Reviewerly/exHarmony huggingface dataset:
Designing With, For, and Without Communities

In this chapter, I present three case studies which I explored within the context of Lebanon, as a part of my doctoral research journey, revolving around social innovation within spaces of contestation. These case studies entail participatory research engagements within three distinct spaces: (1) an international humanitarian and development organisation, (2) two local organisations in Lebanon and (3) the social movement of 2019 in Lebanon. Drawing upon the analysis of these case studies, I propose a potential design framework examining four dimensions: power, co-creation of value, enactment of agency, and sustainable pathways of collective agency and action. Additionally, I discuss how within such spaces of contestation, a researcher has to constantly shift roles in order to navigate inherent complexities while being truthful to their own research and its embedded values.
Comparing Classical and Bayesian Panel Kink Regression Frameworks in Estimating the Impact of Economic Freedom on Economic Growth

This study aims to accomplish three main tasks. Firstly, it seeks to determine the more appropriate choice between classical and Bayesian methods in estimating a pooled panel kink regression model under the condition of a known but bounded policy variable choice that serves as a kink point. Secondly, as a product of the first target, the study seeks to provide empirical evidence for the economic growth–economic freedom nexus in five top-performing economies in Sub-Saharan Africa. Using index explanatory variables, which are bounded between 0 and 100, and using both numerical and graphical methods, the findings show that the use of the Bayesian method is more appropriate in characterizing the data than the classical OLS framework, as the former better accounts for randomness via the use of posterior distributions. Finally, the study further employed both threshold and Bayesian pooled panel kink regressions, with mixed results. The Bai–Perron test confirmed that the economic freedom index has a single threshold value of 56.70. Whereas the threshold estimates show a negative impact of economic freedom on growth in both low and high regimes, the Bayesian estimates reveal that economic freedom has a negative impact on growth in a low regime but a positive impact in a high regime. Our novel findings show that there exists a nonlinear impact of economic freedom on growth. This provides some guidance and caution in charting policy paths that seek to achieve economic growth.
Application of MPEG-Based 3D Animation Compression Algorithm in Architectural Animation Customization

In recent years, 3D animation technology has developed at a rapid pace and has been used in a variety of fields. In the field of architecture, the use of bespoke 3D animation techniques to simulate the exterior and interior details of a building can not only help designers to communicate their design ideas, but can also be used in marketing to give purchasers a more visual understanding of the building. However, the production process of 3D animation is limited by storage capacity and bandwidth. In order to reduce the load of 3D data on the bandwidth, it is necessary to choose an appropriate compression method to compress the 3D data. This study introduces the Motion Picture Experts Group format algorithm to optimise the traditional algorithm for compressing 3D animation in the hope of achieving better compression results. The experimental results showed that for the quantization table filling method, linear function compression worked best. For the choice of sub-block size, the compression effect of sub-block size of A is the best and the reconstruction error is lower. The compression effect is significantly improved by introducing a local linear embedding algorithm for dimensionality reduction, and the combination of a digital zero processing algorithm and a discrete cosine transform results in better compression. In comparative experiments of 3D animation compression based on the Motion picture expert group format algorithm, the Motion Picture Experts Group format algorithm outperforms other algorithms. The experiments confirm the superior compression performance of the Motion picture expert group format algorithm and show that the Motion picture expert group formatalgorithm has some application value in architectural animation adaptation.
Deep learning-based sentiment classification in Amharic using multi-lingual datasets

The analysis of emotions expressed in natural language text, also known as sentiment analysis, is a key application of natural language processing (NLP). It involves assigning a positive, negative (sometimes also neutral) value to opinions expressed in various contexts such as social media, news, blogs, etc. Despite its importance, sentiment analysis for under-researched languages like Amharic has not received much attention in NLP yet due to the scarcity of resources required to train such methods. This paper examines various deep learning methods such as CNN, LSTM, FFNN, BiLSTM, and transformers, as well as memory-based methods like cosine similarity, to perform sentiment classification using the word or sentence embedding techniques. This research includes training and comparing mono-lingual or cross-lingual models using social media messages in Amharic on Twitter. The study concludes that the lack of training data in the target language is not a significant issue since the training data 1) can be machine translated from other languages using machine translation as a data augmentation technique [33], or 2) cross-lingual models can capture the semantics of the target language, even when trained on another language (e.g., English). Finally, the FFNN classifier, which combined the sentence transformer and the cosine similarity method, proved to be the best option for both 3-class and 2-class sentiment classification tasks, achieving 62.0% and 82.2% accuracy, respectively.
Dual-channel interaction learning with a span-level for aspect sentiment triplet extraction

