nils-herrmann/clean_scirepeval huggingface dataset:
Transgenic Crops : Implications for Biodiversity and Sustainable Agriculture

The potential for genetically modified (GM) crops to threaten biodiversity conservation and sustainable agriculture is substantial. Megadiverse countries and centers of origin and/or diversity of crop species are particularly vulnerable regions. The future of sustainable agriculture may be irreversibly jeopardized by contamination of in situ preserved genetic resources threatening a strategic resource for the world’s food security. Because GM crops are truly biological novelties, their release into the environment poses concerns about the unpredictable ecological and evolutionary responses that GM species themselves and the interacting biota may express in the medium and long term. One of the consequences of these processes may be a generalized contamination of natural flora by GM traits and a degradation and erosion of the commonly owned genetic resources available today for agricultural development. GM plants carrying pharmaceutical and industrial traits will pose even more dangerous risks if released in the environment.
Neural Networks for Information Retrieval

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many approaches to many IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. The aim of this full- day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR.
The FinTech Opportunity

This paper assesses the potential impact of FinTech on the finance industry, focusing on financial stability and access to services. I document first that financial services remain surprisingly expensive, which explains the emergence of new entrants. I then argue that the current regulatory approach is subject to significant political economy and coordination costs, and therefore unlikely to deliver much structural change. FinTech, on the other hand, can bring deep changes but is likely to create significant regulatory challenges.
Applying convolutional neural network for network intrusion detection

Recently, Convolutional neural network (CNN) architectures in deep learning have achieved significant results in the field of computer vision. To transform this performance toward the task of intrusion detection (ID) in cyber security, this paper models network traffic as time-series, particularly transmission control protocol / internet protocol (TCP/IP) packets in a predefined time range with supervised learning methods such as multi-layer perceptron (MLP), CNN, CNN-recurrent neural network (CNN-RNN), CNN-long short-term memory (CNN-LSTM) and CNN-gated recurrent unit (GRU), using millions of known good and bad network connections. To measure the efficacy of these approaches we evaluate on the most important synthetic ID data set such as KDDCup 99. To select the optimal network architecture, comprehensive analysis of various MLP, CNN, CNN-RNN, CNN-LSTM and CNN-GRU with its topologies, network parameters and network structures is used. The models in each experiment are run up to 1000 epochs with learning rate in the range [0.01-05]. CNN and its variant architectures have significantly performed well in comparison to the classical machine learning classifiers. This is mainly due to the reason that CNN have capability to extract high level feature representations that represents the abstract form of low level feature sets of network traffic connections.
Educational data mining: A survey and a data mining-based analysis of recent works

0957-4174/$ see front matter 2013  La Escalera, Gustavo A. Madero, D.F., C.P. 07320,  2611 (cellular); fax: +52 55 5694 0916. E-mail address: apenaa@ipn.mx URL: http://www.wolnm.org/apa 1 AIWBES: adaptive and intelligent web-based educational systems; BKT: Bayesian knowledge tracing; CBES: computer-based educational systems; CBIS: computerbased information system,; DM: data mining; DP: dynamic programming; EDM: educational data mining; EM: expectation maximization; HMM: hidden Markov model; IBL: instances-based learning; IRT: item response theory; ITS: intelligent tutoring systems; KDD: knowledge discovery in databases; KT: knowledge tracing; LMS: learning management systems; SNA: social network analysis; SWOT: strengths, weakness, opportunities, and threats; WBC: web-based courses; WBES: web-based educational systems. Alejandro Peña-Ayala ⇑
A Survey of Question Answering for Math and Science Problem

Turing test was long considered the measure for artificial intelligence. But with the advances in AI, it has proved to be insufficient measure. We can now aim to measure machine intelligence like we measure human intelligence. One of the widely accepted measure of intelligence is standardized math and science test. In this paper, we explore the progress we have made towards the goal of making a machine smart enough to pass the standardized test. We see the challenges and opportunities posed by the domain, and note that we are quite some ways from actually making a system as smart as a even a middle school scholar.
Sustainable supply chain network design : An optimization-oriented review

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Sustainable supply chain network design: An optimization-oriented review Majid Eskandarpour, Pierre Dejax, Joe Miemczyk, Olivier Péton
INITIAL VALUE PROBLEMS IN DISCRETE FRACTIONAL CALCULUS

