Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Object Recognition and Segmentation using Enhanced Mask R-CNN for Intricate Image Scenes

Vision based object revealing and dissection have been imperative investigation area for autonomous driving atmosphere detection, although conventional object detection and segmentation methods suffer from low finding precision and insufficient mask dissection value to find and segment multiple objects. Complex traffic views. This paper upgraded the R-CNN mask to solve this problem by supplanting the ResNet trunk network through a ResNeXt network that uses group convolution to significantly improve model and feature discrimination. Ensemble convolution significantly progresses model and feature resolution. In this investigation, the R-CNN mask was improved by using the ResNeXt network instead of the ResNet trunk network. Taken from the visibly presented CityScapes dataset for independent driving. The study outcomes disclosed that the modified Mask R-CNN algorithm outperformed the original algorithm by 4.73% and 3.96%, respectively, achieving 67.92% mAP for object finding and 62.59% mAP for segmentation accuracy.
Power Flow Evaluation in Selayar Island Power System as a Result of Wind Farm Installation

Wind speeds between 4 to 15 metres per second provide a significant opportunity for renewable energy generation on Selayar Island. Wind power plants are able to make advantage of different wind speeds. There are 11 diesel generators on Selayar Island, and their combined capacity is 13.17 megawatts (MW). This is the status of the island’s electric power system. When there is a high peak load on Selayar Island, the status of the electric power system means that it is still unable to supply the demand for electrical energy that exists on the island. In addition, the fact that Selayar Island is a popular tourist destination causes a rise in the amount of electrical energy that is required on the island due to the great number of visitors that come to the island. The purpose of this research is to conduct an investigation of how the wind farm plant on Selayar Island is connected to the island’s electrical distribution network. The wind farm that was used for this research has a capacity of 3 MW. At peak loads, the circumstances of the load on the electric power system are modelled and simulated. Changes in the power flow, voltage, and power factor on each bus are all caused by the connectivity of the wind farm to the power system on Selayar Island, which has an effect on these variables. The approach involves installing the wind farm in a variety of different locations on the buses. The objective is to locate the wind farm in the most advantageous location possible. When erected in the incorrect location, wind farms have the potential to produce either an excess or a deficit of voltage. For the purpose of this investigation, several configurations of the wind farm were positioned on bus 2, bus 4, and bus 5 in a randomised order. According to the findings of this research, the location of the wind farm on bus 2 yields the greatest results when compared to the locations of the wind farm on the other buses. There is neither an overvoltage nor an undervoltage that exceeds the tolerance value of safe operating limits at the location of the wind farm that is located on bus 2.
Conduct an In-Depth Investigation into the Utilization of Satellite Data and Machine Learning Techniques for the Prediction of Rainfall

This research aimed to explore the viability of utilizing machine learning algorithms for predicting rainfall. The study involved the examination of four distinct models: linear regression, multilayer perceptron, k-nearest neighbors, and decision tree. These models underwent training using a historical dataset containing recorded rainfall and were subsequently assessed against a separate holdout dataset. The findings revealed that the linear regression model demonstrated exceptional performance, achieving an impressive R2 score, an exceptionally low RMSE, and a modest MAE. Additionally, both the MLP and KNN models displayed commendable accuracy respectively. This study underscores the potential effectiveness of employing linear regression for rainfall forecasting. In forthcoming research endeavours, this method could be harnessed to estimate rainfall in localized regions, contributing to more precise predictions.
A Real-time Deep Learning-based Fault Diagnosis Framework in Power Distribution System with PVs

Detecting and identifying faults is essential for protection systems to minimize downtime and prevent cascading failures. This work proposes a real-time deep learning-based fault diagnosis framework to detect the fault and identify the fault type information. Ground faults, high impedance faults and downed conductors are identified. The framework comprises dynamic state estimation-based fault detection and supervised learning for fault classification. The identified fault is utilized in a time domain dynamic state estimation to estimate the fault location. The fault locating method is not addressed in this paper. The fault classification method uses a 1D convolutional neural network (1D-CNN) for offline training and online classification. With the sample data obtained from merging units, this framework can automatically run dynamic state estimation algorithm to detect the internal fault in a protection zone. After that, the faulted samples are fed into the CNN model to output the fault type. The method is demonstrated with three events which include different types of faults, including downed conductors. The results have demonstrated the capability of the proposed framework to detect faults and identify the fault type and faulted phase.
An Efficient Active Learning based Method for Deepfake Vidoes Model Attribution

