Reviewerly/exHarmony huggingface dataset:
Building Tolerance into Hate Speech Laws

UN monitoring bodies frequently pose questions about Australia’s compliance with the hate speech mandates of key UN conventions. Recently, the Human Rights Committee enquired about inconsistencies across Australian state and territory anti-vilification legislation, as raising issues under the International Covenant on Civil and Political Rights (‘ICCPR’). This article examines the implications of those inconsistencies, both legal and practical, for Australia’s ICCPR compliance. At a time when hate speech is not abating but becoming a common feature of an increasingly fragmented society, this article asks the following questions: Are the settings for anti-vilification legislation at state and territory levels appropriate in the balance achieved between applicable human rights? Can Australian state and territory legislation be better targeted to distinguish between prohibited and preserved free speech? Do Australian state and territory laws conform to the requirements of the ICCPR and other UN instruments? The article concludes that the inconsistencies are problematic and lead to public uncertainty, exacerbated by the unpredictable application by some competent authorities. It proposes legislative solutions that focus less on the scope of prohibition (which is dependent on terminology lacking definitional precision) and more on bringing clarity to the scope of excluded conduct, in conformity with ICCPR demands protecting freedom of expression.
Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.
Semi-supervised Contrast Learning in Renal Pathological Image Classification

Deep learning has been implemented in many applications for diagnosis assistant of renal pathological images, but most of them are based on supervised learning models. Semi-supervised learning may lessen labour of data labelling and ease burden on pathologists. By introducing a positive-example based contrast learning algorithm and a small portion of image labels, a semi-supervised contrast learning model is proposed for renal pathological image classification. A training dataset is consisted of pathological images from 313 patients of IgA chronic kidney disease in Fukushima Medical University Hospital. A self-supervised algorithm BYOL was applied for training on a ResNet-50 backbone to obtain feature representation of images with glomeruli. Semi-supervised training was performed on the pre-trained weights with 30% labelled data to obtain a binary classification model. Experimental results showed that the proposed method achieved a classification accuracy of 82.6%, close to a supervised learning model using ResNet50 (accuracy of 86.6%). In conclusion, this study achieved the classification of renal pathological images with a high accuracy using a semi-supervised learning method.
FLDP: A differentiated privacy security approach based on flow model

Sensitive data in standard deep learning training can be subject to assaults. These attacks take advantage of the disclosure of gradients and intermediate representations during the training process, potentially resulting in data breaches and unauthorized access to sensitive information. Concerns about the privacy and security of sensitive data during the training process have grown in importance as deep learning has grown in popularity. This study describes a novel strategy FLDP for improving the privacy and security of deep learning training by combining differential privacy (DP) with stochastic gradient descent (SGD) and flow model. Our strategy improves privacy and security without compromising deep learning model training efficiency or scalability. Individual data points are indistinguishable in our methodology, reducing the danger of privacy breaches.Instead of applying a consistent privacy level to all data points, our technique enables for privacy protection to be differentiated based on data sensitivity. This adaptive privacy approach promotes value while preserving the most sensitive data's privacy.
Risk Assessment for Power Grid Infrastructure Projects based on Graph Attention Networks

Current risk assessment methods of power grid infrastructure projects are limited to the post-incident stage, while less research has been conducted on the pre-construction risk assessment for power grid infrastructure projects. To address this limitation, this paper proposed a pre-construction risk assessment method based on the Graph Attention Networks (GAT) model, which can consider the coupling relationship between projects. Firstly, a pre-construction risk assessment index system for power grid infrastructure projects was constructed to extract their nodal features. Secondly, we built the relationship network to extract two types of relationships between projects. Thirdly, we proposed the GAT model based on each type of relationship to predict the risk level of the project. Finally, the final evaluation results were obtained after combining model outputs based on each edge type. The empirical results showed that this method can quickly and accurately evaluate the risk level of power grid infrastructure projects before construction.
Intelligent Household Peritoneal Dialysis System

