Reviewerly/exHarmony huggingface dataset:
Privacy-preserving Mechanisms of Continuous Location Queries Based on LBS: A Comprehensive Survey

recently, the fast growth of mobile devices and location technology has improved users’ lives and created enormous prospects for diverse applications. Users’ lives have been made simpler by technologies like mobile devices and location-based services (LBS). Users prefer Continuous Location Queries Based on LBS (Continuous LBS). However, when users utilize connected applications, they must constantly submit relevant information to location-based services’ provider (LBSP). In terms of spatial and temporal correlations, in comparison to snapshot LBSs, continuous LBSs present a greater barrier for maintaining location privacy. The adversary can more easily assess the user’s private information, including lifestyles, work address, interests, and even health conditions, through the query content. As a result, one of the current study topics for academics is how to preserve mobile users’ location information to ensure that they do not disclose personal privacy while enjoying associated services. This work comprehensively expounds the research state and development trend of location privacy protection mechanisms in continuous LBSs, classifies five current technologies, and describes their technological solutions, privacy efficiency, and application scenarios based on a timeline. At the same time, this study outlines the challenges that will need to be overcome by future research.
Deep Learning Approaches for Unobtrusive Human Activity Recognition using Insole-based and Smartwatch Sensors

In this digital age, human activity recognition (HAR) has been playing an increasingly important role in almost all aspects of life. Healthcare systems for monitoring the activities of daily living (ADL), security environments for automatically detecting abnormal activity and notifying the pertinent authorities, and increasing human contact with computers are all examples of HAR applications. The data collecting tools used in HAR research can also be categorized (sensors or cameras). In sensor-based HAR, various wearable sensors have been investigated for their usefulness in effectively detecting both simple and complex human activities. In this work, we studied HAR using signal data acquired from insole-based and smartwatch sensors. To achieve our research goal, we proposed a ResNeXt inspired deep learning (DL) network called HARNeXt. The proposed model was evaluated on a public data set named 19NonSense data that collected activity signal data recorded from insole-based and smartwatch sensors. In addition, we compared the proposed model to baseline DL models. The results showed that the HARNeXt outperformed other DL models and achieved the highest F1-score of 97.03%.
Collision-free Path Planning For Welding Manipulator Via Deep Reinforcement Learning

In the narrow industrial welding scene, it is difficult for the 6DOF manipulator to realize intelligent obstacle avoidance planning. This paper proposes an adaptive reinforcement learning on the path planning method of welding manipulator to find a collision-free path in the limited scene. The sub-actor network is designed to conduct guided search on the main actor-network to achieve effective obstacle avoidance. The overestimation of Q value is alleviated by embedding the return distribution function into maximum entropy to replace the shear-double Q learning of SAC. We evaluate our approach on a group path planning experiment, the experiments demonstrate that our method increases learning efficiency and obtains safer policies.
Solving The Travelling Salesman Problem by Using Hybrid Simulated Annealing with Tabu Technique

This paper presents a hybrid approach which mainly based on concepts of the simulated annealing algorithm for solving the travelling salesman problem (TSP). This article intends to improve the performance of the original of the simulated annealing algorithm (SA). To enhance the performance of the SA, two new features have been used. 1) The Tabu search is embedded in the simulated annealing algorithm. 2) The two swap techniques are present. The proposed algorithm was evaluated and compared to the other two state-of-the-art algorithms in the literature. The experimental results on 5 sets of well-known standard benchmarks shown that the proposed algorithm is very effective than the comparison methods in term of the average percentage deviation from the lower bound criteria.
A Study on the Feature-level Perception Sharing of Autonomous Vehicles

Recently, remarkable progress has been made in autonomous driving. However, there are still many things to do for fully autonomous driving. One of them is perception sharing, which utilizes vehicular communication to maximize the cognitive ability of the vehicle. In this paper, we identify recent research trends by surveying studies on perception sharing. Moreover, we introduce papers dealing with more practical scenarios and present the future works given to perception sharing.
Learning an Inference-accelerated Network from a Pre-trained Model with Frequency-enhanced Feature Distillation

