Reviewerly/exHarmony huggingface dataset:
Covid-19 Safety Protocol Monitoring System using AI

The entire globe is going through a severe health crisis due to the rapid spread of Covid-19 Disease, which in turn creating a deep impact on the human lives and their day-to-day livelihood. Only hope of preventing it from further spread of disease is by following all the precautionary measures provided by the WHO. One of the most important safety measures is to wear face mask and maintain social distancing. Hence, we have proposed to develop a system can monitor whether a person is wearing a facemask correctly/not in real time. This will help to reduce the rapid spread of the disease in public places and various other organizations. We have proposed a solution that uses Artificial Intelligence and has a capability to detect the violation of wearing a face mask in real-time using Image Recognition and Video Techniques. The main intention of the proposed work is to provide the front-end software (Web or mobile application) for administrators to monitor the violation. The system will capture the violated instance and store the data. Our system will also include a User registration form where the users have to register their information along with their face images. This will be an input to the face recognition model, which will train and alert the user whenever violation occurs.
A meta-synthesis of the use of activity theory in design for sustainable behaviour

Abstract Over the past decade, the field of design for sustainable behaviour (DfSB) has gained a growing amount of research interest. However, as the field evolves, new challenges also arise. A suitable unit of analysis is needed to contextualize users’ behaviour issues in a broader socio-cultural and long-term perspective. This paper explores the use of activity theory (AT) as a potential lens for guiding empirical analysis and design exploration in DfSB. By employing a meta-synthesis approach, we systematically search and synthesize existing studies that adopted AT in design for sustainability. Key findings show that AT’s principles and theoretical implications are especially useful for helping design researchers frame and address DfSB challenges. We argue that by taking activity as the unit of analysis, the AT lens can enable researchers to incorporate users’ dynamic, multi-level and complex activity systems into DfSB considerations.
Linking the Linguistic Resources Using Graph Structure for Multilingual Sentiment Analysis

Sentiment Analysis aims to analyse a large amount of data to infer and deduce the different opinions expressed therein. It would be interesting to link several linguistic resources and tools to combine their advantages and obtain a result as close as possible to reality. Moreover, creating a new lexicon either manually or automatically is time-consuming and needs considerable efforts. This paper proposes a new approach to build a polar lexicon by using the existing resources to generate a rich and time-saving one. Nevertheless, not all linguistic resources have the same structure. It is then challenging to use them together at once. We propose to map the resources into the graph structure, where the nodes represent the words, and the edges represent the relations between the nodes, thus the words. The obtained results are promising, F1-measure was 0.82 for the English language, and accuracy ranged from 0.76 to 0.98 for all other languages used in the experiment.
Augmented Reality Based Supply Chain Management System

This electronic document explains that augmented reality has the potential to change the way we look at mobile applications and also how augmented reality can be utilized for supply chain management. This paper explains the creation of a Markerless augmented reality supply chain management system. It also describes how augmented reality has a huge scope and can be used for an use case like supply chain, how different stakeholders of a supply chain can utilize the potential of augmented reality for fulfilling their role and perform tasks assigned to them at ease, by providing right kind of data visibility to different stakeholders. The paper presents a contextual analytics-oriented supply chain management system.
Design and Implementation of a Cross-Media Image to Sound Algorithm and Objective Evaluation Algorithm

Abstract With the joining of digital media and Internet, the application of the conversion of cross-media information becomes wider and wider, which makes the integration of multimedia information possible. The transformation of image information to sound information is also in the stage of exploration. It can be applied to all aspects of the industry. Most importantly, it can be used to guide the blind. In this paper, an improved image-to-sound algorithm based on original method is proposed to judge whether there are obstacles in front of the road and test the sound segment through human ears. Among different mapping methods, the highest accuracy can reach 84.2%, which is much higher than the original method. At the same time, an objective evaluation algorithm is designed based on the neural network, which can replace the human ear to judge a large number of image information, and the matching degree of this model can reach 68%.
Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining

Many real-world applications involve the use of Optical Character Recognition (OCR) engines to transform handwritten images into transcripts on which downstream Natural Language Processing (NLP) models are applied. In this process, OCR engines may introduce errors and inputs to downstream NLP models become noisy. Despite that pre-trained models achieve state-of-the-art performance in many NLP benchmarks, we prove that they are not robust to noisy texts generated by real OCR engines. This greatly limits the application of NLP models in real-world scenarios. In order to improve model performance on noisy OCR transcripts, it is natural to train the NLP model on labelled noisy texts. However, in most cases there are only labelled clean texts. Since there is no handwritten pictures corresponding to the text, it is impossible to directly use the recognition model to obtain noisy labelled data. Human resources can be employed to copy texts and take pictures, but it is extremely expensive considering the size of data for model training. Consequently, we are interested in making NLP models intrinsically robust to OCR errors in a low resource manner. We propose a novel robust training framework which 1) employs simple but effective methods to directly simulate natural OCR noises from clean texts and 2) iteratively mines the hard examples from a large number of simulated samples for optimal performance. 3) To make our model learn noise-invariant representations, a stability loss is employed. Experiments on three real-world datasets show that the proposed framework boosts the robustness of pre-trained models by a large margin. We believe that this work can greatly promote the application of NLP models in actual scenarios, although the algorithm we use is simple and straightforward. We make our codes and three datasets publicly available (https://github.com/tal-ai/Robust-learning-MSSHEM).
BERT-Based Tagging Method for Social Issues in Web Articles

In recent years, public collaboration to address social challenges has become increasingly important for the sustainable development of local and global communities. When discussing how to tackle social challenges, it is essential to investigate relevant existing activities. This study aims to collect web articles on various social issues to support such discussions and research. For this purpose, we have developed an automatic tagging system for web articles on social issues and evaluated it using the Bidirectional Encoder Representations from Transformers model (BERT), Wikidata, and Wikipedia. Specifically, our method classifies each sentence in a web article into social issue tags using BERT, which was trained beforehand using the Japanese Wikipedia. We constructed a training corpus using Wikidata and Wikipedia and extracted candidate social issue tags from the “Social issues” class of Wikidata. Hierarchical tagging is now possible, thanks to the structure of Wikidata. The results of our evaluation showed that the proposed method obtained F1 scores of 0.94 for zeroth-level classifications, 0.85 for first-level classifications, and 0.85 or better for all second-level classifications.
Doodling Based CAPTCHA Authentication System

CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a widely used challenge-measures to distinguish humans and computer automated programs apart. Several existing CAPTCHAs are reliable for normal users, whereas visually impaired users face a lot of problems with the CAPTCHA authentication process. CAPTCHAs such as Google reCAPTCHA alternatively provides audio CAPTCHA, but many users find it difficult to decipher due to noise, language barrier, and accent of the audio of the CAPTCHA. Existing CAPTCHA systems lack user satisfaction on smartphones thus limiting its use. Our proposed system potentially solves the problem faced by visually impaired users during the process of CAPTCHA authentication. Also, our system makes the authentication process generic across users as well as platforms.
An Uncertainty Estimation Framework for Probabilistic Object Detection

In this paper, we introduce a new technique that combines two popular methods to estimate uncertainty in object detection. Quantifying uncertainty is critical in realworld robotic applications. Traditional detection models can be ambiguous even when they provide a high-probability output. Robot actions based on high-confidence, yet unreliable predictions, may result in serious repercussions. Our framework employs deep ensembles and Monte Carlo dropout for approximating predictive uncertainty, and it improves upon the uncertainty estimation quality of the baseline method. The proposed approach is evaluated on publicly available synthetic image datasets captured from sequences of video.
Predicting COVID-19 impact on demand and supply of cryptocurrency using machine learning

In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the demand and supply of cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Decision Trees Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory and Noise Bagging (NBG). The study employed Noise filters to enhance the performance of Decision Trees Bagging named NBG. Dataset utilized for this analysis were obtained from the website of Coin Market Cap, including: Binance Coin (BCN), BitCoin Cash (BCH), BitCoin (BTC), BitCoinSV (BSV), Cardano (CDO), Chainlink (CLK), CryptoCoin (CCN), EOS (EOS), Ethereum (ETH), LiteCoin (LTC), Monero (MNO), Stellar (SLR), Tether (TTR), Tezos (TZS), XRP (XRP), and daily data collected from exchange markets platforms spans from 2nd January 2018 to 7th July 2020. Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.
Quantified Boolean Solving for Achievement Games

Recent developments in the propositional representation of achievement games have renewed interest in applying the latest advances in Quantified Boolean Formula technologies to solving these games. However, the number of quantifier alternations necessary to explore the solution space still impairs and limits the applicability of these methods. In this paper, we show that one can encode blocking strategies for the second player and express the last moves of the play with a single string of existential quantifiers, instead of the usual alternations of universal and existential quantifiers. We experimentally show that our method improves the performance of state-of-the-art Quantified Boolean Formula solvers on Harary’s Tic-Tac-Toe, a well-known achievement game.
Location-based Human Activity Recognition Using Long-term Deep Learning Invariant Mapping

