Reviewerly/exHarmony huggingface dataset:
Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning

This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified.
Room for Me?: Mobile Navigation for Entering a Confined Space Using Deep Reinforcement Learning

We consider the navigation problem where a robot tries to enter a confined space where obstacles are populated randomly. The objective is to move into and stay inside the confined space while not colliding with obstacles. Since the robot does not know the exact locations of the obstacles beforehand, the goal location inside the space and the path to the goal cannot precomputed. Instead of determining the goal and planning path once the configuration space is exactly known, we propose a method based on deep reinforcement learning (DRL). The method directly generates the control input of the robot from the state and sensing information. We train the DRL agent in a simulated environment with a mobile robot in a confined space with randomly located obstacles (up to three). The robot achieves at most 95% of success rate where the robot enters the space without collisions and stays inside for a certain period of time.
Puncturable Key-Policy Attribute-Based Encryption Scheme for Efficient User Revocation

Cloud computing, which provides a brand-new service model, has become an important infrastructure in the information age, and has been widely used in numerous fields. The Key-Policy Attribute-Based Encryption (KP-ABE) scheme allows the encrypted data with fine-grained access control in the cloud environment. However, achieving large-scale user revocation in the application scenario of KP-ABE becomes one of the thorny problems. Furthermore, the computation and communication costs of the previous user revocation schemes were generally high, especially when a large number of users were revoked. To address these problems, an enhanced high-performance user-revocable KP-ABE scheme combined with the puncture method was proposed. In this article, the user could be revoked by the fine-grained restriction policy. When revoking the user, the cloud would run the puncture algorithm to embed the restriction policy defined by the data owner into the ciphertext. This method could effectively omit the re-encryption and key updating processes, by which the computation and communication overhead of the user revocation are efficiently reduced, and the user revocation becomes more flexible and efficient. Moreover, the Chosen-Plaintext Attack (CPA) security proof and extensive simulation results demonstrate the reliability and efficiency of the proposed scheme for user revocation in a cloud environment.
Privacy Evaluation of Blockchain Based Privacy Cryptocurrencies: A Comparative Analysis of Dash, Monero, Verge, Zcash, and Grin

Privacy is important to financial industry, so as to blockchain based cryptocurrencies. Bitcoin can provide only weak identity privacy. To overcome privacy challenges of Bitcoin, some privacy focused cryptocurrencies are proposed, such as Dash, Monero, Zcash, Grin and Verge. Private address, confidential transaction, and network anonymization service are adopted to improve privacy in these privacy focused cryptocurrencies. We propose four privacy metrics for blockchain based cryptocurrencies as identity anonymity, transaction confidentiality, transaction unlinkability, and network anonymity. Then make a comparative analysis on privacy of Bitcoin, Dash, Monero, Verge, Zcash, and Grin from these privacy metrics. Finally, open challenges and future directions on blockchain based privacy cryptocurrencies are discussed. In the future, multi-level privacy enhancement schemes can be combined in privacy cryptocurrencies to improve privacy, performance and scalability.
Ask-AC: An Initiative Advisor-in-the-Loop Actor–Critic Framework

Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or predefined rules, which inevitably result in a cumbersome and expensive learning process. In this article, we introduce a novel initiative advisor-in-the-loop actor–critic (AC) framework, termed as Ask-AC, that replaces the unilateral advisor-guidance mechanism with a bidirectional learner-initiative one, and thereby enables a customized and efficacious message exchange between learner and advisor. At the heart of Ask-AC are two complementary components, namely, action requester and adaptive state selector, that can be readily incorporated into various discrete AC architectures. The former component allows the agent to initiatively seek advisor intervention in the presence of uncertain states, while the latter identifies the unstable states potentially missed by the former especially when environment changes, and then learns to promote the ask action on such states. Experimental results on both stationary and nonstationary environments and across different AC backbones demonstrate that the proposed framework significantly improves the learning efficiency of the agent, and achieves the performances on par with those obtained by continuous advisor monitoring.
Slice-to-slice context transfer and uncertain region calibration network for shadow detection in remote sensing imagery

