Reviewerly/exHarmony huggingface dataset:
A secure crossing two qubits protocol based on quantum homomorphic encryption

Abstract In order to solve the information leakage caused by dishonest intermediate nodes in quantum network coding, we apply quantum homomorphic encryption to the butterfly network, and propose a secure protocol for crossing two qubits. Firstly, in the communication process between two senders and the first intermediate node, two senders encrypt their measured particles and send them to the first intermediate node for encoding. If two intermediate nodes are dishonest and know the encryption rules between two senders and two receivers, or there is an external eavesdropper, none of them can recover the transmitted qubits of two senders from the encrypted transmitted particles. In this way, our protocol can transmit two qubits safely and crossly in the butterfly network. Secondly, by analyzing the internal participant attack and the external eavesdropper attack launched by dishonest intermediate nodes and an external eavesdropper respectively, it is confirmed that our protocol is secure. Finally, the experimental simulation results based on the Qiskit framework prove that our protocol is feasible.
Achieving the Integrity of the Educational Process Through Its Modeling and Interaction of Blocks

The article describes the method of modeling as a way to visualize the unity and integrity of the process of activation of cognitive process of students. The author points out that the implementation of the principle of integrity by the teacher is impossible without his ability to organize the unity of all components of the process of training and education, as well as without the student himself, an active participant in the process, whose activity is an important condition for mastering their professional competencies and knowledge accumulated by mankind in all their diversity. The study emphasizes the relevance of the modeling method used in terms of its visualization. The author proposes to use this method to solve various pedagogical problems. The study presents the structure of the phenomenon "cognitive interest" and the model of its activation. The author's complex of technologies (digital, cognitive-visual and complex technologies), due to which it is possible to increase the level of cognitive interest of students, should be noted. Along with that the author underlines the peculiarities of modeling method application, its properties and characteristics in order to facilitate the perception and understanding of the complex process of knowledge acquisition, as well as the nature of the variability of built models. In the presented model the author traced the logic and sequence, interrelation and interdependence of the selected blocks, which can be transformed and adapted to other pedagogical goals and objectives.
Optimized intellectual resource scheduling using deep reinforcement Q‐learning in cloud computing

Abstract Cloud computing has recently attracted both academics and industrialists in the field of research. Virtualization allows cloud service providers (CSPs) with their own data centers to supply infrastructures, resources, and services to users by converting real servers into virtual machines (VMs). Profit‐driven CSPs charge users for VM leasing and service access while reducing energy consumption to increase profits. But CSPs even face challenges like minimizing the energy cost for the data center. Several different algorithms were introduced for minimizing the energy cost by using task scheduling (TS) and/or resource provisioning. However, scalability issues were encountered, or TS with task dependencies were not considered, which is a critical factor in assuring exact parallel execution of tasks in parallel. This article introduces a novel artificial algorithm, called deep reinforcement Q‐learning for resource scheduling which integrates the features of the Q‐learning and reinforcement learning approaches. The objective of this new approach is to provide a solution to the problem of handling energy consumption in a cloud computing environment. Based on advancements in WorkflowSim, experiments are carried out comparatively by considering the variance of make‐span, time, cost analysis, deadline overflow, and load balance in resource scheduling. The proposed method tends to be effective in terms of cost, energy consumption, resource utilization, and response time. The resource reuse capability of the proposed methodology is 63% higher when compared to the modified particle swarm optimization and modified cat swarm optimization technique. The task approval rate of the proposed methodology is 54% higher than the crow search‐based load balancing algorithm and 50% higher than the task duplication‐based scheduling algorithm.
Towards a Benchmarking System for Comparing Automatic Hate Speech Detection with an Intelligent Baseline Proposal

