Reviewerly/exHarmony huggingface dataset:
A Decentralised Blockchain-Based Secure Authentication Scheme for IoT Devices

Internet of things (IoT) is a paradigm comprising smart and embedded devices that communicate with each other over communication channels. As the connectivity is mostly through vulnerable channels, these devices may have authentication issues. With the exponential growth in the number of IoT devices in the market, alongside exposed vulnerabilities of constantly improving technology, the adversary has found numerous ways to pose a threat to the IoT communication channel. Authentication is essential in order to ensure the security of IoT, comprising identification of both users and devices. The reliance on single servers and centralised authorities for network-wide registration and authentication of IoT devices exposes current authentication schemes to adversarial threats. We present a blockchain-based decentralised authentication scheme for peer-to-peer IoT networks. The scheme is designed to foster secure authentication for IoT devices in an integrated IoT-cloud network topology.
Handwritten Character Recognition for Tamil Language Using Convolutional Recurrent Neural Network

The process of conversion of printed Tamil character from a handwritten Tamil character is known as handwritten Tamil character recognition. Handwritten characters are challenging to analyze because of the wide range of writing styles, as well as the varying sizes and orientation angles of the characters. This paper has the ability to recognize offline handwritten characters that presently supports Tamil language that has high accuracy and minimum validation loss by the system. The optical character recognition system developed for the Tamil language carry a poor recognition rate because of its numerous writing designs and enormous number of characters. The goal of this system is to notice a digital format for Tamil written document. The proposed method is to achieve a accuracy of ninety six percentage with the help of CRNN. It is the foremost difficult task even although, numerous researches have been planned for written Tamil text. The trendy analysis during this domain has used the deep learning algorithm rule to improve the accuracy for the input document. This survey grants associate in fair review of the Tamil written character recognition, challenges, and diverse skills.
A Comprehensive Review on Automatic Image Captioning Using Deep Learning

Image captioning is the process of producing the sentence description from an image. It consists of two approaches: image based and language based. Extracting the features of an input image is image-based model. The feature and objects obtained from image-based model are translated into a natural sentence by language-based model. Image captioning is a challenging task as it has to determine the objects in the image and it also has to capture and express their attributes and relationships in natural languages. Deep learning-based technique paves the way for handling the challenges and complexities of image captioning. The goal is to present a literature review of machine learning and deep learning-based image captioning techniques and discusses the performances, strengths, and weaknesses. This paper also highlights the comparison of deep learning-based techniques along with image and language model with respect to the evaluation metrics like BLUE-1, BLUE-2 and METEOR on Flickr8k and MSCOCO datasets.
Cascade fuzzy control of a tubular chemical reactor

Cascade control is a multi-loop control structure often used in industrial applications, which offers a possibility for applying advanced controllers. This paper compares cascade control with type-1 fuzzy controllers, type-2 fuzzy controllers, and PID controllers on the case study of a tubular chemical reactor. The primary controllers are type-1 fuzzy PID and PD controllers, type-2 fuzzy PID and PD controllers, or conventional PID controllers. The secondary controllers are type-1 fuzzy P, type-2 fuzzy P or conventional P controllers. Simulation results demonstrate that cascade control with both types of fuzzy controllers can assure better values of followed performance indices and higher energy savings measured by the coolant consumption during control of the tubular chemical reactor.
Deep Learning-Based Big Data Analytics Model for Activity Monitoring of Elderly People

Human activity recognition (HAR) has grown in popularity over recent years, owing to the recent advancements in communication and wearable sensors. In order to handle the assemblage of big data and to analyze in real-time, deep learning is vital to extract useful information from complex systems. Deep learning-based HAR is one of the most promising assistive technology tools for supporting elderly people in their daily lives by monitoring their cognitive and physical function. In particular, fall detection is a major challenging issue since elderly people are more likely to fall. Therefore, a dedicated monitoring system is highly desirable in order to improve independent living. In this paper, a video-based fall detection system in an indoor environment is created using a convolution neural network (CNN). It also describes the design of a wearable-based fall detection system that uses an accelerometer and a gyroscope as motion sensors to detect body rotation and movement. The development of artificial neural network (ANN) for fall detection, employing wearable sensors is also investigated in this research. Experimental analysis on the ‘UR fall detection dataset’ proved the significance and robustness of the proposed approach in terms of accuracy and precision.
Two-Tier Securing Mechanism Against Web Application Attacks

