Reviewerly/exHarmony huggingface dataset:
Feature fusion and Ensemble learning-based CNN model for mammographic image classification

In recent times, the world has faced an alarming situation regarding breast cancer patients. The early diagnosis of this deadly disease can make the treatment more accessible and practical. In this regard, a Computer-Aided Diagnosis (CAD) system can assist the radiologists in distinguishing the normal and abnormal tissues and diagnosing the pathological stages. The classification task is challenging in CAD systems because of noisy and low contrast mammogram images, tumors' shape and location variations, and the high resemblance between the normal and tumor regions of interest (ROI). We propose a novel deep convolution neural network (DCNN) approach based on feature fusion and ensemble learning strategies to improve the detection and classification of abnormalities in mammographic scans. The feature fusion helps to detect discriminative features between the classes properly, while ensemble learning in the last block better classify the normal and tumor ROIs to get more authenticated results. Moreover, the role of spatial dropout and depthwise separable convolution is investigated for mammogram classification to better deal with overfitting and small dataset problems in medical images. The proposed model is evaluated on two publicly available datasets, MIAS and BCDR, getting high sensitivity, specificity, and accuracy of 0.995, 0.994, and 0.994, respectively, on the MIAS dataset.
Artificial Intelligence based Cervical Cancer Risk Prediction Using M1 Algorithms

Cervical cancer growth is the fourth maximum of regular diseases in females. It is brought about by long haul disease in skin cells and mucous film cells of the genital region. The World Health Organization (WHO) considers malignant growth a nonexclusive term for a huge gathering of infections that can influence any piece of the body, which is profoundly risky. In 2018, an expected 5,70,000 females were determined to have cervical malignancy worldwide, and around 3,11,000 females passed on from the illness. Hence proposing a model with high precision and high accuracy for diagnosing at the right phase of contamination will help a lot. This paper aims to develop machine learning(ML) algorithms like Support Vector Machine(SVM), Random Forest(RF) and Deep Learning (DL)models like Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN) using python, which gives more accurate results compared to existing models. The accuracy of each model SVM, CNN, RF and ANN obtained was 97&#x0025;, 95.3&#x0025;, 94&#x0025; and 9 5.2&#x0025;, respectively, where SVM has higher precision among ML algorithms similarly, CNN has the highest precision among the neural network algorithms, So to anticipate the cervical disease and to help in its initial judgments which can shield women in huge scope from being affected to this disease.
Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol

This paper presents a high-speed and low-area accelerator architecture for shared key generation using an elliptic-curve Diffie-Hellman protocol over GF(2233). Concerning the high speed, the proposed architecture employs a two-stage pipelining and a Karatsuba finite field multiplier. The use of pipelining shortens the critical path which ultimately improves the clock frequency. Similarly, the employment of a Karatsuba multiplier decreases the required number of clock cycles. Moreover, an efficient rescheduling of point addition and doubling operations avoids data hazards that appear due to pipelining. Regarding the low area, the proposed architecture computes finite field squaring and inversion operations using the hardware resources of the Karatsuba multiplier. Furthermore, two dedicated controllers are used for efficient control functionalities. The implementation results after place-and-route are provided on Virtex-7, Spartan-7, Artix-7 and Kintex-7 FPGA (field-programmable gate arrays) devices. The utilized FPGA slices are 5102 (on Virtex-7), 5634 (on Spartan-7), 5957 (on Artix-7) and 6102 (on Kintex-7). In addition to this, the time required for one shared-key generation is 31.08 (on Virtex-7), 31.68 (on Spartan-7), 31.28 (on Artix-7) and 32.51 (on Kintex-7). For performance comparison, a figure-of-merit in terms of throughputarea is utilized which shows that the proposed architecture is 963.3 and 2.76 times faster as compared to the related architectures. In terms of latency, the proposed architecture is 302.7 and 132.88 times faster when compared to the most relevant state-of-the-art approaches. The achieved results and performance comparison prove the significance of presented architecture in all those shared key generation applications which require high speed with a low area.
The impact of news media on Bitcoin prices: modelling data driven discourses in the crypto-economy with natural language processing

