Reviewerly/exHarmony huggingface dataset:
Live Sign Language Recognition: Using Convolution Neural Networks

Sign language is an effective way to communicate for the communities with speech and hearing difficulties. There has been a significant improvement in sign languages in recent years. Though the method is convenient, there is no consistent or uniform way for interpretation. This issue may be addressed using a computer aided system that facilitates hearing impaired persons to interact with others in the community. In India, the Indian Sign Language (ISL) has evolved over the decades to consider such issues. Here we discuss a vision-based hand gesture recognition method, as hands play crucial role in interpretation. The vision-based systems have their inherent limitations over conventional hardware methods, though improvements are possible through pattern and vision recognition methods. In this work, we consider Convolution Neural Networks (CNN) to arrange different signs into legible English text. The step-by-step methodology and the results are provided.
Blind Digital Modulation Classification for Cooperative STBC-OFDM Systems based on Random Subspace and AdaBoost Classifiers

Software defined radio (SDR) allows software to control physical layer modulation and waveforms for wireless communications. Intelligent radio is the fusion of machine learning (ML) with cognitive radio (CR) which enables SDR to more complex and autonomous tasks, such as channel estimation and automatic modulation classification (AMC).In this paper, we investigate Adaptive Boosting (AdaBoost) and Random Subspace Classifier (RSC) using cooperative space-time bloc coding (STBC) Orthogonal frequency Division Multiplexing (OFDM) applying Amplify and Forward (AF) protocol in wireless communication systemsBased on the results, the best-performing algorithm is determined to provide a simple AMC method, which is then evaluated employing HOS originated from received signal to discriminate between modulations type and order namely {2PSK, 8PSK, 8PAM, 16QAM}.
3D Face Recognition using Inception Networks for Service Robots

The field of face recognition has significantly advanced as deep learning methods, such as those using CNNs, continuously show improvements. However, despite face recognition&#x2019;s promising potential, there are still many concerns regarding privacy and safety. Moreover, the first 2D algorithms, besides having good performance, turned out to be influenced by several factors like the environment&#x2019;s lighting conditions, pose, and facial expression of the subjects, compromising the model&#x2019;s accuracy. This work describes the development of a computer vision system using Deep Learning methods to detect and recognise human faces in 3D in real-time. The RGB images and depth maps from several subjects were captured using an Intel RealSense D455, processed, and consequently provided into two independent CNNs, an Inception-Resnet V1 to deal with the RGB images and an Inception V3 to deal with depth maps. The final algorithm was implemented on the anthropomorphic domestic and healthcare service robot CHARMIE (Collaborative Home Assistant Robot by Minho Industrial Electronics) to perform its tasks according to the recognised user.
Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Moving target tracking technology is an important topic in the field of time domain sensors, which is widely used in medical diagnosis, intelligent robots, human-computer interaction, education, and entertainment. This article is based on the time domain vision sensor to explore the methods of sports target tracking and speed measurement. The article introduces the regional gray-scale correlation matching method in the vision sensor algorithm, the noise model of the time domain vision sensor, and the method of eliminating noise. At the same time, the tracking effects of SIFT algorithm, Mean Shift algorithm, and MSO algorithm on moving targets are studied, analyzing the ANF algorithm, TSML algorithm, and IHTLS based on the time domain visual sensor to compare the TUD-Crossing test set, the ETHBAHNHOF test set, and the SMOT data set; then the image matching was purified and analyzed based on the time domain visual sensor. The research results show that, in the method of exploring moving target tracking based on time domain vision sensor, when the improved SIFT algorithm uses feature adaptive fusion to describe the target, it can automatically allocate the proportion of color features and texture features, which improves the accuracy and reliability of moving target tracking. Among them, the DNP algorithm can control the final accuracy rate above 80%, and its recall rate is basically greater than 80%. In the case that the accuracy of rough matching is less than 50%, the final matching accuracy after processing by multiple purification algorithms is above 98%.
Automatic Detection of Small- and Medium-Sized Targets in High-Resolution Images Based on Computer Vision and Deep Learning Energy