Hate Speech is a frequent problem occurring among Internet users. Recent regulations are being discussed by U.K. representatives (“Online Safety Bill”) and by the European Commission, which plans on introducing Hate Speech as an “EU crime”. The recent legislation having passed in order to combat this kind of speech places the burden of identification on the hosting websites and often within a tight time frame (24 h in France and Germany). These constraints make automatic Hate Speech detection a very important topic for major social media platforms. However, recent literature on Hate Speech detection lacks a benchmarking system that can evaluate how different approaches compare against each other regarding the prediction made concerning different types of text (short snippets such as those present on Twitter, as well as lengthier fragments). This paper intended to deal with this issue and to take a step forward towards the standardization of testing for this type of natural language processing (NLP) application. Furthermore, this paper explored different transformer and LSTM-based models in order to evaluate the performance of multi-task and transfer learning models used for Hate Speech detection. Some of the results obtained in this paper surpassed the existing ones. The paper concluded that transformer-based models have the best performance on all studied Datasets.
Towards efficient use of data, models and tools in food microbiology

Food microbiology researchers, risk assessment agencies and food business operators rely heavily on the reuse of knowledge that is available as data, models and tools. Unfortunately, such knowledge reuse remains challenging, as food safety data sets, models and tools are usually only available in platform-dependent or software-dependent formats that rarely comply to the Findability, Accessibility, Interoperability, and Reusability data principles. In recent years, the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP) Initiative developed the so-called Food Safety Knowledge Exchange (FSKX) format. This development was accompanied by the creation of open-source software that facilitates the adoption of FSKX. Future work within RAKIP will focus on creating semantic interoperability in FSKX-related solutions and on the extension of the FSKX format towards other food microbiology knowledge.
Planning the Emergency Collision Avoidance Strategy Based on Personal Zones for Safe Human-Machine Interaction in Smart Cyber-Physical System

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of autonomous vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are (i) the recommendation of an active stopping planner using different smooth generators from a modelling study, (ii) the validation of their physical characteristics and (iii) the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing.
Virtual Reality and Construction Industry: Review of Current State-of-Practice and Future Applications

Virtual reality systems are making significant headway in the construction industry for design, training, planning, and management applications. With research continuing to show the virtues of adopting virtual reality in blue-collar occupational environments, the industry is making rapid and substantial investments to facilitate institutional innovation that supports adoption. Despite a push from both the research and professional community, the widespread adoption of virtual reality remains hindered because details on practical application remains unclear. This study used a social constructionist approach to capture the perspectives of industry professionals on the challenges and opportunities associated with use of virtual reality for construction safety applications. The interviews with professionals revealed that the application of virtual reality in the workplace is currently in its infancy stage and lacks standardization primarily due to scalability and quality concerns. Additionally, the cost associated with large-scale adoption also remains prohibitive to date. The experiential learnings of professionals are used in this paper to determine where future research and practice efforts need to focus to pave the way for future of virtual reality within the construction industry.
A Differential Fault Attack on Security Vehicle System Applied SIMON Block Cipher

With the application of new technologies in vehicles such as wireless communications, microelectronics, Artificial Intelligence (AI) and Autonomous Vehicles (AV), there are lots of embedded hardware and software in vehicles, and so cryptographic technologies such as block ciphers are also applied to the vehicle system to protect its security and privacy. In order to better find potential security vulnerabilities and improve security of the vehicle system which applied SIMON block cipher, in this paper, a novel Differential fault attack (DFA) method is presented. By inducing a random bit fault in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L^{T-4}$ </tex-math></inline-formula> (the fourth round from the last), we first demonstrate the process of identifying the fault-induced position. Then, on the basis of the determined position, we describe how to retrieve three rounds of keys by inducing only one round of faults. For retrieving the SIMON family, compared with previous DFAs under the random bit fault model, the lowest numbers of fault-induced and fault locations (or rounds) are required by our attack. In particular, when the key words <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is 2 or 3, the proposed attack only needs to induce one round of faults. Finally, we carry out simulation verification and a comparison to illustrate the correctness and effectiveness of the proposed attack.
Calibration and validation of the Angstrom–Prescott model in solar radiation estimation using optimization algorithms

