Reviewerly/exHarmony huggingface dataset:
Sign Language Translator Using Deep Learning Techniques

Sign language is mainly used by hearing impaired people for communication. And it has been an active research topic for the past 2 decades. Although sign language seems to be familiar in recent times, it's still quite a challenge for non-signers to communicate with signers leading to the widening of communication gap between them. Deep learning models are widely being used for motion and gesture recognition. With advancement in deep learning techniques and computer vision, this topic is gaining more attention. The main aim of the proposed model in this paper is to implement a sign language translator using deep learning techniques like Convolutional Neural Network (CNN) and help to bridge the gap between signers and non-signers. The model recognizes all the numbers[0-9], alphabets[a-z] and custom user-defined unigram symbols with better accuracy both in real time and for static sign image classification. A web application is built with the trained model to make it accessible to everyone. The front end of the web application is built with the help of a python library called gradio for simple and user friendly interface.
Comparison of Gaussian Hidden Markov Model and Convolutional Neural Network in Sign Language Recognition System

Sign language Recognition is the study to help bridging communication of deaf-mute people. Sign Language Recognition uses techniques to convert gestures of sign language into words or alphabet. In Indonesia, there are two types of sign languages which are used, Bahasa Isyarat Indonesia (BISINDO) and Sistem Isyarat Bahasa Indonesia (SIBI). The purpose of this research is comparing sign language recognition methods between Gaussian Hidden Markov Model and Convolutional Neural Network using indonesian sign language SIBI as a dataset. The dataset comes from 200 videos from 2 signers. Each signer performs 10 signs with 10 repetitions. To improve the recognition accuracy, modified histogram equalization is used as an image enhancement. Skin detection was used to track the movement of the gesture as input features in the Gaussian Hidden Markov Model and fine tuning was used in Convolutional Neural Network using transfer learning, freeze layer, and dropout. The results of the research are the Gaussian Hidden Markov Model provides accuracy value of 84.6% and Convolutional Neural Network provides accuracy value of 82%.
Analysis of Feature Selection Techniques for Classification Problems

Feature Selection problem is increasingly recognized as one of the key areas in academic research that employs data-driven or computationally-intensive approaches. Huge collection of data available for the research community and access to data aggregation techniques that help to easily get vast amounts of data, bring number of other problems. More data is not always better. Data mainly comes with noise distracting from what is more important. Data cleaning and pre-processing is long and resource consuming process. When it comes to widely used supervised machine learning approach, more data requires more time and more computation power for the training. This is why right data that represents entire population of cases is better than just more data. Feature selection is the process that helps to address two key issues associated with data-driven systems: dimensionality reduction of the data to increase performance and selecting features that produce most accurate model. The main purpose of this study is to review feature selection methods applied on real data.
Yanji: An Automated Mobile Meeting Minutes System

With the development of intelligent phones and speech recognition technology, there is a great demand for generating meeting minutes automatically. In this paper, we design and implement Yanji, an automated system for generating meeting minutes based on speech and speaker recognition. The Yanji system realizes the following functions: recording the audio of the conference, uploading audios to IBM cloud in real time, transcribing audios to texts and identifying various speakers with IBM Speech to Text API, and finally generating complete meeting minutes. Yanji greatly reduces the recording storage space and the labor cost of listening and writing, and improves the meeting efficiency.
Poisson kernel: Avoiding self-smoothing in graph convolutional networks

Graph convolutional network is now an effective tool to deal with non-Euclidean data, such as social behavior analysis, molecular structure analysis, and skeleton-based action recognition. Graph convolutional kernel is one of the most significant factors in graph convolutional networks to extract nodes' feature, and some variants of it have achieved highly satisfactory performance theoretically and experimentally. However, there was limited research about how exactly different graph structures influence the performance of these kernels. Some existing methods used an adaptive convolutional kernel to deal with a given graph structure, which still not explore the internal reasons. In this paper, we start from theoretical analysis of the spectral graph and study the properties of existing graph convolutional kernels, revealing the self-smoothing phenomenon and its effect in specific structured graphs. After that, we propose the Poisson kernel that can avoid self-smoothing without training any adaptive kernel. Experimental results demonstrate that our Poisson kernel not only works well on the benchmark datasets where state-of-the-art methods work fine, but also is evidently superior to them in synthetic datasets.
Denigrate Comment Detection in Low-Resource Hindi Language Using Attention-Based Residual Networks

