title: Hybrid gate dielectric materials for unconventional electronic circuitry.

abstract: Recent advances in semiconductor performance made possible by organic π-electron molecules, carbon-based nanomaterials, and metal oxides have been a central scientific and technological research focus over the past decade in the quest for flexible and transparent electronic products. However, advances in semiconductor materials require corresponding advances in compatible gate dielectric materials, which must exhibit excellent electrical properties such as large capacitance, high breakdown strength, low leakage current density, and mechanical flexibility on arbitrary substrates. Historically, conventional silicon dioxide (SiO2) has dominated electronics as the preferred gate dielectric material in complementary metal oxide semiconductor (CMOS) integrated transistor circuitry. However, it does not satisfy many of the performance requirements for the aforementioned semiconductors due to its relatively low dielectric constant and intransigent processability. High-k inorganics such as hafnium dioxide (HfO2) or zirconium dioxide (ZrO2) offer some increases in performance, but scientists have great difficulty depositing these materials as smooth films at temperatures compatible with flexible plastic substrates. While various organic polymers are accessible via chemical synthesis and readily form films from solution, they typically exhibit low capacitances, and the corresponding transistors operate at unacceptably high voltages. More recently, researchers have combined the favorable properties of high-k metal oxides and π-electron organics to form processable, structurally well-defined, and robust self-assembled multilayer nanodielectrics, which enable high-performance transistors with a wide variety of unconventional semiconductors. In this Account, we review recent advances in organic-inorganic hybrid gate dielectrics, fabricated by multilayer self-assembly, and their remarkable synergy with unconventional semiconductors. We first discuss the principals and functional importance of gate dielectric materials in thin-film transistor (TFT) operation. Next, we describe the design, fabrication, properties, and applications of solution-deposited multilayer organic-inorganic hybrid gate dielectrics, using self-assembly techniques, which provide bonding between the organic and inorganic layers. Finally, we discuss approaches for preparing analogous hybrid multilayers by vapor-phase growth and discuss the properties of these materials.


title: Hybrid intrusion detection in connected self-driving vehicles

abstract: Emerging self-driving vehicles are vulnerable to different attacks due to the principle and the type of communication systems that are used in these vehicles. These vehicles are increasingly relying on external communication via vehicular ad hoc networks (VANETs). VANETs add new threats to self-driving vehicles that contribute to substantial challenges in autonomous systems. These communication systems render self-driving vehicles vulnerable to many types of malicious attacks, such as Sybil attacks, Denial of Service (DoS), black hole, grey hole and wormhole attacks. In this paper, we propose an intelligent security system designed to secure external communications for self-driving and semi self-driving cars. The proposed scheme is based on Proportional Overlapping Score (POS) to decrease the number of features found in the Kyoto benchmark dataset. The hybrid detection system relies on the Back Propagation neural networks (BP), to detect a common type of attack in VANETs: Denial-of-Service (DoS). The experimental results show that the proposed BP-IDS is capable of identifying malicious vehicles in self-driving and semi self-driving vehicles.


title: An overview of end-to-end language understanding and dialog management for personal digital assistants

abstract: Spoken language understanding and dialog management have emerged as key technologies in interacting with personal digital assistants (PDAs). The coverage, complexity, and the scale of PDAs are much larger than previous conversational understanding systems. As such, new problems arise. In this paper, we provide an overview of the language understanding and dialog management capabilities of PDAs, focusing particularly on Cortana, Microsoft's PDA. We explain the system architecture for language understanding and dialog management for our PDA, indicate how it differs with prior state-of-the-art systems, and describe key components. We also report a set of experiments detailing system performance on a variety of scenarios and tasks. We describe how the quality of user experiences are measured end-to-end and also discuss open issues.


title: Quantifying the Financial Value of Cloud Investments: A Systematic Literature Review

abstract: The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. This paper conducts a multidisciplinary systematic literature review drawing from CS, IS, and Business disciplines to understand the current evidence on the quantification of financial value from cloud computing investments. The study identified 53 articles, which were coded in an analytical framework across six themes (measurement type, costs, benefits, adoption type, actor and service model). Future research directions were presented for each theme. The review highlights the need for multi-disciplinary research which both explores and further develops the conceptualization of value in cloud computing research, and research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios.


title: Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

abstract: Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention. Most previous machine learning studies use hand-designed features, such as functional connectivity, which does not maintain the potential useful information in the spatial relationship between brain regions and the temporal profile of the signal in each region. Here we propose a new method based on recurrent-convolutional neural networks to automatically learn useful representations from segments of 4-D fMRI recordings. Our goal is to exploit both spatial and temporal information in the functional MRI movie (at the whole-brain voxel level) for identifying patients with schizophrenia.


title: Data envelopment analysis with missing values : An interval DEA approach

abstract: Missing values in inputs, outputs cannot be handled by the original data envelopment analysis (DEA) models. In this paper we introduce an approach based on interval DEA that allows the evaluation of the units with missing values along with the other units with available crisp data. The missing values are replaced by intervals in which the unknown values are likely to belong. The constant bounds of the intervals, depending on the application, can be estimated by using statistical or experiential techniques. For the units with missing values, the proposed models are able to identify an upper and a lower bound of their efficiency scores. The efficiency analysis is further extended by estimating new values for the initial interval bounds that may turn the unit to an efficient one. The proposed methodology is illustrated by an application which evaluates the efficiency of a set of secondary public schools in Greece, a number of which appears to have missing values in some inputs and outputs.


title: Learning from positive and unlabeled examples

abstract: In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any, target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications.


title: Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial"Bottleneck"Structure

abstract: Deep convolutional neural networks achieve remarkable visual recognition performance, at the cost of high computational complexity. In this paper, we have a new design of efficient convolutional layers based on three schemes. The 3D convolution operation in a convolutional layer can be considered as performing spatial convolution in each channel and linear projection across channels simultaneously. By unravelling them and arranging the spatial convolution sequentially, the proposed layer is composed of a single intra-channel convolution, of which the computation is negligible, and a linear channel projection. A topological subdivisioning is adopted to reduce the connection between the input channels and output channels. Additionally, we also introduce a spatial "bottleneck" structure that utilizes a convolution-projection-deconvolution pipeline to take advantage of the correlation between adjacent pixels in the input. Our experiments demonstrate that the proposed layers remarkably outperform the standard convolutional layers with regard to accuracy/complexity ratio. Our models achieve similar accuracy to VGG, ResNet-50, ResNet-101 while requiring 42, 4.5, 6.5 times less computation respectively.


title: Recovery of drawing order in handwritten digit images

abstract: This paper presents recovery of drawing order that converts offline handwritten text to their online handwritten format. The offline text images are preprocessed using stages as size normalization, noise removal and thinning of text. We have proposed a traversal algorithm to recover drawing order of digit images that convert offline image to online handwriting format and find trajectory direction using chain code features. The chain code features are extracted from the recovered trajectories and support vector machine has used as recognition technique to recognize text images. Our approach has been implemented with MNIST database and we have achieved an overall error rate as 2.61%.


title: Drag and Drop: A Better Approach to CAPTCHA

abstract: A Completely Automated Public Turing test to tell Computers and Humans Apart is a test use to determine whether the user is a human or a malicious computer program. In simple terms CAPTCHA is a program that can generate and grade tests that most humans can pass, yet current computer programs can't pass. They can be found as blurred or distorted random images, in free email signups, online polls or commenting on blogs; these images are difficult to recognize by computer programs but easy for most humans. They work as basic tool for preventing Denial of Service, brute force attacks and advertising spams. CAPTCHAs have evolved from simple text based to recent advancements like audio and visual tests. Text based CAPTCHAs have been broken successfully with the help of Optical Character Recognition and every approach is vulnerable against laundry attacks or redirection. The suggested approach 'Drag and Drop CAPTCHA' or 'DnD' is inclusive solution against OCR and laundry attacks. This HIP challenge uses conventional mouse events to recognize human intervention proof. If an attacker computer knows the answer of test but it can't pass this test without human intervention. This technique can be considered as an Artificial Intelligence challenge. It creates win-win situation either this tough AI problem gets solved or DnD technique is distinction tool for human & computers.


