Reviewerly/exHarmony huggingface dataset:
Cross-Domain Aware Approach for Improving Word Embeddings-Application Oriented Analysis

Context based semantic and syntactic information extraction using word embedding techniques are commonly used in natural language processing (NLP) activities. The current universal word embedding strategy for obtaining context-oriented word embeddings obtains word vectors largely from semantic and syntactic information in the context available, which results in a failure to capture essential sentiment information. Some techniques model words by grouping a large number of vectors based on context. Recent neural approaches, on the other hand, rarely focus on making the NLP algorithm's produced vectors readily available for consumption. Although some algorithms model sentiment data, they neglect specific phrases in certain fields. The sentiment classification performance will certainly degrade if the generic word vector is only implemented on the review oriented sentiment analysis when there is a change in emotion. This study provides a thorough analytical examination of the findings achieved while doing sentiment analysis with the Word2vec method in both similar domain and inter-domain aspects.
Secure Content-Based Image Retrieval Scheme for Multi-User Scenario Over Cloud

Since the public cloud is not a credible entity when providing image retrieval services through the public cloud, it may steal the sensitive information of the image data. In recent years, cipher text image retrieval methods have been proposed to protect image privacy. However, the traditional privacy protection image retrieval scheme has low search efficiency and cannot support multi-user scenarios. Therefore, a safe and efficient multi-user outsourcing image retrieval scheme based on access control is proposed. This scheme adopts one-time encryption and matrix transformation methods to realize cipher text image retrieval based on the similarity of Euclidean distance (referred to as Euclidean distance). It uses matrix decomposition and proxy re-encryption to learn multi-user outsourced image retrieval. In addition, the local sensitive hash algorithm is used to construct the index to improve the efficiency of cipher text image retrieval. In particular, a lightweight access control strategy based on role polynomial functions is proposed, which can flexibly set image access permissions and prevent malicious users from stealing private information. Security analysis demonstrates that the proposed scheme can protect the confidentiality of images and query requests; the experimental results show that the proposed method can achieve efficient image retrieval.
A User-based Collaborative Filtering Method to deal with Sparsity in Recommendation Systems by an unsupervised learning of Users’ Hidden Preferences

This paper focusses on sparsity in User-based Collaborative Filtering (UCF) type of Recommendation Systems. UCF mainly depends on Users&#x2019; Similarity Calculation (USC). The idea of the proposed method to overcome the sparsity problem of UCF is, overcoming the sparsity effect on USC through imputation of missing ratings. In the proposed method imputation is carried out by finding the hid-den preferences of users through an unsupervised learning using K-Means, basing on given ratings and item features. The proposed method aims to provide adequate information to the similarity metric used for USC, even with sparse rating data. The proposed method is compared with some other approaches in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error (MSE) values with varying levels of Sparsity and the RMSE, MAE and MSE are found to be the least for the proposed method. Also the RMSE of the proposed method for all the levels is close to 1 with the rating range of the dataset used being [1], [5] and the fact that the error-rate being almost constant across the sparsity levels shows that the proposed method is not greatly affected by sparsity.
BloSecR: A Conceptual Framework of Identity Documents' Security on Blockchain in Refugee Issues and Conserving Fundamental Rights

Identity documents are vital credentials to the people, which assists to fulfil their demands and conserve their rights. Besides the benefits of the amplification of modern technologies, the tendency of forging IDs is becoming a profitable business for criminals. They utilise the technology to make counterfeit ID cards for non-native people (refugees) by taking advantage of their physical or cultural similarities. The aim of this paper is to propose a blockchain-based security model which will ensure the authenticity of identity documents and conserve fundamental rights for refugees. In the construction of the model, a secure environment for identification is demonstrated, where the authorized controller, the third party's data request, reviewing and assisting refugees functions. The detailed proposal of the aid request and donation on the Aid Management (AM) System is presented to complete peer-to-peer transactions between the hosting country (HC) and the refugee upon the request of the refugee and donation of a third party (TP). This model will certainly have a major impact as a wider cyber-defense framework for refugees' identification and basic rights, preventing forged IDs and criminal activities.
Temperature Prognosis Using Regression Techniques

