title: Improvements to the SMO algorithm for SVM regression

abstract: This paper points out an important source of inefficiency in Smola and Scholkopf's (1998) sequential minimal optimization (SMO) algorithm for support vector machine regression that is caused by the use of a single threshold value. Using clues from the Karush-Kuhn-Tucker conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.


title: Robust decision-support tools for airport surface traffic

abstract: Forecasts of departure demand are one of the driving inputs to tactical decision-support tools (DSTs) for airport surface traffic. While there are well-known results on average- or worst-case forecast uncertainty, it is the forecast errors which occur under best-case minimum-uncertainty conditions which constrain robust DST design and the achievable traffic benefits. These best-case errors have never previously been characterized. ::: Several quantitative models and techniques for computing pushback forecasts are developed. These are tested against a dataset of 17,344 real-world airline ground operations covering 3 months of Lufthansa flights transiting Frankfurt International Airport. The Lufthansa dataset includes detailed timing information on all of the turn processes, including deboarding, catering, cleaning, fueling and boarding. The dataset is carefully filtered to obtain a sample of 3820 minimum-uncertainty ground events. The forecast models and techniques are tested against this sample, and it is observed that current pushback forecast errors (on the order of ±15min) cannot be reduced by a factor of more than 2 or 3. Furthermore, for each ground event, only 3 observations are necessary to achieve this best-case performance: the available ground-time between actual onblock and scheduled offblock; the time until deboarding begins; and the time until boarding ends. ::: Any DST used in real-world operations must be robust to this “noise floor”. To support the development of robust DSTs, a unified framework called ceno-scale modelling is developed. This class of models encodes a wide range of observed delay mechanisms using multi-resource synchronization (MRS) feedback networks. A ceno-scale model instance is created for Newark International Airport, and the parameter sensitivity and model fidelity are tested against a detailed real-world dataset. Based on the validated model framework, several robust dual control strategies are proposed for airport surface traffic. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)


title: Adaptive key frame extraction using unsupervised clustering

abstract: Key frame extraction has been recognized as one of the important research issues in video information retrieval. Although progress has been made in key frame extraction, the existing approaches are either computationally expensive or ineffective in capturing salient visual content. We first discuss the importance of key frame selection; and then review and evaluate the existing approaches. To overcome the shortcomings of the existing approaches, we introduce a new algorithm for key frame extraction based on unsupervised clustering. The proposed algorithm is both computationally simple and able to adapt to the visual content. The efficiency and effectiveness are validated by large amount of real-world videos.


title: Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling

abstract: Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data. Experimental results show that our method outperforms existing methods. We use our method to generate Proposition Banks with high to reasonable quality for 7 languages in three language families and release these resources to the research community.


title: JDiff: A differencing technique and tool for object-oriented programs

abstract: During software evolution, information about changes between different versions of a program is useful for a number of software engineering tasks. For example, configuration-management systems can use change information to assess possible conflicts among updates from different users. For another example, in regression testing, knowledge about which parts of a program are unchanged can help in identifying test cases that need not be rerun. For many of these tasks, a purely syntactic differencing may not provide enough information for the task to be performed effectively. This problem is especially relevant in the case of object-oriented software, for which a syntactic change can have subtle and unforeseen effects. In this paper, we present a technique for comparing object-oriented programs that identifies both differences and correspondences between two versions of a program. The technique is based on a representation that handles object-oriented features and, thus, can capture the behavior of object-oriented programs. We also present JDiff, a tool that implements the technique for Java programs. Finally, we present the results of four empirical studies, performed on many versions of two medium-sized subjects, that show the efficiency and effectiveness of the technique when used on real programs.


Collaboration: A Literature Review Research Report


title: Luminance-difference-based adaptive subpixel rendering algorithm for matrix displays

abstract: This paper presents an adaptive subpixel rendering algorithm that uses the luminance difference between the current subpixel and neighboring subpixels. The proposed method reduces both the color fringing artifact and blurring, thereby enhancing greatly the image quality.


title: Robust identification of fuzzy duplicates

abstract: Detecting and eliminating fuzzy duplicates is a critical data cleaning task that is required by many applications. Fuzzy duplicates are multiple seemingly distinct tuples, which represent the same real-world entity. We propose two novel criteria that enable characterization of fuzzy duplicates more accurately than is possible with existing techniques. Using these criteria, we propose a novel framework for the fuzzy duplicate elimination problem. We show that solutions within the new framework result in better accuracy than earlier approaches. We present an efficient algorithm for solving instantiations within the framework. We evaluate it on real datasets to demonstrate the accuracy and scalability of our algorithm.


Basic Human Values : An Overview


title: Speculative Markov blanket discovery for optimal feature selection

abstract: In this paper we address the problem of learning the Markov blanket of a quantity from data in an efficient manner Markov blanket discovery can be used in the feature selection problem to find an optimal set of features for classification tasks, and is a frequently-used preprocessing phase in data mining, especially for high-dimensional domains. Our contribution is a novel algorithm for the induction of Markov blankets from data, called Fast-IAMB, that employs a heuristic to quickly recover the Markov blanket. Empirical results show that Fast-IAMB performs in many cases faster and more reliably than existing algorithms without adversely affecting the accuracy of the recovered Markov blankets.


title: An integrated fast battery charger for Electric Vehicle

abstract: Under the requirements of reducing emissions, air pollution and achieving higher fuel economy, companies are developing electric, hybrid electric, and plug-in hybrid electric vehicles. However, the high cost of these technologies and the low autonomy are very restrictive. In this paper a new concept of fast on-board battery charger for Electric Vehicles (EVs) is proposed which uses the electric motor like filter and the same converter for charging and traction mode.


title: SYMDIFF: a language-agnostic semantic diff tool for imperative programs

abstract: In this paper, we describe SymDiff, a language-agnostic tool for equivalence checking and displaying semantic (behavioral) differences over imperative programs. The tool operates on an intermediate verification language Boogie, for which translations exist from various source languages such as C, C# and x86. We discuss the tool and the front-end interface to target various source languages. Finally, we provide a brief description of the front-end for C programs.


title: On the Power of Claw-Free Permutations

abstract: The popular random-oracle-based signature schemes, such as Probabilistic Signature Scheme (PSS) and Full Domain Hash (FDH), output a signature of the form , where y somehow depends on the message signed (and pub) and f is some public trapdoor permutation (typically RSA). Interestingly, all these signature schemes can be proven asymptotically secure for an arbitrary trapdoor permutation f, but their exact security seems to be significantly better for special trapdoor permutations like RSA. This leads to two natural questions: (1) can the asymptotic security analysis be improved with general trapdoor permutations?; and, if not, (2) what general cryptographic assumption on f - enjoyed by specific functions like RSA - is responsible for the improved security? We answer both these questions. First, we show that if f is a black-box trapdoor permutation, then the poor exact security is unavoidable. More specifically, the security loss for general trapdoor permutations is Ω(q hash ), where q hash is the number of random oracle queries made by the adversary (which could be quite large). On the other hand, we show that all the security benefits of the RSA-based variants come into effect once f comes from a family of claw-free permutation pairs. Our results significantly narrow the current gap between general trapdoor permutations and RSA to the gap between trapdoor permutations and claw-free permutations. Additionally, they can be viewed as the first security/efficiency separation between these basic cryptographic primitives. In other words, while it was already believed that certain cryptographic objects can be built from claw-free permutations but not from general trapdoor permutations, we show that certain important schemes (like FDH and PSS) provably work with either, but enjoy a much better tradeoff between security and efficiency when deployed with claw-free permutations.


title: A capacity theory of comprehension: Individual differences in working memory

abstract: A theory of the way working memory capacity constrains comprehension is proposed. The theory proposes that both processing and storage are mediated by activation and that the total amount of activation available in working memory varies among individuals. Individual differences in working memory capacity for language can account for qualitative and quantitative differences among college-age adults in several aspects of language comprehension. One aspect is syntactic modularity: The larger capacity of some individuals permits interaction among syntactic and pragmatic information, so that their syntactic processes are not informationally encapsulated. Another aspect is syntactic ambiguity: The larger capacity of some individuals permits them to maintain multiple interpretations. The theory is instantiated as a production system model in which the amount of activation available to the model affects how it adapts to the transient computational and storage demands that occur in comprehension. Working memory plays a central role in all forms of complex thinking, such as reasoning, problem solving, and language comprehension. However, its function in language comprehension is especially evident because comprehension entails processing a sequence of symbols that is produced and perceived over time. Working memory plays a critical role in storing the intermediate and final products of a reader's or listener's computations as she or he constructs and integrates ideas from the stream of successive words in a text or spoken discourse. In addition to its role in storage, working memory can also be viewed as the pool of operational resources that perform the symbolic computations and thereby generate the intermediate and final products. In this article, we examine how the human cognitive capacity accommodates or fails to accommodate the transient computational and storage demands that occur in language comprehension. We also explain the differences among individuals in their comprehension performance in terms of their working memory capacity. The major thesis is that cognitive capacity constrains comprehension, and it constrains comprehension more for some people than for others. This article begins with a general outline of a capacity theory of language comprehension. In the second section we use the capacity theory to account for several phenomena relating individual differences in language processing to working memory


