Reviewerly/exHarmony huggingface dataset:
A Survey on the Optimization of Neural Network Accelerators for Micro-AI On-Device Inference

Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI) tasks including computer vision, data analytics, robotics, etc.The efficacy of DNNs coincides with the fact that they can provide state-ofthe-art inference accuracy for these applications.However, this advantage comes from the high computational complexity of the DNNs in use.Hence, it is becoming increasingly important to scale these DNNs so that they can fit on resource-constrained hardware and edge devices.The main goal is to allow efficient processing of the DNNs on low-power micro-AI platforms without compromising hardware resources and accuracy.In this work, we aim to provide a comprehensive survey about the recent developments in the domain of energy-efficient deployment of DNNs on micro-AI platforms.To this extent, we look at different neural architecture search strategies as part of micro-AI model design, provide extensive details about model compression and quantization strategies in practice, and finally elaborate on the current hardware approaches towards efficient deployment of the micro-AI models on hardware.The main takeaways for a reader from this article will be understanding of different search spaces to pinpoint the best micro-AI model configuration, ability to interpret different quantization and sparsification techniques, and the realization of the micro-AI models on resource-constrained hardware and different design considerations associated with it.
Minimum threshold determination method based on dataset characteristics in association rule mining

Abstract Association rule mining is a technique that is widely used in data mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. Before the rule is formed, it must be determined in advance which items will be involved or called the frequent itemset. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, user determines the minimum support value randomly. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. It causes a lot of memory and time consumption. This is because the rule formation process is repeated until it finds the desired number of rules. The value of minimum support in the adaptive support model is determined based on the average and total number of items in each transaction, as well as their support values. Furthermore, the proposed method also uses certain criteria as thresholds, therefore, the resulting rules are in accordance with user needs. The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics. The trial of the proposed adaptive support method uses 2 basic algorithms in the association rule, namely Apriori and Fpgrowth. The test is carried out repeatedly to determine the highest and lowest minimum support values. The result showed that 6 out of 8 datasets produced minimum and maximum support values for the apriori and fpgrowth algorithms. This means that the value of the proposed adaptive support has the ability to generate a rule when viewed from the quality as adaptive support produces at a lift ratio value of &gt; 1. The dataset characteristics obtained from the experimental results can be used as a factor to determine the minimum threshold value.
Quantum entanglement on photonic chips: a review

Entanglement is one of the most vital properties of quantum mechanical systems, and it forms the backbone of quantum information technologies. Taking advantage of nano/microfabrication and particularly complementary metal-oxide-semiconductor manufacturing technologies, photonic integrated circuits (PICs) have emerged as a versatile platform for the generation, manipulation, and measurement of entangled photonic states. We summarize the recent progress of quantum entanglement on PICs, starting from the generation of nonentangled and entangled biphoton states, to the generation of entangled states of multiple photons, multiple dimensions, and multiple degrees of freedom, as well as their applications for quantum information processing.
Toward Quantum Secured Distributed Energy Resources: Adoption of Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD)

Quantum computing is a game-changing technology that affects modern cryptography and security systems including distributed energy resources (DERs) systems. Since the new quantum era is coming soon in 5–10 years, it is crucial to prepare and develop quantum-safe DER systems. This paper provides a comprehensive review of vulnerabilities caused by quantum computing attacks, potential defense strategies, and remaining challenges for DER networks. First, new security vulnerabilities and attack models of the cyber-physical DER systems caused by quantum computing attacks are explored. Moreover, this paper introduces potential quantum attack defense strategies including Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC), which can be applied to DER networks and evaluates defense strategies. Finally, remaining research opportunities and challenges for next-generation quantum-safe DER are discussed.
Control of mental processes

