Reviewerly/exHarmony huggingface dataset:
Extending the Horizon by Empowering Government Customer Service Officers with ACQAR for Enhanced Citizen Service Delivery

A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath X SLA predictor and CQAS (a type of Question Answer model) as critical inputs to ChatGPT engine, known as AI Based Citizen Question-Answer Recommender (ACQAR).Empath X SLA predictor anticipates the expected service response time based on citizens’ emotional state. These valuable inputs coupled with the recommended answer provided by the CQAS will serve as prompt inputs to ChatGPT to craft contextually aware responses. This ensures that the final response considers the citizen’s emotional needs, expected service timeline, and recommended answers from official government documents.While the full-scale deployment of this pilot system, ACQAR, is pending, this paper presents a comprehensive blueprint for governments seeking to modernize citizen service delivery. By fusing sentiment analysis, SLA prediction, question-answer models, and ChatGPT, this system design aims to revolutionize government-citizen interactions, delivering more empathetic, efficient, and tailored responses, while not violating SLA.This paper serves as a foundational step towards the practical development and implementation of an intelligent system (ACQAR) that holds the potential to significantly enhance citizen satisfaction, foster trust in government services, and strengthen overall government-citizen relationships.
A Case Study on ChatGPT Question Generation

The advent of transformers and the subsequent development of Large Language Models (LLMs) based on these technologies has revolutionized the field of Natural Language Processing (NLP). These models are able to understand and generate coherent natural language and hold conversations with humans continuously. Meanwhile, ChatGPT has become famous among many LLMs for its general-purpose characteristics and versatility. With that in mind, we investigate the capabilities of ChatGPT, which is very successful in many downstream NLP tasks on the task of Question Generation (QG). In particular, our experiments show that appropriate context through our designed prompts makes ChatGPT an appropriate tool for accurately performing the QG task. We compare ChatGPT’s question generation results with the state-of-the-art models, particularly on the SQuAD and car manual datasets. The results show that ChatGPT is able to compete with or even outperform some of the baseline models. Furthermore, we illustrate that we may improve ChatGPT through additional fine-tuning of the prompts. Finally, we also investigate the use of ChatGPT to evaluate QG models. While the use of ChatGPT for such purposes is still in its early stages, our results demonstrate that ChatGPT can potentially be a strong QG accuracy evaluator comparable to human evaluators.
Modeling and Forecasting Photovoltaic Power Production

As the world's population grows, there is a need to find new sources of energy that are more sustainable. Photovoltaic (PV) energy is one of the renewable energy sources (RES) expected to have the greatest margin for growth in the near future. Given their intermittency, RES bring uncertainty and instability to the management of the power system, therefore it is essential to predict their behavior for different time frames. This paper aims to find the most effective forecasting method for PV energy production that could be applied to different time frames. PV energy production is directly dependent on solar radiation and temperature. Several forecasting approaches are proposed in this paper. A multiple linear regression (MLR) model is proposed to predict the monthly energy production based on the climatic parameters of the previous year. Different approaches are proposed based on first predicting the temperature and radiation and then applying the PV mathematical models to predict the produced energy. Three methods are proposed to predict the climatic parameters: using the average values, the additive decomposition, or the Holt-Winters method. Comparing the errors of the four proposed forecasting methods, the best model is the Holt-Winters, which presents smaller errors for radiation, temperature, and produced energy. This method is close to additive decomposition.
Outdoor Long Range Ubiquitous Projectiles Tracking System Using P-MPLR and Computer Vision

A growing body of research has been dedicated to investigating systems for quickly detecting and pinpointing the location of projectiles aimed at a target. Various studies and products have demonstrated the feasibility of predicting the point of origin for a shot. However, existing tools come with limitations and inconveniences. This study aims to introduce a model that achieves highly accurate and low-latency tracking of shot origins during long-range shooting, free from constraints, by incorporating LoRa, computer vision, and sound detection technologies. The proposed system commences with an initial photograph captured by a camera positioned near the target. Whenever the adjacent acoustic sensor detects a fired projectile, the camera captures a new image, triggering the Raspberry Pi located beside the target to calculate the coordinates of the shot by comparing it with the previous image. When compared to the actual bullet mark, the predicted bullet mark’s error remains under 1 mm. This paper’s proposal showcases accuracy suitable for integration into military intelligence training systems, offering a cost-effective solution.
Symbolic Analysis Based Pipeline for EEG Data