Cyberspace has been recognized as a conducive environment for use of various hostile, direct, and indirect behavioural tactics to target individuals or groups. Denigration is one of the most frequently used cyberbullying ploys to actively damage, humiliate, and disparage the online reputation of target by sending, posting, or publishing cruel rumours, gossip, and untrue statements. Previous pertinent studies report detecting profane, vulgar, and offensive words primarily in the English language. This research puts forward a model to detect online denigration bullying in low-resource Hindi language using attention residual networks. The proposed model Hindi Denigrate Comment–Attention Residual Network (HDC-ARN) intends to uncover defamatory posts (denigrate comments) written in Hindi language which stake and vilify a person or an entity in public. Data with 942 denigrate comments and 1499 non-denigrate comments is scraped using certain hashtags from two recent trending events in India: Tablighi Jamaat spiked Covid-19 (April 2020, Event 1) and Sushant Singh Rajput Death (June 2020: Event 2). Only text-based features, that is, the actual content of the post, are considered. The pre-trained word embedding for Hindi language from fastText is used. The model has three ResNet blocks with an attention layer that generates a post vector for a single input, which is passed through a sigmoid activation function to get the final output as either denigrate (positive class) or non-denigrate (negative class). An F-1 score of 0.642 is achieved on the dataset.
The Important Role Of Internet Algorithm In English Language Learning

In recent years, with the continuous development and progress of Internet information technology, relevant English learning resources are more and more abundant and learning methods are more and more intelligent. The continuous increase of information data will bring difficulties and challenges to the autonomous learning of English learners. In the Context of English language learning, due to the large amount of data and information on the Internet, the weak links in the learning process of learners are screened out, and appropriate learning programs are recommended according to their own conditions, so as to carry out appropriate English training for learners and improve their learning efficiency and effect. The Internet + classroom English teaching model improves students' interest in and confidence in English learning by cultivating their language learning ability. By cultivating students to study independently, Internet teaching can answer questions for students in time. Although there are differences in the degree of dependence on the Internet, they are all student-centered and pay attention to giving play to the guiding role of teachers and cultivating students' learning ability. Therefore, the integration of Internet algorithm and English teaching philosophy is conducive to the completion of classroom teaching, so as to realize the deep combination of Internet technology and English language learning.
Withers height estimation in medieval horse samples from Poland: Comparing the internal cranial cavity‐based modified Wyrost and Kucharczyk method with existing methods

Abstract Horse withers height is frequently estimated based on skeletal remains during archeozoological analyses. The routinely used methods (Vitt and Kiesewalter) do not allow withers height estimation without complete long bone and skull osteometry. The modified Wyrost and Kucharczyk formula is based on the internal dimensions of the cranial cavity. This method can be used even when the neurocranium is the only surviving portion of the skull. Earlier investigation demonstrated that it can be used as a substitute for the Kiesewalter method. The statistical analyses of the results achieved using metapodial bones are strongly correlated with the results achieved using the Wyrost and Kucharczyk method. A unique horse skull assemblage, dating back to medieval times, was unearthed from several archeological sites in Poland (Silesia, Kuyavia, Grater Poland, Western Pomerania, and Eastern Pomerania). This study aimed to estimate the withers height of this assemblage using the modified Wyrost and Kucharczyk method. Subsequently, these estimations were compared with accessible literature data from both medieval Poland and surrounding territories. The literature indicates that the horses from Western Pomerania, Silesia, and Kuyavia were larger than the animals from Eastern Pomerania. Our results show that horses from Western Pomerania were larger than those from Silesia, Kuyavia, and Eastern Pomerania. In both cases, the Western Pomeranian horses are the largest. In general, according to our results and the accessible literature, it seems true that Polish medieval horse populations can be described as medium‐ and small‐sized, according to Vitt's classification. The modified Wyrost and Kucharczyk formula can be used as an additional or alternative method of calculating withers height in routine archeozoological studies.
MLEFlow: Learning from History to Improve Load Balancing in Tor