In the area of human activity recognition based on mobile phone data, the Sussex-Huawei locomotion dataset[6][19] has been gaining popularity over recent years. This dataset contains sensors’ readings from several mobile phones that have been collected while their users participated in eight types of their everyday offline activities: staying still (while sitting or standing), walking, running, riding a bike, staying in a car, on a bus, the train or taking an underground. The main goal of this project was to recognize those activities based on human location and wireless signal receivers data (such as Wi-Fi, GPS, LTE, CDMA receivers). This paper proposes using an automatic feature extraction deep learning algorithm to extract long-term patterns and features that maximize differentiation between activities. We first determined essential data characteristics (mean, standard deviation, min and max values, difference, distance, velocity, etc.) and trained an ensemble of Siamese LSTM feature extractors on training data during experiments. Then we selected the best features using validation data and trained the classifier on training and validation datasets. According to the model evaluation results, as the LPEM team, we achieved 79.89% of the F1 score using 4-fold shuffling.
The mediating role of task-technology fit (TTF) in the effect of knowledge sharing intention on job satisfaction and employee performance

Public service offices are currently required to provide services effectively and efficiently. Therefore, technology plays a role in helping employees to be able to finish the job. The use of technology makes employees able to achieve high satisfaction and productivity. To be able to take advantage of existing technology, a collaboration between employees is needed through knowledge sharing. This study focuses on the context of the role of task-technology fit in mediating the relationship of knowledge sharing intention to employee satisfaction and performance. This is explanatory research was conducted through a survey of 412 employees of the State Treasury Service Office (KPPN) in the East Java Province. The structural Equation Model (SEM) approach was used and 214 questionnaires were processed with WarpPLS. The results of this study indicate that knowledge sharing, intention affects task-technology fit (TTF) and task-technology fit (TTF) also has a significant effect on employee satisfaction and performance.
The Relevance of Bitcoin to the Regression Theorem: A Reply to Luther

Given Bitcoin’s apparent lack of non-monetary uses, Luther (2018) argues that its emergence as a medium of exchange invalidates the regression theorem, or at least severely limits its relevance to identifying which commodities could emerge as media of exchange in the absence of State intervention. However, this view misinterprets both the regression theorem itself and the problem it was developed to address. The goal of the regression theorem was not to identify which commodities could become monies, but to provide a subjectivist explanation of the purchasing power of money. To do this, it requires only that some individuals valued the good in question before its use as a medium of exchange, not that it had some objective pre-monetary use.
JaCaMo Builders: Team Description for the Multi-agent Programming Contest 2020/21

This paper describes the JaCaMo Builders team and its participation in the Multi-Agent Programming Contest 2020/21 based on the Agents Assemble II scenario. The paper presents the analysis of the scenario and design of the solution; the software architecture, including the tools used during the development of the team; the main strategies; and the results achieved by the team, with challenges and directions for future editions of the contest.
Image Channel as an Input Method for Deep Learning Ensemble

The application of computer vision (CV) in many fields is becoming an important factor in development, and the demand for high accuracy CV products is increasing. Therefore, a new method that ensembles three trained models with the same architecture but different inputs is proposed. Each of the different channels of the images were used as input. The first channel of the image was used in the dataset for the first model, the second channel was used for the second model, and the third channel was used for the third model. The classification probabilities were achieved after training three different models with three channels. The model with the highest accuracy was selected as the main model, and it was added to the classification probabilities of certain classes of the main model. Applying this method to the classification task resulted in 75.45% accuracy, while using the entire image for the model resulted in 70.9% accuracy. The advantage of using this ensemble method is that it can be used simultaneously with other ensemble methods and achieves better results. Dividing an image into three different channels than simply using the entire image helps the model learn the image better.
Detecting anomalous road traffic conditions using VGG19 CNN Model

