Detection of camera artifacts from camera images

Cameras are frequently used in state-of-the-art systems in order to get detailed information of the environment. However, when cameras are used outdoors they easily get dirty or scratches on the lens which leads to image artifacts that can deteriorate a system's performance. Little work has yet been done on how to detect and cope with such artifacts. Most of the previous work has concentrated on detecting a specific artifact like rain drops on the windshield of a car. In this paper, we show that on moving systems most artifacts can be detected by analyzing the frames in a stream of images from a camera for static image parts. Based on the observation that most artifacts are temporally stable in their position in the image we compute pixel-wise correlations between images. Since the system is moving the static artifacts will lead to a high correlation value while the pixels showing only scene elements will have a low correlation value. For testing this novel algorithm, we recorded some outdoor data with the three different artifacts: raindrops, dirt and scratches. The results of our new algorithm on this data show, that it reliably detects all three artifacts. Moreover, we show that our algorithm can be implemented efficiently by means of box-filters which allows it to be used as a self-checking routine running in background on low-power systems such as autonomous field robots or advanced driver assistant systems on a vehicle.


FLAP: An End-to-End Event Log Analysis Platform for System Management

Many systems, such as distributed operating systems, complex networks, and high throughput web-based applications, are continuously generating large volume of event logs. These logs contain useful information to help system administrators to understand the system running status and to pinpoint the system failures. Generally, due to the scale and complexity of modern systems, the generated logs are beyond the analytic power of human beings. Therefore, it is imperative to develop a comprehensive log analysis system to support effective system management. Although a number of log mining techniques have been proposed to address specific log analysis use cases, few research and industrial efforts have been paid on providing integrated systems with an end-to-end solution to facilitate the log analysis routines.
 In this paper, we design and implement an integrated system, called FIU Log Analysis Platform (a.k.a. FLAP), that aims to facilitate the data analytics for system event logs. FLAP provides an end-to-end solution that utilizes advanced data mining techniques to assist log analysts to conveniently, timely, and accurately conduct event log knowledge discovery, system status investigation, and system failure diagnosis. Specifically, in FLAP, state-of-the-art template learning techniques are used to extract useful information from unstructured raw logs; advanced data transformation techniques are proposed and leveraged for event transformation and storage; effective event pattern mining, event summarization, event querying, and failure prediction techniques are designed and integrated for log analytics; and user-friendly interfaces are utilized to present the informative analysis results intuitively and vividly. Since 2016, FLAP has been used by Huawei Technologies Co. Ltd for internal event log analysis, and has provided effective support in its system operation and workflow optimization.


Communication in reactive multiagent robotic systems

Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and e ectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three di erent types of communication for three di erent tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can signi cantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as e ective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.


A Compact Highly Reconfigurable CMOS MMIC Directional Coupler

This paper presents a tunable CMOS directional coupler that utilizes lumped-element L-C sections. The lumped-element approach used to build the directional coupler makes it possible to integrate the coupler onto a single monolithic microwave integrated circuit (MMIC), as it occupies a small area compared to printed designs. The directional coupler uses varactors and tunable active inductors (TAIs) to synthesize the series and shunt reactances, respectively, which allows extensive electronic control over the coupling coefficient, while insuring a low return loss and a very high isolation. Furthermore, using varactors and TAIs allows the directional coupler to be reconfigured for operation over a wide range of frequencies. Moreover, the symmetric configuration of the coupler allows it to switch from forward to backward operation by simply exchanging the bias voltages applied across the series varactors. The MMIC coupler was fabricated in a standard 0.13-mum CMOS process and operates from a 1.5-V supply. The circuit occupies 730 mum times 600 mum, and is capable of achieving tunable coupling coefficients from 1.4 to 7.1 dB, while maintaining an isolation higher than 41 dB. The MMIC coupler is also capable of operating at any center frequency over the 2.1-3.1-GHz frequency range with higher than 40-dB isolation. The coupler achieves a -4.1-dBm 1-dB compression point while operating from a 1.5-V supply.


Reconstructing the feedback polynomial of a linear scrambler with the method of hypothesis testing

Blind identification of the feedback polynomial in a linear scrambler is an important component in the wireless communication systems which have the function of automatic standard recognition and parameter estimation. The following research contains three parts. In the first part, promotion model is deduced based on Cluzeau’s model. Comparative analysis shows the advantage of computational complexity by use of the promotion model. In the second part, the improved method of estimation for the normalised standard deviation is proposed, which plays an important role for the computation time and precision. In the final part, simulation results explain the better practical application of my algorithm compared with Cluzeau’s algorithm and XiaoBei-Liu’s algorithm.


An analysis of developer metrics for fault prediction

Background: Software product metrics have been widely used as independent variables for constructing a fault prediction model. However, fault injection depends not only on characteristics of the products themselves, but also on characteristics of developers involved in the project. Aims: The goal of this paper is to study the effects of developer features on software reliability. Method: This paper proposes developer metrics such as the number of code churns made by each developer, the number of commitments made by each developer and the number of developers for each module. By using the eclipse project dataset, we experimentally analyzed the relationship between the number of faults and developer metrics. Second, the effective of developer metrics for performance improvements of fault prediction models were evaluated. Results: The result revealed that the modules touched by more developer contained more faults. Compared with conventional fault prediction models, developer metrics improved the prediction performance. Conclusions: We conclude that developer metrics are good predictor of faults and we must consider the human factors for improving the software reliability.


Happiness Economics , Eudaimonia and Positive Psychology : From Happiness Economics to Flourishing Economics

A remarkable current development, happiness economics focuses on the relevance of people’s happiness in economic analyses. As this theory has been criticised for relying on an incomplete notion of happiness, this paper intends to support it with richer philosophical and psychological foundations. Specifically, it suggests that happiness economics should be based on Aristotle’s philosophical eudaimonia concept and on a modified version of ‘positive psychology’ that stresses human beings’ relational nature. First, this analysis describes happiness economics and its shortcomings. Next, it introduces Aristotle’s eudaimonia and takes a look at positive psychology with this lens, elaborating on the need to develop a new approach that goes beyond the economics of happiness: the economics of flourishing. Finally, the paper specifies some possible socio-economic objectives of a eudaimonic economics of happiness.


Compact dual-band microstrip bandpass filter without external feeds

A compact dual-band microstrip bandpass filter is proposed and designed to operate at 2.4 and 5.2GHz without needing any external impedance-matching block. The modified half-wavelength stepped-impedance resonator with sinuous configuration is constructed to simultaneously excite the dual resonances at these two specified frequencies with miniaturized overall size. The parallel-coupled microstrip line is properly characterized to minimize the return losses within both dual passbands. The optimized results exhibit the good dual-band filtering performances with return losses higher than 20dB as well being confirmed by experimentation with a fabricated filter circuit.


3D Object Proposals for Accurate Object Class Detection

In the Supplementary Material we first provide a description of the architecture of our object detection network. We then describe some details about our road estimation method. We also introduce the class-agnostic variant of our approach. We provide comprehensive results on object detection and orientation estimation, and recall statistics for both 2D and 3D box proposals. Finally, a visualization of success cases as well as failure modes are presented, followed by a detailed error analysis of our detector.


