Reviewerly/exHarmony huggingface dataset:
MDAS_DBRCC: Data Backup and Recovery Technique in Cloud Computing for Education Industry

Cloud computing also offers several services to its clients in departments such as education, healthcare, military, government, and sports. The storage of each service, which keeps a huge amount of electronic data in a cloud database, is one of the services provided by cloud infrastructure. In the main cloud, this type computes massive amounts of private data. As a result, information retrieval solutions that are both effective and efficient are required. When the server deletes the lost data and cannot transmit any information to the client, the recovery method's main goal is to assist the user in collecting data from each server backup. Users can benefit from cloud computing by storing data, accessing data from anywhere, and retrieving data at any time. As data on the cloud is generated at such a quick rate, speed is one of the most important aspects of cloud computing. It's simple to save files in the cloud and then retrieve it again. However, the difficult problem is to safely store and retrieve data from the cloud. We suggested a way for securely storing data in the cloud utilizing encryption and decryption methods in this work. We also employed a compression module to compress data before putting it in the cloud.
Ship Detection from Satellite Images using Deep Learning

The features of synthetic aperture radar (SAR) has been widely used in maritime surveillance. While various object detection techniques have been proposed, currently, the techniques for detecting ships are inconsiderate to the small-scale vessels. Due to the complexity of images, traditional classification techniques often fail to classify complex images. Also, the intricacies of numerous ships, varying imaging settings, and limited tagged images have made ship categorization difficult in optical images. While the traditional classification approaches rely on feature extraction; however, they frequently fail to design well-performing features for complicated images,this paper presents a method that learns discriminative features and achieves strong classification accuracy using deep networks. A novel multi-scale method that uses an R-CNN network has been applied to extract the features of the images. We studied various aspects of ship photography and present the findings in the experiments. The R-CNN-based technique works well for ship categorization, capable of learning discriminative features and attaining strong classification accuracy using deep networks. The proposed work has been undertaken numerous investigations and produce two minor datasets of optical ship pictures for training and test data. The results of the experimentshave shown that the proposed method is effective for ship recognition than the state of the art approaches.
Equilibrium Allocation Approaches of Quantum Key Resources With Security Levels in QKD-Enabled Optical Data Center Networks

In this article, the network performance of equilibrium allocation of quantum key resources was investigated in quantum key distribution (QKD)-enabled optical data center networks. To effectively use quantum key resources, we propose three novel efficient load balancing routing, wavelength, and time-slot assignment (LB-RWTA) approaches, including LB-RWTA with flexible security level (LB-RWTA-FSL), LB-RWTA with specific security level (LB-RWTA-SSL), and LB-RWTA without security level (LB-RWTA-NSL). Particularly, the proposed LB-RWTA-FSL approach is uniquely featured by adaptive security level classification (ASLC) and load balancing (LB), aiming to demarcate the security level (SL) and reduce the overall network congestion. We introduce an existing routing, wavelength, and time-slot assignment (E-RWTA) approach without SL, called E-RWTA, for comparison. Simulation results show the effectiveness of our proposed approaches in terms of much higher quantum key resource efficiency and thus much higher network security performance than the state-of-the-art quantum key resource allocation approaches compared with the E-RWTA approach in QKD-enabled optical data center networks.
A Multi-Agent Trust and Reputation Mechanisms for the Management of Smart Urban Lighting Systems

In this paper, an adaptive urban smart lighting architecture allowing municipalities to manage and control public street lighting lamps is developed. In order to reduce energy consumption, the system is designed to autonomously adapt street lamps' brightness on the basis of the presence of vehicles in specific segments of the streets/roads of interest. To this end, a hybrid traffic model is here proposed to estimate in each segment of the road the number of vehicles crossing the segment. The estimation task is carried out by means of a bank of switching observers and corresponding sensors. One observer at a time is selected from the bank and put in order. Such a selection is based on a recently developed multi-agent reputation mechanism for a class of networked systems. As one of its merits, this methodology is capable to reveal those sensors that have the highest information contents (Quality of Service) on the basis of their reputation (trust) shared among agents.
A Recommender for Research Collaborators Using Graph Neural Networks

As most great discoveries and advancements in science and technology invariably involve the cooperation of a group of researchers, effective collaboration is the key factor. Nevertheless, finding suitable scholars and researchers to work with is challenging and, mostly, time-consuming for many. A recommender who is capable of finding and recommending collaborators would prove helpful. In this work, we utilized a life science and biomedical research database, i.e., MEDLINE, to develop a collaboration recommendation system based on novel graph neural networks, i.e., GraphSAGE and Temporal Graph Network, which can capture intrinsic, complex, and changing dependencies among researchers, including temporal user–user interactions. The baseline methods based on LightGCN and gradient boosting trees were also developed in this work for comparison. Internal automatic evaluations and external evaluations through end-users' ratings were conducted, and the results revealed that our graph neural networks recommender exhibits consistently encouraging results.
Optimization of College English Teaching Plan under the Background of Micro-Video and Big Data

