Reviewerly/exHarmony huggingface dataset:
Diamond hands to the moon: Idiocultural mobilization and politicization of personal finance on r/wallstreetbets

In January 2021, the shares of the brick-and-mortar video games retail chain GameStop exploded in value. At the same time, billboards on highways and ads in Times Square in New York City used cryptic visuals and seemingly meaningless emojis with diamonds and rockets. Mainstream media soon had an explanation: This was the story of how the subreddit r/wallstreetbets had mobilized thousands of retail investors in a fight against evil hedge funds. Based on a case study of r/wallstreetbets and the GameStop incident, we analyze how idiosyncratic internet culture was incorporated into a broadly resonant and emotionally inflected narrative that lionized the ‘little man’, focusing on both individual profits and collective grievances. Through a theoretical framework combining sociological theories of internet culture and framing analysis, we identify an overall communication structure that drew on three interconnected discursive layers: idiocultural memes, investment-specific information, and a moralized, collectivized injustice frame with heroes and villains. We further argue that the GameStop (GME) incident instantiates a case of the politicization of personal finance, where the investment practices and strategies of ordinary people were transformed into a political issue. As such, the article makes two contributions to the existing literature. First, we contribute to the nascent literature on internet cultures related to personal finance by looking at a specific subreddit devoted to stock trading and investing. Second, we show how idiocultural elements, such as emojis and memes, can function both as contested and exclusionary material aimed at insiders and as flexible components of communications framed for broad mobilization through emotionally resonant notions of grievance and injustice.
Single‐image deraining algorithm based on multi‐stage recurrent network

Abstract The damage caused by the rain streak attached to the rainy image to the background seriously affects the analysis of image information and subsequent research. Aiming at the problem that the current single image rain removal methods cannot extract the deep rain streak features, a single image deraining algorithm based on multi‐stage recurrent network is proposed, which is committed to separating the rain streak layer and the background layer. The network consists of three stages, each stage is mainly composed of upper and lower scale dilation blocks, the upper scale dilation block includes multi‐scale dilated convolution and scale fusion, the lower scale dilation block is set the same, and the stages are connected using improved gated recurrent units to facilitate parameter sharing between them. The experimental results show that the proposed algorithm outperforms the comparative algorithm in both quantitative indicators and qualitative analysis on synthetic and real datasets, and the restored image retains more detailed information.
Simplifying Microservices Migration with Advanced Genetic Algorithms

The increasing complexity and size of software systems have revealed the limitations of monolithic architectures, leading to the adoption of microservices as a more flexible, scalable, and maintainable alternative. This paper introduces an innovative approach to microservices identification and migration from monolithic architecture using advanced multi-objective genetic algorithms. By formulating the microservices identification problem as a multi-objective Optimization task, we harness the power of genetic algorithms to search for Pareto-optimal solutions, ultimately leading to an efficient decomposition of monolithic systems. Our proposed methodology offers a systematic approach to the migration process, ensuring minimal downtime and maximum efficiency. We present real-world case studies showcasing our approach’s successful application alongside examining its limitations and future research directions. This work encourages further exploration and application of multi-objective genetic algorithms in software engineering and system architecture, ultimately simplifying the transition from monolithic to microservices architectures.
How to Analyse Emojis, gif s, Embedded Images, Videos, and url s: A Bakhtinian Methodological Approach

Abstract This article offers a means of analysing social networking, visual dialogues of emojis, gif s (images in the Graphics Interchange Format), embedded images, videos, and url s (Uniform Resource Locators). Doing so addresses these often overlooked and undervalued forms of visual communication, suggesting a unique means of gaining insights into their use within online interactions. Utilising a Bakhtinian methodology, the author extracts excerpts from her research, situated within Facebook, to demonstrate a Bakhtinian genre analysis, a framework that the author contends is adaptable to multiple social networking spaces. Highlighting emojis, gif s, embedded images, videos, and url s as integral components of online communication, an emphasis is placed on how the text dances with the visual, presenting a nuanced framework for such an analysis. Consequently, an argument is developed for the significance of visual dialogues in contemporary online spaces, and the need for researchers to better understand these dynamic forms of communication, offered through Bakhtinian dialogism.
The Role of Social Media in Improving Institutional Reputation

