nils-herrmann/clean_scirepeval huggingface dataset:
Information needs in bug reports: improving cooperation between developers and users

For many software projects, bug tracking systems play a central role in supporting collaboration between the developers and the users of the software. To better understand this collaboration and how tool support can be improved, we have quantitatively and qualitatively analysed the questions asked in a sample of 600 bug reports from the MOZILLA and ECLIPSE projects. We categorised the questions and analysed response rates and times by category and project. Our results show that the role of users goes beyond simply reporting bugs: their active and ongoing participation is important for making progress on the bugs they report. Based on the results, we suggest four ways in which bug tracking systems can be improved.
Re-thinking ethical leadership : An interdisciplinary integrative approach ☆

Received 21 March 2011 Revised 28 February 2012  The purpose of this paper is (1) to identify critical issues in the current literature on ethical leadership — i.e., the conceptual vagueness of the construct itself and the focus on a Western-based perspective; and (2) to address these issues and recent calls for more collaboration between normative and empirical-descriptive inquiry of ethical phenomena by developing an interdisciplinary integrative approach to ethical leadership. Based on the analysis of similarities between Western and Eastern moral philosophy and ethics principles of the world religions, the present approach identifies four essential normative reference points of ethical leadership— the four central ethical orientations: (1) humane orientation, (2) justice orientation, (3) responsibility and sustainability orientation, and (4) moderation orientation. Research propositions on predictors and consequences of leader expressions of the four central orientations are offered. Real cases of ethical leadership choices, derived from in-depth interviews with international leaders, illustrate how the central orientations play out in managerial practice.
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

We present a primal-dual interior-point algorithm with a filter line-search method for nonlinear programming. Local and global convergence properties of this method were analyzed in previous work. Here we provide a comprehensive description of the algorithm, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix. Heuristics are also considered that allow faster performance. This method has been implemented in the IPOPT code, which we demonstrate in a detailed numerical study based on 954 problems from the CUTEr test set. An evaluation is made of several line-search options, and a comparison is provided with two state-of-the-art interior-point codes for nonlinear programming.
Soft Pneumatic Actuator Fascicles for High Force and Reliability

Soft pneumatic actuators (SPAs) are found in mobile robots, assistive wearable devices, and rehabilitative technologies. While soft actuators have been one of the most crucial elements of technology leading the development of the soft robotics field, they fall short of force output and bandwidth requirements for many tasks. In addition, other general problems remain open, including robustness, controllability, and repeatability. The SPA-pack architecture presented here aims to satisfy these standards of reliability crucial to the field of soft robotics, while also improving the basic performance capabilities of SPAs by borrowing advantages leveraged ubiquitously in biology; namely, the structured parallel arrangement of lower power actuators to form the basis of a larger and more powerful actuator module. An SPA-pack module consisting of a number of smaller SPAs will be studied using an analytical model and physical prototype. Experimental measurements show an SPA pack to generate over 112 N linear force, while the model indicates the benefit of parallel actuator grouping over a geometrically equivalent single SPA scale as an increasing function of the number of individual actuators in the group. For a module of four actuators, a 23% increase in force production over a volumetrically equivalent single SPA is predicted and validated, while further gains appear possible up to 50%. These findings affirm the advantage of utilizing a fascicle structure for high-performance soft robotic applications over existing monolithic SPA designs. An example of high-performance soft robotic platform will be presented to demonstrate the capability of SPA-pack modules in a complete and functional system.
Reinforcement learning for long-run average cost

