Reviewerly/exHarmony huggingface dataset:
A Survey Paper on Moving Object Detection Using Deep Learning

Moving object detection in Python using deep learning is a powerful technique for accurately identifying and localizing moving objects in images or videos. By leveraging pre-trained models like YOLO or SSD, developers can implement this task efficiently. The Python implementation allows for customization and extension to handle real-time video streams and complex scenarios. This approach is valuable for researchers, practitioners, and enthusiasts interested in moving object detection using deep learning. Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. This survey paper provides study of various methodologies for object detection. This paper provides systematic analysis of various existing object detection techniques with precise and arranged representation.
Key Elements for E-Learning Success in the Context of the Coronavirus Crisis

Using the Analytical Multi-Criteria Hierarchy (AHP) approach and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this article seeks to discover crucial success criteria for e-learning during COVID-19 in order to enhance process education. Based on established assessment criteria and multi-channel e-learning methodologies, data was gathered by interviewing 69 e-learning leaders in educational institutions during COVID-19. We discovered that managing technology, assisting management, educating students about the usage of the e-learning platform, and requiring a high level of information technology exchange from the lecturer are all important. Universities, staff, and students have had the biggest influence on online learning during COVID-19. The most useful of the five learning models is blended learning. These findings show that e-learning preparation plays a significant impact in boosting the educational process during the COVID-19 pandemic, regardless of the specific technology used by an educational institution.
Toward a Federated Model for Human Context Recognition on Edge Devices

Federated Learning is a promising technology to address crucial problems, such as those related to data privacy, involved in training Machine Learning (ML)/Deep Learning (DL) models in a distributed way. On the other hand, Human Activity Recognition (HAR) has recently gained more attention due to the evolution of the technologies involved, such as sensor availability, advances in ML/DL/Edge AI, and IoT. Due to computational resource constraints, techniques must be employed to reduce the effort required to train the model on the device. Meanwhile, there’s the need to customize the ML model of each Federated Learning (FL) client with the specific data collected by that client. The present work explores the FL of an ML model for HAR in a set of twelve simulated FL clients, each with its own set of data from smartphone sensors. The FL loop starts from a global model that was previously trained in a centralized way, using a large dataset, different from the data used individually by each client during the FL. In this way, the FL constitutes a fine-tuning of the base model. The metrics collected are balanced accuracy and loss. Data is pulled from the ExtraSensory dataset, creating a benchmark for future applications across device farms and in-the-wild devices. The results show that our models achieve equivalent or better performance than most methods found in the literature, using a relatively simple Multilayer Perceptron (MLP) model. The proposed method can then reduce the time needed to retrain the model when data is acquired from the device’s own sensors.
Desenvolvimento de um Modelo Ensemble para Responder Questões via Bancos de Dados Estruturados (KB-QA)

The virtual sales market (E-commerce) has expanded a lot nowadays due to the ease and practicality provided by this way of purchase, and the fact that technologies are becoming increasingly accessible. With this, the implementation of virtual assistants by companies can add benefits for both sides of the negotiation (consumers-companies), since the use of virtual assistants can allow the automation of tasks (involving means of communication), thus accelerating solving problems and increasing productivity for customer service, in addition to being able to provide personalized experiences tailored to each customer. In this work we discuss some models and strategies that are relevant to the topic of Knowledge Base Question Answering (KB-QA), and means of developing a KB-QA model capable of answering user questions provided in natural language, based on information contained in knowledge bases (KBs) that can be implemented as part of a virtual assistant. In order to develop a superior KB-QA model via Ensemble by majority vote, the search, analysis and selection of KB-QA models are carried out for the Ensemble’s composition. For the analysis of the KB-QA models, the WebQuestionsSP question bank is used, which is developed to be answered through queries to the Freebase KB. As the main result of this work, three different Ensembles are generated, called Simple Ensemble, Ensemble with Countermeasure and Ensemble with Total Countermeasure, which are state-of-the-art for the KB-QA task considering the WebQuestionsSP database with F1-scores of 75.40%, 78.72% and 81.43% respectively.
Aprendizado few shot learning com redes neurais siamesas aplicado na classificação para E-commerce