In present scenario, weather and temperature prediction is considered as one of the most important aspects on daily life basis. Accurate prediction of weather and temperature at the very right time is very important. Nobody knows when and where; the things might go worse naturally. Thus, pre scenario prediction is the key to mark all the key strategies. In this work, temperature prediction is achieved by using multiple well known regression techniques namely Multi Linear Regression (MLR), Polynomial Regression (PR) and Support Vector Regression (SVR). During the implementation of the experiment, it was observed that all the above-mentioned regression techniques came up with almost the similar type of the performance in terms of the accuracy, so to differentiate the performance of each regression technique in detail, the comparison was done on the basis of Mean Square Error (MSE). After the completion of the experiment, it was observed that all the regression techniques came up with a better performance result and the training accuracy achieved of MLR, PR and SVR came out to be 99%, 99.71% and 99.77% respectively whereas talking about the MSE of all three regression techniques came out to be 0.900937, 0.275822 and 2.022358 respectively.
MalNet – an Optimized CNN based method for Malaria Diagnosis

Due to the sheer advancement of modern computing, deep learning has emerged as one of the most promising technologies for machine learning. This work concentrates on applying deep learning to accurately diagnose disease. Our proposed method employs a Convolutional Neural Network (CNN) along with the optimal number and size of convolution and spooling layers. We have focused on a case study of the malaria diagnosis dataset containing pathological samples. This work utilizes Adam optimizer to train and validate the model. The choice of Adam optimization along with its important features is discussed. A suitable dataset is used and images are fed into the CNN without reducing their size or color and their performance is evaluated. An architectural comparison is performed between the proposed CNN model and some popular CNN architectures, resulting in a clearer vision of the proposed model's correctness while using a smaller number of hyperparameters. Furthermore, we also compared the proposed model to existing state-of-the-art models to assess its competency in terms of key performance metrics. This comparison reveals that this model's mechanism requires significantly fewer evaluation parameters, enabling the proposed method to become a time-efficient and computationally accurate model in terms of predicate accuracy.
Weighted Averaging Ensemble Model for Concept Drift Adaptation in Streaming Data

In the present world., companies in every industry are overwhelmed by an unprecedented amount of data. The surge of sensor and various network data is a great challenge in deploying a well-fit machine learning (ML) model for data analysis and prediction. These models are subjected to data streams where the statistical properties of the underlying data keep changing. This dynamism results in the decreased performance of the ML models as they fail to generalize to the changes in data distributions. The change in the data distribution is known as concept drift. While there have been many methods introduced to address concept drift., ML models keep suffering from adaptation to the drifts. This paper proposes an error-based weighted averaging ensemble model for adaptation to the concept drift. The model tries to adapt to domains that share the same feature space with varying data distributions. The model is implemented and tested on two real-world datasets and one synthetic dataset. The experimental results illustrate that the proposed ensemble model achieves higher performance compared to the models considered for the study.
A Distributed Formation Controller with Multi-Obstacle Avoidance for Multi-Mobile Robot System

Multi-robot system has great potential than one single robot due to the flexibility. By adjusting formations of the system, complex tasks can be accomplished. However, formation control may conflict with obstacle avoidance in clutter environments. Therefore, we propose a distributed motion planner for multi-mobile robot systems to balance formation control and obstacle avoidance. Caging behavior is designed for the system to achieve desired formations. For obstacle avoidance, the artificial potential filed algorithm is improved, in which the force caused by formation control is considered. Thus, conflicted forces can be avoided and tangential directional forces can be enrolled. Simulation results demonstrate the system can keep desired formations in multi-obstacle environments. Moreover, the proposed algorithms is distributed, and the priority between the two tasks can be adjusted.
Performance Improvement of PID Controller for PMSM Using ANFIS Controller

PID motor controllers are largely implemented in industrial applications because of their uncomplicated structure and simplicity in their design. PID controllers are generally preferred where the system characteristics are linear. But when system characteristics are non-linear, we have to change PID control gains according to system requirements. Moreover, the noise in the feedback sensing system makes the PID controller less suitable for system control. This paper aims to provide an advanced approach to improve the dynamic speed response of permanent magnet synchronous motor (PMSM) by replacing the PID controller with an adaptive neuro-fuzzy inference system (ANFIS) controller. This specific technique delivers the benefits fuzzy logic and neural network-based control system.
PENINGKATAN KETERAMPILAN MENULIS TEKS EKSPLANASI MELALUI MEDIA BERITA MENGGUNAKAN METODE STAD (STUDENT TEAM ACHIEVEMENT DIVISION) PADA SISWA KELAS X1 SMKN 1 BINONG

