nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting Generative Models in Discriminative Classifiers

Generative probability models such as hidden ~larkov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand , discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification performance superior to that of the model based approaches. An ideal classifier should combine these two complementary approaches. In this paper, we develop a natural way of achieving this combination by deriving kernel functions for use in discriminative methods such as support vector machines from generative probability models. We provide a theoretical justification for this combination as well as demonstrate a substantial improvement in the classification performance in the context of D~A and protein sequence analysis.
Unpacking the Privacy Paradox: Irrational Decision-Making within the Privacy Calculus

Understanding privacy is important to individuals and organizations. This research in progress develops a theoretical model that incorporates both actual disclosure behavior and the potential for irrational behavior induced by overriding situational factors presented at the time of the privacy disclosure decision. Actual disclosure behavior is seldom measured in privacy research, and most prior work has assumed the disclosure decision to be a fully rational one. This research challenges that assumption, and the eventual completion of the project will contribute uniquely to our understanding of the privacy calculus in online transactions.
New VF-power system architecture and evaluation for future aircraft

Conventional aircraft power system is a constant-frequency (CF) supply based on mechanical-regulated constant-speed mechanism that has relatively low efficiency. Replacing the CF system with variable-frequency (VF) power improves overall efficiency, and reduce the system's weight and volume. However, this creates a new tier of requirements and design challenges. Novel VF-power architecture is developed with minimization of the power losses throughout the stages of power conversions. Optimal partitioning and grouping of onboard AC loads has been discussed with specific system data. New VF-input multi-functional power converters are also briefly discussed.
Multi-image matching using invariant features

This thesis concerns the problems of automatic image stitching and 3D modelling from multiple views. These are basic problems of computer vision, with applications in robotics, architecture, industrial inspection, surveillance, computer graphics and film. Recent work has brought increasing automation to these tasks, but despite a large amount of progress, state-of-the-art algorithms still require some form of user input or assumptions about the image sequence. For example, the best image stitchers currently require an ordered set of input images, or user input to identify the matching images, before automatic registration can proceed. In this work we show how such tasks can be performed automatically and without any user input at all. We formulate the multi-image matching problem as one of finding all matching images, subject to the constraint that they are consistent views from a perspective camera. We use invariant features as a mechanism for finding correspondences, and indexing techniques to efficiently find matches between multiple views. We then find all sets of geometrically consistent feature matches, using a probabilistic model for verification. This allows us to identify each object or scene in the dataset using only the structure already present in the data. The major contributions of this thesis are the development of a system that can automatically recognise and stitch 2D panoramas in unordered image datasets, and a new class of invariant features for this purpose.
Procedural Content Generation: Goals, Challenges and Actionable Steps

This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multi-content PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately.
Real World BCI: Cross-Domain Learning and Practical Applications

In order to develop real-world BCI solutions machine learning models must generalize not only to unseen users but also to unseen scenarios. In this concept paper we describe our initial investigation into Deep Learning tools to create generalized models for both cross-subject and cross-domain learning. We demonstrate our approach using two different, laboratory grade data sets to train a learning model that we then apply to a third more complex scenario. While our results indicate that cross-domain learning is possible, we also identify potential avenues for further research and development (such as disentangling spatially or temporally overlapping responses). Finally, we describe our work to implement a system that uses cross-domain learning to develop a real-time application for performing BCI-based Human-Centric Scene Analysis.
Real-time 3D scene reconstruction with dynamically moving object using a single depth camera

Online 3D reconstruction of real-world scenes has been attracting increasing interests from both the academia and industry, especially with the consumer-level depth cameras becoming widely available. Recent most online reconstruction systems take live depth data from a moving Kinect camera and incrementally fuse them to a single high-quality 3D model in real time. Although most real-world scenes have static environment, the daily objects in a scene often move dynamically, which are non-trivial to reconstruct especially when the camera is also not still. To solve this problem, we propose a single depth camera-based real-time approach for simultaneous reconstruction of dynamic object and static environment, and provide solutions for its key issues. In particular, we first introduce a robust optimization scheme which takes advantage of raycasted maps to segment moving object and background from the live depth map. The corresponding depth data are then fused to the volumes, respectively. These volumes are raycasted to extract views of the implicit surface which can be used as a consistent reference frame for the next iteration of segmentation and tracking. Particularly, in order to handle fast motion of dynamic object and handheld camera in the fusion stage, we propose a sequential 6D pose prediction method which largely increases the registration robustness and avoids registration failures occurred in conventional methods. Experimental results show that our approach can reconstruct moving object as well as static environment with rich details, and outperform conventional methods in multiple aspects.
RECOGNITION WITH WEIGHTED FINITE-STATE TRANSDUCERS

This chapter describes a general representation and algorithmic framework for speech recognition based on weighted finite-state transducers. These transducers provide a common and natural representation for major components of speech recognition systems, including hidden Markov models (HMMs), context-dependency models, pronunciation dictionaries, statistical grammars, and word or phone lattices. General algorithms for building and optimizing transducer models are presented, including composition for combining models, weighted determinization and minimization for optimizing time and space requirements, and a weight pushing algorithm for redistributing transition weights optimally for speech recognition. The application of these methods to large-vocabulary recognition tasks is explained in detail, and experimental results are given, in particular for the North American Business News (NAB) task, in which these methods were used to combine HMMs, full cross-word triphones, a lexicon of forty thousand words, and a large trigram grammar into a single weighted transducer that is only somewhat larger than the trigram word grammar and that runs NAB in real-time on a very simple decoder. Another example demonstrates that the same methods can be used to optimize lattices for second-pass recognition.
A Critique of the Balanced Scorecard as a Performance Measurement Tool

Given the dynamic nature of business markets, organizations have witnessed a rapid change in their performance measurement systems in the last three decades with most of the performance measurement tools being more sophisticated. One of the most widespread tools is the Balanced Scorecard (BSC). Within a decade since its emergence, a majority of the Fortune 1000 companies was implementing the BSC. Despite the widespread adoption, a growing body of scholarship finds limitations in the BSC, particularly in its concept, application, and practice. The same body of scholarship contends these limitations could either undermine the effectiveness of the BSC or cause firms to abandon the BSC altogether for better performance measurement alternatives. Hence, the main aim of this paper is to provide a review or rather a critique of the BSC as a performance measurement tool and debating whether the BSC is in fact a universal solution for corporate performance measurement.
Motion compensation and efficient array design for TDMA FMCW MIMO radar systems

In this paper we present methods for the design of planar frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) arrays with an emphasis on the problem of moving targets in time-division multiple-access (TDMA) systems. We discuss the influence of target motion and boundaries of operation and present a method to compensate for its effects, which requires special attention in the array design and in signal processing. Array design techniques, examples including an implementation, and measurement results are also covered in this article.
Optimising QoS-assurance, Resource Usage and Cost of Fog Application Deployments

Identifying the best application deployment to distribute application components in Fog infrastructures – spanning the IoT-to-Cloud continuum – is a challenging task for application deployers. Indeed, it requires fulfilling all application requirements, whilst determining a trade-off among different objectives (i.e., QoS assurance, Fog resource consumption and cost), resulting in a complex and time-consuming decision-making process to be tuned manually. In this paper, we present a simple multi-objective optimisation scheme that permits selecting the best placement of application components, balancing the trade-off among QoS-assurance, Fog resource consumption and monthly deployment costs. We exploit our prototype, extended with parallel Monte Carlo simulations, and a motivating example to show how IT experts can benefit from our approach.
Multi-perspective Anomaly Detection in Business Process Execution Events

Ensuring anomaly-free process model executions is crucial in order to prevent fraud and security breaches. Existing anomaly detection approaches focus on the control flow, point anomalies, and struggle with false positives in the case of unexpected events. By contrast, this paper proposes an anomaly detection approach that incorporates perspectives that go beyond the control flow, such as, time and resources (i.e., to detect contextual anomalies). In addition, it is capable of dealing with unexpected process model execution events: not every unexpected event is immediately detected as anomalous, but based on a certain likelihood of occurrence, hence reducing the number of false positives. Finally, multiple events are analyzed in a combined manner in order to detect collective anomalies. The performance and applicability of the overall approach are evaluated by means of a prototypical implementation along and based on real life process execution logs from multiple domains.
THE IMPACT OF HUMAN RESOURCE MANAGEMENT PRACTICES ON PERCEPTIONS OF ORGANIZATIONAL PERFORMANCE

