Reviewerly/exHarmony huggingface dataset:
Practices of Luganda Language Educators in Using Emerging Technologies at the School of Education, Makerere University

The study explored practices of Luganda language educators at the School of Education, Makerere University in using emerging technologies.The study analyzed the practices of Luganda language educators in using emerging technologies; identified the emerging technologies used in Luganda language instruction; and established the factors influencing the practices of Luganda language educators in emerging technologies.The study paradigm was interpretivism with a case design.The study participants were purposively selected Luganda language teacher educators and conveniently sampled students specializing in Luganda Language as one of the teaching subjects.Data were collected using interview guide, focused group discussion guide and observation check list and analyzed using thematic content data analysis technique.Findings revealed that Luganda language educators practiced use of emerging technologies, such as Zoom and Google meet apps, MUELE platform, mails, Telegrams, WhatsApp, laptops, smart phones as well as computers, among others for lectures through sharing learning material with learners, providing feedback, conducting online assessment, and conducting lectures via the online platforms.The study concluded that Luganda teacher educators practiced emerging technologies to teach, send materials, course works, and submission of course works and assessment and grading of learners.Luganda teacher educators practiced emerging technologies through preparation for teaching, selection of Luganda content and teaching aids.The study concluded that the most practiced emerging technologies during Luganda language teaching were the moodle system in form of MUELE, video conferencing, Zoom, and Google meet and WhatsApp, Facebook, and Youtube, among others.The study also concluded that the factors influencing practices of Luganda language teachers were; favorable University policy, availability of electric power, internet connectivity, availability of merging technological tools such as smart mobile phones, pads, laptops and or computers, among others, as well as use of offline platforms, such as distributing class notes via CDs and flash disks for use on online computers.From the study findings, the study recommends that Luganda language educators should adopt, apply and maintain excellent practices with use of the emerging technologies.This could be done through encouragement of online lectures via platforms such as MUELE; they should also continuously upload academic material on online platforms such as Whatsapp, hold academic engagements / sessions via google meet, conduct online assessment and provide feedback to learners online.Such practices will not only improve their skills with emerging technologies, but shall also deepen their knowledge as well as shape a positive attitude and adoption of emerging technologies for effective Luganda language teaching and learning.
Fusion-Ann: A Novel Feature-Independent Parametric Artificial Neural Network for Creative 3d Boundary Representation Modelling

In the era of competitive digital innovation, product manufacturing companies need rapid customisation and ability to create uniqueness in new product development to stay competitive in the consumer market. Till recent times, this requirement heavily depended on CAD designer’s ability and experience to produce creative designs. This paper presents a novel feature-independent CAD design/modelling approach combined with a neural network that enables the creation of random parametric 3D CAD design variants using the Boundary Representation (B-Rep) method. This method is rapid and thus offers about 10 to 100 concept alternatives to the client in 10 to 30 minutes. Additionally, the paper also highlights the suitability of the proposed autoencoding method in creating 3D deep learning datasets to train generative design models like 3D GANs to further enhance 3D designs specifically targeted in product design problems.
A Fuzzy Navigation Approach Using the Intelligent Lights Algorithm for an Autonomous Mobile Robot

In recent years, research toward autonomous vehicles has undergone unparalleled growth. In addition, the ongoing attempt by corporations to lessen the energy consumption of their vehicles is meant to render them more economical for drivers. Our article discusses the problem of autonomous navigation of mobile robots (vehicles) in a common environment using a combination of fuzzy logic and intelligent lights. A mobile robot can be defined as an instrument managed by software. The latter applies all sorts of technologies to recognize and wander in the environment in which it was placed Mobile robots are the result of the perfect mixture of artificial intelligence (AI) and physical robotic components. Now the main problems are navigation and positioning. The first can be interpreted as directing a robot from the departure point to the arrival. The second is a closely related discipline that offers a way to display the most optimal path from its current position to the goal destination. As it may appear, both of the aforementioned are of quite an importance to vehicle navigation systems. The means used for navigation are the same since man first sailed the oceans, nonetheless, the equipment has developed throughout the ages. Nowadays, navigational techniques undergo a massive impact from the improvement in network and communications technology, especially for vehicle navigation. We combined the intelligent lights with the fuzzy logic to create an acceptable solution for the autonomous mobile robot navigation problem the results were discussed and compared to those given by similar algorithms.
Deep-Learning Methods for the Classification of Normal and Pathological Blood Cells and Bone-Marrow Cells: A Comprehensive Review