The Study aims to identify the role of social media in improving the reputation of the institutions. The study reached many results, the most important of which are: negative attacks that affect that reputation such as all false or negative comments and creating also affect the level degree of credibility and trust between the Institutions and customers, through a strong network of digital public relations, and establishment and creation of an identity for the institutions on social media and digital networks to protect the brand through a positive presence on the Internet and Social Media, in addition to it erases the ambiguity about that mark in the search results in the engine.
A Lightweight Block Cipher for Securing Sensitive Healthcare Data for Hospitals in Karnataka

The AES has deemed the development of new block ciphers obsolete; it is currently the de facto standard for the majority of block cipher applications. Even though there have been improvements in the AES technology, it is still not the best fit for situations that have limitations such as RFID tags and sensor networks. In this paper, we offer SHC (Simple Hybrid Cipher) a new type of encryption method has been developed utilising a key size of 128 bits and a block size of 64 bits. It provides a hardware solution that uses limited resources efficiently, making it perfect for use as a sensor in a WSN or an RFID tag. SHC’s main function is built on S-Box-based, composite field arithmetic technology, which consumes relatively little hardware while still delivering adequate security as a robust encryption technique. When used on various controller devices such as ASIC and FPGA, the hardware implementation of SHC-64 requires less gates or LUTs. On the Xilinx-powered Artix-7 FPGA development board, SHC-64 generates a maximum operating frequency of 515.995 MHz the NIST-recommended standard algorithm AES (advanced encryption standard) on the other hand, requires 3645 LUTs and has a maximum working frequency of 277.369 MHz. The SHC-64 encryption is also resistant to known cryptanalytic assaults, making it a perfect contender for safeguarding sensitive healthcare information over the internet.
Verifiable delay functions and delay encryptions from hyperelliptic curves

Abstract Verifiable delay functions (VDFs) and delay encryptions (DEs) are two important primitives in decentralized systems, while existing constructions are mainly based on time-lock puzzles. A disparate framework has been established by applying isogenies and pairings on elliptic curves. Following this line, we first employ Richelot isogenies and non-degenerate pairings from hyperelliptic curves for a new verifiable delay function, such that no auxiliary proof and interaction are needed for the verification. Then, we demonstrate that our scheme satisfies all security requirements, in particular, our VDF can resist several attacks, including the latest attacks for SIDH. Besides, resorting to the same techniques, a secure delay encryption from hyperelliptic curves is constructed by modifying Boneh and Frankiln’s IBE scheme, which shares the identical setup with our VDF scheme. As far as we know, these schemes are the first cryptographic applications from high-genus isogenies apart from basic protocols, i.e., hash functions and key exchange protocols.
Patch-based deep learning models for breast mammographic mass classification

Background: Breast cancer is one of the greatest health threats to women worldwide. Mammography is an effective and inexpensive tool for breast cancer early detection. Mammography-based breast cancer screening requires a lot of manpower from professional experts. Thus, computer-aided diagnosis tools, especially accurate classifiers which can distinguish the breast masses from the background tissues, are needed. However, since the sample size of publicly available mammography data sets is relatively small, the performance of the published breast mass identification models was not great, and the models were not well-embraced by clinical practice due to their low interpretability. Methods: In this work, using two independent and well-known mammography data sets, the CBIS-DDSM and the INbreast, we proposed a novel patch generation method for data augmentation and negative case generation. We implemented two successful deep learning models, the ResNet and the ViT, to classify the generated mass and non-mass patches. We also proposed to apply the patch-level model to the full-view mammogram screening in a sliding window manner and visualize/interpret the prediction results using a heatmap so that the clinic practice could potentially benefit from the well-trained model. Result: For the CBIS-DDSM dataset, we compared the performance of the ResNet and the ViT with and without data augmentation. The F1 score is 0.91, 0.86, 0.85, and 0.70, respectively. We also evaluated our models using other metrices such as accuracy, precision, recall, and ROC curve. The results show that the ResNet model outperforms the ViT model. And the data augmentation improves the overall performance of the models. The similar conclusions are further supported using the independent INbreast data. Furthermore, we also explored to use probability-based heatmaps to visualize the potential mass regions in mammogram images. Conclusion: The study shows that our patch-level data augmentation is effective in improving the classification performance of the deep learning models. The comparable performance on the CBIS-DDSM data and the independent INbreast data demonstrates the generalizability of our methods. The proposed heatmap visualization tool increases the interpretability of our results and could be a potential approach for clinic utilization.
A Disaggregate Data Collecting Approach for Loss-Tolerant Applications

