Reviewerly/exHarmony huggingface dataset:
Novel Image Cryptography Method to Improve Security in Cloud Computing

Cloud is a practice of storing information and accessing requests using internet-supporting devices from anywhere around the globe. Due to the simplicity and integrity of the cloud, the user trusts and decides to reside his personal and confidential data on the cloud instead of storing the same on his local computer or local server. But several times, the cloud safety is hampered when this personal information is compromised often due to the indulgence of uninvited anonymities, leading to the security breach of the cloud. To safeguard this private and confidential data, several encryption–decryption methodologies have been used for a long time. In cryptography, typically, the user's private information is transformed into a cipher text using a specific key and then transformed back into its original form using the same key. The major disadvantage of this technique is that, if the key is in the open and known by any particular untrusted entity, then the personal and confidential data will likely be exposed. The main purpose of the study in this paper is to propose a novel encryption-decryption methodology that generates a more robust and challenging to break cipher image from the private text instead of a simple plain cipher text. This proposed encryption–decryption methodology will provide higher security to the cloud, thereby maintaining its security and integrity.
Media and psychoanalysis: methodological interaction

Introduction. Various areas of media significantly effects on person in the 21st century, using numerous tools of communication and influence. There are quite a lot of media technologies that productively apply the theory and practice of psychoanalysis, its basic techniques, principles and tools, which proves the methodological interaction between media and psychoanalysis. The media sphere is an effective platform for successful discussion of many problems, which makes it closer to psychoanalysis as a theory of talk therapy, that is capable to free the human of unconscious, fears, anxieties, insecurities, and traumas. Relevance of research. The relevance of the presented materials is determined by studying the possibilities of mass media to accumulate and transmit information, interpret it, create numerous focuses and visions of presenting events, influence the recipient of information by tools, methods, techniques and technologies productively used by psychoanalysis – discussion, transference, distancing, manipulation, trauma. Results and discussion. A main condition that ensures the intraction between the socio-communication continuum and psychoanalysis is the presence of appropriate media material. The methods, techniques, and tools of psychoanalysis are quite appropriate to use in the process of analysis and interpretation of stereotypes, symbols, identification, categories of fear, anxiety, complex, death, justice, desire, destruction, deviation, pathology, addiction, trauma, which are widely expressed in various types of media – photography, cinema, audio, video and text messaging, radio, television, the Internet. Conclusions and prospects. The media productively implement communication, various narratives, numerous forms of talking therapy (dialogue, polylogue, conversation, interview), discussion of socially significant problems, conflicts, individual, group, society, nation traumas, the interpretation of which is possible and expedient from the standpoint of the theory of psychoanalysis.
Comparative Analysis of Minimax Algorithm with Alpha–Beta Pruning Optimization for Chess Engine

The chess game engine is introduced which makes use of a minimax algorithm for searching game trees and makes use of alpha–beta pruning algorithm to reduce the search space of game states in the game state tree. The first part of this paper is an expository presentation that demonstrates and explains the minimax algorithm. The work in this paper demonstrates that the alpha–beta pruning method improves the efficiency of the chess engine algorithm based on the results obtained using rigorous experimentation.
Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of Machine Learning Classifiers

In the supplied ASD dataset, it is typically seen that there is an extremely large imbalance in the number of samples for two classes, leading to an imbalance. Without addressing this issue, applying binary classification algorithms to such data would produce an extremely biased result. It affects the relationships between features as well. These misclassifications could affect the decision regarding medical treatment and result in a protracted delay for those who urgently require medical intervention. In the current study, we use a variety of resampling strategies to address the issue of class imbalance. Precision, Recall, and F1-score are used as evaluation measures for all models. We have also looked at AUC score, which demonstrates encouraging outcomes for the use of resampling methods for imbalanced dataset.
Unleashing the Predators: Autonomous Predation and Manipulation Through Algorithms

This research paper presents new findings concerning the autonomous acquisition of anti-competitive behavior by algorithms. We first demonstrate that algorithms trained through reinforcement learning are capable of independently formulating predatory strategies without any human involvement or intention. Further, and beyond the usual predation-accommodation trade-off, we provide evidence that algorithms are capable of engaging in adversarial attacks: when predation is too costly, an algorithm used by an incumbent may, autonomously, manipulate a new entrant's perception of its environment, thereby generating supracompetitive profits. We propose solutions and highlight the pressing need to revise antitrust regulations relating to abuse of dominance in a world increasingly dominated by algorithmic decision-making.
DMDD: A Large-Scale Dataset for Dataset Mentions Detection