A large class of sequential decision-making problems under uncertainty can be modeled as Markov and Semi-Markov Decision Problems, when their underlying probability structure has a Markov chain. They may be solved by using classical dynamic programming methods. However, dynamic programming methods suffer from the curse of dimensionality and break down rapidly in face of large state spaces. In addition, dynamic programming methods require the exact computation of the so-called transition probabilities, which are often hard to obtain and are hence said to suffer from the curse of modeling as well. In recent years, a simulation-based method, called reinforcement learning, has emerged in the literature. It can, to a great extent, alleviate stochastic dynamic programming of its curses by generating near-optimal solutions to problems having large state-spaces and complex transition mechanisms. In this paper, a simulation-based algorithm that solves Markov and Semi-Markov decision problems is presented, along with its convergence analysis. The algorithm involves a step-size based transformation on two time scales. Its convergence analysis is based on a recent result on asynchronous convergence of iterates on two time scales. We present numerical results from the new algorithm on a classical preventive maintenance case study of a reasonable size, where results on the optimal policy are also available. In addition, we present a tutorial that explains the framework of reinforcement learning in the context of semi-Markov decision problems for long-run average cost.
77-GHz Dual-Layer Transmit-Array for Automotive Radar Applications

A 77-GHz transmit-array on dual-layer printed circuit board (PCB) is proposed for automotive radar applications. Coplanar patch unit-cells are etched on opposite sides of the PCB and connected by through-via. The unit-cells are arranged in concentric rings to form the transmit-array for 1-bit in-phase transmission. When combined with four-substrate-integrated waveguide (SIW) slot antennas as the primary feeds, the transmit-array is able to generate four beams with a specific coverage of ±15°. The simulated and measured results of the antenna prototype at 76.5 GHz agree well, with gain greater than 18.5 dBi. The coplanar structure significantly simplifies the transmit-array design and eases the fabrication, in particular, at millimeter-wave frequencies.
Scaffolding game-based learning: Impact on learning achievements, perceived learning, and game experiences

One of the central challenges of integrating game-based learning in school settings is helping learners make the connections between the knowledge learned in the game and the knowledge learned at school, while maintaining a high level of engagement with game narrative and gameplay. The current study evaluated the effect of supplementing a business simulation game with an external conceptual scaffold, which introduces formal knowledge representations, on learners’ ability to solve financial-mathematical word problems following the game, and on learners’ perceptions regarding learning, flow, and enjoyment in the game. Participants (Mage 1⁄4 10.10 years) were randomly assigned to three experimental conditions: a “study and play” condition that presented the scaffold first and then the game, a “play and study” condition, and a “play only” condition. Although no significant gains in problem-solving were found following the intervention, learners who studied with the external scaffold before the game performed significantly better in the post-game problem-solving assessment. Adding the external scaffold before the game reduced learners’ perceived learning. However, the scaffold did not have a negative impact on reported flow and enjoyment. Flow was found to significantly predict perceived learning and enjoyment. Yet, perceived learning and enjoyment did not predict problem-solving and flow directly predicted problem solving only in the “play and study” condition. We suggest that presenting the scaffold may have “problematized” learners’ understandings of the game by connecting them to disciplinary knowledge. Implications for the design of scaffolds for game-based learning are discussed.
A tag in the hand: supporting semantic, social, and spatial navigation in museums

Designers of mobile, social systems must carefully think about how to help their users manage spatial, semantic, and social modes of navigation. Here, we describe our deployment of MobiTags, a system to help museum visitors interact with a collection of "open storage" exhibits, those where the museum provides little curatorial information. MobiTags integrates social tagging, art information, and a map to support navigation and collaborative curation of these open storage collections. We studied 23 people's use of MobiTags in a local museum, combining interview data with device use logs and tracking of people's movements to understand how MobiTags affected their navigation and experience in the museum. Despite a lack of social cues, people feel a strong sense of social presence--and social pressure--through seeing others' tags. The tight coupling of tags, item information, and map features also supported a rich set of practices around these modes of navigation.
Deep Fisher Kernels -- End to End Learning of the Fisher Kernel GMM Parameters