In recent years, U.S. companies have been urged to adopt a variety of performance-enhancing or progressive human resource management (HRM) practices to improve their competitiveness in the global marketplace (U.S. Department of Labor, 1993). Such recommendations are unsurprising given that professionals and academics have long asserted that the way in which an organization manages people can influence its performance. Spurred by Peters and Waterman’s (1982) description and assessment of “excellent” organizations, the past decade has produced many testimonials to the value of progressive HRM practices and systems of such practices. In particular, employee participation and empowerment and job redesign, including teambased production systems, extensive employee training, and performancecontingent incentive compensation, are widely believed to improve the performance of organizations (Pfeffer, 1994). Moreover, a developing body of research has reported positive associations between firm-level measures of HRM systems and organizational performance (Arthur, 1994; Cutcher-Gershenfeld, 1991; Delaney, forthcoming; Huselid, 1995; Huselid & Becker, 1994; Ichniowski, Shaw, & Prennushi, 1994; MacDuffie, 1995). Substantial uncertainty remains, however, as to how HRM practices affect organizational
Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor

In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions. & 2014 Elsevier B.V. All rights reserved.
eWOM: THE EFFECTS OF ONLINE CONSUMER REVIEWS ON PURCHASING DECISION OF ELECTRONIC GOODS

Internet has become the primary source of information for a large number of consumers and it has dramatically changed the consumer behaviour. One of the main changes in modern consumer behaviour has been the transition from a passive to an active and informed consumer. Internet enables customers to share their opinions on, and experiences with, goods and services with a multitude of other consumers. Online consumer reviews are used by prospective buyers of related products who are interested in obtaining more information from people who have purchased and used a product of interest. Word-of-mouth (WOM) is one of the most important information sources when a consumer is making a purchase decision. The arrival and expansion of the Internet has extended consumers' options for gathering product information by including other consumers' comments, posted on the Internet, and has provided consumers opportunities to offer their own consumption-related advice by engaging in electronic word-of-mouth (eWOM). eWOM can be defined as all informal communications directed at consumers through Internet-based technology related to the usage or characteristics of particular goods and services, or their sellers. The aim of this study is to assess the impact of, one type of electronic word-of-mouth (eWOM), the online consumer review, on purchasing decision of electronic products. This empirical study also focuses on the relationship between reviews and purchasing behaviour. An instrument was prepared to measure the proposed constructs, with questionnaire items taken from prior studies but adapted to fit the context of e-commerce. The survey was applied to academicians in Turkey through internet. The data was analyzed using the SPSS package. The results show that consumer reviews have a causal impact on consumer purchasing behaviour and they have an effect on choosing the products by consumer. Finally, the results and their implications are discussed.
A typology for visualizing uncertainty

Information analysts, especially those working in the field of intelligence analysis, must assess not only the information presented to them, but the confidence they have in that information. Visual representations of information are challenged to incorporate a notion of confidence or certainty because the factors that influence the certainty or uncertainty of information vary with the type of information and the type of decisions being made. Visualization researchers have no model or framework for describing uncertainty as it relates to intelligence analysis, thus no consistent basis for constructing visualizations of uncertainty. This paper presents a typology describing the aspects of uncertainty related to intelligence analysis, drawing on frameworks for uncertainty representation in scientific computing.
AGA: Attribute-Guided Augmentation

We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.
On the Representation and Estimation of

This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arisingfrom diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached, to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those/rom an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, i f not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.
Video shot boundary detection: Seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other contentbased operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example.
Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns

Local binary patterns LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.
Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks

Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two datasets to enhance robustness: one with fourteen cryptocurrencies beginning from 9 November 2017, and a subset with nine cryptocurrencies starting 9 September 2016, both ending 6 March 2018. Separately analysing the subset of cryptocurrencies raised the number of data points from 115 to 537, and improved robustness to changes in relationships over time. Excluding USD Tether, the results showed a positive association between different cryptocurrencies that was statistically significant. Robust, strong positive associations were observed for six cryptocurrencies where one was a fork of the other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the existence of a group of cryptocurrencies particularly associated with Cardano, and a separate group correlated with Ethereum. The data was not consistent with a token’s functionality or creation mechanism being the dominant determinants of the evolution of prices over time but did suggest that factors other than speculation contributed to the price.