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice context transfer and uncertainty region calibration network (SCUCNet). First, an interslice context semantic transfer (SCT) module is proposed to explore the connections among spatial pixel information and extract sufficient global contextual information via interslice feature transfer. This process helps alleviate the interference of multiscale differences and complex backgrounds on the detection results. Second, to further optimize the initial output mapping, calibrate the uncertainty region, and enhance the feature representation, we propose a plug-and-play uncertainty region awareness and calibration (URAC) module combined with a distance transform algorithm, which introduces only 0.09 M parameters. The quantitative results show that the proposed model outperforms state-of-the-art methods for shadow detection in RSIs, achieving an Intersection over Union (IoU) of 88.33% on the aerial imagery dataset for shadow detection (AISD). In addition, extensive experiments and visualizations further prove the validity and interpretability of our method, and the proposed method can effectively mitigate the interference caused by multiscale differences, morphological diversity, spectral similarity and heterogeneity.
Cformer: An underwater image enhancement hybrid network combining convolution and transformer

Abstract Underwater images are the most direct and effective ways to obtain underwater information. However, underwater images typically suffer from contrast reduction and colour distortion due to the absorption and scattering of water by light, which seriously limits the further development of underwater visual tasks. Recently, the convolutional neural network has been extensively applied in underwater image enhancement for its powerful local information extraction capabilities, but due to the locality of convolution operation, it cannot capture the global context well. Although the recently emerging Transformer can capture global context, it cannot model local correlations. Cformer is proposed, which is an Unet‐like hybrid network structure. First, a Depth Self‐Calibrated block is proposed to extract the local features of the image effectively. Second, a novel Cross‐Shaped Enhanced Window Transformer block is proposed. It captures long‐range pixel interactions while dramatically reducing the computational complexity of feature maps. Finally, the depth self‐calibrated block and the cross‐shaped enhanced window Transformer block are ingeniously fused to build a global–local Transformer module. Extensive ablation studies are performed on public underwater datasets to demonstrate the effectiveness of individual components in the network. The qualitative and quantitative comparisons indicate that Cformer achieves superior performance compared to other competitive models.
Optimized byzantine fault tolerant algorithm for improving performance and security with trust values

Abstract Blockchain is considered to be a promising technology due to its decentralization, open, and non tamperation. Nevertheless, the current blockchain system cannot replace traditional financial institutions because its performance and security indicators in the consensus algorithm are not perfect. As one of the emerging capacity expansion technologies, sharding technology improves throughput by grouping users and processing transactions in parallel. This paper proposes an improved Byzantine Fault Tolerance algorithm TV‐BRAFT. TV‐BRAFT calculates a trust value for each user through an incentive mechanism. The sharding scheme based on the trust value is adopted to reduce the probability of malicious nodes being allocated to the same partition, thus improving the security of the system. In the consensus, the system selects users with qualified trust values to form a verification group to supervise the consensus process and avoid submitting error transactions to the blockchain. In order to prove that the TV‐BRAFT can resist Byzantine attacks, a recursive formula is used to prove the security of sharding and the security of consensus within one shard. Finally, the simulation results show that the TV‐BRAFT not only guarantees the fairness of incentives but is also better than the Raft and PBFT algorithms in efficiency and throughput.
Machine translation and culture-bound texts in translator education: a pilot study

Mass adoption of neural machine translation (NMT) tools in the translation workflow has exerted a significant impact on the language services industry over the last decade. There are claims that with the advent of NMT, automated translation has reached human parity for translating news (see, e.g. Popel et al. 2020). Moreover, some machine translation (MT) research has already been done in the context of literary texts. In this paper, we share the results of a pilot study carried out with two groups (a pre-course group and post-course group) of MA-level students participating in a course that involved translating culture-bound texts. The students’ role was to post-edit and evaluate two machine-translated stories (Polish legends), marking their comprehensibility and accuracy. We discuss the lessons learnt during this pilot study, the critical errors detected by the students and their perceptions of the end products and the experiment itself. We report noticeable differences found between the pre-course group and the post-course group in terms of language awareness and the speed and quality of their post-editing (PE) performance. Our results also show that the task of post-editing culture-bound texts offers students a unique and enjoyable setting, enabling them to assess translation technology and hone their translation skills at the same time.
Text Recognition Model Based on Multi-Scale Fusion CRNN

