Reviewerly/exHarmony huggingface dataset:
An End-To-End Analysis of Deep Learning-Based Remaining Useful Life Algorithms for Satefy-Critical 5G-Enabled IIoT Networks

Remaining Useful Life (RUL) prediction is a critical task that aims to estimate the amount of time until a system fails, where the latter is formed by three main components, that is, the application, communication network, and RUL logic. In this paper, we provide an end-to-end analysis of an entire RUL-based chain. Specifically, we consider a factory floor where Automated Guided Vehicles (AGVs) transport dangerous liquids whose fall may cause injuries to workers. Regarding the communication infrastructure, the AGVs are equipped with 5G User Equipments (UEs) that collect real-time data of their movements and send them to an application server. The RUL logic consists of a Deep Learning (DL)-based pipeline that assesses if there will be liquid falls by analyzing the collected data, and, eventually, sending commands to the AGVs to avoid such a danger. According to this scenario, we performed End-to-End 5G NR-compliant network simulations to study the Round-Trip Time (RTT) as a function of the overall system bandwidth, subcarrier spacing, and modulation order. Then, via real-world experiments, we collect data to train, test and compare 7 DL models and 1 baseline threshold-based algorithm in terms of cost and average advance. Finally, we assess whether or not the RTT provided by four different 5G NR network architectures is compatible with the average advance provided by the best-performing one-Dimensional Convolutional Neural Network (1D-CNN). Numerical results show under which conditions the DL-based approach for RUL estimation matches with the RTT performance provided by different 5G NR network architectures.
Structural Architecture and Mineralogical Relationship in Chitradurga Schist Belt, Dharwar Cration, South India

Abstract: The succession in the late Archaean supracrustal belts in the Chitradurga region begins with basal quartz-pebble conglomerates and quartzites which were deposited on a basement of tonalitic--granitic gneisses, ca. 3000 Ma, containing tracts and enclaves of older supracrustal rocks. Thebasal beds are overlain by basic metalavas and amphibolites interbeddedwith crossbedded quartzites and siliceous phyllites. This series passes up into siliceous phyllites, locally containing a large lenticular mass of polymict conglomerate (Talya Conglomerate), overlain by banded iron formations and siliceous phyllites including sporadic chert pebble conglomerates. Greater subsidence in part of the basin accommodated greater thicknesses of basic volcanic and acid pyroclastic rocks, the latter being more abundant at higher levels. With progressive deepening of the whole basin, finegrained, graded greywackes and local polymict conglomerates (Aimangala Conglomerate) were deposited ; the turbidite sequence forms the youngest sedimentary rocks exposed.Estimated total stratigraphic thickness is at least 10 km indeeper parts of the basin. Deformation led to a series of upright anticlines and synclines and relatedLS fabrics with extreme variations in plunge of the coaxial fold axes and Lfabrics within axial surfaces which maintain a steep orientation. Low-grademetamorphism outlasted the deformation. Sedimentation and volcanism in the Chitradurga supracrustal belt is linkedto variable subsidence of the basement foundation, probably as a result of rifting. The structural evolution of the belt followed as a direct continuation of events that controlled the depositional and volcanic history. Variable displacement of the basement during theselate Archaean events in the Karnataka craton took place on shear zones and related fractures that acted as channels for water and CO: asindicated by vein minerals and alteration products. Uplift of basement segments and their distension by granitic diapirs led to deformation of thesupracrustal cover made ductile by its high water content
Effects of Cyberattacks on Virtual Reality and Augmented Reality Technologies for People with Disabilities

Virtual Reality (VR) and Augmented Reality (AR) technologies offer transformative solutions for individuals with disabilities, empowering them with enhanced accessibility and immersive experiences.The importance of VR and AR for accessibility provides assistive solutions for disabled users through accessibility enhancements, personalized assistive technologies to support education, rehabilitation support, and social inclusion and empathy building.However, limitations and security challenges are inherent in the current integration of VR and AR.That includes inadequate authentication, insecure communication channels, software errors, device incompatibilities, keystroke errors on controllers, poor network speed, and cyberattacks, potentially jeopardising vulnerable users' safety and wellbeing.The paper explores the impact of cyberattacks on VR and AR technologies for disabled users.The novelty contribution of the paper is threefold.First, we analyze existing VR and AR technologies and their immersive environments, including their vulnerabilities.Secondly, we consider the various cyberattacks being deployed to exploit the vulnerabilities in the settings and their impact on users with disabilities.Finally, we implement an attack to exploit a vulnerability in the AR and VR environment to determine security and recommend control mechanisms.The paper raises awareness of the importance of securing VR and AR to safeguard the inclusivity and independence of disabled users.
CCMT2023 Tibetan-Chinese Machine Translation Evaluation Technical Report