According to the "Renal Disease Annual Report" by the Taiwan Society of Nephrology, it is estimated that about 12% of Taiwanese suffer from kidney disease, it has become an important medical issue in the context of Taiwan's aging population. Peritoneal dialysis is a self-care home treatment for treating kidney disease, peritoneal dialysis can be performed manually by a trained caregiver or patient. However, with an improper operation, that could cause thrombosis or peritonitis in patients. Therefore, we propose an intelligent household peritoneal dialysis system to automatically execute the process of peritoneal dialysis. The voice-controlled system is easy in use for patients, who can be decreasing reliance on caregivers. Especially, the automatic operation can avoid possible improper controls by user in the process of peritoneal dialysis.
Facial Expression Segmentation Labeling and Modeling Using Movement Analysis

Deep learning-based facial synthesis can generate realistic and detailed facial images. Nonetheless, altering the expression of a pre-existing facial image can be challenging. The primary challenge is preserving the original Image’s characteristics, such as skin tone, eye color, moles, freckles, or birthmarks. Limiting the editing area is an efficient method for preserving the identity of a facial image. And according to physiological research on facial expression, the most important areas are the eye and mouth. Concentrating on the eye and mouth can effectively reduce the modifying area. However, modifications beyond the eye and mouth are still being considered for a realistic facial image. Analysis of movement patterns is a potential method for locating the expression region or editing area. This study evaluated facial expression movement from the MMI Facial Expression Database to identify the expression area and presented a segmentation strategy that uses deep learning to extract the expression area.
Character Detection in First Person Shooter Game Scenes Using YOLO-v5 and YOLO-v7 Networks

Game character detection methods have been the basis in the design of artificial intelligence in video games. With real-time character detection methods based on in-game views, the game AIs can become more realistic and life-like compared to traditional information-gathering methods on memory-level. This is particularly the case in first person shooting (FPS) games, where it is extremely important to extract information directly from the in-game view the players have. However, there are difficulties in in-game view based character detections, such as viewpoint variation, ambient illumination and occlusion. The purpose of this research is to compare the performance of two famous versions of a CNN-based model named You-Only-Look-once (YOLO), including the most popular version named YOLOv5 and the state-of-the-art version, YOLO-v7, to find out the currently optimal method to be used in the further development of game AIs.
Dual-Polarity ViT for Fine-Grained Visual Categorization

Identifying delicate yet discriminative features is the main task of fine-grained visual categorization (FGVC). The Transformer’s long-range receptive field makes it appropriate for FGVC. Existing Transformer-based efforts heavily rely on attention weights to mine fine-grained local features. However, the approach will highlight irrelevant, even adversarial, regions. In this paper, we propose a new formulation that optimizes the token-importance indicator to distinguish the contribution and suppression of tokens and effectively select discriminative patches. Given a network, the Part Selection Module(PSM) introduces gradients to attention weights for token polarity and utilizes the fusion rule to aggregate multi-layers information. The Dual-Polarity ViT(DP ViT) can reduce misleading attention and focus on subtle yet discriminative parts. Our method outperforms existing methods and improves baselines.
Smart Footwear Types Detection for Automatic Venue Entry Approval

The Internet of Things (IoT) is a new and popular approach for incorporating the Internet into our daily life. Using IoT, many smart systems are developed, such as smart homes, smart cities, smart traffic management, and many more. This brings us closer to seeing many implementations of technologies in our daily life. In our research, we employ object detection technology using YOLOv8 to detect the footwear type automatically. By detecting the footwear type, a system can be designed and developed to remind, reject, or give access to certain places based on the footwear they wear. This research poses a challenge because the type of footwear often looks the same and is difficult to differentiate. Our experiments found that YOLOv8 can detect and determine the three types of footwear used in this research. YOLOv8m is the best model in this research, with a precision of 77%, recall of 76%, and mAP@50 of 76%.
Efficient Wi-Fi Device RF Fingerprint Detection and Classification Using Wigner-Ville Distribution

In the realm of Internet of Things (IoT) applications, Radio Frequency Fingerprint Identification (RFFI) is emerging as a robust technique for enhancing wireless security based on the inherent hardware attributes in wireless devices. We proposed and demonstrated a robust scheme achieved by the statistical extraction method based on Wigner- Ville Distribution analysis for Wi - Fi devices. The proposed framework attains full authentication using a low-complexity feature extractor and employs K-nearest neighbor algorithm for both detecting rogue devices and classifying legitimate devices. This work shows the suitability of statistical feature extraction for rogue device detection. An excellent recognition accuracy of 97.64 % is experimentally demonstrated, with a false alarm rate of 3.29% and a miss alarm rate of 0%, using 11 wireless network interface controllers. Notably, the computational efficiency of our RFFI scheme, achieved through lightweight and straightforward feature extraction method, positions it as a viable solution for security enhancement in resource-constrained wireless communications.
Dehazing Algorithm for UAV Image Based on Smooth Dilated Convolution

