Reviewerly/exHarmony huggingface dataset:
Group Similar Images Based on Skeletal Information Extracted from Person's Tracking Path in Real-Time Surveillance System

In modern society, it is important to count the number of people to monitor absence in class and estimate the number of customers in the store or guests at a wedding. Thus, an application of the real-time surveillance system to discern people is required. In this research, we used an object detection method (YOLO) and the people-tracking method (DeepSort) for tracking people on any occasion. The number of images for each person corresponding to a single ID may be too large to categorize these images. Therefore, we proposed a method for the occlusions of body parts with lightweight OpenPose to group images. This proposed method effectively reduced the amounts of images in each folder by moving the original folder of images into different groups of images. This method also integrated new images. For example, new images were classified to each group and checked if the image appeared before. By grouping the images, we reduced repeated comparisons since the new image was searched in a subgroup rather than in every folder. When images were small and blurry, they might be classified into wrong groups. Therefore, we enlarged and deblurred images for restoration. We employed three different methods for in experiments. We used a set of 10 images with 5 originally classified correctly and the other 5 classified incorrectly. The experimental results showed that 'bilinear interpolation' not only effectively enlarged the images but also extracted accurate features, leading to more correct classifications.
Research on Human-Computer Interaction Collaborative Model of College English Learning Platform

The knowledge system and learners in a human-computer interaction exhibit complex and dynamically evolving collaborative relationships. In this study, the elements of foreign language learners were integrated based on Hartson's four levels of generative mechanisms to investigate the ecological coupling of cognition behavior. Based on the present research, a human-machine interaction and collaboration model was constructed on language knowledge platforms.
Multimodal Framework for Fake Messages Detection with Textual and Facial Data

Besides sharing text messages, the direct and rapid dissemination of images and video has become increasingly common in social media. However, it has become less common for people to critically evaluate and confirm the authenticity of a message before receiving it. Previous research focused on text recognition or account authenticity but less focused on integrated multimedia promotional materials. In this article, we proposed a framework to recognize face data embedded with text in a message and deal with multimodal information. We combined Tesseract Optical Character Recognition (OCR) with Local Binary Pattern Histogram (LBPH) face training to recognize texts and faces. By extracting and analyzing pictures or posters circulated on social media, we identified and confirmed the information of a specific individual, as well as the text content related to it. We developed a system in which Tesseract OCR and LBPH face recognition showed an accuracy of 95.5 and 92.7%.
Evaluating Freeform Creativity versus Goal-Directed Exercises in VR: An Examination of Immersion Levels and Effectiveness

As virtual reality (VR) continues to evolve, the study on user interaction within the VR environment becomes increasingly important. Thus, we investigated two interactions "Exploration and Browsing" and "Purpose and Guidance," using VR platforms to compare immersion levels and exercise effectiveness. We analyzed the results from 33 participants using Gravity Sketch 3D for freeform creativity, and Unity3D generated program for goal-directed exercises. We assessed four factors of 'concentration,' 'enjoyment,' 'sense of time distortion,' and 'control' for immersion levels and self-recognized exercise intensity, and participants' preference between the two VR formats. Preliminary results indicated that Freeform Creativity scored high overall in Immersion Levels and Exercise Effectiveness compared to Goal-Directed Exercises. The data also suggested that when participants used creative VR tools for exploration and browsing, stronger Immersion Levels were triggered, which in turn, led to more exercise. However, providing participants with exercise goals did not guarantee a higher level of exercise.
Unsupervised Image-to-Image Translation Based on Bidirectional Style Transfer

Image-to-image translation (I2I) is an image synthesis technique to map a source image to the style of the target domain while preserving its content information. Existing image-to-image translation study results showed excellent image synthesis performance using generative adversarial network (GAN) based models, but they are not capable of efficiently handling the style of the target domain. To overcome this limitation, a bidirectional style transfer network has been developed to perform image synthesis by intersecting images of two domains with each other's styles, but the type of applicable dataset is limited due to supervised learning-based training. We proposed an unsupervised image-to-image translation method by employing a bidirectional style transfer network with a cyclic collaborative loss to train the model. Experimental results showed that the proposed network accurately reflected the style of the target domain in the image synthesis task.
BTDA: Automatic Solver for Math Word Problems Based On Back-Translation Data Augmentation Mechanism