Abstract The recognition of dataset names is a critical task for automatic information extraction in scientific literature, enabling researchers to understand and identify research opportunities. However, existing corpora for dataset mention detection are limited in size and naming diversity. In this paper, we introduce the Dataset Mentions Detection Dataset (DMDD), the largest publicly available corpus for this task. DMDD consists of the DMDD main corpus, comprising 31,219 scientific articles with over 449,000 dataset mentions weakly annotated in the format of in-text spans, and an evaluation set, which comprises 450 scientific articles manually annotated for evaluation purposes. We use DMDD to establish baseline performance for dataset mention detection and linking. By analyzing the performance of various models on DMDD, we are able to identify open problems in dataset mention detection. We invite the community to use our dataset as a challenge to develop novel dataset mention detection models.
Evaluating a Century of Progress on the Cognitive Science of Adjective Ordering

Abstract The literature on adjective ordering abounds with proposals meant to account for why certain adjectives appear before others in multi-adjective strings (e.g., the small brown box). However, these proposals have been developed and tested primarily in isolation and based on English; few researchers have looked at the combined performance of multiple factors in the determination of adjective order, and few have evaluated predictors across multiple languages. The current work approaches both of these objectives by using technologies and datasets from natural language processing to look at the combined performance of existing proposals across 32 languages. Comparing this performance with both random and idealized baselines, we show that the literature on adjective ordering has made significant meaningful progress across its many decades, but there remains quite a gap yet to be explained.
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification

Abstract Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/ few-shots cross-lingual transfer). Nowadays, cross-lingual text classifiers are typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. However, the performance of these models varies significantly across languages and classification tasks, suggesting that the superposition of the language modelling and classification tasks is not always effective. For this reason, in this paper we propose revisiting the classic “translate-and-test” pipeline to neatly separate the translation and classification stages. The proposed approach couples 1) a neural machine translator translating from the targeted language to a high-resource language, with 2) a text classifier trained in the high-resource language, but the neural machine translator generates “soft” translations to permit end-to-end backpropagation during fine-tuning of the pipeline. Extensive experiments have been carried out over three cross-lingual text classification datasets (XNLI, MLDoc, and MultiEURLEX), with the results showing that the proposed approach has significantly improved performance over a competitive baseline.
Solving strongly convex-concave composite saddle-point problems with low dimension of one group of variable

Algorithmic methods are developed that guarantee efficient complexity estimates for strongly convex-concave saddle-point problems in the case when one group of variables has a high dimension, while another has a rather low dimension (up to 100). These methods are based on reducing problems of this type to the minimization (maximization) problem for a convex (concave) functional with respect to one of the variables such that an approximate value of the gradient at an arbitrary point can be obtained with the required accuracy using an auxiliary optimization subproblem with respect to the other variable. It is proposed to use the ellipsoid method and Vaidya's method for low-dimensional problems and accelerated gradient methods with inexact information about the gradient or subgradient for high-dimensional problems. In the case when one group of variables, ranging over a hypercube, has a very low dimension (up to five), another proposed approach to strongly convex-concave saddle-point problems is rather efficient. This approach is based on a new version of a multidimensional analogue of Nesterov's method on a square (the multidimensional dichotomy method) with the possibility to use inexact values of the gradient of the objective functional. Bibliography: 28 titles.
Decision Support System for Tuition Subsidy in XYZ Senior High School Student with Families Affected by Pandemic

According to UNICEF 2020, in the percentage of children aged 7–18 years according to their school status, 938 Indonesian children have dropped out of school due to the Covid-19 pandemic. Children from families whose parents have been laid off and need tuition. Regarding this issue, the authorities anticipate it with several policies; one of the policies that can be carried out is the tuition subsidy program with selected students considering the impact of Covid-19. To get optimal results, a decision support system (DSS) is needed to support policies until decisions are taken. Simple Additive Weighting (SAW) is used in designing the system design. In designing the assessment criteria system, it is based on previous literature studies. There are four selected assessment criteria in a row, namely KIP card recipients, Parental Income, number of Parent Dependents, and arrears in tuitions. In addition, system development is carried out using the Rapid Application Development (RAD) method, which has several phases: Requirements Planning, User Design, Construction, and Cutover. This research aims to assist XYZ High School in selecting prospective students who receive the tuition subsidy program by predetermined criteria and conditions. This research resulted in an application for selecting students who receive subsidies for tuition.
Deep Learning Implementation for Rami Fiber Image Classification Using Convolutional Neural Network Algorithm