Abstract Tor has millions of daily users seeking privacy while browsing the Internet. It has thousands of relays to route users’ packets while anonymizing their sources and destinations. Users choose relays to forward their traffic according to probability distributions published by the Tor authorities . The authorities generate these probability distributions based on estimates of the capacities of the relays. They compute these estimates based on the bandwidths of probes sent to the relays. These estimates are necessary for better load balancing. Unfortunately, current methods fall short of providing accurate estimates leaving the network underutilized and its capacities unfairly distributed between the users’ paths. We present MLEFlow , a maximum likelihood approach for estimating relay capacities for optimal load balancing in Tor. We show that MLEFlow generalizes a version of Tor capacity estimation, TorFlow - P , by making better use of measurement history. We prove that the mean of our estimate converges to a small interval around the actual capacities, while the variance converges to zero. We present two versions of MLEFlow : MLEFlow - CF , a closed-form approximation of the MLE and MLEFlow - Q , a discretization and iterative approximation of the MLE which can account for noisy observations. We demonstrate the practical benefits of MLEFlow by simulating it using a flow-based Python simulator of a full Tor network and packet-based Shadow simulation of a scaled down version. In our simulations MLEFlow provides significantly more accurate estimates, which result in improved user performance, with median download speeds increasing by 30%.
Circulant preconditioners for mean curvature-based image deblurring problem

The mean curvature-based image deblurring model is widely used to enhance the quality of the deblurred images. However, the discretization of the associated Euler–Lagrange equations produces a nonlinear ill-conditioned system which affects the convergence of the numerical algorithms such as Krylov subspace methods (generalized minimal residual etc.) To overcome this difficulty, in this paper, we present three new circulant preconditioners. An efficient algorithm is presented for the mean curvature-based image deblurring problem, which combines a fixed point iteration with new preconditioned matrices to handle the nonlinearity and ill-conditioned nature of the large system. The eigenvalues analysis is also presented in the paper. Fast convergence has shown in the numerical results by using the proposed new circulant preconditioners.
Monitoring of Workplace Safety Using IoT

Abstract Security is the most critical thing in today’s working conditions for the workers who are working in process industries and manufacturing industries. In order to have a safe workplace, a suitable safety system is yet to be developed for Micro, Small, and Medium Enterpiises-level companies. Therefore, a safety system is developed to monitor the temperature of the employee while entering the workplace and company gate. Even for the single parameter health check, the control system to get the data from the sensor to the database and the database needs to be checked for the limitation of range whether it is acceptable or not. Based on the data condition, the sound alarm system needs to be connected and at the same time, a Short message service needs to be sent to the higher authorities. This information is sent to top management in an email form for their record and the information. The database contains data for the long period to have a statistical analysis of health issues for the employees. This statistical data analysis can help to improve the safety standard at companies. Considering the low-cost estimation, this safety system can be used in any MSME-level company.
Calculation method of projection point of circle center in camera calibration

In order to eliminate the asymmetric projection error, this paper proposes a method to calculate projection point of circle center by use of concentric circles. Firstly, the basic principles and mathematical model are introduced briefly. Next, take arbitrary secants on a circle, the relationship between the secant midpoint and the projection point of the infinity point on the imaging plane is obtained based on the properties of projective geometry. The equations which contain parameters of secant midpoints are established according to the geometric constraints. Then the coordinate of projection point of circle center is derived from the coordinates of secant midpoints. Finally, in order to eliminate the algorithm error in the calculation process, the coordinates of all solved projection points are averaged to get the final coordinates. The simulation results show that the method can get the projection point of circle center accurately and stably, and does not have intrinsic projection error. Besides, the accuracy of the proposed method is 1.71nm higher than that of the direct ellipse fitting method.
Research on the Development of China's AI+ Education Industry under the Framework of Supply and Demand Theory

This article first identifies the definition and historical evolution of AI+ education)artificial intelligence education(. As following, the author starts to make research on China's current situation of AI+ education based on the theory of supply and demand. On the demand side, this paper mainly analyzes from three aspects: policy, economy and society. On the supply side, a diagram of the upper, middle and lower reaches of the AI+ education industry, which was designed by the author. And it took adaptive research as an example. Then, the importance of education model, underlying technology and teaching environment was been advocated by the author. Finally, based on the above research, the author analyzes the future development trend of the industry, and proposes students’ learning outcomes could be improved and optimized by AI+ education. Useful suggestions for practitioners in the AI+ education industries were proposed based on this article's perspectives.
Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification

Mobility data analysis has received significant attention in the last few years. Enriching spatial-temporal trajectory data with semantic information, which is the definition of Multiple Aspect Trajectories, presents lots of opportunities, but also many challenges. Regarding trajectory classification, the state-of-the-art method called MASTERMovelets has shown to have the best classification accuracy over several datasets. Indeed, this method generates interpretable patterns called movelets which are the most discriminant sequences of points. Despite its increased performance, the method is computationally expensive and does not scale well, which makes its application unfeasible for large datasets. In this paper we propose a pivot based approach to reduce the search space, selecting only most promising trajectory points to extract movelets. We additionally provide a method to define a limited number of semantic dimensions for movelets. Experiments show that the proposed method is at least \(50\%\) faster for extracting the movelets, and shows a average drop of \(82\%\) of input to the classification models while keeping a similar classification accuracy level. Additionally, our scalability analysis with respect to computation time shows that the proposed method scales better than the other methods as the dataset grows in number of points, trajectories and dimensions.
A Permissioned Consensus Algorithm Based DAGs-to-Blockchain in Hierarchical Architecture for Decentralized Internet of Things

In today’s modern world, distributed ledgers have become dominant in several sophisticated applications to remove the centralized controlling entity (e.g., Cloud Computing). The currents are problematic to meet the dramatic growth of the Internet of Things (IoT), for instance, Blockchain and Tangle. The former is very secure but has a very slow confirmation time, and low throughput. The latter has more throughput; however, sometimes records are getting left behind. On the other hand, the existing consensus algorithms that determine who can add new records into a ledger, are particularly energy-intensive, unfair, or deemed infeasible for resource-constrained Things. In this paper, a novel consensus algorithm based on Directed Acyclic Graphs (DAGs)-to-Blockchain is proposed in horizontal architecture to mitigate the scalability issues, in which every limited number of nodes are grouped. It consists of five permissioned nodes of different responsibilities: (i) Things send data from sensors with signature to Edge. Every interval, (ii) each Edge node chooses a random number, and auto-decrement it. The first node reaches zero, becomes the winner that can add the verified data temporally to the DAG ledger (Cobweb) depends on uniform distribution. The rest of the nodes who are waiting for the leader, rate it based on its work (rate scores from 1-to-5) after accepting Cobweb from it. (iii) Fog node is chosen randomly from Edge to send Cobweb periodically with authentication to reduce the required storage. (iv) Cloud nodes receive and add it permanently to the Blockchain ledger with global accessibility. (v) Queen node is located between Fog and Cloud, which monitors the network. The proposal is emulated and evaluated in the real-time scenario.
Simple DL: A Toolkit to Create Simple Digital Libraries

Digital library systems are not always successfully implemented and sustainable in low resource environments, such as in poor countries and in organisations without resources. As a result, some archives with important collections are short-lived while others never materialise. This paper presents a new toolkit for the creation of simple digital libraries, based on a long trajectory of research into architectural styles. It is hoped that this system and approach will lower the barrier for the creation of digital libraries and provide an alternative architecture for experiments and the exploration of new design ideas.
A Comprehensive Review of Blockchain Technology Implementation in the EV Charging Infrastructure

Charging infrastructure is a key factor in successful electric vehicle adoption. Charging stations are still a fragmented market in terms of ownership, lack of standards, and charging protocols. The increasing decentralised grid has made energy and communication flow bi-directional. Challenges arise in maintaining the increasing decentralised structure, security, and privacy of the network. Blockchain facilitates the interconnectedness of such a distributed and decentralised network. Blockchain's versatility lies in its transparent and immutable decentralized architecture that enables direct transactions between users without the need of a middleman. It provides powerful safeguards against cyberattacks with its advanced cryptography enabling privacy-preserving authentication. This chapter presents a comprehensive review on the application of blockchain technology in EV charging infrastructure such as facilitating the peer-to-peer energy exchange, increased security and privacy, immutable transactions, and mitigating trust issues among the participants in the charging infrastructure.
Multi-Label Feature Selection Combining Three Types of Conditional Relevance

With the rapid growth of the Internet, the curse of dimensionality caused by massive multi-label data has attracted extensive attention. Feature selection plays an indispensable role in dimensionality reduction processing. Many researchers have focused on this subject based on information theory. Here, to evaluate feature relevance, a novel feature relevance term (FR) that employs three incremental information terms to comprehensively consider three key aspects (candidate features, selected features, and label correlations) is designed. A thorough examination of the three key aspects of FR outlined above is more favorable to capturing the optimal features. Moreover, we employ label-related feature redundancy as the label-related feature redundancy term (LR) to reduce unnecessary redundancy. Therefore, a designed multi-label feature selection method that integrates FR with LR is proposed, namely, Feature Selection combining three types of Conditional Relevance (TCRFS). Numerous experiments indicate that TCRFS outperforms the other 6 state-of-the-art multi-label approaches on 13 multi-label benchmark data sets from 4 domains.