It is difficult to implement an automatic solution for math word problems (MWP) in natural language processing. In the last few years, effective results have been obtained but most of the work was to study an unknown problem. We proposed a math word problem solver BTDA based on the encoder-decoder framework with back-translation data augmentation to solve MWPs with two unknowns. In addition, we incorporated background knowledge to improve the accuracy of the solution. We built a high-quality data set HBLE with two unknown word problems. The experiment result showed that the proposed model improved the accuracy by 2.11% compared to other models.
Transient Group Recommendation for Shared Environment using Check-in and Tag Data

Group Recommendation Systems (GRSs) are crucial in facilitating decision-making processes for shared playlists. However, recommending appropriate items is challenging when it comes to dynamic and transient groups such as restaurant customers. Thus, we proposed a hybrid GRS designed to address these difficulties. By combining collaborative filtering and content-based recommendations enriched with tag information, the proposed system used historical and current check-in data to predict future group composition and tailor recommendations accordingly. The system generated top-N music playlists for every period of the week and provided diverse and novel recommendations prepared before customers’ arrival. We enhanced recommendation performance by extending the traditional user-item matrix with tags and combining memory-based and model-based computation. The proposed method was tested with two real-world datasets. The result showed the improvement of the performance over popularity-based item selection, offering a promising approach for transient group recommendation in a shared environment.
Knowledge Distillation Boosted Ultra-light Weight Road Object Detection on Edge Device

Road object detection is required for automatic driving and intelligent traffic control and detecting illegal behavior. However, many edge computing devices do not have high computing power. Therefore, it is necessary to deploy a lightweight object detection model while maintaining its performance. Thus, we developed a scenario on the 24-hour automatic patrol camcorder for the corners and alleys. The device instantly identified and detected suspicious vehicles or pedestrians and sent notifications. Hence, the model had low complexity and efficiency to meet the requirement of real-time detection. We improved the model's precision within the limited inference time. In the experiment, YOLOv5 was selected as the base model for lightweight model construction on the Snapdragon 660 Mobile Platform on Xiaomi Mi A2. The designed model YOLOv5p with parameter size 0.7 M boosted knowledge distillation from mean average precision (mAP) of 18 to 19.3% on the BDD100K dataset.
Towards Explainable AI Validation in Industry 4.0: A Fuzzy Cognitive Map-based Evaluation Framework for Assessing Business Value

The development of Artificial Intelligence (AI) systems in Industry 4.0 has gained momentum due to their potential for increasing efficiency and productivity. However, AI systems can be just as complex and opaque, leading to concerns about their reliability, trustworthiness, and accountability. To address these issues, this paper proposes a validation framework for Explainable AI (XAI) in Industry 4.0 based on Fuzzy Cognitive Maps (FCMs). The proposed framework aims to evaluate Key Performance Indicators (KPIs) based on a set of AI metrics and XAI metrics. The FCM-based approach enables the representation of causal-effect relationships between the different concepts of the system using expert knowledge. The presented validation framework provides a theoretical background for evaluating and optimizing the business values of AI systems based on multiple criteria in the manufacturing industry, demonstrating its effectiveness. The main contributions of this paper are: i) the development of an FCM-based validation framework for XAI in Industry 4.0; ii) the identification of relevant AI and XAI metrics for the evaluation of the KPIs of the theoretical graph model; and iii) the demonstration of the effectiveness of the proposed framework through a case study. The results of this study provide valuable insights into the importance of considering not only accuracy but also efficiency and transparency when developing AI pipelines that generate higher business value. Overall, this paper offers a theoretical foundation and practical insights for organizations seeking to evaluate the business values of their AI systems in Industry 4.0. It emphasizes the importance of explainability and the integration of AI and XAI metrics in achieving transparent and accountable AI solutions that deliver optimal results for the manufacturing industry and beyond.
Industrial Asset Management and Secure Sharing for an XAI Manufacturing Platform

Explainable AI is an emerging field that aims to address how black-box decisions of AI systems are made, by attempting to understand the steps and models involved in this decision-making. Explainable AI in manufacturing is supposed to deliver predictability, agility, and resiliency across targeted manufacturing apps. In this context, large amounts of data, which can be of high sensitivity and various formats need to be securely and efficiently handled. This paper proposes an Asset Management and Secure Sharing solution tailored to the Explainable AI and Manufacturing context in order to tackle this challenge. The proposed asset management architecture enables an extensive data management and secure sharing solution for industrial data assets. Industrial data can be pulled, imported, managed, shared, and tracked with a high level of security using this design. This paper describes the solution´s overall architectural design and gives an overview of the functionalities and incorporated technologies of the involved components, which are responsible for data collection, management, provenance, and sharing as well as for overall security.
Report on the 6th International Workshop on Narrative Extraction from Texts (Text2Story 2023) at ECIR 2023

