Query: The Utilibot Project : An Autonomous Mobile Robot Based on Utilitarianism

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On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. <=SEP=> Well-being is a complex construct that concerns optimal experience and functioning. Current research on well-being has been derived from two general perspectives: the hedonic approach, which focuses on happiness and defines well-being in terms of pleasure attainment and pain avoidance; and the eudaimonic approach, which focuses on meaning and self-realization and defines well-being in terms of the degree to which a person is fully functioning. These two views have given rise to different research foci and a body of knowledge that is in some areas divergent and in others complementary. New methodological developments concerning multilevel modeling and construct comparisons are also allowing researchers to formulate new questions for the field. This review considers research from both perspectives concerning the nature of well-being, its antecedents, and its stability across time and culture.

Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. <=SEP=> Human beings can be proactive and engaged or, alternatively, passive and alienated, largely as a function of the social conditions in which they develop and function. Accordingly, research guided by self-determination theory has focused on the social-contextual conditions that facilitate versus forestall the natural processes of self-motivation and healthy psychological development. Specifically, factors have been examined that enhance versus undermine intrinsic motivation, self-regulation, and well-being. The findings have led to the postulate of three innate psychological needs--competence, autonomy, and relatedness--which when satisfied yield enhanced self-motivation and mental health and when thwarted lead to diminished motivation and well-being. Also considered is the significance of these psychological needs and processes within domains such as health care, education, work, sport, religion, and psychotherapy.

Mean and Covariance Structures (MACS) Analyses of Cross-Cultural Data: Practical and Theoretical Issues. <=SEP=> Practical and theoretical issues are discussed for testing (a) the comparability, or measurement equivalence, of psychological constructs and (b) detecting possible sociocultural difference on the constructs in cross-cultural research designs. Specifically, strong factorial invariance (Meredith, 1993) of each variable's loading and intercept (mean-level) parameters implies that constructs are fundamentally the same in each sociocultural group, and thus comparable. Under this condition, hypotheses about the nature of sociocultural differences and similarities can be confidently and meaningfully tested among the constructs' moments in each sociocultural sample. Some of the issues involved in making such tests are reviewed and explicated within the framework of multiple-group mean and covariance structures analyses.

Daily Well-Being : The Role of Autonomy , Competence , and Relatedness <=SEP=> Emotional well-being is most typically studied in trait or traitlike terms, yet a growing literature indicates that daily (withinperson) fluctuations in emotional well-being may be equally important. The present research explored the hypothesis that daily variations may be understood in terms of the degree to which three basic needs—autonomy, competence, and relatedness—are satisfied in daily activity. Hierarchical linear models were used to examine this hypothesis across 2 weeks of daily activity and well-being reports controlling for trait-level individual differences. Results strongly supported the hypothesis. The authors also examined the social activities that contribute to satisfaction of relatedness needs. The best predictors were meaningful talk and feeling understood and appreciated by interaction partners. Finally, the authors found systematic day-of-the-week variations in emotional well-being and need satisfaction. These results are discussed in terms of the importance of daily activities and the need to consider both trait and day-level determinants of well-being.

Personal projects, happiness, and meaning: on doing well and being yourself. <=SEP=> Personal Projects Analysis (B. R. Little, 1983) was adapted to examine relations between participants' appraisals of their goal characteristics and orthogonal happiness and meaning factors that emerged from factor analyses of diverse well-being measures. In two studies with 146 and 179 university students, goal efficacy was associated with happiness and goal integrity was associated with meaning. A new technique for classifying participants according to emergent identity themes is introduced. In both studies, identity-compensatory predictors of happiness were apparent. Agentic participants were happiest if their goals were supported by others, communal participants were happiest if their goals were fun, and hedonistic participants were happiest if their goals were being accomplished. The distinction between happiness and meaning is emphasized, and the tension between efficacy and integrity is discussed. Developmental implications are discussed with reference to results from archival data from a sample of senior managers.