title: Bayesian Multinomial Logistic Regression for Author Identification

abstract: Motivated by high‐dimensional applications in authorship attribution, we describe a Bayesian multinomial logistic regression model together with an associated learning algorithm.


title: Fingerprint Matching Incorporating Ridge Features With Minutiae

abstract: This paper introduces a novel fingerprint matching algorithm using both ridge features and the conventional minutiae feature to increase the recognition performance against nonlinear deformation in fingerprints. The proposed ridge features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type. These ridge features have some advantages in that they can represent the topology information in entire ridge patterns existing between two minutiae and are not changed by nonlinear deformation of the finger. For extracting ridge features, we also define the ridge-based coordinate system in a skeletonized image. With the proposed ridge features and conventional minutiae features (minutiae type, orientation, and position), we propose a novel matching scheme using a breadth-first search to detect the matched minutiae pairs incrementally. Following that, the maximum score is computed and used as the final matching score of two fingerprints. Experiments were conducted for the FVC2002 and FVC2004 databases to compare the proposed method with the conventional minutiae-based method. The proposed method achieved higher matching scores. Thus, we conclude that the proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used in conjunction with existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.


title: A UHF RFID antenna for a wireless sensor platform with a near-isotropic radiation pattern

abstract: An antenna designed for a spherical surface with a near-isotropic radiation pattern is presented in this work. The antenna is designed for wireless, UHF RFID sensors. It is electrically small with a diameter of 52 mm and operates in the UHF RFID frequency band. The antenna consists of a spherical ABS plastic shell surrounding a standard FR4 printed circuit board. This allows ample space for sensors and electronic circuitry. A prototype sensor platform has been manufactured and tested. The outer shell was produced using a 3D printer. An Alien Technology Higgs 3 RFID IC was used to test the basic functionality of the antenna. Simulation and measured results are presented.


title: Scalable Time-Decaying Adaptive Prediction Algorithm

abstract: Online learning is used in a wide range of real applications, e.g., predicting ad click-through rates (CTR) and personalized recommendations. Based on the analysis of users' behaviors in Video-On-Demand (VoD) recommender systems,we discover that the most recent users' actions can better reflect users' current intentions and preferences. Under this observation, we thereby propose a novel time-decaying online learning algorithm derived from the state-of-the-art FTRL-proximal algorithm, called Time-Decaying Adaptive Prediction (TDAP) algorithm. To scale Big Data, we further parallelize our algorithm following the data parallel scheme under both BSP and SSP consistency model. We experimentally evaluate our TDAP algorithm on real IPTV VoD datasets using two state-of-the-art distributed computing platforms, TDAP achieves good accuracy: it improves at least 5.6% in terms of prediction accuracy, compared to FTRL-proximal algorithm; and TDAP scales well: it runs 4 times faster when the number of machines increases from 2 to 10.


title: Computerized Adaptive Test based on Item Response Theory in E-Learning System

abstract: Adaptive Test (CAT) is a computer-based test framework which has ability to customize questions items given to the learner based on their estimated ability. In this research, the CAT system is build using Item Response Theory (IRT) techniques to develop an adaptive system based on question item's difficulty level and students' ability level. Moreover, to figure out the effectiveness of this CAT system, we do some experiments by comparing the average post-test score of students in CAT system and conventional system. The experiments result reveals that the average post-test score of students in the CAT system is much higher than the average post-test score of students in traditional test system.


Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network


title: Security Enhancement in Image Steganography a MATLAB Approach

abstract: Steganography helps in communication of secured data in several carries like images, videos and audio. It undergoes many useful applications and well known for ill intentions. It was mainly proposed for the security techniques in the increase of computational power, in order to have security awareness like individuals, groups, agencies etc. The factors that are separated from cryptography and water making are data is not detectable; capacity of hidden data is unknown and robustness of medium. The steganography provides different methods existing and guidelines. The current technology of image steganography involves techniques of LSB in image domain but once the attacker acknowledges that medium is containing embedded data he will attack the medium and breaks into the secured content. In this paper we are discussing how to protect the steganography image by embedding it into another medium using mat lab. Here we work on image matrices to perform the steganography. Lightness adjustment on the matrix is done to reduce the brighter pixels in image. The lightness decreased image then embedded into another cover image by matrix difference technique (will be discussed in detail). This enhances the security to a higher level because to acquire the steganography image embedded we need to have the key image which will be having only by the receiver. And from millions of images on the internet it is impossible for an attacker to guess the key image. And this enhances the level of security.


Deep Convolution Neural Network for Extreme Multi-label Text Classification


title: Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains

abstract: New data driven framework for simulating exploratory attacks on black box classifiers.Algorithms for simple evasion attacks, to more sophisticated reverse engineering attacks.Formal adversarial model and metrics for baseline evaluation of secure learning strategies.Experimental evaluation on 10 datasets, with linear and non-linear defender models.Experimental evaluation on the black box Google Cloud Platform classifier system. While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, with machine learning forming the core of many cybersecurity systems. Machine learning was not designed with security in mind and the essential assumption of stationarity, requiring that the training and testing data follow similar distributions, is violated in an adversarial domain. In this paper, an adversarys view point of a classification based system, is presented. Based on a formal adversarial model, the Seed-Explore-Exploit framework is presented, for simulating the generation of data driven and reverse engineering attacks on classifiers. Experimental evaluation, on 10 real world datasets and using the Google Cloud Prediction Platform, demonstrates the innate vulnerability of classifiers and the ease with which evasion can be carried out, without any explicit information about the classifier type, the training data or the application domain. The proposed framework, algorithms and empirical evaluation, serve as a white hat analysis of the vulnerabilities, and aim to foster the development of secure machine learning frameworks.


title: StyLit: illumination-guided example-based stylization of 3D renderings

abstract: We present an approach to example-based stylization of 3D renderings that better preserves the rich expressiveness of hand-created artwork. Unlike previous techniques, which are mainly guided by colors and normals, our approach is based on light propagation in the scene. This novel type of guidance can distinguish among context-dependent illumination effects, for which artists typically use different stylization techniques, and delivers a look closer to realistic artwork. In addition, we demonstrate that the current state of the art in guided texture synthesis produces artifacts that can significantly decrease the fidelity of the synthesized imagery, and propose an improved algorithm that alleviates them. Finally, we demonstrate our method's effectiveness on a variety of scenes and styles, in applications like interactive shading study or autocompletion.


title: Assessing degree of intrusion scope (DIS): a statistical strategy for anomaly based intrusion detection

abstract: Intrusion detection system (IDS) is a type of security management system which analyzes information gathered from various areas within a computer or a network to identify possible security breaches. In the last decades an unprecedented increase in the volume and sophistication of network attacks are witnessed. As the quality of the training data greatly influences the quality of the learned models it is difficult to collect high quality training data. New attacks leveraging newly discovered security vulnerabilities emerge quickly and frequently, and also it is not possible to collect data related to these new attacks to train a detection model before the attacks are discovered and understood. The exponential growth of zero-day attacks emphasizes the need of defence mechanisms that can accurately detect previously unseen attacks in real-time. In this regard, a meta-heuristic assessment model called assessing degree of intrusion scope, which is aimed to estimate the degree of intrusion scope threshold from optimal features of given network transaction for training. In order to evaluate the proposed approach, widely used dataset for evaluation of IDS, NSL-KDD data set is used which reflects the network traffic and provides considerable and consistent accuracy improvements in detecting the new and existing attacks. The experimental results indicating that the feature correlation is having significant impact towards minimizing the computational and time complexity of measuring Intrusion Impact Scale.


