Reviewerly/exHarmony huggingface dataset:
Region selection for occluded person re-identification via policy gradient

Person re-identification (re-id) has attracted lots of attention in the past few years. However, most of work in this area focus on the re-identification of the holistic pedestrian images. In the real application scenarios, the task of re-identification is usually influenced by the occlusion problems. Most of previous work tackle occlusion problem through pose estimation, human parsing or manually labelling occlusion objects. Such reliance on additional annotation information severely limits the generalization ability on practical usage. To address this problem, we propose a novel region selection learning strategy based on the policy gradient to remove irrelevant parts without using any extra information outside dataset. A transformer-based feature extractor is also constructed to learn discriminate features with self-attention mechanism. We evaluate the performance of the proposed method on three occluded re-identification datasets. The experiments show that we achieve 87%, 85.2% and 68.3% in Rank-1 accuracy on Occluded-REID, Partial-REID and Occluded-Duke datasets respectively.
A Hybrid Approach for Protection Against Rumours in a IoT Enabled Smart City Environment

Smart cities have become a new ideal for maximising machine-to-machine, machine-to-human, and human-to-human communications to provide better amenities and quality of life. These ideas use diverse networks, sensor nodes, processing devices, and data repositories that collect, transfer, store, and process real-time information. Smart cities along with latest technology offer applications and services for health care, education, energy usage, and transportation. Smart cities offer potential, but security and privacy must be addressed. Our research aims to identify Intrusion Detection systems that detect cyber threats.
A Comprehensive Comparative Study of Machine Learning Classifiers for Spam Filtering

In July 2021, the daily spam count globally reached 283 billion and constitutes 84.12% of the total email volume. The increasing surge in the spam or unsolicited emails that can hamper communication has led to an intrinsic requirement for robust and reliable antispam filters. In recent years, spam filtration and monitoring have become significant concerns for mail and other internet services. Machine learning strategies are being employed to act as safeguards against internet spam. This study provides a systematic survey of spam filtering methods using machine learning techniques. Logistic Regression, Random Forest, Naive Bayes, and Decision Tree methods used for spam filtering have been compared based on precision, recall, and accuracy on a dataset composed of Twitter tweets, Facebook posts, and YouTube comments. The preliminary discussion involves a background study of the related work on spam filtering and the research gaps in the current literature. Further, a detailed discussion on each method has been provided in this study. The results of our experiments indicate that Decision Trees provide the best accuracy at 97.02% and precision at 98.83%, and Logistic Regression has the highest recall at 99.89%.
The impact of organizational culture and leadership styles on the performance of public organizations

When comparing actual outputs of an organization to its expected results, aims, and objectives, this is referred to as organizational performance (Abuzarqa, 2019; Al Khajeh, 2018). Therefore, this study is to determine the impact of organizational culture and leadership styles on the performance of Jordanian government organizations. The total number of respondents in this survey was 168, and they were divided into Jordanian government employees. The quantitative analysis test, which includes the validity test, reliability test, classic assumptions test, and hypothesis test, is used in the data analysis process. Organizational culture and leadership styles are independent variables in this study. Organizations’ performance is the dependent variable in this study. The findings of this study reveal that organizational culture and leadership styles have a significant or minor impact on the performance of Jordanian public organizations. The two independent variables have a significant point that supports the hypothesis. As a result, it is widely assumed that organizational culture and leadership styles have an impact on the functioning of Jordanian government organizations. Future studies may examine managerial support as a moderating variable between organizational culture and leadership styles and performance.
Labelled Calculi for the Logics of Rough Concepts

We introduce sound and complete labelled sequent calculi for the basic normal non-distributive modal logic $$\textbf{L}$$ and some of its axiomatic extensions, where the labels are atomic formulas of the first order language of enriched formal contexts, i.e., relational structures based on formal contexts which provide complete semantics for these logics. We also extend these calculi to provide a proof system for the logic of rough formal contexts.
Appraising the Manifestation of Optimism Bias and Its Impact on Human Perception of Cyber Security: A Meta Analysis

Cyber threats and risks are increasing exponentially with time.For preventing and defense against these threats and risks, precise risk perception for effective mitigation is the first step.Risk perception is necessary requirement to mitigate risk as it drives the security strategy at the organizational level and human attitude at individual level.Sometime, individuals understand there is a risk that a negative event or incident can occur, but they do not believe there will be a personal impact if the risk comes to realization but instead, they believe that the negative event will impact others.This belief supports the common belief that individuals tend to think of themselves as invulnerable, i.e., optimistically bias about the situation, thus affecting their attitude for taking preventive measures due to inappropriate risk perception or overconfidence.The main motivation of this meta-analysis is to assess that how the cyber optimistic bias or cyber optimism bias affects individual's cyber security risk perception and how it changes their decisions.Applying a meta-analysis, this study found that optimistic bias has an overall negative impact on the cyber security due to the inappropriate risk perception and considering themselves invulnerable by biasing that the threat will not occur to them.Due to the cyber optimism bias, the individual will sometimes share passwords by considering it will not be maliciously used, lack in adopting of preventive measures, ignore security incidents, wrong perception of cyber threats and overconfidence on themselves in the context of cyber security.
Effects of Modalities in Detecting Behavioral Engagement in Collaborative Game-Based Learning

Collaborative game-based learning environments have significant potential for creating effective and engaging group learning experiences. These environments offer rich interactions between small groups of students by embedding collaborative problem solving within immersive virtual worlds. Students often share information, ask questions, negotiate, and construct explanations between themselves towards solving a common goal. However, students sometimes disengage from the learning activities, and due to the nature of collaboration, their disengagement can propagate and negatively impact others within the group. From a teacher's perspective, it can be challenging to identify disengaged students within different groups in a classroom as they need to spend a significant amount of time orchestrating the classroom. Prior work has explored automated frameworks for identifying behavioral disengagement. However, most prior work relies on a single modality for identifying disengagement. In this work, we investigate the effects of using multiple modalities to detect disengagement behaviors of students in a collaborative game-based learning environment. For that, we utilized facial video recordings and group chat messages of 26 middle school students while they were interacting with Crystal Island: EcoJourneys, a game-based learning environment for ecosystem science. Our study shows that the predictive accuracy of a unimodal model heavily relies on the modality of the ground truth, whereas multimodal models surpass the unimodal models, trading resources for accuracy. Our findings can benefit future researchers in designing behavioral engagement detection frameworks for assisting teachers in using collaborative game-based learning within their classrooms.
The Role of Gender in Students’ Privacy Concerns about Learning Analytics

The protection of students' privacy in learning analytics (LA) applications is critical for cultivating trust and effective implementations of LA in educational environments around the world. However, students' privacy concerns and how they may vary along demographic dimensions that historically influence these concerns have yet to be studied in higher education. Gender differences, in particular, are known to be associated with people's information privacy concerns, including in educational settings. Building on an empirically validated model and survey instrument for student privacy concerns, their antecedents and their behavioral outcomes, we investigate the presence of gender differences in students' privacy concerns about LA. We conducted a survey study of students in higher education across five countries (N = 762): Germany, South Korea, Spain, Sweden and the United States. Using multiple regression analysis, across all five countries, we find that female students have stronger trusting beliefs and they are more inclined to engage in self-disclosure behaviors compared to male students. However, at the country level, these gender differences are significant only in the German sample, for Bachelor's degree students, and for students between the ages of 18 and 24. Thus, national context, degree program, and age are important moderating factors for gender differences in student privacy concerns.
When the Tutor Becomes the Student: Design and Evaluation of Efficient Scenario-based Lessons for Tutors

Tutoring is among the most impactful educational influences on student achievement, with perhaps the greatest promise of combating student learning loss. Due to its high impact, organizations are rapidly developing tutoring programs and discovering a common problem- a shortage of qualified, experienced tutors. This mixed methods investigation focuses on the impact of short (∼15 min.), online lessons in which tutors participate in situational judgment tests based on everyday tutoring scenarios. We developed three lessons on strategies for supporting student self-efficacy and motivation and tested them with 80 tutors from a national, online tutoring organization. Using a mixed-effects logistic regression model, we found a statistically significant learning effect indicating tutors performed about 20% higher post-instruction than pre-instruction (β = 0.811, p < 0.01). Tutors scored ∼30% better on selected compared to constructed responses at posttest with evidence that tutors are learning from selected-response questions alone. Learning analytics and qualitative feedback suggest future design modifications for larger scale deployment, such as creating more authentically challenging selected-response options, capturing common misconceptions using learnersourced data, and varying modalities of scenario delivery with the aim of maintaining learning gains while reducing time and effort for tutor participants and trainers.
Instruction-Embedded Assessment for Reading Ability in Adaptive Mathematics Software

Adaptive educational software is likely to better support broader and more diverse sets of learners by considering more comprehensive views (or models) of such learners. For example, recent work proposed making inferences about "non-math" factors like reading comprehension while students used adaptive software for mathematics to better support and adapt to learners. We build on this proposed approach to more comprehensive learning modeling by providing an empirical basis for making inferences about students' reading ability from their performance on activities in adaptive software for mathematics. We lay out an approach to predicting middle school students' reading ability using their performance on activities within Carnegie Learning's MATHia, a widely used intelligent tutoring system for mathematics. We focus on how performance in an early, introductory activity as an especially powerful place to consider instruction-embedded assessment of non-math factors like reading comprehension to guide adaptation based on factors like reading ability. We close by discussing opportunities to extend this work by focusing on particular knowledge components or skills tracked by MATHia that may provide important "levers" for driving adaptation based on students' reading ability while they learn and practice mathematics.
Improving the University Digital Infrastructure Management System: Practice of Social Network Analysis

This article presents the results of applied research aimed at developing topical approaches to solving the problem of dysfunctionality of universities’ digital infrastructures in social media. In the context of the large-scale digitalization and networking processes of modern society, higher education institutions are faced with new challenges that threaten their further sustainability and reduce their competitiveness. As a possible solution to this problem, the authors propose a comprehensive study of university network structures in social media and further implementation of actual practices of online profile management and digital communities. By applying the method of social graphs and automated tools for processing open digital data, a universal algorithm for building and interpreting the resulting visual models is defined. The article substantiates the basic problems faced by modern universities in developing their own digital structures, as well as the ways to solve them, aimed at improving digital infrastructure management, establishing effective communication with target groups, consolidating the organizational structure, developing educational and scientific communities, and promoting the university brand. The proposed interactive digital maps have a high practical potential for direct implementation in the management process of the Russian universities.
Sentiment analysis on land and forest fire management in Twitter using Naïve Bayes method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Indah Fitri Astuti, Putut Pamilih Widagdo, Muhammad Luckman Ramadhan Tanro, Dedy Cahyadi, Aad Aries Suntara; Sentiment analysis on land and forest fire management in Twitter using Naïve Bayes method. AIP Conf. Proc. 21 February 2023; 2482 (1): 140004. https://doi.org/10.1063/5.0110588 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Optimized instance segmentation by super-resolution and maximal clique generation

The rise of surveillance systems has led to exponential growth in collected data, enabling several advances in Deep Learning to exploit them and automate tasks for autonomous systems. Vehicle detection is a crucial task in the fields of Intelligent Vehicle Systems and Intelligent Transport systems, making it possible to control traffic density or detect accidents and potential risks. This paper presents an optimal meta-method that can be applied to any instant segmentation model, such as Mask R-CNN or YOLACT++. Using the initial detections obtained by these models and super-resolution, an optimized re-inference is performed, allowing the detection of elements not identified a priori and improving the quality of the rest of the detections. The direct application of super-resolution is limited because instance segmentation models process images according to a fixed dimension. Therefore, in cases where the super-resolved images exceed this fixed size, the model will rescale them again, thus losing the desired effect. The advantages of this meta-method lie mainly in the fact that it is not required to modify the model architecture or re-train it. Regardless of the size of the images given as input, super-resolved areas that fit the defined dimension of the object segmentation model will be generated. After applying our proposal, experiments show an improvement of up to 8.1% for the YOLACT++ model used in the Jena sequence of the CityScapes dataset.
Improving Students' Self-Study Capacity in Online Teaching at the University of Technology, Vietnam National University Ho Chi Minh City: A Discussion

Today, the strong development of information technology has led to important changes in education in general and higher education in particular, associated with the development of the Internet and the formation of the E-learning teaching method (online teaching). Online teaching is becoming the new training trend in many Vietnamese universities. Online teaching is a form of transmitting knowledge through an Internet connection for studying, exchanging materials, and communicating among students and between students and lecturers. The practice of online teaching over the years shows that the E-learning method can only be highly effective when students' self-study ability is promoted because students' self-study capacity is a combination of students' self-study abilities to dominate knowledge in the best way. Within the framework of this article, the author shares some measures to improve students' self-study capacity in online learning at the University of Technology, Vietnam National University-Ho Chi Minh City.
A transferable turbidity estimation method for estimating clear-sky solar irradiance

