Reviewerly/exHarmony huggingface dataset:
Unbiased Validation of the Algorithms for Automatic Needle Localization in Ultrasound-Guided Breast Biopsies

Automatic localization of the biopsy needle in ultrasound images has become an important medical image analysis task, because it can directly translate to reducing the risk of damage to the tissues surrounding the lesion and spreading cancer cells. Although the algorithms to tackle this problem has been emerging at a steady pace, we lack a standardized way of validating them. In this paper, we tackle this issue and raise the attention of the research community to this experimental flaw which is especially important for the applications that can be ultimately deployed in the clinical settings. Our study, which involves a range of different training-test dataset splits performed over heterogeneous image data, showed that the incorrectly designed validation procedures can easily lead to overly optimistic conclusions concerning the abilities of such algorithms. We believe that our efforts will be an important step toward designing reproducible, rigorous, and fair approach for confronting the needle localization techniques in an unbiased way.
Subjective Assessment Of High Dynamic Range Videos Under Different Ambient Conditions

High Dynamic Range (HDR) videos can represent a much greater range of brightness and color than Standard Dynamic Range (SDR) videos and are rapidly becoming an industry standard. HDR videos have more challenging capture, transmission, and display requirements than legacy SDR videos. With their greater bit depth, advanced electro-optical transfer functions, and wider color gamuts, comes the need for video quality algorithms that are specifically designed to predict the quality of HDR videos. Towards this end, we present the first publicly released large-scale subjective study of HDR videos. We study the effect of distortions such as compression and aliasing on the quality of HDR videos. We also study the effect of ambient illumination on perceptual quality of HDR videos by conducting the study in both a dark lab environment and a brighter living-room environment. A total of 66 subjects participated in the study and more than 20,000 opinion scores were collected, which makes this the largest in-lab study of HDR video quality ever. We anticipate that the dataset will be a valuable resource for researchers to develop better models of perceptual quality for HDR videos.
Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems

Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities in the field of health care. Computer vision and pattern recognition communities are particularly interested in fall automated recognition. Human sensing and artificial intelligence have both paid great attention to human posture detection (HPD). The health status of elderly people can be remotely monitored using human posture detection, which can distinguish between positions such as standing, sitting, and walking. The most recent research identified posture using both deep learning (DL) and conventional machine learning (ML) classifiers. However, these techniques do not effectively identify the postures and overfits of the model overfits. Therefore, this study suggested a deep convolutional neural network (DCNN) framework to examine and classify human posture in health monitoring systems. This study proposes a feature selection technique, DCNN, and a machine learning technique to assess the previously mentioned problems. The InceptionV3 DCNN model is hybridized with SVM ML and its performance is compared. Furthermore, the performance of the proposed system is validated with other transfer learning (TL) techniques such as InceptionV3, DenseNet121, and ResNet50. This study uses the least absolute shrinkage and selection operator (LASSO)-based feature selection to enhance the feature vector. The study also used various techniques, such as data augmentation, dropout, and early stop, to overcome the problem of model overfitting. The performance of this DCNN framework is tested using benchmark Silhouettes of human posture and classification accuracy, loss, and AUC value of 95.42%, 0.01, and 99.35% are attained, respectively. Furthermore, the results of the proposed technology offer the most promising solution for indoor monitoring systems.
UAV safe route planning based on PSO-BAS algorithm

In order to solve the current situation that unmanned aerial vehicles (UAVs) ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace, a UAV route planning method that considers regional risk assessment is proposed.Firstly, the low-altitude airspace is discretized based on rasterization, and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map.The path risk value is taken as the cost, the particle swarm optimization-beetle antennae search (PSO-BAS) algorithm is used to plan the spatial 3D route, and it effectively reduces the generated path redun dancy.Finally, cubic B-spline curve is used to smooth the plan ned discrete path.A flyable path with continuous curvature and pitch angle is generated.The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost.At the same time, the path redundancy is low, and the curvature and pitch angle continuously change.It is a flyable path that meets the UAV performance constraints.
CINI MINIS

Observation and manipulation of physical characteristics are well-known and powerful threats to cryptographic devices. While countermeasures against passive side-channel and active fault-injection attacks are well understood individually, combined attacks, i.e., the combination of fault injection and side-channel analysis, is a mostly unexplored area. Naturally, the complexity of analysis and secure construction increases with the sophistication of the adversary, making the combined scenario especially challenging. To tackle complexity, the side-channel community has converged on the construction of small building blocks, which maintain security properties even when composed. In this regard, Probe-Isolating Non-Interference (PINI) is a widely used notion for secure composition in the presence of side-channel attacks due to its efficiency and elegance. In this work, we transfer the core ideas behind PINI to the context of fault and combined security and, from that, construct the first trivially composable gadgets in the presence of a combined adversary.
"Is your explanation stable?"

