Reviewerly/exHarmony huggingface dataset:
Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm

The self-adjusting (1 + (λ, λ)) GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax . It uses a parameter control mechanism for the parameter λ that governs the mutation strength and the number of offspring. However, on multimodal problems, the parameter control mechanism tends to increase λ uncontrollably. We study this problem for the standard Jump k benchmark problem class using runtime analysis. The self-adjusting (1 + (λ, λ)) GA behaves like a (1 + n ) EA whenever the maximum value for λ is reached. This is ineffective for problems where large jumps are required. Capping λ at smaller values is beneficial for such problems. Finally, resetting λ to 1 allows the parameter to cycle through the parameter space. We show that resets are effective for all Jump k problems: the self-adjusting (1 + (λ, λ)) GA performs as well as the (1 + 1) EA with the optimal mutation rate and evolutionary algorithms with heavy-tailed mutation, apart from a small polynomial overhead. Along the way, we present new general methods for translating existing runtime bounds from the (1 + 1) EA to the self-adjusting (1 + (λ, λ)) GA. We also show that the algorithm presents a bimodal parameter landscape with respect to λ on Jump k . For appropriate n and k , the landscape features a local optimum in a wide basin of attraction and a global optimum in a narrow basin of attraction. To our knowledge this is the first proof of a bimodal parameter landscape for the runtime of an evolutionary algorithm on a multimodal problem.
A DIC-based method for measuring the thermal expansion coefficient of porous flexible foam material board

Porous flexible foam material board, represented by polyimide, has the characteristics of lightweight, vibration damping, and heat insulation, which is widely used in the cushioning and heat insulation structure of super-static, super-stable, and super-precision satellites. As the thermal insulation and buffer layer of satellite, the coefficient of thermal expansion is the key performance parameter and the important index of satellite structure design under the space environment with large temperature variation. However, the existing thermal expansion coefficient measurement technology has some problems, such as contact force interference, low measurement accuracy, complex measurement process. To solve the above measurement problems, this paper designs a thermal expansion coefficient measurement method of porous flexible materials based on digital image correlation (DIC) measurement technology. This method uses the temperature loading system with adjustable size to control the temperature of the measured object, and combines multi-target processing method and DIC technology to realize the thermal expansion coefficient measurement of porous flexible materials. Through the uncertainty evaluation and verification experiment of the method, it is shown that the method is accurate and reliable.
Foreign Language Teaching Model (FLTM) evaluation system based on Internet of Things (IOT) information technology

With the progress and popularization of information technology(IT), the evaluation of FLTM is becoming a new challenge facing China's English education. The progress of FLT system based on IOT technology is important to cultivate students' comprehensive quality, improve teachers' professional level and stimulate their interest in learning in an information environment. Firstly, this paper introduces the current situation and characteristics of FLTM, then expounds the existing problems of FLTM, studies the relevant applications of IOT, expounds the characteristics of IOT, and designs the evaluation system of FLTM based on IOT. After that, simulation experiments are used to test the effect of the system. Finally, the test results show that the FLTM evaluation system has good performance in three aspects: evaluation function, statistical function and feedback function (the full score is 5). This shows that the FLTM evaluation system based on IOT can meet the evaluation needs.
Construction and application of dynamic risk knowledge graph of large diameter emergency rescue well

The construction of a large-diameter emergency rescue well dynamic risk knowledge map is a strong support for the rapid drilling of large-diameter emergency rescue wells in a big data environment. Aiming at the problem of static mapping of existing drilling knowledge graphs, this paper introduces the time dimension to characterize the dynamic, sudden and time-effective evolution characteristics of the risk events in the drilling process of large-diameter emergency rescue wells. After large-diameter rescue well drilling risk knowledge extraction, knowledge fusion, ontology construction and dynamic knowledge reasoning, etc., the dynamic risk knowledge map of large-diameter emergency rescue wells is systematically constructed from top to bottom. In the process of constructing the knowledge graph, aiming at the imbalanced classification problem of the entity samples of the domain data set, this paper proposes a dynamic knowledge reasoning method and applies the knowledge graph to the intelligent question answering system of large-diameter rescue wells.
Comprehensive Analysis of Various Distance Metrics on Colour-Based CBIR System

