Reviewerly/exHarmony huggingface dataset:
Improving Student Learning Outcomes Through Innovative Textbooks In Writing Persuasive Texts

The ability to write persuasive texts is a skill that is not easy to master. The low level of students' understanding and memory of the material taught orally and in writing. In addition, the lack of learning resources is also an obstacle for students in learning. This study aims to develop a persuasive text writing book and analyze the potential effects of a persuasive text writing book. This research method uses the Jolly and Bolitho and Tesmer development model. This research is research and development research. Methods This research adapts the Jolly and Bolitho development and Tessmer development models. Data collection techniques used three techniques, questionnaire techniques, interviews, and tests. The data collection instrument is a questionnaire. The technique used to analyze the data is descriptive qualitative analysis quantitative and inferential statistics. The study results, the expert validation assessment, obtained very good results from content, language, presentation, and graphics feasibility. The field test results through the paired simple test showed a significant difference between the scores before and after using this developed textbook. It was concluded that writing persuasive textbooks could make it easier for students to understand writing to improve student learning outcomes.
Distributed Stochastic Inertial-Accelerated Methods with Delayed Derivatives for Nonconvex Problems

Stochastic gradient methods (SGMs) are predominant approaches for solving stochastic optimization. On smooth nonconvex problems, a few acceleration techniques have been applied to improve the convergence rate of SGMs. However, little exploration has been made on applying a certain acceleration technique to a stochastic subgradient method (SsGM) for nonsmooth nonconvex problems. In addition, few efforts have been made to analyze an (accelerated) SsGM with delayed derivatives. The information delay naturally happens in a distributed system, where computing workers do not coordinate with each other. In this paper, we propose an inertial proximal SsGM for solving nonsmooth nonconvex stochastic optimization problems. The proposed method can have guaranteed convergence even with delayed derivative information in a distributed environment. Convergence rate results are established for three classes of nonconvex problems: weakly convex nonsmooth problems with a convex regularizer, composite nonconvex problems with a nonsmooth convex regularizer, and smooth nonconvex problems. For each problem class, the convergence rate is $O(1/K^{\frac{1}{2}})$ in the expected value of the gradient norm square, for $K$ iterations. In a distributed environment, the convergence rate of the proposed method will be slowed down by the information delay. Nevertheless, the slow-down effect will decay with the number of iterations for the latter two problem classes. We test the proposed method on three applications. The numerical results clearly demonstrate the advantages of using the inertial-based acceleration. Furthermore, we observe higher parallelization speed-up in asynchronous updates over the synchronous counterpart, though the former uses delayed derivatives. Our source code is available at https://github.com/RPI-OPT/Inertial-SsGM.
edaGAN: Encoder-Decoder Attention Generative Adversarial Networks for Multi-contrast MR Image Synthesis

Magnetic resonance imaging (MRI) is the preferred modality among radiologists in the clinic due to its superior depiction of tissue contrast. Its ability to capture different contrasts within an exam session allows it to collect additional diagnostic information. However, such multi-contrast MRI exams take a long time to scan, resulting in acquiring just a portion of the required contrasts. Consequently, synthetic multi-contrast MRI can improve subsequent radiological observations and image analysis tasks like segmentation and detection. Because of this significant potential, multi-contrast MRI synthesis approaches are gaining popularity. Recently, generative adversarial networks (GAN) have become the de facto choice for synthesis tasks in medical imaging due to their sensitivity to realism and high-frequency structures. In this study, we present a novel generative adversarial approach for multi-contrast MRI synthesis that combines the learning of deep residual convolutional networks and spatial modulation introduced by an attention gating mechanism to synthesize high-quality MR images. We show the superiority of the proposed approach against various synthesis models on multi-contrast MRI datasets.
A Secured Key Management Scheme for Mobile Ad Hoc Networks with Modified Montgomery Modular Arithmetic