Neural networks have become increasingly popular. Nevertheless, understanding their decision process turns out to be complicated. One vital method to explain a models' behavior is feature attribution, i.e., attributing its decision to pivotal features. Although many algorithms are proposed, most of them aim to improve the faithfulness (fidelity) to the model. However, the real environment contains many random noises, which may cause the feature attribution maps to be greatly perturbed for similar images. More seriously, recent works show that explanation algorithms are vulnerable to adversarial attacks, generating the same explanation for a maliciously perturbed input. All of these make the explanation hard to trust in real scenarios, especially in security-critical applications.
The Metaverse: A Systematic Literature Review to Map Scholarly Definitions

Metaverse is the new buzz word in the field of technology, particularly in the media and industry. Studies of virtual spaces have been going on in academia for decades, and industry has been creating such spaces as commercial products. However, the specific term "metaverse" has been used sparingly, and when used, the definitions have been inconsistent. In an effort to understand how researchers are using this term, we conducted a systematic literature review and reviewed articles that used and defined the term metaverse. We categorized the main characteristics that researchers have defined as being properties of the metaverse, finding broad overlaps but divergence in details.
DeepScaling

Cloud service providers conservatively provision excessive resources to ensure service level objectives (SLOs) are met. They often set lower CPU utilization targets to ensure service quality is not degraded, even when the workload varies significantly. Not only does this potentially waste resources, but it can also consume excessive power in large-scale cloud deployments. This paper aims to minimize resource costs while ensuring SLO requirements are met in a dynamically varying, large-scale production microservice environment. We propose DeepScaling, which introduces three innovative components to adaptively refine the target CPU utilization to a level that is maintained at a stable value to meet SLO constraints while using minimum resources. First, DeepScaling forecasts the workload for each service using a Spatio-temporal Graph Neural Network. Second, DeepScaling estimates the CPU utilization by mapping the workload intensity to an estimated CPU utilization with a Deep Neural Network, while taking into account multiple factors in the cloud environment (e.g., periodic tasks and traffic). Third, DeepScaling generates an autoscaling policy for each service based on an improved Deep Q Network (DQN). The adaptive autoscaling policy updates the target CPU utilization to be a maximum, stable value, while ensuring SLOs is not violated. We compare DeepScaling with state-of-the-art autoscaling approaches in the large-scale production cloud environment of the Ant Group. It shows that DeepScaling outperforms other approaches both in terms of maintaining stable service performance, and saving resources, by a significant margin. The deployment of DeepScaling in Ant Group's real production environment with 135 microservices saves the provisioning of over 30,000 CPU cores per day, on average.
Fashion intelligence system: An outfit interpretation utilizing images and rich abstract tags

In recent years, it has become common for consumers to familiarize themselves with the latest fashion trends through the internet and engage in their own fashion-inspired shopping activities. Therefore, making fashion-inspired shopping and browsing activities (internet surfing in the fashion domain) comfortable is essential because it leads to interactions in the fashion industry. However, fashion is a fuzzy and complex domain that contains many abstract elements, and this ambiguity and complexity can hinder users' deep interest in the fashion industry. Therefore, we define a novel technology and domain called "fashion intelligence" and propose a system based on a visual-semantic embedding method for automatically learning and interpreting fashion and obtaining answers to users' questions. Our proposed method can embed the abundant abstract tag information in the same projective space as outfit images. Mapping of images and tags in a projective space helps search for outfit images using fashion-specific abstract words. In addition, visually estimating the degree of relevance between images and tags helps interpret abstract words. As a result, this research helps decrease fashion-specific ambiguity and complexity and supports the marketing activities and fashion choices of both experts and non-experts.
CMANet: Cross-Modality Attention Network for Indoor-Scene Semantic Segmentation

