title: Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

text: Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.


title: Anterior cingulate cortex, error detection, and the online monitoring of performance.

text: An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.


title: QR code location based reverse car-searching route recommendation model

text: To deal with the reverse car-searching issue in large buildings and parking lots, a Quick Response Code (QR code) based reverse car-searching route recommendation model is designed. By scanning the deployed QR codes, a Smartphone can pinpoint the host location and parking location efficiently. Based on the submitted location information, the central control system can finally return the recommended routes, which facilitates a host to reach the parking location effectively. In our model, the reverse car-searching route is divided into two parts: choosing the optimal exports (elevator) and computing the shortest walking distance route. Based on the optimal export selection algorithm and regional shortest path algorithm, our model can choose the prior exports (elevator) effectively, and then recommend the optimal walking route in the buildings and parking lots. The simulation shows that this low-cost system can effectively solve the reverse car-searching problem in large buildings and parking lots, save the driver's car-searching time and improve the utilization rate of parking facilities.


title: Ant supervised by PSO and 2-Opt algorithm, AS-PSO-2Opt, applied to Traveling Salesman Problem

text: AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum. Applied to TSP, the method allowed retrieving a valuable path solution and a set of fitted parameters for ACO. The performance of the AS-PSO-2Opt is tested on nine different TSP test benches. Experimental results based on a statistical analysis showed that the new proposal performs better than key state of art methods using Genetic algorithm, Neural Network and ACO algorithm. The AS-PSO-2Opt performs better than close related methods such as PSO-ACO-3Opt [9] and ACO with ABC [19] for various test benches.


title: Using association rules for ontology extraction from a Quran corpus

text: Ontology can be seen as a formal representation of knowledge. They have been investigated in many arti ficial intelligence studies including semantic web, softwa re engineering, and information retrieval. The aim of ontology is t o develop knowledge representations that can be shared and re used. This research project is concerned with the use of assoc iati n rules to extract the Quran ontology. The manual acquisition of ontologies from Quran verses can be very costly; therefore, we ne d an intelligent system for Quran ontology construction using patternbased schemes and associations rules to discover Qu ran concepts and semantics relations from Quran verses. Our system i s based on the combination of statistics and linguistics methods t o extract concepts and conceptual relations from Quran. In particular, linguistic pattern-based approach is exploited to extract spec ific concepts from the Quran, while the conceptual relations are found based on association rules technique. The Quran ontology wil l offer a new and powerful representation of Quran knowledge, and the association rules will help to represent the relations between all classes of connected concepts in the Quran ontology. Keywords— Ontology; Concepts Extraction ; Quran; Association Rules; Text Mining.


title: Learning Image Descriptors with Boosting

text: We propose a novel and general framework to learn compact but highly discriminative floating-point and binary local feature descriptors. By leveraging the boosting-trick we first show how to efficiently train a compact floating-point descriptor that is very robust to illumination and viewpoint changes. We then present the main contribution of this paper—a binary extension of the framework that demonstrates the real advantage of our approach and allows us to compress the descriptor even further. Each bit of the resulting binary descriptor, which we call BinBoost, is computed with a boosted binary hash function, and we show how to efficiently optimize the hash functions so that they are complementary, which is key to compactness and robustness. As we do not put any constraints on the weak learner configuration underlying each hash function, our general framework allows us to optimize the sampling patterns of recently proposed hand-crafted descriptors and significantly improve their performance. Moreover, our boosting scheme can easily adapt to new applications and generalize to other types of image data, such as faces, while providing state-of-the-art results at a fraction of the matching time and memory footprint.


title: SMART VOTING SYSTEM WITH FACE RECOGNITION

text: Manual voting system has been deployed for many yea rs in our country. However in many parts of our cou ntry people cannot attend the voting because of several r asons. In order to solve these problems there is a need of online election voting system in addition to manual voting system. The long-term goal of this project is to g reatly reduce the cost and complexity of running elections and increase th e accuracy of results by removing the direct involv ement of humans in gathering and counting of votes. The voters will be a le to give their votes at any field areas by usi ng the system if they prefer online voting In this system, the user votes using android applic ation which can be downloaded over internet. The us er has to login before giving his vote. Authentication will b e done using SMS confirmation and face recognition. Users can view parties, enter votes, can verify vote via SMS or email and can view result after result is declared.


title: The Nature, Importance, and Difficulty of Machine Ethics

text: The question of whether machine ethics exists or might exist in the future is difficult to answer if we can't agree on what counts as machine ethics. Some might argue that machine ethics obviously exists because humans are machines and humans have ethics. Others could argue that machine ethics obviously doesn't exist because ethics is simply emotional expression and machines can't have emotions. A wide range of positions on machine ethics are possible, and a discussion of the issue could rapidly propel us into deep and unsettled philosophical issues. Perhaps, understandably, few in the scientific arena pursue the issue of machine ethics. As we expand computers' decision-making roles in practical matters, such as computers driving cars, ethical considerations are inevitable. Computer scientists and engineers must examine the possibilities for machine ethics because, knowingly or not, they've already engaged in some form of it. Before we can discuss possible implementations of machine ethics, however, we need to be clear about what we're asserting or denying


title: Integrating Conflicting Data: The Role of Source Dependence

text: Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sourc es can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers pendencebetween data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources ( e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracyof data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.


title: Teenager ’ s Addiction to Smart Phones and Its Integrated Therapy Method

text: The fundamental cause of teenagers’ addiction to smart phone is due to stress arising from family and school. Smartphone can cause many problems including musculoskeletal symptom which can develop due to staying in one position for a long time. Furthermore, starring at a small screen for an extended time can cause decreased eye sight, along with amblyopia. Smart phone addiction causes depression and anxiety which will lead to severe mental problems. This will lead to problems associated to one’s social life and human network. Fundamental treatment of cognitive and emotional elements needed to become harmonized as a whole in order to increase the effectiveness of the treatment results. The therapy has been verified to cure the behavioral and emotional aspect related to depression, sense of control, and assimilation in school. From these integrated treatment approaches, this report compared the treatment’s upsides and downsides and also, assessed the possibility the integrated treatment process. We can presume that this report provided the opportunity and support for the creation of a fundamental treatment method that can help teenagers live a healthy life by overcoming addiction to smart phones.


title: Communication-efficient algorithms for statistical optimization

text: We study two communication-efficient algorithms for distributed statistical optimization on large-scale data. The first algorithm is an averaging method that distributes the N data samples evenly to m machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing that under a reasonable set of conditions, the combined parameter achieves mean-squared error that decays as O(N-1 + (N/m)-2). Whenever m ≤ √N, this guarantee matches the best possible rate achievable by a centralized algorithm with access to all N samples. The second algorithm is a novel method, based on an appropriate form of bootstrap. Requiring only a single round of communication, it has mean-squared error that decays as O(N-1 + (N/m)-3), and so is more robust to the amount of parallelization.


title: Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems

text: We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible.To date, reinforcement learning approaches for such <i>independent </i> agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results (Claus & Boutilier 1998) by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.


title: Characterization of log periodic planar dipole array antenna

text: This paper proposes a analysis approach for characterizing the broadband Log Periodic Planar Dipole Array (LPPDA) antenna useful for C-band applications. A simplest geometry of the edge fed Log Periodic Planar Dipole Array is designed and developed. Parameters like length, width of the elements, and spacing between elements are the varied using defined scale factor (τ) and a spacing factor (σ), keeping others constant and their impact was noted down. A parametric analysis is performed. using CST software. A transmission line equivalent circuit of the proposed LPPDA is constructed. Log periodicity behaviour of the proposed antenna is checked by plotting input impedance with respect to logarithm of frequency. An antenna is fabricated and measured in laboratory.


