Reviewerly/exHarmony huggingface dataset:
A context-based image segmentation using multiverse optimization and joint entropy

One of the most commonly used approaches for image segmentation is multilevel thresholding. Histogram segmentation is the most often used approach in image segmentation. While histogram-based techniques only examine intensity frequency and ignore spatial information. Contextual knowledge helps improve the segmented image by helping users see how vital each pixel is and comprehend the context of other pixels. Spatial information is built into a curve with the same characteristics as a histogram. This work proposes Joint entropy and a multilevel energy curve for segmenting colour images. Multiverse optimization is employed as an optimization algorithm to find out the threshold. The energy curve based method is compared with a histogram-based method and variational mode decomposition-based method. The numerical metrics used to evaluate the proposed algorithm's output include structural similarity index, feature similarity index, peak signal to noise ratio, uniformity and a quality index based on local variance and computing time. Experiments show that the proposed algorithm produces more consistent results than existing techniques. The proposed algorithm delivers more consistent results than the other two techniques, according to the experiments.
Vision Based Polygon Detection System for Industrial Robots under Complex Illumination

In this paper, we design a polygon detection and localization system for torpedo car capping in steelmaking scenarios. The existing target detection algorithms are, to a large extent, susceptible to the interference from illumination in complex industrial scenarios, which highly motivates our work. First, we enhance the image using histogram key regions based histogram specification. The method can adaptively analyze the illumination conditions and perform histogram specification using different mapping functions set up in advance. Then, a line segment detector is utilized to extract image features and complete the polygon fitting. Finally, we present experiments conducted on image datasets and grabbing tests, which shows both the accuracy and robustness of our algorithm.
Assisted Driving for Power Wheelchair: A Segmentation Network for Obstacle Detection on Nvidia Jetson Nano

Recently, an ever-growing focus and attention is given to accessibility and enabling technologies for people with disabilities. In the mobility field, people with motor skill impairments exploit power wheelchairs to move in indoor and outdoor scenarios. AI-Drive aims to design an assistive power wheelchair for outdoor use, providing obstacle detection and avoidance in urban scenarios, leveraging low-cost digital cameras and artificial intelligence. This paper focuses on the implementation of a convolutional neural network for semantic segmentation of urban scenes, to detect obstacles in an outdoor setting. A U-Net-like architecture was trained on GPU over multiple datasets, representative of the final environment. The selected trained network was then customised to perform inference on the Nvidia Jetson Nano hardware accelerator, to be mounted directly on the wheelchair. The resulting model achieves an accuracy of around 85% and inference time of 35 ms, thus providing a concrete solution towards the target assisted power wheelchair.
AND: Effective Coupling of Accuracy, Novelty and Diversity in the Recommender System

At present, most of the research on recommender system (RS) based on artificial intelligence focus on the algorithm, but the evaluation metrics being an important process to evaluate the performance of RS are usually ignored. Specifically, independent evaluation metrics cannot effectively reflect the differences between algorithms, so how to effectively couple these evaluation metrics needs further improvement. In order to reflect the difference in RS performance, this paper proposes a rational evaluation framework for RS performance, named AND, which can reflect metrics of accuracy, novelty and diversity. Through comparative experiments, it is verified that the proposed framework, on the basis of the hypothetical model and the state-of-the-art algorithms, can effectively reflect the difference between the recommendation performance with similar seemingly accuracy between different algorithms.
A Forensic Way to Find Solutions for Security Challenges in Cloudserver Through MapReduce Technique

Cloud computing is a large and distributed platform repository of user information. But it also extensively serves the security threats in the research aspect. This chapter attempts to find the solution to the security challenges through the MapReduce technique in a forensic way. Four security challenges are included in this chapter: losing the user information during the mapping process for different reasons such as the shutdown of the server, which causes parallel or unrelated services to get interrupted; the velocity of attack, which enables security threats to amplify and spread quickly in the cloud; injecting malicious code; and finally information deletion. MapReduce and dynamic decomposition-based distributed algorithm with the help of Hadoop and JavaBeans in the live forensic method is used to find solution to the problem. MapReduce is a software framework and live forensics is the method attempting to discover, control, and eliminate threats in a live system environment. This chapter uses Hadoop's cloud simulation techniques that can give a live result.
Monthly Solar Radiation Maps for Morocco Based on the Visualization of Clustering

