Reviewerly/exHarmony huggingface dataset:
A Fusion Approach Based on HOG and Adaboost Algorithm for Face Detection under Low- Resolution Images

Detecting human faces in low-resolution images is more difficult than high quality images because people appear smaller and facial features are not as clear as high resolution face images. Furthermore, the regions of interest are often impoverished or blurred due to the large distance between the camera and the objects which can decrease detection rate and increase false alarms. As a result, the performance of face detection (detection rate and the number of false positives) in low-resolution images can affect directly subsequent applications such as face recognition or face tracking. In this paper, a novel method, based on cascade Adaboost and Histogram of Oriented Gradients (HOG), is proposed to improve face detection performance in low resolution images, while most of researches have been done and tested on high quality images. The focus of this work is to improve the performance of face detection by increasing the detection rate and at the same time decreasing the number of false alarms. The concept behind the proposed combination is based on the a-priori rejection of false positives for a more accurate detection. In other words in order to increase human face detection performance, the first stage (cascade Adaboost) removes the majority of the false alarms while keeping the detection rate high, however many false alarms still exist in the final output. To remove existing false alarms, a stage (HOG+SVM) is added to the first stage to act as a verification module for more accurate detection. The method has been extensively tested on the Carnegie Melon University (CMU) database and the low-resolution images database. The results show better performance compared with existing techniques.
Abnormal Behavior Recognition Based on Hybrid Depth Model with SE Block

The detection and recognition of human abnormal behavior in surveillance video based on deep learning has become a research hotspot. Due to the complex background in the video and the variety of targets, it is difficult to accurately identify abnormal behavior in surveillance video. The current abnormal behavior recognition models have some shortcomings such as complex structure, big calculation cost and low recognition rate. Given the aforementioned obstacles, this paper proposes an ISE-R3D-LSTM network based on R3D model for abnormal behavior detection in surveillance video, which integrates the improved SE block and LSTM network. The contribution of the proposed algorithm consists of three parts: First, considering the global information extraction and feature calibration, the improved SE block with higher accuracy is obtained by substituting the FC layer of the conventional SE block with the GAP; Second, a residual module is added to the 3D CNN so as to avoid gradient dispersion, and the GAP is utilized to reduce the number of the network parameters; Finally, to effectively learn feature information, LSTM is introduced to carry out the timing modeling for high-level features, yielding our ISE-R3D-LSTM network. The RGB-only video data from UCF Crime dataset is employed for the experimental evaluation. The experimental results present that the recognition rate of our ISE-R3D-LSTM network reaches 92.30%. Comparative results present that our model is competitive in terms of computation cost and recognition accuracy. (Abstract)
Highlighting a few educators

I think of Dr. Clarence "Skip" Ellis often, a distinguished computer scientist, outstanding educator, and one of my mentors. As alluded to in the Historical Highlight on page seven of this issue, a wealth of professional and personal information about Skip is easily found on the internet. I've highlighted a few of his professional achievements in the accompanying highlight. In this inaugural Acknowledging Our Social Good Mentors piece 1 , I'd like to add my small part to the narrative associated with this wonderful person by relating a few things that, perhaps, only I know or think about. All of which, either overtly or subtlety, have influenced my passion and work related to computing for the common good. I'll begin at the end, so to speak.
CNN-CA: Convolutional Neural Network Combined with Active Contour for Image RGB-D Segmentation

Convolutional neural networks (CNNs) usually obtain the best results in image segmentation without a priori. However, they have two weaknesses: they do not take into account edge and local features. These weaknesses are even truer in the case of RGB-D (colour and depth) image segmentation because the complexity is higher. RGB-D segmentation CNNs have, at most, an average IoU below 60.0. To improve the result, the proposed new method is a combination of two existing methods: CNN and active contours. Active contours take into account the edges of objects by using customisable forces. These forces are specific to colour or depth images. CNN predicts the initial segmentation and active contours, using RGB-D forces, adjust the segmentation to the edges of the objects. The proposed method improves the CNN segmentation by 8.0 IoU on average. The proposed solution is effective in some cases. The exploration and analysis of the forces allow to find the essential forces to improve the segmentation of the objects. These forces have to be refined in order to have an optimal segmentation.
XML Interactive Data Analysis of Online Solutions for Students' Innovative Ability Training Integrating Engineering Intelligent Information Algorithms

This paper conducts a questionnaire survey from the perspective of student management work, aiming to understand the cognition of college students on the cultivation of innovation ability, to clarify the various factors that hinder the cultivation of college students' innovation ability, and to provide a basis for building a system for the cultivation of students' innovation ability in engineering schools. Improve the mathematical modeling training system, build a mathematical modeling innovation research platform, a teacher’s cross-professional cooperative research platform and other measures to practice training mode, and finally store and manage XML documents through Oracle XML DB database, thus effectively realizing the unstructured data management.
Research on C++ System Practice of Online Auxiliary Teaching Platform based on Trusted Data Analysis

