the interacting elements of a complex system and regularities that are observed in the global behavior of that system, making it virtually impossible to derive the global behavior from the local rules. It is arguable that intelligent systems must involve some amount of complexity, and so the global behavior of AI systems would therefore not be expected to have an analytic relation to their constituent mechanisms. This has serious implications for the methodology of AI. This paper suggests that AI researchers move toward a more empirical research paradigm, referred to as “theoretical psychology,” in which systematic experimentation is used to discover how the putative local mechanisms of intelligence relate to their global performance. There are reasons to expect that this new approach may allow AI to escape from a trap that has dogged it for much of its history: on the few previous occasions that something similar has been tried, the results were both impressive and quick to arrive.


title: Efficient Recommendation of Aggregate Data Visualizations

abstract: Data visualization is a common and effective technique for data exploration. However, for complex data, it is infeasible for an analyst to manually generate and browse all possible visualizations for insights. This observation motivated the need for automated solutions that can effectively recommend such visualizations. The main idea underlying those solutions is to evaluate the utility of all possible visualizations and then recommend the top-k visualizations. This process incurs high data processing cost, that is further aggravated by the presence of numerical dimensional attributes. To address that challenge, we propose novel view recommendation schemes, which incorporate a hybrid multi-objective utility function that captures the impact of numerical dimension attributes. Our first scheme, Multi-Objective View Recommendation for Data Exploration (MuVE), adopts an incremental evaluation of our multi-objective utility function, which allows pruning of a large number of low-utility views and avoids unnecessary objective evaluations. Our second scheme, upper MuVE (uMuVE), further improves the pruning power by setting the upper bounds on the utility of views and allowing interleaved processing of views, at the expense of increased memory usage. Finally, our third scheme, Memory-aware uMuVE (MuMuVE), provides pruning power close to that of uMuVE, while keeping memory usage within a pre-specified limit.


title: Vehicle Safety Distance Warning System: A Novel Algorithm for Vehicle Safety Distance Calculating Between Moving Cars

abstract: A novel algorithm for vehicle safety distance between driving cars for vehicle safety warning system is presented in this paper. The presented system concept includes a distance obstacle detection and safety distance calculation. The system detects the distance between the car and the in front of vehicles (obstacles) and uses the vehicle speed and other parameters to calculate the braking safety distance of the moving car. The system compares the obstacle distance and braking safety distance which are used to determine the moving vehicle's safety distance is enough or not. This paper focuses on the solution algorithm presentation.


title: Extending web applications with a lightweight zero knowledge proof authentication

abstract: User authentication is a crucial requirement for secure transactions and access to the sensitive resources on the Web. We propose, implement and evaluate a Zero-Knowledge Proof Authentication (ZKP) algorithm based on isomorphic graphs. The proposed mechanism allows for authentication with varying confidence and security levels. We suggest that most of the computations should be carried out by the user's web browser without revealing password or login at any point in time; instead generated random isomorphic graphs and permutation functions based on the user login/password can be exchanged. Our experimental evaluation shows that by combining the asynchronous web with ZKP protocols, it is feasible to satisfy existing usability standards on the web.


title: Modeling of wind energy system with MPPT control

abstract: This paper presents the modeling of wind energy systems using MATLAB Simulink. The model considers the MPPT (Maximum Power Point Tracking) technique to track the maximum power that could be extracted from the wind energy, due the non-linear characteristic of the wind turbine. The model consists of wind generation model, converter model (DC-DC converter), and MPPT controller. The main contribution of our work is in the model of DC-DC converter (buck converter) which is developed in rather details, which allows the MPPT controller output (duty cycle) adjusts the voltage input of the converter to track the maximum power point of the wind generator. The simulation results show that the developed model complies with the theoretical one. Further the MPPT control shows a higher power output compared to the system without MPPT.


title: State-Regularized Recurrent Neural Networks

abstract: Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) nonregular languages such as balanced parentheses, palindromes, and the copy task where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition, and language modeling. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN's state transition dynamics; (b) forces RNNs to operate more like automata with external memory and less like finite state machines; (c) makes RNNs have better interpretability and explainability.


title: Optimization of MEMS intraocular capacitive pressure sensor

abstract: MEMS intraocular capacitive pressure sensor is used for monitoring glaucoma disease. The structure of the diaphragm of MEMS capacitive pressure sensor is one of the important factors which could affect the sensor's performance. In this paper, Taguchi and Two-Level Factorial approach are employed to optimize the size of diaphragm thickness, slot width, and slot length. The typical range of intraocular pressure is at 0 – 60 mmHg and applied on 550 × 550 µm four slotted diaphragms. The effects of sensitivity and linearity on these parameters are investigated. From this study, it is found that the optimized parameters are 4.2µm, 25µm, and 100µm for diaphragm thickness, slot width, and slot length respectively. Simulated results by COMSOL Multiphysics indicate that the optimized parameters produce more sensitivity with high linearity compared to the initial parameters condition.