title: Using Suffix Arrays to Compute Term Frequency and Document Frequency for All Substrings in a Corpus

text: Bigrams and trigrams are commonly used in statistical natural language processing; this paper will describe techniques for working with much longer n-grams. Suffix arrays (Manber and Myers 1990) were first introduced to compute the frequency and location of a substring (n-gram) in a sequence (corpus) of length N. To compute frequencies over all N(N+1)/2 substrings in a corpus, the substrings are grouped into a manageable number of equivalence classes. In this way, a prohibitive computation over substrings is reduced to a manageable computation over classes. This paper presents both the algorithms and the code that were used to compute term frequency (tf) and document frequency (df) for all n-grams in two large corpora, an English corpus of 50 million words of Wall Street Journal and a Japanese corpus of 216 million characters of Mainichi Shimbun. The second half of the paper uses these frequencies to find interesting substrings. Lexicographers have been interested in n-grams with high mutual information (MI) where the joint term frequency is higher than what would be expected by chance, assuming that the parts of the n-gram combine independently. Residual inverse document frequency (RIDF) compares document frequency to another model of chance where terms with a particular term frequency are distributed randomly throughout the collection. MI tends to pick out phrases with noncompositional semantics (which often violate the independence assumption) whereas RIDF tends to highlight technical terminology, names, and good keywords for information retrieval (which tend to exhibit nonrandom distributions over documents). The combination of both MI and RIDF is better than either by itself in a Japanese word extraction task.


title: QDFS: A quality-aware distributed file storage service based on HDFS

text: On the basis of the Hadoop distributed file system (HDFS), this paper presents the design and implementation of QDFS, a distributed file storage service system that employs a backup policy based on recovery volumes and a quality-aware data distribution strategy. The experimental results show that QDFS increases the storage utilization ratio and improves the storage performance by being aware of the quality of service of the DataNodes. The improvements of QDFS compared with HDFS make the Hadoop distributed computing model more suitable to unstable wide area network environments.


title: Effects of psychological and social factors on organic disease: a critical assessment of research on coronary heart disease.

text: An extensive research literature in the behavioral sciences and medicine suggests that psychological and social factors may play a direct role in organic coronary artery disease (CAD) pathology. However, many in the medical and scientific community regard this evidence with skepticism. This chapter critically examines research on the impact of psychological and psychosocial factors on the development and outcome of coronary heart disease, with particular emphasis on studies employing verifiable outcomes of CAD morbidity or mortality. Five key variables identified as possible psychosocial risk factors for CAD are addressed: acute and chronic stress, hostility, depression, social support, and socioeconomic status. Evidence regarding the efficacy of psychosocial interventions is also presented. It is suggested that, taken as a whole, evidence for a psychological and social impact on CAD morbidity and mortality is convincing. However, continued progress in this area requires multidisciplinary research integrating expertise in cardiology and the behavioral sciences, and more effective efforts to communicate research findings to a biomedical audience.


title: Learning Theory and Instructional Design

text: Designing effective instruction goes beyond systematically executing various steps within an instructional design model. Among a host of considerations, effective instructional design should take into consideration the theoretical bases in which it is grounded. This is not to say that learning theory offers instructional designers answers to design problems but instead, offers clarity, direction and focus throughout the instructional design process. Merrill (2001, p. 294) explains that a “theoretical tool, in and of itself, is not an instructional design theory but defines instructional components that can be used to define instructional prescriptions more precisely.” Likewise, Merriam and Caffarella (1999, p. 250) make the point that “[learning] theories do not give us solutions, but they do direct our attention to those variables that are crucial in finding solutions.” Thus, understanding theoretical frameworks and properly incorporating them within the scope of instructional design is important for designers to effectively prepare and present instruction as well as for organizational entities to more precisely and efficiently address training-appropriate issues.


title: Oil content, tocopherol composition and fatty acid patterns of the seeds of 51 Cannabis sativa L. genotypes

text: The oil content, the tocopherol composition, the plastochromanol-8 (P-8) content and the fatty acid composition (19 fatty acids) of the seed of 51 hemp (Cannabis sativa L.) genotypes were studied in the 2000 and 2001 seasons. The oil content of the hemp seed ranged from 26.25% (w/w) to 37.50%. Analysis of variance revealed significant effects of genotype, year and of the interaction (genotype × year) on the oil content. The oil contents of the 51 genotypes in 2000 and 2001 were correlated (r = 0.37**) and averaged 33.19 ± 1.45% in 2000 and 31.21 ± 0.96% in 2001. The γ-tocopherol, α-tocopherol, δ-tocopherol, P-8- and β-tocopherol contents of the 51 genotypes averaged 21.68 ± 3.19, 1.82 ± 0.49, 1.20 ± 0.40, 0.18 ± 0.07 and 0.16 ± 0.04 mg 100g−1 of seeds, respectively (2000 and 2001 data pooled). Hierarchical clustering of the fatty acid data did not group the hemp genotypes according to their geographic origin. The γ-linolenic acid yield of hemp (3–30 kg ha−1) was similar to the γ-linolenic acid yield of plant species that are currently used as sources of γ-linolenic acid (borage (19–30 kg ha−1), evening primrose (7–30 kg ha−1)). The linoleic acid yield of hemp (129–326 kg ha−1) was similar to flax (102–250 kg ha−1), but less than in sunflower (868–1320 kg ha−1). Significant positive correlations were detected between some fatty acids and some tocopherols. Even though the average content of P-8 in hemp seeds was only 1/120th of the average γ-tocopherol content, P-8 content was more closely correlated with the unsaturated fatty acid content than γ-tocopherol or any other tocopherol fraction. The average broad-sense heritabilities of the oil content, the antioxidants (tocopherols and P-8) and the fatty acids were 0.53, 0.14 and 0.23, respectively. The genotypes Fibrimon 56, P57, Juso 31, GB29, Beniko, P60, FxT, Félina 34, Ramo and GB18 were capable of producing the largest amounts of high quality hemp oil.


title: Learning to Remember Translation History with a Continuous Cache

text: Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.


title: A fast method of fog and haze removal

text: Fog and haze degrade the quality of preview and captured image by reducing the contrast and saturation. As a result the visibility of scene or object degrades. The objective of the present work is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. We propose a method that uses single frame for enhancing foggy images using multilevel transmission map. The method is fast and free from noise or artifacts that generally arise in such enhancement techniques. A comparison with existing methods shows that the proposed method performs better in terms of both processing time and quality. The proposed method works in real time for VGA resolution. The proposed work also presents a scheme to remove fog, rain and snow in real-time.


title: Snapshots in a flash with ioSnap

text: Snapshots are a common and heavily relied upon feature in storage systems. The high performance of flash-based storage systems brings new, more stringent, requirements for this classic capability. We present ioSnap, a flash optimized snapshot system. Through careful design exploiting common snapshot usage patterns and flash oriented optimizations, including leveraging native characteristics of Flash Translation Layers, ioSnap delivers low-overhead snapshots with minimal disruption to foreground traffic. Through our evaluation, we show that ioSnap incurs negligible performance overhead during normal operation, and that common-case operations such as snapshot creation and deletion incur little cost. We also demonstrate techniques to mitigate the performance impact on foreground I/O during intensive snapshot operations such as activation. Overall, ioSnap represents a case study of how to integrate snapshots into a modern, well-engineered flash-based storage system.