This report presents the evaluation results of the Tibetan-Chinese machine translation task in the 19th Machine Translation Conference (CCMT2023) by Tibetan Language Information Technology Ministry of Education Engineering Research Center and NLP Lab at Northeastern University. This system uses data augmentation, ensemble and iterative Fine-tune to experiment with Tibetan-Chinese machine translation. The results showed that compared with the baseline model, the method used in this system can improve the performance of the translation quality.
Spam Review Detection Using Machine Learning

With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviors of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviors of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews with and without features extracted from users behaviors. In both cases, we compare the performance of several classifiers; KNN, Naive Bayes (NB), SVM, Logistic Regression and Random forest. Also, different language models of n-gram in particular bi-gram and tri-gram are taken into considerations during the evaluations. The results reveal that KNN(K=7) outperforms the rest of classifiers in terms of f-score achieving best f-score 82.40%. The results show that the f-score has increased by 3.80% when taking the extracted reviewers behavioral features into consideration.
Relational Learning with Attribute-Based Multi-Head Attention Regulator for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC) has received increasing attention as it uses a few reference triplets about a few-shot relation to infer unseen query triplets of the relation. Most previous FKGC models neglect the attribute values of the dataset itself, which result in underutilization of the information provided by supporting triples. Furthermore, earlier methodologies primarily concentrate on acquiring the connection between entities and relations, neglecting the correlations among entities. In this paper, we propose our AMAR, a novel FKGC framework based on attribute-based multi-head attention regulator and neighbor encoder with the entity-level and the relation-level correlations. Our model effectively captures attribute relations supporting triples and obtains more informative representations through a multi-head attention mechanism. Furthermore, we utilize the correlations between entities and between entities and relations to obtain representations of the support set. Experimental results on two public datasets show that we have better results than the current baseline approach.
A Publicly Verifiable Leveled Fully Homomorphic Signcryption Scheme

With the deepening of research, how to construct a fully homomorphic signcryption scheme based on standard assumptions is a problem that we need to solve. For this question, recently, Jin et al. proposed a leveled fully homomorphic signcryption scheme from standard lattices. However, when verifying, it is supposed to unsigncrypt first as they utilize sign-then-encrypt method. This leads to users being unable to verify the authenticity of the data first, which resulting in the waste of resources. This raises another question of how to construct an fully homomorphic signcryption (FHSC) scheme with public verifiability. To solve this problem, we propose a leveled fully homomorphic signcryption scheme that can be publicly verified and show its completeness, IND-CPA security, and strong unforgeability.
Real-Time Object Detection for ITS Applications

Object detection models have been widely applied in intelligent transportation systems, such as vehicle detection, pedestrian detection, driver behaviour recognition, road damage detection, etc. The development of communications networks (5G networks) has increased data transfer rates, while the large-scale application of sensors in roadside units and connected-automated vehicles has generated huge amounts of data. This makes it possible to train object detection models with high performance in intelligent transportation systems (ITS). However, high performance models typically increase computation and the number of parameters, which makes the problem of under-performance of embedded devices in ITS more obvious. This chapter introduces common object detection algorithms. Moreover, this chapter introduces lightweight methods that focus on reducing the consumption of inference. The knowledge distillation algorithm is described in detail and applied to the YOLOv5 model. Finally, this chapter describes the YOLOv5 knowledge distillation experiment and analyzes the experimental results.
AI-Empowered Metaverse Learning Simulation Technology Application