The Angstrom-Prescott (A-P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The aim of this research was calibration and validation of the coefficients of the A-P model at six meteorological stations across arid and semi-arid regions of Iran. This model has improved by adding the air temperature and relative humidity terms. Besides, the coefficients of the A-P model and improved models have calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error, and coefficient of determination (R2) have used to analyze the models ability in estimating Rs. The results indicated that the performance of the A-P model had more precision and less error than improved models in all the stations. In addition, the best results have obtained for the A-P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m-2 day-1 for the A-P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively.
Simultaneous resolution of photon numbers and positions with series-connected superconducting nanowires

In this Letter, we report on a device with which to resolve photon numbers and positions simultaneously, using single-channel readout from superconducting nanowire single-photon detectors (SNSPDs). The nanowires in the SNSPDs are connected in series with parallel resistors for producing response pulses with different amplitudes, whose values obey a distribution of 1:2:4:,..,2n-1. In single-photon detection, a saturated counting rate is obtained at a low dark count rate (&amp;lt;10 cps). Furthermore, we observed 16 output modes corresponding one-to-one with 16 states (15 photon responses + 1 state without photons) in multiphoton mode. This structure not only inherits the advantages of high quantum efficiency and low dark count rate of traditional SNSPD, but also realizes the resolution of photon position and number simultaneously without complicated optical system. Thus, this proposal offers a promising platform for realizing scalable and practical quantum information chips.
Spike propagation in a nanolaser-based optoelectronic neuron

With the recent development of artificial intelligence and deep neural networks, alternatives to the Von Neumann architecture are in demand to run these algorithms efficiently in terms of speed, power and component size. In this theoretical study, a neuromorphic, optoelectronic nanopillar metal-cavity consisting of a resonant tunneling diode (RTD) and a nanolaser diode (LD) is demonstrated as an excitable pulse generator. With the proper configuration, the RTD behaves as an excitable system while the LD translates its electronic output into optical pulses, which can be interpreted as bits of information. The optical pulses are characterized in terms of their width, amplitude, response delay, distortion and jitter times. Finally, two RTD-LD units are integrated via a photodetector and their feasibility to generate and propagate optical pulses is demonstrated. Given its low energy consumption per pulse and high spiking rate, this device has potential applications as building blocks in neuromorphic processors and spiking neural networks.
AVSeeker: An Active Video Retrieval Engine at VBS2022

Exploring video clips in a vast collection of videos is a difficult task. It is necessary to provide an efficient system for users to express the information need for sought events in that video collection. Thus, we propose to develop AVSeeker – an active video retrieval engine – to assist users in finding appropriate moments in videos with two main query types: textual descriptions and visual examples. The main characteristic of AVSeekeris that we change the retrieval engine from a passive system to an active one, which narrows the search space by gaining clues from users through an interactive relevance feedback manner. The AVSeeker is based on the LifeSeeker system from the annual Lifelog Search Challenge with the addition of an interactive relevance feedback via concept recommendation, enriched temporal concepts, and query-by-sketch functionality.
Web Service Composition Optimization with the Improved Fireworks Algorithm

Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> <mo>−</mo> <mn>1</mn> </math> individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.
Reinforcement Learning-Based Adaptive Streaming Scheme with Edge Computing Assistance

Dynamic Adaptive Streaming over HTTP (DASH) is a promising scheme for improving the Quality of Experience (QoE) of users in video streaming. However, the existing schemes do not perform coordination among clients and depend on fixed heuristics. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme improves the overall QoE of clients and QoE fairness among clients based on a state-of-the-art reinforcement learning algorithm. Edge computing assistance plays a role in providing client-side observations to the mobile edge, making agents utilize this information when generating a policy for multi-client adaptive streaming. We evaluated the proposed scheme through simulation-based experiments under various network conditions. The experimental results show that the proposed scheme achieves better performance than the existing schemes.
IoT based Medicine Reminder and Dispensing Machine