title: Analysis of back-end flash in a 1.5b/stage pipelined ADC

text: An analysis of the impact of last stage flash in a conventional pipeline ADC is performed in this paper. The performance of a pipeline ADC can be altered significantly by calibrating the comparators in the back-end flash. Also, realizing that the input to the back-end flash (in a pipeline ADC) is not uniformly distributed, this paper proposes alternative back-end flash references to improve the overall performance of the ADC. An analysis of the performance of the pipeline with large offsets in the MDAC stages is also presented in this paper.


title: A Preliminary Taxonomy of Crowdsourcing

text: Many firms are now asking how they can benefit from the new form of outsourcing labelled “crowdsourcing”. Like many other forms of outsourcing, crowdsourcing is now being “talked up” by a somewhat credulous trade press. However, the term crowdsourcing has been used to describe several related, but different phenomena, and what might be successful with one form of crowdsourcing may not be with another. In this paper the notion of crowdsourcing is decomposed to create a taxonomy that expands our understanding of what is meant by the term. This taxonomy focuses on the different capability levels of crowdsourcing suppliers; different motivations; and different allocation of benefits. The management implications of these distinctions are then considered in light of what we know about other forms of outsourcing.


title: Being John Malkovich

text: Given a photo of person A, we seek a photo of person B with similar pose and expression. Solving this problem enables a form of puppetry, in which one person appears to control the face of another. When deployed on a webcam-equipped computer, our approach enables a user to control another person’s face in real-time. This image-retrievalinspired approach employs a fully-automated pipeline of face analysis techniques, and is extremely general—we can puppet anyone directly from their photo collection or videos in which they appear. We show several examples using images and videos of celebrities from the Internet.


title: Common-Duty-Ratio Control of Input-Parallel Output-Parallel (IPOP) Connected DC–DC Converter Modules With Automatic Sharing of Currents

text: Input-parallel output-parallel (IPOP) connected converter systems allow the use of low-power converter modules for high-power applications, with interleaving control scheme resulting in smaller filter size, better dynamic performance, and higher power density. In this paper, a new IPOP converter system is proposed, which consists of multiple dual-active half-bridge (DAHB) dc-dc converter modules. Moreover, by applying a common-duty-ratio control scheme, without a dedicated current-sharing controller, the automatic sharing of input currents or load currents is achieved in the IPOP converter even in the presence of substantial differences of 10% in various module parameters. The current-sharing performance of the proposed control method is analyzed using both a small-signal model and a steady-state dc model of the IPOP system. It is concluded that the equal sharing of currents among modules can be achieved by reducing the mismatches in various module parameters, which is achievable in practice. The current-sharing performance of the IPOP converter is also verified by Saber simulation and a 400-W experimental prototype consisting of two DAHB modules. The common-duty-ratio control method can be extended to any IPOP system that consists of three or more converter modules, including traditional dual-active bridge dc-dc converters, which have a characteristic of current source.


title: Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

text: The need for more security on mobile devices is increasing with new functionalities and features made available. To improve the device security we propose gait recognition as a protection mechanism. Unlike previous work on gait recognition, which was based on the use of video sources, floor sensors or dedicated high-grade accelerometers, this paper reports the performance when the data is collected with a commercially available mobile device containing low-grade accelerometers. To be more specific, the used mobile device is the Google G1 phone containing the AK8976A embedded accelerometer sensor. The mobile device was placed at the hip on each volunteer to collect gait data. Preproccesing, cycle detection and recognition-analysis were applied to the acceleration signal. The performance of the system was evaluated having 51 volunteers and resulted in an equal error rate (EER) of 20%.


title: Estimation of Planar Curves, Surfaces, and Nonplanar Space Curves Defined by Implicit Equations with Applications to Edge and Range Image Segmentation

text: This paper addresses the problem of parametric representation and estimation of complex planar curves in 2-D, surfaces in 3-D and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, and we use the latter representation. A planar curve is the set of zeros of a smooth function of two variables X-Y, a surface is the set of zeros of a smooth function of three variables X-~-Z, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables X-!/-Z. For example, the surface of a complex object in 3D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. We show how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of “interest regions” that are more complex than high curvature regions and, hence, more useful as features for object recognition. Fitting implicit curves and surfaces to data would be ideally based on minimizing the mean square distance from the data points to the curve or surface. Since the distance from a point to a curve or surface cannot be computed exactly by direct methods, the approximate distance, which is a first-order approximation of the real distance, is introduced, generalizing and unifying previous results. We fit implicit curves and surfaces to data minimizing the approximate mean square distance, which is a nonlinear least squares problem. We show that in certain cases, this problem reduces to the generalized eigenvector fit, which is the minimization of the sum of squares of the values of the functions that define the curves or surfaces under a quadratic constraint function of the data. This fit is computationally reasonable to compute, is readily parallelizable, and, hence, is easily computed in real time. In general, the generalized eigenvector lb provides a very good initial estimate for the iterative minimization of the approximate mean square distance. Although we are primarily interested in the 2-D and 3-D cases, the methods developed herein are dimension independent. We show that in the case of algebraic curves and surfaces, i.e., those defined by sets of zeros of polynomials, the minimizers of the approximate mean square distance and the generalized eigenvector fit are invariant with respect to similarity transformations. Thus, the generalized eigenvector lit is independent of the choice of coordinate system, which is a very desirable property for object recognition, position estimation, and the stereo matching problem. Finally, as applications of the previous techniques, we illustrate the concept of “interest regions” Manuscript received April 13, 1988; revised April 30, 1991. This work was supported by an IBM Fellowship and NSF Grant IRI-8715774. The author was with the Laboratory for Engineering Man/Machine Systems, Department of Engineering, Brown University, Providence, RI 02912. He is now with the Exploratory Computer Vision Group, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598. IEEE Log Number 9102093. for object recognition and describe a variable-order segmentation algorithm that applies to the three cases of interest.


title: What Is Coefficient Alpha ? An Examination of Theory and Applications

text: Psychological research involving scale construction has been hindered considerably by a widespread lack of understanding of coefficient alpha and reliability theory in general. A discussion of the assumptions and meaning of coefficient alpha is presented. This discussion is followed by a demonstration of the effects of test length and dimensionality on alpha by calculating the statistic for hypothetical tests with varying numbers of items, numbers of orthogonal dimensions, and average item intercorrelations. Recommendations for the proper use of coefficient alpha are offered.


title: Biometric authentication on a mobile device: a study of user effort, error and task disruption

text: We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods.


title: Is Smart Beta Really Smart ?

text: BURTON G. MALKIEL is the Chemical Bank Chairman’s Professor Emeritus of Economics at Princeton University in Princeton, NJ. bmalkiel@princeton.edu There is a popular new investment strategy in portfolio management called smart beta. With a catchy title and a promise of improved portfolio performance, the strategy has already attracted hundreds of billions of dollars and is growing by leaps and bounds. Unfortunately, smart beta portfolios do not consistently outperform and when they do produce appealing results, they f lunk the risk test.


title: Exploring the Regularity of Sparse Structure in Convolutional Neural Networks

text: Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accelerators such as TPU[1]. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as well as the prediction accuracy. Coarse-grained pruning creates regular sparsity patterns, making it more amenable for hardware acceleration but more challenging to maintain the same accuracy. In this paper we quantitatively measure the trade-off between sparsity regularity and prediction accuracy, providing insights in how to maintain accuracy while having more a more structured sparsity pattern. Our experimental results show that coarse-grained pruning can achieve a sparsity ratio similar to unstructured pruning without loss of accuracy. Moreover, due to the index saving effect, coarse-grained pruning is able to obtain a better compression ratio than fine-grained sparsity at the same accuracy threshold. Based on the recent sparse convolutional neural network accelerator (SCNN), our experiments further demonstrate that coarse-grained sparsity saves∼ 2× the memory references compared to fine-grained sparsity. Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.


