nils-herrmann/clean_scirepeval huggingface dataset:
Modularity in design of products and systems

Modularity refers to the use of common units to create product variants. As companies strive to rationalize engineering design, manufacturing, and support processes and to produce a large variety of products at a lower cost, modularity is becoming a focus. However, modularity has been treated in the literature in an abstract form and it has not been satisfactorily explored in industry. This paper aims at the development of models and solution approaches to the modularity problem for mechanical, electrical, and mixed process products (e.g., electromechanical products). To interpret various types of modularity, e.g., component-swapping, component-sharing, and bus modularity, a matrix representation of the modularity problem is presented. The decomposition approach is used to determine modules for different products. The representation and solution approaches presented are illustrated with numerous examples. The paper presents a formal approach to modularity allowing for optimal forming of modules even in the situation of insufficient availability of information. The modules determined may be shared across different products.
Designing Classrooms to Maximize Student Achievement

Improving student achievement is vital for our nation’s competitiveness. Scientific research shows how the physical classroom environment influences student achievement. Two findings are key: First, the building’s structural facilities profoundly influence learning. Inadequate lighting, noise, low air quality, and deficient heating in the classroom are significantly related to worse student achievement. Over half of U.S. schools have inadequate structural facilities, and students of color and lower income students are more likely to attend schools with inadequate structural facilities. Second, scientific studies reveal the unexpected importance of a classroom’s symbolic features, such as objects and wall décor, in influencing student learning and achievement in that environment. Symbols inform students whether they are valued learners and belong within the classroom, with far-reaching consequences for students’ educational choices and achievement. We outline policy implications of the scientific findings—noting relevant policy audiences—and specify critical features of classroom design that can improve student achievement, especially for the most vulnerable students.
Computer Applications in Archaeological Pottery : A Review and New Perspectives

During the past few years, many publications about computer applications in the field of drawing, classification and analysis of archaeological pottery have been presented at various congresses by various researchers. This paper will review and analyze the most relevant works published so far. It focuses on computer applications oriented towards the graphical visualization and analysis of data relevant to archaeological pottery. The intention is to order and systematize these data and to review those publications that are most relevant to computerized systems of archaeological pottery. This review and analysis will introduce the methodology used in the CATA project (Archaeological Wheel­made Pottery of Andalusia), the procedures used in the CATA project for the representation, archiving, analysis and retrieval of data concerning pottery vessels and their fragments. The main aim of the CATA project is to provide a scientific tool for the analysis of pottery finds in eastern Andalusia. These findings will be introduced into a database with documentational and graphical capabilities for visualizing pottery fragments and vessels. The objective is to create a general tool that can be applied to any kind of ceramic material found in any geographical location.
Cyber Bullying Detection Using Social and Textual Analysis

Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.
Economy-Wide Estimates of the Implications of Climate Change : Sea Level Rise

The economy-wide implications of sea level rise in 2050 are estimated using a static computable general equilibrium model. Overall, general equilibrium effects increase the costs of sea level rise, but not necessarily in every sector or region. In the absence of coastal protection, economies that rely most on agriculture are hit hardest. Although energy is substituted for land, overall energy consumption falls with the shrinking economy, hurting energy exporters. With full coastal protection, GDP increases, particularly in regions that do a lot of dike building, but utility falls, least in regions that build a lot of dikes and export energy. Energy prices rise and energy consumption falls. The costs of full protection exceed the costs of losing land.
Line-based extrinsic calibration of range and image sensors