Uncrewed Aerial Vehicles (UAVs) are widely used for tasks such as remote sensing mapping, ground target detection, and environmental information extraction. Their advantages lie in the broad aerial perspective, versatile shooting angles, and suitability for different scenarios. In regions characterized by frequent rain and fog, the presence of significant moisture significantly affects the image quality of UAV aerial imagery. This results in reduced contrast, changes in tonality, and obscured details, severely limiting the ability to derive meaningful information from these images. Traditional deep learning-based image-dehazing algorithms rely too much on atmospheric scattering models and are prone to color distortion and detail loss. The end-to-end image-dehazing algorithm presented in this paper uses a smooth dilation convolution approach. Unlike conventional methods based on the atmospheric scattering model, this technique achieves a direct end-to-end dehazing process, seamlessly transforming the input image into a clear output. The color distortion phenomenon can be effectively solved through experiments.
Honey Badger Algorithm with Voting Ensemble Based Computational Linguistics Enabled YouTube Video Popularity Prediction

Computational linguistics dealing with or designing models for language for precise machine translation of language or looking at the ways that machine treats natural language, or the simulation of artificial intelligence. YouTube is an income source for several people, and so video's popularity eventually develops the high importance to sustaining a stable income, offered that the eminence of videos remains at the maximum. Researchers and analysts utilize distinct approaches and techniques for predicting the maximal viewers of famous videos. The significance of identifying the celebrity of online videos and predicting the achievement of videos or conversely is an undeniable detail. Researchers and analysts utilize distinct techniques and models for predicting the maximal viewers of famous videos. In this aspect, a new Honey Badger Algorithm with Voting Ensemble based Machine Learning (HBA-VEML) model for YouTube Video Popularity Prediction is proposed in this study. The goal of the presented HBA-VEML technique is the prediction of YouTube video popularity accurately and promptly. It encompasses a series of processes such as pre-processing, feature extraction, prediction, and parameter optimization. Initially, the HBA-VEML technique undergoes data pre-processing and feature extraction. Next, a voting ensemble classifier is derived using three machine learning (ML) models such as logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM). To enhance the classification outcomes, the HBA is utilized as parameter optimizer. The performance validation of HBA-VEML technique is assessed on YouTube dataset. An extensive set of comparison results demonstrate the enhanced outcomes of the HBA-VEML method over other recent approaches.
Deer Hunting Optimization with Path Augmented RetinaNet Model for Scale-Invariant Object Detection and Tracking

In the zone of pattern recognition and machine learning, object detection and tracking are gaining interest because of its range of applications like visual surveillance. Recently, several deep learning-related techniques are provided for object classification however, there exists some set of concerns or problems that minimizes the overall classifier accuracy. Congest conditions, complex backgrounds, and similarities among various objects become some challenging problems. Large scale variation over object samples, and particularly, the task of detecting small objects turns out to be one such factor for the performance differences. This study introduces an Artificial Intelligence Enabled Scale Invariant Object Detection and Tracking (AIE-SIODC) model on Color Images. The presented AIE-SIODC model intends to recognize and track the objects exist in the color images irrespective of scaling. It follows a two stage process like object detection and objects classification. Initially, the presented AIE-SIODC technique applies path augmented RetinaNet (PA-RetinaNet) based object detection module, which improves the feature extraction process. To improvise the network potentiality of the PA-RetinaNet method, the deer hunting optimization (DHO) algorithm is utilized. Finally, extreme gradient boosting (XGBoost) classifier is exploited for classification procedures. For examining the effectual object detection outcomes of the AIE-SIODC technique, a wide ranging experimental validation process can be performed. The simulation values confirmed the enhancements of the AIE-SIODC method over other DL models.
Automated Osteosarcoma Detection and Classification Using Advanced Deep Learning with Remora Optimization Algorithm