title: Ensemble clustering using factor graph

abstract: In this paper, we propose a new ensemble clustering approach termed ensemble clustering using factor graph (ECFG). Compared to the existing approaches, our approach has three main advantages: (1) the cluster number is obtained automatically and need not to be specified in advance; (2) the reliability of each base clustering can be estimated in an unsupervised manner and exploited in the consensus process; (3) our approach is efficient for processing ensembles with large data sizes and large ensemble sizes. In this paper, we introduce the concept of super-object, which serves as a compact and adaptive representation for the ensemble data and significantly facilitates the computation. Through the probabilistic formulation, we cast the ensemble clustering problem into a binary linear programming (BLP) problem. The BLP problem is NP-hard. To solve this optimization problem, we propose an efficient solver based on factor graph. The constrained objective function is represented as a factor graph and the max-product belief propagation is utilized to generate the solution insensitive to initialization and converged to the neighborhood maximum. Extensive experiments are conducted on multiple real-world datasets, which demonstrate the effectiveness and efficiency of our approach against the state-of-the-art approaches. HighlightsIntroduce the super-object representation to facilitate the consensus process.Probabilistically formulate the ensemble clustering problem into a BLP problem.Propose an efficient solver for the BLP problem based on factor graph.The cluster number of the consensus clustering is estimated automatically.Our method achieves the state-of-the-art performance in effectiveness and efficiency.


title: A Performance Evaluation of Container Technologies on Internet of Things Devices

abstract: The use of virtualization technologies in different contexts - such as Cloud Environments, Internet of Things (IoT), Software Defined Networking (SDN) - has rapidly increased during the last years. Among these technologies, container-based solutions own characteristics for deploying distributed and lightweight applications. This paper presents a performance evaluation of container technologies on constrained devices, in this case, on Raspberry Pi. The study shows that, overall, the overhead added by containers is negligible.


title: 1d camera geometry and its application to circular motion estimation

abstract: This paper describes a new and robust method for estimating circular motion geometry from an uncalibrated image sequence. Under circular motion, all the camera centers lie on a circle, and the mapping of the plane containing this circle to the horizon line in the image can be modelled as a 1D projection. A 2◊ 2 homography is introduced in this paper to relate the projections of the camera centers in two 1D views. It is shown that the two imaged circular points and the rotation angle between the two views can be derived directly from the eigenvectors and eigenvalues of such a homography respectively. The proposed 1D geometry can be nicely applied to circular motion estimation using either point correspondences or silhouettes. The method introduced here is intrinsically a multiple view approach as all the sequence geometry embedded in the epipoles is exploited in the computation of the homography for a view pair. This results in a robust method which gives accurate estimated rotation angles and imaged circular points. Experimental results are presented to demonstrate the simplicity and applicability of the new method.


title: A theoretical model of GSM network based vehicle tracking system

abstract: This paper presents a novel idea of vehicle tracking system based on the existing GSM cellular networks. A software based system is proposed that sends specialized request to the GSM cellular networks to call any particular vehicle ID. The vehicle ID is actually a particular SIM kept in a special kit inside the vehicle that is capable of receiving a phone call automatically. As soon as the call is established, the particular cell information is available to the BSC which is then passed to the software. Based on the information collected, the software will initiate a forced handover of the call to another suitable cell and then receive the information of that cell too. Upon completion of two consecutive forced handovers, i.e., receiving cell information of the vehicle ID from three different cells and sending them to the software, it will automatically disconnect the call. The software will analyze the cell info and extract three timing advances (TA) data along with the GPS locations of the individual cells. An algorithm has been developed for this system, which then calculates the exact location of the vehicle. The accuracy of the vehicle right at the moment of the call establishment is expected to be within a circle of 200 meters radius.


title: A Comparative Study of Bug Algorithms for Robot Navigation

abstract: This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory -- relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO) methods. We compare a selection of Bug Algorithms in a simulated robot and environment where they endure different types noise and failure-cases of their on-board sensors. From the simulation results, we conclude that the implemented Bug Algorithms' performances are sensitive to many types of sensor-noise, which was most noticeable for odometry-drift. This raises the question if Bug Algorithms are suitable for real-world, on-board, robotic navigation as is. Variations that use multiple sensors to keep track of their progress towards the goal, were more adept in completing their task in the presence of sensor-failures. This shows that Bug Algorithms must spread their risk, by relying on the readings of multiple sensors, to be suitable for real-world deployment.


title: Design and Performance of LQR and LQR based Fuzzy Controller for Double Inverted Pendulum System

abstract:  Abstract—The objective of this paper is to compare performance between two type of controller for a double inverted pendulum system. Double inverted pendulum is a non-linear, unstable and fast reaction system. DIP is stable when its two pendulums allocated in vertically position and have no oscillation and movement and also inserting force should be zero. The objective is to determine the control strategy that to delivers better performance with respect to pendulum angle’s and cart position. In this paper LQR based fuzzy controller is designed and its performance is compared with Linear Quadratic Regulator controller using Matlab and Simulink. The results shows that LQR based fuzzy controller produced better response as compared to LQR control strategy. 


title: Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments

abstract: Abstract The following study both synthesizes and builds on the efforts to conceptualize the effects of quality, satisfaction, and value on consumers’ behavioral intentions. Specifically, it reports an empirical assessment of a model of service encounters that simultaneously considers the direct effects of these variables on behavioral intentions. The study builds on recent advances in services marketing theory and assesses the relationships between the identified constructs across multiple service industries. Several competing theories are also considered and compared to the research model. A number of notable findings are reported including the empirical verification that service quality, service value, and satisfaction may all be directly related to behavioral intentions when all of these variables are considered collectively. The results further suggest that the indirect effects of the service quality and value constructs enhanced their impact on behavioral intentions.


title: Power Control for Induction Heating by Asymmetrical Pulse Density Modulation

abstract: To adjust the power of serial-resonant inverters for induction heating applications, asymmetrical Pulse Density Modulation (APDM) power controlled strategy is described, analysed and designed. The Pulse Density Modulation (PDM) is introduced to overcome the drawbacks of the classical control modes such as Pulse Width Modulation, Pulse Frequency Modulation and Phase Shift Modulation. One characteristic of PDM is that it operates at resonant frequency with Zero-current and zero-voltage switching for a wide range of output power variations. Normal PDM often considered as symmetry pattern for the power switches. While in some case of low quality of the resonant loads, the load current is incontinuous in stage of low density, the phase loop locked circuit can not work efficiently, and the advantage of PDM will disappear. APDM provides two times wide range of output power variations at the same operation period of PDM, and the switches also cut off at Zero Current Switching and open at Zero Voltage Switching.


title: Research on digital museum for the intangible cultural heritage

abstract: China is rich in intangible cultural heritage with distinguishable characteristics. Its protection is very important. This paper is going to discuss the application and researches of digital in the protection of intangible cultural heritage, and stresses on the issues involved in the construction of digital museum, such as conceive a management information system, structure of the system, and the method to recover ancient intangible cultural heritage.


title: Intracellular features predicted by extracellular recordings in the hippocampus in vivo

abstract: Multichannel tetrode array recording in awake behaving animals provides a powerful method to record the activity of large numbers of neurons. The power of this method could be extended if further information concerning the intracellular state of the neurons could be extracted from the extracellularly recorded signals. Toward this end, we have simultaneously recorded intracellular and extracellular signals from hippocampal CA1 pyramidal cells and interneurons in the anesthetized rat. We found that several intracellular parameters can be deduced from extracellular spike waveforms. The width of the intracellular action potential is defined precisely by distinct points on the extracellular spike. Amplitude changes of the intracellular action potential are reflected by changes in the amplitude of the initial negative phase of the extracellular spike, and these amplitude changes are dependent on the state of the network. In addition, intracellular recordings from dendrites with simultaneous extracellular record...


title: A Unified Model with Structured Output for Fashion Images Classification

abstract: A picture is worth a thousand words. Albeit a cliche, for the fashion industry, an image of a clothing piece allows one to perceive its category (e.g., dress), sub-category (e.g., day dress) and properties (e.g., white colour with floral patterns). The seasonal nature of the fashion industry creates a highly dynamic and creative domain with evermore data, making it unpractical to manually describe a large set of images (of products). In this paper, we explore the concept of visual recognition for fashion images through an end-to-end architecture embedding the hierarchical nature of the annotations directly into the model. Towards that goal, and inspired by the work of [7], we have modified and adapted the original architecture proposal. Namely, we have removed the message passing layer symmetry to cope with Farfetch category tree, added extra layers for hierarchy level specificity, and moved the message passing layer into an enriched latent space. We compare the proposed unified architecture against state-of-the-art models and demonstrate the performance advantage of our model for structured multi-level categorization on a dataset of about 350k fashion product images.