This paper is concerned with developing an analysis tool for electroencephalogram (EEG) data. It explores the possibility of using the self-organizing map algorithm as starting point in a symbolic analysis based approach, with the aim of reducing the challenges brought by the high dimensionality and complexity of EEG data. The solution represents a pipeline integrating different steps for accomplishing an in-depth analysis: self-organizing map training, clustering, color sequence generation, pattern specificity index, pattern triggered average and peristimulus time histogram computation.
Text Mining of South Korea YouTube Comments on Sidewalk Autonomous Delivery Robots

Sidewalk autonomous delivery robots (SADRs) are set to commence formal operations in South Korea, allowing the general public to encounter and interact with them in public spaces. Consequently, understanding the opinions of the populace regarding SADRs has become imperative, and one viable approach is the analysis of user-generated online comments. This research involved the analysis of 6,598 comments from nine YouTube videos to uncover the perspectives of South Korean YouTube users on SADRs. The text mining was executed through word frequency analysis and LDA topic modeling. Word frequency analysis revealed that, beyond search terms like
Vision-Language Assisted Attribute Learning

Attribute labeling at scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the missing labels as negative or simply ignore them all during training, either of which could hamper the model performance to great extent. To overcome these limitations, in this paper we leverage the available vision-language knowledge to explicitly disclose the missing labels for enhancing model learning. Given an image, we predict the likelihood of each missing attribute label assisted by an off-the-shelf vision-language model, and randomly select to ignore those with high scores in training. Our strategy strikes a good balance between fully ignoring and negatifying the missing labels, as these high scores are found to be informative on revealing label ambiguity. Extensive experiments show that our proposed vision-language assisted loss can achieve state-of-the-art performance on the newly cleaned VAW dataset. Qualitative evaluation demonstrates the ability of the proposed method in predicting more complete attributes.
Research on MBTI Testing System Based on the Combination of Flask Framework and ORM Technology

Aiming at the problem that the existing personality test system can only give the conclusion of personality type and lacks the consideration of matching personality type with career choice, this paper develops a new MBTI career personality test system. The system uses Flask framework to build the system page, Jinja2 template engine to achieve separation of page display and data transmission, ORM technology to achieve the interaction between the test question bank and the user interface, and finally, Python recommendation algorithm combined with MBTI theory to analyze the tester's career personality. The results of the personality test and the career recommendation are given to provide practical suggestions for the testers. The system test results show that the system can accurately and effectively recommend matching occupations for the tester's personality type, with good matching and high accuracy, achieving the expected purpose.
Hierarchical Inductive Bias in the L2 Textbook-T5 and Child-T5 Language Model: A Study of Data and Architecture

This study aims to investigate neural language models in alignment with Chomsky"s (1965, 1980) proposition regarding the innate human tendency to acquire syntactic rules based on hierarchical structures rather than linear order. We have examined the architectural and dataset factors influencing the acquisition of a syntactic inductive bias during the pre-training of the T5 model. To achieve this objective, particularly concerning the pre-training dataset, we inquire whether there are differences when using datasets of varying complexity. We use two distinct pre-training datasets: Child-Directed Speech and L2-Textbook dataset. Upon examination, we observe that these datasets exhibit different levels of syntactic complexity. Then, with our models, we employ two syntactic transformation tasks: (i) question formation and (ii) passivization. Our results indicate that model depth (number of layers) exerts a more significant influence on hierarchical generalization compared to other model components. Furthermore, we observed that models learn the hierarchical aspects of language more efficiently when exposed to the simpler complexity of the dataset.
ML and Natural Language Processing : Cyberbullying Detection System for Safer and Culturally Adaptive Digital Communities

Abstract Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a multi-tiered system that reliably detects and classifies instances of cyberbullying on a variety of platforms by utilising cutting-edge machine learning and natural language processing approaches. Our algorithm, which was trained on a wide range of datasets, shows excellent accuracy in differentiating between instances of cyberbullying and non-bullying situations while taking linguistic and cultural quirks into account. Furthermore, our flexible system guarantees applicability by adjusting to changing cyberbullying patterns. By promoting safer and more inclusive digital communities, our research helps to design proactive treatments that lessen the effects of online harassment. This study introduces a robust multi-tiered system designed for the detection and classification of cyberbullying across diverse digital platforms. Leveraging state-of-the-art machine learning and natural language processing techniques, our algorithm, trained on extensive datasets, exhibits exceptional accuracy in distinguishing cyberbullying instances from non-bullying scenarios while accommodating linguistic and cultural nuances. The system’s adaptability to evolving cyberbullying patterns ensures continued efficacy. By fostering safer and more inclusive online environments, our research contributes to proactive measures and mitigates the impact of digital harassment.
Extracting Keywords From Text Using NLP On Azure Virtual Machine