In the recent decades, lots of images have been added to the database and is growing rapidly. Since the database is very huge, hence retrieval and querying of these images become difficult. The content-based image retrieval (CBIR) system provides an efficient option, that is based on extraction of image features and compare. The primary method of feature extraction in CBIR system is based on the colour content of images. Finding the similarity among image features is mainly based on distance metrics, and it plays crucial role in image retrieval. There are many such similarity metrics found from the literature, few of them perform well on some specific cases. Thus, it is important to know, the appropriate metrics for optimal image retrieval. This article presents a comprehensive survey on various popular distance metrics on wide range of image database. The survey is based on finding the similarity of image features, that are based on colour content of an image. The survey gives good insight into the similarity measuring metrices. The popular set of large image database is used for analysis purpose. The results show, superiority of Canberra and Bray–Curtis distances over other distance metrics.
Gesture Recognition System for Real-Time Interaction in Dynamic Environment

Hand gesture recognition provides a significant impact in the field of human–computer interaction. It introduces the information, tools, and systematic design techniques, by which accuracy and easy implementation of daily tasks can be achieved. Gesture recognition is the approach by which computers can detect hand gestures. Human–computer interaction provides appropriateness in feedback, effortless implementation, and timely completion of the goal. Computer vision plays an important role in extracting high levels of comprehension from electronic images and videos. It is applied to a hand gesture recognition system to provide input to the computer to manipulate virtual objects by simply moving hand parts which act as a command. Providing a low-cost infrastructure device that alters the need for keyboards and mouse in laptops and computers.
Chaos-Based Image Encryption with Salp Swarm Key Optimization

Substantial data is being transferred across the unsecured channel; with this considerable data transfer comes the need to protect this data. Thus, to achieve security during transmission, several encryption algorithms have been proposed. Chaos-based maps are widely employed for multimedia encryption due to their characteristics, like pseudo-randomness sensitivity to initial conditions. Inspired by researchers, we proposed an image security algorithm based on a chaotic tent map integrated with the Salp Swarm Algorithm (SSA) for key generation and optimization, for grayscale images. A diffusion and permutation are carried out in each round to make it secure. A simple XOR function is applied to encrypt and decrypt the data. Different statistical analysis has been applied to images, and results has been discussed to justify proposed techniques effectiveness.
Syntactic grammar distraction usage or misusage: Definite article

Contributors: Michael Lewis Schneir (author), Wendy Kingdom (section editor), Amy Wheareat (section editor)
Smart Spy in the Online Video Calls Without Notifying the Concerned and Alerting the Nearest Receiver Contacts

Now-a-days, video calls are used by everywhere and it became more essential service. In the video calling, the receivers suppose not able to lift the call due to many constraints. The sender may get vex with this kind of unresponsive possessed by the receiver. There are dangerous scenarios when the receiver is in danger or critical stage not able to communicate. The sender might want to know what the receiver is doing although the video call is ringing the receiver will not attempt it. This curiosity leads to develop a novel feature called hidden activation of the camera without notifying the receiver during the video call ringing but the receiver has to enable the spy feature. This feature may also help when the important person is kidnapped and trapped by some violent gang. The feature might show the things that are in front of the camera as well as the backside of it although the receiver mobile is switched off but having reserved battery power. This feature will recognize the scenes that are related to the danger zone clip or the normal clip. If the clip is a suspected zone, it automatically alerts the police near the location of the receiver. If not, the sender will be notified the receiver is busy. The proposed feature aims to know the environment of the receiver. This will benefit society a lot in the issues of the contemporary environment. The results will guide the necessity of this feature and its performance. There are apps to track the victim by the police control room by dialing before entering into the dangerous location but the user cannot know odd things to have happened in advance that time alerts the family contacts as well as close friends contacts.
An Overview of Cybercrimes and Its Impact: Indian Scenario

The phenomenal rise in the field of communication and the digital world has resulted in possible issues in the domain of cyber law and its ramifications. Nothing comes without a price and disadvantages. Though it makes people's lives easier by allowing them to do a variety of tasks with a single click of a button, it has also created new obstacles. Everything, including business, industries, government operations, and so on, comes to a halt without technology, particularly computers. Our economy and government, as well as various sorts of businesses, rely on computers for the efficient operation of their businesses and economic advancement, while criminals are actively involved in various types of cybercrime. As a result, the current study discusses a comprehensive knowledge and overview of cybercrime, particularly computer-related cybercrime, and its effects on numerous fields.
NO PHISHING! Noise Resistant Data Resampling in Majority-Biased Detection of Malicious Websites

