nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Frame skip Deep Q Network

Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE, [Bellemare et al., 2013]) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of k allows the agent to repeat a selected action k number of times. The current state of the art architectures like Deep QNetwork (DQN, [Mnih et al., 2015]) and Dueling Network Architectures (DuDQN, [Wang et al., 2015]) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest.
A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair

Fuzzy-match repair (FMR), which combines a human-generated translation memory (TM) with the flexibility of machine translation (MT), is one way of using MT to augment resources available to translators. We evaluate rule-based, phrase-based, and neural MT systems as black-box sources of bilingual information for FMR. We show that FMR success varies based on both the quality of the MT system and the type of MT system being used.
Closed-loop brain training: the science of neurofeedback

Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain–machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.
Practical Modbus Flooding Attack and Detection

The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.
Semantic Document Distance Measures and Unsupervised Document Revision Detection

In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED). Our revision detection system is designed for a large scale corpus and implemented in Apache Spark. We demonstrate that our system can more precisely detect revisions than state-of-the-art methods by utilizing the Wikipedia revision dumps 1 and simulated data sets.
Multi-Person Tracking by Multicut and Deep Matching

In , we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem. In this paper, we modify and extend  in three ways: 1) We introduce a novel local pairwise feature based on local appearance matching that is robust to partial occlusion and camera motion. 2) We perform extensive experiments to compare different pairwise potentials and to analyze the robustness of the tracking formulation. 3) We consider a plain multicut problem and remove outlying clusters from its solution. This allows us to employ an efficient primal feasible optimization algorithm that is not applicable to the subgraph multicut problem of . Unlike the branch-and-cut algorithm used there, this efficient algorithm used here is applicable to long videos and many detections. Together with the novel feature, it eliminates the need for the intermediate tracklet representation of . We demonstrate the effectiveness of our overall approach on the MOT16 benchmark [2], achieving state-of-art performance.
Daily activity recognition with inertial ring and bracelet: An unsupervised approach

Daily activity recognition can help people to maintain a healthy lifestyle and robot to better interact with users. Robots could therefore use the information coming from the activities performed by users to give them some custom hints to improve lifestyle and daily routine. The pervasiveness of smart things together with advances in cloud robotics can help the robot to perceive and collect more information about the users and the environment. In particular thanks to the miniaturization and low cost of Inertial Measurement Units, in the last years, body-worn activity recognition has gained popularity. In this work, we investigated the performances with an unsupervised approach to recognize eight different gestures performed in daily living wearing a system composed of two inertial sensors placed on the hand and on the wrist. In this context our aim is to evaluate whether the system is able to recognize the gestures in more realistic applications, where is not possible to have a training set. The classification problem was analyzed using two unsupervised approaches (K-Mean and Gaussian Mixture Model), with an intra-subject and an inter-subject analysis, and two supervised approaches (Support Vector Machine and Random Forest), with a 10-fold cross validation analysis and with a Leave-One-Subject-Out analysis to compare the results. The outcomes show that even in an unsupervised context the system is able to recognize the gestures with an averaged accuracy of 0.917 in the K-Mean inter-subject approach and 0.796 in the Gaussian Mixture Model inter-subject one.
Easier contagion and weaker ties make anger spread faster than joy in social media

Similar to face-to-face communication in daily life, more and more evidence suggests that human emotions also spread in online social media through virtual interactions. However, the mechanism underlying the emotion contagion, like whether different feelings spread unlikely or how the spread is coupled with the social network, is rarely investigated. Indeed, due to the costly expense and spatio-temporal limitations, it is challenging for conventional questionnaires or controlled experiments. While given the instinct of collecting natural affective responses of massive connected individuals, online social media offer an ideal proxy to tackle this issue from the perspective of computational social science. In this paper, based on the analysis of millions of tweets in Weibo, a Twitter-like service in China, we surprisingly find that anger is more contagious than joy, indicating that it can sparkle more angry follow-up tweets; and anger prefers weaker ties than joy for the dissemination in social network, indicating that it can penetrate different communities and break local traps by more sharings between strangers. Through a simple diffusion model, it is unraveled that easier contagion and weaker ties function cooperatively in speeding up anger’s spread, which is further testified by the diffusion of realistic bursty events with different dominant emotions. To our best knowledge, for the first time we quantificationally provide the long-term evidence to disclose the difference between joy and anger in dissemination mechanism and our findings would shed lights on personal anger management in human communication and collective outrage control in cyber space.
Learning and Teaching Styles In Foreign and Second Language Education

