Reviewerly/exHarmony huggingface dataset:
Dynamic Environment Simulation for Database Performance Evaluation

The wide popularity and the maturity of cloud platform promote the development of Cloud Native database systems. On-demand resource configuration or application is an attractive feature of cloud platforms, but its complexity in resource management challenges the benchmarking of database performance, which is no longer in a stand-alone test environment. Sharing or contending of resources aggravates the dynamics of environment, which can influence database performance much. In order to expose the real performance in production environment, environment simulation is prerequisite for benchmarking databases. Although Docker Containers have been promoted to isolate resources, we still cannot achieve the true resource isolation. In this paper, we first define four kinds of workload generators corresponding to the key environmental dimensions, then builds a multi-factor linear regression model to calculate the correlation among workloads, and finally designs an algorithm to simulate the dynamical changes of environment. It is the first work to provide a complete and dynamic simulation to environment. We conduct comprehensive experiments on the open source DBMSs by running the standard benchmarks to verify the effectiveness of our work.
Face Filters as Augmented Reality Art on Social Media

This chapter explores the emergence of face filters as augmented reality (AR) art. AR “face filters”—a mask-like augmented reality layer that adds virtual objects to an individual’s face—have become wildly popular on Instagram, Snapchat, and even video calling on Zoom. Up to now, far too little attention has been paid to face filters as a form of AR art. Often seen as play, AR face filters can provide an engaging and personal art experience that allows users to actively participate in creating art online. Further, AR filters enable users to experiment with creating a variety of online identities as self-portraiture. This chapter will generate fresh insight into the current trends in AR art on social media. It is likely that AR face filters will radically change how we see ourselves online and how we engage with art in general.
Analyzing the Usefulness of Public Web Camera Video Sequences for Calibrating and Validating Pedestrian Dynamics Models

Calibrating and validating pedestrian dynamics models is usually conducted using data obtained in experiments with groups of people. An interesting alternative to using data obtained in such a way is using data from public web cameras. The article presents a case study of using public web cameras in the analysis of pedestrian dynamics and social behavior. We have applied YOLOv3 object detector trained on the COCO dataset in order to identify objects in the video frame and to determine their position.
Digital Blockchain for Smart Surveillance in Resilient Cities

Digital blockchain is an assessment parameter that will encompass computational strategies to assess the issue of how to recognize the integrity of Smart surveillance information for a resilient city. Smart surveillance in resilient cities gives spatiotemporally-based representation as of the source of surveillance which can bring about restricted alter of the assembled data from an advanced camera. Distributed plan of blockchain is to spread, video outlines as information output over resilient cities. Through choosing most fitting cryptographical calculations utilizing the blockchain model, it can make feasible computational strategies for Digital blockchain and make solid answers for the current Digital information. In this research, the commitments are to advance the security of surveillance data by utilizing most fitting cryptographic calculation to accomplish the data integrity and alter resistance, a web-based prototype is made through Digital blockchain to attest to the truth and steadfastness of Digital data from computerized surveillance.
Studying Ergonomic Gestures on Interactive Learning Tabletop and Tablets from a Situated Approach. A Taxonomic Table and an Exploratory Study

We investigate the ergonomic gestures produced during recreational and educational activities on two types of touchscreens surfaces from a situated perspective based on activity theory: an interactive learning tabletop (ILT) and a tablet. We firstly identified an exploratory taxonomic table of ergonomic gestures performed on touchscreen. Our results indicate that the mean ranks of observed gestures (classified according to their duration) are significantly similar between participants and some limited contextual effects. Secondly, analyses led us to conclude to some significant differences in terms of the ergonomic gestures (overall ranking of gestures) performed between ILT and tablet, but not between learning and recreational activities. More precisely, we found that: no complex gesture were observed overall; single finger gestures were preferred during tablet activities, in contrast to many-finger gestures that were preferred during ILT activities.
ADAM: An Attentional Data Augmentation Method for Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a fundamental text mining task, which aims to assign multiple labels related to the given text from a large-scale label set. Various models and many data augmentation methods are proposed to improve classification performance. However, the classification performance is limited due to the long tail distribution of labels, which is an essential characteristic of XMC. To address this problem, we propose a novel data augmentation method named Attentional Data Augmentation Method (ADAM) for long tail labels. Specifically, we split each sentence into several segments of equal length and use an attention-based neural network to explore the core segments of long tail labels. The unimportant segments of each instance from the dataset are considered to be replaced by those segments related to the long tail labels. Extensive experiments show that ADAM has an improvement based on the XMC method, especially on the prediction of long tail labels.
Audiovisual Dependency Attention for Violence Detection in Videos