Quasi-Dense Reconstruction from Image Sequence <=SEP=> This paper proposes a quasi-dense reconstruction from uncalibrated sequence. The main innovation is that all geometry is computed based on re-sampled quasi-dense correspondences rather than the standard sparse points of interest. It not only produces more accurate and robust reconstruction due to highly redundant and well spread input data, but also fills the gap of insufficiency of sparse reconstruction for visualization application. The computational engine is the quasi-dense 2-view and the quasi-dense 3-view algorithms developed in this paper. Experiments on real sequences demonstrate the superior performance of quasi-dense w.r.t. sparse reconstruction both in accuracy and robustness.

Novel Dataset for Fine-Grained Image Categorization : Stanford Dogs <=SEP=> We introduce a 120 class Stanford Dogs dataset, a challenging and large-scale dataset aimed at fine-grained image categorization. Stanford Dogs includes over 22,000 annotated images of dogs belonging to 120 species. Each image is annotated with a bounding box and object class label. Fig. 1 shows examples of images from Stanford Dogs. This dataset is extremely challenging due to a variety of reasons. First, being a fine-grained categorization problem, there is little inter-class variation. For example the basset hound and bloodhound share very similar facial characteristics but differ significantly in their color, while the Japanese spaniel and papillion share very similar color but greatly differ in their facial characteristics. Second, there is very large intra-class variation. The images show that dogs within a class could have different ages (e.g. beagle), poses (e.g. blenheim spaniel), occlusion/self-occlusion and even color (e.g. Shih-tzu). Furthermore, compared to other animal datasets that tend to exist in natural scenes, a large proportion of the images contain humans and are taken in manmade environments leading to greater background variation. The aforementioned reasons make this an extremely challenging dataset.

The importance of being flexible: the ability to both enhance and suppress emotional expression predicts long-term adjustment. <=SEP=> Researchers have documented the consequences of both expressing and suppressing emotion using between-subjects designs. It may be argued, however, that successful adaptation depends not so much on any one regulatory process, but on the ability to flexibly enhance or suppress emotional expression in accord with situational demands. We tested this hypothesis among New York City college students in the aftermath of the September 11th terrorist attacks. Subjects' performance in a laboratory task in which they enhanced emotional expression, suppressed emotional expression, and behaved normally on different trials was examined as a prospective predictor of their adjustment across the first two years of college. Results supported the flexibility hypothesis. A regression analysis controlling for initial distress and motivation and cognitive resources found that subjects who were better able to enhance and suppress the expression of emotion evidenced less distress by the end of the second year. Memory deficits were also observed for both the enhancement and the suppression tasks, suggesting that both processes require cognitive resources.

OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles <=SEP=> We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

Training RNN simulated vehicle controllers using the SVD and evolutionary algorithms <=SEP=> We describe an approach to creating a controller for The Open Car Racing Simulator (TORCS), based on The Simulated Car Racing Championship (SCRC) client, using unsupervised evolutionary learning for recurrent neural networks. Our method of training the recurrent neural network controllers relies on combining the components of the singular value decomposition of two different neural network connection matrices.

Label Embedding Trees for Large Multi-Class Tasks <=SEP=> Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a treestructure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.

Dependency Grammar and Dependency Parsing <=SEP=> Despite a long and venerable tradition in descriptive lingu stics, dependency grammar has until recently played a fairly marginal role both in t heoretical linguistics and in natural language processing. The increasing interes t in dependency-based representations in natural language parsing in recent year s appears to be motivated both by the potential usefulness of bilexical relations in d isambiguation and by the gains in efficiency that result from the more constrained par sing problem for these representations. In this paper, we will review the state of the art in dependenc y-based parsing, starting with the theoretical foundations of dependency gr ammar and moving on to consider both grammar-driven and data-driven methods fo r dependency parsing. We will limit our attention to systems for dependency parsin g i a narrow sense, i.e. systems where the analysis assigned to an input sentenc akes the form of a dependency structure. This means that we will not discuss sy tems that exploit dependency relations for the construction of another type o f r presentation, such as the head-driven parsing models of Collins (1997, 1999). M oreover, we will restrict ourselves to systems for full parsing, which means that we will not deal with systems that produce a partial or underspecified repres ntation of dependency structure, such as Constraint Grammar parsers (Karlsson, 1 990; Karlsson et al., 1995).

