Reviewerly/exHarmony huggingface dataset:
Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices

In this paper, we investigate different scenarios of anomaly detection on decentralised Internet of Things (IoT) applications. Specifically, an anomaly detector is devised to detect different types of anomalies for an IoT data management system, based on the decentralised alternating direction method of multipliers (ADMM), which was proposed in our previous work. The anomaly detector only requires limited information from the IoT system, and can be operated using both a mathematical-rule-based approach and the deep learning approach proposed in the paper. Our experimental results show that detection based on mathematical approach is simple to implement, but it also comes with lower detection accuracy (78.88%). In contrast, the deep-learning-enabled approach can easily achieve a higher detection accuracy (96.28%) in the real world working environment.
Study on the Lightweighting Strategy of Target Detection Model with Deep Learning

Aiming at the high miss detection and false detection rate of traditional SSD (single shot multibox detector) target detection algorithm in target detection, this paper proposes a lightweight detection algorithm for deep learning target detection model in order to improve the detection accuracy. Firstly, the real-time and efficient target detection backbone network VoVNet is used to replace the feature extraction network VGG16. The residual structure is integrated to solve the problem of VoVNet network degradation to improve network performance. Secondly, self-attention mechanism is introduced to capture multiscale local and global information to obtain richer image semantic features. According to the characteristics of the target sample size, the anchor frame is designed by using a priori information. In the network training, the anchor frame by selection enhancement based on IoU optimization is used to fully train the target information and strengthen the reading ability of the network to small-scale targets. Experiments on the public data set AI-TOD to show that the target detection lightweight model of deep learning has stronger detection ability and higher average detection accuracy than other algorithms, which proves the applicability and effectiveness of this algorithm.
Mitotic cells detection in H&amp;E-stained breast carcinoma images

Breast cancer is the most common cancer occurring in women, and is the second leading cause of cancer related deaths in women. Grading of breast cancer is carried out based on characteristics such as the gland formation, nuclear features, and mitotic activities, all of which need to be correctly detected first. In this paper, we proposed a system to detect mitotic cells from H&E-stained whole-slide images of breast carcinoma. The system consists of three stages, namely superpixel segmentation to group similar pixels into superpixel regions, blob analysis to separate the cells from the tissues and the background, and shape analysis and classification to distinguish mitotic cells from non-mitotic cells. The proposed system, with the histogram of oriented gradients (HOGs) and Fourier descriptor (FD) as features, is able to detect mitotic cells reliably, with more than 90% true positive rate, true negative rate and overall accuracy.
Sentiment Analysis of COVID-19 using Multimodal Fusion Neural Networks

The purpose of this study creates a Sentiment Analysis model of COVID-19 using Multimodal Fusion Neural Networks in real time to model and visualize COVID-19 in Indonesia. This study obtained 87 percent accuracy using the Multimodal Fusion Neural Networks model, a higher 5 percent than the benchmarking model Convolutional Neural Networks. This study proves that the sentiment model built is quite promising and relevant to be implemented.
QMA-Hardness of Consistency of Local Density Matrices with Applications to Quantum Zero-Knowledge

We provide several advances to the understanding of the class of Quantum Merlin-Arthur proof systems (QMA), the quantum analogue of NP. Our central contribution is proving a longstanding conjecture that the Consistency of Local Density Matrices (CLDM) problem is QMA-hard under Karp reductions. The input of CLDM consists of local reduced density matrices on sets of at most k qubits, and the problem asks if there is an n-qubit global quantum state that is consistent with all of the k-qubit local density matrices. The containment of this problem in QMA and the QMA-hardness under Turing reductions were proved by Liu [APPROX-RANDOM 2006]. Liu also conjectured that CLDM is QMA-hard under Karp reductions, which is desirable for applications, and we finally prove this conjecture. We establish this result using the techniques of simulatable codes of Grilo, Slofstra, and Yuen [FOCS 2019], simplifying their proofs and tailoring them to the context of QMA. In order to develop applications of CLDM, we propose a framework that we call locally simulatable proofs for QMA: this provides QMA proofs that can be efficiently verified by probing only k qubits and, furthermore, the reduced density matrix of any k-qubit subsystem of an accepting witness can be computed in polynomial time, independently of the witness. Within this framework, we show advances in quantum zero-knowledge. We show the first commit-and-open computational zero-knowledge proof system for all of QMA, as a quantum analogue of a "sigma" protocol. We then define a Proof of Quantum Knowledge, which guarantees that a prover is effectively in possession of a quantum witness in an interactive proof, and show that our zero-knowledge proof system satisfies this definition. Finally, we show that our proof system can be used to establish that QMA has a quantum non-interactive zero-knowledge proof system in the secret parameter setting.
A Mobile Intelligent Mine Platform with a Hybrid Fuzzy NN and ATT-CNN Prewarning Model