Creating rich representations of environments requires integration of multiple sensing modalities with complementary characteristics such as range and imaging sensors. To precisely combine multisensory information, the rigid transformation between different sensor coordinate systems (i.e., extrinsic parameters) must be estimated. The majority of existing extrinsic calibration techniques require one or multiple planar calibration patterns (such as checkerboards) to be observed simultaneously from the range and imaging sensors. The main limitation of these approaches is that they require modifying the scene with artificial targets. In this paper, we present a novel algorithm for extrinsically calibrating a range sensor with respect to an image sensor with no requirement of external artificial targets. The proposed method exploits natural linear features in the scene to precisely determine the rigid transformation between the coordinate frames. First, a set of 3D lines (plane intersection and boundary line segments) are extracted from the point cloud, and a set of 2D line segments are extracted from the image. Correspondences between the 3D and 2D line segments are used as inputs to an optimization problem which requires jointly estimating the relative translation and rotation between the coordinate frames. The proposed method is not limited to any particular types or configurations of sensors. To demonstrate robustness, efficiency and generality of the presented algorithm, we include results using various sensor configurations.
Cord blood NK cells engineered to express IL-15 and a CD19-targeted CAR show long-term persistence and potent antitumor activity

Chimeric antigen receptors (CARs) have been used to redirect the specificity of autologous T cells against leukemia and lymphoma with promising clinical results. Extending this approach to allogeneic T cells is problematic as they carry a significant risk of graft-versus-host disease (GVHD). Natural killer (NK) cells are highly cytotoxic effectors, killing their targets in a non-antigen-specific manner without causing GVHD. Cord blood (CB) offers an attractive, allogeneic, off-the-self source of NK cells for immunotherapy. We transduced CB-derived NK cells with a retroviral vector incorporating the genes for CAR-CD19, IL-15 and inducible caspase-9-based suicide gene (iC9), and demonstrated efficient killing of CD19-expressing cell lines and primary leukemia cells in vitro, with marked prolongation of survival in a xenograft Raji lymphoma murine model. Interleukin-15 (IL-15) production by the transduced CB-NK cells critically improved their function. Moreover, iC9/CAR.19/IL-15 CB-NK cells were readily eliminated upon pharmacologic activation of the iC9 suicide gene. In conclusion, we have developed a novel approach to immunotherapy using engineered CB-derived NK cells, which are easy to produce, exhibit striking efficacy and incorporate safety measures to limit toxicity. This approach should greatly improve the logistics of delivering this therapy to large numbers of patients, a major limitation to current CAR-T-cell therapies.
MUSE: Modularizing Unsupervised Sense Embeddings

This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.
Why do people play mobile social games? An examination of network externalities and of uses and gratifications

The purpose of this paper is to identify the factors that influence people to play socially interactive games on mobile devices. Based on network externalities and theory of uses and gratifications (U&G), it seeks to provide direction for further academic research on this timely topic.  Based on 237 valid responses collected from online questionnaires, structural equation modeling technology was employed to examine the research model.  The results reveal that both network externalities and individual gratifications significantly influence the intention to play social games on mobile devices. Time flexibility, however, which is one of the mobile device features, appears to contribute relatively little to the intention to play mobile social games.  This research successfully applies a combination of network externalities theory and U&G theory to investigate the antecedents of players’ intentions to play mobile social games. This study is able to provide a better understanding of how two dimensions – perceived number of users/peers and individual gratification – influence mobile game playing, an insight that has not been examined previously in the mobile apps literature.
Provable Optimal Algorithms for Generalized Linear Contextual Bandits

Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search. Generalized linear models (logistical regression in particular) have demonstrated stronger performance than linear models in many applications where rewards are binary. However, most theoretical analyses on contextual bandits so far are on linear bandits. In this work, we propose an upper confidence bound based algorithm for generalized linear contextual bandits, which achieves an Õ( √ dT ) regret over T rounds with d dimensional feature vectors. This regret matches the minimax lower bound, up to logarithmic terms, and improves on the best previous result by a √ d factor, assuming the number of arms is fixed. A key component in our analysis is to establish a new, sharp finite-sample confidence bound for maximumlikelihood estimates in generalized linear models, which may be of independent interest. We also analyze a simpler upper confidence bound algorithm, which is useful in practice, and prove it to have optimal regret for certain cases.
Forensic analysis of social networks (case study)

This paper deals with a new challenge for digital forensic experts - the forensic analysis of social networks. There is a lot of identity theft, theft of personal data, public defamation, cyber stalking and other criminal activities on social network sites. This paper will present a forensic analysis of social networks and cloud forensics in the internet environment. For the purpose of this research one case study is created like - a common practical scenario where the combination of identity theft and public defamation through Facebook activity is explored. Investigators must find the person who stole some others profile, who publish inappropriate and prohibited contents performing act of public defamation and humiliation of profile owner.
Joint 3D Tracking of a Deformable Object in Interaction with a Hand

