nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing Factorization Machines for Top-N Context-Aware Recommendations

Context-aware Collaborative Filtering (CF) techniques such as Factorization Machines (FM) have been proven to yield high precision for rating prediction. However, the goal of recommender systems is often referred to as a top-N item recommendation task, and item ranking is a better formulation for the recommendation problem. In this paper, we present two collaborative rankers, namely, Ranking Factorization Machines (RankingFM) and Lambda Factorization Machines (LambdaFM), which optimize the FM model for the item recommendation task. Specifically, instead of fitting the preference of individual items, we first propose a RankingFM algorithm that applies the cross-entropy loss function to the FM model to estimate the pairwise preference between individual item pairs. Second, by considering the ranking bias in the item recommendation task, we design two effective lambda-motivated learning schemes for RankingFM to optimize desired ranking metrics, referred to as LambdaFM. The two models we propose can work with any types of context, and are capable of estimating latent interactions between the context features under sparsity. Experimental results show its superiority over several state-of-the-art methods on three public CF datasets in terms of two standard ranking metrics.
Detecting hot topics from Twitter: A multiview approach

Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people’s daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets’ features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network

a r t i c l e i n f o a b s t r a c t We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks.
DL-Store: A Distributed Hybrid OLTP and OLAP Data Processing Engine

There has been a recent push in the database community towards supporting real-time analytical queries (OLAP) while sustaining a large volume of fine-grained updates (OLTP). Supporting these types of workloads require both an efficient data storage layer as well as a distributed architecture. In this demo, we address the latter point with our Distributed Lineage-based Data Store (DL-Store), which is a distributed data processing engine. DL-Store is built on top of L-Store, which is a lineage-based storage architecture designed to handle mixed OLTP and OLAP workloads, and provides scalability and elasticity by supporting multiple L-Store nodes. To maintain the desired consistency semantics, DL-Store employs a distributed transaction handler component which can horizontally scaled by provisioning additional transaction manager nodes. We leverage partitioning in the record space of the transactions to minimize communication across transaction managers while ensuring consistent execution. The demo shows our implementation of DL-Store over Apache Spark using a variety of use cases.
Learning for control from multiple demonstrations

We consider the problem of learning to follow a desired trajectory when given a small number of demonstrations from a sub-optimal expert. We present an algorithm that (i) extracts the---initially unknown---desired trajectory from the sub-optimal expert's demonstrations and (ii) learns a local model suitable for control along the learned trajectory. We apply our algorithm to the problem of autonomous helicopter flight. In all cases, the autonomous helicopter's performance exceeds that of our expert helicopter pilot's demonstrations. Even stronger, our results significantly extend the state-of-the-art in autonomous helicopter aerobatics. In particular, our results include the first autonomous tic-tocs, loops and hurricane, vastly superior performance on previously performed aerobatic maneuvers (such as in-place flips and rolls), and a complete airshow, which requires autonomous transitions between these and various other maneuvers.
IL-17 and Th17 Cells.

CD4+ T cells, upon activation and expansion, develop into different T helper cell subsets with different cytokine profiles and distinct effector functions. Until recently, T cells were divided into Th1 or Th2 cells, depending on the cytokines they produce. A third subset of IL-17-producing effector T helper cells, called Th17 cells, has now been discovered and characterized. Here, we summarize the current information on the differentiation and effector functions of the Th17 lineage. Th17 cells produce IL-17, IL-17F, and IL-22, thereby inducing a massive tissue reaction owing to the broad distribution of the IL-17 and IL-22 receptors. Th17 cells also secrete IL-21 to communicate with the cells of the immune system. The differentiation factors (TGF-beta plus IL-6 or IL-21), the growth and stabilization factor (IL-23), and the transcription factors (STAT3, RORgammat, and RORalpha) involved in the development of Th17 cells have just been identified. The participation of TGF-beta in the differentiation of Th17 cells places the Th17 lineage in close relationship with CD4+CD25+Foxp3+ regulatory T cells (Tregs), as TGF-beta also induces differentiation of naive T cells into Foxp3+ Tregs in the peripheral immune compartment. The investigation of the differentiation, effector function, and regulation of Th17 cells has opened up a new framework for understanding T cell differentiation. Furthermore, we now appreciate the importance of Th17 cells in clearing pathogens during host defense reactions and in inducing tissue inflammation in autoimmune disease.
Painting Outside the Box: Image Outpainting with GANs

The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion). Accordingly, we present a deep learning approach based on  for adversarially training a network to hallucinate past image boundaries. We use a three-phase training schedule to stably train a DCGAN architecture on a subset of the Places365 dataset. In line with [4], we also use local discriminators to enhance the quality of our output. Once trained, our model is able to outpaint 128 × 128 color images relatively realistically, thus allowing for recursive outpainting. Our results show that deep learning approaches to image outpainting are both feasible and promising.
Tap-Tap and Pay ( TTP ) : Preventing Man-InThe-Middle Attacks in NFC Payment Using Mobile Sensors