title: Development of a new controller with FPGA for PUMA 560 robot

abstract: Abstract In this paper is presented architecture of the controller for controlling the base configuration of PUMA 560 robot (three axes). Developed controller (designated for student education) has hardware part based on Altera Cyclon II FPGA chip implemented on DE2 development board and software part MATLAB® with corresponding toolboxes. Matlab with installed Robotics Toolbox was used for the purposes such as generating trajectory of movement, calculating inverse kinematics, gravity compensation and simulation of manipulator movements. The control structures were which have been previously devised in Matlab/DSP Builder using FPGA Real Time Toolbox were implemented on FPGA chip. Performed experimental results showed that proposed concept of the controller design satisfied appointed requirements such as easy simulation, programming and testing controller performances, so the next objective of research will be design of the improved control algorithms of robot and design of new Teachbox.


title: How to make unforgeable money in generalised probabilistic theories

abstract: We discuss the possibility of creating money that is physically impossible to counterfeit. Of course,"physically impossible"is dependent on the theory that is a faithful description of nature. Currently there are several proposals for quantum money which have their security based on the validity of quantum mechanics. In this work, we examine Wiesner's money scheme in the framework of generalised probabilistic theories. This framework is broad enough to allow for essentially any potential theory of nature, provided that it admits an operational description. We prove that under a quantifiable version of the no-cloning theorem, one can create physical money which has an exponentially small chance of being counterfeited. Our proof relies on cone programming, a natural generalisation of semidefinite programming. Moreover, we discuss some of the difficulties that arise when considering non-quantum theories.


title: Towards autonomous flight of micro aerial vehicles using ORB-SLAM

abstract: In the last couple of years a novel visual simultaneous localisation and mapping (SLAM) system, based on visual features, has emerged as one of the best, if not the best, systems for estimating the 6D camera pose whilst building a 3D map of the observed scene. This method is called ORB-SLAM and one of its key ideas is to use the same visual descriptor, a binary descriptor called ORB, for all the visual tasks, this is, for feature matching, relocalisation and loop closure. On the top of this, ORB-SLAM combines local and graph-based global bundle adjustment, which enables a scalable map generation whilst keeping real-time performance. Therefore, motivated by its performance in terms of processing speed, robustness against erratic motion and scalability, in this paper we present an implementation of autonomous flight for a low-cost micro aerial vehicle (MAV), where ORB-SLAM is used as a visual positioning system that feeds a PD controller that controls pitch, roll and yaw. Our results indicate that our implementation has potential and could soon be implemented on a bigger aerial platform with more complex trajectories to be flown autonomously.


title: Plasmonic waveguide array: Simulating topological photonic states and massless Dirac Fermion

abstract: In the binary plasmonic waveguide array (PWA), we experimentally demonstrated a topologically protected optical mode with immunity against structural disorders, and theoretically proposed an optical analogue of massless Dirac Fermion by alternating positive and negative couplings.


title: Understanding intention to continuously share information on weblogs

abstract: Purpose – Weblogs, or blogs, have been a significant new development in recent years. Many businesses have begun using blogs to stimulate discussions, garner ideas, and provide further visibility. Thus, blog usage as well as individual motives behind continued usage is an important area of research. This research aims to examine behavioral motivations underlying individual intention to keep using blogs.Design/methodology/approach – Based on social cognitive theory and related technology adoption literature, this study considered knowledge self‐efficacy, subjective norms, feedback, and personal outcome expectations as the determinants of continuing to share information on blogs. The proposed model was empirically evaluated using randomized survey data collected from 155 users of a popular web site in Taiwan (WRETCH blog).Findings – The results revealed that although both self‐efficacy and personal outcome expectations affected directly the intention of sharing information on blogs, personal outcome expecta...


title: Scalable Test Problems for Evolutionary Multi-Objective Optimization

abstract: After adequately demonstrating the ability to solve dierent two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their ecacy in handling problems having more than two objectives. In this paper, we have suggested three dierent approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Pareto-optimal front, and introduction of controlled diculties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of the above features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing dierent MOEAs, and better understanding of the working principles of MOEAs.


title: iSAM: Incremental Smoothing and Mapping

abstract: In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.


A Conceptual Analysis of Capabilities: Identifying and Classifying Sources of Competitive Advantage in the Wood Industry


title: Collective Cell Migration: A Mechanistic Perspective

abstract: Collective cell migration is fundamental to gaining insights into various important biological processes such as wound healing and cancer metastasis. In particular, recent in vitro studies and in silico simulations suggest that mechanics can explain the social behavior of multicellular clusters to a large extent with minimal knowledge of various cellular signaling pathways. These results suggest that a mechanistic perspective is necessary for a comprehensive and holistic understanding of collective cell migration, and this review aims to provide a broad overview of such a perspective.


