Reviewerly/exHarmony huggingface dataset:
Opinion Mining on Paul W. S. Anderson’s Monster Hunter from Chinese Social Media Using Sentiment Analysis

The growth of social media platforms has enabled people to publicly respond to matters such as racial and gender inequalities. On a recent case in China, individuals used online platforms to express their outrage at racial insults against the Chinese in Paul W. S. Anderson’s Monster Hunter (2020). This article uses sentiment analysis and natural language processing to analyze Chinese users’ views and responses on short-text social media platform called Weibo. More than 3,000 users’ reviews are collected through a web crawler tool. An unsupervised way is chosen to carry out Sentiment Analysis. Findings reveal that the overall proportion of negative reviews about the movie is not high. Positive comments are relatively normal, and most of them are comments on the movie itself. There were negative comments before the movie was released but these comments did not contain content related to “辱华”. The negative reviews after the release of the movie include different types of content: some are about the movie itself, some are because of “辱华”, and some mention “腾讯”. This study can benefit large-scale decision-makers on matters related censorship and filtering.
User-Based Algorithmic Auditing

In the artificial intelligence and cloud computing age, digital platforms like Meta, Google, and Amazon wield immense social, economic, and political power, shaping users’ daily lives. As these platforms gather vast amounts of user data and utilize sophisticated algorithms to personalize services, they also expose users to risks of bias and manipulation. Policymakers seek ways to hold platforms accountable, and algorithmic auditing is emerging as a key approach. However, existing regulations often rely on self-audits by the platforms themselves, leading to conflicts of interest. The shift towards third-party auditing is promising but still falls short of resolving these conflicts. To address this challenge, this article introduces, typologizes and explores a unique and underutilized approach to regulatory algorithmic oversight: “user-based algorithmic auditing.” According to this auditing approach, the platforms’ users lead the audit or assist external auditors in the process. User-based auditing is impartial, as it is entirely independent of the audited platforms. User-based audits are also valuable for corroborating the information the platforms provide in their self-auditing reports. The article explores regulatory frameworks, scrutinizes auditing approaches, and delves into the potential of user-based auditing to shape algorithmic oversight policies effectively.
Safety vs. Security – Why Separation of Concerns is a Good Strategy for Safety-Critical Systems

Cybersecurity plays an increasing role in critical infrastructure, in particular safety systems. Hence, it is necessary to compose systems that fulfill security and safety requirements, which are partially conflicting. Safety related software will rarely be changed after approval, whereas security related software needs almost permanently updates. This leads to problems that are hard to solve. Recently CENELEC TS 50701 “Cybersecurity for Railways” has been released that proposes separation concepts. In this paper we will discuss this approach and show, how a suitable architecture can help to satisfy the security as well as the safety requirements. We consider some examples of such architectures and show, how systems can be constructed that on the one hand side contain a safety code that is not changed and on the other hand side security software that can easily be patched.
Safer Than Perception: Assuring Confidence in Safety-Critical Decisions of Automated Vehicles

We address one of the key challenges in assuring safety of autonomous cyber-physical systems that rely on learning-enabled classification within their environmental perception: How can we achieve confidence in the perception chain, especially when dealing with percepts safe-guarding critical manoeuvres? We present a methodology which allows to mathematically prove that the risk of misevaluating a safety-critical guard conditions referring to environmental artefacts can be bounded to a considerably lower frequency than the risk of individual misclassifications, and can thereby be adjusted to a value less than a given level of societally accepted risk.
On Likelihood Ratio Tests for Dimensionality Selection

Many multivariate statistical models have dimensional structures. Such models typically require judicious choice of dimensionality. Likelihood ratio tests are often used for dimensionality selection. However, to this day there is still a great deal of confusion about the asymptotic distributional properties of the log-likelihood ratio (LR) statistics in some areas of psychometrics. Although in many cases the asymptotic distribution of the LR statistic representing the difference between the correct model (of specific dimensionality) and the saturated model is guaranteed to be chi-square, that of the LR statistic representing the difference between the correct model and the one with one dimension higher than the correct model is not likely to be chi-square due to a violation of one of regularity conditions. In this paper, we attempt to clarify the misunderstanding that the latter is also assured to be asymptotically chi-square. This common misunderstanding has occurred repeatedly in various fields, although in some areas it has been corrected.
Behaviormetrics in Blockchains: A Novel Protocol for Recording, Processing, and Rewarding Valuable Behavior

