nils-herrmann/clean_scirepeval huggingface dataset:
Exploring the Context of Locations for Personalized Location Recommendations

Conventional location recommendation models rely on users’ visit history, geographical influence, temporal influence, etc., to infer users’ preferences for locations. However, systematically modeling a location’s context (i.e., the set of locations visited before or after this location) is relatively unexplored. In this paper, by leveraging the Skipgram model, we learn the latent representation for a location to capture the influence of its context. A pair-wise ranking loss that considers the confidences of observed user preferences for locations is then proposed to learn users’ latent representations for personalized top-N location recommendations. Moreover, we also extend our model by taking into account temporal influence. Stochastic gradient descent based optimization algorithms are developed to fit the models. We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location recommendation methods.
Chinese Grammatical Error Diagnosis Using Single Word Embedding

Automatic grammatical error detection for Chinese has been a big challenge for NLP researchers. Due to the formal and strict grammar rules in Chinese, it is hard for foreign students to master Chinese. A computer-assisted learning tool which can automatically detect and correct Chinese grammatical errors is necessary for those foreign students. Some of the previous works have sought to identify Chinese grammatical errors using templateand learning-based methods. In contrast, this study introduced convolutional neural network (CNN) and long-short term memory (LSTM) for the shared task of Chinese Grammatical Error Diagnosis (CGED). Different from traditional word-based embedding, single word embedding was used as input of CNN and LSTM. The proposed single word embedding can capture both semantic and syntactic information to detect those four type grammatical error. In experimental evaluation, the recall and f1-score of our submitted results Run1 of the TOCFL testing data ranked the fourth place in all submissions in detection-level.
The Use of Twitter to Track Levels of Disease Activity and Public
                    Concern in the U.S. during the Influenza A H1N1 Pandemic

Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's "tweets," or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.
A survey of network anomaly detection techniques

Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection. & 2015
MIMO rate adaptation in 802.11n wireless networks

This paper studies MIMO based rate adaptation (RA) in 802.11n wireless networks. Our case study shows that existing RA algorithms offer much lower throughput than even a fixed-rate scheme. The fundamental problem is that, all such algorithms are MIMO oblivious; they do not consider the characteristics of diversity-oriented, single-stream mode and the spatial multiplexing driven, double-stream mode. We propose MiRA, a novel MIMO RA scheme that zigzags between intra- and inter-mode rate options. Our experiments show that MiRA consistently outperforms three representative RA algorithms, SampleRate, RRAA and Atheros MIMO RA, in static, mobility and collision settings.
Virtual reality treatment versus exposure in vivo: a comparative evaluation in acrophobia.

The aim of the present study was to evaluate the effectiveness of low-budget virtual reality (VR) exposure versus exposure in vivo in a between-group design in 33 patients suffering from acrophobia. The virtual environments used in treatment were exactly copied from the real environments used in the exposure in vivo program. VR exposure was found to be as effective as exposure in vivo on anxiety and avoidance as measured with the Acrophobia Questionnaire (AQ), the Attitude Towards Heights Questionnaire (ATHQ) and the Behavioral Avoidance Test (BAT). Results were maintained up to six months follow-up. The present study shows that VR exposure can be effective with relatively cheap hardware and software on stand-alone computers currently on the market. Further studies into the effectiveness of VR exposure are recommended in other clinical groups as agoraphobics and social phobics and studies in which VR exposure is compared with more emerging virtual worlds as presented in CAVE-type systems.
Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks

Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets.
Analysis of Docker Security

Over the last few years, the use of virtualization technologies has increased dramatically. This makes the demand for efficient and secure virtualization solutions become more obvious. Container-based virtualization and hypervisor-based virtualization are two main types of virtualization technologies that have emerged to the market. Of these two classes, container-based virtualization is able to provide a more lightweight and efficient virtual environment, but not without security concerns. In this paper, we analyze the security level of Docker, a well-known representative of container-based approaches. The analysis considers two areas: (1) the internal security of Docker, and (2) how Docker interacts with the security features of the Linux kernel, such as SELinux and AppArmor, in order to harden the host system. Furthermore, the paper also discusses and identifies what could be done when using Docker to increase its level of security.
CAPTCHA Recognition with Active Deep Learning

CAPTCHAs are automated tests to tell computers and humans apart. They are designed to be easily solvable by humans, but unsolvable by machines. With Convolutional Neural Networks these tests can also be solved automatically. However, the strength of CNNs relies on the training data that the classifier is learnt on and especially on the size of the training set. Hence, it is intractable to solve the problem with CNNs in case of insufficient training data. We propose an Active Deep Learning strategy that makes use of the ability to gain new training data for free without any human intervention which is possible in the special case of CAPTCHAs. We discuss how to choose the new samples to re-train the network and present results on an auto-generated CAPTCHA dataset. Our approach dramatically improves the performance of the network if we initially have only few labeled training data.
Ultimately Fast Accurate Summation

We present two new algorithms FastAccSum and FastPrecSum, one to compute a faithful rounding of the sum of floating-point numbers and the other for a result “as if” computed in K-fold precision. Faithful rounding means the computed result either is one of the immediate floating-point neighbors of the exact result or is equal to the exact sum if this is a floating-point number. The algorithms are based on our previous algorithms AccSum and PrecSum and improve them by up to 25%. The first algorithm adapts to the condition number of the sum; i.e., the computing time is proportional to the difficulty of the problem. The second algorithm does not need extra memory, and the computing time depends only on the number of summands and K. Both algorithms are the fastest known in terms of flops. They allow good instruction-level parallelism so that they are also fast in terms of measured computing time. The algorithms require only standard floating-point addition, subtraction, and multiplication in one working precision, for example, double precision.
Noninvasive Blood Glucose Measurement Based on NIR Spectrums and Double ANN Analysis

This paper presents a new noninvasive blood glucose monitoring method based on four near infrared spectrums and double artificial neural network analysis. We choose four near infrared wavelengths, 820 nm, 875 nm, 945 nm, 1050 nm, as transmission spectrums, and capture four fingers transmission PPG signals simultaneously. The wavelet transform algorithm is used to remove baseline drift, smooth signals and extract eight eigenvalues of each PPG signal. The eigenvalues are the input parameters of double artificial neural network analysis model. Double artificial neural network regression combines the classification recognition algorithm with prediction algorithm to improve the accuracy of measurement. Experiments show that the root mean square error of the prediction is between 0.97 mg/dL 6.69 mg/dL, the average of root mean square error is 3.80 mg/dL.
Named entity recognition in query

This paper addresses the problem of Named Entity Recognition in Query (NERQ), which involves detection of the named entity in a given query and classification of the named entity into predefined classes. NERQ is potentially useful in many applications in web search. The paper proposes taking a probabilistic approach to the task using query log data and Latent Dirichlet Allocation. We consider contexts of a named entity (i.e., the remainders of the named entity in queries) as words of a document, and classes of the named entity as topics. The topic model is constructed by a novel and general learning method referred to as WS-LDA (Weakly Supervised Latent Dirichlet Allocation), which employs weakly supervised learning (rather than unsupervised learning) using partially labeled seed entities. Experimental results show that the proposed method based on WS-LDA can accurately perform NERQ, and outperform the baseline methods.
Modified Hysteretic Current Control (MHCC) for Improving Transient Response of Boost Converter

This paper proposes a modulated hysteretic current control (MHCC) technique to improve the transient response of a DC-DC boost converter, which suffers from low bandwidth due to the existence of the right-half-plane (RHP) zero. The MHCC technique can automatically adjust the on-time value to rapidly increase the inductor current, as well as to shorten the transient response time. In addition, based on the characteristic of the RHP zero, the compensation poles and zero are deliberately adjusted to achieve fast transient response in case of load transient condition and adequate phase margin in steady state. Experimental results show the improvement of transient recovery time over 7.2 times in the load transient response compared with the conventional boost converter design when the load current changes from light to heavy or vice versa. The power consumption overhead is merely 1%.
PARA-SNORT : A MULTI-THREAD SNORT ON MULTI-CORE IA PLATFORM

As security threats and network bandwidth increase in a very fast pace, there is a growing interest in designing highperformance network intrusion detection system (NIDS). This paper presents a parallelization strategy for the popular open-source Snort to build a high performance NIDS on multi-core IA platform. A modular design of parallel NIDS based on Snort is proposed in this paper. Named Para-Snort, it enables flexible and easy module design. This paper also analyzed the performance impact of load balancing and multi-pattern matching. Modified-JSQ and AC-WM algorithms are implemented in order to resolve the bottlenecks and improve the performance of the system. Experimental results show that Para-Snort achieves significant speedup of 4 to 6 times for various traces with a 7-thread parallelizing test setup.
Improving network intrusion detection system performance through quality of service configuration and parallel technology

This paper outlines an innovative software development that utilizes Quality of Service (QoS) and parallel technologies in Cisco Catalyst Switches to increase the analytical performance of a Network Intrusion Detection and Protection System (NIDPS) when deployed in highspeed networks. We have designed a real network to present experiments that use a Snort NIDPS. Our experiments demonstrate the weaknesses of NIDPSes, such as inability to process multiple packets and propensity to drop packets in heavy traffic and high-speed networks without analysing them. We tested Snort’s analysis performance, gauging the number of packets sent, analysed, dropped, filtered, injected, and outstanding. We suggest using QoS configuration technologies in a Cisco Catalyst 3560 Series Switch and parallel Snorts to improve NIDPS performance and to reduce the number of dropped packets. Our results show that our novel configuration improves performance.
A Model-based K-means Algorithm for Name Disambiguation