title: Dependent and multiple scattering in transmission and backscattering optical coherence tomography

abstract: We use transmission and backscattering optical coherence tomography (OCT) to distinguish and quantify dependent and multiple scattering effects in turbid media. With transmission OCT the dependent scattering coefficients for a range of monodisperse silica particle suspensions are determined. An excellent agreement is observed between the measured dependent scattering coefficients and calculations based on Mie calculations, the Percus-Yevick radial distribution function, and coherent light scattering theory. Backscattering OCT measurements are fitted using the extended Huygens-Fresnel (EHF) model with the dependent scattering coefficients obtained from the transmission OCT measurements as input parameters. Good agreement between the EHF model and the backscattering OCT measurements is observed. For large particles, the rms scattering angle θrms obtained from the EHF fit is in fair agreement with θrms calculated from the transmission OCT data.


title: Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms

abstract: ✓ Forty patients with previously untreated intracranial glial neoplasms underwent stereotaxic serial biopsies assisted by computerized tomography (CT) and magnetic resonance imaging (MRI). Tumor volumes defined by computer reconstruction of contrast enhancement and low-attenuation boundaries on CT and T1 and T2 prolongation on MRI revealed that tumor volumes defined by T2-weighted MRI scans were larger than those defined by low-attenuation or contrast enhancement on CT scans. Histological analysis of 195 biopsy specimens obtained from various locations within the volumes defined by CT and MRI revealed that: 1) contrast enhancement most often corresponded to tumor tissue without intervening parenchyma; 2) hypodensity corresponded to parenchyma infiltrated by isolated tumor cells or in some instances to tumor tissue in low-grade gliomas or to simple edema; and 3) isolated tumor cell infiltration extended at least as far as T2 prolongation on magnetic resonance images. This information may be useful in plann...


title: Bilateral robotic priming before task-oriented approach in subacute stroke rehabilitation: a pilot randomized controlled trial

abstract: OBJECTIVES ::: To investigate the treatment effects of bilateral robotic priming combined with the task-oriented approach on motor impairment, disability, daily function, and quality of life in patients with subacute stroke. ::: ::: ::: DESIGN ::: A randomized controlled trial. ::: ::: ::: SETTING ::: Occupational therapy clinics in medical centers. ::: ::: ::: SUBJECTS ::: Thirty-one subacute stroke patients were recruited. ::: ::: ::: INTERVENTIONS ::: Participants were randomly assigned to receive bilateral priming combined with the task-oriented approach (i.e., primed group) or to the task-oriented approach alone (i.e., unprimed group) for 90 minutes/day, 5 days/week for 4 weeks. The primed group began with the bilateral priming technique by using a bimanual robot-aided device. ::: ::: ::: MAIN MEASURES ::: Motor impairments were assessed by the Fugal-Meyer Assessment, grip strength, and the Box and Block Test. Disability and daily function were measured by the modified Rankin Scale, the Functional Independence Measure, and actigraphy. Quality of life was examined by the Stroke Impact Scale. ::: ::: ::: RESULTS ::: The primed and unprimed groups improved significantly on most outcomes over time. The primed group demonstrated significantly better improvement on the Stroke Impact Scale strength subscale ( p = 0.012) and a trend for greater improvement on the modified Rankin Scale ( p = 0.065) than the unprimed group. ::: ::: ::: CONCLUSION ::: Bilateral priming combined with the task-oriented approach elicited more improvements in self-reported strength and disability degrees than the task-oriented approach by itself. Further large-scale research with at least 31 participants in each intervention group is suggested to confirm the study findings.


title: Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning

abstract: This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment. Extracting a relevant set of features from these observations is a challenging task and may require substantial domain knowledge. One way to tackle this problem is to store sequences of past observations and actions in the state vector, making it high dimensional, and apply techniques from deep learning. This paper investigates the capabilities of different deep learning techniques, such as convolutional neural networks and recurrent neural networks, to extract relevant features for finding near-optimal policies for a residential heating system and electric water heater that are hindered by sparse observations. Our simulation results indicate that in this specific scenario, feeding sequences of time-series to an LSTM network, which is a specific type of recurrent neural network, achieved a higher performance than stacking these time-series in the input of a convolutional neural network or deep neural network.


Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics


title: Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism

abstract: This paper reviews the use of socially interactive robots to assist in the therapy of children with autism. The extent to which the robots were successful in helping the children in their social, emotional and communication deficits was investigated. Child–robot interactions were scrutinized with respect to the different target behaviors that are to be elicited from a child during therapy. These behaviors were thoroughly examined with respect to a child’s development needs. Most importantly, experimental data from the surveyed works were extracted and analysed in terms of the target behaviors and of how each robot was used during a therapy session to achieve these behaviors. The study concludes by categorizing the different therapeutic roles that these robots were observed to play, and highlights the important design features that enable them to achieve high levels of effectiveness in autism therapy.


title: Discovering different types of topics: Factored topic models

abstract: In traditional topic models such as LDA, a word is generated by choosing a topic from a collection. However, existing topic models do not identify different types of topics in a document, such as topics that represent the content and topics that represent the sentiment. In this paper, our goal is to discover such different types of topics, if they exist. We represent our model as several parallel topic models (called topic factors), where each word is generated from topics from these factors jointly. Since the latent membership of the word is now a vector, the learning algorithms become challenging. We show that using a variational approximation still allows us to keep the algorithm tractable. Our experiments over several datasets show that our approach consistently outperforms many classic topic models while also discovering fewer, more meaningful, topics.


