Reviewerly/exHarmony huggingface dataset:
Improved Arabic image captioning model using feature concatenation with pre-trained word embedding

Abstract Automatic captioning of images contributes to identifying features of multimedia content and helps in the detection of interesting patterns, trends, and occurrences. English image captioning has recently made incredible progress; however, Arabic image captioning is still lagging. In the field of machine learning, Arabic image-caption generation is generally a very difficult problem. This paper presents a more accurate model for Arabic image captioning by using transformer models in both the encoder and decoder phases as feature extractors from images in the encoder phase and a pre-trained word embedding model in the decoder phase. The models are demonstrated, and all of them are implemented, trained, and tested on Arabic Flickr8k datasets. For the image feature extraction subsystem, we compared using three different individual vision models (SWIN, XCIT, and ConvNexT) with concatenation to get among them the most expressive extracted feature vector of the image, and for the caption generation lingual subsystem, which is tested by four different pre-trained language embedding models: (ARABERT, ARAELECTRA, MARBERTv2, and CamelBERT), to select from them the most accurate pre-trained language embedding model. Our experiments showed that building an Arabic image captioning system that uses a concatenation of the three transformer-based models ConvNexT combined with SWIN and XCIT as an image feature extractor, combined with the CamelBERT language embedding model produces the best results among the other combinations, having scores of 0.5980 with BLEU-1 and with ConvNexT combined with SWIN the araelectra language embedding model having a score of 0.1664 with BLEU-4 which are higher than the previously reported values of 0.443 and 0.157.
Analysing Cyber Threats: A Comprehensive Literature Review on Data-Driven Approaches

This literature review paper aims to examine and analyze the existing research on prediction models for different types of cyber-attacks. Four key research papers have been selected as the base for this review: "A Prediction Model of DoS Attack's Distribution Discrete Probability," "Apriori Viterbi Model for Prior Detection of Socio-Technical Attacks," "Cyber Attacks Prediction Model Based on Bayesian Network," and "Applying Data Science to Cybersecurity Network Attacks &amp; Events.” An overview of the value of prediction models in cybersecurity and their function in reducing potential threats come first in the review. The methodology section outlines the search strategy used to identify relevant literature and the selection criteria for the base papers. The subsequent sections provide an overview of the field, highlighting the historical development and key theories or frameworks related to cyber-attack prediction. The themes or subtopics identified in the literature are discussed, focusing on the discrete probability distribution model of DoS attacks, the Apriori Viterbi model for detecting socio-technical attacks, the Bayesian network-based prediction model, and the application of data science in analyzing network attacks and events. The review critically evaluates the selected papers, analyzing their methodologies, findings, and limitations. It identifies gaps, controversies, and conflicting findings in the literature, paving the way for further research in the field. The synthesis and interpretation section integrates the findings from the different studies, compares various perspectives, and discusses the implications and significance of the literature for cyber-attack prediction. In conclusion, this literature review paper provides a comprehensive analysis of prediction models for cyber-attacks, based on the selected base papers. It highlights the strengths and weaknesses of existing approaches, identifies research gaps, and offers recommendations for future studies. This review contributes to the advancement of knowledge in the field of cybersecurity and aids in the development of more effective prediction models to combat evolving cyber threats.
Extraction of Character from Visuals and Images Using OpenCV

To develop a computer vision system that can accurately extract characters from document pages and image data using the OpenCV library. The system is designed to process a wide range of visual inputs and extract characters with high precision and efficiency. The techniques used to implement the character extraction system, including pre-processing, feature extraction, and classification. The performance of the system is evaluated using a dataset of visual image data from a different type of visual inputs and the output of the system can be accurately extracting characters. In Future, Automated conversion of an image input into a machine-readable file.
Physiotherapeutic Aid using ML and AI

In the recent year 2020 due to corona many people were not able to have appointments with their doctors and they were locked in their house. The younger generation can possibly maintain their health even if they do not go outside for days, but the older generation have to do regular exercise to maintain their health and well-being; prescribed by the doctors. It is not just because of corona that we have experienced this but we have also come to a situation where older people cannot visit their doctors. So, as a solution for these problems/challenges, we came up with an idea so that they can do their exercise and maintain their health without visiting their doctors or physiotherapist. Artificial Intelligence is technically defined as the development of technology which is used to perform technology operations which require involvement of human intelligence. Machine learning is one of the key components of artificial intelligence and it provides us with the ability of both supervised and unsupervised learning for training our model. AI technology today can be in different forms such as software programs as well as hardware interfaces to develop a system which is capable of learning from their own datasets. In our project AI with machine learning can be used for posture detection and then assessment of patients. We provided physiotherapy using AI and ML. We used normal running feed and we got good frame rates. These older generations have to do live exercise in front of the camera, our software will detect their pose/position whether they are doing their exercises correctly or not. By comparing the poses obtained from the live feed to the images or the videos obtained from the dataset. If the pose is not matched to the dataset, it is terminated or denied and deemed as wrong exercise. Our software is going to tell them where they went wrong. Now we have to decide a perfect algorithm/method to detect/estimate the pose with much higher accuracy. We are going to compare all the algorithms present till date regarding pose estimation and select the algorithms which give best accuracy.
A Study on Teacher Information System

