Reviewerly/exHarmony huggingface dataset:
Using Augmented Reality Applications in the Greek Mythology Courses: The Case of Türkiye

The study aims to reveal the advantages, disadvantages, requirements, and possible problems of using augmented reality applications in Greek Mythology courses. Firstly, an open-ended question form was created by authors who are both professional tourist guides. To ensure the content validity on August 25, 2022, the form was submitted to five experienced academicians from different universities via e-mail. On September 13, 2022, the forms were evaluated and revised. Between September 14 and 23, 2022, a pilot study was carried out with five academicians who had teaching experience in Greek Mythology courses. On September 23, 2022, the data of the pilot study was analyzed, and the final form comprised of four questions was submitted via e-mail to the academicians from different universities in Türkiye. As a result of data collection which took approximately one and a half months, 20 participants (11 female, 9 male) returned. Consequently, four codes (pre-training on augmented reality, augmented reality-supported classes, and financial support) under the theme of requirements; six codes (arouse interest, making lessons enjoyable, increasing the memorability, facilitating learning, visualization, provide remote experience) under the theme of advantages; two codes (being the application costly and misrepresentation) under the theme of disadvantages and three codes (inadequacies in physical infrastructure, Internet disconnections, and finding budget) under the theme of possible problems were highlighted.
Performance analysis of modified wavelet difference reduction methods in image compression and transmission

The wavelet difference reduction (WDR) method, a variant of run-length coding, finds its significance in data transmission applications. Over time, numerous enhanced iterations of WDR methods have emerged. Notably, the Adaptive Scalable WDR method exhibits superior coding gains, as evidenced by the peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), when compared to its predecessors. This paper conducts an exhaustive examination, encompassing both coding performance and time complexity, of various WDR methods vis-à-vis the conventional image compression algorithm SPIHT. Furthermore, it delves into the performance assessment of diverse coding techniques in the realm of encoding arbitrary-shaped objects. The analysis underscores that modified WDR variants demonstrate remarkable prowess in compression, rendering them invaluable for rapid transmission in bandwidth-constrained networks. To substantiate these findings, coding results (measured in terms of PSNR) are derived from the application of these algorithms to standard test images, MRI images, and video still images. The results reveal coding gains ranging from 0.5 dB to 1 dB for regular resolution images and a substantial 2 dB to 12 dB for scalable resolution scenarios, in comparison to traditional coding approaches. Consequently, this analysis underscores the convenience and superiority of modified WDR methods, not only for still images but also for encoding and transmitting arbitrary-shaped objects.
A Kvisoft Flipbook Maker-Assisted E-Book to Increase Students' Interest In Elementary Schools

Students' interest in studying science subjects with material on Earth and the Universe in sixth class is still low. It is caused by various factors, one of which is the media of teaching materials that are still conventional. This research aims to develop media in ebooks assisted by Kvisoft Flipbook Maker. This research uses the R&amp;D (Research and Development) method with the ADDIE model. The research subjects consisted of material validators, media validators, language validators, and 62 class VI students. Data collection techniques in this study using observation and tests. The instrument used in this study was a questionnaire given to the validator and a pretest and posttest given to students. Data analysis was carried out in a quantitative descriptive manner. The results of the research are the validation of media experts in a 92% very feasible category, material experts in a 99% very feasible category, and linguists in an 86.15% very feasible category. The results showed that ebook development was very good, so there was an increase in learning outcomes with ebook teaching materials assisted by Kvisoft Flipbook Maker. This research proves that this research contributes to increasing the interest of elementary school students.
Who Has to Be Technologically Competent in Arbitration?

This chapter reflects on the role assigned to legal assistants in contemporary arbitration and the level of digital competence required from them. It also meditates on whether technological advances require a restructuring of the relationship between arbitrators and their legal assistants. Light is also shed on the existing relationship between arbitrators and a broad group of professionals who have not legal but technical expertise (IT experts, technology secretaries, outside technology consultants, vendors, etc.) Nowadays arbitrators need to have contact with these non-legal professionals not only in the day-to-day business of their increasingly multidisciplinary law firms, but also during the arbitration proceedings. This chapter explains that at key stages such as remote hearings, arbitrators have to interact more closely with other professionals and take on additional supervisory duties.
What Is Technological Competence in the Current Arbitration World?