Detection on the real time road traffic has tremendous application possibilities in metropolitan road safety and traffic management. Due to the effect of numerous factors, for example: climate, viewpoints and road conditions in real-time traffic scene, Anomaly detection actually faces many difficulties. There are many reasons for vehicle accidents, for example: crashes, vehicle on flames and vehicle breakdowns, which exhibits distinctive and obscure behaviours. In this paper, we approached with a model to identify oddity in street traffic by monitoring the vehicle movement designs in two unmistakable yet associated modes which is 1. The vehicle’s dynamic mode and 2. The vehicle’s Static mode. The vehicle’s static mode investigation is gained using the background modelling after the detection of a vehicle, this strategy is useful to locate the unusual vehicle movement which keep still out and about. The dynamic mode vehicle examination is gained from identified and followed vehicle directions to locate the strange direction which is distorted from the predominant movement designs. The outcomes from the double mode investigations are at long last fused together by driven a distinguishing proof model to get the last peculiarity. For this research we are using traffic-net Dataset, VGG19 CNN model along with ImageNet weights and OpenCV.
Multi-subpopulation coevolutionary algorithm with adaptive Cauchy-Polynomial mutation

In order to solve the difficulties of multiobjective optimization problems (MOPs) such as high dimensionality and complex calculation, this paper designs a multiobjective optimization algorithm of multi-subpopulation coevolution. In this paper, the MOPs is divided into several sub-problems, and multiple sub-populations are used to optimize the sub-problems separately, and an information exchange strategy is designed for coevolution among multiple subpopulations; Aiming at the problem of multiobjective particle swarm optimization easily falling into local optima, an adaptive Cauchy mutation is designed to enhance the diversity and convergence of the algorithm.
Feature extraction and enhancement for real‐time semantic segmentation

Abstract Most of the semantic segmentation real‐time networks improve the segmentation speed by reducing the spatial resolution, leading to the accuracy being significantly reduced as a result. To solve this problem, we propose feature enhancement module (FEM), feature extraction and fusion module (FEFM). By extracting and enhancing the future map before the image down‐sample on the backbone and fusing the low‐level features with rich details and the high‐level features with more semantic information. Based on the FEM and FEFM, we introduce a real‐time semantic segmentation network feature extraction and enhancement network. In the experiment, using Cityscapes and CamVid datasets, the proposed network achieves a balance between computing speed and accuracy. Without additional processing and pretraining, it achieves 75.47% Mean IoU on the Cityscapes test dataset with only 29.96G Flops and a speed of 94 frames per second on a single RTX 2080Ti card. Code is available at https://github.com/favoMJ/FEENet.
The Model of Business Intelligence Development by Applying Cooperative Society Based Financial Technology

Technological advances seem to have no limit so that its power can penetrate many sectors, one of them is business. On the one hand, the development of financial technology has proven to be of benefit to consumers, business actors and the world economy, but on the other hand it has potential risks that if it does not adequately follow technological developments it could disrupt the financial system. Cooperatives are understood as legal entities that are established based on the principle of kinship and also adheres to the principles of social economy with the aim to improve their members in carrying out dominant transactions using cash, then modelled for the future will rely on cashless and FinTech virtual accounts. So that a new model is formed in the movement of business intelligence that is FinTech-based cooperative society that is able to increase the movement of cooperative society, reduce the operational costs of cooperative society and prosper the community with one mobile device.
Velocity Planning for Autonomous Vehicle

Autonomous vehicle has been one of the most active study with the potential to enhance safety and convenience. As one of the safety-critical tasks that must be executed by autonomous vehicle, the motion planning is generally divided into path planning and velocity planning. The main purpose of velocity planning is to determine a safe and comfortable velocity change mode for autonomous vehicle, which is a crucial task. A velocity planning method based on pre-set model is proposed in this paper. The exponential function model with undetermined parameters is constructed as the velocity planning model in this method. We take comfort and time-effectiveness as evaluation indexes and construct model functions respectively, and the objective function is constructed in the form of weighted sum to determine the optimal parameters of the velocity planning model in each scenario. The exponential model is suitable for most application scenes, and its closed form expression form is easy to implement in real-time applications. By analysis with actual data, the model is proved to be reasonable.
Analytical Study of Success Rate of I.T Projects Developed using Agile Methodology

Revolutionary software developing methodologies have captured the attention of many information technology industries nowadays. The complexity of software has increased to a higher level but the time limit and budget are also increasing. The customer also demands maintenance and quality of software. Henceforth, the requirements cannot be given at the starting and this is the main drawback of heavyweight methods. In heavyweight methods, changing and incomplete requirements create difficulty for developers. The recent methodology aims to remove all these challenges. Recently, agile methodology is adopted by various organizations for improving efficiency of software. While many researchers have gathered various information on Agile and suggested their own contribution, this paper has identified the success rate of projects, which are using any one of the agile methodology.