Indoor-scene semantic segmentation is of great significance to indoor navigation, high-precision map creation, route planning, etc. However, incorporating RGB and HHA images for indoor-scene semantic segmentation is a promising yet challenging task, due to the diversity of textures and structures and the disparity of multi-modality in physical significance. In this paper, we propose a Cross-Modality Attention Network (CMANet) that facilitates the extraction of both RGB and HHA features and enhances the cross-modality feature integration. CMANet is constructed under the encoder–decoder architecture. The encoder consists of two parallel branches that successively extract the latent modality features from RGB and HHA images, respectively. Particularly, a novel self-attention mechanism-based Cross-Modality Refine Gate (CMRG) is presented, which bridges the two branches. More importantly, the CMRG achieves cross-modality feature fusion and produces certain refined aggregated features; it serves as the most crucial part of CMANet. The decoder is a multi-stage up-sampled backbone that is composed of different residual blocks at each up-sampling stage. Furthermore, bi-directional multi-step propagation and pyramid supervision are applied to assist the leaning process. To evaluate the effectiveness and efficiency of the proposed method, extensive experiments are conducted on NYUDv2 and SUN RGB-D datasets. Experimental results demonstrate that our method outperforms the existing ones for indoor semantic-segmentation tasks.
E-learning course recommendation based on sentiment analysis using hybrid Elman similarity

Online learning is also referred to as E-learning which has gained huge attention and attracted most people during the COVID-19 lockdowns. Due to the excess of online information, users face severe challenges and difficulties realizing the best course that is being competitive in the global market. Therefore, it is necessary to develop an online recommendation system that supports the users in selecting the finest course with E-learning. Thus, the proposed work develops a robust RS model using different approaches. Initially, the pre-processing stage is performed to reduce the presented noise in the website data. Then, the feature extraction stage is done to extract the needed features using Improved TF-IDF, W2V (Word 2 Vector), and Hybrid N-gram. Finally, Elman Minimal Redundancy Maximum Relevance and Enhanced Aquila Optimization (EMRMR_EAO) model is proposed to provide Robust course recommendations. In this work, the ERNN method is used to classify the sentiments based on the similarity measure of the MRMR model. The top course recommendation is afforded depending on the similarity scores like Jaccard similarity, cosine similarity and euclidean similarity. Also, the loss function in the classifier is reduced by optimizing the weight parameters using the EAO approach. The performance analysis shows that the proposed recommendation model obtains improved results in terms of accuracy of 99.98%, recall of 99.81%, precision of 99.65%, and F-measure of 99.95%. The comparative analysis exhibit that the proposed EMRMR_EAO model attains better performance than the other existing works in the literature.
A theoretical demonstration for reinforcement learning of PI control dynamics for optimal speed control of DC motors by using Twin Delay Deep Deterministic Policy Gradient Algorithm

To benefit from the advantages of Reinforcement Learning (RL) in industrial control applications, RL methods can be used for optimal tuning of the classical controllers based on the simulation scenarios of operating conditions. In this study, the Twin Delay Deep Deterministic (TD3) policy gradient method, which is an effective actor-critic RL strategy, is implemented to learn optimal Proportional Integral (PI) controller dynamics from a Direct Current (DC) motor speed control simulation environment. For this purpose, the PI controller dynamics are introduced to the actor-network by using the PI-based observer states from the control simulation environment. A suitable Simulink simulation environment is adapted to perform the training process of the TD3 algorithm. The actor-network learns the optimal PI controller dynamics by using the reward mechanism that implements the minimization of the optimal control objective function. A setpoint filter is used to describe the desired setpoint response, and step disturbance signals with random amplitude are incorporated in the simulation environment to improve disturbance rejection control skills with the help of experience based learning in the designed control simulation environment. When the training task is completed, the optimal PI controller coefficients are obtained from the weight coefficients of the actor-network. The performance of the optimal PI dynamics, which were learned by using the TD3 algorithm and Deep Deterministic Policy Gradient algorithm, are compared. Moreover, control performance improvement of this RL based PI controller tuning method (RL-PI) is demonstrated relative to performances of both integer and fractional order PI controllers that were tuned by using several popular metaheuristic optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization and Differential Evolution.
Sigstore

Software supply chain compromises are on the rise. From the effects of XCodeGhost to SolarWinds, hackers have identified that targeting weak points in the supply chain allows them to compromise high-value targets such as U.S. government agencies and corporate targets such as Google and Microsoft. Software signing, a promising mitigation for many of these attacks, has seen limited adoption in open-source and enterprise ecosystems.
Cybersecurity and Blockchain Usage in Contemporary Business