Feedback is always needed but not always received, especially when it is not required. But often it is very valuable, most notably when it is spontaneous and active feedback. This can help any entity and the society as a whole grow, and that is why it makes sense to reward it. The work here proposes a new blockchain protocol called the Proof of Feedback, which customers can use to provide feedback that is evaluated by a set of jurors and rewarded. In this paper, a prototype has been presented with the help of smart contracts in an EVM blockchain and a DApp has been developed for the users to interact with the protocol. The proposed protocol is technologically feasible, secure and economically viable. Such an approach and technology encourages novel aspects of transparency and reproducibility for the wider behaviormetrics community as well.
“They see me scrollin”—Lessons Learned from Investigating Shoulder Surfing Behavior and Attack Mitigation Strategies

Abstract Mobile computing devices have become ubiquitous; however, they are prone to observation and reconstruction attacks. In particular, shoulder surfing, where an adversary observes another user’s interaction without prior consent, remains a significant unresolved problem. In the past, researchers have primarily focused their research on making authentication more robust against shoulder surfing—with less emphasis on understanding the attacker or their behavior. Nonetheless, understanding these attacks is crucial for protecting smartphone users’ privacy. This chapter aims to bring more attention to research that promotes a deeper understanding of shoulder surfing attacks. While shoulder surfing attacks are difficult to study under natural conditions, researchers have proposed different approaches to overcome this challenge. We compare and discuss these approaches and extract lessons learned. Furthermore, we discuss different mitigation strategies of shoulder surfing attacks and cover algorithmic detection of attacks and proposed threat models as well. Finally, we conclude with an outlook of potential next steps for shoulder surfing research.
The Expanding Renewable Energy Economy

This chapter explores the expanding renewable energy economy. It begins by discussing the identifiability and homogeneity of electricity, drawing similarities to the concept of money. The chapter delves into the essence of money, examining its role in facilitating transactions and value exchange. It then explores how the FinTech revolution, which revolutionized the financial industry, is now reaching the energy sector. The emergence of blockchain technology is highlighted as a game-changer in the energy industry. The chapter explores the application of blockchain to the Digital Grid, demonstrating its potential to enhance transparency, security, and efficiency in energy transactions. Overall, this chapter offers insights into the growing renewable energy economy, drawing parallels to the world of finance and showcasing the transformative potential of blockchain technology in the energy sector.
Fractal fuzzy logic using Kelly plots

Strictly defined, the concept of self-similarity or self-similarity applies only to mathematical fractals - which arise from the iteration of simple formulae but lead to very complex structures, Cantor Dust, Peano Curve, Koch Snowflake, whereas in natural or physical fractals - those found in nature, such as a fern leaf, an arborisation, capillaries - the concept of self-similarity applies, since their fractality is only statistical and they possess, consequently, an anisotropic scaling,(not having the same properties in all dimensions of analysis), which does not allow an amplified part of a figure to maintain exactly the characteristics of the figure as a whole, is where we find Kelly plots.
Understanding blockchain: Definitions, architecture, design, and system comparison

The explosive advent of the blockchain technology has led to hundreds of blockchain systems in the industry, thousands of academic papers published over the last few years, and an even larger number of new initiatives and projects. Despite the emerging consolidation efforts, the area remains highly turbulent without systematization, educational materials, or cross-system comparative analysis. In this paper, we provide a systematic and comprehensive study of four popular yet widely different blockchain systems: Bitcoin, Ethereum, Hyperledger Fabric, and IOTA. The study is presented as a cross-system comparison, which is organized by clearly identified aspects: definitions, roles of the participants, entities, and the characteristics and design of each of the commonly used layers in the cross-system blockchain architecture. Our exploration goes deeper compared to what is currently available in academic surveys and tutorials. For example, we provide the first extensive coverage of the storage layer in Ethereum and the most comprehensive explanation of the consensus protocol in IOTA. The exposition is due to the consolidation of fragmented information gathered from white and yellow papers, academic publications, blogs, developer documentation, communication with the developers, as well as additional analysis gleaned from the source code. We hope that this survey will help the readers gain in-depth understanding of the design principles behind blockchain systems and contribute towards systematization of the area.
Hybrid feature fusion neural network integrating transformer for DCE-MRI super resolution

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is an essential medical imaging technique that provides multiple time-series images of the same anatomical structures and function without causing ionizing radiation. However, due to the physical nature of the DCE-MRI process, the scan time can be as long as tens of minutes, which severely affects the patient experience. Therefore, reducing scanning time has become a hot research topic, which super-resolution reconstruction from post-processing of the resulting low-resolution images to obtain high-resolution images. However, some existing methods exhibit general limitations in capturing long-range dependencies and loss of localized details. In this paper, we propose a Hybrid Feature Fusion Neural Network Integrating Transformer for DCE-MRI Super Resolution Reconstruction, we call it HybridF. HybridF combines Transformer and CNN, where both global dependency and low-level spatial details can be efficiently captured. Moreover, where a Hybrid Fusion Block (HFB) is proposed to effectively aggregate multi-level features from Transformer and CNN. The method is evaluated by the data collection of clinical DCE-MRI and public IXI dataset. The DCE-MRI dataset consists of 212 cases of patients images. The reconstruction quality of our network is higher than that of the SRCNN, EDSR and RCAN method (＋2.24 dB, ＋2.79, ＋1.09). It is demonstrated that our network outperforms state-of-the art approaches it will contribute to the development of DCE-MRI super-resolution reconstruction.
New 3-Layer Low Complexity Cryptosystem for Color Images Based on Chao, Algebraic Diffusion and ECBC

