Reviewerly/exHarmony huggingface dataset:
How to Fool Your Robot: Designing Exploitable Sensory Systems

Abstract Based on real-world interactions in our lives and in the lives of our ancestors, humans have developed a multitude of psychological, social, and reflexive actions for efficient living. We consider the integration of similar behaviours into embodied robots through the design of their sensory systems, evaluating their impact through a novel lens: how magicians exploit these human behaviours in order to fool their spectators into experiencing impossible events. We explore the consequences of designing agents which can experience magic effects, and argue that such design facilities lifelike actions.
Open to Evolve Embodied Intelligence

Abstract For the goal of automatically evolving Embodied Intelligence (EI), we investigate an open software architecture inspired by the high surface area to volume ratio of animal lungs, which aims to avoid information theoretic limits on long term evolution experiments (LTEE) encountered with monolithic genetic programming trees. Instead individuals are teams composed of 1023 trees whose inputs and outputs are linked by a low entropy loss branching data (air) pathway. Most trees are shallow and software engineering’s failed disruption propagation (FDP) is observed in the small fraction of deep trees. After initial search, most improvements are at intermediate depths and performance is still rising even after 100 000 generations. Despite the use of double precision for the bifurcating data interconnect, some information loss is seen, particularly in early generations. The static optimisation benchmark, appears to encourage early convergence, which locks the population into possibly sub-optimal phenotypes. Later thousands of small improvements, sometimes in large bloated ensemble members, appear to compensate for early overfitting. Using tournament fitness selection and subtree crossover, we target pure nested side-effect free floating point functions, which are known to have low FDP, and high fidelity data paths, in the hope of generating code which is not too robust so as to prevent on going improvement. However, we again find genetic changes deep within trees are silent. For single precision, we find a maximum evolvability sweet spot with trees of depth 10 to 100. Accordingly, we suggest to evolve very large very complex programs needed for Embodied Intelligence, an open structure with a high surface area permitting most mutation sites to be within 10-100 levels of the organism’s environment, and many better placed test oracles to monitor the impact of mutated code, will be needed.
Using Informative AI to Understand Camouflaged Object Detection and Segmentation

Explainable artificial intelligence is crucial for trust-building and gaining insights into machine learning applications, allowing for the identification of improvement opportunities. Recent advances in computer vision have incorporated XAI into object detection, providing explanations to validate system predictions. However, informative artificial intelligence, which focuses on understanding the underlying world behind data, has not been integrated into camouflaged object detection and segmentation research. We propose leveraging the Self-Explaining Decision Architecture alongside localization and ranking techniques to bridge this gap. By combining explainable artificial intelligence with informative artificial intelligence concepts into camouflaged object detection and segmentation, we strive to deepen our understanding of visual cues that undermine camouflage.
Efficiency Evaluation of ChatGPT for Adverb Type Categorization

Recent generative pre-trained transformer models provide outstanding performance in many different natural language tasks. This paper presents an evaluation of the efficiency of the ChatGPT 3.5 language model, and Dictionary and ML-based methods, in the task of adverb type categorization within natural language processing. This evaluation provides insights into the strengths and weaknesses of ChatGPT 3.5 and its potential applications in English sentence parsing and language understanding tasks. In this evaluation scenario, the average test score for the ChatGPT-based method is found to be 3.5 out of 5, indicating the overall evaluation by the experts. Similarly, the Dictionary and ML-based method achieves an average test score of 3.6 out of 5, reflecting the collective assessment by the evaluators. These findings of our test results contribute to our understanding of the performance and effectiveness of both approaches in adverb-type categorization.
Exploring Interpretable XAI Algorithms for Image Classification and Prediction Explanations

This paper investigates various eXplainable Artificial Intelligence (XAI) algorithms and how they simplify image classification tasks and provide interpretable explanations for the predictions made by classifiers. Specifically, we examine the Glance and Focus Network (GFNet), which employs image segmentation to facilitate classification simplification, and the novel Local Interpretable Model-Agnostic Explanations (LIME) technique, which locally learns an interpretable model to explain predictions. Additionally, the study incorporates the utilization of the CARE framework, which animates attentional convolutional neural networks through transformations, and Integrated Gradient, a method based on semantic segmentation. The primary focus of these XAI methods lies in object detection within images and the visualization of regions responsible for the identification of specific features by trained neural networks. To bridge the gap between technical experts and end-users, this thesis endeavours to develop a user-friendly interface that facilitates experimentation with convolutional neural networks and enhances understanding of their behaviour. By promoting accessibility and comprehensibility in artificial intelligence, a broader audience will be able to engage with AI systems and gain a clearer understanding of their potential benefits and limitations. This research contributes to the responsible and ethical development and implementation of artificial intelligence systems.
Tuning-less Object Naming with a Foundation Model