A transferable turbidity estimation method is proposed for estimating the turbidity and clear-sky solar irradiance. Instead of using on-site irradiance measurements (i.e., the local model), a transferable model is developed involving stations with sufficient information, and then applied at locations with limited data availability. Compared with the local method, the transferable model yields results with slightly higher discrepancies regrading normalized root mean squared error (nRMSE, 2.80% vs 2.75%). When compared with the Ineichen–Perez (PVLIB) model, the nRMSE of clear-sky global horizontal irradiance (GHIcs) estimation is reduced from 4.99% to 2.44%, and the normalized mean bias error (nMBE) is improved from -3.37% to 0.57%. The GHIcs estimation is comparable with physical models (i.e., McClear and REST2), where the McClear produces a nRMSE of 3.32% and the nMBE is 2.10%, while the REST2 generates results with an nRMSE of 2.55% and an nMBE of 1.30%. We further compare aforementioned models for day-ahead GHIcs forecasts using a day persistent way. GHIcs forecast from the transferable method has slightly lower discrepancies of nRMSE and nMBE than the physical models. Considering the complexity of physical models, the transferable turbidity estimation method with comparable performance demonstrates valuable potential for solar resourcing and forecasting applications.
A Graphical Approach for the Selection of the Number of Clusters in the Spectral Clustering Algorithm

The selection of the number of clusters K in a data set is a fundamental issue in data clustering. In this paper we consider the problem of the selection of the optimal number of clusters in the spectral clustering algorithm. To this end, we propose a multi-graphic method that takes into account geometric characteristics derived from the similarity matrix and from the Laplacian embedding among data. Our approach is supported by some mathematical properties developed in the context of spectral clustering theory. Finally, the performance of our proposal is illustrated on ground of some numerical studies based on both synthetic and real datasets.
Co-designing an Embodied e-Coach With Older Adults: The Tangible Coach Journey

This article describes a tangible interface for an e-coach, co-designed in four countries to meet older adults' needs and expectations.The aim of this device is to coach the user by giving recommendations, personalized tasks and to build empathy through vocal, visual, and physical interaction.Through our co-design process, we collected insights that helped identifying requirements for the physical design, the interaction design and the privacy and data control.In the first phase, we collected users' needs and expectations through several workshops.Requirements were then transformed into three design concepts that were rated and commented by our target users.The final design was implemented and tested in three countries.We discussed the results and the open challenges for the design of physical e-coaches for older adults.To encourage further developments in this field, we released the research outputs of this design process in an open-source repository.
Optimal Complex Morlet Wavelet Parameters for Quantitative Time-Frequency Analysis of Molecular Vibration

When the complex Morlet function (CMOR) is used as a wavelet basis, it is necessary to select optimal bandwidth and center frequency. However, the method to select the optimal CMOR wavelet parameters for one specific frequency is still unclear. In this paper, we deeply investigate the essence of CMOR wavelet transform and clearly illustrate the time-frequency resolution and edge effect. Then, the selection method of the optimal bandwidth and center frequency is proposed. We further perform the quantitative time-frequency (QTF) analysis of water molecule vibration based on our method. We find that the CMOR wavelet parameters obtained by our method can not only meet the requirement of frequency resolution but also meet the limit of edge effect. Moreover, there is an uphill energy relaxation in the vibration of the water molecule, which agrees well with the experimental results. These results demonstrate that our method can accurately find the optimal CMOR wavelet parameters for the target frequency.
Unsupervised Learning of Disentangled Representation via Auto-Encoding: A Survey

In recent years, the rapid development of deep learning approaches has paved the way to explore the underlying factors that explain the data. In particular, several methods have been proposed to learn to identify and disentangle these underlying explanatory factors in order to improve the learning process and model generalization. However, extracting this representation with little or no supervision remains a key challenge in machine learning. In this paper, we provide a theoretical outlook on recent advances in the field of unsupervised representation learning with a focus on auto-encoding-based approaches and on the most well-known supervised disentanglement metrics. We cover the current state-of-the-art methods for learning disentangled representation in an unsupervised manner while pointing out the connection between each method and its added value on disentanglement. Further, we discuss how to quantify disentanglement and present an in-depth analysis of associated metrics. We conclude by carrying out a comparative evaluation of these metrics according to three criteria, (i) modularity, (ii) compactness and (iii) informativeness. Finally, we show that only the Mutual Information Gap score (MIG) meets all three criteria.