In learning to write at school, especially at SMKN I Binong based on the results of interviews and observations that have been carried out, it shows the low writing ability of students because students pay less attention to cohesion and coherence in paragraphs. This research was obtained from one class, namely class XI which was used as the experimental class. Data were obtained from 9 students to write explanatory texts and the results of interviews with Indonesian language teachers. Furthermore, the data were analyzed using the formula for finding the average score, normality test, homogeneity test, hypothesis testing, and N-Gain test. The results of the students' pretest in learning to write explanatory texts before using the STAD (Student Team Achievement Division) method were still very less than the data on the entire object found. The lowest score is 40, the highest score is 80, and students get an average score of 63.33 for writing explanatory text, the score is said to be very low. Because the Minimum Completeness Criteria (KKM) in schools is 70. The results of the posttest in writing explanatory texts the lowest score is 60, the highest score is 90, and the average posttest in the experimental class is 74.44. The improvement of explanatory text writing skills using the STAD (Student Team Achievement Division) method has been proven by researchers to improve learning to write explanatory texts. The STAD (Student Team Achievement Division) method makes the learning atmosphere in the classroom more active in asking questions, is conducive, collaboration between students, solving problems, and can make it easier for students when retelling an incident so as to produce better explanatory text writing results.
Semantic Segmentation of Road Scene Using Deep Learning

The semantic segmentation task is the process of predicting each pixel in the image to some class. Classes could be road, person, car, tree, etc. Road scene segmentation has a vast application in the autonomous driving system. Researchers emphasize autonomous vehicles to reduce the human effort of driving and reduce the chances of road accidents caused by human error. Much research has been done to segment and classify objects in the road scene. We humans can easily detect and understand the objects around the road scene, but it is quite a challenging task for any machine. This article presents some state-of-the-art deep learning-based semantic segmentation techniques, namely FCN, SegNet, and UNet. And a new architecture named PNet for road scene semantic segmentation is proposed. The proposed network is trained end-to-end and comparatively evaluated with the state-of-the-art by using segmentation performance matrices mean Intersection over Union (mIoU) and Dice Coefficient measure (DCM). The publicly available CamVid dataset is used to train and validate the proposed PNet. We have achieved 70.7% mIoU and 80.9% DCM, which is comparatively better than the state-of-the-art.
Cybercrimes against Women in India: How Can the Law and the Technology Help the Victims?

Women, the half population of the world, are sometimes referred to as second-class citizens. This unpleasantly differential treatment is not only in higher social and political aspirations of womankind but sadly also in the very basic right of every human being, i.e., living with dignity. This unfairness is noticeable in most parts of the world, especially where “might is right” is practised. Women could not have anticipated the same status to be accorded in the cyber world, which apparently does not work on muscle power. However, on observing the cyber world in the 21st century, it is evident that the struggles, which used to haunt women in the real world, continue to distress them in the virtual world as well. However, unlike in the real world, in the cyber world, women netizens face threats from distant perpetrators as well. Stalking, harassments, bullying, blackmailing, defamation, pornography, obscenity, morphing, e-mail spoofing, revenge porn, slut shaming, and sexting are some of the known crimes committed against women in the online environment. The Indian Parliament passed the Information Technology Act in 2000 to combat cybercrimes. Though the Act was initially aimed at providing a law-enabled environment for e-commerce, at a later point in the year 2008, it was amended to cover various cybercrimes as well. India's National Crimes Records Bureau (NCRB) has identified that Cybercrimes are a new distinct category of crimes quickly swelling due to widespread use of Internet and information technology-enabled services. Dealing with such incidents can leave invisible scars on the mind of the victims. The increasing rate of offenses committed over the Internet and the unpreparedness of the legal system to take on the offenders and support victims is palpable. In general speaking, cybercrimes are criminal wrongs committed with the help of computers. Irrespective of the fact that computer device is the target of the crime or a means of crime, it will be identified as cybercrime. Cybercrimes can be generally categorized as crimes against i) property, ii) person, and iii) State. This paper focuses on crimes against ii) persons and particularly against women in India. This chapter analyses the role of the Indian legislative system and the help that technology can provide in dealing with the issue of cybercrimes against women and in providing an essential support system to the victims of these crimes.
Electric Street Cart with Covid-19 Protocol