Aspect Sentiment Triple Extraction (ASTE) is a new subtask for fine-grained sentiment analysis aimed to extract the triples of aspect, opinion and sentiment terms from sentences. Previous work has extracted the target triple in an end-to-end approach, but such rely significantly on the interactions between aspect words and opinion words. Thus, they do not handle well the aspect and opinion terms containing multiple words. Recently, the span-level based models have performed well on ASTE tasks by utilizing the prediction of all possible spans. In this paper, we propose a span-level dual-channel interactive learning network for extracting target triples which uses all possible spans in a sentence as input and dual extraction of target terms using a target-aware attention mechanism to complement each other. Our model makes some contributions to both ASTE as well as ATE and OTE tasks. Experimental results show that the framework achieves better performance in extracting triples.
Autocorrelation phase retrieval function of light field for improving rendering quality

In this paper, we show an autocorrelation phase retrieval function (APRF) of light field to improve the rendering quality of novel views for light field rendering (LFR) technology. The phase of light field signal carries more abundant scene information, such as for scenes with occlusion, non-Lambertian, complex geometry and color texture. We apply the autocorrelation theory to convert the phase retrieval of the light field signal into the phase retrieval of the light field signal autocorrelation function, so as to facilitate the phase retrieval in the frequency domain. Therefore, we first derive the autocorrelation function of the light field signal and the influence of scene attributes. Based on the autocorrelation function of the light field, the phase retrieval of the light field signal is performed in the frequency domain for multiple iterations. By the phase retrieval optimization of the light field, the estimation and recovery of the scene information carried by the phase are realized, and the rendering quality of multi-view images is optimized. Additionally, to verify the effectiveness of this method, we compare the APRF with other reconstruction algorithms. The experimental results show the effectiveness of APRF in improving the reconstruction quality.
Digital signal processing techniques for image enhancement and restoration

The image processing technology is widely used in digital signal processing, biomedical engineering, and industrial automation control. In image processing, image enhancement and restoration techniques are used in many imaging applications, such as astrophotography, medical imaging camera, film production, and infrared imaging. At the same time, image enhancement and restoration technology based on both deep learning and neural network is a major research hotspot in the area of image processing. This paper reviews the related research of image enhancement and restoration technology. In terms of image enhancement, the working principle of its technology based on spatial domain and frequency domain and contrast enhancement technology, including color image contrast enhancement, are introduced. In image restoration, median filtering technology and the Lucy-Richardson (LR) algorithm are introduced, including the adaptive median filtering algorithm and the improved LR algorithm based on the Gaussian low-pass filter. Finally, the merits and demerits of these technologies are discussed, and the development prospect of image enhancement and restoration technology has prospected.
A Secure and Efficient Charitable Donation System Based on Ethereum Blockchain and Searchable Encryption

With the flourishing development of philanthropy, more and more people are actively involved in charitable activities. However, traditional charitable systems face several challenges, including low levels of information, lack of transparency in the flow of funds, lack of accountability and vulnerability to tampering. To address these issues, Ethereum provides a distributed platform that can execute smart contracts, ensuring adherence to predefined rules and mitigating the possibility of fraud and third-party interference. Blockchain, as a decentralized distributed ledger technology, stores data records across multiple computer nodes and employs cryptographic methods to ensure data security and reliability. Its main features include decentralization, immutability, transparency and traceability. However, in the construction of a secure, reliable, and third-party-independent charitable donation system, challenges associated with privacy protection and search efficiency need to be addressed. To tackle these issues, this paper proposes a solution based on the Ethereum blockchain, highlighting the role of searchable encryption methods. This solution employs searchable encryption methods, enabling search operations to perform without decrypting the ciphertext, effectively safeguarding participants’ privacy information, and enhancing search efficiency. This system is more secure, trustworthy, and efficient compared to traditional charitable donation systems.In the implementation of the proposed solution, we conduct a detailed analysis of the security of the searchable encryption scheme. We successfully counter key guessing attacks and multi-keyword selection attacks. Furthermore, we perform a security analysis of the smart contract to verify the system’s feasibility and security. Additionally, we comprehensively compare the proposed solution with existing alternatives and evaluate its performance. By introducing Ethereum blockchain technology and searchable encryption methods, we provide a secure and reliable solution for the charitable donation field, significantly enhancing transparency and efficiency in charitable endeavors. This initiative not only safeguards the interests of donors and recipients but also makes a positive contribution to the development of social public welfare activities.
Enhancing Visual Security: An Image Encryption Scheme based on Parallel Compressive Sensing and Edge Detection Embedding

