nils-herrmann/clean_scirepeval huggingface dataset:
On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies.

Even at young ages, children self-report experiencing math anxiety, which negatively relates to their math achievement. Leveraging a large dataset of first and second grade students' math achievement scores, math problem solving strategies, and math attitudes, we explored the possibility that children's math anxiety (i.e., a fear or apprehension about math) negatively relates to their use of more advanced problem solving strategies, which in turn relates to their math achievement. Our results confirm our hypothesis and, moreover, demonstrate that the relation between math anxiety and math problem solving strategies is strongest in children with the highest working memory capacity. Ironically, children who have the highest cognitive capacity avoid using advanced problem solving strategies when they are high in math anxiety and, as a result, underperform in math compared with their lower working memory peers.
Associating Genes and Protein Complexes with Disease via Network Propagation

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.
Exploring the comparative salience of restaurant attributes: A conjoint analysis approach

This study explores how travelers select a restaurant for dining out, given that restaurant customers consider diverse attributes in making a selection. By applying a conjoint analysis, an exploratory multiplecase study is conducted for three restaurants in New York City. Findings from Study 1 (an overall travelers group) and Study 2 (two different country-of-residence groups: foreign and domestic travelers) show that food, value, atmosphere, and service are considered as substantially important criteria in selecting restaurants, in that order. However, results from Study 3 examining different restaurant types (lowpriced food stand, low-priced indoor, and high-priced indoor) reveal that the food attribute is the most important factor, regardless of restaurant types, whereas the other attributes’ rankings vary. Results from ypes of restaurants and travelers
Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland

With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland's electricity demand forecasting.
Enhancing Neural Disfluency Detection with Hand-Crafted Features

In this paper, we apply a bidirectional Long Short-Term Memory with a Conditional Random Field to the task of disfluency detection. Long-range dependencies is one of the core problems for disfluency detection. Our model handles long-range dependencies by both using the Long Short-Term Memory and hand-crafted discrete features. Experiments show that utilizing the hand-crafted discrete features significantly improves the model’s performance by achieving the state-of-the-art score of 87.1% on the Switchboard corpus.
A Better Baseline for AVA

We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features – in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3% of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.
Discriminative and consistent similarities in instance-level Multiple Instance Learning

In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are highly and consistently similar to each other and dissimilar to negative instances. Our approach takes advantage of a discriminative notion of pairwise similarity coupled with a structural cue in the form of a consistency metric that measures the quality of each similarity. We learn a similarity function for every pair of instances in positive bags by how similarly they differ from instances in negative bags, the only certain labels in MIL. Our experiments demonstrate that our method consistently outperforms state-of-the-art MIL methods both at bag-level and instance-level predictions in standard benchmarks, image category recognition, and text categorization datasets.
Android Forensics: Simplifying Cell Phone Examinations.

It is hardly appropriate to call the devices many use to receive the occasional phone call a telephone any more. The capability of these devices is growing, as is the number of people utilizing them. By the end of 2009, 46.3% of mobile phones in use in the United States were reported to be smart phones (AdMob, 2010). With the increased availability of these powerful devices, there is also a potential increase for criminals to use this technology as well. Criminals could use smart phones for a number of activities such as committing fraud over e-mail, harassment through text messages, trafficking of child pornography, communications related to narcotics, etc. The data stored on smart phones could be extremely useful to analysts through the course of an investigation. Indeed, mobile devices are already showing themselves to have a large volume of probative information that is linked to an individual with just basic call history, contact, and text message data; smart phones contain even more useful information, such as e-mail, browser history, and chat logs. Mobile devices probably have more probative information that can be linked to an individual per byte examined than most computers -and this data is harder to acquire in a forensically proper fashion. Part of the problem lies in the plethora of cell phones available today and a general lack of hardware, software, and/or interface standardization within the industry. These differences range from the media on which data is stored and the file system to the operating system and the effectiveness of certain tools. Even different model cell phones made by the same manufacture may require different data cables and software to access the phone's information. The good news is there are numerous people in the field working on making smart phone forensics easier. Already there is material available on how to conduct an examination on Blackberry phones and a growing number of resources about the iPhone. However, there is a new smart phone OS on the market named Android and it will likely gain in appeal and market share over the next year. While Android initially launched with only one phone on T-Mobile, phones are now available on Sprint, Verizon and AT&T as well.
Machine Learning with World Knowledge: The Position and Survey

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.
Secure Coding Practices in Java: Challenges and Vulnerabilities

