title: Determination of the spread parameter in the Gaussian kernel for classification and regression

abstract: Based on statistical learning theory, Support Vector Machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. In the RBF case, the Gaussian kernel is commonly used, while the spread parameter σ in the Gaussian kernel is essential to generalization performance of SVMs. In this paper, determination of σ is studied based on discussions of the influence of σ on generalization performance. For classification problems, the optimal σ can be computed on the basis of Fisher discrimination. And for regression problems, based on scale space theory, we demonstrate the existence of a certain range of σ, within which the generalization performance is stable. An appropriate σ within the range can be achieved via dynamic evaluation. In addition, the lower bound of iterating step size of σ is given. Simulation results show the effectiveness of the presented method.


title: Targeting Neural Circuits

abstract: Optogenetic methodology enables direct targeting of specific neural circuit elements for inhibition or excitation while spanning timescales from the acute (milliseconds) to the chronic (many days or more). Although the impact of this temporal versatility and cellular specificity has been greater for basic science than clinical research, it is natural to ask whether the dynamic patterns of neural circuit activity discovered to be causal in adaptive or maladaptive behaviors could become targets for treatment of neuropsychiatric diseases. Here, we consider the landscape of ideas related to therapeutic targeting of circuit dynamics. Specifically, we highlight optical, ultrasonic, and magnetic concepts for the targeted control of neural activity, preclinical/clinical discovery opportunities, and recently reported optogenetically guided clinical outcomes.


The ns Manual (formerly ns Notes and Documentation) 1 The VINT Project


title: JavaScript at ten years

abstract: This talk presents the tumultuous history of JavaScript, from its first appearance in Netscape 2 beta releases in the fall of 1995 through the present, with emphasis on the unvarnished, real-world side of designing, implementing, shipping, and standardizing a functional programming language used by millions of people. JavaScript was conceived of as an "object-based scripting language", but its inspiration came originally from Scheme, with an admixture of Self. Designing a language to fit the constraints of the target audience of HTML authors, the embedding browser application, and the market conditions of that time was challenging. We discuss what worked and what did not, and how the language evolved due to competition and de jure standardization. The talk ranges widely over the history of the web, including the recent renaissance of JavaScript and its development in the ECMA standards group. We close by presenting new work likely to appear in the next version of the language.


title: Bilateral areolar sebaceous hyperplasia in a post-menopausal woman

abstract: In the breast, sebaceous glands can be seen at the tip of the nipple or as 10 to 15 small rounded elevations in the areola. These structures can undergo atrophy with age, being of little importance in medical practice since pathological lesions develop rarely at this site. However, sebaceous hyperplasia at this level may lead to confusion with breast Paget's disease.A 67-year-old woman sought medical attention for bilateral multiple papules coalescing into plaques on areolas, which were noticed two [...]


title: Cognitive Control Signals for Neural Prosthetics

abstract: Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices. In this study, higher level signals related to the goals of movements were decoded from three monkeys and used to position cursors on a computer screen without the animals emitting any behavior. Their performance in this task improved over a period of weeks. Expected value signals related to fluid preference, the expected magnitude, or probability of reward were decoded simultaneously with the intended goal. For neural prosthetic applications, the goal signals can be used to operate computers, robots, and vehicles, whereas the expected value signals can be used to continuously monitor a paralyzed patient's preferences and motivation.


title: Censorship in the Wild: Analyzing Internet Filtering in Syria

abstract: Internet censorship is enforced by numerous governments worldwide, however, due to the lack of publicly available information, as well as the inherent risks of performing active measurements, it is often hard for the research community to investigate censorship practices in the wild. Thus, the leak of 600GB worth of logs from 7 Blue Coat SG-9000 proxies, deployed in Syria to filter Internet traffic at a country scale, represents a unique opportunity to provide a detailed snapshot of a real-world censorship ecosystem. This paper presents the methodology and the results of a measurement analysis of the leaked Blue Coat logs, revealing a relatively stealthy, yet quite targeted, censorship. We find that traffic is filtered in several ways: using IP addresses and domain names to block subnets or websites, and keywords or categories to target specific content. We show that keyword-based censorship produces some collateral damage as many requests are blocked even if they do not relate to sensitive content. We also discover that Instant Messaging is heavily censored, while filtering of social media is limited to specific pages. Finally, we show that Syrian users try to evade censorship by using web/socks proxies, Tor, VPNs, and BitTorrent. To the best of our knowledge, our work provides the first analytical look into Internet filtering in Syria.


title: DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling

abstract: We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.


title: Teaching Phishing-Security: Which Way is Best?

abstract: Ever more processes of our daily lives are shifting into the digital realm. Consequently, users face a variety of IT-security threats with possibly severe ramifications. It has been shown that technical measures alone are insufficient to counter all threats. For instance, it takes technical measures on average 32 h before identifying and blocking phishing websites. Therefore, teaching users how to identify malicious websites is of utmost importance, if they are to be protected at all times. A number of ways to deliver the necessary knowledge to users exist. Among the most broadly used are instructor-based, computer-based and text-based training. We compare all three formats in the security context, or to be more precise in the context of anti-phishing training.


title: Inspiratory muscle dysfunction and unexplained dyspnea in systemic lupus erythematosus.

abstract: The role of inspiratory muscle dysfunction in lung volume restriction and unexplained dyspnea was studied in 16 consecutive patients with systemic lupus erythematosus. Maximal mouth inspiratory pressure (PIM) and maximal transdiaphragmatic pressure (Pdi max) were measured. Pdi and its components were determined during quiet breathing. No significant association was found between the activity of the disease, several serologic markers, and the inspiratory muscle dysfunction. No specific anti-skeletal muscle antibody was found in these patients. Significant correlations were found between the degree of dyspnea and PIM (r = -0.69, P less than 0.01) and Pdi max (r = -0.75, P less than 0.001); however, dyspnea did not correlate with specific lung compliance. Vital capacity correlated significantly with the degree of dyspnea (r = -0.813, P less than 0.001) and with Pdi max (r = 0.544, P less than 0.05). No correlation was found between vital capacity and specific lung compliance. We conclude that inspiratory muscle dysfunction can be an important mechanism in the pathogenesis of the lung volume restriction and dyspnea in patients with systemic lupus erythematosus.


title: Threshold-Voltage Modeling of Body-Tied FinFETs (Bulk FinFETs)

abstract: The threshold voltages Vth of the body-tied double/triple-gate MOSFETs (bulk FinFETs) implemented on bulk silicon (Si) wafers were modeled systematically and compared with data obtained from 3-D device simulation. The threshold-voltage behaviors of the bulk FinFETs were modeled, for the first time, based on charge sharing. For the simplified Vth model, we considered not only short-channel effect (SCE) and narrow-width effect but also 3-D charge sharing at the corner. Only one fitting parameter is introduced to reflect the SCE in the fin body. The model predicted the Vth behavior with fin body thickness, body doping concentration, gate height, gate length, and corner shape of the fin body well. Our compact model makes an accurate prediction of Vth and shows good agreement with 3-D simulation data


title: Botz-4-sale: surviving organized DDoS attacks that mimic flash crowds

abstract: Recent denial of service attacks are mounted by professionals using Botnets of tens of thousands of compromised machines. To circumvent detection, attackers are increasingly moving away from bandwidth floods to attacks that mimic the Web browsing behavior of a large number of clients, and target expensive higher-layer resources such as CPU, database and disk bandwidth. The resulting attacks are hard to defend against using standard techniques, as the malicious requests differ from the legitimate ones in intent but not in content. We present the design and implementation of Kill-Bots, a kernel extension to protect Web servers against DDoS attacks that masquerade as flash crowds. Kill-Bots provides authentication using graphical tests but is different from other systems that use graphical tests. First, Kill-Bots uses an intermediate stage to identify the IP addresses that ignore the test, and persistently bombard the server with requests despite repeated failures at solving the tests. These machines are bots because their intent is to congest the server. Once these machines are identified, Kill-Bots blocks their requests, turns the graphical tests off, and allows access to legitimate users who are unable or unwilling to solve graphical tests. Second, Kill-Bots sends a test and checks the client's answer without allowing unauthenticated clients access to sockets, TCBs, and worker processes. Thus, it protects the authentication mechanism from being DDoSed. Third, Kill-Bots combines authentication with admission control. As a result, it improves performance, regardless of whether the server overload is caused by DDoS or a true Flash Crowd.