Our paper comprises a state-of-art study of the Deep-Learning methods for medical imaging and more precisely classification of blood cells based on microscopic images. The Blood composition is considered complex and diverse; however, it remains a pertinent factor in diagnosing a patient’s health. Detecting all blood disorders without Artificial Intelligence necessities years of training and experience. As it takes a lot of time to analyse blood samples, making it harder for modern laboratories to meet high demand. In this context, numerous researches were run to implement AI systems that may assist pathologists and biologists in blood analysis. To date, these provided solutions didn’t meet all the needs of researchers regarding the classification of normal and pathological blood cells. Firstly, these proposed solutions only treat and target specific issues, and each solution has been integrated using a microscope that is dedicated entirely to that specific task. Therefore, a more flexible and general classification system must be implemented. Secondly, smart microscopes are expensive which makes them hard to purchase. In this paper, we cover the proposed methods and models related our context, as well as the data sets that best fit it using Artificial Intelligence and Deep-Learning methods.
Vision Sensor Surveillance for Online Stationary Object Detection Based on Basic Sequential Clustering

Maintaining security in public, crowded areas is becoming a key priority with the new terrorist threats. To this end, important studies on computer support for the field of special event detection are proposed to support human-assisted camera surveillance. This study introduces a vision sensor surveillance system that detects and localizes stationary objects (such as abandoned luggage) with their dwell time. The proposed method starts with the classification of pixels of each position of the current frame using a basic sequential clustering as a first step. Then newly appearing clusters, with continuous update, are marked as stationary sites for the stationary mask. The latter is improved in the second step by the MRF framework (Markov Random Field). A stationary event alarm is then signaled for this object if it keeps its shape unchanged until a timeout. The method shows interesting results in stationary baggage detection in real time.
A New Collective Simulated Annealing with Adapted Objective Function for Web Service Selection

The continual increase of web services on the global web has created new challenges for both academicians and industrials. Indeed, the existence of similar web services from a functional point of view but different from a Quality of Service (QoS) point of view requires using optimization techniques in order to retain the best compositions of the requested services. In order to contribute for solving such a problem, we propose in this paper a collective version of simulated annealing algorithm with adapted objective function for the computing of the optimal service composition. Such collective version allows to speed-up the algorithm without losing in its exploration and exploitation capabilities. Furthermore the expressions of the introduced objective function allow obtaining the best selection of web services that meet the customer's requirements. The results obtained by using service web data from the literature are very satisfactory and encourage enhancing the current work in the future research.
A Paring-Free ID-Based Authenticated Key Agreement Protocol for IoT Environment

The Internet of Things (IoT) has become a growing technology now a days. IoT security aspect needs an authenticated session key in view of the communication among IoT nodes. In result, numerous authenticated key agreement protocols have been propounded for IoT environment. However, these protocols failed to deliver adequate security and their efficiency is comparatively low as well due to the presence of various expensive operations like bilinear pairing, etc. In addition, these protocols impose many communications rounds to compute the session key, resulting in greater message latency. As a result, we motivate to introduce an ID-based authenticated key agreement scheme without pairing which is suitable for the IoT environment due to its lightweight nature. The proposed scheme overcomes the security issues of existing state-of-the-art protocols. The security analysis shows the robustness of the proposed protocol and makes it appropriate for node-to-node communication in the IoT environment.
Emotion Analysis of Social Media Content for Mental Health Improvement

Natural language processing is mostly used to assess the author’s mood based on the context. The purpose of sentiment analysis (SA) is to discover the exactness of the underlying emotion in a situation. It has been applied in a variety of fields, including stock market predictions, social media data on product evaluations, psychology, judiciary, forecasting, illness prediction, agriculture, and more. Numerous researchers have worked on these topics and generated substantial findings. These outcomes are useful in their domains because they help people comprehend the general summary in a short amount of time. SA aids in limiting the harmful effect of some posts on various social media sites such as Facebook, Twitter, and others. The major goal of this comprehensive research work is to examine our proposed solution that we have been conducting and evaluate some of the most important research that had been done previously and summarize SA models in emotion AI-driven SA.
Effectiveness of zero-shot models in automatic Arabic Poem generation

