Reviewerly/exHarmony huggingface dataset:
The risk assessment on the security of industrial internet infrastructure under intelligent convergence with the case of G.E.'s intellectual transformation

The industrial internet depends on the development of cloud computing, artificial intelligence, and big data analysis. Intelligent fusion is dependent on the architecture and security features of the industrial internet. Firstly, the paper studies the infrastructure mode that needs to be solved urgently in the industrial internet and provides a possible infrastructure mode and related security evaluation system. Secondly, it analyses the digital transformation process with the case of G.E.os industrial nternet development practice. It clarifies that G.E. is forming a new value closed-loop through digital and strategy mixed channels. Thirdly, industrial internet security research is described within multiple viewpoints based on industrial internet applications, the security service and security assurance defense systemos architecture, and the non-user entrance probability model. Finally, the paper illustrates the changes in knowledge workflow and social collaboration caused by the industrial internet under intelligent manufacture.
An Empirical Study for Method-Level Refactoring Prediction by Ensemble Technique and SMOTE to Improve Its Efficiency

Code refactoring is the modification of structure without altering its functionality. The refactoring task is critical for enhancing the qualities for non-functional attributes, such as efficiency, understandability, reusability, and flexibility. The research aims to build an optimized model for refactoring prediction at the method level with seven ensemble techniques and varieties of SMOTE techniques. This research has considered five open source java projects to investigate the accuracy of the proposed model, which forecasts refactoring applicants by the use of ensemble techniques (BAG-KNN, BAG-DT, BAG-LOGR, ADABST, EXTC, RANF, GRDBST). Data imbalance issues are handled using three sampling techniques (SMOTE, BLSMOTE, SVSMOTE) to improve refactoring prediction efficiency and also focused all features and significant features. The mean accuracy of the classifiers like BAG-DT is 99.53%, RANF is 99.55%, and EXTC is 99.59%. The mean accuracy of the BLSMOTE is 97.21%. The performance of classifiers and sampling techniques are shown in terms of the box-plot diagram.
Fundamental tools for developing likelihood functions within ACT-R

Likelihood functions are an integral component of statistical approaches to parameter estimation and model evaluation. However, likelihood functions are rarely used in cognitive architectures due, in part, to challenges in their derivation, and the lack of accessible tutorials. In this tutorial, we present fundamental concepts and tools for developing analytic likelihood functions for the ACT-R cognitive architecture. These tools are based on statistical concepts such as serial vs. parallel process, convolution, minimum/maximum processing time, and mixtures. Importantly, these statistical concepts are highly composable, allowing them to be combined to form likelihood functions for many models. We demonstrate how to apply these tools within the context of Bayesian parameter estimation using five models taken from the standard ACT-R tutorial. Although the tutorial focuses on ACT-R due to its prevalence, the concepts covered within the tutorial are applicable to other cognitive architectures.
Dual-filtering (DF) schemes for learning systems to prevent adversarial attacks

Abstract Defenses against adversarial attacks are essential to ensure the reliability of machine-learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. We proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through anomaly (outlier) detectors before making the final decision. Experimental results (using benchmark data-sets) demonstrated that our dual-filtering strategy could mitigate adaptive or advanced adversarial manipulations for wide-range of ML attacks with higher accuracy. Moreover, the output decision boundary inspection with a classification technique automatically affirms the reliability and increases the trustworthiness of any ML-based decision support system. Unlike other defense techniques, our dual-filtering strategy does not require adversarial sample generation and updating the decision boundary for detection, makes the ML defense robust to adaptive attacks.
Exploration of Teaching and Education: Integration of Sports and Medicine in Physical Education in Colleges and Universities

Based on the concept and definition of “integration of sports and medicine,” this paper analyzes the current situation of the integration, where it lacks innovative physical education curriculum content and physical education teachers with conventional medical professional knowledge; in addition, physical education disciplines with medical characteristics are not highlighted. Therefore, relevant measures are put forward for reference and discussion.
Ambient intelligence governance review: from service-oriented to self-service