In order to solve the problem that it is difficult for traditional manual feature algorithms to deal with complex image features quickly and automatically in high-resolution images, an automatic detection method for small objects in high-resolution images based on computer vision and deep learning energy is proposed. Starting from the deep learning target detection system, this paper studies many problems in the system according to the characteristics of target spatial deformation, more small targets, easy confusion, and rough regional proposal in high-score remote sensing images. The results showed that a combination of spatial deformation strength with spatial deformation resistance and multiphase coupling were all proposed to be comparable with spatial deformation. The process of tracking international products in the system has been extended to study local channels. Pay attention to the operation of the machine, make full use of rich semantic data of local characteristics and the world of spatial points, which will help to improve the accuracy of the purpose of the chaw, and identify ways to create a shared database. The proposed method is evaluated on three attribute, classification joint datasets; ACCNN on the AC-AID dataset, the performance of ACCNN is suboptimal on AC-AID and is very close to the best method for DCA fusion. The average error rate of DCA fusion is 5.67%; on the AC-UCM dataset, the error rate of ACCNN is 2.11% lower than the suboptimal method DCA fusion, and the error rate of DCA fusion is 6.16%. On the AC-Sydney dataset, the error rate of ACCNN is 1.86% lower than the suboptimal method DCA fusion, and the error rate of DCA fusion is 5.99%, which obtains quite competitive results, experimenting with high-resolution images.
Advantages of cooperative methods in teaching professionally-oriented foreign languages to students of technical universities

The major cause of the given study is the urgent necessity to develop effective information and communication technologies to employ in teaching professionally-oriented foreign language to students of technical universities. The digital transformation in teaching practices makes both educators and theorists search for new teaching methods and technologies. Collaborative learning or leaning in cooperation is an advanced educational approach that aims to facilitate students learning. This study is focused on describing theoretical foundations, analyzing practical application, and examining advantages of collaborative learning technologies in comparison with traditional ones. The paper defines the concept, main principles, and implementation stages of this method, as well as advantages and potential of collaborative learning. We have revealed that collaborative learning greatly contributes to an increase of the language proficiency level and to the development of professional communicative competence in foreign languages. The study was carried out using a wide array of theoretical methods, including investigation, description, review and analysis of literature in the field of teaching foreign languages to students of technical universities. The findings of our study might be useful for both education theorists and practitioners who are interested in cooperative methods and their usage as classroom activities. To further our research, we intend to analyze the use of collaborative learning technology, expand its possibilities and enhance the efficiency of its application in teaching foreign languages at technical universities.
University Network Education and School-Based Platform Based on Smart Campus Data Platform Architecture

With the vigorous development of college informatization construction, many colleges and universities have established and applied digital campus platforms. The main research of this paper is the construction and application of the school-based platform of university network education based on smart campus data platform architecture. This article conducts interviews and classroom observations with teachers of different teaching ages. The construction of school-based curriculum system is reflected in the text as the school’s curriculum plan. The average test score of the experimental class is 7.94 points higher than the average score of the control class, which shows that the test score of the experimental class is higher than that of the control class. The experimental data show that the school-based platform of college network education based on the smart campus data platform architecture can effectively improve the learning effect of students, and popularizing it in the construction of the existing school-based network education platform can promote the in-depth development of smart education.
Chapter 2. “A geography of names”

We investigate discourses surrounding venereal disease in a wide body of seventeenth-century texts in the one-billion-word Early English Books Online (EEBO) corpus. By combining quantitative methods with close reading of texts within EEBO, we explore whether perceptions of sufferers and responses to the illness itself shifted as the century progressed. In order to uncover the kinds of written works in which references to venereal disease appear, we undertake a genre analysis with the help of genre categories added to the corpus. The analysis shows that at the beginning of the century, references to these diseases were most likely to appear within historical works, while the end of the century witnesses an increase in references to venereal disease in scientific and medical texts.
A Secured Storage and Communication System for Cloud Using ECC, Polynomial Congruence and DSA