We present a novel method that is able to track a complex deformable object in interaction with a hand. This is achieved by formulating and solving an optimization problem that jointly considers the hand, the deformable object and the hand/object contact points. The optimization evaluates several hand/object contact configuration hypotheses and adopts the one that results in the best fit of the object’s model to the available RGBD observations in the vicinity of the hand. Thus, the hand is not treated as a distractor that occludes parts of the deformable object, but as a source of valuable information. Experimental results on a dataset that has been developed specifically for this new problem illustrate the superior performance of the proposed approach against relevant, state of the art solutions.
A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering

Personalized recommendation systems can help people to find interesting things and they are widely used with the development of electronic commerce. Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommender systems in recent years. With the gradual increase of customers and products in electronic commerce systems, the time consuming nearest neighbor collaborative filtering search of the target customer in the total customer space resulted in the failure of ensuring the real time requirement of recommender system. At the same time, it suffers from its poor quality when the number of the records in the user database increases. Sparsity of source data set is the major reason causing the poor quality. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalized recommendation approach joins the user clustering technology and item clustering technology. Users are clustered based on users’ ratings on items, and each users cluster has a cluster center. Based on the similarity between target user and cluster centers, the nearest neighbors of target user can be found and smooth the prediction where necessary. Then, the proposed approach utilizes the item clustering collaborative filtering to produce the recommendations. The recommendation joining user clustering and item clustering collaborative filtering is more scalable and more accurate than the traditional one.
Data Mining Based Strategy for Detecting Malicious PDF Files

Portable Document Format (PDF) is one of the widely-accepted document format. However, it becomes one of the most attractive targets for exploitation by malware developers and vulnerability researchers. Malicious PDF files can be used in Advanced Persistent Threats (APTs) targeting individuals, governments, and financial sectors. The existing tools such as intrusion detection systems (IDSs) and antivirus packages are inefficient to mitigate this kind of attacks. This is because these techniques need regular updates with the new malicious PDF files which are increasing every day. In this paper, a new algorithm is presented for detecting malicious PDF files based on data mining techniques. The proposed algorithm consists of feature selection stage and classification stage. The feature selection stage is used to the select the optimum number of features extracted from the PDF file to achieve high detection rate and low false positive rate with small computational overhead. Experimental results show that the proposed algorithm can achieve 99.77% detection rate, 99.84% accuracy, and 0.05% false positive rate.
CHAMPS: A peer support program for children of parents with a mental illness

Having a parent with a mental illness can create considerable risks in the mental health and wellbeing of children. While intervention programs have been used effectively to reduce children’s psychopathology, particularly those whose parents have a specific diagnosis, little is known about the effectiveness of these early interventions for the wellbeing of children of parents who have a mental illness from a broad range of parents. Here we report on an evaluation of CHAMPS (Children And Mentally ill ParentS), a pilot intervention program offered in two formats (school holiday and after school peer support programs) to children aged 8-12 whose parents have a mental illness. The wellbeing of 69 children was evaluated at the beginning of the programs and four weeks after program completion, on instruments examining self-esteem, coping skills, connections (total, within and outside the family) and relationship problems (total, within and outside the family). Post intervention, there were significant improvements in self-esteem, coping and connections within the family, and reductions in relationship problems. The impact on children’s wellbeing differed according to the intensity of the program (consecutive days or weekly program). The results are discussed in the context of providing interventions for children whose parents have a mental illness and the implications for service provision generally.
LINK – CRIMINAL INTELLIGENCE ENVIRONMENT WITH SPATIAL/GEOGRAPHICAL ANALYSIS TOOLS