The reader-and-ghost attack is a real concern in mobile NFC payment applications. A malicious reader relays the user's NFC-enabled mobile phone to a remote legitimate reader to charge for a higher amount than what the user expects to pay. Using an NFC shield cannot prevent the attack, since the user consciously instantiates the NFC payment, though without realizing that the reader is controlled by an attacker. Recent solutions generally involve using ambient sensors to measure the ambient properties of the surrounding environment to ensure that the NFC-enabled phone and the reader are at nearby locations. Unfortunately, all these solutions fail completely once the attacker's reader and the legitimate reader are located in the same or similar physical environment. In this paper, we propose the first and currently the only viable technical solution to defeat the reader-and-ghost attack even when the attacker' reader and the legitimate one are located in the same physical environment. Our solution is called "Tap-Tap and Pay" (TTP). It works by asking the user to physically tap the reader twice in succession to initiate an NFC payment. The physical tapping causes random but correlated vibrations at both devices, which are hard to forge (or reproduce) and can be reliably measured by accelerometers. Accordingly, we design the TTP protocol such that the NFC transaction will proceed only if the two vibration signals are found sufficiently similar. As compared with previous solutions, ours is fast, simple to use, easy to deploy, and above all, prevents attacks even if the attacker's reader and the legitimate one are located in the same environment.
A clinical decision support system based on support vector machine and binary particle swarm optimisation for cardiovascular disease diagnosis

Cardiovascular diseases have been known as one of the main reasons of mortality all around the world. Nevertheless, this disease is preventable if it can be diagnosed in an early stage. Therefore, it is crucial to develop Clinical Decision Support Systems (CDSSs) that are able to help physicians diagnose the disease and its related risks. This study focuses on cardiovascular disease diagnosis in an Iranian community by developing a CDSS, based on Support Vector Machine (SVM) combined with Binary Particle Swarm Optimisation (BPSO). We used SVM as the classifier and benefited enormously from optimisation capabilities of BPSO in model development as well as feature selection. Finally, experiments were carried out on the proposed system using Isfahan Healthy Heart Program (IHHP) dataset and the performance of the system is compared with other commonly used classification algorithms in term of classification accuracy, sensitivity, specificity and GMean.
In search of causality: a systematic review of the relationship between the built environment and physical activity among adults

Empirical evidence suggests that an association between the built environment and physical activity exists. This evidence is mostly derived from cross-sectional studies that do not account for other causal explanations such as neighborhood self-selection. Experimental and quasi-experimental designs can be used to isolate the effect of the built environment on physical activity, but in their absence, statistical techniques that adjust for neighborhood self-selection can be used with cross-sectional data. Previous reviews examining the built environment-physical activity relationship have not differentiated among findings based on study design. To deal with self-selection, we synthesized evidence regarding the relationship between objective measures of the built environment and physical activity by including in our review: 1) cross-sectional studies that adjust for neighborhood self-selection and 2) quasi-experiments.



In September 2010, we searched for English-language studies on built environments and physical activity from all available years in health, leisure, transportation, social sciences, and geographical databases. Twenty cross-sectional and 13 quasi-experimental studies published between 1996 and 2010 were included in the review.



Most associations between the built environment and physical activity were in the expected direction or null. Land use mix, connectivity and population density and overall neighborhood design were however, important determinants of physical activity. The built environment was more likely to be associated with transportation walking compared with other types of physical activity including recreational walking. Three studies found an attenuation in associations between built environment characteristics and physical activity after accounting for neighborhood self-selection.



More quasi-experiments that examine a broader range of environmental attributes in relation to context-specific physical activity and that measure changes in the built environment, neighborhood preferences and their effect on physical activity are needed.
Network navigability in the social Internet of Things

The Internet of Things is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner. However, in the resulting network, every object will still have to manage a large number of friends, slowing down the search of the services. In this work, we intend to address this issue by analyzing possible strategies to drive the objects to select the appropriate links for the benefit of overall network navigability.
Symmetry Breaking in Constraint Programming

We describea methodfor symmetrybreakingduring search(SBDS) in constraintprogramming.It hasthe greatadvantageof not interfering with heuristicchoices.It guaranteesto return a unique solution from eachset of symmetricallyequivalent ones,which is theonefoundfirst by thevariableandvalueordering heuristics.Wedescribeanimplementationof SBDSin ILOG Solver, andapplicationsto low autocorrelationbinarysequences andthe queensproblem.We discusshow SBDScanbe appliedwhenthere aretoomany symmetriesto reasonwith individually, andgive applicationsin graphcolouringandRamsey theory.
An Efficient Algorithm for Calculating the Exact Hausdorff Distance

The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees.
Tetris game design based on the FPGA

Tetris game is a classic game of logic control. This article gives a programming design of Tetris game based on the FPGA using VHDL. Game players can move and rotate blocks with the PS/2 interface keyboard, and the game video is showed in a VGA monitor. The game realized the function of the movement and rotation of blocks, randomly generating next blocks, eliminating rows, getting scores and speeding up. It also contained the normal mode corresponding to 7 types of blocks and the expert mode corresponding to 11 types of blocks. The successful transplant of Tetris game provides a template for the development of other visual control systems in the FPGA.
Unauthorized origin crossing on mobile platforms: threats and mitigation