Scene text recognition is a crucial area of research in computer vision. However, current mainstream scene text recognition models suffer from incomplete feature extraction due to the small downsampling scale used to extract features and obtain more features. This limitation hampers their ability to extract complete features of each character in the image, resulting in lower accuracy in the text recognition process. To address this issue, a novel text recognition model based on multi-scale fusion and the convolutional recurrent neural network (CRNN) has been proposed in this paper. The proposed model has a convolutional layer, a feature fusion layer, a recurrent layer, and a transcription layer. The convolutional layer uses two scales of feature extraction, which enables it to derive two distinct outputs for the input text image. The feature fusion layer fuses the different scales of features and forms a new feature. The recurrent layer learns contextual features from the input sequence of features. The transcription layer outputs the final result. The proposed model not only expands the recognition field but also learns more image features at different scales; thus, it extracts a more complete set of features and achieving better recognition of text. The results of experiments are then presented to demonstrate that the proposed model outperforms the CRNN model on text datasets, such as Street View Text, IIIT-5K, ICDAR2003, and ICDAR2013 scenes, in terms of text recognition accuracy.
Exploring digital literacy in the era of digital civilization: A framework for college students in China

Cultivating the digital literacy of all citizens, especially for college students, would contribute to the construction of a ‘Learning Society’ where everybody loves learning and would offer a powerful impetus for building a modern country. First, this paper reviews the origins and general definitions of computer literacy, network literacy, media literacy, information literacy and digital literacy. Compared with similar terminologies, digital literacy features the subjective initiatives to actively improve one’s and others’ skills, competence, awareness and thinking mode for adapting to, qualifying for and creating a new digital age and society. Second, the internal logic in the Digital Literacy Framework of College Students (DLFCS) can be summarized as ‘Skills-Competencies-Awareness’ attributing to the evolution progress of ‘digital technologies utilization, actual problems solving, and digital awareness cultivation’. Following this logic, this paper develops the DLFCS through scene-based requirements analysis and professional consultations, including three areas (i.e. operational skills, applied competencies, thinking &amp; awareness), identifying fifteen descriptors and their examples with key performances. Third, it measures the self-perception and actual performance of college students digital literacy by questionnaire, Q&amp;A tests and task evaluation, and validates the completeness and validity of the DLFCS by Pearson correlation analysis of datasets collected in three modes. The results indicate that (a) the fifteen descriptors in DLFCS basically cover the essential areas of digital literacy with extremely weak correlations among them, (b) the relationships of progressive and intertwined ‘Skills-Competencies-Awareness’ demonstrate the validity of the internal logic and DLFCS itself, (c) to be digitally literate requires long-term and gradually-progressed cultivation and improvement, as achieving one descriptor of digital literacy proficiently does not guarantee good performance on the other descriptors.
Investigating the effect of Region of Interest coding on the QoE of FPV drone piloting under adverse network conditions

In this paper we evaluate an approach for increasing Quality of Experience for drone pilots. We look at the use case in which the drone is controlled in first-person view over an IP based network such as 5G. In case of congestion, the video sent by the drone needs to be encoded at a lower bitrate so as to avoid severe video artefacts and Quality of Experience degradation. We evaluate methods of how to maximize Quality of Experience for the drone pilots in degraded network conditions with low bandwidth. We evaluate the impact of Region of Interest coding and greyscale coding on subjective metrics of video quality. The testing is performed through a drone driving simulator and using commercial first-person view googles. Our findings, based on a sample size of (n = 24), indicate that ROI and greyscale coding have, in almost all cases, either no impact or a negative impact on perceived video quality of a drone pilot.
Current Technologies and Applications of Digital Image Processing