The Sixth International Workshop on Narrative Extraction from Texts (Text2Story'23) was held on April 2 nd , 2023, in conjunction with the 45 th European Conference on Information Retrieval (ECIR 2023) in Dublin, Ireland. Continuing the tradition of past years, the workshop was held as a hybrid event. Online participation was allowed using the Zoom platform. During the course of the day, more than 50 attendees had the opportunity to follow up and discuss the recent advances in topics related to representation, extraction, and generation of narratives. The workshop program included two invited keynotes and nineteen paper presentations. The proceedings of the workshop are available online 1 . Date: 2 April 2023. Website: https://text2story23.inesctec.pt/.
Report on the 45th European Conference on Information Retrieval (ECIR 2023)

This paper reports on the 45th European Conference on Information Retrieval (ECIR 2023), held in Dublin, Ireland, during April 2--6, 2023. The conference was the largest ECIR ever, and brought together hundreds of researchers from Europe and abroad. For those who like numbers: First, we received 489 submissions in total (including 228 full and 153 short papers) excluding further workshop submissions. Second, the technical program committee consisted of 624 reviewers in total (including 27 chairs, 124 SPC, 473 reviewers) with many serving on multiple tracks. Third, the proceedings necessitated a third volume, and contains a total of 175 papers in 2,151 pages. The rest of this report details the conference program and events. Date: 2--6 April 2023. Website: https://ecir2023.org/.
E-Learning: on the Issue of the Effectiveness of the Organization of the Educational Process at the University

The subject of the study is a quantitative assessment of the effectiveness of the educational process in the form of E-learning in relation to classical and distance forms of its organization on the example of the elective course "Physical Culture and Sports". The object of the study is a new form of organization of the educational process at the university - e-learning. The authors consider the features of the organization of the educational process in the form of E-learning: the setting of an individual educational goal by the student himself, its formalization by means of the virtual educational space of the university, the calculation of an individual educational trajectory and its practical implementation by "playing" the student given pedagogical situations. Special attention is paid to proving the practical feasibility in the educational process of the main provisions of the well-known pedagogical concepts of the organization of e-learning and obtaining quantitative data on the real effectiveness of the organization of the educational process in the form of E-learning. The novelty of the pedagogical experiment conducted by the authors lies in the fact that for the first time the elective course "Physical Culture and Sports" was fully implemented in the form of e-learning, which made it possible to obtain quantitative values of the parameters of the educational process (the time the student reaches an individual educational goal, the percentage of participants guaranteed to achieve the educational goal in relation to the total number of students, etc.), proving a higher efficiency of e-learning compared to the "classical" and "distance" forms. A number of negative factors have been identified that are not sufficiently covered in theoretical studies on E-learning, but have a serious impact on the effectiveness of the educational process in this form. The directions of improving the educational process in the form of E-learning for its implementation in educational institutions of various levels are determined.
Ensemble Learning Methods for Dirty Data: A Keynote at CIKM 2022

Neural network ensemble is a collaborative learning paradigm that utilizes multiple neural networks to solve a complex learning problem. Constructing predictive models with high generalization performance is an important and yet most challenging goal for robust AI systems in the presence of dirty data. Given a target learning task, popular approaches have been dedicated to designing and finding the top performing model. However, it is difficult in general to estimate the best model when available data is finite, possibly dirty, or insufficient for the problem. The problem of dirty data in machine learning (ML) can be characterized by the out of distribution data and the digital or physical deception of data. Such dirty data may cause unintended or harmful behavior for well trained ML models. In this paper, a curated version of my keynote at ACM CIKM 2022, I will first give a brief overview of ensemble learning methodology. Then I will review different types of dirty data that could deceive well-trained ML models. Finally, I will describe a focal diversity optimized ensemble learning framework, developed at Georgia Tech, for measuring, enforcing, and combining multiple neural networks, delivering high generalization performance of ensemble learner, while maximizing ensemble utility and resilience to dirty data. Date: 17 October 2022.
A privacy preserving recommendation and fraud detection method based on graph convolution