Implementing factorization concept in Montgomery Modular Arithmetic improves the efficiency of standard modular arithmetic, which occurs in many cryptographic algorithms that are applied frequently for key management in Mobile Ad Hoc Network (MANET). In this paper Elliptic Curve Cryptography with Diffie Hellman key exchanging mechanism (ECC-DH) is implemented with Modified Montgomery Modular Arithmetic (MMECC-DH) for secure key management and performance enhancement of MANET. Metrics like Key calculation time, Key update time, Packet Delivery Radio, Average Power Consumption per Node, number of cycles per key exchange, Operations per second, processing time and Security Levels of ECC-DH, Montgomery Elliptic Curve Cryptography with Diffie Hellman key exchange (MECC-DH) and MMECC-DH methods are measured in Haswell and Sandy Bridge Architecture based processors.
Deep Learning-based Channel State Information Prediction with Incomplete History

Outdated and inaccurate channel state information (CSI) prevents the transmitter from adapting to current channel conditions and impairs reliable communication. This paper proposes a deep learning-based methodology for real-time prediction of future CSI, which enables it to be fed back in advance and mitigates the effect of the feedback and processing delay. Predicting the future CSI at the user equipment may also help the gNodeB (gNB) send reference signals less frequently and may help reduce the reference signal transmission overhead. Our approach utilizes Long Short-Term Memory (LSTM) type recurrent neural networks (RNN) to predict the CSI from past channel estimations. However, the knowledge of past channel gains is assumed to be limited, and only partial channel data is available. We propose a new algorithm that utilizes LSTMs and remedies incomplete channel history problem, which we denote by RNN-I. We evaluate the performance of the proposed framework by comparing it with two conventional techniques: persistence and auto-regressive (AR) model-based prediction methods. The results show that the proposed RNN-I algorithm performs well under strong channel mismatch scenarios, while AR methods diverge and can not keep up with fast varying channels if the mismatch in the channel characteristics used in training and inference is significant.
Digital Ethnographic Art(i)Facts as Evidence: Anthropological Entanglements between Techne and Episteme

This chapter addresses the ethical question to what extend we are allowed to fabricate evidence in order to make the ethnographic narration more accurate and more accessible to the wider public. It reexamines the persistent issues of field research, interpretation and meaning articulation in anthropology, and frames them in an expanded understanding of research ethics that leaves room for imagination, creativity, omission and “tampering with evidence” as part of a viable ethnographic practice. In doing so, it discusses the central epistemological division between episteme and techne (as art and as technology), which has been posing tantalizing questions to the humanities and social sciences since Aristotle. It introduces the idea of the ethnographic artifact (a notional and tangible object that stands both etymologically and epistemologically midway between art and fact) as a tentative resolution of the tension between a politics of truth and a politics of fabrication and storytelling. The author posits that, since academic writing has often failed to do justice to the research subjects or adequately engage with nonacademic audiences, research-based fiction may be an answer to a wider politics of representation and knowledge dissemination. The analysis draws on the research project “Ethnography and/as hypertext fiction: representing surrogate motherhood” (HYFRESMO), which focuses on the emerging social practice of surrogate motherhood in order to critically address digital ethnographic textuality by producing nonnormative ethnographic “texts.”
Computer Science Education in Angola: The Key Challenges

The increasing adoption of technology in the classroom offers several advantages, but also challenges all stakeholders, including instructors, students and decision makers. E-learning has gained momentum, especially in the last two years with the onset of the pandemic, forcing lectures to migrate to the internet and requiring new methodologies to support knowledge transfer between instructors and students. Thus, to verify the situation of Angola in relation to practices and methodologies related to the teaching of informatics, we search to identify existing practices through the application of a questionnaire to teachers of informatics subjects in secondary schools, colleges, and universities of the country. This study aims to discover the effects of using technology as a support for teaching computer science classes, including software engineering, programming, and computer networks. Therefore, 82 participants were involved in this study answering an online survey. Results shown that, although digital technologies are revolutionizing the world and consequently education, institutions in Angola still face several challenges, such as, lack of laboratories for practical classes, lack of electricity, lack of internet, and lack of computers for students. In addition, the adoption of technologies in the classroom proved ineffective, perhaps because of the absence of determining factors for technological inclusion by the institutions, or even because of the training of teachers in the area, whether weak, inefficient, or incomplete. In this sense, although the use of software in the classroom facilitates the acquisition of knowledge, there are still several barriers to be overcome for this to happen broadly. This study concludes that it is necessary to consider technology as a basic need for the facilitating role it plays in society.
Improving modeling of stochastic processes by smart denoising