In this paper, based on chaotic dynamical systems coupled with pseudo-random permutations and built using a three-layer architecture, a new color image cryptosystem is proposed. The first layer process performs a confusion using a bitwise XOR of the original image and a Piece Wise Linear Chaotic Map (PWLCM). The second layer process further enhances the consequent confusion using a one-to-one permutation function over the ring Z/nZ. The third and final layer process performs a diffusion using an enhanced cipher block-chaining mode (ECBC). Evaluation of the proposed cryptosystem proves that with low complexity the diffusion chaotically and sequentially changes the pixels of the image and proves that the avalanche effect is fully ensured. It follows that a high robustness against different attacks is guaranteed.
Role of Public Policy Communication in Poverty Alleviation

&#x0D; &#x0D; &#x0D; Poverty alleviation has become one of key global agendas for achieving sustainable development goals. Implementation of poverty alleviation programs, however, is often impeded by poor public policy communication. This study evaluates how public policy communication helps ensure the successful implementation of the programs, by reviewing (a) the importance of public policy communication, (b) the type of communication strategies implemented, and (c) the results gained. Using qualitative descriptive method, the authors review communication practices and results in Indonesia and other countries.&#x0D; &#x0D; &#x0D;
Risks and preventive measures for manufacturing enterprises in the Context of cloud accounting

Abstract The wide application of “Internet+” makes the organic integration of accounting and cloud computing a trend, and cloud accounting is also favored by enterprises for its efficient information sharing and high scalability; however, in the actual promotion due to the openness of the Internet and other special reasons, it also reveals such problems and risks as confusion in choosing cloud services, confusion in service level agreement, confusion in system irregularities and insufficient legal compliance. This paper takes the risk of transmission, storage, and use of financial information in the process of applying cloud accounting in manufacturing enterprises as the research object, to identify, confirm and evaluate the risk factors of cloud accounting and propose counter strategies. The results show that the relationship between the risk factors of cloud accounting are: risk of decision making in applying cloud services &gt; risk of data information security &gt; risk caused by insufficient understanding of service level agreement &gt; risk of system Therefore, the risk of using cloud accounting in manufacturing enterprises is moderate and controllable, and as long as the risk factors are controlled reasonably, the use of cloud accounting is beneficial to the development of manufacturing enterprises.
The Research on Lightweight Traffic Sign Recognition Algorithm Based on Improved YOLOv5 Model

Traffic sign detection is an important research direction in object detection, which has been widely used in intelligent transportation system, driving assistance, automatic driving and other fields. In practical applications, traffic sign detection algorithms are required to complete detection and recognition tasks quickly and accurately, which requires the algorithm model to be lightweight to meet the deployment conditions. Aiming at the existing traffic sign detection problems, a lightweight traffic sign detection network based on YOLOv5s model was constructed, which improved the detection performance of the network model on the premise of guaranteeing the computing speed. In order to ensure lightweight, YOLOv5s model was selected. Firstly, Dense CSP Module (DCM) was designed to enhance the effect of feature fusion. At the same time, the feature pyramid is improved, and reduced the number of parameters in the model. Experimental results show that compared with the original algorithm, the detection efficiency of the proposed algorithm is improved by 5.28%, and the experimental results on multiple data sets show obvious improvement effect. This is a lightweight model that works well in the area of traffic sign detection.
Augmenting Radar Data via Sampling from Learned Latent Space

Data augmentation is a widely used technique to regularize deep learning models. It is especially famous in computer vision due to its simplicity to apply. Literature suggests numerous ways of transforming images without changing the characteristic semantics. However, for data coming from sensors such as radar these approaches are not applicable leading to data augmentation being not commonly performed. To solve this problem and close the gap we investigate how a Variational Autoencoder (VAE) can be trained on radar data to sample from the learned latent space and use the resulting data to regularize the training of a classifier. We run our experiments on two radar gesture datasets and show that the introduction of generated data can increase generalization. We investigate whether the learned embedded space is sufficient and propose how to sample from the latent space while preserving labels for successful supervised training.
Transformer-based Temporal Knowledge Graph Completion

