Reviewerly/exHarmony huggingface dataset:
Session Key Agreement Protocol for Secure D2D Communication

Device to device (D2D) communication offers numerous benefits such as low latencies, high throughputs, spectral efficiency boosting, and base station energy reduction. However, lack of encryption and authentication during device discovery, link setup, and data transmission exposes the transmitted data to numerous security and privacy threats. To counter these security threats, a number of schemes have been developed based on public key infrastructure, bilinear pairing operations, batch verification, pre-computed lookup tables, symmetric cryptography, and elliptic curve cryptography (ECC) among others. However, these techniques experience very high communication and computation overheads or fail to address privacy and security challenges effectively. In this paper, a session key agreement protocol is proposed to address some of these issues. Simulation results showed that the proposed protocol exhibits the lowest communication costs and average computation costs. In addition, security analysis showed that our protocol is resilient against message replays, masquerade, identity theft, message forgery, and cloning attacks.
Security, privacy and blockchain applications in COVID-19 detection and social distancing

The coronavirus (COVID-19) outbreak has posed serious challenges to healthcare systems around the world. Due to the COVID pandemic, there are a huge number of technologies implemented in our real life to prevent impacts of viruses such as contact tracing, camera surveillance and location detection. However, the use of technologies in the detection and prevention of COVID-19 also poses new problems related to the security and privacy of users. In this context, blockchain has emerged as a potential solution for supporting the prevention of the COVID-19 epidemic by providing security and privacy solutions for facilitating COVID-19 detection and social distancing. Blockchain can help combat the pandemic by offering a number of promising solutions, such as outbreak monitoring, user privacy protection, safe day-to-day operations, medical supply chain and social distancing. This chapter focuses on the applications of blockchain for the COVID-19 pandemic and discusses the key roles of blockchain for COVID-19 detection and social distancing.
Attribute-based Hybrid Encryption &amp; Privacy Preservation Framework for Securing Broadcast Communication Channels

Broadcast communication channels allow users to transact data packets with large groups of people. Which indicates that a greater number of nodes participate in communication, thereby increasing probability of spoofing, spying, and other network attacks. Due to which, encryption strength for this data must be higher than node-to-node unicast communications. A wide variety of models are available to perform this task, but these models compromise on communication quality of service (QoS) in order to enhance encryption performance, due to which they have limited scalability. In order to overcome this drawback, a novel attribute-based hybrid encryption & privacy preservation framework (ABHEPB) is proposed in this text. The proposed model uses attribute-based encryption with packet-lifetime awareness in order to avoid any intermediate attacks. This is achieved via combination of Fernet model with initialization vector (IV) based encryption. The ABHEPB model uses node-level attributes from all broadcasting nodes in order to create a broadcast key. This broadcast key is recreated during decryption via node-level attributes, which can mitigate key-leakage issues from the communication channel. The IV encryption model uses stochastic values of the same key-vector in order to further improve encryption strength, which imbibes automatic key-validation in the network. The encrypted data is further secured using an exponential mechanism to achieve differential privacy. Due to which, the broadcasting parties cannot accidentally gather identifiable information about other nodes. The proposed model was evaluated under a wide variety of attack scenarios, and its attack resilience along with QoS metrics were evaluated. It was observed that the proposed model had 23% lower delay, 19% better attack resilience, and 20% lower complexity when compared with state-of-the-art methods, thereby facilitating its use for real-time deployment scenarios.
Room Layout Estimation based on Planar Semantic Segmentation

Layout estimation is one of fundamental tasks for understanding indoor scenes. In a single image of indoor scenes, key information, such as key points and boundaries for inferring the layout is often severely occluded or completely invisible. In this paper, a novel end-to-end deep learning network, which is extended from Xception module, is constructed for segmentation of semantic planes to infer the room layout structure. Moreover, the network contains an optimized depth wise separable convolution (DSC) in Xception module and a reinforced spatial attention module (SAM), so that it obtained good learning ability in severely occluded areas. Tests on public datasets show that the proposed network has a lower pixel error (PE) and higher generalization compared with existing algorithms. Ablation experiments prove that optimized DSC and SAM in the network are effective in improving the accuracy of layout estimation.
A Framework for Digital Forensics Using Blockchain to Secure Digital Data

