Reviewerly/exHarmony huggingface dataset:
Finding Significant Influencing Factors of Core Quality and Ability Development of Teachers Based on Improved Genetic Algorithm

The professional development of teachers is crucial for the education development of a country. For understanding the core qualities and ability development of teachers, it is very important to identify the associated influencing factors. In this paper, an improved genetic algorithm is used to find out the significant influencing factors among the many influencing factors of teachers’ core qualities and ability development, the idea of the improvement is to introduce elite solution retention strategy and adaptive mutation probability on the basis of traditional genetic algorithm. At the same time, the collected samples are processed by back propagation neural network, and the accuracy of its prediction is taken as the criterion to measure the significant degree of the influencing factors. The experimental results show that the improved genetic algorithm is more effective than traditional genetic algorithm in finding the significant influencing factors.
Introduction and Beginners Guide to Quantum Computing

Quantum computing is a fascinating topic, and the authors attempt to give a detailed explanation about the history of quantum computing, double slit experiment, introduction about quantum computers, quantum super position, quantum entanglement, overview about quantum supremacy, building quantum computing, single bit gates, multi-bit gates, model of quantum computing with applications, and case study. The quantum computing was developed using quantum algorithms and quantum devices. Firstly, the quantum algorithms are developed as mathematical models, and the performance is evaluated through simulations, algebraic algorithms (cryptography, Diffie-Hellman, shor algorithm, for RSA) and amplitude amplification (database searching, pattern matching, etc.). Secondly, the quantum devices are the original physical devices built with optical connections instead of electrical connections. The qubit control uses the microwave for superconducting, laser, quantum dots, and photonics (optical instrument).
Role of Technology for Formal Education in Bangladesh

We are using technology for e-learning platforms, blended platforms, and formal education platforms. Formal education is a structured and systematic form of learning. For formal education, we can say face-to-face or on-campus education. Formal education is classroom based, meaning everything a student learns comes from books and other educational materials with the sole purpose of educating students. Internet gives us the option to meet around the world. Internet distance education is a natural consequence of industrial transformations from a manufacturing economy, in which standard educational practices are based, to an information economy, in which greater autonomy, collaboration, flexibility, and a project orientation to work are the norm. The internet did not cause changes in education, but rather enabled educators to meet new demands for instructional practices and outcomes and adapt to a rapidly changing economic and social environment that was beginning to outpace the academy.
Recent Developments in Quantum Computing and Their Challenges

Today, major corporations such as IBM, Google, Microsoft, and Amazon are racing to build cloud quantum computers. They integrate quantum computers with cloud computing to create a system that can be accessible via a network without the need for quantum computing power in a cloud computing environment. The authors describe cloud quantum computing in this chapter and conduct research to compare cloud quantum computing providers across the globe. In this study, the authors look at the various cloud quantum computing options and the outcomes they provide. At the conclusion, each cloud quantum computing service will be compared based on its performance, amplitudes, times, and architecture. The study will primarily focus on IBM and Qutech cloud quantum computing technologies.
Hoax Detection and Review Analysis Using Machine Learning

A common trouble of the prevailing time is that, organizations from distinct domain names are struggling to acquire powerful answers for detecting online-primarily based fake news. it is quite problematic to distinguish faux facts on the net as it's perfect and regularly written to deceive customers. Preceding review papers were primarily based on statistics mining and system mastering strategies, scarcely exploring the deep gaining knowledge of techniques for faux information detection. However, emerging deep learning techniques- is primarily based on processes such as attention, Generative adversarial Networks, and Bidirectional Encoder Representations for Transformers are absent from preceding surveys. We begin with highlighting the fake news results. Then, we proceed with the dialogue on the dataset utilized in preceding studies and their NLP techniques. A complete overview of deep getting to know primarily based techniques has been bestowed to arrange consultant methods into diverse categories. The distinguished assessment metrics in fake news detection are also mentioned. The algorithms used are TF-IDF and LEMMETISATION using NLP. Deep Learning is used to abstract the data set which holds information ranging from any product categories in selected Ecommerce website or news(tweets) spread through twitter. Most fake news follow a common pattern which can be identified from the data set by the NLP algorithm A large amount of computational energy and memory is required to analyze a big variety of variables. category algorithms can also overfit the training samples and set off poorly to new samples. characteristic extraction is a system of building combos of variables to conquer those difficulties while nevertheless representing the facts with good enough precision. feature extraction and characteristic selection are often utilized in textual content of fake review analysis of Amazon food review works by BERT , which analyses common word pattern , after which Vectorization and Tokenization is also used to separate them and analyze what individual word is and after that using Sentimental Analysis, it can differentiate positive and negative review and repetition in pattern to validate the scores of a review.
A Study on ChinaBond Threat Intelligence Platform Construction

