Reviewerly/exHarmony huggingface dataset:
Data Investigation and Analysis Based on Grounded Theory

Abstract The task of building rural teachers team is an important part of the rural revitalization strategy and the key to developing local education. Based on grounded theory approach, theoretical sampling was applied in semi-structured interviews of teachers in 12 counties (cities, districts) of minority region in southwest Guangxi. In this study, through the survey data analysis and coding ,through open coding, axial coding and selective coding on the interview data, we found some problems in the minority region of southwest Guangxi such as shortage of teachers, structural imbalance, poor teaching quality, unsuitable training mode. To tackle these problems, we propose some solutions to the development of excellent teachers team and the directional and localized training that highlights national advantages in order to provide a good reference for the development of the rural teaching body in China’s minority regions.
Application of Corpus-Based Text Statistics Analyzer in Sports English

Abstract The link between corpora and ESP is crucial and even more for some time to come, due to the current patterns of academic and professional mobility which create a need for ESP expertise and teaching materials. The problem in Sports English in China since 2004 is the lack of competent teachers and proper-leveled textbooks, which hindered the development of the major of Sports English greatly. This essay discusses four corpus-based online text analyzers to offer statistics of individual texts and vocabulary profile to help English teachers choose proper-leveled texts and modify materials to meet their needs in Sports English teaching.
Violence Content Detection Based on Audio using Extreme Learning Machine

In this paper, we proposed an audio based violent scene detection system. As visual based approach has been widely used in identification of violent scenes from video data, audio-based approach; on the other hand, has not been explored as much as visual approach of the video data. In some applications such as video surveillance, visual scenes can be absent because of environmental situations. Also, in many approaches different systems are proposed for movies and real time videos. Therefore, we practiced the audio approach of video data to decide whether a video scene is violent or not from movies and real time videos. For this purpose, we propose an Extreme Learning Machine (ELM) method to detect video scenes as “violent” or “non-violent” using two types of datasets Standardized Media Eval VSD-2014 and other is Customized dataset for the same classifier. After successful training and testing, 85.7% accuracy is achieved by ELM for VSD-2014 dataset and 88.89% for Customized dataset respectively.
Bias of Particle Approximations to Optimal Filter Derivative

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 26 September 2018Accepted: 30 November 2020Published online: 25 February 2021Keywordsparticle methods, bias, optimal filter, optimal filter derivative, nonlinear state-space modelsAMS Subject Headings93E11, 62M20, 65C05Publication DataISSN (print): 0363-0129ISSN (online): 1095-7138Publisher: Society for Industrial and Applied MathematicsCODEN: sjcodc
A Four-Feature Keyword Extraction Algorithm Based on Word Length Priority Ratio

With the rapid development of Internet technology and the advent of the information age, it has become a research hotspot to obtain key information from numerous data. Due to the diversity and irregularity of network data, it is difficult for people to find the literature they want, especially knowledge scholars working in the frontier field of science and technology, who have higher requirements on the accuracy and efficiency of literature keyword extraction than ordinary people. The feature values selected by the current keyword extraction algorithm are usually limited to word frequency and word length, which is incomplete and affects the accuracy of the algorithm. Given this phenomenon, this paper, by comparing with TF-IDF and KEA algorithm, define the concept of word length priority ratio, and applies this concept to the calculation of word length-weight, proposes a four-feature keyword extraction algorithm (WPR-TOC algorithm) based on word frequency, word length, word position and the degree of association between words. Through experiments, compared with the KEA algorithm, KEA++ algorithm, and four features extraction algorithm, the precision of the WPR-TOC algorithm is improved by 40%, 30%, and 10% respectively, and the recall rate is also increased by 40%, 30%, and 10% respectively.
Analysis for Malicious URLs Using Machine Learning and Deep Learning Approaches