Convolution neural networks (CNNs) have achieved great success in various computer vision tasks, but they are still suffering from the heavy computation costs, which are mainly resulted from the substantial redundancy of the feature maps. In order to reduce these redundancy, we proposed a simple but effective frequency-enhanced feature distillation strategy to train an inference-accelerated network with a pre-trained model. Traditionally, one CNN can be regarded as a hierarchical structure, which can generate the low-level, middle-level and high-level feature maps from different convolution layers. In order to accelerate the inference time of CNNs, in this paper, we propose to resize the low-level and middle-level feature maps to smaller scales to reduce the spatial computation costs of CNNs. A frequency-enhanced feature distillation training strategy with a pre-trained model is then used to help the inference-accelerated network to maintain the core information after resizing the feature maps. To be specific, the original pre-trained network and the inference-accelerated network with resized feature maps are regarded as the teacher network and student network respectively. Considering that the low-frequency domain of the feature maps contribute the most parts to the final classification, we then transform the feature maps of different levels into a frequency-enhanced feature space, which highlights the low-frequency features for both the teacher and student networks. The frequency-enhanced features are used to transfer the knowledge from the teacher network to the student network. At the same time, knowledge for the final classification, i.e., the classification feature and predicted probabilities, are also used for distillation. Experiments on multiple databases based on various network structure types, e.g., ResNet, Res2Net, MobileNetV2, and ConvNeXt, have shown that with the proposed frequency-enhanced feature distillation training strategy, our method could get an inference-accelerated network with comparable performance and much less computation cost.
TWIZ

This paper introduces TWIZ, a multimodal conversational task wizard that supports an engaging experience, where users are guided through a multimodal conversation, towards the successful completion of recipes and DIY tasks. TWIZ leverages task guides from WikiHow and Recipe sources, as well as dialog AI-based methods to deliver a rich, compelling, and engaging experience when guiding users through complex manual tasks. TWIZ participated in the Amazon Alexa Prize Taskbot 2021.
Codes from symmetric polynomials

We define and study a class of Reed–Muller type error-correcting codes obtained from elementary symmetric functions in finitely many variables. We determine the code parameters and higher weight spectra in the simplest cases.
Future leadership characteristics among the principals of basic education schools

The current study aimed to investigate the degree of availability of the future leadership characteristics among the principals of basic education schools. A questionnaire was used to collect the data from a study sample of (600) school principals. The study results showed that the degree of availability of the future leadership characteristics among the principals of basic education schools was average. The results also showed that there were statistically significant differences related to the academic qualification variable in favour of master’s degree and above. There were statistically significant differences related to the variable of the Specialized Center for Professional Training of Teachers in favour of those enrolled in the centre. The study recommended the need to offer programs targeting school principals in dealing with scenarios of future events and issues. The study recommended training of school principals on future planning and future predictions based on the current data. Keywords: Characteristics, Future Leaders, Basic Education Schools.
3D Video QoE Based Adaptation Framework for Future Communication Networks

A thorough investigation of the characteristics of 3D videos and the circumstances that support them in content-related contexts is necessary to help advance 3D video adaption systems. In order to create an advanced 3D video Quality of Experience (QoE) based adaptation framework for smart service management of future communication networks, various elements and situations related to content can be employed as milestones. Given this knowledge, the spatial resolution of a color + depth map 3D video representation is taken into account in this study as a factor to suggest a 3D video QoE based adaption framework. In order to construct this framework, the content-related contexts—namely, the motion and structure of a color video and the relative depth and aerial perspective of a depth map are taken into consideration. Under the condition that certain requirements are met and the 3D video QoE is maintained at an ideal level, the performance assessment results obtained using the suggested framework demonstrate its efficacy for selecting the best spatial resolutions for the color + depth map videos.
Lightweight Block Cipher for Resource Constrained IoT Environment—An Survey, Performance, Cryptanalysis and Research Challenges

Nowadays IoT (Internet of Things) is becoming a more popular environment and has a variety of applications like Smart Home, Smart Healthcare, Vehicles, and many Industries. There is plenty of information shared among the devices with the use of the internet. Due to this importance of data sharing, there is a possibility of security attacks, and threats. IoT environments have many security challenges including Providing Confidentiality, Integrity, and Availability in addition Privacy, Authentication. These challenges can be fulfilled by many cryptographic algorithms. Since IoT has limited memory, resources, power, those cryptographic primitives may not be suitable. The best solution for this problem is lightweight cryptographic algorithms. This paper presents the importance of lightweight cryptography algorithms. We analysed the performance of current algorithms in terms of throughput, latency, ROM/RAM, software efficiency, and energy. We are comparing the cryptanalysis of some popular algorithms. Also, we are discussing the research challenges and research gaps in the area of lightweight cryptography for providing better performance, cost and software implementation without affecting high security.
Improved Lightweight Cryptography Authentication Based Secure Data Transmission in IoT Networks