From the perspective of AI, this large-scale complex data is used to infer students' learning needs by analyzing students' learning behavior data, provide students with different levels of personalized learning content and assessment indicators in addition to the basic content, and establish a set of existing teaching methods. The assessment mode is an intelligent system that can be beneficially supplemented, and combined with the characteristics of students and the needs of teaching, an online and offline hybrid teaching implementation plan has been developed, and then the C++ oversampling method combined with the Tomek Link Removal undersampling method is used to generate a balanced data set.
Support Vector Machine Face Recognition Application based on Particle Swarm optimization Algorithm

Today’s society is in the era of big data, and the data generated by all walks of life contains massive knowledge. However, such knowledge has the characteristics of high redundancy and insignificant feature expression, so useful knowledge cannot be directly obtained from it, which means that efficient and general methods are needed for data analysis. Classification algorithm is an important means for computer to recognize and recognize the real world, and it is also an efficient method to classify and organize massive data. Support vector machine is one of many classification algorithms, which is widely used because of its relatively complete theoretical support and good classification performance on data sets. However, the parameter selection of the algorithm directly affects the classification accuracy and generalization ability of the model. Therefore, optimizing the model parameters to improve the classification accuracy and generalization ability is a research focus of the current help vector machine algorithm.
Photonic Fock state generation using superradiance

Photonic Fock states are the most basic quantum states of a radiation field, but arbitrary number states are still difficult to produce. Here we propose to use superradiant atoms in a chiral waveguide to generate multi-photon Fock states deterministically. We calculate the explicit forms of the output quantum photonic states and their correlation functions. We further establish the conditions for the output optical fields to approach the Fock states asymptotically.
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design

The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, with the rapid development of defense technologies, it also tends to be more difficult to evaluate the robustness of the defensed model due to the weak performance of existing manually designed adversarial attacks. To address the challenge, given the defensed model, the efficient adversarial attack with less computational burden and lower robust accuracy is needed to be further exploited. Therefore, we propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models. Firstly, the more general mathematical model of auto adversarial attack optimization design is constructed, where the search space includes not only the attacker operations, magnitude, iteration number, and loss functions but also the connection ways of multiple adversarial attacks. In addition, we develop a multi-objective memetic algorithm combining NSGA-II and local search to solve the optimization problem. Finally, to decrease the evaluation cost during the search, we propose a representative data selection strategy based on the sorting of cross entropy loss values of each images output by models. Experiments on CIFAR10, CIFAR100, and ImageNet datasets show the effectiveness of our proposed method.
The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis (Preprint)

<sec> <title>BACKGROUND</title> During the COVID-19 pandemic, there was limited adoption of contact-tracing apps (CTAs). Adoption was particularly low among vulnerable people (eg, people with a low socioeconomic position or of older age), while this part of the population tends to have lesser access to information and communication technology and is more vulnerable to the COVID-19 virus. </sec> <sec> <title>OBJECTIVE</title> This study aims to understand the cause of this lagged adoption of CTAs in order to facilitate adoption and find indications to make public health apps more accessible and reduce health disparities. </sec> <sec> <title>METHODS</title> Because several psychosocial variables were found to be predictive of CTA adoption, data from the Dutch CTA CoronaMelder (CM) were analyzed using cluster analysis. We examined whether subgroups could be formed based on 6 psychosocial perceptions (ie, trust in the government, beliefs about personal data, social norms, perceived personal and societal benefits, risk perceptions, and self-efficacy) of (non)users concerning CM in order to examine how these clusters differ from each other and what factors are predictive of the intention to use a CTA and the adoption of a CTA. The intention to use and the adoption of CM were examined based on longitudinal data consisting of 2 time frames in October/November 2020 (N=1900) and December 2020 (N=1594). The clusters were described by demographics, intention, and adoption accordingly. Moreover, we examined whether the clusters and the variables that were found to influence the adoption of CTAs, such as health literacy, were predictive of the intention to use and the adoption of the CM app. </sec> <sec> <title>RESULTS</title> The final 5-cluster solution based on the data of wave 1 contained significantly different clusters. In wave 1, respondents in the clusters with positive perceptions (ie, beneficial psychosocial variables for adoption of a CTA) about the CM app were older (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), had a higher education level (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), and had higher intention (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001) and adoption (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001) rates than those in the clusters with negative perceptions. In wave 2, the intention to use and adoption were predicted by the clusters. The intention to use CM in wave 2 was also predicted using the adoption measured in wave 1 (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001, β=–2.904). Adoption in wave 2 was predicted by age (&lt;i&gt;P&lt;/i&gt;=.022, exp(B)=1.171), the intention to use in wave 1 (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001, exp(B)=1.770), and adoption in wave 1 (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001, exp(B)=0.043). </sec> <sec> <title>CONCLUSIONS</title> The 5 clusters, as well as age and previous behavior, were predictive of the intention to use and the adoption of the CM app. Through the distinguishable clusters, insight was gained into the profiles of CM (non)intenders and (non)adopters. </sec> <sec> <title>CLINICALTRIAL</title> OSF Registries osf.io/cq742; https://osf.io/cq742 </sec>
AUTOMATIC TEXT SUMMARIZATION USING SEQUENCE-TO-SEQUENCE MODELS