Code injection-based attacks like cross-site scripting (XSS) and Structured Query Language Injection (SQLi) are among the most critical security issues for web applications. Web application firewalls (WAFs) are installed to defend against injection attacks. The WAF has a predefined rule set to detect malicious content in HTTP requests. Nevertheless, attackers use cleverly crafted payload modifications to evade such rule sets. This project presents a novel approach that extracts user inputs from HTTP requests to find sophisticated XSS and SQLi attack vectors. The proposed solution is a two-tier securing mechanism that uses both a deep learning model called Bidirectional Encoder Representations from Transformers (BERT), which is fine-tuned to classify SQLi and XSS attacks, and a context-aware classifier which looks for a change in the structure of the intended query to detect SQL injections. This work is a server-side solution and implemented as a reverse proxy thus requiring no changes in the server code. The BERT model achieves detection accuracy of 98.98% and a precision rate of 99.14% on a real-world dataset after fivefold cross-validation. Also, the context-aware classifier produced zero false positives and false negatives during testing.
Achieving Data Privacy Using Extended NMF

Data mining plays a vital role today for decision making and analysis in education, health care, business and more. It is very important to protect the data before the mining process such that it is protected from security threats and produces correct and desirable results. Privacy-preserving data mining (PPDM) allows securing data, thus maintaining data privacy. In this paper, we have used perturbation-based methods for data transformation, making it secure before applying the data mining process. The authors have proposed extended non-negative matrix factorization (NMF), which includes the NMF method followed by double-reflecting data perturbation (DRDP) method to distort data. This gives higher protection levels compared to NMF alone based upon various privacy measures. We have used R language for the implementation of the research work. We have evaluated and compared various privacy parameters to show that the proposed method of extended NMF (NMF followed by DRDP), provides higher level of protection to nonnegative numeric data compared to NMF alone.
Issues in Machine Translation—A Case Study of the Kashmiri Language

Machine translation has emerged as a promising research area, attracting considerable interest from researchers all over the world. Many techniques have been developed for machine translation of various languages around the world, which can be used alone or in combination. Besides that, there are several languages, including Kashmiri, in which little work has been done in terms of machine translation. The purpose of this paper is to provide an overview of the Kashmiri language, including its history, character set, and writing systems. This paper presents a brief introduction to machine translation and core issues associated with machine translation, with Kashmiri as a participating language. The study presented in this paper will be extremely useful to researchers involved in machine translation of the Kashmiri language.
Pedestrian Detection with Anchor-Free and FPN Enhanced Deep Learning Approach

Rapid development in artificial intelligence has been a huge benefit for optimizing autonomous automobiles. Although safety equipment like airbags, ABS and pre-tensioned seat belts have widely been used, pedestrian detection algorithms are an undeniably important feature to be incorporated in vehicles as driver assistance systems to avoid accidents that may injure or risk people’s lives. Though sliding window and anchor-based detectors have vastly been applied in the domain of object detection, these require several configurations for the bounding boxes and are moreover sensitive to background features around the object of interest in the image. To overcome these limitations, we use Center and Scale Prediction (CSP), an anchor-free object detection technique to overcome the limitations of bounding box approaches. We extract features with a ResNet-101, a deeper convolutional neural network and use Feature Pyramid Networks to build semantically richer feature maps. Experiments performed on the CityPersons and Caltech pedestrian datasets show that our model provides promising performance compared to existing state of the art models and is robust to various occlusion levels.
Reducing Error Rate for Eye-Tracking System by Applying SVM

Electrooculography (EOG) is widely considered the most effective signal-processing technique for identifying distinct eye movements. The EOG signal was used to extract functionality to provide dependable assistance to visually impaired patients. In EOG studies, the extraction of new features is an adequate and reasonable phenomenon. The EOG system is less expensive than any other signal-processing system. Still, it has significant drawbacks, such as a high error rate. In our study, we measured the Euclidean distance error. We found that it is 3.95 cm, which is significantly less than the standard error rate. The main objective of our study is to investigate an EOG analysis with the least possible error rate. EOG is substantially less expensive than other eye-tracking systems, and the proposed method can be used to provide a consistent user experience for visually impaired patients at a low cost with a minimum error rate. Moreover, this method can be applied in drone controllers, mouse controllers, and wheelchair controllers.
Facial Recognition with Computer Vision

