nils-herrmann/clean_scirepeval huggingface dataset:
Identification of milestone papers through time-balanced network centrality

Citations between scientific papers and related bibliometric indices, such as the h-index for authors and the impact factor for journals, are being increasingly used – often in controversial ways – as quantitative tools for research evaluation. Yet, a fundamental research question remains still open: to which extent do quantitative metrics capture the significance of scientific works? We analyze the network of citations among the 449, 935 papers published by the American Physical Society (APS) journals between 1893 and 2009, and focus on the comparison of metrics built on the citation count with network-based metrics. We contrast five article-level metrics with respect to the rankings that they assign to a set of fundamental papers, called Milestone Letters, carefully selected by the APS editors for “making long-lived contributions to physics, either by announcing significant discoveries, or by initiating new areas of research”. A new metric, which combines PageRank centrality with the explicit requirement that paper score is not biased by paper age, is the best-performing metric overall in identifying the Milestone Letters. The lack of time bias in the new metric makes it also possible to use it to compare papers of different age on the same scale. We find that networkbased metrics identify the Milestone Letters better than metrics based on the citation count, which suggests that the structure of the citation network contains information that can be used to improve the ranking of scientific publications. The methods and results presented here are relevant for all evolving systems where network centrality metrics are applied, for example the World Wide Web and online social networks.
State-of-the-art Chinese Word Segmentation with Bi-LSTMs

A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neuralnetwork architectures. Furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes. Instead, more effort should be made on exploring resources for further improvement.
S3ORAM: A Computation-Efficient and Constant Client Bandwidth Blowup ORAM with Shamir Secret Sharing

Oblivious Random Access Machine (ORAM) enables a client to access her data without leaking her access patterns. Existing client-efficient ORAMs either achieve O(log N) client-server communication blowup without heavy computation, or O(1) blowup but with expensive homomorphic encryptions. It has been shown that O(log N) bandwidth blowup might not be practical for certain applications, while schemes with O(1) communication blowup incur even more delay due to costly homomorphic operations.
 In this paper, we propose a new distributed ORAM scheme referred to as Shamir Secret Sharing ORAM (S3ORAM), which achieves O(1) client-server bandwidth blowup and O(1) blocks of client storage without relying on costly partial homomorphic encryptions. S3ORAM harnesses Shamir Secret Sharing, tree-based ORAM structure and a secure multi-party multiplication protocol to eliminate costly homomorphic operations and, therefore, achieves O(1) client-server bandwidth blowup with a high computational efficiency. We conducted comprehensive experiments to assess the performance of S3ORAM and its counterparts on actual cloud environments, and showed that S3ORAM achieves three orders of magnitude lower end-to-end delay compared to alternatives with O(1) client communication blowup (Onion-ORAM), while it is one order of magnitude faster than Path-ORAM for a network with a moderate bandwidth quality. We have released the implementation of S3ORAM for further improvement and adaptation.
Effective explanations of recommendations: user-centered design

This paper characterizes general properties of useful, or Effective, explanations of recommendations. It describes a methodology based on focus groups, in which we elicit what helps moviegoers decide whether or not they would like a movie. Our results highlight the importance of personalizing explanations to the individual user, as well as considering the source of recommendations, user mood, the effects of group viewing, and the effect of explanations on user expectations.
Design of CPW fed bow-tie slot antenna for ground penetrating radar application

This paper presents the antenna for ground penetrating radar (GPR) for soil characteristics measurement application. The objective is operating frequency having range from 250-1250 MHz whilst restricting antenna lobs radiate to ground direction. The demand of ground penetrating radar design is antenna with ultra wide bandwidths (UWB). Designing optimum gain consideration of 5dBi thus the conventional antennas such as dipoles cannot be used effectively throughout the frequency band. The main objective of this proposed antenna is to use it for agriculture soil characteristics extraction. This paper proposes the practicability of building a ultra-wide band bow-tie slot antenna for GPR system is explored. Aluminum back cavity is considered to achieve unidirectional radiation pattern and enhance the gain of antenna. Considering best simulation results antenna is configured and implemented.
Deep Feature Consistent Deep Image Transformations: Downscaling, Decolorization and HDR Tone Mapping

