Reviewerly/exHarmony huggingface dataset:
Persona Agents from Playtraces and Emotion

This paper proposes a novel pipeline for generating agents that simulate player behaviour. By clustering player traces and using evolutionary algorithms to evolve parametric agents to best represent those clusters, our pipeline creates persona agents that represent the behavioural space of players. We here propose clustering playtraces based on behaviour, emotional experience and a mixture of both. We implement the pipeline on a test bed game and using 182 collected player traces with both behavioural and emotional information, we demonstrate that our persona agents can generate diverse player-like behaviour both in the level used to evolve them but also in a previously unseen level. We further find that using emotional information leads to better behavioural coverage on both levels. Although on its early stages, our approach offers a new perspective on how game developers and testers can gather insights on player behaviour without having to rely on extensive user testing.
Causal Necessity as a Narrative Planning Step Cost Function

Narrative planning generates a sequence of actions which must achieve the author's goal for the story and must be composed only of actions that make sense for the characters who take them. A causally necessary action is one that would make the plan impossible to execute if it were left out. We hypothesize that action sequences which are solutions to narrative planning problems are more likely to feature causally necessary actions than those which are not solutions. In this paper, we show that prioritizing sequences with more causally necessary actions can lead to solutions faster in ten benchmark story planning problems.
Data-To-Question Generation Using Deep Learning

Many publicly available datasets exist that can provide factual answers to a wide range of questions that benefit the public. Indeed, datasets created by governmental and nongovernmental organizations often have a mandate to share data with the public. However, these datasets are often underutilized by knowledge workers due to the cumbersome amount of expertise and embedded implicit information needed for everyday users to access, analyze, and utilize their information. To seek solutions to this problem, this paper discusses the design of an automated process for generating questions that provide insight into a dataset. Given a relational dataset, our prototype system architecture follows a five-step process from data extraction, cleaning, pre-processing, entity recognition using deep learning, and questions formulation. Through examples of our results, we show that the questions generated by our approach are similar and, in some cases, more accurate than the ones generated by an AI engine like ChatGPT, whose question outputs while more fluent, are often not true to the facts represented in the original data. We discuss key limitations of our approach and the work to be done to bring to life a fully generalized pipeline that can take any data set and automatically provide the user with factual questions that the data can answer.
An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare Applications and Use-Cases

Deep learning has had a major impact in a wide range of research domains across the world, including healthcare and medicine. From aiding radiologists, by acting as clinical assistants, to analyzing electronic health records, deep learning models have proved to be beneficial in identifying health abnormalities and aiding diagnostics. This chapter discusses a framework that can be used to explore the design space of embedded neural network models for healthcare applications and use-cases, given the user quality requirements, such as accuracy or precision, and hardware constraints of the target execution platform. The models explored by the framework are successful in reducing the hardware overhead of network by a factor of 53 × while achieving a quality loss of <0.2% compared to state of the art.
Problems of Improving the Quality of Training of Future Primary Education Teachers

A key factor in the dynamic development of the economy is the improvement of the staffing process in domestic higher education. In accordance with this, Kazakhstan's higher education faces the task of ensuring the necessary quality and recognition of education at the world level. The article will be devoted to the problem of improving the quality of training of future primary education teachers. The article analyzes the current state of the quality of education in the higher education system based on the study of scientific literature, the experience of the accreditation system for quality assurance in the field of higher education is considered. The main pedagogical factors affecting the quality of students' education are determined. The analysis of the rating of educational programs at the NAAR (independent agency for accreditation and rating) of primary education programs Abai Kazakh National Pedagogical University for 2015–2020, 2022 years. The opinions of students of the primary education program related to factors directly affecting the quality of education are analyzed based on the results of the questionnaire. The results of the survey of 3rd-year students of the Abai Kazakh National Pedagogical University in the direction of “Primary education programs” clearly showed the attitude of students to the quality of education. It substantiates the need for adequate financial, social and academic support for the teacher and the student, support for students from small groups, provision of consulting services.
Features of Preparing Future Mathematics Teachers for the Implementation of Logical Tasks