There has been significant advances in the field of image and video processing over the past few decades. The term "image processing" is used to describe multiple signal-processing methodologies where images (such as video or picture frames) serves as the input, resulting to another image or a collection of image-related parameters or features. The majority of methodologies to image processing include reducing the picture to a two-dimensional signal and processing it in the same way as any other signal. The term "video processing" on the other hand is used to describe a particular type of signal processing where video files or video streams are utilized as output or input signals. Video recorders, televisions, video codecs, digital versatile, disc players, and other devices all utilize video processing algorithms. This paper provides a survey of the components of Digital Image Processing (DIP) as well as the recent developments in Image Processing technology and DIP applications.
Extended Reality (XR) in the Digital Fashion Landscape

Abstract The fashion industry is progressively investing in technology, and the benefits of Extended Reality (XR) are not going unnoticed. Despite widespread interest in the topic, the literature is limited and the topic is poorly understood. This study brings XR in fashion to the forefront. Based on a review of several sources (latest news, reports, and literature), it describes the current landscape, presents the advantages and downsides of immersive technologies, and provides useful advice for practitioners. The results suggest that brands can leverage XR to establish close connections with consumers through personalised experiences and compelling stories. Moreover, some of the intricacies of XR are discussed, namely, costs and unresolved technical issues. Crafting meaningful XR experiences requires expertise in multiple domains, adding value to consumers through enhanced features and striving for quality.
Voice Biometric System - Authentication Over the Voice Command from Remote Place - A Case Study

The entire globe has been focused on the security of the data system for the past decade. With every passing day, the requirement to secure virtual data becomes much more difficult. Professionals are exploring towards advanced techniques to secure the access to system infrastructure as the threat to key information such as customer data. Numerous cases are reported daily in which an unauthorized users and fraudsters gained illegal access to system and then used confidential data such as customer information and compromised them. Lakhs of rupees have indeed been lost as a result of this personification, as well as legal issues that are best avoided especially in the banking sector. For a firm, these occurrences frequently result in a decrease in market value and a loss of trust. Passwords, OTPs are no more secure.
Hardware-friendly tone-mapping operator design and implementation for real-time embedded vision applications

Tone-mapping is required to convert High Dynamic Range (HDR) images to Low Dynamic Range (LDR) images for viewing on standard displays. Most recent works in the domain include the efficient implementation of tone-mapping algorithms on Graphics Processing Units (GPUs) for throughput improvements. However, their downside is significantly high power consumption, making such solutions unsuitable for displays on embedded and general consumer electronic devices. In contrast, Field Programmable Gate Arrays (FPGAs) have demonstrated significant speedup on data-intensive image and video applications with a much lower power consumption than GPUs. This paper proposes a modular, scalable and resource-efficient FPGA implementation of a global Tone-Mapping Operator (TMO) based on histogram of the scene luminance and characteristics of the Human Visual System (HVS). Compared to GPU based implementation of a state-of-the-art TMO, our FPGA implementation achieved a higher throughput in the range of 2.5× to 4.5× with power savings of more than two orders of magnitude. Moreover, tonal quality of the proposed solution is better than the baseline TMO in approximately 80% of a dataset of 288 HDR images. The proposed solution also outperformed the state-of-the-art FPGA implementations of other TMOs in throughput, resource usage and scalability.
Detection and Classification of Aggressive Comments and Hate Speech

The prevalence of social media has led to an increase in aggressive online behaviour such as cyberbullying, trolling, hate speech, and flaming. The anonymity and convenience of social media platforms have allowed individuals to express their aggressive tendencies without fear of retaliation. This issue is not limited to certain sections of society and has impacted people globally. Cyberbullies now have the power to affect the lives of millions of people worldwide, which can be detrimental to the internet's importance as a communication and networking tool. Therefore, it is critical to recognize and restrict this negative behaviour. It is essential to detect and prevent cyberbullying and hate speech in Indian native languages, considering India's diverse population and multiple languages. Social media platforms have brought about significant benefits, including improved communication and networking, but the increased use of these platforms has led to an increase in harmful behaviour that can negatively impact mental health. Additionally, misinformation and propaganda can cause severe consequences for society. This paper discusses the detection and classification of aggressive comments in Hindi-English code-mixed and Hindi data from popular social media platforms such as Facebook, Twitter, and Instagram.