title: Targeting metabolic transformation for cancer therapy

abstract: Cancer therapy has long relied on the rapid proliferation of tumour cells for effective treatment. However, the lack of specificity in this approach often leads to undesirable side effects. Many reports have described various 'metabolic transformation' events that enable cancer cells to survive, suggesting that metabolic pathways might be good targets. There are currently several drugs under development or in clinical trials that are based on specifically targeting the altered metabolic pathways of tumours. This Review highlights pathways against which there are already drugs in different stages of development and also discusses additional druggable targets.


title: DWDM Hybrid-Integrated TOSA and ROSA for 10 $\,\times\,$10.7-Gb/s Transmission Over 75-km Links

abstract: Compact parallel transmitters and receivers with an aggregate capacity of 107 Gb/s are built through hybrid integration of arrays of ten 100-GHz spaced directly modulated lasers, arrays of ten avalanche photodiodes, and high-index contrast silica arrayed waveguide grating multi- and demultiplexers. Unamplified transmission over 75 km of standard single-mode fiber and 155-km amplified links is demonstrated in the C-band, by using a modulation format based on spectral offset filtering and electronic dispersion compensation.


title: Chip design of an 8 MHz CMOS switched-capacitor low-pass filter for signal receiver applications

abstract: In this paper, a fifth-order elliptical low-pass filter using switched-capacitor (SC) architecture is proposed. The filter has a pass-band of 8 MHz and clock frequency of 80 MHz. The proposed filter is realized by cascades of first-order and second-order biquad building blocks. In order to reach the largest possible input dynamic range and save chip area, the method of dynamic range scaling and minimum capacitor scaling is used. Measurement results show that the proposed SC low-pass filter achieves a pass-band frequency of 8.72 MHz. The chip area including pads is 0.895 mm2 and the power dissipation is 44.2 mW at the supply voltage 1.8 V.


title: Viral hepatitis C.

abstract: Identification of a cDNA clone and the genome of hepatitis C virus in 1988-1989 allowed the development of clinical tests that are now used to detect and quantify hepatitis C virus. This has largely eliminated post-transfusion hepatitis C virus infection; however, the overall incidence of chronic hepatitis C and its complications has greatly increased because of its transmission by other means, lack of a protective vaccine, and inadequate virucidal therapy. Drug abuse is the most common cause of hepatitis C; an etiologic mechanism, however, remains unknown in one third of patients referred to the New Jersey Medical School Liver Center. Response to treatment depends on the viral subtype, immune reactivity of the host, and hepatic pathologic alterations. Many patients with hepatitis C improve or are cured by administering an interferon with or without ribavirin; patients refractory to these measures exhibit persistent elevation of serum cytokines and progressive liver disease. New measures, including protease inhibitors and adjunct immunotherapy, should increase effectiveness of therapy, diminishing hepatitis C virus-induced cirrhosis and hepatocellular cancer. Populations, including the underserved, who harbor and transmit hepatitis C virus require special assistance. This is best achieved by community support groups organized through medical schools, physician associations, and churches to help prevent, detect, and treat chronic hepatitis C.


title: Influence of the temperature and hydraulic retention time in bioelectrochemical anaerobic digestion of sewage sludge

abstract: Abstract At ambient temperature (25 °C), bioelectrochemical anaerobic digestion of sewage sludge was investigated with a hydraulic retention time (HRT) of 10 days and compared to that at a mesophilic condition (35 °C). The methane production and methane content in the biogas at ambient temperature were 612.8 mL/L·d and 73.3%, respectively, which were not significantly lower than that of the mesophilic condition. Additionally, the VS removal was 54.5% which was similar for both temperature conditions. However, for a HRT of 20 days, the bioelectrochemical anaerobic digestion at ambient temperature became more stable, and the VS removal improved up to 65.0%. For the HRT of 10 days, the net energy production at ambient temperature was about 168 kJ/L·d, which was similar to the mesophilic condition; however, the apparent energy efficiency at ambient temperature was 249.2% which was significantly higher than 197.7% at the mesophilic condition. The bioelectrochemical anaerobic digestion that can save the thermal energy input at ambient temperature is recommended for the treatment of organic waste including sewage sludge in moderate and cold climate regions.


title: Analysis of streaming social networks and graphs on multicore architectures

abstract: Analyzing static snapshots of massive, graph-structured data cannot keep pace with the growth of social networks, financial transactions, and other valuable data sources. We introduce a framework, STING (Spatio-Temporal Interaction Networks and Graphs), and evaluate its performance on multicore, multisocket Intel®-based platforms. STING achieves rates of around 100 000 edge updates per second on large, dynamic graphs with a single, general data structure. We achieve speedups of up to 1000× over parallel static computation, improve monitoring a dynamic graph's connected components, and show an exact algorithm for maintaining local clustering coefficients performs better on Intel-based platforms than our earlier approximate algorithm.


title: Random synaptic feedback weights support error backpropagation for deep learning

abstract: The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.