Given the existence of coal production risk, effective prewarning is important to the reliability and safety of coal mine. So, the development of a risk prewarning system has become an important safety management tool. To improve the prediction ability and the supervision level of safety production, and handle different multidimensional (temporal and spatial) information for risk prewarning, we built a new platform based on the Internet, cloud platform, mobile communication, GIS, and artificial intelligence technology, i.e., a mobile intelligent mine platform. The terminal of the platform provides real-time queries and procedures of coal mine production and risk prewarning and provides data support and technical means for daily supervision, remote networking analysis, law enforcement inspection, and emergency rescue. The prewarning model of safety risk is an essential means to realize prewarning. The complexity of production of coal mine leads to the dynamic characteristics, fuzziness, and randomness of coal mine accidents. The complex nonlinear relationship between index and risk level leads to low accuracy of the traditional back propagation (BP) neural network prewarning method. A novel model based on a compensation fuzzy neural network (NN) and an attention mechanism-convolutional neural network (ATT-CNN) are a critical part of the new design. First, to full use of the convolutional network to get a larger receptive field, one-dimensional time series is transformed into two-dimensional matrix as the input of the CNN network by mapping. The neural network is utilized to extract the advanced features of the input signal. The results are finally output through a fully connected classifier. The model fuses multisource data at the feature level, employs the temporal and spatial relationships of monitoring data, and dynamically evaluates the risk. The experiment shows that the proposed model achieves impressive performance in both quantitative and qualitative evaluations and has improved the model generalization ability. The combination of data integration, remote examinations, and approval from existing information systems enables this platform to provide dynamic reminders of approval information, various risk prewarning, and management process automation through a mobile network.
A Comparative Study on Three Selective Cloud Providers

Cloud Computing means a place where we can store our valuable information of data and access the computing and networking services following the pay-as-you-go method without a physical environment. In the present day, cloud computing offers us powerful computing and storage, high availability and security, instant accessibility and adaptation, guaranteed scalability and interoperability, and cost and time effectiveness. Cloud computing has three platforms (IaaS, PaaS, SaaS) with exclusive features which assure to make easy their work for a client, Organization or Trade to build up any kind of IT business. In this paper, we managed a comparison of cloud service features and after the comparison, It's simple to select a certain cloud service from the available features by comparison with three selective cloud providers like Amazon, Microsoft Azure and Digital Ocean. Using the result of this survey to not only find the similarities and differences between various elements of cloud computing but also to propose some topics to look into for further research.
Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using Natural Language Processing

One of the most crucial aspects of communication in daily life is speech recognition. Speech recognition that is based on natural language processing is one of the essential elements in the conversion of one system to another. In this paper, we created an interface that transforms speech and other auditory inputs into text using a digital filter. Contrary to the many methods for this conversion, it is also possible for linguistic faults to appear occasionally, gender recognition, speech recognition that is unsuccessful (cannot recognize voice), and gender recognition to fail. Since technical problems are involved, we developed a program that acts as a mediator to prevent initiating software issues in order to eliminate even this little deviation. Its planned MFCC and HMM are in sync with its AI system. As a result, technical errors have been avoided.
A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

Abstract Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely. On the other hand, estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components. In this research, a novel hybrid forecasting model, namely complete ensemble empirical mode decomposition with adaptive noise–Gaussian process regression (CEEMDAN–GPR), has been developed for daily global solar radiation prediction. The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets. After that, the GPR model uses these subsets as inputs to perform its prediction. According to the results of this research, the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting, namely wavelet–GPR and wavelet packet–GPR, in terms of mean square error, root mean square error, coefficient of determination and relative root mean square error values, which reached 3.23 MJ/m2/day, 1.80 MJ/m2/day, 95.56%, and 8.80%, respectively (for one-step forward forecasting). The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.
Resources for Turkish natural language processing: A critical survey