Geodesic weighted Bayesian model for salient object detection

In recent years, a variety of salient object detection methods under Bayesian framework have been proposed and many achieved state of the art. However, those ignore spatial relationships and thus background regions similar to the objects are also highlighted. In this paper, we propose a novel geodesic weighted Bayesian model to address this issue. We consider spatial relationships by attaching more importance to regions which are more likely to be parts of a salient object, thus suppressing background regions. First, we learn a combined similarity via multiple features to measure similarity of adjacent regions. Then, we apply the combined similarity as edge weight to construct an undirected weighted graph and compute geodesic distance. Last, we utilize the geodesic distance to weight the observation likelihood to infer a more precise saliency map. Experiments on several benchmark datasets demonstrate the effectiveness of our model.


An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition

Video-based traffic sign detection, tracking, and recognition is one of the important components for the intelligent transport systems. Extensive research has shown that pretty good performance can be obtained on public data sets by various state-of-the-art approaches, especially the deep learning methods. However, deep learning methods require extensive computing resources. In addition, these approaches mostly concentrate on single image detection and recognition task, which is not applicable in real-world applications. Different from previous research, we introduce a unified incremental computational framework for traffic sign detection, tracking, and recognition task using the mono-camera mounted on a moving vehicle under non-stationary environments. The main contributions of this paper are threefold: 1) to enhance detection performance by utilizing the contextual information, this paper innovatively utilizes the spatial distribution prior of the traffic signs; 2) to improve the tracking performance and localization accuracy under non-stationary environments, a new efficient incremental framework containing off-line detector, online detector, and motion model predictor together is designed for traffic sign detection and tracking simultaneously; and 3) to get a more stable classification output, a scale-based intra-frame fusion method is proposed. We evaluate our method on two public data sets and the performance has shown that the proposed system can obtain results comparable with the deep learning method with less computing resource in a near-real-time manner.


Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates

This paper introduces a new real-time traffic light recognition system for on-vehicle camera applications. This approach has been tested with good results in urban scenes. Thanks to the use of our generic “Adaptive Templates” it would be possible to recognize different kinds of traffic lights from various countries.


Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization

When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience. By adding sparsity constraints on the number of output vectors, we can generate condensed information which can be treated as word salience. Fine-grained and coarse-grained sentence compression strategies are incorporated to produce compressive summaries. Experiments on some benchmark data sets show that our framework achieves better results than the state-of-the-art methods.


Facial Performance Capture with Deep Neural Networks

We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor’s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character.


1 Sex , Syntax , and Semantics

Many languages have a grammatical gender system whereby all nouns are assigned a gender (most commonly feminine, masculine, or neuter). Two studies examined whether (1) the assignment of genders to nouns is truly arbitrary (as has been claimed), and (2) whether the grammatical genders assigned to nouns have semantic consequences. In the first study, English speakers’ intuitions about the genders of animals (but not artifacts) were found to correlate with the grammatical genders assigned to the names of these objects in Spanish and German. These findings suggest that the assignment of genders to nouns is not entirely arbitrary but may to some extent reflect the perceived masculine or feminine properties of the nouns’ referents. Results of the second study suggested that people’s ideas about the genders of objects are strongly influenced by the grammatical genders assigned to these objects in their native language. Spanish and German speakers’ memory for object--name pairs (e.g., apple--Patricia) was better for pairs where the gender of the proper name was congruent with the grammatical gender of the object name (in their native language), than when the two genders were incongruent. This was true even though both groups performed the task in English. These results suggest that grammatical gender may not be as arbitrary or as purely grammatical as was previously


Ayahuasca, dimethyltryptamine, and psychosis: a systematic review of human studies.

Ayahuasca is a hallucinogen brew traditionally used for ritual and therapeutic purposes in Northwestern Amazon. It is rich in the tryptamine hallucinogens dimethyltryptamine (DMT), which acts as a serotonin 5-HT2A agonist. This mechanism of action is similar to other compounds such as lysergic acid diethylamide (LSD) and psilocybin. The controlled use of LSD and psilocybin in experimental settings is associated with a low incidence of psychotic episodes, and population studies corroborate these findings. Both the controlled use of DMT in experimental settings and the use of ayahuasca in experimental and ritual settings are not usually associated with psychotic episodes, but little is known regarding ayahuasca or DMT use outside these controlled contexts. Thus, we performed a systematic review of the published case reports describing psychotic episodes associated with ayahuasca and DMT intake. We found three case series and two case reports describing psychotic episodes associated with ayahuasca intake, and three case reports describing psychotic episodes associated with DMT. Several reports describe subjects with a personal and possibly a family history of psychosis (including schizophrenia, schizophreniform disorders, psychotic mania, psychotic depression), nonpsychotic mania, or concomitant use of other drugs. However, some cases also described psychotic episodes in subjects without these previous characteristics. Overall, the incidence of such episodes appears to be rare in both the ritual and the recreational/noncontrolled settings. Performance of a psychiatric screening before administration of these drugs, and other hallucinogens, in controlled settings seems to significantly reduce the possibility of adverse reactions with psychotic symptomatology. Individuals with a personal or family history of any psychotic illness or nonpsychotic mania should avoid hallucinogen intake.


Identifying Potential CSCW Applications by Means of Activity Theory Concepts: A Case Example

The paper presents some novel concepts and models derived from Activity Theory for to identify a potential CSCW application. It is suggested that the six elements of the structure of the activity concept might be useful for differentiating between areas of support, and that three levels of support are needed in order to cope with both routine and emergent features of cooperative work situations. Thus a 3x6 support type classification is formed and its usefulness studied by means of a real-world example. A work situation is analyzed, problems identified and possible areas of support defined. A temporary solution is produced and, by evaluating it, possible directions for the development of a “real” new CSCW application and the usefulness of the classification am discussed.


Image Captioning with Object Detection and Localization

Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperform previous benchmark models.


Building Occlusion Detection From Ghost Images

This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation.


Evaluation of supply chain structures through modularization and postponement

This paper introduces a conceptual framework for evaluating dierent supply chain structures in the context of modularization and postponement. In our analysis, modularization is linked to inbound logistics as the combination of dierent components (or modules) allow for the assembly of the ®nal product. Postponement, however, corresponds with the outbound logistics since it is through the distribution function that customers' speci®c demand is satis®ed. Given this perspective, we introduce a taxonomy and develop a corresponding framework for the characterization of four supply chain structures, de®ned according to the combined levels of modularization and postponement: Rigid, Postponed, Modularized and Flexible. We also illustrate the applicability of the resulting framework through quantifying the total cost dierential for utilizing a particular supply chain structure. By quantifying the total cost for employing a particular supply chain structure we can numerically illustrate structural results that allow for an objective comparison among them. As such, our ®ndings substantiate the empirical evidence that vertical integration along the supply chain is not always desirable. A point of emphasis in this paper is the notion that ®rms may be better o making combined modularization (outsourcing) and postponement decisions as opposed to separate ones (as they currently do). This also suggests that modularization and postponement decisions need not be independent and that their joint consideration extends operational advantages worthy of contemplation. Ó 2000 Elsevier Science B.V. All rights reserved.


Database Description with SDM: A Semantic Database Model

SDM is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it.
SDM is designed to enhance the effectiveness and usability of database systems. An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system.


