nils-herrmann/clean_scirepeval huggingface dataset:
Paper Prototyping: The Fast and Easy Way to Design and Refine User Interfaces

Paper prototyping highlights cost-effective usability testing techniques that produce fast results for improving an interface design. Practitioners and students interested in the design, development, and support of user interfaces will appreciate Snyder’s text for its focus on practical information and application. This book’s best features are the real life examples, anecdotes, and case studies that the author presents to demonstrate the uses of paper prototyping and its many benefits. While the author advocates paper prototyping, she also notes that paper prototyping techniques are one of many usability evaluation methods and that paper prototyping works best only in certain situations. Snyder reminds her readers that paper prototyping does not produce precise usability measurements, but rather it is a “blunt instrument” that rapidly uncovers qualitative information from actual users performing real tasks (p. 185). Hence, this book excludes in-depth theoretical discussions about methods and validity, but its pragmatic discussion on test design prepares the practitioner for dealing with several circumstances and making sound decisions based on testing method considerations.
Cluster-based concept invention for statistical relational learning

We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Entities derived from clusters increase the expressivity of feature spaces by creating new first-class concepts which contribute to the creation of new features. For example, in CiteSeer, papers can be clustered based on words or citations giving "topics", and authors can be clustered based on documents they co-author giving "communities". Such cluster-derived concepts become part of more complex feature expressions. Out of the large number of generated features, those which improve predictive accuracy are kept in the model, as decided by statistical feature selection criteria. We present results demonstrating improved accuracy on two tasks, venue prediction and link prediction, using CiteSeer data.
IKNN: Informative K-Nearest Neighbor Pattern Classification

The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to measure the closeness between objects, two novel KNN procedures, Locally Informative-KNN (LI-KNN) and Globally Informative-KNN (GI-KNN), are proposed. By selecting a subset of most informative objects from neighborhoods, our methods exhibit stability to the change of input parameters, number of neighbors(K) and informative points (I). Experiments on UCI benchmark data and diverse real-world data sets indicate that our approaches are application-independent and can generally outperform several popular KNN extensions, as well as SVM and Boosting methods.
Commercial Applications of Natural Language Processing

Vast quantities of text are becoming available in electronic form, ranging from published documents (e.g., electronic dictionaries, encyclopedias, libraries and archives for information retrieval services), to private databases (e.g., marketing information, legal records, medical histories), to personal email and faxes. Online information services are reaching mainstream computer users. There were over 15 million Internet users in 1993, and projections are for 30 million in 1997. With media attention reaching all-time highs, hardly a day goes by without a new article on the National Information Infrastructure, digital libraries, networked services, digital convergence or intelligent agents. This attention is moving natural language processing along the critical path for all kinds of novel applications.
Clutchable series-elastic actuator: Design of a robotic knee prosthesis for minimum energy consumption

The cyclic and often linear torque-angle relationship of locomotion presents the opportunity to innovate on the design of traditional series-elastic actuators (SEAs). In this paper, a novel modification to the SEA architecture was proposed by adding a clutch in parallel with the motor within the SEA - denoted as a CSEA. This addition permits bimodal dynamics where the system is characterized by an SEA when the clutch is disengaged and a passive spring when the clutch is engaged. The purpose of the parallel clutch was to provide the ability to store energy in a tuned series spring, while requiring only reactionary torque from the clutch. Thus, when the clutch is engaged, a tuned elastic relationship can be achieved with minimal electrical energy consumption. The state-based model of the CSEA is introduced and the implementation of the CSEA mechanism in a powered knee prosthesis is detailed. The series elasticity was optimized to fit the spring-like torque-angle relationship of early stance phase knee flexion and extension during level ground walking. In simulation, the CSEA knee required 70% less electrical energy than a traditional SEA. Future work will focus on the mechanical implementation of the CSEA knee and an empirical demonstration of reduced electrical energy consumption during walking.
Intuitionistic Fuzzy Aggregation Operators