title: The Sparse Manifold Transform

abstract: We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maintaining approximate invertibility. The sparse manifold transform is an unsupervised and generative framework that explicitly and simultaneously models the sparse discreteness and low-dimensional manifold structure found in natural scenes. When stacked, it also models hierarchical composition. We provide a theoretical description of the transform and demonstrate properties of the learned representation on both synthetic data and natural videos.


title: The mucosal immune system: master regulator of bidirectional gut‐brain communications

abstract: Communication between the brain and gut is not one-way, but a bidirectional highway whereby reciprocal signals between the two organ systems are exchanged to coordinate function. The messengers of this complex dialogue include neural, metabolic, endocrine and immune mediators responsive to diverse environmental cues, including nutrients and components of the intestinal microbiota (microbiota-gut-brain axis). We are now starting to understand how perturbation of these systems affects transition between health and disease. The pathological repercussions of disordered gut-brain dialogue are probably especially pertinent in functional gastrointestinal diseases, including IBS and functional dyspepsia. New insights into these pathways might lead to novel treatment strategies in these common gastrointestinal diseases. In this Review, we consider the role of the immune system as the gatekeeper and master regulator of brain-gut and gut-brain communications. Although adaptive immunity (T cells in particular) participates in this process, there is an emerging role for cells of the innate immune compartment (including innate lymphoid cells and cells of the mononuclear phagocyte system). We will also consider how these key immune cells interact with the specific components of the enteric and central nervous systems, and rapidly respond to environmental variables, including the microbiota, to alter gut homeostasis.


title: Survey Toward a Smart Campus Using the Internet of Things

abstract: IoT with its ability to make objects be sensed and connected is inevitable in the smart campus market. Even though the smart campus market has not taken off yet, there is an enormous research that is going on now all over the world to explore such technology. Several factors are driving investigators to study smart campus including: deliver high quality services, protect the environment, and save cost. In this paper, not only we explore the research conducted in this area, but we also investigatechallengesandprovidepossibleresearchopportunities regarding smart campus.


title: VDDA: automatic visualization-driven data aggregation in relational databases

abstract: Contemporary RDBMS-based systems for visualization of high-volume numerical data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of large data sets disregard the spatial properties of visualizations, resulting in visualization errors. In this work, we introduce VDDA, a visualization-driven data aggregation that models visual aggregation at the pixel level as data aggregation at the query level. Based on the M4 aggregation for producing pixel-perfect line charts from highly reduced data subsets, we define a complete set of data reduction operators that simulate the overplotting behavior of the most frequently used chart types. Relying only on the relational algebra and the common data aggregation functions, our approach is generic and applicable to any visualization system that consumes data stored in relational databases. We demonstrate our visualization-driven data aggregation using real-world data sets from high-tech manufacturing, stock markets, and sports analytics, reducing data volumes by up to two orders of magnitude, while preserving pixel-perfect visualizations, as producible from the raw data.


title: Game programming in introductory courses with direct state manipulation

abstract: While the introduction of object-oriented programming slowly moves down the age groups - starting from advanced university courses, to introductory courses, and now into high schools - many attempts are being made to make object-oriented programming introduction less abstract and theoretical. Visualisation and interaction techniques are being applied in an attempt to give students engaging and concrete experiences with objects. Recently, the greenfoot environment has been proposed as another step in this development. In this paper, we describe new functionality in the greenfoot environment, especially the addition of user interaction programming via direct state manipulation. Direct state manipulation provides very low overhead graphical I/O handling at a level that makes it feasible to guide students to simple graphical game programming within a few weeks, while concentrating on fundamental object-oriented concepts in the structure of the program.


Lexical Predictors Of Personality Type


Growth and structure of the thyroid gland in the common seal (Phoca vitulina).


title: Cultural differences in the online behavior of consumers

abstract: Understanding how different cultures use the Net---as well as perceive the same Web sites---can translate to truly global e-commerce.


title: A semantic similarity measure for recommender systems

abstract: In the past few years, recommender systems and semantic web technologies have become main subjects of interest in the research community. In this paper, we present a domain independent semantic similarity measure that can be used in the recommendation process. This semantic similarity is based on the relations between the individuals of an ontology. The assessment can be done offline which allows time to be saved and then, get real-time recommendations. The measure has been experimented on two different domains: movies and research papers. Moreover, the generated recommendations by the semantic similarity have been evaluated by a set of volunteers and the results have been promising.


title: Design and Implementation of a Simulation System Based on Deep Q-Network for Mobile Actor Node Control in Wireless Sensor and Actor Networks

abstract: A Wireless Sensor and Actor Network (WSAN) is a group of wireless devices with the ability to sense physical events (sensors) or/and to perform relatively complicated actions (actors), based on the sensed data shared by sensors. This paper presents design and implementation of a simulation system based on Deep Q-Network (DQN) for mobile actor node control in WSANs. DQN is a deep neural network structure used for estimation of Q-value of the Q-learning method. In this work, we implement the proposed simulating system by Rust programming language. We describe the design and implementation of the simulation system, and show some simulation results to evaluate its performance.


A Non-Sequential Neural Click Model for Web Search


title: Subharmonic Analysis for Buck Converters With Constant On-Time Control and Ramp Compensation

abstract: This paper presents a new subharmonic analysis for buck converters with constant on-time control and ramp compensation. For constant on-time control, subharmonic oscillation can be eliminated by adding a compensation ramp with a fixed slope during the off time and a fixed level during the on time. Based on the inductor current information, the compensation ramp, and the charge variations of the output capacitor, the minimum amount of compensation ramp to avoid subharmonic is derived, and the effect of circuit propagation delay is quantified. A prototype of buck converter is built by using constant on-time control with ramp compensation. Experimental results demonstrate the detailed theoretical analysis.


title: Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research

abstract: Amazon Mechanical Turk (AMT) is an online crowdsourcing service where anonymous online workers complete web-based tasks for small sums of money. The service has attracted attention from experimental psychologists interested in gathering human subject data more efficiently. However, relative to traditional laboratory studies, many aspects of the testing environment are not under the experimenter's control. In this paper, we attempt to empirically evaluate the fidelity of the AMT system for use in cognitive behavioral experiments. These types of experiment differ from simple surveys in that they require multiple trials, sustained attention from participants, comprehension of complex instructions, and millisecond accuracy for response recording and stimulus presentation. We replicate a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT. While most of replications were qualitatively successful and validated the approach of collecting data anonymously online using a web-browser, others revealed disparity between laboratory results and online results. A number of important lessons were encountered in the process of conducting these replications that should be of value to other researchers.


title: Market efficiency and accounting research: a discussion of ‘capital market research

abstract: Much of capital market research in accounting over the past 20 years has assumed that the price adjustment process to information is instantaneous and/or trivial. This basic assumption has had an enormous influence on the way we select research topics, design empirical tests, and interpret research findings. In this discussion, I argue that price discovery is a complex process, deserving of more attention. I highlight significant problems associated with a naive view of market efficiency, and advocate a more general model involving noise traders. Finally, I discuss the implications of recent evidence against market efficiency for future capital market research in accounting.


title: Dynamic Adaptive Streaming over HTTP/2.0

abstract: MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard that has been recently ratified as an international standard (IS). In comparison to other streaming systems, e.g., HTTP progressive download, DASH is able to handle varying bandwidth conditions providing smooth streaming. Furthermore, it enables NAT and Firewall traversal, flexible and scalable deployment as well as reduced infrastructure costs due to the reuse of existing Internet infrastructure components, e.g., proxies, caches, and Content Distribution Networks (CDN). Recently, the Hypertext Transfer Protocol Bis (httpbis) working group of the IETF has officially started the development of HTTP 2.0. Initially three major proposals have been submitted to the IETF i.e., Googles' SPDY, Microsofts' HTTP Speed+Mobility and Network-Friendly HTTP Upgrade, but SPDY has been chosen as working draft for HTTP 2.0. In this paper we implemented MPEG-DASH over HTTP 2.0 (i.e., SPDY), demonstrating its potential benefits and drawbacks. Moreover, several experimental evaluations have been performed that compare HTTP 2.0 with HTTP 1.1 and HTTP 1.0 in the context of DASH. In particular, the protocol overhead, the performance for different round trip times, and DASH with HTTP 2.0 in a lab test scenario has been evaluated in detail.


title: A Novel Approach for Rogue Access Point Detection on the Client-Side

abstract: There is a big risk for public Wi-Fi users being tricked into connecting to rogue access points. Rogue access point is one of the most serious threats in WLAN, since it exposes a large number of users to MITM and evil twin attack. In this paper we propose a practical method that warns users to avoid connecting to the rogue access points. Proposed method compares the gateways and the routes that a packet travels to determine whether an access point is legitimate or not. This method can easily detect Man-In-The-Middle and evil twin attack without any assistance from the WLAN operator.