The Human Genetic History of East Asia: Weaving a Complex Tapestry

East Asia encompasses a wide variety of environments, peoples, cultures and languages. Although this review focuses on East Asia, no geographic region can be considered in isolation in terms of human population history, and migrations to and from East Asia have had a major impact. Here, we review the following topics: the initial colonization of East Asia, the direction of migrations between southeast Asia and northern Asia, the genetic relationships of East Asian hunter-gatherers and the genetic impact of various social practices on East Asian populations. By necessity we focus on insights derived from mitochondrial DNA and/or Y-chromosome data; ongoing and future studies of genome-wide SNP or multi-locus re-sequencing data, combined with the use of simulation, model-based methods to infer demographic parameters, will undoubtedly provide additional insights into the population history of East Asia.


Winter 2003 COMPETENCE , CULPABILITY , AND PUNISHMENT : IMPLICATIONS OF A TKINS FOR EXECUTING AND SENTENCING ADOLESCENTS

The Supreme Court has explored the issues of culpability, proportionality, and deserved punishment most fully in the context of capital punishment. In death penalty decisions addressing developmental impairments and culpability, the Court has considered the cases of defendants with mental retardation and older adolescents, and has created an anomalous inconsistency by reaching opposite conclusions about the deserved punishment for each group of defendants. Recently, in Atkins v. Virginia, the Court relied on both empirical and normative justifications to categorically prohibit states from executing defendants with mental retardation. Atkins reasoned that mentally retarded offenders lacked the reasoning, judgment, and impulse control necessary to equate their culpability with that of other death-eligible criminal defendants. This Article contends that the same psychological and developmental * Centennial Professor of Law, University of Minnesota, B.A., University of Pennsylvania, 1966; J.D., University of Minnesota Law School, 1969; Ph.D. (Sociology), Harvard University, 1973. 1 would like to thank Steven Drizin, Richard Frase, Wayne Logan, Monte Norgaard, and Victor Streib for critical and helpful comments on an earlier draft of this article. The questions and comments of faculty and student participants at the University of Houston Law School Symposium, January, 2004, helped to sharpen the analysis. I received outstanding research assistance from Brittany Stephens, Class of 2005. 1 Feld: Competence, Culpability, and Punishment: Implications of Atkins f Published by Scholarly Commons at Hofstra Law, 2003


WOMEN ’ S VIOLENCE TO MEN IN INTIMATE RELATIONSHIPS Working on a Puzzle

Different notions among researchers about the nature of intimate partner violence have long been the subjects of popular and academic debate. Research findings are contradictory and point in two directions, with some revealing that women are as likely as men to perpetrate violence against an intimate partner (symmetry) and others showing that it is overwhelmingly men who perpetrate violence against women partners (asymmetry). The puzzle about who perpetrates intimate partner violence not only concerns researchers but also policy makers and community advocates who, in differing ways, have a stake in the answer to this question, since it shapes the focus of public concern, legislation, public policy and interventions for victims and offenders. The question of who are the most usual victims and perpetrators rests, to a large extent, on ‘what counts’ as violence. It is here that we begin to try to unravel the puzzle, by focusing on concept formation, definitions, forms of measurement, context, consequences and approaches to claim-making, in order better to understand how researchers have arrived at such apparently contradictory findings and claims. The question also turns on having more detailed knowledge about the nature, extent and consequences of women’s violence, in order to consider the veracity of these contradictory findings. To date, there has been very little in-depth research about women’s violence to male partners and it is difficult, if not impossible, to consider this debate without such knowledge. We present quantitative and qualitative findings from 190 interviews with 95 couples in which men and women reported separately upon their own violence and upon that of their partner. Men’s and women’s violence are compared. The findings suggest that intimate partner violence is primarily an asymmetrical problem of men’s violence to women, and women’s violence does not equate to men’s in terms of frequency, severity, consequences and the victim’s sense of safety and well-being. But why bother about the apparent contradictions in findings of research? For those making and implementing policies and expending public and private resources, the apparent contradiction about the very nature of this problem has real consequences for what might be done for those who are its victims and those who


Deep joint demosaicking and denoising

Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moiré or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.


Fingerprinting Information in JavaScript Implementations

To date, many attempts have been made to fingerprint users on the web. These fingerprints allow browsing sessions to be linked together and possibly even tied to a user’s identity. They can be used constructively by sites to supplement traditional means of user authentication such as passwords; and they can be used destructively to counter attempts to stay anonymous online. In this paper, we identify two new avenues for browser fingerprinting. The new fingerprints arise from the browser’s JavaScript execution characteristics, making them difficult to simulate or mitigate in practice. The first uses the innate performance signature of each browser’s JavaScript engine, allowing the detection of browser version, operating system and microarchitecture, even when traditional forms of system identification (such as the user-agent header) are modified or hidden. The second subverts the whitelist mechanism of the popular NoScript Firefox extension, which selectively enables web pages’ scripting privileges to increase privacy by allowing a site to determine if particular domains exist in a user’s NoScript whitelist. We have experimentally verified the effectiveness of our system fingerprinting technique using a 1,015-person study on Amazon’s Mechanical Turk platform.


Modeling trend progression through an extension of the Polya Urn Process

Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.


A Benchmark for Geometric Facial Beauty Study


WebQual: An Exploration of Web-Site Quality

The issue of web-site quality is tackled from the perspective of the ‘voice of the customer’. Quality function deployment (QFD) is adopted as a framework for identifying web-site qualities demanded by users, which are gathered through a quality workshop. From the workshop an instrument for assessing web-site quality is developed (WebQual) and tested in the domain of UK business schools. The results of the WebQual survey are presented and analyzed, leading to the generation of a WebQual Index of web-site quality. Work under way to extend and refine the WebQual instrument includes electronic commerce evaluation, where web-site service quality is proposed as a key issue.


Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks

This paper proposes a self-organized power allocation technique to solve the interference problem caused by a femtocell network operating in the same channel as an orthogonal frequency division multiple access cellular network. We model the femto network as a multi-agent system where the different femto base stations are the agents in charge of managing the radio resources to be allocated to their femtousers. We propose a form of real-time multi-agent reinforcement learning, known as decentralized Q-learning, to manage the interference generated to macro-users. By directly interacting with the surrounding environment in a distributed fashion, the multi-agent system is able to learn an optimal policy to solve the interference problem. Simulation results show that the introduction of the femto network increases the system capacity without decreasing the capacity of the macro network.


Guarding Against the Erosion of Competitive Advantage: A Knowledge Leakage Mitigation Model

A critical objective of knowledge-intensive organizations is to prevent erosion of their competitive knowledge base through leakage. Our review of the literature highlights the need for a more refined conceptualization of perceived leakage risk. We propose a Knowledge Leakage Mitigation (KLM) model to explain the incongruity between perceived high-risk of leakage and lack of protective actions. We argue that an organization’s perceived risk of leakage increases if competitors can benefit from leakage incidents. Further, perceived leakage risk decreases if the organization is shielded from impact due to their diversity of knowledge assets and their ability to reconfigure knowledge resources to refresh their competitive knowledge base. We describe our approach to the design of a large-scale survey instrument that has been tested and refined in two stakeholder communities: 1) knowledge managers responsible for organizational strategy, and 2) Information security management consultants.