Unambiguous identities of resources are important aspect for semantic web. This paper addresses the personal identity issue in the context of bibliographies. Because of abbreviations or misspelling of names in publications or bibliographies, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of identity matching, document retrieval and database federation, and causes improper attribution of research credit. This paper describes a new K-means clustering algorithm based on an extensible Naïve Bayes probability model to disambiguate authors with the same first name initial and last name in the bibliographies and proposes a canonical name. The model captures three types of bibliographic information: coauthor names, the title of the paper and the title of the journal or proceeding. The algorithm achieves best accuracies of 70.1% and 73.6% on disambiguating 6 different J Anderson s and 9 different "J Smith" s based on the citations collected from researchers publication web pages.
Recurrent Attentional Reinforcement Learning for Multi-Label Image Recognition

Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks. The step of hypothesis regions (region proposals) localization in these existing multi-label image recognition pipelines, however, usually takes redundant computation cost, e.g., generating hundreds of meaningless proposals with nondiscriminative information and extracting their features, and the spatial contextual dependency modeling among the localized regions are often ignored or over-simplified. To resolve these issues, this paper proposes a recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions. Besides, our method explicitly models longterm dependencies among these attentional regions that help to capture semantic label co-occurrence and thus facilitate multilabel recognition. Extensive experiments and comparisons on two large-scale benchmarks (i.e., PASCAL VOC and MSCOCO) show that our model achieves superior performance over existing state-of-the-art methods in both performance and efficiency as well as explicitly identifying image-level semantic labels to specific object regions.
The Spam-Filtering Accuracy Plateau at 99 . 9 % Accuracy and How to Get Past It .

Bayesian filters have now become the standard for spam filtering; unfortunately most Bayesian filters seem to reach a plateau of accuracy at 99.9 percent. We experimentally compare the training methods TEFT, TOE, and TUNE, as well as pure Bayesian, token-bag, tokensequence, SBPH, and Markovian ddiscriminators. The results deomonstrate that TUNE is indeed best for training, but computationally exorbitant, and that Markovian discrimination is considerably more accurate than Bayesian, but not sufficient to reach four-nines accuracy, and that other techniques such as inoculation are needed. MIT Spam Conference 2004 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.  Bayesian filters have now become the standard for spam filtering; unfortunately most Bayesian filters seem to reach a plateau of accuracy at 99.9%. We experimentally compare the training methods TEFT, TOE, and TUNE, as well as pure Bayesian, token-bag, token-sequence, SBPH, and Markovian discriminators. The results demonstrate that TUNE is indeed best for training, but computationally exorbitant, and that Markovian discrimination is considerably more accurate than Bayesian, but not sufficient to reach four-nines accuracy, and that other techniques such as inoculation are needed. Bayesian filters have now become the standard for spam filtering; unfortunately most Bayesian filters seem to reach a plateau of accuracy at 99.9%. We experimentally compare the training methods TEFT, TOE, and TUNE, as well as pure Bayesian, token-bag, token-sequence, SBPH, and Markovian discriminators. The results demonstrate that TUNE is indeed best for training, but computationally exorbitant, and that Markovian discrimination is considerably more accurate than Bayesian, but not sufficient to reach four-nines accuracy, and that other techniques such as inoculation are needed.
Context and leadership : an examination of the nine-factor full-range leadership theory using the Multifactor Leadership Questionnaire $

In this study, we examined the validity of the measurement model and factor structure of Bass and Avolio’s Multifactor Leadership Questionnaire (MLQ) (Form 5X). We hypothesized that evaluations of leadership—and hence the psychometric properties of leadership instruments—may be affected by the context in which leadership is observed and evaluated. Using largely homogenous business samples consisting of 2279 pooled male and 1089 pooled female raters who evaluated same-gender leaders, we found support for the nine-factor leadership model proposed by Bass and Avolio. The model was configurally and partially metrically invariant—suggesting that the same constructs were validly measured in the male and female groups. Mean differences were found between the male and female samples on four leadership factors (Study 1). Next, using factor-level data of 18 independently gathered samples (N = 6525 raters) clustered into prototypically homogenous contexts, we tested the nine-factor model and found it was stable (i.e., fully invariant) within homogenous contexts (Study 2). The contextual factors comprised environmental risk, leader–follower gender, and leader hierarchical level. Implications for use of the MLQ and nine-factor model are discussed. D 2003  This study is based in part on the doctoral dissertation of the first author.  Corresponding author. Present address: Faculty of Economics and Business Administration, Ecoles des Hautes Etudes Commerciales—HEC, University of Lausanne, BFSH-1, Lausanne, CH-1015, Switzerland. 21-692-3300. E-mail address: john.antonakis@hec.unil.ch ( The Leadership Quarterly 14 (2003) 261–295 262
The effects of transformational and change leadership on employees' commitment to a change: a multilevel study.

The effects of transformational leadership on the outcomes of specific change initiatives are not well understood. Conversely, organizational change studies have examined leader behaviors during specific change implementations yet have failed to link these to broader leadership theories. In this study, the authors investigate the relationship between transformational and change leadership and followers' commitment to a particular change initiative as a function of the personal impact of the changes. Transformational leadership was found to be more strongly related to followers' change commitment than change-specific leadership practices, especially when the change had significant personal impact. For leaders who were not viewed as transformational, good change-management practices were found to be associated with higher levels of change commitment.