Reviewerly/exHarmony huggingface dataset:
Neural Network Predictor for Fast Channel Change on DVB Set-Top-Boxes

With the generalization of digital Television (TV), keeping the channel change delay as low as possible gradually became a difficult requisite in what concerns the resulting user’s Quality-of-Experience (QoE). Frequently, this latency may be higher than 2 s. While many state-of-the-art Set-top-Boxes (STBs) already include a shadow tuner to anticipate the tuning of the next channel, they strive to predict which channel should be pre-tuned, generally opting for one of the adjacent channels. The presented research proposes the use of a predictive system to assist the STB in the forecast of the channel(s) the user will select next. The implemented predictor is based on a Recurrent Neural Network (RNN) and makes use of STB log data concerning the user’s channel changes history to train (and adjust) the model every week. To attain this objective, the most convenient hyperparameter combination that not only fulfilled the aimed prediction accuracy but also suited the rather limited computational constraints of most current STBs had to be identified. The obtained experimental results, validated using four embedded processor families commonly equipping commercial STBs, showed a prediction accuracy of 50.2% for a single-channel prediction and 67.7% when five channels were simultaneously predicted. When combined with the existing dual-tuning system of current STBs, the proposed predictor can save as much as 1000 s per month in TV channel change delays, greatly improving the resulting user’s QoE.
Incivility from the UK Prime Minister’s Dispatch Box: A Comparative Study of PM Question Time Attacks, 2010–2020

Despite the widespread notion that political incivility is on the rise, including the British Parliament, there is scarce research that systematically examines political incivility over time within and outside of the British context. As such, this chapter presents a rare longitudinal study of adversarial communication in the British Parliament. We explore the extent to which Prime Ministers (PMs) and Members of Parliament (MPs) used trait attacks and uncivil attacks during Prime Minister Questions (PMQs) in the period between 2010 and 2020. Our results show that despite the official ban on unparliamentary language, PMQs are filled with adversarial communication, including uncivil discourse. While we do not identify a linear increase in political incivility, we do find that the use of uncivil attacks fluctuates and becomes more frequent when elections draw closer. Furthermore, we uncover vast differences among PMs. Former Prime Minister Boris Johnson used most trait and uncivil attacks, while former Prime Minster Theresa May employed the fewest uncivil attacks of all prime ministers. The results contribute to academic research on political incivility and the broader debate on the communication style of political leaders, especially in British politics and more particularly, during PMQs.
Emergency Remote EFL Instruction in Brazil, China, and Indonesia: What Teachers Learned and How?

It is widely accepted that language teachers’ pedagogical knowledge bases are dynamic and ever-evolving (Freeman and Johnson, 1998; Johnson and Golombek, 2020; Moradkhani et al., 2013; Schachter & Freeman, 2020; Varghese et al., 2016). With the swift integration of technology-supported language teaching practices under emergency lockdown conditions, the dynamicity of teacher development, both formal and autonomous, augmented in exciting and unanticipated ways (Meskill, et al., 2023a). This inquiry set out to discover how six EFL educators’ pedagogical knowledges and actions were shaped by their quick, unexpected migration to online teaching. Teachers from Brazil, China, and Indonesia were interviewed to probe what and how they learned about their practices. Forced to discover new ways to interact with learners, they found technological means to implement student-centered materials and instructional activities online. Findings point to the cyclical dynamic of pedagogical knowledge and action while underscoring teacher freedom and autonomy as integral components of professional development.
Intra-Group Diversity and How it is Managed by an Outlaw Motorcycle Club

A deviant subculture such as an outlaw motorcycle club must be able to accommodate internal diversity because a repertoire of differing talents, ideas, and attitudes facilitates an adequate response to the disparate demands placed upon the organization. At the same time that intra-member variation is an asset, however, diversity frequently must be camouflaged in order to maintain the integration necessary to survive in a hostile environment. The need for camouflage varies in relation to the degree of perceived threat. This theme is examined in three different contexts: the clubhouse, the club bar, and violent encounters with outside society.
Technology Threat Avoidance Factors Affecting Cybersecurity Professionals' Willingness to Share Information