title: Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights

abstract: Quantitative evaluation of the ability of soccer players to contribute to team offensive performance is typically based on goals scored, assists made, and shots taken. In this paper, we describe a novel player ranking system based entirely on the value of passes completed. This value is derived based on the relationship of pass locations in a possession and shot opportunities generated. This relationship is learned by applying a supervised machine learning model to pass locations in event data from the 2012-2013 La Liga season. Interestingly, though this metric is based entirely on passes, the derived player rankings are largely consistent with general perceptions of offensive ability, e.g., Messi and Ronaldo are near the top. Additionally, when used to rank midfielders, it separates the more offensively-minded players from others.


title: The anterior knee: normal variants, common pathologies, and diagnostic pitfalls on MRI

abstract: The anterior aspect of the knee is host to an array of normal variants and potential pathology. These normal anatomic variants are often encountered and may mimic pathologies, leading to unnecessary work-up and treatments. On the other hand, there are several subtle abnormalities that may be easily overlooked or mistaken for variants or other injuries or diseases. Recognition of these diagnostic challenges is essential for radiologists to make an accurate diagnosis. This article reviews normal anatomical variants of ligaments, tendons, bones, and other important structures of the anterior knee, focusing on magnetic resonance imaging features. Commonly encountered injuries and abnormalities of the anterior knee and their diagnostic pitfalls are also discussed, highlighting findings on magnetic resonance imaging.


title: COMMUNICATION THEORY Communication Theory ISSN 1050-3293 ORIGINAL ARTICLE Campaigns, Reflection, and Deliberation: Advancing an O-S-R-O-R Model of Communication Effects

abstract: Recent communication research concerning participatory politics has found that the effects of media, especially campaign ads, conventional news, and online political resources, are largely mediated through interpersonal discussion about politics. This article extends this line of theorizing about the role of political conversation in citizen competence by testing an O-S-R-O-R model of campaign communication mediation, a modification and extension of the longstanding O-S-O-R model of communication effects. This model combines insights from iterations of the communication mediation model (McLeod et al., 2001; Shah et al., 2007) and cognitive mediation model (Eveland, 2001; Eveland, Shah, & Kwak, 2003) to theorize a set of the interrelated reasoning (R) processes that channel the influences of campaign exposure and news consumption on political engagement. Three key mediators of campaign and news influence are postulated: face-to-face political conversation, online political messaging, and cognitive reflection. We provide empirical evidence to test this model by merging two datasets: (1) tracking of the content and placement of campaign messages in the 2000 and 2004 election cycles, and (2) surveys of traditional and digital media consumption and levels of campaign participation during these same elections. Findings reveal that political conversation, political messaging, and cognitive reflection mediate the effects of campaign advertising exposure and news consumption on political participation and knowledge, providing considerable support for our theory. This O-S-R-O-R model helps organize a large body of theorizing and research on campaigns and conversation in the communication sciences.


title: Revisiting Visual Question Answering Baselines

abstract: Visual question answering (VQA) is an interesting learning setting for evaluating the abilities and shortcomings of current systems for image understanding. Many of the recently proposed VQA systems include attention or memory mechanisms designed to perform “reasoning”. Furthermore, for the task of multiple-choice VQA, nearly all of these systems train a multi-class classifier on image and question features to predict an answer. This paper questions the value of these common practices and develops a simple alternative model based on binary classification. Instead of treating answers as competing choices, our model receives the answer as input and predicts whether or not an image-question-answer triplet is correct. We evaluate our model on the Visual7W Telling and the VQA Real Multiple Choice tasks, and find that even simple versions of our model perform competitively. Our best model achieves state-of-the-art performance of \(65.8\,\%\) accuracy on the Visual7W Telling task and compares surprisingly well with the most complex systems proposed for the VQA Real Multiple Choice task. Additionally, we explore variants of the model and study the transferability of the model between both datasets. We also present an error analysis of our best model, the results of which suggest that a key problem of current VQA systems lies in the lack of visual grounding and localization of concepts that occur in the questions and answers.


title: Real-Time Sign Language Recognition Using a Consumer Depth Camera

abstract: Gesture recognition remains a very challenging task in the field of computer vision and human computer interaction (HCI). A decade ago the task seemed to be almost unsolvable with the data provided by a single RGB camera. Due to recent advances in sensing technologies, such as time-of-flight and structured light cameras, there are new data sources available, which make hand gesture recognition more feasible. In this work, we propose a highly precise method to recognize static gestures from a depth data, provided from one of the above mentioned devices. The depth images are used to derive rotation-, translation- and scale-invariant features. A multi-layered random forest (MLRF) is then trained to classify the feature vectors, which yields to the recognition of the hand signs. The training time and memory required by MLRF are much smaller, compared to a simple random forest with equivalent precision. This allows to repeat the training procedure of MLRF without significant effort. To show the advantages of our technique, we evaluate our algorithm on synthetic data, on publicly available dataset, containing 24 signs from American Sign Language(ASL) and on a new dataset, collected using recently appeared Intel Creative Gesture Camera.


Video Segmentation with Background Motion Models


title: Leadership Styles of World's most Admired Companies A Holistic Approach to Measuring Leadership Effectiveness

abstract: The fundamental influence of leadership on several organizational outcomes, in spite of many empirical studies, has been only partially addressed mainly because no one of the existing models has so far found a fully convincing empirical evidence. In this paper we have identified ten fundamental instruments able to encompass the full complexity of the leadership role, considering also their situational impacts. Moreover, our model allows for the construction of leadership measures which make possible the comparison of the effects of different styles. In this paper we discuss the results of a study conducted in order to analyze the perceived leadership styles of some of the world's most admired companies in the 2007 Fortune ranking, plus Google (the best company to work for in the 2007 Fortune ranking) and four Italian companies in the 2006 Fortune Global 500. The key questions we have addressed are related to the extent in which the ten instruments suffice in providing a full understanding of the leadership role and its influence on performance. Results showed that the leaders' influence on the functioning of an organization may take four different routes, each addressed to a specific area of intervention and therefore with a different effect. We provided a limited but convincing evidence that our model is able to explain the pattern of statistical correlations among the leadership instruments and the relationships among those instruments and companies performances. Our results also proved that high leadership indexes are not related to past performance records, but associated both to higher potentiality of enhanced performance and to higher reputation of organizations, pointing in the direction of a meaningful influence of behavioral complexity and dynamics on the leadership perceived levels.


title: Strengths Gym: The impact of a character strengths-based intervention on the life satisfaction and well-being of adolescents

abstract: This preliminary research study examined the impact of Strengths Gym, a character strengths-based positive psychological intervention program, on adolescent life satisfaction. Using a quasi-experimental treatment-control condition design, the study compared student outcomes for life satisfaction, positive and negative affect, and self-esteem for 319 adolescent students aged 12‐14 (M!12.98): 218 adolescent students who participated in character strengths-based exercises in the school curriculum, and 101 adolescent students who did not participate in character strengths-based exercises in the school curriculum. Results revealed that adolescents who participated in character strengths-based exercises experienced significantly increased life satisfaction compared to adolescents who did not participate in character strengths-based exercises. Overall, results provide encouraging preliminary support for the application of character strengths-based exercises in the school curriculum as a means of increasing life satisfaction and well-being among youths.


title: A 240 GHz ultra-wideband FMCW radar system with on-chip antennas for high resolution radar imaging

abstract: Nowadays emerging industrial radar applications demand for high-resolution, high-precision and at the same time low-cost radar sensors. Recent advances in semiconductor technology allow highly integrated radar sensors at frequencies up to several hundred GHz in mass-production suitable and cost-effective SiGe bipolar technologies. In this contribution, a SiGe MMIC-based 240 GHz radar sensor with more than 60GHz bandwidth is presented. It consists of a MMIC chip including the high-frequency components and a digital control module with the PLL stabilisation, ramp generation, and data acquisition. The antenna is realized by on-chip patch antennas, which are focused by using an additional dielectric lens. The radar allows fast and highly linear frequency sweeps from 204 GHz to 265 GHz with an maximum output power of ≈ -1dBm EIRP (patch only). A phase noise of 1 kHz offset) is achieved over the complete tuning range. Additionally range profile, jitter and imaging measurements are presented to demonstrate the achieved system performance.