title: Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles

abstract: This paper presents a novel curvilinear-coordinate-based approach to improve object and situation assessment performance for highly automated vehicles under various curved road conditions. The approach integrates object information from radars and lane information from a camera with three steps: track-to-track fusion, curvilinear coordinate conversion, and lane assessment. The track-to-track fusion is achieved through a nearest neighbor filter that updates the target state estimation and covariance with the nearest neighbor measurement, and a cross-covariance method that merges the duplicate tracks using error covariance. In order to determine in which lane the fused tracks are located accurately and reliably, the curvilinear coordinate conversion process is performed. The curvilinear coordinates are generated in the form of a cubic Hermite spline lane model from the lane information of the camera. Based on the converted track information and the lane model in the curvilinear coordinates, the probability distribution of the threat levels in each lane is determined though a probabilistic lane association and threat assessment. The developed algorithm is verified and evaluated through experiments using a real-time embedded system. The results show that the proposed curvilinear-coordinate-based approach provides excellent performance of object and situation assessment, in respect of accuracy and computational efficiency, in real-time operation.


title: A morphological segmentation and curve-let features extraction on text region classification using SVM

abstract: Text Extraction from different background is always a challenging problem in these emerging field of research area of image processing. In our project we have implemented upon Noise Removal segmentation, Curve-let Transformation features and Support Vector Machine classifier. In that we calculated on successful result purpose on still images and some real time images for different background location image on features extraction and classified techniques. Previously, Different methodologies have been used for texture feature extraction. The method which we proposed gives a better accuracy and efficiency towards extract texture feature analysis as with wavelet transform. Curve-let, Gradient Edge Operator on Support Vector Machine Classifier analysis and performance measure is completely new in this paper.


title: Detection of text on road signs from video

abstract: A fast and robust framework for incrementally detecting text on road signs from video is presented in this paper. This new framework makes two main contributions. 1) The framework applies a divide-and-conquer strategy to decompose the original task into two subtasks, that is, the localization of road signs and the detection of text on the signs. The algorithms for the two subtasks are naturally incorporated into a unified framework through a feature-based tracking algorithm. 2) The framework provides a novel way to detect text from video by integrating two-dimensional (2-D) image features in each video frame (e.g., color, edges, texture) with the three-dimensional (3-D) geometric structure information of objects extracted from video sequence (such as the vertical plane property of road signs). The feasibility of the proposed framework has been evaluated using 22 video sequences captured from a moving vehicle. This new framework gives an overall text detection rate of 88.9% and a false hit rate of 9.2%. It can easily be applied to other tasks of text detection from video and potentially be embedded in a driver assistance system.


Automatic Mapping of Remote Crowd Gaze to Stimuli in the Classroom


title: 140-GHz ${\rm TE}_{20}$-Mode Dielectric-Loaded SIW Slot Antenna Array in LTCC

abstract: A ${\rm TE}_{20}$ -mode substrate integrated waveguide (SIW) fed slot antenna array is proposed at 140 GHz for fabrication ease and cost reduction. The antenna array consists of a feeding network, radiators, and dielectric loadings. The proposed antenna array and an SIW-waveguide transition are integrated into the multilayered low-temperature co-fired ceramic (LTCC). The feeding network comprises a power divider and $E$ -plane couplers that support the ${\rm TE}_{20}$ mode. Each pair of symmetrical longitudinal radiating slots is fed by the ${\rm TE}_{20}$ mode in a doubled-width SIW. The pair of radiating slots is loaded by a doubled-width open-ended via-fence structure in which a higher order mode is also excited. Experimental results show that an 8 $\times$ 8 slot array with the transition achieves a maximum boresight gain of 21.3 dBi at 140.6 GHz and an impedance matching bandwidth of 129.2–146 GHz with a boresight gain above 18.3 dBi.


Type Abstraction for Relaxed Noninterference


title: Multi-layer text classification with voting for consumer reviews

abstract: As social media has become increasingly popular in the modern world, people are using these platforms to express their opinions about products, businesses, and services. The need for categorizing these consumer reviews has been prominent. One effective solution is sentiment analysis (SA), which has been an active research topic. The goal of SA is to automatically extracting and classifying user opinions. Pervious research works however have not shown satisfied results. In this paper, a multilayer architecture is proposed to increase the performance of multiclass classification. The framework includes data-preprocessing, feature extraction and selection, and classifier building. The framework is a two-layer classification, choosing from Naive Bayes, Support Vector Machine, Random Forest, and Logistic Regression as base models, and using a voting scheme to obtain the final predicted class. The proposed model is applied to more than 1.3 million restaurant reviews from the Yelp Challenge dataset. We have achieved a high accuracy of 86% for cross validation, and using real-world online review data as test data, we have achieved an accuracy of 80%. The results show that the proposed framework has greatly improved classification accuracy while comparing with those using single-layer architectures. We believe that the proposed method may be applied to, and would have significant contributions to other areas of opinion mining.


title: Spoofing Detection with Reinforcement Learning in Wireless Networks

abstract: In this paper, we investigate the PHY-layer authentication in wireless networks, which exploits PHY-layer channel information such as the received signal strength indicators to detect spoofing attacks. The interactions between a legitimate receiver node and a spoofer are formulated as a PHY- authentication game. More specifically, the receiver chooses the test threshold in the hypothesis test of the spoofing detection to maximize its expected utility based on Bayesian risk to detect the spoofer. On the other hand, the spoofing node decides its attack strength, i.e., the frequency to send a spoofing packet that claims to use another node's MAC address, based on its individual utility in the zero-sum game. As it is challenging for most radio nodes to obtain the exact channel models in advance in a dynamic radio environment, we propose a spoofing detection scheme based on reinforcement learning techniques, which achieves the optimal test threshold in the spoofing detection via Q-learning and implement it over universal software radio peripherals (USRP). Experimental results are presented to validate its efficiency in spoofing detection.


title: Study of close space sublimation (CSS) Grown SnS thin-films for solar cell applications

abstract: We report ou characterization of SnS thin films grown by high temperature close space sublimation (CSS) method. The CSS grown films are slightly Sn-rich and show high degree of crystalmity with orthorhombic (Herzeubergite) crystal structure. The as deposited SnS has direct transition at about 1.3 eV with a very high absorption (α ∼104 cm−1) in risible light range. Hall measurements in Van der Pau configuration suggest p-type conductivities with carrier concentrations in the order of 1017 – 1018 cm−3. Despite baring poor adhesion, C dS/SnS solar cells synthesized on FTO-coated glass substrates in superstrate configuration have demonstrated non-zero cell efficiencies. With application of SnCl4 treatment and Au back contact, we achieved a 0.1% cell efficiency with a VOC of 108 mV, a JSC of 2.2 mA/cm2 and a FF of 36.2%. The large band offsets, high carrier concentration, and poor adhesion are the limiting factors for the solar cell performance.


