nils-herrmann/clean_scirepeval huggingface dataset:
Prerequisites between learning objects: Automatic extraction based on a machine learning approach

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task. In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.
Advances on localization techniques for wireless sensor networks: A survey

Localization in Wireless Sensor Networks (WSNs) is regarded as an emerging technology for numerous cyberphysical system applications, which equips wireless sensors with the capability to report data that is geographically meaningful for location based services and applications. However, due to the increasingly pervasive existence of smart sensors in WSN, a single localization technique that affects the overall performance is not sufficient for all applications. Thus, there have been many significant advances on localization techniques in WSNs in the past few years. The main goal in this paper is to present the state-of-the-art research results and approaches proposed for localization in WSNs. Specifically, we present the recent advances on localization techniques in WSNs by considering a wide variety of factors and categorizing them in terms of data processing (centralized vs. distributed), transmission range (range free vs. range based), mobility (static vs. mobile), operating environments (indoor vs. outdoor), node density (sparse vs dense), routing, algorithms, etc. The recent localization techniques in WSNs are also summarized in the form of tables. With this paper, readers can have a more thorough understanding of localization in sensor networks, as well as research trends and future research directions in this area.
Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix

A a new type of word-of-mouth information, online consumer product review is an emerging market phenomenon that is playing an increasingly important role in consumers’ purchase decisions. This paper argues that online consumer review, a type of product information created by users based on personal usage experience, can serve as a new element in the marketing communications mix and work as free “sales assistants” to help consumers identify the products that best match their idiosyncratic usage conditions. This paper develops a normative model to address several important strategic issues related to consumer reviews. First, we show when and how the seller should adjust its own marketing communication strategy in response to consumer reviews. Our results reveal that if the review information is sufficiently informative, the two types of product information, i.e., the seller-created product attribute information and buyer-created review information, will interact with each other. For example, when the product cost is low and/or there are sufficient expert (more sophisticated) product users, the two types of information are complements, and the seller’s best response is to increase the amount of product attribute information conveyed via its marketing communications after the reviews become available. However, when the product cost is high and there are sufficient novice (less sophisticated) product users, the two types of information are substitutes, and the seller’s best response is to reduce the amount of product attribute information it offers, even if it is cost-free to provide such information. We also derive precise conditions under which the seller can increase its profit by adopting a proactive strategy, i.e., adjusting its marketing strategies even before consumer reviews become available. Second, we identify product/market conditions under which the seller benefits from facilitating such buyer-created information (e.g., by allowing consumers to post user-based product reviews on the seller’s website). Finally, we illustrate the importance of the timing of the introduction of consumer reviews available as a strategic variable and show that delaying the availability of consumer reviews for a given product can be beneficial if the number of expert (more sophisticated) product users is relatively large and cost of the product is low.
The best advice I ever got.

A young manager faces an impasse in his career. He goes to see his mentor at the company, who closes the office door, offers the young man a chair, recounts a few war stories, and serves up a few specific pointers about the problem at hand. Then, just as the young manager is getting up to leave, the elder executive adds one small kernel of avuncular wisdom--which the junior manager carries with him through the rest of his career. Such is the nature of business advice. Or is it? The six essays in this article suggest otherwise. Few of the leaders who tell their stories here got their best advice in stereotypical form, as an aphorism or a platitude. For Ogilvy & Mather chief Shelly Lazarus, profound insight came from a remark aimed at relieving the tension of the moment. For Novartis CEO Daniel Vasella, it was an apt comment, made on a snowy day, back when he was a medical resident. For publishing magnate Earl Graves and Starwood Hotels' Barry Sternlicht, advice they received about trust from early bosses took on ever deeper and more practical meaning as their careers progressed. For Goldman Sachs chairman Henry Paulson, Jr., it was as much his father's example as it was a specific piece of advice his father handed down to him. And fashion designer Liz Lange rejects the very notion that there's inherent wisdom in accepting other people's advice. As these stories demonstrate, people find wisdom when they least expect to, and they never really know what piece of advice will transcend the moment, profoundly affecting how they later make decisions, evaluate people, and examine--and reexamine--their own actions.
Third symmetric power L-functions for GL ( 2 )

In this paper we shall prove two results. The first one is of interest in number theory and automorphic forms, while the second is a result in harmonic analysis on p-adic reductive groups. The two results, even though seemingly different, are fairly related by a conjecture of Langlands [13]. To explain the first result let F be a number field and denote by AF its ring of adeles. Given a place v of F, we let Fv denote its completion at v. Let 03C0 be a cusp form on GL2(AF). Write n = Q9v1tv. · For an unramified v, let diag(cxv, 03B2v) denote the diagonal element in GL2(C), the L-group of GL2, attached to 1tv. For a fixed positive integer m, let rm denote the m-th symmetric power representation of the standard representation r, of GL2(C) which is an irreducible (m + 1 )-dimensional representation. Then, for a complex number s, the local Langlands L-function [14] attached to 1tv and rm is
Evidence-based practice: beliefs, attitudes, knowledge, and behaviors of physical therapists.