We implement a real-time object naming system that enables learning a set of named entities never seen. Our approach employs an existing foundation model that we consider ready to see anything before starting. It turns seen images into relatively small feature vectors that we associate with index to a gradually built vocabulary without any training of fine-tuning of the model. Our contribution is using the association mechanism known from transformers as attention. It has features that support generalization from irrelevant information for distinguishing the entities and potentially enable associating with much more than indices to vocabulary. As a result, the system can work in a one-shot manner and correctly name objects named in different contents. We also outline implementation details of the system modules integrated by a blackboard architecture. Finally, we investigate the system’s quality, mainly how many objects it can handle in this way.
Understanding the Interplay of Clinical Features and Cognitive Behavior in Schizophrenia: A Machine Learning Study

Schizophrenia is a complex mental disorder characterized by cognitive and behavioral impairments. Understanding the interplay between clinical features and cognitive behavior in schizophrenia is crucial for improving diagnosis and treatment strategies. In this study, we present a machine learning approach to analyze the relationship between clinical features and cognitive behavior patterns in schizophrenia. Our dataset comprises 3001 patients diagnosed with schizophrenia, and it includes information such as age, birth season, and clinical symptom scores. We employ various machine learning algorithms to extract meaningful insights from the data and identify patterns that characterize different cognitive behavior profiles. Our findings highlight the potential of machine learning techniques in unraveling the heterogeneity of schizophrenia and shed light on the association between clinical features and cognitive behavior. The results contribute to a deeper understanding of the disorder and pave the way for personalized interventions and targeted therapeutic approaches. This research underscores the importance of leveraging machine learning to explore the complexities of schizophrenia and improve patient outcomes.
Movement Vector Computing of Vehicles on Mast-Mounted Camera Systems

Vehicle detection is an essential part of collecting valuable data for traffic management. Many sensors and computational architectures are used for this purpose. However, many of them have limitations in providing data in the real-time scenario. This paper surveys the architectures of similar problems. We used this survey to propose our own vehicle detection architecture using edge-computing and service-oriented architecture. The proposed architecture was implemented and tested in real-world conditions in the mast-mounted surveillance system. The deployment of services is provided via containerization.
Comparison of modern and traditional Slovak children’s speech recognition

We compare two distinct speech recognition approaches, namely Hidden Markov models mixed with deep neural networks and modern end-to-end neural speech recognition architectures. Our evaluation focuses on the metrics of performance in the context of low-resource data. Here, we utilize newly completed Slovak speech recognition dataset containing children’s speech from Slovak public TV talk show intended for children. Our objective is to assess the feasibility of utilizing the Slovak children dataset within the framework of state-of-the-art end-to-end speech recognition, specifically by employing ESPnet2 framework. We anticipate the end-to-end model’s performance to be inferior in comparison with any tailored hybrid Hidden Markov model. The results from the baseline experiments motivate us to employ other advanced techniques that we propose to further enhance quality of models moving forward.
A survey of generative adversarial networks and their application in text-to-image synthesis

&lt;abstract&gt;&lt;p&gt;With the continuous development of science and technology (especially computational devices with powerful computing capabilities), the image generation technology based on deep learning has also made significant achievements. Most cross-modal technologies based on deep learning can generate information from text into images, which has become a hot topic of current research. Text-to-image (T2I) synthesis technology has applications in multiple fields of computer vision, such as image enhancement, artificial intelligence painting, games and virtual reality. The T2I generation technology using generative adversarial networks can generate more realistic and diverse images, but there are also some shortcomings and challenges, such as difficulty in generating complex backgrounds. This review will be introduced in the following order. First, we introduce the basic principles and architecture of basic and classic generative adversarial networks (GANs). Second, this review categorizes T2I synthesis methods into four main categories. There are methods based on semantic enhancement, methods based on progressive structure, methods based on attention and methods based on introducing additional signals. We have chosen some of the classic and latest T2I methods for introduction and explain their main advantages and shortcomings. Third, we explain the basic dataset and evaluation indicators in the T2I field. Finally, prospects for future research directions are discussed. This review provides a systematic introduction to the basic GAN method and the T2I method based on it, which can serve as a reference for researchers.&lt;/p&gt;&lt;/abstract&gt;
Algebra of Vectors

Tensors are characterized by their orders. Scalars are zeroth-order tensors with only magnitude and no other characteristics. They are extensively used in physical relations and usually designated by small and capital lightface Latin or Greek letters; examples of which include mass, time and temperature. In contrast, vectors are physical quantities that are completely characterized by both magnitude and direction. These mathematical objects, that are first-order tensors, obey the parallelogram law of vector addition; examples of which include force, velocity and momentum.
A Survey on Sign Language Recognition and Training Module