Abstract4Through the Cybersecurity Information Sharing Act of 2015, a DHS information-sharing program was mandated to protect U.S. businesses and critical infrastructure and mitigate cyberattacks.The present study examined cybersecurity professionals9 willingness to collaborate and share information regarding cybersecurity threats via that program.The technology threat avoidance theory (TTAT) served as the study9s theoretical framework.This research examined to what extent technology threat avoidance factors affect cybersecurity professionals9 willingness to collaborate and share information regarding cybersecurity threats.Threat avoidance factors consisted of perceived susceptibility, perceived severity, perceived threat, prevention effectiveness, prevention cost, and self-efficacy.This cross-sectional study used partial least squaresstructural equation modeling to analyze data collected from 137 cybersecurity professionals with a minimum of five years of cybersecurity experience.The data analysis indicated that perceived susceptibility and perceived severity significantly predicted participants9 perceptions of cybersecurity threats, and perceived threat explained 44% of the variance in avoidance motivation.Prevention effectiveness, prevention cost, and selfefficacy were not significant predictors of avoidance motivations and the willingness to participate in the DHS9s information-sharing program.These results indicate that more research is necessary to understand the factors influencing information sharing among cybersecurity professionals working in U.S. organizations.
Digital Forensic Readiness of Cybercrime Investigating Institutions in Nigeria: A Case Study of the Economic and Financial Crimes Commission (EFCC) and the Nigeria Police Force

Abstract4This case study investigates Nigeria's cybercrime agencies' digital forensic readiness and forensic capability and how this affects the cybercrime caseloads and prosecution.The Routine Activity Theory (RAT) and the Technology, Organization, and Environment (TOE) theories were applied.This study used the TOE framework to examine the digital forensic technology adoption and forensic readiness of cybercrime investigators in Nigeria and relates this with the RAT framework and the effectiveness of law enforcement agencies as capable guardians.The research population of this study was the Nigerian Internet fraud investigative agencies 3 the Economic and Financial Crimes Commission (EFCC) and the Nigeria Police Force (NPF).Eighteen participants from the two organizations were interviewed.The paper concluded that the cybercrime investigators in Nigeria are not forensically ready given the established lack of digital forensic resources (technological gap, human resources gap, skills gap, funding gap), particularly when juxtaposed with the high cybercrime caseloads in the country.
Energy-Efficient Object Tracking Using Adaptive ROI Subsampling and Deep Reinforcement Learning

Recent innovations in ROI camera systems have opened up the avenue for exploring energy optimization techniques like adaptive subsampling. Generally speaking, image frame capture and read-out demand high power consumption. ROI camera systems make it possible to exploit the inverse relation between energy consumption and spatiotemporal pixel readout to optimize the power efficiency of the image sensor. To this end, we develop a reinforcement learning (RL) based adaptive subsampling framework which predicts ROI trajectories and reconfigures the image sensor on-the-fly for improved power efficiency of the image sensing pipeline. In our proposed framework, a pre-trained convolutional neural network (CNN) extracts rich visual features from incoming frames and a long short-term memory (LSTM) network predicts the region of interest (ROI) and subsampling pattern for the consecutive image frame. Based on the application and the difficulty level of object motion trajectory, the user can utilize either the predicted ROI or coarse subsampling pattern to switch off the pixels for sequential frame capture, thus saving energy. We have validated our proposed method by adapting existing trackers for the adaptive subsampling framework and evaluating them as competing baselines. As a proof-of-concept, our method outperforms the baselines and achieves an average AUC score of 0.5090 on three benchmarking datasets. We also characterize the energy-accuracy tradeoff of our method vs. the baselines and show that our approach is best suited for applications that demand both high visual tracking precision and low power consumption. On the TB100 dataset, our method achieves the highest AUC score of 0.5113 out of all the competing algorithms and requires a medium-level power consumption of approximately 4 W as per a generic energy model and an energy consumption of 1.9 mJ as per a mobile system energy model. Although other baselines are shown to have better performance in terms of power consumption, they are ill-suited for applications that require considerable tracking precision, making our method the ideal candidate in terms of power-accuracy tradeoff.
Multi-Circle Detection Guided by Multimodal Optimization Scheme

Automatic circle detection is one of the most important construction elements to design more complex industrial image tasks such as target detection, manufacture inspection and process control. Most of the literature concerning multi-circle detection can be categorized under deterministic or stochastic perspectives. Deterministic approaches combine geometric and histogram information to identify circle shapes. However, deterministic techniques are unable to detect circles in presence of noise, shape variance, or occlusion. On the other hand, stochastic methodologies such as metaheuristic algorithms have been also proposed as alternatives to deterministic approaches for circle detection. Although these techniques are in general more robust, they allow detecting only one circle per execution, translating the detection problem into a unimodal optimization problem. Under such conditions, it is necessary to execute multiple times the algorithm to identify all the circles contained in the image. In this paper, the multi-circle detection problem has been reformulated as a multimodal optimization problem. The proposed approach adopts the Multimodal Flower Pollination Algorithm (MFPA) to detect all the circular instances contained in the image. Experimental results suggest that the proposed approach significantly improves the multi-circle detection problem.
Improving Complex Scene Generation by Enhancing Multi-Scale Representations of GAN Discriminators

