nils-herrmann/clean_scirepeval huggingface dataset:
Collective Entity Disambiguation with Structured Gradient Tree Boosting

We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many topperforming natural language processing systems. Surprisingly, most works limit the use of gradient tree boosting as a tool for regular classification or regression problems, despite the structured nature of language. To the best of our knowledge, our work is the first one that employs the structured gradient tree boosting (SGTB) algorithm for collective entity disambiguation. By defining global features over previous disambiguation decisions and jointly modeling them with local features, our system is able to produce globally optimized entity assignments for mentions in a document. Exact inference is prohibitively expensive for our globally normalized model. To solve this problem, we propose Bidirectional Beam Search with Gold path (BiBSG), an approximate inference algorithm that is a variant of the standard beam search algorithm. BiBSG makes use of global information from both past and future to perform better local search. Experiments on standard benchmark datasets show that SGTB significantly improves upon published results. Specifically, SGTB outperforms the previous state-of-the-art neural system by near 1% absolute accuracy on the popular AIDA-CoNLL
A dataset of human decision-making in teamwork management

Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under different situations have not yet been fully explored, partially due to a lack of detailed large-scale data. In this paper, we describe a multi-faceted large-scale dataset to bridge this gap. It is derived from a game simulating complex project management processes. It presents the participants with different conditions in terms of team members' capabilities and task characteristics for them to exhibit their decision-making strategies. The dataset contains detailed data reflecting the decision situations, decision strategies, decision outcomes, and the emotional responses of 1,144 participants from diverse backgrounds. To our knowledge, this is the first dataset simultaneously covering these four facets of decision-making. With repeated measurements, the dataset may help establish baseline variability of decision-making in teamwork management, leading to more realistic decision theoretic models and more effective decision support approaches.
DO OFFENSIVE WORDS HARM PEOPLE ?

The harm thesis—the assumption that words harm people—is a defining feature of sexual harassment, hate speech, verbal abuse, and obscene telephone call (OTC) offenses. This thesis ignores the possibility that swearing can be advantageous, cathartic, or an acceptable substitute for physical aggression. Observational data, courtroom evidence and verbal abuse research reviewed here produce conflicting conclusions on the question of harm. The best evidence of harm resides in harassment and OTC studies, but verbal abuse research is indeterminate because of flawed research methodology. Public swearing research reveals that swearing is a common conversational practice resulting in no obvious harm. “Common sense” (folk psychology) views of swearing are mistaken and inadequate for some decisions regarding harm. Meanwhile, efforts to restrict speech in media and instructional settings continue, despite the lack of a convincing need to do so. Harm from offensive speech is contextually determined; therefore attempts to restrict speech on a universal basis are misguided. Psychologists’ research needs to be informed by public policy and courtroom practices, and public policy and litigation need to be better informed by psychologists’ research.
Bisimulation Minimisation Mostly Speeds Up Probabilistic Model Checking

This paper studies the effect of bisimulation minimisation in model checking of monolithic discrete-time and continuous-time Markov chains as well as variants thereof with rewards. Our results show that—as for traditional model checking—enormous state space reductions (up to logarithmic savings) may be obtained. In contrast to traditional model checking, in many cases, the verification time of the original Markov chain exceeds the quotienting time plus the verification time of the quotient. We consider probabilistic bisimulation as well as versions thereof that are tailored to the property to be checked.
Severity Analyses of Single-Vehicle Crashes Based on Rough Set Theory

A single-vehicle crash is a typical pattern of traffic accidents and tends to cause heavy loss. The purpose of this study is to identify the factors significantly influencing single-vehicle crash injury severity, using a data selected from Beijing city for a 4-year period. Rough set theory was applied to complete the injury severity analysis, and followed by applying cross-validation method to estimate the prediction accuracy of extraction rules. Results show that it is effective for analyzing the severity of Single-vehicle crashes with rough set theory.
Coloring-based coalescing for graph coloring register allocation

Graph coloring register allocation tries to minimize the total cost of spilled live ranges of variables. Live-range splitting and coalescing are often performed before the coloring to further reduce the total cost. Coalescing of split live ranges, called sub-ranges, can decrease the total cost by lowering the interference degrees of their common interference neighbors. However, it can also increase the total cost because the coalesced sub-ranges can become uncolorable. In this paper, we propose coloring-based coalescing, which first performs trial coloring and next coalesces all copyrelated sub-ranges that were assigned the same color. The coalesced graph is then colored again with the graph coloring register allocation. The rationale is that coalescing of differently colored sub-ranges could result in spilling because there are some interference neighbors that prevent them from being assigned the same color. Experiments on Java programs show that the combination of live-range splitting and coloring-based coalescing reduces the static spill cost by more than 6% on average, comparing to the baseline coloring without splitting. In contrast, well-known iterated and optimistic coalescing algorithms, when combined with splitting, increase the cost by more than 20%. Coloring-based coalescing improves the execution time by up to 15% and 3% on average, while the existing algorithms improve by up to 12% and 1% on average.
BOSCH RARE SOUND EVENTS DETECTION SYSTEMS FOR DCASE 2017 CHALLENGE

