nils-herrmann/clean_scirepeval huggingface dataset:
Discovering and Ranking Semantic Associations over a Large RDF Metabase

Information retrieval over semantic metadata has recently received a great amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this data is becoming an active research topic. Just as ranking of documents is a critical component of today’s search engines, the ranking of relationships will be essential in tomorrow’s semantic analytics engines. Building upon our recent work on specifying these semantic relationships, which we refer to as Semantic Associations, we demonstrate a system where these associations are discovered among a large semantic metabase represented in RDF. Additionally we employ ranking techniques to provide users with the most interesting and relevant results.
A survey on sensor networks

Wireless micro sensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.  Collaborative signal processing, micro sensors, net-work routing and control, querying and tasking, sensor networks, tracking and classification, wireless networks.
Optimization of Tree Ensembles

Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are controllable and are also decision variables. In this paper, we study the problem of tree ensemble optimization: given a tree ensemble that predicts some dependent variable using controllable independent variables, how should we set these variables so as to maximize the predicted value? We formulate the problem as a mixed-integer optimization problem. We theoretically examine the strength of our formulation, provide a hierarchy of approximate formulations with bounds on approximation quality and exploit the structure of the problem to develop two large-scale solution methods, one based on Benders decomposition and one based on iteratively generating tree split constraints. We test our methodology on real data sets, including two case studies in drug design and customized pricing, and show that our methodology can efficiently solve large-scale instances to near or full optimality, and outperforms solutions obtained by heuristic approaches. In our drug design case, we show how our approach can identify compounds that efficiently trade-off predicted performance and novelty with respect to existing, known compounds. In our customized pricing case, we show how our approach can efficiently determine optimal store-level prices under a random forest model that delivers excellent predictive accuracy.
Si-Based Technologies for mmWave Automotive Radar

Continued advancements in Si-based technologies - in particular SiGe BiCMOS technologies - have enabled mmWave integrated circuits designed for 77GHz automotive radar systems to reach production-level maturity. This paper will discuss the technology requirements for mmWave automotive radar products, and also present the evolution of the respective key figures of merit. Si-based CMOS challenges and opportunities will also be discussed.
Drift Time Measurement in the ATLAS Liquid Argon Electromagnetic Calorimeter using Cosmic Muons

The ionization signals in the liquid argon of the ATLAS electromagnetic calorimeter are studied in detail using cosmic muons. In particular, the drift time of the ionization electrons is measured and used to assess the intrinsic uniformity of the calorimeter gaps and estimate its impact on the constant term of the energy resolution. The drift times of electrons in the cells of the second layer of the calorimeter are uniform at the level of 1.3% in the barrel and 2.8% in the endcaps. This leads to an estimated contribution to the constant term of (0.29+0.05 −0.04)% in the barrel and (0.54 +0.06 −0.04)% in the endcaps. The same data are used to measure the drift vee-mail: atlas.secretariat@cern.ch locity of ionization electrons in liquid argon, which is found to be 4.61 ± 0.07 mm/μs at 88.5 K and 1 kV/mm.
Online spatial concept and lexical acquisition with simultaneous localization and mapping

In this paper, we propose an online learning algorithm based on a Rao-Blackwellized particle filter for spatial concept acquisition and mapping. We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in the theoretical framework of the Bayesian generative model. The proposed method can simultaneously learn place categories and lexicons while incrementally generating an environmental map. Furthermore, the proposed method has scene image features and a language model added to SpCoA. In the experiments, we tested online learning of spatial concepts and environmental maps in a novel environment of which the robot did not have a map. Then, we evaluated the results of online learning of spatial concepts and lexical acquisition. The experimental results demonstrated that the robot was able to more accurately learn the relationships between words and the place in the environmental map incrementally by using the proposed method.
Use of LEGO as a therapeutic medium for improving social competence.