The ways in which an individual characteristically acquires, retains, and retrieves information are collectively termed the individual’s learning style. Mismatches often occur between the learning styles of students in a language class and the teaching style of the instructor, with unfortunate effects on the quality of the students’ learning and on their attitudes toward the class and the subject. This paper defines several dimensions of learning style thought to be particularly relevant to foreign and second language education, outlines ways in which certain learning styles are favored by the teaching styles of most language instructors, and suggests steps to address the educational needs of all students in foreign language classes. Students learn in many ways—by seeing and hearing; reflecting and acting; reasoning logically and intuitively; memorizing and visualizing. Teaching methods also vary. Some instructors lecture, others demonstrate or discuss; some focus on rules and others on examples; some emphasize memory and others understanding. How much a given student learns in a class is governed in part by that student’s native ability and prior preparation but also by the compatibility of his or her characteristic approach to learning and the instructor’s characteristic approach to teaching. The ways in which an individual characteristically acquires, retains, and retrieves information are collectively termed the individual’s learning style. Learning styles have been extensively discussed in the educational psychology literature (Claxton & Murrell 1987; Schmeck 1988) and specifically in the context Richard M. Felder (Ph.D., Princeton University) is the Hoechst Celanese Professor of Chemical Engineering at North Carolina State University,
AUTOMATED INNOVATIVE WHEELCHAIR

New development in sensors, radar and ultrasonic technologies has proved to be a boon for electronics travelling aids (ETAs). These devices are widely used by blind and physically challenged peoples. C5 laser cane, Mowat sensor, belt and binaural sonic aid, NAV guide cane are among popular electronic travelling aids used by blind peoples. For physically challenged person electric wheel chairs controlled by joystick, eye movement and voice recognition are also available but they have their own limitation in terms of operating complexity, noise environment and cost. Our paper proposes an automated innovative wheelchair controlled by neck position of person. It uses simple LEDs, photo sensor, motor and microcontroller to control the movement of wheelchair.
Clustering scientific documents with topic modeling

Topic modeling is a type of statistical model for discovering the latent “topics” that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities.
Inception Recurrent Convolutional Neural Network for Object Recognition

Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an InceptionRecurrent Convolutional Neural Network (IRCNN), which utilizes the power of an inception network combined with recurrent layers in DCNN architecture. We have empirically evaluated the recognition performance of the proposed IRCNN model using different benchmark datasets such as MNIST, CIFAR-10, CIFAR100, and SVHN. Experimental results show similar or higher recognition accuracy when compared to most of the popular DCNNs including the RCNN. Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset. We report about 3.5%, 3.47% and 2.54% improvement in classification accuracy when compared to the RCNN, equivalent Inception Networks, and InceptionResidual Networks on the augmented CIFAR100 dataset respectively.
An Ontological Interpretation of Non-Functional Requirements

Non-functional requirements (NFRs) have been the focus of research in Requirements Engineering (RE) for more than 20 years. Despite this attention, their ontological nature is still an open question, thereby hampering efforts to develop concepts, tools and techniques for eliciting, modeling, and analyzing them, in order to produce a specification for a system-to-be. In this paper, we propose to treat NFRs as qualities, based on definitions of the UFO foundational ontology. Furthermore, based on these ontological definitions, we provide guidelines for distinguishing between non-functional and functional requirements, and sketch a syntax of a specification language that can be used for capturing NFRs.
Alveolar bone dimensional changes of post-extraction sockets in humans: a systematic review.

To review the literature to assess the amount of change in height and width of the residual ridge after tooth extraction.