English education has gained greater popularity due to the demands of international economy as well as international politics. College English education is also a part of national English education. However, the English education mode in colleges and universities has certain defects, and it still adopts traditional English education methods to impart knowledge to college students. This limits the interest of college students in learning English. The class hours and assessment mode of English education in colleges and universities limit the time for English learning in colleges and universities, which is also one of the reasons for the disadvantages of college English. Today, with the development of information technology, college English education should consider more technological elements to improve quality of English education in colleges and universities. Micro-video technology is a kind of video with interactive and real-time characteristics, which can be free from geographical restrictions. There are also many English education videos in the micro-video technology, which has a richer background of English knowledge and local customs. This research uses micro-video technology to assist English class level in universities. This research also considers big data theory to extract relevant features in micro-video technology to improve level of college English education. The research results show that micro-video technology can improve learning interest of English students in colleges and universities. CNN and LSTM methods can better predict student grades, student performance characteristics, and teacher performance characteristics. The hybrid CNN-LSTM algorithm has higher accuracy in predicting relevant features of college English teaching compared to the single CNN method. This also shows that the micro-video technology has great temporal characteristics in English teaching. This is a friendly study for improving the quality of English teaching.
Securing medical records based on inter-planetary file system and blockchain

In general, health records include important information like the patient's history, findings of examinations and assessments, diagnosis reports, documentation of consent, and treatment plans.Sharing this information has grown to be a challenge concerning data security, as it could result in compromising patient privacy.Therefore, the patient's information should not be misused or tampered with.In this paper, a full process of storing and retrieving medical records is proposed using a decentralized system through the integration of two emerging technologies: Blockchain and Inter-Planetary File System (IPFS).The system provides solutions for the major security concerns associated with medical files, including authentication and authorization, database breaches, data integrity of local and cloud storage, and data availability.The obtained results indicate a high level of safety by adding security layers such as confidentiality, authentication, authorization and access control, based on different factors.All these aspects contribute to reaching the aim of the proposed system, which is storing and retrieving medical records in a decentralized and safe manner.
Automated information retrieval and services of graduate school using chatbot system

&lt;span&gt;Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research.&lt;/span&gt;
Adaptive Graph Embedded Preserving Projection Learning for Feature Extraction and Selection

Preserving projection learning has been widely used in feature extraction and selection for unsupervised image classification. Generally, some related methods constructed a graph to represent the nearest neighbor relationships of the data based on the Euclidean distances among different samples, which used 0 or 1 to predefine whether two samples are from the same class. Since a simple Euclidean distance is sensitive to noise, the predefined graph cannot produce exact correlations between the two samples. What is more, the predefined graph cannot reflect the structure of the projected data on a latent subspace when the projection matrix is learned. To solve these problems, in this article a novel adaptive graph embedded preserving projection learning (AGE_PPL) method is proposed, first combining the sparsity-based graph learning and the projection learning as an integral framework for feature extraction and feature selection. In particular, a sparse representation term with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm is exploited in AGE_PPL to achieve the adaptive graph of the data to preserve the local structures among different samples while the projection matrix is learned. Meanwhile, a global-scale constraint is imposed to preserve the global structure of the data on a latent subspace. Therefore, the transformed samples will be more discriminative, allowing margins of the same class to be reduced, and margins among different classes to be enlarged. Experimental results proved the effectiveness of the proposed algorithm by obtaining competitive performances over other baseline and state-of-the-art methods. In addition, the proposed method is very flexible for feature selection and dimensionality reduction.
Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia

Assessing the numbers and distribution of at-risk megafauna such as the black rhino (Diceros bicornis) is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Satellite monitoring is emerging as a potential tool for very large animals (e.g., elephant) but detecting smaller species requires higher resolution imaging. Drones can deliver the required resolution and speed of monitoring, but challenges remain in delivering automated monitoring systems where internet connectivity is unreliable or absent. This study describes a model built to run on a drone to identify in situ images of megafauna. Compared with previously reported studies, this automated detection framework has a lower hardware cost and can function with a reduced internet bandwidth requirement for local network communication. It proposes the use of a Jetson Xavier NX, onboard a Parrot Anafi drone, connected to the internet throughout the flight to deliver a lightweight web-based notification system upon detection of the target species. The GPS location with the detected target species images is sent using MQ Telemetry Transport (MQTT), a lightweight messaging protocol using a publisher/subscriber architecture for IoT devices. It provides reliable message delivery when internet connection is sporadic. We used a YOLOv5l6 object detection architecture trained to identify a bounding box for one of five objects of interest in a frame of video. At an intersection over union (IoU) threshold of 0.5, our model achieved an average precision (AP) of 0.81 for black rhino (our primary target) and 0.83 for giraffe (Giraffa giraffa). The model was less successful at identifying the other smaller objects which were not our primary targets: 0.34, 0.25, and 0.42 for ostrich (Struthio camelus australis), springbok (Antidorcas marsupialis) and human respectively. We used several techniques to optimize performance and overcome the inherent challenge of small objects (animals) in the data. Although our primary focus for the development of the model was rhino, we included other species classes to emulate field conditions where many animal species are encountered, and thus reduce the false positive occurrence rate for rhino detections. To constrain model overfitting, we trained the model on a dataset with varied terrain, angle and lighting conditions and used data augmentation techniques (i.e., GANs). We used image tiling and a relatively larger (i.e., higher resolution) image input size to compensate for the difficulty faced in detecting small objects when using YOLO. In this study, we demonstrated the potential of a drone-based AI pipeline model to automate the detection of free-ranging megafauna detection in a remote setting and create alerts to a wildlife manager in a relatively poorly connected field environment.
Upper Bounds on Key Rates in Device-Independent Quantum Key Distribution Based on Convex-Combination Attacks