title: License plate recognition using MSER and HOG based on ELM

abstract: In this paper, an effective method for automatic license plate recognition (ALPR) is proposed, on the basis of extreme learning machine (ELM). Firstly, morphological Top-Hat filtering operator is applied to do the image pre-processing. Then candidate character regions are extracted by means of maximally stable extremal region (MSER) detector. Thirdly, most of the noise character regions are removed according to the geometrical relationship of characters in standard license plates. Finally, the histograms of oriented gradients (HOG) features are extracted from each character of every plate detected and the characters are recognized by the classifier trained though the ELM. Experimental evaluation shows that our approach significantly performs well in the ALPR systems.


title: An accurate two-tier classifier for efficient duty-cycling of smartphone activity recognition systems

abstract: Our vision is a smartphone service that accurately tracks users' physical activities and transportation modes using accelerometer-based activity recognition carried out on the phone. A key challenge that needs to be addressed to realize this vision is the high power consumption of keeping the smartphone awake in order to perform sensor sampling, feature extraction, and activity classification. In this paper, we propose a two-tier classifier for reducing the wake time percentage of the activity recognition system, which we define as the percentage of time the activity recognition system keeps the smartphone awake. We compare our two-tier classifier to (i) a conventional single-tier classifier, and (ii) a confidence-based multi-tier classifier designed to reduce wake time. We evaluate our approaches using activity-labeled smartphone accelerometer data traces from 2 subjects over a total of 60 hours performing 7 different physical activities. Given an accuracy lower bound of 91%, our two-tier approach achieves the lowest wake times and is able to reduce the wake time percentage of the best single-tier classifier by 93.0% and 70.1% respectively for the 2 subjects.


title: An Implementation of Faster RCNN with Study for Region Sampling

abstract: We adapted the join-training scheme of Faster RCNN framework from Caffe to TensorFlow as a baseline implementation for object detection. Our code is made publicly available. This report documents the simplifications made to the original pipeline, with justifications from ablation analysis on both PASCAL VOC 2007 and COCO 2014. We further investigated the role of non-maximal suppression (NMS) in selecting regions-of-interest (RoIs) for region classification, and found that a biased sampling toward small regions helps performance and can achieve on-par mAP to NMS-based sampling when converged sufficiently.


title: Milan criteria in liver transplantation for hepatocellular carcinoma: An evidence-based analysis of 15 years of experience

abstract: Hepatocellular carcinoma (HCC) is the seventh most common cancer worldwide and the third most common cause of cancer-related deaths; the number of new cases per year is approaching 750,000. The magnitude of the incidence of HCC has discouraged any attempts to apply liver transplantation (LT) as the prevailing curative therapy for HCC worldwide because of the limited sources of donated organs (deceased and living donors) and the poor access to sophisticated health care systems in some geographical areas. If these limitations continue to prevail throughout the world, any attempt to significantly reduce HCC-related mortality rates through the application of LT will be delusional. International experiences have confirmed, however, the potential of LT to definitively cure HCC because it presents a unique opportunity to remove both the tumor (HCC is associated with 695,000 deaths per year) and the underlying cirrhosis. Despite its limited access, LT has become the standard of care for patients with small HCCs and the main driving force for alternative strategies offered to patients with intermediate HCCs. In 1996, a prospective cohort study defined restrictive selection criteria that led to superior survival for transplant patients in comparison with any other previous experience with transplantation or other options for HCC. Since then, these selection criteria have become universally known as the Milan criteria (MC) in recognition of their origin. Ever since their adoption in clinical practice, the MC have helped doctors to single out early-stage HCC as a prognostic category of cancer presentation that is amenable to curative treatments. After their implementation, the favorable posttransplant outcomes that were observed in cohort series were so convincing that the MC immediately became the standard of care for early HCC, and further validation by randomized controlled trials (RCTs) was prevented. After the passage of approximately a decade, researchers began to challenge the MC with other proposals designed to capture those patients not meeting the MC who could achieve similar posttransplant survival rates through the expansion of the accepted tumor limits for transplant eligibility. None of these expanded criteria have become the new reference standard for selecting LT candidates with HCC; any broadening of the selection criteria for transplantation is inevitably hampered by severe


title: Standardized Extensions of High Efficiency Video Coding (HEVC)

abstract: This paper describes extensions to the High Efficiency Video Coding (HEVC) standard that are active areas of current development in the relevant international standardization committees. While the first version of HEVC is sufficient to cover a wide range of applications, needs for enhancing the standard in several ways have been identified, including work on range extensions for color format and bit depth enhancement, embedded-bitstream scalability, and 3D video. The standardization of extensions in each of these areas will be completed in 2014, and further work is also planned. The design for these extensions represents the latest state of the art for video coding and its applications.


title: AOP++: a generic aspect-oriented programming framework in c++

abstract: This paper presents AOP++, a generic aspect-oriented programming framework in C++. It successfully incorporates AOP with object-oriented programming as well as generic programming naturally in the framework of standard C++. It innovatively makes use of C++ templates to express pointcut expressions and match join points at compile time. It innovatively creates a full-fledged aspect weaver by using template metaprogramming techniques to perform aspect weaving. It is notable that AOP++ itself is written completely in standard C++, and requires no language extensions. With the help of AOP++, C++ programmers can facilitate AOP with only a little effort.