Fisher Kernels and Deep Learning were two developments with significant impact on large-scale object categorization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object categorization datasets, such as ImageNet. Conceptually, however, they are perceived as very different and it is not uncommon for heated debates to spring up when advocates of both paradigms meet at conferences or workshops. In this work, we emphasize the similarities between both architectures rather than their differences and we argue that such a unified view allows us to transfer ideas from one domain to the other. As a concrete example we introduce a method for learning a support vector machine classifier with Fisher kernel at the same time as a task-specific data representation. We reinterpret the setting as a multi-layer feed forward network. Its final layer is the classifier, parameterized by a weight vector, and the two previous layers compute Fisher vectors, parameterized by the coefficients of a Gaussian mixture model. We introduce a gradient descent based learning algorithm that, in contrast to other feature learning techniques, is not just derived from intuition or biological analogy, but has a theoretical justification in the framework of statistical learning theory. Our experiments show that the new training procedure leads to significant improvements in classification accuracy while preserving the modularity and geometric interpretability of a support vector machine setup.
Craniofacial Superimposition in Forensic Identification using Genetic Algorithms

Craniofacial superimposition is a process that aims to identify a person by overlaying a photograph and a model of the skull. This process is usually carried out manually by forensic anthropologists; thus being very time consuming and presenting several difficulties in finding a good fit between the 3D model of the skull and the 2D photo of the face. In this paper we present a fast and automatic procedure to tackle the superimposition problem. The proposed method is based on real-coded genetic algorithms. Synthetic data are used to validate the method. Results on a real case from our Physical Anthropology lab of the University of Granada are also presented.
Graphene and graphene oxide: synthesis, properties, and applications.

There is intense interest in graphene in fields such as physics, chemistry, and materials science, among others. Interest in graphene's exceptional physical properties, chemical tunability, and potential for applications has generated thousands of publications and an accelerating pace of research, making review of such research timely. Here is an overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.
Measuring password guessability for an entire university

Despite considerable research on passwords, empirical studies of password strength have been limited by lack of access to plaintext passwords, small data sets, and password sets specifically collected for a research study or from low-value accounts. Properties of passwords used for high-value accounts thus remain poorly understood.
 We fill this gap by studying the single-sign-on passwords used by over 25,000 faculty, staff, and students at a research university with a complex password policy. Key aspects of our contributions rest on our (indirect) access to plaintext passwords. We describe our data collection methodology, particularly the many precautions we took to minimize risks to users. We then analyze how guessable the collected passwords would be during an offline attack by subjecting them to a state-of-the-art password cracking algorithm. We discover significant correlations between a number of demographic and behavioral factors and password strength. For example, we find that users associated with the computer science school make passwords more than 1.5 times as strong as those of users associated with the business school. while users associated with computer science make strong ones. In addition, we find that stronger passwords are correlated with a higher rate of errors entering them.
 We also compare the guessability and other characteristics of the passwords we analyzed to sets previously collected in controlled experiments or leaked from low-value accounts. We find more consistent similarities between the university passwords and passwords collected for research studies under similar composition policies than we do between the university passwords and subsets of passwords leaked from low-value accounts that happen to comply with the same policies.
Predicting Bike Usage for New York City's Bike Sharing System

Bike sharing systems consist of a fleet of bikes placed in a network of docking stations. These bikes can then be rented and returned to any of the docking stations after usage. Predicting unrealized bike demand at locations currently without bike stations is important for effectively designing and expanding bike sharing systems. We predict pairwise bike demand for New York City’s Citi Bike system. Since the system is driven by daily commuters we focus only on the morning rush hours between 7:00 AM to 11:00 AM during weekdays. We use taxi usage, weather and spatial variables as covariates to predict bike demand, and further analyze the influence of precipitation and day of week. We show that aggregating stations in neighborhoods can substantially improve predictions. The presented model can assist planners by predicting bike demand at a macroscopic level, between pairs of neigh-
Image Features Detection, Description and Matching

Feature detection, description and matching are essential components of various computer vision applications, thus they have received a considerable attention in the last decades. Several feature detectors anddescriptors have beenproposed in the literaturewith a variety of definitions forwhat kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection and description algorithms.
A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.
Short-circuit ruggedness of high-voltage IGBTs