Digital forensics (DF) requires evidence integrity and provenance across boundaries of jurisdiction, and blockchain technology is ideal for ensuring that. As part of this paper, we discussed a digital forensic framework designed to help prevent duplication of data and secure digital data. In order to accomplish such forensic capabilities, we provide a block-based forensics framework. Using it, examinations are validated, irreversible, traceable, robust, and demonstrate high levels of confidence among examiners and evidence entities.
Robotic Arm with Obstacle Detection Designed for Assistive Applications

With the growing popularity in manufacturing, medical or aerospace applications, Robotic Arm machinery is a growing industry and field of focus. In this paper, a low-cost robotic arm, HiWonder xArm 2.0, provides a proof-of-concept assistive system for use towards individuals with visual impairment. The system takes the camera and ultrasonic sensor input to collect objects of interest while maintaining user safety, automatically detecting obstacles or humans nearby and providing a requested object as close to a human as possible. This system correctly identifies and retrieves the objects based on the YOLOv4-tiny (You only look Once) deep-learning object detection network, Intel&#x0027;s pyrealsense2 library for the Intel RealSense D435i camera, and four HC-S04 ultrasonic sensors connected to an Arduino Uno.
A Technology Recommender System Based on Web Crawling and Natural Language Processing

The goal of this study is to develop a prototype for a recommendation system that could assist individuals and organizations without in-depth knowledge of technology products and services to choose an appropriate tool/service that best suits their needs. Recommendation systems, as they are popularly known, are personalized information filtering systems which are usually integrated into various consumer and commercial applications. These personalized systems play a vital role, especially when the user is unsure of what to search for. Our work focuses in particular on web-based recommendation systems for end-users in academia and industries who need recommendations for their software tools and services. This paper focuses on extracting information from the web using Apache Nutch, an open-source web crawler which extracts data from websites widely used for software recommendations. The information extracted is indexed in Elasticsearch, whose Natural Language Processing (NLP) and text mining capabilities are applied to provide appropriate recommendations to the end-users. Kibana dashboards and visualizations are used to visualize the recommendations in a format that is conducive for the end-users.
Performance Analysis of Weighted Low-Rank Approximation Models for Robust Face Recognition

Face recognition has become a research hotspot in the fields of computer vision, pattern recognition, and machine learning. The accuracy of recognizing faces with varying expressions and illumination as well as occlusions and noise, on the other hand, presents a unique challenge in face recognition systems. Facial images must be preprocessed for improved recognition accuracy. A major issue with the existing approaches is that they have limited capacity that cannot handle large-scale occlusion and noise situations adequately. In this paper, we present Low -rank matrix approximation (LRMA) models like Robust principal component analysis (RPCA), Weighted Nuclear Norm Minimization (WNNM), and Weighted Schatten p-norm (WSNM) for Robust face recognition. A confusion matrix is used for calculating the accuracy of face recognition. Experiments are conducted and the performance of these LRMA models is compared using the Yale database with facial occlusions, poor illumination, expressions, and noise. The results show, both intuitively and numerically, that WSNM outperforms RPCA in removing facial occlusions, resulting in restored low-rank images with greater PSNR and SSIM.
Evolutionary Generative Adversarial Networks for Sentence Generation from Keyword

Natural language processing has been recognized as an important goal for computers. Especially, generating sentence as we needed is very practical goal. However, the current GAN implementations for sentence generation ignore context. Generating sentences based on a simple corpus by specifying keywords will bring us one step closer to achieving generating along context. This research has made the following two contributions. First, we confirmed the effectiveness of genetic programming in keyphrase selection, built a non-black-box keyphrase selection model. Secondly, we improved the evolutionary GAN and realized sentence generation based on keywords. There is little integration of natural language processing by evolutionary computation methods, so this work is a good step.
Classification Accuracy in Sentiment Analysis using Hybrid and Ensemble Methods