In this report, we describe three systems designed at BOSCH for rare audio sound events detection task of DCASE 2017 challenge. The first system is an end-to-end audio event segmentation using embeddings based on deep convolutional neural network (DCNN) and deep recurrent neural network (DRNN) trained on Mel-filter banks and spectogram features. Both system 2 and 3 contain two parts: audio event tagging and audio event segmentation. Audio event tagging selects the positive audio recordings (containing audio events), which are later processed by the audio segmentation part. Feature selection method has been deployed to select a subset of features in both systems. System 2 employs Dilated convolutional neural network on the selected features for audio tagging, and an audio-codebook approach to convert audio features to audio vectors (Audio2vec system) which are then passed to an LSTM network for audio events boundary prediction. System 3 is based on multiple instance learning problem using variational auto encoder (VAE) to perform audio event tagging and segmentation. Similar to system 2, here a LSTM network is used for audio segmentation. Finally, we have utilized models based on score-fusion among different systems to improve the final results.
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games

Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on cooccurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of using general purpose word-embeddings for language learning in robots. We propose using text-based games as a proxy to evaluating word embedding on real robots. Based in a risk-reward setting, we review the effectiveness of the embeddings in navigating tasks in fantasy games, as an approximation to their performance on more complex scenarios, like language assisted robot navigation.
X-band FMCW radar system with variable chirp duration

For application in a short range ground based surveillance radar a combination between frequency modulated continuous wave (FMCW) transmit signals and a receive antenna array system is considered in this paper. The target echo signal is directly down converted by the instantaneous transmit frequency. The target range R will be estimated based on the measured frequency shift fB between transmit and receive signal. Due to an extremely short chirp duration Tchirp, the target radial velocity υτ has only a very small influence to the measured frequency shift fB. Therefore the radial velocity υτ will not be measured inside a single FMCW chirp but in a sequence of chirp signals and inside each individual range gate. Finally, the target azimuth angle is calculated utilizing the receive antenna array and applying a digital beamforming scheme. Furthermore, in order to unambiguously measure even high radial velocities, a variable chirp duration is proposed on a dwell to dwell basis.
Effects of Lectures with PowerPoint or Prezi Presentations on Cognitive Load , Recall , and Conceptual Learning

Received 17.02.2016 Received in revised form 12.07.2016 Accepted 07.08.2016 Available online Tarih girmek için burayı tıklatın. The purpose of this study is to investigate and compare the effects of two types of presentations as created by PowerPoint or Prezi, which are used during lectures at the higher education level, on the students' cognitive load, the level of recall of the learning and conceptual learning. Presentations prepared by PowerPoint have linear slides with a limited canvas, while Prezi allows for non-linear, multiple paths and unlimited canvas design. This study is of a quasi-experimental design. The participants were 50 university students enrolled in a Computer Programming program at a vocational school. Data were collected using the Cognitive Load Scale, achievement tests and concept maps. According to the results, students lectured by presentations created by Prezi had more conceptual learning and less cognitive load whilst teaching computer network systems’ content. Recall the level of learning was measured by an achievement test which did not significantly differ across the groups. Prezi may be a better alternative for more conceptual learning and to reduce the cognitive load.
Do burnout and work engagement predict depressive symptoms and life satisfaction? A three-wave seven-year prospective study.

Burnout and work engagement have been viewed as opposite, yet distinct states of employee well-being. We investigated whether work-related indicators of well-being (i.e. burnout and work engagement) spill-over and generalize to context-free well-being (i.e. depressive symptoms and life satisfaction). More specifically, we examined the causal direction: does burnout/work engagement lead to depressive symptoms/life satisfaction, or the other way around?



Three surveys were conducted. In 2003, 71% of all Finnish dentists were surveyed (n=3255), and the response rate of the 3-year follow-up was 84% (n=2555). The second follow-up was conducted four years later with a response rate of 86% (n=1964). Structural equation modeling was used to investigate the cross-lagged associations between the study variables across time.



Burnout predicted depressive symptoms and life dissatisfaction from T1 to T2 and from T2 to T3. Conversely, work engagement had a negative effect on depressive symptoms and a positive effect on life satisfaction, both from T1 to T2 and from T2 to T3, even after adjusting for the impact of burnout at every occasion.



The study was conducted among one occupational group, which limits its generalizability.



Work-related well-being predicts general wellbeing in the long-term. For example, burnout predicts depressive symptoms and not vice versa. In addition, burnout and work engagement are not direct opposites. Instead, both have unique, incremental impacts on life satisfaction and depressive symptoms.
Context-free Attacks Using Keyboard Acoustic Emanations

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.
Nested LSTM : Modeling Taxonomy and Temporal Dynamics in Location-Based Social Network

