nils-herrmann/clean_scirepeval huggingface dataset:
TPOT-RL Applied to Network Routing

This work is a survey on the usage of machine learning techniques in wireless sensor networks (WSNs) and mobile ad-hoc networks (MANETs). Its focus lies on approaches for data routing. The goal of the work is two-fold: first, to classify and evaluate the most important existing and on-going research in the area and, second, to provide a guide for researchers wanting to apply machine learning techniques. For this, it also gives short description of the most appropriate algorithms and suggests for which scenarios they can be best used.
Towards Improving Top-N Recommendation by Generalization of SLIM

Sparse Linear Methods (SLIM) are state-of-the-art recommendation approaches based on matrix factorization, which rely on a regularized !1-norm and !2-norm optimization –an alternative optimization problem to the traditional Frobenious norm. Although they have shown outstanding performance in Top-N recommendation, existent works have not yet analyzed some inherent assumptions that can have an important effect on the performance of these algorithms. In this paper, we attempt to improve the performance of SLIM by proposing a generalized formulation of the aforementioned assumptions. Instead of directly learning a sparse representation of the user-item matrix, we (i) learn the latent factors’ matrix of the users and the items via a traditional matrix factorization approach, and then (ii) reconstruct the latent user or item matrix via prototypes which are learned using sparse coding, an alternative SLIM commonly used in the image processing domain. The results show that by tuning the parameters of our generalized model we are able to outperform SLIM in several Top-N recommendation experiments conducted on two different datasets, using both nDCG and nDCG@10 as evaluation metrics. These preliminary results, although not conclusive, indicate a promising line of research to improve the performance of SLIM recommendation.
A Hierarchical Approach to Adaptive Local Refinement in Isogeometric Analysis

Adaptive local refinement is one of the key issues in Isogeometric Analysis. In this article we present an adaptive local refinement technique for Isogeometric Analysis based on extensions of hierarchical B-splines. We investigate the theoretical properties of the spline space to ensure fundamental properties like linear independence and partition of unity. Furthermore, we use concepts well-established in finite element analysis to fully integrate hierarchical spline spaces into the isogeometric setting. This also allows us to access a posteriori error estimation techniques. Numerical results for several different examples are given and they turn out to be very promising.
A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications

In recent few years, the antenna and sensor communities have witnessed a considerable integration of radio frequency identification (RFID) tag antennas and sensors because of the impetus provided by internet of things (IoT) and cyber-physical systems (CPS). Such types of sensor can find potential applications in structural health monitoring (SHM) because of their passive, wireless, simple, compact size, and multimodal nature, particular in large scale infrastructures during their lifecycle. The big data from these ubiquitous sensors are expected to generate a big impact for intelligent monitoring. A remarkable number of scientific papers demonstrate the possibility that objects can be remotely tracked and intelligently monitored for their physical/chemical/mechanical properties and environment conditions. Most of the work focuses on antenna design, and significant information has been generated to demonstrate feasibilities. Further information is needed to gain deep understanding of the passive RFID antenna sensor systems in order to make them reliable and practical. Nevertheless, this information is scattered over much literature. This paper is to comprehensively summarize and clearly highlight the challenges and state-of-the-art methods of passive RFID antenna sensors and systems in terms of sensing and communication from system point of view. Future trends are also discussed. The future research and development in UK are suggested as well.
Multimedia Data Management for Disaster Situation Awareness

To raise awareness in disaster situations, the quality and analysis of disaster-related big data are essential. Recent developments in the collection, analysis, and visualization of multimedia data have led to a significant enhancement in disaster management systems. Crowdsourcing tools, for instance, allow citizens to perform an active role in reporting information relevant to disaster events at a global scale through popular social media sites such as Twitter and Facebook. As multimedia data analysis becomes further advanced, it can augment the disaster situation awareness and provide an efficient and timely response. This paper describes how multimedia data management plays a prominent role in improving the capabilities to readily manage disaster situations. Specifically, visualization provides a more convenient and user-friendly means for individuals who have limited experience in disaster situations. A case study introducing a 3D animation system is presented, which simulates the impacts of storm surge near coastal areas.
What are Digital Libraries? Competing Visions

Research and practice in digital libraries (DL) has exploded worldwide in the 1990s. Substantial research funding has become available, libraries are actively involved in DL projects and conferences, journals and online news lists proliferate. This article explores reasons for these developments and the in ̄uence of key players, while speculating on future directions. We ®nd that the term `digital library' is used in two distinct senses. In general, researchers view digital libraries as content collected on behalf of user communities, while practicing librarians view digital libraries as institutions or services. Tensions exist between these communities over the scope and concept of the term `library'. Research-oriented de®nitions serve to build a community of researchers and to focus attention on problems to be addressed; these de®nitions have expanded considerably in scope throughout the 1990s. Library community de®nitions are more recent and serve to focus attention on practical challenges to be addressed in the transformation of research libraries and universities. Future trends point toward the need for extensive research in digital libraries and for the transformation of libraries as institutions. The present ambiguity of terminology is hindering the advance of research and practice in digital libraries and in our ability to communicate the scope and signi®cance of our work.
Backpage and Bitcoin: Uncovering Human Traffickers

Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.
BodyScope: a wearable acoustic sensor for activity recognition

Accurate activity recognition enables the development of a variety of ubiquitous computing applications, such as context-aware systems, lifelogging, and personal health systems. Wearable sensing technologies can be used to gather data for activity recognition without requiring sensors to be installed in the infrastructure. However, the user may need to wear multiple sensors for accurate recognition of a larger number of different activities. We developed a wearable acoustic sensor, called BodyScope, to record the sounds produced in the user's throat area and classify them into user activities, such as eating, drinking, speaking, laughing, and coughing. The F-measure of the Support Vector Machine classification of 12 activities using only our BodyScope sensor was 79.5%. We also conducted a small-scale in-the-wild study, and found that BodyScope was able to identify four activities (eating, drinking, speaking, and laughing) at 71.5% accuracy.
Internet-of-Things security: Denial of service attacks

Internet of Things (IoT) is a network of sensors, actuators, mobile and wearable devices, simply things that have processing and communication modules and can connect to the Internet. In a few years time, billions of such things will start serving in many fields within the concept of IoT. Self-configuration, autonomous device addition, Internet connection and resource limitation features of IoT causes it to be highly prone to the attacks. Denial of Service (DoS) attacks which have been targeting the communication networks for years, will be the most dangerous threats to IoT networks. This study aims to analyze and classify the DoS attacks that may target the IoT environments. In addition to this, the systems that try to detect and mitigate the DoS attacks to IoT will be evaluated.
WikiRelate! Computing Semantic Relatedness Using Wikipedia

Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.
Loads and Responses for Planing Craft in Waves

Experimental and numerical analysis of loads and responses for planing craft in waves is considered. Extensive experiments have been performed on a planing craft, in full-scale as well as in model scale. The test set-ups and significant results are reviewed. The required resolution in experiments on planing craft in waves, concerning sampling frequencies, filtering and pressure transducer areas, is investigated. The aspects of peak identification in transient signals, fitting of analytical cumulative distribution functions to sampled data, and statistical convergence are treated. A method for reconstruction of the momentary pressure distribution at hull-water impact, from measurements with a limited number of transducers, is presented. The method is evaluated to full-scale data, and is concluded to be applicable in detailed evaluation of the hydrodynamic load distribution in time-domain simulations. Another suggested area of application is in full-scale design evaluations, where it can improve the traceability, i.e. enable evaluation of the loads along with the responses with more confidence. The presented model experiment was designed to enable time-domain monitoring of the complete hydromechanic pressure distribution on planing craft in waves. The test set-up is evaluated by comparing vertical forces and pitching moments derived from acceleration measurements, with the corresponding forces derived with the pressure distribution reconstruction method. Clear correlation is found. An approach for direct calculations of loads, as well as motion and structure response, is presented. Hydrodynamic loads and motion responses are calculated with a non-linear timedomain strip method. Structure responses are calculated by applying momentary distributed pressure loads, formulated from hydrodynamic simulations, on a global finite element model with inertia relief. From the time series output, limiting conditions and extreme responses are determined by means of short term statistics. Promising results are demonstrated in applications, where extreme structure responses derived by the presented approach, are compared with responses to equivalent uniform rule based loads, and measured responses from the full-scale trials. It is concluded that the approach is a useful tool for further research, which could be developed into a rational design method.
Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs

In order to match shoppers with desired products and provide personalized promotions, whether in online or offline shopping worlds, it is critical to model both consumer preferences and price sensitivities simultaneously. Personalized preferences have been thoroughly studied in the field of recommender systems, though price (and price sensitivity) has received relatively little attention. At the same time, price sensitivity has been richly explored in the area of economics, though typically not in the context of developing scalable, working systems to generate recommendations. In this study, we seek to bridge the gap between large-scale recommender systems and established consumer theories from economics, and propose a nested feature-based matrix factorization framework to model both preferences and price sensitivities. Quantitative and qualitative results indicate the proposed personalized, interpretable and scalable framework is capable of providing satisfying recommendations (on two datasets of grocery transactions) and can be applied to obtain economic insights into consumer behavior.
One Representation per Word - Does it make Sense for Composition?

In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone. We evaluate the performance of off-the-shelf singlevector and multi-sense vector models on a benchmark phrase similarity task and a novel task for word-sense discrimination. We find that single-sense vector models perform as well or better than multi-sense vector models despite arguably less clean elementary representations. Our findings furthermore show that simple composition functions such as pointwise addition are able to recover sense specific information from a single-sense vector model remark-
Measuring scientific impact beyond academia: An assessment of existing impact metrics and proposed improvements

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature.
What is stress, and how does it affect reproduction?

Stress is revealed by the inability of an animal to cope with its environment, a phenomenon that is often reflected in a failure to achieve genetic potential. Field data from dairy cows show that stressors such as milk fever or lameness increase the calving to conception interval by 13-14 days, and an extra 0.5 inseminations are required per conception. We suggest that a variety of endocrine regulatory points exist whereby stress limits the efficiency of reproduction. Transport produces an immediate constant increase in arginine vasopressin (AVP) and corticotrophin-releasing hormone (CRH) secretion in ewes, but adrenocorticotrophic hormone (ACTH) reaches a maximum in the first hour while cortisol is highest during the second hour. In contrast, after an insulin injection, the hypothalamo-pituitary-adrenal (HPA) response is delayed occurring only after glucose decreases below a threshold. Changes in AVP, CRH and ACTH each follow a similar time course, but eventually the secretion of AVP and CRH decreases while glucose is still at a nadir. Negative feedback effects appear to operate mainly at the pituitary level during transport but at the hypothalamus during hypoglycaemia. We also have endocrine evidence to show that stressors interfere with precise timings of reproductive hormone release within the follicular phase. Transport, or insulin, reduce the frequency and amplitude of gonadotrophin-releasing hormone and LH pulses, suggesting that these stressors exert effects at the hypothalamus or higher centres in the brain. Both stressors also delay the onset of the luteinising hormone (LH) surge. Preliminary results suggest that opioids mediate these effects but progesterone/glucocorticoid receptors are not involved because the antagonist, RU486, is unable to reverse insulin-induced delays in the LH surge. There is also evidence to support effects at pituitary level because exogenous ACTH, or transport, reduce the amount of LH released by challenges with GnRH. The reduction in endogenous GnRH/LH secretion ultimately deprives the ovarian follicle of adequate gonadotrophin support leading to reduced oestradiol production by slower growing follicles. Thus, there is a level of interference by stressors at the ovary. Reproduction is such an important physiological system that animals have to ensure that they can respond to their surroundings; thus, it is advantageous to have several protein mechanisms, i.e. at higher brain, hypothalamus, pituitary and target gland levels. However, when pushed too far, subfertility occurs.
Assessing the Financial Sustainability of China ’ s Rural Pension System

Considering the rapid growth of China’s elderly rural population, establishing both an adequate and a financially sustainable rural pension system is a major challenge. Focusing on financial sustainability, this article defines this concept of financial sustainability before constructing sound actuarial models for China’s rural pension system. Based on these models and statistical data, the analysis finds that the rural pension funding gap should rise from 97.80 billion Yuan in 2014 to 3062.31 billion Yuan in 2049, which represents an annual growth rate of 10.34%. This implies that, as it stands, the rural pension system in China is not financially sustainable. Finally, the article explains how this problem could be fixed through policy recommendations based on recent international experiences.
How Challenging is Sarcasm versus Irony Classification ? : An Analysis From Human and Computational Perspectives

Sarcasm and irony, although similar, differ in that sarcasm has an impact on sentiment (because it is used to ridicule a target) while irony does not. Past work treats the two interchangeably. In this paper, we wish to validate if sarcasm versus irony classification is indeed a challenging task. To this end, we use a dataset of quotes from English literature, and conduct experiments from two perspectives: the human perspective and the computational perspective. For the former, we show that three human annotators have lower agreement for sarcasm versus irony as compared to sarcasm versus philosophy. Similarly, sarcasm versus irony classification performs with a lower F-score as compared to another classification task where labels are not related: sarcasm versus philosophy classification. Another key point that our paper makes is that features designed for sarcasm versus non-sarcasm classification do not work well for sarcasm versus irony classification.
Is There Social Capital in a Social Network Site?: Facebook Use and College Students' Life Satisfaction, Trust, and Participation

This study examines if Facebook, one of the most popular social network sites among college students in the U.S., is related to attitudes and behaviors that enhance individuals’ social capital. Using data from a random web survey of college students across Texas (n = 2, 603), we find positive relationships between intensity of Facebook use and students’ life satisfaction, social trust, civic engagement, and political participation. While these findings should ease the concerns of those who fear that Facebook has mostly negative effects on young adults, the positive and significant associations between Facebook variables and social capital were small, suggesting that online social networks are not the most effective solution for youth disengagement from civic duty and democracy.
Sentiment Analysis and Opinion Mining: A Survey

In this age, in this nation, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions (Abraham Lincoln, 1858 ) . It is apparent from President Lincoln's well known quote that legislators understood the force of open assumption quite a while prior. In today world, the Internet is the main source of information. An enormous amount of information and opinion online is scattered and unstructured with no machine to arrange it. Because of demand the public to know opinions about exact product and services, political issues, or social scientists. That’s led us to study of field Opining Mining and Sentiment Analysis.  Mining and Sentiment Analysis have recently played a significant role for researchers because analysis of online text is beneficial for the market research political issue, business intelligence, online shopping, and scientific survey from psychological. Sentiment Analysis identifies the polarity of extracted public opinions. This paper presents a survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification.