title: Transparent dynamic instrumentation

abstract: Process virtualization provides a virtual execution environment within which an unmodified application can be monitored and controlled while it executes. The provided layer of control can be used for purposes ranging from sandboxing to compatibility to profiling. The additional operations required for this layer are performed clandestinely alongside regular program execution. Software dynamic instrumentation is one method for implementing process virtualization which dynamically instruments an application such that the application's code and the inserted code are interleaved together. DynamoRIO is a process virtualization system implemented using software code cache techniques that allows users to build customized dynamic instrumentation tools. There are many challenges to building such a runtime system. One major obstacle is transparency. In order to support executing arbitrary applications, DynamoRIO must be fully transparent so that an application cannot distinguish between running inside the virtual environment and native execution. In addition, any desired extra operations for a particular tool must avoid interfering with the behavior of the application. Transparency has historically been provided on an ad-hoc basis, as a reaction to observed problems in target applications. This paper identifies a necessary set of transparency requirements for running mainstream Windows and Linux applications. We discuss possible solutions to each transparency issue, evaluate tradeoffs between different choices, and identify cases where maintaining transparency is not practically solvable. We believe this will provide a guideline for better design and implementation of transparent dynamic instrumentation, as well as other similar process virtualization systems using software code caches.


title: Evaluating Recommendation Systems

abstract: Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recommenders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and finally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the properties that they evaluate.


title: Dual Deep Network for Visual Tracking

abstract: Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work. To highlight geometric contours of the target, we integrate the hierarchical feature maps with an edge detector as the coarse prior maps to further embed local details around the target. To leverage the robustness of our dual network, we train it with random patches measuring the similarities between the network activation and target appearance, which serves as a regularization to enforce the dual network to focus on target object. The proposed dual network is updated online in a unique manner based on the observation, that the target being tracked in consecutive frames should share more similar feature representations than those in the surrounding background. It is also found that for a target object, the prior maps can help further enhance performance by passing message into the output maps of the dual network. Therefore, an independent component analysis with reference algorithm is employed to extract target context using prior maps as guidance. Online tracking is conducted by maximizing the posterior estimate on the final maps with stochastic and periodic update. Quantitative and qualitative evaluations on two large-scale benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-arts.


title: 2D-3D Pose Estimation of Heterogeneous Objects Using a Region Based Approach

abstract: Recently, region based methods for estimating the 3D pose of an object from a 2D image have gained increasing popularity. They do not require prior knowledge of the object's texture, making them particularity attractive when the object's texture is unknown a priori. Region based methods estimate the 3D pose of an object by finding the pose which maximizes the image segmentation in to foreground and background regions. Typically the foreground and background regions are described using global appearance models, and an energy function measuring their fit quality is optimized with respect to the pose parameters. Applying a region based approach on standard 2D-3D pose estimation databases shows its performance is strongly dependent on the scene complexity. In simple scenes, where the statistical properties of the foreground and background do not spatially vary, it performs well. However, in more complex scenes, where the statistical properties of the foreground or background vary, the performance strongly degrades. The global appearance models used to segment the image do not sufficiently capture the spatial variation. Inspired by ideas from local active contours, we propose a framework for simultaneous image segmentation and pose estimation using multiple local appearance models. The local appearance models are capable of capturing spatial variation in statistical properties, where global appearance models are limited. We derive an energy function, measuring the image segmentation, using multiple local regions and optimize it with respect to the pose parameters. Our experiments show a substantially higher probability of estimating the correct pose for heterogeneous objects, whereas for homogeneous objects there is minor improvement.


title: Low-Latency Successive-Cancellation Polar Decoder Architectures Using 2-Bit Decoding

abstract: Polar codes have emerged as important error correction codes due to their capacity-achieving property. Successive cancellation (SC) algorithm is viewed as a good candidate for hardware design of polar decoders due to its low complexity. However, for (n, k) polar codes, the long latency of SC algorithm of (2n-2) is a bottleneck for designing high-throughput polar decoder. In this paper, we present a novel reformulation for the last stage of SC decoding. The proposed reformulation leads to two benefits. First, critical path and hardware complexity in the last stage of SC algorithm is significantly reduced. Second, 2 bits can be decoded simultaneously instead of 1 bit. As a result, this new decoder, referred to as 2b-SC decoder, reduces latency from (2n-2) to (1.5n-2) without performance loss. Additionally, overlapped-scheduling, precomputation and look-ahead techniques are used to design two additional decoders referred to as 2b-SC-Overlapped-scheduling decoder and 2b-SC-Precomputation decoder, respectively. All three architectures offer significant advantages with respect to throughput and hardware efficiency. Compared to known prior least-latency SC decoder, the 2b-SC-Precomputation decoder has 25% less latency. Synthesis results show that the proposed (1024, 512) 2b-SC-Precomputation decoder can achieve at least 4 times increase in throughput and 40% increase in hardware efficiency.


title: Imaging the Visual Network in the Migraine Spectrum

abstract: The involvement of the visual network in migraine pathophysiology has been well known for more than a century. Not only is the aura phenomenon linked to cortical alterations primarily localized in the visual cortex; but also migraine without aura has shown distinct dysfunction of visual processing in several studies in the past. Further, the study of photophobia, a hallmark migraine symptom, has allowed unravelling of distinct connections that link retinal pathways to the trigeminovascular system. Finally, visual snow, a recently recognised neurological disorder characterized by a continuous visual disturbance, is highly comorbid with migraine and possibly shares with it some common pathophysiological mechanisms. Here we review the most relevant neuroimaging literature to date, considering studies that have either attempted to investigate the visual network or have indirectly shown visual processing dysfunctions in migraine. We do this by taking into account the broader spectrum of migrainous biology, thus analysing migraine both with and without aura, focusing on light sensitivity as the most relevant visual symptom in migraine, and finally analysing the visual snow syndrome. We also present possible hypotheses on the underlying pathophysiology of visual snow, for which very little is currently known.


title: Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

abstract: State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant contextual dependence. In this work, we advocate the use of spatially recurrent layers (i.e. ReNet layers) which directly capture global contexts and lead to improved feature representations. We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet (N-ReNet), which achieves competitive performance on Stanford Background dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including full-image receptive fields, end-to-end training, and efficient network execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6%, 2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20 object classes.


title: Preparing a collection of radiology examinations for distribution and retrieval

abstract: Objective Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. ::: ::: Materials and Methods The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals’ picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. ::: ::: Results The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. ::: ::: Conclusion Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine ( ).


title: Neural systems of reinforcement for drug addiction: from actions to habits to compulsion

abstract: Drug addiction is increasingly viewed as the endpoint of a series of transitions from initial drug use—when a drug is voluntarily taken because it has reinforcing, often hedonic, effects—through loss of control over this behavior, such that it becomes habitual and ultimately compulsive. Here we discuss evidence that these transitions depend on interactions between pavlovian and instrumental learning processes. We hypothesize that the change from voluntary drug use to more habitual and compulsive drug use represents a transition at the neural level from prefrontal cortical to striatal control over drug seeking and drug taking behavior as well as a progression from ventral to more dorsal domains of the striatum, involving its dopaminergic innervation. These neural transitions may themselves depend on the neuroplasticity in both cortical and striatal structures that is induced by chronic self-administration of drugs. *Note: In the version of this article initially published, there is an error in Figure 1. Please see the PDF for details.


title: Detecting version number attacks in RPL-based networks using a distributed monitoring architecture

abstract: The concept of Internet of Things involves the deployment of Low power and Lossy Networks (LLN) allowing communications among pervasive devices such as embedded sensors. The IETF designed the Routing Protocol for Low power and Lossy Networks (RPL) for supporting these constrained networks. Keeping in mind the different requirements of such networks, the protocol supports multiple routing topologies, called DODAGs, built using different objective functions, so as to optimize routing based on several metrics. A DODAG versioning system is incorporated into RPL in order to ensure an optimized topology. However, an attacker can exploit this mechanism to damage the network and reduce its lifetime. In this paper we propose a detection strategy based on a distributed monitoring architecture with dedicated algorithms that is able to identify malicious nodes performing such attacks in RPL-based environments. The performance of this solution is evaluated through extensive experiments and its scalability is quantified considering a monitoring node placement method.


