Reviewerly/exHarmony huggingface dataset:
Understanding the Challenges of Addressing the Digital Divide through Telecentres

Constituency Innovation Hubs (CIHs) is a project by the Kenyan government to establish digital hubs in each of its constituencies to promote digital literacy and to bridge the digital divide between the urban and rural areas. Each CIH provides digital equipment with free IT and Internet access for the local community and form a central part of the government's agenda to transform Kenya into an information society, preparing its citizens for a digital economy. The main emphasis has been on implementation of the hubs with little consideration for their ongoing maintenance and support, leading many of them to underperform or fail. The aim of this research was to investigate the challenges associated with a CIH and explore the reasons behind this with a view to proposing a more sustainable approach. The study adopted a case study approach focusing on a typical rural CIH in Ndhiwa. Using a qualitative approach, interviews were conducted with the local community (staff, users, and non-users of the CIH; n=53). Thematic analysis applied to the resulting data identified several key challenges: infrastructure, lack of knowledge, understanding, motivation and trust among users, and inadequate levels of tutor and technical support. This study recommends it is critical to work in partnership with each community to ensure the CIH is sustainable and meets that community's needs.
Visualizations and Filtering to Help People Find their Path

Constraint satisfaction problems (CSPs) are problems with a set of variables that have constraints and the goal is to assign a value to each of the variables so the constraints are satisfied. CSPs are found in many areas and domains of computer science. When given a single solution out of the solution space from a general-purpose solver, users may want to further explore other alternatives to find ones that better meet their aims or to verify the believability of the existing result. However, users attempting to examine the solution space are often confronted with a state explosion problem, leaving them with the unrealistic task of manually sifting through each state. We aimed to assist users in exploring state spaces more efficiently. Using the concrete semantics of requirements modeling in the Evolving Intentions framework, we contribute two visualization techniques: (1) valuation-based coloring to assist users in interpreting the results of the path-based analysis and selecting states from the solution space more efficiently, and (2) valuation-based filtering to reduce the number of states in the solution space. Using a motivating example, we demonstrate the need for state space visualization and we provide an initial validation of our results. These visualization techniques can be used beyond the Evolving Intentions framework and adapted to other areas in software engineering.
Coding as a Self-Expression Tool

This lighting talk discusses the potential for using coding as a new means of self-expression. Coding has been identified as a key 21st-century skill, and as such, it is expected to be vastly acquired already in the early ages of formal education. Coding is a language that adheres to a system of symbols and rules, which, when combined, convey meaning. This postulation has led a few scholars to sustain that coding is a language functioning like any other language. Thus, coding might enable its coders to produce expressive artifacts, similarly to any other natural language, potentially offering a new form of creative writing in the 21st century. A celebrated use of creative writing is in the field of emotional therapy. Writing has been proven an efficient tool in both clinical and non-clinical populations. Nevertheless, despite its effectiveness, some populations are deprived of the great benefits creative writing may enable. Among these are children who do not know how to read and write, as well as adults who struggle with expressive writing. Therefore, we suggest a way for expanding the boundaries of coding to the field of self-expressive writing. We believe that apart from promoting its acquisition as an essential tool in the 21st century, knowing coding at an early age may also endorse general well-being, offering a new, somewhat more reachable, means of self-expression.
Designing, Deploying, and Analyzing Adaptive Educational Field Experiments

Digital experiments can be used in CSedu to test hypotheses about interventions and conditions' efficacy (or inefficacy). This workshop will discuss and deconstruct the design process and analysis for various experiments conducted in CS1. E.g., experiments testing which explanations students find helpful, which emails get them to start homework early, or which webpages effectively encourage and motivate students. This workshop teaches participants how to conduct, interpret, and analyze adaptive field experiments. These adaptive experiments employ machine learning algorithms to analyze experiments during deployment and dynamically shift the allocation of arms/conditions to give future students better conditions more rapidly. Adaptive field experiments can accelerate scientific discovery by enabling more complex experimental designs and increasing statistical power by phasing conditions in and out more efficiently. The workshop is supported by a 5-year NSF grant to build software tools and a digital community, gathering instructors, domain scientists and methodologists to teach them how to run adaptive experiments. The methodological focus includes understanding: (1) which algorithms are best for adaptive experiments that meet domain scientists' needs in specific experimental designs and data sets; (2) which hypothesis tests and Bayesian analyses to choose. Software companies use these innovative methodologies extensively to continuously improve product design. This workshop demonstrates how the same methods can be used in CSedu to improve research rigor and accelerate educational research implementation, ultimately improving student outcomes.
Exploring AI Techniques on Game of Thrones