Communication among the deaf and non-verbal communities has long reliedon sign language recognition. From all researchers around from early electric signal-based sign language identification to more recent recognition using machine/deep learning techniques, the globe has tried to automate this process. The main objective of this research is Recognition of sign language based on key point detection (SLR).American Sign Language (ASL), primarily ASL pickle data, is the subject of this work. The model was trained using a variety of machine learning algorithms, including randomforest, support vector machine, and k closest neighbor. Lastly, utilizing evaluation criteria such as f1score, precision, and recall, the best model is chosen from the model testing. A straightforward GUI is created to collect user input, and the best machine learning model makes the forecast. Also, a Training tool is created for the purpose of learning the American sign language which will create a major difference for non-verbalcommunities
A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees

Phishing performs by trying to trick the victim into accessing any computing information which looks original, then instructing them to send important data to unrestricted/unwanted privacy resource. For prevention, it is essential to develop a phishing detection system. Recent phishing detection systems are based on data mining and machine learning techniques. Most of the related work literature require collection of previous phishing attack logs, analyze them and create a list of such activities and block traffic from such sources. But this is a cumbersome task because the data size is very large, continue changing and dynamic nature. [1]. Instead of using single algorithm approach it would be better to use a hybrid approach. A hybrid approach would be better at mitigating phishing attacks because classification of different format of data is handled; whether the intruder want to use images or textural input to gain into another user system for phishing. Hybrid recommendation decision tress enhances any of machine learning and deep learning algorithms performance. The decision path of the model followed a series of if/else/then statements that connect the predicted class from the root of the tree through the branches of the tree to detect true positive and false negatives of phishing attempts. 10 decision trees were considered and used the features to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach provided an overall true positive rate of the model of 92.28 % and false negative rate is 7.4%.
Counting rational points of two classes of algebraic varieties over finite fields

&lt;abstract&gt;&lt;p&gt;Let $ p $ stand for an odd prime and let $ \eta\in \mathbb Z^+ $ (the set of positive integers). Let $ \mathbb F_q $ denote the finite field having $ q = p^\eta $ elements and $ \mathbb F_q^* = \mathbb F_q\setminus \{0\} $. In this paper, when the determinants of exponent matrices are coprime to $ q-1 $, we use the Smith normal form of exponent matrices to derive exact formulas for the numbers of rational points on the affine varieties over $ \mathbb F_q $ defined by&lt;/p&gt; &lt;p&gt;&lt;disp-formula&gt; &lt;label/&gt; &lt;tex-math id="FE1"&gt; \begin{document}$ \left\{ \begin{aligned} &amp;amp;a_1x_1^{d_{11}}...x_n^{d_{1n}}+... +a_sx_1^{d_{s1}}...x_n^{d_{sn}} = b_1,\\ &amp;amp;a_{s+1}x_1^{d_{s+1,1}}...x_n^{d_{s+1,n}}+... +a_{s+t}x_1^{d_{s+t,1}}...x_n^{d_{s+t,n}} = b_2 \end{aligned} \right. $\end{document} &lt;/tex-math&gt;&lt;/disp-formula&gt;&lt;/p&gt; &lt;p&gt;and&lt;/p&gt; &lt;p&gt;&lt;disp-formula&gt; &lt;label/&gt; &lt;tex-math id="FE2"&gt; \begin{document}$ \left\{ \begin{aligned} &amp;amp;c_1x_1^{e_{11}}...x_m^{e_{1m}}+... +c_rx_1^{e_{r1}}...x_m^{e_{rm}} = l_1,\\ &amp;amp;c_{r+1}x_1^{e_{r+1,1}}...x_m^{e_{r+1,m}}+... +c_{r+k}x_1^{e_{r+k,1}}...x_m^{e_{r+k,m}} = l_2,\\ &amp;amp;c_{r+k+1}x_1^{e_{r+k+1,1}}...x_m^{e_{r+k+1,m}}+... +c_{r+k+w}x_1^{e_{r+k+w,1}}...x_m^{e_{r+k+w,m}} = l_3, \end{aligned} \right. $\end{document} &lt;/tex-math&gt;&lt;/disp-formula&gt;&lt;/p&gt; &lt;p&gt;respectively, where $ d_{ij}, e_{i'j'}\in \mathbb Z^+, a_i, c_{i'}\in \mathbb F_q^*, i = 1, ..., s+t,$ $j = 1, ..., n, i' = 1, ..., r+k+w, j' = 1, ..., m, $ and $ b_1, b_2, l_1, l_2, l_3\in \mathbb F_q $. These formulas extend the theorems obtained by Q. Sun in 1997. Our results also give a partial answer to an open question posed by S.N. Hu, S.F. Hong and W. Zhao [The number of rational points of a family of hypersurfaces over finite fields, &lt;italic&gt;J. Number Theory&lt;/italic&gt; &lt;bold&gt;156&lt;/bold&gt; (2015), 135–153].&lt;/p&gt;&lt;/abstract&gt;
Handwritten Script Detection and Translation