PRIVACY, TRUST AND SELF-DISCLOSURE ONLINE


title: Alternative formulations and improved bounds for the multi-depot fleet size and mix vehicle routing problem

abstract: In this paper, we compare different formulations of the multi-depot fleet size and mix vehicle routing problem (MDFSMVRP). This problem extends the multi-depot vehicle routing problem and the fleet size and mix vehicle routing problem, two logistics problems that have been extensively studied for many decades. This difficult vehicle routing problem combines complex assignment and routing decisions under the objective of minimizing fixed vehicle costs and variable routing costs. We first propose five distinct formulations to model the MDFSMVRP. We introduce a three-index formulation with an explicit vehicle index and a two-index formulation in which only vehicle types are identified. Other formulations are obtained by defining aggregated and disaggregated loading variables. The last formulation makes use of capacity-indexed variables. For each formulation, we summarize known and propose new valid inequalities, including symmetry breaking, lexicographic ordering, routing, and rounded capacity cuts. We then implement branch-and-cut and branch-and-bound algorithms for these formulations, and we fed them into a general purpose solver. We compare the bounds provided by the formulations on a commonly used set of instances in the MDFSMVRP literature, containing up to nine depots and 360 customers, and on newly generated instances. Our in-depth analysis of the five formulations shows which formulations tend to perform better on each type of instance. Moreover, our results have considerably improved available lower bounds on all instances and significantly improved quality of upper bounds that can be obtained by means of currently available methods.


title: High-Speed LED Driver for ns-Pulse Switching of High-Current LEDs

abstract: This letter analyzes the intrinsic optical switching response of state-of-the-art high power, multiquantum wells, thin-film, and surface-textured infrared light emitting diodes (LEDs). For the switching time response of such devices, the theoretical basics are presented and their predictions are experimentally verified. In the second part of this letter, a novel, fast switching LED driving circuit is presented which enables nanosecond-pulse operation. The circuit reduces significantly the optical rise and fall time of LEDs from 10/15 ns, respectively, down to 2.6 ns at 2-A pulse current without compromising the peak output power.


Using Potential to Guide Mobile Nodes in Wireless Sensor Networks


title: Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information

abstract: Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance.


title: Dictionary learning based software defect prediction

abstract: In order to improve the quality of a software system, software defect prediction aims to automatically identify defective software modules for efficient software test. To predict software defect, those classification methods with static code attributes have attracted a great deal of attention. In recent years, machine learning techniques have been applied to defect prediction. Due to the fact that there exists the similarity among different software modules, one software module can be approximately represented by a small proportion of other modules. And the representation coefficients over the pre-defined dictionary, which consists of historical software module data, are generally sparse. In this paper, we propose to use the dictionary learning technique to predict software defect. By using the characteristics of the metrics mined from the open source software, we learn multiple dictionaries (including defective module and defective-free module sub-dictionaries and the total dictionary) and sparse representation coefficients. Moreover, we take the misclassification cost issue into account because the misclassification of defective modules generally incurs much higher risk cost than that of defective-free ones. We thus propose a cost-sensitive discriminative dictionary learning (CDDL) approach for software defect classification and prediction. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that CDDL outperforms several representative state-of-the-art defect prediction methods.


title: A multi-fpga 10x-real-time high-speed search engine for a 5000-word vocabulary speech recognizer

abstract: Today's best quality speech recognition systems are implemented in software. These systems fully occupy the resources of a high-end server to deliver results at real-time speed: each hour of audio requires a significant fraction of an hour of computation for recognition. This is profoundly limiting for applications that require extreme recognition speed, for example, high-volume tasks such as video indexing (e.g., YouTube), or high-speed tasks such as triage of homeland security intelligence. We describe the architecture and implementation of one critical component -- the backend search stage -- of a high-speed, large-vocabulary recognizer. Implemented on a multi-FPGA Berkeley Emulation Engine 2 (BEE2) platform, we handle a standard 5000-word Wall Street Journal speech benchmark. Our backend search engine can decode on average 10 times faster than real-time running at 100 MHz, i.e, 10x faster than real-time, with negligible degradation in accuracy, running at a clock rate ~ 30x slower than a conventional server. To the best of our knowledge, this is both the most complex, and the fastest recognizer ever to be realized in a hardware form.


The Effect of Vitamin B 12 Infusion on Prevention of Nitrous Oxide-induced Homocysteine Increase : A Double-blind Randomized Controlled Trial


title: Electro-hydrostatic actuators with Series Dissipative property and their application to power assist devices

abstract: The actuators used in wearable robots must behave compliant. This, however, is not easy mainly due to two reasons: lack of force measurement capability and lack of backdrivability. Both of above needs to be realized, but the latter issue lies in the actuation principle of conventional robot actuators. We have been investigating in the electro-hydrostatic actuators to realize backdrivability by minimizing friction due to the reduction. Still, the way to design actuator, considering backdrivability, including the one due to the internal leakage, was an open problem. We introduced the actuator model “Series Dissipative Actuator” to explain the actuator behavior. This model enables us to understand the roll of internal leakage in the hydraulic system and it shows the design guideline considering the backdrivability. In this paper, we show the method to apply SDA (Series Dissipative Actuator)concept on EHAs (Electro-Hydrostatic Actuators). Development of the actuator prototype targeted for use in lower limb is also presented and preliminary results in backdrivability evaluation is performed.


title: An S-Opsin Knock-In Mouse (F81Y) Reveals a Role for the Native Ligand 11-cis-Retinal in Cone Opsin Biosynthesis

abstract: In absence of their natural ligand, 11- cis -retinal, cone opsin G-protein-coupled receptors fail to traffic normally, a condition associated with photoreceptor degeneration and blindness. We created a mouse with a point mutation (F81Y) in cone S-opsin. As expected, cones with this knock-in mutation respond to light with maximal sensitivity red-shifted from 360 to 420 nm, consistent with an altered interaction between the apoprotein and ligand, 11- cis -retinal. However, cones expressing F81Y S-opsin showed an ∼3-fold reduced absolute sensitivity that was associated with a corresponding reduction in S-opsin protein expression. The reduced S-opsin expression did not arise from decreased S-opsin mRNA or cone degeneration, but rather from enhanced endoplasmic reticulum (ER)-associated degradation of the nascent protein. Exogenously increased 11- cis -retinal restored F81Y S-opsin protein expression to normal levels, suggesting that ligand binding in the ER facilitates proper folding. Immunohistochemistry and electron microscopy of normal retinas showed that Mueller cells, which synthesize a precursor of 11- cis -retinal, are closely adjoined to the cone ER, so they could deliver the ligand to the site of opsin synthesis. Together, these results suggest that the binding of 11- cis -retinal in the ER is important for normal folding during cone opsin biosynthesis.