This paper examines the relationship between events reported in international news via categorical discourses and Bitcoin price. Natural language processing was adopted in this study to model data-driven discourses in the crypto-economy, specifically the Bitcoin market. Using topic modelling, namely Latent Dirichlet Allocation, a text analysis of cryptocurrency articles (N = 4218) published from 60 countries in international news media identified key topics associated with cryptocurrency in the international news media from 2018 to 2020. This study provides empirical evidence that across the corpora of international news articles, 18 key topics were framed around the following categorical macro discourses: crypto-related crime, financial governance, and economy and markets. Analysis shows that the identified discourses may have had a 'social signal' effect on movements in the crypto-financial markets, particularly on Bitcoin's price volatility. Results show these specific discourses proved to have a negative effect on Bitcoin's market price, within 24 h of when the crypto news articles were published. Further, the study found that in some cases, the source of the news may have amplified the volatility effect, particularly in terms of geographical region, relative to broader market conditions.
Supervised learning for analysing movement patterns in a virtual reality experiment

The projection into a virtual character and the concomitant illusionary body ownership can lead to transformations of one's entity. Both during and after the exposure, behavioural and attitudinal changes may occur, depending on the characteristics or stereotypes associated with the embodied avatar. In the present study, we investigated the effects on physical activity when young students experience being old. After assignment (at random) to a young or an older avatar, the participants' body movements were tracked while performing upper body exercises. We propose and discuss the use of supervised learning procedures to assign these movement patterns to the underlying avatar class in order to detect behavioural differences. This approach can be seen as an alternative to classical feature-wise testing. We found that the classification accuracy was remarkably good for support vector machines with linear kernel and deep learning by convolutional neural networks, when inserting time sub-sequences extracted at random and repeatedly from the original data. For hand movements, associated decision boundaries revealed a higher level of local, vertical positions for the young avatar group, indicating increased agility in their performances. This occurrence held for both guided movements as well as achievement-orientated exercises.
Real time COVID-19 facemask detection using deep learning

The COVID-19 pandemic is producing a global health pandemic. According to the World Health Organization (WHO), the utmost effective protection is to wear a face mask in crowded regions/areas. During this pandemic, it is compulsory for every person to wear a mask and maintain social distancing. In the field of Image Processing, Convolutional Neural Networks (CNNs) have risen to prominence as the most common type of image realization/recognition model. Our project's purpose is to research and assess Machine Learning (ML) technologies for identification and recognition of people wearing face masks in any pre-recorded videos, photos, or in actual-time (real- time) circumstances. Our project aims to create a real-time Graphics User Interface based Automated Facial Recognition as well as Mask Detection System. The algorithms used in the proposed methodology are Principal Component Analysis (PCA) and the HAAR Cascade Algorithm. Finally, the result is indicated by a “GREEN” color rectangle box, which would be drawn around the section of the face, which indicates that the person on the camera is wearing a mask, or a “RED” color rectangle box, which indicates that the person on the camera is not wearing a mask. This model achieves 99% accuracy.
A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.
On Eliciting a Sense of Self when Integrating with Computers

While some Human-Computer Integration (HInt) systems have successfully demonstrated that humans and technology can be physically and functionally integrated, we find that these integrations are not necessarily part of the users identity (i.e. self-judgment) or felt as part the user (i.e. with a sense of self) and that they can even create feelings of self-dissociation. Literature on how to elicit these self-experiences is often inconsistent and vague, which complicates the metric for success and hinders the advancement of research. To help designers elicit and systematically evaluate in particular a sense of self, we draw metrics and theory from phenomenology and cognitive science. We find that experiential structures such as "pre-reflective experience", "sense of body-ownership" and "sense of agency" are to be designed for as they together seem to elicit a "sense of self".
2D versus 3D: A Comparison of Needle Navigation Concepts between Augmented Reality Display Devices