Applying artificial intelligence to e-commerce is a trend, however, algorithms that deal with small databases are lacking. This work proposes a model with Siamese neural network in a few shot learning context to classify new samples from an online store. In order to choose the best representative sample to be used in the Siamese network, the random choice and centroid calculation approaches were tested. In addition, an ensemble structure of Siamese network considering 3 and 5 representants was also proposed. Finally, the k-Means algorithm was used to calculate the centroids of the clusters of each class and use them as representative samples in the ensemble. The best result was with the centroid, improving performance from 93% to over 98% accuracy. Different structures were also proposed for the internal network, the best being with three hidden layers and applying the DropOut technique. In addition, it is possible to develop other alternatives to choose representatives and improve feature extractors, improving data quality, being useful to reduce the size of the analyzed data to reduce the complexity of the network in order to reduce costs with the use of servers and data storage in the cloud.
Inteligência Artificial em Robôs de Software Aplicada em Processos Administrativos e Regulatórios do Setor Elétrico

Now a days, more and more the companies need to invest in new information and control technologies to ensure operation, management and performance. This text presents a research and development project that demonstrated the strategic advantage in the use of innovative techniques of Artificial Intelligence, Machine Learning and Robotic Process Automation (RPA-software robots) in administrative and regulatory activities in an energy distribution utility. The combination of these cutting- edge technologies was implemented in two company processes, the application cases reported in the following text. These cases demonstrate how these technologies can be used to add value to companies in short-, medium- and long-term benefits.
Classificação de Produtos Utilizando Técnicas de Few-Shot Learning

E-commerce platforms (marketplaces) receive daily thousands of products belonging to new classes that have not participated in the training process of the algorithm responsible for automating the classification of products. However, it is difficult to constantly update the system with these products, because the cost of retraining the classifiers currently in operation is high due to the large size of the databases. In this sense, the use of product classifiers that use few- shot learning algorithms is an interesting option, as they are capable of being trained using only new classes containing one or few samples per class. Therefore, the k-nearest neighbor (KNN), Matching Networks (MN) and DPGN (Distribution Propagation Graph Network) algorithms were compared in the product classification problem using a database, with 312 classes and 3120 samples, from a marketplace. The tests were performed with k-fold cross-validation, of which the matching networks presented the best result with 93.78% accuracy.
A Report on the E-C Translation of Stance Adverbs with Suffix -ly in Academic Text Teacher-Made Assessment

This paper selects the first, second, and third chapters and some parts of Chapter Four and Seven of the book Teacher-Made Assessment as the material for translation practice, aiming at summarizing and analyzing the translation strategies of -ly stance adverbs in the academic text. Academic texts are mainly used to convey information and are characterized by professionalism, objectivity, interactivity, and conciseness. They are formal, precise, and concise in wording, clear in expression, strong in logic with many complex and long sentences. This paper analyzes three categories of -ly stance adverbs: epistemic, attitudinal, and style adverbs. Epistemic stance adverbs emphasize accuracy, confidence, seriousness, and persuasiveness. Translation techniques include grammatical adjustment and semantic enrichment, showing the accuracy of academic texts. Attitudinal adverbs are a means of value judgment and emotional expression. Their translations reflect the interactive and explicit characteristics of the academic text. Two translation techniques, namely selection of proper attitudinal words and split of sentences, are adopted. Style stance adverbs refer to how information is given. Their translations reflect the professional and standard characteristics of the academic text. Translation techniques of omitting -ly stance adverbs and using formulaic expression are adopted. This paper furthers the investigation into the translation of academic text from the new perspective of -ly stance adverbs. This paper is expected to provide references for translators and readers of academic texts, and to deepen peoples understanding of -ly stance adverbs.
Superconducting route to quantum computing

The basics of the hardware for superconducting quantum computing are described.
JPEG Artifact Removal for Hyperspectral Images Based on Spatial-Spectral Regularization

A hyperspectral image (HSI) contains vast data, storing hundreds of spectral information with higher resolution than an RGB image. Therefore, considering practical aspects such as data transmission and storage, it is essential to explore the application of standard JPEG compression to an HSI. Conventional JPEG artifact removal methods, such as block and mosquito noise, recover a JPEG image by improving spatial smoothness under the quantization constraint of the DCT coefficients. However, these methods do not consider the application of JPEG compression to HSIs and the low rank of the spectral characteristics of HSIs. This paper proposes a novel optimization method for removing JPEG artifacts from an HSI. Specifically, we formulate a convex optimization problem by introducing a regularization term based on the nuclear norm to the conventional optimization problems. This regularization promotes low rankness in the spectral domain. To solve the proposed problem, we utilize the alternating direction method of multipliers (ADMM). Experiments demonstrate the effectiveness of the proposed method compared to several existing methods applied to both remote and non-remote sensing HSIs.
Comparison of improved variational autoencoder models for human face generation