This paper is devoted to the study of discrete fractional calculus; the particular goal is to define and solve well-defined discrete fractional difference equations. For this purpose we first carefully develop the commutativity properties of the fractional sum and the fractional difference operators. Then a ν-th (0 < ν ≤ 1) order fractional difference equation is defined. A nonlinear problem with an initial condition is solved and the corresponding linear problem with constant coefficients is solved as an example. Further, the half-order linear problem with constant coefficients is solved with a method of undetermined coefficients and with a transform method.
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation

Recent work on evaluating representation learning architectures in NLP has established a need for evaluation protocols based on subconscious cognitive measures rather than manually tailored intrinsic similarity and relatedness tasks. In this work, we propose a novel evaluation framework that enables large-scale evaluation of such architectures in the free word association (WA) task, which is firmly grounded in cognitive theories of human semantic representation. This evaluation is facilitated by the existence of large manually constructed repositories of word association data. In this paper, we (1) present a detailed analysis of the new quantitative WA evaluation protocol, (2) suggest new evaluation metrics for the WA task inspired by its direct analogy with information retrieval problems, (3) evaluate various state-of-the-art representation models on this task, and (4) discuss the relationship between WA and prior evaluations of semantic representation with well-known similarity and relatedness evaluation sets. We have made the WA evaluation toolkit publicly available.
Separating fact from fiction: an examination of deceptive self-presentation in online dating profiles.

This study examines self-presentation in online dating profiles using a novel cross-validation technique for establishing accuracy. Eighty online daters rated the accuracy of their online self-presentation. Information about participants' physical attributes was then collected (height, weight, and age) and compared with their online profile, revealing that deviations tended to be ubiquitous but small in magnitude. Men lied more about their height, and women lied more about their weight, with participants farther from the mean lying more. Participants' self-ratings of accuracy were significantly correlated with observed accuracy, suggesting that inaccuracies were intentional rather than self-deceptive. Overall, participants reported being the least accurate about their photographs and the most accurate about their relationship information. Deception patterns suggest that participants strategically balanced the deceptive opportunities presented by online self-presentation (e.g., the editability of profiles) with the social constraints of establishing romantic relationships (e.g., the anticipation of future interaction).
Enhancing System Thinking in Education Using System Dynamics

System thinking skills are a prerequisite for acting successfully and responsibly in a complex world. However, traditional education largely fails to enhance system thinking skills whereas learner-centered educational methods seem more promising. Several such educational methods are compared with respect to their suitability for improving system thinking. It is proposed that integrated learning environments consisting of system dynamics models and additional didactical material have positive learning effects.This is exemplified by the illustration and validation of two learning sequences.
Biophysics: for HTS hit validation, chemical lead optimization, and beyond.

There are many challenges to the drug discovery process, including the complexity of the target, its interactions, and how these factors play a role in causing the disease. Traditionally, biophysics has been used for hit validation and chemical lead optimization. With its increased throughput and sensitivity, biophysics is now being applied earlier in this process to empower target characterization and hit finding.  In this article, the authors provide an overview of how biophysics can be utilized to assess the quality of the reagents used in screening assays, to validate potential tool compounds, to test the integrity of screening assays, and to create follow-up strategies for compound characterization. They also briefly discuss the utilization of different biophysical methods in hit validation to help avoid the resource consuming pitfalls caused by the lack of hit overlap between biophysical methods.  The use of biophysics early on in the drug discovery process has proven crucial to identifying and characterizing targets of complex nature. It also has enabled the identification and classification of small molecules which interact in an allosteric or covalent manner with the target. By applying biophysics in this manner and at the early stages of this process, the chances of finding chemical leads with novel mechanisms of action are increased. In the future, focused screens with biophysics as a primary readout will become increasingly common.
A low-power fully-integrated SP10T-RF-switch-IC

A new architecture has been designed and demonstrated for a low-power SP10T-RF-Switch-IC using 0.18μm SOI-CMOS, implementing an RF-Switch, negative voltage generator, and MIPI in a chip. Clock frequency of the negative voltage generator is controlled to increase only in a switch transition and drop at other times in order to reduce power consumption. Results of an evaluation of a trial chip confirmed a 33% reduction in power consumption compared with conventional architecture while RF performance is maintained.
HIGH-CONVERSION-RATIO BIDIRECTIONAL DC – DC CONVERTER WITH COUPLED INDUCTOR

