nils-herrmann/clean_scirepeval huggingface dataset:
Mediating the Undercurrents: Using Social Media to Sustain a Social Movement

While studies of social movements have mostly examined prevalent public discourses, undercurrents' the backstage practices consisting of meaning-making processes, narratives, and situated work-have received less attention. Through a qualitative interview study with sixteen participants, we examine the role of social media in supporting the undercurrents of the Umbrella Movement in Hong Kong. Interviews focused on an intense period of the movement exemplified by sit-in activities inspired by Occupy Wall Street in the USA. Whereas the use of Facebook for public discourse was similar to what has been reported in other studies, we found that an ecology of social media tools such as Facebook, WhatsApp, Telegram, and Google Docs mediated undercurrents that served to ground the public discourse of the movement. We discuss how the undercurrents sustained and developed public discourses in concrete ways.
Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models

The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for an expert’s risk sensitivity. To this end, we propose a flexible class of models based on coherent risk metrics, which allow us to capture an entire spectrum of risk preferences from risk-neutral to worst-case. We propose efficient algorithms based on Linear Programming for inferring an expert’s underlying risk metric and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with ten human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk-averse to risk-neutral in a data-efficient manner. Moreover, comparisons of the RiskSensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively.
Benchmarking Distributed Stream Processing Engines

Over the last years, stream data processing has been gaining atten— tion both in industry and in academia due to its wide range of appli— cations. To fulfill the need for scalable and efficient stream analyt— ics, numerous open source stream data processing systems (SDPSs) have been developed, with high throughput and low latency being their key performance targets. In this paper, we propose a frame— work to evaluate the performance of three SDPSs, namely Apache Storm, Apache Spark, and Apache Flink. Our evaluation focuses in particular on measuring the throughput and latency of windowed operations. For this benchmark, we design workloads based on real—life, industrial use—cases. The main contribution of this work is threefold. First, we give a definition of latency and throughput for stateful operators. Second, we completely separate the system under test and driver, so that the measurement results are closer to actual system performance under real conditions. Third, we build the first driver to test the actual sustainable performance of a system under test. Our detailed evaluation highlights that there is no single winner, but rather, each system excels in individual use—cases.
Domal stabilization suture in tip rhinoplasty.

To investigate use of the domal stabilization suture as a complementary suture modification technique for refining and securing the nasal tip.



A single permanent or absorbable suture is placed via an open or cartilage delivery approach. The suture is placed along the cephalic borders of the domes at the medial third of the lateral crura bilaterally just posterior to the junction of the intermediate and lateral crura as a final step in tip rhinoplasty.



The domal stabilization suture provided a means to help maintain dome symmetry in the setting of variable healing and scarring forces with no complications and no effect on tip rotation or projection.



Use of the domal stabilization suture enables correction of subtle changes in mild tip asymmetry and irregularities in domal height and provides subtle narrowing of the interdomal distance.
Minimal Coflow Routing and Scheduling in OpenFlow-Based Cloud Storage Area Networks

Researches affirm that coflow scheduling/routing substantially shortens the average application inner communication time in data center networks(DCNs). The commonly desirable critical features of existing coflow scheduling/routing framework includes (1) coflow scheduling, (2) coflow routing, and (3) per-flow rate-limiting. However, to provide the 3 features, existing frameworks require customized computing frameworks, customized operating systems, or specific external commercial monitoring frameworks on software-defined networking(SDN) switches. These requirements defer or even prohibit the deployment of coflow scheduling/routing in production DCNs. In this paper, we design a coflow scheduling and routing framework, MinCOF which has minimal requirements on hosts and switches for cloud storage area networks(SANs) based on OpenFlow SDN. MinCOF accommodates all critical features of coflow scheduling/routing from previous works. The deployability in production environment is especially taken into consideration. The OpenFlow architecture is capable of processing the traffic load in a cloud SAN. Not necessary requirements for hosts from existing frameworks are migrated to the mature commodity OpenFlow 1.3 Switch and our coflow scheduler. Transfer applications on hosts only need slight enhancements on their existing connection establishment and progress reporting functions. Evaluations reveal that MinCOF decreases the average coflow completion time (CCT) by 12.94% compared to the latest OpenFlow-based coflow scheduling and routing framework.
Standard operation procedures for conducting the on-the-road driving test, and measurement of the standard deviation of lateral position (SDLP)

