Reviewerly/exHarmony huggingface dataset:
Impact of Persona Creation by Celebrity Endorsement on Buying Behavior of Educated Young &amp;amp; Middle Age Consumers towards Beauty Soaps in Karachi City

This research paper focused on the impact of persona creation by celebrity endorsement on consumer buying behavior and on their perceptions towards beauty soaps particularly in Karachi city. Pakistan is a country where people would love and proud to follow or copy or be look like their favorite celebrities. Unilever Pakistan has cashed or used this psyche of Pakistanis very beautifully for their beauty soap named LUX but on the other hand for its beauty soap, named Dove, it has taken commoners to promote that product. In this research it is founded that in Pakistan especially in Karachi city there are more users of LUX than Dove because the persona marketers of LUX has created in the market through celebrities in terms of fragrances which attract spouse and help users to be confident regarding freshness for whole day, this person a creation has created strong positive image of LUX in consumers of young age as well as middle age educated people. Results of this paper indicates that when companies choose celebrities based on factors such as, Credibility (physical attractiveness, trustworthiness, expertise), Emotional Involvement (passion, dedication), Meaning Transfer (effective communication, field of celebrity endorsed that best match with the product attributes) this would have a positive impact on consumers’ buying behavior and especially for beauty soap named LUX young ones as well as middle aged educated gentry both have same perception and likeness regarding usage due to the persona creation by celebrities.Recommendation for companies regarding selection of celebrities is that companies should do that after ensure that the celebrities’ image and overall personalities must match with the brand personality to create a desired persona and buying behavior of users for a brand.
Features of distance learning by the disciplines in the Earth Sciences direction at a technical university

: Distance education has become widely used both in Russia and abroad in recent decades. A particularly powerful impetus to distance education technology development and spread was given by the coronavirus pandemic in 2019. To improve learning efficiency in the system of distance education, it was required to refine various and accessible forms of education. It was also necessary to make a wider use of new educational technologies. The purpose of this study is to analyze the use of the latest educational technologies in the distance learning of students in the disciplines of the Earth Sciences direction at a technical university, as well as to form a modern educational space in online learning and stimulate students' independent work based on the assignments supervised by teachers. The study described in this paper deals with the following application features of distance learning at Irkutsk National Research Technical University: the presentation specifics of the theory and the introduction forms of material for laboratory works, choice of the most effective teaching aids, methodology for the current and final control of students' knowledge. Irkutsk National Research Technical University has chosen Moodle from a wide variety of distance learning systems. The advantage of this system is the ability to perform laboratory, practical and course works, which is important for a technical university, also to conduct tests, the results of which can be monitored by both teachers and students in an electronic gradebook. The distance learning system turned out to be more multisided, but more laborious than in-person training. Moreover, it is also complicated by a number of technical problems. The distance learning process requires the establishment of a certain parity of interests, abilities and opportunities between the teaching (professor/lecturer) and the learning (student) parties. The distance learning system has a number of features that should be taken into account when introducing theoretical and lecture material, which requires a more rigorous selection of files or their conversion into less heavy ones, as well as conciseness and clarity of material presentation. The use of virtual laboratories for carrying out laboratory works, availability of distance technologies for training disabled students and corresponding students is gaining great importance. The research results imply that today distance learning is a necessary part of the educational system along with in-person (full-time) or correspondent (part-time) training and one of the main goals of modern distance learning is the ability to work with information and selfimprovement.
A Secure Authentication Scheme for Mobile Cloud Computing Against CCA

Authentication schemes employing identity-based encryption are widely used in mobile cloud computing environments. It enables users to get umpteen services from different cloud servers by registering only once with a third-party. However, in existing schemes, the security against chosen-ciphertext attacks and malicious private key generator is not well-considered. These security pitfalls may cause security and privacy issues in some application scenarios since an active attacker can send a number of adaptively chosen ciphertext and tries to distinguish the target ciphertext; succeeds may cause the exposure of the underlying secret key. In this paper, we propose a secure and efficient authentication scheme for mobile cloud computing with security against chosen-ciphertext attacks and malicious private key generator based on adaptive one-wayness trapdoor function and learning-parity with noise hardness assumption. Our comprehensive security proof demonstrates that our scheme is indistinguishable against chosen-ciphertext attacks.
Action Recognition Network Based on Temporal Spatial Temporal Mode