HOW TECHNOLOGY READINESS EXPLAINS ACCEPTANCE AND SATISFACTION OF DIGITAL SERVICES IN B2B HEALTHCARE SECTOR


title: BEOL scaling limits and next generation technology prospects

abstract: This paper presents the major limitations to the interconnect technology scaling at future technology generations and demonstrates both evolutionary and radical potential solutions to the BEOL scaling problem. To address the local interconnect challenges, a novel hybrid Al-Cu interconnect technology is introduced. Performances of carbon-based interconnects are evaluated as a more radical solution. The impact of interconnects and the optimal interconnect options are investigated for emerging next generation devices. Interconnects for new state variables, namely spintronic interconnects, are studied and their potential performances in an all-spin logic system are evaluated.


title: Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data

abstract: Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.


title: A New Bayesian Classifier for Skin Detection

abstract: Skin detection has different applications in computer vision such as face detection, human tracking and adult content filtering. One of the major approaches in pixel based skin detection is using Bayesian classifiers. Bayesian classifiers performance is highly related to their training set. In this paper, we introduce a new Bayesian classifier skin detection method. The main contribution of this paper is creating a huge database to create color probability tables and new method for creating skin pixels data set. Our database consists of about 80000 images containing more than 5 billions pixels. Our tests shows that the performance of Bayesian classifier trained on our data set is better than Compaq data set which is one of the currently greatest data sets.


title: An Approach to Glove-Based Gesture Recognition

abstract: Nowadays, computer interaction is mostly done using dedicated devices. But gestures are an easy mean of expression between humans that could be used to communicate with computers in a more natural manner. Most of the current research on hand gesture recognition for Human-Computer Interaction rely on either the Neural Networks or Hidden Markov Models (HMMs). In this paper, we compare different approaches for gesture recognition and highlight the major advantages of each. We show that gestures recognition based on the Bio-mechanical characteristic of the hand provides an intuitive approach which provides more accuracy and less complexity.


title: Airline Crew Scheduling: State-of-the-Art

abstract: The airline industry is faced with some of the largest scheduling problems of any industry. The crew scheduling problem involves the optimal allocation of crews to flights. Over the last two decades the magnitude and complexity of crew scheduling problems have grown enormously and airlines are relying more on automated mathematical procedures as a practical necessity. In this paper we survey different approaches studied and discuss the state-of-the-art in solution methodology for the airline crew scheduling problem. We conclude with a discussion about promising areas for further work to make it possible to get very good solutions for the crew scheduling problem.


title: Boomerang: resourceful lenses for string data

abstract: A lens is a bidirectional program. When read from left toright, it denotes an ordinary function that maps inputs to outputs. When read from right to left, it denotes an ''update translator'' that takes an input together with an updated output and produces a new input that reflects the update. Many variants of this idea have been explored in the literature, but none deal fully with ordered data. If, for example, an update changes the order of a list in theoutput, the items in the output list and the chunks of the input that generated them can be misaligned, leading to lost or corrupted data. We attack this problem in the context of bidirectional transformations over strings, the primordial ordered data type. We first propose a collection of bidirectional string lens combinators, based on familiar operations on regular transducers (union, concatenation, Kleene-star) and with a type system based on regular expressions. We then design anew semantic space of dictionary lenses, enriching the lenses of Foster et al. (2007) with support for two additional combinators for marking ''reorderable chunks'' andtheir keys. To demonstrate the effectiveness of these primitives, we describe the design and implementation of Boomerang, a full-blown bidirectional programming language with dictionary lenses at its core. We have used Boomerang to build transformers for complex real-world data format sincluding the SwissProt genomic database. We formalize the essential property of resourcefulness-the correct use of keys to associate chunks in the input and output-by defining a refined semantic space of quasi-oblivious lenses. Several previously studied properties of lenses turn out to have compact characterizations in this space.


title: Personalized Dialogue Generation with Diversified Traits

abstract: Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. ::: To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. ::: Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.


title: Quercetin improves the postthaw characteristics of cryopreserved sex-sorted and nonsorted stallion sperm.

abstract: Excessive reactive oxygen species generation during sex sorting and cryopreservation of stallion sperm leads to DNA fragmentation, lipid peroxidation, and motility loss. In this study we investigated whether antioxidant supplementation during sex sorting and cryopreservation could ameliorate the effects of reactive oxygen species on stallion sperm. In experiment 1, the postthaw characteristics of stallion sperm (N = 9) cryopreserved in the presence or absence of catalase (200 U/mL), cysteine (0.2 mg/mL), or quercetin (0.15 mM) was examined. Motility and acrosome integrity were assessed at 0, 1, and 3 hours after thawing. The sperm chromatin structure assay (SCSA; detectable DNA fragmentation index [DFI], mean DFI, and DFI) was used to assess DNA integrity immediately after thawing. Quercetin increased the total postthaw motility (25.3% vs. 20.9%; P < 0.05), but there was no beneficial effect of catalase or cysteine. Based on these results, the effect of quercetin during cryopreservation on the postthaw zona binding ability of sperm was assessed using a heterologous (bovine) zona binding assay. Quercetin increased the number of sperm bound per oocyte (13.6 vs. 9.2; P < 0.05) compared with the control. In experiment 2, the effect of quercetin (0.15 mM) in the media used during semen storage and transport, Hoechst 33342 staining and cryopreservation of stallion sperm (N = 9) was investigated. Motility, acrosome integrity, and viability were assessed at 0, 1, and 3 hours after thawing and SCSA was performed at 0 hours after thawing. Quercetin supplementation during sex sorting and cryopreservation improved DNA integrity (SCSA; detectable DFI of 54.9% vs. 74.6%, P < 0.05; mean DFI of 270.2 vs. 288.1, P < 0.05; and DFI of 26.3% vs. 28.5%, P < 0.05) compared with control sex-sorted sperm. There was no beneficial effect of quercetin on the motility, acrosome integrity, or viability of sex-sorted sperm. In conclusion, quercetin significantly improved the motility and zona binding ability of cryopreserved stallion sperm, and reduced DNA fragmentation in sex-sorted, cryopreserved stallion sperm.


title: Wheelchair navigation assisted by Human-Machine shared-control and a P300-based Brain Computer Interface

abstract: This paper presents a new shared-control approach for assistive mobile robots, using Brain Computer Interface (BCI) as the Human-Machine Interface (HMI). A P300-based paradigm that allows the selection of brain-actuated commands to steer a Robotic Wheelchair (RW), is proposed. At least one specific motor skill, such as the control of arms, legs, head or voice, is required to operate a conventional HMI. Due to this reason, they are not suited for people suffering from severe motor disorders. BCI may open a new communication channel to these users, since it does not require any muscular activity. The number of decoded symbols per minute (SPM) in a BCI is still very low, which means that users can only provide sparse, and discrete commands. The RW must rely on the navigation system to validate user commands effectively. A two-layer shared-control approach is proposed. The first, a virtual-constraint layer, is responsible for enabling/disabling the user commands, based on certain context restrictions. The second layer is an user-intent matching responsible for determining the suitable steering command, that better fits the user command, taking the user competence on steering the wheelchair into account. Experimental results using Robchair, the RW platform developed at ISR-UC [1], [2] are presented, showing the effectiveness of the proposed methodologies.


title: Social media cultivating perceptions of privacy: A 5-year analysis of privacy attitudes and self-disclosure behaviors among Facebook users:

abstract: In light of the omnipresence of personal information exchange in the virtual world, this study examines the effects of Facebook use on privacy perceptions and self-disclosure behaviors across a 5-year period from 2010 to 2015. Findings at the global level support the socializing role of Facebook in cultivating more relaxed privacy attitudes, subsequently increasing self-disclosure in both offline and online contexts. However, longitudinal trends indicate that while risk perceptions increased for heavy users, they remained stable for light users. Furthermore, the negative relationship between privacy concerns and self-disclosure weakened across time. Implications for the application of cultivation theory to a contemporary social media context and the year-to-year changes in the impact of Facebook use on privacy attitudes and self-disclosure are discussed.


title: Building Emotional Intelligence: A Strategy for Emerging Nurse Leaders to Reduce Workplace Bullying

abstract: Bullying is one of the most concerning forms of aggression in health care organizations. Conceptualized as an emotion-based response, bullying is often triggered by today's workplace challenges. Unfortunately, workplace bullying is an escalating problem in nursing. Bullying contributes to unhealthy and toxic environments, which in turn contribute to ineffective patient care, increased stress, and decreased job satisfaction among health care providers. These equate to a poor workforce environment, which in turn increases hospital costs when nurses choose to leave. Nurse managers are in positions of power to recognize and address negative workplace behaviors, such as bullying. However, emerging leaders in particular may not be equipped with the tools to deal with bullying and consequently may choose to overlook it. Substantive evidence from other disciplines supports the contention that individuals with greater emotional intelligence are better equipped to recognize early signs of negative behavior, such as bullying. Therefore, fostering emotional intelligence in emerging nurse leaders may lead to less bullying and more positive workplace environments for nurses in the future.