This review discusses the methodology of the standardized on-the-road driving test and standard operation procedures to conduct the test and analyze the data. The on-the-road driving test has proven to be a sensitive and reliable method to examine driving ability after administration of central nervous system (CNS) drugs. The test is performed on a public highway in normal traffic. Subjects are instructed to drive with a steady lateral position and constant speed. Its primary parameter, the standard deviation of lateral position (SDLP), ie, an index of 'weaving', is a stable measure of driving performance with high test-retest reliability. SDLP differences from placebo are dose-dependent, and do not depend on the subject's baseline driving skills (placebo SDLP). It is important that standard operation procedures are applied to conduct the test and analyze the data in order to allow comparisons between studies from different sites.
The Rapid Growth of Cybercrimes Affecting Information Systems in the Global : Is this a Myth or Reality in Tanzania ?

The main objective of this study was to determine w hether the rapid growth of cybercrimes affecting in formation systems in the global: is a myth or reality in Tanz ia. The study was undertaken using a mixed resear ch methods. The research findings reveal that 12.8 % of users are victims of cybercrimes due to visiting unhealthy websites in cyberspace, more than 90.89% of users have been denied access to torrents /p2p applications (with malicious codes); and more than 63.29% of e-mails received by users are spam. It has been noted that Internet users has risen to 5.63 million users in 2012 out of 46.9 million of population in Tanzania; and is increasing at the ra t of 416.98% per year thus expectation of 7.34 mil lion users of Internet by December 2015. The increase in the number of Intern e users has direct implication to the increase in cybercrimes affecting information systems. The Government of Tanzania has declared that “about 320 people were apprehended b etween July and December 2011of which over 1bn/, Euro 8,897 and U SD 551,777 reported to have been stolen through cyb er”. The study concluded that there is a need for a holistic appro ach in addressing cybercrimes in a developing count ry like Tanzania. ; information systems; cyberspace; cyber security.
Deep Learning using Rectified Linear Units (ReLU)

We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. However, there have been several studies on using a classification function other than Softmax, and this study is an addition to those. We accomplish this by taking the activation of the penultimate layer hn−1 in a neural network, then multiply it by weight parameters θ to get the raw scores oi . Afterwards, we threshold the raw scores oi by 0, i.e. f (o) = max(0,oi ), where f (o) is the ReLU function. We provide class predictions ŷ through argmax function, i.e. argmax f (x).
Single-session gamified virtual reality exposure therapy for spider phobia vs. traditional exposure therapy: study protocol for a randomized controlled non-inferiority trial

Traditional one-session exposure therapy (OST) in which a patient is gradually exposed to feared stimuli for up to 3 h in a one-session format has been found effective for the treatment of specific phobias. However, many individuals with specific phobia are reluctant to seek help, and access to care is lacking due to logistic challenges of accessing, collecting, storing, and/or maintaining stimuli. Virtual reality (VR) exposure therapy may improve upon existing techniques by facilitating access, decreasing cost, and increasing acceptability and effectiveness. The aim of this study is to compare traditional OST with in vivo spiders and a human therapist with a newly developed single-session gamified VR exposure therapy application with modern VR hardware, virtual spiders, and a virtual therapist.



Participants with specific phobia to spiders (N = 100) will be recruited from the general public, screened, and randomized to either VR exposure therapy (n = 50) or traditional OST (n = 50). A behavioral approach test using in vivo spiders will serve as the primary outcome measure. Secondary outcome measures will include spider phobia questionnaires and self-reported anxiety, depression, and quality of life. Outcomes will be assessed using a non-inferiority design at baseline and at 1, 12, and 52 weeks after treatment.