title: Lazy Modeling of Variants of Token Swapping Problem and Multi-agent Path Finding through Combination of Satisfiability Modulo Theories and Conflict-based Search

text: We address item relocation problems in graphs in this paper. We assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be moved across edges while various constraints depending on the type of relocation problem must be satisfied. We introduce a general problem formulation that encompasses known types of item relocation problems such as multi-agent path finding (MAPF) and token swapping (TSWAP). In this formulation we express two new types of relocation problems derived from token swapping that we call token rotation (TROT) and token permutation (TPERM). Our solving approach for item relocation combines satisfiability modulo theory (SMT) with conflict-based search (CBS). We interpret CBS in the SMT framework where we start with the basic model and refine the model with a collision resolution constraint whenever a collision between items occurs in the current solution. The key difference between the standard CBS and our SMT-based modification of CBS (SMT-CBS) is that the standard CBS branches the search to resolve the collision while in SMT-CBS we iteratively add a single disjunctive collision resolution constraint. Experimental evaluation on several benchmarks shows that the SMT-CBS algorithm significantly outperforms the standard CBS. We also compared SMT-CBS with a modification of the SAT-based MDD-SAT solver that uses an eager modeling of item relocation in which all potential collisions are eliminated by constrains in advance. Experiments show that lazy approach in SMT-CBS produce fewer constraint than MDD-SAT and also achieves faster solving run-times.


title: Data Mining in Healthcare : Current Applications and Issues By

text: The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper provides a survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths, and detection of fraudulent insurance claims.


title: Using the Kinect for detecting tremors: Challenges and opportunities

text: The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.


title: Automated Pyramid Scoring of Summaries using Distributional Semantics

text: The pyramid method for content evaluation of automated summarizers produces scores that are shown to correlate well with manual scores used in educational assessment of students’ summaries. This motivates the development of a more accurate automated method to compute pyramid scores. Of three methods tested here, the one that performs best relies on latent semantics.


title: Large eddy simulations of the HyShot II scramjet

text: The present work is part of a broad effort toward predictive simulations of complex flows at high Reynolds numbers. The main objective of the Stanford PSAAP Center (Pre-dictive Science Academic Alliance Program) is to predict the reacting flow in the HyShot II scramjet experiment carried out in the HEG facility of the German Aerospace Agency DLR (cf. Laurence et al. 2011). Specifically, the objective is to predict the best-estimate of the flow, to estimate the uncertainties from a range of sources in this prediction, and, finally , to quantify the margin to engine unstart. Unstart occurs in supersonic internal flows when the flow becomes choked (e.g., due to excessive friction or heat addition), which initiates an unsteady unstart process that eventually leads to subsonic flow throughout. The unstart process involves a propagation of shock waves upstream in the combustor, the speed of which depends strongly on the amount of heat addition. The experiments by Laurence et al. (2011) indicate propagation velocities of 31 m/s at equivalence ratio (ER) of 0.5, 93 m/s at ER=0.7, and above 200 m/s at ER=1.1. For this problem with fixed mass flux of incoming air, the equivalence ratio is proportional to the fuel mass flux. The length of the HyShot combustor is about 300 mm; thus the unstart process takes about 10 ms at ER=0.5. The experiments were carried out in a shock tube with a test time of about 2.5 ms; thus the unstart process is significantly longer than the test time near the unstart boundary. For this reason, it is clear that shock tube experiments can not give a precise unstart bound. An alternative approach is to use a validated large eddy simulation (LES) method to explore and determine the unstart bound. This is the approach taken here, and the motivation of the work described in this brief. The complexity of the flow (turbulence, shocks, mixing, combustion) implies that a careful multi-stage validation plan is needed. Moreover, the available quantitative experimental data for the HyShot case is limited to measurements of pressure (mean and rms) and heat transfer (although these are highly uncertain) along two lines in the combustor. Therefore, validation on the HyShot problem alone is not sufficient. The first step in the validation is to compute a supersonic flat plate boundary layer as a basic check on the LES method and, specifically, the modeling of the friction and heat transfer at …


title: Context-free Attacks Using Keyboard Acoustic Emanations

text: The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.


title: Marriage, honesty, and stability

text: Many centralized two-sided markets form a matching between participants by running a stable marriage algorithm. It is a well-known fact that no matching mechanism based on a stable marriage algorithm can guarantee truthfulness as a dominant strategy for participants. However, as we will show in this paper, in a probabilistic setting where the preference lists of one side of the market are composed of only a constant (independent of the the size of the market) number of entries, each drawn from an arbitrary distribution, the number of participants that have more than one stable partner is vanishingly small. This proves (and generalizes) a conjecture of Roth and Peranson [23]. As a corollary of this result, we show that, with high probability, the truthful strategy is the best response for a given player when the other players are truthful. We also analyze equilibria of the deferred acceptance stable marriage game. We show that the game with complete information has an equilibrium in which a (1 - o(1)) fraction of the strategies are truthful in expectation. In the more realistic setting of a game of incomplete information, we will show that the set of truthful strategies form a (1 + o(1))-approximate Bayesian-Nash equilibrium. Our results have implications in many practical settings and were inspired by the work of Roth and Peranson [23] on the National Residency Matching Program.


title: A personalized recommender system for pervasive social networks

text: The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. On the one hand, users tend to be always connected for sharing experiences and conducting their social interactions with friends and acquaintances, through so-called Mobile Social Networks, further improving their social inclusion. On the other hand, the pervasiveness of communication infrastructures spreading data (cellular networks, direct device-to-device contacts, interactions with ambient devices as in the Internet-of-Things) makes compulsory the deployment of solutions able to filter off undesired information and to select what content should be addressed to which users, for both (i) better user experience, and (ii) resource saving of both devices and network. In this work, we propose a novel framework for pervasive social networks, called Pervasive PLIERS (p-PLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. p-PLIERS exploits the recently proposed PLIERS tag-based recommender system [2] as context a reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. p-PLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes’ knowledge during proximity contacts and through device-to-device communications. We evaluated p-PLIERS by simulating its behavior in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD’15), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterize the generation and sharing of user contents.


title: Recent Advancements in Retinal Vessel Segmentation

text: Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.


title: Deep Learning with Low Precision by Half-Wave Gaussian Quantization

text: The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximating a classical non-linearity, the hyperbolic tangent, by two functions: a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagation step during network learning. The problem of approximating the widely used ReLU non-linearity is then considered. An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations. To overcome the problem of gradient mismatch, due to the use of different forward and backward approximations, several piece-wise backward approximators are then investigated. The implementation of the resulting quantized network, denoted as HWGQ-Net, is shown to achieve much closer performance to full precision networks, such as AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision networks, with 1-bit binary weights and 2-bit quantized activations.


title: Resource Allocation Optimization for Device-to-Device Communication Underlaying Cellular Networks

text: Device-to-Device (D2D) communication will become an important technology in future networks with the increase of the requirements of local communication services. The interference between cellular communication and D2D communication can be coordinated by proper power control and resource allocation. In this paper, we analyze the resource allocation methods for D2D communication underlaying cellular networks. A novel resource allocation method that D2D can reuse the resources of more than one cellular user is proposed. After that, we discuss the selection of the optimal resource allocation method from the proposed method and the conventional methods. Finally, the performance of different methods is evaluated through numerical simulation. The simulation results show that the proposed method is the optimal resource allocation method when the D2D pair locates at the most part of the cell area in both uplink and downlink. The proposed method can improve the sum throughput of cellular communication and D2D communication significantly.