1 Summary. The paper presents the LINK application, which is a decision-support system dedicated for operational and investigational activities of homeland security services. The paper briefly discusses issues of criminal analysis, possibilities of utilizing spatial (geographical) information together with crime mapping and spatial analyses. LINK – ŚRODOWISKO ANALIZ KRYMINALNYCH WYKORZYSTUJĄCE NARZĘRZIA ANALIZ GEOPRZESTRZENNYCH Streszczenie. Artykuł prezentuje system LINK będący zintegrowanym środowi-skiem wspomagania analizy kryminalnej przeznaczonym do działań operacyjnych i śledczych służb bezpieczeństwa wewnętrznego. W artykule omówiono problemy analizy kryminalnej, możliwość wykorzystania informacji o charakterze przestrzen-nym oraz narzędzia i metody analiz geoprzestrzennych.
The personnel factor : Exploring the personal attributes of highly successful employment specialists who work with transition-age youth

Employment specialists play a pivotal role in assisting youth and adults with disabilities find and retain jobs. This requires a unique combination of skills, competencies and personal attributes. While the fields of career counseling, vocational rehabilitation and special education transition have documented the ideal skills sets needed to achieve desired outcomes, the authors characterize these as essential mechanics. What have not been examined are the personal qualities that effective employment specialists possess. Theorizing that these successful professionals exhibit traits and behaviors beyond the mechanics, the authors conducted a qualitative study incorporating in-depth interviews with 17 top-performing staff of a highly successful national program, The Marriott Foundation’s Bridges from school to work. Four personal attributes emerged from the interviews: (a) principled optimism; (b) cultural competence; (c) business-oriented professionalism; and (d) networking savvy. In presenting these findings, the authors discuss the implications for recruitment, hiring, training, and advancing truly effective employment specialists, and offer recommendations for further research.
DYNAMIC BEHAVIOR OF TALL BUILDINGS UNDER WIND : INSIGHTS FROM FULL-SCALE MONITORING

The wind-induced response of tall buildings is inherently sensitive to structural dynamic properties like frequency and damping ratio. The latter parameter in particular is fraught with uncertainty in the design stage and may result in a built structure whose acceleration levels exceed design predictions. This reality has motivated the need to monitor tall buildings in full-scale. This paper chronicles the authors’ experiences in the analysis of full-scale dynamic response data from tall buildings around the world, including full-scale datasets from high rises in Boston, Chicago, and Seoul. In particular, this study focuses on the effects of coupling, beat phenomenon, amplitude dependence, and structural system type on dynamic properties, as well as correlating observed periods of vibration against fi nite element predictions. The fi ndings suggest the need for time–frequency analyses to identify coalescing modes and the mechanisms spurring them. The study also highlighted the effect of this phenomenon on damping values, the overestimates that can result due to amplitude dependence, as well as the comparatively larger degree of energy dissipation experienced by buildings dominated by frame action.
Approximate Stream Analytics in Apache Flink and Apache Spark Streaming

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic trade-off between the output accuracy and computation efficiency. Unfortunately, the state-of-the-art systems for approximate computing primarily target batch analytics, where the input data remains unchanged during the course of sampling. Thus, they are not well-suited for stream analytics. This motivated the design of StreamApprox— a stream analytics system for approximate computing. To realize this idea, we designed an online stratified reservoir sampling algorithm to produce approximate outputwith rigorous error bounds. Importantly, our proposed algorithm is generic and can be applied to two prominent types of stream processing systems: (1) batched stream processing such asApache Spark Streaming, and (2) pipelined stream processing such as Apache Flink. To showcase the effectiveness of our algorithm,we implemented StreamApprox as a fully functional prototype based on Apache Spark Streaming and Apache Flink. We evaluated StreamApprox using a set of microbenchmarks and real-world case studies. Our results show that Sparkand Flink-based StreamApprox systems achieve a speedup of 1.15×—3× compared to the respective native Spark Streaming and Flink executions, with varying sampling fraction of 80% to 10%. Furthermore, we have also implemented an improved baseline in addition to the native execution baseline — a Spark-based approximate computing system leveraging the existing sampling modules in Apache Spark. Compared to the improved baseline, our results show that StreamApprox achieves a speedup 1.1×—2.4× while maintaining the same accuracy level. This technical report is an extended version of our conference publication .