A repeated-measures, waiting list control design was used to assess efficacy of a social skills intervention for autistic spectrum children focused on individual and group LEGO play. The intervention combined aspects of behavior therapy, peer modeling and naturalistic communication strategies. Close interaction and joint attention to task play an important role in both group and individual therapy activities. The goal of treatment was to improve social competence (SC) which was construed as reflecting three components: (1) motivation to initiate social contact with peers; (2) ability to sustain interaction with peers for a period of time: and (3) overcoming autistic symptoms of aloofness and rigidity. Measures for the first two variables were based on observation of subjects in unstructured situations with peers; and the third variable was assessed using a structured rating scale, the SI subscale of the GARS. Results revealed significant improvement on all three measures at both 12 and 24 weeks with no evidence of gains during the waiting list period. No gender differences were found on outcome, and age of clients was not correlated with outcome. LEGO play appears to be a particularly effective medium for social skills intervention, and other researchers and clinicians are encouraged to attempt replication of this work, as well as to explore use of LEGO in other methodologies, or with different clinical populations.
The use of soft silicone solid implant molded intraoperatively for pectus excavatum surgical repair

To describe a new surgical technique to treat pectus excavatum utilizing low hardness solid silicone block that can be carved during the intraoperative period promoting a better aesthetic result.



Between May 1994 and February 2013, 34 male patients presenting pectus excavatum were submitted to surgical repair with the use of low hardness solid silicone block, 10 to 30 Shore A. A block-shaped parallelepiped was used with height and base size coinciding with those of the bone defect. The block was carved intraoperatively according to the shape of the dissected space. The patients were followed for a minimum of 120 days postoperatively. The results and the complications were recorded.



From the 34 patients operated on, 28 were primary surgeries and 6 were secondary treatment, using other surgical techniques, bone or implant procedures. Postoperative complications included two case of hematomas and eight of seromas. It was necessary to remove the implant in one patient due to pain, and review surgery was performed in another to check prothesis dimensions. Two patients were submitted to fat grafting to improve the chest wall contour. The result was considered satisfactory in 33 patients.



The procedure proved to be fast and effective. The results of carved silicone block were more effective for allowing a more refined contour as compared to custom made implants.
Are hardware performance counters a cost effective way for integrity checking of programs

In this paper, we propose to use hardware performance counters (HPC) to detect malicious program modifications at load time (static) and at runtime (dynamic). HPC have been used for program characterization and testing, system testing and performance evaluation, and as side channels. We propose to use HPCs for static and dynamic integrity checking of programs.. The main advantage of HPC-based integrity checking is that it is almost free in terms of hardware cost; HPCs are built into almost all processors. The runtime performance overhead is minimal because we use the operating system for integrity checking, which is called anyway for process scheduling and other interrupts. Our preliminary results confirm that HPC very efficiently detect program modifications with very low cost.
Gaze Estimation in the 3 D Space Using RGB-D sensors Towards Head-Pose And User Invariance

We address the problem of 3D gaze estimation within a 3D environment from remote sensors,which is highly valuable for applications in human-human and human-robot interactions. To the contrary of most previous works, which are limited to screen gazing applications, we propose to leverage the depth data of RGB-D cameras to perform an accurate head pose tracking, acquire head pose invariance through a 3D rectification process that renders head pose dependent eye images into a canonical viewpoint, and computes the line-ofsight in 3D space. To address the low resolution issue of the eye image resulting from the use of remote sensors, we rely on the appearance based gaze estimation paradigm, which has demonstrated robustness against this factor. In this context, we do a comparative study of recent appearance based strategies within our framework, study the generalization of these methods to unseen individual, and propose a cross-user eye image alignment technique relying on the direct registration of gaze-synchronized eye images. We demonstrate the validity of our approach through extensive gaze estimation experiments on a public dataset as well as a gaze coding task applied to natural job interviews.
Opportunistic storage maintenance