title: Eleos: ExitLess OS Services for SGX Enclaves

text:
Intel Software Guard extensions (SGX) enable secure and trusted execution of user code in an isolated enclave to protect against a powerful adversary. Unfortunately, running I/O-intensive, memory-demanding server applications in enclaves leads to significant performance degradation. Such applications put a substantial load on the in-enclave system call and secure paging mechanisms, which turn out to be the main reason for the application slowdown. In addition to the high direct cost of thousands-of-cycles long SGX management instructions, these mechanisms incur the high indirect cost of enclave exits due to associated TLB flushes and processor state pollution.
 We tackle these performance issues in Eleos by enabling exit-less system calls and exit-less paging in enclaves. Eleos introduces a novel Secure User-managed Virtual Memory (SUVM) abstraction that implements application-level paging inside the enclave. SUVM eliminates the overheads of enclave exits due to paging, and enables new optimizations such as sub-page granularity of accesses.
 We thoroughly evaluate Eleos on a range of microbenchmarks and two real server applications, achieving notable system performance gains. memcached and a face verification server running in-enclave with Eleos, achieves up to 2.2× and 2.3× higher throughput respectively while working on datasets up to 5× larger than the enclave's secure physical memory.


title: When sex is more than just sex: attachment orientations, sexual experience, and relationship quality.

text: The authors explored the contribution of individual differences in attachment orientations to the experience of sexual intercourse and its association with relationship quality. In Study 1, 500 participants completed self-report scales of attachment orientations and sexual experience. The findings indicated that whereas attachment anxiety was associated with an ambivalent construal of sexual experience, attachment avoidance was associated with more aversive sexual feelings and cognitions. In Study 2, 41 couples reported on their attachment orientations and provided daily diary measures of sexual experiences and relationship interactions for a period of 42 days. Results showed that attachment anxiety amplified the effects of positive and negative sexual experiences on relationship interactions. In contrast, attachment avoidance inhibited the positive relational effect of having sex and the detrimental relational effects of negative sexual interactions. The authors discuss the possibility that attachment orientations are associated with different sex-related strategies and goals within romantic relationships.


title: Hierarchical graph indexing

text: Traffic analysis, in the context of Telecommunications or Internet and Web data, is crucial for large network operations. Data in such networks is often provided as large graphs with hundreds of millions of vertices and edges. We propose efficient techniques for managing such graphs at the storage level in order to facilitate its processing at the interface level(visualization). The methods are based on a hierarchical decomposition of the graph edge set that is inherited from a hierarchical decomposition of the vertex set. Real time navigation is provided by an efficient two level indexing schema called the gkd*-tree. The first level is a variation of a kd-tree index that partitions the edge set in a way that conforms to the hierarchical decomposition and the data distribution (the gkd-tree). The second level is a redundant R-tree that indexes the leaf pages of the gkd-tree. We provide computational results that illustrate the superiority of the gkd-tree against conventional indexes like the kd-tree and the R*-tree both in creation as well as query response times.


title: Detection of copy-move image forgery based on discrete cosine transform

text: Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human’s naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forged areas and threshold value used for the research.


title: Analysis and validation of proteomic data generated by tandem mass spectrometry

text: The analysis of the large amount of data generated in mass spectrometry–based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.


title: A three-level, T-type, power electronics building block using Si-SiC hybrid switch for high-speed drives

text: This paper presents the design and implementation of a 100 kW three-level T-type (3L-T2), single-phase power electronics building block (PEBB). The PEBB features switching power devices with associated gate drivers, DC-link capacitors and interconnection busbar. A hybrid switch (HyS), which includes a Si IGBT and a SiC MOSFET in parallel, forms the switching power device in the PEBB. An low-inductive multi-layer laminated busbar was designed to have symmetrical current commutation loops. A major contribution is that this is achieved while no three-level T-type power module was used. A loop inductance of 29 nH was achieved which is lower than the existing state-of-the-art three-level designs in literature. The fabricated prototype PEBB achieves a specific power density of 27.7 kW/kg and a volumetric power density of 308.61 W/in3. Single-phase operation of the PEBB has been demonstrated at the switching frequency of 28 kHz.


title: Trends in camera based Automotive Driver Assistance Systems (ADAS)

text: Advance Driver Assistance Systems (ADAS), once limited to high end luxury automobiles are fast becoming popular with Mid and entry level segments driven in part by legislation coming in to effect in the latter part of this decade. These systems require support for a wide variety of applications, from surround-view visual systems to safety critical vision applications (eg Pedestrian Detect, automatic braking etc). In this white paper we describe some of the existing and emerging trends and applications in each of these segments along with the requirements and motivations for each of these features. We also highlight TI's automotive class TDA2x device, a state of the art automotive grade device capable of handling complex ADAS applications within a low power and cost budget.


title: Modeling and control active suspension system for a full car model

text: The purpose of this paper is to investigate the performance of a full car model active suspension system using LQR controller. Dynamic model used in this study is a linear model. A linear model can capture basic performances of vehicle suspension such as body displacement, body acceleration, wheel displacement, wheel deflection, suspension travels, pitch and yawn. Performance of suspension system is determined by the ride comfort and vehicle handling. It can be measured by car body displacement and wheel displacement performance. Two types of road profiles are used as input for the system. Simulation is based on the mathematical model by using MATLAB/SIMULINK software. Results show that the performance of body displacement and wheel displacement can be improved by using Linear Quadratic Regulator control (LQR).


title: Asymmetric division of labor in human skilled bimanual action: the kinematic chain as a model.

text: This article presents a tentative theoretical framework for the study of asymmetry in the context of human bimanual action. It is emphasized that in man most skilled manual activities involve two hands playing different roles, a fact that has been often overlooked in the experimental study of human manual lateralization. As an alternative to the current concepts of manual preference and manual superiority-whose relevance is limited to the particular case of unimanual actions-the more general concept of lateral preference is proposed to denote preference for one of the two possible ways of assigning two roles to two hands. A simple model describing man's favored intermanual division of labor in the model are the following. 1) The two hands represent two motors, that is, decomplexity is ignored in the suggested approach. 2) In man, the two manual motors cooperate with one another as if they were assembled in series, thereby forming a kinematic chain: In a right-hander allowed to follow his or her lateral preferences, motion produced by the right hand tends to articulate with motion produced by the left. It is suggested that the kinematic chain model may help in understanding the adaptive advantage of human manual specialization.


title: CONDENSATION—Conditional Density Propagation for Visual Tracking

text: The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.


