nils-herrmann/clean_scirepeval huggingface dataset:
On Data Integrity Attacks Against Real-Time Pricing in Energy-Based Cyber-Physical Systems

In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.
Online Media Use of False News to Frame the 2016 Trump Presidential Campaign

The 2016 U.S. presidential election campaigns witnessed an unprecedented viral false news -- a type of misinformation referred to as "factitious information blend" that is motivated to discredit political rivals. Despite the different speculations of factors that might have influenced Donald Trump's surprised victory, empirical and theoretical research on the potential impacts of false news propagated by online news media during election campaigns on influencing voters' attitudes and public opinion is seriously lacking. By drawing on the literature on framing political-effects research and by developing our computational text analytics programs, we addressed questions regarding how online news media used false news to negatively frame the Trump presidential campaign. Our text analytics results indicate that although the negative frames against Trump far outnumbered those against Hillary Clinton, weak frames of unverifiable misinformation might have failed to influence the mass audience, leaving them to the power of Trump's direct political communications via Twitter.
Door recognition and deep learning algorithm for visual based robot navigation

In this paper, a new method based on deep learning for robotics autonomous navigation is presented. Different from the most traditional methods based on fixed models, a convolutional neural network (CNN) modelling technique in Deep learning is selected to extract the feature inspired by the working pattern of the biological brain. This neural network model has muti-layer features where the ambient scenes can be recognized and useful information such as the location of door can be identified. The extracted information can be used for robot navigation, so does the robot can approach the target accurately. In the field experiments, detecting doors and predicting the door poses such tasks are designed in the indoor environment to verify the proposed method. The experimental results demonstrate that the doors can be identified with good performance and the deep learning model is suitable for robot navigation.
Balancing control with neuro-fuzzy approach for electrical cart-seesaw system

The objectives of the research are to exploit a novel cart-seesaw laboratory apparatus which is driven by servo motors and gear transmission, and develop balancing approach for such system. In order to stabilize the system, the two controls approaches are implemented as examples of how the system can be used to demonstrate controls concepts. Experimental results indicate that utilizing the proposed novel cart-seesaw system play a superior performance than the existed pneumatic type in balancing and tracking operation. Moreover, the proposed control methodology significantly signifying the usefulness of the fuzzy controller in suppressing the seesaw tilt angle in the time domain. The development the proposed software/hardware platform can be beneficial for standardizing laboratory equipment, and the development of amusement apparatus.
An Incentive Mechanism for Crowdsensing Markets With Multiple Crowdsourcers

In this paper, we design an incentive mechanism for data collection in smart cities. We propose an incentive mechanism for crowdsensing with multiple crowdsourcers. We model the incentive mechanism as a noncooperative game. We consider two different pricing mechanisms when the crowdsourcers fixed the rewards in advance, and when the crowdsourcers dynamically set the rewards in order to maximize their own utilities. A discrete time dynamic inspired by the well known best response dynamic, called elite strategy dynamics, is proposed to compute a Nash equilibrium of the modeled game. Comprehensive simulations were presented to evaluate the performance of the proposed incentive mechanism.
SDN Based Collaborative Scheme for Mitigation of DDoS Attacks

Software Defined Networking (SDN) has proved itself to be a backbone in the new network design and is quickly becoming an industry standard. The idea of separation of control plane and data plane is the key concept behind SDN. SDN not only allows us to program and monitor our networks but it also helps in mitigating some key network problems. Distributed denial of service (DDoS) attack is among them. In this paper we propose a collaborative DDoS attack mitigation scheme using SDN. We design a secure controller-to-controller (C-to-C) protocol that allows SDN-controllers lying in different autonomous systems (AS) to securely communicate and transfer attack information with each other. This enables efficient notification along the path of an ongoing attack and effective filtering of traffic near the source of attack, thus saving valuable time and network resources. We also introduced three different deployment approaches i.e., linear, central and mesh in our testbed. Based on the experimental results we demonstrate that our SDN based collaborative scheme is fast and reliable in efficiently mitigating DDoS attacks in real time with very small computational footprints.
A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain.



This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset.



We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models.



The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average.