In a complex international environment, the increasingly severe threat from internal and external attacks has become a “new normal”. The “Cyber-security 2.0 Era” surrounding the enhancement of defensive capabilities has arrived. CCDC is building its active defense capabilities for corporate security and protecting its assets in line with the tiered cyber-security policy. As commercial threat intelligence is not timely, targeted or accurate enough, CCDC has independently developed the ChinaBond Threat Intelligence Platform with knowledge graph and other technologies to collect, analyze and retain intelligence clues and deliver targeted tactical, operational and strategic intelligence to capture security risks, identify potential threats, make clear the security situation and detect the true intent of attackers, thus providing strong support for the overall security protection under real-world scenarios.
Cognition-Driven Multiagent Policy Learning Framework for Promoting Cooperation

Many attempts have been made to promote cooperation for multiagent systems. However, several issues that draw less attentions but may dramatically degrade the cooperation performance still exist, such as redundant information interactions among neighbors, and difficulties in understanding complex and dynamic environments from high-level cognition. To address these limitations, a cognition-driven multiagent policy (CDMAP) learning framework is proposed in this article. It includes a cognition difference network (CDN), a coupling cognition network (CCN), and a policy optimization network (PON). CDN is designed based on a variational autoencoder, where a concept of cognition difference is defined to prune redundant interactions among agents for more efficient communication. Based on the pruned topology, CCN captures the hidden representations of the surrounding environment. Several coupling graph attention layers are incorporated in CCN, each layer with different but coupling adjacent matrices, yielding a comprehensive state understanding from multiple representation spaces. Based on the captured hidden states, PON generates the final policies, where QMIX is adopted as a value factorization method to alleviate the credit-assignment problem. At last, CDMAP is evaluated through two representative multiagent games including Google Research Football and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StarCraft II</i> . The results demonstrate its superior effectiveness compared with existing methods.
MS-LSTMEA: Predicting Clinical Events for Hypertension Using Multi-Sources LSTM Explainable Approach

Hypertension is a major risk factor for cardiovascular disorders and diseases. Since it is expected to increase dramatically, effective Hypertension management becomes more and more critical. Early detection of patients with uncontrolled Hypertension would allow to employ personalized medicine for anticipating and providing the adequate drug class.In this paper, we propose MS-LSTMEA, a Multi-Source and Explainable Hypertension prediction Approach based on Long Short-Term Memory algorithm, for predicting both the drug class for patients and the date of their next medical appointment. MS-LSTMEA can successfully combine different sources of medical information about patients, represented by tabular data, while processing them separately and differently, and taking into account the temporal aspect of Electronic Health Records. In addition, it integrates an Attention mechanism which allows to improve the model results and to explain its outcomes.Experiments have been conducted on real Electronic Health Records data from 429,087 patients with the support of experts in healthcare. The results show significant improvements in accuracy over several existing models.
Improving Address Clustering in Bitcoin by Proposing Heuristics

The Bitcoin system uses anonymous transactions to protect users’ privacy, but attackers can use this defect of bitcoin transactions to discover the association between bitcoin addresses. At present, address clustering methods can make use of these vulnerabilities to associate the address as an entity to a certain extent. However, these address clustering methods have problems such as an insufficient inference rate of change addresses, inability to identify mixing transactions, and low efficiency of algorithm implementation. We propose some solutions to these problems. 1) We improve the method of change address identification to identify and mark more of them. 2) We propose a heuristic address clustering method related to mixing transactions, which can identify their privacy vulnerabilities. 3) We propose an incremental address clustering method that can store the historical state and more quickly discover the anonymity defect of Bitcoin. We use real Bitcoin transaction data to demonstrate our method’s feasibility and reliability.
A Countrywide Descriptive Survey of Agile Software Development in Brazil