title: Vehicle License Plate Recognition Based on Text-line Construction and Multilevel RBF Neural Network

abstract: License plate localization and character segmentation and recognition are the research hotspots of vehicle license plate recognition (VLPR) technology. A new method to VLPR is presented in this paper. In license plate localization section, Otsu binarization is operated to get the plate-candidates regions, and a text-line is constructed from the candidate regions. According to the text-line construction result and the characteristics of the license plate character arrangement, the license plate location will be determined. And then the locally optimal adaptive binarization is utilized to make more accurate license plate localization. After the license plate localization, the segment method of vertical projection information with prior knowledge is used to slit characters and the statistical features are extracted. Then the multilevel classification RBF neural network is used to recognize characters using the feature vector as input. The results show that this method can recognize characters precisely and improve the ability of license plate character recognition effectively.


title: Random Walk Based Fake Account Detection in Online Social Networks

abstract: Online social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive fake accounts (also called Sybils) and uses them to perform various malicious activities. Therefore, Sybil detection is a fundamental security research problem in online social networks. Random walk based methods, which leverage the structure of an online social network to distribute reputation scores for users, have been demonstrated to be promising in certain real-world online social networks. In particular, random walk based methods have three desired features: they can have theoretically guaranteed performance for online social networks that have the fast-mixing property, they are accurate when the social network has strong homophily property, and they can be scalable to large-scale online social networks. However, existing random walk based methods suffer from several key limitations: 1) they can only leverage either labeled benign users or labeled Sybils, but not both, 2) they have limited detection accuracy for weak-homophily social networks, and 3) they are not robust to label noise in the training dataset. In this work, we propose a new random walk based Sybil detection method called SybilWalk. SybilWalk addresses the limitations of existing random walk based methods while maintaining their desired features. We perform both theoretical and empirical evaluations to compare SybilWalk with previous random walk based methods. Theoretically, for online social networks with the fast-mixing property, SybilWalk has a tighter asymptotical bound on the number of Sybils that are falsely accepted into the social network than all existing random walk based methods. Empirically, we compare SybilWalk with previous random walk based methods using both social networks with synthesized Sybils and a large-scale Twitter dataset with real Sybils. Our empirical results demonstrate that 1) SybilWalk is substantially more accurate than existing random walk based methods for weakhomophily social networks, 2) SybilWalk is substantially more robust to label noise than existing random walk based methods, and 3) SybilWalk is as scalable as the most efficient existing random walk based methods. In particular, on the Twitter dataset, SybilWalk achieves a false positive rate of 1.3% and a false negative rate of 17.3%.


title: AGDISTIS - Graph-Based Disambiguation of Named Entities Using Linked Data

abstract: Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback by presenting AGDISTIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can efficiently detect the correct URIs for a given set of named entities within an input text. We evaluate our approach on eight different datasets against state-of-the-art named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.


title: Tactile Sensing—From Humans to Humanoids

abstract: Starting from human ?sense of touch,? this paper reviews the state of tactile sensing in robotics. The physiology, coding, and transferring tactile data and perceptual importance of the ?sense of touch? in humans are discussed. Following this, a number of design hints derived for robotic tactile sensing are presented. Various technologies and transduction methods used to improve the touch sense capability of robots are presented. Tactile sensing, focused to fingertips and hands until past decade or so, has now been extended to whole body, even though many issues remain open. Trend and methods to develop tactile sensing arrays for various body sites are presented. Finally, various system issues that keep tactile sensing away from widespread utility are discussed.


How Far Is the Human Hand ? A Review on Anthropomorphic Robotic End-effectors


title: Ethics and tactics of professional crowdwork

abstract: Paid crowd workers are not just an API call---but all too often, they are treated like one.


title: SELF- EFFICACY, ACHIEVEMENT MOTIVATION AND ACADEMIC PROCRASTINATION AS PREDICTORS OF ACADEMIC ACHIEVEMENT IN PRE-COLLEGE STUDENTS

abstract: The purpose of this study was to determine the relationship between academic Selfefficacy, achievement motivation and academic procrastination with academic achievement and investigate predictive validity of them with academic achievement and interaction of them with gender to Academic achievement. To achieve this aim, Samples of 200 (100 male and 100 female) students were selected by multi-stage cluster sampling from high schools of Orumieh. All participants were asked to complete lay’s academic procrastination scale, Hermans achievement motivation scale and self efficacy scale. The data were analysed using, mean, standard deviation, one-way ANOVA, t-test and regression analyses. The result of multiple regression analysis revealed that academic self-efficacy and gender were the best predicators and Academic procrastination inversely is a significant predictor of academic achievement. Also, extra result of t- test revealed that there is no significant between the mean score of girls and boys in academic procrastination (t= 0.47, p=0.64). There was significant difference among boys and girls, in terms of the level of achievement motivation (t=2.06, P=.04), academic achievement (t=.54, p=.000) and academic self-efficacy (t=094, P=0/01).


title: The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family

abstract: Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly with the number of output classes, which can restrict the size of problems we are able to tackle with current hardware. Second, it remains unclear how close it matches the task loss such as the top-k error rate or other non-differentiable evaluation metrics which we aim to optimize ultimately. In this paper, we introduce an alternative classification loss function, the Z-loss, which is designed to address these two issues. Unlike the log-softmax, it has the desirable property of belonging to the spherical loss family (Vincent et al., 2015), a class of loss functions for which training can be performed very efficiently with a complexity independent of the number of output classes. We show experimentally that it significantly outperforms the other spherical loss functions previously investigated. Furthermore, we show on a word language modeling task that it also outperforms the log-softmax with respect to certain ranking scores, such as top-k scores, suggesting that the Z-loss has the flexibility to better match the task loss. These qualities thus makes the Z-loss an appealing candidate to train very efficiently large output networks such as word-language models or other extreme classification problems. On the One Billion Word (Chelba et al., 2014) dataset, we are able to train a model with the Z-loss 40 times faster than the log-softmax and more than 4 times faster than the hierarchical softmax.


title: Taming the Noise in Reinforcement Learning via Soft Updates

abstract: Model-free reinforcement learning algorithms, such as Q-learning, perform poorly in the early stages of learning in noisy environments, because much effort is spent unlearning biased estimates of the state-action value function. The bias results from selecting, among several noisy estimates, the apparent optimum, which may actually be suboptimal. We propose G-learning, a new off-policy learning algorithm that regularizes the value estimates by penalizing deterministic policies in the beginning of the learning process. We show that this method reduces the bias of the value-function estimation, leading to faster convergence to the optimal value and the optimal policy. Moreover, G-learning enables the natural incorporation of prior domain knowledge, when available. The stochastic nature of G-learning also makes it avoid some exploration costs, a property usually attributed only to on-policy algorithms. We illustrate these ideas in several examples, where G-learning results in significant improvements of the convergence rate and the cost of the learning process.


title: Asthma-predictive-index, bronchial-challenge, sputum eosinophils in acutely wheezing preschoolers

abstract: Summary ::: Background ::: ::: Most preschoolers with viral wheezing exacerbations are not atopic. ::: ::: ::: ::: Aim ::: ::: To test in a prospective controlled trial whether wheezing preschoolers presenting to the ED are different from the above in three different domains defining asthma: the atopic characteristics based on stringent asthma predictive index (S-API), the characteristics of bronchial hyper-responsiveness (BHR), and airway inflammation. ::: ::: ::: ::: Methods ::: ::: The S-API was prospectively collected in 41 preschoolers (age 31.9 ± 17.4 months, range; 1–6 years) presenting to the ED with acute wheezing and compared to healthy preschoolers (n = 109) from our community (community control group). Thirty out of the 41 recruited preschoolers performed two sets of bronchial challenge tests (BCT)-(methacholine and adenosine) within 3 weeks and following 3 months of the acute event and compared to 30 consecutive ambulatory preschoolers, who performed BCT for diagnostic workup in our laboratory (ambulatory control group). On presentation, induced sputum (IS) was obtained from 22 of the 41 children. ::: ::: ::: ::: Outcomes ::: ::: Primary: S-API, secondary: BCTs characteristics and percent eosinophils in IS. ::: ::: ::: ::: Results ::: ::: Significantly more wheezing preschoolers were S-API positive compared with the community control group: 20/41 (48.7%) versus 15/109 (13.7%, P < 0.001). All methacholine-BCTs—30/30 (100%) were positive compared with 13/14 (92.8%) in the ambulatory control group (P = 0.32). However, 23/27 (85.2%) were adenosine-BCT positive versus 3/17 (17.5%) in the ambulatory control group (P < 0.001). Diagnostic IS success rate was 18/22 (81.8%). Unexpectedly, 9/18 (50.0%) showed eosinophilia in the IS. ::: ::: ::: ::: Conclusions ::: ::: Wheezing preschoolers presenting to the ED is a unique population with significantly higher rate of positive S-API and adenosine-BCT compared with controls and frequently (50%) express eosinophilic airway inflammation. Pediatr Pulmonol. 2014; 49:952–959. © 2013 Wiley Periodicals, Inc.