Building on crucial insights into the determining factors of the visual integrity of an image and the property of deep convolutional neural network (CNN), we have developed the Deep Feature Consistent Deep Image Transformation (DFC-DIT) framework which unifies challenging one-to-many mapping image processing problems such as image downscaling, decolorization (colour to grayscale conversion) and high dynamic range (HDR) image tone mapping. DFC-DIT uses one CNN as a non-linear mapper to transform an input image to an output image following the deep feature consistency principle which is enforced through another pretrained and fixed deep CNN. Unlike applications in related literature, none of these problems has a known ground truth or target for a supervised learning system. For each problem, we reason about a suitable learning objective function and develop an effective solution under the DFC-DIT framework. This is the first work that uses deep learning to solve and unify these three common image processing tasks. We present experimental results to demonstrate the effectiveness of the DFC-DIT technique and its state of the art performances.
A Deep Learning Approach for the Prediction of Retail Store Sales

The purpose of this research is to construct a sales prediction model for retail stores using the deep learning approach, which has gained significant attention in the rapidly developing field of machine learning in recent years. Using such a model for analysis, an approach to store management could be formulated. The present study uses three years' worth of point-of-sale (POS) data from a retail store to construct a sales prediction model that, given the sales of a particular day, predicts the changes in sales on the following day. As a result, a deep learning model that considers the L1 regularization achieved a sale forecasting accuracy rate of 86%. The products at the retail store have been finely categorized. Even if the attributes of the product categories are increased in number from tens to thousands, the predictive accuracy did not fall by more than about 7%. In contrast, the accuracy decreased by around 13% when the logistic regression model was used. These results indicate that deep learning is highly suitable for constructing models that include multi-attribute variables. The present research demonstrates that deep learning is effective for analyzing the POS data of retail stores.
Business Sustainability Maturity Model

This conceptual paper in sustainable business research introduces a business sustainability maturity model as an innovative solution to support companies move towards sustainable development. Such model offers the possibility for each firm to individually assess its position regarding five sustainability maturity levels and, as a consequence, build a tailored as well as a common strategy along its network of relationships and influence to progress towards higher levels of sustainable development. The maturity model suggested is based on the belief that business sustainability is a continuous process of evolution in which a company will be continuously seeking to achieve its vision of sustainable development in uninterrupted cycles of improvement, where at each new cycle the firm starts the process at a higher level of business sustainability performance. The referred model is therefore dynamic to incorporate changes along the way and enable its own evolution following the firm’s and its network partners’ progress towards the sustainability vision. The research on which this paper is based combines expertise in science and technology policy, R&D and innovation management, team performance and organisational learning, strategy alignment and integrated business performance, knowledge management and technology foresighting.
Unsupervised Neural Machine Translation with Weight Sharing

Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, EnglishFrench and Chinese-to-English translation tasks.
Impulse voltage generator modelling using MATLAB

MATLAB is specifically designed for simulating dynamic systems. This paper describes a method of modelling impulse voltage generator using Simulink, an extension of MATLAB. The equations for modelling have been developed and a corresponding Simulink model has been constructed. It shows that Simulink program becomes very useful in studying the effect of parameter changes in the design to obtain the desired impulse voltages and waveshapes from an impulse generator.
Deep Embedding Forest: Forest-based Serving with Deep Embedding Features

Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require additional hardware that are not in the mainstream design of most commercial applications. In contrast, tree or forest-based models are widely adopted because of low serving cost, but heavily depend on carefully engineered features. This work proposes a Deep Embedding Forest model that benefits from the best of both worlds. The model consists of a number of embedding layers and a forest/tree layer. The former maps high dimensional (hundreds of thousands to millions) and heterogeneous low-level features to the lower dimensional (thousands) vectors, and the latter ensures fast serving.
 Built on top of a representative DNN model called Deep Crossing, and two forest/tree-based models including XGBoost and LightGBM, a two-step Deep Embedding Forest algorithm is demonstrated to achieve on-par or slightly better performance as compared with the DNN counterpart, with only a fraction of serving time on conventional hardware. After comparing with a joint optimization algorithm called partial fuzzification, also proposed in this paper, it is concluded that the two-step Deep Embedding Forest has achieved near optimal performance. Experiments based on large scale data sets (up to 1 billion samples) from a major sponsored search engine proves the efficacy of the proposed model.
A slacks-based measure of super-efficiency in data envelopment analysis : An alternative approach

The current paper proposes a slack-based version of the Super SBM, which is an alternative superefficiency model for the SBM proposed by Tone. Our two-stage approach provides the same superefficiency score as that obtained by the Super SBM model when the evaluated DMU is efficient and yields the same efficiency score as that obtained by the SBM model when the evaluated DMU is inefficient. The projection identified by the Super SBM model may not be strongly Pareto efficient; however, the projection identified from our approach is strongly Pareto efficient. & 2012 Elsevier Ltd. All rights reserved.
The Effect of Relaxation Exercises for the Masticator Muscles on
Temporomandibular Joint Dysfunction (TMD)

The purpose of this study was to identify the influence of relaxation exercises for the masticator muscles on the limited ROM and pain of temporomandibular joint dysfunction (TMD).  The subjects were 10 men and 31 women in their 20s and 30s. They were randomly divided into no treatment, active exercises and relaxation exercise for the masticator muscle groups. The exercise groups performed exercises three times or more a day over a period of four weeks, performing exercise for 10 minutes each time. Before and after the four weeks, all the subjects were measured for ROM, deviation, occlusion, and pain in the temporomandibular joint.  ROM, deviation and pain showed statistically significant in improvements after the intervention in the active exercise and relaxation exercise for the masticator muscle groups. Deviation also showed a statistically significant difference between the active exercise and relaxation exercise groups.  The results verify that as with active exercises, relaxation exercises for the masticatory muscles are an effective treatment for ROM and pain in TMD. Particularly, masticatory muscle relaxation exercises were found to be a treatment that is also effective for deviation.
Instagram Use, Loneliness, and Social Comparison Orientation: Interact and Browse on Social Media, But Don't Compare

Ever since the emergence of social networking sites (SNSs), it has remained a question without a conclusive answer whether SNSs make people more or less lonely. To achieve a better understanding, researchers need to move beyond studying overall SNS usage. In addition, it is necessary to attend to personal attributes as potential moderators. Given that SNSs provide rich opportunities for social comparison, one highly relevant personality trait would be social comparison orientation (SCO), and yet this personal attribute has been understudied in social media research. Drawing on literature of psychosocial implications of social media use and SCO, this study explored associations between loneliness and various Instagram activities and the role of SCO in this context. A total of 208 undergraduate students attending a U.S. mid-southern university completed a self-report survey (Mage = 19.43, SD = 1.35; 78 percent female; 57 percent White). Findings showed that Instagram interaction and Instagram browsing were both related to lower loneliness, whereas Instagram broadcasting was associated with higher loneliness. SCO moderated the relationship between Instagram use and loneliness such that Instagram interaction was related to lower loneliness only for low SCO users. The results revealed implications for healthy SNS use and the importance of including personality traits and specific SNS use patterns to disentangle the role of SNS use in psychological well-being.
Stigma: ignorance, prejudice or discrimination?

