Reviewerly/exHarmony huggingface dataset:
Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications

Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.
YOUTH DIGITAL INCLUSION

Contradictions of digital transformation: 1. New technologies and old systems: Problems often arise when trying to integrate new technologies into already existing systems, especially if the latter are outdated. 2.Data security and privacy: The growth of digital data also raises security threats and privacy issues. 3. Staff Training and Upgrading: The implementation of new technologies often requires staff training, which can be a difficult process. 4. Costly investments: Digital transformation may require significant investments in technology that may not immediately return to profit. Ways of digital transformation: 1. Strategic planning: Developing a clear strategy is key to successful digital transformation. 2. Innovation and ecosystems: Cooperation with innovative startups and creation of ecosystems can stimulate innovative solutions. 3. Agile approach: The use of agile development methods allows you to quickly implement changes and adapt to new conditions. 4. Education and training of staff: Investing in training and development of staff will ensure their ability to work effectively with new technologies. 5. Digital technologies for customer interaction: Focusing on improving customer interactions using digital technologies can improve the customer experience. Digital transformation is a complex but necessary process in order to maintain competitiveness in today's digital world.
Multi-strategy enhanced kernel search optimization and its application in economic emission dispatch problems

Abstract The Kernel Search Optimizer (KSO) is a recent metaheuristic optimization algorithm that has been proposed in recent years. The KSO is based on kernel theory, eliminating the need for hyper-parameter adjustments, and demonstrating excellent global search capabilities. However, the original KSO exhibits insufficient accuracy in local search, and there is a high probability that it may fail to achieve local optimization in complex tasks. Therefore, this paper proposes a Multi-Strategy Enhanced Kernel Search Optimizer (MSKSO) to enhance the local search ability of the KSO. The MSKSO combines several control strategies, including chaotic initialization, chaotic local search mechanisms, the High-Altitude Walk Strategy (HWS), and the Levy Flight (LF), to effectively balance exploration and exploitation. The MSKSO is compared with ten well-known algorithms on fifty benchmark test functions to validate its performance, including single-peak, multi-peak, separable variable, and non-separable variable functions. Additionally, the MSKSO is applied to two real engineering economic emission dispatch (EED) problems in power systems. Experimental results demonstrate that the performance of the MSKSO nearly optimizes that of other well-known algorithms and achieves favorable results on the EED problem. These case studies verify that the MSKSO outperforms other algorithms and can serve as an effective optimization tool.
G-Mail Spam Detection using Natural Language Processing

Spam has gotten out of control and is now a serious problem that affects information security and user experience due to the exponential rise of email communication. Effective spam detection systems are essential to combating this issue. This study suggests a sophisticated method for Gmail spam detection that makes use of feature engineering and machine learning methods to improve the precision and dependability of email filtering. It significantly reduces user internet speed. Takes crucial information, such as user phone number. Identifying these spammers and the spam material can be a time-consuming and laborious process. Email spam is the practice of sending messages in mass via email. Because most of the expense of spam is borne by the recipient, it is essentially postage due advertising. Commercial advertising includes spam email. The model that is suggested may determine whether a message is spam or not by using Bayes’ theorem and the Naive Bayes’ Classifier.
Convolutional Neural Network Classification of Liver Fibrosis Stages Using Ultrasonic Images Colorized by Features of Echo-Envelope Statistics

The progression of liver fibrosis is the most important indicator that determines the prognosis of patients with diffuse liver disease. Variations in tissue structure triggered by liver fibrosis severely affect the texture and contrast of the ultrasound image. Therefore, progression can be non-invasively evaluated by analyzing ultrasound images. The convolutional neural network (CNN) classification of liver fibrosis stages using ultrasound images has also been studied. In previous studies, grayscale ultrasound images obtained using conventional ultrasound scanners were adopted as the input images. In this study, the modulation and colorization of the ultrasound images by the echo-envelope statistics that correspond to the texture and contrast of the ultrasound images have been proposed. In the proposed method, the colorized ultrasound image in RGB representation comprises the original image and two images modulated by different features of the echo-envelope statistics. Accordingly, the effect enhancement of tissue-structure variation by the colorization of the ultrasound images is promising in improving the accuracy of CNN classification. Therefore, CNN classification of the ultrasound images colorized by their 1st- and 3rd-order moments is demonstrated via the transfer learning of the VGG-16 pretrained network.
Preservasi Digital Warisan Budaya: Sebuah Ulasan