Sentiment Analysis (SA) is a relatively young subject of research in the science of text mining. SA is seeking for opinions, feelings, and subjectivity in the content that it reads. The main purpose of sentiment analysis is to look at product and service reviews to see how many ratings they get. The ensemble model (EM) is proposed in this paper to improve machine learning (ML) results by combining many ML models into a single optimal predictive model. To improve the accuracy of the data mining model, two or more analytical models that are related but not identical are run, and the results are aggregated into a single score. Ensemble methods such as bagging, boosting, stacking, and voting are commonly utilized. Voting is best for classification problems, while regression is usually done by averaging. As a result, the ensemble model using the majority voting technique is used to improve sentiment analysis classification accuracy. For classification, the proposed ensemble classifier incorporates the results of prior work as well as AdaBoosting in SVM. These algorithms&#x2019; classification results are fed into the majority voting procedure as inputs. Each classifier&#x2019;s forecast for test phrases is taken, and the final output prediction with more than half of the votes is deemed the winner. The suggested method outperforms both individual machine learning algorithms and previously proposed hybrid methods in terms of classification accuracy.
A comparative Study of Handwritten Devanagari Script Character Recognition Techniques

In the discipline of pattern recognition, optical character recognition is a critical task. A significant amount of research has been done on character recognition in the English language but in the Indian context, the research has been limited. Devanagari is a commonly used Indian script that is the foundation of languages like Hindi, Sanskrit, Kashmiri, and Marathi. Several researchers have published their work on this topic in recent years with some promising results. To expand upon the existing work and to provide a benchmark for future studies, a comparative study of four different classifiers and two different feature extraction techniques have been proposed in this paper. Multi-Layer Perceptron, K-Nearest Neighbor, Support Vector Machine, and Random Forest algorithms are used as classifiers whereas Convolutional Neural Network and Histogram of Oriented Gradients are used as feature extraction techniques.
ResStorage - Blockchain Based Decentralized Resume Storage Application

Blockchain is the technology that has had the greatest influence on our lives in the last decade. It is a technique that makes it difficult to hack the system or alter the data it stores, keeping it safe and unchangeable at all times. Our research exploits this unique feature provided by blockchain to ensure the privacy of job applicants. We extract text from resumes in PDF format, encrypt it and store the hash value in a blockchain. Our implementation involves the usage of Ganache, Truffle and Metamask with a front end designed using ReactJS. The resumes can be accessed via the blockchain from the administrator side and viewed in its original format. The data integrity of the resumes can then be checked at any point by converting a resume to its unique hash and comparing it with the hash stored inside the blockchain.
Phishing Websites Detection using Machine Learning with URL Analysis

Phishing has become a vital concern for security researchers due to the anonymous structure of the Internet. Phishing attacks are becoming more prevalent, and due to rapid technological advancements, hackers are developing increasingly sophisticated phishing kits. Therefore, more attention to be paid to preventing phishing scams. This study uses URLs as a dataset to detect phishing websites. The dataset contains 6000 URLs, from which ten features were extracted and utilized to determine if the website was phishing or not. Eight machine learning algorithms were designed for this research. The performance analysis results show that the Multilayer perceptron algorithm got the highest accuracy of 85.41% and an F1 score of 85.17% compared with other algorithms.
A Local Feature Extraction Mechanism for Effective Content Based Image Retrieval

A CBIR system works on the primitive visual features extracted from the images of a concerned database. A CBIR system should provide better results and need to have a close resemblance with human perception. So, to keep these points in mind, in this paper, we have proposed a CBIR system that works on local image features to provide better efficiency. In contrast, the HSV color model has been incorporated as it is closest to human perception. Various statistical parameters are evaluated from H and S components for the color features extraction. Now, for texture and shape features BDIP and BVLC are deployed. This work has been validated through various benchmark databases, and overlying results are capable enough to demonstrate its superiority over contemporaries.
Text Categorization of Telugu News Headlines

The era of digitization has generated huge amounts of data in every field in the range of petabytes, and the news is one of them. To adopt a classification technique using only human intervention is impossible and also like many other Indian languages, the Telugu language is belonging to the Dravidian family which is rich in morphological content. While Natural Language Processing deals with the textual format of data, different types of word embedding features are considered and passed to the models. Existing work on this problem statement is accomplished only with count-based algorithm word embeddings. In this study, several methods were performed to obtain the best model for categorization of the newspaper articles. These methods include building custom-based Machine Learning and Deep Learning models with both count and prediction based word embeddings.
Applying Enhanced Machine Learning Mechanisms to Improve Performance of Statistical Data Processing over Cloud System