This chapter is based on a range of sources including studies carried out by international organizations, documents endorsed by professional associations and scholarly contributions. The chapter starts from broad definitions such as digital literacy in contemporary society and technological competence for judges and lawyers. Building on these notions, the overall aim is to sketch out a working definition of technological competence that is specifically applicable to modern arbitrators. The difficulty of creating a universal and immutable definition due to a range of factors such as continuous technological advances and differences within the broad arbitrator collective is acknowledged. In spite of these circumstances, the arbitration community would be well-advised to move towards a consensus on a definition of technological competence for the profession. This chapter proposes a working definition of the minimum level to be expected from arbitrators, which is sufficiently flexible to be nuanced as technological and legal advances require. Drawing a distinction between “basic” and “premium” technological competence might be a useful starting point and one that will be further developed in Chaps. 4 – 6 .
Cross-Modal Sentiment Analysis

This chapter explores multimodal sentiment analysis in social computing, highlighting the limitations of traditional text analysis and the importance of incorporating nonverbal modalities. It proposes an automatic MSA approach with MMix strategy, leveraging supervised and unsupervised data to accurately predict sentiment and reduce manual efforts. The results show state-of-the-art performance. The second section introduces a hierarchical model for acoustic analysis, combining frame-level and utterance-level features. It validates the model on IEMOCAP and MELD datasets. In the third section, this chapter presents CM-BERT, fine-tuning BERT with multimodal information. Experimental results on CMU-MOSI and CMU-MOSEI datasets show significant improvements. This chapter concludes by discussing the effectiveness of masked multimodal attention.
Text Mining

Convolutional neural networks (CNNs) are a type of deep learning model specifically designed for processing and analyzing visual data, such as images and videos. CNNs allow us to extract high-level information from visual data by detecting and recognizing common patterns found across large volumes of images. They utilize convolution layers that perform operations similar to image processing filters and are applied in a variety of tasks related to images such as image classification, object detection, and semantic segmentation. They are powerful tools for understanding, processing, and extracting meaningful information from visual data, making them invaluable in numerous fields, and can be applied to a wide variety of tasks in automation and information extraction. In this chapter, we will cover how to develop your own or finetune existing CNNs for your own specific tasks.
Unimodal Sentiment Analysis

This chapter explores the realm of sentiment analysis, covering diverse domains such as text, audio, and facial expressions. It introduces novel approaches that address the limitations of existing methods, emphasizing the significance of semantic relationships, effective fusion of heterogeneous features, and the benefits of multitask learning. The proposed techniques, including word2vec and SVMperf for sentiment classification, RCMSA and CHFFM for audio sentiment analysis, and CMCNN with CCAM and SCAM for facial expression recognition, exhibit superior performance in their respective domains. This chapter sets the stage for the book, showcasing innovative methods that advance the field of sentiment analysis and provide valuable insights for researchers and practitioners alike.
Fake Review Detection Using Machine Learning

Abstract: Customer reviews on ecommerce platforms and online services are invaluable for both users and vendors. They enhance brand loyalty and provide insight into product experiences. Reviews also empower vendors by boosting sales through positive feedback. Unfortunately, this system can be exploited, with fake reviews being used to manipulate reputations. Instead of a limited dataset, it employ a diverse range of vocabulary sourced from various subjects. Raw data is collected from multiple channels and refined, removing irrelevant, redundant, and unreliable information. This application implement sentiment analysis to categorize reviews, detecting and classifying fake ones. Testing involves Naive Bayes, Logistic Regression, Support Vector Machine, and Random Forest algorithms. The solution's core is machine learning, achieving highest accuracy with Random forest and sentiment analysis. Additionally, the application also extract post frequency and reviewer response times for further analysis.
AUTHORSHIP OF AI-GENERATED WORKS IN ARTISTIC DOMAIN

The article analyzes the question of authorship of AI-generated works in the artistic domain. As usage of AI is projected to increase in many different industries, sometimes regardless of presence of any tangible benefits to the user, we have to evaluate potential impact of an AI on the artistic domain, mostly from the perspective of it being used as a tool by various artists. Discussion is held from the perspective of the evaluation of actual work done and the quality of the result, accounting for the time spent to achieve that result, along with human-made edits where necessary. Discussions about legal standards and decisions of determining sufficient authorship of the artistic work are largely outside of the scope of this article, however, the decision by the U.S. Copyright Office is used as an example of a sufficiently good middle ground found as an attempt to acknowledge existence and importance of AI-generated art, while avoiding any decisions that could prevent its use in both commercial and non-profit environments.
CLIP-Based Composed Image Retrieval with Comprehensive Fusion and Data Augmentation

Composed image retrieval (CIR) is a challenging task where the input query consists of a reference image and its corresponding modification text. Recent methodologies harness the prowess of visual-language pre-training models, i.e., CLIP, yielding commendable performance in CIR. Despite their promise, several shortcomings linger. First, a salient domain discrepancy between the CLIP's pre-training data and the CIR's training data leads to suboptimal feature representation. Second, the existing multimodal fusion mechanisms solely rely on weighted summing and feature concatenation, neglecting the intricate higher-order interactions inherent in the multimodal query. This oversight poses challenges in modeling complex modification intents. Additionally, the paucity of data impedes model generalization. To address these issues, we propose a CLIP-based composed image retrieval model with comprehensive fusion and data augmentation (CLIP-CD), consisting of two training stages. In the first stage, we fine-tune both the image and text encoders of CLIP to alleviate the aforementioned domain discrepancy. In the second stage, we propose a comprehensive multimodal fusion module that enables the model to discern complex modification intentions. Furthermore, we propose a similarity-based data augmentation method for CIR, ameliorating data scarcity and enhancing the model's generalization ability. Experimental results on the Fashion-IQ dataset demonstrate the effectiveness of our method.
Generating Collective Motion Behaviour Libraries Using Developmental Evolution

This paper presents an evolutionary framework for generating diverse libraries of collective motion behaviours. It builds upon recent advancements in machine recognition of collective motion and the transformation of random motions into structured collective behaviours. The paper describes the design of the framework, including the use of a fitness function and diversity metrics specifically tailored for this purpose. The proposed framework generates diverse behaviours with distinct collective motion characteristics. Analysing the relationship between genotypic and behavioural diversity, we observed that greater diversity emerges after a moderate number of evolutionary generations. Our findings highlight the effectiveness of task non-specific fitness functions in distinguishing structured collective behaviours in an evolutionary setting.
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline

Accurate and efficient object detection is crucial for safe and efficient operation of earth-moving equipment in mining. Traditional 2D image-based methods face limitations in dynamic and complex mine environments. To overcome these challenges, 3D object detection using point cloud data has emerged as a comprehensive approach. However, training models for mining scenarios is challenging due to sensor height variations, viewpoint changes, and the need for diverse annotated datasets. This paper presents novel contributions to address these challenges. We introduce a synthetic dataset SimMining-3D [1] specifically designed for 3D object detection in mining environments. The dataset captures objects and sensors positioned at various heights within mine benches, accurately reflecting authentic mining scenarios. An automatic annotation pipeline through ROS interface reduces manual labor and accelerates dataset creation. We propose evaluation metrics accounting for sensor-to-object height variations and point cloud density, enabling accurate model assessment in mining scenarios. Real data tests validate our model's effectiveness in object prediction. Our ablation study emphasizes the importance of altitude and height variation augmentations in improving accuracy and reliability. The publicly accessible synthetic dataset [1] serves as a benchmark for supervised learning and advances object detection techniques in mining with complimentary pointwise annotations for each scene. In conclusion, our work bridges the gap between synthetic and real data, addressing the domain shift challenge in 3D object detection for mining. We envision robust object detection systems enhancing safety and efficiency in mining and related domains.
Competitive Collaboration for Complex Task Learning in Agent Systems

This paper presents a novel competitive collaboration-based reinforcement learning strategy to improve the performance of goal-oriented autonomous agent systems. Competitive characteristics are introduced while agents are required to collaboratively share and update a single knowledge pool. Experimental evaluations are conducted in a reward collection task where the goal is to collect as many reward points as possible across a set space navigating through multiple channels while avoiding costs associated with channel switching. Evaluations across 50 challenge levels of the task in comparison to state-of-the-art reinforcement learning models reveal that introducing a combination of competition and collaboration attributes facilitate statistically significant performance improvements. The compared incremental models carry forward knowledge from previous challenge levels to learn more complex challenges. However, they resort to sub-optimal solutions where cost minimisation is given priority over higher reward collection. Traditional reinforcement learning is generally incapable of identifying a balance between reward collection and cost minimisation leading to poor performance. In contrast, the proposed competitive collaboration-based model is capable of strategically planning the best route while taking appropriate risks with higher costs for better rewards in the long run. Further, investigations with multiple reward schemes illustrate that frequent rewards to guide the agents' strategies as opposed to delayed rewards that are awarded at less frequent intervals are imperative for agent performance. The presented evaluations lend insights into future research for optimising agent behaviours in complex real-world applications with competitive collaboration.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads

With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge. Modern trackers are required to operate on more and more complicated scenes. According to the MOT20 challenge result, the pedestrian is 4 times denser than the MOT17 challenge. Hence, improving the ability to detect and track in extremely crowded scenes is the aim of this work. In light of the occlusion issue with the human body, the heads are usually easier to identify. In this work, we have designed a joint head and body detector in an anchor-free style to boost the detection recall and precision performance of pedestrians in both small and medium sizes. Innovatively, our model does not require information on the statistical head-body ratio for common pedestrians detection for training. Instead, the proposed model learns the ratio dynamically. To verify the effectiveness of the proposed model, we evaluate the model with extensive experiments on different datasets, including MOT20, Crowdhuman, and HT21 datasets. As a result, our proposed method significantly improves both the recall and precision rate on small and medium sized pedestrians, and achieves state-of-the-art results in these challenging datasets.
Causal Disentanglement for Adversarial Defense

Representation learning that seeks the high accuracy of a classifier is a key contribute to the success of state-of-the-art DNNs. However, DNNs face the threat of adversarial attacks and their robustness is in peril. While the adversarial defense has been widely studied, much of the research is based on a statistical association and causality based defense approach is a relatively open area. We present CDAD ( $$\underline{C}$$ ausal $$\underline{D}$$ isentanglement for $$\underline{A}$$ dversarial $$\underline{D}$$ efense), a novel defense method that learns and utilizes causal representations for robust prediction. We take inspiration from a recent study that takes a causal perspective on the adversarial problem and considers the susceptibility of DNNs to adversarial examples come from their reliance on spurious associations between non-causal features and labels, such that an adversary exploits the associations to succeed in the attack. Causal representations are robust as the causal relationship between a cause of the label and the label is invariant under different environments. However, discovering causal representations is a challenging task, especially in the context of image data. Harnessing the recent advancement in representation learning with VAE (Variational AutoEncoder), we design CDAD as a VAE based causal disentanglement representation learning method to decouple causal and non-causal representations. CADA uses the invariance property of causal features as a constraint in the disentanglement of causal features and non-causal features. Experimental results show CDAD's highly competitive performance compared to the state-of-the-art defense methods, while possessing a causal foundation.
Part-Aware Prototype-Aligned Interpretable Image Classification with Basic Feature Domain

In recent years, the interpretive this looks like that structure has gained significant attention. It refers to the human tendency to break down images into key parts and make classification decisions by comparing them to pre-existing concepts in their minds. However, most existing prototypical-based models assign prototypes directly to each category without considering that key parts with the same meaning may appear in images from different categories. To address this issue, we propose dividing prototypes with the same meaning into the same latent space (referred to as Basic Feature Domain) since different category parts only slightly affect the corresponding prototype vectors. This process of integrating prototypes based on the feature domain is referred to as prototype alignment. Additionally, we introduce the concept of part-aware optimization, which prioritizes prototypical parts of images over simple category labels during optimizing prototypes. Moreover, we present two feature aggregation methods, by row and by cluster, for the basic feature domain. We demonstrate competitive results compared to other state-of-the-art prototypical part methods on the CUB-2011-200 dataset and Stanford Cars dataset using our proposed self-explanatory part-aware proto-aligned network (PaProtoPNet).
Transformed Successor Features for Transfer Reinforcement Learning

Reinforcement learning algorithms require an extensive number of samples to perform a specific task. To achieve the same performance on a new task, the agent must learn from scratch. Transfer reinforcement learning is an emerging solution that aims to improve sample efficiency by reusing previously learnt knowledge in new tasks. Successor feature is a technique aiming to reuse representations to leverage that knowledge in unseen tasks. Successor feature has achieved outstanding results on the assumption that the transition dynamics must remain across tasks. Initial Successor feature approach omits settings with different environment dynamics, common among real-life tasks in reinforcement learning problems. Our approach transformed successor feature projects a set of diverse dynamics into a common dynamic distribution. Hence, it is an initial solution to relax the restriction of transference across fixed environment dynamics. Experimental results indicate that the transformed successor feature improves the transfer of knowledge in environments with fixed and diverse dynamics under the control of a simulated robotic arm, a robotic leg, and the cartpole environment.