Design and Verification of USB 3 . 0 Link Layer ( LTSSM )


title: Music improves sleep quality in students.

abstract: AIM ::: This paper is a report of a study to investigate the effects of music on sleep quality in young participants with poor sleep. ::: ::: ::: BACKGROUND ::: Sleep disorders may result in fatigue, tiredness, depression and problems in daytime functioning. Music can reduce sympathetic nervous system activity, decrease anxiety, blood pressure, heart and respiratory rate and may have positive effects on sleep via muscle relaxation and distraction from thoughts. Control groups have not been used in most previous studies. ::: ::: ::: METHODS ::: We used a three-group repeated measures design. Ninety-four students (aged between 19 and 28 years) with sleep complaints were studied in 2006. Participants listened for 45 minutes either to relaxing classical music (Group 1) or an audiobook (Group 2) at bedtime for 3 weeks. The control group (Group 3) received no intervention. Sleep quality was measured using the Pittsburg Sleep Quality Index before the study and weekly during the intervention. Depressive symptoms in experimental group participants were measured using the Beck Depression Inventory. ::: ::: ::: RESULTS ::: Repeated measures anova revealed a main effect of TIME (P < 0.0001) and an interaction between TIME and GROUPS (P < 0.0001). Post hoc tests with Bonferroni correction showed that music statistically significantly improved sleep quality (P < 0.0001). Sleep quality did not improve statistically significantly in the audiobook and the control group. Depressive symptoms decreased statistically significantly in the music group (P < 0.0001), but not in the group listening to audiobooks. ::: ::: ::: CONCLUSION ::: Relaxing classical music is an effective intervention in reducing sleeping problems. Nurses could use this safe, cheap and easy to learn method to treat insomnia.


title: A novel cost-sensitive framework for customer churn predictive modeling

abstract: Customer churn predictive modeling deals with predicting the probability of a customer defecting using historical, behavioral and socio-economical information. This tool is of great benefit to subscription based companies allowing them to maximize the results of retention campaigns. The problem of churn predictive modeling has been widely studied by the data mining and machine learning communities. It is usually tackled by using classification algorithms in order to learn the different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art classification algorithms are not well aligned with commercial goals, in the sense that, the models miss to include the real financial costs and benefits during the training and evaluation phases. In the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive power, does not yield to the best results when measured by the actual financial cost, ie. investment per subscriber on a loyalty campaign and the financial impact of failing to detect a real churner versus wrongly predicting a non-churner as a churner. In this paper, we present a new cost-sensitive framework for customer churn predictive modeling. First we propose a new financial based measure for evaluating the effectiveness of a churn campaign taking into account the available portfolio of offers, their individual financial cost and probability of offer acceptance depending on the customer profile. Then, using a real-world churn dataset we compare different cost-insensitive and cost-sensitive classification algorithms and measure their effectiveness based on their predictive power and also the cost optimization. The results show that using a cost-sensitive approach yields to an increase in cost savings of up to 26.4 %.


title: Scrutinizing WPA2 password generating algorithms in wireless routers

abstract: A wireless router is a networking device that enables a user to set up a wireless connection to the Internet. A router can offer a secure channel by cryptographic means which provides authenticity and confidentiality. Nowadays, almost all routers use a secure channel by default that is based on Wi-Fi Protected Access II (WPA2). This is a security protocol which is believed not to be susceptible to practical key recovery attacks. However, the passwords should have sufficient entropy to avert bruteforce attacks. ::: ::: In this paper, we compose a strategy on how to reverse-engineer embedded routers. Furthermore, we describe a procedure that can instantly gather a complete wireless authentication trace which enables an offline password recovery attack. Finally, we present a number of use cases where we identify extremely weak password generating algorithms in various routers which are massively deployed in The Netherlands. ::: ::: The algorithms are used to generate the default WPA2 password. Such a password is loaded during device initialization and hardware reset. Users that did not explicitly change their wireless password are most likely vulnerable to practical attacks which can recover their password within minutes. A stolen password allows an adversary to abuse someone else's internet connection, for instance compromising the firewall, making a fraudulent transaction or performing other criminal activities. ::: ::: Together with the Dutch National Cyber Security Centre we have initiated a responsible disclosure procedure. However, since these routers are also used by many other companies in various countries, our findings seem to relate an international industry wide security issue.


title: Multiple Kernel Learning for vehicle detection in wide area motion imagery

abstract: Vehicle detection in wide area motion imagery (WAMI) is an important problem in computer science, which if solved, supports urban traffic management, emergency responder routing, and accident discovery. Due to large amount of camera motion, the small number of pixels on target objects, and the low frame rate of the WAMI data, vehicle detection is much more challenging than the task in traditional video imagery. Since the object in wide area imagery covers a few pixels, feature information of shape, texture, and appearance information are limited for vehicle detection and classification performance. Histogram of Gradients (HOG) and Haar descriptors have been used in human and face detection successfully, only using the intensity of an image, and HOG and Haar descriptors have different advantages. In this paper, we propose a classification scheme which combines HOG and Haar descriptors by using Generalized Multiple Kernel Learning (GMKL) that can learn the trade-off between HOG and Haar descriptors by constructing an optimal kernel with many base kernels. Due to the large number of Haar features, we first use a cascade of boosting classifier which is a variant of Gentle AdaBoost and has the ability to do feature selection to select a small number of features from a huge feature set. Then, we combine the HOG descriptors and the selected Haar features and use GMKL to train the final classifier. In our experiments, we evaluate the performance of HOG+Haar with GMKL, HOG with GMKL, Haar with GMKL, and also the cascaded boosting classifier on Columbus Large Image Format (CLIF) dataset. Experimental results show that the fusion of the HOG+Haar with GMKL outperforms the other three classification schemes.


title: Low-power network-on-chip for high-performance soc design

abstract: An energy-efficient network-on-chip (NoC) is presented for possible application to high-performance system-on-chip (SoC) design. It incorporates heterogeneous intellectual properties (IPs) such as multiple RISCs and SRAMs, a reconfigurable logic array, an off-chip gateway, and a 1.6-GHz phase-locked loop (PLL). Its hierarchically-star-connected on-chip network provides the integrated IPs, which operate at different clock frequencies, with packet-switched serial-communication infrastructure. Various low-power techniques such as low-swing signaling, partially activated crossbar, serial link coding, and clock frequency scaling are devised, and applied to achieve the power-efficient on-chip communications. The 5 /spl times/5 mm/sup 2/ chip containing all the above features is fabricated by 0.18-/spl mu/m CMOS process and successfully measured and demonstrated on a system evaluation board where multimedia applications run. The fabricated chip can deliver 11.2-GB/s aggregated bandwidth at 1.6-GHz signaling frequency. The chip consumes 160 mW and the on-chip network dissipates less than 51 mW.


title: A visual approach for driver inattention detection

abstract: Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver's visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.


title: Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods

abstract: Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition. For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show our method outperforms humans in the ME recognition task by a large margin, and achieves comparable performance to humans at the very challenging task of spotting and then recognizing spontaneous MEs.


title: Microstrip Circuits with a Modified Ground Plane

abstract: Different microstrip circuits with modified ground plane are considered in this article. The ground plane aperture (GPA), ground plane lossy aperture (GPLA), and defected ground structure (DGS) provide numerous advantages for microstrip elements and devices. DGS resonators find application in a large number of microstrip circuits where a frequency notch can reduce complexity and enhance performance. GPA is mainly used to increase the impedance in a section of a microstrip, allowing higher impedance lines and higher Q printed inductors. The applications of the GPA and GPLA include termination, attenuator, LPF, directional coupler, phase shifter, and Wilkinson divider.


title: A Forensic Text Comparison in SMS Messages: A Likelihood Ratio Approach with Lexical Features.

abstract: Due to its convenience and low cost, short message service (SMS) has been a very popular medium of communication for quite some time. Unfortunately, however, SMS messages are sometimes used for reprehensible purposes, e.g. communication between drug dealers and buyers, or in illicit acts such as extortion, fraud, scams, hoaxes, and false reports of terrorist threats. In this study, we perform a likelihood-ratio-based forensic text comparison of SMS messages focusing on lexical features. The likelihood ratios (LRs) are calculated in Aitken and Lucy’s (2004) multivariate kernel density procedure, and are calibrated. The validity of the system is assessed based on the magnitude of the LRs using the log-likelihood-ratio cost (Cllr). The strength of the derived LRs is graphically presented in Tippett plots. The results of the current study are compared with those of previous studies.