&lt;p align="justify"&gt;Digital preservation plays an important role in safeguarding cultural heritage. Along with the development of the times, cultural heritage as a valuableassetcannot be separated from potential threats in the form of damage and loss. Digital preservation is an aspect of opportunity in maintaining the sustainability of cultural heritage values. Through a qualitative approach with the literature study method, this research seeks to provide an overview of aspects of digital preservation in relation to efforts to protect cultural heritage. The results of the study illustrate that digital preservation has strong relevance in terms of protecting cultural heritage values from damage and loss. The involvement of technology in digital preservation results in convenience in efforts to save cultural heritage by representing cultural material in digital formats. Digital preservation has various challenges including technological obsolescence, loss or damage to data, intellectual property rights, preservation costs, metadata and contextual information, skills and expertise, social and ethical considerations, utilization, and various other things according to the context of implementation. A digital preservation strategy that includes aspects of technological environmental conservation for future use and handling of technical limitations is an integral part of overcoming existing challenges. By implementing various strategies and actively participating in the realm of digital preservation activities, the existence of cultural heritage with its noble values can be maintained and gain long-term accessibility for the sustainability of future generations.&lt;/p&gt;
How Well Can Masked Language Models Spot Identifiers That Violate Naming Guidelines?

Using meaningful identifiers in source code reduces the risk of errors, the cognitive load of developers, and speeds up the development process. Therefore, recent research has looked into an AI-based analysis of identifiers, for which large-scale language models appear to offer great potential. Based on tokens’ probabilities, such models can suggest identifiers that are likely to appear in a given context. While current research has used language models to predict the most likely identifier names, studies on assessing the quality of given identifiers are scarce. To this end, we explore adherence to identifier naming guidelines as a proxy for identifier quality and propose and evaluate two unsupervised approaches for spotting violations: First, a generative approach, which uses the probability distribution of the language model directly without fine-tuning. Second, a discriminative method, which fine-tunes the model’s encoder to discriminate between original identifiers and similar drop-in replacements suggested by a weak AI. We demonstrate that the proposed approaches can successfully detect violations of common guidelines for identifier naming. To do so, we have developed a dataset built on widely accepted identifier naming guidelines. The manually annotated dataset contains more than 6000 dense annotations of identifiers for 28 common guidelines. Using the data, we show that the generative approach achieves the best results, but that the particular masking strategy and scoring method matter substantially. Also, we demonstrate our approach to outperform other recent code transformers. In a per-guideline analysis, we highlight the potential and limitations of language models, and provide a blue-print for training and evaluating their ability to identify bad identifier names in source code. We make our dataset and models’ implementation publicly available to encourage future research on AI-based identifier quality assessment.
LocoMixer: A Local Context MLP-Like Architecture For Image Classification

While Convolutional Neural Networks (CNNs) have been the de-facto architecture for computer vision tasks, recent works have shown that self-attention-based models, such as the Vision Transformer (ViT), have achieved competitive results on various visual benchmarks compared to CNNs. More recently, MLP-Mixer replaces the self-attention layer with a spatial MLP block as a token-mixing module. It attains good scores on image classification tasks when training on large datasets with strong regularization. In this paper, we propose a Local Context MLP-like architecture (LocoMixer). Unlike MLP-Mixer, where global spatial information is captured for information exchange through token-mixing MLP, we primarily focus on the local dependencies between the patch embeddings. Our LocoMixer independently encodes the spatial features along the rows and columns of 2D image patches with fully-connected layers. Meanwhile, we apply a scale-transformation operation to learn multi-scale feature representations in our proposed multiscale token-MLP. Furthermore, we further model local cross-channel interaction by absorbing a context block into the proposed dynamic channel-MLP. When only trained on the ImageNet-1K dataset, the proposed LocoMixer achieves state-of-the-art performace compared with other MLP-like models. Moreover, the proposed LocoMixer achieves 82.3% top-1 accuracy with only 26M parameters, which is much better than most CNNs and vision Transformers under the same model size constraint. When scaling up to 90M parameters, LocoMixer achieves 84.2% top1 accuracy, which outperforms the state-of-the-art Deformable Attention Transformer (DAT).
Leveraging ChatGPT and Amazon Alexa to Empower Healthcare Workers in Sierra Leone