In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge.
A robotic knee exoskeleton for walking assistance and connectivity topology exploration in EEG signal

Disability of lower limb affects voluntary movements and results in a low quality of life. Although people with disabled lower limb can restore their movements through assistive devices like wheelchair, such manner is not as natural as that healthy people perform locomotion. It would be better to regain movements by restoring the function of lower limb itself. Hence, an orthosis that can be worn on the disable lower limb might be a good choice. In addition, it is of interest to explore brain activities during walking with and without an orthosis. In this paper, we introduced a robotic knee exoskeleton that can provide assistive torque to facilitate walking. Three different conditions were employed here, namely free walking (FW, without exoskeleton), zero force (ZF, with the exoskeleton but no torque was provided), and assistive force (AF, torque was provided by the exoskeleton). During the walking, electrophysiological signals were simultaneously recorded. Partial directed coherence (PDC) was employed to measure connectivity strengths between channels and the obtained effective connectivity network was quantitatively assessed through a standard graph theoretical analysis framework. We found that the clustering coefficient was significantly increased when assistive torque was provided by the exoskeleton compared with FW and ZF. A decrease in path length was also found when assistive torque was provided. Our findings demonstrated that topological patterns of brain activities were distinctly different when people received assistive torque during walking. This study could be of meaningful significance on further orthosis development from the perspective of brain electrophysiological activity and insight into the understanding for brain plasticity and neural rehabilitation.
A taxonomy and rating system to measure situation awareness in resuscitation teams

Team SA involves a common perspective between two or more individuals regarding current environmental events, their meaning, and projected future status. Team SA has been theorized to be important for resuscitation team effectiveness. Accordingly, multidimensional frameworks of observable behaviors relevant to resuscitation teams are needed to understand more deeply the nature of team SA, its implications for team effectiveness, and whether it can be trained. A seven-dimension team resuscitation SA framework was developed following a literature review and consensus process using a modified Delphi approach with a group of content experts. We applied a pre-post design within a day-long team training program involving four video-recorded simulated resuscitation events and 42 teams across Canada. The first and fourth events represented "pre" and "post" training events, respectively. Teams were scored on SA five times within each 15-minute event. Distractions were introduced to investigate whether SA scores would be affected. The current study provides initial construct validity evidence for a new measure of SA and explicates SA's role in resuscitation teams.
The rise of "big data" on cloud computing: Review and open research issues

Cloud computing is a powerful technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Massive growth in the scale of data or big data generated through cloud computing has been observed. Addressing big data is a challenging and timedata processing and analysis. The rise of big data in cloud computing is reviewed in this study. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The relationship between big data and cloud computing, big data storage systems, and Hadoop technology are also discussed. Furthermore, research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance. Lastly, open research issues that require substantial research efforts are summarized. & 2014 Elsevier Ltd. All rights reserved.
3D depth estimation from a holoscopic 3D image

This paper presents an innovative technique for 3D depth estimation from a single holoscopic 3D image (H3D). The image is captured with a single aperture holoscopic 3D camera, which mimics a fly's eye technique to acquire an optical 3D model of a true 3D scene. The proposed method works by extracting of optimum viewpoints images from a H3D image and it uses the shift and integration function in up-sampling the extracted viewpoints and then it performs the match block functions to match correspondence features between the stereoscopic 3D images. Finally, the 3D depth is estimated through a smoothing and optimizing process to produce a final 3D depth map. The proposed method estimates the full 3D depth information from a single H3D image, which makes it reliable and suitable for trend autonomous robotic applications.
Enterprise Content Management (ECM): Needs, challenges and recommendations

Availability of information, agility of business process, interoperability of diverse business functions and conformity of the legal requirements are few of the critical success factors for enterprises to survive in the modern and competing global market space. Enterprises while striving to achieve these critical success factors are further exposed to the complexity of massive volumes of variant data and information, which exists in a broad array of formats. Complex and extended business processes spanning across the business functions and partners around the globe, need for integration and interoperability, fulfillment of compliance to legal and regulatory requirements. Enterprise content management (ECM) answers the above questions and offers the capabilities to efficiently and effectively manage the challenges imposed by the demanding business requirements of the modern world. The study in-hand reviews and analyzes the existing literature on enterprise content management domain. In order to conduct a structured literature review, three broader areas of considerations for ECM are identified i.e. business needs & drivers, challenges and recommendations. The review inhand aims at consolidating valuable domain knowledge and research work accomplished in the recent years for the benefits of the industry and academia.
Motor control exercises, sling exercises, and general exercises for patients with chronic low back pain: a randomized controlled trial with 1-year follow-up.

Exercise benefits patients with chronic nonspecific low back pain; however, the most effective type of exercise remains unknown.



