nils-herrmann/clean_scirepeval huggingface dataset:
One VM to rule them all

Building high-performance virtual machines is a complex and expensive undertaking; many popular languages still have low-performance implementations. We describe a new approach to virtual machine (VM) construction that amortizes much of the effort in initial construction by allowing new languages to be implemented with modest additional effort. The approach relies on abstract syntax tree (AST) interpretation where a node can rewrite itself to a more specialized or more general node, together with an optimizing compiler that exploits the structure of the interpreter. The compiler uses speculative assumptions and deoptimization in order to produce efficient machine code. Our initial experience suggests that high performance is attainable while preserving a modular and layered architecture, and that new high-performance language implementations can be obtained by writing little more than a stylized interpreter.
Knowledge sharing : A review and directions for future research

a r t i c l e i n f o a b s t r a c t The success of knowledge management initiatives depends on knowledge sharing. This paper reviews qualitative and quantitative studies of individual-level knowledge sharing. Based on the literature review we developed a framework for understanding knowledge sharing research. The framework identifies five areas of emphasis of knowledge sharing research: organizational context, interpersonal and team characteristics, cultural characteristics, individual characteristics, and motivational factors. For each emphasis area the paper discusses the theoretical frameworks used and summarizes the empirical research results. The paper concludes with a discussion of emerging issues, new research directions, and practical implications of knowledge sharing research. Knowledge is a critical organizational resource that provides a sustainable competitive advantage in a competitive and dynamic economy (e. To gain a competitive advantage it is necessary but insufficient for organizations to rely on staffing and training systems that focus on selecting employees who have specific knowledge, skills, abilities, or competencies or helping employees acquire them (e.g., Brown & Duguid, 1991). Organizations must also consider how to transfer expertise and knowledge from experts who have it to novices who need to know (Hinds, Patterson, & Pfeffer, 2001). That is, organizations need to emphasize and more effectively exploit knowledge-based resources that already exist within the organization As one knowledge-centered activity, knowledge sharing is the fundamental means through which employees can contribute to knowledge application, innovation, and ultimately the competitive advantage of the organization (Jackson, Chuang, Harden, Jiang, & Joseph, 2006). Knowledge sharing between employees and within and across teams allows organizations to exploit and capitalize on knowledge-based resources Research has shown that knowledge sharing and combination is positively related to reductions in production costs, faster completion of new product development projects, team performance, firm innovation capabilities, and firm performance including sales growth and revenue from new products and services (e. Because of the potential benefits that can be realized from knowledge sharing, many organizations have invested considerable time and money into knowledge management (KM) initiatives including the development of knowledge management systems (KMS) which use state-of-the-art technology to facilitate the collection, storage, and distribution of knowledge. However, despite these investments it has been estimated that at least $31.5 billion are lost per year by Fortune 500
Consensus clustering in complex networks

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.
A hierarchical control approach for a quadrotor tail-sitter VTOL UAV and experimental verification

We present a hierarchical control approach that can be used to fulfill autonomous flight, including vertical takeoff, landing, hovering, transition, and level flight, of a quadrotor tail-sitter vertical takeoff and landing unmanned aerial vehicle (VTOL UAV). A unified attitude controller, together with a moment allocation scheme between elevons and motor differential thrust, is developed for all flight modes. A comparison study via real flight tests is performed to verify the effectiveness of using elevons in addition to motor differential thrust. With the well-designed switch scheme proposed in this paper, the aircraft can transit between different flight modes with negligible altitude drop or gain. Intensive flight tests have been performed to verify the effectiveness of the proposed control approach in both manual and fully autonomous flight mode.
Digital Image Authentication From JPEG Headers

It is often desirable to determine if an image has been modified in any way from its original recording. The JPEG format affords engineers many implementation trade-offs which give rise to widely varying JPEG headers. We exploit these variations for image authentication. A camera signature is extracted from a JPEG image consisting of information about quantization tables, Huffman codes, thumbnails, and exchangeable image file format (EXIF). We show that this signature is highly distinct across 1.3 million images spanning 773 different cameras and cell phones. Specifically, 62% of images have a signature that is unique to a single camera, 80% of images have a signature that is shared by three or fewer cameras, and 99% of images have a signature that is unique to a single manufacturer. The signature of Adobe Photoshop is also shown to be unique relative to all 773 cameras. These signatures are simple to extract and offer an efficient method to establish the authenticity of a digital image.
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling

Access to word–sentiment associations is useful for many applications, including sentiment analysis, stance detection, and linguistic analysis. However, manually assigning finegrained sentiment association scores to words has many challenges with respect to keeping annotations consistent. We apply the annotation technique of Best–Worst Scaling to obtain real-valued sentiment association scores for words and phrases in three different domains: general English, English Twitter, and Arabic Twitter. We show that on all three domains the ranking of words by sentiment remains remarkably consistent even when the annotation process is repeated with a different set of annotators. We also, for the first time, determine the minimum difference in sentiment association that is perceptible to native speakers of a language.
Inferring character from faces: a developmental study.

Human adults attribute character traits to faces readily and with high consensus. In two experiments investigating the development of face-to-trait inference, adults and children ages 3 through 10 attributed trustworthiness, dominance, and competence to pairs of faces. In Experiment 1, the attributions of 3- to 4-year-olds converged with those of adults, and 5- to 6-year-olds' attributions were at adult levels of consistency. Children ages 3 and above consistently attributed the basic mean/nice evaluation not only to faces varying in trustworthiness (Experiment 1) but also to faces varying in dominance and competence (Experiment 2). This research suggests that the predisposition to judge others using scant facial information appears in adultlike forms early in childhood and does not require prolonged social experience.
An experience report on using gamification in technical higher education

Technical universities, especially in Europe, are facing an important challenge in attracting more diverse groups of students, and in keeping the students they attract motivated and engaged in the curriculum. We describe our experience with gamification, which we loosely define as a teaching technique that uses social gaming elements to deliver higher education. Over the past three years, we have applied gamification to undergraduate and graduate courses in a leading technical university in the Netherlands and in Europe. Ours is one of the first long-running attempts to show that gamification can be used to teach technically challenging courses. The two gamification-based courses, the first-year B.Sc. course Computer Organization and an M.Sc.-level course on the emerging technology of Cloud Computing, have been cumulatively followed by over 450 students and passed by over 75% of them, at the first attempt. We find that gamification is correlated with an increase in the percentage of passing students, and in the participation in voluntary activities and challenging assignments. Gamification seems to also foster interaction in the classroom and trigger students to pay more attention to the design of the course. We also observe very positive student assessments and volunteered testimonials, and a Teacher of the Year award.
Invariant visual representation by single neurons in the human brain

It takes a fraction of a second to recognize a person or an object even when seen under strikingly different conditions. How such a robust, high-level representation is achieved by neurons in the human brain is still unclear. In monkeys, neurons in the upper stages of the ventral visual pathway respond to complex images such as faces and objects and show some degree of invariance to metric properties such as the stimulus size, position and viewing angle. We have previously shown that neurons in the human medial temporal lobe (MTL) fire selectively to images of faces, animals, objects or scenes. Here we report on a remarkable subset of MTL neurons that are selectively activated by strikingly different pictures of given individuals, landmarks or objects and in some cases even by letter strings with their names. These results suggest an invariant, sparse and explicit code, which might be important in the transformation of complex visual percepts into long-term and more abstract memories.
PolyUnpack: Automating the Hidden-Code Extraction of Unpack-Executing Malware

Modern malware often hide the malicious portion of their program code by making it appear as data at compile-time and transforming it back into executable code at runtime. This obfuscation technique poses obstacles to researchers who want to understand the malicious behavior of new or unknown malware and to practitioners who want to create models of detection and methods of recovery. In this paper we propose a technique for automating the process of extracting the hidden-code bodies of this class of malware. Our approach is based on the observation that sequences of packed or hidden code in a malware instance can be made self-identifying when its runtime execution is checked against its static code model. In deriving our technique, we formally define the unpack-executing behavior that such malware exhibits and devise an algorithm for identifying and extracting its hidden-code. We also provide details of the implementation and evaluation of our extraction technique; the results from our experiments on several thousand malware binaries show our approach can be used to significantly reduce the time required to analyze such malware, and to improve the performance of malware detection tools.
FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering

Aspect-based opinion mining from online reviews has attracted a lot of attention recently. Given a set of reviews, the main task of aspect-based opinion mining is to extract major aspects of the items and to infer the latent aspect ratings from each review. However, users may have different preferences which might lead to different opinions on the same aspect of an item. Even if fine-grained aspect rating analysis is provided for each review, it is still difficult for a user to judge whether a specific aspect of an item meets his own expectation. In this paper, we study the problem of estimating personalized sentiment polarities on different aspects of the items. We propose a unified probabilistic model called Factorized Latent Aspect ModEl (FLAME), which combines the advantages of collaborative filtering and aspect based opinion mining. FLAME learns users' personalized preferences on different aspects from their past reviews, and predicts users' aspect ratings on new items by collective intelligence. Experiments on two online review datasets show that FLAME outperforms state-of-the-art methods on the tasks of aspect identification and aspect rating prediction.
A Survey on Time-of-Flight Stereo Fusion

Due to the demand for depth maps of higher quality than possible with a single depth imaging technique today, there has been an increasing interest in the combination of different depth sensors to produce a “super-camera” that is more than the sum of the individual parts. In this survey paper, we give an overview over methods for the fusion of Time-ofFlight (ToF) and passive stereo data as well as applications of the resulting high quality depth maps. Additionally, we provide a tutorial-based introduction to the principles behind ToF stereo fusion and the evaluation criteria used to benchmark these methods.
1 9 INPUT TECHNOLOGIES AND TECHNIQUES

s. Putz-Anderson, V. 1988. Cumulative trauma disorders: A manual for musculoskeletal diseases of the upper limbs. Bristol, PA: Taylor & Francis. Ramos, G., and R. Balakrishnan. 2005.  Fluid Zooming and Sliding for High Precision Parameter Manipulation. Paper read at UIST 2006. Ramos, G., and R. Balakrishnan. 2006. Pressure Marks. Paper read at UNPUBLISHED MANUSCRIPT (under review). Ramos, G., M. Boulos, and R. Balakrishnan. 2004.  Paper read at CHI 2004. Raskar, R., P. Beardsley, J. van Baar, Y. Wang, P. Dietz, J. Lee, D. Leigh, and T. Willwacher. 2004.  interacting with a self-describing world via photosensing wireless tags and projectors. 23 (3): 406-415. Raskar, Ramesh, Jeroen van Baar, Paul Beardsley, Thomas Willwacher, Srinivas Rao, and Clifton Forlines. 2005. iLamps: geometrically aware and self-configuring projectors.    New Directions for Designing Interactive Systems: ACM Press. Rekimoto, J., Y. Ayatsuka, M. Kohno, and Haruo Oba. 2003.  A Direct Manipulation Technique for Wireless Networking. Paper read at INTERACT 2003. Rekimoto, Jun. 1996. Tilting operations for small screen interfaces.  A multiple-device approach for supporting whiteboard based interaction.  Effects of forearm pronation/supination on carpal tunnel pressure. J Hand Surgery 23 (1):38-42. Rime, B., and L. Schiaratura. 1991. Gesture and speech. In Fundamentals of Nonverbal Behaviour.  Press Syndacate of the University of Cambridge. Rutledge, J., and T. Selker. 1990. Force-to-Motion Functions for Pointing.  The IFIP Conf. on Human-Computer Interaction, Aug. 27-31. Rutledge, Joseph D., and Ted Selker. 1990. Force-to-motion functions for pointing. In Proceedings of the IFIP TC13 Third Interational Conference on Human-Computer Interaction: North-Holland Publishing Co. Ryall, Kathy, Clifton Forlines, Chia Shen, and Meredith Ringel Morris. 2004. Exploring the effects of group size and table size on interactions with tabletop shared-display groupware. In  Chicago, Illinois, USA: ACM. Saponas, T. Scott, Desney S. Tan, Dan Morris, Ravin Balakrishnan, Jim Turner, and James A. Landay. 2009. Enabling always-available input with muscle-computer interfaces. In Proceedings of the 22nd annual ACM symposium on User interface software and technology. Victoria, BC, Canada: ACM. Saund, Eric, and Edward Lank. 2003. Stylus input and editing without prior selection of mode. In Proceedings of the 16th annual ACM symposium on User interface software and technology. Vancouver,  Speech and Audio Interaction for Contextual Messaging in Nomadic Environments. ACM Transactions on Computer-Human Interaction 7 (3):353-383.
A Deep Hierarchical Approach to Lifelong Learning in Minecraft

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledgebase. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et al. 2015) for learning skills. Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et al. 2015) in sub-domains of Minecraft.
Mind wandering and reading comprehension: examining the roles of working memory capacity, interest, motivation, and topic experience.