The cutting-edge age of innovation has developed at high speed to make our lives quiet. The progression in security administrations of the advanced world has prompted the working of frameworks and gadgets all the more effectively and precisely by giving the greatest to most extreme wellbeing and security. The significant method of getting our frameworks and gadgets is passwords or passphrases however this has a few bugs which can undoubtedly be broken or hacked, this prompted the appropriation of further developed procedures, i.e., biometric finger impression scanner, this gives sufficient measure of safety to frameworks like opening the telephone and section in schools or workplaces. In any case, examining the current situation of this gigantic pandemic Covid-19, some control is needed for the protection of individuals which advances touchless registration into workplaces, schools, and universities. This examination paper denotes the use of Computer Vision innovation by carrying out python programming language and its libraries.
Incorporating deep learning into capacitive images for smartphone user authentication

We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.
How Can I Help You Today?

The pandemic context has fast-tracked the digital transformation of many organizations that pursued to dramatically change their organizational processes to survive in a global digital economy. While virtual assistants (VA), a specialized artificial intelligence-based chatbot, such as Alexa or Siri, have penetrated our private lives, many organizations are still trying to understand and evaluate why and how to integrate these technologies into their employees' workday. The study explores whether VAs can be used to support human resources (HR) trainee management software in a German organization and how it can be done. Four key HR areas of self-service, onboarding, training, and knowledge management were explored. Interviews were conducted to analyze which VAs' functions can be reused to support trainee management software in these four areas. The technology affordances and constraints theory were used to analyze data collected. The results showed that a VA's functions can support trainee management software especially in the areas of self-service, onboarding, and training.
Unsupervised Domain Adaptation via bidirectional generation and middle domains alignment

The research involved Unsupervised Domain Adaptation (UDA) has huge emerged. However, almost of them assume that the domain distributions could be transformed into a shared representation space, which does not always hold, making domain-invariant representations challenging to generate directly on the original domains. We propose a novel UDA algorithm introducing a bidirectional generation and middle domains alignment (BGMA) that uses the idea of transmitting middle domains to reduce a cross-domain gap. To explore the middle domains (i.e., fake source/target domain), we embed supervised knowledge in the constructed task network to train a bidirectional generator. Besides, we replace the source and target domains with the generated middle domains, and we reassign the relative influence of each sample to the clusters' centers. Then we specifically design an algorithm to optimize our method with generalization bound analysis and demonstrate its effectiveness through a comprehensive evaluation.
SISTEM PENDUKUNG KEPUTUSAN PENERIMA BANTUAN LANGSUNG TUNAI (BLT) MENGGUNAKAN METODE TOPSIS DAN SAW (STUDI KASUS DI KANTOR LURAH LIMBUNGAN)

Cash Direct Assistance is a government assistance program in the form of providing cash or various other assistance for the poor. The Limbungan sub-district has an important role in the proverty management program and has a role in the selection of BLT recipient candidates. However, in its implementation, there is still a form ofsubjectivity in the determination of prospective BLT recipients because the data for prospective recipients is a recommendation of the local household that is reported to the Limbungan sub-district and selected based on considerations without an accurate assessment. Likewise, the quota for BLT recipients from the government is not comparable to the list of BLT recipients submitted by the local RT. In this case, there is a need for a decision support system to find out who really deserves to receive BLT so that the allocation of BLT funds is right on target in accordance with predetemined criteria. The criteria used in this study are income, expenses, number of dependents, number of family members of productive age, residence status, house area, and land area. The sample used is 5 people who recommend direct cash assistance is more targeted because it is based on the weight of the criteria that have been determined, so that accurate result will be obtained.
A Ternary Sentiment Classification of Bangla Text Data using Support Vector Machine and Random Forest Classifier

