Reviewerly/exHarmony huggingface dataset:
Emotional Analysis using Deep Learning

Emotions are mental states that accompany physiological changes in the face, resulting in facial expressions. Sympathy, anger, worry, joy, fright, and other significant emotions are a few examples. Facial expressions play a significant role in non-verbal communication because they encapsulate a person's emotions. There has been a great deal of research done on computer modelling of human emotions. Computer modelling of human emotions has been made possible by computer technology. However, it is still in its infancy. The authors attempted to overcome limitations and create new opportunities as well as gain a better understanding and implement this simple form of human interaction in proposed computer-aided world. It has been made possible to evaluate and interpret genuine facial expressions in real time thanks to new techniques for collecting facial expressions and quick, highresolution pictures. The FER (Facial Expression Recognition) method currently relies on motionless frames, which makes it very hard to recognize foreground from background in the absence of motion information. This study describes a real-time facial expression identification system that detects faces using HAAR cascading classification and classifies facial expressions using convolutional neural networks. The system utilizes a webcam to dynamically display emotion text and accurately categorizes seven major emotions, including anger, disgust, fear, happiness, sadness, surprise, and neutrality. Real-time facial expression recognition may be utilised in a number of real-world applications, including as airport security, trade, and medical monitoring.
A Survey on Various Approaches for Edge Detection

Edge detection is a fundamental image processing technique used to spot sudden shifts in color or intensity in image. It is utilized to detect and highlight boundaries between various items or regions in image, as well as to detect features such as corners, circles and lines. Edge detection approaches typically work by applying a filter to an image to detect areas where the image undergoes an immediate shift in magnitude. Applications for edge detection techniques include recognizing objects, healthcare images, and background segmentation. Many techniques have been presented based on the classical approaches (such as Sobel, Prewitt, and Roberts, Canny, Laplacian of Gaussian (LOG), etc.) and soft computing approaches (SCA), which are the two main approaches for detection of edge. This paper provides an overview of studies carried out on edge detection using various approaches. That will assist brand-new researchers in learning about these techniques and selecting one from among them to evolve or improve according to their field of study.
Similarity in action with an Embodied Conversational Agent; can synchronous speech yield higher levels of rapport? An exploratory eHealth study

Self-guided eHealth has the benefit of providing autonomy to patients. However, the autonomy comes with a cost; elevated attrition rates. Embodied Conversational Agents (‘robots on screen’), have technological capabilities to build rapport with eHealth users and to support them, but are costly to realize and their evidence is inconclusive. We investigated a novel and low-technological method to build rapport. eHealth users synchronized their speech with a monologue-style ECA, a method for which there exists evidence within the human-to-human communication domain. User experiences were investigated using predominantly qualitative methods. As our study results show, users are fairly positive about speaking synchronously with the ECA. However, the experimental task needs refinements. Users need to priorly hear, at least once, the pace of their artificial interlocutor in order to synchronize. Future studies can further examine the refined synchronous speech task and its potential for more widely accessible rapport-building ECA’s aiming for eHealth adherence.
Participatory Design and Critical Media Studies: a Convivial Conversation

In the age of platform society, Critical Media Studies are faced with the challenge of developing not only theoretical proposals capable of critically addressing the power exerted on society by GAFAM-owned digital platforms, but also of envisioning a praxis capable of changing the political economy of this corporate-driven digital media ecosystem. To do this, we believe that Participatory Design and Critical Media Studies should dialogue when it comes to theoretical frameworks and intervention practices. In this paper we propose to start this dialogue building upon the work of Ivan Illich, especially his book Tools for Conviviality. We find that in order to imagine a possible digital media design process able to avoid the creation of radical monopolies, it is necessary to adopt a Convivial Participatory Design approach where issues related to scale, people needs and power inequalities are considered by designers in collaboration with the users.
How do Human and Contextual Factors Affect the Way People Formulate Queries?

This research will examine the factors that affect how people formulate their queries. The main objective is to determine whether query variations for the same information need are influenced by the type of tasks, human characteristics, or contextual information. In initial experiments, the UQV100 dataset was used to examine whether specific properties of search tasks influenced how people phrased their queries. The properties considered include the length and the degree of readability of the backstories, as well as a Jaccard similarity between the backstories and query variations. It was found that the properties have an impact on user queries even when users are in the same context. Such properties should be taken into consideration when designing tasks. In the future, we will conduct research involving users to examine their characteristics such as demographics. Additionally, the influence of contextual information on users will be studied.
What Does AI Think of My Paper?