title: Development of the Gubernaculum During Testicular Descent in the Rat

abstract: Gubernacular elongation during inguinoscrotal testicular descent and cremaster muscle development remains poorly described in mammals. The role of the genitofemoral nerve (GFN) remains elusive. We performed detailed histological analysis of testicular descent in normal rats to provide a comprehensive anatomical description for molecular studies. Fetuses and neonatal male offspring (5–10 per group) from time-mated Sprague-Dawley dams (embryonic days 15, 16, and 19; postnatal days 0, 2, and 8) were prepared for histology. Immunohistochemistry was performed for nerves (Class III tubulin, Tuj1) and muscle (desmin). At embryonic days 15 and 16, the gubernaculum and breast bud are adjacent and both supplied by the GFN. By embryonic day 19, the breast bud has regressed and the gubernacular swelling reaction is completed. Postnatally, the gubernacular core regresses, except for a cranial proliferative zone. The cremaster is continuous with internal oblique and transversus abdominis. By postnatal day 2 (P2), the gubernaculum has everted, locating the proliferative zone caudally and the residual mesenchymal core externally. Eversion creates the processus vaginalis, with the everted gubernaculum loose in subcutaneous tissue but still remote from the scrotum. By P8, the gubernaculum has nearly reached the scrotum with fibrous connections attaching the gubernaculum to the scrotal skin. A direct link between GFN, gubernaculum, and breast bud suggests that the latter may be involved in gubernacular development. Second, the cremaster muscle is continuous with abdominal wall muscles, but most of its growth occurs in the distal gubernacular tip. Finally, gubernacular eversion at birth brings the cranial proliferative zone to the external distal tip, enabling gubernacular elongation similar to a limb bud. Anat Rec, 2011. © 2011 Wiley-Liss, Inc.


title: Biometric Gait Recognition

abstract: Psychological studies indicate that people have a small but statistically significant ability to recognize the gaits of individuals that they know. Recently, there has been much interest in machine vision systems that can duplicate and improve upon this human ability for application to biometric identification. While gait has several attractive properties as a biometric (it is unobtrusive and can be done with simple instrumentation), there are several confounding factors such as variations due to footwear, terrain, fatigue, injury, and passage of time. This paper gives an overview of the factors that affect both human and machine recognition of gaits, data used in gait and motion analysis, evaluation methods, existing gait and quasi gait recognition systems, and uses of gait analysis beyond biometric identification. We compare the reported recognition rates as a function of sample size for several published gait recognition systems.


title: Development of the Perceived Stress Questionnaire: a new tool for psychosomatic research.

abstract: Abstract A 30-question Perceived Stress Questionnaire (PSQ) was validated, in Italian and English, among 230 subjects. Test—retest reliability was 0.82 for the General (past year or two) PSQ, while monthly Recent (past month) PSQs varied by a mean factor of 1.9 over 6 months; coefficient alpha > 0.9. General and/or Recent PSQ scores were associated with trait anxiety ( r = 0.75), Cohen's Perceived Stress Scale ( r = 0.73), depression ( r = 0.56), self-rated stress ( r = 0.56), and stressful life events ( p p r > 0.5), and were higher, among 27 asymptomatic ulcerative colitis patients, in the seven who had rectal inflammation than in those with normal proctoscopy ( p = 0.03). Factor analysis yielded seven factors, of which those reflecting interpersonal conflict and tension were significantly associated with health outcomes. The Perceived Stress Questionnaire may be a valuable addition to the armamentarium of psychosomatic researchers.


title: A Novel Compact Tapered-Slot Antenna for GPR Applications

abstract: A novel compact planar tapered-slot antenna for ground penetrating radar (GPR) system is proposed. The antenna is fed by a coplanar waveguide (CPW) and evolved from one slotline of the compact CPW. Another slotline of the compact CPW forms a magnetic antenna as a supplement to the tapered-slot antenna. Some discontinuous points on the compact antenna structure will produce a relatively strong reflection, so the resistive loading is introduced. In order to validate the antenna performance, one prototype is fabricated and measured. The results demonstrate that the impedance bandwidth of the tapered slot antenna can span 0.64-6 GHz with the VSWR below 2, which agrees well with the simulated result. The radiation characteristics of the proposed antenna are measured, and its application for moving target tracking is also validated in a GPR system.


title: Pocket companion to textbook of medical physiology

abstract: I. Introduction to Physiology: The Cell and General Physiology II. Membrane Physiology, Nerve, and Muscle III. The Heart IV. The Circulation V. The Kidneys and Body Fluids VI. Blood Cells, Immunity, and Blood Clotting VII. Respiration VIII. Aviation, Space, and Deep Sea Diving Physiology IX. The Nervous System A. General Principles and Sensory Physiology X. The Nervous System B. The Special Senses XI. The Nervous System C. Motor and Integrative Neurophysiology XII. Gastrointestinal Physiology XIII. Metabolism and Temperature Regulation XIV. Endocrinology and Reproduction XV. Sports Physiology Index


title: A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

abstract: With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.


title: From Google Street View to 3D city models

abstract: We present a structure-from-motion (SfM) pipeline for visual 3D modeling of a large city area using 360° field of view Google Street View images. The core of the pipeline combines the state of the art techniques such as SURF feature detection, tentative matching by an approximate nearest neighbour search, relative camera motion estimation by solving 5-pt minimal camera pose problem, and sparse bundle adjustment. The robust and stable camera poses estimated by PROSAC with soft voting and by scale selection using a visual cone test bring high quality initial structure for bundle adjustment. Furthermore, searching for trajectory loops based on co-occurring visual words and closing them by adding new constraints for the bundle adjustment enforce the global consistency of camera poses and 3D structure in the sequence. We present a large-scale reconstruction computed from 4,799 images of the Google Street View Pittsburgh Research Data Set.


title: Nurse educators and professional ethics--ethical principles and their implementation from nurse educators' perspectives.

abstract: Summary This study describes nurse educators' knowledge of the ethical principles of professional codes of ethics and educators' assessment of the implementation of principles of fairness and human respect. Data for this study was collected from nurse educators in Finland. The data was analyzed by SPSS (15.0) for Windows. A total of 342 nurse educators participated. The response rate was 46%. Nurse educators knew well the ethical principles of professional codes governing their work. Older and more experienced educators knew the principles better than younger and less experienced. According to the educators the principle of fairness was implemented the best whereas fair treatment of nurse educators and respect for educators' opinions in the society were implemented the weakest. Educators who knew the principles well assessed themselves to act in a fairer way and to respect other persons' opinions in a better way than educators who knew these principles less well. They also felt themselves to be better treated than educators having less knowledge of the principles. These findings can be utilized to develop nurse educators' ethics education. Further research should focus on students', colleagues' and superiors' assessments of nurse educators' ethical knowledge base to gain comparative data on the phenomenon.


title: Humor: Prosody Analysis And Automatic Recognition For F*R*I*E*N*D*S*

abstract: We analyze humorous spoken conversations from a classic comedy television show, FRIENDS, by examining acoustic-prosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we automatically label speaker turns in our corpus that are followed by laughs as humorous and the rest as non-humorous. Our humor-prosody analysis reveals significant differences in prosodic characteristics (such as pitch, tempo, energy etc.) of humorous and non-humorous speech, even when accounted for the gender and speaker differences. Humor recognition was carried out using standard supervised learning classifiers, and shows promising results significantly above the baseline.


title: Measuring ability to enhance and suppress emotional expression: The Flexible Regulation of Emotional Expression (FREE) Scale.

abstract: Flexibility in self-regulatory behaviors has proved to be an important quality for adjusting to stressful life events and requires individuals to have a diverse repertoire of emotion regulation abilities. However, the most commonly used emotion regulation questionnaires assess frequency of behavior rather than ability, with little evidence linking these measures to observable capacity to enact a behavior. The aim of the current investigation was to develop and validate a Flexible Regulation of Emotional Expression (FREE) Scale that measures a person's ability to enhance and suppress displayed emotion across an array of hypothetical contexts. In Studies 1 and 2, a series of confirmatory factor analyses revealed that the FREE Scale consists of 4 first-order factors divided by regulation and emotional valence type that can contribute to 2 higher order factors: expressive enhancement ability and suppression ability. In Study 1, we also compared the FREE Scale to other commonly used emotion regulation measures, which revealed that suppression ability is conceptually distinct from suppression frequency. In Study 3, we compared the FREE Scale with a composite of traditional frequency-based indices of expressive regulation to predict performance in a previously validated emotional modulation paradigm. Participants' enhancement and suppression ability scores on the FREE Scale predicted their corresponding performance on the laboratory task, even when controlling for baseline expressiveness. These studies suggest that the FREE Scale is a valid and flexible measure of expressive regulation ability. (PsycINFO Database Record.