AUTOMATIC TEXT SUMMARIZATION USING SEQUENCE-TO-SEQUENCE MODELS Yerzhan Uteshov Master of Faculty of Information Technology, Kazakh-British Technical University, Kazakhstan, Almaty &nbsp; ABSTRACT As we acquire more digitization over
LINGUISTIC DETERMINANTS OF HEALTH OF TRANSGENDER PEOPLE

Significance. During the last few decades epidemiology has come a long way from taking into account only biological mechanisms influencing human health towards inclusion of socio-economic, legal and political factors. However, linguistic determinants of health have received little scholarly attention. The present article highlights some challenges in the design of epidemiological studies that would credibly demonstrate the impact of linguistic factors on health outcomes on the example of transgender issues. The purpose of the study is to analyze the impact of linguistic factors on health outcomes of transgender individuals. Identify methodological challenges in the design of epidemiological studies that would credibly prove causation between linguistic factors and health outcomes. Material and methods. Content analysis of literature on the impact of hate speech, misgendering and deadnaming on health outcomes of transgender individuals. Results. Qualitative and quantitative data shows a negative impact of such linguistic practices as hate speech, misgendering and deadnaming on mental and physical health of transgender individuals. Nevertheless, the lack of clear definitions of those concepts, reliance on self-reported scales and existence of confounders (physical appearance and legal gender/name) makes it impossible to generate evidence of high credibility.
A reflection on noncognitive factors affecting spatial cognitive testing: Examples from nonmodel species.

Probing for spatial cognitive processes in model rodent species has a long history in the psychological literature, with well-established protocols and paradigms successfully revealing the mechanisms underlying spatial learning and memory. There has also been much interest in examining the ecological and evolutionary context of spatial cognition, with a focus on how selection has molded spatial cognitive abilities in nonmodel species, how spatial cognitive traits vary across species, the neural mechanisms underlying spatial cognitive abilities, and the fitness outcomes of spatial cognition. Behavioral ecologists have been able to take advantage of paradigms from experimental psychology's rich history of spatial cognitive testing for use in nonmodel species. However, as the field advances, it is important to highlight noncognitive factors that can impact performance on spatial cognitive tasks (e.g., motivation to perform the task, switching navigational strategies, variation across protocols, ecological relevance of the task), as these factors may explain discrepancies in findings among some studies. This review highlights how these noncognitive factors can differentially modulate performance on spatial cognitive tests in different nonmodel species. Accounting for these factors when creating protocols and paradigms allows for a more nuanced approach with more explanatory power when probing for spatial cognitive abilities in nonmodel species. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Cognitive Systems

Developmental sub-stages, formalFormal and relational concepts analysis, and neural coding are considered here as cognitiveCognitive systemsSystem. The roadmapRoadmap for developmentDevelopment sub-stages outline: Single, Mapping, SystemsSystem, System of Systems, SoS, and Systems of Systems of Systems, SoSoS. The last sub-stageStage is less explored and specific to polytopicPolytopic referenceReference architectures. For conceptConcept analysis and for neural coding the roadmapsRoadmap include: Objects, Attributes, ConceptConcept LatticeLattice, Concept CubeCube and Concept 4D-Cube. ReferenceReference architectures, Periodic TablesTable and roads to higher dimensionality have been specified.
Quantum coherence in a superconducting circuit coupled with a dissipative cavity field

Abstract Quantum coherence represents a basic feature of a quantum system that is not present in the classical world. Here, we explore the dynamic behaviors of quantum coherence in two charge qubits who are strongly coupled with a single-mode dissipative cavity field. The results show that quantum coherence is sensitive to the coupled system parameters including qubit dissipation rate, initial qubit distribution angle, and coherent state intensity of the cavity field. Additionally, during the dynamic evolution, quantum coherence behaves periodically in the case of the qubit distribution angle, and this periodicity depends on the qubit dissipation rate. Also, the increasing coherent state intensity of cavity field can enhance the magnitude of quantum coherence, meaning that coherence resource in dissipative solid state quantum system can be controlled to some extent. This controllable coherence resource in engineering applications may quantify the advantage enabled in the superconducting circuit for processing the remarkable quantum information tasks.
Online Identity Theft Index Scorer and Recommendation Service

Online search engines have paved the way for accessing people's profile information more precisely than ever before. This is a blessing in disguise. In this paper, we crawl the internet to get more accurate details on people's identity, rank them based on the quotient of information captured, and assign them weights to develop a score using weighted models. We first crawl major websites, narrow down user profiles, and then mine the resultant data based on the top website's search engine rankings by rank and then weigh them. As a result, an identity theft score is generated, which directly maps to the risk associated with the malicious interpretation of user detail online. We also recommend a susceptibility rate for users to privatize these pages by tracking websites that could be blocked for third-party users. Also, a privacy graph page is given for standard websites in which the user holds his information at risk. Once the user incorporates the changes, the same recommendation engine can be rerun to track better online theft scores. This entire engine is provided as a service and can be used as a plug-in feature in any consuming website.
A Study on the Drama by Park Jaebum: Focusing on Anti-hero Trilogy &lt;Good Manager&gt;, &lt;The Fiery Priest&gt; and &lt;Vincenzo&gt;

Park Jaebum made his debut as a drama writer through &lt;KBS Drama City&gt; in 2003 and established a distinctive world of works starting from &lt;Good Manager&gt; (KBS2, 2017). &lt;Good Manager&gt;, &lt;The Fiery Priest&gt;, and &lt;Vincenzo&gt;. In conclusion, the anti-hero trilogy by Park Jaebum based on the primitive vengeance of the anti-hero and showed the characteristics of varied heroic narrative, and displayed similarity where the anti-hero and ordinary citizens are in unity and block out cartel, the antagonist. Moreover, the anti-hero trilogy by Park Jaebum contains the nature of crimes inference investigation, but it stands out from regular genre drama as comedy is actively applied. Especially, Park Jaebum’s anti-hero trilogy actualizes laughter through verbal humor, situational humor, and satire and parody and this shows similarity with entertainment effects and the relief of dramatic tension.
Self-narratives Using Literary Writing

This study developed and reflected on the class model of ‘literary writing’ as a methodology of literary education and creative writing. Literary writing started with four goals. To learn to appreciate literary works (tales) with an open mind, to link problems that hinder self-realization with reactions through appreciation of stories, to properly reflect on one’s own problems in the process of creating a narrative, and to discover problems was to solve. To what extent these goals were achieved was checked through the questionnaires of the learners. As a result, it was confirmed that literary writing is a meaningful means as a writing methodology that allows learners to develop their own imagination and creativity and freely reveal their inner self.
A Study on the Expansion Method of Storytelling in the Drama “Chunhyang You’re in Hanyang”

This article looks at the storytelling extension method of MBC’s “Chunhyang, You’re in Hanyang”(1996). This is because considering the type of storytelling conversion method can produce meaningful results when converting well-known theories such as “Chunhyangjeon” into video media. “Chunhyangjeon” was produced as a sequel to “Chunhyangjeon” with a story about a new time zone, focusing on Chunhyang and Yumeryu who became adults. It did not transform the story of the original book “Chunhyangjeon,” but expanded the story world with the story of imagination after that. “Chunhyangjeon” is a three-act, eight-chapter (A-H) structure, and each chapter was narrated according to the diagram of “Meeting - Love - Farewell - Suffering - Reunion,” the basic epic structure of “Chunhyangjeon.” In the first part, the main characters of “Mongryeong Parent-Wolmae-Mongryeong,” “Mongryeong Parent- Chunhyang-Mongryeong” and “Byeonghakdo-Mongryeong Uncle” and “Byeonghakdo-Mongryeong -Chunhyang” are maintaining the original characters and story world. The story was expanded by repeating the existing narrative structure of “Chunhyangjeon” and naturally composed the story after “Chunhyangjeon” through conflicts between characters.
A Case Study on the Use of Problem-Based Learning (PBL) in Essay Literature Education

The purpose of this study is to educate many readers on essay literature that has been read and positively recognized in recent years. This purpose is to make learners understand the genre characteristics of essay literature, enjoy it as literature, and recognize its value. Problem-Based Learning was used to help learners actively understand the genre of essay literature. As a result of the PBL application of essay literature education, learners were able to smoothly enter problem-solving learning by recognizing the meaning and value of essay writing while parodying essay works. In the problem-solving process for each team that followed, the genre characteristics of essay literature were clearly understood, the utility of essay literature was acquired, and the learner's sense of achievement in self-directed learning was also revealed in the self-evaluation.