As a process of categorizing and labeling groups of images, image classification can be applied in various work fields to make work easier. PT Retota Sakti Magelang is a company that processes rami fiber into sun shade curtains, carpets, upholstery basketry and wallcovering. Currently, in this company after the bleaching process, there is still no inspection process to ensure that all fibers are perfectly bleached so there are needs to be an inspection after bleaching process. To realize a fast and effective rami fiber inspection process, a deep learning implementation in the form of image classification is used. In this study, image classification was formed using a pre-trained Convolutional Neural Network (CNN) which is constructed using the GoogLeNet architecture. Two hundred and six pieces of augmented fiber images in JPEG format data were processed into two classifications to assist in the inspection and separation process of bleached and unbleached rami fiber. Rami fiber database was divided into two sets 70% data for training set and 30% for validation set, then processed using the algorithm. As a result from the validation accuracy, the value of the training and testing that has been carried out is 100% as well as the core of accuracy, precision, recall, specificity, and F1-score obtained from confusion matrix. After the training results are used to test other images besides the input data, it is found that the results of the training managed to classify most of the images correctly. Of 25 images, only 2 images of unbleached rami fiber being wrongly predicted as bleached, which means the accuracy in classifying rami fiber is 92%.
Artificial Intelligence and Evolution of the Virtual University

In this chapter we invite the reader to consider the importance of context in the history and evolution of both artificial intelligence and the virtual university. There are histories in the terminology as much as what they refer to. Thus, the virtual universities that emerged late last century are very different in scope and vision compared to contemporary practice. There are likewise different theoretical perspectives with Gartner’s “Hype Cycle,” one among many methods that provides perspective on the stages of AI-driven advances in technological innovation. An imperative to foreground questioning of the domain is what frames our discussion. One key question among many concerns the affordances and challenges that AI presents for a virtual university in the coming decades. A university must prepare for AI-based auditing of the integrity of its student records. AI can help spot plagiarism and fake IDs and support academic integrity. Policy-makers might use AI to spot potentially impossible situations that could give rise to big penalties. But as AI merges into our environment, it is no longer just a tool with new ethical and social issues arising. There already exist forms of colonial biases embedded into AI and machine learning systems. What practical choices exist now? Every virtual university might consider collaborating on how to ensure trustworthiness of their operational systems that use AI. Emerging public policy such as the EU’s Artificial Intelligence Act provides a legal mechanism for building a reliable AI ecosystem. Research and innovation into both AI and the kinds of services virtual universities will need into the future are now proliferating. Many futures are possible. From an educational perspective developing human agency through this next era should be a priority. Students could be taught how to use AI-supported tools to solve increasingly challenging questions. Even more imperative will be the questions they ask.
Digital Inclusion in School Education in India: Key Issues and Challenges

Education is presently facing a critical challenge in terms of the literacy of children for adapting to digital culture and citizenship. Demand for a more attractive instructional process for the millennial already familiar with personal computers, navigating on the internet, and using mobile has been the need of the hour. The results of a regression analysis explained that there is no connection between school-level factors and use of digital education. The content analysis here discusses the key issues and challenges for schoolteachers and students in primary and secondary education with respect to digital usage.
Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

Despite increases in qubit count and connectivity in quantum annealers, a quantum speedup has yet to be observed for problems of practical significance. In order to further improve annealer performance, some researchers focus on tuning annealer parameters, such as the annealing schedule. In this work, we focus on pausing, an annealing schedule modification that has been shown to improve the probability of solving an optimization problem by orders of magnitude. However, a challenge associated with pausing is selecting an appropriate pause location, as pausing is only effective in problem-dependent regions and ineffective elsewhere. Moreover, there is little advice on how to determine where pausing is effective. Thus, pausing effectively is difficult and often inaccessible to the majority of users. To address these issues, we propose a data-driven method that leverages machine learning to predict optimal pause locations. First, we construct a dataset consisting of optimization problems and their corresponding optimal pause locations. The optimal pause locations are determined using spin-vector Monte Carlo, a method known to yield results similar to quantum annealing. Next, we train a convolutional neural network on this dataset, demonstrating its ability to distinguish between problem types and accurately predict the optimal pause location. Finally, we evaluate the model on multiple types of optimization problems. Our results show that the pause locations predicted by our method improve solution quality for all selected problem types. Additionally, our model can be pre-trained and easily distributed, making the power of pausing accessible to users unfamiliar with annealing schedule modifications.
LumiNet: Multispatial Attention Generative Adversarial Network for Backlit Image Enhancement