title: Surround-screen projection-based virtual reality: the design and implementation of the CAVE

text: Several common systems satisfy some but not all of the VR definition above. Flight simulators provide vehicle tracking, not head tracking, and do not generally operate in binocular stereo. Omnimax theaters give a large angle of view [8], occasionally in stereo, but are not interactive. Head-tracked monitors [4][6] provide all but a large angle of view. Head-mounted displays (HMD) [7][13] and BOOMs [9] use motion of the actual display screens to achieve VR by our definition. Correct projection of the imagery on large screens can also create a VR experience, this being the subject of this paper. This paper describes the CAVE (CAVE Automatic Virtual Environment) virtual reality/scientific visualization system in detail and demonstrates that projection technology applied to virtual-reality goals achieves a system that matches the quality of workstation screens in terms of resolution, color, and flicker-free stereo. In addition, this format helps reduce the effect of common tracking and system latency errors. The off-axis perspective projection techniques we use are shown to be simple and straightforward. Our techniques for doing multi-screen stereo vision are enumerated, and design barriers, past and current, are described. Advantages and disadvantages of the projection paradigm are discussed, with an analysis of the effect of tracking noise and delay on the user. Successive refinement, a necessary tool for scientific visualization, is developed in the virtual reality context. The use of the CAVE as a one-to-many presentation device at SIGGRAPH '92 and Supercomputing '92 for computational science data is also mentioned. Previous work in the VR area dates back to Sutherland [12], who in 1965 wrote about the “Ultimate Display.” Later in the decade at the University of Utah, Jim Clark developed a system that allowed wireframe graphics VR to be seen through a headmounted, BOOM-type display for his dissertation. The common VR devices today are the HMD and the BOOM. Lipscomb [4] showed a monitor-based system in the IBM booth at SIGGRAPH '91 and Deering [6] demonstrated the Virtual Portal, a closets ized three-wal l project ion-based system, in the Sun Microsystems' booth at SIGGRAPH '92. The CAVE, our projection-based VR display [3], also premiered at SIGGRAPH '92. The Virtual Portal and CAVE have similar intent, but different implementation schemes.


title: The Representation and Matching of Pictorial Structures

text: The primary problem dealt with in this paper is the following. Given some description of a visual object, find that object in an actual photograph. Part of the solution to this problem is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.


title: Research directions in virtual environments: report of an NSF Invitational Workshop, March 23-24, 1992, University of North Carolina at Chapel Hill

text: At the request of NSF's Interactive Systems Program, a two-day invitational workshop was held March 23-24, 1992 at UNC Chapel Hill to identify and recommend future research directions in the area of "virtual environments" (VE) *. Workshop participants included some 18 experts (plus 4 NSF officials) from universities, industry, and other leading technical organizations. The two-day schedule alternated between sessions of the entire group and sessions in the following specialty areas, around which the recommendations came to be organized: 1) Perception , 2) Human-Machine Software Interface , 3) Software, 4) Hardware, and 5) Applications. Also, two participants developed a taxonomy of VE applications that is included as an appendix to the report. 1. Collaborative science-technology development programs should be established at several sites around the country to encourage closer collaboration between developers and scientists. 2. Theoretical research should focus on development of metrics of performance and task demands in VE. 3. Paradigmatic applications and theoretical questions that illustrate the science-technology synergy need identification. Audition Spatial Sound 1.Theoretical research should emphasize the role of individual differences in Head-Related Transfer Functions (HRTF's), critical cues for distance and externalization, spectral cues for enhancing elevation and disambiguating the cone-of-confusion, head-motion, and intersensory interaction and adaptation in the accurate perception of virtual acoustic sources. The notion of artificially enhanced localization cues is also a promising area. 2. A fruitful area for joint basic and applied research is the development of perceptually-viable methods of simplifying the synthesis technique to maximize the efficiency of algorithms for complex room modeling. 3. Future effort should still be devoted to developing more realistic models of acoustic environments with implementation on more powerful hardware platforms. Nonspeech Audio 1. Theoretical research should focus on lower-level sensory and higher-level cognitive determinants of acoustic perceptual organization, with particular emphasis on how acoustic parameters interact to determine the identification, segregation, and localization of multiple, simultaneous sources. 2. Technology development should focus on hardware and software systems specifically aimed at real-time generation and control for acoustic information display. Haptics 1. Development should be encouraged of a variety of computer-controlled mechanical devices for either basic scientific investigation of the human haptic system or to serve as haptic interfaces for virtual environments and teleloperation. 2. Research programs should be initiated to encourage collaboration among engineers who are capable of building high precision robotic devices and scientists who can conduct biomechanical and perceptual experiments with the devices. 3. Research …


title: Bilingualism, Biliteracy, and Learning to Read: Interactions Among Languages and Writing Systems

text: Four groups of children in first grade were compared on early literacy tasks. Children in three of the groups were bilingual, each group representing a different combination of language and writing system, and children in the fourth group were monolingual speakers of English. All the bilingual children used both languages daily and were learning to read in both languages. The children solved decoding and phonological awareness tasks, and the bilinguals completed all tasks in both languages. Initial differences between the groups in factors that contribute to early literacy were controlled in an analysis of covariance, and the results showed a general increment in reading ability for all the bilingual children but a larger advantage for children learning two alphabetic systems. Similarly, bilinguals transferred literacy skills across languages only when both languages were written in the same system. Therefore, the extent of the bilingual facilitation for early reading depends on the relation between the two languages and writing systems.


title: Position Sensorless Control for Four-Switch Three-Phase Brushless DC Motor Drives

text: This paper proposes a position sensorless control scheme for four-switch three-phase (FSTP) brushless DC (BLDC) motor drives using a field programmable gate array (FPGA). A novel sensorless control with six commutation modes and novel pulsewidth modulation scheme is developed to drive FSTP BLDC motors. The low cost BLDC driver is achieved by the reduction of switch device count, cost down of control, and saving of hall sensors. The feasibility of the proposed sensorless control for FSTP BLDC motor drives is demonstrated by analysis and experimental results.


title: Social Media Use and Mental Health among Young Adults

text: In recent years many parents, advocates and policy makers have expressed concerns regarding the potential negative impact of social media use. Some studies have indicated that social media use may be tied to negative mental health outcomes, including suicidality, loneliness and decreased empathy. Other studies have not found evidence for harm, or have indicated that social media use may be beneficial for some individuals. The current correlational study examined 467 young adults for their time spent using social media, importance of social media in their lives and tendency to engage in vaguebooking (posting unclear but alarming sounding posts to get attention). Outcomes considered included general mental health symptoms, suicidal ideation, loneliness, social anxiety and decreased empathy. Results indicated that social media use was not predictive of impaired mental health functioning. However, vaguebooking was predictive of suicidal ideation, suggesting this particular behavior could be a warning sign for serious issues. Overall, results from this study suggest that, with the exception of vaguebooking, concerns regarding social media use may be misplaced.


title: Discriminative and consistent similarities in instance-level Multiple Instance Learning

text: In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are highly and consistently similar to each other and dissimilar to negative instances. Our approach takes advantage of a discriminative notion of pairwise similarity coupled with a structural cue in the form of a consistency metric that measures the quality of each similarity. We learn a similarity function for every pair of instances in positive bags by how similarly they differ from instances in negative bags, the only certain labels in MIL. Our experiments demonstrate that our method consistently outperforms state-of-the-art MIL methods both at bag-level and instance-level predictions in standard benchmarks, image category recognition, and text categorization datasets.


title: Polyhedral Extraction Tool

text: We present a new library for extracting a polyhedral model from C source. The library is based on clang, the LLVM C frontend, and isl, a library for manipulating quasi-affine sets and relations. The use of clang for parsing the C code brings advanced diagnostics and full support for C99. The use of isl allows for an easy construction and a powerful and compact representation of the polyhedral model. Besides allowing arbitrary piecewise quasi-affine index expressions and conditions, the library also supports some data dependent constructs and has special treatment for unsigned integers. The library has been successfully used to obtain polyhedral models for use in an equivalence checker, a tool for constructing polyhedral process networks, a parallelizer targeting GPUs and an interactive polyhedral environment.