Due to urbanization and industrialization, there is a rapid acceleration of migration from rural to urban area. So urban centers are unable to provide employment to all workforces, so they have to find other opportunities for the settlement in informal sector. Street vendors are often those who are unable to get regular jobs in the remunerative formal sector on account of their low level of education and skills. They try to solve their livelihoods problems through their own meager financial resource. They are the main distribution channel for a large variety of products of daily consumption like fruits, vegetables, readymade garments, shoes, household gadgets, toys, stationery, newspapers, and magazines and so on. If they were to be eliminated from the urban markets, it would lead to a severe crisis for fruit and vegetable farmers, as well as small scale industries which cannot afford to retail their products through expensive distribution networks in the formal sector. Corona virus sickness has become a big public health issue in 2019. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face mask detection software based on AI and image processing techniques. For face mask detection, sanitization and social distancing the approaches mentioned in the article utilize Machine learning, Deep learning, and many other approaches. It will be simple to distinguish between persons having masks and those who are not having masks using all of these ways. The effectiveness of mask detectors must be improved immediately. In this article, we will explain the techniques for face mask detection with a literature review and drawbacks for each technique..
Transformasi Pengelolaan Arsip Statis Pada Lembaga Kearsipan Melalui Penerapan RIC: Menuju Interoperabilitas dan Open Data Kearsipan

This research was aimed to examine how the work system of archival institutions in terms of the management of archives must be transformed into digital/electronic systems in the current era of information and communication technology advancements . Archival descriptions have an important role in processing archives at archival institutions. However, archive descriptions are not organized efficiently and effectively in a digital environment. Metadata of archive is often lacking in archive descriptions of digital formats, making it difficult for users to locate and access archives of digital formats. This research uses a qualitative approach with literature studies. Relevant sources are collected, processed, selected, and used to analyze the standard concept of records in contect (RiC) descriptions and digital archive representations in the processing of digital-based archives at archival institutions . The result of this research showed that the application of RiC description standards in digital archive processing provides more accurate and quality archive representations, thereby increasing accessibility and retrieval of digital archive at archival institutions.
SFNet: Clothed Human 3D Reconstruction via Single Side-to-Front View RGB-D Image

Front-view human information is critical for reconstructing a detailed 3D human body from a single RGB/RGB-D image. However, we sometimes struggle to access the front-view portrait in practice. Thus, in this work, we propose a bidirectional network (SFNet), one branch to transform side-view RGB image to front-view and another to transform side-view depth image to front-view. Since normal maps typically encode more 3D surface detail information than depth maps, we leverage an adversarial learning framework conditioned on normal maps to improve the performance of predicting front-view depth. Our method is end-to-end trainable, resulting in high fidelity front-view RGB-D estimation and 3D reconstruction.
The Improved Adaptive Algorithm of Deep Learning with Barzilai-Borwein Step Size

To solve the problem that it is difficult to determine the learning rate when training a neural network model, this paper proposes an improved adaptive algorithm based on the Barzilai-Borwein (BB) step size. In this paper, the new algorithm accelerates the model's training through the second-order momentum and adapts the learning rate according to the BB step size. We also set an adequate range for the learning rate to ensure the stability of adaptive adjustment and reduce the error of step size. Compared with different algorithms in a series of popular models, the new algorithm significantly avoids the tediousness of manually adjusting the learning rate and helps to improve the convergence speed. The results show that the new algorithm is feasible and effective.
UOUU: User-Object and User-User Distance-Combined Method for Augmented Reality Collaborative Task

Augmented reality superimposes digital information on objects in the physical world and stimulates multiuser collaboration by enabling a shared augmented space. Despite extensive studies that separately explored user-object and user-user distances, few are concerned about how combining these distances affects multiuser collaboration in augmented reality. To fill the gap, we present the UOUU, the user-object and user-user distance-combined collaboration method. The method integrates two types of distances to assign tasks dynamically. Furthermore, we conducted empirical studies with 16 publicly recruited participants to evaluate the method's influence on overall task performance and collaboration. The results showed a significant increase in collaboration occurrences and task performance, indicating that the simultaneous comparison of UOD and UUD in UOUU helped users collaborate efficiently. Besides, we also found that UOUU reduced user's perceived workload and facilitated the development of user's relations with their collaborators in terms of increased social presence on collaborators and reduced psychological distance between users and collaborators.
A New Parameter Model for Consumer-Level Virtual Reality Equipment Evaluation

This paper explores the parameter model of Vir-tual Reality (short as VR) equipment evaluation, it focuses on the comfortable feeling of VR users. Because the keyword comfortable feeling has strong personal subjective imagination, therefore in this research, we draw on existing methods such as comfortable feeling evaluation model of vehicle or other riding equipment, also the existing and traditional VR evaluation method was referred. According to the current experience and past achievement, a new evaluation model by parameter was establish. In the model, three macro directions (Image, Sound, Tactile) and six influence parameters (Sound delay, Horizon, Picture delay, Weight, Pressure, Sound source deviation) were included. Based on these parameters and thus the calculation of algorithm's area, we can get a relatively objective and reasonable evaluation of the performance of VR devices.