NLP methods (Natural Language Processing) are used in this project to approach fetching the all keywords by written content and are deployed on an Azure Virtual Machine (VM). Both texts summarization and information retrieval, keyword extraction is essential. The project seeks to create an accurate and efficient method for identifying and retrieving keywords from several sources text sources by leveraging the plus point of NLP libraries and tools. By utilizing Azure VM's scalability and computational power, the project provides reliable processing of significant amounts of text data. The project's conclusion provides a useful tool for improving document categorization, subject analysis, and content summarization, with the added benefit of utilizing cloud resources for top performance. As a way to determine distinctions between every single viewpoint of the individual by algorithms, we have considered numerous factors models and their implementation in this work. We initially learned how to convert PDF files to text before learning how to get keywords out of documents using a variety of algorithms, including RAKE YAKE TFIDF and modifying them in a way that ensures we receive the best-extracted keywords that relate to the document and aid in comprehending it. In the end, we evaluated various methods using mined keywords. Additionally, it gave us the ability to judge which algorithm was the most effective overall. Given the actual facts, we could make an educated judgment as to which method produces the best keywords.
Translingual Image-to-Text Conversion: Bridging Visual and Multilingual Semantic Representations

This paper presents a novel approach for translingual image-to-text conversion to establish a connection between visual content and multilingual semantic representations. The suggested strategy makes use of deep learning methods to extract valuabledata from images and subsequently generates linguistically diverse textual descriptions in multiple languages. By bridging the gap between visual and linguistic domains, our approach offers a comprehensive solution to the challenge of conveying image content across different languages. Experimental results illustrate the suggested method's efficiency in accurately converting images to multilingual text, thus contributing to enhanced cross-lingual information retrieval and content accessibility.
Imagery contents descriptions for People with visual impairments

Image descriptions are crucial in assisting individuals without eyesight by providing verbal representations of visual content. While manual and Artificial Intelligence (AI)-generated descriptions exist, automatic description generators have not fully met the needs of visually impaired People. In this study, we have examined the problems related to image descriptions reported in existing literature using the Snowballing technique. Through this method, we have identified thirteen issues, including ethical concerns surrounding physical appearance, gender and identity, race, and disability. Furthermore, we have identified five reasons why sighted Individuals often fail to provide descriptions for visual content, highlighting the necessity for accessibility campaigns that raise awareness about the social significance of descriptive sentences. We conducted interviews with eight low-vision volunteers, in which we analyzed the characteristics of descriptive sentences for 25 indoor images and gathered participants’ expectations regarding image descriptions. As a result, we propose a set of Good Practices for writing descriptive sentences aimed to assist automatic tools and sighted Individuals in generating more satisfactory and high-quality image descriptions. We hope our results will emphasize the societal importance of imagery descriptions and inspire the community to pursue further interdisciplinary research to address the issues identified in our study.
A Case Study with a Riverside Community to Improve Apps Instant Payments Access

Computational systems have revolutionized essential services to society. The inaccessibility of instant payment applications can cause obstacles in financial activities between customers. This work aims to improve the accessibility of instant payment services for emergent users. We carried out a case study in a riverside community with 12 participants using the Semiotic Engineering theory (SemEng) across the Communicability Evaluation Method (CEM), identifying the accessibility problems caused by communicability disruptions. Data collection has been organized in three steps: interviews about social aspects, interactions with a payment proposal, and interviews to gather interaction challenges and opportunities. The payment proposal was developed with accessibility guidelines, a report, and manuals of a Brazilian financial institution. With this, we developed 14 design considerations for instant payment applications, which software developers can easily and quickly implement. Lastly, we highlighted the social, regional, and geographic factors that shape the digital interactions of emergent users.
YOLOv5s-FBG: A Small Object Detection Method in Complex Road Background