This paper presents a comprehensive survey of corpora and lexical resources available for Turkish. We review a broad range of resources, focusing on the ones that are publicly available. In addition to providing information about the available linguistic resources, we present a set of recommendations, and identify gaps in the data available for conducting research and building applications in Turkish Linguistics and Natural Language Processing.
A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems

Metaheuristics (MHs) are proven powerful algorithms for solving non-linear optimisation problems over discrete, continuous, or mixed domains. Applications have ranged from basic sciences to applied technologies. Nowadays, the literature contains plenty of MHs based on exceptional ideas, but often, they are just recombining elements from other techniques. An alternative approach is to follow a standard model that customises population-based MHs, utilising simple heuristics extracted from well-known MHs. Different approaches have explored the combination of such simple heuristics, generating excellent results compared to the generic MHs. Nevertheless, they present limitations due to the nature of the metaheuristic used to study the heuristic space. This work investigates a field of action for implementing a model that takes advantage of previously modified MHs by learning how to boost the performance of the tailoring process. Following this reasoning, we propose a hyper-heuristic model based on Artificial Neural Networks (ANNs) trained with processed sequences of heuristics to identify patterns that one can use to generate better MHs. We prove the feasibility of this model by comparing the results against generic MHs and other approaches that tailor unfolded MHs. Our results evidenced that the proposed model outperformed an average of 84 % of all scenarios; in particular, 89 % of basic and 77 % of unfolded approaches. Plus, we highlight the configurable capability of the proposed model, as it shows to be exceptionally versatile in regards to the computational budget, generating good results even with limited resources.
Word2vec Fuzzy Clustering Algorithm and Its Application in Credit Evaluation

Fuzzy value is much important in credit risk management of commercial banks. However, there is not a perfect method of cluster analysis to analyze the data. The traditional clustering method is not suitable for fuzzy value clustering. Although lattice clustering uses lattice degree instead of Euclidean metric to calculate distance, it also has some problems, such as obscure concept of grid progress, complex calculation process, long calculation time, and so on. Therefore, the Word2vec model is introduced to convert the fuzzy value into a probability matrix that can be measured by Euclidean distance, then we analyze the probability matrix clustering. From the experiments, we can find Word2vec is much better for fuzzy value.
A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer

The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, including GWO, TLBO, PIO, Jaya, CFPSO, CFWPSO, ETLBO, CTLBO, NTLBO and DOLJaya were used to make comparisons with DOLGWO algorithm. Results indicate that the new model has strong robustness and adaptability, and has the significant advantage of converging to the global optimum, which demonstrates that the DOL strategy greatly improves the performance of original GWO algorithm.
Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

An intelligent wheelchair is a kind of service robot. The most critical part of its research field is the safe navigation control of a wheelchair, and path planning is the core part of the navigation control system. The use of a reasonable obstacle avoidance algorithm can not only ensure the safe navigation of wheelchair but also protect the personal safety of users. This paper analyzes the actual needs of the wheelchair following system according to the application scenarios and determines the way to follow the target. The sensor scheme and the wheelchair mobile platform are selected through the analysis of the following methods and the characteristics of each sensor, and the overall architecture of the wheelchair hardware system is designed. Then, the key components such as processor, laser ranging radar, signal strength positioning module, and ultrasonic sensor in the hardware system are selected, and the corresponding peripheral circuit is designed according to the interface type and working voltage of the sensor. In order to solve the problems of the complex calculation process, one-way reasoning, and poor accuracy of the original fault tree method, this paper designs an expert knowledge inference machine based on the Bayesian network. After completing the definition of component vulnerability, the inference engine can be activated, the construction of the directed acyclic network is completed based on the knowledge model mapping of the fault tree, the conditional probability table definition of the directed edge is completed, and the bidirectional probability inference of the Bayesian network can be started. Diagnostic inference obtains the posterior probability to characterize the system impact degree of the bottom event failure. Based on a comprehensive analysis of the research status and research trend of intelligent wheelchair obstacle avoidance path planning methods, this paper mainly studies the intelligent wheelchair path planning method. The environment modeling and path planning of global path planning for static obstacles and dynamic obstacles are realized, and a simulation platform for wheelchair obstacle avoidance path planning based on MATLAB is designed, and simulation experiments are carried out on this basis. The effectiveness of the studied algorithms and strategies in the dynamic obstacle environment is verified by simulation experiments.
Short Non-Malleable Codes from Related-Key Secure Block Ciphers, Revisited