In this paper, a high-conversion-ratio bidirectional dc–dc converter with coupled inductor is proposed. In the boost mode, two capacitors are parallel charged and series discharged by the coupled inductor. Thus, high step-up voltage gain can be achieved with an appropriate duty ratio. The voltage stress on the main switch is reduced by a passive clamp circuit. Therefore, the low resistance RDS (ON) of the main switch can be adopted to reduce conduction loss. In the buck mode, two capacitors are series charged and parallel discharged by the coupled inductor. The bidirectional converter can have high step-down gain. Aside from that, all of the switches achieve zero voltage-switching turn-on, and the switching loss can be improved. Due to two active clamp circuits, the energy of the leakage inductor of the coupled inductor is recycled. The efficiency can be further improved. The operating principle and the steady-state analyses of the voltage gain are discussed.
SemSearch: A Search Engine for the Semantic Web

Semantic search promises to produce precise answers to user queries by taking advantage of the availability of explicit semantics of information in the context of the semantic web. Existing tools have been primarily designed to enhance the performance of traditional search technologies but with little support for naive users, i.e., ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query languages.  In contrast with existing semantic-based keyword search engines which typically compromise their capability of handling complex user queries in order to overcome the problem of knowledge overhead, SemSearch not only overcomes the problem of knowledge overhead but also supports complex queries. Further, SemSearch provides comprehensive means to produce precise answers that on the one hand satisfy user queries and on the other hand are self-explanatory and understandable by end users. A prototype of the search engine has been implemented and applied in the semantic web portal of our lab. An initial evaluation shows promising results.
Congenitally missing maxillary lateral incisors and orthodontic treatment considerations for the single-tooth implant.

Implant restorations have become a primary treatment option for the replacement of congenitally missing lateral incisors. The central incisor and canine often erupt in less than optimal positions adjacent to the edentulous lateral incisor space, and therefore preprosthetic orthodontic treatment is frequently required. Derotation of the central incisor and canine, space closure and correction of root proximities may be required to create appropriate space in which to place the implant and achieve an esthetic restoration. This paper discusses aspects of preprosthetic orthodontic diagnosis and treatment that need to be considered with implant restorations.
Virginity Testing Beyond a Medical Examination

Apart from religious values, virginity is important in different communities because of its prominent role in reducing sexually transmitted diseases and teen pregnancies. Even though virginity testing has been proclaimed an example of violence against women by the World Health Organization, it is still conducted in many countries, including Iran. 16 in-depth, semi-structured interviews were conducted with participants aged 32 to 60 years to elucidate the perceptions and experiences of Iranian examiners of virginity testing.The perception and experience of examiners were reflected in five main themes. The result of this study indicated that virginity testing is more than a medical examination, considering the cultural factors involved and its overt and covert consequences. In Iran, testing is performed for both formal and informal reasons, and examiners view such testing with ambiguity about the accuracy and certainty of the diagnosis and uncertainty about ethics and reproductive rights. Examiners are affected by the overt and covert consequences of virginity testing, beliefs and cultural values underlying virginity testing, and informal and formal reasons for virginity testing.
Temporal Connectives in a Discourse Context

We examine the role of temporal connectives in multi-sentence discourse. In certain contexts, sentences containing temporal connectives that are equivalent in temporai structure can fail to be equivalent in terms of discourse coherence. We account for this by offering a novel, formal mechanism for accommodating the presuppositions in temporal subordinate clauses. This mechanism encompasses both accommodation by discourse aftachme,f and accommodation by temporal addition. As such, it offers a precise and systematic model of interactions between presupposed material, discourse context, and the reader's background knowledge. We show how the results of accommodation help to determine a discou~e's coherence.
Plasma brain natriuretic peptide as a prognostic indicator in patients with primary pulmonary hypertension.

Plasma brain natriuretic peptide (BNP) level increases in proportion to the degree of right ventricular dysfunction in pulmonary hypertension. We sought to assess the prognostic significance of plasma BNP in patients with primary pulmonary hypertension (PPH).



Plasma BNP was measured in 60 patients with PPH at diagnostic catheterization, together with atrial natriuretic peptide, norepinephrine, and epinephrine. Measurements were repeated in 53 patients after a mean follow-up period of 3 months. Forty-nine of the patients received intravenous or oral prostacyclin. During a mean follow-up period of 24 months, 18 patients died of cardiopulmonary causes. According to multivariate analysis, baseline plasma BNP was an independent predictor of mortality. Patients with a supramedian level of baseline BNP (>/=150 pg/mL) had a significantly lower survival rate than those with an inframedian level, according to Kaplan-Meier survival curves (P<0.05). Plasma BNP in survivors decreased significantly during the follow-up (217+/-38 to 149+/-30 pg/mL, P<0. 05), whereas that in nonsurvivors increased (365+/-77 to 544+/-68 pg/mL, P<0.05). Thus, survival was strikingly worse for patients with a supramedian value of follow-up BNP (>/=180 pg/mL) than for those with an inframedian value (P<0.0001).



A high level of plasma BNP, and in particular, a further increase in plasma BNP during follow-up, may have a strong, independent association with increased mortality rates in patients with PPH.
A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools

A huge repository of terabytes of data is generated each day from modern information systems and digital technologies such as Internet of Things and cloud computing. Analysis of these massive data requires a lot of efforts at multiple levels to extract knowledge for decision making. Therefore, big data analysis is a current area of research and development. The basic objective of this paper is to explore the potential impact of big data challenges, open research issues, and various tools associated with it. As a result, this article provides a platform to explore big data at numerous stages. Additionally, it opens a new horizon for researchers to develop the solution, based on the challenges and open research issues. Big data analytics; Hadoop; Massive data; Structured data; Unstructured Data
Enhancing fingerprint biometrics in Automated Border Control with adaptive cohorts

Automated Border Control (ABC) systems are being increasingly used to perform a fast, accurate, and reliable verification of the travelers' identity. These systems use biometric technologies to verify the identity of the person crossing the border. In this context, fingerprint verification systems are widely adopted due to their high accuracy and user acceptance. Matching score normalization methods can improve the performance of fingerprint recognition in ABC systems and mitigate the effect of non-idealities typical of this scenario without modifying the existing biometric technologies. However, privacy protection regulations restrict the use of biometric data captured in ABC systems and can compromise the applicability of these techniques. Cohort score normalization methods based only on impostor scores provide a suitable solution, due to their limited use of sensible data and to their promising performance. In this paper, we propose a privacy-compliant and adaptive normalization approach for enhancing fingerprint recognition in ABC systems. The proposed approach computes cohort scores from an external public dataset and uses computational intelligence to learn and improve the matching score distribution. The use of a public dataset permits to apply cohort normalization strategies in contexts in which privacy protection regulations restrict the storage of biometric data. We performed a technological and a scenario evaluation using a commercial matcher currently adopted in real ABC systems and we used data simulating different conditions typical of ABC systems, obtaining encouraging results.
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

References 1. Guan, P., Weiss, A., Balan, A., Black, M.J.: Estimating human shape and pose from a single image. ICCV 2009 2. Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.: DeepCut: Joint subset partition and labeling for multi person pose estimation. CVPR 2016 3. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black,  A skinned multi-person linear model. SIGGRAPH Asia 2015 4. Akhter, I., Black, M.J.: Pose-conditioned joint angle limits for 3D human pose reconstruction. CVPR 2015 5. Ramakrishna, V., Kanade, T., Sheikh, Y.: Reconstructing 3D Human Pose from 2D Image Landmarks. ECCV 2012 6. Zhou, X., Zhu, M., Leonardos, S., Derpanis, K., Daniilidis, K.: Sparse representation for 3D shape estimation: A convex relaxation approach.  Projected joints from 1000 synthetic 3D models + noise.
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition

This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images. Research trends to date are summarized, and challenges confronting the development of more accurate three-dimensional face recognition are identified. These challenges include the need for better sensors, improved recognition algorithms, and more rigorous experimental methodology.
Advertising and Media Market Concentration *

In media markets, the value of advertisement exposure depends on circulation, and media consumers’ valuation is affected by advertising. This paper analyzes media market competition in a duopoly framework. There exist symmetric and asymmetric equilibria in terms of firm size, and sometimes a natural monopoly may emerge . There is less scope for asymmetry when products are more differentiated or of higher intrinsic quality. Some media exhibit public good features. This increases the scope for asymmetry when consumers value advertising positively. If their valuation is negative only symmetric equilibria exist. Regulations limiting price competition increase the scope for natural monopoly.
Unravelling Names of Fictional Characters

In this paper we explore the correlation between the sound of words and their meaning, by testing if the polarity (‘good guy’ or ‘bad guy’) of a character’s role in a work of fiction can be predicted by the name of the character in the absence of any other context. Our approach is based on phonological and other features proposed in prior theoretical studies of fictional names. These features are used to construct a predictive model over a manually annotated corpus of characters from motion pictures. By experimenting with different mixtures of features, we identify phonological features as being the most discriminative by comparison to social and other types of features, and we delve into a discussion of specific phonological and phonotactic indicators of a character’s role’s polarity.