The time has come to test ChatGPT, the new AI application that everyone has been talking about, for something more than recreative fun. Over the past few weeks, it consistently impressed me over numerous random tasks that I demanded, some of which are downright unthinkable for humans, e.g., analyze a 7,000-word research paper in 30 seconds and list the findings and methods in bullet points. It is not flawless, but it is indeed powerful.
Reinforcing Chemistry Learning in Sub-Atomic Particle Concept Using Educational Board Game

Recent innovations in the field of education recognized game-based learning as an approach that facilitates the attainment of educational objectives by creating student-centered activities through games, particularly in the field of Chemistry. In this regard, the Department of Education in the Philippines released the Most Essential Learning Competencies (MELCs) used nationwide by field implementers. One of these learning competencies that students need to learn is the number of sub-atomic particles of an atom. Hence, in this study, the researchers determined the usefulness of an educational board game as a supplement to achieve learning competency; determine the number of protons, neutrons, and electrons in a particular atom. The subjects of the study were thirty (30) students consisting of eleven (11) males and nineteen (19) females of Grade - 8 students under the Technical-Vocational Program enrolled for the academic year 2022-2023. Based on the pre-test and post-test results, statistical evidence shows that there is a significant difference in the PreTest (M =12.61, SD =1.783) and PostTest (M =14.48, SD = .851) scores of students; t(30) = -5.720, p = 0.000. This means that the educational board game intervention implemented to reinforce the learning of the students in Chemistry, particularly in the learning competency “determine the number of protons, neutrons, and electrons in a particular atom”, is effective.
Potentially toxic elements (PTE) in soils on the megaprofile Eastern Donbass – Azov sea

An expedition was carried out to take soil samples of Eastern Donbass and other districts of the Rostov region. The main goal was to study the distribution of potentially toxic elements (PTE) in soils. The total content of mineral components in solid-phase samples was determined by using the X-ray fluorescence method. It was found that, according to the average concentrations, PTE`s line up in the following series: Fe&gt;Mn&gt;Zn&gt;Cr&gt;V&gt;Cu&gt;Ni&gt;Pb&gt;Co. The contents of Zn and Cu in the soil were compared by seasons. It was determined that PTE concentrations in the upper layer of soils were higher in winter than in summer. Geochemical spectra were constructed. They demonstrated that the concentration Clarks of Cr, Cu, Zn, Co, Pb exceeded the Clarks of these elements in the Earth’s crust. Against this background, Pb stands out with contrast, the concentration in the soil of which exceeds those in the earth’s crust by 2.8 times. The tightness of the relationship between the content of PTE in soil samples was calculated, which turned out to be a high straight line.
Discrete Optimization: Limitations of Existing Quantum Algorithms

We investigate the limitations of existing quantum algorithms to solve discrete optimization problems. First, we discuss the quantum counting algorithm of Brassard et al. (1998), and show that it has performance that is equivalent to that of a brute-force approach when approximating the number of valid solutions. In addition, we show that a straightforward application of Grover's algorithm (referred to as GUM by Creemers and Pérez (2023b)) dominates any quantum counting algorithm when verifying whether a valid solution exists. Next, we discuss the nested quantum search algorithm of Cerf et al. (2000), and show that it is dominated by a classical nested search that uses an approach such as GUM to find (partial) solutions to (nested) problems. Last but not least, we also discuss amplitude amplification (a procedure that generalizes Grover's algorithm), and show (once more) that it may not be possible to outperform GUM.
Automatic Sight Distance Estimation for Overtaking Maneuvers

Abstract New assistance systems, as a preliminary step towards automated driving, require a complete knowledge of the environment. To do this, perception sensors are used, but positioning on digital maps is also a valuable tool as a secondary sensor. In this sense, digital maps must contain increasingly greater precision and detail. A variable that is not usually included is the sight distance, which is conditioned by static and dynamic elements. This paper presents a method for its estimation considering the road geometry and other elements that can interfere with driver or sensors vision such as obstacles in the surroundings of the road or a vehicle in front of the ego vehicle (before an overtaking maneuver). The method is applied to data from a real road with the advantage of being able to work in real time so results could be obtained based on the dynamic elements detected by the vehicle’s sensors. From the sight distance information, the possibility of carrying out overtaking maneuvers is estimated.
The Role of Adult Learning and Education in (Post-)Covid Times

The COVID-19 pandemic has caused unprecedented disruptions to global education systems, with adult education shifting to a virtual world and relying heavily on digital and ICT tools to maintain continuity of learning. However, the prolonged pandemic raises important questions about the future of adult education. The current essay examines the most significant changes in three key areas of adult education: health, citizenship, and digital technologies. It argues that adult education should not only equip people with the knowledge and skills necessary to navigate a crisis, but also play a proactive role in shaping the future of education and the world. It should critically analyse and challenge mainstream ideas and offer constructive alternatives to proposed solutions.
Evaluating the Added Value of Digital Contact Tracing Support Tools for Citizens: Framework Development