The majority of the population in Sierra Leone lives below the poverty line, with poor access to secondary education and the public healthcare system. Community Health Workers bridge the gap between the communities and clinics that are led by Clinical Officers or nurses. These frontline healthcare workers receive little formal training themselves and are often unable to provide adequate guidance to their communities. How might we leverage rapidly growing Artificial Intelligence (AI) tools and technologies to educate and empower frontline healthcare workers and their communities? AI tools can be affordable, easy to use, quick to distribute, and can share healthcare information verbally, particularly helpful in regions with low literacy rates and strong oral traditions. This article describes an early attempt at using Amazon Alexa devices to respond to health-related questions with vetted responses in English and Krio. For the first phase, our application focuses on women’s health issues such as pregnancy, cervical cancer, and STIs. The initial bank of ~350 questions and answers were generated by the AI chatbot, ChatGPT, and validated through conversations with nonprofit leaders and an informal group of healthcare workers who were fascinated by a small device talking to them in their local language!
Automated facial characterization and image retrieval by convolutional neural networks

Introduction Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks. Methods We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation. Results and discussion Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.
Complementary monogamy and polygamy properties among multipartite systems

Abstract Monogamy and polygamy relations are essential properties of quantum entanglement, which characterize the distributions of entanglement in multipartite systems. In this paper, we establish the general monogamy relations for $\gamma$th $(0\leq\gamma \leq\alpha, \alpha\geq 1)$ power of quantum entanglement based on unified-$(q,s)$ entanglement and polygamy relations for $\delta$th $(\delta\geq \beta, 0\leq\beta\leq1)$ power of entanglement of assistance based on unified-$(q,s)$ entanglement of assistance, which provides a complement to the previous research in terms of different parameter regions of $\gamma$ and $\delta$. These results are then applied to specific quantum correlations, e.g. entanglement of formation, Renyi-$q$ entanglement of assistance and Tsallis-$q$ entanglement of assistance to get the corresponding monogamy and polygamy inequalities. Moreover, typical examples are presented for illustration.
The law of naming: who is who according to their actual intellectual and spiritual potential

The article analyses fundamental differences between different types of man on the basis of metaphysical theory of personality: dependent and mediocre, personality and genius. Differences in understanding of ideas, truth, morality, duties, responsibility, ability to perceive and understand philosophical categories and concepts have been revealed. Different content of consciousness and self-consciousness, professional and intellectual capabilities, motives, thinking, ability to reflection have been well-proven. Fundamental differences in the attitude toward society, the process of making decisions, existential aspects of human vital functions have been revealed. It has been grounded that metaphysical theory of personality is able to set forth criteria for understanding of human essence.
An integrated photonics reservoir computing using chaotic semiconductor laser hybrid with Si3N4 micro-resonator

Reservoir computing (RC) is a computational framework for information processing based on neural network. It can be implemented with different physical platforms, principally, electronic architectures and photonic architectures. Photonic RC shows potential path to ultra-fast and efficient processing beyond the traditional Turing-von Neumann computer architecture. Typical photonics RC consider specifically a semiconductor laser (SL) with delayed feedback as reservoir substrate. Basically, the SL is a kind of type B laser, needing enough long delay feedback for the high dimensional chaos generation and for the RC mapping. But on the other hand, long delay feedback leads to the setup big size, being nonconductive of integration implement and stable operation performance in real world. To solve the problem of a huge size, we propose a new photonics RC scheme that using chaotic SL hybrid with Si3N4 micro-resonator, which works as the storage layer and feedback loop. The Si3N4 micro-resonator could help SL producing high-dimensional chaos and reaching high-complexity RC. Meanwhile, the size of Si3N4 micro-resonator is highly compressed at the level of ten micrometers, thereby realizing a size compression of over ten times than that of typical photonics RC setup. In our experiment, we make the free spectrum range (FSR) of micro-resonator is 35GHz, reaching the nonlinear frequency of SL. Then, with careful operation, two-mode mixing chaos can be realized, being very conductive for the photonics RC applications. These results are conducive for the development of on-chip photonic RC.
Machine Translation System Based on Intelligent Language Model