Datacenter generates operation data at an extremely high rate, and data center operators collect and analyze them for problem diagnosis, resource utilization improvement, and performance optimization. However, existing data collection methods fail to efficiently aggregate and store data at extremely high speed and scale. In this paper, we explore a new approach that leverages programmable switches to aggregate data and directly write data to the destination storage. Our proposed data collection system, ALT, uses programmable switches to control NVMe SSDs on remote hosts without the involvement of a remote CPU. To tolerate loss, ALT uses an elegant data structure to enable efficient data recovery when retrieving the collected data. We implement our system on a Tofino-based programmable switch for a prototype. Our evaluation shows that ALT can saturate SSD’s peak performance without any CPU involvement.
To Possess or Not to Possess - WhatsApp for Android Revisited with a Focus on Stickers

WhatsApp stickers are a popular hybrid of images and emoticons that can contain user-created content. Stickers are mostly sent for legitimate reasons, but are also used to distribute illicit content such as Child Sexual Abuse Material (CSAM). As the process of creating stickers becomes easier for users from version to version, a digital forensic analysis is still lacking. Therefore, we present the first comprehensive digital forensic analysis of WhatsApp’s sticker handling on Android, with a special focus on the legal context, i.e. the definition of possession of illicit content. Our analysis is based on 40 scenarios that reflect the full lifecycle of community-created stickers. We show how the distribution channel of a sticker found on a device can be reconstructed, partially even when its traces have been removed from WhatsApp and are not visible through WhatsApp’s user interface. In addition, we show that Google Drive backups recover stickers, making device seizure dispensable; however, stickers can still be permanently deleted. Most importantly, we show that simply finding a sticker on a device is not sufficient to meet the requirements of the legal definition of possession. Therefore, prosecution for possession of a sticker requires additional evidence, which we provide.
A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate Activation Maps (AMs) and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing AMs could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies homomorphic encryption on the AMs before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.
Smart Noise Detection for Statistical Disclosure Attacks

While anonymization systems like mix networks can provide privacy to their users by, e.g., hiding their communication relationships, several traffic analysis attacks can deanonymize them. In this work, we examine Statistical Disclosure Attacks and introduce a new implementation called the Smart Noise Statistical Disclosure Attack. This attack can improve results by examining how often other users send together with the attacker’s target to better filter out the noise caused by them. We evaluate this attack by comparing it to previous variants in various simulations and thus show how it can improve upon them. Further, we demonstrate how other implementations can be improved by combing them with our approach to noise calculation. Finally, we critically review used evaluation metrics to determine their significance.
A method to characterize turquoise deposits in china using sedimentary environment sensitive elements

Abstract ‘Fingerprint’ identification is the key to turquoise provenance studies, but previous studies have met with limited success in China. Turquoises in China mainly occur in altered sedimentary rocks and are formed by weathering, leaching and sedimentation. This paper evaluates the potential for sedimentary environment sensitive elements to be used to distinguish between turquoise deposits in China. In this paper, the ratios of V/Mo, Ce/Ce*, Ni/Co, Sr/Ba, Tl/Ga, Li/Ba and V/Cr were calculated for turquoise of different origins. Our results show that these parameters when used alongside Ga concentration permit effective discrimination between turquoise sources in China. This approach brings to light a new means of analyzing trace element databases and an untapped wealth of information that can be used for future turquoise provenance studies.
Tor network anonymity evaluation based on node anonymity