“Is there any pattern in location-based, mobile check-in activities?” “If yes, is it possible to accurately forecast the intention of a user’s next check-in, given his/her check-in history?” To answer these questions, we crawl and analyze probably the largest mobile checkin datasets, containing 20million check-in activities from 0.4million users. We provide two observations—“work-n-relax” and “diurnaln-nocturnal”—showing that the intentions of users’ check-ins are strongly associated with time. Furthermore, the category of each check-in venue, which reveals users’ intentions, has structure and forms taxonomy. In this paper, we proposeNested LSTM that takes both (a) time and (b) taxonomy structure from check-in sequences into consideration, providing accurate predictions on the category of a user’s next check-in location. Nested LSTM also projects each category into an embedding space, providing a new representation with strong semantic meanings. Experimental results are poised to demonstrate the effectiveness of the proposed Nested LSTM: (a) Nested LSTM improves Accuracy@5 by 4.22% on average, and (b) Nested LSTM learns a better taxonomy embedding for clustering categories, which improves Silhouette Coefficient by 1.5X. Both results (a)(b) are compared with LSTM-based, state-of-the-art approaches.
Cross-Domain 3D Equivariant Image Embeddings

Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations making them ideally suited for applications where 3D data may be observed in arbitrary orientations. In this paper we learn 2D image embeddings with a similar equivariant structure: embedding the image of a 3D object should commute with rotations of the object. We introduce a crossdomain embedding from 2D images into a spherical CNN latent space. Our model is supervised only by target embeddings obtained from a spherical CNN pretrained for 3D shape classification. The trained model learns to encode images with 3D shape properties and is equivariant to 3D rotations of the observed object. We show that learning only a rich embedding for images with appropriate geometric structure is in and of itself sufficient for tackling numerous applications. Evidence from two different applications, relative pose estimation and novel view synthesis, demonstrates that equivariant embeddings are sufficient for both applications without requiring any task-specific supervised training.
Approximating displacement with the body velocity integral

In this paper, we present a technique for approximating the net displacement of a locomoting system over a gait without directly integrating its equations of motion. The approximation is based on a volume integral, which, among other benefits, is more open to optimization by algorithm or inspection than is the full displacement integral. Specifically, we develop the concept of a body velocity integral (BVI), which is computable over a gait as a volume integral via Stokes’s theorem. We then demonstrate that, given an appropriate choice of coordinates, the BVI for a gait approximates the displacement of the system over that gait. This consideration of coordinate choice is a new approach to locomotion problems, and provides significantly improved results over past attempts to apply Stokes’s theorem to gait analysis.
Finding minimal enclosing boxes

The problem of finding minimal volume boxes circumscribing a given set of three-dimensional points is investigated. It is shown that it is not necessary for a minimum volume box to have any sides flush with a face of the convex hull of the set of points, which makes a naive search problematic. Nevertheless, it is proven that at least two adjacent box sides are flush with edges of the hull, and this characterization enables anO(n 3) algorithm to find all minimal boxes for a set ofn points.
A Wideband Magnetoelectric Dipole Antenna With Microstrip Line Aperture-Coupled Excitation

In this communication, a novel wideband magnetoelectric (ME) dipole antenna is proposed and investigated. A microstrip line aperture-coupled feeding structure is used to excite the antenna. This simple feeding structure can significantly enhance the impedance bandwidth. An antenna prototype is fabricated and exemplified to validate the proposed concept. Measured results show that the proposed ME dipole antenna with microstrip line aperture-coupled excitation achieves an impedance bandwidth of 85% for VSWR < 2 from 2.5 to 6.2 GHz. Further, a measured maximum boresight gain up to 8 dBi is obtained and a 1 dB gain bandwidth is about 67% from 2.5 to 5 GHz. The antenna radiation patterns perform well over the 1 dB gain bandwidth range. A low cross-polarization level is obtained over the operating band.
Optimum Placement of DF Antenna Elements for Accurate DOA Estimation in a Harsh Platform Environment

This paper presents a systematic approach to the optimum placement of direction finding (DF) antennas in a harsh platform environment. A circular array with omni-directional elements is considered for azimuthal direction of arrival (DOA) estimation based on the multiple signal classification (MUSIC) algorithm. The element positions are optimized by the use of a genetic algorithm (GA) in conjunction with the FEKO electromagnetic simulator. The proposed approach is first tested with a generic platform shape. It is then extended to a more realistic military aircraft model with a strong scattering object. The optimized results show a significant performance improvement over the uniform circular array in both platform environments.
Extension of trajectory planning in parameterized spaces to articulated vehicles

The main objective of this research is to study a novel method for safe maneuvering of articulated vehicles in warehouses. The presented method extends the concept of probabilistic planning on manifolds to articulated vehicles, which will be capable of driving, maneuvering and performing obstacle avoidance in any scenario. The proposed technique involves the extension of a parameterized space, developed for the reactive navigation of differential driven vehicles, to include an additional degree of freedom and use a probabilistic planner to calculate kinematically feasible trajectories. As a result, the algorithm is able to successfully generate maneuvers for an articulated truck and to navigate towards specific target points. The approach was validated using three problems representing different driving scenarios, demonstrating the possible utilization of the method in real-case scenarios. The solutions have been further benchmarked on multiple runs to evaluate success rate and to demonstrate the validity of the algorithm.
Functional brain network efficiency predicts intelligence.

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.
Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator’s business models and provide a wide range of new markets to enter. This paper analyzes the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them, operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analyzed. Conclusions show that the operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.