This article describes the types of logical tasks in Mathematics textbooks of the main school according to the updated program and the characteristic features of the methodological training of future mathematics teachers in their implementation. The issue of preparing students for the implementation of logical tasks is considered. Methods and techniques for improving the logical thinking of students studying in the specialty of a mathematics teacher are outlined. The main task of teaching mathematics is the development of logical thinking and logical training of students. Accounting in the ability to think logically, along with the interest of a student who comes to school, is the main task of a mathematics teacher. Therefore, it is necessary to train future teachers of mathematics as young specialists capable of developing logical literacy of students using effective teaching technologies. This leads to the need to increase the requirements for the quality of teaching students in general schools and, accordingly, the quality of training of future teachers in pedagogical universities. However, only supplementing the process of training students with new content, increasing the amount of knowledge communicated to students is not able to solve the task of raising the quality of teacher training to the level of modern requirements. The solution to this problem, in our opinion, lies in the systematization of fundamental training of future teachers, should be logical training that allows students to be confident in critical thinking, analysis, reasoning and reasoning, maintain and justify their point of view.
Ecosense: An IoT System for Detecting Suitable and Sustainable Living Conditions

The EcoSense project intends to create a sustainable IoT system to detect and track whether living circumstances are suitable for individuals. The system gathers sensor data and measures important aspects including temperature, air quality, and noise level. The degree of sustainable air pollution is determined by air pollution sensors, which monitor several dangerous chemicals including CO and CO2 in the atmosphere. The noise sensor measures the level of noise and pinpoints the source of excessive noise. Environmental temperature and humidity are measured using sensors. These sensors sustainably and continuously track all of these parameters, sending out notifications when specified thresholds are reached or exceeded. In order to assess the habitat’s general compatibility, the sensor data is processed, saved in the cloud, and then examined. Following that, this information is used to give locals immediate feedback so they may decide for themselves how best to enhance their living arrangements as well as the environment and public health. their general wellbeing. This initiative helps to spread the word about living sustainably. The EcoSense initiative has the potential to have a big impact on the environmental sustainability industry. The initiative can assist people and communities in making educated decisions about their environment and taking constructive action to enhance it by offering real-time data on quality of life. The project has the potential to increase public awareness of environmental problems and inspire individuals to take action to safeguard the environment.
Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction

Digital imaging has become an essential element in every medical institution. Therefore, medical image retrieval such as chest X-ray (CXR) must be improved via novel feature extraction and annotation activities before they are stored into image databases. To date, many methods have been introduced to annotate medical images using spatial relationships after these features are extracted. However, the annotation performance for each method is inconsistent and does not show promising achievement to retrieve images. It is noticed that each method is still struggling with at least two big problems. Firstly, the recommended annotation model is weak because the method does not consider the object shape and rely on gross object shape estimation. Secondly, the suggested annotation model can only be functional for simple object placement. As a result, it is difficult to determine the spatial relationship feature after they are extracted to annotate images accurately. Hence, this study aims to propose a new model to annotate nodule location within lung zone for CXR image with extracted spatial relationship feature to improve image retrieval. In order to achieve the aim, a methodology that consists of six phases of CXR image annotation using the extracted spatial relationship features is introduced. This comprehensive methodology covers all cycles for image annotation tasks starting from image pre-processing until determination of spatial relationship features for the lung zone in the CXR. The outcome from applying the methodology also enables us to produce a new semi-automatic annotation system named CHEXRIARS which acts as a tool to annotate the extracted spatial relationship features in CXR images. The CHEXRIARS performance is tested using a retrieval test with two common tests namely the precision and recall (PNR). Apart from CHEXRIARS, three other annotation methods that are object slope, object projection and comparison of region boundaries are also included in the retrieval performance test. Overall, the CHEXRIARS interpolated PNR curve shows the best shape because it is the closest curve approaching the value of 1 on the X-axis and Y-axis. Meanwhile the value of area under curve for CHEXRIARS also revealed that this system attained the highest score at 0.856 as compared to the other three annotation methods. The outcome from the retrieval performance test indicated that the proposed annotation model has produced outstanding outcome and improved the image retrieval.
A 1920×1080 129fps 4.3pJ/pixel Stereo-Matching Processor for Pico Aerial Vehicles

This paper presents an energy-efficient stereo matching processor that fully leverages disparity and RGB images to achieve high-speed and low-power depth prediction in 28nm CMOS process. A data compression-based guided image filter is proposed to compensate for the accuracy degradation caused by insufficient aggregation paths with slightly extra hardware resources. Two SRAM architectures tailored for the aggregation and multi-linebuffer in stereo matching achieve a compact chip area of only $1.26 \mathrm{~mm}^{2}$, which enables 30-129fps dense depth throughput on full HD resolution $(1920 \times 1080)$ with only $57-147 \mathrm{~mW}$ power dissipation. Besides, its normalized energy dissipation outperforms the state-of-the-art in $28 \mathrm{~nm}$ CMOS by $9.6 \times$ even with a larger on-chip SRAM footprint.
Integrating Renewable Energy and Computer Science: Innovations and Challenges in a Sustainable Future

