Reviewerly/exHarmony huggingface dataset:
Web-based expert system to determine digital forensics tool using rule-based reasoning approach

Abstract Digital forensics is a method to trace the digital evidence using knowledge of science. There are several stages in the method of digital forensics. Each stage has their own way to use the method collaborate with the tool of digital forensics. Nowadays, there are tools that we can use in digital forensics. Therefore, not all the tool coming with the help document on how to use the tool. This situation makes the investigator have to check the feature of the tool one by one in order to suit which one is the best tool to use in some stage. To overcome this problem, we made a system to determine the right tool in digital forensics using rule-based reasoning approach. The result of this paper is web-based system to determine the right tool in digital forensics. The system shows that only 40% in suitability to help the investigator to determine the right tool. This cause by lacking of the rule consists in the reasoning approach.
RETRACTED ARTICLE: Green city economic efficiency based on cloud computing and machine learning

In the early development process, my country destroyed the development of the natural environment and the allocation of natural resources in pursuit of the speed of economic development. With the acceleration of development, my country's economic construction and social development have achieved good results. To achieve sustainable economic and ecological development in the future, green and high-quality development must be achieved to meet the content of the development strategy of realizing the resource-based and economical society in our country. In the long-term development process, many regions rely on resources to create a lot of opportunities for the development of the region, but with the high consumption of resources, many resources are non-renewable or the regeneration process is very slow, resulting in many regions entering the resources. During the period of shortage, it is necessary to advocate green development. This article analyzes the green economic benefits achieved by the innovative development of a certain province in my country by studying the relevant knowledge of machine learning and some important issues in the theoretical development of cloud computing, which can provide a certain reference for the development of economic efficiency theory and provide help for people to formulate green development strategies for cities and regions.
Matyas–Meyer–Oseas Skein Cryptographic Hash Blockchain-Based Secure Access Control for E-Learning in Cloud

A learning system is that depends on formalized teaching with the assist of electronic resources which is called e-learning. E-learning employs electronic technologies to access educational curriculum. E-learning is a promising and growing area that permits the rapid integration of smart learning and the teaching process. Since cloud computing is the main paradigm to deliver more efficiently learning content in an integrated environment. E-learning comprises all kinds of educational technology but security analysis is required to achieve higher data confidentiality, fine-grained access control. In order to increase security of data access, A Matyas–Meyer Skein Cryptographic Hash Blockchain and Modified Connectionist Double Q-learning (MMSCHB-MCDQL) technique is introduced. The main aim of the MMSCHB-MCDQL technique is to increase the secure access and academic performance analysis using e-learning data. The IoT devices are deployed for sensing and monitoring the student activities during the e-learning process. At first, the sensing data are collected from the IoT devices and apply the Matyas–Meyer–Oseas Skein Cryptographic Hash Blockchain technique for secure data transmission. A Skein Cryptographic Hash is applied to a blockchain technology to generate the hash for each input data using Matyas–Meyer–Oseas compression function. In addition, the smart contract theory is applied to Blockchain technology to guarantee access control without believing external third parties and it helps to achieve a higher data confidentiality rate. After that, a Modified Connectionist Double Q-learning algorithm is applied for analyzing the student activities to make optimal action with higher accuracy. Based on the learning process, the student’s performance levels are correctly predicted. Experimental evaluation is carried out on certain factors such as data confidentiality rate, execution time, and prediction accuracy with respect to a number of student data. The experimental results and discussion demonstrate that the proposed MMSCHB-MCDQL technique offers an efficient solution for secure decentralized access control in the cloud. The experimental results evidence that the MMSCHB-MCDQL technique improves data confidentiality rate and prediction accuracy by 9.5% and 10% and reduces the execution time by 15% as compared to the conventional methods.
Data Analysis on Cancer Disease Using Machine Learning Techniques