Violence detection in videos can help maintain public order, detect crimes, or provide timely assistance. In this paper, we aim to leverage multimodal information to determine whether successive frames contain violence. Specifically, we propose an audiovisual dependency attention (AVD-attention) module modified from the co-attention architecture to fuse visual and audio information, unlike commonly used methods such as the feature concatenation, addition, and score fusion. Because the AVD-attention module’s dependency map contains sufficient fusion information, we argue that it should be applied more sufficiently. A combination pooling method is utilized to convert the dependency map to an attention vector, which can be considered a new feature that includes fusion information or a mask of the attention feature map. Since some information in the input feature might be lost after processing by attention modules, we employ a multimodal low-rank bilinear method that considers all pairwise interactions among two features in each time step to complement the original information for output features of the module. AVD-attention outperformed co-attention in experiments on the XD-Violence dataset. Our system outperforms state-of-the-art systems.
On Ranking of Image Quality Metrics

In different problems of image processing, it needs to evaluate the quality of images. For a ground-truth case, the result is evaluated in comparison with the original image. Different metrics realize different ideas of what a more quality image is. On the other side, the human quality evaluation of images isn’t so precise numerically. Therefore, may be better to use measurements in ordinal scales. Based on this approach, the known Kemeny’s median is proposed here to coordinate image rankings from different metrics. In this paper, the modification of Kemeny’s algorithm is developed. The coordination of the median ranking with rankings by a human is statistically tested for images from the public database. As a result, it can be recommended to use some of the preferred metrics from the given set to compare the results of different processing algorithms.
Sentiment Analysis of Film Reviews Based on Text Mining—Taking the Shawshank Redemption as an Example

Based on the user text evaluation data of Douban and IMDB, this paper realizes the judgment of movie tendency and the mining and analysis of hidden information by judging the emotional tendency in movie reviews, so as to provide guidance and Suggestions for other movie viewers. This paper mainly USES the emotional tendency to analyze and explore whether this can become a way to understand China’s foreign cultural exchanges which will be the content of active exploration and research.
The Effects of Required Security on Software Development Effort

The software industry has been under pressure to adopt security practices and reduce software vulnerabilities. But despite recognizing the importance of secure software development, such practices have not been broadly embraced. Costs are frequently pointed out as a barrier, although studies show that there is a lack of knowledge about the amount of resources needed to achieve a determined level of security assurance. This study quantifies the effort required to develop secure software in increasing levels of rigor and scope. We built a statistical cost model based on a dataset with 1,140 maintenance projects from two large companies. The model calibration revealed that the application of software security practices can impact the cost estimations ranging from 19% additional effort, on the first level of the scale, to 102% additional effort, on the highest level of the scale. These results suggest that the effort required to develop secure software is lower than it was estimated in previous studies, especially when considering the domain of Information Systems. The resulting model can be used by practitioners to estimate proper resources for adopting security practices and provide valuable information for the research on secure software development investment models.
Locality Sensitive Hashing-Driven Multifactorial Evolutionary Algorithms for Multitask Optimization

In recent years, evolutionary multitasking optimization (EMTO) has attracted considerable attention. Different from the traditional evolutionary algorithms that solve one optimization problem at a time, EMTO aims at solving multiple optimization problems simultaneously, through transferring useful information among individuals. The most well-known algorithm in this area is the multifactorial evolutionary algorithm (MFEA). However, when designing the information sharing mechanism, MFEA and its variants solely focus on the properties of individuals in the objective space, while neglecting the information in the decision space. This paper presents a novel EMTO paradigm by using the locality sensitive hashing (LSH) method to analyze the population distribution in the decision space and then guide the information sharing in the individualsâ€™ reproduction process. Specifically, LSH uses hash functions to map the individuals in the decision space into several buckets, ensuring that the similar individuals have a higher probability to be hashed into the same bucket than the dissimilar ones. Then, based on the respective buckets to which the two parent individuals are hashed, as well as their skill factors in the objective space, different reproduction strategies are exerted on the individuals. This way, the knowledge from both the decision space and the objective space are utilized for the purpose of better manipulating the individuals. Additionally, an opposition-based learning methods is integrated in this algorithm to enhance the exploration of individuals in the search space. The experimental studies have confirmed the efficacy and efficiency of the proposed algorithm on both single-objective multi-task benchmarks and multi-objective multi-task benchmarks.
Usability of Antivirus Tools in a Threat Detection Scenario