Little research has been done regarding the attitudes and behaviors of physical therapists relative to the use of evidence in practice. The purposes of this study were to describe the beliefs, attitudes, knowledge, and behaviors of physical therapist members of the American Physical Therapy Association (APTA) as they relate to evidence-based practice (EBP) and to generate hypotheses about the relationship between these attributes and personal and practice characteristics of the respondents.



A survey of a random sample of physical therapist members of APTA resulted in a 48.8% return rate and a sample of 488 that was fairly representative of the national membership. Participants completed a questionnaire designed to determine beliefs, attitudes, knowledge, and behaviors regarding EBP, as well as demographic information about themselves and their practice settings. Responses were summarized for each item, and logistic regression analyses were used to examine relationships among variables.



Respondents agreed that the use of evidence in practice was necessary, that the literature was helpful in their practices, and that quality of patient care was better when evidence was used. Training, familiarity with and confidence in search strategies, use of databases, and critical appraisal tended to be associated with younger therapists with fewer years since they were licensed. Seventeen percent of the respondents stated they read fewer than 2 articles in a typical month, and one quarter of the respondents stated they used literature in their clinical decision making less than twice per month. The majority of the respondents had access to online information, although more had access at home than at work. According to the respondents, the primary barrier to implementing EBP was lack of time.



Physical therapists stated they had a positive attitude about EBP and were interested in learning or improving the skills necessary to implement EBP. They noted that they needed to increase the use of evidence in their daily practice.
How the relationship between the crisis life cycle and mass media content can better inform crisis communication .

Crises are unpredictable events that can impact on an organisation’s viability, credibility, and reputation, and few topics have generated greater interest in communication over the past 15 years. This paper builds on early theory such as Fink (1986), and extends the crisis life-cycle theoretical model to enable a better understanding and prediction of the changes and trends of mass media coverage during crises. This expanded model provides a framework to identify and understand the dynamic and multi-dimensional set of relationships that occurs during the crisis life cycle in a rapidly changing and challenging operational environment. Using the 2001 Ansett Airlines’ Easter groundings as a case study, this paper monitors mass media coverage during this organisational crisis. The analysis reinforces the view that, by using proactive strategies, public relations practitioners can better manage mass media crisis coverage. Further, the understanding gained by extending the crisis life cycle to track when and how mass media content changes may help public relations practitioners craft messages and supply information at the outset of each stage of the crisis, thereby maintaining control of the message.
Critical Failure Factors in ERP Implementation

This study firstly examines the current literature concerning ERP implementation problems during implementation phases and causes of ERP implementation failure. A multiple case study research methodology was adopted to understand “why” and “how” these ERP systems could not be implemented successfully. Different stakeholders (including top management, project manager, project team members and ERP consultants) from these case studies were interviewed, and ERP implementation documents were reviewed for triangulation. An ERP life cycle framework was applied to study the ERP implementation process and the associated problems in each phase of ERP implementation. Fourteen critical failure factors were identified and analyzed, and three common critical failure factors (poor consultant effectiveness, project management effectiveness and poo555îr quality of business process re-engineering) were examined and discussed. Future research on ERP implementation and critical failure factors is discussed. It is hoped that this research will help to bridge the current literature gap and provide practical advice for both academics and practitioners.
CNN- and LSTM-based Claim Classification in Online User Comments

When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.
"It's complicated": collective memories of transgender, queer, and questioning youth in high school.

Using the qualitative participatory action methodology, collective memory work, this study explored how transgender, queer, and questioning (TQQ) youth make meaning of their sexual orientation and gender identity through high school experiences. Researchers identified three major conceptual but overlapping themes from the data generated in the transgender, queer, and questioning youth focus group: a need for resilience, you should be able to be safe, and this is what action looks like! The researchers discuss how as a research product, a documentary can effectively "capture voices" of participants, making research accessible and attractive to parents, practitioners, policy makers, and participants.
Relaying Protocols for Wireless Energy Harvesting and Information Processing