Surgical procedures requiring needle navigation assistance suffer from complicated hand-eye coordination and are mentally demanding. Augmented reality (AR) can help overcome these issues. How-ever, only an insufficient amount of fundamental research has focused on the design and hardware selection of such AR needle navigation systems. This work contributes to this research area by presenting a user study (n=24) comparing three state-of-the-art navigation concepts displayed by an optical see-through head-mounted display and a stereoscopic projection system. A two-dimensional glyph visualization resulted in higher targeting accuracy but required more needle insertion time. In contrast, punctures guided by a three-dimensional see-through vision concept were less accurate but faster and were favored in a qualitative interview. The third concept, a static representation of the correctly positioned needle, showed too high target errors for clinical accuracy needs. This concept per-formed worse when displayed by the projection system. Besides that, no meaningful differences between the evaluated AR display devices were detected. User preferences and use case restrictions, e.g., sterility requirements, seem to be more crucial selection criteria. Future work should focus on improving the accuracy of the see-through vision concept. Until then, the glyph visualization is recommended.
How to Take a Brake from Embodied Locomotion – Seamless Status Control Methods for Seated Leaning Interfaces

Embodied locomotion, especially leaning, has one major problem. Effectively the regular functionality of the utilized body parts is overwritten. Thus, in this work, we propose 6 different status control methods that seamlessly switch off (brake) a seated leaning locomotion interface. Different input modalities, such as a physical button, voice, and gestures/metaphors, are used and evaluated against a baseline condition and a leaning interface with a bilateral transfer function. In a user study, participants were encouraged, but not forced to use these interfaces. They did, and the most diegetic interface, a hover-board metaphor, was the most preferred. However, the overall results are more heterogeneous and the interfaces vary in their suitability for different applications/scenarios. Therefore, we summarized the results in a guideline.
A Summary of PID Control Algorithms Based on AI-Enabled Embedded Systems

Proportional-integral-derivative (PID) controllers are extensively used in engineering practices for their simple structures, robustness to model errors, and easy operations. At present, there is a great variety of PID controllers. Companies have developed intelligent regulators with functions for automatically tuning PID parameters. For present PID controllers, strategies such as intelligence, self-adaptation, and self-correction are extended to transmission PID. PID controllers and corresponding improved ones are utilized in 90% of industrial control processes. In this paper, PID control algorithms are summarized. This paper focuses on advanced control strategies such as PID control, predictive PID control, adaptive PID control, fuzzy PID control, neural network PID control, expert intelligent PID control, PID control based on genetic algorithms, and PID control based on ant colony algorithms. Besides, these kinds of algorithms are compared, and prospects of PID algorithms are forecast at the end of this paper.
Predicting l-CrossSold products using connected components: A clustering-based recommendation system

Recommender systems (RSs) are an integral part of the online retail industry. They create tremendous potentials for cross-selling, including increasing sales revenue, improving consumer fulfillment, increasing customer lifetime value, controlling product consumption to optimize resources, and dynamically adapting to consumer behaviors. Online retailers have relied on the position and presence of RSs to assist users in better decision-making and thereby increase their revenues. However, with dynamic changes in consumer behavior patterns, the quality and accuracy of recommendations have become a significant challenge for e-commerce retailers. Moreover, with the rapid evolution of online data, most RSs suffer from data sparsity and scalability problems. This paper introduces a new model of applying clustering analysis concepts to RSs along with advancements in graph theory to react effectively to user changes and business challenges in the the online retail industry. A clustering-based RS using the notion of the cross-sold score—namely, “l-CrossSold”—is proposed and tested with the aim of enhancing the quality of recommendations, handling data sparsity, improving consumer profiling and addressing scalability in the current recommendation methods in order to generate a more practical set of personalized recommendations. The proposed algorithm shows a significant improvement in both the prediction accuracy and the speedup as compared to the state-of-art collaborative filtering RSs, clustering-based collaborative filtering RSs, and rule-based RSs.
Ensemble Federated Learning for Non-II D COVID-19 Detection