Realized Volatility and Variance : Options via Swaps <=SEP=> Let St denote the value of a stock or stock index at time t. Given a variance/volatility option to be priced and hedged, let us designate as time 0 the start of its averaging period, and time T the end. For t ∈ [0, T ], and τ ≤ t, let R2 τ,t denote the realized variance of returns over the time interval [τ, t]. The mathematical results about the synthesis of volatility and variance swaps will hold exactly if R2 refers to the continuously-monitored variance. This means the quadratic variation of logS, ∗Bloomberg L.P. and NYU Courant Institute pcarr@nyc.rr.com. †University of Chicago. RL@math.uchicago.edu. We thank the International Securities Exchange, Kris Monaco,

Single-Stage Single-Switch PFC Flyback Converter Using a Synchronous Rectifier <=SEP=> A single-stage single-switch power factor correction (PFC) flyback converter with a synchronous rectifier (SR) is proposed for improving power factor and efficiency. Using a variable switching-frequency controller, this converter is continuously operated with a reduced turn-on switching loss at the boundary of the continuous conduction mode and discontinuous conduction mode (DCM). The proposed PFC circuit provides relatively low dc-link voltage in the universal line voltage, and also complies with Standard IEC 61000-3-2 Class D limits. In addition, a new driving circuit as the voltage driven-synchronous rectifier is proposed to achieve high efficiency. In particular, since a driving signal is generated according to the voltage polarity, the SR driving circuit can easily be used in DCM applications. The proposed PFC circuit and SR driving circuit in the flyback converter with the reduced switching loss are analyzed in detail and optimized for high performance. Experimental results for a 19 V/90 W adapter at the variable switching-frequency of 30~70 kHz were obtained to show the performance of the proposed converter.

Current and Emerging Topics in Sports Video Processing <=SEP=> Sports video processing is an interesting topic for research, since the clearly defined game rules in sports provide the rich domain knowledge for analysis. Moreover, it is interesting because many specialized applications for sports video processing are emerging. This paper gives an overview of sports video research, where we describe both basic algorithmic techniques and applications.

Improving Business Process Quality through Exception Understanding, Prediction, and Prevention <=SEP=> Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and mining techniques. We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.

A Service-Oriented Architecture for Virtualizing Robots in Robot-as-a-Service Clouds <=SEP=> Exposing software and hardware computing resources as services through a cloud is increasingly emerging in the recent years. This comes as a result of extending the service-oriented architecture (SOA) paradigm to virtualize computing resources. In this paper, we extend the paradigm of the SOA approach to virtualize robotic hardware and software resources to expose them as services through the Web. This allows non-technical users to access, interact and manipulate robots simply through a Web browser. The proposed RoboWeb system is based on a SOAP-based Web service middleware that binds robots computing resources as services and publish them to the end-users. We consider robots that operates with the Robotic Operating System (ROS), as it provides hardware abstraction that makes easier applications development. We describe the implementation of RoboWeb and demonstrate how researchers can use it to interact remotely with the robots. We believe that this work consistently contributes to enabling remote robotic labs using the cloud paradigm.

TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process <=SEP=> Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.

Behavior Analysis Using a Multilevel Motion Pattern Learning Framework <=SEP=> The increasing availability of video data, through existing traffic cameras or dedicated field data collection, paves the way for the collection of massive datasets about the microscopic behaviour of road users using computer vision techniques. Analysis of such datasets helps to understand the normal road user behaviour, and it can be used for realistic prediction of future motion and computing surrogate safety indicators. A multi-level motion pattern learning framework is developed that enables automated scene interpretation, anomalous behaviour detection and surrogate safety analysis. Firstly, Points of interest (POI) are learnt based on Gaussian Mixture Model and the Expectation Maximization algorithm and then used to form activity paths (APs). Secondly, motion patterns, represented by trajectory prototypes, are learnt from road users’ trajectories in each AP using a two-stage trajectory clustering method based on spatial then temporal (speed) information. Finally, motion prediction relies on matching at each instant partial trajectories to the learnt prototypes to evaluate the potential for collision by computing indicators. An intersection case study demonstrates the framework ability in many ways: it helps to reduce the computation cost up to 90 %, clean trajectories dataset from tracking outliers, use actual trajectories as prototypes without any preand post-processing and predict future motion realistically to compute surrogate safety indicators.