A structured semantic knowledge base called a temporal knowledge graph contains several quadruple facts that change throughout time. To infer missing facts is one of the main challenges with temporal knowledge graph, i.e., temporal knowledge graph completion (TKGC). Transformer has strong modeling abilities across a variety of domains since its self-attention mechanism makes it possible to model the global dependencies of input sequences, while few studies explore Transformer encoders for TKGC tasks. To address this problem, we propose a novel end-to-end TKGC model named Transbe-TuckERTT that adopts an encoder-decoder architecture. Specifically, t he proposed model employs the Transformer-based encoder to facilitate interaction between entities, relations, and temporal information within the quadruple to generate highly expressive embeddings. The TuckERTT decoder uses encoded embeddings to predict missing facts in the knowledge graph. Experimental results demonstrate that our proposed model outperforms several state-of-the-art TKGC methods on three public benchmark datasets, verifying the effectiveness of the self-attention mechanism in the Transformer-based encoder for capturing dependencies in the temporal knowledge graph.
A Simulation-Based Multi-CPU Architecture Virtual Machine Management System for OpenStack

The rapid development of cloud scenarios and applications has led to increased demand for computing power. However, a single X86 CPU architecture can no longer meet the diverse business needs of users. As a result, mainstream cloud infrastructure platforms have begun to support other CPU architectures, such as ARM and RISC-V. Nevertheless, the financial pressure associated with purchasing CPU hardware of different architectures to assemble cloud infrastructure with multi-CPU architectures is a challenge for scientific research institutions or small enterprises. To address this issue, this paper proposes and implements a simulation-based multi-CPU architecture virtual machine management system based on the open source cloud operating system OpenStack. With this system, multiple CPU architecture virtual machines can be created and managed in a single CPU architecture hardware environment, thus offering a cost-effective solution for multi-CPU architecture cloud infrastructure.
Fairness and Effectiveness in Federated Learning on Non-independent and Identically Distributed Data

Federated learning is a distributed machine learning method that protects privacy by allowing participants to train models locally rather than uploading data. However, federated learning has a significant barrier because of the non-independent and identically distributed (Non-IID) nature of each participant’s local data. FedFE, a novel fair and effective federated optimization algorithm, is presented in this paper. FedFE introduces momentum gradient descent in the federated training process and proposes a fair weighting strategy based on participant performance in training to eliminate the unfairness caused by the preference for some participants in the federated aggregation process. Experiments on a large number of Non-IID datasets have demonstrated that the proposed algorithm improves on existing baseline algorithms in terms of fairness, effectiveness, and convergence speed.
Research on Motion Trend Enhanced 2D Detection on Drones

Inspired by the human visual system, we proposed a motion information-based enhancement mechanism for drone detection, named Collaborative Filtering Mechanism (CFM). CFM enhances small object features through GAN-based image translation which is based on a Cycle Generative Adversarial Network (CycleGAN), and filters out unrelated features during the feature extraction cascade of YOLO-V5s, thus improving the performance of object detection. In the experiments, we verified the performance improvement brought by the proposed CFM module on the VisDrone dataset.
Federated Learning Empowered Resource Allocation in UAV-Assisted Edge Intelligent Systems

Mobile edge computing (MEC) has been considered a promising advanced technology to support delay-sensitive tasks of user equipment (UE) in the internet of things (IoT) systems, it is necessary to allow multiple UEs to offload their computationally intensive tasks to a flexible edge computing server, such as an unmanned aerial vehicle (UAV)-assisted edge computing server. However, most existing works mainly focused on minimising energy consumption under the transmission and/or processing delay constraints while ignoring privacy-preserving, which will be challenging when dealing with large volumes of raw data. In this paper, we consider a federated learning (FL) empowered UAV-assisted edge intelligent system to minimise the maximum utility cost (which indicates the relationship between latency and energy consumption) to the selected UE for task processing. We propose to jointly optimise the FL task offloading decisions among all UEs and the communication resource allocation under each epoch. This is achieved by devising a federated learning-based edge intelligence offloading decision optimisation algorithm (FEOA). Simulation results show that our proposed schemes outperform the benchmarks in terms of the maximum cost efficiency among all UEs.
Comparable GPU: Optimizing the BERT Model with AMX Feature

BERT is widely used in natural language processing (NLP) tasks in AI field. BERT has wide range of application scenarios. The performance of BERT determines the user experience feeling of application directly. AMX technology is a new feature introduced by Intel CPU, which supports two dimensional vector operations to optimize matrix operations. This paper uses AMX features, combined with optimization techniques such as operator fusion and quantization, to significantly improve the inference performance of BERT. Under the premise of a certain accuracy, compared with NVIDIA’s T4 GPU, in the BF16 small batch size scenario, the performance is improved by 1.2 times; Similarly, the performance of INT8 small batch size scene is 1.48 times higher.