The Malicious Uniform Resource Locator (URL) is the primary mechanism for hosting unsolicited content, for example, spam, malicious ads, phishing, drive-outs, etc. Blacklisting, signature matching and regular expression techniques have been used by past studies. These methods are totally ineffective when detecting variants of the existing malicious URLs or new URLs. The machine-based learning approach provides a simple solution to this problem. This type of approach requires a broad analysis in feature engineering and security artifact type representation, for example, URLs of this Web site. These conventional mechanisms are based on the combination of keywords and URL syntax. Such traditional methods cannot however deal efficiently with ever-changing developments and techniques of Web access. This article also provides a timely and comprehensive survey to a variety of audiences to improve their knowledge of the state of the art and to encourage their own work and practices, not just machine learning researchers and academic engineers, but also cybersecurity professionals and specialists.
On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies–Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score.
Preliminary Analysis of the OPENCARE System Architecture in Residential Facilities

The constant increase in the age of the population requires the construction of a model of care able to satisfy ever-increasing requirements of hospitality for the elderly in care facilities. At the same time, however, a demand for great transparency comes from families regarding the type and quality of care provided to patients. The OPENCARE project, now in its third year of activity, has the objective of creating an automatic tool to promote communication between residential structures and families, so as to increase the exchange of information without further weighing on the workload of the operators. This paper describes the technological platform that has been developed, based on the acquisition of data by sensors and on a WEB application through which operators memorize the performed activities. All the information acquired is the basis for processing communications to be sent to families, via messages or apps on smartphones.
CAE: Towards Crowd Anarchism Exploration

Towards effective violence detection in densely crowded scenes, we introduce two novel computationally inexpensive real time pipelines. One is for automatic crowd violence detection and another for capturing the region with highest crowd concentration. The proposed violence detection architecture uses dense Histogram of Oriented Gradients (HOG) and dense Histogram of Motion Gradients (HMG) for feature extraction and Radial Bias Function Support Vector machine (RBF SVM) for classification. We further contribute by introducing a benchmark dataset, Dense Crowd Turbulence (DCT), having 120 videos of crowd violence and 120 for non-violence. DCT achieves an accuracy of 100% when evaluated with deep learning based violence detection frameworks. The violence detection architecture achieved a near the state of art accuracy of 87.3% on DCT.
Anti-Interpolation: An Attack Facilitator Hiding Adversaries Into Images

Deep learning models show the vulnerability to adversarial examples, which can be attacked to make incorrect predictions. As one of the key image preprocessing techniques, interpolation may weaken the robustness of the attack and even disable the perturbation. Thus, the malicious images cannot achieve the attack purpose. In this work, anti-interpolation, as an attack facilitator, is proposed, where interpolation does not affect the attack results intended by the perturbation. Based on anti-interpolation, an attack framework is extended to achieve adversarial attacks, including targeted attack, non-targeted attack, and auxiliary attack. Furthermore, gradient derivation is developed based on the interpolation output of large-sized images, which can infer the interpolation mode in the black box attack scenario. Compared with the current adversarial attacks, anti-interpolation can achieve a higher attack success rate. Moreover, anti-interpolation is capable of attacking defensive models with various model corrections.
WITHDRAWN: Comparative and experimental study in identifying the similarity between languages for plagiarism detection and efficient language translation

The comparison of multilingual documents which are used in plagiarism detection and bilingual lexicon extraction is compared through this article. Parallel corpus is used for the comparison of multilingual text which is a collection of allied sentences and sentences which are translation of each other. In this paper we have presented a comparative study on the proposed three techniques like Sequence matcher, Fuzzy-Wuzzy (Ratio) and Spacy similarity techniques in order to find out the similarity between the sentences, words in a multi lingual content. The Comparative study proposed in this article includes the comparison of proposed methods with the similar kind of work implemented with techniques in the literature. The Fuzzy-Wuzzy (Ratio) out performs in terms of Accuracy compared to the sequence matcher and Spacy similarity for the identification of similarity between the languages.
Relay Protection Service Support Platform to Meet Multiple Business Needs