Government agencies hire a considerable number of teachers each year and assign them to various government schools. The need to maintain their information increases as the number of teachers rises. We are developing an app that will track teachers' employment information. In order to do this, the user app for teachers will have sections for accounts, education, careers, expertise, and more. A check-in option for attendance is also available. Since everyone has a smartphone these days, teachers will find it simpler to use the application. The admin side is a web portal. The administrator will verify the files and data that the teacher uploads and will maintain the validated data in the database acquired from the user app. If the information turns out to be incorrect, the administrator may take additional steps to reject it. This system will help reduce the amount of manual work currently needed.
A Discriminative Image Registration Model with Hybrid Feature Detector

Image registration is a manner of aligning two or more images of the same scene which achieves maximum accuracy. Analysis of geometric distortion, caused by the difference in viewing angle, sensor resolution and distance. The critical step in image registration is the collection of feature points and evaluate a spatial transformation mainly when outliers are present. In our proposed framework we have used the hybrid feature-based and area-based methods together. Before applying those methods, the Bilateral Filter is applied for the preprocessing of both the images, the reference image and the image that needs to be registered in order to make the images noise free. For feature detection, the ORB (Oriented FAST and Rotated BRIEF) is used which is basically a fusion of FAST key point detector and BRIEF descriptor with many modifications to enhance the performance. KNN (K-Nearest Neighbors) is used for matching similar points and for reducing the miss matches the efficient algorithm is used which is Random Sample Consensus (RANSAC). The template matching method is applied for the area-based matching.
Music theoretical approach to auditory interface design: progressive, explainable, and accessible

Music activities offer individuals an opportunity to engage in various cognitive interactions with sound, a phenomenon which has been studied from music theoretical perspectives. This cognitive trait is not limited to the arts but also extends to the development of auditory interfaces for everyday products. However, uncertainties still exist regarding the current theoretical framework to move beyond abstract concepts and towards practical design methodologies. To address this issue, my doctoral research focuses on exploring a method for sound production that employs tonal music theories, creating an accessible and explainable system for auditory interface design. This approach will result in a systematic methodology covering sound progression that can respond to user scenarios, allowing designers and researchers to better describe their design and analysis. Ultimately, this research aims to enhance the end-user experience by making sound design more accessible to a broader people, opening the door to diverse and logical sound creation.
Transforming Healthcare Infrastructure for Enhanced Energy Efficiency and Privacy

The Internet of Medical Things (IoMT) is a revolutionary technique for integrating the IT infrastructure of healthcare organisations with medical apps and equipment. Rapid advancements in this approach in recent years have resulted in game-changing improvements in the healthcare system, illness management, and patient care standards. Both achievements have been made possible by the Internet of Medical Things. People can use the IoMT to access a variety of cloud-based services, including file sharing, patient monitoring, data collection, information gathering, and hospital cleaning. Wireless sensor networks (WSNs), which collect and transmit data, are critical to system operation. In the healthcare system, patients’ privacy and security must be preserved at all costs. Wireless data transmission from these cutting-edge devices may have been intercepted and manipulated without consent. The hybrid and improved (Elliptic Curve Cryptography ECC) Energy-Efficient Routing Protocol (EERP) method, which is based on the elliptic curve encryption protocol, may provide enough protection for sensitive information. ECC-EERP uses pairs of public and private keys known only to each other to decode and encrypt data delivered across a network. As a result, the energy needed to sustain WSNs has dropped. To assess the efficacy of the recommended plan, we did an extensive study and compared our findings to the many other viable courses of action. We did the analysis while taking a variety of aspects into account. The study's findings and conclusion all point to the strategy's ability to significantly increase energy efficiency and security. ECC-EERP is a novel encryption method that increases data security while consuming less energy. Because of its efficacy in improving the whole healthcare system, this strategy has a lot of potential for the future of patient care, illness management, and healthcare delivery in general.
Fluid Transformers and Creative Analogies: Exploring Large Language Models’ Capacity for Augmenting Cross-Domain Analogical Creativity