title: Age estimation in facial images using histogram equalization

abstract: The digital world draws sensitive attention in the field of face recognition and facial age estimation. Despite of various transpiring researches in this domain, invigoration is indeed required. With respect to various challenges, the estimation of age requires consideration of several aspects. The estimation of age can be refined by considering both geometrical measures and texture analysis. The steps to be followed are normalization, face cropping, filtering, feature extraction and finally classification to provide the refined age of the face image. The normalization of face is done using histogram equalization to study the intensity component. From the image the face is cropped. The median filter is applied on the image to detach noise in the image and to maintain the edge details. The requisite features are extracted from the image such as geometric measures, texture details. The structure of texture features and the intensity transitions are analyzed. With these descriptors, the age of the image is classified.


title: Low cost tangible glove for translating sign gestures to speech and text in Hindi language

abstract: Vocal Communication is a way to convey our thoughts, messages and information. However, each of us is not gifted to be able to share our thoughts in verbal mode with others due to some physical disabilities. The deaf and the mute are the one who face extreme difficulty in conveying their messages to others. Normally deaf and the mute people use sign language for communication but it turns out to be a difficult when others are not able to understand the sign language. A number of devices are available that converts the human gestures to English speech and text but no such device is available for Hindi Language. Hindi is the most spoken and National language in India and is widely used in the country. Keeping a pace with advent of technology there is a desperate need of cost effective devices which could convert the sign language into speech/text in Hindi to establish an independent communication and movement for deaf and mute people in every sphere of society. In this paper, we propose a tangible interface using a customized glove equipped with flex sensors. These sensors are placed across the length of fingers and the thumb. Hand gestures can be captured by the digital glove which are then converted to speech/text so that it could easily be understood by the common people. The experiments conducted with the design prototype have shown significant interaction improvements among the society and deaf-mute people. The experiments on modeling and improving the prototype is going on to improvise the interaction specially for mobile devices.


title: Towards a Planetary Spatial Data Infrastructure

abstract: Planetary science is the study of planets, moons, irregular bodies such as asteroids and the processes that create and modify them. Like terrestrial sciences, planetary science research is heavily dependent on collecting, processing and archiving large quantities of spatial data to support a range of activities. To address the complexity of storing, discovering, accessing, and utilizing spatial data, the terrestrial research community has developed conceptual Spatial Data Infrastructure (SDI) models and cyberinfrastructures. The needs that these systems seek to address for terrestrial spatial data users are similar to the needs of the planetary science community: spatial data should just work for the non-spatial expert. Here we discuss a path towards a Planetary Spatial Data Infrastructure (PSDI) solution that fulfills this primary need. We first explore the linkage between SDI models and cyberinfrastructures, then describe the gaps in current PSDI concepts, and discuss the overlap between terrestrial SDIs and a new, conceptual PSDI that best serves the needs of the planetary science community.


Parametric Analysis of Different Configurations of Loop Elements in Frequency-Selective Surfaces


MRI Brain Image Segmentation based on Thresholding


title: LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network

abstract: We present a method for skin lesion segmentation for the ISIC 2017 Skin Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional Network architecture which is trained end to end, from scratch, on a limited dataset. Our semantic segmentation architecture utilizes several recent innovations in particularly in the combined use of (i) use of \emph{atrous} convolutions to increase the effective field of view of the network's receptive field without increasing the number of parameters, (ii) the use of network-in-network $1\times1$ convolution layers to increase network capacity without incereasing the number of parameters and (iii) state-of-art super-resolution upsampling of predictions using subpixel CNN layers for accurate and efficient upsampling of predictions. We achieved a IOU score of 0.642 on the validation set provided by the organisers.


title: MOOD AND MEMORY

abstract: This article describes experiments in which happy or sad moods were induced in subjects by hyp- notic suggestion to investigate the influence of emo- tions on memory and thinking. One result was that subjects exhibited mood-state-dependent memory in recall of word lists, personal experiences recorded in a daily diary, and childhood experiences; people recalled a greater percentage of those experiences that were affectively congruent with the mood they were in dur- ing recall. Second, emotion powerfully influenced such cognitive processes as free associations, imaginative fantasies, social perceptions, and snap judgments about others' personalities (e.g., angry subjects generated an- gry associates, told hostile stories, and were prone to find fault with others). Third, when the feeling-tone of a narrative agreed with the reader's emotion, the salience and memorability of events in that narrative were increased. Thus, sad readers attended more to sad material, identified with a sad character from a story, and recalled more about that character. An associative network theory is proposed to account for these several results. In this theory, an emotion serves as a memory unit that can enter into associations with coincident events. Activation of this emotion unit aids retrieval of events associated with it; it also primes emotional themata for use in free association, fantasies, and per- ceptual categorization.


title: A scheme for robust distributed sensor fusion based on average consensus

abstract: We consider a network of distributed sensors, where where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with a weighted average of its neighbors' data (they maintain the same data structure). At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected.


title: Protein therapeutics: new applications for pharmacogenetics

abstract: Pharmacogenetic studies have traditionally focused on genes involved in processes that affect the pharmacokinetics of small-molecule drugs, such as drug metabolism. However, attention is shifting to the effects of genetic variations in drug targets and associated pathway components on drug responses. We describe how these variations are important for understanding differences in responses to the growing number of protein therapeutics that are entering clinical practice. Pharmacogenetic studies of these drugs are surveyed, and issues important to the success of such endeavours are discussed. As novel protein therapeutics are introduced, we anticipate that the use of pharmacogenetics will assume a key role in their development and clinical application.


title: Qualitative data analysis.

abstract: Qualitative data is extremely varied in nature. It includes virtually any information that can be captured that is not numerical in nature. Here are some of the major categories or types: ::: ::: In-Depth Interviews ::: In-Depth Interviews include both individual interviews (e.g., one-on-one) as well as "group" interviews (including focus groups). The data can be recorded in a wide variety of ways including stenography, audio recording, video recording or written notes. In depth interviews differ from direct observation primarily in the nature of the interaction. In interviews it is assumed that there is a questioner and one or more interviewees. The purpose of the interview is to probe the ideas of the interviewees about the phenomenon of interest. ::: ::: Direct Observation ::: Direct observation is meant very broadly here. It differs from interviewing in that the observer does not actively query the respondent. It can include everything from field research where one lives in another context or culture for a period of time to photographs that illustrate some aspect of the phenomenon. The data can be recorded in many of the same ways as interviews (stenography, audio, video) and through pictures, photos or drawings (e.g., those courtroom drawings of witnesses are a form of direct observation). ::: ::: Written Documents ::: Usually this refers to existing documents (as opposed transcripts of interviews conducted for the research). It can include newspapers, magazines, books, websites, memos, transcripts of conversations, annual reports, and so on. Usually written documents are analyzed with some form of content analysis. ::: ::: sumber : http://www.socialresearchmethods.net/kb/qualdata.php


title: Evaluating CALM M5-based vehicle-to-vehicle communication in various road settings through field trials

abstract: Future cooperative Intelligent Transport Systems (ITS) applications aimed to improve safety, efficiency and comfort on our roads put high demands on the underlying wireless communication system. To gain better understanding of the limitations of the 5.9 GHz frequency band and the set of communication protocols for medium range vehicle to vehicle (V2V) communication, a set of field trials with CALM M5 enabled prototypes has been conducted. This paper describes five different real vehicle traffic scenarios covering both urban and rural settings at varying vehicle speeds and under varying line-of-sight (LOS) conditions and discusses the connectivity (measured as Packet Reception Ratio) that could be achieved between the two test vehicles. Our measurements indicate a quite problematic LOS sensitivity that strongly influences the performance of V2V-based applications. We further discuss how the awareness of these context-based connectivity problems can be used to improve the design of possible future cooperative ITS safety applications.


title: Radiomics: Images Are More than Pictures, They Are Data

abstract: This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.


title: ALIVE: artificial life interactive video environment

abstract: In this video we demonstrate a novel system which allows wireless full-body interaction between a human participant and a graphical world inhabited by autonomous agents. The system is called "ALIVE", an acronym for Artificial Life Interactive Video Environment. The goal of ALIVE is to present a virtual environment in which a user can interact, in natural and believable ways, with autonomous semi-intelligent agents whose behavior is equally natural and believable.


title: Quantum Annealing and Analog Quantum Computation

abstract: We review here the recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations. The concept is introduced in successive steps through the studies of mapping of such computationally hard problems to the classical spin glass problems. The quantum spin glass problems arise with the introduction of quantum fluctuations, and the annealing behavior of the systems as these fluctuations are reduced slowly to zero. This provides a general framework for realizing analog quantum computation.