Assessment of the solar radiation potential is a necessity for the establishment of solar projects. It is difficult to evaluate the solar energy potential for areas far from the meteorological stations. Current methods for estimating solar energy are site-dependent or limited to certain areas. Therefore, a method for solar radiation maps using unsupervised machine learning techniques and satellite products is proposed. The method uses the hierarchical dendrogram to determine the suitable number of distinct areas for each month, then this number is used as input for Agglomerative Hierarchical Clustering (AHC) that clusters areas into the given number for each month. The maps are homogeneous in space and reflect the seasonal character of solar radiation. The resulting solar maps cover all regions of Morocco and can help for preliminary assessment and decision making, especially for areas far from solar radiation ground stations.
Research on the Design of Smart Classroom Mode from the Perspective of Big Data

Abstract: Big data technology is an important force to promote the reform of teaching. Smart classroom is the inevitable result of education modernization condensed in classroom teaching, education informatization converges on teacher-student interaction, and education intelligence converges on students' smart thinking in the background of big data era. The hotspot of education intelligence research. The rapid development of artificial intelligence has made sufficient preparations for its involvement in school education and realization of smart classrooms. Based on the intelligent architecture of artificial intelligence, it will redefine classroom teaching with its closed-loop architecture formed by intelligent interaction and intelligent deep learning. To further strengthen the ideological education of college students is necessary to conform to the development situation of the big data era. Therefore, it is necessary to actively explore ideological education strategies for college students based on the era of big data by building network positions, applying social media, developing application software, relying on communication tools, and building smart classrooms. This article is based on the smart classroom information technology platform to design teaching goals and plans, implement interactive teaching, and innovate the teaching design and implementation strategies of smart classrooms.
Quantifying Polish Anti-semitism in Twitter: A Robust Unsupervised Approach with Signal Processing

Anti-Semitism has long been a controversial topic in Poland, and remains so to the present day. Holocaust research continues in Poland today, attempting to face up to the truth about the nature and extent of any Polish complicity with the Nazis in the persecution and massacre of Jews during World War II. But such research faces significant headwinds in various forms, including popular Polish nationalism as well as the passage in 2018 by the current Polish nationalist government of a law against defaming the ‘Polish nation’ in connection with the Holocaust. A recent court case against a Poland-based Holocaust researcher again brought this to the fore, lighting up the Polish Twittersphere with chatter on the topic. In this paper, we set out to analyze this chatter, to shed light – not on any past Polish anti-Semitism – but on present-day Polish attitudes. Along the way, we argue that unsupervised techniques from signal processing are the ideal tool for wading into such a politically and emotionally charged topic. Such techniques simply reflect, in condensed and quantifiable form, what is in the data. It is then up to the human analyst to draw the appropriate conclusions.
Targeted Attentional Adversarial Attack

Class activation map (CAM) indicates the discriminative regions used for classification. Recent studies show that the CAM can benefit adversarial example generation in both attack robustness and undetectability. However, existing studies focus only on untargeted attacks. Since the CAM of a weak-confident label is less convincing than that of a strong-confident label, utilizing the CAM of the target label directly in attacking is not efficient enough. To address this issue, we designed a novel attention map generation strategy tailored for targeted attacks. The entire attacking process is composed of two stages. In the first stage, the CAM of the original label is used to generate an intermediate image, from which the CAM of the target label is inferred. In the second stage, we combine the CAMs of both the original and target label to guide the adversarial example generation. We validate the proposed scheme by mounting targeted attacks on the ImageNet dataset. Experiments show that the proposed scheme significantly improves the baseline in terms of both white-box/black-box success rate and robustness to image processing.
Application of HGR Rock-Paper-Scissors Model