The current generation of connected devices and the Internet of Things augment people's capabilities through ambient intelligence. Ambient Intelligence (AmI) support systems contain applications consuming available services in the environment to serve users. A well-known design of these applications follows a service architecture style and implement artificial intelligence mechanisms to maintain an awareness of the context: The service architecture style enables the distribution of capabilities and facilitates interoperability. Intelligence and context-awareness provide an adaptation of the environment to improve the interaction. Smart objects in distributed deployments and the increasing machine awareness of devices and people context also lead us to architectures, including self-governed policies providing self-service. We have systematically reviewed and analyzed ambient system governance considering service-oriented architecture (SOA) as a reference model. We applied a systematic mapping process obtaining 198 papers for screening (out of 712 obtained after conducting searches in research databases). We then reviewed and categorized 68 papers related to 48 research projects selected by fulfilling ambient intelligence and SOA principles and concepts. This paper presents the result of our analysis, including the existing governance designs, the distribution of adopted characteristics, and the trend to incorporate service in the context-aware process. We also discuss the identified challenges and analyze research directions.
Space-based Applications of Remote Sensing in Studying Opencast Mining and Ecology at Deposits of Non-ferrous Metal Ores

Data on mining landscapes at large deposits of non-ferrous metal ores, both abandoned and opencast, in the mining regions of Russia based on the remote sensing have been presented. On the basis of high-resolution satellite images, the number of mining and conveyor equipment in opencast quarries at deposits of non-ferrous metal ores and gold has been determined. Through mining logistics indicators determied via satellite images, diesel fuel consumption by haul trucks involved in pit-run rock transportation has been calculated. The toxic substances emission by haul truck at deposits of non-ferrous metal ores has been rated.
Matlab Based Potent Algorithm for WBc cancer Detection and classification

This paper aims to automate the detection of cancer using digital image processing techniques in MATLAB software. The analysis of white blood cells (WBC) is a powerful diagnostic tool for the prediction of Leukemia. The automatic detection of leukemia is a challenging task, which remains an unresolved problem in the medical imaging field. This Automation in Biological laboratories can be done by extracting the features of the blood film images taken from the digital microscopes and processed using MATLAB software. The aim of this approach is to discover the WBC cancer cells in an earlier stage and to reduce the discrepancies in diagnosis, by improving the system learning methodology. This paper presents the potent algorithm, which will eliminate the dubiety, in diagnosing the cancers with similar symptoms. This Algorithm concentrates on major WBC cancers, such as Acute Lymphocytic Leukemia, Acute Myeloid Leukemia, Chronic Lymphocytic Leukemia and Chronic Myeloid Leukemia. As they are life threatening diseases, rapid and precise differentiation is necessary in clinical settings. These cancers are categorized by segmentation and feature extraction, which will be further, classified using Random forest classification (RFC). RFC will classify the cancer using a decision tree learning method, which uses predictors at each node to make better decision.
RSIS: A Secure and Reliable Secret Image Sharing System Based on Extended Hamming Codes in Industrial Internet of Things

In the industrial Internet of Things (IoT), large amounts of image data are collected, stored, and transmitted by low-power cooperative sensors and heterogeneous devices, such as monitoring pictures taken by surveillance cameras in intelligent transportation. Thus, the increasing image data would be outsourced and shared between different distributed servers. Recently, some secret image sharing with authentication (SISA) schemes were proposed, which can provide image sharing in the distributed IoT system. However, if the IoT data are tampered with by malicious attackers, the error cannot be corrected, which causes unreliability and data loss. Therefore, in order to solve the above issues, we propose a secure and reliable secret image-sharing system based on extended Hamming codes (RSIS) in IoT. In RSIS, a novel distributed IoT architecture-based secret sharing is proposed. A secret image is shared into a series of stego-images. These stego-images can be distributed among multiple edge servers in IoT, which is not easy to attract attentions of attackers. In RSIS, check bits generated by authentication bits and hamming code are embedded into the stego-images to identify the tampered places with a high probability. Theoretical analysis and experiments prove that the proposed scheme achieves a high authentication capability, and the bit errors of data can be accurately detected and corrected. Generally, the scheme is effective and practical.
Structure‐aware multiple salient region detection and localization for autonomous robotic manipulation

This paper proposes a multiple salient region detection and localization approach for unstructured industrial robot work environments with arbitrarily located and orientated objects. Different from the existing, the authors' novel technique to detect multiple salient regions performs locally adaptive center-surround operations on proto-object partitions obtained through color consistency and spatial proximity analysis. The multi-scale center-surround operations are done by masks that are local structure-aware yielding regions with precise and accurate boundaries as required for robotic manipulation. First, experiments to evaluate the multiple salient region detection performance are carried out using four standard databases having images with multiple salient objects. Quantitative result analysis using F-measure, shuffled F-measure, shuffled AUC and MAE, and subjective result inspection suggests that the proposed approach is in general better at collectively detecting multiple salient regions than the state-of-the-art, including those based on deep learning. Then, real-life experiments involving robotic manipulation are carried out to demonstrate the utility of the multiple salient region detection method. For robotic manipulation, object localization is improved after salient region detection by employing a fast shadow detection algorithm proposed based on hue analysis, and recognition through existing matching techniques is applied only at the localized salient regions. The benefit of the novel multiple salient region detection approach in the robotic manipulation system is shown using localization and pose estimation accuracy, rates of detection and recognition, positional and angular errors, and processing speed.
An Efficient Ant Colony System for Multi-Robot Task Allocation with Large-scale Cooperative Tasks and Precedence Constraints