Abstract The variational autoencoder (VAE) model has evolved a number of VAE improved models in the past 10 years, including CVAE, WAE, NVAE, etc. These models have greatly improved the calculation speed of VAE and the resolution of generated images. The main goal of this paper is to compare the principles of these different models and the effect of generating images. The principle analysis method is mainly used to study the improvement direction of the VAE model in different papers. The main ideas for improving the VAE model include optimizing the loss function, optimizing the objective function, introducing other parameters, or improving code efficiency. Some models even add many algorithms in computer vision in for improving image effect. In the experiments in this paper, results show the image results processed by different models. In the current optimal model NVAE, this algorithm has solved most of the VAE image blur problems, and has achieved perfection in image details. The improvement of the VAE model in the future may require innovative ideas. Under the current principle and model structure, the room for improvement of the VAE model is relatively limited. The final experiment shows that to improve the quality of VAE generated images, it is required to optimize the objective function, optimize the algorithm and add image visual processing, and then the generated images will be significantly improved.
Optimization approach in quadratic assignment and graph isomorphism

Abstract Graph matching problem, also known as quadratic assignment problem (QAP) in a practical context, aims at finding the best correspondence between two given graphs. If two nodes are connected in one graph, it would be ideal if their corresponding images are connected in the other. The optimal solution should maximize the number of matches between the two graphs, while minimizing the weight of mismatches. Graph isomorphism is a particular case where an exact correspondence with no mismatches could be found. The quadratic assignment problem itself is hard to solve due to its high computational complexity. In this paper, the researcher analyzes two optimization approaches in detail: convex relaxation and the Fast Approximate QAP algorithm, both trying to solve the problem with lower computational effort. The performances of these two methods are further compared through numerical experiments. This paper focuses on pairs of equal-sized undirected graphs. ρ correlated graphs are constructed to simulate the noise in inexact graph matching problems. After comparing two existing methods, we propose an even faster optimization approach with comparable accuracy. This new approach provides a particular way to approximate an ideal initialization for the traditional Fast Approximate QAP algorithm. The significant reduction in the runtime makes obtaining good correspondence between large graphs feasible.
Machine Learning Through Quantum Computing: Validation of a New and Sustainable Approach

The onset of quantum fever in India is ever growing and fascinating. Being a common chapter to subjects like Computer Science, physics and engineering, the basics of so called "quantum advantage" or "quantum supremacy" foundation is being laid. The usage of quantum computers is keeping on increasing and is no more a theoretical utopia since real quantum computers are now available which have been proved to serve results faster than regular modern-day computers. The workings demand well established such as Shor's and Grover's Algorithms. This paper is a short epiphany of the degree and heights of usage of quantum computers in the field of machine learning. An exponential rise has taken place in understanding machine learning through quantum computing due to its vast ranges through which it can be applied. For instance, if a classical artificial neuron can produce results up to N dimensions, then a corresponding quantum perception has the ability of processing results in about 2 N dimensions. This paper rather excavates the above-mentioned possibilities and tries for understanding new prospects, keeping in mind that quantum machine intelligence will probably be the pioneering research field in the years to come.
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation

Scene understanding is an essential and challenging task in computer vision. To provide the visually- grounded graphical structure of an image, the scene graph has received increased attention due to offering explicit grounding of visual concepts. Previous works commonly get scene graphs by using ground-truth annotations or generating from the target images. However, drawing a proper scene graph for image retrieval, image generation, and multi-modal applications is difficult. The conventional scene graph annotation interface is not easy to use and hard to revise the results. The automatic scene graph generation methods using deep neural networks only focus on the objects and relationships while disregarding attributes. In this work, we propose SGDraw, a scene graph drawing interface that uses object- oriented representation to help users interactively draw and edit scene graphs. SGDraw provides a web-based scene graph annotation and creation tool for scene understanding applications. To verify the effectiveness of the proposed interface, we conducted a comparison study with the conventional tool and the user experience study. The results show that SGDraw can help create scene graphs with richer details and describe the images more accurately than traditional bounding box annotations. We believe the proposed SGDraw can be useful in various vision tasks, such as image generation and retrieval. The project source code is available at https://github.com/zty0304/SGDraw .
Effective history and efficient integration for conversational machine reading comprehension