Design and Analysis of Swapped Port Coupler and Its Application in a Miniaturized Butler Matrix

This paper presents a novel size reduction technique for designing a Butler matrix using a new type of coupler. This coupler is half the length of a conventional coupler and has a swapped port characteristic wherein the locations of the isolation and coupled ports are switched. These features make the new coupler, called the "swapped port coupler," effective for reducing the size of the Butler matrix. The behavior of the swapped port coupler was mathematically analyzed, and its design equation was derived. Furthermore, the proposed coupler was employed to design a miniaturized Butler matrix. The area of this Butler matrix was 70 × 70 mm2, and its operating frequency was 1 GHz. When the area was normalized to the frequency, the area of the proposed Butler matrix was 10% of a conventional Butler matrix.


Learning to Make Predictions on Graphs with Autoencoders

We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction and node classification. Our autoencoder architecture is efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification, whereas previous related methods require multiple training steps that are difficult to optimize. We provide a comprehensive empirical evaluation of our models on nine benchmark graph-structured datasets and demonstrate significant improvement over related methods for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning.


Culture, corruption, suicide, happiness and global social media use: a cross-cultural perspective

This study was conducted to answer this simple question: ‘Can cultural values explain global social media use?’ Along with cultural dimensions introduced by past studies we also added several demographic, socio-economic and personality variables into this study that generated quite interesting findings. We found that there are low levels of suicide, more happiness and more corruption in societies that use social media heavily. We also observed that GDP per capita and median age are negatively related with social media use. Self-esteem stood out as important variable related to social media use intensity along with emotional expressiveness, openness and conscientiousness. Contrary to the common view, nation-level social capital achievement was negatively related with social media use and there was absolutely no relationship between high-context and low-context communication characteristics and local social media use. Some other findings also indicated that conservative and collectivistic countries use social media more often than do individualistic and developed countries. Schwartz’s cultural dimensions and the results of the GLOBE study accounted for a considerable amount of variation in country-level social media use where Hofstede and Trompenaars’ cultural dimensions were insignificant. Since most of the cultural values failed to explain the intensity of social media use, we also developed a cross-cultural online communication framework called cross-cultural self and others’ worth.


Running head : SOCIAL NETWORKING SITE USE Psychological Predictors of Young Adults ' Use of Social Networking Sites

Psychological predictors of young adults': use of social networking sites. Acknowledgements: The authors would like to thank Shari Walsh for assistance in the design of the study and Eric Livingston for assistance in data collection. Abstract Young people are increasingly using social networking sites (SNSs), like Myspace and Facebook, to engage with others. The use of SNSs can have both positive and negative effects on the individual; however, few research studies identify the types of people who frequent these Internet sites. This study sought to predict young adults' use of SNSs and addictive tendency towards the use of SNSs from their personality characteristics and levels of self-esteem. University students (N = 201), aged 17 to 24 years, reported their use of SNSs and addictive tendencies for SNSs use, and completed the NEO Five-Factor Personality Inventory 1 and the Coopersmith Self-Esteem Inventory. 2 Multiple regression analyses revealed that, as a group, the personality and self-esteem factors significantly predicted both level of SNS use and addictive tendency but did not explain a large amount of variance in either outcome measure. The findings indicated that extraverted and unconscientious individuals reported higher levels of both SNS use and addictive tendencies. Future research should attempt to identify which other psychosocial characteristics explain young people's level of use and propensity for addictive tendencies for these popular Internet sites. Social networking site use 3 The proliferation of social networking sites (SNSs) has created a phenomenon that engages millions of Internet users around the world, especially young people. Given the popularity of these sites and their importance in young people's lives to facilitate communication and relationships, it is important to understand the factors influencing SNS use, especially at higher levels, and to identify those who may be prone to developing addictive tendencies towards new communication technologies. 5 As with other communication technologies, 6,7 a useful starting point may be to examine the role of personality traits and self-esteem on young people's SNS use. Researchers have confirmed repeatedly that the five-factor model of personality adequately accounts for and explains personality by taking the approach that personality consists of five traits; openness to experience (pursuing and appreciating all types of experience), conscientiousness (control, regulation and direction of goals and impulses), extraversion (amount and intensity of interpersonal interactions), agreeableness (the type of interactions a person prefers to have with others), and neuroticism (degree of emotional adjustment and instability). 8 Self esteem …


Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales

We address therating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars” is intuitively closer to “four stars” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labelingformulation of the problem, that alters a given n-ary classifier’s output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. Publication info: Proceedings of the ACL, 2005.


Learning Distributed Representations of Sentences from Unlabelled Data

Unsupervised methods for learning distributed representations of words are ubiquitous in today’s NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-bilinear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.


Wristband-Type Driver Vigilance Monitoring System Using Smartwatch

Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.


A Framework for Detecting MAC and IP Spoofing Attacks with Network Characteristics

This paper presents a spoofing attack detection framework based on physical network characteristics (e.g., received signal strength indicator round trip time and link quality indicator) that cannot easily be mimicked by artificial means. Unlike most previous studies that are sensitive to changes in network conditions, we propose a spoofing attack detection method, which is highly robust to the changes of network conditions over time. The proposed framework can monitor devices' physical network characteristics in real time and check if any significant changes have been made in the monitored measurements to effectively detect device spoofing attacks. To demonstrate the feasibility of the proposed framework, we analyzed how the RSSI values of packets were changed with varying physical distances in a real ZigBee (IEEE 802.15.4) network environment.


Summarization from medical documents: a survey

OBJECTIVE
The aim of this paper is to survey the recent work in medical documents summarization.

BACKGROUND
During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc.

METHODOLOGY
This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics.

DISCUSSION AND CONCLUSIONS
The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.


TrustOTP: Transforming Smartphones into Secure One-Time Password Tokens

Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.


Keyword Search in Spatial Databases: Towards Searching by Document

This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords. Given a set of keywords from a document, mCK query can be very useful in geotagging the document by comparing the keywords to other geotagged documents in a database. To answer mCK queries efficiently, we introduce a new index called the bR*-tree, which is an extension of the R*-tree. Based on bR*-tree, we exploit a priori-based search strategies to effectively reduce the search space. We also propose two monotone constraints, namely the distance mutex and keyword mutex, as our a priori properties to facilitate effective pruning. Our performance study demonstrates that our search strategy is indeed efficient in reducing query response time and demonstrates remarkable scalability in terms of the number of query keywords which is essential for our main application of searching by document.


Free-form shape design using triangulated surfaces

We present an approach to modeling with truly mutable yet completely controllable free-form surfaces of arbitrary topology. Surfaces may be pinned down at points and along curves, cut up and smoothly welded back together, and faired and reshaped in the large. This style of control is formulated as a constrained shape optimization, with minimization of squared principal curvatures yielding graceful shapes that are free of the parameterization worries accompanying many patch-based approaches. Triangulated point sets are used to approximate these smooth variational surfaces, bridging the gap between patch-based and particle-based representations. Automatic refinement, mesh smoothing, and re-triangulation maintain a good computational mesh as the surface shape evolves, and give sample points and surface features much of the freedom to slide around in the surface that oriented particles enjoy. The resulting surface triangulations are constructed and maintained in real time.


