nils-herrmann/clean_scirepeval huggingface dataset:
Diagnostic Evaluation of Information Retrieval Models

Developing effective retrieval models is a long-standing central challenge in information retrieval research. In order to develop more effective models, it is necessary to understand the deficiencies of the current retrieval models and the relative strengths of each of them. In this article, we propose a general methodology to analytically and experimentally diagnose the weaknesses of a retrieval function, which provides guidance on how to further improve its performance. Our methodology is motivated by the empirical observation that good retrieval performance is closely related to the use of various retrieval heuristics. We connect the weaknesses and strengths of a retrieval function with its implementations of these retrieval heuristics, and propose two strategies to check how well a retrieval function implements the desired retrieval heuristics. The first strategy is to formalize heuristics as constraints, and use constraint analysis to analytically check the implementation of retrieval heuristics. The second strategy is to define a set of relevance-preserving perturbations and perform diagnostic tests to empirically evaluate how well a retrieval function implements retrieval heuristics. Experiments show that both strategies are effective to identify the potential problems in implementations of the retrieval heuristics. The performance of retrieval functions can be improved after we fix these problems.
Enterprise Resource Planning (ERP) Systems: An Empirical Analysis of Benefits

This paper uses a database, derived from a data repository, in order to do an analysis of enterprise resource planning (ERP) system benefits. ERP benefits are important for a number of reasons, including establishing a match between what ERP systems benefits areas compared to ERP expectationssetting a benchmark for other firms, and measuring those benefits. ERP benefits also are central to the business case for deciding whether a firm will invest in an ERP system. It is found that some benefits vary across industry, while others seem to be important to firms independent of industry. In particular, tangible benefits are largely industry-independent, while intangible benefits vary across industry. In addition, when compared to an earlier study by Deloitte Consulting, the results are statistically consistent with their findings, but find substantial additional intangible benefits.
Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes

With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this research, a time series prediction model by combining nonlinear independent component analysis (NLICA) and neural network is proposed to forecast Asian stock markets. NLICA is a novel feature extraction technique to find independent sources from observed nonlinear mixture data where no relevant data mixing mechanisms are available. In the proposed method, we first use NLICA to transform the input space composed of original time series data into the feature space consisting of independent components representing underlying information of the original data. Then, the ICs are served as the input variables of the neural network to build prediction model. Among the Asian stock markets, Japanese and China’s stock markets are the biggest two in Asia and they respectively represent the two types of stock markets. Therefore, in order to evaluate the performance of the proposed approach, the Nikkei 225 closing index and Shanghai B-share closing index are used as illustrative examples. Experimental results show that the proposed forecasting model not only improves the prediction accuracy of the neural network approach but also outperforms the three comparison methods. The proposed stock index prediction model can be therefore a good alternative for Asian stock market indexes.
A Multimodal Emotion Detection System during Human-Robot Interaction

In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human-robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human-robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately.
Chronic, multisite, multielectrode recordings in macaque monkeys.

A paradigm is described for recording the activity of single cortical neurons from awake, behaving macaque monkeys. Its unique features include high-density microwire arrays and multichannel instrumentation. Three adult rhesus monkeys received microwire array implants, totaling 96-704 microwires per subject, in up to five cortical areas, sometimes bilaterally.  and a signal-to-noise ratio of better than 5:1. As many as 247 cortical neurons were recorded in one session, and at least 58 neurons were isolated from one subject 18 months after implantation. This method should benefit neurophysiological investigation of learning, perception, and sensorimotor integration in primates and the development of neuroprosthetic devices.
Pushing the contextual envelope: developing and diffusing IS theory for health information systems research