AI (Artificial intelligence) has entered a new stage of development, approaching the level of human intelligence as never before in history, from academia to application, from specialty to generality. Natural language processing is one of the main application fields of AI, which has made significant progress in recent years. It combines the progress of language theory and statistics with hardware technology, which makes Machine translation technology have a new change. This paper discusses the Machine translation system based on the intelligent language model. First, the concepts of the intelligent language model and the Machine translation system are introduced respectively. Then, the operation performance of the Machine translation system based on the intelligent language model is verified through experiments. Finally, it shows that the Machine translation system based on the intelligent language model is more meaningful for research.
A High Speed Post-Quantum Digital Signature At 180 Sig/Sec On ARM Cortex-M4

This paper presents a high speed implementation of a recently developed quantum-safe multivariate polynomial digital signature algorithm on the ARM Cortex-M4 processor. The structure of the algorithm and its different security levels are demonstrated, with a focus on its layered implementation architecture and optimization techniques. The results demonstrate the superior performance of the new algorithm on resources-constrained devices compared to the three standardized NIST PQC digital signature algorithms in terms of processing speed, key and signature size, flash and RAM memory usage. Overall, the performance metrics presented in this study demonstrate the promising potential of that algorithm for securing digital communications in a post-quantum era.
Projects and Technologies: Shaping the Worldview of Modern Russian Students

In order to improve the quality of teaching social sciences, since 2022, a number of conferences and seminars have been held in leading universities. The discussion centered on the teaching methodology and content of the newly introduced course “Fundamentals of Russian Statehood”. The deepest and most open involvement of teaching and research community in this work will be able to contribute to the development of Russian socio-political knowledge, domestic scientific schools and research centers.
Analysis and Forecast of Subway Construction Cost Based on Bayesian Network

In the process of subway construction, there is a complex nonlinear relationship among the engineering quantity, material cost, labor cost and total cost, which cannot be accurately calculated by manpower alone. Therefore, the reasonable deployment of human, material, capital and other resources in the future requires a high-precision and highly reliable analysis and prediction model. Aiming at this problem, this paper proposes a Bayesian network model of subway construction project cost prediction based on principal component analysis and K2 algorithm on the basis of analyzing the financial data, contract data and the existing engineering quantity calculation data in the information data system. Firstly, the principal component analysis method is used to screen the influencing factors of subway mechanical and electrical engineering and extract the core data features. Based on the extracted features, a Bayesian network model was built according to the principle of K2 algorithm to dynamically analyze the engineering cost, and the actual subway engineering cost data was used to train, verify and predict the model. The experimental results show that the accuracy of the prediction model proposed in this paper is 87.5%, and the prediction results are completely within the controllable range, thus proving the effectiveness of the model and realizing the dynamic tracking of the construction cost of electromechanical engineering.
Amber Gemstones Colour Classification by CNN

This paper demonstrates the implementation of a convolutional neural network (CNN) for classifying amber gemstone data. We show two strategies on how to solve multiclass color classification problem by CNN. At the beginning the data images are rescaled to 60x60 px and the network is trained by using a 5-fold approach to minimize categorical cross entropy loss. This resulted in an average accuracy of 88.5%, which was improved to 90% by adjusting the training loss function by adding Dice score component. In the second attempt, the original image data was split into 60x60 px patches and the predictions from these patches were combined using a majority voting rule. The final classification resulted to 89.6% of accuracy and was similar to that of the first attempt, but the advantage of the patching approach was a reduction of standard deviation, indicating that the trained classifier is less sensitive to data divisions and background noise.