Through many years of operation, the power grid regulation system has accumulated abundant operation data. It includes measurement data, alarm data, failure and operation event data, power data and planned prediction data. The data of power grid business system exists independently in EMS, OMS, statistical analysis platform, TMR and other different systems. The lack of cross-professional, cross-unit and cross-business data sharing mechanism which forms the information island. At present, through the construction of model data cloud platform, the control center extracts and collects all kinds of data from EMS, OMS, statistical analysis platform and other business systems. However, the data exchange capacity is still inadequate. Therefore, it is urgent to build dispatching data exchange platform. Through the model data cloud platform, the regulator center provides unified service of data receiving/extracting, transformation and publishing which "configurable, monitored and controllable" to form a data exchange hub for the internal and external business systems of the control center. In addition, accessing statistical analysis system to protect information ledger, protection model and operation data. The data of the fault recording network system forms the data exchange hub of the control center. The logical business layer realizes model management, fault and action analysis, operation monitoring, intelligent diagnosis and other application scenarios. At the same time, to meet the needs for friendly data exchange with national research vertically and meet the needs for friendly data exchange with all departments of the provincial company horizontally.
A Learnable Blur Kernel for Remote Sensing Image Retrieval

With the explosive increase of remote sensing images, content-based remote sensing image retrieval (CBRSIR) has aroused widespread attention. Convolutional Neural Network (CNN) based methods are widely used in CBRSIR due to the development of deep learning. However, common used CNN models have difficulties in holding shift-invariant property due to the widely used down-sampling method, which means a little shift of input may cause a mutation of feature representation. To mitigate the absence of shift-invariant in down-sampling, we propose the learnable blur kernel (LBK), that can enhance the feature extraction capability by leveraging more context information. We build on this concept without extra cost, which can be simply integrated with modern CNNs architecture. Our method is validated on the public remote sensing dataset and compared with other retrieval methods. The overall experimental results show that the proposed method achieves outstanding performance.
Entropy-parametric control of stochastic system

Abstract The paper contains material about controlled methods of stochastic systems. The article considers the features of establishing control over a stochastic system, which are found when using parametric and informational criteria. It is proposed to use the potential of the Renyi entropy when building control over a complex multilevel system. The possibility of combining entropy and parametric criteria to increase the stability of control over the system is discussed.
NoiseSenseDNN: Modeling DNN for Sensor Data to Mitigate the Effect of Noise in Edge Devices

Edge computing usage in many applications, such as transportation and healthcare, has been becoming popular nowadays. These applications often use deep learning (DL) prediction, which are highly dependent on time-series data collected by the sensors in the edge devices. However, the presence of noise in the on-device sensors negatively affects the sensing output of the DL models. Recently proposed time-series based DL approaches (e.g., SADeepSense) address this issue with the assumption that in the presence of noise, the correlation of sensor inputs in an edge device changes. In this paper, through real experiments, we notice that this assumption may not hold true in the presence of shot noise. To handle this problem, in order to further improve the prediction accuracy, we propose a DL model, namely NoiseSenseDNN, which more accurately extracts the correlation between different sensor inputs over time in the presence of both shot and white noise due to its unique architecture. We further propose a compressed version of NoiseSenseDNN that minimizes the inference time and consumed energy of the edge device while meeting the accuracy requirement. Our experiments on a workstation and a real edge device and three real traces show that NoiseSenseDNN outperforms SADeepSense in accuracy, and the compressed NoiseSenseDNN significantly reduces inference time and energy consumption while meeting the required accuracy.
Goal-driven Look-alike Modelingfor Mobile Consumers

Look-alike modeling (LAM) is a well-adopted practice in the marketing industry to reach the right audience or target users of specific interests who fits the profile of a potential customer. In this paper, we propose a goal-driven LAM (GLAM) approach which not only caters to the audience extension problem but also gives businesses the flexibility to choose the basis for similarity. In addition, the proposed solution has been implemented at a large scale with sublinear execution time - an essential requirement as the order of complexity of such problems can be computationally high. We tested the efficacy of our solution by statistically computing and comparing features <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> distribution between different sets of audiences. Our experimental results show that there is close proximity between seed audience and GLAM selected look-alike audience compared to the initial search population.
Enforcement of Highly Secured Dynamic ATM Pin Entry