The healthcare sector is a crucial and socially challenging component of modern economies. Information systems (IS) research could contribute to the effective development, application and use of information technologies to manage and coordinate health services. Healthcare also provides opportunities to develop or refine IS theory because of its unique institutional context. To profile IS research in health-related settings, we examine the publication of health information systems research (HISR) in 17 IS journals since 1985. Our analysis revealed a small but growing body of HISR literature. These publications are concentrated in ‘‘HISR-friendly journals’’ and employ a variety of strategies for balancing general IS theories and knowledge with attention to the institutional characteristics of healthcare. We consider the strengths and limitations of these strategies in advancing HISR within the IS field and for contributing to multidisciplinary HISR knowledge.
THE CONSUMER LOAN ’ S PAYMENT DEFAULT PREDICTIVE MODEL : AN APPLICATION IN A TUNISIAN COMMERCIAL BANK

For the alarming growth in consumer credit in recent years, consumer credit scoring is the term used to describe methods of classifying credits’ applicants as `good' and `bad' risk classes.. In the current paper, we use the logistic regression as well as the discriminant analysis in order to develop predictive models allowing to distinguish between “good” and “bad” borrowers. The data have been collected from a commercial Tunisian bank over a 3-year period, from 2010 to 2012. These data consist of four selected and ordered variables. By comparing the respective performances of the Logistic Regression (LR) and the Discriminant Analysis (DA), we notice that the LR model yields a 89% good classification rate in predicting customer types and then, a significantly low error rate (11%), as compared with the DA approach (where the good classification rate is only equal to 68.49%, leading to a significantly high error rate, i.e. 31.51%).
Virtual reality and tactile augmentation in the treatment of spider phobia: a case report.

This is the first case report to demonstrate the efficacy of immersive computer-generated virtual reality (VR) and mixed reality (touching real objects which patients also saw in VR) for the treatment of spider phobia. The subject was a 37-yr-old female with severe and incapacitating fear of spiders. Twelve weekly 1-hr sessions were conducted over a 3-month period. Outcome was assessed on measures of anxiety, avoidance, and changes in behavior toward real spiders. VR graded exposure therapy was successful for reducing fear of spiders providing converging evidence for a growing literature showing the effectiveness of VR as a new medium for exposure therapy.
The benefit of interleaved mathematics practice is not limited to superficially similar kinds of problems.

Most mathematics assignments consist of a group of problems requiring the same strategy. For example, a lesson on the quadratic formula is typically followed by a block of problems requiring students to use that formula, which means that students know the appropriate strategy before they read each problem. In an alternative approach, different kinds of problems appear in an interleaved order, which requires students to choose the strategy on the basis of the problem itself. In the classroom-based experiment reported here, grade 7 students (n = 140) received blocked or interleaved practice over a nine-week period, followed two weeks later by an unannounced test. The mean test scores were greater for material learned by interleaved practice rather than by blocked practice (72 % vs. 38 %, d = 1.05). This interleaving effect was observed even though the different kinds of problems were superficially dissimilar from each other, whereas previous interleaved mathematics studies had required students to learn nearly identical kinds of problems. We conclude that interleaving improves mathematics learning not only by improving discrimination between different kinds of problems, but also by strengthening the association between each kind of problem and its corresponding strategy.
Optimal User Scheduling and Power Allocation for Millimeter Wave NOMA Systems

This paper investigates the application of non-orthogonal multiple access (NOMA) in millimeter wave (mm-Wave) communications by exploiting beamforming, user scheduling, and power allocation. Random beamforming is invoked for reducing the feedback overhead of the considered system. A non-convex optimization problem for maximizing the sum rate is formulated, which is proved to be NP-hard. The branch and bound approach is invoked to obtain the  $\epsilon$ -optimal power allocation policy, which is proved to converge to a global optimal solution. To elaborate further, a low-complexity suboptimal approach is developed for striking a good computational complexity-optimality tradeoff, where the matching theory and successive convex approximation techniques are invoked for tackling the user scheduling and power allocation problems, respectively. Simulation results reveal that: 1) the proposed low complexity solution achieves a near-optimal performance and 2) the proposed mm-Wave NOMA system is capable of outperforming conventional mm-Wave orthogonal multiple access systems in terms of sum rate and the number of served users.
S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters

Availability of large data sets like ImageNet and massively parallel computation support in modern HPC devices like NVIDIA GPUs have fueled a renewed interest in Deep Learning (DL) algorithms. This has triggered the development of DL frameworks like Caffe, Torch, TensorFlow, and CNTK. However, most DL frameworks have been limited to a single node. In order to scale out DL frameworks and bring HPC capabilities to the DL arena, we propose, S-Caffe; a scalable and distributed Caffe adaptation for modern multi-GPU clusters. With an in-depth analysis of new requirements brought forward by the DL frameworks and limitations of current communication runtimes, we present a co-design of the Caffe framework and the MVAPICH2-GDR MPI runtime. Using the co-design methodology, we modify Caffe's workflow to maximize the overlap of computation and communication with multi-stage data propagation and gradient aggregation schemes. We bring DL-Awareness to the MPI runtime by proposing a hierarchical reduction design that benefits from CUDA-Aware features and provides up to a massive 133x speedup over OpenMPI and 2.6x speedup over MVAPICH2 for 160 GPUs. S-Caffe successfully scales up to 160 K-80 GPUs for GoogLeNet (ImageNet) with a speedup of 2.5x over 32 GPUs. To the best of our knowledge, this is the first framework that scales up to 160 GPUs. Furthermore, even for single node training, S-Caffe shows an improvement of 14\% and 9\% over Nvidia's optimized Caffe for 8 and 16 GPUs, respectively. In addition, S-Caffe achieves up to 1395 samples per second for the AlexNet model, which is comparable to the performance of Microsoft CNTK.
Patient risk factors for pressure ulcer development: systematic review.

To identify risk factors independently predictive of pressure ulcer development in adult patient populations?



A systematic review of primary research was undertaken, based upon methods recommended for effectiveness questions but adapted to identify observational risk factor studies.



Fourteen electronic databases were searched, each from inception until March 2010, with hand searching of specialist journals and conference proceedings; contact with experts and a citation search. There was no language restriction.



Abstracts were screened, reviewed against the eligibility criteria, data extracted and quality appraised by at least one reviewer and checked by a second. Where necessary, statistical review was undertaken. We developed an assessment framework and quality classification based upon guidelines for assessing quality and methodological considerations in the analysis, meta-analysis and publication of observational studies. Studies were classified as high, moderate, low and very low quality. Risk factors were categorised into risk factor domains and sub-domains. Evidence tables were generated and a summary narrative synthesis by sub-domain and domain was undertaken.



Of 5462 abstracts retrieved, 365 were identified as potentially eligible and 54 fulfilled the eligibility criteria. The 54 studies included 34,449 patients and acute and community patient populations. Seventeen studies were classified as high or moderate quality, whilst 37 studies (68.5%) had inadequate numbers of pressure ulcers and other methodological limitations. Risk factors emerging most frequently as independent predictors of pressure ulcer development included three primary domains of mobility/activity, perfusion (including diabetes) and skin/pressure ulcer status. Skin moisture, age, haematological measures, nutrition and general health status are also important, but did not emerge as frequently as the three main domains. Body temperature and immunity may be important but require further confirmatory research. There is limited evidence that either race or gender is important.



Overall there is no single factor which can explain pressure ulcer risk, rather a complex interplay of factors which increase the probability of pressure ulcer development. The review highlights the limitations of over-interpretation of results from individual studies and the benefits of reviewing results from a number of studies to develop a more reliable overall assessment of factors which are important in affecting patient susceptibility.
Sentiment analysis of arabic social media content: a comparative study

The Internet became an indispensable part of people's lives because of the significant role it plays in the ways individuals interact, communicate and collaborate with each other. Over recent years, social media sites succeed in attracting a large portion of online users where they become not only content readers but also content generators and publishers. Social media users generate daily a huge volume of comments and reviews related to different aspects of life including: political, scientific and social subjects. In general, sentiment analysis refers to the task of identifying positive and negative opinions, emotions and evaluations related to an article, news, products, services, etc. Arabic sentiment analysis is conducted in this study using a small dataset consisting of 1,000 Arabic reviews and comments collected from Facebook and Twitter social network websites. The collected dataset is used in order to conduct a comparison between two free online sentiment analysis tools: SocialMention and SentiStrength that support Arabic language. The results which based on based on the two of classifiers (Decision tree (J48) and SVM) showed that the SentiStrength is better than SocialMention tool.
A Hand-held Instrument to Maintain Steady Tissue Contact during Probe-Based Confocal Laser Endomicroscopy