title: Determination of a new podophyllotoxin derivative, TOP-53, and its metabolite in rat plasma and urine by high-performance liquid chromatography with electrochemical detection.

abstract: A high-performance liquid chromatographic method was developed for the determination of a new podophyllotoxin derivative, TOP-53 (I), and TOP-53 glucuronide (II) as its major metabolite in rat plasma and urine. For the analysis of I, the sample was chromatographed on a reversed-phase C18 column with electrochemical detection after consecutive two-step liquid-liquid extractions. Compound II was determined as I after enzymatic hydrolysis of II. This method was validated sufficiently with respect to specificity, accuracy, and precision. The limits of quantitation for both I and II were 2 ng/ml in plasma and 10 ng/ml in urine. The method is thus useful for the pharmacokinetic study of I.


title: A generalized LSTM-like training algorithm for second-order recurrent neural networks

abstract: The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM's original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting its applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory(LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks.


title: Online Groups and Political Discourse: Do Online Discussion Spaces Facilitate Exposure to Political Disagreement?

abstract: To what extent do online discussion spaces expose participants to political talk and to cross-cutting political views in particular? Drawing on a representative national sample of over 1000 Americans reporting participation in chat rooms or message boards, we examine the types of online discussion spaces that create opportunities for cross-cutting political exchanges. Our findings suggest that the potential for deliberation occurs primarily in online groups where politics comes up only incidentally, but is not the central purpose of the discussion space. We discuss the implications of our findings for the contributions of the Internet to cross-cutting political discourse.


title: Sieve: cryptographically enforced access control for user data in untrusted clouds

abstract: Modern web services rob users of low-level control over cloud storage--a user's single logical data set is scattered across multiple storage silos whose access controls are set by web services, not users. The consequence is that users lack the ultimate authority to determine how their data is shared with other web services. ::: ::: In this paper, we introduce Sieve, a new platform which selectively (and securely) exposes user data to web services. Sieve has a user-centric storage model: each user uploads encrypted data to a single cloud store, and by default, only the user knows the decryption keys. Given this storage model, Sieve defines an infrastructure to support rich, legacy web applications. Using attribute-based encryption, Sieve allows users to define intuitively understandable access policies that are cryptographically enforceable. Using key homomorphism, Sieve can reencrypt user data on storage providers in situ, revoking decryption keys from web services without revealing new keys to the storage provider. Using secret sharing and two-factor authentication, Sieve protects cryptographic secrets against the loss of user devices like smartphones and laptops. The result is that users can enjoy rich, legacy web applications, while benefiting from cryptographically strong controls over which data a web service can access.


title: Using fuzzy super-efficiency slack-based measure data envelopment analysis to evaluate Taiwan's commercial bank efficiency

abstract: Data envelopment analysis (DEA) mainly utilizes envelopment technology to replace production function in microeconomics. The input and output of decision making units (DMUs) are projected into the attributes to evaluate or measure their performance. However, if the inputs and outputs are linguistically termed or are fuzzy-numbered, conventional DEA can not easily measure the performance. Therefore, we propose the use of a fuzzy super-efficiency slack-based measure DEA to analyze the operational performance of 24 commercial banks facing problems on loan and investment parameters with vague characteristics. After our analysis, we find that the fuzzy slack-based measure of efficiency (Fuzzy SBM)/fuzzy super-efficiency slack-based measure of efficiency (Fuzzy Super SBM) can not only effectively characterize uncertainty, but also have a higher capability to evaluate bank efficiency than the conventional Fuzzy DEA approach.


title: The Fast Bilateral Solver

abstract: We present the bilateral solver, a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms. Our technique is capable of matching or improving upon state-of-the-art results on several different computer vision tasks (stereo, depth superresolution, colorization, and semantic segmentation) while being 10–1000\(\times \) faster than baseline techniques with comparable accuracy, and producing lower-error output than techniques with comparable runtimes. The bilateral solver is fast, robust, straightforward to generalize to new domains, and simple to integrate into deep learning pipelines.


title: A video forensic technique for detecting frame deletion and insertion

abstract: We propose a method for detecting insertion and deletion of whole frames in digital videos. We start by strengthening and extending a state of the art method for double encoding detection, and propose a system that is able to locate the point in time where frames have been deleted or inserted, discerning between the two cases. The proposed method is applicable even when different codecs are used for the first and second compression, and performs well even when the second encoding is as strong as the first one.


title: Monoclonal Antibodies Directed to the Calcium-Free Conformation of Human Protein S

abstract: Four mouse hybridomas secreting monoclonal antibodies specific for human protein S (PS) have been generated. The antibodies, all of the IgG1 subclass, were designated S2, S3, S8, and S10. In a fluid phase radioimmunoassay, the binding of monoclonal antibodies to PS was about 30% greater in the presence of EDTA and totally inhibited in presence of Ca2+. Using the same technique, we performed displacement curves of 125I-labeled PS by purified PS, thrombin-cleaved PS, normal plasma, plasma from a patient on warfarin therapy, and plasma from a patient with no free PS and only PS bound to C4b-binding protein. The slopes of the curves show that the monoclonal antibodies reacted equally with all the tested forms of PS indicating that the antigenic site(s) to which the monoclonal antibodies are directed are present and exposed in free and bound PS, in thrombin-cleaved PS, and in the coumarin form of the protein. Each EDTA-dependent antibody, immobilized on Sepharose 4B-CNBr was used to purify PS from the barium citrate-absorbed, ammonium sulphate-soluble fraction of plasma. The fraction eluted from the immunoabsorbent with a buffer containing 4 mmol/l CaCl2 and analysed by SDS-PAGE, contained two bands, one migrating with conventionally purified PS and the other with purified C4b-binding protein. Homogeneous PS was obtained by chromatography of the barium citrate adsorbate on a DEAE-Sephadex column. The protein peak containing the bulk of PS was subsequently applied to the immunoadsorbent and eluted with 4 mmol/l CaCl2.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Design of a Large Range XY Nanopositioning System

abstract: Achieving large motion range (> 1 mm) along with nanometric motion quality (< 10 nm), simultaneously, has been a key challenge in nanopositioning systems. Practical limitations associated with the individual physical components (flexure bearing, actuators, and sensors) and their integration, particularly in the case of multi-axis systems, have restricted the range of current nanopositioning systems to about 100 μm. This paper presents a novel physical system layout, with a parallel-kinematic XY flexure mechanism at its heart, that provides a high degree of decoupling between the two motion axes by avoiding geometric over-constraints, provides actuator isolation that allows the use of large-stroke single-axis actuators, and enables a complementary end-point sensing scheme that employs commonly available sensors. These attributes help achieve an unprecedented 10 mm × 10 mm motion range in the proposed nanopositioning system. Having overcome the physical system design challenges, a dynamic model of proposed nanopositioning system is created and verified via system identification methods. In particular, dynamic non-linearities associated with the large displacements of the flexure mechanism and resulting controls challenges are identified. The physical system is fabricated, assembled, and tested to validate its simultaneous large range and nanometric motion capabilities. Preliminary closed-loop test results, which highlight the potential of this new design configuration, are presented.Copyright © 2010 by ASME


title: RCD: Rapid Close to Deadline Scheduling for Datacenter Networks

abstract: Datacenter-based Cloud Computing services provide a flexible, scalable and yet economical infrastructure to host online services such as multimedia streaming, email and bulk storage. Many such services perform geo-replication to provide necessary quality of service and reliability to users resulting in frequent large inter-datacenter transfers. In order to meet tenant service level agreements (SLAs), these transfers have to be completed prior to a deadline. In addition, WAN resources are quite scarce and costly, meaning they should be fully utilized. Several recently proposed schemes, such as B4 [1], TEMPUS [2], and SWAN [3] have focused on improving the utilization of inter-datacenter transfers through centralized scheduling, however, they fail to provide a mechanism to guarantee that admitted requests meet their deadlines. Also, in a recent study, authors propose Amoeba [4], a system that allows tenants to define deadlines and guarantees that the specified deadlines are met, however, to admit new traffic, the proposed system has to modify the allocation of already admitted transfers. In this paper, we propose Rapid Close to Deadline Scheduling (RCD), a close to deadline traffic allocation technique that is fast and efficient. Through simulations, we show that RCD is up to 15 times faster than Amoeba, provides high link utilization along with deadline guarantees, and is able to make quick decisions on whether a new request can be fully satisfied before its deadline.