Security is necessary for performing the effective data communications in internet and cloud environment. The data security in cloud is becoming very challenging task due to the presence of huge volume of cloud users. Even though, the various secured cloud data storage mechanisms are available and tried to fulfill the current requirement in terms of security level and efficiency. However, the available works are not obtained the enough security level and the efficiency while storing and sharing the data in cloud. For this purpose, this work proposes a new secured storage method for providing data security to store the data securely in cloud. The proposed system consists of three different algorithms for performing prime number generation, digital signature creation for generating keys, encryption, decryption and authorization. Here, a new technique to find a co-prime number which is used to generate keys in key generation process and also useful for performing encryption and decryption process. Moreover, a new key generation technique is also introduced for elliptic curve cryptography and digital signature algorithm using polynomial congruence for performing key generation process securely. In addition, a new elliptic curve and polynomial congruence based encryption/decryption algorithms for performing data encryption and decryption in the process of data storage and communication. Finally, the user authenticity is verified by the proposed digital signature algorithm in this work. The experiments have been conducted for evaluating the proposed secured storage and communication system and proved as better than others in terms of efficiency and security level.
Uncovering the conceptual evolution of sentiment analysis research field during the period 2017–2021

Sentiment analysis has gained much attention from both academics and industry in the last years. The increasing availability of data on the opinion of users in social platforms such as Twitter, Facebook and others have enabled the development of artificial intelligence to uncover hidden knowledge in social media and perform strategical business decisions. In this way, due to the impact on business strategic decisions, the research advancements in the field of sentiment analysis has quickly grown, and consequently 5293 documents have been published in the last five years, making an impossible task to stay up to date on these advancements. Therefore, given the implications of the new techniques and approaches in the field, in this contribution, we performed a science mapping analysis to uncover the conceptual structure of the area, showing the noteworthy techniques and detecting the possible applications of them. Results show how the sentiment analysis research field paid great attention to topics related to the pandemic (COVID-19, vaccinations) and mental health. Also, advanced artificial techniques such as deep-learning and transfer learning have been widely used in the last years.
Online Continual Learning via the Meta-learning update with Multi-scale Knowledge Distillation and Data Augmentation

Continual learning aims to rapidly and continually learn the current task from a sequence of tasks, using the knowledge obtained in the past, while performing well on prior tasks. A key challenge in this setting is the stability–plasticity dilemma existing in current and previous tasks, i.e., a high-stability network is weak to learn new knowledge in an effort to maintain previous knowledge. Correspondingly, a high-plasticity network can easily forget old tasks while dealing with well on the new task. Compared to other kinds of methods, the methods based on experience replay have shown great advantages to overcome catastrophic forgetting. One common limitation of this method is the data imbalance between the previous and current tasks, which would further aggravate forgetting. Moreover, how to effectively address the stability–plasticity dilemma in this setting is also an urgent problem to be solved. In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA). Specifically, we apply multi-scale knowledge distillation to grasp the evolution of long-range and short-range spatial relationships at different feature levels to alleviate the problem of data imbalance. Besides, our method mixes the samples from the episodic memory and current task in the online continual training procedure, thus alleviating the side influence due to the change of probability distribution. Moreover, we optimize our model via the meta-learning update by resorting to the number of tasks seen previously, which is helpful to keep a better balance between stability and plasticity. Finally, our extensive experiments on four benchmark datasets show the effectiveness of the proposed MMKDDA framework against other popular baselines, and ablation studies are also conducted to further analyze the role of each component in our framework.
Deep Learning based Classification of Cervical Cancer using Transfer Learning

Cervical cancer is responsible for 90% of all deaths in low- and middle-income nations (LMIC). Cervical cancer is still one of the most common cancers in women's that develops in the cervix lower part of the womb that connects to the vagina canal. Cervical cancers are caused by continual infection on their cervix with one of the human papilloma viruses (HPVs). The most commonly used screening test for early detection of abnormal cells and cancer is the Pap smear test. Manual screening, on the other hand, leads to human errors. It is possible to save lives by detecting cancer early and accurately. Transfer learning has made significant progress in the field of machine learning in recent years, and the use of transfer learning technology to cervical cancer image classification has emerged as a new experimental domain. This work presents a study of transfer learning frameworks InceptionResNetV2, VGG19, DenseNet201 and Xception networks pre-trained on ImageNet, to classify cervical images using the SIPaKMeD dataset.
DeepFake Video Detection