Fourier transforms on the motion groups


title: LUT-Lock: A Novel LUT-Based Logic Obfuscation for FPGA-Bitstream and ASIC-Hardware Protection

abstract: In this work, we propose LUT-Lock, a novel Look-Up-Table-based netlist obfuscation algorithm, for protecting the intellectual property that is mapped to an FPGA bitstream or an ASIC netlist. We, first, illustrate the effectiveness of several key features that make the LUT-based obfuscation more resilient against SAT attacks and then we embed the proposed key features into our proposed LUT-Lock algorithm. We illustrate that LUT-Lock maximizes the resiliency of the LUT-based obfuscation against SAT attacks by forcing a near exponential increase in the execution time of a SAT solver with respect to the number of obfuscated gates. Hence, by adopting LUT-Lock algorithm, SAT attack execution time could be made unreasonably long by increasing the number of utilized LUTs.


title: Virtual Reality Therapy Controlled Study for War Veterans with PTSD. Preliminary Results

abstract: More than 30 years after signing truces, there are still around 20,000 Portuguese war veterans that fill PTSD (Posttraumatic Stress Disorder) diagnose criteria. Despite many of them attending therapy, the outcome is not cheerful. In this way, a research protocol was devised to investigate the opportunity of adopting virtual reality exposure therapy (VRET) to reduce PTSD symptomathology. This protocol consists on a controlled study (VRET vs. traditional psychotherapy vs. waiting list), where in the VRET condition patients will be graded by being exposed to a virtual reality jungle scenario. The activating episodes, that are comprised of three cues (ambush, mortar blasting and waiting for injured rescue), are repeated 3 times each session. The cues' intensity and frequency increase from session to session. Patients are exposed to the VR world through a HMD (Head Mounted Display). This paper reports on the ongoing research where 4 VRET patients that filled CAPS DSM-IV PTSD criteria were assessed at pretreatment and at the middle of treatment (5th session). Results from IES and SCL-90R dimensions showed no statistical significant differences between assessments, with exception to obsession-compulsion dimension of SCL-90R (F(1;3)=21.235; p<.05), indicating a decrease in obsessive thoughts. However, through descriptive analysis, it was observed a reduction in all IES and SCL-90R dimensions, except for hostility and psychoticism of SCL-90R.


title: Using k-Poselets for Detecting People and Localizing Their Keypoints

abstract: A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.


title: Nodulation of legumes in inland valley soils of Ghana.

abstract: Because most African farmers cannot afford the purchase of chemical fertilizers to correct soil nutrient deficiencies, alternatives such as increased use of biological nitrogen fixation are most useful. For best results, sound recommendations are needed on the use of different legumes, their rhizobial requirement and management for optimum nitrogen fixation. In West Africa however, except for the highly popular legumes such as cowpea, soybean and groundnut, few reliable data are available on the abilities of other legumes to nodulate and fix nitrogen with naturally occurring strains of rhizobia in various soils and thus on the need for rhizobial inoculation. A screen-house study was conducted at the University of Ghana, Legon, to assess the potential of 13 commonly grown legumes in Ghana to form nodules with native rhizobia from eight inland valley soils and to identify those for which further research would be needed to enhance their nodulation. Two of the soils had indigenous rhizobia that induced formation of nodules on all the legumes evaluated. Rhizobia in the remaining soils induced the formation of nodules on more than 75% of the legumes studied. However, numbers of nodules plant -1 on most of the legumes were low and were influenced by (i) the type of soil, (ii) legume and (iii) the soil x legume interaction. Population densities of rhizobia capable of forming nodules with legumes in the studied estimated by the most probable number method (MPN) ranged from very low (negligible to 1.7 X 10 1 cells g -1 soil for those that could form nodules with mucuna) to high (up to 1.7 X 10 5 cells g -1 soil for cowpea and green gram, respectively). Nodule number per plant correlated significantly with some important indices of soil fertility ; namely %N, available NO 3 - and % organic C, with r values of 0.39*, 0.60* and 0.40*, respectively. Inoculation of mucuna increased nodulation by between 3 to 20-fold and N 2 fixation by 18 to 98 percent depending on the soil type. Non-inoculated mucuna absorbed a high proportion of its N (up to 60%) from the soil and may mine the soil of native N if harvested for seed. The low rhizobial populations and poor nodulation are indications for the necessity for inoculating many of these legumes in similar soils in West Africa.


title: A Naive Bayes Approach for Detecting Coordinated Attacks

abstract: Alert correlation is a very useful mechanism to reduce the high volume of reported alerts and to detect complex and coordinated attacks. Existing approaches either require a large amount of expert knowledge or use simple similarity measures that prevent detecting complex attacks. They also suffer from high computational issues due, for instance, to a high number of possible scenarios. In this paper, we propose a Naive Bayes approach to alert correlation. Our modeling only needs a small part of expert knowledge. It takes advantage of available historical data, and provides efficient algorithms for detecting and predicting most plausible scenarios. Our approach is illustrated using the well known DARPA 2000 data set.