A High-Power Broadband Passive Terahertz Frequency Doubler in CMOS <=SEP=> To realize a high-efficiency terahertz signal source, a varactor-based frequency-doubler topology is proposed. The structure is based on a compact partially coupled ring that simultaneously achieves isolation, matching, and harmonic filtering for both input and output signals at f0 and 2f0. The optimum varactor pumping/loading conditions for the highest conversion efficiency are also presented analytically along with intuitive circuit representations. Using the proposed circuit, a passive 480-GHz frequency doubler with a measured minimum conversion loss of 14.3 dB and an unsaturated output power of 0.23 mW is reported. Within 20-GHz range, the fluctuation of the measured output power is less than 1.5 dB, and the simulated 3-dB output bandwidth is 70 GHz (14.6%). The doubler is fabricated using 65-nm low-power bulk CMOS technology and consumes near zero dc power.

Anonymous CoinJoin Transactions with Arbitrary Values <=SEP=> Bitcoin, the arguably most popular cryptocurrency to date, allows users to perform transactions using freely chosen pseudonymous addresses. Previous research, however, suggests that these pseudonyms can easily be linked, implying a lower level of privacy than originally expected. To obfuscate the links between pseudonyms, different mixing methods have been proposed. One of the first approaches is the CoinJoin concept, where multiple users merge their transactions into one larger transaction. In theory, CoinJoin can be used to mix and transact bitcoins simultaneously, in one step. Yet, it is expected that differing bitcoin amounts would allow an attacker to derive the original single transactions. Solutions based on CoinJoin therefore prescribe the use of fixed bitcoin amounts and cannot be used to perform arbitrary transactions.In this paper, we define a model for CoinJoin transactions and metrics that allow conclusions about the provided anonymity. We generate and analyze CoinJoin transactions and show that with differing, representative amounts they generally do not provide any significant anonymity gains. As a solution to this problem, we present an output splitting approach that introduces sufficient ambiguity to effectively prevent linking in CoinJoin transactions. Furthermore, we discuss how this approach could be used in Bitcoin today.

Implementation of a Restricted Boltzmann Machine in a Spiking Neural Network <=SEP=> Restricted Boltzmann Machines (RBMs) have been demonstrated to perform efficiently on a variety of applications, such as dimensionality reduction and classification. Implementing RBMs on neuromorphic hardware has certain advantages, particularly from a concurrency and lowpower perspective. This paper outlines some of the requirements involved for neuromorphic adaptation of an RBM and attempts to address these issues with suitably targeted modifications for sampling and weight updates. Results show the feasibility of such alterations which will serve as a guide for future implementation of such algorithms in VLSI arrays of spiking neurons.

Blockchain — Literature survey <=SEP=> In the modern world that we live in everything is gradually shifting towards a more digitized outlook. From teaching methods to online transactions, every small aspect of our life is given a more technological touch. In such a case “money” is not left behind either. An approach towards digitizing money led to the creation of “bitcoin”. Bitcoin is the most efficient crypto-currency so far in the history of digital currency. Ruling out the interference of any third party which monitors the cash flow, Bitcoin is a decentralized form of online currency and is widely accepted for internet transactions all over the world. The need for digital money would be extensive in the near future.