Multiple heterogeneous robots can work together to execute complex tasks. Given multiple heterogeneous tasks and heterogeneous robots, the allocation of tasks to robots is a challenging optimization problem. Lots of methods have been proposed for the multi-robot task allocation (MRTA) problem. However, most existing methods only consider small-scale tasks without precedence constraints. Hence, this paper tracks the time-extended MRTA problem with large-scale cooperative tasks and precedence constraints, and proposes an efficient ant colony system (ACS) to solve the problem. In the proposed algorithm, we adopt a permutation with task-robot alliance pairs as the encode scheme to represent a feasible solution. A pheromone matrix is initialized by a hierarchical greedy strategy and iteratively updated to record historical experience. Heuristic information related to the optimization objective is also designed to help algorithm find better solutions according to the current state. Through combining pheromone and heuristic information, the ACS is able to search high-quality solutions from a global perspective. Experimental results on multiple problem instances are reported to show the advantage of the proposed method. The proposed ACS method can well solve the MRTA problem with large-scale cooperative tasks and precedence constraints.
Ensuring the Big Data Integrity Through Verifiable Zero-Knowledge Operations

Information from databases is exposed to threats at all stages of its existence: from recording and storing in the database to processing and returning to the user. Big Data systems combine multiple DBMSs and databases. The problem of data integrity is the most acute in them. The article describes an approach to control the integrity of Big Data during their processing, based on verifiable zero knowledge operations. It can be applied to various complex systems containing heterogeneous Big Data databases. The proposed approach implements prospective data integrity protection against existing and hypothetical future threats.
Automated Search and Analysis of the Stylometric Features That Describe the Style of the Prose of 19th–21st Centuries

This article is devoted to the comparison of stylometric features of several levels, which are markers of the style of the prose text and analysis of the stylistic changes in Russian and British prose of the 19th–21st centuries. Stylometric features include low-level features based on the words and symbols and high-level features based on rhythm. These features model the style of a text and are indicators of the time when the text was created. Calculations of all the features are performed completely automatically, so they allow conducting large-scale experiments with artworks of a large volume and speeds up the work of a linguist. To calculate the stylometric features, including ones based on the search results for rhythm figures, the ProseRhythmDetector program is used. As a result of its work, each text is presented as a set of the same features of three levels: characters, words, and rhythm. Texts are combined by decades, and average values of stylometric features are found for each decade. The obtained models of decades are compared using standard similarity metrics, results of comparison are visualized in the form of heatmaps and dendrograms. Experiments with two corpora of Russian and British texts show that during the 19th–21st centuries there are general trends in style change for both corpora, for example, a decrease in the number of rhythm figures per sentence, and also particular trends for each language, for example, dynamics of change of the word and sentence lengths. Stylometric features of all levels reveal the similarity in the style of texts published in one century. Also, features of three levels in the complex better demonstrate the uniqueness of each decade than features of a particular level. This study shows the importance of stylometric features as style markers of the different eras and allows identifying trends in style during several centuries.
Aircraft Rotation Detection in Remote Sensing Image Based on Multi-Feature Fusion and Rotation-Aware Anchor

Due to the variations of aircraft types, sizes, orientations, and complexity of remote sensing images, it is still difficult to effectively obtain accurate position and type by aircraft detection, which plays an important role in intelligent air transportation and digital battlefield. Current aircraft detection methods often use horizontal detectors, which produce significant redundancy, nesting, and overlap of detection areas and negatively affect the detection performance. To address these difficulties, a framework based on RetinaNet that combines a multi-feature fusion module and a rotating anchors generation mechanism is proposed. Firstly, the multi-feature fusion module mainly realizes feature fusion in two ways. One is to extract multi-scale features by the feature pyramid, and the other is to obtain corner features for each layer of feature map, thereby enriching the feature expression of aircraft. Then, we add a rotating anchor generation mechanism in the middle of the framework to realize the arbitrary orientation detection of aircraft. In the last, the framework connects two sub-networks, one for classifying anchor boxes and the other for regressing anchor boxes to ground-truth aircraft boxes. Compared with state-of-the-art methods by conducting comprehensive experiments on a publicly available dataset to validate the proposed method performance of aircraft detection. The detection precision (P) of proposed method achieves 97.06% on the public dataset, which demonstrates the effectiveness of the proposed method.
A Research of I/Q Imbalance based RF Fingerprint Identification with LTE-RACH Signals