Imitation and Mechanisms of Joint Attention : A Developmental Structure for Building Social Skills on a Humanoid Robot

Adults are extremely adept at recognizing social cues, such as eye direction or pointing gestures, that establish the basis of joint attention. These skills serve as the developmental basis for more complex forms of metaphor and analogy by allowing an infant to ground shared experiences and by assisting in the development of more complex communication skills. In this chapter, we review some of the evidence for the developmental course of these joint attention skills from developmental psychology, from disorders of social development such as autism, and from the evolutionary development of these social skills. We also describe an on-going research program aimed at testing existing models of joint attention development by building a human-like robot which communicates naturally with humans using joint attention. Our group has constructed an upper-torso humanoid robot, called Cog, in part to investigate how to build intelligent robotic systems by following a developmental progression of skills similar to that observed in human development. Just as a child learns social skills and conventions through interactions with its parents, our robot will learn to interact with people using natural social communication. We further consider the critical role that imitation plays in bootstrapping a system from simple visual behaviors to more complex social skills. We will present data from a face and eye finding system that serves as the basis of this developmental chain, and an example of how this system can imitate the head movements of an individual.


Suicide prevention strategies: a systematic review.

CONTEXT
In 2002, an estimated 877,000 lives were lost worldwide through suicide. Some developed nations have implemented national suicide prevention plans. Although these plans generally propose multiple interventions, their effectiveness is rarely evaluated.

OBJECTIVES
To examine evidence for the effectiveness of specific suicide-preventive interventions and to make recommendations for future prevention programs and research.

DATA SOURCES AND STUDY SELECTION
Relevant publications were identified via electronic searches of MEDLINE, the Cochrane Library, and PsychINFO databases using multiple search terms related to suicide prevention. Studies, published between 1966 and June 2005, included those that evaluated preventative interventions in major domains; education and awareness for the general public and for professionals; screening tools for at-risk individuals; treatment of psychiatric disorders; restricting access to lethal means; and responsible media reporting of suicide.

DATA EXTRACTION
Data were extracted on primary outcomes of interest: suicidal behavior (completion, attempt, ideation), intermediary or secondary outcomes (treatment seeking, identification of at-risk individuals, antidepressant prescription/use rates, referrals), or both. Experts from 15 countries reviewed all studies. Included articles were those that reported on completed and attempted suicide and suicidal ideation; or, where applicable, intermediate outcomes, including help-seeking behavior, identification of at-risk individuals, entry into treatment, and antidepressant prescription rates. We included 3 major types of studies for which the research question was clearly defined: systematic reviews and meta-analyses (n = 10); quantitative studies, either randomized controlled trials (n = 18) or cohort studies (n = 24); and ecological, or population- based studies (n = 41). Heterogeneity of study populations and methodology did not permit formal meta-analysis; thus, a narrative synthesis is presented.

DATA SYNTHESIS
Education of physicians and restricting access to lethal means were found to prevent suicide. Other methods including public education, screening programs, and media education need more testing.

CONCLUSIONS
Physician education in depression recognition and treatment and restricting access to lethal methods reduce suicide rates. Other interventions need more evidence of efficacy. Ascertaining which components of suicide prevention programs are effective in reducing rates of suicide and suicide attempt is essential in order to optimize use of limited resources.


A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.


The Traveling Salesman Problem: A Survey

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Improved algorithm for mining maximum frequent patterns based on FP-Tree

Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space. Keywordsdata mining;association rule;maximum frequent pattern;FP-Tree


Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis

Twitter sentiment analysis offers organizations ability to monitor public feeling towards the products and events related to them in real time. The first step of the sentiment analysis is the text pre-processing of Twitter data. Most existing researches about Twitter sentiment analysis are focused on the extraction of new sentiment features. However, to select the pre-processing method is ignored. This paper discussed the effects of text pre-processing method on sentiment classification performance in two types of classification tasks, and summed up the classification performances of six pre-processing methods using two feature models and four classifiers on five Twitter datasets. The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words. The Naive Bayes and Random Forest classifiers are more sensitive than Logistic Regression and support vector machine classifiers when various pre-processing methods were applied.


Alzheimer's Disease and the Impact of Music Therapy: A Systematic Literature Review