Probe-based confocal laser endomicroscopy (pCLE) provides high-resolution in vivo imaging for intraoperative tissue characterization. Maintaining a desired contact force between target tissue and the pCLE probe is important for image consistency, allowing large area surveillance to be performed. A hand-held instrument that can provide a predetermined contact force to obtain consistent images has been developed. The main components of the instrument include a linear voice coil actuator, a donut load-cell, and a pCLE probe. In this paper, detailed mechanical design of the instrument is presented and system level modeling of closed-loop force control of the actuator is provided. The performance of the instrument has been evaluated in bench tests as well as in hand-held experiments. Results demonstrate that the instrument ensures a consistent predetermined contact force between pCLE probe tip and tissue. Furthermore, it compensates for both simulated physiological movement of the tissue and involuntary movements of the operator's hand. Using pCLE video feature tracking of large colonic crypts within the mucosal surface, the steadiness of the tissue images obtained using the instrument force control is demonstrated by confirming minimal crypt translation.
Gas Leakage Detection and Smart Alerting and prediction using IoT

IoT is an expanding network of physical devices that are linked with different types of sensors and with the help of connectivity to the internet, they are able to exchange data. Through IoT, internet has now extended its roots to almost every possible thing present around us and is no more limited to our personal computers and mobile phones. Safety, the elementary concern of any project, has not been left untouched by IoT. Gas Leakages in open or closed areas can prove to be dangerous and lethal. The traditional Gas Leakage Detector Systems though have great precision, fail to acknowledge a few factors in the field of alerting the people about the leakage. Therefore we have used the IoT technology to make a Gas Leakage Detector having Smart Alerting techniques involving calling, sending text message and an e-mail to the concerned authority and an ability to predict hazardous situation so that people could be made aware in advance by performing data analytics on sensor readings.
Underwater Wireless Optical Communication Channel Modeling and Performance Evaluation using Vector Radiative Transfer Theory

This paper presents the modeling of an underwater wireless optical communication channel using the vector radiative transfer theory. The vector radiative transfer equation captures the multiple scattering nature of natural water, and also includes the polarization behavior of light. Light propagation in an underwater environment encounters scattering effect creating dispersion which introduces inter-symbol-interference to the data communication. The attenuation effect further reduces the signal to noise ratio. Both scattering and absorption have adverse effects on underwater data communication. Using a channel model based on radiative transfer theory, we can quantify the scattering effect as a function of distance and bit rate by numerical Monte Carlo simulations. We also investigate the polarization behavior of light in the underwater environment, showing the significance of the cross-polarization component when the light encounters more scattering.
Minimizing the Total Weighted Completion Time of Coflows in Datacenter Networks

Communications in datacenter jobs (such as the shuffle operations in MapReduce applications) often involve many parallel flows, which may be processed simultaneously. This highly parallel structure presents new scheduling challenges in optimizing job-level performance objectives in data centers. Chowdhury and Stoica introduced the coflow abstraction to capture these communication patterns, and recently Chowdhury et al. developed effective heuristics to schedule coflows. In this paper, we consider the problem of efficiently scheduling coflows with release dates so as to minimize the total weighted completion time, which has been shown to be strongly NP-hard. Our main result is the first polynomial-time deterministic approximation algorithm for this problem, with an approximation ratio of 67/3, and a randomized version of the algorithm, with a ratio of 9+16√2/3. Our results use techniques from both combinatorial scheduling and matching theory, and rely on a clever grouping of coflows. We also run experiments on a Facebook trace to test the practical performance of several algorithms, including our deterministic algorithm. Our experiments suggest that simple algorithms provide effective approximations of the optimal, and that our deterministic algorithm has near-optimal performance.