An intuitionistic fuzzy set, characterized by a membership function and a non-membership function, is a generalization of fuzzy set. In this paper, based on score function and accuracy function, we introduce a method for the comparison between two intuitionistic fuzzy values and then develop some aggregation operators, such as the intuitionistic fuzzy weighted averaging operator, intuitionistic fuzzy ordered weighted averaging operator, and intuitionistic fuzzy hybrid aggregation operator, for aggregating intuitionistic fuzzy values and establish various properties of these operators.
Camera Calibration with Known Rotation

We address the problem of using external rotation information with uncalibrated video sequences. The main problem addressed is, what is the benefit of the orientation information for camera calibration? It is shown that in case of a rotating camera the camera calibration problem is linear even in the case that all intrinsic parameters vary. For arbitrarily moving cameras the calibration problem is also linear but underdetermined for the general case of varying all intrinsic parameters. However, if certain constraints are applied to the intrinsic parameters the camera calibration can be computed linearily. It is analyzed which constraints are needed for camera calibration of freely moving cameras. Furthermore we address the problem of aligning the camera data with the rotation sensor data in time. We give an approach to align these data in case of a rotating camera.
A Trust Model Based on Subjective Logic

In order to resolve the trust problem due to the opening, the dynamics, the anonymity and uncertainty of p2p system, this paper proposes a new p2p trust model by analyzing and improving the subjective logic, a new trust quantification formula with the negative events effect and the time effect is given, and a definition of risk is also presented, By doing this, we can evaluate the trust relationships between peers more precisely and prevent the hidden security dangers of cooperative cheat and slander more effectively, thus can resolve security problems exist in p2p environment more effectively.
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE. Our results can be reproduced with the source code and trained models available at https://imatge-upc.github. io/saliency-salgan-2017/.
A theory-based framework for evaluating exergames as persuasive technology

Exergames are video games that use exertion-based interfaces to promote physical activity, fitness, and gross motor skill development. The purpose of this paper is to describe the development of an organizing framework based on principles of learning theory to classify and rank exergames according to embedded behavior change principles. Behavioral contingencies represent a key theory-based game design principle that can be objectively measured, evaluated, and manipulated to help explain and change the frequency and duration of game play. Case examples are presented that demonstrate how to code dimensions of behavior, consequences of behavior, and antecedents of behavior. Our framework may be used to identify game principles which, in the future, might be used to predict which games are most likely to promote adoption and maintenance of leisure time physical activity.
Learning to Parse and Translate Improves Neural Machine Translation

There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.
Real-time reactive motion generation based on variable attractor dynamics and shaped velocities

This paper describes a novel method for motion generation and reactive collision avoidance. The algorithm performs arbitrary desired velocity profiles in absence of external disturbances and reacts if virtual or physical contact is made in a unified fashion with a clear physically interpretable behavior. The method uses physical analogies for defining attractor dynamics in order to generate smooth paths even in presence of virtual and physical objects. The proposed algorithm can, due to its low complexity, run in the inner most control loop of the robot, which is absolutely crucial for safe Human Robot Interaction. The method is thought as the locally reactive real-time motion generator connecting control, collision detection and reaction, and global path planning.
Applying an extended model of deterrence across cultures: An investigation of information systems misuse in the U.S. and South Korea

Intentional employee misuse of IS is a global problem. Research suggests that security countermeasures can deter misuse by increasing the perceived certainty and severity of punishment for such behavior. However, the value of generalizing this work beyond Western cultures is open to question. In our study, we examined whether national culture influenced the deterrent capabilities of security policies, security education, training, and awareness programs and computer monitoring. Using U.S. and Korean samples, we found evidence that the deterrent effect of certain security countermeasures varied between the two countries, as did the influence of age and gender. The results have implications for information security management practices in global businesses.
Design of spoke type BLDC motors with high power density for traction applications