Abstract In this paper, a novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform. Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher-Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decrypt.
Artificial Intelligence, Robotics And Its Applications In Green Libraries

Technologies and engineering scholars are still debating the prevalence and consequences of ICT (Information and Communications Technology) on every aspect of human life. This line of thinking is shared by experts in the field of library as well as information science/technology, who see opportunities to improve the effectiveness of librarianship via the use of AI and robots. Because of the expanding complexity of the digital environment, the rise in library use, and the urgent need for effective service delivery, this is of the highest significance in the twenty-first century. There is a pressing need to prioritise the incorporation of AI and robots in Green libraries in order to address the problems and difficulties associated with big data, overload of information, and the explosion of information. In light of the facts above, the purpose of this research is to investigate the academic discourse around the integration of AI and robotics-based technologies in knowledge institutions.
Ego-Body Pose Estimation via Ego-Head Pose Estimation

Estimating 3D human motion from an ego-centric video, which records the environment viewed from the first-person perspective with a front-facing monocular camera, is critical to applications in VR/AR. However, naively learning a mapping between egocentric videos and full-body human motions is challenging for two reasons. First, modeling this complex relationship is difficult; unlike reconstruction motion from third-person videos, the human body is often out of view of an egocentric video. Second, learning this mapping requires a large-scale, diverse dataset containing paired egocentric videos and the corresponding 3D human poses. Creating such a dataset requires meticulous instrumentation for data acquisition, and unfortunately, such a dataset does not currently exist. As such, existing works have only worked on small-scale datasets with limited motion and scene diversity (yuan20183d; yuan2019ego; luo2021dynamics).
<scp>IoT</scp> forensics: Exploiting log records from the <scp>DAHUA</scp> technology <scp>CCTV</scp> systems

CCTV surveillance systems are ubiquitous IoT appliances. Their forensic examination has proven critical for investigating crimes. DAHUA Technology is a well-known manufacturer of such products. Despite its global market share, research regarding digital forensics of DAHUA Technology CCTV systems is scarce and currently limited to extracting their video footage, overlooking the potential presence of valuable artifacts within their log records. These pieces of evidence remain unexploited by major commercial forensic software, yet they can hide vital information for an investigation. For instance, these log records document user actions, such as formatting the CCTV system's hard drive or disabling camera recording. This information can assist in attributing nefarious actions to specific users and hence can be invaluable for understanding the sequence of events related to incidents. Therefore, in this paper, several DAHUA Technology CCTV systems are thoroughly analyzed for these unexplored pieces of evidence, and their forensic value is presented.
AHFL-UAVs: adaptive loss isomorphism federated learning algorithm for UAV swarms

UAV swarms have crucial applications in modern military, geological exploration, and 5G/6G communication fields. As communication nodes, drones frequently exchange wireless data with other drones, and data privacy protection is currently one of the most urgent research topics. Based on this, this article proposes an efficient isomorphic federated learning algorithm for unmanned aerial vehicle clusters. First, set the adaptive loss differential adaptive parameter, with the initial value set to a floating point number not greater than 0.01, then activate it with the Tanh function, participate in the training as part of the loss function during the training process, and optimize it by gradient descent. When the UAV node uploads the gradient to the wingman, sum the adaptive loss differential parameter with the gradient matrix as a disturbance term. Then, based on the dynamic confidence matrix, high-quality drones are selected to participate in the gradient security aggregation of drones, achieving the selection of high-quality drones. The built-in global neural network of the drone transmits shared parameters indiscriminately to each drone through broadcast to achieve updates to the shared parameters. The comparative experiments of our algorithm on the Fashion and Cifar10 datasets show that our algorithm has higher accuracy, with the highest accuracy improvement of 4.42% on the Mnist dataset and 8.22% on the Cifar10 dataset.
A new hierarchical algorithm based on CapsGAN for imbalanced image classification