This project involves the design & development of medicine reminder and dispensing machines based on the healthcare system. Because of the pandemic situation, this innovative system is proposed because patients are given medicines by nurses and doctors to alleviate pain, boost immunity, and reduce symptoms. However, this puts the healthcare warriors at risk when they administer the medication to patients, so a machine that could take in the medication doses for an entire week has been created. Thus, ensuring distance from patients and nurses. The data of when the medicine is taken is been stored in an SD card for further reference by doctors. This machine can also be used in homes where elderly patients forget to take their medications on time and require a caregiver to administer each dose. Food and exercise times are beneficial to patients because certain medications must be taken at specific intervals before or after food.
Named Entity Recognition for Tamil text Using Deep Learning

A common foundation for Natural Language Processing (NLP) applications is Named-Entity-Recognition (NER). Organizing data into pre-defined labels is a challenging task, and due to the Tamil language&#x0027;s unique characteristics and complexity, it becomes even more difficult. With the Multilingual Universal Sentence Encoder, we constructed a GRU model using transfer learning and deep neural networks. The enormous amount of unstructured data transmitted every day necessitates the development of effective information retrieval and extraction methods. The act of categorizing the data into pre-defined categories, known as named entity recognition, is a tough task. This work provides a novel Deep Learning technique for Standard Arabic Named Entity Recognition that outperforms previous studies. The main purpose of creating a new model is to provide more fine-grained results for Natural Language Processing applications. We compared RNN, LSTM, GRU, Bi-LSTM, and GRU using deep neural networks. (Abstract)
Triangle-based outlier detection

For the last decades, anomaly detection has been one of the most common problems in data mining and computer science projects. The scientific community has made a great effort to develop methods and techniques for the detection of elements that deviate from the norm. Many of these techniques follow an unsupervised approach since anomalies are scarce and not easily reproducible. In this paper, a novel unsupervised anomaly detection method based on geometric reasoning is proposed. Triangle-based Outlier Detection (TOD) falls within the group of distance-based anomaly detection techniques and has been compared to state of the art methods for unsupervised anomaly detection. Results achieved prove that TOD offers greater robustness than alternative methods for the selection of the decision thresholds that condition the detection of outliers.
Sender-controlled measurement-device-independent multiparty quantum communication

Multiparty quantum communication is an important branch of quantum networks. It enables private information transmission with information-theoretic security among legitimate parties. We propose a sender-controlled measurement-device-independent multiparty quantum communication protocol. The sender Alice divides a private message into several parts and delivers them to different receivers for secret sharing with imperfect measurement devices and untrusted ancillary nodes. Furthermore, Alice acts as an active controller and checks the security of quantum channels and the reliability of each receiver before she encodes her private message for secret sharing, which makes the protocol convenient for multiparity quantum communication.
Research Perspectives and Challenges of Blockchain for Data-Intensive and Resource-Constrained Devices

Blockchain technology in recent years has become potentially pervasive in the cryptocurrency market, thus providing tamper-proof security to decentralized transaction management systems. Structurally, the design foundation is an ideal advancement of the distributed ledger technology that maintains a set of global states across nodes. As technology expands with a higher trend towards mobile computing, the development of new applications demands understanding the current progression, especially concerning performance, data management, and storage prospects. Here, we report the principle design structure of the blockchain technology combined with the state of the arts, thus characterizing their original topological contexts. We depart from the fundamental concepts of the technology and analyze performance of the Ethereum blockchain on two devices having different computing power. Our presentation is tailored to provide a systematic review of the technology, thus facilitating their possible adoption into the new application domains like the Internet of Things (IoT). Further, we developed Debug-Bench, the first VSCode (Visual Studio Code) extension that enables benchmarking and profiling of the blockchain applications. Finally, we demonstrate several critical challenges concerning the design space of the current blockchain platforms for their implementation over resource-constrained devices.