The IGBT can run into different short-circuit types (SC I, SC II, SC III). Especially in SC II and III, an interaction between the gate drive unit and the IGBT takes place. A self-turn-off mechanism after short-circuit turn on can occur. Parasitic elements in the connection between the IGBT and the gate unit as well as asymmetrical wiring of devices connected in parallel are of effect to the short-circuit capability. In high-voltage IGBTs, filament formation can occur at short-circuit condition. Destructive measurements with its failure patterns and short-circuit protection methods are shown.
Real-Time Upper-Body Human Pose Estimation Using a Depth Camera

 Real-time Upper-body Human Pose Estimation using a Depth Camera Himanshu Prakash Jain, Anbumani Subramanian HP Laboratories HPL-2010-190 Haar cascade based detection, template matching, weighted distance transform and pose estimation Automatic detection and pose estimation of humans is an important task in HumanComputer Interaction (HCI), user interaction and event analysis. This paper presents a model based approach for detecting and estimating human pose by fusing depth and RGB color data from monocular view. The proposed system uses Haar cascade based detection and template matching to perform tracking of the most reliably detectable parts namely, head and torso. A stick figure model is used to represent the detected body parts. Then, the fitting is performed independently for each limb, using the weighted distance transform map. The fact that each limb is fitted independently speeds-up the fitting process and makes it robust, avoiding the combinatorial complexity problems that are common with these types of methods. The output is a stick figure model consistent with the pose of the person in the given input image. The algorithm works in real-time and is fully automatic and can detect multiple non-intersecting people. External Posting Date: November 21, 2010 [Fulltext] Approved for External Publication Internal Posting Date: November 21, 2010 [Fulltext]
The Document Components Ontology (DoCO)

The availability in machine-readable form of descriptions of the structure of documents, as well as of the document discourse (e.g. the scientific discourse within scholarly articles), is crucial for facilitating semantic publishing and the overall comprehension of documents by both users and machines. In this paper we introduce DoCO, the Document Components Ontology, an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements to describe both structural and rhetorical document components in RDF. In addition to describing the formal description of the ontology, this paper showcases its utility in practice in a variety of our own applications and other activities of the Semantic Publishing community that rely on DoCO to annotate and retrieve document components of scholarly articles.
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi-domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in singledomain dialogue tracking tasks.
Monitoring body positions and movements during sleep using WISPs

Sleep monitoring is very important for elderly people as inadequate and irregular sleep are often related to serious diseases such as depression and diabetes. In many cases, it is necessary to monitor the body positions and movements made while sleeping because of their relationships to particular diseases (i.e., sleep apnea and restless legs syndrome). Analyzing movements during sleep also helps in determining sleep quality and irregular sleeping patterns. This paper presents a sleep monitoring system based on the WISP platform - active RFID-based sensors equipped with accelerometers. We show how our system accurately infers fine-grained body positions from accelerometer data collected from the WISPs attached to the bed mattress. Movements and their duration are also detected by the system. We present the results of our empirical study from 10 subjects on three different mattresses in controlled experiments to show the accuracy of our inference algorithms. Finally, we evaluate the accuracy of the movement detection and body position inference for six nights on one subject, and compare these results with two baseline systems: one that uses bed pressure sensors and the other is an iPhone application.
Multilingual deep neural network based acoustic modeling for rapid language adaptation

This paper presents a study on multilingual deep neural network (DNN) based acoustic modeling and its application to new languages. We investigate the effect of phone merging on multilingual DNN in context of rapid language adaptation. Moreover, the combination of multilingual DNNs with Kullback-Leibler divergence based acoustic modeling (KL-HMM) is explored. Using ten different languages from the Globalphone database, our studies reveal that crosslingual acoustic model transfer through multilingual DNNs is superior to unsupervised RBM pre-training and greedy layer-wise supervised training. We also found that KL-HMM based decoding consistently outperforms conventional hybrid decoding, especially in low-resource scenarios. Furthermore, the experiments indicate that multilingual DNN training equally benefits from simple phoneset concatenation and manually derived universal phonesets.