We propose an enhanced object detection algorithm, YOLOv5s-FBG, to address the challenges associated with false detections and missed detections caused by numerous small targets within complex road backgrounds. Firstly, we introduce a shallow detection layer dedicated to detecting smaller targets, augmenting the original algorithm's three-scale detection to four-scale. This enhancement extends the algorithm's capacity to learn and recognize features of smaller targets. Secondly, inspired by the feature fusion concept of the Weighted Bidirectional Feature Pyramid Network (BiFPN), we introduce a modified version called de-weighted BiFPN. This innovation optimally harnesses deep, shallow, and original feature information, bolstering feature fusion while minimizing information loss during the convolution process, consequently elevating detection accuracy. Lastly, drawing inspiration from the principles of GhostNet, we refine the original Bottleneck architecture to reduce the network's size, parameters, and computational complexity while maintaining detection accuracy. Our experimental findings demonstrate that the refined algorithm, as presented in this paper, achieves a detection accuracy of 94.6% and 96.5% on two distinct datasets: the publicly available autonomous driving dataset KITTI and our self-made rider helmet dataset, Helmet. When compared to the original algorithm, the enhanced algorithm yields an improvement of 1.6% and 1.8% in detection accuracy, achieving detection speeds of 73 FPS and 69 FPS, respectively. This signifies excellent detection accuracy and real-time performance. Furthermore, in comparison to some mainstream object detection algorithms, the improved algorithm in this article exhibits notable advantages.
WDSN: A Weighted Difference Scaling Network Based on the Prototypical Networks for Few-Shot Learning

This paper presented a weighted difference scaling network (WDSN) based on the prototypical network. Firstly, for the intra-class distribution information, we designed a weighting module that assigned different weights to all samples in the same class according to the representativeness of the samples to generate more representative class prototypes. Secondly, we designed a differential scaling module aimed at further improving the classification performance of the prototype network by reducing intra-class differences and amplifying interclass differences. We conducted experiments on two benchmark datasets, and the experimental results showed that WDSN achieved significant progress compared to the prototypical network, and also outperformed some related methods in recent years.
Improvement Reinforcement Learning for Solving Multisensor Collaborative Scheduling Problems

The essence of multi-sensor scheduling is a multi-objective optimization problem, which aims to finding the optimal matching relationship between sensors and multiple motion targets within a period time. several literatures proposed multi-sensor scheduling schemes with various reactive algorithms, but the most of them overlook the changes brought to the scheduling scheme by dynamic environments. To address this issue, first an evaluation module between sensor detection advantage, sensor load, and scheduling timeliness are established in dynamic environments. Second, a reinforcement learning algorithm is investigated to improve the collaborative Scheduling. Furthermore, an improvement Actor-Critic algorithm is proposed, which create a centralized evaluation network, that is built to the requirements of task demand. This effectively enhancement the interaction between sensors and the external environment. Finally, simulations show that the models and algorithm proposed in this paper. It strengthens cooperation between sensors, achieve better convergence, reinforce the impact of dynamic environments on sensors, and generates batter multi-sensor scheduling schemes.
Improved structure on feature extraction for small object detection

Object detection in aerial images is a challenging task, primarily due to the presence of a large number of small-sized objects. In natural images, general-purpose detectors have achieved impressive performance. However, even state-of-the-art general-purpose detectors fall short of expected performance in aerial images. In this paper, we propose an improved framework to enhance the small object detection performance of YOLOv7 in aerial images. We introduce Bi-Level Routing Attention into the YOLOv7 base architecture to efficiently extract features at a finer granularity. Additionally, we employ a more suitable metric for small object detection to enhance the loss function and improve detection performance. We train and test the network on the publicly available VisDrone dataset and conduct ablation and comparative experiments for the proposed improvements. Experimental results demonstrate that the proposed improvements enhance the small object detection capability of the general-purpose detector.
Development of a Shopping Assist Robot

In this study, we developed a shopping assistance robot system. First, we attempted to create an algorithm to find an efficient route to visit multiple sales floors of a product. This algorithm is based on the solution of the Traveling Salesman Problem. The Traveling Salesman Problem is the problem of finding the shortest path that visits all given cities one at a time. By combining the 2-opt method, which is one of the methods for solving the traveling salesman problem, and the A* algorithm, which is a method for finding the shortest path between two points, we were able to create a route that efficiently visits the sales points of multiple products.