Abstract For years, industry institutions and academic researchers have been surveying software practitioners on agile software development methods adoption. These surveys have been useful in describing the characteristics, challenges, and impacts of agile adoption, mainly in Europe and North America. Latin American practitioners miss information on the state of agile adoption. This study aims to fill this gap by describing agile software development adoption in Brazil. We collected data from 897 countrywide-distributed practitioners. We used descriptive statistics and machine learning algorithms to understand our dataset. Results show the profile of companies and teams, characteristics of agile usage, perception of success, applied principles and practices, and reasons, challenges and impacts of agile adoption. We also explore the relevance of principles in software process improvements. We contribute by mapping the state-of-the-practice of agile adoption in Brazil and by contrasting our results to previous literature, which points out how we further current knowledge in academia.
A Hyperparameter Adaptive Genetic Algorithm Based on DQN

The hyperparameters of the metaheuristic algorithm are difficult to determine when solving optimization problems. The existing methods mainly adjust hyperparameters through preset rules or traditional RL. The performance of the above methods is unsatisfactory and the generalization is poor. This work proposes a deep Q-learning network (DQN)-based dynamic setting framework for combinatorial hyperparameters, and applies it to a Genetic algorithm (GA) to improve its performance. By defining the four elements of the environment, state, action and reward required for learning strategy in advance, the parametrized strategy can be trained offline and different DQN models can be studied. Our method was compared with other algorithms and achieved the shortest path on 14 of 15 public TSP instances. Meanwhile, the test results on our simulation TSP validation dataset revealed that Category DQN achieved the best performance. This means the proposed method can effectively solve the problem of combinatorial hyperparameters setting, and bring more solving advantages to the GA.
A Case for Data-Driven Agile Transformations: Can Longitudinal Backlog Data Help Guide Organizational Improvement Journeys?

Abstract Context: Almost every organization with a strong digital capability has embarked on an agile transformation journey. But do these changes actually deliver on the envisioned transformation goals? What conclusions can we draw from measurements and observations? Objective: The ambition of this report is to (1) assess whether tooling data can be used to guide a transformation towards improved organizational performance; (2) verify claimed benefits of agile transformations using tooling data in the presented case study. Method: We measure productivity, time-to-market, and quality as transformation objectives by analyzing longitudinal Jira backlog tooling data within an embedded multiple-unit case study. Results: By analyzing over 57,000 Jira issues from eight agile release trains over a period of three years, we (1) provide a proof of concept of how tooling data can be used to guide agile transformations; (2) provide empirical evidence on the assessment of transformation objectives over time and organizational layers at FinOrg; and (3) connect measurement results with available literature. Conclusions: We may conclude that tooling data is a viable addition to guide transformations through identification of improvement opportunities on the set objectives. We connected the case study results to existing literature and identified similarities. We argue that there is a need for a measurement framework and better understanding of the dynamics between measurement and performance.
Driver Secondary Driving Task Behavior Recognition

In this chapter, we present a driver activity recognition framework that can steadily extract the driver skeleton data from low-cost monocular cameras, while also, accurately recognizing different driving-related tasks in real time. However, several difficulties should be overcome to achieve this goal. First, the videos collected by the onboard monocular camera always contain noises because of non-negligible environmental factors such as illumination and camera motions, which will directly affect skeleton joints detection. Second, the driver activity recognition network should be of a concise framework with accurate classification performances since the computing resource on-board is usually limited. Third, there usually exists a huge data imbalance among normal driving and other secondary driving tasks in real-time applications, and this will largely affect the recognition performance of small-scale categories.
Use of multilayer recursive model for non-linear dynamic system identification

In practice, the dynamics of the system are uncertain due to nonlinear and dynamic characteristics. It is difficult to establish accurate identification and prediction of the nonlinear plants that require dynamic modelling of the system. Extreme learning machine (ELM) as the recursive model due to its fast training and convergence speed is utilized in this work. However, its limitation is that it has only 1 hidden neuron which tends to make evolution speed low. Further, Multi-layer ELM (ML-ELM) model is applied on a nonlinear Auto-regressive complex benchmark system. The performance of ML-ELM is compared with dynamic recurrent functional link neural network (DRFLNN), functional link neural network (FLNN), nonlinear auto-regressive moving average (NARAX), multi-layer perception (MLP), radial basis function network (RBFN), Elman recurrent neural network (ERNN), and basic ELM models. From the comparison table, it can be seen that ML-ELM has better performance as compared with other models.
Enhancing the teleportation of quantum Fisher information under correlated amplitude damping decoherence