This research examines a two-player combinatorial game, Game of Thrones: Hand of the King, and applies several different techniques to develop an effective AI player for this game. We used several approaches including simple game state analysis, heuristic search, minimax tree search, and Monte Carlo tree search. A game framework that supports random board generation and multi-game tournaments was used to test different players against each other. Our results show that a hybrid player using both minimax tree search and Monte Carlo tree search is effective at winning against a variety of other AI players.
Reinforcement Learning Algorithms for Effective Resource Management in Cloud Computing

Cloud has gained enormous significance today due to its computing abilities and service-oriented paradigm. The end-users opt for the cloud to execute their tasks rather than on their local machines or servers. To provide the best results, the cloud uses resource scheduling algorithms to process the tasks on the cloud Virtual Machines (VM). The cloud experiences several inefficiencies and damages due to inappropriate resource scheduling and/or several faults generated by the tasks when they are being processed dynamically, thereby becoming fragile and fault-intolerant. Due to these issues, the cloud produces meager or occasionally no results. The main objective of this research paper is to propose and compare the behavior of the hybrid resource scheduling algorithms – Reinforcement Learning − First Come First Serve (RL − FCFS) and Reinforcement Learning − Shortest Job First (RL– SJF) which are implemented by combining the resource scheduling algorithms FCFS and SJF with Reinforcement Learning (RL) mechanism. To compare the proposed algorithms, heavy tasks are executed on the cloud VMs under various scenarios, and the results obtained are compared with each other with respect to various performance metrics. With the implementation of these algorithms, the cloud will initially go into a learning phase. With proper feedback and a trial-and-error mechanism, the cloud can perform appropriate resource scheduling and also handle the tasks, irrespective of their faults, thereby enhancing the entire cloud performance and making the cloud fault-tolerance. With this, the Quality of Service (QoS) is improved while meeting the SLAs.
A Method for Workflow Segmentation and Action Prediction from Video Data - AR Content

This paper proposes an approach to segment workflows from video content data and outputs textual summary which is nothing but action prediction of these workflows from the speech recognized module. The method of workflow segmentation requires less computation power and achieves good accuracy compared to using other complex deep learning approaches of feature extraction and processing of sequential data. The method provides segmented action frames and video clips based on workflow segmentation on video frames data. The video segmentation module has intelligible video analysis methods which helps the system to provide a summary of important description of workflows in the frames and situation in the frames in order create an easily understandable outline. The workflow content will be helpful to create the AR content for maintenance and repair of different manufacturing systems.
Graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images

The analysis of hematoxylin and eosin (H&E) stained images obtained from breast tissue biopsies is one of the most dependable ways to obtain an accurate diagnosis for breast cancer. Several deep learning methods have been explored to perform the analysis of breast histopathology images to automate and improve the efficiency of computer aided diagnoses (CAD) of breast cancer. Cell nuclei segmentation is one of the predominant tasks of a CAD system. Traditional convolutional neural networks (CNNs) have seen remarkable success when it comes to this task. However, there has been recent success suggested by several studies that made use of graph convolutional neural networks (GCNN) with computer vision tasks. This paper aimed to design an architecture called GraphSegNet, a GCNN that follows the encoder-decoder structure of the well-known network for image segmentation, SegNet to perform nuclei segmentation. The proposed model achieved an accuracy, Dice coefficient, Intersection over Union (IoU), precision and recall scores of 90.75%, 83.74%, 83.2%, 75.40% and 79.93% respectively on a UCSB bio-segmentation benchmark breast histopathology dataset.
Visual Foresight with a Local Dynamics Model

Model-free policy learning has been shown to be capable of learning manipulation policies which can solve long-time horizon tasks using single-step manipulation primitives. However, training these policies is a time-consuming process requiring large amounts of data. We propose the Local Dynamics Model (LDM) which efficiently learns the state-transition function for these manipulation primitives. By combining the LDM with model-free policy learning, we can learn policies which can solve complex manipulation tasks using one-step lookahead planning. We show that the LDM is both more sample-efficient and outperforms other model architectures. When combined with planning, we can outperform other model-based and model-free policies on several challenging manipulation tasks in simulation.
Control and Estimation of Renewable Energy