The internet of things (IoT) devices is becoming more common in our daily lives. At the same time, attacks on these devices lead people’s lives in danger and cost much money. Man-in-the-Middle (MITM) attacks have been primarily examined, especially in IoT networks. In this paper, the framework is constructed using the smart environment monitoring system (SEMS), created with the help of ten nodes, and communicated through the message queuing telemetry transport (MQTT) protocol. An enhanced lightweight encryption algorithm (ELWEA) is proposed to mitigate the MITM attack on IoT devices. The ELWEA comprises (i) key scheduling and (ii) encryption and decryption. The performance metrics of the proposed method provide less time complexity of 10.5 ns compared to the literature. Substantial cryptography methods are applied to maintain the secrecy of sensor data. The proposed method protects against the MITM attack and works against a wide range of typical applications.
An Efficient Algorithm for Multi Class Classification in Deep Neural Network

In comparison to other machine learning techniques, deep neural networks are effective in classifying non-linearly separable data. Because of its simplicity, contemporary gradient-based algorithms such as momentum Stochastic Gradient Descent (SGD) are commonly employed in Deep Neural Networks (DNN). However, the process of convergence is slowed by the choice of an appropriate learning rate and the local minima problem. To address these issues, this research proposes a unique approach for training DNNs called Simulated Annealing Based Gradient Descent (SAGD), which involves optimizing weights and biases. The SAGD technique optimizes the function by combining gradient information with the simulated annealing notion. The learning rate does not need to be manually adjusted with this method. Instead, using the simulated annealing approach, the learning rate is modified automatically for each epoch. The approach is tested utilizing VGG16, ResNet 18 and InceptionV3 architectures on typical multi-class classification data sets such as Iris, MNIST, and CIFAR10. The performance of SAGD and other state-of-the-art gradient descent optimization methods is compared, and it is demonstrated that SAGD performs comparably to existing gradient descent methods.
Modeling and Control of Induction Machine and Drive in the Combined Domain with New Chaotic Gorilla Troop Optimizer

In this work, a new chaotic version of gorilla troop optimizer is developed. The position equation of the algorithm is modified with the help chaotic maps. Around ten widely cited chaos maps, one-dimensional in nature, are considered to develop new chaotic algorithms. Two unimodal and three multi-modal test functions are employed to validate the efficacy of the proposed technique. A 50 hp induction motor model is also reduced and further its controller is designed utilizing the benefit of delta operator with the help of this proposed algorithm. Finally, a practical test system of induction motor drive is taken up for modeling and control action as well. The controller realization in both cases is carried out using approximate model matching framework. The convergence speed and accuracy of the proposed techniques are better as compared to the standard and latest methods. Thus, the results in all experimentations performed show great promise.
Navbot—College Navigation Chatbot Using Deep Neural Network

In terms of size, college campuses are massive. It is an uphill task to identify in which block a particular room is located or where the block is located within the campus. A chatbot has been developed to address this problem. A chatbot is an interactive artificial intelligence program that attempts to mimic human behavior by interpreting input and responding appropriately in text format. Deep learning model and natural language processing algorithms are used to develop the chatbot. The knowledge base for this chatbot will be in JavaScript Object Notation (JSON) format. Users can request college navigation-related queries. The query is processed using natural language processing techniques such as tokenization and lemmatization. The processed query after spell correction is given as an input to the Deep Neural Network (DNN) algorithm. The proposed chatbot will search the processed query in the knowledge base and respond with the corresponding answer using a sequential DNN model with five hidden layers. User interface of the chatbot is developed using Hyper Text Markup Language (HTML), Cascading Style Sheets (CSS) and Java Script. The proposed model will help in navigating people inside the college to different blocks through google map links and inside the blocks through textual directions. The model works with an accuracy of 98%.
Deep Learning Based Hand Sign Recognition in the Context of Indian Greetings and Gestures

Communication through sign language can be orchestrated in a variety of ways. There are certain words of the spoken language that can be directly represented and interpreted through simple gestures. Words like (Namaste) (good) (bad) etc. have designated signs or gestures. However, there are certain words that don’t have predefined signs. Prior to the development of deep learning methodologies and algorithms, research in the field of sign language interpretation and translation was few. The most typical method for interpretation is to extract features from coordinated movements using Image Processing Algorithms, then use Convolutional Neural Networks to learn these features and increase utility. Advances in deep learning have led to the creation of Object Detection Algorithms that, when used in conjunction with neural networks, can identify all types of objects. You Only Look Once (YOLO) is one such algorithm that excels in identifying custom objects. It's utilized in conjunction with Darknet, a neural network architecture. People who use sign language frequently need to rely on a translation to get their message across to someone who does not understand sign language. Dependency on a translator can create issues and potentially render the person incapable of acting independently. The creation of a system that can help people use sign language without depending on another person can really help them be independent and ignite the confidence to present themselves to the world without any fear. Thus in a country like India, it is very important that we develop a hand gesture detection system that can identify Indian gestures.
A Deep Learning Framework for Social Distance Monitoring and Face Mask Detection