From the perspective of quantum information transmission, one may be interested in the teleportation of quantum Fisher information (QFI) which provides the optimal precision of parameter estimation. In this paper, we investigate the teleportation of QFI under the correlated amplitude damping (CAD) decoherence. It is found that the correlated effects play a positive role in improving the teleported QFI, but the impact of decoherence is still serious. Therefore, we propose two schemes, which are based on weak measurement (WM) and environment-assisted measurement (EAM), to enhance the teleportation of QFI under the CAD decoherence. The results show that both schemes can significantly improve the teleported QFI with a certain success probability. The findings of our study suggest that the correlated effects can significantly increase the success probabilities of these two schemes. A detailed comparison confirms that the EAM scheme is more efficient than the WM scheme in improving the teleportation of QFI.
Improved Unet in Lithology Identification of Coal Measure Strata

Abstract The lithology of underground formations can be determined using logging data, which is important for a variety of subsurface geoscience and industrial applications. Deep learning technology offers the advantage of discovering a potential relationship between input and output variables, making it a great choice for generating fast and cost-effective lithology classification models. To automatically characterize lithologies, a multiclass image segmentation problem is considered and an improved Unet as a solution is adopted. The model’s input data is two-dimensional images composed of rock feature data at different depths, and the outcome is a result of one-dimensional rock lithology classification. The algorithm’s practicality was tested using the logging data set from the Xinjing mining area in Shanxi Province, in north-central China, and an open-source data set of Canadian strata. Our model is tested against the 1D-convolutional neural network (CNN) and XGBoost algorithms using a good logging data set of the same depth and different depths for testing. The results show that the improved Unet method outperforms the 1D-CNN and XGBoost algorithms in the classification of rock lithologies. This algorithm has high application potential in the automatic interpretation of rock lithologies.
Recommendation of Regression Techniques for Software Maintainability Prediction With Multi-Criteria Decision-Making

Context: Successful project management requires accurate estimation of maintenance effort and cost. Software Maintainability Prediction (SMP) plays a very important role in controlling software maintenance costs by detecting software modules with low maintainability. In previous research, numerous regression techniques were applied to predict software maintainability. The results with respect to various accuracy or performance measures are conflicting. Thus, there is a dire need to develop a method that can recommend regression techniques for predicting software maintainability in the presence of conflicting performance or accuracy measures. Objective: This paper aims to recommend suitable regression techniques for SMP based on the Multi-Criteria Decision-Making (MCDM) approach. Methodology: In our proposed approach, selecting a regression technique for SMP is modeled as the MCDM problem. To validate the proposed approach, an empirical study is done using three MCDM methods, 22 regression techniques, and eight performance measures over five software maintainability datasets. Before applying MCDM methods, a statistical test, namely the Friedman test, was conducted to ensure the significant difference between regression techniques. Results: The results of our study show that SVR, IBK, REPTree, and MLP-SVM achieve the highest-ranking score value one and are recommended as top-ranked approaches for SMP based on MCDM rankings. Conclusion: The main outcome of this study is that the proposed MCDM-based approach can be used as an efficient tool for selecting regression techniques among different available regression techniques for SMP modeling in the presence of more than one conflicting accuracy or performance measure.
Postdigital Ecopedagogies of Attainment and Progress

Human attainment is based on a particular model of chronological achievements. People and society are assessed in terms of making progress towards ‘something better’. This approach through modernity sees technology treated as a resource to harness for gain regardless of environmental costs. In education, this linear progress model is mirrored: accessing learning, completing study in a timeframe, attaining an award and progress beyond education. Though Covid-19 has interrupted these components of ‘success’, a consensus that children, students, workers and the economy all need to ‘catch up’ after the pandemic exists, even when people are not catching up from an equal positionality. In this competitive, neoliberal progress model attempts to widen participation in education have only had limited success. Additionally, new convergences between digitalisation and biological sciences now provide a broader world view on relations between technologies, progress and humans. This chapter examines the possible demise of a model of progress based on linear hierarchies. It reviews related assumptions, and considers implications, for ecopedagogies of attainment, when unpredictable developments in technology now begin to alter how we might understand progress itself.
Modelling and control of <scp>fuzzy‐based</scp> systems using intelligent water drop algorithm

Abstract Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques.