Reviewerly/exHarmony huggingface dataset:
Source-Code Generation Using Deep Learning: A Survey

In recent years, the need for writing effective, reusable, and high-quality source code has grown exponentially. Writing source code is an integral part of building any software system; the development phase of the software lifecycle contains code implementation, refactoring, maintenance, and fixing bugs. Software developers implement the desired solution by turning the system requirements into viable software products. For the most part, the implementation phase can be challenging as it requires a certain level of problem-solving skills and the ability to produce high-quality outcomes without decreasing productivity rates or not meeting the business plans and deadlines. Programmers’ daily tasks might also include writing large amounts of repetitive boilerplate code, which can be tedious, not to mention the potential bugs that could arise from human errors during the development process. The ability to automatically generate source code will save significant time and effort invested in the software development process by increasing the speed and efficiency of software development teams. In this survey, we review and summarize the recent studies on deep learning approaches used to generate source code in different programming languages such as Java, Python, and SQL (Structured Query Language). We categorize the surveyed work into two groups, Natural Language-based solutions for approaches that use natural text as input and Computer Vision-based solutions which generate code based on images as input.
Quantum computing perspective for electromagnetic wave propagation in cold magnetized plasmas

Electromagnetic waves are an inherent part of all plasmas—laboratory fusion plasmas or astrophysical plasmas. The conventional methods for studying properties of electromagnetic waves rely on discretization of Maxwell equations suitable for implementing on classical, present day, computers. The traditional methodology is not efficient for quantum computing implementation—a future computational source offering a tantalizing possibility of enormous speed up and a significant reduction in computational cost. This paper addresses two topics relevant to implementing Maxwell equations on a quantum computer. The first is on formulating a quantum Schrödinger representation of Maxwell equations for wave propagation in a cold, inhomogeneous, and magnetized plasma. This representation admits unitary, energy preserving, evolution and conveniently lends itself to appropriate discretization for a quantum computer. Riding on the coattails of these results, the second topic is on developing a sequence of unitary operators which form the basis for a qubit lattice algorithm (QLA). The QLA, suitable for quantum computers, can be implemented and tested on existing classical computers for accuracy as well as scaling of computational time with the number of available processors. In order to illustrate the QLA for Maxwell equations, results are presented from a time evolving, full wave simulation of propagation and scattering of an electromagnetic wave packet by non-dispersive dielectric medium localized in space.
Underwater Image Enhancement Based on Color Feature Fusion

The ever-changing underwater environment, coupled with the complex degradation modes of underwater images, poses numerous challenges to underwater image enhancement efforts. Addressing the issues of low contrast and significant color deviations in underwater images, this paper presents an underwater image enhancement approach based on color feature fusion. By leveraging the properties of light propagation underwater, the proposed model employs a multi-channel feature extraction strategy, using convolution blocks of varying sizes to extract features from the red, green, and blue channels, thus effectively learning both global and local information of underwater images. Moreover, an attention mechanism is incorporated to design a residual enhancement module, augmenting the capability of feature representation. Lastly, a dynamic feature enhancement module is designed using deformable convolutions, enabling the network to capture underwater scene information with higher precision. Experimental results on public datasets demonstrate the outstanding performance of our proposed method in underwater image enhancement. Further, object detection experiments conducted on pre- and post-enhanced images underscore the value of our method for downstream tasks.
Internet of Vehicle Moving Objects Detection System for the Rural Road Networks

The paper presents the concept of a system for detecting moving objects on rural road networks. Pedestrians, wild animals and domestic animals are the most common causes of traffic accidents on country roads. The system is designed to warn the driver in a timely manner about the possibility of the movement of living objects on the road, in order to protect the lives and ensure the health of living beings, as well as the driver's property on rural roads.
Exploring Decentralized Governance: A Framework Applied to Compound Finance

This research proposes a methodology which can be used for measuring governance decentralization in a Decentralized Autonomous Organization (DAO). DAOs, commonly, have the ambition to become more decentralized as time progresses. Such ambitions led to the creation of decentralized governance models that use governance tokens to represent voting power. Relevant research suggests that the distribution of the governance tokens follows centralized accumulations in a few wallets. By studying the accumulations of voting power from a DeFi protocol, this research presents a framework for identifying and measuring decentralization via analyzing all the various governance sub-systems instead of focusing on one or a small group. Governance within a DAO is a multi-layered process. By examining the decentralization of each layer or subsystem within the overarching governance structure, we can compose a comprehensive understanding of the entire protocol. To demonstrate this method, this paper uses the Compound Finance protocol as a case study. The first sub-system that this research discusses is the delegated and self-delegated wallets which are the only entities that can participate in the voting process in the Compound platform. The second sub-system is the actual proposals and votes that have taken place in the protocol's governance. Data is derived directly from the protocol's web data and for two time periods.
Event Extraction for Portuguese: A QA-Driven Approach Using ACE-2005