We construct non-malleable codes in the split-state model with codeword length m + 3λ or m + 5λ, where m is the message size and λ is the security parameter, depending on how conservative one is. Our scheme is very simple and involves a single call to a block cipher meeting a new security notion which we dub entropic fixed-related-key security, which essentially means that the block cipher behaves like a pseudorandom permutation when queried upon inputs sampled from a distribution with sufficient min-entropy, even under related-key attacks with respect to an arbitrary but fixed key relation. Importantly, indistinguishability only holds with respect to the original secret key (and not with respect to the tampered secret key).In a previous work, Fehr, Karpman, and Mennink (ToSC 2018) used a related assumption (where the block cipher inputs can be chosen by the adversary, and where indistinguishability holds even with respect to the tampered key) to construct a nonmalleable code in the split-state model with codeword length m + 2λ. Unfortunately, no block cipher (even an ideal one) satisfies their assumption when the tampering function is allowed to be cipher-dependent. In contrast, we are able to show that entropic fixed-related-key security holds in the ideal cipher model with respect to a large class of cipher-dependent tampering attacks (including those which break the assumption of Fehr, Karpman, and Mennink).
Blockchain Powered Vaccine Efficacy for Pharma Sector

Infectious and contagious diseases exist in humanity for many centuries which causes a curb in the growth of the population. Immunization plays a vital role to prevent mortality and morbidity against infectious diseases. COVID-19 pandemic continues to rage the urgency of developing a vaccine that should ensure the safety, efficacy, swift and fair deployment, implementation, and monitoring of vaccines across the globe. In the present context, the vaccine production to immunization campaign is a critical challenge. Therefore, an effective vaccine supply chain mechanism is required to address issues such as counterfeit vaccines, reduce vaccine wastages, and vaccine record fraud. In this paper, a blockchain-enabled vaccine supply chain is proposed to ensure the correctness, transparency, trust, and immutable log and improve the efficiency of vaccine distribution in the cold chain. The uniqueness of the proposed system is to provide distributed system to verify the reliability and efficacy of the vaccine from production to end beneficiaries’ feedback about the vaccine. Our proposed system gives a clear view to the users as well as to the healthcare provider about the vaccination and ensures the anticounterfeit vaccine. The proposed system minimizes counterfeit vaccines and records, provides transparent communication between stakeholders in the supply chain, and improves the security of the vaccine supply chain and immutable feedback system about the vaccine.
Which Biases and Reasoning Pitfalls Do Explanations Trigger? Decomposing Communication Processes in Human–AI Interaction

Collaborative human–AI problem-solving and decision making rely on effective communications between both agents. Such communication processes comprise explanations and interactions between a sender and a receiver. Investigating these dynamics is crucial to avoid miscommunication problems. Hence, in this article, we propose a communication dynamics model, examining the impact of the sender’s explanation intention and strategy on the receiver’s perception of explanation effects. We further present potential biases and reasoning pitfalls with the aim of contributing to the design of hybrid intelligence systems. Finally, we propose six desiderata for human-centered explainable AI and discuss future research opportunities.
Electronic Health Data Security in the Internet of Things through Watermarking: An Introduction

Patient digital records, known as electronic health data (EHD), have security and privacy concerns in the existing Internet of Things (IoT)-based healthcare systems. These issues are attracting much attention and need to be resolved urgently. This article aims to introduce IoT-enabled watermarking, addressing the ownership conflicts, and data security concerns of the smart healthcare framework. Further, a comprehensive review of current studies related to IoT-based healthcare with watermarking techniques, along with their merits and limitations, is provided. The contributions of the reviewed approaches are then summarized and compared from different technical perspectives. Lastly, recent challenges, along with several directions for potential research, are detailed and commented on.