title: Reverse Engineering Aggregation Queries

abstract: Query reverse engineering seeks to re-generate the SQL query that produced a given query output table from a given database. In this paper, we solve this problem for OLAP queries with group-by and aggregation. We develop a novel three-phase algorithm named REGAL 1 for this problem. First, based on a lattice graph structure, we identify a set of group-by candidates for the desired query. Second, we apply a set of aggregation constraints that are derived from the properties of aggregate operators at both the table-level and the group-level to discover candidate combinations of group-by columns and aggregations that are consistent with the given query output table. Finally, we find a multi-dimensional filter, i.e., a conjunction of selection predicates over the base table attributes, that is needed to generate the exact query output table. We conduct an extensive experimental study over the TPC-H dataset to demonstrate the effectiveness and efficiency of our proposal.


title: Hop Client-Side Compilation

abstract: Hop is a new language for programming interactive Web applications. It aims to replace HTML, JavaScript, and server-side scripting languages (such as PHP, JSP) with a unique language that is used for client-side interactions and server-side computations. A Hop execution platform is made of two compilers: one that compiles the code executed by the server, and one that compiles the code executed by the client. This paper presents the latter. In order to ensure compatibility of Hop graphical user interfaces with popular plain Web browsers, the client-side Hop compiler has to generate regular HTML and JavaScript code. The generated code runs roughly at the same speed as handwritten code. Since the Hop language is built on top of the Scheme programming language, compiling Hop to JavaScript is nearly equivalent to compiling Scheme to JavaScript. SCM2JS, the compiler we have designed, supports the whole Scheme core language. In particular, it features proper tail recursion. However complete proper tail recursion may slow down the generated code. Despite an optimization which eliminates up to 40% of instrumentation for tail call intensive benchmarks, worst case programs were more than two times slower. As a result Hop only uses a weaker form of tail-call optimization which simplifies recursive tail-calls to while-loops. The techniques presented in this paper can be applied to most strict functional languages such as ML and Lisp. SCM2JS can be downloaded at http://www-sop.inria.fr/mimosa/personnel/Florian.Loitsch/scheme2js/. It is also distributed along with Hop which can be found at http://hop.inria.fr. 1Inria Sophia Antipolis, 2004 route des Lucioles BP93, F-06902 Sophia Antipolis, Cedex, France; Email: {florian.loitsch, manuel.serrano}@inria.fr


title: Autonomous Helicopter Flight via Reinforcement Learning

abstract: Autonomous helicopter flight represents a challenging control problem, with complex, noisy, dynamics. In this paper, we describe a successful application of reinforcement learning to autonomous helicopter flight. We first fit a stochastic, nonlinear model of the helicopter dynamics. We then use the model to learn to hover in place, and to fly a number of maneuvers taken from an RC helicopter competition.


Instant Radiosity for Real-Time Global Illumination


title: SkyNET: A 3G-Enabled Mobile Attack Drone and Stealth Botmaster

abstract: SkyNET is a stealth network that connects hosts to a bot-master through a mobile drone. The network is comprised of machines on home Wi-Fi networks in a proximal urban area, and one or more autonomous attack drones. The SkyNET is used by a botmaster to command their botnet(s) without using the Internet. The drones are programmed to scour an urban area and compromise wireless networks. Once compromised, the drone attacks the local hosts. When a host is compromised it joins both the Internet-facing botnet, and the sun-facing SkyNET. Subsequent drone flights are used to issue command and control without ever linking the botmaster to the botnet via the Internet. Reverse engineering the botnet, or enumerating the bots, does not reveal the identity of the botmaster. An analyst is forced to observe the autonomous attack drone to bridge the command and control gap. In this paper we present a working example, SkyNET complete with a prototype attack drone, discuss the reality of using such a command and control method, and provide insight on how to prevent against such attacks.


title: Tag Disentangled Generative Adversarial Network for Object Image Re-rendering

abstract: In this paper, we propose a principled Tag Disentangled Generative Adversarial Networks (TDGAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression, etc.). The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are completely/partially tagged (i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts disentangled and interpretable representations, which are then used to generate images by the generative network. In order to boost the quality of disentangled representations, the tag mapping net is integrated to explore the consistency between the image and its tags. Furthermore, the discriminative network is introduced to implement the adversarial training strategy for generating more realistic images. Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.


title: Imaging of spinal stenosis: neurogenic intermittent claudication and cervical spondylotic myelopathy.

abstract: Spinal stenosis in either the cervical or lumbar spinal segments is one of the most common indications for spine imaging and intervention, particularly among the elderly. This article examines the pathophysiology and imaging of the corresponding clinical syndromes, cervical spondylotic myelopathy or neurogenic intermittent claudication. The specificity fault of spine imaging is readily evident in evaluation of spinal stenosis, as many patients with anatomic cervical or lumbar central canal narrowing are asymptomatic. Imaging also may be insensitive to dynamic lesions. Those imaging features that identify symptomatic patients, or predict response to interventions, are emphasized.


title: Synthetic Text Generation for Sentiment Analysis

abstract: Natural language is a common type of input for data processing systems. Therefore, it is often required to have a large testing data set of this type. In this context, the task to automatically generate natural language texts, which maintain the properties of real texts is desirable. However, current synthetic data generators do not capture natural language text data sufficiently. In this paper, we present a preliminary study on different generative models for text generation, which maintain specific properties of natural language text, i.e., the sentiment of a review text. In a series of experiments using different data sets and sentiment analysis methods, we show that generative models can generate texts with a specific sentiment and that hidden Markov model based text generation achieves less accuracy than Markov chain based text generation, but can generate a higher number of distinct texts.


title: Characterizing scholar popularity: a case study in the computer science research community

abstract: A common live debate among scholars regards the popularity, productivity and impact of research. This paper aims to contribute to such discussion by quantifying the impact of various academic features on a scholar popularity throughout her career. Using a list of over 2 million publications in the Computer Science research area obtained from two large digital libraries, we analyze how features that capture the number and rate of publications, number and quality of publication venues, and the importance of the scholar in the co-authorship network relate to the scholar popularity. We also investigate the temporal dynamics of scholar popularity, identifying a few common profiles, and characterizing scholars in each profile according to their academic features.


title: A new paradigm: cloud agile manufacturing

abstract: Cloud Agile Manufacturing is a new paradigm proposed in this article. The main objective of Cloud Agile Manufacturing is to offer industrial production systems as a service. Thus users can access any functionality available in the cloud of manufacturing (process design, production, management, business integration, factories virtualization, etc.) without knowledge — or at least without having to be experts — in managing the required resources. The proposal takes advantage of many of the benefits that can offer technologies and models like: Business Process Management (BPM), Cloud Computing, Service Oriented Architectures (SOA) and Ontologies. To develop the proposal has been taken as a starting point the Semantic Industrial Machinery as a Service (SIMaaS) proposed in previous work. This proposal facilitates the effective integration of industrial machinery in a computing environment, offering it as a network service. The work also includes an analysis of the benefits and disadvantages of the proposal.


title: Unsupervised learning of acoustic features via deep canonical correlation analysis

abstract: It has been previously shown that, when both acoustic and articulatory training data are available, it is possible to improve phonetic recognition accuracy by learning acoustic features from this multi-view data with canonical correlation analysis (CCA). In contrast with previous work based on linear or kernel CCA, we use the recently proposed deep CCA, where the functional form of the feature mapping is a deep neural network. We apply the approach on a speaker-independent phonetic recognition task using data from the University of Wisconsin X-ray Microbeam Database. Using a tandem-style recognizer on this task, deep CCA features improve over earlier multi-view approaches as well as over articulatory inversion and typical neural network-based tandem features. We also present a new stochastic training approach for deep CCA, which produces both faster training and better-performing features.


title: Exposing digital forgeries in video by detecting double MPEG compression

abstract: With the advent of sophisticated and low-cost video editing software, it is becoming increasingly easier to tamper with digital video. In addition,an ever-growing number of video surveillance cameras is giving rise to an enormous amount of video data. The ability to ensure the integrity and authenticity of this data poses considerable challenges. Here we begin to explore techniques for detecting traces of tampering in digital video. Specifically, we show how a doublycompressed MPEG video sequence introduces specific static and temporal statistical perturbations whose presence can be used as evidence of tampering.