title: Printed Rectangular Monopole Antenna with Slots for UWB Applications

text: This paper presents various slotted rectangular Monopole Antenna. The proposed antenna is designed in order to meet ultra wideband (UWB) requirements and applications. This antenna is small in size enabling it to be fixed into wireless Personal Area Network (PAN) devices. There are 3 types of proposed slots; T-slot, Π-slot and I-slot for patch. The T & I slots produce more vertical current, thus improve the matching impedance performance at higher frequencies. The Π-slot on rectangular patch has shown dual frequency performance. For all proposed antennas, their measured return loss and radiation pattern meet the UWB requirements.


title: Cultivating Competence , Self-Efficacy , and Intrinsic Interest Through Proximal Self-Motivation

text: The present experiment tested the hypothesis that self-motivation through proximal goal setting serves as an effective mechanism for cultivating competencies, self-percepts of efficacy, and intrinsic interest. Children who exhibited gross deficits and disinterest in mathematical tasks pursued a program of self-directed learning under conditions involving either proximal subgoals, distal goals, or no goals. Results of the multifaceted assessment provide support for the superiority of proximal self-influence. Under proximal subgoals, children progressed rapidly in self-directed learning, achieved substantial mastery of mathematical operations, and developed a sense of personal efficacy and intrinsic interest in arithmetic activities that initially held little attraction for them. Distal goals had no demonstrable effects. In addition to its other benefits, goal proximity fostered veridical self-knowledge of capabilities as reflected in high congruence between judgments of mathematical self-efficacy and subsequent mathematical performance. Perceived self-efficacy was positively related to accuracy of mathematical performance and to intrinsic interest in arithmetic activities. Article: Much human behavior is directed and sustained over long periods, even though the external inducements for it may be few and far between. Under conditions in which external imperatives are minimal and discontinuous, people must partly serve as agents of their own motivation and action. In social learning theory (Bandura, 1977b, in press), self-directedness operates through a self system that comprises cognitive structures and subfunctions for perceiving, evaluating, motivating, and regulating behavior. An important, cognitively based source of self-motivation relies on the intervening processes of goal setting and self-evaluative re-actions to one's own behavior. This form of self-motivation, which operates largely through internal comparison processes, re-quires personal standards against which to evaluate ongoing performance. By making self-satisfaction conditional on a certain level of performance, individuals create self-inducements to persist in their efforts until their performances match internal standards. Both the anticipated satisfactions for matching attainments and the dissatisfactions with insufficient ones provide incentives for self-directed actions. Personal goals or standards do not automatically activate the evaluative processes that affect the level and course of one's behavior. Certain properties of goals, such as their specificity and level, help to provide clear standards of adequacy (Latham & Yukl, 1975; Locke, 1968; Steers & Porter, 1974). Hence, explicit goals are more likely than vague intentions to engage self-reactive influences in any given activity. Goal proximity, a third property, is especially critical because the more closely referential standards are related to ongoing behavior, the greater the likelihood that self-influences will be activated during the process. Some suggestive evidence exists that the impact of goals on behavior is indeed determined by how far into the future they are projected (Bandura & Simon, 1977; Jeffery, 1977). In the social learning view, adopting proximal subgoals for one's own behavior can have at least three major psychological effects. As already alluded to, such goals have motivational effects. One of the propositions tested in the present experiment is that self-motivation can be best created and sustained by attainable subgoals that lead to larger future ones. Proximal subgoals provide immediate incentives and guides for performance, whereas distal goals are too far re-moved in time to effectively mobilize effort or to direct what one does in the here and now. Focus on the distant future makes it easy to temporize and to slacken efforts in the present. Proximal subgoals can also serve as an important vehicle in the development of self-percepts of efficacy. Competence in dealing with one's environment is not a fixed act or simply knowing what to do. Rather, it involves a generative capability in which component skills must be selected and organized into integrated courses of action to manage changing task demands. Operative competence thus requires flexible orchestration of multiple subskills. Self-efficacy is concerned with judgments about how well one can organize and execute courses of action required to deal with prospective situations containing many ambiguous, unpredictable, and often stressful elements. Self-percepts of efficacy can affect people's choice of activities, how much effort they expend, and how long they will persist in the face of difficulties (Bandura, 1977a; Brown & Inouye, 1978; Schunk, 1981). Without standards against which to measure their performances, people have little basis for judging how they are doing or for gauging their capabilities. Subgoal attainments provide indicants of mastery for enhancing selfefficacy. By contrast, distal goals are too far removed in time to provide sufficiently clear markers of progress along the way to ensure a growing sense of self-efficacy. The processes underlying the development of intrinsic interest may similarly be governed, at least in part, by goal proximity. Most of the activities that people enjoy doing for their own sake originally had little or no interest for them. Young children are not innately interested in singing operatic arias, playing tubas, deriving mathematical equations, writing sonnets, or propelling heavy shotput balls through the air. However, through favorable continued involvement, almost any activity can become imbued with consuming significance. One can posit at least two ways in which proximal goals might contribute to enhancement of interest in activities. When people aim for, and master, desired levels of performance, they experience a sense of satisfaction (Locke, Cartledge, & Knerr, 1970). The satisfactions derived from subgoal attainments can build intrinsic interest. When performances are gauged against lofty, distal goals, the large negative disparities between standards and attainments are likely to attenuate the level of self-satisfaction experienced along the way. Conceptual analyses of intrinsic interest within the framework of both self-efficacy theory (Bandura, 1981) and intrinsic motivation theory (Deci, 1975; Lepper & Greene, 1979) assign perceived competence a mediating role. A sense of personal efficacy in mastering challenges is apt to generate greater interest in the activity than is selfperceived inefficacy in producing competent performances. To the extent that proximal subgoals promote and authenticate a sense of causal agency, they can heighten interest through their effects on perception of personal causation. Investigations of intrinsic interest have been concerned almost exclusively with the effects of extrinsic rewards on interest when it is already highly developed. Although results are somewhat variable, the usual finding is that rewards given regardless of quality of performance tend to reduce interest, whereas rewards for performances signifying competence sustain high interest (Boggiano & Ruble, 1979; Enzle & Ross, 1978; Lepper & Greene, 1979; Ross, 1976). The controversy over the effects of extrinsic rewards on preexisting high interest has led to a neglect of the issue of how intrinsic interest is developed when it is lacking. One of the present study's aims was to test the notion that proximal subgoals enlist the type of sustained involvement in activities that build selfefficacy and interest when they are absent. Children who displayed gross deficits in mathematical skills and strong disinterest in such activities engaged in self-directed learning over a series of sessions. They pursued the self-paced learning under conditions involving either proximal subgoals, distal goals, or bids to work actively without any reference to goals. It was predicted that self-motivation through proximal subgoals would prove most effective in cultivating mathematical competencies, self-percepts of efficacy, and intrinsic interest in mathematical activities. For reasons given earlier, distal goals were not expected to exceed bids to work actively in promoting changes. It was hypothesized further that strength of self-efficacy would predict subsequent accuracy on mathematical tasks and level of intrinsic interest.


title: Efficient segmentation and surface classification of range images

text: Derivation of geometric structures from point clouds is an important step towards scene understanding for mobile robots. In this paper, we present a novel method for segmentation and surface classification of ordered point clouds. Data from RGB-D cameras are used as input. Normal based region growing segments the cloud and point feature descriptors classify each segment. Not only planar segments can be described but also curved surfaces. In an evaluation on indoor scenes we show the performance of our approach as well as give a comparison to state of the art methods.