VR exposure therapy has previously been evaluated as a treatment for specific phobias, but there has been a lack of high-quality randomized controlled trials. A new generation of modern, consumer-ready VR devices is being released that are advancing existing technology and have the potential to improve clinical availability and treatment effectiveness. The VR medium is also particularly suitable for taking advantage of recent phobia treatment research emphasizing engagement and new learning, as opposed to physiological habituation. This study compares a market-ready, gamified VR spider phobia exposure application, delivered using consumer VR hardware, with the current gold standard treatment. Implications are discussed.
Using Conceptual Class Attributes to Characterize Social Media Users

We describe a novel approach for automatically predicting the hidden demographic properties of social media users. Building on prior work in common-sense knowledge acquisition from third-person text, we first learn the distinguishing attributes of certain classes of people. For example, we learn that people in the Female class tend to have maiden names and engagement rings. We then show that this knowledge can be used in the analysis of first-person communication; knowledge of distinguishing attributes allows us to both classify users and to bootstrap new training examples. Our novel approach enables substantial improvements on the widelystudied task of user gender prediction, obtaining a 20% relative error reduction over the current state-of-the-art.
Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing

Intel has introduced a hardware-based trusted execution environment, Intel Software Guard Extensions (SGX), that provides a secure, isolated execution environment, or enclave, for a user program without trusting any underlying software (e.g., an operating system) or firmware. Researchers have demonstrated that SGX is vulnerable to a page-fault-based attack. However, the attack only reveals page-level memory accesses within an enclave. In this paper, we explore a new, yet critical, sidechannel attack, branch shadowing, that reveals finegrained control flows (branch granularity) in an enclave. The root cause of this attack is that SGX does not clear branch history when switching from enclave to nonenclave mode, leaving fine-grained traces for the outside world to observe, which gives rise to a branch-prediction side channel. However, exploiting this channel in practice is challenging because 1) measuring branch execution time is too noisy for distinguishing fine-grained controlflow changes and 2) pausing an enclave right after it has executed the code block we target requires sophisticated control. To overcome these challenges, we develop two novel exploitation techniques: 1) a last branch record (LBR)-based history-inferring technique and 2) an advanced programmable interrupt controller (APIC)-based technique to control the execution of an enclave in a finegrained manner. An evaluation against RSA shows that our attack infers each private key bit with 99.8% accuracy. Finally, we thoroughly study the feasibility of hardwarebased solutions (i.e., branch history flushing) and propose a software-based approach that mitigates the attack.
Limits to community participation in the tourism development process in developing countries

This study deals with a normative concept of participatory development approach, which originates in the developed world. In particular, it analyses and explains the limitations to the participatory tourism development approach in the context of developing countries. It was found that there are operational, structural and cultural limits to community participation in the TDP in many developing countries although they do not equally exist in every tourist destination. Moreover, while these limits tend to exhibit higher intensity and greater persistence in the developing world than in the developed world, they appear to be a re#ection of prevailing socio-political, economic and cultural structure in many developing countries. On the other hand, it was also found that although these limitations may vary over time according to types, scale and levels of tourism development, the market served, and cultural attributes of local communities, forms and scale of tourism developed are beyond the control of local communities. It concludes that formulating and implementing the participatory tourism development approach requires a total change in sociopolitical, legal, administrative and economic structure of many developing countries, for which hard political choices and logical decisions based on cumbersome social, economic and environmental trade-o!s are sine qua non alongside deliberate help, collaboration and co-operation of major international donor agencies, NGOs, international tour operators and multinational companies.
Traffic flows analysis and visualization based on data from an advanced Vilnius traveller ’ s information system

As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real-time traffic information to travellers. This paper analyses traffic flows data, describes methodology of traffic flows data processing and visualization in digital ArcGIS online maps. Calculation based on real time traffic data from equipped traffic sensors in Vilnius city streets. The paper also discusses about traffic conditions and impacts for Vilnius streets network from the point of traffic flows view. Furthermore, a comprehensive traffic flow GIS modelling procedure is presented, which relates traffic flows data from sensors to street network segments and updates traffic flow data to GIS database. GIS maps examples and traffic flows analysis possibilities in this paper presented as well.
Shock-Based Causal Inference in Corporate Finance and Accounting Research