The Covid outbreak has caused a worldwide calamity with its poisonous spreading. It has become very important to protect ourselves and the people around us from this infection. The dangers of contagiousness can be limited only by following Covid rules such as wearing facemask and keeping up social distance. This paper proposes a system to distinguish whether the person is wearing a facemask or not and also if the people are maintaining a social distance. The framework used is MobileNetV2 for object recognition. The model is prepared on an image dataset and tested with live real time video with a decent precision. The precision is represented by red and green bounding boxes which indicates facemask accuracy as well as the depth for social distance. Red bounding box appears when the particular object is not wearing a mask or not following social distance and green bounding box displays if the object is following the criteria.
Review on Web Application Based Infant Health Management

The neonatal phase is the most vulnerable time of life and is the important period for child survival. Therefore, improving newborn survival is very important and is a priority. It is also very important to monitor the postnatal health condition of the baby and manage and store the data to examine the health condition and consider the baby as healthy. The health parameters on which infant is monitored are temperature, pulse rate, humidity, and motion capture. To ensure the good health of the newborn, certain health parameters need to be monitored. In this paper Infant health monitoring system consists of a web application-based health management system for a neonatal that keeps track of the neonatal health is reviewed.
PDF Steganography Using Hybrid Crypto Encryption Technique

Recently, for all script and content writing, and transferring purposes, the Portable Document Format (PDF) has been widely used. This article presents PDF steganography based on a hybrid crypto encryption technique. The proposed method does not modify the portable document structure or content while concealing the secret data at any stage. Before the stego operation is performed, the confidential data undergo hybrid crypto encryption. Encryption consists of an advanced encryption standard 256-bit key and RSA encryption algorithm. As the document structure is not modified throughout this technique, it is not suspicious to technocrats during communication. Only the size of the PDF document increases as the size of the secret data increases. The human vision system cannot differentiate between the stego and a standard document, as the content and structure are unaltered.
A Study on the Activation of ISO Standards to Achieve SDGs:Using Text Mining Technique

SDGs(지속가능발전목표)는 인류의 보편적 문제와 지구 환경문제를 해결하기 위한 목표이다. 전 세계 국가들과 기업들의 SDGs에 대한 관심이 증가하고 다양한 연구들이 진행되고 있다. ISO에서는 ISO 국제표준이 SDGs 달성에 이바지할 수 있다고 설명하며 SDGs 17개의 목표에 맞게 ISO 국제표준을 분류하였다. 본 연구에서는SDGs와 분류된 ISO 국제표준의 연계성을 파악하기 위하여 ISO 국제표준 문서를 발췌하여 텍스트마이닝 분석을 하였으며, SDGs 17개의 목표에 중복으로 분류되는 ISO 국제표준의 관계를 파악하기 위하여 상관 분석을 하였다. SDGs를 달성하기 위해 ISO 국제표준이 제정된 것이 아니라 기존에 제정된 ISO 국제표준을 SDGs의 개념에 맞춰 제시하고 있기에 때문에 SDGs 목표와 ISO 국제표준 간의 연관성을 정량적인 지표로 제시하기 어려운 것으로 파악되며, 체계적인 연구를 통해 SDGs를 달성하기 위한 ISO 국제표준의 재정립이 필요하다.The SDGs (Sustainable Development Goals) are aimed at solving the universal problems of humanity and the global environment. Countries and companies around the world are increasingly interested in SDGs and various studies are being conducted. ISO explained that ISO international standards can contribute to the achievement of SDGs and classified ISO standards according to the 17 goals of SDGs. However, no criteria have been given for this classification. Therefore, in this study, ISO standard documents were extracted to understand the connection between SDGs and classified ISO international standards, and correlation analysis was performed to understand the relationship between ISO standards classified as overlapping with 17 SDGs goals. According to the text mining analysis results, most of the keywords were overlapping, and the number of keywords that appeared alone was not large. As a result of correlation analysis, it was confirmed that there were many overlapping standards between 17 goals. Therefore, since ISO international standards have not been established to achieve SDGs, but existing ISO international standards have been presented according to the concept of SDGs, it is difficult to present a quantitative indicator between SDGs goals and ISO international standards, and it is necessary to re-establish ISO standards to achieve SDGs through systematic research.