Individual differences in mind wandering and reading comprehension were examined in the current study. In particular, individual differences in mind wandering, working memory capacity, interest in the current topic, motivation to do well on the task, and topic experience and their relations with reading comprehension were examined in the current study. Using confirmatory factor analysis and structural equation modeling it was found that variation in mind wandering while reading was influenced by working memory capacity, topic interest, and motivation. Furthermore, these same factors, along with topic experience, influenced individual differences in reading comprehension. Importantly, several factors had direct effects on reading comprehension (and mind wandering), while the relation between reading comprehension (and mind wandering) and other factors occurred via indirect effects. These results suggest that both domain-general and domain-specific factors contribute to mind wandering while reading and to reading comprehension.
Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study.
Two-Stage License Plate Detection Using Gentle Adaboost and SIFT-SVM

This paper presents a two-stage method to detect license plates in real world images. To do license plate detection (LPD), an initial set of possible license plate character regions are first obtained by the first stage classifier and then passed to the second stage classifier to reject non-character regions. 36 Adaboost classifiers (each trained with one alpha-numerical character, i.e. A..Z, 0..9) serve as the first stage classifier. In the second stage, a support vector machine (SVM) trained on scale-invariant feature transform (SIFT) descriptors obtained from training sub-windows were employed. A recall rate of 0.920792 and precision rate of 0.90185 was obtained.
Bias in analytic research.

CASE-CONTF~OL studies are highly attractive. They can be executed quickly and at low cost, even when the disorders of interest are rare. Furthermore, the execution of pilot case-control studies is becoming automated; strategies have been devised for the ‘computer scanning’ of large files of hospital admission diagnoses and prior drug exposures, with more detailed analyses carried out in the same data set on an ad hoc basis [l]. As evidence of their growing popularity, when one original article was randomly selected from each issue of The New England Journal of Medicine, The Lancet, and the Journal of the American Medical Association for the years, 1956, 1966 and 1976, the proportion reporting case-control analytic studies increased fourfold over these two decades (2-8”;) whereas the proportion reporting cohort analytic studies fell by half (30-159); incidentally, a general trend toward fewer study subjects but more study authors was also noted . If an ebullition of case-control studies is in progress, a review of their merits and shortcomings is of more than academic interest, and this symposium was well-timed. Because this meeting also coincided with the completion of some work we had been doing on biases in analytic research (, I offered to summarize a portion of this work for presentation and discussion here. A first draft of a catalog of biases which may distort the design, execution, analysis and interpretation of research appears as an appendix to this paper (additions, corrections and citations of examples would be welcomed by the author). For this paper, I have considered those biases which arise in analytic studies and have focused on two subsets which affect the specification and selection of the study sample and the measurement of exposures and outcomes, since these attributes most clearly distinguish the case-control study from its relatives.  Furthermore. I have included occasional discussions of cohort analytic studies because they represent a common, alternative, subexperimental approach to determining causation. Finally, after describing the prospects for the prevention (or at least the measurement) of these biases in these two forms of analytic studies, this paper closes with suggestions for further methodologic research.
Sparse Communication for Distributed Gradient Descent

We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense updates. Gradient updates are positively skewed as most updates are near zero, so we map the 99% smallest updates (by absolute value) to zero then exchange sparse matrices. This method can be combined with quantization to further improve the compression. We explore different configurations and apply them to neural machine translation and MNIST image classification tasks. Most configurations work on MNIST, whereas different configurations reduce convergence rate on the more complex translation task. Our experiments show that we can achieve up to 49% speed up on MNIST and 22% on NMT without damaging the final accuracy or BLEU.