Radio frequency fingerprint (RFF) based wireless device identification has been extensively researched. Long-Term Evolution (LTE) and 5th Generation (5G) mobile communication systems have similar random access channel (RACH) signals, which could be employed for RFF based device identifications. This paper proposes a practical I/Q imbalance-based RFF extraction method using LTE-RACH signals. We initially evaluate the performance of the proposed method by simulating LTE-RACH signals with RFF model. The relationship between I/Q imbalance characteristics and LTE-RACH signal format is explored. Secondly, we build an experimental system including 12 LTE mobile phones from 3 brands and 6 Universal Software Radio Peripheral (USRP) devices from 2 brands. The I/Q imbalance features are extracted for classifications. The experimental results show that the I/Q imbalance feature is distinguishable among the devices produced by different manufacturers, especially the commercial LTE mobile phones and experimental USRP devices. The classification accuracy rate of LTE mobile phones and USRP devices can reach 96.01%, and the accuracy rate of LTE mobile phones from different brands is 89.80%.
Individualized Learning Patterns Require Individualized Conversations – Data-Driven Insights from the Field on How Chatbots Instruct Students in Solving Exercises

Chatbots can foster the learning success of students in educational settings. This has been shown in prior research studies, e.g., using laboratory studies in online learning settings. Actual evaluations of using educational chatbots in the field are nevertheless rarely reported. Thus, insights into the students’ interactions with chatbots in long-term field settings are missing. In this research study, we aim at gaining insights into the students’ interactions with an educational chatbot in a programming course. To this aim, we follow an explorative data-driven discourse analysis approach and show how students interacted with the chatbot during a field study lasting several months. We ground our analysis on a dataset from 54 students interacting with a chatbot while working on programming exercise tasks. We reveal how students interact with the chatbot and identify different types of usage patterns. The results imply that adaptive learning paths are one of the most important aspects of educational chatbot design for dealing with heterogeneous usage patterns.
Efficiency Improvement of the GLOBAL Optimization Method by Local Search Changes

There are many suitable global optimization approaches to find the minimum value of an objective function.In this paper, the improvement of the GLOBAL Optimization Method is studied, which is based on stochastic clustering.Through its three main components, which are sampling, clustering, and local search the algorithm aims to find the global minimum of the objective function.Local search methods significantly influence the efficiency of the GLOBAL method.The efficiency of our proposal may be improved by dividing the system into modules and by creating new variants of both the local and line search methods.The main contribution of this work is to show the achievements of modularization and the efficiency of the new variants of both local and line search methods.
How Does Social Media Platforms Help to Improve Business Performances during the COVID-19 Pandemic? A Study of TikTok

The study investigates to which extent social media platforms, specifically TikTok, have driven users’ satisfaction and lead to business performance improvements. Entrepreneurs, such as small business owners, agents, and drop shippers were facing tremendous challenges to carry out their businesses during the COVID-19 pandemic. The existence of social media applications has allowed them to promote their products or services in various forms despite the failure to conduct businesses physically. However, they tend to use certain social media application only if it satisfies their business needs. This study employs 100 samples of data from students in Universiti Sains Malaysia collected through an online questionnaire. Subsequently, SPSS analysis was conducted and generated descriptive, Pearson correlation, and regression analysis to support the hypotheses. The findings of this study revealed that users’ satisfaction towards social media, specifically TikTok, have led to customer engagement, personal branding, and effective promotion. Thus, entrepreneurs shall first adopt social media application that provides the highest level of satisfaction to motivate continuous usage of the application which then gradually improves their business performance.
High-Level Planning for Object Manipulation With Multi Heterogeneous Robots in Shared Environments

Multi-robot systems are becoming increasingly popular in warehouses and factories, since they potentially enable the development of more versatile and robust systems than single robots. Multiple robots allow performing complex tasks with greater efficiency. However, this leads to increased complexity in planning and dispatching actions to robots. In this letter, we tackle such complexity using a hierarchical planning framework: the task is first planned at an abstract level and then refined by local motion planning. We propose a framework based on a state-transition system formalism that abstracts the problem by removing unnecessary details and, hence, considerably reduces planning space complexity. Forward search from an initial state allows the robot to find a sequence of actions to accomplish the assigned task. These actions can be planned at a lower level employing any motion planning technique available in the literature. The proposed method is validated through experiments in several operating conditions and scenarios.