Exploring the exciting opportunities and formidable challenges of achieving a sustainable future, the research paper “Integrating Renewable Energy and Computer Science: Innovations and Challenges in a Sustainable Future” delves into the crucial intersection of renewable energy and computer science. Computer science emerges as a key facilitator, providing novel solutions to maximize the efficiency, integration, and management of renewable energy systems as the world acknowledges the urgent need to transition from fossil fuels to renewable energy sources. New developments in solar photovoltaics, wind power, hydropower, and geothermal energy, as well as the transformative role that computer science plays in optimizing their performance, lowering costs, and fostering scalability, are the focus of this interdisciplinary investigation. This article examines vital facets of this integration, highlighting their potential in boosting renewable energy consumption and system stability. These facets include smart grid technology, energy storage solutions, demand-side management, and predictive analytics. The research report also examines the difficulties that come when trying to create a sustainable future by combining renewable energy sources and modern computer science. To achieve successful implementation and widespread adoption, comprehensive solutions are required to address these hurdles, which include technical complications, interoperability issues, cyber security concerns, and socio-economic variables. This paper seeks to improve comprehension of the synergistic relationship between renewable energy and computer science by reviewing state-of-the-art research and providing concrete examples of its application toward the goal of building a sustainable future. The paper also highlights the importance of researchers, policymakers, and industry stakeholders working together to overcome obstacles and realize the full potential of this integration, so laying the groundwork for a cleaner, greener, and more resilient energy future.
Secure Information System for Visitor Access Recognition using Machine Learning

Typical visitor access apps are usually passive/manual. The security guards in highly populated communities have to verify the identity of the visitors every single time, be it the residents or new people. This process is also time-consuming. Availability of the resident is also essential to approve the entry. VISITOR ACCESS is a better substitute for the regular visitor access systems. The process starts with choosing the house number and the name initially. When there is a known person, the visitor is verified, recognized, and approved to enter. If the visitor is unknown, he or she is asked to enter a few more details, which are sent to the resident’s mobile. A message is sent to the visitor’s mobile regarding the approval status to deal with the case of multiple visitors. Open CV is used to verify the identity of the visitors using face recognition, and the app acts as an interface between the users and the system.
Automated Opencv-Based Presentation Controller

Présentations have proved to be an effective way of communication in today’s world. Effective présentations are essential as they express one’s idea or analysis in professional meetings. A keyboard or a remote-controlled physical device is generally used to operate the presentations. The display can be performed using hand gestures by utilizing the features provided by Computer Vision (CV). The slides in the presentation can be navigated using specific hand gestures for each operation, like navigating forward or backward in slides. This model also allows the user to virtually draw on the screen in real-time by using their hands. So this model helps eliminate the use of a physical device and is cost-effective. This automation results in a software product that works as an efficient controller. Using this, the user can operate the slides efficiently without any physical contact. This automated model is cost-effective, easy to use, and doesn't require any physical device. This can also see tremendous market potential as presentations have become necessary in every field today. Many features and gestures can be added to further development of this model.
Automated Caption Generation for Video Call with Language Translation

In the modern era, virtual communication between individuals is common. Many people’s lives have been made simpler in a number of circumstances by providing subtitles, generating automated captions for social media videos, and language translation from a source language to a targeted language. Both are included, which offers face-to-face translated captions during video conversations. React is used for application development. To send the data, socket programming is utilized. Context is understood and translated using Google translate API and speech recognition modules. With OpenAI and Whisper, captions are generated. This paper will directly create the translated user voice rather than translating the text and creating subtitles.
Deep Learning Approach for Detection of Fraudulent Credit Card Transactions