This study compared outcomes after motor control exercises, sling exercises, and general exercises for low back pain.



This was a randomized controlled trial with a 1-year follow-up.



The study was conducted in a primary care setting in Norway.



The participants were patients with chronic nonspecific low back pain (n=109).



The interventions in this study were low-load motor control exercises, high-load sling exercises, or general exercises, all delivered by experienced physical therapists, once a week for 8 weeks.



The primary outcome measure was pain reported on the Numeric Pain Rating Scale after treatment and at a 1-year follow-up. Secondary outcome measures were self-reported activity limitation (assessed with the Oswestry Disability Index), clinically examined function (assessed with the Fingertip-to-Floor Test), and fear-avoidance beliefs after intervention.



The postintervention assessment showed no significant differences among groups with respect to pain (overall group difference) or any of the outcome measures. Mean (95% confidence interval) group differences for pain reduction after treatment and after 1 year were 0.3 (-0.7 to 1.3) and 0.4 (-0.7 to 1.4) for motor control exercises versus sling exercises, 0.7 (-0.6 to 2.0) and 0.3 (-0.8 to 1.4) for sling exercises versus general exercises, and 1.0 (-0.1 to 2.0) and 0.7 (-0.3 to 1.7) for motor control exercises versus general exercises.



The nature of the interventions made blinding impossible.



This study gave no evidence that 8 treatments with individually instructed motor control exercises or sling exercises were superior to general exercises for chronic low back pain.
Mixing Confidential Transactions: Comprehensive Transaction Privacy for Bitcoin

The public nature of the blockchain has been shown to be a severe threat for the privacy of Bitcoin users. Even worse, since funds can be tracked and tainted, no two coins are equal, and fungibility, a fundamental property required in every currency, is at risk. With these threats in mind, several privacyenhancing technologies have been proposed to make Bitcoin more private. However, they either require a deep redesign of the currency, breaking many currently deployed features, or they address only specific privacy issues and consequently provide only very limited guarantees when deployed separately. The goal of this work is to overcome this trade-off. Building on CoinJoin, we design ValueShuffle, the first coin mixing protocol compatible with Confidential Transactions, a proposal to hide payment values in transactions. ValueShuffle ensures a mixing participant’s anonymity and the confidentiality of her payment values not only against an attacker observing the blockchain but also against the other possibly malicious mixing participants and against network attackers. By combining ValueShuffle with the proposal for Confidential Transactions and additionally Stealth Addresses, our solution provides comprehensive privacy (payer anonymity, payee anonymity, and payment value privacy) without breaking with the design or the features of Bitcoin. We demonstrate that the combination of these three privacyenhancing technologies creates synergies that overcome the two major obstacles which so far have prohibited the deployment of coin mixing in practice, namely that users need to mix funds of the same value, and need to do so before they can actually spend the funds. As a result, our approach unleashes the full potential of coin mixing as a privacy solution for Bitcoin.
Recognizing and Imitating Programmer Style: Adversaries in Program Authorship Attribution

Source code attribution classifiers have recently become powerful. We consider the possibility that an adversary could craft code with the intention of causing a misclassification, i.e., creating a forgery of another author’s programming style in order to hide the forger’s own identity or blame the other author. We find that it is possible for a non-expert adversary to defeat such a system. In order to inform the design of adversarially resistant source code attribution classifiers, we conduct two studies with C/C++ programmers to explore the potential tactics and capabilities both of such adversaries and, conversely, of human analysts doing source code authorship attribution. Through the quantitative and qualitative analysis of these studies, we (1) evaluate a state-of-the-art machine classifier against forgeries, (2) evaluate programmers as human analysts/forgery detectors, and (3) compile a set of modifications made to create forgeries. Based on our analyses, we then suggest features that future source code attribution systems might incorporate in order to be adversarially resistant.
Deep learning architecture for air quality predictions.

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.
Deep neural networks on graph signals for brain imaging analysis

Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation of the recorded data. This work proposes a new method to learn the low-dimensional representations from the noise-degraded measurements. In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers. Our work leverages efficient graph filter design using Chebyshev polynomial and recent work on convolutional nets on graph-structured data. Our approach exploits graph structure as the prior side information, localized graph filter for feature extraction and neural networks for high capacity learning. Experiments on real MEG datasets show that our approach can extract more discriminative representations, leading to improved accuracy in a supervised classification task.
Analyzing User Modeling on Twitter for Personalized News Recommendations

How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.