Osteosarcoma is considered a primary malignant bone tumor which affects young people and adults. Manual osteosarcoma identification is time consuming and necessitates expert knowledge. Since earlier detection of osteosarcoma reduces death rate, computer aided diagnosis (CAD) models can be developed to examine the medical images for decision making. The latest breakthroughs in machine learning (ML) and deep learning (DL) methods find to be useful for enhancing detection performance and diagnostic time. In this view, this article introduces an Automated Osteosarcoma Detection and Classification using metaheuristics with Advanced Deep Learning (AODC-MADL) algorithm. The proposed AODC-MADL model makes use of recent DL models with hyperparameter optimization algorithms to detect and classify osteosarcoma. For achieving that, the presented AODC-MADL algorithm pre-processes the input images to optimize the image quality. Moreover, the Dense-EfficientNet architecture is utilized to form a set of feature vectors. Besides, attention based bidirectional recurrent neural network (ABiRNN) model receives the feature vectors and performs classification process. At last, the remora optimization algorithm (ROA) is used to optimally choose the hyperparameters related to the ABiRNN model. A series of experiments have been conducted to depict the outstanding performance of the AODC-MADL algorithm. The experimental outcome emphasized that the AODC-MADL algorithm has obtained higher performance over other approaches.
Maximizing Cognitive Readiness of Individuals in Project Management

This study explores how aligned factors such as goal/desire, identity, values, and beliefs maximize cognitive readiness, which is a critical factor in the ability of project managers to process situational information or project signals in an increasingly turbulent post-COVID environment and improve decision-making. We show that cognitive readiness is highest when project goals dovetail with the personal goals and identity of the team. This could create intrinsic motivation in team members and enhance the clarity of choices that supports attaining the desired individual and project goals. The findings indicate that if individuals in project management set goals congruent and aligned with their highest value, their sensory awareness ( <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Sensory awareness — the direct focus on specific sensory aspects of the body or outer or inner environment. R. T. Hurlburt Christopher L. Heavey and A. Bensaheb, Sensory Awareness, Article in Journal of Consciousness Studies, January 2009, Accessed June 2023, [Online]. Available: https://www.researchgate.net/publication/233693913 selective bias) filters out noise, they are present, mindful and aware, they are in flow, focused and diligent, they see opportunities, they take advantage of the synchronicities, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Synchronicity is a concept first introduced by analytical psychologist Carl G. Jung “to describe circumstances that appear meaningfully related yet lack a causal connection.” https://en.wikipedia.org/wiki/Synchronicity make decisions quick, and take inspired actions.
Cross-University-Business Platform as a Part of the Digital Education Ecosystem

Today's world is rapidly changing, and educational institutions face many challenges that have yet to be solved. One of these tasks is the optimal organization of support for students in the matter of their future employment. The authors of this article consider the development and implementation of the Cross-Universities-Business (CUB) platform on the base of the Digital Education Ecosystem (DEE) to be the optimal solution to this problem. To check the relevance of this idea, the authors conducted a survey, the purpose of which was to find out the attitude of employers from different countries to the implementation of the CUB platform. The results of the survey showed a positive attitude of employers towards the project, although they highlighted some additional issues and tasks that will need to be resolved during the work.
Towards a Conceptual Model for the FAIR Digital Object Framework

The FAIR principles define a number of expected behaviours for the data and services ecosystem with the goal of improving the findability, accessibility, interoperability, and reusability of digital objects. A key aspiration of the principles is that they would lead to a scenario where autonomous computational agents are capable of performing a “self-guided exploration of the global data ecosystem,” and act properly with the encountered variety of types, formats, access mechanisms and protocols. The lack of consistent support for some of these expected behaviours by current information infrastructures such as the internet and the World Wide Web motivated the emergence, in the last years, of initiatives such as the FAIR Digital Object (FDO) movement. The FDO aims at defining an infrastructure where digital objects can be exposed and explored according to the FAIR principles. In this paper, we report the current status of the work towards an ontology-driven conceptual model for FAIR Digital Objects. The conceptual model covers aspects of digital objects that are relevant to the FAIR principles such as the distinction between metadata and the digital object it describes, the classification of digital objects in terms of both their informational value and their computational representation format, and the relation between different types of FAIR Digital Objects.