title: A low-voltage low-offset dual strong-arm latch comparator

abstract: A dual strong-arm (DSA) comparator is designed targeting at low-voltage operation in deeply-scaled technologies. The addition of a second regenerative latch helps reduce both offset sensitivity and offset while maintaining comparable or better performance as a conventional double-tail latch across a wide range of voltages. A large comparator offset measurement array is fabricated in a 28nm FDSOI process. The DSA offset is measured to be 8.5mV across 6 dies, approximately 30% lower than the conventional topology at iso-area and iso-capacitance conditions. It is also shown to scale well with supply and commonmode voltage, achieving up to 65% lower offset across the voltage range.


title: Towards an IoT-based water quality monitoring system with brokerless pub/sub architecture

abstract: This paper investigates a real-time water quality monitoring system by using a proposed brokerless publisher-subscriber (pub/sub) architecture framework. On the system, sensors sense the water measurement metrics, including temperature, pH, and dissolved oxygen level. All collected data are stored in a database and computed stochastically for further analysis on water quality. A complementary experiment compares the proposed pub/sub architecture and MQTT, a lightweight protocol on which IoT mostly uses, to show better performance of the proposed architecture in case of network latency and throughput for diverse message payload size, thus suggesting the future IoT implementation of the system. To complete the experiment, the relationship among temperature, pH, and dissolved oxygen is analyzed, and the experiment summarizes that water temperature is inversely proportional to pH and dissolved oxygen value.


title: High Accuracy Monocular SFM and Scale Correction for Autonomous Driving

abstract: We present a real-time monocular visual odometry system that achieves high accuracy in real-world autonomous driving applications. First, we demonstrate robust monocular SFM that exploits multithreading to handle driving scenes with large motions and rapidly changing imagery. To correct for scale drift, we use known height of the camera from the ground plane. Our second contribution is a novel data-driven mechanism for cue combination that allows highly accurate ground plane estimation by adapting observation covariances of multiple cues, such as sparse feature matching and dense inter-frame stereo, based on their relative confidences inferred from visual data on a per-frame basis. Finally, we demonstrate extensive benchmark performance and comparisons on the challenging KITTI dataset, achieving accuracy comparable to stereo and exceeding prior monocular systems. Our SFM system is optimized to output pose within 50 ms in the worst case, while average case operation is over 30 fps. Our framework also significantly boosts the accuracy of applications like object localization that rely on the ground plane.


title: Automatically Repairing Network Control Planes Using an Abstract Representation

abstract: The forwarding behavior of computer networks is governed by the configuration of distributed routing protocols and access filters---collectively known as the network control plane. Unfortunately, control plane configurations are often buggy, causing networks to violate important policies: e.g., specific traffic classes (defined in terms of source and destination endpoints) should always be able to reach their destination, or always traverse a waypoint. Manually repairing these configurations is daunting because of their inter-twined nature across routers, traffic classes, and policies. Inspired by recent work in automatic program repair, we introduce CPR, a system that automatically computes correct, minimal repairs for network control planes. CPR casts configuration repair as a MaxSMT problem whose constraints are based on a digraph-based representation of a control plane's semantics. Crucially, this representation must capture the dependencies between traffic classes arising from the cross-traffic-class nature of control plane constructs. The MaxSMT formulation must account for these dependencies whilst also accounting for all policies and preferring repairs that minimize the size (e.g., number of lines) of the configuration changes. Using configurations from 96 data center networks, we show that CPR produces repairs in less than a minute for 98% of the networks, and these repairs requiring changing the same or fewer lines of configuration than hand-written repairs in 79% of cases.


title: Optimizing Statistical Machine Translation for Text Simplification

abstract: Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.


title: A new circuit simplification method for error tolerant applications

abstract: Starting from a functional description or a gate level circuit, the goal of the multi-level logic optimization is to obtain a version of the circuit that implements the original function at a lower cost. For error tolerant applications — images, video, audio, graphics, and games — it is known that errors at the outputs are tolerable provided that their severities are within application-specified thresholds. In this paper, we perform application level analysis to show that significant errors at the circuit level are tolerable. Then we develop a multi-level logic synthesis algorithm for error tolerant applications that minimizes the cost of the circuit by exploiting the budget for approximations provided by error tolerance. We use circuit area as the cost metric and use a test generation algorithm to select faults that introduce errors of low severities but provide significant area reductions. Selected faults are injected to simplify the circuit for the experiments. Results show that our approach provides significant reductions in circuit area even for modest error tolerance budgets.


Multi-class Generative Adversarial Networks with the L2 Loss Function.


title: A Tutorial on Multi-Label Classification Techniques

abstract: Most classification problems associate a single class to each example or instance. However, there are many classification tasks where each instance can be associated with one or more classes. This group of problems represents an area known as multi-label classification. One typical example of multi-label classification problems is the classification of documents, where each document can be assigned to more than one class. This tutorial presents the most frequently used techniques to deal with these problems in a pedagogical manner, with examples illustrating the main techniques and proposing a taxonomy of multi-label techniques that highlights the similarities and differences between these techniques.


title: HIV-AIDS in minorities.

abstract: HIV-AIDS has disproportionately affected minority populations in the United States. Significant disparities in case rates and mortality have been noted. This article reviews the magnitude of the problem and the many factors involved in the development and perpetuation of these disparities. Possible measures to help correct the problem are also reviewed.


A Review of Knowledge Management Models


title: Integration of Legacy Appliances into Home Energy Management Systems

abstract: The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.


title: SLTP: A Fast Descriptor for People Detection in Depth Images

abstract: This paper presents a new feature descriptor for real-time people detection in depth images. The shape cue in depth images can reduce negative impacts of variations of clothing, lighting conditions and the complexity of backgrounds. The proposed Simplified Local Ternary Patterns (SLTP) can take advantage of depth images to describe human body shape with low computational cost. To evaluate the SLTP feature, we establish a dataset with 7260 positive samples. A series of experiments are carried out on this dataset, and the results show that the SLTP feature can achieve a high detection rate with a low false positive rate. Besides, SLTP is easy to implement, and performs fast (over 80 frames per second) on a standard desktop computer.


title: Brachytherapy in accelerated partial breast irradiation (APBI) – review of treatment methods

abstract: Breast conserving surgery (BCS) with following radiotherapy (EBRT) of the conserved breast became widely accepted in the last decades as the treatment of early invasive breast cancer. In an early stage of breast cancer, research has shown that the area requiring radiation treatment to prevent cancer from local recurrence is the breast tissue that surrounds the area where the initial cancer was removed. Accelerated partial breast irradiation (APBI) is an approach that treats only the lumpectomy bed with 1-2 cm margin, rather than the whole breast and as a result allows accelerated delivery of the radiation dose in four to five days. Published results of APBI are very promising. It is evident that APBI will play a role in the management of a selected group of early breast cancer. We discuss current status, indications, technical aspects and recently published results of APBI using different brachytherapy techniques.


title: Global Ridge Orientation Modeling for Partial Fingerprint Identification

abstract: Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. First, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.


title: The capacity optimality of beam steering in large millimeter wave MIMO systems

abstract: Millimeter wave (mmWave) systems must overcome the heavy attenuation at high frequency to support high-throughput wireless communication. The small wavelength in mmWave systems enables beamforming using large antenna arrays to combat path loss with large array gain. Beamforming in traditional microwave systems is often done at baseband for maximum flexibility. Such baseband processing requires a dedicated transceiver chain per antenna element. The high cost of radio frequency (RF) chains in mmWave systems, however, makes supporting each antenna with a dedicated RF chain expensive. This mismatch between the number of antennas and transceiver chains makes baseband processing infeasible; thus mmWave systems typically rely on a traditional approach known as beam steering which can be done at RF using inexpensive phase shifters. Unlike baseband precoding, however, traditional beam steering is not explicitly designed to achieve the capacity of the mmWave channel. In this paper, we consider both beamforming and multi-stream precoding in single user systems with large mmWave antenna arrays at both transmitter and receiver. Using a realistic channel model, we show that the unconstrained capacity-achieving precoding solutions converge to simple beam steering solutions. Therefore, in large mmWave systems, no rate loss is incurred by adopting the traditional lower-complexity solution.