Coherent and systematic analysis for finding complex patterns in structured and unstructured cancer data has seen quite a rich and diverse implementation of distinct techniques in the recent past. The delicate and life-threatening aspect of Cancer has led to the huge need as well as the attraction of everyone to propose optimized techniques to garner a commendable result for the prediction of cancer subtypes. As a result, several Data Analysis techniques have led a revolution to provide the best outcomes, among which several have shown mammoth results. In this chapter, the focus is put directly on such techniques that have been implemented and adapted for cancer data analysis. The chapter goes into quite an in-depth review of each of the proposed architectures which have been very precisely screened by us and also do quite a lot to develop a concrete sense of each one of these taxonomies by putting each of them under scanning through various evaluation metrics. Furthermore, the chapter issues several future scopes and recommendations from the perspective of the authors to ignite the thought of the ones interested in pushing this field into further sub-stratum.
Design of Side-Channel-Resistant Power Monitors

In modern computing platforms, power monitors (PwrMons) are employed to deliver online power estimates to support different runtime power-performance optimization methodologies. However, the possibility of setting up a successful side-channel attack by analyzing the power estimates imposes the use of a suitable and systematic approach in the design of such PwrMons. This article proposes a design methodology to automatically identify and implement side-channel-resistant PwrMons at the hardware level, for generic computing platforms. The methodology works by designing a PwrMon for which the switching activity of the signals used to compute the power estimates is not a function of both the secret key and the plaintext/ciphertext values processed by the computing platform. According to the most recent standardized methodologies to assess the side-channel security, our experimental validation leverages both correlation power analysis and <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-test analysis considering a general purpose System on Chip executing different cryptographic primitives and an application-specific accelerator implementing the AES-128 algorithm. Our results confirm the impossibility of retrieving the secret key from the power estimates provided by our side-channel-resistant PwrMon. Considering several temporal resolutions, we highlight an accuracy error of the power estimates limited to less than 2.7&#x0025;, as well as an average area and power overheads for the protected PwrMons lower than 6&#x0025; and 5&#x0025;, respectively. To this end, the proposed methodology is able to deliver a side-channel-resistant PwrMon within state-of-the-art accuracy error and overheads.
An Improved Q-RRT* Algorithm Based on Virtual Light

The Rapidly-exploring Random Tree (RRT) algorithm is an efficient path-planning algorithm based on random sampling.The RRT* algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal solution.However, it has been proven to take an infinite time to do so.An improved Quick-RRT* (Q-RRT*) algorithm based on a virtual light source is proposed in this paper to overcome this problem.The virtual light-based Q-RRT* (LQ-RRT*) takes advantage of the heuristic information generated by the virtual light on the map.In this way, the tree can find the initial solution quickly.Next, the LQ-RRT* algorithm combines the heuristic information with the optimization capability of the Q-RRT* algorithm to find the approximate optimal solution.LQ-RRT* further optimizes the sampling space compared with the Q-RRT* algorithm and improves the sampling efficiency.The efficiency of the algorithm is verified by comparison experiments in different simulation environments.The results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.
Fine-Grained Image Retrieval Via Multiple Part-Level Feature Ensemble

Fine-Grained Image Retrieval (FGIR) has become a crucial research area of fine-grained analysis. Despite the extensive progress in FGIR, the main problem remains. Existing methods rely mainly on object-level representations, which are interrupted by background clutters. Instead of object-level features, in this paper, we propose to learn part-level representations. We first present a novel Attention-Activation-based Part Detector (AAPD) without any part-level annotations to extract part-level features and to remove background noises. AAPD not only localizes the discriminative part by attention mechanism automatically but also selects the part with high activation values in an unsupervised way. Then we propose a novel unified Multiple Part-level Feature Ensemble (MPFE) framework to assemble serval part-level features extracted by AAPDs. Finally, the proposed MPFE is evaluated on two widely-used benchmark datasets, including CUB-200-2011 and Cars-196. The MPFE Framework is capable of localizing discriminative parts and achieves state-of-the-art performances on two datasets.
Lightweight fog‐centric auditing scheme to verify integrity of <scp>IoT</scp> healthcare data in the cloud environment