title: Design of a Dual-Band Microstrip Antenna With Enhanced Gain for Energy Harvesting Applications

text: In this letter, a dual-band circular patch antenna is introduced. The antenna consists of a circular patch with a direct feed through microstrip feedline designed to radiate at 2.45 GHz with fractional bandwidth of 4.5%. A circular slot is inserted into the ground plane that radiates by capacitive coupling between the patch and the ground plane. This slot radiates at 1.95 GHz with fractional impedance bandwidth of 5%. The antenna achieves good radiation characteristics by inserting a reflecting plane at optimum position behind it. The antenna has gain of 8.3 and 7.8 dBi at 1.95 and 2.45 GHz, respectively. This antenna is proposed for the rectenna; then it is designed to direct the main beam in a certain direction by increasing front-to-back (F/B) ratio with low cross-polarization levels by using defected reflector structure in the reflecting plane. The equivalent circuit of the proposed antenna is introduced to model the electrical behavior of the antenna. The proposed antenna can be used to harvest the energy from Wi-Fi and widely spread mobile networks. The proposed antenna was designed using CST Microwave Studio. The simulated and measured results show good agreement.


title: Trend analysis model: trend consists of temporal words, topics, and timestamps

text: This paper presents a topic model that identifies interpretable low dimensional components in time-stamped data for capturing the evolution of trends. Unlike other models for time-stamped data, our proposal, the trend analysis model (TAM), focuses on the difference between temporal words and other words in each document to detect topic evolution over time. TAM introduces a latent trend class variable into each document and a latent switch variable into each token for handling these differences. The trend class has a probability distribution over temporal words, topics, and a continuous distribution over time, where each topic is responsible for generating words. The latter class uses a document specific probabilistic distribution to judge which variable each word comes from for generating words in each token. Accordingly, TAM can explain which topic co-occurrence pattern will appear at any given time, and represents documents of similar content and timestamp as sharing the same trend class. Therefore, TAM projects them on a latent space of trend dimensionality and allows us to predict the temporal evolution of words and topics in document collections. Experiments on various data sets show that the proposed model can capture interpretable low dimensionality sets of topics and timestamps, take advantage of previous models, and is useful as a generative model in the analysis of the evolution of trends.


title: Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

text: In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also the method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at https://github.com/openai/baselines.


title: Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

text: Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results.


title: User Based Aggregation for Biterm Topic Model

text: Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user’s preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several stateof-the-art baselines.


title: Network medicine: a network-based approach to human disease

text: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.


title: Development of a capacitive-type 6-axis force-torque sensor

text: Force sensing is a crucial task for robots, especially when end effectors such as fingers and hands need to interact with unknown environments; to sense such forces, a force-torque (F/T) sensor is an essential component. In this paper, we propose a small-sized 6-axis F/T sensor with a novel arrangement of 12 transducers using the force transducer we have previously developed. The copper beryllium used in our sensor reduces hysteresis in each transducer. Additionally, the sensor provides digital output via I2C bus to reduce the susceptibility to noise, and reduce the number of required wires. Sensor characteristics such as its sensitivity, signal-to-noise ratio, linearity, and hysteresis are determined. More importantly, we showed that our sensor can detect and measure the 6-axis F/T.


title: Service-Oriented Computing: State of the Art and Research Challenges

text: Service-oriented computing promotes the idea of assembling application components into a network of services that can be loosely coupled to create flexible, dynamic business processes and agile applications that span organizations and computing platforms. An SOC research road map provides a context for exploring ongoing research activities.


title: An implementation of the 802.1AE MAC Security Standard for in-car networks

text: The continuous increase in complexity in automotive electronics has led to cars that include up to 80 Electronic Control Units (ECUs). As a consequence, in-car networks are currently up to their limit in terms of data load, flexibility and bandwidth. The Ethernet backbone is thus considered as the best performing solution. On the other hand, the growing interconnection of cars with the external world requires high security standards in order to prevent safety risks for car passengers. The IEEE 802.1AE MAC Security Standard (MACSec) solves the security issues of Ethernet networks by providing confidentiality, authenticity and integrity of data. This paper presents an efficient hardware implementation of the MACSec standard for the automotive world. The system was synthesized on a Stratix V FPGA and on a 28nm standard-cell CMOS technology. In terms of maximum throughput, the FPGA results in 1.1 Gbps while the standard-cell technology reaches 3.9 Gbps. The FPGA implementation occupies 4.5% of the Adaptive Logic Modules (ALMs) while the standard-cell one gives a 285 kgate size. The proposed architecture represents a suitable implementation for a low area and high-performance solution as usually required by in-car network controllers.


title: Contextual correlates of synonymy

text: Experimentol corroboration was obtained for the hypothesis that the proportion of words common to the contexts of word A and to the contexts of word B is a function of the degree to which A and B are similar in meaning. The tests were carried out for variously defined contexts. The shapes of the functions, however, indicate that similarity of context is reliable as criterion only for detecting pairs of words that are very similar in meaning.


title: Stock market prediction exploiting microblog sentiment analysis

text: This paper proposes a stock market prediction method exploiting sentiment analysis using financial microblogs (Sina Weibo). We analyze the microblog texts to find the financial sentiments, then combine the sentiments and the historical data of the Shanghai Composite Index (SH000001) to predict the stock market movements. Our framework includes three modules: Microblog Filter (MF), Sentiment Analysis (SA), and Stock Prediction (SP). The MF module is based on LDA to get the financial microblogs. The SA module first sets up a financial lexicon, then gets the sentiments of the microblogs obtained from the MF module. The SP module proposes a user-group model which adjusts the importance of different people, and combines it with stock historical data to predict the movement of the SH000001. We use about 6.1 million microblogs to test our method, and the result demonstrates our method is effective.


title: A Critical Review of Recurrent Neural Networks for Sequence Learning

text: Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translating natural language, engaging in dialogue, and controlling a robot, often demand both capabilities. Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes. Unlike standard feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window. Although recurrent neural networks have traditionally been difficult to train, and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with them. In recent years, systems based on long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition. In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we reconcile conflicting notation and nomenclature. Our goal is to provide a selfcontained explication of the state of the art together with a historical perspective and references to primary research.


title: Open Graphs and Monoidal Theories

text: String diagrams are a powerful tool for reasoning about physical processes, logic circuits, tensor networks, and many other compositional structures. The distinguishing feature of these diagrams is that edges need not be connected to vertices at both ends, and these unconnected ends can be interpreted as the inputs and outputs of a diagram. In this paper, we give a concrete construction for string diagrams using a special kind of typed graph called an open-graph. While the category of open-graphs is not itself adhesive, we introduce the notion of a selective adhesive functor, and show that such a functor embeds the category of open-graphs into the ambient adhesive category of typed graphs. Using this functor, the category of open-graphs inherits “enough adhesivity” from the category of typed graphs to perform double-pushout (DPO) graph rewriting. A salient feature of our theory is that it ensures rewrite systems are “type-safe” in the sense that rewriting respects the inputs and outputs. This formalism lets us safely encode the interesting structure of a computational model, such as evaluation dynamics, with succinct, explicit rewrite rules, while the graphical representation absorbs many of the tedious details. Although topological formalisms exist for string diagrams, our construction is discrete, finitary, and enjoys decidable algorithms for composition and rewriting. We also show how open-graphs can be parameterised by graphical signatures, similar to the monoidal signatures of Joyal and Street, which define types for vertices in the diagrammatic language and constraints on how they can be connected. Using typed open-graphs, we can construct free symmetric monoidal categories, PROPs, and more general monoidal theories. Thus open-graphs give us a tool for mechanised reasoning in monoidal categories.