title: Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

abstract: Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.


title: Principles of Light Field Imaging: Briefly revisiting 25 years of research

abstract: Light field imaging offers powerful new capabilities through sophisticated digital processing techniques that are tightly merged with unconventional optical designs. This combination of imaging technology and computation necessitates a fundamentally different view of the optical properties of imaging systems and poses new challenges for the traditional signal and image processing domains. In this article, we aim to provide a comprehensive review of the considerations involved and the difficulties encountered in working with light field data.


title: pH-ZONE REFINING COUNTERCURRENT CHROMATOGRAPHY

abstract: A recently developed preparative technique, pH-zone-refining countercurrent chromatography (CCC), separates organic acids and bases according to their pKa and hydrophobicity. The hydrodynamic mechanism of pH-zone-refining CCC is illustrated in two elution modes along with a simple mathematical analysis. Separations include acidic and basic derivatives of amino acids and peptides, hydroxyxanthene dyes, alkaloids, indole auxins, and structural and geometrical isomers. In addition, recently developed affinity ligand separations of enantiomers, polar catecholamines, and free peptides are also described. Technical guidance is provided for interested users so that they can conduct a systematic search for the optimum solvent system and experimental conditions. Advantages as well as limitations of the present technique are discussed.


title: Bias in clinical epidemiological study designs.

abstract: Systematic error, or bias, is error that occurs in each measurement made and which has a direction, i.e., the measured value is always either greater or smaller than the true value. The presence of systematic error directly affects the internal validity of the study, and indirectly affects the external validity of the results obtained. In general, such error can be classified as selection bias, classification bias or confounding bias. It is essential to deal with possible bias in the research design phase, since only confounding bias can be controlled in the phase corresponding to analysis of the results.


title: Efficiency improvement of wireless power transfer via magnetic resonance using transmission coil array

abstract: The transfer efficiency between a transmission coil and a reception coil for wireless power transfer via magnetic resonance is a function of the orientation between the coils. When two coils share a single axis, the transfer efficiency is maximal, but otherwise the efficiency becomes lower. In this paper, a wireless power transfer system via magnetic resonance using a transmission coil array is proposed to improve the transfer efficiency. The transmission coils of the array are excited with appropriate phase weights by transmission circuit, according to the orientation among the transmission coils and the receiving coil. The effect of the proposed system is confirmed by simulation by a method of moment and measurement using a vector network analyzer.


title: Integration of three visualization methods based on co-word analysis

abstract: Visualization of subject structure based on co-word analysis is used to explore the concept network and developmental tendency in certain field. There are many visualization methods for co-word analysis. However, integration of results by different methods is rarely reported. This article addresses the knowledge gap in this field of study. We compare three visualization methods: Cluster tree, strategy diagram and social network maps, and integrate different results together to one result through co-word analysis of medical informatics. The three visualization methods have their own character: cluster trees show the subject structure, strategic diagrams reveal the importance of topic themes in the structure, and social network maps interpret the internal relationship among themes. Integration of different visualization results to one more readable map complements each other. And it is helpful for researchers to get the concept network and developmental tendency in a certain field.


title: Searchbased procedural content generation

abstract: Recently, a small number of papers have appeared in which the authors implement stochastic search algorithms, such as evolutionary computation, to generate game content, such as levels, rules and weapons. We propose a taxonomy of such approaches, centring on what sort of content is generated, how the content is represented, and how the quality of the content is evaluated. The relation between search-based and other types of procedural content generation is described, as are some of the main research challenges in this new field. The paper ends with some successful examples of this approach.


title: Requirements engineering for embedded systems: an investigation of industry needs

abstract: [Context and Motivation] Requirements engineering (RE) research is expected to provide methods that address the specific challenges of industrial systems engineering. [Question/problem] For this purpose, researchers need a detailed understanding of the needs and expectations that the industry has regarding RE methods. [Principal ideas/results] To identify the key industry needs, we have conducted an in-depth study with representatives from large, internationally operating companies in the domain of embedded systems in Germany. [Contribution] This paper reports on the identified industry needs related to the topics natural language vs. requirements models, support for high system complexity, quality assurance of requirements, and intertwining of RE and design.


title: LocoFS: a loosely-coupled metadata service for distributed file systems

abstract: Key-Value stores provide scalable metadata service for distributed file systems. However, the metadata's organization itself, which is organized using a directory tree structure, does not fit the key-value access pattern, thereby limiting the performance. To address this issue, we propose a distributed file system with a loosely-coupled metadata service, LocoFS, to bridge the performance gap between file system metadata and key-value stores. LocoFS is designed to decouple the dependencies between different kinds of metadata with two techniques. First, LocoFS decouples the directory content and structure, which organizes file and directory index nodes in a flat space while reversely indexing the directory entries. Second, it decouples the file metadata to further improve the key-value access performance. Evaluations show that LocoFS with eight nodes boosts the metadata throughput by 5 times, which approaches 93% throughput of a single-node key-value store, compared to 18% in the state-of-the-art IndexFS.


title: An elementary proof of a theorem of

abstract: A result of Johnson and Lindenstrauss [13] shows that a set of n points in high dimensional Euclidean space can be mapped into an O(log n/e2)-dimensional Euclidean space such that the distance between any two points changes by only a factor of (1 ± e). In this note, we prove this theorem using elementary probabilistic techniques.


title: Perceptual-Cognitive Expertise in Sport: A Meta-Analysis

abstract: Research focusing on perceptual-cognitive skill in sport is abundant. However, the existing qualitative syntheses of this research lack the quantitative detail necessary to determine the magnitude of differences between groups of varying levels of skills, thereby limiting the theoretical and practical contribution of this body of literature. We present a meta-analytic review focusing on perceptual-cognitive skill in sport (N = 42 studies, 388 effect sizes) with the primary aim of quantifying expertise differences. Effects were calculated for a variety of dependent measures (i.e., response accuracy, response time, number of visual fixations, visual fixation duration, and quiet eye period) using point-biserial correlation. Results indicated that experts are better than nonexperts in picking up perceptual cues, as revealed by measures of response accuracy and response time. Systematic differences in visual search behaviors were also observed, with experts using fewer fixations of longer duration, including prolonged quiet eye periods, compared with nonexperts. Several factors (e.g., sport type, research paradigm employed, and stimulus presentation modality) significantly moderated the relationship between level of expertise and perceptual-cognitive skill. Practical and theoretical implications are presented and suggestions for empirical work are provided.


title: Agonal Sequences in Eight Filmed Hangings: Analysis of Respiratory and Movement Responses to Asphyxia by Hanging*

abstract: It has been proposed that filmed hangings may hold the key to a better understanding of human asphyxia, and The Working Group on Human Asphyxia was formed to systematically review and compare these video recordings. This study analyzed eight filmed hangings. Considering time 0 to represent the onset of the final hanging, rapid loss of consciousness was observed (at 8-18 sec), closely followed by convulsions (at 10-19 sec). A complex pattern of decerebrate rigidity and decorticate rigidity then followed. Between 1 min 38 sec and 2 min 15 sec, muscle tone seemed to be lost, the body becoming progressively flaccid. From then on, isolated body movements were observed from time to time, the last one occurring between 1 min 2 sec and 7 min 31 sec. As for the respiratory responses, all cases presented deep rhythmic abdominal respiratory movements (last one between 1 min 2 sec and 2 min 5 sec).


title: On the Design and Analysis of the Privacy-Preserving SVM Classifier

abstract: The support vector machine (SVM) is a widely used tool in classification problems. The SVM trains a classifier by solving an optimization problem to decide which instances of the training data set are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training data set, releasing the SVM classifier for public use or shipping the SVM classifier to clients will disclose the private content of support vectors. This violates the privacy-preserving requirements for some legal or commercial reasons. The problem is that the classifier learned by the SVM inherently violates the privacy. This privacy violation problem will restrict the applicability of the SVM. To the best of our knowledge, there has not been work extending the notion of privacy preservation to tackle this inherent privacy violation problem of the SVM classifier. In this paper, we exploit this privacy violation problem, and propose an approach to postprocess the SVM classifier to transform it to a privacy-preserving classifier which does not disclose the private content of support vectors. The postprocessed SVM classifier without exposing the private content of training data is called Privacy-Preserving SVM Classifier (abbreviated as PPSVC). The PPSVC is designed for the commonly used Gaussian kernel function. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the sensitive attribute values possessed by support vectors. By applying the PPSVC, the SVM classifier is able to be publicly released while preserving privacy. We prove that the PPSVC is robust against adversarial attacks. The experiments on real data sets show that the classification accuracy of the PPSVC is comparable to the original SVM classifier.


title: VNF-P: A model for efficient placement of virtualized network functions

abstract: Network Functions Virtualization (NFV) is an upcoming paradigm where network functionality is virtualized and split up into multiple building blocks that can be chained together to provide the required functionality. This approach increases network flexibility and scalability as these building blocks can be allocated and reallocated at runtime depending on demand. The success of this approach depends on the existence and performance of algorithms that determine where, and how these building blocks are instantiated. In this paper, we present and evaluate a formal model for resource allocation of virtualized network functions within NFV environments, a problem we refer to as Virtual Network Function Placement (VNF-P). We focus on a hybrid scenario where part of the services may be provided by dedicated physical hardware, and where part of the services are provided using virtualized service instances. We evaluate the VNF-P model using a small service provider scenario and two types of service chains, and evaluate its execution speed. We find that the algorithms finish in 16 seconds or less for a small service provider scenario, making it feasible to react quickly to changing demand.


title: Transforming Question Answering Datasets Into Natural Language Inference Datasets

abstract: Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.


The Case for a Visual Discovery Assistant: A Holistic Solution for Accelerating Visual Data Exploration


title: Improving Opinion-Target Extraction with Character-Level Word Embeddings

abstract: Fine-grained sentiment analysis is receiving increasing attention in recent years. Extracting opinion target expressions (OTE) in reviews is often an important step in fine-grained, aspect-based sentiment analysis. Retrieving this information from user-generated text, however, can be difficult. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system's performance. Specifically, we obtain an increase by 3.3 points F1-score with respect to our baseline model. In further experiments, we reveal encoded character patterns of the learned embeddings and give a nuanced view of the performance differences of both models.


title: Cost Model and Analysis of Iterative MapReduce Applications for Hybrid Cloud Bursting

abstract: A popular and cost-effective way to deal with the increasing complexity of big data analytics is hybrid cloud bursting that leases temporary off-premise cloud resources to boost the overall capacity during peak utilization. The main challenge of hybrid cloud bursting is that the network link between the on-premise and the off-premise computational resources often exhibit high latency and low throughput ("weak link") compared to the links within the same data-center. This paper introduces a cost model that is specifically designed for iterative MapReduce applications running in a hybrid cloud bursting scenario, which are a popular class of large-scale data-intensive applications that provides near real-time responsiveness. Using this cost model, users can discover trends that can be leveraged to reason about how to balance performance, accuracy and cost such that it op-timizes their requirements. We illustrated this approach through a cost analysis that focuses on two real-life iterative MapReduce applications using extensive horizontal scalability experiments that involve multiple hybrid cloud bursting strategies.


title: A Load-Adaptive Class-G Headphone Amplifier With Supply-Rejection Bandwidth Enhancement Technique

abstract: This paper presents a load-adaptive Class-G headphone amplifier to improve the power efficiency for different headphone impedances. For an impedance value, the amplifier utilizes two techniques, voltage rails selection and Class-G switching activity control, to reduce the power dissipation of the amplifier and the power loss of a Class-G power generator, respectively. In addition, a supply-rejection bandwidth enhancement technique for the Class-AB output stage is proposed to suppress the out-of-band noise by the Class-G supply rails switching. Compared with the conventional fixed 2-level Class-G amplifier, the integrated $0.18~\mu \text {m}$ CMOS amplifier improves the total-path efficiency to 37% from 31% (1.45 mA current can be saved from a 1.8 V supply) while delivering 5 mW into a $16~\Omega $ . The PSRR of the amplifier is 130 dB at 217 Hz and is still larger than 100 dB up to 20 kHz. Finally, the decoder and amplifier together achieve 108 dB DR and 95 dB SNDR with a $16~\Omega $ headphone load.


title: RCS Patterns of Pedestrians at 76¿77 GHz

abstract: 76 GHz to 77 GHz vehicular radars are useful in pedestrian collision avoidance for their longer detection range, higher spatial resolution, and tolerances to different weather and illumination conditions. Such radars can be used to provide early warning to the driver, or to just take over the control of the vehicle in emergency conditions. In order to have a high detection rate and a low false-alarm rate, it is very important to identify unique pedestrian radar signatures. However, experimentally characterizing pedestrian radar signatures at such short wavelengths is very challenging and often not reliable, because the radar response becomes very sensitive to the pedestrian's small motions. In this paper, the horizontal scattering patterns of pedestrians are investigated via both full-wave numerical model simulations, and actual measurements of human subjects of different sizes in different postures and clothing conditions. The contributions to the radar responses from different major body parts are also identified. The resultant radar cross section patterns exhibited unique features that could be used for pedestrian detection.


title: Theoretical perspectives in medical education: past experience and future possibilities

abstract: Medical Education 2011: 45: 60–68 ::: ::: Context Pedagogical practices reflect theoretical perspectives and beliefs that people hold about learning. Perspectives on learning are important because they influence almost all decisions about curriculum, teaching and assessment. Since Flexner’s 1910 report on medical education, significant changes in perspective have been evident. Yet calls for major reform of medical education may require a broader conceptualisation of the educational process. ::: ::: Past and current perspectives Medical education has emerged as a complex transformative process of socialisation into the culture and profession of medicine. Theory and research, in medical education and other fields, have contributed important understanding. Learning theories arising from behaviourist, cognitivist, humanist and social learning traditions have guided improvements in curriculum design and instruction, understanding of memory, expertise and clinical decision making, and self-directed learning approaches. Although these remain useful, additional perspectives which recognise the complexity of education that effectively fosters the development of knowledge, skills and professional identity are needed. ::: ::: Future perspectives Socio-cultural learning theories, particularly situated learning, and communities of practice offer a useful theoretical perspective. They view learning as intimately tied to context and occurring through participation and active engagement in the activities of the community. Legitimate peripheral participation describes learners’ entry into the community. As learners gain skill, they assume more responsibility and move more centrally. The community, and the people and artefacts within it, are all resources for learning. Learning is both collective and individual. Social cognitive theory offers a complementary perspective on individual learning. Situated learning allows the incorporation of other learning perspectives and includes workplace learning and experiential learning. Viewing medical education through the lens of situated learning suggests teaching and learning approaches that maximise participation and build on community processes to enhance both collective and individual learning.


title: Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition

abstract: This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding methods.


title: Grid connected Photovoltaic system

abstract: Renewable energy resources such as Solar, wind and hydro are pollution free, easily erectable, and limitless so they represent reliable alternatives to conventional energy sources e.g. oil and natural gas. However, the efficiency and the performance of these systems are still under development. Among them, Photovoltaic systems are mostly used as they are light, clean and easily installable. This systems connection to the grid requires special conditions to obtain a high-quality electric power system. This paper presents interfacing of three-phase grid connected PV system. DC-DC boost converter with maximum power point tracking (MPPT) is used to extract the maximum power obtained from the sun and transfer it to the grid. In any PV based system, the inverter is a critical component responsible for the control of electricity flow between the dc source, and loads or grid so a voltage source inverter (VSI) is used to convert the dc power into AC power before injecting it into the grid. A comprehensive simulation and implementation of a three-phase grid-connected inverter are presented to validate the proposed controller for the grid-connected PV system.


title: Automated Bug Triaging in an Industrial Context

abstract: There is an increasing need to introduce some formof automation within the bug triaging process, so that no timeis wasted on the initial assignment of issues. However, there is agap in current research, as most of the studies deal with opensource projects, ignoring the industrial context and needs. In this paper, we report our experience in dealing with theautomation of the bug triaging process within a research-industrycooperation. After reporting the requirements and needs thatwere set within the industrial project, we compare the analysisresults with those from an open source project used frequentlyin related research (Firefox). In spite of the fact that theprojects have different size and development process, the datadistributions are similar and the best models as well. We foundout that more easily configurable models (such as SVM+TF -- IDF) are preferred, and that top-x recommendations, number of issuesper developers, and online learning can all be relevant factorswhen dealing with an industrial collaboration.


title: Middle class and marginal? Socioeconomic status, stigma, and self-regulation at an elite university.

abstract: In four studies, the authors investigated the proposal that in the context of an elite university, individuals from relatively lower socioeconomic status (SES) backgrounds possess a stigmatized identity and, as such, experience (a) concerns regarding their academic fit and (b) self-regulatory depletion as a result of managing these concerns. Study 1, a correlational study, revealed the predicted associations between SES, concerns about academic fit, and self-regulatory strength. Results from Studies 2 and 3 suggested that self-presentation involving the academic domain is depleting for lower (but not higher) SES students: After a self-presentation task about academic achievement, lower SES students consumed more candy (Study 2) and exhibited poorer Stroop performance (Study 3) relative to their higher SES peers; in contrast, the groups did not differ after discussing a nonacademic topic (Study 3). Study 4 revealed the potential for eliminating the SES group difference in depletion via a social comparison manipulation. Taken together, these studies support the hypothesis that managing concerns about marginality can have deleterious consequences for self-regulatory resources.


Author ' s personal copy Perceived academic effects of instant messaging use


title: Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity

abstract: The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.


title: Isoform‐level gene expression patterns in single‐cell RNA‐sequencing data

abstract: RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single-cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform-level expression patterns at the single-cell level. In this study, we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and describe a method for differential-pattern analysis. We demonstrate ISOP through analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform-pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop-out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential-pattern analysis were not detected by differential-expression analysis. Finally, the effects of drop-out events and expression levels of isoforms on ISOP's performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform-level preference, commitment and heterogeneity in single-cell RNA-sequencing data. The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL-3 license. Supplementary data are available at Bioinformatics online.


title: Photoplethysmography-based in-ear sensor system for identification of increased stress arousal in everyday life

abstract: In this work, we present an in-ear system for physiological and psychological stress detection based on photoplethysmography, acceleration, and temperature measurements. The complete system is used to extract vital signs from healthy subjects, who are exposed to psychologically demanding tasks. The newly developed sensor system is integrated into our IPANEMA body sensor network and, thus, can be used in combination with several sensor modalities. The capability of the stress level estimation is validated in an human stress experiment. To obtain information on the current stress level, several well-known indicators are utilized like the heart rate variability, surgical stress index or the Oliva and Roztocil index.


title: Imagine This! Scripts to Compositions to Videos

abstract: Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval, and Fusion Network (CRAFT), a model capable of learning this knowledge from video-caption data and applying it while generating videos from novel captions. CRAFT explicitly predicts a temporal-layout of mentioned entities (characters and objects), retrieves spatio-temporal entity segments from a video database and fuses them to generate scene videos. Our contributions include sequential training of components of CRAFT while jointly modeling layout and appearances, and losses that encourage learning compositional representations for retrieval. We evaluate CRAFT on semantic fidelity to caption, composition consistency, and visual quality. CRAFT outperforms direct pixel generation approaches and generalizes well to unseen captions and to unseen video databases with no text annotations. We demonstrate CRAFT on FLINTSTONES, a new richly annotated video-caption dataset with over 25000 videos. For a glimpse of videos generated by CRAFT, see https://youtu.be/688Vv86n0z8.


title: Modular Generative Adversarial Networks

abstract: Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains. However, most existing methods have limited scalability and robustness, since they require building independent models for each pair of domains in question. This leads to two significant shortcomings: (1) the need to train exponential number of pairwise models, and (2) the inability to leverage data from other domains when training a particular pairwise mapping. Inspired by recent work on module networks, this paper proposes ModularGAN for multi-domain image generation and image-to-image translation. ModularGAN consists of several reusable and composable modules that carry on different functions (e.g., encoding, decoding, transformations). These modules can be trained simultaneously, leveraging data from all domains, and then combined to construct specific GAN networks at test time, according to the specific image translation task. This leads to ModularGAN's superior flexibility of generating (or translating to) an image in any desired domain. Experimental results demonstrate that our model not only presents compelling perceptual results but also outperforms state-of-the-art methods on multi-domain facial attribute transfer.


title: Multilevel image thresholding by fireworks algorithm

abstract: This paper presents implementation of the recent fireworks algorithm adjusted for solving multilevel image thresholding problem. This is an important problem since it is often used in image processing for the purpose of image segmentation. Since the number of possible threshold combinations grows exponentially with the number of desirable thresholds, standard deterministic methods could not generate satisfying results when tackling this problem. To test the performance of our proposed approach, we employed Kapur's maximum entropy thresholding function on standard benchmark images where the optimal solutions are known (up to five thresholding points) from the exhaustive search. Results show that our approach has great potential in this field.


title: Toward a Moving Target Defense for Web Applications

abstract: Web applications are a critical component of the security ecosystem as they are often the front door for many companies, as such, vulnerabilities in web applications allow hackers access to companies' private data, which contains consumers' private financial information. Web applications are, by their nature, available to everyone, at anytime, from anywhere, and this includes attackers. Therefore, attackers have the opportunity to perform reconnaissance at their leisure, acquiring information on the layout and technologies of the web application, before launching an attack. However, the defender must be prepared for all possible attacks and does not have the luxury of performing reconnaissance on the attacker. The idea behind Moving Target Defense (MTD) is to reduce the information asymmetry between the attacker and defender, ultimately rendering the reconnaissance information misleading or useless. In this paper we take the first steps of applying MTD concepts to web applications in order to create effective defensive layers. We first analyze the web application stack to understand where and how MTD can be applied. The key issue here is that an MTD application must actively prevent or disrupt a vulnerability or exploit, while still providing identical functionality. Then, we discuss our implementation of two MTD approaches, which can mitigate several classes of web application vulnerabilities or exploits. We hope that our discussion will help guide future research in applying the MTD concepts to the web application stack.


title: Coarse Grained Exponential Variational Autoencoders

abstract: Variational autoencoders (VAE) often use Gaussian or category distribution to model the inference process. This puts a limit on variational learning because this simplified assumption does not match the true posterior distribution, which is usually much more sophisticated. To break this limitation and apply arbitrary parametric distribution during inference, this paper derives a \emph{semi-continuous} latent representation, which approximates a continuous density up to a prescribed precision, and is much easier to analyze than its continuous counterpart because it is fundamentally discrete. We showcase the proposition by applying polynomial exponential family distributions as the posterior, which are universal probability density function generators. Our experimental results show consistent improvements over commonly used VAE models.


title: Hidden Markov model decomposition of speech and noise

abstract: The problem of automatic speech recognition in the presence of interfering signals and noise with statistical characteristics ranging from stationary to fast changing and impulsive is discussed. A technique of signal decomposition using hidden Markov models is described. This is a generalization of conventional hidden Markov modeling that provides an optimal method of decomposing simultaneous processes. The technique exploits the ability of hidden Markov models to model dynamically varying signals in order to accommodate concurrent processes, including interfering signals as complex as speech. This form of signal decomposition has wide implications for signal separation in general and improved speech modeling in particular. The application of decomposition to the problem of recognition of speech contaminated with noise is emphasized. >


title: Exploring goal-setting, rewards, self-monitoring, and sharing to motivate physical activity

abstract: Many people have turned to technological tools to help them be physically active. To better understand how goal-setting, rewards, self-monitoring, and sharing can encourage physical activity, we designed a mobile phone application and deployed it in a four-week field study (n=23). Participants found it beneficial to have secondary and primary weekly goals and to receive non-judgmental reminders. However, participants had problems with some features that are commonly used in practice and suggested in the literature. For example, trophies and ribbons failed to motivate most participants, which raises questions about how such rewards should be designed. A feature to post updates to a subset of their Facebook NewsFeed created some benefits, but barriers remained for most participants.