Event extraction is an Information Retrieval task that commonly consists of identifying the central word for the event (trigger) and the event's arguments. This task has been extensively studied for English but lags behind for Portuguese, partly due to the lack of task-specific annotated corpora. This paper proposes a framework in which two separated BERT-based models were fine-tuned to identify and classify events in Portuguese documents. We decompose this task into two sub-tasks. Firstly, we use a token classification model to detect event triggers. To extract event arguments, we train a Question Answering model that queries the triggers about their corresponding event argument roles. Given the lack of event annotated corpora in Portuguese, we translated the original version of the ACE-2005 dataset (a reference in the field) into Portuguese, producing a new corpus for Portuguese event extraction. To accomplish this, we developed an automatic translation pipeline. Our framework obtains F1 marks of 64.4 for trigger classification and 46.7 for argument classification setting, thus a new state of the art reference for these tasks in Portuguese.
CoBertTC: Covid-19 Text Classification Using Transformer-Based Language Models

Covid-19 has significantly impacted human life, decreasing face-to-face communication and causing an exponential rise in virtual interactions. Consequently, online platforms like news websites, blogs, and social media have become the primary source of information for many aspects, particularly Covid-19-related news. Nonetheless, accurately categorizing Covid-19-related text data is an ongoing research challenge during and after the pandemic. This paper introduces a Covid-19-related text classification system named CoBerTC to address this issue, which consists of three primary modules: transformer-based language model fine-tuning, transformer-based language model inference, and best-performing model selection. Six transformer-based language models are exploited for the text classification task, including mBERT, XML-RoBERTa, mDistilBERT, IndicBERT, MuRIL, and mDeBERTa-V3 on the English Covid-19 text classification corpus (ECovC). The findings reveal that XML-RoBERTa achieved the highest accuracy of 94.22% for the Covid text classification task among the six models.
ETSR-YOLO: An improved multi-scale traffic sign detection algorithm based on YOLOv5

In the application of driverless technology, current traffic sign recognition methods are susceptible to the influence of ambient light interference, target size changes and complex backgrounds, resulting in reduced recognition accuracy. To address these challenges, this study introduces an optimisation algorithm called ETSR-YOLO, which is based on the YOLOv5s algorithm. First, this study improves the path aggregation network (PANet) of YOLOv5s to enhance multi-scale feature fusion by generating an additional high-resolution feature layer to improve the recognition of YOLOv5s for small-sized objects. Second, the study introduces two improved C3 modules that aim to suppress background noise interference and enhance the feature extraction capabilities of the network. Finally, the study uses the Wise-IoU (WIoU) function in the post-processing stage to improve the learning ability and robustness of the algorithm to different samples. The experimental results show that ETSR-YOLO improves mAP@0.5 by 6.6% on the Tsinghua-Tencent 100K (TT100K) dataset and by 1.9% on the CSUST Chinese Traffic Sign Detection Benchmark 2021 (CCTSDB2021) dataset. In the experiments conducted on the embedded computing platform, ETSR-YOLO demonstrates a short average inference time, thereby affirming its capability to deliver dependable traffic sign detection for intelligent vehicles operating in real-world traffic scenes. The source code and test results of the models used in this study are available at https://github.com/cbrook16/ETSR-YOLO.
Community-Engaged User Experience Pedagogy: Stories, Emergent Strategy, and Possibilities

In this article, we discuss the unique challenges of Community-Engaged User Experience (CEUX) by using storytelling and present a framework of emergent patterns (brown, 2017) to make visible labor, practice, and messiness of the process of building, maintaining, and renewing partnerships with community members and partners. We share three models for CEUX engagements: one-to-many, many-to-many, and one-to-plural. Within the models, we detail the structure of each CEUX engagement, what students did, and the affordances and constraints of each model. In addition, we share thoughts or voices from the community partners or collaborators or students engaged in the projects. We conclude by connecting the models to the elements of Emergent Strategy in the section From Patterns to Possibilities where we call on fellow instructors and community partners to embrace abundance-oriented questions.
Fermionic Reduced Density Low-Rank Matrix Completion, Noise Filtering, and Measurement Reduction in Quantum Simulations