title: Forecasting in the NBA and other team sports: Network effects in action

abstract: The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a a 14p rank prediction accuracy improvement over our best competitor.


title: Exact acceleration of linear object detectors

abstract: We describe a general and exact method to considerably speed up linear object detection systems operating in a sliding, multi-scale window fashion, such as the individual part detectors of part-based models. The main bottleneck of many of those systems is the computational cost of the convolutions between the multiple rescalings of the image to process, and the linear filters. We make use of properties of the Fourier transform and of clever implementation strategies to obtain a speedup factor proportional to the filters' sizes. The gain in performance is demonstrated on the well known Pascal VOC benchmark, where we accelerate the speed of said convolutions by an order of magnitude.


title: Public Key Encryption with Keyword Search

abstract: We study the problem of searching on data that is encrypted using a public key system. Consider user Bob who sends email to user Alice encrypted under Alice’s public key. An email gateway wants to test whether the email contains the keyword “urgent” so that it could route the email accordingly. Alice, on the other hand does not wish to give the gateway the ability to decrypt all her messages. We define and construct a mechanism that enables Alice to provide a key to the gateway that enables the gateway to test whether the word “urgent” is a keyword in the email without learning anything else about the email. We refer to this mechanism as Public Key Encryption with keyword Search. As another example, consider a mail server that stores various messages publicly encrypted for Alice by others. Using our mechanism Alice can send the mail server a key that will enable the server to identify all messages containing some specific keyword, but learn nothing else. We define the concept of public key encryption with keyword search and give several constructions.


title: Magnetic rare earth superlattices

abstract: Abstract Advances in molecular beam epitaxy deposition techniques have recently made it possible to grow, an atomic plane at a time, single crystalline superlattices composed of alternating layers of a magnetic rare earth, such as Gd, Dy, Ho, or Er, and metallic Y, which has an identical chemical structure. The primary goal of this article is to review the new and interesting magnetic structures which have been discovered in these novel superlattice systems and to consider what implications the observed phases have on our understanding of the underlying microscopic magnetic interactions. In particular, the effects of the artificial periodicity or compositional modulation, finite layer thickness, and epitaxial strain on the resulting long range magnetic order of Gd-Y, Dy-Y, Ho-Y, Er-Y, and Gd-Dy superlattices are described.


A Practical Generic Relay Attack on Contactless Transactions by Using NFC Mobile Phones


title: Large-scale visual sentiment ontology and detectors using adjective noun pairs

abstract: We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.


title: Application Concept of Social Capital Theory and Social Exchange Theory on Organizational Trust, Willingness To Share, Membership Involvement, and Knowledge Obtaining Dimensions in Industry Cluster

abstract: The originality of this research lies in the application of the grand theories {Social Capital and ::: Social Exchange theories) in revealing types of variables which affects knowledge obtaining ::: in industrial clusters. The practical implication of this research is an attempt to produce a ::: new strategy, the cooperation strategy for knowledge obtaining that can be applied to the ::: company in addition to a conceptual foundation for further research about the collaboration ::: between the companies. Limitations of this study is, this is the study with limited to an ::: assessment and conceptual literature excavation that remains to be proved further by ::: research in the field.


title: Flow Map Layout via Spiral Trees

abstract: Flow maps are thematic maps that visualize the movement of objects, such as people or goods, between geographic regions. One or more sources are connected to several targets by lines whose thickness corresponds to the amount of flow between a source and a target. Good flow maps reduce visual clutter by merging (bundling) lines smoothly and by avoiding self-intersections. Most flow maps are still drawn by hand and only few automated methods exist. Some of the known algorithms do not support edge-bundling and those that do, cannot guarantee crossing-free flows. We present a new algorithmic method that uses edge-bundling and computes crossing-free flows of high visual quality. Our method is based on so-called spiral trees, a novel type of Steiner tree which uses logarithmic spirals. Spiral trees naturally induce a clustering on the targets and smoothly bundle lines. Our flows can also avoid obstacles, such as map features, region outlines, or even the targets. We demonstrate our approach with extensive experiments.


title: Effective attack models for shilling item-based collaborative filtering systems

abstract: Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers who cannot be readily distinguished from ordinary users may introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. A handful of simple attack models have, so far, been identified, and there appear to be significant differences in the susceptibility of different recommendation techniques to these attacks. In particular, item-based collaborative filtering has been found to offer some security advantages over user-based collaborative filtering. Our research in secure personalization is examining a range of more complex attack models and recommendation techniques, paying particular attention to the costs and benefits of mounting an attack. In this paper, we take a closer look at item-based collaborative filtering. In particular, we propose a new attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against an item-based algorithm.


title: Empowering Leadership in Management Teams: Effects on Knowledge Sharing, Efficacy, And Performance

abstract: We surveyed management teams in 102 hotel properties in the United States to examine the intervening roles of knowledge sharing and team efficacy in the relationship between empowering leadership and team performance. Team performance was measured through a time-lagged market-based source. Results showed that empowering leadership was positively related to both knowledge sharing and team efficacy, which, in turn, were both positively related to performance.


title: ARTT taxonomy and cyber-attack Framewok

abstract: The security of computer systems has become essential especially in front of the critical issues of cyber-attacks that can result in the compromise of these systems because any act on a system intends to harm one of the security properties (Confidentiality, Integrity and availability). Studying computer attacks is also essential to design models of attacks aiming to protect against these attacks by modeling the last ones. The development of taxonomies leads to characterize and classify the attacks which lead to understand them. In computer's security taxonomy, we can have two broad categories: cyber-attack's taxonomy and cyber-security taxonomy. In this paper, we propose taxonomy for cyber-attacks according to an attacker vision and the aspect of achieving an attack. This taxonomy is based on 4 dimensions: Attack vector, Result, Type and Target. To generalize our approach, we have used the framework of the Discrete EVent system Specification DEVS. This framework depicts the overall vision of cyber-attacks. To partially validate our work, a simulation is done on a case study of buffer overflow. A DEVS model is described and a simulation is done via this formalism. This case study aims to reinforce our proposal.


title: Soft Tissue Wounds and Principles of Healing

abstract: Wound healing is a complex interchange, orchestrated between cellular components that play their respective parts signaled by and mediated by different cellular instruments of healing. When healing is performed well, the final product is a thing of beauty. When healing is delayed, interrupted, or excessive, then unsightly scars of chronic painful wounds that are frustrating to the patient and physician occur.


title: Human activity analysis: Concentrating on Motion History Image and its variants

abstract: The Motion History Image (MHI) method is a view-based temporal template approach, which is simple and robust in representing movements, and hence MHI is widely employed by various research groups for action recognition, motion analysis, etc. This paper overviews motion history image (MHI)-based human motion recognition techniques. Since the inception of the motion history image template for motion recognition, various progresses have been adopted to improve this basic MHI technique. We have presented almost all the important variants of the MHI method in this paper. This paper has pointed some areas for further research at the top of this method.


title: Data mining techniques for medical data: A review

abstract: Data mining is an important area of research and is pragmatically used in different domains like finance, clinical research, education, healthcare etc. Further, the scope of data mining have thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare which is an active interdisciplinary area of research. In fact, the task of knowledge extraction from the medical data is a challenging endeavor and it is a complex task. The main motive of this review paper is to give a review of data mining in the purview of healthcare. Moreover, intertwining and interrelation of previous researches have been presented in a novel manner. Furthermore, merits and demerits of frequently used data mining techniques in the domain of health care and medical data have been compared. The use of different data mining tasks in health care is also discussed. An analytical approach regarding the uniqueness of medical data in health care is also presented.


title: An overview of anomaly detection techniques: Existing solutions and latest technological trends

abstract: As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems-the cyberspace's equivalent to the burglar alarm-join ranks with firewalls as one of the fundamental technologies for network security. However, today's commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system/network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or ''zero day'' attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area.