Currently ranked as the sixth leading cause of death in the United States, Alzheimer’s disease (AD) has become the most prevalent form of dementia, a term commonly associated with memory loss and other progressive cognitive deficits that compromise patients’ lives. What may begin as a mindlessly misplaced object or momentary inability to recall newly learned information will eventually advance to a loss of personal identity, forgotten loved ones, and utter misperception of reality. Although a cure for AD has yet to be discovered, there are several nonpharmacological treatments that can improve patients’ quality of life and provide temporary relief from the disabling manifestations, one of which is music therapy, the topic of this literature review. Music can be deeply connected with emotional processing and memory recall, and, when utilized as an interventional therapy for AD patients, can yield numerous cognitive and behavioral symptomatic benefits. The purpose of this project is to conduct a systematic literature review that evaluates the therapeutic relationship between AD and music therapy with a narrowed focus on familiar music therapy, the potential mechanisms of action that explain the efficacy of this intervention, and the resulting nursing implications that may be utilized in practice. MUSIC THERAPY AND ALZHEIMER’S DISEASE 6 Methods An exhaustive literature review was conducted using the database CINAHL (EBSCO). The search terms included: “Alzheimer’s disease AND music therapy”, “Alzheimer’s AND music therapy”, “Alzheimer’s disease symptoms AND music therapy”, “Alzheimer’s disease AND music therapy AND familiar music”, and “Alzheimer’s disease AND active music therapy.” The literature search included full text articles from academic journals in the English language, including international articles. Only articles published between 2007 through 2017 were reviewed, with the exception of one relevant article from 2005. From the initial search results, 35 articles matched the preliminary criteria, and were further reviewed if they addressed: a) Alzheimer’s disease, b) elements and types of music therapy, c) outcomes of music therapy, d) scientific explanation of music therapy efficacy and, e) nursing implications. A total of 13 articles met the inclusion criteria and were included in the literature review. Six of these articles focused on familiar music therapy, and are summarized in table 1. MUSIC THERAPY AND ALZHEIMER’S DISEASE 7 Alzheimer’s Disease Alzheimer’s Disease (AD), an irreversibly progressive brain disorder, has an unrelenting course, adversely impacting every aspect of a patient’s reality, often beginning with the inability to perform the most mundane tasks and resulting in utter dependency on others for the totality of self care. However, what may be even more staggering than the relentless nature of this disease are the associated statistics, for the prevalence of new cases is growing at an alarming rate – every 66 seconds to be exact. Currently, AD is the sixth leading cause of death in the United States, affecting more than 5 million individuals; however, this number is estimated to more than triple by 2050 (Alzheimer’s Association, 2017). While this disease often progresses gradually through the three stages – mild, moderate, and severe – every patient endures the associated manifestations differently, which can make it difficult to discern which stage a person is experiencing. An individual displaying the early symptoms of AD may show signs of forgetfulness, possibly misplacing objects or failing to recall a friend’s name, but will likely remain independent through the first stage of the disease. As the patient’s condition progresses into the middle stage, personality and behavior changes will be noted, such as frustration, anger, refusing to shower, etc., as well as a greater need for assistance with daily activities. The most disabling manifestations of the disease appear in the final stage, including a loss of environmental awareness, severe communication impairments, crippling physical limitations, and the need for constant supervision and care (Alzheimer’s Association, 2017). Perhaps one of the most troubling aspects of AD lies in the inevitable imminence of death once the disease begins to unfold, despite the pharmacological options that exist. Treatment efforts for AD patient currently focus on symptom management utilizing a multidisciplinary MUSIC THERAPY AND ALZHEIMER’S DISEASE 8 approach, combining both pharmacological and non-pharmacological interventions to achieve the highest possible quality of life. While somewhat beneficial, most pharmacological treatments yield limited improvement and may result in further patient deterioration due to the adverse effects associated with antipsychotic and/or psychotropic drugs (Guetin, 2013). However, various non-pharmacological therapies for symptom management have been identified and implemented to improve patients’ quality of life, and universal research efforts vigorously continue in the pursuit of answers to better understand the disease, determine effective treatment options, and prevent its development entirely. MUSIC THERAPY AND ALZHEIMER’S DISEASE 9 Music Therapy The use of non-pharmacological strategies as a potential treatment method for cognitive and behavioral disorders has become a more prevalent occurrence in patient care plans, with a specific emphasis on music therapy as an exceptionally effective intervention for AD, as acknowledged by the French Agency for Health Accreditation and Evaluation (Narme et al., 2013). Music therapy, as defined by the World Federation of Music Therapy, is “the use of music and/or its musical elements (sound, rhythm, melody and harmony) by a qualified music therapist, with a client or group, in a process designed to facilitate and promote communication, relationships, learning, mobilization, expression, organization and other relevant therapeutic objectives in order to meet physical, emotional, mental, social and cognitive needs” (Guetin et al., 2013, p. 621). While music therapy has gained popularity in recent years as an effective tool to manage a variety of health care needs, such as procedural pain control, behavioral disorder manifestations, and physical rehabilitation, this non-pharmacological intervention has ancient roots dating as far back as the sixth century BC (Cox, Nowak, & Buettner, 2011). There is hieroglyphic evidence suggesting the Egyptians treated a variety of ailments, such as pain, depression, and sleep disorders, with melodic hymns and incantations (Guetin et al., 2013). Plato, during ancient Grecian times, dubbed musical training a form of “mental hygiene,” as well as “medicine for the soul” (Guetin et al., 2013, p. 622). The 20 century brought about the scientific discovery of the physiological effects music can produce on blood pressure and heart rate, which helps explain the earlier findings detected by dental surgeons, who noticed their patients seemed to experience decreased pain and anxiety during procedures if a phonograph was playing simultaneously (Guetin et al., 2013). MUSIC THERAPY AND ALZHEIMER’S DISEASE 10 Although primitive, these historical efforts laid the foundation to legitimize music therapy professionally. The National Association for Music Therapy, created in the U.S. during the mid-20 century, became the original representative professional organization and was shortly followed by the emergence of the American Association for Music Therapy in 1971. Since then, these two associations have been consolidated into the present-day American Music Therapy Association (AMTA), which is responsible for setting the educational requirements for aspiring music therapists. In order to become Music Therapist-Board Certified (MT-BC) in the United States, these individuals must receive a bachelor’s, master’s, or doctoral degree from an accredited program, complete a clinical internship, and partake in 1200 clinical training hours before sitting for the board certification examination (Ahn & Ashida, 2012). In order to maintain the quality of services provided by the music therapy profession, the AMTA also established professional competencies, standards of clinical practice, and a code of ethics. The standards of clinical practice outline the general order in which music therapy services are delivered: 1) referral and acceptance, 2) assessment, 3) treatment planning, 4) implementation, 5) documentation, and 6) termination (American Music Therapy Association, 2015). MUSIC THERAPY AND ALZHEIMER’S DISEASE 11 Active vs. Receptive Music Therapy Although the exact physiological mechanisms responsible for the efficacy of music therapy on Alzheimer’s disease are not yet fully understood, the behavioral, cognitive, and physical improvements that have been noted during both therapy sessions and research studies are undeniably significant. The degree of improvement following musical intervention is primarily influenced by music therapists’ understanding of how best to utilize the distinct elements within music, which is further based on their knowledge of how each aspect will impact a patient’s cognitive and behavioral status (Guetin et al., 2009). The broader concept of music therapy encompasses two fundamental methods, active and receptive. Active music therapy requires the patient to be physically engaged, such as through the use of sound-producing objects, singing, dance-like movement, or playing instruments, for example. (Guetin et al., 2013). Receptive music therapy, considered a “controlled method for listening to music,” is comprised of specifically selected songs or live music played for the patient, with song choice based on individually meaningful elements, such as generation, culture, or personal history (Guetin et al., 2009). Both active and receptive music therapy can be used to target specific AD symptoms, such as memory loss, language deficits, depression, anx


Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features

Parkinson’s Disease is a neurodegenerative disease affecting millions of people globally, most of whom present difficulties producing speech sounds. In this paper, we describe a system to identify the degree to which a person suffers from the disease. We use a number of automatic phone recognition-based features and we augment these with i-vector features and utterancelevel acoustic aggregations. On the Interspeech 2015 ComParE challenge corpus, we find that these features allow for prediction well above the challenge baseline, particularly under crossvalidation evaluation.


High-dimensional signature compression for large-scale image classification

We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 105 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases — ImageNet and a dataset of lM Flickr images — showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.


Using Smart Sensor Strings for Continuous Monitoring of Temperature Stratification in Large Water Bodies

A "smart" thermistor string for continuous long-term temperature profiling in large water bodies is described allowing highly matched yet low-cost spatial and temporal temperature measurements. The sensor uses the three-wire SDI-12 communications standard to enable a low-powered radio or data logger on supporting buoys to command measurements and retrieve high-resolution temperature data in digital form. Each "smart" temperature sensor integrates a thermistor element, measurement circuitry, power control, calibration coefficient storage, temperature computation, and data communications. Multiple addressable sensors at discrete vertical depths are deployed along a three-wire cable that provides power and allows data transfer at regular intervals. Circuit, manufacturing, and automated calibration techniques allow temperature measurements with a resolution of plusmn0.003degC, and with intersensor matching of plusmn0.006degC. The low cost of each sensor is achieved by using poor tolerance thermistor and circuit components in conjunction with a 15-bit charge-balance analog-to-digital converter. Sensor inaccuracies and temperature coefficients are corrected by a two-point calibration procedure made possible by a standard-curve generator within the sensor, based upon the method of finite differences. This two-point calibration process allows in-field sensor string calibration in stratified water bodies and provides a means to correct for long-term calibration drift without having to return the string to a laboratory


Animating fluid sediment mixture in particle-laden flows

In this paper, we present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows. We develop a Multigrid Preconditioned fluid solver for the Locally Averaged Navier Stokes equation. This is discretized purely on a semi-staggered standard MPM grid. Sedimentation is modeled with the Drucker-Prager elastoplasticity flow rule, enhanced by a novel particle density estimation method for converting particles between representations of either continuum or discrete points. Fluid and sediment are two-way coupled through a momentum exchange force that can be easily resolved with two MPM background grids. We present various results to demonstrate the efficacy of our method.