IMPLICIT SLICING METHOD FOR ADDITIVE MANUFACTURING PROCESSES <=SEP=> All additive manufacturing processes involve a distinct preprocessing stage in which a set of instructions, or GCode, that control the process specific manufacturing tool are generated, otherwise known as slicing. In regards to fused deposition modeling, the GCode defines many crucial parameters which are needed to produce a part, including tool path, infill density, layer height, feed rate, tool head and plate temperature. The majority of current commercial slicing programs generate tool paths explicitly, and do not consider particular geometric properties of parts, such as thin walls, small corners and round profiles that can result in critical voids leading to part failure. This work replicates an implicit slicing algorithm in which functionally derived infill patterns are overlaid onto each layer of a part, reducing the possibility of undesired voids and flaws. This work also further investigates the effects that varying implicitly derived infill patterns have on a part’s mechanical properties through tensile testing dog bone specimens with three different infill patterns and comparing ultimate stress and elastic modulus properties.

Adversarial Cross-Modal Retrieval <=SEP=> Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of cross-modal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two processes. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.

Key drivers of internet banking services use <=SEP=> Purpose – The purpose of this paper is to analyse the determinants of internet banking use, paying special attention to the role of product involvement, perceived risk and trust. Design/methodology/approach – The impact of trust, perceived risks, product involvement and TAM beliefs (ease of use and usefulness) on internet banking adoption is tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users and the data are collected through an internet survey. Risk is measured as a formative construct. Findings – Data analysis shows that TAM beliefs and perceived risks (security, privacy, performance and social) have a direct influence on e-banking adoption. Trust appears as a key variable that reduces perceived risk. Involvement plays an important role in increasing perceived ease of use. Practical implications – This research provides banks with knowledge of what aspects to highlight in their communications strategies to increase their internet banking services adoption rates. The research findings show managers that web contents and design are key tools to increase internet banking services adoption. Practical recommendations to increase web usefulness and trust, and guidelines to reduce perceived risk dimensions are also provided. Originality/value – Despite the importance of trust issues and risk perceptions for internet banking adoption, only limited work has been done to identify trust and risk dimensions in an online banking context. We have evaluated the impact of each risk dimension instead of treating risk as a whole. Furthermore, risk has been measured as a formative construct because there is no reason to expect that risk dimensions in online financial services are correlated.

Information technology and organizational learning: a review and assessment of research <=SEP=> This paper reviews and assesses the emerging research literature on information technology and organizational learning. After discussing issues of meaning and measurement, we identify and assess two main streams of research: studies that apply organizational learning concepts to the process of implementing and using information technology in organizations; and studies concerned with the design of information technology applications to support organizational learning. From the former stream of research, we conclude that experience plays an important, yet indeterminate role in implementation success; learning is accomplished through both formal training and participation in practice; organizational knowledge barriers may be overcome by learning from other organizations; and that learning new technologies is a dynamic process characterized by relatively narrow windows of opportunity. From the latter stream, we conclude that conceptual designs for organizational memory information systems are a valuable contribution to artifact development; learning is enhanced through systems that support communication and discourse; and that information technologies have the potential to both enable and disable organizational learning. Currently, these two streams flow independently of each other, despite their close conceptual and practical links. We advise that future research on information technology and organizational learning proceeds in a more integrated fashion, recognizes the situated nature of organizational learning, focuses on distributed organizational memory, demonstrates the effectiveness of artifacts in practice, and looks for relevant research findings in related fields.

Cyber attack modeling and simulation for network security analysis <=SEP=> Cyber security methods are continually being developed. To test these methods many organizations utilize both virtual and physical networks which can be costly and time consuming. As an alternative, in this paper, we present a simulation modeling approach to represent computer networks and intrusion detection systems (IDS) to efficiently simulate cyber attack scenarios. The outcome of the simulation model is a set of IDS alerts that can be used to test and evaluate cyber security systems. In particular, the simulation methodology is designed to test information fusion systems for cyber security that are under development.

Using Knowledge Representation and Reasoning Tools in the Design of Robots <=SEP=> The paper describes the authors’ experience in using knowledge representation and reasoning tools in the design of robots. The focus is on the systematic construction of models of the robot’s capabilities and its domain at different resolutions, and on establishing a clear relationship between the models at the different resolutions.

An Experience Teaching a Graduate Course in Cryptography <=SEP=> This article describes an experience of teaching a graduate-level course in cryptography and computer security at New York University. The course content as well as lessons learned and plans for the future are discussed.