Usability of antivirus (AV) tools has not received much attention yet. We conducted a laboratory study with 34 German students to investigate how they experience notifications and interventions of their AV when a threat is detected. During the study, a specifically designed harmless file triggered AV on participants’ laptops. Out of 34 participants, 19 users noticed AV messages, and 8 of them understood that the message communicated threat detection concerning a specific file. Moreover, only 6 users understood that this file was removed by the AV tool. Additionally, most participants were distracted by Windows OS messages that were unintelligible to them. We investigate reasons for incomprehension in our sample, and give recommendations for improved user interaction design of AV tools.
A new multimodal planning algorithm based on PRM

The robot motion planning problem has a unique multimodal structure, where the space of feasible configuration consists of intersecting submanifolds. The planning problem of reconfigurable robot leg motions was considered as a multimodal structure composed of intersecting submanifolds in different dimensions. After that, a new multimodal planning algorithm based on PRM was proposed. Simulation results showed that the new algorithm had a shorter running time than PRM in different modes.
A Fuzzy Meta Model for Adjusting Ant Colony System Parameters

Metaheuristic algorithms have become an important choice for solving complex optimization problems which are difficult to solve by conventional methods. But, like many other metaheuristic algorithms, ant colony system (ACS) has the problem of parameters setting. In the last few years, different approaches have been proposed to deal whit this problem. Recently the use of fuzzy logic in dynamic parameters adaptation of metaheuristic algorithms is gaining a considerable interest from the researchers. In this paper, a meta model for modifying the parameters of ACS during runtime based on fuzzy logic concept is presented. The main idea is to study the effect of modifying all the parameters of the ACS on the same time on its performance. To compare the efficiency of the proposed approaches, they were applied to a set of traveling salesman problem instances. Also, a comparison with the standard ACS and some literature results are discussed.
A Comparative Analysis of Automatic Extractive and Abstractive Text Summarization

With the advancement of the web, a large amount of data is being generated by people on the web. A shorter yet logical and consistent version of such a large amount of text is very beneficial to easily draw significant conclusions. Summarization is a way to derive a coherent and fluent summary. Summarization can be categorized as Extractive summarization and Abstractive summarization. In this paper, we compare a few Extractive and Abstractive methods of summarization on WikiHow-Dataset. The experiment results show superiority of abstractive summarization methods was observed over the extractive summarization methods when the average precision score was compared. Though pre-trained transformer technology was applied for abstractive summaries, even then, basic extractive summarization techniques performed well. This Analysis helps the researchers to understand the summarization process and performance comparison of the summarization techniques.
Smart Correlated Feature Selector [SCFS]: An Approach to Eliminate the Redundant Features from a Correlated Feature

Ab s t r a c tIn recent trends, globally the volume of dataset is increasing vastly due to enhancement of information and technologies. The datasets are accumulated with number of relevant and irrelevant features which can affect the performance of the accuracy of the model. The features in the dataset are having different types of datatypes like numeric, nominal and mixed data. Dealing with huge volume of features will be difficult for the researchers to obtain the knowledge for the model. The best solution to overcome these issues is selecting the best features by removing the redundant and irrelevant features. This study proposed a Smart Correlated Feature Selector (SCFS) method which helps to select the best features and to improve the classification performance for the classifiers for the mixed type of datasets. Basically, the Na誰ve Bayes method are implemented to analyze Class Conditional Probability for each feature (CCP). The features having the higher-Class Conditional Probability values more than the threshold values are considered as the relevant features as a subset feature. This reduced subset features are executed with various classifiers like Na誰ve Bayes (NB), Multilayer Perceptron Classifier (MLP), K Nearest Neighbor (KNN), Decision Tree (DT), SMO, J48, KStar, JRip and OneR classifiers. From UCI repository the mixed datatype of datasets like Credit, Dermatology, Hepatitis and weather nominal are used for this study to show that the proposed Extended Class Conditional Probability method is better in terms of selecting the best features and to obtain the best classification performance for the classifiers.
Which Visual Features Impact the Performance of Target Task in Self-supervised Learning?