This paper proposes a novel method for modeling stochastic processes, which are known to be notoriously hard to predict accurately. State of the art methods quickly overfit and create big differences between train and test datasets. We present a method based on smart noise addition to the data obtained from unknown stochastic process, which is capable of reducing data overfitting. The proposed method works as an addition to the current state of the art methods in both supervised and unsupervised setting. We evaluate the method on equities and cryptocurrency datasets, specifically chosen for their chaotic and unpredictable nature. We show that with our method we significantly reduce overfitting and increase performance, compared to several commonly used machine learning algorithms: Random forest, General linear model and LSTM deep learning model.
Multi-level out-of-vocabulary words handling approach

Distributed representation models can generate a vector representation only for words that belong to a finite vocabulary collected from the training data. If out-of-vocabulary (OOV) words are not handled properly, they can impair the performance of machine learning methods in a given natural language processing task. This study offers a new methodology based on the consolidated top-down human reading theory, which may serve as a strong basis for developing new techniques to deal with the OOV problem. For this, we present MLOH, a Multi-Level OOV Handling approach, based on three chained strategies: analogy, decoding, and prediction. The techniques available in the literature, in general, are limited since they often resolve specific types of OOV words, such as those that can be inferred by analyzing their morphological structure or context. Compared to the process used by human readers to infer unknown words, using a single strategy is generally not effective. We evaluated MLOH performance on tasks that can be highly affected by OOV words, such as part-of-speech tagging, named entity recognition, and text categorization of short and noisy texts. The results indicate that the proposed approach is promising since it could handle most of the OOV words presented, is more generalist, and obtained competitive performance in all experiments.
CONCEPTUALIZATION OF CRITERIA FOR ASSESSING THE LEVEL OF READINESS OF FUTURE LAWYERS TO LAW-MAKING ACTIVITIES

Relevance. The article discusses the criteria for assessing the level of formation of the readiness of future lawyers for law-making activities based on the application of various scientific conceptualization methods. Understanding the effectiveness of ongoing measures of pedagogical stimulation is possible on the basis of generalization of information about the control of the level of formation of readiness for law-making activities, which is carried out using a set of criteria for its assessment.&#x0D; Materials and methods. The main methodological approaches on which the pedagogical stimulation of the formation of students’ readiness for law-making activities are based are activity, comprehensive, systemic, axiological, sign-contextual and individually differentiated approaches, which are a necessary and sufficient basis for solving the problems of such a study. The main methods of obtaining and processing scientific information were: the dialectical and system method; systematization and conceptualization; generalization and classification. When substantiating the criteria for assessing the readiness of future lawyers for law-making activities, the starting point was the structure of the readiness of future lawyers for law-making activities.&#x0D; Results. The study identified and substantiated the following criteria: cognitive-legal; orientation-value; activity-creative. The levels of each of the criteria are: familiarization and information; professionally necessary; optimal-sufficient. The general functions of the criteria under study include: teleological; structural and organizational; generalizing-analytical; evaluation-level; information and diagnostic. The special functions of the criteria are: reflexive-corrective and prognostic; orientation-value; logical and argumentative.&#x0D; Findings. The practical application of the criteria for assessing the level of formation of the readiness of future lawyers for law-making activities can be effectively carried out using the principles of consistency, continuity, algorithmic, objectivity, and combination with other diagnostic tools.
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users’ historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model.
Codes

This chapter introduces linear codes and some of their properties, surveys how the Tutte polynomial determines these properties, and provides an overview of more general results as well as applications to coding theory. Linear code, codeword weight, support and weight enumerator, dual code. The MacWilliams identity. The coboundary polynomial, Greene's theorem and the critical theorem. Ordered tuples and subcodes. Equivalence between the Tutte polynomial and generalized weights. Orbital polynomials. Generalizations and applications of Greene's theorem. Wei's duality theorem.
Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

Dynamic environments are still a big challenge for optimization algorithms. In this paper, a Genetic Algorithm using both Multiploid representation and the Bayesian Decision method is proposed. By Multiploid representation, an implicit memory scheme is introduced to transfer useful information to the next generations. In this representation, there are more than one genotypes and only one phenotype. The phenotype values are determined based on the corresponding genotypes values. To determine phenotype values, the well-known Bayesian Optimization Algorithm (BOA) has been injected into our algorithm to create a Bayes Network by using the previous population to exploit interactions between variables. With this algorithm, we have solved the well-known Dynamic Knapsack Problem (DKP) with 100, 250, and 500 items. Also, we have compared our algorithm with the most recent algorithm in the literature by using the DKP with 100 items. Experiments have shown that the proposed algorithm is efficient and faster than the peer algorithms in the manner of tracking moving optima without using an explicit memory scheme. In conclusion, using relationships between variables within the optimization algorithms is useful when concerning dynamic environments.
Current and Future Biometrics: Technology and Applications