MEDLINE-PubMed and the Cochrane Central register of controlled trials (CENTRAL) were searched through up to March 2009. Appropriate studies which data reported concerning the dimensional changes in alveolar height and width after tooth extraction were included. Approximal height change, mid-buccal change, mid-crestal change, mid-lingual change, Alveolar width change and socket fill were selected as outcome variables. Mean values and if available standard deviations were extracted. Weighted mean changes were calculated.



Independent screening of the titles and abstracts of 1244 MEDLINE-PubMed and 106 Cochrane papers resulted in 12 publications that met the eligibility criteria. The reduction in width of the alveolar ridges was 3.87 mm. The mean clinical mid-buccal height loss was 1.67 mm. The mean crestal height change as assessed on the radiographs was 1.53 mm. Socket fill in height as measured relative to the original socket floor was on an average 2.57 mm.



During the post-extraction healing period, the weighted mean changes as based on the data derived from the individual selected studies show the clinical loss in width to be greater than the loss in height, assessed both clinically as well as radiographically.
Can humans fly? Action understanding with multiple classes of actors

Can humans fly? Emphatically no. Can cars eat? Again, absolutely not. Yet, these absurd inferences result from the current disregard for particular types of actors in action understanding. There is no work we know of on simultaneously inferring actors and actions in the video, not to mention a dataset to experiment with. Our paper hence marks the first effort in the computer vision community to jointly consider various types of actors undergoing various actions. To start with the problem, we collect a dataset of 3782 videos from YouTube and label both pixel-level actors and actions in each video. We formulate the general actor-action understanding problem and instantiate it at various granularities: both video-level single- and multiple-label actor-action recognition and pixel-level actor-action semantic segmentation. Our experiments demonstrate that inference jointly over actors and actions outperforms inference independently over them, and hence concludes our argument of the value of explicit consideration of various actors in comprehensive action understanding.
Politics , Markets and Schools : Quasi-Experimental Evidence on the Impact of Autonomy and Competition from a Truly Revolutionary UK Reform

Supporters of market-based education reforms argue that school autonomy and between-school competition can raise student achievement. Yet U.S. reforms based in part on these ideas charter schools, school-based management, vouchers and school choice are limited in scope, complicating evaluations of their impact. In contrast, a series of remarkable reforms enacted by the Thatcher Government in Britain in the 1980s provide an ideal testing ground for examining the effects of school autonomy and between-school competition. In this paper I study one reform described by Chubb and Moe (1992) as ‘truly revolutionary’ that allowed public high schools to ‘opt out’ of the local school authority and become quasi-independent, funded directly by central Government. In order to opt out schools had to first win a majority vote of current parents, and I assess the impact of school autonomy via a regression discontinuity design, comparing student achievement levels at schools where the vote barely won to those where it barely lost. To assess the effects of competition I use this same idea to compare student achievement levels at neighbouring schools of barely winners to neighbouring schools of barely losers. My results suggest two conclusions. First, there were large gains to schools that won the vote and opted out, on the order of a onequarter standard deviation improvement on standardised national examinations. Since results improved for those students already enrolled in the school at the time of the vote, this outcome is not likely to be driven by changes in student-body composition (cream-skimming). Second, the gains enjoyed by the opted-out schools appear not to have spilled over to their neighbours I can never reject the hypothesis of no spillovers and can always reject effects bigger than one half of the ‘own-school’ impact. I interpret my results as supportive of education reforms that seek to hand power to schools, with the caveat that I do not know precisely what opted-out schools did to improve. With regards to competition, although I cannot rule out small but economically important competition effects, my results suggest caution as to the likely benefits.
Aminoglycoside antibiotics.