In recent years, it has become a trend to train an action recognition network with large-scale datasets, but under the condition of limited computing resources, the batch size cannot take a larger value, which will lead to the degradation of recognition performance. Based on this phenomenon, this paper proposes a brain-like convolution kernel module more suitable for small batch size training, which adopts the "Temporal - Spatial-Temporal" mode of thinking (TST). For small batch size, the proposed network using TST convolution module outperforms the baseline ST-GCN and the RA-GCN methods in the NTU-RGB+D dataset, showing its efficiency and feasibility.
Effects of Reputation Systems and Group Decision Making Systems Usage on Online Social Networks

The use of online social networks is one of daily activity for people. OSNs' users share contents which sometimes has other users' information on. This type of data sharing is called co-owned data sharing and it is one of the most popular reasons for privacy leakages in OSNs platforms. OSNs' users claim that the responsibility of preserving users' privacy should be taken by those platforms. To do so, there are different approaches taken by OSNs for protecting users' privacy and providing the most secure environment to their users. This study proposes a model which should be adopted by OSNs platforms for a more secure environment. The model is developed on top of a hypothesis, the proof of the hypothesis is done with analysis on two questionnaires. Results of the analysis show that group decision making and reputation systems are needed to have more secure online social network platforms.
Security Analysis and Improvement of Designated Verifier CLAS in Network Security Course Education

Cryptographic algorithm plays an important role in the teaching of network security course. Certificateless aggregate signature (CLAS) scheme is an important cryptographic algorithm in cryptography. Many CLAS schemes have been proposed with some property, such as designated verifier property. Designated verifier property makes it possible for a specific person to verify the signature which can be applied in many scenes, such as medicine and biology system and et al. In this paper, we analyze a CLAS scheme with designated verifier property proposed in 2020 which satisfies designated verifiability property, unforgeability property and non-transitivity property. However, we point out that the CLAS scheme does not satisfy the unforgeability as they claimed. We present two improved methods to overcome these existed security drawbacks. By analyzing the security of a CLAS scheme and proposing some improved methods, it can let students know how to better design a secure cryptographic algorithm in process of network security course.
Disappearing Volatility of Bitcoin

Bitcoin market capitalization has recently surpassed $1 trillion. According to the popular belief one of the key characteristics of bitcoin is its excessive volatility. This paper provides evidence that high volatility of bitcoin is largely a misperception. We show that bitcoin return fluctuations are lower than those of roughly 900 different stocks in the S&P1500 and 190 stocks in the S&P500. Moreover, we find that bitcoin is less volatile than commodities such as oil and silver, US Treasuries, AAA-rated corporate bonds, EU carbon credits, and some of the most popular technology and media stocks: Apple, Twitter, and Netflix. Equally important, we find that during the March 2020 stock market crash triggered by COVID-19, bitcoin volatility was lower than most of the above-mentioned asset classes. Significant decline in bitcoin volatility over the last decade renders it more “investable” by conservative investors.
Design of a Prediction based Pedestrian Tracking System by UAV

At present, fixed cameras are widely used to monitor pedestrians, but they are unable to monitor the moving pedestrians, making it possible to violate the regulations by moving to areas fixed camera cannot cover. To reduce the number of fixed cameras and continuously supervise moving pedestrian's misbehaviors, a UAV system with pedestrian tracking is introduced. By pose recognizing, turning prediction is proposed. Aiming at tracking loss about corners which is common in cities, effective enhancements on corner occlusion are put forward. This paper introduces the body key point extraction and pose recognization method, and give the control scheme with movement prediction and corner mode. Finally, the design of the software and hardware of the UAV system is proposed and the experimental UAV platform is built and tested. The testing result reveals the turning prediction and corner mode to be effective, and the system realizes a fastest tracking speed of 1.2 meters per second, and a pose recognition rate of 89%.
Leading Athlete Following UAV Using Transfer Learning Approach

Nowadays Unmanned Aerial Vehicles (UAVs) have tremendous applications to make human life easier or to automate the process, similar to other technologies. In this paper we are developing a vision-based navigation system for UAVs, so that a UAV can follow a human athlete autonomously. This work involves two sub tasks 1) To detect and mark the leading athlete and 2) To navigate the UAV in such a way that it follows this marked athlete and continuously changes its position with respect to the person which has been marked. Since detecting the person in the video is one of the important tasks while tracking an athlete, we need a model which gives quick and reliable outputs in low resource-constraint environment. We will perform person detection on live video and one of the most popular models for this purpose is MobileNet with SSD, object detection with convolutional neural network-based characteristics because using SSD (Single Shot Detector) makes detecting objects much faster compared to 2 shot detectors like Faster R-CNN. A control algorithm uses the bounding box position to change the UAV flight parameters in order to keep the runner in the field of centre view. When this model has been tested on 100 images of side view of various running races, 95% of the time the athlete in the lead was marked correctly.
Impact of Rider Optimization Algorithm Towards Optimal Container Resource Allocation