Although great progress has been made by leveraging the use of sensors in various fields like human body tracking, robust, accurate, and efficient hand gesture recognition still remains an open problem. The authors draw a comparison on how the HSV (hue, saturation, value) model used in hand gesture recognition is better than the other models. Also, the focus will be laid on the different segmentation techniques that are being used nowadays and how thresholding is an important part of it. The extraction of noisy hand contours is really challenging, and for the same reason, the authors propose the usage of convexity defects to record deviation from contours and the dilution and erosion of the binary images to recognize hand gestures like rock-paper-scissors. This model has been tried and tested on different devices with various hand gestures and yields a positive result.
Incremental Learning Model for Load Forecasting without Training Sample

This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input.However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained.To solve this problem, a synthesis of the initial training sample is proposed.The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data to create new and sufficient samples.Then the synthesis samples are used to initial train the OS-ELM.This proposed method is compared with Fully Online Extreme Learning Machine (FOS-ELM), which is an incremental learning model that also does not require the initial training samples.Both the proposed method and FOS-ELM are used for hourly load forecasting from the Hourly Energy Consumption dataset.Experiments have shown that the proposed method with a wide range of noise levels, can forecast hourly load more accurately than the FOS-ELM.
The Evolution of Cryptocurrencies in the Global Political Economy

This paper, issue focus on the emerging effects of cryptocurrencies on globalization. Cryptocurrencies change social and economic processes throughout the world and within developing countries. This process can be expressed as a part of globalization in general terms. The concept of undertaking both opportunities and problems together due to the paradoxes it carries is a common result of many commercial, social, cultural, geographic, and political influences. The development process of blockchain technology, the infrastructure of cryptocurrencies, is different in countries. For example, Turkey has been studied as an example of developing countries’ approaches to blockchain technology and cryptocurrencies. Consequently, cryptocurrency is in request in developing countries, with a different perception and expectation. This has various implications for globalization.
The Relationship Between Depth and Broadness Based on Artificial Neural Networks

Deep learning is the main focus of the machine learning field. However, it is noticeable that some researchers utilize broad learning to efficiently solve some problems in this field. Under such conditions, even though deep learning has obtained some breakthroughs, a comprehensive learning system is still valuable. Comparing the advantages and disadvantages of these two methods is difficult to achieve a general conclusion. However, outcomes about the performances of models are possible for a particular problem or data set. In this article, all the experiments are based on MNIST data set, using Multilayer Perceptron models to test the differences of broadness and depth. Nevertheless, the result of experiments shows it is tough to choose the best from these models. This may derive from the complexity of model structures.
Codewords Detection in Microblogs Focusing on Differences in Word Use Between Two Corpora

In recent years, the number of drug trafficking using microblogs has been increasing, which has become a social problem. While cyber patrols have been conducted to crack down on such crimes, those who post crime-inducing messages use terms that camouflage their criminal intentions so-called “codewords” to avoid keywords such as “enjo kosai,” “marijuana,” and “methamphetamine” that may be monitored and attract police attention. These codewords change once they become popular, so it is always necessary to keep track of the latest codewords. Therefore, we propose a new method for detecting the latest codewords. In this paper, we offer a new way of detecting code words from the differences in the words used in posts to detect codewords used in a crime. Specifically, we propose a new method in which we divide words into two corpora, depending on whether a post containing a word has a criminal intention and detect codewords from the differences between similar words of the same word between two corpora. To confirm the effectiveness of the proposed method, we conducted an experiment to detect codewords. The experimental results showed that the proposed method was able to detect codewords with an accuracy of 0.56 persentages points higher than that of the baseline method. The experiment shows that the proposed method can reduce the burden of continuously monitoring code words by rapidly and automatically detecting new codewords that change with time; thus, it provides the possibility of showing clues for crimes.
Architecture and Implementation of Electricity Statistics Knowledge Graphs

With the continuous advancement of the digital power business, the data and knowledge in the field of power show explosive growth. To better manage the scattered data and exploit its potential value, an electricity statistics knowledge graph is structured, in which the data is extracted into triplet. And the electricity knowledge graph is stored by the index matrix and the adjacency matrix for the sake of fast search. On the basis of the constructed graph, four applications have been developed, namely search function, perspective generation, perspective recommendation and electricity forecasting. Search function provides the basis for information acquisition. Perspective is statistical analysis of data flow based on knowledge graph. Perspective recommendation and electricity forecasting are advanced applications of knowledge graph which is combined with artificial intelligence. Perspective recommendation use graph convolution neural network to recommend noteworthy perspectives based on historical operations. Electricity forecast is a bottom-up forecast based on the integration of knowledge graph and RNN. The knowledge graph constructed based on the data of a municipal power grid shows that the constructed knowledge graph has faster search speed and more functions.
Fault diagnosis of wave sensor based on wavelet packet and improved RBF neural network