In the 50 years since their discovery, the aminoglycoside antibiotics have seen unprecedented use. Discovered in the 1940s, they were the long-sought remedy for tuberculosis and other serious bacterial infections. The side effects of renal and auditory toxicity, however, led to a decline of their use in most countries in the 1970s and 1980s. Nevertheless, today the aminoglycosides are still the most commonly used antibiotics worldwide thanks to the combination of their high efficacy with low cost. This review first summarizes the history, chemistry, antibacterial actions and acute side effects of the drugs. It then details the pathophysiology of aminoglycoside ototoxicity including experimental and clinical observations, risk factors and incidence. Pharmacokinetics, cellular actions and our current understanding of the underlying molecular mechanisms of ototoxicity are discussed at length. The review concludes with recent advances towards therapeutic intervention to prevent aminoglycoside ototoxicity.
High-level synthesis with LegUp: a crash course for users and researchers

High-level synthesis (HLS) has been gaining traction recently as a design methodology for FPGAs, with the promise of raising the productivity of FPGA hardware designers, and ultimately, opening the door to the use of FPGAs as computing devices targetable by software engineers. In this tutorial, we introduce LegUp, an open-source HLS tool for FPGAs developed at the University of Toronto. With LegUp, a user can compile a C program completely to hardware, or alternately, he/she can choose to compile the program to a hybrid hardware/software system comprising a processor along with one or more accelerators. LegUp supports the synthesis of most of the C language to hardware, including loops, structs, multi-dimensional arrays, pointer arithmetic, and floating point operations. The LegUp distribution includes the CHStone HLS benchmark suite, as well as a test suite and associated infrastructure for measuring quality of results, and for verifying the functionality of LegUp-generated circuits. LegUp is freely downloadable at www.legup.org, providing a powerful platform that can be leveraged for new high-level synthesis research.
An Efficient Evolutionary Algorithm for the Degree-Constrained Minimum Spanning Tree Problem

The representation of candidate solutions and the variation operators are fundamental design choices in an evolutionary algorithm (EA). This paper proposes a novel representation technique and suitable variation operators for the degree-constrained minimum spanning tree problem. For a weighted, undirected graphG(V, E), this problem seeks to identify the shortest spanning tree whose node degrees do not exceed an upper bound d ≥ 2. Within the EA, a candidate spanning tree is simply represented by its set of edges. Special initialization, crossover, and mutation operators are used to generate new, always feasible candidate solutions. In contrast to previous spanning tree representations, the proposed approach provides substantially higher locality and is nevertheless computationally efficient; an offspring is always created in O(|V |) time. In addition, it is shown how problemdependent heuristics can be effectively incorporated into the initialization, crossover, and mutation operators without increasing the time-complexity. Empirical results are presented for hard problem instances with up to 500 vertices. Usually, the new approach identifies solutions superior to those of several other optimization methods within few seconds. The basic ideas of this EA are also applicable to other network optimization tasks.
A Blockchain-Based Framework for Data Sharing With Fine-Grained Access Control in Decentralized Storage Systems

In traditional cloud storage systems, attribute-based encryption (ABE) is regarded as an important technology for solving the problem of data privacy and fine-grained access control. However, in all ABE schemes, the private key generator has the ability to decrypt all data stored in the cloud server, which may bring serious problems such as key abuse and privacy data leakage. Meanwhile, the traditional cloud storage model runs in a centralized storage manner, so single point of failure may leads to the collapse of system. With the development of blockchain technology, decentralized storage mode has entered the public view. The decentralized storage approach can solve the problem of single point of failure in traditional cloud storage systems and enjoy a number of advantages over centralized storage, such as low price and high throughput. In this paper, we study the data storage and sharing scheme for decentralized storage systems and propose a framework that combines the decentralized storage system interplanetary file system, the Ethereum blockchain, and ABE technology. In this framework, the data owner has the ability to distribute secret key for data users and encrypt shared data by specifying access policy, and the scheme achieves fine-grained access control over data. At the same time, based on smart contract on the Ethereum blockchain, the keyword search function on the cipher text of the decentralized storage systems is implemented, which solves the problem that the cloud server may not return all of the results searched or return wrong results in the traditional cloud storage systems. Finally, we simulated the scheme in the Linux system and the Ethereum official test network Rinkeby, and the experimental results show that our scheme is feasible.