Considering the fatality of malicious websites, the current literature supports the application of machine learning-based architectures to perform automatic classification of websites. Irregular distribution of the data often results in a majority-biased classification and eventually decreases a model’s classification performance. In this paper, we addressed the problem in detecting malicious websites by employing Tomek Link-based resampling techniques to avoid the sampling of noisy examples. The experiment is conducted with three popular classification techniques and compared with different oversampling and undersampling frameworks. The fatal effect of the irregular class distribution in malicious website detection is further experimentally justified with the increasing level of irregularity.
Incorporation of Secure Channel Communications Over Multi-tenant Database

The Enterprise IT industry is undergoing a paradigm shift—with the help of cloud computing which is one of the main reasons. The popular IT giants such as Google, IBM, Microsoft, and Amazon have started their cloud computing infrastructure. Software as a Service (SaaS) has been one of the major business models to provide cost saving Enterprise services to small and medium enterprises (SME). In SaaS cloud provider provides their services to their customer’s on rental basis which is usually very less as compared as paying for licensed applications. Multi-tenancy which is an important feature of cloud computing provides a concept named multi-tenant database; is a relational model-based database architecture where single instance of the database servers multiple customers called Tenants. The application is planned to virtually partition its data and configuration and tenant is provided with a customized virtual application. However, the customers are often reluctant to store their highly confidential data using multi-tenant database in the fear of their information being exposed to other tenants either due to some application bug or any passive or active attack on the database. So, here we define a highly secured model of a multi-tenant database protected by different encryption algorithms. Every stream of data that travels through the channel is encrypted using AES encryption and is stored in database in encrypted form. Further, the key generated by AES algorithm has been encrypted using RSA encryption and stored in database data. The particular key corresponds to a particular User ID, So, even if the data if exposed to some unauthorized user due to some application bug that will not be decrypted. Further, role-based access control (RBAC) defines the roles of different tenants and sub-tenants using the application.
Energy Minimization of Cloud Computing Data Center Strategies, Research Questions: A Survey

Large client models use cloud computing because it has several benefits, including minimizing cost of construction resources and its elasticity property which enables services to also be up or down to current demand. And order to provide cloud services, which meet all the requirements specified in service level agreements, there are so many challenges to still be overcome from the cloud-provider viewpoint (SLAs). As data centers absorb huge quantities of power, it is a major challenge in cloud computing to increase their energy efficiency. The main purpose of this systematic analysis is to present and analyze many algorithms in this cloud computer environment used to reduce the energy from data center and to compare solutions for research challenges. In the energy-conscious cloud applications of the data center, a new combination of the Virtual Machine Image Constructor (VMIC) is possible to evaluate component efficiency. In addition, the capacity to satisfy the required SLAs would ideally reduce energy on various host machines using VM implementations. Using less VM migration and PM shutdowns than a common heuristics method. The ongoing research work on the minimizing of energy strategies used in cloud computing is a comparative research study.
Technical Review and Data Analysis of Expert System Development

In this modern world, expert systems are one of the well-known research areas of artificial intelligence. Most of the expert systems are highly responsive, reliable, easily understandable and high performance result oriented. This paper contains the design methodology, classification and development strategy of an expert system. Here we try to present some abstract analysis, structure of knowledge acquisition process with in the domain of expert system. We tried to review data analysis and express such kind of beneficial model that not only defend the innovative attributes of expert system but also described some of their constraint.
Social Engineering Attacks

We live in an Internet-based world, and our personal and professional lives are more intertwined. Systems of information technology (OS). These mechanisms are frequently subjected to public scrutiny, to avoid being hacked or infected with a virus, we are all on the lookout for the greatest antivirus and anti-spyware software. Install the programme, yet the malware still exists. Our system has been hacked into. The most influential people Social Engineering is a type of system attack. These attacks aim to tricking individuals or businesses to perform acts that benefit the attackers or to provide them with sensitive information such as social security numbers, medical records, and passwords. Because it takes advantage of the inherent human desire to trust, social engineering is one of the most difficult problems in network security. This paper provides a comprehensive overview of social engineering attacks, including classifications, detection and prevention procedures.
Upgrading Search Link Priority by Content Analysis