title: Techniques for RF localization of wireless capsule endoscopy

abstract: Location estimation of a wireless capsule endoscope at 400 MHz MICS band is implemented here using both RSSI and TOA-based techniques and their performance investigated. To improve the RSSI-based location estimation, a maximum likelihood (ML) estimation method is employed. For the TOA-based localization, FDTD coupled with continuous wavelet transform (CWT) is used to estimate the time of arrival and localization is performed using multilateration. The performances of the proposed localization algorithms are evaluated using a computational heterogeneous biological tissue phantom in the 402MHz–405MHz MICS band. Our investigations reveal that the accuracy obtained by TOA based method is superior to RSSI based estimates. It has been observed that the ML method substantially improves the accuracy of the RSSI-based location estimation.


title: MIMO-Net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images

abstract: We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows us to deal with variable intensity cell boundaries and highly variable cell size in the mouse pancreatic tissue by adding extra convolutional layers which bypass the max-pooling operation. The results show that our method outperforms state-of-the-art deep learning based approaches for segmentation.


title: A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy

abstract: This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE?+?KC-MLP?+?ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.


title: Efficient trajectory extraction and parameter learning for data-driven crowd simulation

abstract: We present a trajectory extraction and behavior-learning algorithm for data-driven crowd simulation. Our formulation is based on incrementally learning pedestrian motion models and behaviors from crowd videos. We combine this learned crowd-simulation model with an online tracker based on particle filtering to compute accurate, smooth pedestrian trajectories. We refine this motion model using an optimization technique to estimate the agents' simulation parameters. We highlight the benefits of our approach for improved data-driven crowd simulation, including crowd replication from videos and merging the behavior of pedestrians from multiple videos. We highlight our algorithm's performance in various test scenarios containing tens of human-like agents.


title: A pairwise ranking based approach to learning with positive and unlabeled examples

abstract: A large fraction of binary classification problems arising in web applications are of the type where the positive class is well defined and compact while the negative class comprises everything else in the distribution for which the classifier is developed; it is hard to represent and sample from such a broad negative class. Classifiers based only on positive and unlabeled examples reduce human annotation effort significantly by removing the burden of choosing a representative set of negative examples. Various methods have been proposed in the literature for building such classifiers. Of these, the state of the art methods are Biased SVM and Elkan & Noto's methods. While these methods often work well in practice, they are computationally expensive since hyperparameter tuning is very important, particularly when the size of labeled positive examples set is small and class imbalance is high. In this paper we propose a pairwise ranking based approach to learn from positive and unlabeled examples (LPU) and we give a theoretical justification for it. We present a pairwise RankSVM (RSVM) based method for our approach. The method is simple, efficient, and its hyperparameters are easy to tune. A detailed experimental study using several benchmark datasets shows that the proposed method gives competitive classification performance compared to the mentioned state of the art methods, while training 3-10 times faster. We also propose an efficient AUC based feature selection technique in the LPU setting and demonstrate its usefulness on the datasets. To get an idea of the goodness of the LPU methods we compare them against supervised learning (SL) methods that also make use of negative examples in training. SL methods give a slightly better performance than LPU methods when there is a rich set of negative examples; however, they are inferior when the number of negative training examples is not large enough.


title: Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis

abstract: We propose an image super resolution (ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of 16. This facilitates more accurate automated image analysis, especially for small or blurred landmarks and pathologies. Local saliency maps, which define each pixel’s importance, are used to define a novel saliency loss in the GAN cost function. Experimental results show the resulting SR images have perceptual quality very close to the original images and perform better than competing methods that do not weigh pixels according to their importance. When used for retinal vasculature segmentation, our SR images result in accuracy levels close to those obtained when using the original images.


title: Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

abstract: Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.


title: Open System Categorical Quantum Semantics in Natural Language Processing

abstract: Originally inspired by categorical quantum mechanics (Abramsky and Coecke, LiCS'04), the categorical compositional distributional model of natural language meaning of Coecke, Sadrzadeh and Clark provides a conceptually motivated procedure to compute the meaning of a sentence, given its grammatical structure within a Lambek pregroup and a vectorial representation of the meaning of its parts. The predictions of this first model have outperformed that of other models in mainstream empirical language processing tasks on large scale data. Moreover, just like CQM allows for varying the model in which we interpret quantum axioms, one can also vary the model in which we interpret word meaning. ::: In this paper we show that further developments in categorical quantum mechanics are relevant to natural language processing too. Firstly, Selinger's CPM-construction allows for explicitly taking into account lexical ambiguity and distinguishing between the two inherently different notions of homonymy and polysemy. In terms of the model in which we interpret word meaning, this means a passage from the vector space model to density matrices. Despite this change of model, standard empirical methods for comparing meanings can be easily adopted, which we demonstrate by a small-scale experiment on real-world data. This experiment moreover provides preliminary evidence of the validity of our proposed new model for word meaning. ::: Secondly, commutative classical structures as well as their non-commutative counterparts that arise in the image of the CPM-construction allow for encoding relative pronouns, verbs and adjectives, and finally, iteration of the CPM-construction, something that has no counterpart in the quantum realm, enables one to accommodate both entailment and ambiguity.


title: Local Monotonic Attention Mechanism for End-to-End Speech Recognition

abstract: Recently, sequence-to-sequence model by using encoder-decoder neural network has gained popularity for automatic speech recognition (ASR). The architecture commonly uses an attentional mechanism which allows the model to learn alignments between source speech sequence and target text sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in speech recognition task. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results demonstrate that encoder-decoder based ASR with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.


title: Human-level CMR image analysis with deep fully convolutional networks

abstract: Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing a wealth of information for sensitive and specific diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a dataset of unprecedented size, consisting of 4,875 subjects with 93,500 pixelwise annotated images, which is by far the largest annotated CMR dataset. By combining FCN with a large-scale annotated dataset, we show for the first time that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinical measures. We anticipate this to be a starting point for automated and comprehensive CMR analysis with human-level performance, facilitated by machine learning. It is an important advance on the pathway towards computer-assisted CVD assessment.


title: Fusion of laser and monocular camera data in object grid maps for vehicle environment perception

abstract: Occupancy grid maps provide a reliable vehicle environmental model and usually process data from range finding sensors. Object grid maps additionally contain information about the classes of objects, which is crucial for applications like autonomous driving. Unfortunately, they lack the precision of occupancy grid maps, since they mostly process classification results from camera data by projecting the corresponding images onto the ground plane. This paper proposes a modular framework to create precise object grid maps. The presented algorithm creates classical occupancy grid maps and object grid maps. In a combination step, it transforms both maps into the same frame of discernment based on the Dempster-Shafer theory of evidence. This allows fusing the maps to one object grid map, which contains valuable object information and at the same time benefits from the precision of the occupancy grid map.


title: A dual-input full-bridge current-source isolated DC/DC converter based on Quasi-Switched-Capacitor circuit for Photovoltaic systems with energy storage

abstract: In this paper, a dual-input full-bridge current-source isolated dc/dc converter is proposed to combine the Photovoltaic (PV) system with the Energy Storage System (ESS). The proposed converter utilizes a full-bridge as the primary side, and the Quasi-Switched-Capacitor circuit as the secondary side. Compared to other multiple-input dc/dc converters with galvanic isolation function, the proposed converter utilizes less number of switches and passive components, reduces voltage stress on switches, and can achieve a high efficiency at a high switching frequency. The operation principle of the converter is presented, and a power sharing strategy between two input sources is proposed. A 2.0 kW, 1 MHz, 40 V/ 400 V prototype utilizing Gallium Nitride (GaN) switching devices is built in the lab, and experimental results show that a peak efficiency of 95.9% is achieved at 500 kHz.