Storage systems rely on maintenance tasks, such as backup and layout optimization, to ensure data availability and good performance. These tasks access large amounts of data and can significantly impact foreground applications. We argue that storage maintenance can be performed more efficiently by prioritizing processing of data that is currently cached in memory. Data can be cached either due to other maintenance tasks requesting it previously, or due to overlapping foreground I/O activity.
 We present Duet, a framework that provides notifications about page-level events to maintenance tasks, such as a page being added or modified in memory. Tasks use these events as hints to opportunistically process cached data. We show that tasks using Duet can complete maintenance work more efficiently because they perform fewer I/O operations. The I/O reduction depends on the amount of data overlap with other maintenance tasks and foreground applications. Consequently, Duet's efficiency increases with additional tasks because opportunities for synergy appear more often.
CALVIS32: Customizable assembly language visualizer and simulator for intel x86-32 architecture

This research paper discusses the extensibility and customizability of CALVIS32, a simulation tool that was developed to visualize x86-32 assembly programs. CALVIS32 can support various sets of instructions with limitations aligned with the developer's knowledge in assembly overall. This is done by means of adjusting configuration files affecting the types of assembly instructions, registers, memory sizes that could be used in CALVIS32. After configuring the simulation environment, CALVIS32 can then simulate an assembly program being executed in the specified environment. Also, CALVIS32 will check for errors, and display crucial information such as register, flag, and memory values that were affected during the simulation. Overall, extended assembly instructions can be managed and created with CALVIS32, which would be essential in today's ideology of extensibility and reusability of a software system in the industrial world.
Sparse Online Learning via Truncated Gradient

We propose a general method called truncated gradient to induce sparsity in the weights of online learning algorithms with convex loss functions. This method has several essential properties: 1. The degree of sparsity is continuous a parameter controls the rate of sparsi cation from no sparsi cation to total sparsi cation. 2. The approach is theoretically motivated, and an instance of it can be regarded as an online counterpart of the popular L1-regularization method in the batch setting. We prove that small rates of sparsi cation result in only small additional regret with respect to typical online learning guarantees. 3. The approach works well empirically. We apply the approach to several datasets and nd that for datasets with large numbers of features, substantial sparsity is discoverable.
Comparison of upper arm kinematics during a volleyball spike between players with and without a history of shoulder injury.

Volleyball players are at high risk of overuse shoulder injuries, with spike biomechanics a perceived risk factor. This study compared spike kinematics between elite male volleyball players with and without a history of shoulder injuries. Height, mass, maximum jump height, passive shoulder rotation range of motion (ROM), and active trunk ROM were collected on elite players with (13) and without (11) shoulder injury history and were compared using independent samples t tests (P < .05). The average of spike kinematics at impact and range 0.1 s before and after impact during down-the-line and cross-court spike types were compared using linear mixed models in SPSS (P < .01). No differences were detected between the injured and uninjured groups. Thoracic rotation and shoulder abduction at impact and range of shoulder rotation velocity differed between spike types. The ability to tolerate the differing demands of the spike types could be used as return-to-play criteria for injured athletes.
Recommender systems based on user reviews: the state of the art

In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers’ emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system’s ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building. In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed algorithms are evaluated. In addition, we discuss several future trends based on the survey, which may inspire investigators to pursue additional studies in this area.
EyeSwipe: Dwell-free Text Entry Using Gaze Paths

Text entry using gaze-based interaction is a vital communication tool for people with motor impairments. Most solutions require the user to fixate on a key for a given dwell time to select it, thus limiting the typing speed. In this paper we introduce EyeSwipe, a dwell-time-free gaze-typing method. With EyeSwipe, the user gaze-types the first and last characters of a word using the novel selection mechanism "reverse crossing." To gaze-type the characters in the middle of the word, the user only needs to glance at the vicinity of the respective keys. We compared the performance of EyeSwipe with that of a dwell-time-based virtual keyboard. EyeSwipe afforded statistically significantly higher typing rates and more comfortable interaction in experiments with ten participants who reached 11.7 words per minute (wpm) after 30 min typing with EyeSwipe.
Continuous depth estimation for multi-view stereo