Abstract Imbalanced image datasets consist of image datasets where there is a significant disparity in the number of samples across different classes. With imbalanced image datasets, learning algorithms often tend to be biased toward the majority class samples. This leads to poor classification of minority class samples as their training is not properly conducted. It becomes more complicated when the number of samples in the minority class is very low. In this paper, a novel hierarchical algorithm is proposed for generating new data using Capsule Generative Adversarial Networks (CapsGAN) to address the class imbalance problem in imbalanced image datasets. Unlike common GAN models, the proposed method incorporates an auxiliary CapsNet to identify high‐value images in both minority and majority classes. This identification is based on the ability to detect complex relationships between low‐level and high‐level features present in capsule networks. Furthermore, the proposed CapsGAN model is conditioned to generate minority class samples based on feature vectors of last capsule layer to achieve a more balanced image dataset. For evaluating the performance of the proposed model, an image dataset called CICS was collected and introduced. Extensive experiments were also conducted using various online image datasets from different domains, with varying numbers of classes and data sizes. The experimental results demonstrated that the proposed model can generate high‐quality samples in cases where the image dataset or the number of minority class samples is relatively small. Furthermore, the proposed model has maintained an accuracy of over 80% in an imbalanced ratio of 1:60.
Maximizing reusability of learning objects through machine learning techniques

Abstract Maximizing the reusability of learning objects through machine learning techniques has significantly transformed the landscape of e-learning systems. This progress has fostered authentic resource sharing and expanded opportunities for learners to explore these materials with ease. Consequently, a pressing need arises for an efficient categorization system to organize these learning objects effectively. This study consists of two primary phases. Firstly, we extract metadata from learning objects using web exploration algorithms, specifically employing feature selection techniques to identify the most relevant features while eliminating redundant ones. This step drastically reduces the dataset’s dimensionality, enabling the creation of practical and useful models. In the second phase, we employ machine learning algorithms to categorize learning objects based on their specific forms of similarity. These algorithms are adept at accurately classifying objects by measuring their similarity using Euclidean distance metrics. To evaluate the effectiveness of learning objects through machine learning techniques, a series of experimental studies were conducted using a real-world dataset. The results of this study demonstrate that the proposed machine learning approach surpasses traditional methods, yielding promising and efficient outcomes for enhancing learning object reusability.
Human-Browser Interaction: Investigating Whether the Current Browser Application’s Design Actually Make Sense for Its Users?

As a society, we can get so engrossed in using the World Wide Web (WWW) also known as the web. We look for, read about and interact with different people, text, images, videos etc. that we often take for granted how we are doing this. We all use a web browser, we must, to get around the web. Yet, do we ever stop to give much thought to this gateway to the web? The web browser can be fittingly described as a transparent technology. This is in the sense that we don’t really stop to think about the browser and its features, we just unconsciously absorb and use it. This paper reports on a study that explores just how effectively we understand and use our web browser applications. The study was focused on people’s perceived awareness of desktop web browser functionality, what features they are using/aware of and most importantly what understanding they take from these? The findings show that the majority (69%) of the five hundred and twenty-eight participants studied do not fully understand what the padlock feature on their web browsers represents. In fact, the findings highlight that many of the participants feel that the padlock represents a safe website which it clearly does not. This paper succinctly draws attention to the fact that the current desktop web browser application design is not fit for purpose. In summary, the research pushes for more effective web browser application designs; it provides design recommendations aimed at achieving web browser consistency and creating designs that promote safety, trust, and confidence.
Research on multi-hop question answering algorithm based on knowledge graph

With the continuous increase of Internet information, the traditional single-hop query is not enough to meet the needs of users. For complex problems, multi-hop KGQA requires reasoning at multiple edges of the KG to arrive at the correct answer. KGS often lack many links, which poses additional challenges for KGQAs especially multi-jump KGQAs. In this paper, the main multi-hop question answering algorithms are divided into two categories: embedded-based multi-hop knowledge question answering reasoning and linked multi-hop knowledge question answering reasoning. The results show that the EmbedKGQA model performs better in prediction reasoning under the knowledge map with missing links by analyzing and comparing the performance of the subgraph matching and embedding prediction models on the Meta and WebQestionSP data sets. Finally, in view of the absence of knowledge graph data in practical application, we propose the development prospect of knowledge graph multi-hop algorithm from the two directions of combining pre-training with knowledge graph and using multi-modal model to expand the data in knowledge graph from multiple dimensions of the same entity.