The term stigma refers to problems of knowledge (ignorance), attitudes (prejudice) and behaviour (discrimination). Most research in this area has been based on attitude surveys, media representations of mental illness and violence, has only focused upon schizophrenia, has excluded direct participation by service users, and has included few intervention studies. However, there is evidence that interventions to improve public knowledge about mental illness can be effective. The main challenge in future is to identify which interventions will produce behaviour change to reduce discrimination against people with mental illness.
The Simulation Method of the Galvanic Coupling Intrabody Communication With Different Signal Transmission Paths

The simulation method plays an important role in the investigation of the intrabody communication (IBC). Due to the problems of the transfer function and the corresponding parameters, only the simulation of the galvanic coupling IBC along the arm has been achieved at present. In this paper, a method for the mathematical simulation of the galvanic coupling IBC with different signal transmission paths has been introduced. First, a new transfer function of the galvanic coupling IBC was derived with the consideration of the internal resistances of the IBC devices. Second, the determination of the corresponding parameters used in the transfer function was discussed in detail. Finally, both the measurements and the simulations of the galvanic coupling IBC along the different signal transmission paths were carried out. Our investigation shows that the mathematical simulation results coincide with the measurement results over the frequency range from 100 kHz to 5 MHz, which indicates that the proposed method offers the significant advantages in the theoretical analysis and the application of the galvanic coupling IBC.
Decision support from financial disclosures with deep neural networks and transfer learning

Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long shortterm memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response ∗Corresponding author.  mathias.kraus@is.uni-freiburg.de; Tel: +49 761 203 2395;  Email addresses: mathias.kraus@is.uni-freiburg.de (Mathias Kraus),  (Stefan Feuerriegel) Preprint submitted to Decision Support Systems July 6, 2018 ar X iv :1 71 0. 03 95 4v 1 [ cs .C L ] 1 1 O ct 2 01 7 to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives.
POS Tagging of English-Hindi Code-Mixed Social Media Content

Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling variations, transliteration and non-adherance to formal grammar. We describe our initial efforts to create a multi-level annotated corpus of Hindi-English codemixed text collated from Facebook forums, and explore language identification, back-transliteration, normalization and POS tagging of this data. Our results show that language identification and transliteration for Hindi are two major challenges that impact POS tagging accuracy.
A Proactive DDoS Attack Detection Approach Using Data Mining Cluster Analysis

Detection and preventing Distributed Denial of Service Attack (DDoS) becomes a crucial process for the commercial organization that using the internet these days. Different approaches have been adopted to process traffic information collected by a monitoring stations (Routers and Servers) to distinguish the misbehaving of malicious traffic of DDoS attacks in Intrusion Detection Systems (IDS). In general, data mining techniques can be designed and implemented with the intrusion systems to protect the organizations from malicious. Specifically, unsupervised data mining clustering techniques allow to effectively distinguish the normal traffic from malicious traffic in a good accuracy. In this paper, we present a hybrid approach called centroid-based rules to detect and prevent a real-world DDoS attacks collected from “CAIDA UCSD " DDoS Attack 2007 Dataset” and normal traffic traces from “CAIDA Anonymized Internet Traces 2008 Dataset” using unsupervised k-means data mining clustering techniques with proactive rules method. Centroid-based rules are used to effectively detect the DDoS attack in an efficient time. The Result of experiments shows that the centroid-based rules method perform better than the centroid-based method in term of accuracy and detection rate. In term of false alarm rates, the proposed solution obtains very low false positive rate in the training process and testing phases. Results of accuracy were more than 99% in training and testing processes. The proposed centroid-based rules method can be used in a real-time monitoring as DDoS defense system.
Terahertz band: Next frontier for wireless communications

This paper provides an in-depth view of Terahertz Band (0.1–10 THz) communication, which is envisioned as a key technology to satisfy the increasing demand for higher speed wireless communication. THz Band communication will alleviate the spectrum scarcity and capacity limitations of current wireless systems, and enable new applications both in classical networking domains as well as in novel nanoscale communication paradigms. In this paper, the device design and development challenges for THz Band are surveyed first. The limitations and possible solutions for high-speed transceiver architectures are highlighted. The challenges for the development of new ultra-broadband antennas and very large antenna arrays are explained. When the devices are finally developed, then they need to communicate in the THz band. There exist many novel communication challenges such as propagation modeling, capacity analysis, modulation schemes, and other physical and link layer solutions, in the THz band which can be seen as a new frontier in the communication research. These challenges are treated in depth in this paper explaining the existing plethora of work and what still needs to be tackled.