With the progress in mobile computing, web services are increasingly delivered to their users through mobile apps, instead of web browsers. However, unlike the browser, which enforces origin-based security policies to mediate the interactions between the web content from different sources, today's mobile OSes do not have a comparable security mechanism to control the cross-origin communications between apps, as well as those between an app and the web. As a result, a mobile user's sensitive web resources could be exposed to the harms from a malicious origin. In this paper, we report the first systematic study on this mobile cross-origin risk. Our study inspects the main cross-origin channels on Android and iOS, including intent, scheme and web-accessing utility classes, and further analyzes the ways popular web services (e.g., Facebook, Dropbox, etc.) and their apps utilize those channels to serve other apps. The research shows that lack of origin-based protection opens the door to a wide spectrum of cross-origin attacks. These attacks are unique to mobile platforms, and their consequences are serious: for example, using carefully designed techniques for mobile cross-site scripting and request forgery, an unauthorized party can obtain a mobile user's Facebook/Dropbox authentication credentials and record her text input. We report our findings to related software vendors, who all acknowledged their importance. To address this threat, we designed an origin-based protection mechanism, called Morbs, for mobile OSes. Morbs labels every message with its origin information, lets developers easily specify security policies, and enforce the policies on the mobile channels based on origins. Our evaluation demonstrates the effectiveness of our new technique in defeating unauthorized origin crossing, its efficiency and the convenience for the developers to use such protection.
Maturity Models for Systems Thinking

Recent decades have seen a rapid increase in the complexity of goods, products, and services that society has come to demand. This has necessitated a corresponding growth in the requirements demanded of organizational systems and the people who work in them. The competence a person requires to be effective in working in such systems has become an area of increased interest to scholars and practitioners in many disciplines. How can we assess the degree to which a person is executing the competencies required to do good systems work? Several industries now utilize maturity models in the attempt to evaluate and cultivate people’s ability to effectively execute complex tasks. This paper will examine current thought regarding the value and pitfalls of maturity models. It will identify principles and exemplars that could guide the development of a Maturity Model of Systems Thinking Competence (MMSTC) for the varied roles people inhabit in systems contexts.
A Crosstab-based Statistical Method for Effective Fault Localization

Fault localization is the most expensive activity in program debugging. Traditional ad-hoc methods can be time-consuming and ineffective because they rely on programmers' intuitive guesswork, which may neither be accurate nor reliable. A better solution is to utilize a systematic and statistically well-defined method to automatically identify suspicious code that should be examined for possible fault locations. We present a crosstab-based statistical method using the coverage information of each executable statement and the execution result (success or failure) with respect to each test case. A crosstab is constructed for each executable statement and a statistic is computed to determine the suspiciousness of the corresponding statement. Statements with a higher suspiciousness are more likely to contain bugs and should be examined before those with a lower suspiciousness. Three case studies using the Siemens suite, the Space program, and the Unix suite, respectively, are conducted. Our results suggest that the crosstab-based method is effective in fault localization and performs better (in terms of a smaller percentage of executable statements that have to be examined until the first statement containing the fault is reached) than other methods such as Tarantula. The difference in efficiency (computational time) between these two methods is very small.
Effect of horseback riding versus a dynamic and static horse riding simulator
on sitting ability of children with cerebral palsy: a randomized controlled
trial

A randomized controlled trial was conducted to investigate the immediate effects of horseback riding (HR) and a dynamic (DHS) and static (SHS) horse riding simulator (OSIM uGallop, Taiwan) on sitting ability of children with cerebral palsy.  Thirty children with cerebral palsy were recruited and randomly assigned into three groups. Children received 30 minutes of exercise according to their assigned group. The Segmental Assessment of Trunk Control (SATCo) and Gross Motor Function Measure-66 (GMFM-66) sitting dimension were used to assess children in all groups both before and after the interventions.  Sitting abilities were significantly improved after all interventions. Horseback riding showed the most improvement, followed by the dynamic and static horse riding simulator groups. Horseback riding also showed a significant improvement in the GMFM sitting dimension.  Horseback riding was the best intervention for promoting sitting ability of children with spastic cerebral palsy. However, a dynamic horse riding simulator can be a good surrogate for horseback riding when horseback riding is not available.
Natural Toxins for Use in Pest Management

Natural toxins are a source of new chemical classes of pesticides, as well as environmentally and toxicologically safer molecules than many of the currently used pesticides. Furthermore, they often have molecular target sites that are not exploited by currently marketed pesticides. There are highly successful products based on natural compounds in the major pesticide classes. These include the herbicide glufosinate (synthetic phosphinothricin), the spinosad insecticides, and the strobilurin fungicides. These and other examples of currently marketed natural product-based pesticides, as well as natural toxins that show promise as pesticides from our own research are discussed.