Text generation is one of the most challenging applications in artificial intelligence and natural language processing. In recent years, text generation has gotten much attention thanks to the advances in deep learning and language modeling approaches. However, writing poetry is a challenging activity for humans that necessitates creativity and a high level of linguistic ability. Therefore, automatic poem generation is an important research issue that has piqued the interest of the Natural Language Processing (NLP) community. Several researchers have examined automatic poem generation using deep learning approaches, but little has focused on Arabic poetry. In this work, we exhibit how we utilize various GPT-2 and GPT-3 models to automatically generate Arabic poems. BLEU scores and human evaluation are used to evaluate the results of four GPT-based models. Both BLEU scores and human evaluations indicate that fine-tuned GPT-2 outperforms GPT-3 and fine-tuned GPT-3 models, with GPT-3 model having the lowest value in terms of Poeticness. To the best of the authors' knowledge, this work is the first in literature that employs and fine-tunes GPT-3 to generate Arabic poems.
Statement of Retraction: QFFT quantum circuits for exact solutions of the black-hole singularity-mass schrödinger equations on quantum kerr- (A) ds galilean myers– perr driven gravitational transformations in a lorentzian path integral for the anti-COVID- 19 roccuffirnatm, roccuttirnaTM, and eplerotiffirnaTM drug designs

This article refers to:RETRACTED ARTICLE: QFFT quantum circuits for exact solutions of the black-hole singularity-mass schrödinger equations on quantum kerr- (A) ds galilean myers– perr driven gravitational transformations in a lorentzian path integral for the anti-COVID-19 roccuffirnatm, roccuttirnaTM, and eplerotiffirnaTM drug designs
A Hybrid Human-Machine System for Image-Based Multi-weather Detection

Accurate determination of weather from images or video is of prime significance in applications such as autonomous vehicles drive or atmospheric pollution estimation. In contrast to image classification approaches generally adopted in the literature based on neural networks acting directly on the input, we propose a novel joint learning approach combining features extracted from the input image, sensitive to the human visual system (HVS) and those generated by the CNN model trained on benchmark datasets. The features work in a joint collaboration that is able to detect the presence of weather features during learning. The novel approach outperforms many state-of-the-art methods which use only CNN-based extracted features in order to classify the images. Experimental results with publicly available benchmark datasets establish the robustness and effectiveness of the proposed method in multi-weather classification.
M3T: Multi-class Multi-instance Multi-view Object Tracking for Embodied AI Tasks

In this paper, we propose an extended multiple object tracking (MOT) task definition for embodied AI visual exploration research task - multi-class, multi-instance and multi-view object tracking (M3T). The aim of the proposed M3T task is to identify the unique number of objects in the environment, observed on the agent’s way, and visible from far or close view, from different angles or visible only partially. Classic MOT algorithms are not applicable for the M3T task, as they typically target moving single-class multiple object instances in one video and track objects, visible from only one angle or camera viewpoint. Thus, we present the M3T-Round algorithm designed for a simple scenario, where an agent takes 12 image frames, while rotating 360° from the initial position in a scene. We, first, detect each object in all image frames and then track objects (without any training), using cosine similarity metric for association of object tracks. The detector part of our M3T-Round algorithm is compatible with the baseline YOLOv4 algorithm [1] in terms of detection accuracy: a 5.26 point improvement in AP75. The tracker part of our M3T-Round algorithm shows a 4.6 point improvement in HOTA over GMOTv2 algorithm [2], a recent, high-performance tracking method. Moreover, we have collected a new challenging tracking dataset from AI2-Thor [3] simulator for training and evaluation of the proposed M3T-Round algorithm.
Medical VQA: MixUp Helps Keeping it Simple

Recently, Medical Visual Question Answering (VQA) became an active area of research with the induction of several publicly available benchmark datasets and the organization of challenges. Like many competitions, the quest for success has driven the use of increasingly complex neural networks. Winning strategies generally leverage multi-scale architectures and model ensembling to achieve state-of-the-art performance. However, several studies have established the capability of simpler architectures in learning more meaningful features and avoiding over-parameterization. Specifically, the use of MixUp based image augmentation with a simple VGG16 network helped achieve significant improvement in performance for medical VQA. Inspired by this finding, we propose a modified version, VQAMixUp, that leverages both images and questions for augmenting VQA datasets. VQAMixUp combined with a few enhanced training strategies help simple models (with $$\approx 65$$ % reduced parameters) achieve state-of-the-performance on benchmark ImageCLEF-VQA-MED validation datasets.
TTF-ST: Diversified Text to Face Image Generation Using Best-Match Search and Latent Vector Transformation