People are familiar with the English language, as it has been used frequently since the British era. Yet, those who only use their native language or who are so adamant about using their native language that they do so in all contexts—including marketing, teaching, business transactions, etc.—face challenges. Only used for business purposes, English has established itself as one of the top twenty languages spoken worldwide. Yet, using one's native language appears to be more critical in nations like India. Application cases for recognizing and translating have spread to insurance, banking, online libraries, and the healthcare and pharmaceutical industries. In order to better grasp a document, be it in any field, individuals began to realize the necessity of translating it into their preferred language. An attempt is performed to recognize the provided text before it is translated into the target language. OCR technology is utilized to identify and recognize the text, resulting in the intended outcome. The given text is first detected for language by the model, which then results in translation into the native language. This flow will ease in recognition of handwriting written in various ways that will lower labor costs and conserve valuable human time
Deciphering Handwritten Text: A Convolutional Neural Network Framework for Handwritten Character Recognition

HCR (Handwritten Character Recognition) is considered one of the most challenging research areas, given the vast array of potential applications. Character recognition has been the focus of research since the beginning of Artificial Intelligence. Numerous studies, including HCR, have been conducted in this sector. A typical procedure requires two steps: feature extraction and Classification. Many forms of neural networks have been used in this cause over the years, with notable results. CNN has altered the scenario in recent years. It has had remarkable success in this industry due to its cutting-edge extraction of features and Classification. To produce recognized characters, CNN uses images for input and sends them through a sequence of layers, including a convolutional layer, a nonlinear function, a pooling layer, and interconnected layers. We utilized a dataset containing 372,450 handwritten character images covering the entire alphabet in English. We created a model using the CNN model and achieved 99% test accuracy. CNN is an efficient and powerful approach for HCR. Our model's high accuracy suggests that it has the potential to be applied in various practical scenarios such as postal address reading, digital libraries, and traffic sign detection.
Robust Vehicle Speed Estimation Based on Vision Sensor Using YOLOv5 and DeepSORT

For driving safety purposes, the policymakers make regulations on the speed limit on the highway because vehicles high speed may cause an accident. Vision-based vehicle speed estimation requires limited human resources and minimizes human error. Therefore, the authors of this research propose a vehicle speed estimation pipeline using You Only Look Once version 5 (YOLOv5) for vehicle detection, tracking vehicles using Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT), and speed measurement process. The dataset built for this work is taken in Gadjah Mada University using a camera in bird view orientation. The detection precision and recall of the of YOLOv5l pre-trained model used are both about 100% and has fps 10.762. The speed value reaches a Root Mean Square Error (RMSE) of 3.926 and a Mean Absolute Error (MAE) of 3.155. The RMSE obtained is compared with previous research and has the most significant value.
A Comparative Study of Deep Learning Algorithms and SARIMA Models for Forecasting Monthly Solar Radiation and UV Index: Case Study for Mauritius

Forecasting solar radiation and UV index accurately have proven in various studies to be crucial in this evolving world where solar energy is being exploited to its core for various applications. The objective of this paper is to apply different artificial neural network architectures to the modelling of the two monthly time series data aforementioned, with their structures and training processes fully described. A comparative analysis of the results, including that of a seasonal autoregressive auto moving model, are afterwards thoroughly carried out, which is finally followed by a forecast for the next twelve months. The optimal models for modelling solar radiation and UV are concluded to be the feedforward neural network, with a RMSE of 13.5 and the hybrid model of gated recurrent unit and long short term memory network, with a RMSE of 0.198 respectively for this study.
Digital Revolution Through Computational Intelligence: Innovative Applications and Trends

Computational Intelligence is sometimes also referred to as soft computing, which is a specific field of study where the task is to make computers learn some real-life or complex problems from the experimental data or observations. In computational intelligence, there is a set of approaches or methodologies used to address real-life or complex problems. Generally, it is impossible to solve real-life problems using traditional computing methods because of complexity, uncertainty, or problems that don’t have a proper definition. Considering cognitive computing is an indispensable technology to develop these smart systems, this paper proposes human-centered computing assisted by cognitive computing and cloud computing. Computational Intelligence has significantly extended the possibility of computing, encompassing it from traditional computing on data to progressively diverse computing paradigms such as cognitive intelligence and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years. Intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing.
Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing

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