The exploitation of containers in the cloud has gained great attention due to increased portability, restricted overheads and faster and easier deployment. Regardless of the huge count of solutions, there are several problems, which are not entirely addressed in container allocation. This paper utilizes Rider Optimization Algorithm (ROA) for optimizing the allocation of container resources. A novel rescaled objective function is portrayed in this work, which takes account of “Threshold Distance, Balanced Cluster Use, System Failure and Total Network Distance”. At last, the superiority of the adopted scheme is validated over the other traditional schemes in terms of cost analysis.
Classifying User Personality Based on Media Social Posts Using Support Vector Machine Algorithm Based on DISC Approach

Twitter is one of the largest social media with 326 million active users in January 2019. Indonesia emerged as one of the largest countries in terms of Twitter users. Every day more than millions of tweets are published by Twitter users. This study tries to analyze Tweets to get the personalities from chosen Twitter accounts by using the DISC character approach. The classification algorithm that will be used is Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) weighting on the dataset. This research starts with preprocessing stages such as Data Cleansing and Case Folding. We involved psychologists to validate the personality approach of 109 Twitter accounts to determine each Twitter user character. The character classification results used in this study are Dominance, Influence, Steadiness, Compliance (DISC). From 109 Twitter accounts, we considered as the final dataset, we obtain an accuracy of 36.37%, average precision of 23.11%, and average recall performance of 35.25%.
Handwritten Gujarati Character Detection Using Image Processing and Recognition Using HOG, DHV Features and KNN

This paper presents a compendious system which can be used to detect and recognize Gujarati handwritten scripts using image processing techniques and machine learning concepts. It focuses on the essential steps that are involved in the entire process. The complexity of the entire process is on account of variety of handwritings and the curves of letters involved in Gujarati scripts. This paper presents the essential steps of image processing including thresholding, noise removal, character detection and comprehensive steps of machine learning including training and testing of the system for recognition purpose in order to deal with the difficulties and challenges possessed by the script. This paper can be used by researchers and technology enthusiasts to develop systems for Gujarati script recognition. The paper aims to present and deal with special properties associated with Gujarati script.
The Human Factor Assessment of Consumer Air Purifier Panel Using Eye Tracking Device

Among the various types of air purifiers available in the market, some products are more complex and difficult to use than others. This study aims to suggest a method to derive products, which are convenient to use, through the evaluation of an air purifier control panel using eye-tracking. An experiment was conducted with 28 housewives, in which various tasks using an air purifier were presented to them. The usage errors were measured and the tasks with low success rates were analyzed. As a result, the preferred icon and button names could be identified in Steps 5 and 6, where the success rates were low. Therefore, the convenience of using the control panel was accurately evaluated. Based on the obtained results, the study proposes the use of eye tracking as an evaluation method to derive qualitative data to increase the usability of control panels on home appliances such as air purifiers.
Capped ℓ1-norm regularized least squares classification with label noise

Since label noise can hurt the performance of supervised learning (SL), how to train a good classifier to deal with label noise is an emerging and meaningful topic in machine learning field. Although many related methods have been proposed and achieved promising performance, they have the following drawbacks: (1) They can lead to data waste and even performance degradation if the mislabeled instances are removed; and (2) the negative effect of the extremely mislabeled instances cannot be completely eliminated. To address these problems, we propose a novel method based on the capped ℓ1 norm and a graph-based regularizer to deal with label noise. In the proposed algorithm, we utilize the capped ℓ1 norm instead of the ℓ1 norm. The used norm can inherit the advantage of the ℓ1 norm, which is robust to label noise to some extent. Moreover, the capped ℓ1 norm can adaptively find extremely mislabeled instances and eliminate the corresponding negative influence. Additionally, the proposed algorithm makes full use of the mislabeled instances under the graph-based framework. It can avoid wasting collected instance information. The solution of our algorithm can be achieved through an iterative optimization approach. We report the experimental results on several UCI datasets that include both binary and multi-class problems. The results verified the effectiveness of the proposed algorithm in comparison to existing state-of-the-art classification methods.
Simulation Technology for the Testability of Inertial Navigation Systems and Its Application