Lightweight Memory Tracing

Dynamic binary instrumentation is a technique that consists of translating the binary code of executables on-the-fly, including all the loaded libraries. The original binary code is instrumented in the process of translation. Applications of this technique range from sandboxes to debugging and profiling tools. In this thesis we describe lMem, a lightweight binary translation framework for the IA-32/x64 architectures. One of the challenges when writing instrumentation tools is that resources such as registers and memory have to be shared between the instrumented program and the instrumentation logic. A possible way to approach this problem is to translate a given machine code snippet in an intermediate representation (IR), add instrumentation code written in this IR and then recompile the snippet to machine code and run it. Tools such as Valgrind successfully make use of this technique. These IR-based methods however are heavyweight in the sense that a significant amount of work done by the compiler has to be replicated at runtime. IR-based recompilation increases the runtime overhead and the complexity of the binary translation framework itself. A second approach consists of saving and restoring some of the state before and after the inserted instrumentation code. This approach, which is used for example by the selDebug debugger, is simple but it suffers from overhead due to frequent saving and restoring of state. A third approach is to let the instrumentation code make use of hardware resources that are not used by the instrumented program. The minemu taint checking tool achieves good performance by exploiting the fact that some 32 bit machines lack SSE registers. This enables it to fool the application into thinking that it is running on such a machine while in reality these registers are used by the instrumentation code. lMem translates IA-32 programs to x64 and uses additional registers and memory for instrumentation. To gauge the performance of this approach we implement a simple application on top of lMem which enables to insert an arbitrary number of watchpoints. We observe that this lMem-based application has overheads of only 50% to 200% on SPEC CPU 2006 benchmarks.


An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.


Kinematics analysis of a novel 4-dof stabilized platform

This paper presents a novel stabilized platform with three UPS-type and one RPU-type active limbs based on the shipboard helicopter's landing process and its kinematics are studied systematically. First, a prototype of this stabilized platform is constructed and its dof is analyzed, Second, analytic formulas for solving the inverse displacement kinematics are derived Third, analytic formulas for solving the forward displacement kinematics are derived and verified. Theoretical formulas and results provide a basis for the further optimization, control, and manufacturing.


AP16-OL7: A multilingual database for oriental languages and a language recognition baseline

We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research.


Accurate odometry and error modelling for a mobile robot

This paper presents a low cost novel odometry design capable of achieving high accuracy deadreckoning. It also develops a statistical error model for estimating position and orientation errors of a mobile robot using odometry. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Closed form error covariance matrix is developed for a general circular arc and two special cases : (I) straight line and (II) turning about the centre of axle of the robot. Other paths can be composed of short segments of constant curvature arcs without great loss of accuracy. The model assumes that wheel distance measurement errors are exclusively random zero mean white noise. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark [BorFen94]. Experimental results show that, despite its low cost, our system’s performance, with regard to dead-reckoning accuracy, is comparable to some of the best, award-winning vehicles around. The statistical error model, on the other hand, needs to be improved in light of new insights.


An Approximate Analysis of the LRU and FIFO Buffer Replacement Schemes

In this paper, we develop approximate analytical models for predicting the buffer hit probability under the Least Recently Used (LRU) and First In First Out (FIFO) buffer replacement policies under the independent reference model. In the case of the analysis of the LRU policy, the computational complexity for estimating the buffer hit probability is O(KB) where B is the size of the buffer and K denotes the number of items having distinct access probabilities. In the case of the FIFO policy, the solution algorithm is iterative and the computational complexity of each iteration is O(K). Results from these models are compared to exact results for models originally developed by King [KING71] for small values of the buffer size, B, and the total number of items sharing the buffer, D. Results are also compared with results from a simulation for large values of B and D. In most cases, the error is extremely small (less than 0.1%) for both LRU and FIFO, and a maximum error of 3% is observed for very small buffer size (less than 5) when the access probabilities are extremely skewed. To demonstrate the usefulness of the model, we consider two applications. In our first application, we compare the LRU and FIFO policies to an optimal static buffer allocation policy for a database consisting of two classes of data items. We observe that the performance of LRU is close to that of the optimal allocation. As the optimal allocation requires knowledge of the access probabilities, the LRU policy is preferred when this information is unavailable. We also observe that the LRU policy always performs better than the FIFO policy in our experiments. In our second application, we show that if multiple independent reference streams on mutually disjoint sets of data compete for the same buffer, it is better to partition the buffer using an optimal allocation policy than to share a common buffer.


100 000 Frames/s 64 × 32 Single-Photon Detector Array for 2-D Imaging and 3-D Ranging

We report on the design and characterization of a multipurpose 64 × 32 CMOS single-photon avalanche diode (SPAD) array. The chip is fabricated in a high-voltage 0.35-μm CMOS technology and consists of 2048 pixels, each combining a very low noise (100 cps at 5-V excess bias) 30-μm SPAD, a prompt avalanche sensing circuit, and digital processing electronics. The array not only delivers two-dimensional intensity information through photon counting in either free-running (down to 10-μs integration time) or time-gated mode, but can also perform smart light demodulation with in-pixel background suppression. The latter feature enables phase-resolved imaging for extracting either three-dimensional depth-resolved images or decay lifetime maps, by measuring the phase shift between a modulated excitation light and the reflected photons. Pixel-level memories enable fully parallel processing and global-shutter readout, preventing motion artifacts (e.g., skew, wobble, motion blur) and partial exposure effects. The array is able to acquire very fast optical events at high frame-rate (up to 100 000 fps) and at single-photon level. Low-noise SPADs ensure high dynamic range (up to 110 dB at 100 fps) with peak photon detection efficiency of almost 50% at 410 nm. The SPAD imager provides different operating modes, thus, enabling both time-domain applications, like fluorescence lifetime imaging (FLIM) and fluorescence correlation spectroscopy, as well as frequency-domain FLIM and lock-in 3-D ranging for automotive vision and lidar.


CMOS SPADs: Design Issues and Research Challenges for Detectors, Circuits, and Arrays

Solid-state single photon detectors are playing a significant role in the development of high-performance single-photon imaging systems for fluorescence lifetime imaging, time-of-flight positron emission tomography and Raman Spectroscopy applications. The main driving factors are the unparalleled levels of miniaturization and portability, low fabrication costs, and high overall performance resulting from the integration of single-photon avalanche diodes (SPADs) with mixed-signal circuits in deep-submicron (DSM) complementary metal-oxide-semiconductor (CMOS) technology. At the heart of such imaging systems is the SPAD, capable of single-photon sensitivity and sub-nanosecond time resolution, and its associated circuitry, which in DSM CMOS, is capable of high-speed, low-power mixed-mode signal processing. In this paper, we review and discuss the most recent developments in DSM CMOS SPAD detectors, circuits and arrays and investigate issues of scalability, miniaturization and performance trade-offs involved in designing SPAD imaging systems. Design considerations, research challenges, and future directions for CMOS SPAD image sensors will be highlighted and addressed.


Photon counting and direct ToF camera prototype based on CMOS SPADs

This paper presents a camera prototype for 2D/3D image capture in low illumination conditions based on single-photon avalanche-diode (SPAD) image sensor for direct time-offlight (d-ToF). The imager is a 64×64 array with in-pixel TDC for high frame rate acquisition. Circuit design techniques are combined to ensure successful 3D image capturing under low sensitivity conditions and high level of uncorrelated noise such as dark count and background illumination. Among them an innovative time gated front-end for the SPAD detector, a reverse start-stop scheme and real-time image reconstruction at Ikfps are incorporated by the imager. To the best of our knowledge, this is the first ToF camera based on a SPAD sensor fabricated and proved for 3D image reconstruction in a standard CMOS process without any opto-flavor or high voltage option. It has a depth resolution of 1cm at an illumination power from less than 6nW/mm2 down to 0.1nW/mm2.