Recently, deepfake face-swapping techniques are widely used, which allow to easily create buinesslike fake videos. Determining the rightfulness of a video is becoming increasingly important due to the potential distructive impact it can have on the world. we used more than technique and compared between them to detect fake videos. we applied different techniques like YOLO-CRNN, LSTM and in this paper, we compared between them in some techniques EfficientNet-B5 is used to pluck out the spatial options of those faces they are fed as a batch of input series into a two-way long- and short-term memory (BiLSTM) to extract temporal characteristics. The scheme is then tested on a a huge new dataset in; CelebDFFaceForencics++ (c23), based on a mash-up of two well-known records; FaceForencies++ (c23) and CelebDF. Achieved Area Under Receiver Operating Characteristic (AUROC) curve 89.35% result, 89.38 accuracy, 83.13% recovery, 85.54% accuracy and 84.23 F1-measure to insert data focus.
The End-to-End Segmentation on Automotive Radar Imagery

Segmentation and classification of surfaces and objects in automotive radar imagery are key techniques to identify the passable region for path planning in autonomous driving. The end-to-end segmentation on automotive radar imagery is proposed in this paper, where the input B-scope automotive radar map is processed to output the segmented radar map with labeled area classes. The algorithm discussed in this paper is the extension of our previous published work [1], where we proposed two-stage segmentation processed including (i) pre-segmentation using watershed transformation (WT), and (ii) the region classification based on the Multivariate Gaussian Distribution (MGD) classifier and the extracted distribution features. In the current paper, we use the B-scope radar map representation to simplify the coordinate transformation procedure as compared to PPI image representation. Secondly to improve classification of low-return regions two-tier segmentation process is introduced, where after the first classification of regions of high return, the more subtle classification is made between classes of low returns. Radar test dataset, collected in outdoor driving scenarios and labeled according to optical ground truth is used for assessing the Jaccard similarity coefficient (JSC) performance of segmentation results, which show higher accuracy of classification than in our previous algorithm [1].
BetterReg: An R package for Useful Regression Statistics

Statistics such as squared semi partial correlations, tolerance, and Mahalanobis Distances are useful for reporting the results of OLS (Ordinary Least Squares) Regression (Tabachnick et al., 2019) as well as Likelihood Ratio Chi-square (Cohen et al., 2002) and Likelihood R-square (Menard, 2010).Such statistics are not part of base R (R Core Team, 2022) popular packages such as car (Fox & Weisberg, 2019).To fill these gaps, the BetterReg package is developed to provide these statistics and measures.
Age Prediction Model using Convolutional Neural Network

Age bunch grouping is a perplexing assignment that is utilized to order facial picture into foreordained age classifications. It is a significant undertaking because of its various applications like wellbeing, security, confirmation framework, enlistment, and furthermore in wise social robots. Convolutional Neural Network (CNN) has as of late shown brilliant execution in examining human face pictures. Programmed age arrangement has become pertinent to a rising measure of utilizations, especially since the ascent of social stages and online media. By and by, execution of existing techniques on genuine pictures is still altogether missing, particularly when contrasted with the huge jumps in execution as of late detailed for the connected errand of face acknowledgment. In this paper we show that by learning portrayals using profound convolutional neural network (CNN), a critical expansion in execution can be gotten on these assignments. To this end, we propose a straightforward convolutional net design that can be utilized in an event of predicting age, when how much learning information is restricted. We assess our technique on the new IMDB-WIKI dataset for age assessment and show it to drastically beat present status of-the-craftsmanship strategies.
Almost-optimal sublinear-time edit distance in the low distance regime

We revisit the task of computing the edit distance in sublinear time. In the (k,K)-gap edit distance problem we are given oracle access to two strings of length n and the task is to distinguish whether their edit distance is at most k or at least K. It has been established by Goldenberg, Krauthgamer and Saha (FOCS '19), with improvements by Kociumaka and Saha (FOCS '20), that the (k,k2)-gap problem can be solved in time O(n/k + poly(k)). One of the most natural questions in this line of research is whether the (k,k2)-gap is best-possible for the running time O(n/k + poly(k)).
Phish Block: A Blockchain Framework for Phish Detection in Cloud

The data in the cloud is protected by various mechanisms to ensure security aspects and user's privacy.But, deceptive attacks like phishing might obtain the user's data and use it for malicious purposes.In Spite of much technological advancement, phishing acts as the first step in a series of attacks.With technological advancements, availability and access to the phishing kits has improved drastically, thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the original Uniform Resource Locator (URL), typosquatting the popular domain names, using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud, providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client's end (browser).At the same time, the avalanche effect and immutability of block-chain proves to be a strong source of security.Considering these trends in technology, a block-chain based filtering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91% which assures the security in cloud storage.