The Java platform and its third-party libraries provide useful features to facilitate secure coding. However, misusing them can cost developers time and effort, as well as introduce security vulnerabilities in software. We conducted an empirical study on StackOverflow posts, aiming to understand developers' concerns on Java secure coding, their programming obstacles, and insecure coding practices.
 We observed a wide adoption of the authentication and authorization features provided by Spring Security---a third-party framework designed to secure enterprise applications. We found that programming challenges are usually related to APIs or libraries, including the complicated cross-language data handling of cryptography APIs, and the complex Java-based or XML-based approaches to configure Spring Security. In addition, we reported multiple security vulnerabilities in the suggested code of accepted answers on the StackOverfow forum. The vulnerabilities included disabling the default protection against Cross-Site Request Forgery (CSRF) attacks, breaking SSL/TLS security through bypassing certificate validation, and using insecure cryptographic hash functions. Our findings reveal the insufficiency of secure coding assistance and documentation, as well as the huge gap between security theory and coding practices.
A GENERALIZED DIRECT APPROACH FOR DESIGNING FUZZY LOGIC CONTROLLERS IN MATLAB / SIMULINK GUI ENVIRONMENT

A straightforward approach for designing fuzzy logic based controllers in Matlab/Simulink environment is presented in this paper. The approach focuses on the fuzzy rule base construction using the time varying values of control error (e) and its change (∆e) mapped on e-∆e space. In order to show the generalized model simplicity of the proposed approach, a fuzzy logic controller (FLC) is designed in Matlab/Simulink environment. For validation, the generated FLC block is simulated to control five different systems such as a PMDC motor speed control system, a position control system, a radar tracking control system, a synchronous generator voltage control system, and loadfrequency control of a two-area power system. The simulation results obtained by FLC are also compared with the results obtained using PID controllers for applicability validation of the developed FLC block in Matlab/Simulink environment.
Variation in adaptive traits among and within Spanish and European populations of Castanea sativa: selection of trees for timber production

Phenological, growth, form, and survival traits were assessed in 3-, 4-, 7-, 8-, and 12-year-old seedlings in two provenance–progeny tests of sweet chestnut (Castanea sativa Miller). One trial included six Spanish populations, with two subpopulations corresponding to wild and orchard trees distinguished within each population, and 77 open-pollinated families. The second trial encompassed six European populations and 156 open-pollinated families. Differences between populations were highly significant for phenological, growth, and form traits, and changes in the ranking of some populations were found for growth traits over the years of the study. Significant differences in survival were reported for the first time. Observed geographic variation among chestnut populations confirmed that drought is an important factor in determining traits related to growth and phenology. Significant differences among families were detected for all traits except for survival. Estimated individual heritabilities ranged from 0.21 to 0.43 for flushing, 0.16 to 0.37 for growth traits and straightness, 0.12 to 0.17 for bud set, and 0.08 to 0.24 for apical dominance. Significant, moderate genetic correlations were observed between growth and form traits, apical dominance and straightness, and flushing and bud set. On the basis of volume and form traits, the best trees of the best families within the best-adapted populations were selected for their inclusion in main and elite Galician breeding populations.
Neuromuscular Electrical Stimulation for Skeletal Muscle Function

Lack of neural innervation due to neurological damage renders muscle unable to produce force. Use of electrical stimulation is a medium in which investigators have tried to find a way to restore movement and the ability to perform activities of daily living. Different methods of applying electrical current to modify neuromuscular activity are electrical stimulation (ES), neuromuscular electrical stimulation (NMES), transcutaneous electrical nerve stimulation (TENS), and functional electrical stimulation (FES). This review covers the aspects of electrical stimulation used for rehabilitation and functional purposes. Discussed are the various parameters of electrical stimulation, including frequency, pulse width/duration, duty cycle, intensity/amplitude, ramp time, pulse pattern, program duration, program frequency, and muscle group activated, and how they affect fatigue in the stimulated muscle.
A single genus in the gut microbiome reflects host preference and specificity

Delineating differences in gut microbiomes of human and animal hosts contributes towards understanding human health and enables new strategies for detecting reservoirs of waterborne human pathogens. We focused upon Blautia, a single microbial genus that is important for nutrient assimilation as preliminary work suggested host-related patterns within members of this genus. In our dataset of 57 M sequence reads of the V6 region of the 16S ribosomal RNA gene in samples collected from seven host species, we identified 200 high-resolution taxonomic units within Blautia using oligotyping. Our analysis revealed 13 host-specific oligotypes that occurred exclusively in fecal samples of humans (three oligotypes), swine (six oligotypes), cows (one oligotype), deer (one oligotype), or chickens (two oligotypes). We identified an additional 171 oligotypes that exhibited differential abundance patterns among all the host species. Blautia oligotypes in the human population obtained from sewage and fecal samples displayed remarkable continuity. Oligotypes from only 10 Brazilian human fecal samples collected from individuals in a rural village encompassed 97% of all Blautia oligotypes found in a Brazilian sewage sample from a city of three million people. Further, 75% of the oligotypes in Brazilian human fecal samples matched those in US sewage samples, implying that a universal set of Blautia strains may be shared among culturally and geographically distinct human populations. Such strains can serve as universal markers to assess human fecal contamination in environmental samples. Our results indicate that host-specificity and host-preference patterns of organisms within this genus are driven by host physiology more than dietary habits.
Towards Accurate Marker-Less Human Shape and Pose Estimation over Time