11.5 A time-correlated single-photon-counting sensor with 14GS/S histogramming time-to-digital converter

Time-correlated single photon counting (TCSPC) is a photon-efficient technique to record ultra-fast optical waveforms found in numerous applications such as time-of-flight (ToF) range measurement (LIDAR) [1], ToF 3D imaging [2], scanning optical microscopy [3], diffuse optical tomography (DOT) and Raman sensing [4]. Typical instrumentation consists of a pulsed laser source, a discrete detector such as an avalanche photodiode (APD) or photomultiplier tube (PMT), time-to-digital converter (TDC) card and a FPGA or PC to assemble and compute histograms of photon time stamps. Cost and size restrict the number of channels of TCSPC hardware. Having few detection and conversion channels, the technique is limited to processing optical waveforms with low intensity, with less than one returned photon per laser pulse, to avoid pile-up distortion [4]. However, many ultra-fast optical waveforms exhibit high dynamic range in the number of photons emitted per laser pulse. Examples are signals observed at close range in ToF with multiple reflections, diffuse reflected photons in DOT or local variations in fluorescent dye concentration in microscopy. This paper provides a single integrated chip that reduces conventional TCSPC pile-up mechanisms by an order of magnitude through ultra-parallel realizations of both photon detection and time-resolving hardware. A TDC architecture is presented which combines the two step iterated TCSPC process of time-code generation, followed by memory lookup, increment and write, into one parallel direct-to-histogram conversion. The sensor achieves 71.4ps resolution, over 18.85ns dynamic range, with 14GS/s throughput. The sensor can process 1.7Gphoton/s and generate 21k histograms/s (with 4.6μs readout time), each capturing a total of 1.7kphotons in a 1μs exposure.


Multiple-event direct to histogram TDC in 65nm FPGA technology

A novel multiple-event Time to Digital Converter (TDC) with direct to histogram output is implemented in a 65nm Xilinx Virtex 5 FPGA. The delay-line based architecture achieves 16.3 ps temporal accuracy over a 2.86ns dynamic range. The measured maximum conversion rate of 6.17 Gsamples/s and the sampling rate of 61.7 Gsamples/s are the highest published in the literature. The system achieves a linearity of -0.9/+3 LSB DNL and -1.5/+5 LSB INL. The TDC is demonstrated in a direct time of flight optical ranging application with 12mm error over a 350mm range.


Human-level concept learning through probabilistic program induction

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several “visual Turing tests” probing the model’s creative generalization abilities, which in many cases are indistinguishable from human behavior.


Analyzing Factors Affecting Users ’ Behavior Intention to Use Social Media : Twitter Case

Advancement of technology and the Internet proliferation have visible effects in the world. One of the most important effects is the increased social media usage among the Internet users. For this purpose, factors having impacts on users’ behavior intention to social media usage are investigated within the scope of the study. A research model based on technology acceptance model 2 is proposed and revised by the taking social media platforms into consideration. The effects of perceived ease of use, perceived usefulness, social influence, facilitating conditions, playfulness, and trust are measured. Data collected from 462 respondents are analyzed by structural equation modeling technique. According to results of the measurement and structural model validities, a fit and acceptable model is achieved to measure the related effects of variables. The results of study reveal the both direct and indirect positive impacts of the factors on users’ behavior intention to use social media.


Detecting Topic Drift with Compound Topic Models

The Latent Dirichlet Allocation topic model of Blei, Ng, & Jordan (2003) is well-established as an effective approach to recovering meaningful topics of conversation from a set of documents. However, a useful analysis of user-generated content is concerned not only with the recovery of topics from a static data set, but with the evolution of topics over time. We employ a compound topic model (CTM) to track topics across two distinct data sets (i.e. past and present) and to visualize trends in topics over time; we evaluate several metrics for detecting a change in the distribution of topics within a time-window; and we illustrate how our approach discovers emerging conversation topics related to current events in real data sets.


Spatial reconstruction of single-cell gene expression data

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.


Defending against Internet worms: a signature-based approach

With the capability of infecting hundreds of thousands of hosts, worms represent a major threat to the Internet. The defense against Internet worms is largely an open problem. This paper investigates two important problems. Can a localized defense system detect new worms that were not seen before and moreover, capture the attack packets? How to identify polymorphic worms from the normal background traffic? We have two major contributions here. The first contribution is the design of a novel double-honeypot system, which is able to automatically detect new worms and isolate the attack traffic. The second contribution is the proposal of a new type of position-aware distribution signatures (PADS), which fit in the gap between the traditional signatures and the anomaly-based systems. We propose two algorithms based on expectation-maximization (EM) and Gibbs sampling for efficient computation of PADS from polymorphic worm samples. The new signature is capable of handling certain polymorphic worms. Our experiments show that the algorithms accurately separate new variants of the MSBlaster worm from the normal-traffic background.


USim: a user behavior simulation framework for training and testing IDSes in GUI based systems

Anomaly detection systems largely depend on user profile data to be able to detect deviations from normal activity. Most of this profile data is currently based on command-line instructions/directives executed by users on a system. With the advent and extensive usage of graphical user interfaces (GUIs), command-line data can no longer fully represent user's complete behavior which is essential for effectively detecting the anomalies in these GUI based systems. Collection of user behavior data is a slow and time consuming process. In this paper, we present a new approach to automate the generation of user data by parameterizing user behavior in terms of user intention (malicious/normal), user skill level, set of applications installed on a machine, mouse movement and keyboard activity. The user behavior parameters are used to generate templates, which can be further customized. The framework is called USim which can achieve rapid generation of user behavior data based on these templates for GUI based systems. The data thus generated can be utilized for rapidly training and testing intrusion detection systems (IDSes) and improving their detection precision.


Learning Pulse: Using Wearable Biosensors and Learning Analytics to Investigate and Predict Learning Success in Self-regulated Learning

The Learning Pulse study aims to explore whether physiological data such as heart rate and step count correlate with learning activity data and whether they are good predictors for learning success during self-regulated learning. To verify this hypothesis an experiment was set up involving eight doctoral students at the Open University of the Netherlands. Through wearable sensors, heart rate and step count were constantly monitored and learning activity data were collected. All data were stored in a Learning Record Store in xAPI format. Additionally, with an Activity Rating Tool, the participants rated their learning and working experience by indicating the perceived levels of productivity, stress, challenge and abilities along with the type of activity. These human annotated labels can be used for supervising machine learning algorithms to discriminate the successful learning moments from the unsuccessful ones and eventually discover the attributes that most influence the learning process.


Moving beyond linearity and independence in top-N recommender systems

This paper suggests a number of research directions in which the recommender systems can improve their quality, by moving beyond the assumptions of linearity and independence that are traditionally made. These assumptions, while producing effective and meaningful results, can be suboptimal, as in lots of cases they do not represent the real datasets. In this paper, we discuss three different ways to address some of the previous constraints. More specifically, we focus on the development of methods capturing higher-order relations between the items, cross-feature interactions and intra-set dependencies which can potentially lead to a considerable enhancement of the recommendation accuracy.