Biometric systems provide the means to identify, authenticate and model individuals in a reliable way through the use of unique physiological and behavioural features. This chapter will explore different definitions, types of biometric systems, commercial and experimental technology, as well as the current and potential applications. The current sensing and processing methods devices and setups are summarized for physiological and behavioral biometry: from fingerprint and facial recognition to gait, speech and handwriting. Biometrics have been an important component in different applications such as security, military, and criminology; and new types of biometric systems are emerging as essential in healthcare, smart cities, and industrial business. Some seminal applications in education, neuromarketing and arts could lead all the efforts and the biometrics to become an everyday asset for the increasing of health and human performance in the near future of humanity.
Using Semi-Supervised Learning and Wikipedia to Train an Event Argument Extraction System

The paper presents a methodology for training an event argument extraction system in a semi-supervised setting. We use Wikipedia and Wikidata to automatically obtain a small noisily labeled dataset and a large unlabeled dataset. The dataset consists of event clusters containing Wikipedia pages in multiple languages. The unlabeled data is iteratively labeled using semi-supervised learning combined with probabilistic soft logic to infer the pseudo-label of each example from the predictions of multiple base learners. The proposed methodology is applied to Wikipedia pages about earthquakes and terrorist attacks in a cross-lingual setting. Our experiments show improvement of the results when using the proposed methodology. The system achieves F1-score of 0.79 when only the automatically labeled dataset is used, and F1-score of 0.84 when trained according to the methodology with semi-supervised learning combined with probabilistic soft logic.
Design of a Computer-Based Legal Information Retrieval System

In today’s society, people’s lives are increasingly inseparable from computer information. Due to the continuous improvement of technology and the rapid development of internet technology, the network environment is becoming more and more complex, which makes it easy to cause loopholes in the information retrieval system when people use the network. Therefore, it is especially important to search for legal knowledge by computer. In order to adapt to this change and demand, we need a retrieval system to provide the corresponding search function, legal information content, and management and other services, so as to achieve the purpose of computer legal information retrieval. The legal information retrieval system is computer based, draws conclusions from the analysis of relevant data, and then applies them to judicial trial cases, criminal investigations, and other fields to provide a reference for relevant legal issues. The system is designed to combine computer technology with a criminal investigation and other fields, and then analyze the data to draw the corresponding conclusions. The retrieval algorithms used are mainly image and content retrieval algorithms, and image retrieval algorithms mainly use image segmentation technology, while content retrieval algorithms mainly use the cuckoo algorithm. At present, the information construction and economic and social development in China have become one of the issues of common concern and need to be solved by all countries in the world. The study of the legal information retrieval system is of great importance in the construction of information technology and the development of economic society.
A New Hyperchaotic System Generated by an External Periodic Excitation and its Image Encryption Application

By using a controller of uniformly bounded sine function, the problem of chaos anti-control for continuous linear systems is studied, and the dynamic characteristics of the controlled system are analyzed via the Lyapunov exponent spectrum and bifurcation diagram. The controlled system can be at a state of periodic motion, chaos or hyperchaos with multiple positive Lyapunov exponents when the parameters of controller belong to different intervals. Based on the hyperchaotic system, a new scheme of chaotic image encryption is proposed and it is given in the following aspects: (1) five chaotic sequences are generated from the hyperchaotic system, and the preprocessed pseudo-random sequences are used in the scrambling of the pixel positions; (2) the pixel values of image are encrypted by the combination of multiple pseudo-random sequences; (3) though the double chaotic encryption, the security of the chaotic stream cipher is analyzed by means of key sensitivity analysis, histogram analysis and information entropy analysis, etc. Finally, the experimental results show the scheme is effective and feasible in image encryption, and it can resist some attacks, such as the differential attacks, chosen-plain-text attacks, and clipping attacks.