An emerging solution for prolonging the lifetime of energy constrained relay nodes in wireless networks is to avail the ambient radio-frequency (RF) signal and to simultaneously harvest energy and process information. In this paper, an amplify-and-forward (AF) relaying network is considered, where an energy constrained relay node harvests energy from the received RF signal and uses that harvested energy to forward the source information to the destination. Based on the time switching and power splitting receiver architectures, two relaying protocols, namely, i) time switching-based relaying (TSR) protocol and ii) power splitting-based relaying (PSR) protocol are proposed to enable energy harvesting and information processing at the relay. In order to determine the throughput, analytical expressions for the outage probability and the ergodic capacity are derived for delay-limited and delay-tolerant transmission modes, respectively. The numerical analysis provides practical insights into the effect of various system parameters, such as energy harvesting time, power splitting ratio, source transmission rate, source to relay distance, noise power, and energy harvesting efficiency, on the performance of wireless energy harvesting and information processing using AF relay nodes. In particular, the TSR protocol outperforms the PSR protocol in terms of throughput at relatively low signal-to-noise-ratios and high transmission rates.
Enlarging the useable workspace of planar parallel robots using mechanisms of variable geometry

This paper demonstrates the efficiency of kinematic redundancy used to increase the useable workspace of planar parallel mechanisms. As examples, we propose kinematically redundant schemes of the well known planar 3RRR and 3RPR mechanisms denoted as 3(P)RRR and 3(P)RPR. In both cases, a prismatic actuator is added allowing a usually fixed base joint to move linearly. Hence, reconfigurations can be performed selectively in order to avoid singularities and to affect the mechanisms' performance directly. Using an interval-based method the useable workspace, i.e. the singularity-free workspace guaranteeing a desired performance, is obtained. Due to the interval analysis any uncertainties can be implemented within the algorithm leading to practical and realistic results. It is shown that due to the additional prismatic actuator the useable workspace increases significantly. Several analysis examples clarify the efficiency of the proposed kinematically redundant mechanisms.
Malware clustering using image processing hashes

Malware clustering is a big challenge and because of the large amount of samples, it is a problem in Big Data processing. Since the number of Malwares is huge, general clustering algorithms are not applicable to Malware clustering for its time complexity. The time complexity is high since we need to compute similarity between every pair of samples that costs O (n2). In this paper, we apply image processing perceptual hashing methods that do not need any comparison between samples and are really fast and applicable to a huge number of samples. Comparison results of our method to other methods indicate better scalability and good accuracy.
Contact-Free Support Structures for Part Overhangs in Powder-Bed Metal Additive Manufacturing

This study investigates the feasibility of a novel concept, contact-free support structures, for part overhangs in powder-bed metal additive manufacturing. The intent is to develop alternative support designs that require no or little post-processing, and yet, maintain effective in minimizing overhang distortions. The idea is to build, simultaneously during part fabrications, a heat sink (called “heat support”), underneath an overhang to alter adverse thermal behaviors. Thermomechanical modeling and simulations using finite element analysis were applied to numerically research the heat support effect on overhang distortions. Experimentally, a powderbed electron beam additive manufacturing system was utilized to fabricate heat support designs and examine their functions. The results prove the concept and demonstrate the effectiveness of contact-free heat supports. Moreover, the method has been tested with different heat support parameters and applied to various overhang geometries. It is concluded that the heat support proposed has potential to be implemented in industrial applications.
Brain-computer interface technology: a review of the first international meeting.

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.
A Survey of FPGA Based Neural Network Accelerator

Recent researches on neural network have shown signicant advantage in machine learning over traditional algorithms based on handcraed features and models. Neural network is now widely adopted in regions like image, speech and video recognition. But the high computation and storage complexity of neural network inference poses great diculty on its application. CPU platforms are hard to oer enough computation capacity. GPU platforms are the rst choice for neural network process because of its high computation capacity and easy to use development frameworks. On the other hand, FPGA-based neural network inference accelerator is becoming a research topic. With specically designed hardware, FPGA is the next possible solution to surpass GPU in speed and energy eciency. Various FPGA-based accelerator designs have been proposed with soware and hardware optimization techniques to achieve high speed and energy eciency. In this paper, we give an overview of previous work on neural network inference accelerators based on FPGA and summarize the main techniques used. An investigation from soware to hardware, from circuit level to system level is carried out to complete analysis of FPGA-based neural network inference accelerator design and serves as a guide to future work.
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection

In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing. Instead of treating NER as a sequence labelling problem, we propose a new local detection approach, which rely on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed-size representation. Afterwards, a simple feedforward neural network is used to reject or predict entity label for each individual fragment. The proposed method has been evaluated in several popular NER and mention detection tasks, including the CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Trilingual Entity Discovery and Linking (EDL) tasks. Our methods have yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labelling methods.