title: Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks

abstract: We present an approach for the verification of feed-forward neural networks in which all nodes have a piece-wise linear activation function. Such networks are often used in deep learning and have been shown to be hard to verify for modern satisfiability modulo theory (SMT) and integer linear programming (ILP) solvers. ::: The starting point of our approach is the addition of a global linear approximation of the overall network behavior to the verification problem that helps with SMT-like reasoning over the network behavior. We present a specialized verification algorithm that employs this approximation in a search process in which it infers additional node phases for the non-linear nodes in the network from partial node phase assignments, similar to unit propagation in classical SAT solving. We also show how to infer additional conflict clauses and safe node fixtures from the results of the analysis steps performed during the search. The resulting approach is evaluated on collision avoidance and handwritten digit recognition case studies.


title: Faster gaze prediction with dense networks and Fisher pruning

abstract: Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition. However, as we show in this paper, these networks are highly overparameterized for the task of fixation prediction. We first present a simple yet principled greedy pruning method which we call Fisher pruning. Through a combination of knowledge distillation and Fisher pruning, we obtain much more runtime-efficient architectures for saliency prediction, achieving a 10x speedup for the same AUC performance as a state of the art network on the CAT2000 dataset. Speeding up single-image gaze prediction is important for many real-world applications, but it is also a crucial step in the development of video saliency models, where the amount of data to be processed is substantially larger.


title: Addressing vaccine hesitancy requires an ethically consistent health strategy

abstract: BackgroundVaccine hesitancy is a growing threat to public health. The reasons are complex but linked inextricably to a lack of trust in vaccines, expertise and traditional sources of authority. Efforts to increase immunization uptake in children in many countries that have seen a fall in vaccination rates are two-fold: addressing hesitancy by improving healthcare professional-parent exchange and information provision in the clinic; and, secondly, public health strategies that can override parental concerns and values with coercive measures such as mandatory and presumptive vaccination.Main textIt is argued that such conflicting, parallel approaches seriously risk undermining trust that is crucial for sustaining herd immunity. Although public health strategies can be ethically justified in limiting freedoms, a parent-centered approach seldom acknowledges how it is impacted by contemporaneous coercive measures. In addition, the clinical encounter is not well suited to helping parents consider the public dimensions of vaccination, despite these being important for trust formation and informed decision-making. Efforts to address vaccine hesitancy require more consistent engagement of parental and citizen views. Along with evidence-based information, debates need to be informed by ethical support that equips parents and professionals to respond to the private and public dimensions of vaccination in a more even-handed, transparent manner.ConclusionEfforts to address vaccine hesitancy need to avoid simple reliance on either parental values or coercive public policies. To do this effectively requires increasing citizen engagement on vaccination to help inform a parent-centered approach and legitimize public policy measures. In addition, cultivating a more ethically consistent strategy means moving beyond the current silos of health ethics - clinical and public health ethics.


title: Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation

abstract: Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error correction methods that employ statistical machine translation (SMT) have been proposed in recent years. An SMT system generates candidates with scores for all candidates and selects the sentence with the highest score as the correction result. However, the 1-best result of an SMT system is not always the best result. Thus, we propose a reranking approach for grammatical error correction. The reranking approach is used to re-score N-best results of the SMT and reorder the results. Our experiments show that our reranking system using parts of speech and syntactic features improves performance and achieves state-of-theart quality, with an F0.5 score of 40.0.


title: Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals: Lab Report for PAN at CLEF 2010

abstract: Wikipedia is an online encyclopedia that anyone can edit. In this open model, some people edits with the intent of harming the integrity of Wikipedia. This is known as vandalism. We extend the framework presented in (Potthast, Stein, and Gerling, 2008) for Wikipedia vandalism detection. In this approach, several vandalism indicating features are extracted from edits in a vandalism corpus and are fed to a supervised learning algorithm. The best performing classifiers were LogitBoost and Random Forest. Our classifier, a Random Forest, obtained an AUC of 0.92236, ranking in the first place of the PAN'10 Wikipedia vandalism detection task.


title: Single-Stage Quasi-Resonant Flyback Converter for a Cost-Effective PDP Sustain Power Module

abstract: A single-stage quasi-resonant flyback converter is proposed for a cost-effective plasma display panel (PDP) sustain power module. The proposed converter operates at the boundary of the continuous- and discontinuous-conduction modes to reduce the switching loss of the primary switch. The bulk capacitor voltage can be independent of the output load and maintained within a practical range for the universal line input. By integrating two power conversion stages into one, the proposed converter provides a simple structure and high efficiency for the PDP sustain power module. The operational principle and theoretical analysis are presented. A prototype converter was built and tested for a 32-in PDP, which requires 150-W (180-V/0.83-A) sustain power. The experimental results verify the feasibility of the proposed scheme.


title: DualBlink: A Wearable Device to Continuously Detect, Track, and Actuate Blinking For Alleviating Dry Eyes and Computer Vision Syndrome

abstract: Increased visual attention, such as during computer use leads to less blinking, which can cause dry eyes—the leading cause of computer vision syndrome. As people spend more time looking at screens on mobile and desktop devices, computer vision syndrome is becoming epidemic in today's population, leading to blurry vision, fatigue, and a reduced quality of life. One way to alleviate dry eyes is increased blinking. In this paper, we present a series of glasses-mounted devices that track the wearer's blink rate and, upon absent blinks, trigger blinks through actuation: light flashes, physical taps, and small puffs of air near the eye. We conducted a user study to evaluate the effectiveness of our devices and found that air puff and physical tap actuations result in a 36% increase in participants’ average blink rate. Air puff thereby struck the best compromise between effective blink actuations and low distraction ratings from participants. In a follow-up study, we found that high intensity, short puffs near the eye were most effective in triggering blinks while receiving only low-rated distraction and invasiveness ratings from participants. We conclude this paper with two miniaturized and self-contained DualBlink prototypes, one integrated into the frame of a pair of glasses and the other one as a clip-on for existing glasses. We believe that DualBlink can serve as an always-available and viable option to treat computer vision syndrome in the future.


title: A Survey on Assessment and Ranking Methodologies for User-Generated Content on the Web

abstract: User-generated content (UGC) on the Web, especially on social media platforms, facilitates the association of additional information with digital resources; thus, it can provide valuable supplementary content. However, UGC varies in quality and, consequently, raises the challenge of how to maximize its utility for a variety of end-users. This study aims to provide researchers and Web data curators with comprehensive answers to the following questions: What are the existing approaches and methods for assessing and ranking UGC? What features and metrics have been used successfully to assess and predict UGC value across a range of application domains? What methods can be effectively employed to maximize that value? This survey is composed of a systematic review of approaches for assessing and ranking UGC: results are obtained by identifying and comparing methodologies within the context of short text-based UGC on the Web. Existing assessment and ranking approaches adopt one of four framework types: the community-based framework takes into consideration the value assigned to content by a crowd of humans, the end-user--based framework adapts and personalizes the assessment and ranking process with respect to a single end-user, the designer-based framework encodes the software designer’s values in the assessment and ranking method, and the hybrid framework employs methods from more than one of these types. This survey suggests a need for further experimentation and encourages the development of new approaches for the assessment and ranking of UGC.


title: Bottom-up generalization: a data mining solution to privacy protection

abstract: The well-known privacy-preserved data mining modifies existing data mining techniques to randomized data. In this paper, we investigate data mining as a technique for masking data, therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The idea is simple but novel: we explore the data generalization concept from data mining as a way to hide detailed information, rather than discover trends and patterns. Once the data is masked, standard data mining techniques can be applied without modification. Our work demonstrated another positive use of data mining technology: not only can it discover useful patterns, but also mask private information. We consider the following privacy problem: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. We adapt an iterative bottom-up generalization from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration. Enumerating all candidate generalizations is impractical. We present a scalable solution that examines at most one generalization in each iteration for each attribute involved in the linking.