title: Dissociating Valence of Outcome from Behavioral Control in Human Orbital and Ventral Prefrontal Cortices

abstract: The precise role of orbitofrontal cortex (OFC) in affective processing is still debated. One view suggests OFC represents stimulus reward value and supports learning and relearning of stimulus-reward associations. An alternate view implicates OFC in behavioral control after rewarding or punishing feedback. To discriminate between these possibilities, we used event-related functional magnetic resonance imaging in subjects performing a reversal task in which, on each trial, selection of the correct stimulus led to a 70% probability of receiving a monetary reward and a 30% probability of obtaining a monetary punishment. The incorrect stimulus had the reverse contingency. In one condition (choice), subjects had to choose which stimulus to select and switch their response to the other stimulus once contingencies had changed. In another condition (imperative), subjects had simply to track the currently rewarded stimulus. In some regions of OFC and medial prefrontal cortex, activity was related to valence of outcome, whereas in adjacent areas activity was associated with behavioral choice, signaling maintenance of the current response strategy on a subsequent trial. Caudolateral OFC-anterior insula was activated by punishing feedback preceding a switch in stimulus in both the choice and imperative conditions, indicating a possible role for this region in signaling a change in reward contingencies. These results suggest functional heterogeneity within the OFC, with a role for this region in representing stimulus-reward values, signaling changes in reinforcement contingencies and in behavioral control.


title: Modbus/DNP3 State-Based Intrusion Detection System

abstract: The security of Industrial Critical Infrastructures is become a prominent problem with the advent of modern ICT technologies used to improve the performances and the features of the SCADA systems. In this paper we present an innovative approach to the design of Intrusion Detection Systems. The aim is to be able to detect complex attacks to SCADA systems, by monitoring its state evolution. By complex attack, we mean attacks composed of a set of commands that, while licit when considered in isolation on a single-packet basis, can disrupt the correct behavior of the system when executed in particular operating states. The proposed IDS detects these complex attacks thanks to an internal representation of the controlled SCADA system. We also present the corresponding rule language powerful enough to express the system’s critical states. Furthermore, we present a prototype of the proposed IDS, able to monitor systems using the ModBus and DNP3 communication protocols.


title: A biochemical approach to periodontal regeneration: Tetracycline treatment conditions dentin surfaces

abstract: Selective use of tetracycline HCl was studied to evaluate a potential treatment methodology. Tetracycline HCl adsorbed to dentin surfaces, binding 4.7 μg/mm2 after a 5 min exposure to a 50 mg/ml tetracycline HCl solution. Desorption in a discontinuous flow assay maintained biologically active concentrations of tetracycline HCl in the fluid phase for at least 48 h. The tetracycline HCl bound, and subsequently released from dentin retained antimicrobial activity with an ID50 of 3.7 μg/ml. Tetracycline HCl conditioning removed an amorphous surface layer, thereby exposing dentin with open tubules, as determined by scanning electron microscopy. These data suggest that tetracycline HCl-treated root surfaces may act as a depot for release of active antibiotic, as well as serve as an improved substrate for connective tissue components vital to healing at the interface between hard and soft tissues.


title: A ripple voltage sensing MPPT circuit for ultra-low power microsystems

abstract: We propose a maximum power point tracking (MPPT) circuit for micro-scale sensor systems that measures ripple voltages in a switched capacitor energy harvester. Compared to conventional current mirror type MPPT circuits, this design incurs no voltage drop and does not require high bandwidth amplifiers. Using correlated double sampling, high accuracy is achieved with a power overhead of 5%, even at low harvested currents of 1.4uA based on measured results in 180nm CMOS.


title: Block-Based Steganographic Algorithm Using Modulus Function and Pixel-Value Differencing

abstract: The main purpose in developing the steganographic algorithms lies in achieving most of the steganographic objectives which comprise the embedding capacity, imperceptibility, security, robustness and complexity. In this paper, we propose a high quality steganographic algorithm using new block structure which makes a good use of both modulus function and pixel-value differencing, namely, MF-PVD. We have made many experiments with various test images from several galleries, such as USC-SIPI and UWATERLOO-LINK. The performance of our proposed algorithm is verified using three different performance metrics which include peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and embedding capacity (EC). Experimental results and comparisons with six pertinent state-of-art algorithms are given to prove the validation and efficiency of the proposed algorithm.


title: A Comparison Between V-Ramp TDDB Techniques For Reliability Evaluation

abstract: The key methods used to convert ramped voltage test (RVT) data to long term reliability measurement data are reviewed. During the last years the discussion to replace time consuming constant voltage stress (CVS) tests with ramp testing was pushed forward, mainly due to time constraints during qualification of high-k and also low-k material as well as through results obtained for ultrathin oxides [1]. The aim of this work is to contribute to the discussion with focus on thicker oxides used for automotive applications. While a future fWLR standard currently under discussion should also clearly state applicable oxide thickness ranges, advantages and limitations of various approaches, the principle assumptions and formulas are compared and applied to different data sets with gate oxide thicknesses of 9, 10, 17 and 50 nm, respectively. The results are then analyzed, discussed and compared to work based on material with much thinner/thicker oxides.


