Reviewerly/exHarmony huggingface dataset:
UAV-Based Rescue System and Seismic Zonation for Hazard Analysis and Disaster Management

The rapid growth of population leads to numerous difficulties in case of any natural calamity. Such problems include insecurity, risk assessment and disaster planning of affected regions. Therefore, the critical demand of society is to invent efficient ways to tackle disastrous situations. UAV-based technologies are the only realistic approach to deal with such circumstances. Additionally, UAVs are already acknowledged for low cost-effectiveness and flexibility by executing numerous tasks. This work proposes an autonomous system based on drone rescue (Auto-SBDR). Auto-SBDR model uses UAVs to provide first and quick response to a life-threatening situation in urban areas like residential and commercial setups. However, integrated autonomous rescue systems comprise three main sections: intelligent UAVs, sensor networks and command centers. Furthermore, Auto-SBDR systems are designed in such a manner that UAVs automatically deploy to a specified location based on seismic zonation upon receiving signals from the sensors network. Where, seismic zones are produced through computing earthquake catalogs. Through Interpolation techniques, Kalabagh areas are categorized in events frequency, depth and magnitude. Moreover, the projected UAV rescue system drastically reduces response time as compared to conventional response systems. While operating in earthquake-prone areas, data collected by FANETs are considered to be extremely valuable.
TOOLS OF IMPROVING THE DIGITAL COMPETENCE LEVEL OF CYBER SECURITY PROFESSIONALS IN THE EDUCATIONAL PROCESS

The article analyzes the accrued national and foreign developments regarding the problems of digital competence formation and effective use of information technology in education. The components of digital competence are considered, which provide for a confident, critical and responsible interaction with digital technology for education, work and participation in social activities. The results of a global information security research are presented along with surveys of employees of leading cyber companies around the world, including Ukraine, that testify to the necessity of further application and implementation of an integrated approach to education using organizational measures, software and hardware means and management processes at all activity levels of any organization, as well as using the appropriate tools to raise the digital competence level. The essence of the importance of digital tools in the field of cyber security is formulated, which means a set of Internet tools (resources) to protect network environment entities against various information and cyber threats, ensuring proper organization of countering their effect, formation, functioning and evolution of cyber space and development of educational cyber technology and the information society as a whole. Three main groups of digital cyber security tools were analyzed, identified and proposed (professional cyber tools, education cyber tools and communicative cyber tools) that enable the use, access, filtering, evaluating, creating, programming and communicating digital content, managing and protecting information, content, data and digital identities, as well as working effectively with software, devices, artificial intelligence, robots and more. It is proved that present-day work with digital cyber tools and their content requires a reflective, critical and at the same time inquisitive, open and promising attitude to their development, as well as an ethical, safe, effective and responsible approach to their use
Credit Card Fraud Intelligent Detection Based on Machine Learning

The advancement of technology and the diversification of payment forms have resulted in the emergence of financial fraud, with credit card fraud being more prevalent. In financial fraud detection, there are currently two approaches: 1) Rule-based methods and 2) Machine learning-based methods now. Although both approaches are relatively well developed and have been tested well, there still need human involvement to drive the experiments. Besides, the limitations of the data set can affect the predictive effectiveness of the models. Therefore, combining different strategies is needed to improve prediction performance. This paper summarizes new approaches that have been proposed recently, including the combination of spatial-temporal attention mechanism and GNN, the combination of DNN and spectrum coordinates, the combination of semi-supervised learning and GNN and the hybrid of supervised learning and semi-supervised learning. Some of the challenges faced in financial fraud detection are also addressed in this paper.
Speaker Identification System Using CNN Approach

In this paper, a text independent Speaker Identification (SI) system is proposed using convolutional neural network (CNN). Also, the proposed methodology is tested in a noisy environment. The text independent SI system is used in this work due to its ability to learn features for classification task. Spectrogram images are used at front end for feature extraction. For classification, convolutional neural network is utilized indicating promising results. Dataset used in this work comprise of 5 speakers, with each speaker uttering 4 voice samples. The overall accuracy achieved for the proposed approach is 96.54 &#x0025;.
A New Intelligent Approach for Deaf/Dumb People based on Deep Learning