Text to face generation is a multimodal task which involves generation of facial images of people from the textual description of their faces. It requires joint modeling of the language space and the image space. This task is less studied due to numerous problems, some of which are - high diversity and abstractness in descriptions of human facial features, lack of datasets, and difficulty in independent manipulation of discrete facial features in the image space to produce the desired, high quality human face images. In our work, we attempt to resolve some of the aforementioned challenges that exist in the context of the task. This paper presents the Text-to-Face Search and Transform model, TTF-ST for short, which introduces a novel technique of searching and selecting images from a custom dataset that best match the encoded textual description and subsequently transforming their latent vectors to enhance the accuracy of the generated images. The methodology proposed in this paper also allows for the usage of complex and diversified textual descriptions, while generating images of high resolution of 1024 × 1024 pixels. A new metric is introduced to allow for a more accurate evaluation of the model's performance by accounting for only those facial attributes specified in the description. Extensive experiments and comparisons with the current models demonstrate that the proposed method achieves significant improvements in accuracy while generating diverse high-quality photo-realistic images.
Determining Realism of Procedurally Generated City Road Networks

Cities are both expansive and relatively homogeneous, making them well suited to procedural generation. Road networks are one of the most important components of generated cities, and rely on both exogenous and endogenous factors such as the environment and population. These factors significantly affect the characteristics of the generated city, and thus the evaluation of the realism or plausibility of the generated city is a non-trivial task. Previous work largely relied of subjective assessments of realism. In this paper, we present an extensible approach for procedurally generating city road networks, utilizing real world data and intended to mimic the characteristics of a real city. We also present a set of metrics such as entropy and orientation order suitable for objectively assessing the generated road network in comparison to the real city it is mimicking. We find that employing these metrics can provide a reasonable baseline for future projects exploring realism in procedural city generation.
Reinterpreting Technological Language

This study analysed the use of Facebook emojis to construct meanings of izaci namaqhalo esiXhosa (isiXhosa proverbs and idioms), and the use of Facebook emojis by isiXhosa Facebook subscribers in creating new meanings of isiXhosa expressions. The snowball sampling technique was used to select Facebook posts related to the reinterpretation of Facebook emojis to create meanings of izaci namaqhalo esiXhosa from the period of October 4, 2019 to January 28, 2020. To analyse the data, the study used thematic analysis, where two primary themes emerge: (1) the reinterpretation of Facebook emojis to create meanings of izaci namaqhalo esiXhosa and (2) reinterpretation of Facebook emojis to create new meanings of isiXhosa expressions. Findings show that in some languages, especially Nguni languages, social media users have reinterpreted these emojis by adopting them to their languages and giving them new meanings. Furthermore, because of their visualisation, findings also revealed that proverbs and idioms, especially in isiXhosa language, have been constructed using some of these emojis. Therefore, these are interesting findings, especially after the introduction of the fourth industrial revolution, and the fact that it adds to the big question about the future of the human language, particularly indigenous languages.
Designing camouflage scheme by modeling 3D virtual battlefield environment

A camouflage design strategy based on battlefield environment twinning is proposed to address the camouflage concealment of military equipment in a realistic battlefield environment. The strategy is based on the construction of a three-dimensional battlefield environment digital twin model, and by synthesizing the digital camouflage based on the background primary color and natural camouflage decoration, a camouflage design scheme with a higher camouflage success rate can be generated to fit the realistic battlefield environment. A neural network-based image segmentation network and a target detection network are used to evaluate the performance of the camouflage design scheme. The experiment results show that the camouflage design strategy proposed in this paper has better interference and confrontation with detection technology, and it can provide the most adaptable camouflage design scheme for the target object in the real battlefield environment, which has strong practical value and strategic significance.
Research on knowledge extraction method for government affairs

With the increasing complexity of urban scale and social structure, the phenomenon of "data island", repeated data collection, difficult sharing and other problems occur frequently in government data. This paper studies the knowledge extraction technology in knowledge graph to provide data support for the subsequent construction of knowledge graph of government affairs. Knowledge extraction includes two sub-tasks: named entity recognition and relation extraction. Machine learning method is used to extract entities and relations, and the knowledge graph of government affairs is constructed based on the extracted entity and relation data. Building a good government affairs knowledge graph plays a crucial role in mining the connections between different types of government affairs entities, and it is also of great significance for information extraction in the following application fields.
Research on expression recognition based on deep learning in classroom learning situation analysis

With the rapid development of artificial intelligence technology, the automatic recognition of students' learning state and emotion by target detection and expression recognition technology has attracted more and more attention. In order to solve the problem that the detection accuracy and speed can not be considered in the application, an intelligent lightweight classroom learning situation analysis solution is proposed. Firstly, face is recognized by face detection. By designing a small convolution kernel for continuous convolution and extracting the key feature points of the face in parallel, five kinds of learning expression intensity are output. Finally, the learning emotion score of the weighted sum of the students' overall head up rate obtained by face detection and the expression intensity obtained by expression recognition is used as the evaluation result of learning emotion analysis.