This paper proposes an immersive and adaptive virtual learning system incorporating artificial intelligence and multi-agent technologies to provide personalized and engaging education tailored to individual students. A prototype system was developed and tested for an English language learning task, demonstrating over 30% improvement versus conventional teaching. However, challenges remain for large-scale classroom implementation, including development costs, technology readiness differences, risks of over-reliance on technology lacking human interactions, and formulating new pedagogical models. Further research is required to address these open issues before realizing the full potential of AI-enabled immersive learning environments for transforming education. The paper provides a conceptual framework and system design for intelligent customizable virtual learning tailored to students' knowledge states, skill levels, interests, and learning styles. Preliminary experimentation validated gains in outcomes and engagement versus conventional classrooms. While substantial hurdles exist for translation to mainstream adoption, prudent integration of immersive technologies could enable more stimulating and effective education worldwide. With continued systemic research and adoption, AI-driven VR heralds a new era in technology-enhanced yet human-centric pedagogy to prepare learners for the modern digital world.
Double-direction cyclic controlled quantum communication of single-particle states

The purpose of this paper is to investigate the double-direction cyclic (DDC) controlled quantum communication of single-particle states. We construct 13-particle, 19-particle and 16-particle entangled channels via Hadamard gates and controlled-NOT gates, separately. Based on these quantum channels that we construct, three novel theoretical schemes are proposed for achieving four-party DDC controlled quantum teleportation (QT), remote state preparation (RSP) and hybrid quantum communication, respectively. For any of these three schemes, each correspondent can simultaneously teleport two different single-particle states to two other correspondents under the control of the supervisor, which ensures that cyclic controlled quantum communication is implemented simultaneously in both clockwise and anticlockwise directions. Furthermore, the three presented four-party schemes for DDC controlled quantum communication can be extended to scenarios with n (n>3) correspondents. In each of our schemes, the general analysis formulas for both the sender’s manipulations and the receiver’s transformations are respectively given, and the unit success probability of each scheme is achieved. This article requires specific two-particle projective measurement, single-particle von Neumann measurement, Hadamard gate, controlled-NOT gates and Pauli gates, so our schemes can be physically implemented in terms of current technologies. We also analyzed the inherent efficiency, safety, and control power of the proposed schemes, and the results showed that our schemes are good.
An optimization case study for solving a transport robot scheduling problem on quantum-hybrid and quantum-inspired hardware

We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem. This problem originates from an industrially relevant real-world scenario. We provide three different models for our problem following different design philosophies. In our benchmark, we focus on the solution quality and end-to-end runtime of different model and solver combinations. We find promising results for the digital annealer and some opportunities for the hybrid quantum annealer in direct comparison with Gurobi. Our study provides insights into the workflow for solving an application-oriented optimization problem with different strategies, and can be useful for evaluating the strengths and weaknesses of different approaches.
Novel Ray-tracing Received Power Adjustment Approach Based on the Ratio of Wavelength and Object Size

In this paper, the received power calculated using ray-tracing simulations is compared with measurement data obtained in a factory environment. In order to reduce the errors of the ray-tracing received power at sub-6 GHz frequencies due to the wavelength being larger than many objects, a novel method to adjust the power of reflected and diffracted waves based on the wavelength and the dimensions of the objects is proposed. Through cross-validation of the proposed method at 922 MHz and 4.85 GHz frequency bands, the median absolute error of the ray-tracing received power value could be reduced from 9.7 dB and 4.1 dB to 4.6 dB and 2.2 dB for each frequency band respectively.
Review on Neural Question Generation for Education Purposes

Abstract Questioning plays a vital role in education, directing knowledge construction and assessing students’ understanding. However, creating high-level questions requires significant creativity and effort. Automatic question generation is expected to facilitate the generation of not only fluent and relevant but also educationally valuable questions. While rule-based methods are intuitive for short inputs, they struggle with longer and more complex inputs. Neural question generation (NQG) has shown better results in this regard. This review summarizes the advancements in NQG between 2016 and early 2022. The focus is on the development of NQG for educational purposes, including challenges and research opportunities. We found that although NQG can generate fluent and relevant factoid-type questions, few studies focus on education. Specifically, there is limited literature using context in the form of multi-paragraphs, which due to the input limitation of the current deep learning techniques, require key content identification. The desirable key content should be important to specific topics or learning objectives and be able to generate certain types of questions. A further research opportunity is controllable NQG systems, which can be customized by taking into account factors like difficulty level, desired answer type, and other individualized needs. Equally important, the results of our review also suggest that it is necessary to create datasets specific to the question generation tasks with annotations that support better learning for neural-based methods.
Resource Utilization Comparison of KubeEdge, K3s, and Nomad for Edge Computing