Cross-domain analogical reasoning is a core creative ability that can be challenging for humans. Recent work has shown some proofs-of-concept of Large language Models’ (LLMs) ability to generate cross-domain analogies. However, the reliability and potential usefulness of this capacity for augmenting human creative work has received little systematic exploration. In this paper, we systematically explore LLMs capacity to augment cross-domain analogical reasoning. Across three studies, we found: 1) LLM-generated cross-domain analogies were frequently judged as helpful in the context of a problem reformulation task (median 4 out of 5 helpfulness rating), and frequently (∼ 80% of cases) led to observable changes in problem formulations, and 2) there was an upper bound of ∼ 25% of outputs being rated as potentially harmful, with a majority due to potentially upsetting content, rather than biased or toxic content. These results demonstrate the potential utility — and risks — of LLMs for augmenting cross-domain analogical creativity.
A Walk in Nature: Exploring the Creative Potentials of a Generative Design Tool for Soft Robotic Surfaces that Foster a Connection with Nature.

We are developing soft robotic surfaces to support emotional wellbeing for people with limited access to nature. This work explores how biophilic design (based on the theory that humans have an evolutionary desire to connect with nature) can be applied to interactive systems. We describe a generative design tool that produces a bio-inspired digital prototype of a soft robotic surface. In our workflow, participants walked outdoors to photograph natural features in their environment. The photographs are used to generate a set of corresponding designs. As early validation, we conducted a small user study to test the robustness of our tool with different photographic inputs. We also collected feedback on how the workflow influenced participants’ perceptions of the design outcomes. The results guided the fabrication of a new physical prototype and demonstrated the utility of our approach, which aims to inform the design of future biophilic cyber-physical systems.
Audible Imagery: Creative Contemplations on the Sounds of Home

This pictorial paper explores domestic sounds’ capacity to evoke imagery of everyday inhabited spaces. In recent decades, accelerated digitalization has been transforming our everyday sonic environment, and new interactive possibilities, such as multimodal and embodied interaction, draw attention to sound as a communicative medium. In that context, how can artificial product sounds integrate into the present ecology heard at home?
Supporting Data Visualization Literacy through Embodied Interactions

Data visualization literacy (DVL) is increasingly important in navigating today’s world. Young students are now required to develop data visualization skills (e.g., identifying patterns, collecting, organizing, and analyzing data). Past work focuses on cultivating children’s DVL through playful and gamified approaches. However, these past solutions require hardware (e.g., smartphones) that is not readily replicable within standard classrooms. In addition, the extent of embodied interactions found in past solutions is fairly limited to large-scale interfaces. To address these challenges, we explore how to cultivate children’s DVL through embodied learning that is more apt for a classroom environment. Using a standard Google Chromebook, we designed an interface that utilizes the tracking of a fiducial marker on a wooden arrow to allow a student to author their own bar graphs. We conducted Wizard of Oz tests (n = 3) to gather feedback on the functionality of our design. This project aims to display the benefits of giving students a mode to develop their own visualizations and broaden how they can interact with data.
Revealing the Tuning Practices of Creative Learning Experience Designers

We leverage the visual format and affordances of pictorials to make visible and call attention to the unseen design work of educators and designers of creative learning experiences in making and tinkering spaces. We document the process of design and iteration that took place as researchers and educators co-designed a learning experience focused on the intersection of artmaking, circuitry, and computing for participants in a public library makerspace. We present this visual case study as provocations for interaction designers, educators, and researchers who aim to engage people in creative learning experiences with technologies.
Cogcues: Shifting Perception through Interactive Projected Cues in Still Life Drawing

The ability to visually discern shape, form, and value is fundamental to observational drawing. However, developing this skill requires a drawer to perceive a “raw” version of the scene being drawn, often referred to as regaining the innocence of the eye. This work investigates how interactive projected light cues can be used to alter the perception of drawing objects and understand how users might control their own perception. We introduce an augmented reality system capable of dynamically projecting interactive light cues onto objects within a live drawing studio. We present the design of three cues that address challenging percepts for novice drawers: gauging proportion, discerning shape, and shifting visual attention. In a formal user study with novice and intermediate drawers, we evaluate the effectiveness of these cues in supporting observational drawing. We demonstrate how cues can be designed to correct subconscious errors and visually guide users in learning to draw.
Latent Organism: Embodied Co-Creation with AI