In light of the COVID-19 pandemic, the need for a chest X-ray scans classifier is crucial in order to diagnose patients and classify scans into normal, COVID-infected, and pneumonia. Federated learning was chosen for the classification as it uses a decentralized approach to train the model at the local servers belonging to each entity in various geographic locations. Therefore, information leakage that could happen from the traditional centralized approach of training is prevented, besides saving the huge cost of central storage. However, between the vast difference in the number of X-ray scans per data-silo (i.e. hospital), the dissimilar image-acquisition techniques, and the diverse morphological structures of the human chest, non-IID (non-Independent and Identically Distributed) skews are introduced in the data. In this paper, real-world datasets of COVID and pneumonia scans are used to satisfy all the non-IID data skews. An experiment was then conducted to test the effect of these skews using five federated learning algorithms, FedAvg, FedProx, FedNova, SCAFFOLD, and FedBN, under the same metrics. The obtained accuracy values are 79.5&#x0025;, 76.92&#x0025;, 5.57&#x0025;, 79.18&#x0025;, and 84.4&#x0025;, respectively. In this paper, we present the different effects of non-IID skews on the training process and discuss the different federated learning variations to mitigate the data heterogeneity.
On the design of an AI-driven secure communication scheme for internet of medical things environment

The Internet of Medical Things (IoMT) is a collection of smart healthcare devices, hardware infrastructure, and related software applications, facilitating to connect healthcare information technology system via the Internet. It is also called IoT in healthcare, facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data. In other words, IoMT is an amalgam of medical devices and applications, which improves overall healthcare outcomes. However, this system is prone to security- and privacy-related attacks on healthcare data. Therefore, providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential. To mitigate this, we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT. The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT. Furthermore, we provide the security analysis of the proposed scheme to show its security against different possible attacks. Moreover, a comparative study of the proposed scheme with other similar schemes is presented. Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs, and security and functionality attributes. Finally, we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.
System calibration method for infrared phase measuring deflectometry

Infrared phase measuring deflectometry (IR PMD) is a kind of PMD technique, which exploits IR light as a display screen, therefore it can effectively improve the measurement accuracy by reducing the environmental noise, especially the influence of the ambient light. However, data accuracy of a deflectometric measurement system is mainly determined by calibration procedure, which establishes the relationship between phase map and depth data. In this paper, a novel using fringe projection profilometry (FPP) technology based system calibration method is proposed to obtain the geometrical parameters of the IR PMD system. A composite plane calibration board with diffused and specular surface has been designed to determine the X, Y, and Z coordinates of this specular plane at a reference position, which is located at a certain reference position parallel to the ground glass (used as the IR screen) to obtain the system parameter d. Experimental results demonstrate that 3D shape of specular objects can be effectively and accurately measured using the proposed geometrical calibration method.
Balanced-YOLOv3: Addressing the Imbalance Problem of Object Detection in PCB Assembly Scene

The object detection algorithm of the PCB (Printed Circuit Board) assembly scene based on CNN (Convolutional Neural Network) can significantly improve the production capacity of intelligent manufacturing of electronic products. However, the object class imbalance in the PCB assembly scene, the multi-scale feature imbalance, and the positive/negative sample imbalance in the CNN have become critical problems restricting object detection performance. Based on YOLOv3, this paper proposes a class-balanced Train/Val (Training set/Validation set) split method for object class imbalance, an additional feature fusion strategy for multi-scale feature imbalance, and an efficient anchor concept for positive/negative sample imbalance. These three contributions are Balanced-YOLOv3. After experimental verification, compared with other YOLOv3 series algorithms, the mAP@.5 (Mean Average Precision at Intersection over Union threshold 0.5) and mAP@.5:.95 (average mAP over different Intersection over Union thresholds, from 0.5 to 0.95, step 0.05) of Balanced-YOLOv3 have achieved the best results and ranked third in the metrics of parameter and inference time. Compared with other current anchor-based object detection algorithms, Balanced-YOLOv3 has excellent detection performance and low computational complexity, which effectively solves the problem of imbalanced object detection in PCB assembly scenarios.
Neuroevolution for Parameter Adaptation in Differential Evolution

Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is considered. The artificial neural networks in this study are used for setting the scaling factor and crossover rate values based on the available information about the algorithm performance and previous successful values. The training is performed on a set of benchmark problems, and the testing and comparison is performed on several different benchmarks to evaluate the generalizing ability of the approach. The neuroevolution is enhanced with lexicase selection to handle the noisy fitness landscape of the benchmarking results. The experimental results show that it is possible to design efficient parameter adaptation techniques comparable to state-of-the-art methods, although such an automatic search for heuristics requires significant computational effort. The automatically designed solutions can be further analyzed to extract valuable knowledge about parameter adaptation.