Containers have become increasingly popular as a lightweight form of virtualization technology in the past few years. This surge in popularity aligns closely with the growing use of microservice architectures, primarily due to the scalability, ephemeral nature, and isolation provided by containers. In more recent times, advancements in edge devices have enabled them to support the execution of containerized microservices. These devices maintain a desirable balance between their size and power capabilities, to be implemented in a variety of locations. Research into various container placement strategies within edge networks has been prompted, resulting in the emergence of concepts like osmotic computing. Although these strategies for placing containers are efficient in terms of weight allocation, container orchestrators at present typically demand high resources requirement running on edge devices which have limitations of capabilities in terms of managing processes.This article presents a performance comparison of several container orchestrations consisting of KubeEdge, K3s and Nomad. The comparative aspects include resource utilization like CPU, memory and storage use before and after the deployment process. The experiment’s evaluation indicates that orchestrators favour Nomad because of its exceptional ability to efficiently utilize CPU and memory resources. On the other hand, K3s stands out for its higher efficiency in utilizing storage resources compared to other orchestrators.
Dynamic Data Masking by Two-Step Encryption

Data masking and encryption are crucial techniques in ensuring the security and privacy of sensitive information. This provides a robust layer of protection by transforming data into an unreadable format using cryptographic algorithms. Data masking and encryption play vital roles in safeguarding sensitive data, preserving confidentiality, and maintaining the trust and integrity of data-driven systems. This study used the block cipher method to perform performance analysis with different block models with the AES algorithm. Subsequently, a dynamic data masking system was developed with double encryption. The analyses showed that the fastest encryption and decryption times were ECB, CFB, and CBC, respectively. However, the fact that ECB has low security and CBC is slow compared to others shows that CFB is the best choice among block models. In conclusion, CBC mode with the AES algorithm has proven to be a robust and widely adopted method for secure data encryption. Furthermore, with the increasing importance of data privacy and security, there will likely be continued efforts to bolster encryption protocols and standards. Overall, CBC encryption with the AES algorithm is expected to maintain its relevance and effectiveness in the future, providing a solid foundation for secure data transmission and storage.
Mechanical Neural Network: Making AI Comprehensible for Everyone

Artificial Intelligence (AI) will become more and more an essential part of our everyday technology, but the function of AI is not yet covered in school education. While STEM education often focuses on the mathematical backbone of AI, i.e., probability calculation, students profit from hands-on activities to understand the function of artificial neural networks (ANNs). Making hands-on activities on AI feasible, this paper introduced the Mechanical Neural Network (MNN), a physical implementation of a multilayer perceptron (MLP) with Rectified Linear Units (ReLUs) as activation functions. With two input neurons, four hidden neurons, and two output neurons, the MNN allows students to experience the effect of changing the parameters of the network on the output. Shifting AI from soft to hardware, each component of an MLP is transferred into the real world. Thus, neurons become small wooden levers, connections between the neurons become strings, and summations become guide rollers of a pulley system. In a game based-learning manner, students can adapt the parts of the MNN for, e.g., modeling logical operators like exclusive or (XOR) and starting to understand the function of an ANN.
A Novel Dataset and Benchmark Analysis on Document Image Translation

Document image translation (DIT) deserves more attention on account of its importance in many real-world scenarios. It is a challenging task because of the layout degeneration and noisy text translation problems caused by the optical character recognition (OCR) model. Moreover, due to the task-specific annotation, existing document image datasets usually do not support in-depth DIT analysis and model development. So, to motivate a broader investigation, this paper presents a dataset named DITrans, which provides fine-grained annotations for English-to-Chinese DIT task. It contains 2.8k English document images in three domains: political report, scientific article and paper book. Each document image has been annotated with layout structure, source text and translation references. Based on DITrans, a novel framework, which strengthens the conventional OCR-Translation cascade in layout awareness and noise robustness for better DIT, has been proposed. Furthermore, benchmark evaluations and detailed analysis based on this framework have been conducted. The evaluations and analysis results demonstrate that the dataset is very practical and can facilitate full-stack analysis and long-term research on DIT.