title: Generic framework enabling secure and efficient automotive wireless SW updates

abstract: Future vehicles will be wirelessly connected to nearby vehicles, to the road infrastructure and to the Internet in order to enable new comfort features, safety functions and a number of new vehicle-specific services. The latter will include a fast, secure, and reliable way to remotely diagnose and reconfigure a vehicle as well as to install new software on the electronic control units integrated in a vehicle. Such wireless software updates are beneficial for both automotive OEMs and customers, as they allow to enable new features of the vehicle remotely and to fix software bugs by installing a new software version over the air. Wireless diagnostics and software updates are required in several stages of a vehicle's lifetime: from the manufacturing stage on the assembly line and the maintenance in a workshop to the remote download of up-to-date software directly by the car owner. To support this process over a whole vehicle's lifetime, a generic framework is needed. In this paper we propose a generic framework enabling secure and efficient wireless automotive SW updates and hence supporting a vehicle's whole lifetime. We describe the IEEE 802.11s network used as wireless medium to interconnect vehicles and diagnostic devices in a reliable, trustworthy and fast way and propose a dedicated cross-layer security concept applying strong authentication as well as encryption mechanisms.


title: Efficient Data Management for GPU Databases

abstract: General purpose GPUs are a new and powerful hardware device with a number of applications in the realm of relational databases. We describe a database framework designed to allow both CPU and GPU execution of queries. Through use of our novel data structure design and method of using GPUmapped memory with efficient caching, we demonstrate that GPU query acceleration is possible for data sets much larger than the size of GPU memory. We also argue that the use of an opcode model of query execution combined with a simple virtual machine provides capabilities that are impossible with the parallel primitives used for most GPU database research. By implementing a single database framework that is efficient for both the CPU and GPU, we are able to make a fair comparison of performance for a filter operation and observe speedup on the GPU. This work is intended to provide a clearer picture of handling very abstract data operations efficiently on heterogeneous systems in anticipation of further application of GPU hardware in the relational database domain. Speedups of 4x and 8x over multicore CPU execution are observed for arbitrary data sizes and GPU-cacheable data sizes, respectively.


title: A NEW SCALE OF SOCIAL DESIRABILITY INDEPENDENT OF PSYCHOPATHOLOGY

abstract: It has long been recognized that personality test scores are influenced by non-test-relevant response determinants. Wiggins and Rumrill (1959) distinguish three approaches to this problem. Briefly, interest in the problem of response distortion has been concerned with attempts at statistical correction for "faking good" or "faking bad" (Meehl & Hathaway, 1946), the analysis of response sets (Cronbach, 1946,1950), and ratings of the social desirability of personality test items (Edwards, 19 5 7). A further distinction can be made, however, which results in a somewhat different division of approaches to the question of response distortion. Common to both the Meehl and Hathaway corrections for faking good and faking bad and Cronbach's notion of response sets is an interest in the test behavior of the subject(S). By social desirability, on the other hand, Edwards primarily means the "scale value for any personality statement such that the scale value indicates the position of the statement on the social desirability continuum . . ." (1957, p. 3). Social desirability, thus, has been used to refer to a characteristic of test items, i.e., their scale position on a social desirability scale. Whether the test behavior of 5s or the social desirability properties of items are the focus of interest, however, it now seems clear that underlying both these approaches is the concept of statistical deviance. In the construction of the MMPI K scale, for example, items were selected which differentiated between clinically normal persons producing abnormal te¥Tpfpfiles~snd^cTinically abnormal individuals with abnormal test profiles, and between clinically abnormal persons with normal test profiles and abnormal 5s whose test records were abnormal. Keyed responses to the K scale items tend to be statistically deviant in the parent populations. Similarly, the development of the Edwards Social Desirability Scale (SDS) illustrates this procedure. Items were drawn from various MMPI scales (F, L, K, and the Manifest Anxiety Scale [Taylor, 1953]) and submitted to judges who categorized them as either socially desirable or socially undesirable. Only items on which there was unanimous agreement among the 10 judges were included in the SDS. It seems clear that the items in Edwards SDS would, of necessity, have extreme social desirability scale positions or, in other words, be statistically deviant. Some unfortunate consequences follow from the strict use of the statistical deviance model in the development of-sOcialTtesirSbTBty scales. With items drawn from the MMPI, it is apparent that in addition to their scalability for social desirability the items may also be characterized by their content which,^n a general sense, has pathological implications. When a social desrrabtltty^scale constructed according to this procedure is then applied to a college student population, the meaning of high social desirability scores is not at all clear. When 5s given the Edwards SDS deny, for example, that their sleep is fitful and disturbed (Item 6) or that they worry quite a bit over possible misfortunes (Item 35), it cannot be determined whether these responses are attributable to social desirability or to a genuine absence of such symptoms. The probability of occurrence of the symptoms represented in MMPI items (and incorportated in the SDS)


title: The domino effect of password reuse

abstract: One weak spot is all it takes to open secured digital doors and online accounts causing untold damage and consequences.


title: An Effective Knowledge base system Architecture and issues in representation techniques

abstract: The research in AI is divided in to two categories Knowledge representation and general. For making the computer or Machine as intelligent as human being we require two things Knowledge representation and inference mechanism. Development of an AI system is a crucial task because some time we have incomplete information and it can be ambiguous and uncertain. So solution to these problems is to build a knowledge effective knowledge base and an effective inference mechanism. The objective of this paper is to present the knowledge base system architecture/model for intelligent system and to present the issues in various hybrid KR techniques.


title: A fast block matching algorthim for stereo correspondence

abstract: Stereo correspondence is one of the most active research areas in computer vision. An improved block matching approach for fast stereo correspondence is proposed, where the matching criterion is the sum of absolute difference (SAD). The approach tries to progressively confine the matching operation during the searching process, terminates unnecessary computations of matching criteria between the reference block in the left image and the ineligible candidate block in the right image, and eliminates the ineligible block as early as possible while ensuring the optimal disparity of each pixel. The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing it with the stereo correspondence method based on the standard block matching. Simulation results demonstrate that the disparities obtained by this algorithm are identical to that using standard block matching method, and a reduction of over 55% in computational cost is achieved.


title: C4b-Binding Protein: A Forgotten Factor in Thrombosis and Hemostasis

abstract: Blood coagulation and complement pathways are two important natural defense systems. The high affinity interaction between the anticoagulant vitamin K-dependent protein S and the complement regulator C4b-binding protein (C4BP) is a direct physical link between the two systems. In human plasma, ~70% of total protein S circulates in complex with C4BP; the remaining is free. The anticoagulant activity of protein S is mainly expressed by the free form, although the protein S-C4BP complex has recently been shown to have some anticoagulant activity. The high affinity binding of protein S to C4BP provides C4BP with the ability to bind to negatively charged phospholipid membranes, which serves the purpose of localizing complement regulatory activity close to the membrane. Even though C4BP does not directly affect the coagulation system, it still influences the regulation of blood coagulation through its interaction with protein S. This is particularly important in states of inherited deficiency of protein S where the tight binding of protein S to C4BP results in a pronounced and selective drop in concentration of free protein S, whereas the concentration of protein S in complex with C4BP remains relatively unchanged. This review summarizes the current knowledge on C4BP with respect to its association with thrombosis and hemostasis.


title: Progressive Neural Networks for Image Classification

abstract: The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images. However, in practice, it is highly desirable to establish a progressive structure for deep neural networks which is able to adapt its inference process and complexity for images with different visual recognition complexity. In this work, we develop a multi-stage progressive structure with integrated confidence analysis and decision policy learning for deep neural networks. This new framework consists of a set of network units to be activated in a sequential manner with progressively increased complexity and visual recognition power. Our extensive experimental results on the CIFAR-10 and ImageNet datasets demonstrate that the proposed progressive deep neural network is able to obtain more than 10 fold complexity scalability while achieving the state-of-the-art performance using a single network model satisfying different complexity-accuracy requirements.