Instead of cash, people tend to use credit cards with the swift technological growth in the modern world. This unlocks the door for fraudulent individuals to utilize these cards in a wicked method. Every year, it costs billions of dollars in credit card transaction fraud to card issuers. There are no static patterns in fraud. Their behavior constantly changes. New technologies allow fraudsters to use the online medium and other techniques for implementing frauds. It is vital to learn the behavior patterns. The detection accuracy can be increased with a large dataset and complex features. This chapter addresses the problem of analyzing fraudulent credit card transactions. Explicitly, we propose a deep learning-based framework with various unsupervised learning algorithms. We perform the Hyperparameter optimization of these algorithms using sci-kit-learn machine learning frameworks and popular deep learning framework TensorFlow. In the end, we discussed the applied implementation of detecting the fraudulent transactions on the real-world dataset available on Kaggle.
Multipath Smart Preloading Algorithms in Short Video Peer-to-Peer CDN Transmission Architecture

The rapid growth of short video users has brought high traffic costs to content providers. Saving Content Distribution Network (CDN) costs while maintaining users’ playback Quality of Experience (QoE) is a significant problem for major short video platforms like Douyin (the Chinese counterpart of TikTok). Two commonly utilized cost-saving methods: PCDN (peer-to-peer CDN, fetching data from multiple low-cost but low-bandwidth edge devices with a multipath transmission protocol) and preloading control (minimizing unnecessary future playing data by downloading videos in segmented data ranges), each may hurt users’ experience respectively. Worse still, both methods combined have a synergistic negative effect over QoE. In this paper, we focus on experiences, algorithms, and prospects to solve this cost-QoE dilemma. We first introduce Douyin’s current PCDN multipath architecture and then review learning-based preloading techniques. Second, based on Reinforcement Learning (RL), we propose a Multipath-aware Smart Preloading algorithm, which consists of three schemes: one to decide the best size of the next range of preloaded data, another to design a water level valve algorithm that prioritizes preloading between currently playing video’s unfinished data and the next video’s beginning data, and the last one to determine the bitrate level of the next video. Douyin’s anonymous user feedback shows our Smart Preloading algorithm reduces traffic waste by ~26% while ensuring QoE. Third, we analyze and outlook the future of video systems, including trends in PCDN and other open issues.
Review Paper on Techniques of 2D to 3D Image Reconstruction

Abstract: The review paper emphasize on reducing technologies for 2D and 3D imaging, as well as model conversion. Although the popularity of 3D hardware is growing rapidly in the present era, 3D content is still dominated by its 2D counterpart. There are two main categories of image processing now available in the market, namely analogue and digital image processing. To produce hard copies such as scanned pictures and printouts, with images being the most common output, the analogue IP technique is used. On the other hand, Digital IP is used to manipulate digital images using computers, the outputs are often information related to images, mainly being data on features, edging characteristics, or masks. Image processing techniques, including Machine Learning and Deep Learning, can get more powerful.
Hiding Coded Speech in Color Images using Zaslavsky-Map and AMBTC Technique

The idea of hiding information is to shield a powerful message in public records. The present application-based hidden information is sensitive information disguised in many testing sectors, such as watermarking, fingerprinting, and steganography, which is the art of covered or hidden writing, which is covert contact with coded communications to hide the presence of a document on a communication channel from hostile attackers. This paper presents hiding speech in a coded image, depending on the quantization and discrete wavelet transformation (DWT). The proposed method used the discrete wavelet transform on a three-color band of covered images. Moreover, the term AMBTC which refers to “Absolute Moment Block Truncation Coding” is used for hiding information. The secret information is a short speech (representing a password for example) embedded based on the quantization level modification technique, which will be hidden in a codded image. The three color-bands of the image are transformed using discreet wavelet transform all decomposed regions are used for hiding except the Low-Low band is kept without any change to get high quality of the stego-image with respect to original image. In the bitmap, the secret bits of a speech data are replaced. The experimental results produced an average of PSNR for tested images is about 35.0. Secret speech is compressed using discrete cosine transform (DCT) about (35%-40%), and the result is converted to a binary form that will be embedded in a color image.
A Trajectory Prediction Method via Affine Objective Fuzzy Clustering

For the single-step trajectory prediction of moving objects, a trajectory prediction method via affine objective fuzzy clustering is proposed. Firstly, the affine clustering method and objective clustering analysis are combined to obtain the appropriate number of clusters and clustering centers. Secondly, the results of clustering are integrated with the Fuzzy C-Means algorithm. As a result, the rule number and the premise parameters in T-S fuzzy model are determined. Finally, the Stable Kalman Filter approach is used to estimate the consequent parameters of T-S model. Through the simulation of three-degree-of-freedom flight dynamics trajectory, the presented method is compared with the traditional deep learning model. Then the validity of the presented method is verified.