title: Real-time through-wall imaging using an ultrawideband multiple-input multiple-output (MIMO) phased array radar system

abstract: A real-time acquisition and processing architecture has been developed for an ultrawideband (UWB) S-band (2–4 GHz) multiple-input multiple-output (MIMO) phased array radar system that facilitates greater than 10 Hz imaging rates, providing a video-like radar image of what is behind a concrete wall. Video rate imaging enhances the interpretability of range vs. range through-wall and free-space radar imagery. Images are formed without a-priori information. Video framerate imaging is achieved by designing an electronically switched bi-static array using high-performance microwave components, a multi-threaded data pipeline, and efficient hardware-accelerated processing algorithms. Experiments successfully image low radar cross section (RCS) objects, fast moving objects in free-space, and a human behind a 10 cm-thick solid concrete wall.


title: Work function of boron-doped polycrystalline Si/sub x/Ge/sub 1-x/ films

abstract: The work function of p-type polycrystalline Si/sub x/Ge/sub 1-x/ films deposited by LPCVD using SiH/sub 4/ and GeH/sub 4/ was determined by CV measurements on MOS structures. Boron was introduced in the Si/sub x/Ge/sub 1-x/ films either exsitu by ion implantation or insitu by adding B/sub 2/H/sub 6/ in the reactants during film deposition. The work function of the Si/sub x/Ge/sub 1-x/ films is found to decrease as the Ge content increases; it is 5.16 eV for Si, 4.76 eV for Si/sub 0.49/Ge/sub 0.51/, and 4.67 eV for Ge. The work function of the Si and Ge films coincides well with that of single crystalline Si and Ge, respectively. It is also found that a thin Si adhesion layer of about 3 nm (nominal thickness), deposited prior to the Si/sub x/Ge/sub 1-x/, has a negligible effect on the work function determination.


title: Detecting anomalies in IaaS environments through virtual machine host system call analysis

abstract: Providers and consumers of Infrastructure-as-a-Service (IaaS) virtual machine resources may be the subject of a number of attacks, particularly in public cloud environments. Detecting anomalies is hence critical both to protect against misuse and attacks, but is subject to constraints. These include primarily efficiency, but also legal and contractual restrictions limiting the depth of intrusiveness, which can be achieved by an intrusion detection system. In many cases, the IaaS provider will also have very limited insights into the actual workloads used by clients. In this paper we therefore propose to monitor system calls at the VM host level without requiring any instrumentation within VMs and argue that this level of granularity is sufficient to capture a number of relevant attack classes. This, together with the efficiency and efficacy of the approach is shown through experiments and statistical analysis in a Linux KVM-based reference scenario. The proposed system, unlike other systems such as VM Introspection (VMI), does not require any knowledge about VMs from inside nor requiring any OS or hypervisor modifications.


title: Homology-mediated end joining-based targeted integration using CRISPR/Cas9

abstract: Targeted integration of transgenes can be achieved by strategies based on homologous recombination (HR), microhomology-mediated end joining (MMEJ) or non-homologous end joining (NHEJ). The more generally used HR is inefficient for achieving gene integration in animal embryos and tissues, because it occurs only during cell division, although MMEJ and NHEJ can elevate the efficiency in some systems. Here we devise a homology-mediated end joining (HMEJ)-based strategy, using CRISPR/Cas9-mediated cleavage of both transgene donor vector that contains guide RNA target sites and ∼800 bp of homology arms, and the targeted genome. We found no significant improvement of the targeting efficiency by the HMEJ-based method in either mouse embryonic stem cells or the neuroblastoma cell line, N2a, compared to the HR-based method. However, the HMEJ-based method yielded a higher knock-in efficiency in HEK293T cells, primary astrocytes and neurons. More importantly, this approach achieved transgene integration in mouse and monkey embryos, as well as in hepatocytes and neurons in vivo, with an efficiency much greater than HR-, NHEJ- and MMEJ-based strategies. Thus, the HMEJ-based strategy may be useful for a variety of applications, including gene editing to generate animal models and for targeted gene therapies.


title: IT Project Success from the Management Perspective - A Quantitative Evaluation

abstract: This work investigates the effects that different success criteria and their dimensions may have on the success of IT projects. It focuses on a model that represents the management's view of the success of an IT project. This is of particular interest due to demand for developing and examining such a model. To show the effects of the success criteria and their dimensions a survey of 646 participants was conducted. The effects of the criteria and dimensions on IT project success were subsequently studied with structural equation modeling. Because of some inconsistencies within the original model of IT project success a deducted model had to be developed. Some of the success criteria and dimensions had to be rearranged or removed from the original model due to the results of the study. The new model shows that the perception and the results of a project have a significant impact on the success rating of an IT project.


title: Performance benefits of monolithically stacked 3D-FPGA

abstract: The performance benefits of a monolithically stacked 3D-FPGA, whereby the programming overhead of an FPGA is stacked on top of a standard CMOS layer containing the logic blocks and interconnects, are investigated. A Virtex-II style 2D-FPGA fabric is used as a baseline for quantifying the relative improvements in logic density, delay, and power consumption achieved by such a 3D-FPGA. It is assumed that only the pass-transistor switches and configuration memory cells can be moved to the top layers and that the 3D-FPGA employs the same logic block and programmable interconnect architecture as the baseline 2D-FPGA. Assuming a configuration memory cell that is ≤ 0.7 the area of an SRAM cell and pass-transistor switches having the same characteristics as nMOS devices in the CMOS layer are used, it is shown that a monolithically stacked 3D-FPGA can achieve 3.2 times higher logic density, 1.7 times lower critical path delay, and 1.7 times lower total dynamic power consumption than the baseline 2D-FPGA fabricated in the same 65nm technology node.


title: Object detection on panoramic images based on deep learning

abstract: Panoramic image can be widely used in many applications, such as virtual reality, visual surveillance and autonomous vehicle, because of its large field of view. However, the inherent distortion for panorama causes object detection to be a challenging task. This paper focuses on the multi-class objects detection in panoramic images using deep learning method. The proposed system uses three fisheye cameras to efficiently create panoramas and build a large dataset. A region based convolutional neutral network (R-CNN) is implemented to train and test on an indoor panoramic image dataset. Experiments show great improvement performance on ten categories of distorted indoor objects with a mean average precision of 68.7%.


title: A Heartbeat and Temperature Measuring System for Remote Health Monitoring using Wireless Body Area Network

abstract: This paper presents the design and development of a microcontroller based heartbeat and body temperature monitor using fingertip and temperature sensor. The device uses the optical technology to detect the flow of blood through the finger and offers the advantage of portability over conventional recording systems. However, wireless body area network based remote patient monitoring systems have been presented with numerous problems including efficient data extraction and dynamic tuning of data to preserve the quality of data transmission. Evaluation of the device on real signals shows accuracy in heartbeat measurement, even under intense physical activity. This paper presents these challenges as well as solution to these problems by proposing an architecture which allows a network to be formed between the patient and doctor in order to enable remote monitoring of patient by analyzing the data of patient. The device consists of sensors which are used to measure heartbeat as well as body temperature of a patient and it is controlled by a central unit. The readings from these sensors are further processed and sent via GSM module to a remote location where it is displayed on cell phone. The optical heartbeat sensor counts the heartbeat per minute and temperature sensor measures the temperature from the body and both the measured data are sent to a receiving end utilizing wireless technology where the data is displayed in a cell phone for further processing and patient care. Moreover, the superiority of this device is shown in comparison to traditional system.