title: Connecting Brains and Bodies: Applying Physiological Computing to Support Social Interaction

abstract: Physiological and affective computing propose methods to improve human–machine interactions by adapting machines to the users’ states. Recently, social signal processing (SSP) has proposed to apply similar methods to human–human interactions with the hope of better understanding and modeling social interactions. Most of the social signals employed are facial expressions, body movements and speech, but studies using physiological signals remain scarce. In this paper, we motivate the use of physiological signals in the context of social interactions. Specifically, we review studies which have investigated the relationship between various physiological indices and social interactions. We then propose two main directions to apply physiological SSP: using physiological signals of individual users as new social cues displayed in the group and using inter-user physiology to measure properties of the interactions such as conflict and social presence. We conclude that physiological measures have the potential to enhance social interactions and to connect people.


title: Acquired protein S deficiency in a patient with systemic lupus erythematosus causing central retinal vein thrombosis.

abstract: A 16 year old girl with systemic lupus erythematosus (SLE) developed the rare complication of central retinal vein occlusion. Although classically a disease of older patients, it has been recognised in association with SLE but only in the presence of the lupus anticoagulant or antiphospholipid antibodies. The thrombosis occurred when free protein S concentrations were transiently reduced and there was no family history or other known causes of reduced protein S concentrations. No other prothrombotic risk factors were present.


title: Carotenoid fluorescence in Dunaliella salina

abstract: Dunaliella salina is a halotolerant green alga that is well known for its carotenoid producing capacity. The produced carotenoids are mainly stored in lipid globules. For various research purposes, such as production and extraction kinetics, we would like to determine and/or localise the carotenoid globules in vivo. In this study, we show that the carotenoid-rich globules emit clear green fluorescence, which can be used in, for example, fluorescence microscopy (e.g. CLSM) to obtain pictures of the cells and their carotenoid content.


title: Constraint Relaxation and Chunk Decomposition in Insight Problem Solving

abstract: Insight problem solving is characterized by impasses, states of mind in which the thinker does not know what to do next. The authors hypothesized that impasses are broken by changing the problem representation, and 2 hypothetical mechanisms for representational change are described: the relaxation of constraints on the solution and the decomposition of perceptual chunks. These 2 mechanisms generate specific predictions about the relative difficulty of individual problems and about differential transfer effects. The predictions were tested in 4 experiments using matchstick arithmetic problems. The results were consistent with the predictions. Representational change is a more powerful explanation for insight than alternative hypotheses, if the hypothesized change processes are specified in detail. Overcoming impasses in insight is a special case of the general need to override the imperatives of past experience in the face of novel conditions.


title: WordsEye: an automatic text-to-scene conversion system

abstract: Natural language is an easy and effective medium for describing visual ideas and mental images. Thus, we foresee the emergence of language-based 3D scene generation systems to let ordinary users quickly create 3D scenes without having to learn special software, acquire artistic skills, or even touch a desktop window-oriented interface. WordsEye is such a system for automatically converting text into representative 3D scenes. WordsEye relies on a large database of 3D models and poses to depict entities and actions. Every 3D model can have associated shape displacements, spatial tags, and functional properties to be used in the depiction process. We describe the linguistic analysis and depiction techniques used by WordsEye along with some general strategies by which more abstract concepts are made depictable.


title: Single color extraction and image query

abstract: We propose a method for automatic color extraction and indexing to support color queries of image and video databases. This approach identifies the regions within images that contain colors from predetermined color sets. By searching over a large number of color sets, a color index for the database is created in a fashion similar to that for file inversion. This allows very fast indexing of the image collection by the color contents of the images. Furthermore, information about the identified regions, such as the color set, size, and location, enables a rich variety of queries that specify both color content and spatial relationships of regions. We present the single color extraction and indexing method and contrast it to other color approaches. We examine single and multiple color extraction and image query on a database of 3000 color images.


title: A Simple Domain-Independent Probabilistic Approach to Generation

abstract: We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework. In our approach, we break up the end-to-end generation process into a sequence of local decisions, arranged hierarchically and each trained discriminatively. We deployed our system in three different domains---Robocup sportscasting, technical weather forecasts, and common weather forecasts, obtaining results comparable to state-of-the-art domain-specific systems both in terms of BLEU scores and human evaluation.


title: Predicting seminal quality with artificial intelligence methods

abstract: Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors, as well as life habits, may affect semen quality. Artificial intelligence techniques are now an emerging methodology as decision support systems in medicine. In this paper we compare three artificial intelligence techniques, decision trees, Multilayer Perceptron and Support Vector Machines, in order to evaluate their performance in the prediction of the seminal quality from the data of the environmental factors and lifestyle. To do that we collect data by a normalized questionnaire from young healthy volunteers and then, we use the results of a semen analysis to asses the accuracy in the prediction of the three classification methods mentioned above. The results show that Multilayer Perceptron and Support Vector Machines show the highest accuracy, with prediction accuracy values of 86% for some of the seminal parameters. In contrast decision trees provide a visual and illustrative approach that can compensate the slightly lower accuracy obtained. In conclusion artificial intelligence methods are a useful tool in order to predict the seminal profile of an individual from the environmental factors and life habits. From the studied methods, Multilayer Perceptron and Support Vector Machines are the most accurate in the prediction. Therefore these tools, together with the visual help that decision trees offer, are the suggested methods to be included in the evaluation of the infertile patient.


title: A 17-nW, 0.5V, 500S/s, rail-to-rail SAR ADC with 8.1 effective number of bits

abstract: This paper presents a successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS for low-power applications. The SAR ADC achieves a wide effective resolution bandwidth (ERBW) and a rail-to-rail signal swing by applying a limited number of bootstrapped switches. A robust low-voltage amplifier is proposed as the building block of the comparator. Measurement results show that at a supply voltage of 0.5-V and an output rate of 500S/s, the SAR ADC achieves a peak signal-to-noise-and-distortion ratio of 50.4 dB and an ERBW up to the Nyquist bandwidth (250 Hz). It consumes only 17 nW.


title: Design of a multilink-articulated wheeled inspection robot for winding pipelines: AIRo-II

abstract: This paper presents a multilink-articulated robot with omni and hemispherical wheels (AIRo-II) for inspecting and exploring winding pipes. To quickly adapt to winding pipes, holonomic rolling movement without moving forward and backward is more useful. However, this requires the replacement of driving actuators with rolling actuators at the expense of the driving force. In this paper, we investigate the possibility of high maneuverability of multilink-articulated robots in winding pipes by using less actuators and by designing spring joints. We further validate this by experimental verification.


title: Discovering Opinion Spammer Groups by Network Footprints

abstract: Online reviews are an important source for consumers to evaluate products/services on the Internet (e.g. Amazon, Yelp, etc.). However, more and more fraudulent reviewers write fake reviews to mislead users. To maximize their impact and share effort, many spam attacks are organized as campaigns, by a group of spammers. In this paper, we propose a new two-step method to discover spammer groups and their targeted products. First, we introduce NFS (Network Footprint Score), a new measure that quantifies the likelihood of products being spam campaign targets. Second, we carefully devise GroupStrainer to cluster spammers on a 2-hop subgraph induced by top ranking products. Our approach has four key advantages: (i) unsupervised detection; both steps require no labeled data, (ii) adversarial robustness; we quantify statistical distortions in the review network, of which spammers have only a partial view, and avoid any side information that spammers can easily evade, (iii) sensemaking; the output facilitates the exploration of the nested hierarchy (i.e., organization) among the spammers, and finally (iv) scalability; both steps have complexity linear in network size, moreover, GroupStrainer operates on a carefully induced subnetwork. We demonstrate the efficiency and effectiveness of our approach on both synthetic and real-world datasets from two different domains with millions of products and reviewers. Moreover, we discover interesting strategies that spammers employ through case studies of our detected groups.


title: Human Activity Recognition Based on R Transform

abstract: This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The advantage of the R transform lies in its low computational complexity and geometric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, R transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in recognizing similar activities. Rich experiments have proved the efficiency of the proposed method.