title: Evaluating Flowchart Recognition for Patent Retrieval

abstract: A set of measures for assessing the effectiveness of flowchart recognition methods in the context of patent-related use cases are presented. Two perspectives on the task are envisaged: a traditional, re-use of bitmap flowcharts use-case and a search-related use-case. A graph topology-based measure is analyzed for the first and a particular version of precision/recall for the second. We find that the graph-based measure has a higher discriminating power, but comes at higher computational costs than the search-based measures. The evaluation of the runs in the absence of ground truth is also investigated and found to provide comparable results if runs from the same group are not allowed to unbalance the synthetically generated truth sets.


title: Machine learning in computational docking

abstract: HighlightsThe state-of-the-art machine-learning techniques in computational docking.Various molecular features extracted from molecular databases and software.Potential future research directions e.g. combining more than one ML-model.The inclusion of quantum effects providing rigorous molecular description.Generalizing of bio-molecular applications, e.g., protein-protein docking. ObjectiveThe objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques). MethodsThe methods presented in this paper are specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising. MaterialThe ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind). ResultsIn this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function 35] on PDBbind v2007 achieves a Pearson correlation coefficient between the predicted and experimentally determined binding affinities of 0.803 while the best conventional scoring function achieves 0.644 34]. The best RF-based ranking power 6] ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%. ConclusionsWe conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models.


title: Efficient Resource Mapping Framework over Networked Clouds via Iterated Local Search-Based Request Partitioning

abstract: The cloud represents a computing paradigm where shared configurable resources are provided as a service over the Internet. Adding intra- or intercloud communication resources to the resource mix leads to a networked cloud computing environment. Following the cloud infrastructure as a Service paradigm and in order to create a flexible management framework, it is of paramount importance to address efficiently the resource mapping problem within this context. To deal with the inherent complexity and scalability issue of the resource mapping problem across different administrative domains, in this paper a hierarchical framework is described. First, a novel request partitioning approach based on Iterated Local Search is introduced that facilitates the cost-efficient and online splitting of user requests among eligible cloud service providers (CPs) within a networked cloud environment. Following and capitalizing on the outcome of the request partitioning phase, the embedding phase—where the actual mapping of requested virtual to physical resources is performed can be realized through the use of a distributed intracloud resource mapping approach that allows for efficient and balanced allocation of cloud resources. Finally, a thorough evaluation of the proposed overall framework on a simulated networked cloud environment is provided and critically compared against an exact request partitioning solution as well as another common intradomain virtual resource embedding solution.


title: Acoustic sequences in non-human animals: a tutorial review and prospectus

abstract: This review was developed at an investigative workshop, “Analyzing Animal Vocal Communication Sequences” that took place on October 21–23 2013 in Knoxville, Tennessee, sponsored by the National Institute for Mathematical and Biological Synthesis (NIMBioS). NIMBioS is an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Awards #EF-0832858 and #DBI-1300426, with additional support from The University of Tennessee, Knoxville. In addition to the authors, Vincent Janik participated in the workshop. D.T.B.’s research is currently supported by NSF DEB-1119660. M.A.B.’s research is currently supported by NSF IOS-0842759 and NIH R01DC009582. M.A.R.’s research is supported by ONR N0001411IP20086 and NOPP (ONR/BOEM) N00014-11-1-0697. S.L.DeR.’s research is supported by the U.S. Office of Naval Research. R.F.-i-C.’s research was supported by the grant BASMATI (TIN2011-27479-C04-03) from the Spanish Ministry of Science and Innovation. E.C.G.’s research is currently supported by a National Research Council postdoctoral fellowship. E.E.V.’s research is supported by CONACYT, Mexico, award number I010/214/2012.


title: www.elsevier.com/locate/sigpro Speech enhancement for non-stationarynoise environments

abstract: In this paper, we present an optimally-modi#ed log-spectral amplitude (OM-LSA) speech estimator and a minima controlled recursive averaging (MCRA) noise estimation approach for robust speech enhancement. The spectral gain function, which minimizes the mean-square error of the log-spectra, is obtained as a weighted geometric mean of the hypothetical gains associated with the speech presence uncertainty. The noise estimate is given by averaging past spectral power values, using a smoothing parameter that is adjusted bythe speech presence probabilityin subbands. We introduce two distinct speech presence probabilityfunctions, one for estimating the speech and one for controlling the adaptation of the noise spectrum. The former is based on the time–frequencydistribution of the a priori signal-to-noise ratio. The latter is determined bythe ratio between the local energyof the noisysignal and its minimum within a speci6ed time window. Objective and subjective evaluation under various environmental conditions con6rm the superiorityof the OM-LSA and MCRA estimators. Excellent noise suppression is achieved, while retaining weak speech components and avoiding the musical residual noise phenomena. ? 2001 Elsevier Science B.V. All rights reserved.