The renewable energy is obtained from the sources like hydro, wind, solar and tidal. The way energy is generated is dependent on certain factors. In hydro, the factors are guide vane angles, head available, velocity of water and type of turbine being used. For wind, the factors are the direction of wind, speed or velocity of wind and profile of turbine blade. For solar, the factors are sunlight intensity, position of solar panel, heat generated and weather conditions. In tidal, the factors are size of barrage required, difference in tidal heights and natural fall and rise of tidal. Few factors are controllable by us. The factors are being controlled by the change in the configuration, shape or profile of the equipment. By the integration of AI system, a flawless control can be achieved. By continuously monitoring the factors and making changes according to it, the power output of the system can be changed. This leads to efficient use of renewable energy. With the help of AI system, the control, estimation and monitoring of this renewable energy are possible.
Solar Power Probabilistic Prediction Using Random Forest Regressor

Variation in the energy from renewable energy resources like wind and solar energy sources have major issues in the way the power grid is handled as the output from the plant varies randomly in the short term. Predicting the energy output can reduce the challenges by proper planning the operation of the grid. In this direction, solar power forecasting by various models is gaining much importance. This paper proposes a solar power probabilistic prediction by using Random Forest Regressor model to generate prediction of the solar plant power output. The model performed satisfactorily with R2_score metric above 96% for the given data for two solar plants.
Fusion of Deep Learning Models with Attention for Multi-Resident Activity Recognition in Ambient Assisted Living

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An eclectic analysis of entrepreneurship motivation in conflict and refuge: the Syrian context

This article applies the eclectic theory of entrepreneurship to the study of entrepreneurship motivation under the extreme contexts of violent conflict and subsequent displacement, where the decision to pursue entrepreneurship is modelled as an interplay of the opportunities and external resources in the entrepreneur's environment as well as individual characteristics. The study also adapts Wagner and Sternberg's view of entrepreneurial decision-making in additional to the self-determination theory to discuss the study implications. Findings suggest that conflict-affected entrepreneurs are intrinsically motivated to start new businesses as well as being driven by opportunities and resources in their institutional, economic, cultural, and social environment with no notable difference in motivational drivers between conflict-affected founders in an active conflict versus refuge context. This emphasises the positive potential outcomes of entrepreneurship in conflict and refuge and the need to comprehensively analyse entrepreneurship motivation outside of dichotomies and simple categorisations to allow for actionable recommendations.
Comparison of lossy image compression techniques

Image compression is crucial task in the current era. This is mainly due to the ubiquitous nature of images in the applications we use today and with the need to transmit these images without spending fortunes while also maintaining the quality up to a certain mark. This paper will provide an insight into the problems of image compression, comparing different models. In this paper, autoencoders, SVDs, GANs, JPEG, K-means, and PCA models are used to perform image compression. It further aims to compare the performance of the aforementioned models to understand which model gives the most preferable output.
Is the turn of the month an anomaly on which an investment strategy could be based Evidence from Bitcoin and Ethereum

We examine the turn of the month effect (TOM) in cryptocurrency markets. In contrast to most calendar effect studies, we do not take for granted that the TOM period is the last trading day of the month up to the first three trading days (-1, 3), as Lakonishok and Smidt (1988) proposed in their seminal paper, but we employ an optimisation algorithm which tests several four-day intramonth periods. Our findings confirm the existence of the TOM effect because the most profitable four-day periods are those between the last days of one month and the first trading days of the next one [the (-1, 3) definition is included in these combinations]. We reach the conclusion that the existence of a TOM effect may not always lead to higher profits in comparison with a buy-and-hold (BnH) strategy, but it presents better returns to risk reward and it could be beneficial for investment strategies.
The influence of perceived transparency on the acceptance of work task automation: an example of recruiting chatbots in Germany

This study aims to identify factors influencing recruiters' acceptance of chatbots deployed in their work processes as an example of work task automation technologies. The technology acceptance model (TAM3) was focused and adapted to fit this research context. The developed model examines the effects of the TAM3 aspects of perceived usefulness, perceived ease of use, subjective norms, job relevance, output quality, self-efficacy, technology anxiety, chatbot experience, age, and perceived system transparency as newly introduced variables influencing the acceptance of recruiting chatbots. An online survey was conducted, yielding data from 250 recruiters from German companies, which was analysed via PLS-SEM modelling. The findings confirm that all the examined variables except age are significant. Perceived usefulness, perceived ease of use, subjective norms, job relevance, output quality, self-efficacy, chatbot anxiety, chatbot experience, and the proposed additional perceived system transparency have a significant effect on the behavioural intention to utilise a recruiting chatbot.
​Implementation of Facial Recognition System for Metaverse Using Sbrio Fpga and Nb-Iot Module