This paper presents a method to diagnose faults of wave sensor. Firstly, the simulated fault signal of wave sensors is decomposed by wavelet packet, then the feature vector of the fault signal is constructed, the feature vector is inputted into the improved RBF neural network. The improve RBF neural network adopts a mean shift algorithm to solve the problem of exactly determining the center vector of a traditional RBF neural network. Finally the results are visualized by t-sne dimensionality reduction method. Experiments show that this method can effectively determine the center vector of RBF neural network, and the accuracy of fault type recognition can be boosted.
Know Where Your Current Job Is Taking You

Special Events GaloreVolume 22, Issue 5 p. 8-8 Career Advancement Planning Know Where Your Current Job Is Taking You First published: 07 April 2022 https://doi.org/10.1002/speg.31781Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume22, Issue5May 2022Pages 8-8 RelatedInformation
Regulatory compliance for crypto by relying on on-ramping services

Just like fiat currencies, cryptocurrencies (cryptoassets) may be used in connection with illicit activities. It is thus important to discuss what rules and policies should there be to address that. One possible approach is to apply to crypto the same Know Your Customer (KYC), Anti-Money Laundering (AML), and Countering the Financing of Terrorism (CFT) practices that are already established in traditional finance. One of the component solutions of this framework is the ‘travel rule’.This would be a mistake for several reasons:There is no reliable evidence that the current AML/CFT framework is not merely useful for combating crime connected to traditional finance, but is proportionate (i.e. brings more benefits than costs, compared to alternative solutions). It is even more doubtful that it would be proportionate to apply this framework to crypto.Extending the current AML/CFT framework to crypto, without appropriate adjustments, would effectively stifle innovation and adoption of potentially transformative technologies (especially, decentralised uses of crypto: Web3, DeFi – decentralized finance).It would be illegal under the fundamental law of the European Union (it would require a change of the EU founding treaties). The current AML/CFT framework includes general and indiscriminate data collection and retention obligations, involving sensitive personal data (not merely civil identity of users). As the EU Court of Justice has found in other data retention cases, this is contrary to the EU Charter of the Fundamental Rights. As an alternative, this paper discusses a possible incremental improvement of the travel rule, leveraging the unique advantages of the entry-point into the crypto ecosystem: on-ramping services. The best place to check reliably the identity of a customer is when the customer is buying crypto in exchange for a fiat currency.
Incomplete Information and Justifications

We present a logic for reasoning about higher-order upper and lower probabilities of justification formulas. We provide sound and strongly complete axiomatization for the logic. Furthermore, we show that the introduced logic generalizes the existing probabilistic justification logic $$\mathsf {PPJ}$$ .
L’intelligence artificielle pourrait-elle intervenir dans l’aide au diagnostic des cancers du sein ? – L’exemple de HER2

HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negative categories is well implemented and reproducible, the introduction of a new "HER2-low" category could raise some concerns about its scoring and reproducibility. We herein described the current HER2 testing methods and the application of innovative machine learning techniques to improve these determinations, as well as the main challenges and opportunities related to the implementation of digital pathology in the up-and-coming AI era.
Quantum communications with space encoding technique

Quantum communications are a key enabler for multiple applications, from information theoretic communications to advanced remote quantum simulations. We here report our recent results on generation, transmission and detection of space encoded quantum states multicore.
Vehicle Matching Based on Similarity Metric Learning

With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems.Recent methods explored the vehicle matching based on the feature extraction.Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching.But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times.To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e., Day pattern or Night pattern) for vehicle matching.To exploit properties and relationships among vehicle datasets, we first apply the domain transformer to translate the domain of vehicle images, and then utilize the two-channel Siamese network to extract features from both domains for better feature similarity learning.Experimental results illustrate that our models achieve improvements over state-of-the-arts.