Existing markerless motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, limiting their application scenarios. Here we present a fully automatic method that, given multi-view videos, estimates 3D human pose and body shape. We take the recently proposed SMPLify method \cite{bogo2016keep} as the base method and extend it in several ways. First we fit a 3D human body model to 2D features detected in multi-view images. Second, we use a CNN method to segment the person in each image and fit the 3D body model to the contours, further improving accuracy. Third we utilize a generic and robust DCT temporal prior to handle the left and right side swapping issue sometimes introduced by the 2D pose estimator. Validation on standard benchmarks shows our results are comparable to the state of the art and also provide a realistic 3D shape avatar. We also demonstrate accurate results on HumanEva and on challenging monocular sequences of dancing from YouTube.
Inherent Trade-Offs in the Fair Determination of Risk Scores

Recent discussion in the public sphere about algorithmic cl assification has involved tension between competing notions of what it means for a probabilistic class ification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satis fy these three conditions simultaneously. Moreover, even satisfying all three conditions approximat ely requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. These results suggest some of the ways in which key notions of fairness are incompatible with each other, and hence provide a framework for thinking about the trade-offs between them.
Integrated energy and spectrum harvesting for 5G wireless communications

Two important issues have arisen in 5G wireless communications: spectral efficiency and energy efficiency. The existing wireless communication architectures and technologies may not be able to address these two issues at the same time for 5G multi-tier networks where users in different tiers have different priorities for channel access. In this article, we first present the motivation for our proposal of a new paradigm of integrated spectrum and energy harvesting for 5G wireless networks, in which spectrum and energy resources are efficiently collected and intelligently managed. Then we present an integrated spectrum and energy harvesting 5G architecture for dealing with current challenges. After that, based on the proposed architecture, we provide integrated spectrum and energy control mechanisms for typical 5G networks. Finally, we propose a cooperative medium access control scheme for heterogeneous 5G networks that attracts cooperative sensing participants with heterogeneous energy harvesting capabilities. Illustrative results demonstrate significant energy and spectrum efficiency for 5G wireless networks.
A fuzzy association rule mining approach using movie lens dataset

In the modern era of e-commerce and social networking, people wants to get everything best and better from every other person in their group. There is always an urge within people to find products, services and opportunities better than everyone else. Nowadays, business grows by advertising it online because people are always online and they find it more interesting to review products on a social platform. Mining of association rules is an advanced data mining research area. Currently many works are undergoing in this domain. Completeness is one of the key strengths of fuzzy association rule mining. Fuzzy cannot work with large datasets. The massive amount of candidate item sets sometimes makes it unproductive for a data mining system to analyze them. At the end, it produces a significant number of fuzzy associations. The research focuses on helping the needful to find the best suited movies depending on the previous ratings. Rules have been generated by using fuzzy association rule mining.
Voltage doubler application in isolated resonant converters

Two basic conditions of voltage-doubler adopted in isolated DC/DC converter are analyzed. According to the basic conditions, that voltage-doubler can be both adopted in LC series resonant converter and LLC multi-resonant converter. Operation characteristic of voltage-doubler adopted in isolated DC/DC converter is described in detail based on the analysis of voltage-doubler rectifying LLC multi-resonant converter. Advantages of voltage-doubler rectifying LLC multi-resonant converter are described in detail as follows, structure of transformer, in which one winding is involved on its secondary side, is simple, voltage stress of output capacitor is half of output and extra voltage balancing circuit is excluded. Two rectifier diodes are included on output end, voltage and current stress of diodes respectively equal output voltage and current. Therefore, the topology is a preferable candidate for such application as low-medium power level DC/DC converter with high output voltage. In addition, principle of automatic voltage balancing for output capacitor is presented, moreover design of key parameter is specified as well. Presented characteristics of the very topology are verified by a prototype, whose output is 500 V and efficiency of full load reaches 92.3%.
Cedalion: a language for language oriented programming

Language Oriented Programming (LOP) is a paradigm that puts domain specific programming languages (DSLs) at the center of the software development process. Currently, there are three main approaches to LOP: (1) the use of internal DSLs, implemented as libraries in a given host language; (2) the use of external DSLs, implemented as interpreters or compilers in an external language; and (3) the use of language workbenches, which are integrated development environments (IDEs) for defining and using external DSLs. In this paper, we contribute: (4) a novel language-oriented approach to LOP for defining and using internal DSLs. While language workbenches adapt internal DSL features to overcome some of the limitations of external DSLs, our approach adapts language workbench features to overcome some of the limitations of internal DSLs. We introduce Cedalion, an LOP host language for internal DSLs, featuring static validation and projectional editing. To validate our approach we present a case study in which Cedalion was used by biologists in designing a DNA microarray for molecular Biology research.