The COVID-19 pandemic revealed that with high infection rates, health services conducting contact tracing (CT) could become overburdened, leading to limited or incomplete CT. Digital CT support (DCTS) tools are designed to mimic traditional CT, by transferring a part of or all the tasks of CT into the hands of citizens. Besides saving time for health services, these tools may help to increase the number of contacts retrieved during the contact identification process, quantity and quality of contact details, and speed of the contact notification process. The added value of DCTS tools for CT is currently unknown.To help determine whether DCTS tools could improve the effectiveness of CT, this study aims to develop a framework for the comprehensive assessment of these tools.A framework containing evaluation topics, research questions, accompanying study designs, and methods was developed based on consultations with CT experts from municipal public health services and national public health authorities, complemented with scientific literature.These efforts resulted in a framework aiming to assist with the assessment of the following aspects of CT: speed; comprehensiveness; effectiveness with regard to contact notification; positive case detection; potential workload reduction of public health professionals; demographics related to adoption and reach; and user experiences of public health professionals, index cases, and contacts.This framework provides guidance for researchers and policy makers in designing their own evaluation studies, the findings of which can help determine how and the extent to which DCTS tools should be implemented as a CT strategy for future infectious disease outbreaks.
Explainable Deep Learning for False Information Identification: An Argumentation Theory Approach

To combat false information, social media sites have heavily relied on content moderation, mostly performed by human workers. However, human content moderation entails multiple problems, including huge labor costs, ineffectiveness, and ethical issues. To overcome these concerns, social media companies are aggressively investing in the development of artificial intelligence-powered false information detection systems. Extant efforts, however, have failed to understand the nature of human argumentation, delegating the process of making inferences of the truth to the black box of neural networks. They fail to attend to important aspects of how humans make judgments on the veracity of an argument, creating important challenges. To this end, based on Toulmin’s model of argumentation, we propose a computational framework that helps machine learning for false information identification understand the connection between a claim (whose veracity needs to be verified) and evidence (which contains information to support or refute the claim). The two experiments for testing model performance and explainability reveal that our framework shows stronger performance and better explainability, outperforming cutting-edge machine learning methods and presenting positive effects on human task performance, trust in algorithms, and confidence in decision making. Our results shed new light on the growing field of automated false information identification.
Rice yield prediction using Bayesian analysis on rainfed lands in the Sumbing-Sindoro Toposequence, Indonesia

Since rainfed rice fields typically lack nutrients, frequently experience drought, and require more fund to support farming operations, the production results become erratic and unpredictable. This research aims to construct location-specific rice yield predictions in the rainfed rice fields among the Sumbing-Sindoro Toposequence, Central Java, using a Bayesian method. This study is a survey with an exploratory descriptive methodology based on data from both field and laboratory research. Prediction model analysis using the Bayesian Neural Network (BNN) method on 12geographical units, sampling spots were selected with intention. The following variables were measured: soil (pH level, Organic-C, Total-N, Available-P, Available-K, soil types, elevation, slope) and climate (rainfall, evapotranspiration). According to the statistical analysis used, the BNN model’s performance has the highest accuracy, with an RMSE value of 0.448 t/ha, which compares to the MLR and SR models, indicating the lowest error deviation. To obtain the ideal parameter sampling design, parameter distribution is directly and simultaneously optimised using an optimisation technique based on Pareto optimality. The top 7 data sets (slope, available-P, evapotranspiration, soil type, rainfall, organic-C, and pH) yielded the highest accuracy based on the test results for the three-parameter groups. The coefficient of determination has the highest value, 0.855, while the RMSE test for the model using the top 7 data set has the lowest error value at 0.354 t/ha and 18.71%, respectively. By developing location-specific rice yield predictions using a Bayesian method, farmers and agricultural practitioners can benefit from more accurate and reliable estimates of crop productivity
Three families of self-orthogonal codes and their application in optimal quantum codes

If a linear code is contained in its dual, then it is said to be self-orthogonal. Self-orthogonal codes are an important subclass of linear codes which have many nice applications. In this paper, we mainly study the augmented codes of the linear codes constructed by Hu et al. (2022) [21], and the subcodes of the codes punctured from the irreducible cyclic codes constructed by Ding and Yang (2013) [11]. The parameters and weight distributions of these codes are explicitly determined. These codes are proved to be self-orthogonal under certain conditions. As an application, these self-orthogonal codes are used to derive optimal or almost optimal quantum codes with new parameters.
Experimental Analysis of Skip Connections for SAR Image Denoising