Facial recognition is crucial for identity verification in the metaverse, but it requires significant processing and operation overhead. Transmitting high-definition images to a server PC for processing is not feasible in low-capacity, low-bandwidth, or low-processor virtual environments. To overcome these challenges, we developed a narrow-bandwidth framework integrating embedded FPGA technology with a low-power NB-IOTcommunication module. Our approach uses a DNN-based DeepFace model with front face detection and 7-layer DNN convolution result extraction performed on the Zynq FPGA chip of sbRIO, reducing computational overhead and enabling efficient processing. By leveraging NB-IOT's remote transmission capabilities, classification data is transmitted back to the local server for comparison. Our proposed framework improves speed and accuracy while overcoming bandwidth and processing power challenges, making it a promising solution for facial recognition in immersive virtual environments.
A New Version of the Alkhalil Morpho Sys 2 Analyzer for Contemporary Documents

Morphological analysis is an important step in several applications of natural language processing. The richness of Arabic morphology makes the task of morphological analysis very complex. Arabic context-free morphological parsers widely used in applications suffer from several limitations such as poor coverage and the high average number of solutions per parsed word that negatively impacts the disambiguation phase. In this paper, we have developed a new version of the Alkhalil Morpho Sys analyzer dedicated to contemporary Arabic documents. This version is characterized by a reduced database built on a representative corpus of the standard Arabic language. The tests carried out show the high speed and the good coverage of this new version.
Impact of Feature Vectorization Methods on Arabic Text Readability Assessment

Assessing the readability of texts is a crucial challenge in an information-intensive society. One of the many problems raised in this area is determining whether the models trained for this task are effective for low-resource languages. Since representing texts by vectors composed only of linguistic features is the most explored approach in the field of readability prediction of Arabic texts, word embedding can represent an attractive alternative in this field. It is another popular and efficient method for text classification that generates a vector representation of words based on semantic and syntactic similarities between linguistic units. In this study, we evaluated the impact of different word vectorizations on the readability of texts dedicated to readers of Arabic as a first foreign language. We also examined the effect of combining these representations with classical linguistic features. The test results show that the F-score of the combination-based approach outperforms the classical one by 8%.
A New CBIR Search Engine with a Vision Transformer Architecture

The use of images is starting to grow in the majority of applications, including image search systems. However, the traditional text-based methods used to perform the search process have some limitations. Therefore, CBIR has emerged as an alternative and robust solution. The CBIR systems use the image’s features to perform the search; however, most CBIR systems usually extract low-level features like texture, shape, and color. Such systems have not managed to give an ideal solution that can be effective and fast. Therefore, we propose a newly designed CBIR system that can extract more accurate and meaningful features while responding in a minimum time. Our CBIR system, presented in this paper, uses a transformer-based model to extract the features from images, called ViT (Vision Transformer). Then, we used the principal component analysis (PCA) method to minimize the dimensionality of the feature vector space. Finally, we implemented the Annoy library for similarity searches. The experiments indicate that our CBIR system is more accurate and faster than relevant CBIR systems.
Non-invasive Face Registration for Surgical Navigation

This study aims to propose a precise rigid-body face registration method that does not require an invasive marker attachment for surgical navigation devices. The non-invasive face registration involved the following steps: anatomical feature points such as the eyes and nose are found from the computed tomography (CT) image and the location tracking device attached to the patient
Implementasi Jaringan Hotspot Menggunakan Metode Queve Tree Pada Router Mikrotik (Studi Kasus : SMK Gita Kirtti 1 Jakarta)

The provision of a hotspot network at Gita Kirtti 1 Jakarta Vocational School was made based on problems that often occur in the learning activity, namely unstable internet speed when accessed by many users at once.This of course can disrupt the teaching and learning process, such as when accessing learning materials in the form of videos that require a stable internet speed, so that it can allow some students to open websites that are not related to learning which of course are not related to knowledge.This research aims to provide hotspot facilities for teachers and students at schools by utilizing Mikrotik routers in schools.This.research is using Queue Tree method.The method used to limit bandwidth is more complex than using a simple queue.Queue tree serves to limit bandwidth]on]a]Mikrotik that has.more than one.internet.connections.The]results;of.thisstudy are]the availability of]a hotspot network that can be accessed by teachers and students at several points in the school that can be used to find information related to the world of education.There are different levels of user rights restrictions between teachers and students connected to the network.It is hoped that the school can maintain the hotspot network that has been provided so;that;it;can be useful as.a support for learning at school.