While recent advances of GAN models enabled photo-realistic synthesis of various object images, challenges still remain in modeling more complex image distributions such as scenes with multiple objects. The difficulty lies in the high structural complexity of scene images, where the discriminator carries a heavy burden in discriminating complex structural differences between real and fake scene images. Therefore, enhancing the discriminative capability of the discriminator could be one of the effective strategies to improve the generation performance of GAN models. In this paper, we explore ways to boost the discriminative capability by leveraging two recent paradigms on visual representation learning: self-supervised learning and transfer learning. As the first approach, we propose a self-supervised auxiliary task tailored to enhance the multi-scale representations of the discriminator. In the second approach, we further enhance the discriminator by utilizing pretrained representations from various scene understanding models. To fully utilize knowledge from multiple expert models, we propose a multi-scale feature ensemble to mix multi-sale representations. Empirical results on challenging scene datasets demonstrate that the proposed strategies significantly advance the generation performance, enabling diverse and photo-realistic synthesis of complex scene images.
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which the deep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inference attack. In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally exists in reinforcement learning training data, on the probability of information leakage. Moreover, we compare the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collective</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual</i> membership attacks against the deep reinforcement learning algorithm. Experimental results show that the proposed adversarial attack framework is surprisingly effective at inferring data with an accuracy exceeding 84% in individual and 97% in collective modes in three different continuous control Mujoco tasks, which raises serious privacy concerns in this regard. Finally, we show that the learning state of the reinforcement learning algorithm influences the level of privacy breaches significantly.
ViT-SAPS: Detail-Aware Transformer for Mechanical Assembly Semantic Segmentation

Semantic segmentation of mechanical assembly images provides an effective way to monitor the assembly process and improve the product quality.Compared with other deep learning models, Transformer has advantages in modeling global context, and it has been widely applied in various computer vision tasks including semantic segmentation.However, Transformer pays the same granularity of attention on all the regions of an image, so it has some difficulty to be applied to the semantic segmentation of mechanical assembly images, in which mechanical parts have large size differences and the information quantity distribution is uneven.This paper proposes a novel Transformer-based model called Vision Transformer with Self-Adaptive Patch Size (ViT-SAPS).ViT-SAPS can perceive the detail information in an image and pays finer-grained attention on the regions where the detail information locates, thus meeting the requirements of mechanical assembly semantic segmentation.Specifically, a self-adaptive patch splitting algorithm is proposed to split an image into patches of various sizes.The more detail information an image region has, the smaller patches it is split into.Further, to handle these unfixed-size patches, a position encoding scheme and a non-uniform bilinear interpolation algorithm used after sequence decoding are proposed.Experimental results show that ViT-SAPS has stronger detail segmentation ability than the model with fixed patch size, and achieves an impressive locality-globality trade-off.This study not only provides a practical method for mechanical assembly semantic segmentation, but also has much value for the application of vision Transformers in other fields.The code is available at: https://github.com/QDLGARIM/ViT-SAPS.
Large-capacity image data hiding based on table look-up

Data hiding, also known as information hiding and digital watermarking, refers to the technology of hiding secret information in publicly available media and making it difficult for people to feel its existence. According to the survival result of the hidden information when the stego media is processed, this technology can be divided into robust, semi-fragile, and fragile watermarking. Strong robust digital watermarking is technically difficult to achieve, so it is unrealistic to apply it for digital copyright protection at present. In contrast, fragile (and semi-fragile) watermarking has good feasibility and has been used for authentication and tamper localization of the stego media. However, the existing technology has limitations. To improve the sensitivity and positioning accuracy of tampering, some algorithms embed excessive data, which greatly reduces the fidelity of stego media. To prevent third parties from bypassing integrity authentication and tamper localization, some algorithms add complex encryption algorithms to achieve the confidentiality of hidden data, which not only greatly increases the computational cost, but also destroys the blindness of data extraction. To overcome these shortcomings, this paper proposes a lightweight large-capacity image data-hiding technology. Both the data hiding and data extraction are based on looking up a shared codebook table, so the computational cost on both sides is low. The codebook table can be flexibly customized according to the agreement of the sender and receiver of the stego image. It not only ensures the fidelity of stego image but also naturally provides the confidentiality of hidden data. The experimental results and analysis show that its comprehensive performance, including imperceptibility, capacity, complexity, security, and customizability, is better than various state-of-the-art fragile watermarking techniques. Thus, the proposed technique has obvious advantages when used in image authentication, tampering localization, source tracing, and steganography. The proposed technique can be flexibly extended to hide data in other media.
Exploration of the Ideological and Political Elements of Artificial Intelligence Courses under the Background of Three Comprehensive Education

“Three comprehensive education” is an important measure and mechanism to build a big ideological and political pattern, condense various educational resources, and realize the fundamental goal of morality and cultivating people under the new situation. Curriculum ideology and politics are an effective way for universities to “three comprehensive education,” combining professional construction and curriculum characteristics, grasping the law of students’ growth and talent, and clarifying the direction of education. Curriculum ideology and politics are not only the specific practice of “three comprehensive education” but also the specific embodiment of implementing the goal of morality and talent cultivation. The “three comprehensive education” and the curriculum ideology and politics are organically integrated, represent each other, and complement each other. “Three comprehensive education” grasps the direction of education from a macro perspective, and the practice the education policy from a mesoscopic perspective. The research and exploration under the pattern of “three comprehensive education” effectively promote the supply-side structural work in universities. Pertinence and effectiveness, building a macro, meso-, and micro integrated education system, provide new ideas, new ways, and new methods. In this paper, by discussing the relevant theoretical knowledge of three complete education educating people, the intelligent image recognition algorithm is used to intelligently identify the classroom behavior of teachers and students. Combining intelligent image analysis with educational scenarios not only achieves high accuracy of action recognition in classroom scenarios. According to the process of behavior and action classification and recognition, the system is divided into three steps for research: video-based motion target positioning, human behavior and action feature extraction, and human behavior and action recognition and classification.
Employing Quantile and Probability Plots for Comparing and Assessing Goodness of Fit for Stochastic Models of the DCT Coefficients of Lossy Compressed Images

The paper employs probability plots to performgoodness of fit tests for AC DCT coefficients of compressed images. Attention is paid to various probabilistic models of the DCT coefficients, conventional and rarely used. The Laplacian, generalized Gaussian and doubly Gamma distributions comprise the list. A variation of the method of moments, which involves the 2nd and 4th sample moments as well as Sheppard’s corrections to estimate the shape and scale parameters of the distributions is used. Two types of images are considered, namely, texturelike images and those possessing vast regions of monotonicity. Special effort has been put into adjusting the apparatus of the probability plots to make them suitable for dealing with discrete data, in our case with the quantized DCT coefficients of lossy compressed images. As the source of the DCT coefficients, JPEG images are used. This does not lead us to a significant loss of generality: conclusions drawn in this paper remain applicable to a broad variety of formats of lossy compressed images.
Clustering Using Conditional Generative Adversarial Networks (cGANs)

Clustering is widely acknowledged as an essential process for accomplishing various tasks, such as document clustering, social news clustering, and sentiment analysis. However, conventional clustering algorithms often require the prior determination of the number of clusters to produce satisfactory results. In this study, we propose a novel clustering method that does not necessitate the number of clusters as a compulsory input parameter. Instead, we utilize a conditional Generative Adversarial Network (cGAN) to perform the clustering task. Our experimental results demonstrate that the proposed approach strikes a balance between processing time and accuracy, outperforming conventional clustering methods such as K-Means and Mean-Shift. These methods require the number of clusters to be specified beforehand or demand a significant amount of time to successfully complete the task. In contrast, our proposed method can efficiently cluster data without prior knowledge of the number of clusters, yielding promising outcomes.
Intelligent Companion APP

Virtual assistants are a boon for everyone in this new era of the 21st century. It has paved the way for a new technology where we can ask questions to machines and can interact with IVAs as people do with humans. This new technology attracted almost the whole world in many ways like smart phones, laptops, computers etc. Some of the significant VPs are like Siri, Google Assistant, Cortana, and Alexa. Voice recognition, contextual understanding and human interaction are the issues which are not solved yet in these IVAs. According to those results many services were covered by these assistants but still there are some improvements required in voice recognition, contextual understanding and hand free interaction. After addressing these improvements in IVAs will definitely increase its use is the main goal for this research paper.