Summary IoT healthcare applications have become very popular among medical societies. At regular intervals, the healthcare IoT devices collect the patient's health information and transfer it to cloud storage. Storing the health records in the cloud environment increases the vulnerability. Integrity verification performed at regular intervals ensures the safety and security of the healthcare data stored in the cloud. Existing schemes use RSA and BLS signatures to verify the authenticity. However, the size of the signature is too large in RSA and BLS schemes. Using large signatures for proving the authenticity increases the computation overhead. IoT devices have minimal resource capacity, hence performing heavily weighted integrity schemes will create an unbearable workload. Likewise, private auditing increases the communication overhead between IoT devices and cloud storage. To overcome these issues, a fog‐centric auditing scheme is proposed based on the Cramer–Shoup cryptosystem. The proposed scheme allows the fog nodes to perform integrity verification without compromising security. It reduces the communication overhead by 55%–60%. The size of the CS signature used in the scheme is far lesser than the existing signatures. Moreover, performing the inline deduplication on the fog layer improves the efficiency of cloud storage by 30%.
Simulation and Implementation of SLAM Drawing Based on ROS Wheeled Mobile Robot

Abstract In order to realize the establishment of the unknown terrain map in the indoor environment of the ROS wheeled mobile robot, this article uses Lidar, Raspberry Pi and other hardware to build the wheeled mobile robot, in the ROS environment, the robot is built by the URDF model, and the robot model is configured. Differential controller, keyboard control node, etc. Establish the simulation environment, use different simulation environments to visually simulate the robot, and simulate the two optimization algorithms in the SLAM algorithm to simulate the construction of the map. In order to explore the effect of SLAM’s different optimization algorithms on the construction of graphics, the SLAM algorithm based on filter and the SLAM algorithm based on graph optimization are introduced respectively. The robot is selected. After the robot is built, three different experimental environments are selected. A variety of map-building algorithms are used to experiment, comparing the effects of G-mapping algorithm and Cartographer algorithm to build maps in a small-scale, large-scale and multi-obstacle environment. Use the image processing software under the Linux system to analyse the map created by the robot in the experimental environment, measure the coordinates of the robot feature points in the created map by the number of pixels, and compare the actual coordinates of the location of the special point in the actual environment to evaluate Construction effect. By comparing the effects of the two algorithms on the contours of various obstacles when building a map, the effect of mapping in a multi-obstacle environment is evaluated. The results show that the SLAM graph-building algorithm based on graph optimization has a more stable graph-building effect.
Research on the Application of Computer Technology in Software Technology Talents Training System in Higher Vocational Colleges

Abstract With the advent of the information age, the demand for computer professionals such as network maintenance engineers, security engineers, development and operation engineers has gradually increased, and computer software has become one of the hottest industries at present and in the future. As an important part of the education system, higher vocational colleges are constantly training and transporting computer software technology professionals needed by the society, and exploring the application of efficient talent training mode has become one of the key tasks of higher vocational colleges in the future. While enterprises generally reflect that college graduates can’t meet their own needs, colleges and universities are suffering from being unable to know the real needs of enterprises, and lack of specific training templates and inspection standards, resulting in the disconnection between software talent training and demand. In this paper, the analytic hierarchy process (AHP) is used to analyze the established evaluation index system of software technology specialty in higher vocational colleges, and to explore the effective strategies to strengthen the application of efficient talent training mode of software technology specialty in higher vocational colleges.
Space robot motion path planning based on fuzzy control algorithm

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.
Abstractive Summarization of Malayalam Document using Sequence to Sequence Model

There are different text summarization process available in Natural Language Processing. Among them abstractive text summarization is one of the challenging problems in natural language processing. Abstractive text summarization contains a short and concise summary of a large text document built from the underlying message of the text. The objective of the proposed system is to create a short and understandable abstractive summary of a malayalam text document. A sequence to sequence model is used to create the summary of the document. In this work, the goal was to increase the efficiency and reduce the training loss of a sequence to sequence model thereby implementing a better abstractive text summarizer for a malayalam document.
Taxonomy on RapidMiner Using Machine Learning

Malware Classification is an important factor in security of Computer and Network Systems. Malware Classification means to classify the malware samples into their respective families and subfamilies. In this work we have used malware dataset having multiple signatures. Our objective here is to provide efficient machine learning algorithm which provide best accuracy for classification of malware into their respective families. We use RapidMiner tool and four different machine learning algorithms to analyze dataset. Best accuracy we get is of 64.05% using Decision Tree algorithm and split validation technique for splitting of dataset into training and test set.
Credit Card Fraud Detection via Deep Learning Method Using Data Balance Tools