LSDSem 2017 Shared Task: The Story Cloze Test


title: Heart Rate Estimation Method by Measuring Pulse Wave Through Conductive Fibers Mounted on Forearms

abstract: In this paper, we propose a method for estimating heart rate from pulse wave measured through conductive fibers mounted on forearms. In recent years, heart rate is an effective index for describing mental and physical condition. Accordingly, many types of wearable heart rate measuring devices have been developed. However, there are problems such as uncertainty in the measured waveform caused by contact failure and large burdens on subjects brought by wearing oppression. Hence, it is necessary to have a device that brings less burdens and can measure stably. In this research, we propose a forearm-mounted pulse wave measuring device made of stretchable conductive fibers that can be comfortably worn and can reduce contact failure. In addition, we propose a method for estimating heart rate from pulse wave measured by proposed device. In order to evaluate the effectiveness of the proposed method, we show the accuracy of measuring heart rate and the sample entropy of the heart rate variability waveform. As a result, it was confirmed that pulse wave can be measured stably irrespective of both rest and moving state.


title: Fast Multi-view Face Detection

abstract: Abstract This paper extends the face detection framework proposedby Viola and Jones 2001 to handle proﬁle views and rotatedfaces. As in the work of Rowley et al 1998. and Schneider-man et al. 2000, we build different detectors for differentviews of the face. A decision tree is then trained to deter-mine the viewpoint class (such as right proﬁle or rotated60 degrees) for a given window of the image being exam-ined. This is similar to the approach of Rowley et al. 1998.The appropriate detector for that viewpoint can then be runinstead of running all detectors on all windows. This tech-niqueyields goodresults and maintainsthe speed advantageof the Viola-Jones detector. 1. Introduction There are a number of techniques that can successfullydetect frontal upright faces in a wide variety of images[11, 7, 10, 12, 3, 6]. While the deﬁnition of “frontal” and“upright”mayvaryfromsystem to system, the reality is thatmany natural images contain rotated or proﬁle faces thatare not reliably detected. There are a small number of sys-tems which explicitly address non-frontal, or non-uprightface detection [8, 10, 2]. This paper describes progress to-ward a system which can detect faces regardless of posereliably and in real-time.This paperextendsthe frameworkproposedby Viola andJones [12]. This approach is selected because of its compu-tational efﬁciency and simplicity.One observation which is shared among all previous re-lated work is that a multi-view detector must be carefullyconstructed by combining a collection of detectors eachtrained for a single viewpoint. It appears that a monolithicapproach, where a single classiﬁer is trained to detect allposes of a face, is unlearnable with existing classiﬁers. Ourinformal experiments lend support to this conclusion, sincea classiﬁer trained on all poses appears to be hopelessly in-accurate.This paper addresses two types of pose variation: non-frontal faces, which are rotated out of the image plane, andnon-upright faces, which are rotated in the image plane.In both cases the multi-view detector presented in this pa-per is a combination of Viola-Jones detectors, each detectortrained on face data taken from a single viewpoint.Reliable non-upright face detection was ﬁrst presentedin a paper by Rowley, Baluja and Kanade [8]. They traintwo neural network classiﬁers. The ﬁrst estimates the poseof a face in the detection window. The second is a conven-tional face detector. Faces are detected in three steps: foreach image window the pose of “face” is ﬁrst estimated; thepose estimate is then used to de-rotate the image window;the window is then classiﬁed by the second detector. Fornon-face windows, the poses estimate must be consideredrandom. Nevertheless, a rotated non-faceshouldbe rejectedby the conventional detector. One potential ﬂaw of such asystem is that the ﬁnal detection rate is roughly the productof the correct classiﬁcation rates of the two classiﬁers (sincethe errors of the two classiﬁers are somewhat independent).One could adopt the Rowley et al. three step approachwhile replacingthe classiﬁers with those of Viola andJones.The ﬁnal system would be more efﬁcient, but not signiﬁ-cantly. Classiﬁcation by the Viola-Jones system is so efﬁ-cient, that derotation would dominate the computational ex-pense. In principle derotation is not strictly necessary sinceit should be possible to construct a detector for rotated facesdirectly. Detection becomes a two stage process. First thepose of the window is estimated and then one ofrotationspeciﬁc detectors is called upon to classify the window.In this paper detection of non-upright faces is handledusing the two stage approach. In the ﬁrst stage the pose ofeach window is estimated using a decision tree constructedusing features like those described by Viola and Jones. Inthe second stage one ofpose speciﬁc Viola-Jones dete-tectors are used to classify the window.Oncepose speciﬁc detectors are trained and available,an alternative detection process can be tested as well. In thiscase alldetectors are evaluated and the union of their de-


title: 60 GHz wireless communications: emerging requirements and design recommendations

abstract: Multiple GHz of internationally available, unlicensed spectrum surrounding the 60 GHz carrier frequency has the ability to accommodate high-throughput wireless communications. While the size and availability of this free spectrum make it very attractive for wireless applications, 60 GHz implementations must overcome many challenges. For example, the high attenuation and directional nature of the 60 GHz wireless channel as well as limited gain amplifiers and excessive phase noise in 60 GHz transceivers are explicit implementation difficulties. The challenges associated with this channel motivate commercial deployment of short-range wireless local area networks, wireless personal area networks, and vehicular networks. In this paper we detail design tradeoffs for algorithms in the 60 GHz physical layer including modulation, equalization, and space-time processing. The discussion is enhanced by considering the limitations in circuit design, characteristics of the effective wireless channel (including antennas), and performance requirements to support current and next generation 60 GHz wireless communication applications.


title: SHANK3 mutations identified in autism lead to modification of dendritic spine morphology via an actin-dependent mechanism

abstract: Genetic mutations of SHANK3 have been reported in patients with intellectual disability, autism spectrum disorder (ASD) and schizophrenia. At the synapse, Shank3/ProSAP2 is a scaffolding protein that connects glutamate receptors to the actin cytoskeleton via a chain of intermediary elements. Although genetic studies have repeatedly confirmed the association of SHANK3 mutations with susceptibility to psychiatric disorders, very little is known about the neuronal consequences of these mutations. Here, we report the functional effects of two de novo mutations (STOP and Q321R) and two inherited variations (R12C and R300C) identified in patients with ASD. We show that Shank3 is located at the tip of actin filaments and enhances its polymerization. Shank3 also participates in growth cone motility in developing neurons. The truncating mutation (STOP) strongly affects the development and morphology of dendritic spines, reduces synaptic transmission in mature neurons and also inhibits the effect of Shank3 on growth cone motility. The de novo mutation in the ankyrin domain (Q321R) modifies the roles of Shank3 in spine induction and morphology, and actin accumulation in spines and affects growth cone motility. Finally, the two inherited mutations (R12C and R300C) have intermediate effects on spine density and synaptic transmission. Therefore, although inherited by healthy parents, the functional effects of these mutations strongly suggest that they could represent risk factors for ASD. Altogether, these data provide new insights into the synaptic alterations caused by SHANK3 mutations in humans and provide a robust cellular readout for the development of knowledge-based therapies.


title: Bibliometric analysis of peer-reviewed literature in transgender health (1900 – 2017)

abstract: BACKGROUND ::: Transgender community is marginalized and under-researched. Analysis of peer-reviewed literature in transgender health is needed to better understand health needs and human rights of transgender people. Therefore, the aim of this study was to analyze global research activity in transgender health published in peer-reviewed journals. ::: ::: ::: METHODS ::: Peer-reviewed documents in transgender health were retrieved using Scopus database. VOSviewer was used to map frequently encountered author keywords while ArcGIS 10.1 was used to map the geographical distribution of the retrieved documents. Most active countries, institutions, and authors were presented. The study period was set from 1900 to 2017. ::: ::: ::: RESULTS ::: In total, 5772 peer-reviewed documents were obtained. English (5008; 86.8%) was the most frequently encountered language. A dramatic increase in the number of publications was seen in the last decade. The retrieved documents had an average of 12.1 citations per document and h-index of 92. Most frequently encountered author keywords were Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS), mental health, and discrimination. Authors from 80 different countries contributed to publishing the retrieved documents. Publications originated mainly from Northern America, certain European countries, Australia, and Brazil. Professor Gooren, L.J.G. was the most active author in this field with 104 (1.88%) publications. Top active authors were in the fields of endocrinology, plastic surgery, psychiatry/psychology, public health, and sexology. Five of the top ten active authors were from the USA, three were from the Netherlands, and two were from Belgium. The most active institution was the VU University Medical Center (Netherlands) (184; 3.2%) followed by the University of California, San Francisco (USA) (157; 2.7%). The International Journal of Transgenderism was most active (284; 4.9%) in publishing articles in transgender health. However, documents published in the American Journal of Public Health had the highest impact with 53.5 citations per article. ::: ::: ::: CONCLUSION ::: There was a noticeable growth of research in transgender health in the last decade. Researchers from different world regions need to get involved in health and human rights research of transgender community.


title: On the capacity of thermal covert channels in multicores

abstract: Modern multicore processors feature easily accessible temperature sensors that provide useful information for dynamic thermal management. These sensors were recently shown to be a potential security threat, since otherwise isolated applications can exploit them to establish a thermal covert channel and leak restricted information. Previous research showed experiments that document the feasibility of (low-rate) communication over this channel, but did not further analyze its fundamental characteristics. For this reason, the important questions of quantifying the channel capacity and achievable rates remain unanswered. To address these questions, we devise and exploit a new methodology that leverages both theoretical results from information theory and experimental data to study these thermal covert channels on modern multicores. We use spectral techniques to analyze data from two representative platforms and estimate the capacity of the channels from a source application to temperature sensors on the same or different cores. We estimate the capacity to be in the order of 300 bits per second (bps) for the same-core channel, i.e., when reading the temperature on the same core where the source application runs, and in the order of 50 bps for the 1-hop channel, i.e., when reading the temperature of the core physically next to the one where the source application runs. Moreover, we show a communication scheme that achieves rates of more than 45 bps on the same-core channel and more than 5 bps on the 1-hop channel, with less than 1% error probability. The highest rate shown in previous work was 1.33 bps on the 1-hop channel with 11% error probability.