People who are deaf or have difficulty speaking use sign language, which consists of hand gestures with particular motions that symbolize the "language" they are communicating.A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole hand.In this paper, we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves (IDLG).The approach IDLG offers scientific contributions based deep-learning, a multimode command techniques, real-time, and effective use, and high accuracy rates.For this purpose, smart gloves working in real time were designed.The data obtained from the gloves was processed using deep-learning-based approaches and classified multi-mode commands that allow dumb people to speak with regular people via their smart phone.Internally, the glove has five flex sensors and an accelerometer using to achieve Low-Cost Control System.The flex sensor generates a proportional change in resistance for each individual move.The processing of these hand gestures is in Atmega32A Microcontroller which is an advance version of the microcontroller and the lab view software.IDLG compares the input signal to memory-stored specified voltage values.The performance of the IDLG approach was verified on a dataset created using different hand gestures from 20 different people.In the test using the IDLG approach on 10,000 data points, process time performance of milliseconds was achieved with 97% accuracy.
Privacy-Preserving Classification Scheme Based on Support Vector Machine

As a classifier, support vector machine (SVM) explains a core problem of machine learning, namely sample classification in statistical terms. It has been widely used in machine learning, data mining, pattern recognition, and other fields. With the wide applications of SVMs in machine learning and big data, privacy protection of sensitive data in SVMs is becoming more and more important, such as face recognition and biometric information. At present, the main privacy protection methods in SVMs are homomorphic encryption and secure multiparty computation. However, there are some problems in current research. The computational efficiency is low, and the scalability of the schemes is poor. In addition, the user must stay online in some solutions. To solve the abovementioned problems, this article designs a secure and efficient classification scheme based on SVM to protect the privacy of private data and support vectors in the calculation and transmission process. First, the distributed two trapdoors public-key cryptosystem proposed by Liu is used to realize the distributed double-key decryption function, weaken the decryption capability of a cloud server with the master key, and prevent the server from launching active attacks. Second, we design a universal secure computing protocol for nonlinear SVMs based on the Gaussian kernel function, which can be extended to polynomial kernel function, sigmoid kernel function and is suitable to different kernel functions. Compared with existing schemes, our solution reduces the amount of encrypted data, simplifies the calculation process, and improves calculation efficiency. Third, an introduced cloud server realizes user offline function. Finally, we analyze the security of the scheme and verify its efficiency through experiments. Analysis and experimental results show that the scheme has the advantages of high efficiency, good scalability, and user offline function.
Review of Attribute Based Access Control (ABAC) Models for Cloud Computing

Attribute-Based Encryption (ABE) is one of the new dreams for fine grained access control in cloud computing. A lot of exploration work has been done in both academic and industrial communities. Be that as it may, before ABE can be conveyed in information outsourcing frameworks, efficient enforcement of authorization policies and strategy refreshes are the principal obstacles. Consequently, to take care of this issue, efficient and secure attribute and client revocation ought to be proposed in unique ABE plot, which is yet a test in existing work. In this paper, they propose another cipher text-strategy ABE (CP-ABE) development with efficient attribute and client revocation, which generally eliminates the overhead calculation at information administration chief and information proprietor. Additionally, we present an efficient access control component based on the CP-ABE development with one outsourcing calculation specialist co-op.
TauAud: Test Augmentation of Image Recognition in Autonomous Driving

Deep learning has made great progress in autonomous driving. However, due to the insufficient and unbalanced data used for training and testing, deep learning model will easily overfit and have poor generalization capabilities. The problem of collecting balanced and sufficient data often arises because it is difficult and dangerous to collect data under abnormal situations, such as abnormal weather or camera damage. In addition, neural networks need to be trained on a large amount of accurate and reliable data. Incorrect data may result in poor performance. In this study, we analyze the impact of abnormal situations on the traffic images and perform a series of image processing methods on the normal traffic dataset to generate sufficient and balanced images. Moreover, we use the trained deep learning model to evaluate the effectiveness of the augmented dataset.
Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the web.So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically.The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions.In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation.Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system.Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan).In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem.The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution.We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors.The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.
Finding Semantic Bugs Fast