As all of the valuable information related to different domains are available in the form of hypertext in different web resources. Search engine provides a medium to retrieve this information according to the need of the user. There are various search engines available such as Google search engine, Alta, Vista, Yahoo Search Engine, etc., which provides an interface to access the web resources listing the search result according to the most relevant of the user query is an issue. In this chapter, a new technique is suggested which take the search result of Google search engine and tried to rearrange the search results in increasing order according to the relevancy with the search query. Normalized term frequency of the keywords lying in the particular web documents is used to calculate the rank of a web page.
Interactive Art Design and Color Perception Based on the Sparse Network and the VR System

At present, with the gradual development of science and technology, people's life has also produced a lot of changes, the traditional communication technology has been gradually changed by the new computer technology, people's life has become more intelligent. However, many current artificial intelligence technologies rely on the promotion of network data. In the mobile terminal, especially in the poor state of some data network environment, many users' intelligence needs cannot be met. From the perspective of user interaction experience, this paper analyzes and investigates the interactive standby link in detail and systematically based on the perspective of context awareness and carries out a battle summary with the scientific, systematic, reasonable, and executable design methods suitable for an interactive standby state and puts forward the recommended items of matrix decomposition. The static information is embedded in the model. The status of information is imported as a dimension different from the previous matrix factorization model, and the accumulation of interaction between user's status conditions and project factors is considered, as well as the sensitivity difference between user and information project status. In order to obtain the global situation of user balance, the project prediction deviation caused by the vector and sensitivity to various conditions is needed. Finally, the training model gets the final prediction score value and puts forward the mobile system user interaction experience art design strategy based on context awareness, which provides a certain idea to meet the needs of mobile system users.
ConcentrateNet: Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique

This paper proposes a deep learning based object detection method to locate a distant region in an image in real-time. It concentrates on distant objects from a vehicular front camcorder perspective, trying to solve one of the common problems in Advanced Driver Assistance Systems (ADAS) applications, which is, to detect the smaller and faraway objects with the same confidence as those with the bigger and closer objects. This paper presents an efficient multi-scale object detection network, termed as ConcentrateNet to detect a vanishing point and concentrate on the near-distant region. Initially, the object detection model inferencing will produce a larger scale of receptive field detection results and predict a potentially vanishing point location, that is, the farthest location in the frame. Then, the image is cropped near the vanishing point location and processed with the object detection model for second inferencing to obtain distant object detection results. Finally, the two-inferencing results are merged with a specific Non-Maximum Suppression (NMS) method. The proposed network architecture can be employed in most of the object detection models as the proposed model is implemented in some of the state-of-the-art object detection models to check feasibility. Compared with original models using higher resolution input size, ConcentrateNet architecture models use lower resolution input size, with less model complexity, achieving significant precision and recall improvements. Moreover, the proposed ConcentrateNet architecture model is successfully ported onto a low-powered embedded system, NVIDIA Jetson AGX Xavier, suiting the real-time autonomous machines.
Towards a Policy Development Methodology for Human-Centred IoT Collectives

Embedding ethical concepts into smart Internet-connected devices and making them behave in a more human-centred manner, i.e., ethically and in a socially acceptable manner, has received significant attention in the software industry. To make smart devices behave in more human-centered manners, it is important to develop a methodology for defining smart devices’ key roles and mapping them with socio-ethical and administrative policies. This paper proposes a policy development methodology for making smart devices more human-centred by following its four phases i.e., concept development, defining and mapping policies, implementing the processing of policies, and deploying the devices. The suggested methodology may be used in a variety of situations where smart devices interact with people. For illustration, the proposed methodology has been applied to three different settings, including a supermarket, a children’s hospital, and early learning centers, where each phase defined in the methodology has been followed. The application of the methodology to smart internet-connected devices, including robots, smart cameras, and smart speakers, has shown significant results. It has been observed that the devices behave in more human-centric ways while performing their core functions, adhering to socio-ethical policies.
Proposal of a Novel Python-Based Fuzzy Systems Library - Preliminary Results

In this paper we propose a novel Python-based library for implementing fuzzy systems. The proposed library combines an easy-to-use interface with functionalities capable of conversion between Mamdani and Takagi-Sugeno-Kang fuzzy systems for interpretability.