The task of conversational machine reading comprehension (CMRC) is an extension of single-turn machine reading comprehension to multi-turn settings, to relflect the conversational way in which people seek information. The correlations between multiple rounds of questions mean that the conversation history is critical to solving the CMRC task. However, existing CMRC models ignore the interference that arises from using excessive historical information to answer the current question when incorporating the dialogue history into the current question. In this paper, an effective Question Selection Module (QSM) is designed to select most relevant historical dialogues when answering the current question through question coupling and coarse-to-fine matching. In addition, most existing approaches perform memory inference by stacked RNNs at context word level, without considering semantic information flowing in the direction of conversation flow. In view of this problem, we implement sequential recurrent reasoning at the turn level of the dialogue, where the turn information contains all the filtered historical semantics for the current step. We conduct experiments on two benchmark datasets, QuAC and CoQA, released by Stanford University. The results confirm that our model satisfactorily captures the valid history and performs recurrent reasoning, and our model achieves an F1-score of 83.0% on CoQA dataset and 67.8% on QuAC dataset, outperforming the best alternative model by 4.6% on CoQA and 2.7% on QuAC.
A Design Knowledge Guided Position Encoding Methodology for Implicit Need Identification From User Reviews

Abstract Aspect-based sentiment analysis (ABSA) enables a systematic identification of user opinions on particular aspects, thus enhancing the idea creation process in the initial stages of product/service design. Attention-based large language models (LLMs) like BERT and T5 have proven powerful in ABSA tasks. Yet, several key limitations remain, both regarding the ABSA task and the capabilities of attention-based models. First, existing research mainly focuses on relatively simpler ABSA tasks such as aspect-based sentiment analysis, while the task of extracting aspect, opinion, and sentiment in a unified model remains largely unaddressed. Second, current ABSA tasks overlook implicit opinions and sentiments. Third, most attention-based LLMs like BERT use position encoding in a linear projected manner or through split-position relations in word distance schemes, which could lead to relation biases during the training process. This article addresses these gaps by (1) creating a new annotated dataset with five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI), (2) developing a unified model capable of extracting all five types of labels simultaneously in a generative manner, and (3) designing a new position encoding method in the attention-based model. The numerical experiments conducted on a manually labeled dataset scraped from three major e-Commerce retail stores for apparel and footwear products demonstrate the performance, scalability, and potential of the framework developed. The article concludes with recommendations for future research on automated need finding and sentiment analysis for user-centered design.
Зарубіжні дослідження впливу музики на розвиток емоційного інтелекту особистості

The article examines foreign studies devoted to the study of the influence of music on the development of the emotional intelligence of an individual. Emotional intelligence is defined as a group of mental abilities that are involved in the awareness and understanding of one's own emotions and the emotions of the surrounding people. The review of research begins with an examination of the impact of music on the development of emotionalintelligence in early childhood as a period when all the foundations of personality growth are laid, and music offers a tool for expressing one's thoughts and feelings. The authors regard listening to music at an early age as a bridge between generations. Emphasis is placed on the role of parents in involving children in music and the formation of emotional intelligence. Listening to music and playing a musical instrument can improve emotional intelligence scores in young children, teens, and adults alike. This is due to the neural processes in the cortical and subcortical networks of the brain that are affected by music. Research on the development of emotional intelligence in teenagers and young adults under the influence of playing musical instruments and listening to music of various genres is selectively analyzed. People with high emotional intelligence may, in turn, choose to listen to music to regulate their emotional arousal, to calm themselves. There is some evidence that musical tastes affect emotional intelligence. Most likely, rhythmic music in general is associated with emotions. The article emphasizes that the use of the idea of the influence of music on the development of a person's emotional intelligence, which has gone through a long path of formation and transformation, occupies an important place in the development of the individual, and didactic and psychological tools allow tosignificantly improve not only one's own mental well-being, but also to promote mental and physical the health of society.