Previous studies have shown the effectiveness of deep learning algorithms in improving the detection of credit card fraud, which has become a major issue for financial institutions. This paper discusses how two deep learning methods, Convolutional Neural Network (CNN) and auto-encoder, perform in the fraud detection task using two different datasets. This research utilizes a brand-new dataset with all raw input variables and a Université Libre de Bruxelles (ULB) transaction dataset, which has been preprocessed with PCA technology. Since imbalanced datasets can affect the training quality, we further preprocess the datasets using random under-sampling and the Synthetic Minority Oversampling Techniques (SMOTE) to balance the datasets. Through the experimental results, we find that the networks perform well for the ULB dataset with 93% accuracy in prediction, but perform poorly for the independent input dataset, and the performance can be improved when increasing the complexity of the network. We also notice that the under-sampling method helps improve prediction accuracy better than the oversampling method. The results indicate that more complicated networks are required to detect fraud when the criterion for fraud is stricter, while balancing the dataset before training will improve the results.
Twi Corpus: A Massively Twi-to-Handful Languages Parallel Bible Corpus

This paper presents detailed modeling of massively parallel Bible corpus based on Twi, a common Ghanaian language, to a handful of languages. We discussed some of the common issues we encountered in obtaining, processing, converting, and formatting the corpus and the latent desire for success in NLP. We stored the sentence aligned data in various files based on the Twi to the selected language pairs with a tab-delimited separation. Verses with the same line number in a line pair are mappings of each other. It is often challenging to learn what a “clean” corpus looks like in lower-resource situations, especially where the target corpus is the only sample of the language's parallel text. We, therefore, performed unsupervised measurements on each sentence pair. We engage the squared Mahalanobis distances that predicted parallelism on the dataset. Eventually, we perform a statistical analysis of the corpora collected based on selected text categorization models for text classification by leveraging vector embedding (like Word2vec). Finally, we trained the Twi vocabs for a 2D representation. Similar words find their vectors closer by engaging t-Distributed Stochastic Neighbor embedding (t-SNE).
Performances Combination Schemes AES-Turbo Code Based-on Keys Length

Abstract In data communication the process of sending data in the form of information is susceptible to theft, destruction, modification or repetition of the data sent which causes information to be changed / corrupted according to the original data. One way to guarantee authenticity can be done by utilizing security techniques in the process of sending data, one of which is by using a combination of cryptographic and error correction techniques, namely AES and Turbo Code. This study aims to combine the two techniques and apply them to text data by evaluating based on the length of AES encryption keys. Performance results indicate that AES-Turbo cryptographic combination algorithms can work optimally at SNR greater than or equal to 15 dB, for the best test value for avalanche effects is 24.77 % at 256-bit key lengths and the testing data execution time with an average time of 9.19 seconds, the time required for decryption is longer than the time needed for encryption, and the longer the key the longer it takes for encryption and decryption. For selection of keys length of AES more efficiency and effectiveness that is by using 128-bit key.
Digitized railway ticket verification using facial recognition

The history of the Indian railway dates back to over 160 years. Since then, manual verification of passenger tickets has been practiced by the ticket examiners. This process is very wasteful, in terms of resources, and also tends to be inconvenient for the passengers at times. This project aims at alleviating the problems and modernizing the conventional ticket checking system by the application of computer vision techniques. The system's goal is to create a scalable and efficient approach in order to eliminate the need for carrying a ticket which reduces the usage of paper as well as improves the convenience of passengers. This digitization also provides a clean and organized system throughout the ticket verification life-cycle. With the recent advancements in the field of computer vision and the astounding improvements in the capacity of deep learning algorithms to impart the abilities of human eyes and brain to computer systems, the concept of facial recognition was applied as a solution to railway ticketing modernization. Various face detection and recognition algorithms were studied and a model was created using Haar Cascade Classifiers for human face detection and Local Binary Pattern Histogram (LBPH) method for face recognition. Using this model, this project aims at converting the ticket checking process into a digitized and paperless system, which would be much more convenient for the passengers and efficient for the ticket examiners.