title: Empirical evaluation of vehicular models for ego motion estimation

abstract: Estimating the motion of a vehicle is a crucial requirement for intelligent vehicles. In order to solve this problem using a Bayes filter, an appropriate model of vehicular motions is required. This paper systematically reviews typical vehicular motion models and evaluates their suitability in different scenarios. For that, the results of extensive experiments using accurate reference sensors are presented and discussed in order to provide guidelines for the choice of an optimal model.


title: Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles

abstract: In image classification tasks, the ability of deep CNNs to deal with complex image data has proven to be unrivalled. However, they require large amounts of labeled training data to reach their full potential. In specialised domains such as healthcare, labeled data can be difficult and expensive to obtain. Active Learning aims to alleviate this problem, by reducing the amount of labelled data needed for a specific task while delivering satisfactory performance. We propose DEBAL, a new active learning strategy designed for deep neural networks. This method improves upon the current state-of-the-art deep Bayesian active learning method, which suffers from the mode collapse problem. We correct for this deficiency by making use of the expressive power and statistical properties of model ensembles. Our proposed method manages to capture superior data uncertainty, which translates into improved classification performance. We demonstrate empirically that our ensemble method yields faster convergence of CNNs trained on the MNIST and CIFAR-10 datasets.


Facial blanching after inferior alveolar nerve block anesthesia: an unusual complication


title: RANA: Towards Efficient Neural Acceleration with Refresh-Optimized Embedded DRAM

abstract: The growing size of convolutional neural networks (CNNs) requires large amounts of on-chip storage. In many CNN accelerators, their limited on-chip memory capacity causes massive off-chip memory access and leads to very high system energy consumption. Embedded DRAM (eDRAM), with higher density than SRAM, can be used to improve on-chip buffer capacity and reduce off-chip access. However, eDRAM requires periodic refresh to maintain data retention, which costs much energy consumption. Refresh is unnecessary if the data's lifetime in eDRAM is shorter than the eDRAM's retention time. Based on this principle, we propose a Retention-Aware Neural Acceleration (RANA) framework for CNN accelerators to save total system energy consumption with refresh-optimized eDRAM. The RANA framework includes three levels of techniques: a retention-aware training method, a hybrid computation pattern and a refresh-optimized eDRAM controller. At the training level, CNN's error resilience is exploited in training to improve eDRAM's tolerable retention time. At the scheduling level, RANA assigns each CNN layer with a computation pattern that consumes the lowest energy. At the architecture level, a refresh-optimized eDRAM controller is proposed to alleviate unnecessary refresh operations. We implement an evaluation platform to verify RANA. Owing to the RANA framework, 99.7% eDRAM refresh operations can be removed with negligible performance and accuracy loss. Compared with the conventional SRAM-based CNN accelerator, an eDRAM-based CNN accelerator strengthened by RANA can save 41.7% off-chip memory access and 66.2% system energy consumption, with the same area cost.


title: A Non-Invasive Blood Insulin and Glucose Monitoring System Based on Near-Infrared Spectroscopy with Remote Data Logging

abstract: This paper presents the development of a portable, Non-Invasive device for monitoring patients’ blood Insulin and Glucose concentration. It is based on Near-Infrared(NIR) Spectroscopy. Perkin-Elmer Lambda 750 spectrometer was used to study the absorbance of Insulin and glucose at different wavelengths of NIR region. NIR LED (Light Emitting diode) of appropriate wavelengths were used to construct a finger clip. A separate Printed Circuit Board (PCB) was constructed; that connects to the finger clip and acquires the Photoplethysmography (PPG) signal at three separate NIR wavelengths. The PPG signal can be used to detect the concentartion of Glucose and Insulin in blood. The PCB has a WiFi module interfaced within it; that supports Internet connectivity and uploads the time series data of insulin and glucose to a server. The data can be viewed in a graphical format by authorized users. This can be used by a medical practitioner to understand whether the data shows an increasing or decreasing trend and hence, will enable to prognosticate the approaching critical condition of the patient much before the critical condition actually occurs.


title: STMC: Semantic Tag Medical Concept Using Word2Vec Representation

abstract: In this paper we propose a recognition system of medical concepts from free text clinical reports. Our approach tries to recognize also concepts which are named with local terminology, with medical writing scripts, short words, abbreviations and even spelling mistakes. We consider a clinical terminology ontology (Snomed-CT), as a dictionary of concepts. In a first step we obtain an embedding model using word2vec methodology from a big corpus database of clinical reports. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space, and so the geometrical similarity can be considered a measure of semantic relation. We have considered 615513 emergency clinical reports from the Hospital "Rafael Méndez" in Lorca, Murcia. In these reports there are a lot of local language of the emergency domain, medical writing scripts, short words, abbreviations and even spelling mistakes. With the model obtained we represent the words and sentences as vectors, and by applying cosine similarity we identify which concepts of the ontology are named in the text. Finally, we represent the clinical reports (EHR) like a bag of concepts, and use this representation to search similar documents. The paper illustrates 1) how we build the word2vec model from the free text clinical reports, 2) How we extend the embedding from words to sentences, and 3) how we use the cosine similarity to identify concepts. The experimentation, and expert human validation, shows that: a) the concepts named in the text with the ontology terminology are well recognized, and b) others concepts that are not named with the ontology terminology are also recognized, obtaining a high precision and recall measures.


title: UnrealStereo: A Synthetic Dataset for Analyzing Stereo Vision

abstract: Stereo algorithm is important for robotics applications, such as quadcopter and autonomous driving. It needs to be robust enough to handle images of challenging conditions, such as raining or strong lighting. Textureless and specular regions of these images make feature matching difficult and smoothness assumption invalid. It is important to understand whether an algorithm is robust to these hazardous regions. Many stereo benchmarks have been developed to evaluate the performance and track progress. But it is not easy to quantize the effect of these hazardous regions. In this paper, we develop a synthetic image generation tool and build a benchmark with synthetic images. First, we manually tweak hazardous factors in a virtual world, such as making objects more specular or transparent, to simulate corner cases to test the robustness of stereo algorithms. Second, we use ground truth information, such as object mask, material property, to automatically identify hazardous regions and evaluate the accuracy of these regions. Our tool is based on a popular game engine Unreal Engine 4 and will be open-source. Many publicly available realistic game contents can be used by our tool which can provide an enormous resource for algorithm development and evaluation.


title: Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic Framework

abstract: A system, particularly a decision-making concept, that facilitates highly automated driving on freeways in real traffic is presented. The system is capable of conducting fully automated lane change (LC) maneuvers with no need for driver approval. Due to the application in real traffic, a robust functionality and the general safety of all traffic participants are among the main requirements. Regarding these requirements, the consideration of measurement uncertainties demonstrates a major challenge. For this reason, a fully integrated probabilistic concept is developed. By means of this approach, uncertainties are regarded in the entire process of determining driving maneuvers. While this also includes perception tasks, this contribution puts a focus on the driving strategy and the decision-making process for the execution of driving maneuvers. With this approach, the BMW Group Research and Technology managed to drive 100% automated in real traffic on the freeway A9 from Munich to Ingolstadt, showing a robust, comfortable, and safe driving behavior, even during multiple automated LC maneuvers.


title: Naive Bayes Word Sense Induction

abstract: We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task. The extended model incorporates the idea the words closer to the target word are more relevant in predicting its sense. The proposed model is very simple yet effective when evaluated on SemEval-2010 WSI data.


title: A framework of conversational recommender system based on user functional requirements

abstract: Specifying requirements based on technical features of the product are frequently difficult for most customers in purchasing a multi-function and multi-feature products. A more natural way to identify customers' needs is by asking what they really want to use with the product they are looking for. To address this issue, we propose a framework of conversational recommender system that is based on user functional requirement. This framework covers ontology structure and interaction mechanism that can explore user's preference from their functional requirements. The ontology is composed of three different interacting hierarchical classes: functionality requirement class, component class and product class. Interaction in this system is performed through question-answering dialogue, product recommendation and explanation, similar to the interaction between customer and professional sales support.