We provide an efficient, generic tool for upper-bounding key rates in device-independent quantum key distribution (DIQKD) and prove that Bell nonlocality is not suf-ficient for the security of standard DIQKD protocols.
COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading

The HPC industry is inexorably moving towards an era of extremely heterogeneous architectures, with more devices configured on any given HPC platform and potentially more kinds of devices, some of them highly specialized. Writing a separate code suitable for each target system for a given HPC application is not practical. The better solution is to use directive-based parallel programming models such as OpenMP. OpenMP provides a number of options for offloading a piece of code to devices like GPUs. To select the best option from such options during compilation, most modern compilers use analytical models to estimate the cost of executing the original code and the different offloading code variants. Building such an analytical model for compilers is a difficult task that necessi-tates a lot of effort on the part of a compiler engineer. Recently, machine learning techniques have been successfully applied to build cost models for a variety of compiler optimization problems. In this paper, we present COMPOFF, a cost model that statically estimates the Cost of OpenMP OFFloading using a neural network model. We used six different transformations on a parallel code of Wilson Dslash Operator to support GPU offloading, and we predicted their cost of execution on different GPUs using COMPOFF during compile time. Our results show that this model can predict offloading costs with a root mean squared error in prediction of less than 0.5 seconds. Our preliminary findings indicate that this work will make it much easier and faster for scientists and compiler developers to port legacy HPC applications that use OpenMP to new heterogeneous computing environment.
XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods.
Developing a New Collision-Resistant Hashing Algorithm

Today, cryptographic hash functions have numerous applications in different areas. At the same time, new collision attacks have been developed recently, making some widely used algorithms like SHA-1 vulnerable and unreliable. This article is aiming at the development of a new hashing algorithm that will be resistant to all cryptographic attacks, including quantum collision attacks that potentially pose a threat to some widely used cryptographic hash functions. This algorithm was called Nik-512. The avalanche effect is tested to ensure the cryptographic strength of the developed algorithm. The Nik-512 function is then applied to build a data integrity system which can be used to protect data from malicious users.
Arm movement recognition of badminton players in the third hit based on visual search

In order to overcome the problems of low recognition accuracy, high repetition rate of corner points and long recognition time of badminton players' arm movements in the third beat, a new recognition method based on visual search is proposed in this paper. This method uses visual search technology, binocular visual camera to record video, image stabilisation technology to implement histogram equalisation processing on the recorded visual image, and obtains the visual image after noise elimination. This method also uses scale invariant feature conversion algorithm to collect image sift corner features, combines with support vector machine algorithm to construct feature recognition model, and realises the third shot arm movement of badminton players to distinguish. The experimental results show that the signal-to-noise ratio (SNR) of the collected visual images is less than 10 dB, the repetition rate of corners is always lower than 60%, and the recognition time is basically controlled within 0.8 s.
Cyber Security Challenges, its Emerging Trends on Latest Information and Communication Technology and Cyber Crime in Mobile Money Transaction Services

This paper investigates the challenges emerging trends on latest Information and Communication Technology and cybercrime in mobile money transaction services in Tanzania. The objective of this is to evaluate the challenges associated with this rapid growth in ICT and to determine factors influencing Cybersecurity readiness and Cybercrimes in mobile money transaction services. Cyber Security plays a significant role in the field of Information and Communication Technology especially on mobile money transaction services. The study recognizes the provision of mobile money services by both telecommunication companies and local banks, the fact is that whenever we think about the cyber security, the first thing that comes to our mind is “cybercrimes” which are increasing extremely day to day and become a threat. Cybercrimes are mostly practiced through both internet and mobile money services. Securing the information has become one of the major challenges in the present day. Various Governments and companies are taking measures in order to prevent these cybercrimes. Besides cyber security remains concern to many. This paper mainly focuses on challenges faced by cyber security on the latest information and communication technology and cybercrime especially in mobile money transaction services in Tanzania. Its latest techniques, ethics and trends that change the face of cyber security. Relevant data was collected from the Forensic Section of the Tanzania Police Force, Mobile banking mobile money agents and users of the mobile-money services. This study also used the Pearson correlation and analysis of variance (ANOVA) to establish different facts and determine whether the independent variables had a combined effect on the dependent variable. The findings of the study revealed that there is a positive and significant correlation between users’ awareness, mobile money agents training, top management support, technical and logical controls and cybersecurity readiness. The study also concluded that effective training programs aimed to enlighten the users and mobile money agents on cybersecurity issues are an important ingredient for cybersecurity readiness in cybercrime in mobile money transaction services.