Self-supervised methods gain popularity by achieving results on par with supervised methods using fewer labels. However, their explaining techniques ignore the general semantic concepts present in the picture, limiting to local features at a pixel level. An exception is the visual probing framework that analyzes the vision concepts of an image using probing tasks. However, it does not explain if analyzed concepts are critical for target task performance. This work fills this gap by introducing amnesic visual probing that removes information about particular visual concepts from image representations and measures how it affects the target task accuracy. Moreover, it applies Marr’s computational theory of vision to examine the biases in visual representations. As a result of experiments and user studies conducted for multiple self-supervised methods, we conclude, among others, that removing information about 3D forms from the representation decrease classification accuracy much more significantly than removing textures.
Automatic Schema Construction of Electrical Graph Data Platform Based on Multi-Source Relational Data Models

Data storage and management in power systems usually adopt relational databases. However, the relational database requires ample storage space and has low data retrieval and query efficiency. An electrical graph data platform can describe the complicated relationships between concepts and entities involved in power systems with the form of an association graph, which provides a better ability to organize, manage, and apply massive amounts of information. Since the construction of the top-level ontology model or schema for a specific field graph data platform is cumbersome, complex, and generally requires lots of association analysis and expert system intervention, it is insufficiently automated, time-consuming, and unable to cope with large-scale electric power knowledge. This paper proposes a method for automatically constructing the schema of an electrical graph data platform, which uses the diverse table structure information from the relational database and SQL language descriptions to extract ontologies to form the ontology candidate set automatically. Then the method utilizes ontology clustering and disambiguation to initial an ontology graph model and automatically update ontology and relationship expressions. Meanwhile, the model layering is used to construct a hierarchical model based on different business needs, and the schema optimization is applied according to expert comments.
Data Secrecy: Why Does It Matter in the Cloud Computing Paradigm?

Data secrecy is one of the most prominent issues in security; however, little research has been carried out on data secrecy. User data are precious, and the user never wants their data to reveal any unintended users, including the administrators. However, the administrator can read users’ data which is a violation of data secrecy. In this paper, we discuss the issues on data secrecy and its violation. Moreover, we highlight all the possible problems and challenges of data secrecy. Most of the data exchange platforms do not maintain data secrecy, and therefore, the users’ data are free to read or use for administrators. This issue is least discussed in the scientific forum, and hence, we enlighten the probable data misuse by the administrators. Also, this paper explores end-to-end encryption systems for various data exchange platforms.
Research on Integrated Deeply Design of Mobile Command and Control Vehicle Based on Human Factors Engineering

The command and control vehicle is a complex and intensive human-machine interaction system. The effective cooperation between the commander and the command and control system is the key to the effectiveness of combats. In this paper, it focuses on systematically studying the system integration design technology of the command and control vehicle from the human factors, and solves the problems of comforts during the coordination between the commander and the equipment. The article analyzes the characteristics of the hardware equipment of the command and control vehicle, discusses the physiological and psychological characteristics of the commander, and focuses on the human-machine relationship and the human-environment relationship that need to be paid attention to when the command and control vehicle is integrated. From the perspective of space and operating comfort, the design concept of deep integration with the vehicle chassis is proposed, and the chassis, environmental equipment and information system are integrated into an organic whole. This article focuses on the generic technology based on human factors, such as the modular integrated technology, the bus integrated technology and the operation and maintenance control technology.
A New Similarity Measure for Real Intervals to Solve the Aliasing Problem

These days it is common to work with interval-valued data in areas like fuzzy analysis, clustering, medicine or decision-making. In this paper, we present a new similarity measure between intervals and compare it with other measures found in the literature. We also study some properties such as aliasing, that is obtaining the same similarity value from very different inputs, or the behavior of the similarity when the overlapping degree is varied, among others. The theoretical background could be found in this work as well as some artificial examples in order to a better understanding. Finally, an experiment considering real-life data is displayed.