title: Effects of Virtual Reality Training With Trunk Support Trainer (TruST) on Postural Kinematics

abstract: Improving functional motor deficits in patients with neurological and musculoskeletal disorders has been a primary objective of rehabilitation. Task-oriented training in physical environments is a leading approach for rehab training utilizing tasks that are challenging, adaptable, and meaningful. Although different training paradigms can be utilized in a laboratory setting, they often require expensive training equipment and personnel, limiting the number of training sessions. Creating an effective virtual reality (VR) training environment can greatly increase the repetition, cognitive engagement, and training variability. Specifically, dynamic trunk control is required for the successful completion of everyday tasks. These tasks require coordination between the head, upper and lower trunk, and the pelvis. We have developed a novel robotic device, trunk support trainer (TruST) and a VR gaming environment, which allows training in a seated posture at and beyond an individual's point of stability failure using an assist-as-needed force tunnel strategy. The VR environment consists of challenging reaching tasks where the subject's dominant hand is used to manipulate a VR drone. We conducted a study that involved three groups: ten adult subjects trained using VR, ten adult subjects trained in a physical environment (PR), and ten without TruST assistance (control group) to investigate the changes in trunk kinematics. Our study supports the hypothesis that a single training session with TruST in both VR and PR can increase lower trunk range of motion. This approach may be useful for creating training variability and translating therapy in home settings.


title: Collaborative supply chain management: The most promising practice for building efficient and sustainable supply chains

abstract: Purpose – The aim of this paper is to provide an overview of contemporary supply‐chain management systems.Design/methodology/approach – The paper highlights the examples of state‐of‐the‐art practice in supply‐chain management, and speculates about where this movement is headed. Some of the collaborative supply chain management products generating the most interest will also be examined.Findings – Collaborative planning, forecasting and replenishment (CPFR) is the most recent prolific management initiative that provides supply chain collaboration and visibility. By following CPFR, companies can dramatically improve supply chain effectiveness with demand planning, synchronized production scheduling, logistic planning, and new product design. CPFR will force suppliers to innovate, building on strong one‐to‐one relationships that will drive smarter ways of doing things. Most companies and industries can benefit from CPFR. However, companies that experience variation in demand, buy or sell a product on a perio...


Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks


title: Energy-efficient rate-adaptive GPS-based positioning for smartphones

abstract: Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy. In this paper, we present RAPS, rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine when to turn on GPS. It uses the location-time history of the user to estimate user velocity and adaptively turn on GPS only if the estimated uncertainty in position exceeds the accuracy threshold. It also efficiently estimates user movement using a duty-cycled accelerometer, and utilizes Bluetooth communication to reduce position uncertainty among neighboring devices. Finally, it employs celltower-RSS blacklisting to detect GPS unavailability (e.g., indoors) and avoid turning on GPS in these cases. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone lifetimes by more than a factor of 3.8 over an approach where GPS is always on.


title: Efficient deep models for monocular road segmentation

abstract: This paper addresses the problem of road scene segmentation in conventional RGB images by exploiting recent advances in semantic segmentation via convolutional neural networks (CNNs). Segmentation networks are very large and do not currently run at interactive frame rates. To make this technique applicable to robotics we propose several architecture refinements that provide the best trade-off between segmentation quality and runtime. This is achieved by a new mapping between classes and filters at the expansion side of the network. The network is trained end-to-end and yields precise road/lane predictions at the original input resolution in roughly 50ms. Compared to the state of the art, the network achieves top accuracies on the KITTI dataset for road and lane segmentation while providing a 20× speed-up. We demonstrate that the improved efficiency is not due to the road segmentation task. Also on segmentation datasets with larger scene complexity, the accuracy does not suffer from the large speed-up.


title: A case of true hermaphroditism reveals an unusual mechanism of twinning

abstract: Traditionally twins are classified as dizygous or fraternal and monozygous or identical (Hall Twinning, 362, 2003 and 735-743). We report a rare case of 46,XX/46,XY twins: Twin A presented with ambiguous genitalia and Twin B was a phenotypically normal male. These twins demonstrate a third, previously unreported mechanism for twinning. The twins underwent initial investigation with 17-hydroxyprogesterone and testosterone levels, pelvic ultrasound and diagnostic laparoscopy. Cytogenetic analysis was performed on peripheral blood cells and skin fibroblasts. Histological examination and Fluorescence in situ hybridization studies on touch imprints were performed on gonadal biopsies. DNA analysis using more than 6,000 DNA markers was performed on skin fibroblast samples from the twins and on peripheral blood samples from both parents. Twin A was determined to be a true hermaphrodite and Twin B an apparently normal male. Both twins had a 46,XX/46,XY chromosome complement in peripheral lymphocytes, skin fibroblasts, and gonadal biopsies. The proportion of XX to XY cells varied between the twins and the tissues evaluated. Most significantly the twins shared 100% of maternal alleles and approximately 50% of paternal alleles in DNA analysis of skin fibroblasts. The twins are chimeric and share a single genetic contribution from their mother but have two genetic contributions from their father thus supporting the existence of a third, previously unreported type of twinning.


title: Arithmetic coding for data compression

abstract: The state of the art in data compression is arithmetic coding, not the better-known Huffman method. Arithmetic coding gives greater compression, is faster for adaptive models, and clearly separates the model from the channel encoding.


title: Automatic web news extraction using tree edit distance

abstract: The Web poses itself as the largest data repository ever available in the history of humankind. Major efforts have been made in order to provide efficient access to relevant information within this huge repository of data. Although several techniques have been developed to the problem of Web data extraction, their use is still not spread, mostly because of the need for high human intervention and the low quality of the extraction results.In this paper, we present a domain-oriented approach to Web data extraction and discuss its application to automatically extracting news from Web sites. Our approach is based on a highly efficient tree structure analysis that produces very effective results. We have tested our approach with several important Brazilian on-line news sites and achieved very precise results, correctly extracting 87.71% of the news in a set of 4088 pages distributed among 35 different sites.


title: Routing in Accumulative Multi-Hop Networks

abstract: This paper investigates the problem of finding optimal paths in single-source single-destination accumulative multi-hop networks. We consider a single source that communicates to a single destination assisted by several relays through multiple hops. At each hop, only one node transmits, while all the other nodes receive the transmitted signal, and store it after processing/decoding and mixing it with the signals received in previous hops. That is, we consider that terminals make use of advanced energy accumulation transmission/reception techniques, such as maximal ratio combining reception of repetition codes, or information accumulation with rateless codes. Accumulative techniques increase communication reliability, reduce energy consumption, and decrease latency. We investigate the properties that a routing metric must satisfy in these accumulative networks to guarantee that optimal paths can be computed with Dijkstra’s algorithm. We model the problem of routing in accumulative multi-hop networks, as the problem of routing in a hypergraph. We show that optimality properties in a traditional multi-hop network (monotonicity and isotonicity) are no longer useful and derive a new set of sufficient conditions for optimality. We illustrate these results by studying the minimum energy routing problem in static accumulative multi-hop networks for different forwarding strategies at relays.


title: The multidetermined nature of idiom processing

abstract: Models of idiom comprehension differ in their predictions concerning compositionality: Some claim that idiomatic meaning is the result of compositional analysis initiated at the earliest stages of comprehension, whereas others claim that compositional analysis occurs only at late stages, subsequent to direct retrieval—especially for idioms that are highly familiar. We evaluated these alternatives in four experiments by using a variety of online and offline comprehension measures. In Experiment 1, we analyzed the normative characteristics of 219 idioms with respect to these predictions. Dimensions of interest included several measures of decomposability, familiarity, and word frequency of the idioms’ verbs and nouns. In Experiments 2 through 4, we determined how these dimensions relate to several online measures of idiom comprehension. High familiarity was associated with good comprehension across all experiments; however, facilitative effects of decomposability were found only for tasks that required an overt semantic judgment. Word frequency, but not semantic decomposability of the idiom-initial verb, was associated with comprehension for some measures. These data support a model of idiom comprehension, according to which figurative meaning arises from the time-dependent availability of multiple linguistic constraints, and in which decomposability plays a limited role in the earliest stages of idiom comprehension. Normative data for 210 of the idiomatic phrases may be downloaded from the Psychonomic Society Web archive at www.psychonomic.org/archive/.