In this chapter, the author concerns control of cognitive operations lasting seconds and fractions of a second. At the time-scale of minutes and seconds, the time-scale with which the analysis of cognitive skill has been mostly concerned, a natural unit of analysis of mental activity seems to be the cognitive task. Much of the success of the cognitive psychology has been manifest in its analysis of the performance of the particular tasks, such as comprehension of a spoken sentence, deciding the truth or falsity of a syllogism, deciding whether two visual forms are the same or different, recalling the words in a list, naming a familiar face or object, performing mental arithmetic, searching an array of the visual objects for a target, and many, many others. Norman and Shallice proposed a psychological model based on the production system architecture.
Breast Cancer Detection with Machine Learning

Abstract: Breast cancer is one of the leading cause for the death of women. In women Breast cancer is treated as the most significant issue. According to statistics released by the International Agency for Research on Cancer (IARC) in December 2020, Breast cancer has now overtaken lung cancer as the most commonly diagnosed cancer in women worldwide. Early diagnosis of this helps to prevent the cancer. If breast cancer is detected in early stage, then Survival rate is very high. Machine Learning methods are effective ways to classify data. Especially in the medical field, where those methods are widely used in diagnosis and analysis for decision making. In this paper, Data Visualization and performance comparisons between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree, Naive Bayes (NB), K Nearest Neighbours (k-NN), Adaboost, XGboost and Random Forest conducted on Wisconsin breast cancer Dataset. The main objective is to evaluate the accuracy in the classification of data in terms of efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity. Our aim is to review various Techniques To detect early, efficiently and accurately Using Machine Learning. Experimental results show that XGboost offers the highest accuracy (98.24%) with the lowest error rate. Keywords: Breast Cancer, Machine Learning, Wisconsin, Algorithms, Detection
Blockchain-Based Distributed Information Hiding Framework for Data Privacy Preserving in Medical Supply Chain Systems

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.
Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.
Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues.
Evaluation Challenges for the Application of Extended Reality Devices in Medicine

Augmented and virtual reality devices are being actively investigated and implemented for a wide range of medical uses. However, significant gaps in the evaluation of these medical devices and applications hinder their regulatory evaluation. Addressing these gaps is critical to demonstrating the devices' safety and effectiveness. We outline the key technical and clinical evaluation challenges discussed during the US Food and Drug Administration's public workshop, "Medical Extended Reality: Toward Best Evaluation Practices for Virtual and Augmented Reality in Medicine" and future directions for evaluation method development. Evaluation challenges were categorized into several key technical and clinical areas. Finally, we highlight current efforts in the standards communities and illustrate connections between the evaluation challenges and the intended uses of the medical extended reality (MXR) devices. Participants concluded that additional research is needed to assess the safety and effectiveness of MXR devices across the use cases.
FastSV: A Distributed-Memory Connected Component Algorithm with Fast Convergence

This paper presents a new distributed-memory algorithm called FastSV for finding connected components in an undirected graph. Our algorithm simplifies the classic Shiloach-Vishkin algorithm and employs several novel and efficient hooking strategies for faster convergence. We map different steps of FastSV to linear algebraic operations and implement them with the help of scalable graph libraries. FastSV uses sparse operations to avoid redundant work and optimized MPI communication to avoid bottlenecks. The resultant algorithm shows high-performance and scalability as it can find the connected components of a hyperlink graph with over 134B edges in 30 seconds using 262K cores on a Cray XC40 supercomputer. FastSV outperforms the state-of-the-art algorithm by an average speedup of 2.21× (max 4.27×) on a variety of real-world graphs.
The road to the blockchain technology: Concept and types

As the Bitcoin keeps increasing in value compared to other cryptocurrencies, more attention has been given to Blockchain Technology (BT) which is the infrastructure behind the Bitcoin, especially on its role in addressing the problems of the classical centralized system. As a digital currency, Bitcoin is dependent on the decentralized cryptographic tools and peer-to-peer system. The digital currency implements a distributed ledger using Blockchain when verifying any type of transaction. In this paper, the aim is to describe how digital currency networks such as Bitcoin provides a “trust-less” platform for users to embark on money transfers without necessarily depending on any central trusted establishments such as payment services or financial institutions. Furthermore, this work comprehensively overviewed the basic principle that underly BT, such as transaction, consensus algorithms, and hashing. This study also provided a novel classification for blockchain types according to their system architecture and consensus strategy. For each type, our contribution was provided with an example which clearly describes the blockchain features and the transaction steps. Our classification intended to help researchers understand and choose the blockchain for their application. The paper ends with the discussion of the differences between each type
Immersive technologies for tourism: a systematic review