This paper presents a detailed comparative study of spoke type brushless dc (SPOKE-BLDC) motors due to the operating conditions and designs a new type SPOKE-BLDC with flux barriers for high torque applications, such as tractions. The current dynamic analysis method considering the local magnetic saturation of the rotor and the instantaneous current by pwm driving circuit is developed based on the coupled finite element analysis with rotor dynamic equations. From this analysis, several new structures using the flux barriers are designed and the characteristics are compared in order to reduce the large torque ripple and improve the average torque of SPOKE-BLDC. From these results, it is confirmed that the flux barriers, which are inserted on the optimized position of the rotor, have made remarkable improvement for the torque characteristics of the SPOKE-BLDC.
Business Unit Strategy , Managerial Characteristics , and Business Unit Effectiveness at Strategy Implementation ^

Despite the widespread acceptance of strategy's role in mediating an organization's interaction with its environment (Andrews, 1971; Ansoff, 1965; Chandler, 1962; Child, 1972; Miles & Snow, 1978), the scope of research on strategy "implementation" has remained quite narrow. Following Chandler (1962), the concern has been predominantly with how a firm's organizational structure and control system are, or might be, related to the degree and nature of its product and geographic diversification (Fouraker & Stopford, 1968; Grinyer, Al-Bazzaz, & Yasai-Ardekani, 1980; Rumelt, 1974; Scott, 1973; Vancil, 1980). However^ strategy formulation and implementation take place not just at the level of the diversified firm as a whole, but also at the level of the divisions/strategic business units (SBUs) comprising the firm (Hambrick, 1980; Hofer & Schendel, 1978). In such a context, the near absence of empirical studies on strategy implementation at the SBU level presents a significant research opportunity.
Building LinkedIn's Real-time Activity Data Pipeline

One trend in the implementation of modern web systems is the use of activity data in the form of log or event messages that capture user and server activity. This data is at the heart of many internet systems in the domains of advertising, relevance, search, recommendation systems, and security, as well as continuing to fulfill its traditional role in analytics and reporting. Many of these uses place real-time demands on data feeds. Activity data is extremely high volume and real-time pipelines present new design challenges. This paper discusses the design and engineering problems we encountered in moving LinkedIn’s data pipeline from a batch-oriented file aggregation mechanism to a real-time publish-subscribe system called Kafka. This pipeline currently runs in production at LinkedIn and handles more than 10 billion message writes each day with a sustained peak of over 172,000 messages per second. Kafka supports dozens of subscribing systems and delivers more than 55 billion messages to these consumer processing each day. We discuss the origins of this systems, missteps on the path to real-time, and the design and engineering problems we encountered along the way.
Pedestrian Behavior Understanding and Prediction with Deep Neural Networks

In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance. A pedestrian behavior encoding scheme is designed to provide a general representation of walking paths, which can be used as the input and output of CNN. The proposed Behavior-CNN is trained with real-scene crowd data and then thoroughly investigated from multiple aspects, including the location map and location awareness property, semantic meanings of learned filters, and the influence of receptive fields on behavior modeling. Multiple applications, including walking path prediction, destination prediction, and tracking, demonstrate the effectiveness of Behavior-CNN on pedestrian behavior modeling.
Quantitative evaluation of LDDMM, FreeSurfer, and CARET for cortical surface mapping

Cortical surface mapping has been widely used to compensate for individual variability of cortical shape and topology in anatomical and functional studies. While many surface mapping methods were proposed based on landmarks, curves, spherical or native cortical coordinates, few studies have extensively and quantitatively evaluated surface mapping methods across different methodologies. In this study we compared five cortical surface mapping algorithms, including large deformation diffeomorphic metric mapping (LDDMM) for curves (LDDMM-curve), for surfaces (LDDMM-surface), multi-manifold LDDMM (MM-LDDMM), FreeSurfer, and CARET, using 40 MRI scans and 10 simulated datasets. We computed curve variation errors and surface alignment consistency for assessing the mapping accuracy of local cortical features (e.g., gyral/sulcal curves and sulcal regions) and the curvature correlation for measuring the mapping accuracy in terms of overall cortical shape. In addition, the simulated datasets facilitated the investigation of mapping error distribution over the cortical surface when the MM-LDDMM, FreeSurfer, and CARET mapping algorithms were applied. Our results revealed that the LDDMM-curve, MM-LDDMM, and CARET approaches best aligned the local curve features with their own curves. The MM-LDDMM approach was also found to be the best in aligning the local regions and cortical folding patterns (e.g., curvature) as compared to the other mapping approaches. The simulation experiment showed that the MM-LDDMM mapping yielded less local and global deformation errors than the CARET and FreeSurfer mappings.
Creating a Translation Matrix of the Bible's Names Across 591 Languages