Fermionic reduced density matrices summarize the key observables in Fermionic systems. In electronic systems, the two-particle reduced density matrix (2-RDM) is sufficient to determine the energy and most physical observables of interest. Here, we consider the possibility of using matrix completion to reconstruct the two-particle reduced density matrix to chemical accuracy from partial information. We consider the case of noiseless matrix completion, where the partial information corresponds to a subset of the 2-RDM elements, as well as noisy completion, where the partial information corresponds to both a subset of elements and statistical noise in their values. Through experiments on a set of 24 molecular systems, we find that 2-RDM can be efficiently reconstructed from a reduced amount of information. In the case of noisy completion, this results in a multiple orders of magnitude reduction in the number of measurements needed to determine the 2-RDM with chemical accuracy. These techniques can be readily applied to both classical and quantum algorithms for quantum simulations.
An Overview of Making Decisions on Medical Problems Using Fuzzy Cognitive Maps

This paper provides an overview of how fuzzy logic can be applied to make decisions on medical problems. Fuzzy logic is a mathematical framework that can handle uncertainty and imprecision, making it a useful tool in the field of medicine. The process of using fuzzy logic to make medical decisions involves defining the problem, identifying relevant variables, assigning membership functions, defining rules, fuzzification, inference, and defuzzification. Fuzzy logic can be used to make decisions related to diagnosis, treatment, risk assessment, and prognosis. While fuzzy logic has several advantages, including the ability to handle uncertainty, it requires a high level of expertise to develop accurate models. Overall, fuzzy logic can provide valuable insights for medical professionals making decisions in situations where there is no clear-cut answer. We also highlight the advantages of FCMs in medicine, including their ability to model complex relationships and their ability to handle uncertainty and imprecision. They also discuss the limitations of FCMs, such as the difficulty in obtaining reliable data and the need for expert knowledge to develop accurate models. The methods presented can be used as a guideline for constructing FCMs in medical applications, while the applications discussed can serve as a source of inspiration for future research.
Lightweight Cryptography Concepts and Algorithms: A Survey

Evolutionary technologies have been raised under the umbrella of the Internet, such as Cloud computing, The Internet of Things (IoT), and The Internet of Everything (IoE). All these technologies are extensively data exchange channels, while they are not saving channels. Security plays a vital role; thus, the data must not be easily reached by any type or method of attack. The new trends in data protection are looking for easy-to-use, low-consumption, and high-performance methods. Therefore, Lightweight cryptography algorithms appeared strongly in the protection field; since this type of cipher has the flexibility to be used in all the sharing and using of data channels that have different characteristics and resources. This paper applies a light spot-on various encryption lightweight study. Where the paper presents analyzing the study of common symmetric lightweight algorithms implemented in many recent fields and shows the strength point of each of these algorithms.
Uncovering the Semantics of PD Patients' Movement Data Collected via off-the-shelf Wearables

Wearable sensors are used in monitoring patients with neurodegenerative diseases (ND), such as Parkinson Disease (PD), to collect movement data for the analysis and the assessment of patients' symptoms. To become interoperable and interlinked with other related personal health data, collected data through sensors embedded in wearable devices need to be semantically described in a commonly agreed, explicit, and formal way. Personal health records (PHRs) including patients' Magnetic Resonance Imaging (MRIs), medical prescriptions, and medical advice, can provide a unified view of personal health to health specialists, decreasing their efforts to constantly assess patients' condition via traditional methods. This study aims to present our work for collecting movement data of PD patients through wearables, analyzing them to uncover their inherent semantics, and employing these semantic insights to annotate data in a formal and explicit way to facilitate interlinking with other related heterogeneous data. The movement data was collected via unobstructive wearable technology for health monitoring, and existing formal semantic models were examined for their suitability to be reused and extended for the semantic annotation of the collected movement data. Furthermore, this paper reports early work towards representing such knowledge in the form of a Knowledge Graph (KG) to support rule-based high-level event recognition, such as a missing dose event, for monitoring PD patients and alerting their doctors.
A Machine Learning Approach for Effective Software Defect Detection

This paper examines an efficient Machine Learning (ML) strategy for software defect detection, focusing on identifying defective and non-defective software files and investigating the possibility of using exclusively structural code metrics. The real-life dataset, from the source code of twenty-six N otepad++ releases generated with the LocMetrics tool, was used to classify files as defective or non-defective. Seven ML models, namely Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayes, k-Nearest Neighbors, Random Forest, and Extra Trees, were applied and benchmarked using accuracy, precision, recall, F1-score and their macro/weighted metrics. All models were fine-tuned with Randomized Grid Search, trained using to-fold Cross Validation, and evaluated in two steps: test data evaluation and extra unseen data validation. Results revealed that the Extra Trees model outperformed others with an accuracy of 82.09% and retained its dominance in validation and evaluation procedures. Lastly, the most important predictors were the type of source codes (presence of C++), BLOC, and SLOC-L, illustrating that the programming language, the clarity, and readability of the code (denoted by the number of blank lines), and the logical source lines of code play crucial roles in determining the likelihood of software defects.
YouTube Sentiment Analysis on Healthcare Product Campaigns: Combining Lexicons and Machine Learning Models