Depth-map merging approaches have become more and more popular in multi-view stereo (MVS) because of their flexibility and superior performance. The quality of depth map used for merging is vital for accurate 3D reconstruction. While traditional depth map estimation has been performed in a discrete manner, we suggest the use of a continuous counterpart. In this paper, we first integrate silhouette information and epipolar constraint into the variational method for continuous depth map estimation. Then, several depth candidates are generated based on a multiple starting scales (MSS) framework. From these candidates, refined depth maps for each view are synthesized according to path-based NCC (normalized cross correlation) metric. Finally, the multiview depth maps are merged to produce 3D models. Our algorithm excels at detail capture and produces one of the most accurate results among the current algorithms for sparse MVS datasets according to the Middlebury benchmark. Additionally, our approach shows its outstanding robustness and accuracy in free-viewpoint video scenario.
Hepatitis in Albanian children: molecular analysis of hepatitis A virus isolates.

Hepatitis A is a common disease in developing countries and Albania has a high prevalence of this disease associated to young age. In spite of the occurrence of a unique serotype there are different genotypes classified from I to VII. Genotype characterisation of HAV isolates circulating in Albania has been undertaken, as well as the study of the occurrence of antigenic variants in the proteins VP3 and VP1. To evaluate the genetic variability of the Albanian hepatitis A virus (HAV) isolates, samples were collected from 12 different cities, and the VP1/2A junction amplified and sequenced. These sequences were aligned and a phylogenetic analysis performed. Additionally, the amino half sequence of the protein VP3 and the complete sequence of the VP1 was determined. Anti-HAV IgM were present in 66.2% of all the sera. Fifty HAV isolates were amplified and the analysis revealed that all the isolates were sub-genotype IA with only limited mutations. When the deduced amino acid sequences were obtained, the alignment showed only two amino acids substitutions at positions 22 and 34 of the 2A protein. A higher genomic stability of the VP1/2A region, in contrast with what occurs in other parts of the world could be observed, indicating high endemicity of HAV in Albania. In addition, two potential antigenic variants were detected. The first at position 46 of VP3 in seven isolates and the second at position 23 of VP1 in six isolates.
A Case Study of Paternal Filicide-Suicide: Personality Disorder, Motives, and Victim Choice.

Although evidence with respect to its prevalence is mixed, it is clear that fathers perpetrate a serious proportion of filicide. There also seems to be a consensus that paternal filicide has attracted less research attention than its maternal counterpart and is therefore less well understood. National registries are a very rich source of data, but they generally provide limited information about the perpetrator as psychiatric, psychological and behavioral data are often lacking. This paper presents a fully documented case of a paternal filicide. Noteworthy is that two motives were present: spousal revenge as well as altruism. The choice of the victim was in line with emerging evidence indicating that children with disabilities in general and with autism in particular are frequent victims of filicide-suicide. Finally, a schizoid personality disorder was diagnosed. Although research is quite scarce on that matter, some research outcomes have showed an association between schizoid personality disorder and homicide and violence.
Nutraceuticals and Bioactive Components from Fish for Dyslipidemia and Cardiovascular Risk Reduction

Cardiovascular disease remains the most common health problem in developed countries, and residual risk after implementing all current therapies is still high. Permanent changes in lifestyle may be hard to achieve and people may not always be motivated enough to make the recommended modifications. Emerging research has explored the application of natural food-based strategies in disease management. In recent years, much focus has been placed on the beneficial effects of fish consumption. Many of the positive effects of fish consumption on dyslipidemia and heart diseases have been attributed to n-3 polyunsaturated fatty acids (n-3 PUFAs, i.e., EPA and DHA); however, fish is also an excellent source of protein and, recently, fish protein hydrolysates containing bioactive peptides have shown promising activities for the prevention/management of cardiovascular disease and associated health complications. The present review will focus on n-3 PUFAs and bioactive peptides effects on cardiovascular disease risk factors. Moreover, since considerable controversy exists regarding the association between n-3 PUFAs and major cardiovascular endpoints, we have also reviewed the main clinical trials supporting or not this association.