&lt;abstract&gt; &lt;p&gt;As a typical deep learning technique, Graph Convolutional Networks (GCN) has been successfully applied to the recommendation systems. Aiming at the leakage risk of user privacy and the problem of fraudulent data in the recommendation systems, a Privacy Preserving Recommendation and Fraud Detection method based on Graph Convolution (PPRFD-GC) is proposed in the paper. The PPRFD-GC method adopts encoder/decoder framework to generate the synthesized graph of rating information which satisfies edge differential privacy, next applies graph-based matrix completion technique for rating prediction according to the synthesized graph. After calculating user's Mean Square Error (MSE) of rating prediction and generating dense representation of the user, then a fraud detection classifier based on AdaBoost is presented to identify possible fraudsters. Finally, the loss functions of both rating prediction module and fraud detection module are linearly combined as the overall loss function. The experimental analysis on two real datasets shows that the proposed method has good recommendation accuracy and anti-fraud attack characteristics on the basis of preserving users' link privacy.&lt;/p&gt; &lt;/abstract&gt;
THE ROLE OF TECHNICAL CREATIVITY IN THE DEVELOPMENT OF STUDENTS' CREATIVE ACTIVITY

In the education system, it is necessary to ensure the interconnection, compatibility and prospects of the components of the pedagogical system for the education of a mature, creative personality in all respects, for the continuous development of the student at all stages of the educational process. Socio-pedagogical problems of the development of creativity of students of technical creativity and construction sciences, organizational bases for the activity of technical creativity, invention, discovery, rationalization proposals and patent information, methods for solving creative problems, modeling and design, management of students' technical creativity, technical creativity in various institutions are covered topics related to the organization of activities, methods of development of creativity and it is planned to provide them with knowledge. Providing students with technological education includes general patterns studied in the sciences of the natural science cycle, about the main branches of current production, the device and principles of operation of the latest machines, mechanisms and tools, as well as their practical application use, basic materials and their use, is to provide general information about new technological processes, scientific knowledge and general information about the organization of work on a scientific basis. These tasks are successfully implemented in the process of technical design and modeling of modern technology.
Algorithmic Necropolitics

Abstract This chapter of the author’s 2022 book Racismo algorítmico: inteligência artificial e discriminação nas redes digitais (Algorithmic racism: artificial intelligence and discrimination in digital networks) demonstrates the racism encoded in artificial intelligence. It addresses the material and symbolic, often lethal, violence inflicted upon Black and poor individuals and populations by the deployment of predictive systems built and backfed from datasets that reflect a history of exploitation and segregation. It begins with a historical overview of the normalisation of hypervigilance and violent control over racialized populations in the United States and in Brazil. It then shows the continuity of that control by contemporary algorithmic classification systems such as facial recognition, predictive policing, and health and security risk scores. Aligned with official narratives of racial harmony and the meritocratic justification of colour-blind policy, they are deemed neutral by their developers, private enterprises and the public institutions that employ them, who insidiously ignore their bias.
Multidimensional Behavior Fusion: Joint Probabilistic Generative Modeling

In this work, we aim at building a bridge from coarse behavioral data to an effective, quick-response, and robust behavioral model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we validate that there is a complementary effect among different dimensions of records for modeling users’ behavioral patterns. To deeply exploit such a complementary effect, we propose a joint (instead of fused) model to capture both online and offline features of a user’s composite behavior. We evaluate the proposed joint model by comparing with typical models and their fused model on two real-world datasets: Foursquare and Yelp. The experimental results show that our model outperforms the existing ones, with the AUC values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to $$65.3\%$$ in Foursquare and $$72.2\%$$ in Yelp with the corresponding disturbance rate (False Positive Rate) below $$1\%$$ . It is worth mentioning that these performances can be achieved by examining only one composite behavior, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users’ composite behavioral patterns.
The impact of sustainability reporting on a company’s financial performance: Evidence from the emerging market

The potential impact of sustainability reporting on a company’s financial performance could be measured through its stock price, profitability, or other financial metrics. This research aims to investigate the relationship between sustainability reporting and financial performance, in order to provide insights for companies, investors, and other stakeholders on the potential benefits and drawbacks of sustainability reporting. The research community of this study is formed out of all the 13 Jordanian commercial banks listed in the Amman Stock Exchange, and covering the period from 2012–2021. The study is a census study as it involves collecting data from every member of the study population, which allows for a comprehensive analysis of the relationship between sustainability reporting and financial performance. The data was collected from publicly available sources and analyzed using multiple regression analysis. The results of the study suggest that there is a strong linear relationship between sustainability reporting and the dependent variables return on assets (ROA) and financial leverage (LEV), but the relationship between sustainability reporting (SR) and return on equity (ROE) is not statistically significant. These findings provide insights for companies, investors, and other stakeholders on the potential benefits and drawbacks of sustainability reporting and can inform decision-making around sustainability initiatives.