YouTube comments provide a rich source of data to classify consumers' opinions and emotions towards particular products, brands, and social or health related topics. Previous studies applied either lexicon based or machine learning approaches to classify sentiment through social media comments. This study employs a hybrid strategy that combines machine learning and lexicon-based methods, to classify consumers' sentiment towards health care products promoted through social marketing campaigns in YouTube channels. A total of 59,695 records were exported through the YouTube API, from 18 relevant video campaigns. The comments were labeled using general-purpose sentiment lexicons like TextBlob, VADER and Flair. It was found that 66.4 % of the comments were similarly classified from TextBlob and VADER. However, further analysis revealed that TextBlob generated slightly more accurate results than VADER in the differentiated outcomes. A set of machine learning models were selected to classify the sentiments of the comments, including Logistic Regression (LR), Multinomial Naïve Bayes (Multi.NB), Random Forest (RF), Support Vector Machine (SVM), and Stochastic Gradient Descent Classifier (SGD Classifier). Accuracy, precision, recall, and F1-score were calculated to assess the performance of the algorithms. The most accurate scores were generated from SVM and SGD (accuracy = 91 %, F1-score = 0.89), followed by LR (accuracy = 90%, F1-score = 0.88) and RF (accuracy = 87%, F1-score = 0.84). Implications and limitations are discussed in the paper.
Privacy-Preserving Video Understanding via Transformer-based Federated Learning

Video understanding has been an active area of research over the past several years, which is dominated by deep neural networks like image understanding and other computer vision tasks. In real-world implementations, even after pre-training on large datasets, state-of-the-art deep neural networks for video understanding tasks, such as anomaly detection and action recognition, can greatly benefit from diverse training data from multiple sources to adapt to the specific application. However, sharing videos collected by multiple sources with a central unit may not be feasible in practice due to privacy and communication constraints. Federated Learning (FL),which allows data parties to collaborate on machine learning models while preserving data privacy and reducing communication requirements, can be used to overcome these challenges. Despite significant progress on various computer vision tasks, FL for video understanding tasks has been largely unexplored. To this end, we propose a novel transformer-based approach for video anomaly detection and action recognition, and extensively benchmark the model performance in FL setting. Our results indicate that the proposed approach outperforms all existing state-of-the-art approaches under the centralized (non-FL) setting and performs competitively under the FL setting.
Global-feature Enhanced Network for Fast Semantic Segmentation

Fast-SCNN is a lightweight, real-time dual-branch semantic segmentation network that can capture both global and local information. Global information is essential for segmentation to enhance the semantic understanding. However, but the current deep network branches of Fast-SCNN have limitations in effectively extracting such information. Finding a balance between speed and precision becomes more important as demand for robotics and self-service systems rises. To solve these issues, we propose a Global-feature Enhanced Network (GFEN) for Fast Semantic Segmentation. In particular, the contextual information of the surrounding pixels on the crossover route is retrieved by adding an attention module to the deep branches of the dual branch network, which efficiently extracts global features and achieves a good compromise between accuracy and speed. The attention module known as Criss-Cross Attention (CCA) is capable of efficiently extracting global features with minimal parameter amounts. Extenstive experiments on The efficacy of our suggested strategy is demonstrated by the Cityscapes dataset, and we have obtained 60.546% mIoU and 7.044G FLOPs, with negligible increase in the number of parameters.
GC-MobileSeg: Fast and Accurate Semantic Segmentation Network on Mobile Devices with Global Context Modeling

Transformers have achieved great success in the field of computer vision, such as PP-MobileSeg, a semantic segmentation network on mobile devices, which captures rich semantic information and details through its multi-stage StrideFormer backbone. Despite PP-MobileSeg has shown great performance, its deep network branch captures the global information insufficiently. The fusion module is relatively simple, simply entering the segmentation head for convolution and other operations and it cannot fully fuse multi-scale features. Global information can assist in better describing local semantic information, which is crucial for segmentation. GCBlock integrates global context information into the channel, effectively extracting global contextual information and is lightweight. In this paper, we propose a Fast and Accurate Semantic Segmentation Network on Mobile Devices with Global Context Modeling (GC-MobileSeg). Specifically, we introduce global context modeling into deep network branch of PP-MobileSeg, which can effectively extract global context information, making semantic segmentation accurate and fully extracting global information. Extensive experiments on Cityscapes dataset and ADE20k dataset demonstrate the effectiveness of our proposed GC-MobileSeg. We have obtained 40.20%% mIoU and 2.16(G)FLOPs on ADE20K dataset with base setting, with negligible increase in the number of parameters. 77.58% mIoU and 4.05(G)FLOPs were also obtained on the Cityscapes dataset and base setting, with negligible increase in parameter count.