title: Uptake, translocation, and metabolism of oxabetrinil and CGA-133205 in grain sorghum (Sorghum bicolor) and their influence on metolachlor metabolism

abstract: The uptake, translocation, and metabolism of the oxime ether safeners oxabetrinil and CGA-133205 in grain sorghum (Sorghum bicolor (L.) Moench, var. Funk G-522-DR) were investigated. Following application of ({sup 14}C)oxabetrinil and ({sup 14}C)CGA-133205 to imbibed seeds, it appears that the safeners are conferring protection to grain sorghum by increasing the rate of metolachlor metabolism.


title: A framework for use of wireless sensor networks in forest fire detection and monitoring

abstract: Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance systems for forest fires lack in supporting real-time monitoring of every point of a region at all times and early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather sensory data values, such as temperature and humidity, from all points of a field continuously, day and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor networks face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and communication protocols. The aim of the framework is to detect a fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the environmental conditions that may affect the required activity level of the network. We implemented a simulator to validate and evaluate our proposed framework. Through extensive simulation experiments, we show that our framework can provide fast reaction to forest fires while also consuming energy efficiently.


title: Design and performance evaluation of overcurrent protection schemes for silicon carbide (SiC) power MOSFETs

abstract: Overcurrent protection of silicon carbide (SiC) MOSFETs remains a challenge due to lack of practical knowledge. This paper presents two overcurrent protection methods to improve the reliability and overall cost of the SiC MOSFET based converter. First, a solid state circuit breaker (SSCB) composed primarily by a Si IGBT and a commercial gate driver IC is connected in series with the DC bus to detect and clear overcurrent faults. Second, the desaturation technique using a sensing diode to detect the drain-source voltage under overcurrent faults is implemented as well. The design considerations and potential issues of the protection methods are described and analyzed in detail. A phase-leg configuration based step-down converter is built to evaluate the performance of the proposed protection schemes under various conditions, considering variation of fault type, decoupling capacitance, protection circuit parameters, etc. Finally, a comparison is made in terms of fault response time, temperature dependent characteristics, and applications to help designers select a proper protection method.


title: Dyadic projected spatial augmented reality

abstract: Mano-a-Mano is a unique spatial augmented reality system that combines dynamic projection mapping, multiple perspective views and device-less interaction to support face to face, or dyadic, interaction with 3D virtual objects. Its main advantage over more traditional AR approaches, such as handheld devices with composited graphics or see-through head worn displays, is that users are able to interact with 3D virtual objects and each other without cumbersome devices that obstruct face to face interaction. We detail our prototype system and a number of interactive experiences. We present an initial user experiment that shows that participants are able to deduce the size and distance of a virtual projected object. A second experiment shows that participants are able to infer which of a number of targets the other user indicates by pointing.


title: Shape-based automatic detection of a large number of 3D facial landmarks

abstract: We present an algorithm for automatic detection of a large number of anthropometric landmarks on 3D faces. Our approach does not use texture and is completely shape based in order to detect landmarks that are morphologically significant. The proposed algorithm evolves level set curves with adaptive geometric speed functions to automatically extract effective seed points for dense correspondence. Correspondences are established by minimizing the bending energy between patches around seed points of given faces to those of a reference face. Given its hierarchical structure, our algorithm is capable of establishing thousands of correspondences between a large number of faces. Finally, a morphable model based on the dense corresponding points is fitted to an unseen query face for transfer of correspondences and hence automatic detection of landmarks. The proposed algorithm can detect any number of pre-defined landmarks including subtle landmarks that are even difficult to detect manually. Extensive experimental comparison on two benchmark databases containing 6, 507 scans shows that our algorithm outperforms six state of the art algorithms.


title: Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks

abstract: We are witnessing an explosive development and widespread application of deep neural networks (DNNs) in various fields. However, DNN models, especially a convolutional neural network (CNN), usually involve massive parameters and are computationally expensive, making them extremely dependent on high-performance hardware. This prohibits their further extensions, e.g., applications on mobile devices. In this paper, we present a quantized CNN, a unified approach to accelerate and compress convolutional networks. Guided by minimizing the approximation error of individual layer’s response, both fully connected and convolutional layers are carefully quantized. The inference computation can be effectively carried out on the quantized network, with much lower memory and storage consumption. Quantitative evaluation on two publicly available benchmarks demonstrates the promising performance of our approach: with comparable classification accuracy, it achieves 4 to $6 \times $ acceleration and 15 to $20\times $ compression. With our method, accurate image classification can even be directly carried out on mobile devices within 1 s.


title: Reference system of competence and engagement in adapted physical activities of people with recent spinal cord injury

abstract: AbstractPurpose: This study tested whether persons with Recent Spinal Cord Injury (RSCI) who practice adapted physical activities (APA) and those who do not differ with regard to achievement goals, physical self-perceptions, and global self-esteem. Method: Adults with RSCI in rehabilitation centers voluntarily completed questionnaires of achievement goals and self-esteem. Then, based on whether they engaged or not in APA programs, they were considered participants or non-participants in APA. Results: Compared to participants in APA, non-participants were more oriented toward mastery-avoidance goals and had lower scores of physical self-worth and global self-esteem. No differences were found for other achievement goals and for low-level dimensions of physical self. Conclusions: These findings suggest that mastery-avoidance goals are associated with a maladaptive motivational pattern when intrapersonal comparison conveys a threat for the self. Practical implications for rehabilitation programs for persons w...


title: Deployment strategy study of QB50 network of CubeSats

abstract: QB50 is a network of 50 CubeSats in a string of pearls configuration that will be launched in the mid April of 2015 by a single launch vehicle into a circular orbit at 350 km altitude. The mission lifetime according to the launch date of CubeSats from the initial altitude to 90 km is currently estimated to be about 3 months. The use of a single launch vehicle to deploy CubeSats into a formation is faster and cheaper; however, there is greater risk of collision. The risk must be reduced with the proper deployment direction, sequence and frequency. This paper gives one of the solutions for the minimization of the risk of collision between the deployed CubeSat and the upper stage and the optimization of the distribution of double unit CubeSats all the way around the Earth.


title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer

abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.


title: An InGaAs PIN-diode based broadband traveling-wave switch with high-isolation characteristics

abstract: A high-isolation broadband travelingwave switch using InGaAs PIN diodes is proposed. The circuit design and the analysis of the traveling-wave switch based on an artificial transmission line design are described. The newly proposed MMIC switch provides low insertion loss of less than 3.5 dB and high isolation of better than 39 dB from 15 GHz to 70 GHz. The significantly reduced chip size of 1.0 × 0.8 mm2 including pads has been achieved by using the meandered microstrip lines.


title: Ontological Approach toward Cybersecurity in Cloud Computing

abstract: Widespread deployment of the Internet enabled building of an emerging IT delivery model, i.e., cloud computing. Albeit cloud computing-based services have rapidly developed, their security aspects are still at the initial stage of development. In order to preserve cybersecurity in cloud computing, cybersecurity information that will be exchanged within it needs to be identified and discussed. For this purpose, we propose an ontological approach to cybersecurity in cloud computing. We build an ontology for cybersecurity operational information based on actual cybersecurity operations mainly focused on non-cloud computing. In order to discuss necessary cybersecurity information in cloud computing, we apply the ontology to cloud computing. Through the discussion, we identify essential changes in cloud computing such as data-asset decoupling and clarify the cybersecurity information required by the changes such as data provenance and resource dependency information.


title: Modification of DBSCAN and application to range/Doppler/DoA measurements for pedestrian recognition with an automotive radar system

abstract: We present in this paper modifications of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in order to detect pedestrians with a conventional automotive 77-GHz frequency modulated radar system. These modification include dimension scaling as preprocessing and a generalization of the e-neighborhood notation by introducing a size parameter to constitute the new Ellipsoid DBSCAN (EDBSCAN) algorithm. With these modifications we could successfully cluster real-world measurement data in order to get reasonable cluster representations of pedestrians in a cluttered environment.