A computer system is trained to make right predictions by giving it data through a process known as machine learning. The phrase “cloud computing” refers to on-demand internet access to computer resources kept at a remote data center under the administration of a cloud services provider, such as programs, servers (both real and virtual), data storage, development tools, networking capabilities etc. In order to better understand their target audience, automate aspects of their manufacturing process, and develop better goods in response to market demand, organizations utilize machine learning and analytic. All of these elements contribute to a company's enhanced profitability, providing it an advantage over its rivals. The idea of machine learning is founded on the concept of learning from experience. The cloud computing approach can alter the technological infrastructure by incorporating this technology-based idea. An “intelligent cloud” can be built by merging machine learning with cloud computing.
Hybridization of Cryptographic Algorithms

In present world, revolutions have advanced to such extent which attracts majority of world population to slant in the direction of using these facilities for the betterment of their day-to-day life. Internet is one of the examples of such revolution which becomes the daily need of people to share highly confidential information from one corner of world to another corner of the world. In this transmission of information there is a need of security and a secured passage to maintain the confidentiality of information. At this moment Cryptography came into frame which ensures safety of information by shuffling or changing information into an unreadable format. As our everyday life becomes more dependent upon these transmission networks, problems of security and difficulties also increases. In this paper, we present a very new combination of two differently functioned algorithms which formed a more safe and secure hybrid algorithm. This hybrid algorithm makes the cryptosystem more secure which forms a group of encryptions less vulnerable to any form of attack like Kasiski and Friedman assaults.
A detailed Study of different Clustering Algorithms in Data Mining

Data mining (DM) is a practice in which large data stores are searched automatically to find designs as well as trends that go beyond simple analyses. Data mining is also known as the data clustering of knowledge discovery (KDD), where similar items are grouped into clusters. Clustering is one of the main analytical methods in DM. The clustering method directly influences clustering results. Clustering is the main as well as a key method for the automatic collection of data from enormous amounts. Its task is to recognize groups of indistinguishable objects in a data set, called clusters. Clustering methods, including database shopping, Web inspections, information acquisition, biotechnology, and more are used in a broad spectrum. This paper provides an overview of a few methods: partitioning, hierarchy, density-based method, Grid-based methodology, and data mining model-based method. Some of the clustering techniques have been compared among them based on the time to build a model, several instances, and several cluster formations. All of these algorithm's Expectation Minimization (EM) clustering algorithm takes more time and formed a maximum number of clusters in comparison to other clustering algorithms. But, the OPTICS algorithm is not better to cluster the number of instances.
Resúmenes al Español

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Resúmenes al Español. Am J Intellect Dev Disabil 1 July 2022; 127 (4): 347–348. doi: https://doi.org/10.1352/1944-7558-127.4.347 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Design of Intelligent Home Control System Based on Image Recognition

Thanks to modern life rhythm too fast, people go out to work, the rain fail to close the window, or while sleeping too much noise outside interfere with sleep, and the traditional intelligent window control system design is adopted in the form of humidity sensor and external environment of the detection, misjudgment situation occurs when air humidity is too large, so this article use the embedded technology, Through the establishment of neural network in the system, the image acquisition module to collect pictures for training, real-time access to the weather, determine whether it is raining outside, reduce the probability of the control system to the environment misjudgment, in addition to add harmful gas detection, to meet people's requirements for intelligent home life.
Light U-Net: Network Architecture for Outdoor Scene Semantic Segmentation

We present a novel and efficient semantic segmentation architecture termed Light U-Net in this paper. Light U-Net is less complex with regard to the parameter and model size than the original U-Net. We also propose a network and training strategy based on geometric data augmentation for efficient use of the annotated images from small and limited datasets. Following improvements, we show the use of Light U-Net for semantic segmentation of outdoor scenes. Results reveal that our improvements and data augmentation remarkably enhance the proposed network’s performance. Results from experiments demonstrate that the proposed architecture outperforms some recent segmentation models achieving mIoU scores of 70.06% and 79.84% on the Camvid and Stanford Background datasets respectively.