Abstract In order to address the shortcomings of traditional anonymity network anonymity evaluation methods, which only analyze from the perspective of the overall network and ignore the attributes of individual nodes, we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes, network behavior, and program security monitoring. The anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating status. The anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation features. This paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under attack. Our approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.
SNNTool: A software tool for sampling neural networks algorithms implementation

SNNTool is an efficient utility designed for researchers and developers, providing methods for building, training, and applying various Sampling Neural Networks (SNNs). SNN is a novel neural network with high accuracy, fast convergence, low computational burden, and reliability. SNN extends the diversification of neural networks and their methods, opens up new ideas in neural network bionic research, and provides new application directions. SNNTool provides a simple and user-friendly programming interface and flexible control ability for the structure and parameters of SNNs models. SNNTool brings a positive impetus to SNN-related research work and provides assistance in simplifying and improving SNNs models. The experimental results demonstrated the effectiveness of the models and tools.
Disruptive Technologies for e-Diasporas: Blockchain, DAOs, Data Cooperatives, Metaverse, and ChatGPT

E-diasporas are networks driven by human agency, connecting digital citizens to their home countries and diasporic fellows through digital tools. In contrast, Hyperconnected Diasporas (HD) are data-driven networks engaged in extractive activities, often employed for government (para)diplomacy, heavily relying on social media extractivist data-opolies or Big Tech platforms. This article examines the impact of disruptive technologies on e-diasporas in the context of data extractivism, particularly stemming from HD. The article pursues a dual objective: (i) reviewing existing literature and comparing five disruptive technologies—Blockchain, Decentralized Autonomous Organizations (DAOs), Data Cooperatives, Metaverse, and ChatGPT—in sustaining e-diasporas as networks driven by human agency, and (ii) scrutinizing associated opportunities and risks, including challenges to institutional trust and data privacy arising from HD. The study seeks to elucidate how these technologies may either hinder or exacerbate the impacts of HD on e-diasporas, characterized by their human-driven nature. The article begins with an introduction to HD, followed by a literature review on e-diasporas. Methodologically, it presents a comparative analysis of the five disruptive technologies concerning the research question and discusses their implications for e-diasporic communities, concluding with final remarks.
Optical Character Recognition Using Optimized Convolutional Networks<sup>*</sup>

Optical Character Recognition (OCR) has been a prominent area of research in pattern recognition for several decades, owing to its broad application potential in smart living. To improve offline OCR on mobile devices with limited computing resource, we have optimized Convolutional Neural Networks (CNNs) to efficiently detect text using minimal resources. To achieve this, we employed two distinct pretrained CNN models, namely AlexNet and Inception-V3, for feature extraction. Leveraging these models' unique characteristics and capabilities to extract diverse features, we aimed to enhance the classifier's accuracy. This, in turn, facilitates the development of an efficient edge-device application for faster and higher-quality OCR. Experimental results demonstrate that our proposed optimized algorithm outperforms existing CNN-based methods in the field of OCR, particularly in the categorization and detection of handwritten digits and character recognition. The conducted research yielded impressive accuracy results, with up to 97% accuracy on the MNIST dataset and 95.5% accuracy on the NIST dataset.
Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks

In this position paper, we underscore the critical need for a systematic and structured approach to comparing Federated Learning (FL) frameworks. Given the diversity of FL frameworks currently available, we argue that a comprehensive comparative analysis is essential for two reasons. First, such an analysis can guide decision-makers in identifying the most suitable FL framework for their specific use case by examining the strengths and weaknesses of each framework. Secondly, it can help researchers recognize the potential gaps and shortcomings in existing FL frameworks, thereby directing further research and development in these areas. To illustrate this, we present our preliminary comparative analysis of 14 different FL frameworks, assessing their individual strengths and weaknesses. Our position advocates for a more methodical understanding and selection of FL frameworks, which we believe will substantially benefit both practical applications and future advancements in the field.