Fault diagnosis is becoming increasingly difficult because of the complexity and diversity of electronic products and equipment. Therefore, the testability of products has gradually attracted the attention of manufacturers and users. To evaluate the testability level of products in the development stage, this study performs a failure mode and effect analysis. A testability model is then established for the testability simulation analysis. The feasibility and effectiveness of this method are then verified by taking an inertial navigation system as an example. Results highlight the benefits of the quantitative evaluation of testability in the early stage of product design and the identification of weak links in product testability design so as to provide suggestions for product design improvement.
A New High Performance Approach for Crowd Counting Using Human Filter

One of the tasks of the crowd monitoring system is to estimate the number of people in the crowd and issue a warning when it exceeds the allowed threshold. Previous approaches often used multi-column CNN to estimate density maps and thereby estimate the count. However, the amount of information learned from crowd datasets is very small. On the other hand, the confusion between people and other objects such as buildings, trees, rocks, etc (background noise) will affect the density map estimation. In this paper, we focus to solve these two problems and propose a model called Counting using Human Filter (CHF) which consists of two modules: The first one is a feature extractor from a crowd image based on the VGG-16 model to estimate the density map. This module will take advantage of the features learned from the ImageNet dataset. The second one is the human filter used to weight each pixel of the density map. Two modules are combined by element-wise multiplication. We evaluate the estimated results of the model with MAE, MSE metric and assess the quality of density maps according to PSNR, SSIM. Experiments show that our approach estimates the number of people better than the previous methods when evaluating on the ShanghaiTech, UCF_CC_50, UCF-QRNF datasets. Regarding the complexity of the model, our method shares parameters between two modules so it halved the number of parameters compared to previous methods such as Switch-CNN, SSC, ADCrowdNet.
Cluster-Based Monitoring and Location Estimation for Crowd Counting

Crowd management and monitoring are important research topics in order to ensure personal and community safety by making use of video images. The crowd tracking system (CMS) includes tasks such as density variation in images, irregular distribution of people and objects, overcrowding, exposure estimation. People come together for various purposes in areas designed for socializing, such as parks, stadiums, airports, hospitals, and shopping malls. Generally, these areas are monitored by closed-circuit TeleVision (CCTV). However, this type of system brings problems. The main ones are portability, flexible accessibility, limited coverage area, high power consumption. Crowd density is often examined by people for behavioral analysis or to identify suspicious people. Computer vision and deep learning approaches are maintained in order to prevent mistakes caused by human error and to make a faster evaluation. In this study, a method for detecting event changes with cluster-based inspection in crowd images is proposed. Gaussian YOLOv3 model is used for object recognition. The proposed clustering approach is used to track changes in the number, coordinate, and direction information of the cluster. Behavior change time, location, and classification are achieved as a result of this information extractions. These two event changes are taken into consideration, especially because sudden changes in state occur in walking and running behavior. Six different video sequences in the PET2009 dataset are used for the study. Accuracy performance is achieved between 83.2% and 96.4%. The results obtained to achieve the success that can be compared with similar ones in the literature.
DCNN Based Dual Assurance Model for Age Estimation by Analyzing Facial Images

Advancement in Machine Learning (ML) with improved computing power and large facial image dataset have inspired a lot of research to estimate the age using facial images. These research have also showcased superiority of deeply learned neural networks on large datasets for estimating age. The proposed method utilises the implementation of deep convolutional neural networks (DCNN) using a pre-trained model. It estimates the age using two components called MotherNet (M-Net) and ChildNets (C-Net) which employs a novel dual assurance approach to not only detect but also correct the error in age estimation. Inturn, this strategy improves the overall performance. Experiments have been conducted on two datasets: FG-NET and Chalearn-15. The results show that the proposed approach outperforms the other state-of-the-art methods.
Research on Network Traffic Prediction and Management Based on Logarithmic Barrier Method

In order to effectively describe and manage computer network traffic, this paper proposes a new method based on logarithmic barrier and queuing method (Logarithmic Barrier and Queuing Method, LBQM). This method first analyzes the key factors that affect the network traffic by using the logarithmic barrier function, and then dynamically adjusts the network distributed packet send rate by using the logarithmic barrier method and the node queue congestion situation, so that the network can obtain the maximum aggregate utility. Finally, the simulation results show that, compared network simplex algorithm and distance-directed augmenting path algorithm, LBQM has better adaptability and robustness.