The use of computers is becoming more and more common. The power and maliciousness of powerful attackers are increasing. Organizations must improve their ability to mitigate information security threats. Adequate protection is more critical than ever. With websites and social media platforms holding a wealth of personal information and potentially damaging content, attackers use the internet's sophisticated hacking tools to cause harm to individuals and organizations. Attacks have become stealthier, with more significant economic damage and broader damage. Blockchain must be the solution to the security problem of financial transactions. However, judging from the results and expected answers from the industry and organization, it is crucial to understand how financial institutions deal with these issues by looking at blockchain deployments in cybersecurity. A blockchain is a digital database that records all mentoring transactions. This technology supports many different exchanges that are currently in use. The authors also believe that blockchain should be integrated into many parts of cybersecurity. The primary goal of this chapter is to review relevant research papers and book chapters over the past 10 years to understand how successful cybersecurity and blockchain implementations have been in the enterprise and to identify the different challenges and concerns that enterprise personnel face. They also provide suggestions and solutions to problems.
K-means-G*: Accelerating k-means clustering algorithm utilizing primitive geometric concepts

The k -means is the most popular clustering algorithm, but, as it needs too many distance computations, its speed is dramatically fall down against high-dimensional data. Although, there are some quite fast variants proposed in literature, but, there is still much room for improvement against high-dimensional large-scale datasets. What proposed here, k-means-g*, is based on a simple geometric concept. For four distinct points, if distance between all pairs except one pair are known, then, a lower bound can be determined for the unknown distance. Utilizing this technique in the assignment step of the k -means, many high-dimensional distance computations can be easily ignored, where small amount of memory is used. Both theoretical and experimental results approves speed of the k-means-g* against recently published fast variants. For over than 50 cases, out of 70 cases of performed experiments, it is faster than other algorithms. The C++ source code for k-means-g* is publicly available.
Enhancing Cybersecurity Through Blockchain Technology

Blockchain technology ensures data security through an integrated system whereby it collects, arranges, stores, and disseminates information in different blocks. This technology thus enables adding the data to the network. Once data has been added to the network, no one can alter the data set either by adding or deleting it. Further, this technology also helps to track and check the changes if anything is made to a blockchain, as the changes remain in the database forever. Since this technology uses lots of systems in a blockchain, it will regularly download its data, arranging, and keeping the copy locally. Locating the data errors and cyberattacks in advance by analyzing the data documented, it employs the consent of various participants and accomplishments in cryptography. With this backdrop, the chapter has attempted to disclose the basics of blockchain technology in data security, why blockchain in cybersecurity, how it ensures cybersecurity, its benefits, its innovative uses, and the future of cybersecurity in the online business platforms.
An opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization

Clustering has grown to be a research focus in recent years owing to the challenges of labeling massive collected data. Recent advances such as the emotional preference and migratory behavior clustering model jointly utilized the biogeography-based idea and inertia theorem for handling data clustering tasks. However, as with most clustering methods, the initialization for the solution is often purely random. Moreover, the search results vulnerably converge to local optima due to favorable exploration but short exploitation, which requires an additional process to balance them effectively. Inspired by the differential evolution (DE) strategy and the opposition-based learning (OBL) method, the paper proposed a novelty opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization named OBDE-EPMC. Specifically, we first utilize the OBL method to initialize the population to obtain a closer initial individual to the optimal solution. Thereafter, to ensure population diversity during the optimization, we applied the differential evolution (DE) strategy to expand the scope of the solution and the opposition-based learning method to generate an opposite one. Lastly, the historical solution scheme is added to retain the excellent solution within the population. In addition, the theoretical analysis and convergence property proof of OBDE-EPMC are presented. Numerous experiments were performed to compare the proposed OBDE-EPMC with the other seven clustering algorithms on a host of standard datasets. And several standards demonstrate the rationality and effectiveness of our proposed OBDE-EPMC algorithm.
Refused-knowledge during the COVID-19 Pandemic: Mobilising Experiential Expertise for Care and Well-being

Since the early period of the COVID-19 pandemic concerned groups of people have produced knowledge refused by institutional science of how to manage public health and individual well-being in everyday pandemic life. Research in science and technology studies seeks to understand the social and cultural conditions under which contestation over scientific knowledge claims occurs. In the Italian case, ‘refused’ knowledge claims emerging outside institutionalised science play a performative role in questioning the current models for managing individual and public health. Such refused claims ascribe novel meanings to the COVID-19 pandemic and orient the ways in which people manage their own health and well-being during their everyday life. Two interrelated dimensions are at stake in the production and enactment of refused knowledge: (1) how experiential expertise is mobilised to reframe one’s body in a process of self-care, thus validating a corpus of refused knowledge through personal experience, and (2) how narratives demarcate between a body of refused knowledge and the prevalent biomedical paradigms as a way of gaining experiential epistemic autonomy.