By leveraging recent generative models, Latent Organism suggests a new method for creating 3D objects through tangible interaction. Our artifact enables anyone to create complex and organic 3D shapes using an understandable and sensitive tactile interface. This process of cooperative creation between humans and machines empowers individuals to develop a craftsmanship of artificial imagination modeling. The machine was fed thousands of images of living beings so as to learn a representation of the concept of organic flesh. Then, one can go around in this endless realm of possibility. By employing the machine’s imagination as clay, the subject becomes an active explorer of the latent space.
Gridfinite Possibilities: the Illuminated Button Matrix as Input and Output Device

Gridfinite Possibilities explores the interface of an illuminated 8x8 button grid as simultaneous input and output device. Individually addressable LEDs under a silicone cube button pad serve as a platform for creativity in 8x8 resolution. The demonstration uses a set of RFID cards to allow the user to reconfigure the device’s button programming mode to play a variety of classic grid-based logic and low-resolution games on the system. This paper reviews existing button matrix devices and considers theory of button pushing and remapping. Initial user feedback aligns with a proposal for further study on user cognition in reaction to reassigned buttons on a control device.
Designing Blended Experiences: Laugh Traders

Digital transformation is increasingly blurring the line between what is software and what is the world, requiring designers to harmoniously blend digital and physical products, services and spaces if they want to orchestrate meaningful experiences that are specifically aimed at the interweaving relationships between people, places and things. Traditional approaches to product design, interaction design, and user experience design do not often take this new context into account. The pictorial details the results of a twelve-day workshop focusing on real-world audience and performer problems during the Edinburgh Festival Fringe: it illustrates how two distinct tools, the Blended Experiences Tool and the Evaluation Tool, focusing on the creation of a blended experience and respectively meant to provide a structured way to approach the generative and reflective stages of the design process, can be used to address this gap. This pictorial illustrates the theoretical framing supporting the Blended Experiences Tool; describes how the workshop produced Laugh Traders, a speculative experience centering on attending and reviewing comedy shows; provides a page-by-page pictorial storyboard of the Laugh Trader experience; introduces the Evaluation Tool and applies it to Laugh Trader to measure the relevance, complexity, and attractiveness of the resulting blended experience. Preliminary reflections conclude the pictorial.
Supporting Exploration of Far-Domain Analogical Inspirations with Bridging Analogies

Far domain analogies — relational comparisons between topics that seem very different on the surface, such as the solar system and an atom — can be a powerful source of new ideas. However, people struggle to benefit from them. We explore how bridging analogies, analogies that bridge between a knowledge anchor that is familiar to the problem solver, and a target analogy, can help innovators to benefit from far domain analogies. Utilizing a breadth first search for shortest paths between concepts on Wikipedia, we identified potential bridging analogies that connect a participant’s knowledge anchor to a far domain analogy. In a think aloud study, participants brainstormed on three design challenges, with only far analogies, or far analogies and bridging analogies tailored to their knowledge anchors. We observed that bridging analogies aided participants in producing more abstract solutions vs. more direct translation of the analogies in their solution.
Participatory Design Tools: Leveraging Materiality and Familiarity to Adapt Unconventional Materials into Design Tools

As participatory design approaches proliferate into mainstream industry practice, it becomes important for designers to consider how design tools, methods and practices can be made more accessible for non-designers — fostering participatory mindsets. We explore how materiality and familiarity of design tools impact participation, by investigating an off-the-shelf, familiar material — the Rubik's Cube, for application possibilities as an unconventional design tool. We report on a series of workshops with design practitioners and educators, that find disassociating design methods from traditional materials, opens new possibilities for methodological innovations, while revealing promising avenues to improve participation. We categorise findings into three themes: material properties, staging considerations, and emergent practices. This is supplemented by two Illustrative examples of emergent applications of using Rubik's Cubes to augment (1) de Bono's random input method; and (2) creating dynamic persona states. The emergent applications present new material possibilities enabling design methods to innovate and evolve.
Collaboration on the Tracks: Ethnographically-Informed Design for Computer-Assisted Music Collaboration between Producers and Performers

We suggest that more attention should be given to how musicians collaborate in the studio when designing Digital Audio Workstations (DAWs). We conducted an ethnography in a home studio, gathering three hours of video recordings over four consecutive days of a pro-amateur performer-producer duo during the writing of their debut album. Focusing on how performers and producers structure their interactions around the audio playback of the DAW whilst composing, this paper analyses how their relationship to the software might change their interactions, towards establishing common ground in conversation. Field notes are analysed, and a design brief is suggested where control of the playhead is given to the performer, potentially allowing them to use the audio playback within their utterances to help reference specific audio sections, even when they are not near to the DAW.