Abstract This review provides a comprehensive view of immersive technology in tourism by critically analysing prior scholarly work. More specifically, it identifies the recent use of immersive technology in this field and the potential challenges it poses. This systematic review follows PRISMA guidelines and involves four key steps—identifying research questions, defining keywords, selecting studies based on inclusion and exclusion criteria, and synthesising results. It focuses on immersive technology in tourism-related peer-reviewed journal articles published from 2012 to 2020. The papers were selected from ten prominent journal databases. Some databases used combinations of search queries but with inclusion and exclusion criteria. This systematic review builds on the existing reviews by adding knowledge regarding state-of-the-art immersive technology usage in tourism and its integration with other technology. This review additionally identifies the potential challenges of implementing immersive technology in tourism. Finally, it presents a set of directions for future research in this space. In practice, the findings from this review can make both software developers and tourism providers aware of the potential of immersive technology in tourism. Software developers might consider appropriate designs that suit such usage, and tourism providers might consider using immersive technology to promote tourism destinations and provide a support system to maximise the benefits of immersive technology.
A Survey on Dialogue Summarization: Recent Advances and New Frontiers

Dialogue summarization aims to condense the original dialogue into a shorter version covering salient information, which is a crucial way to reduce dialogue data overload. Recently, the promising achievements in both dialogue systems and natural language generation techniques drastically lead this task to a new landscape, which results in significant research attentions. However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. In detail, we systematically organize the current works according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue. Additionally, we provide an overview of publicly available research datasets as well as organize two leaderboards under unified metrics. Furthermore, we discuss some future directions, including faithfulness, multi-modal, multi-domain and multi-lingual dialogue summarization, and give our thoughts respectively. We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.
A Survey on Terrain Traversability Analysis for Autonomous Ground Vehicles: Methods, Sensors, and Challenges

Understanding the terrain in the upcoming path of a ground robot is one of the most challenging problems in field robotics. Terrain and traversability analysis is a multidisciplinary field combining robotics with image and signal processing, feature extraction, machine learning, three-dimensional (3D) mapping, and 3D geometry. Application scenarios range from autonomous vehicles on urban networks to agriculture, defence, exploration, mining, and search and rescue. Given the broad set of techniques available and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from terrain classification to traversability regression. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects, including open code, available datasets for experimentation and comparisons, and important open research issues.
Knowing your FATE

With the rapid growth and prevalence of social network applications (Apps) in recent years, understanding user engagement has become increasingly important, to provide useful insights for future App design and development. While several promising neural modeling approaches were recently pioneered for accurate user engagement prediction, their black-box designs are unfortunately limited in model explainability. In this paper, we study a novel problem of explainable user engagement prediction for social network Apps. First, we propose a flexible definition of user engagement for various business scenarios, based on future metric expectations. Next, we design an end-to-end neural framework, FATE, which incorporates three key factors that we identify to influence user engagement, namely friendships, user actions, and temporal dynamics to achieve explainable engagement predictions. FATE is based on a tensor-based graph neural network (GNN), LSTM and a mixture attention mechanism, which allows for (a) predictive explanations based on learned weights across different feature categories, (b) reduced network complexity, and (c) improved performance in both prediction accuracy and training/inference time. We conduct extensive experiments on two large-scale datasets from Snapchat, where FATE outperforms state-of-the-art approaches by 10% error and 20% runtime reduction. We also evaluate explanations from FATE, showing strong quantitative and qualitative performance.