Abstract Finding semantic bugs in code is difficult and requires precious expert time. Lacking comprehensive formal specifications, deductive verification is not an option. We propose an incremental specification procedure: With the help of automatic verification tools, a domain expert is guided through program runs and source code locations. The expert validates a run at certain locations and creates lightweight annotations. Formal methods training is not required. We demonstrate by example that this approach is capable to quickly detect different kinds of semantic bugs. We position our approach in the middle ground between fully-fledged deductive verification and bug finding without semantic guidance.
Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks

In this chapter, we mainly investigate the trading between the cloud/fog computing service provider (CFP) and the computationally lightweight devices, i.e., miners. From the system perspective, we aim to maximize the social welfare, which is the total utility of the CFP and all miners in the blockchain network. Social welfare can be interpreted as the system efficiency [1]. For an efficient and sustainable business ecosystem, there are some critical issues about cloud/fog resources allocation and pricing for the service provider. First, which miner can be offered the computing resources? Too many miners will cause service congestion and incur high operation costs to the service provider. By contrast, a tiny group of miners may erode the integrity of the blockchain network. Second, how to set a reasonable service price for miners such that they can be incentivized to undertake the mining tasks? The efficient method is to set up an auction where the miners can actively submit their bids to the CFP for decision making. We should also consider how to make miners truthfully expose their private valuation. A miner's valuation on the computing service is directly related to its privately collected transactional data, which determines its expected reward from the blockchain. We propose an auction-based cloud/fog computing resource market model for blockchain networks to address the above questions.
No-Reference Quality Assessment for Underwater Images

Due to the challenge of underwater environment and the limitations of imaging system, underwater images are often characterized by low contrast, blurring, and other degradations, which may affect their analyses and practical applications. Thus, achieving quality assessment plays a significant role in underwater visual tasks as well as optimizing underwater image sharpening algorithms. In this paper, we propose a novel reference-free underwater image quality assessment metric, dubbed CSN, based on the underwater imaging principles. CSN metric extracts a total of 28 features for contrast measure, sharpness measure and naturalness measure, respectively. Specifically, the contrast measure is calculated by the statistics characteristics of the contrast energy based on the human visual perceptual. In addition, we design a novel sharpness measure by considering perceptual sharpness index and visual saliency to improve the discrimination ability of the sharpness measurement. The naturalness measurement is used to evaluate the spatial structure variation caused by various distortions of the underwater image. After that, we feed these quality-aware features into the trained support vector regression model to perform a regression that maps these features to the image quality. Moreover, we conduct considerable tests and comparative experiments on a publicly large-scale dataset namely UWIQA to demonstrate the performance of the proposed method. Experimental results show that the proposed CSN has a strong correlation with subjective mean opinion score, which validate its superiority.
Exploring students' sensemaking of test case design. An initial study

Testing is the most used process to assure software systems quality. With increasing complexity of software, testing is getting more important. Testing is an intellectual activity that needs to allocate multiple cognitive resources in students, making it a challenging topic to teach in computer science programs. We advocate that testing is both model-based and exploratory, meaning that we can only make useful test models for test case design once we have made enough sense about the testing problem. The latter can only be achieved through exploring, i.e. questioning, studying, observing and inferring. In this paper, we present an initial diagnostic study to understand the sensemaking used by students while creating test models. We found indications of four different approaches used by students when modelling test cases. A plan for further research is presented on how to improve teaching by taking into account the student's sensemaking approaches.
Data and Algorithm Pricing: Incentive Mechanisms Design for Federated Learning

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Adversary Agnostic Robust Deep Reinforcement Learning