For many of the world’s languages, the Bible is the only significant bilingual, or even monolingual, text, making it a unique training resource for tasks such as translation, named entity analysis, and transliteration. Given the Bible’s small size, however, the output of standard word alignment tools can be extremely noisy, making downstream tasks difficult. In this work, we develop and release a novel resource of 1129 aligned Bible person and place names across 591 languages, which was constructed and improved using several approaches including weighted edit distance, machine-translation-based transliteration models, and affixal induction and transformation models. Our models outperform a widely used word aligner on 97% of test words, showing the particular efficacy of our approach on the impactful task of broadly multilingual named-entity alignment and translation across a remarkably large number of world languages. We further illustrate the utility of our translation matrix for the multilingual learning of name-related affixes and their semantics as well as transliteration of named entities.
An integrative approach to the biomechanical function and neuromuscular control of the fingers.

The exquisite mechanical functionality and versatility of the human hand emerges from complex neuro-musculo-skeletal interactions that are not completely understood. I have found it useful to work within a theoretical/experimental paradigm that outlines the fundamental neuro-musculo-skeletal components and their interactions. In this integrative paradigm, the laws of mechanics, the specifications of the manipulation task, and the sensorimotor signals define the interactions among hand anatomy, the nervous system, and manipulation function. Thus, our collaborative research activities emphasize a firm grounding in the mechanics of finger function, insistence on anatomical detail, and meticulous characterization of muscle activity. This overview of our work on precision pinch (i.e., the ability to produce and control fingertip forces) presents some of our findings around three Research Themes: Mechanics-based quantification of manipulation ability; Anatomically realistic musculoskeletal finger models; and Neural control of finger muscles. I conclude that (i) driving the fingers to some limit of sensorimotor performance is instrumental to elucidating motor control strategies; (ii) that the cross-over of tendons from flexors to extensors in the extensor mechanism is needed to produce force in every direction, and (iii) the anatomical routing of multiarticular muscles makes co-contraction unavoidable for many tasks. Moreover, creating realistic and clinically useful finger models still requires developing new computational means to simulate the viscoelastic tendinous networks of the extensor mechanism, and the muscle-bone-ligament interactions in complex articulations. Building upon this neuromuscular biomechanics paradigm is of immense clinical relevance: it will be instrumental to the development of clinical treatments to preserve and restore manual ability in people suffering from neurological and orthopedic conditions. This understanding will also advance the design and control of robotic hands whose performance lags far behind that of their biological counterparts.
Praise for intelligence can undermine children's motivation and performance.

Praise for ability is commonly considered to have beneficial effects on motivation. Contrary to this popular belief, six studies demonstrated that praise for intelligence had more negative consequences for students' achievement motivation than praise for effort. Fifth graders praised for intelligence were found to care more about performance goals relative to learning goals than children praised for effort. After failure, they also displayed less task persistence, less task enjoyment, more low-ability attributions, and worse task performance than children praised for effort. Finally, children praised for intelligence described it as a fixed trait more than children praised for hard work, who believed it to be subject to improvement. These findings have important implications for how achievement is best encouraged, as well as for more theoretical issues, such as the potential cost of performance goals and the socialization of contingent self-worth.
Privacy in mobile technology for personal healthcare

Information technology can improve the quality, efficiency, and cost of healthcare. In this survey, we examine the privacy requirements of mobile computing technologies that have the potential to transform healthcare. Such mHealth technology enables physicians to remotely monitor patients' health and enables individuals to manage their own health more easily. Despite these advantages, privacy is essential for any personal monitoring technology. Through an extensive survey of the literature, we develop a conceptual privacy framework for mHealth, itemize the privacy properties needed in mHealth systems, and discuss the technologies that could support privacy-sensitive mHealth systems. We end with a list of open research questions.