title: Ivy Bridge Server: A Converged Design

abstract: The Intel microarchitecture code named Ivy Bridge (IVB) represents Intel's first processor (CPU) design that services product markets from high-end desktops to mission-critical computing. With one converged design, IVB enables a rich portfolio of products and meets power, performance, and cost targets through multiple die options and configurations. IVB is the first server CPU using Intel's 22-nm process technology. It achieves scalability in die size, core count, cache size, socket count, and memory size while improving power efficiency and decreasing idle power.


title: Adaptive Mixtures of Local Experts

abstract: We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.


title: A hybrid approach for content extraction with text density and visual importance of DOM nodes

abstract: Additional contents in web pages, such as navigation panels, advertisements, copyrights and disclaimer notices, are typically not related to the main subject and may hamper the performance of Web data mining. They are traditionally taken as noises and need to be removed properly. To achieve this, two intuitive and crucial kinds of information--the textual information and the visual information of web pages--is considered in this paper. Accordingly, Text Density and Visual Importance are defined for the Document Object Model (DOM) nodes of a web page. Furthermore, a content extraction method with these measured values is proposed. It is a fast, accurate and general method for extracting content from diverse web pages. And with the employment of DOM nodes, the original structure of the web page can be preserved. Evaluated with the CleanEval benchmark and with randomly selected pages from well-known Web sites, where various web domains and styles are tested, the effect of the method is demonstrated. The average F1-scores with our method were 8.7 % higher than the best scores among several alternative methods.


title: Efficient convolution kernels for dependency and constituent syntactic trees

abstract: In this paper, we provide a study on the use of tree kernels to encode syntactic parsing information in natural language learning. In particular, we propose a new convolution kernel, namely the Partial Tree (PT) kernel, to fully exploit dependency trees. We also propose an efficient algorithm for its computation which is futhermore sped-up by applying the selection of tree nodes with non-null kernel. The experiments with Support Vector Machines on the task of semantic role labeling and question classification show that (a) the kernel running time is linear on the average case and (b) the PT kernel improves on the other tree kernels when applied to the appropriate parsing paradigm.


title: Advanced Medical Displays: A Literature Review of Augmented Reality

abstract: The impressive development of medical imaging technology during the last decades provided physicians with an increasing amount of patient specific anatomical and functional data. In addition, the increasing use of non-ionizing real-time imaging, in particular ultrasound and optical imaging, during surgical procedures created the need for design and development of new visualization and display technology allowing physicians to take full advantage of rich sources of heterogeneous preoperative and intraoperative data. During 90's, medical augmented reality was proposed as a paradigm bringing new visualization and interaction solutions into perspective. This paper not only reviews the related literature but also establishes the relationship between subsets of this body of work in medical augmented reality. It finally discusses the remaining challenges for this young and active multidisciplinary research community.


title: Trends in computer science research

abstract: Keywords in the ACM Digital Library and IEEE Xplore digital library and in NSF grants anticipate future CS research.


title: Teaching Effort and the Future of Cognitive-Behavioral Interventions:

abstract: Cognitive-behavioral interventions (CBIs) are a conceptually related set of educational or therapeutic treatments. When applied to students with emotional and behavioral disorders (EBD), CBIs aim to improve students' social competence (Gresham, 1986) by teaching them to regulate (e.g., see Kanfer & Karoly, 1972; Karoly, 1993), control (e.g., see Polsgrove & Smith, 2004), and manage (e.g., see Reiber & McLaughlin, 2004) their own behavior. Varying treatments that employ CBIs have in common a conviction that students can and do learn to direct their own behavior and, in so doing, ultimately can direct at least a part of their own social development. Although different authors draw on different traditions, most share common though often implicit assumptions about the mutually modifying relationship between unobservable cognition and observable behavior in development and learning (Alexander, Graham, & Harris, 1998; Gerber, 1983). Although they may not acknowledge it, all researchers or therapists who employ CBIs require a foundational assumption that manipulation of overt speech and language can modify unobservable thoughts, which, in turn, can mediate behavior and learning in natural environments. Indeed, serious and chronic behavior problems may represent fundamental impairments in ability to regulate one's behavior. Applications of CBIs would seem, therefore, to provide a powerful approach to a range of behavior problems that otherwise have proved resistant-or only weakly responsive-to more traditional behavior management techniques.CBI BackgroundAlthough there are multiple theoretical origins to the class of procedures we may identify as cogn/i/Ve-behavioral interventions (CBI), they all comprise some application of more basic behavioral principles. When we observe young children, it is readily apparent that speech acts-interpreted by many to be an observable manifestation of thoughts-play some role in the development of certain kinds of problem solving. Meichenbaum (1977) reasoned that the association between thoughts and speech acts confers a mediating power on the speech acts to both guide behavior and, through competent guidance, to modify the underlying thoughts themselves.Despite its reliance on unobservable cognitions as mediators of some kinds of learning, this formulation by Meichenbaum (1977), Mahoney (1974), and others promised a means by which an already well-established technology of behavior intervention could be applied to teaching complex social as well as academic problemsolving. By teaching students to manipulate the same procedures that interventionists had learned to use-for example, objective specification of critical behaviors, direct observation and recording, recognition or modification of antecedent stimuli, verbal prompts or guidance, and management of consequences-CBI would seem to accomplish all the same beneficial results and more. CBI not only gave access to important but invisible cognitive mediators, but it also increased the portability of interventions across time and contexts.For example, in small sample studies, behavior interventions that use selfmanagement techniques have been shown to be more effective than behavior programs that use teacher management alone (e.g., Fantuzzo, Polite, Cook, & Quinn, 1988). Self-management strategies also can be shown to be effective for students with EBD (Smith & Sugai, 2000) under similar controlled circumstances. In another study, students identified as learning disabled were observed by both a research assistant and the teacher after self-management strategies were taught by the research assistant (Dalton, Martel Ia, & Marchland-Martella, 1999). Smith and Sugai (2000) used a similar research design with students identified with EBD and, as is the case in most demonstrations such as this, the students were trained in self-management strategies by the researcher. In fact, the majority of studies that assess CBI techniques also do not use teachers as interventionists (e. …


title: Digital Forensic Model Based On Malaysian Investigation Process

abstract: Summary With the proliferation of the digital crime around the world, numerous digital forensic investigation models already being develop .In fact many of the digital forensic investigation model focus on technical implementation of the investigation process as most of it develop by traditional forensic expert and technologist. As an outcome of this problem most of the digital forensic practitioners focus on technical aspect and forget the core concept of digital forensic investigation model .In this paper we are introducing a new digital forensic model which will be capture a full scope of an investigation process based on Malaysia Cyber Law .The proposed model is also compared with the existing model which currently available and being apply in the investigation process.


title: Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

abstract: Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.


title: An Approach to Computing Ethics

abstract: It might seem impossible to "compute" ideas that humans feel most passionately about and have such difficulty codifying: their ethical beliefs. We've been attempting to make ethics computable for three reasons. First, to avert possible harmful behavior from increasingly autonomous machines, we want to determine whether one can add an ethical dimension to them. Second, we want to advance the study of ethical theory by making it more precise. Finally, we want to solve a particular problem in ethical theory, to develop a decision procedure for an ethical theory that involves multiple, potentially competing, and duties. We've adopted the action-based approach to ethical theory, where the theory tells us how we should act in ethical dilemmas. This approach lends itself to machine implementation by giving the agent either a single principle or several principles to guide its actions, unlike other approaches that don't clearly specify the correct action in an ethical dilemma. The approach to computing ethics that we describe in this article is illustrated by MedEthEx, a system that uses machine learning to resolve a biomedical ethical dilemma. This, we believe, lends support for our approach to computing ethics


title: Framework for Agile Methods Classification

abstract: Agile methods are the response to turbulent software development environments and requirements definitions differs in these methods from what is done in others. The purpose of this paper is to classify these former methods to measure their impact on requirement engineering processes. The approach used in this research has several purposes, the first one is to build a framework to classify and compare the methods. The second is to propose a component based approach to bring agility