The coming up of the ResNet model has helped many researchers in trending fields of deep learning applications and is found to be effective for various computer vision applications as well. ResNet is known for its skip connections or residual connection that solves the vanishing gradient problem using deeper architectures. Most researchers have used the traditional skip connection method in their various works. However, the pattern of these residual blocks varies from application to application. Considering SAR image denoising, several works have incorporated ResNet with different skip connection blocks in their proposed architecture. We analyse the different patterns of skip connection or residual blocks by considering five cases and investigate the performance of the different implementations on denoising images acquired by synthetic aperture radar. We implement each case individually using the BSDS500 benchmark and compare their denoising performances. We also evaluated the performance on real SAR images using the TerraSAR-X data. Results show that an end-to-end residual block effectively improves the performance of image restoration in SAR images by attaining a PSNR of 24.50 and UQI of 0.97 on synthetic data. More importantly, the use of batch normalisation after every convolutional layer in a particular residual block plays a major role in performance enhancement for denoising of SAR images.
On Regenerative and Discriminative Learning from Digital Heritages: A Fractal Dimension Based Approach

In recent years, analyzing historical monuments using computer vision techniques has become extremely important for renovation, tourism experience, and preservation. While such studies are common for European architectural research, their findings are not directly applicable to Indian Monuments as the latter has significant differences in architecture and ornamental design. A key feature of Indian religious monuments such as temples is the heavily ornamented spires containing complex geometrical motifs and sculptures. However, a lack of available datasets prevents a detailed analysis of spires found in Indian monuments. Fractal Dimension can act as a measure of the architectural complexities by engaging the curvature present in the image of a monument. In this paper, we exploit the notion of Fractal Dimension through patch-based image analysis to visualize the architectural complexity of Indian monuments. Further, we present a technique to segment the original image such that the areas with rich architectural patterns stays similar while the regions with no marks or designs change to zero. The segmented image from the initial and fractal-based reconstruction gives a substantial recall improvement to a classifier compared to the original, proving the claim of using the rich segmentation mask from the fractal dimension-based reconstruction.
Person Monitoring by Full Body Tracking in Uniform Crowd Environment

Full body trackers are utilized for surveillance and security purposes, such as person tracking robots. In the Middle East, uniform crowd environments are the norm which challenges state-of-the-art trackers. Despite tremendous improvements in tracker technology documented in the past literature, these trackers have not been trained using a dataset that captures these environments. In this work, we develop an annotated dataset with one specific target per video in a uniform crowd environment. The dataset was generated in four different scenarios where mainly the target was moving alongside the crowd, sometimes occluding with them, and other times the camera’s view of the target is blocked by the crowd for a short period. After the annotations, it was used in evaluating and fine-tuning a state-of-the-art tracker. Our results have shown that the fine-tuned tracker performed better on the evaluation dataset based on two quantitative evaluation metrics, compared to the initial pre-trained tracker.
Sign Language Recognition for Indian Sign Language

The means of communication that consists of hand signals, body movements, and facial expressions are known as sign language. It has specific hand gestures for each letter in the alphabet system and also for some commonly used phrases or words. Deaf-and-dumb people heavily rely on sign language for their day-to-day communication. Most people do not understand sign language, thus creating a barrier to effective communication between deaf-and-dumb people and those who are not. Always having an interpreter for translation is very costly and not feasible enough. Therefore, a system that can detect hand gestures automatically proves to be helpful in this regard. A dataset used consists of 90,000 images for various English alphabets. Several techniques for preprocessing have been used to prepare the dataset for training purposes. The pre-trained model, InceptionV3, available in the Keras library has been used for experimenting on the dataset, and then, OpenCV is used to make real-time predictions using a webcam. The method gives an accuracy of 92.8%.
Segmented $$\varepsilon $$-Greedy for Solving a Redesigned Multi-arm Bandit Environment

The exploration–exploitation trade-off has been one of the most common problems in reinforcement learning. There have been multiple policies in the past that have tried to solve this issue optimally. We propose a redesigned version of the classical multi-arm bandit problem. The new environment formulates the multi-arm bandit problem as an episodic task with the possibility of termination in the middle of the episode. This task tests the ability of the agent to explore the environment as the states change significantly while proceeding through it. We also propose a policy- —segmented $$\varepsilon $$ -Greedy—that allows the agent to pass through the environment while maximizing its returns along the way. This policy has been compared with existing policies on our proposed environment.