Chakraborty, Partha Nawar, Farah Chowdhury, Humayra AfrinSentiment analysis refers to the extraction of the underlying sentiment or emotion associated with an opinion. It is a part of the difficult field in the processing of natural languages (NLP) with several applications in the business, education, and political sectors. Unlike other languages, the amount of research performed on opinion mining for Bangla text is very small. There is also a lack of proper NLP tools for Bangla text processing. To bridge this gap, in this manuscript, a classification system has been developed for predicting the polarity of Bangla text data (i.e., positive, neutral, negative) using the two most efficient algorithms SVM and random forest for opinion mining. In this study, we experimented with unigrams, bigrams, and trigrams to illustrate how contextual information affects the overall performance of the classifiers. The dataset we used in this paper is imbalanced, which resembles natural characteristics of opinions in day-to-day life.
Revitalizing the multivariate elliptical leptokurtic-normal distribution and its application in model-based clustering

We derive an improved unimodality criteria for the elliptical multivariate leptokurtic-normal (MLN) distribution. For finite mixtures of MLN, we prove identifiability. Then we provide a new estimation algorithm and show improvement over existing methods.
Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user's experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results.
A Robust Countermeasures for Poisoning Attacks on Deep Neural Networks of Computer Interaction Systems

In recent years, human–computer interactions have begun to apply deep neural networks (DNNs), known as deep learning, to make them work more friendly. Nowadays, adversarial example attacks, poisoning attacks, and backdoor attacks are the typical attack examples for DNNs. In this paper, we focus on poisoning attacks and analyze three poisoning attacks on DNNs. We develop a countermeasure for poisoning attacks, which is Data Washing, an algorithm based on a denoising autoencoder. It can effectively alleviate the damages inflicted upon datasets caused by poisoning attacks. Furthermore, we also propose the Integrated Detection Algorithm (IDA) to detect various types of attacks. In our experiments, for Paralysis Attacks, Data Washing represents a significant improvement (0.5384) over accuracy increment, and can help IDA detect those attacks, while for Target Attacks, Data Washing makes it so that the false positive rate is reduced to just 1% and IDA can have a high accuracy detection rate of greater than 99%.
Bayesian hierarchical mixture models for detecting non‐normal clusters applied to noisy genomic and environmental datasets

Summary Clustering to find subgroups with common features is often a necessary first step in the statistical modelling and analysis of large and complex datasets. Although follow‐up analyses often make use of complex statistical models that are appropriate for the specific application, most popular clustering approaches are either nonparametric, or based on Gaussian mixture models and their variants, often for reasons of computational efficiency. Certain characteristics in the data, such as the presence of outliers, or non‐ellipsoidal cluster shapes, that are common in modern scientific datasets, often lead these methods to fail to detect the cluster components accurately. In this article, we present two efficient and robust Bayesian clustering approaches that seek to overcome these limitations—a model‐based ‘tight’ clustering approach to cluster points in the presence of outliers, and a hierarchical Laplace mixture‐based approach to cluster heavy‐tailed and otherwise non‐normal cluster components—and illustrate their power and accuracy in detecting meaningful clusters in datasets from genomics, imaging and the environmental sciences.
Solving Bicoloring-Graph Games on Rectangular Boards – Part 2: Impartial Col and Snort

As a sequel to an investigation of the standard (partisan) versions of Col and Snort on rectangular boards, we defined impartial versions of both games (dubbed iCol and iSnort). These have the same coloring conditions as their partisan versions, but either player is allowed to use at any move a black or a white stone. For these two games similar strategies show that with both dimensions odd the first player can win, otherwise it is a second-player win. For both Linear versions, analyses using Combinatorial Game Theory show that the even-length chains have value 0, the odd-length chains value $$*$$ .
Solving Bicoloring-Graph Games on Rectangular Boards – Part 1: Partisan Col and Snort

In this paper we give an overview of results obtained for solving the combinatorial games of Col and Snort on rectangular boards. For Col on boards with at least one dimension even we give a strategy guaranteeing a win for the second player. For Col on general odd $$\times $$ odd boards we found no applicable strategy, though all experimental data show second-player wins. For Linear Col we were able to prove using Combinatorial Game Theory (CGT) that all chains, including odd-length chains, are second-player wins. A similar strategy as for Col guarantees for Snort on boards with both dimensions even a win for the second player and with at least one dimension odd a first-player win. Snort therefore is completely solved.