Abstract The proposes system carries implementing some real-time modifications to the existing systems. It implies with providing an additional special character along with the 4-digit pin rather than the conventional ATM pin. The special character ensures the implementation of a special mathematical algorithm which creates a dynamic security pin during each transaction, making each transaction highly secure. While the transaction is initiated, a set of special characters is displayed in the ATM screen. The display carries our special character (provided with our security pin) along with number of special characters. The count of the special character is to be noted and added along with the provided 4-digit security pin and entered. The importance of this algorithm is the count of the special character is varied dynamically during every transaction. It creates a new security pin during every transaction. The security pin entered during a transaction is stored in the bank server safely. It adds up a safety feature that, if somebody illicitly captures our security pin and illicitly try to enter it, it blocks the ATM and intimates the user. To help the blind person to access the ATM centres, a technique implemented for facial recognition and the transactions carryout securely. The facial recognition helps to initiate the transaction, following it the ATM provides voice command to help them to carry out the transactions with the braille keyboard.
Human, AI, Robot Teaming and the Future of Work: Barriers and Opportunities for Advancement

Global investments in artificial intelligence (AI) and robotics are on the rise, with the results to impact global economies, security, safety, and human well-being. The most heralded advances in this space are more often about the technologies that are capable of disrupting business-as-usual than they are about innovation that advances or supports a global workforce. The Future of Work at the Human-Technology Frontier is one of NSF’s 10 Big Ideas for research advancement. This panel discussion focuses on the barriers and opportunities for a future of human and AI/robot teaming, with people at the center of complex systems that provide social, ethical, and economic value.
Development of a Motion Control Laboratory Focusing on Control Design and Fluid Power Education

Abstract This paper presents the development of a Motion Control Laboratory in the department of Mechanical Engineering at Milwaukee School of Engineering (MSOE). The main objectives of the lab are to 1) Prepare students to work in real-world motion control applications by providing students with hands-on experiences to better understand control system design ideas and concepts, 2) Expose students to electromechanical and fluid power hardware, and 3) Educate students about the benefits and capabilities of fluid power and electromechanical actuation. Students analyze systems in both open-loop and closed-loop operation, implement simulations validated by experimentation, and perform control system design. Hands-on laboratory experiences are used to reinforce control system concepts, introduce students to fluid power and electromechanical hardware, and provide experience with real-time control and industrial strength hardware. The lab experiences begin with a structured inquiry format investigation into numerous control strategies and culminate with an open-ended design lab experience where the students design a closed-loop controller of their choice to meet a set of desired design specifications. This allows students to connect how abstract concepts lead to the realizable control of hardware. Outcomes of a student exit survey are used to provide recommendations for future improvement in class offerings.
Improving Efficiency of Key Enumeration Based on Side-Channel Analysis

Side-channel analysis (SCA) is usually used for analyzing the side-channel resistance of a crypto device. However, it does not mean “practical secure” when a SCA attack fails since SCA only provides a success or failure conclusion. On the basis of the SCA data about scores and ranks of all candidates for each subkey, it is still possible to apply key enumeration (KE) algorithms to search the correct master key at an affordable overhead. Nevertheless, the efficiency of KE is limited by the SCA data in essence. To address the issue, we proposed two methods to exploit the SCA data and Riemann integral of the rank curves of all subkey candidates to update each correct sub key rank before carrying out KE. We applied the proposed methods for different crypto implementations running on different devices to verify their performance. Experimental studies for both mono-channel and multi-channel leakages verified that the proposed methods were effective in improving the efficiency of KE to recover the correct key. The proposed methods are designed for processing the SCA data and can be deemed as a preliminary before executing KE. The work of this paper bridges the gap between SCA and KE.
Target Birth Intensity Estimation in Dense Clutter

Abstract In order to solve the problem of unknown target birth intensity in multi-target tracking, an improved GM-PHD filtering algorithm based on virtual track is proposed. According to the measurement set in the monitoring area, the virtual tracks of the birth targets are established by using the motion model of the targets. The false tracks modeled by the clutter are eliminated by PHD filter, and the states of the birth targets are extracted. The simulation results show that the algorithm performs well in dense clutter environment.
Dynamical Systems and Neural Networks

Neural networks are a way of expressing a variety of nonlinear functions, but can also be thought of as waves of information propagating between layers. In this chapter, we show that such multi-layer propagation can be interpreted as the time evolution of dynamical systems, and hence of Hamiltonian systems, and look at the close relationship between the fundamental concept of “time evolution” in physics and deep neural networks.