Backlit image enhancement is a crucial task in improving the quality and visibility of the underexposed regions in an image caused by the difference in illumination between the background and foreground. Traditional methods struggle to effectively handle the dynamic range compression required for backlit image enhancement and fail to properly balance the exposure between the background and foreground areas. In this paper, we propose a novel backlit image enhancement architecture using a modified UNet and a unique 1x1 discriminator-based conditional generative adversarial network. Our approach incorporates custom hyperparameters, a tailored loss function, and bilateral guided upsampling for efficient enhancement of large images. We evaluated our model on the BAID dataset and achieved superior results in various image evaluation metrics like PSNR, NIQE and ΔE°, demonstrating its effectiveness in enhancing both backlit and low-light images. We have also compared the computational efficiency and resources used by our method with other state-of-the-art methods. Extending our work, we have incorporated our model into a pipeline for enhancing backlit traffic images and videos, which is then used to detect the license plates of vehicles with improved accuracy.
MAIN: Multibranch Attention Integration Network for Degraded Remote-Sensing Image Super-Resolution

Remote sensing images (RSIs) are limited by low-resolution imaging and a variety of degradation effects, posing significant challenges for high-level visual tasks. Traditional deep learning-based super-resolution (SR) algorithms struggle to optimize these issues end-to-end, which inhibits the algorithm’s lightweight design and restricts its application in real-time scenarios. To address this, we introduce a multi-branch attention integration network (MAIN) for the degraded RSI SR. This network features two key functional components: the multi-scale feature perception (MsFP) module and the multi-branch attention integration block (MAIB). The MsFP module, a lightweight convolution-based architecture, is designed primarily to counteract degradation effects and depict low-dimensional features. MAIB, a multi-branch parallel layout, employs attention mechanisms to enable high-level semantic information extraction and context-aware learning. Furthermore, we utilize modulation transfer functions to emulate various degradation effects, thus creating a dedicated dataset for degraded RSI SR, called DeRSSA. Extensive experimental evidence shows that MAIN exceeds the performance of the existing state-of-the-art method for the degraded RSI SR task. Our code and dataset will be accessible at https://github.com/lbo0928/MAIN.
Self-Coding Phase Shift for Absolute Phase Retrieval in Fringe Projection Profilometry

Phase unwrapping is the core of phase shift profilometry. Conventional phase unwrapping usually requires projection of additional patterns or other constrains to unwrap the high-frequency wrapped phase into absolute phase, which increases time consumption and complicates measurement procedures. To solve this problem, we propose a self-coding phase-shift (SCPS) method for retrieving the absolute phase. Two sets of codes are encoded into the projection patterns, namely the De-Bruijn sequence code and the phase gradient code. The fringe order can then be determined by combining these two different encodings. The proposed method does not require any prior information and only demands four phase-shift fringes. Experimental results demonstrate that the SCPS method is highly robust and accurate in measuring both complex and separated objects.
Shake, Shake, I Know Who You Are: Authentication Through Smart Wearable Devices

Information security and user comfort are the games that smartphone manufacturers have always had to face. Explicit authentication (EA) methods, including password, fingerprint recognition, and face recognition, have become the most popular ways to unlock smartphones. However, these methods also incur some troubles in users’ daily lives and even suffer from security problems like shoulder surfing attacks and password reuse attacks. Unlike the EA methods, the implicit authentication (IA) methods employ the user’s behavior, posture, and so on to confirm who the user is. The “Smart Lock (SL),” as a new IA introduced by the biggest smartphone system manufacturer, Google, has effectively increased user comfort while ensuring user information security. However, we found that the SL is not secure enough, where anyone wearing a smart device can be authenticated. In this article, based on the “Trusted Devices (DEVICE)” approach in SL, we design a band with sensors commonly found on smart wearables and two authentication models to improve the security of SL scenarios. We use our designed band to collect data to evaluate our models. The experimental results show that our designed band is universal. Our authentication model for power-constrained devices such as wearables has 97.01% accuracy and power overhead of 0.0195 mAh per time. The designed authentication model for smartphones has an accuracy of 99.67% and power consumption of 0.83 mAh per time. Therefore, our scheme can meet the IA for different security requirements in different scenarios.