We study shock-based methods for credible causal inference in corporate finance research. We focus on corporate governance research, survey 13,461 papers published between 2001 and 2011 in 22 major accounting, economics, finance, law, and management journals; and identify 863 empirical studies in which corporate governance is associated with firm value or other characteristics. We classify the methods used in these studies and assess whether they support a causal link between corporate governance and firm value or another outcome. Only a stall minority of studies have convincing causal inference strategies. The convincing strategies largely rely on external shocks – usually from legal rules – often called “natural experiments”. We examine the 74 shock-based papers and provide a guide to shock-based research design, which stresses the common features across different designs and the value of using combined designs.
SEMANTIC SIMILARITY BETWEEN SENTENCES

The task of measuring sentence similarity is defined as determining how similar the meanings of two sentences are. Computing sentence similarity is not a trivial task, due to the variability of natural language expressions. Measuring semantic similarity of sentences is closely related to semantic similarity between words. It makes a relationship between a word and the sentence through their meanings. The intention is to enhance the concepts of semantics over the syntactic measures that are able to categorize the pair of sentences effectively. Semantic similarity plays a vital role in Natural language processing, Informational Retrieval, Text Mining, Q & A systems, text-related research and application area. Traditional similarity measures are based on the syntactic features and other path based measures. In this project, we evaluated and tested three different semantic similarity approaches like cosine similarity, path based approach (wu – palmer and shortest path based), and feature based approach. Our proposed approaches exploits preprocessing of pair of sentences which identifies the bag of words and then applying the similarity measures like cosine similarity, path based similarity measures. In our approach the main contributions are comparison of existing similarity measures and feature based measure based on Wordnet. In feature based approach we perform the tagging and lemmatization and generates the similarity score based on the nouns and verbs. We evaluate our project output by comparing the existing measures based on different thresholds and comparison between three approaches. Finally we conclude that feature based measure generates better semantic score.
Efficacy of Mulligan ’ s two leg rotation and bent leg raise techniques in hamstring flexibility in subjects with acute non-specific low back pain ( NS-LBP ) : Randomized clinical trial

H would you design an exercise program to promote synthesis and healing of collagen if it were the primary tissue in lesion? Are isometric contractions the most appropriate type of exercise for post-surgical rehabilitation or guarded musculature? What dosage is ideal for mobilization exercises, and how can you localize your forces? These questions and more will be addressed. Exercise is not always emphasized in manual therapy schools of thought. Classically, the focus has been on passive treatments such as articulation, manipulation, and soft tissue massage, all of which aim to increase range of motion, inhibit pain and guarding, and normalize somato-visceral reflexes. Dosed therapeutic exercise is as valuable an approach to achieve these same goals as well as produce numerous additional benefits for your patient. This one-day course will review the history of medical exercise therapy, S.T.E.P principles, exercise physiology, histology, dosage concepts, and a variety of exercise progression examples. This will be an introductory course with several video examples and a few lab activities.
FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test

The maximum mean discrepancy (MMD) is a recently proposed test statistic for the two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner’s theorem and Fourier transform (Rahimi & Recht, 2007). Taking advantage of sampling the Fourier transform, FastMMD decreases the time complexity for MMD calculation from to , where N and d are the size and dimension of the sample set, respectively. Here, L is the number of basis functions for approximating kernels that determines the approximation accuracy. For kernels that are spherically invariant, the computation can be further accelerated to by using the Fastfood technique (Le, Sarlós, & Smola, 2013). The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We also provide a geometric explanation for our method, ensemble of circular discrepancy, which helps us understand the insight of MMD and we hope will lead to more extensive metrics for assessing the two-sample test task. Experimental results substantiate that the accuracy of FastMMD is similar to that of MMD and with faster computation and lower variance than existing MMD approximation methods.
Perceived development of psychological characteristics in Male and Female elite gymnasts

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. Perceived development of psychological characteristics in Male and Female elite gymnasts Claire Calmels, Fabienne D’Arripe-Longueville, Magaly Hars, Nadine Debois