Deep reinforcement learning (DRL) policies have been shown to be deceived by perturbations (e.g., random noise or intensional adversarial attacks) on state observations that appear at test time but are unknown during training. To increase the robustness of DRL policies, previous approaches assume that explicit adversarial information can be added into the training process, to achieve generalization ability on these perturbed observations as well. However, such approaches not only make robustness improvement more expensive but may also leave a model prone to other kinds of attacks in the wild. In contrast, we propose an adversary agnostic robust DRL paradigm that does not require learning from predefined adversaries. To this end, we first theoretically show that robustness could indeed be achieved independently of the adversaries based on a policy distillation (PD) setting. Motivated by this finding, we propose a new PD loss with two terms: 1) a prescription gap maximization (PGM) loss aiming to simultaneously maximize the likelihood of the action selected by the teacher policy and the entropy over the remaining actions and 2) a corresponding Jacobian regularization (JR) loss that minimizes the magnitude of gradients with respect to the input state. The theoretical analysis substantiates that our distillation loss guarantees to increase the prescription gap and hence improves the adversarial robustness. Furthermore, experiments on five Atari games firmly verify the superiority of our approach compared to the state-of-the-art baselines.
Study of Low Resource Language Document Extractive Summarization using Lexical chain and Bidirectional Encoder Representations from Transformers (BERT)

Language is the basic and unique tool of communication for humans. More than 7000 languages exist on our planet. Among these, the languages which lack in linguistic resources for building statistical NLP applications are known as low resource languages (LRL). Written communication is the most popular medium for humans to express and preserve their thoughts. Advancements in technology are bringing the world closer by facilitating remote communication access. Due to increase in the use of internet, with every second new textual information is getting generated. Not all this textual information is useful. With this context the task of summarization is gaining importance. Summary can be generated by two ways: Extractive and Abstractive. In Extractive summarization the key phrases and key sentences in the source document are retained, whereas Abstractive summary is generated by rewriting the key sentences. The task of summarization becomes more challenging in case of LRL documents. The paper focuses on the experiments carried out for extractive summarization of LRL documents using two approaches: Lexical chain and BERT.
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements

Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA's speed and accuracy, including employing probability updating functions, discretizing the probability space, and implementing the "Pursuit" concept. The concept of incorporating "structure" into the ordering of the LA's actions is one of the latest advancements to the field, leading to the $$\epsilon $$ -optimal Hierarchical Continuous Pursuit LA (HCPA) that has superior performance to other LA variants when the number of actions is large. Although the previously proposed HCPA is powerful, its speed has a handicap when the required action probability of an action is approaching unity. The reason for this slow convergence is that the learning parameter operates in a multiplicative manner within the probability space, making the increment of the action probability smaller as its probability becomes close to unity. Therefore, we propose the novel Hierarchical Discrete Learning Automata (HDPA) in this paper, which does not possess the same impediment as the HCPA. The proposed machine infuse the principle of discretization into the action probability vector's updating functionality, where this type of updating is invoked recursively at every depth within a hierarchical tree structure and we pursue the best estimated action in all iterations through utilization of the Estimator phenomenon. The proposed machine is $$\epsilon $$ -optimal, and our experimental results demonstrate that the number of iterations required before convergence is significantly reduced for the HDPA, when compared with the HCPA.
Cancer Detection using Artificial Neural Networking Techniques: A Study

The significant role played by Artificial Neural Networks is not known to all sufficiently. There is a tremendously inspiring future that comes with this application of artificial intelligence. The ANNs are built based on working or animal brain neurons, which gives them the ability to pick up and adapt to patterns through previous experiences. In this paper, we aim at presenting the use of ANNs in different types of cancer detection and treatment, including majorly fatal types such as Lung cancer, prostate cancer, Cervical cancer, Brain cancer, stomach cancer, Skin cancer, Colorectal cancer, Oral cancer, Bladder cancer as well as Breast cancer. The focus will be on the comparison between methods used by ANN to differentiate between malignant and non-malignant cells with high accuracy as compared to current prediction methods