To enhance the forensic investigation of deepfake face-swap videos, it is essential to attribute the specific generation model used to create these videos. Despite the remarkable progress made in data-driven approaches, recent algorithms continue to encounter challenges in learning from limited annotated data, thereby limiting their performance. In this paper, we present a novel active learning framework for the model attribution of deepfake videos. Specifically, our approach leverages active learning to select the most informative videos, thereby effectively reducing the annotation effort. To achieve this, we propose a query function based on clustering, where the clustering features are generated by a projection network trained through supervised contrastive learning. Extensive experiments demonstrate that our method achieves state-of-the-art performance in model attribution of deepfake videos, surpassing other baseline methods in the field of active learning.
Autoencoder-Based Unsupervised Anomaly Detection in Induction Motors Diagnosis

The autoencoder is widely used in anomaly detection. The anomaly detection-based approach assumes a reconstruction error for the anomalous samples higher than the normal ones and can identify abnormal data from normal data. This paper compares two deep autoencoders to earlier anomaly detection in electric motors. Our approach is based on reconstructing the stator current temporal signal for rotor broken bars detection. To evaluate the performance of the Dense and the conv1D autoencoder, we have used an experimental database acquired under varied load conditions and with different types of rotor faults. The results obtained show a promising induction momotornomaly detection approach.
Enhancing Hate Speech Detection through Emoji-based Classification using Bi-LSTM and GloVe Embeddings

The emergence of user-friendly and affordable online networking platforms like YouTube and Instagram have provided people of all ages with a platform to express themselves and share their personal experiences. Regrettably, this has also resulted in surge of harmful and unwarranted statements being made on these platforms, with the aim of damaging someone’s reputation or social status. To combat this, it is crucial for both the government and these platforms to acknowledge and take steps to curb hate speech. However, identifying hate speech automatically can be a daunting task, particularly in tech-savvy countries like India, where non-standard spellings, varied grammatical structures, and code-mixed language with emojis are prevalent. In this study, we delve into the identification of hateful speech within text that incorporates emojis through the implementation of machine learning and deep learning models on code-mixed social media content. Our approach involves analyzing approximately 42,000 tweets that feature both textual and emoji-based features. The results of our recommended approach have been favorable in detecting abusive language in relevant datasets.
PatternRCA: A Pattern-Aware Root Cause Analysis Framework for Multi-Dimensional Time Series

Root cause analysis for multi-dimensional time series from large scale micro-service scenarios aims at identifying the set of anomaly attributes by monitoring operational metrics. The online metrics provide a general indication to investigate these attributes' inter-dependencies and can guide the overall exploration process. However, the problem space for the root cause localization still remains largely challenging due to the combinatorial explosion of possible attribute combinations. This leads researchers and practitioners to (a) assume some prior distributions on the data set; (b) assume some data patterns on the attribute combinations; (c) perform pruning techniques to reduce the search space. Furthermore, state-of-the-art root cause analysis methods are often tied to one or more of these assumptions, which makes it difficult to be robust to general scenarios. In this paper, we conclude the heterogeneity in the data patterns by analyzing several open and industrial datasets. A uniform analytical framework, PatternRCA, is proposed such that it can be aware of the patterns in the metrics while avoiding explicit assumptions about them. We design an offline learning procedure that enables the framework to detect existing data patterns, which then can guide it to do fine-grain exploration in online metrics. Our extensive evaluation results show that PatternRCA outperforms state-of-the-art models with better benchmark results in public datasets. Meanwhile, it can scale to complex root cause analysis tasks on datasets with hybrid patterns in production environments.
Cross Domain Collaborative Filtering: A Graph Neural Network Approach for Accurate and Diverse Recommendations

Collaborative filtering is a widely adopted technique in the field of recommender systems, aiming to predict users’ preferences based on their historical interactions with items. Traditional collaborative filtering methods often face challenges when dealing with cross-domain recommendation scenarios, where user-item interactions are scattered across multiple domains and data sparsity is prevalent. This research paper proposes a novel Graph Neural Network (GNN) approach for Cross-Domain Collaborative Filtering Recommendations to address these issues. The proposed approach leverages the expressive power of GNNs to capture complex and non-linear patterns in user-item interactions while incorporating cross-domain knowledge. The proposed research paper models the recommendation system as a heterogeneous graph, where users and items are represented as nodes, and the interactions between users and items, as well as the relations between domains, are represented as edges. To train the GNN model, mean squared error loss function is used, which jointly optimizes for domain-specific recommendation performance while encouraging knowledge sharing across multiple domains. Experimental results demonstrate the supremacy of the GNN-based method compared to state-of-the-art collaborative filtering techniques, especially in situations where the cold start problem is a major concern. The GNN-based cross-domain collaborative filtering approach not only outperforms traditional collaborative filtering methods but also exhibits robustness in handling heterogeneous and sparse data across multiple domains. The ability to incorporate cross-domain knowledge makes the proposed model a valuable tool for building recommendation systems in complex and diverse environments. In conclusion, this research presents a comprehensive investigation of the application of Graph Neural Networks to cross-domain collaborative filtering recommendations. The results highlight the effectiveness and versatility of the proposed approach, offering valuable insights and paving the way for further advancements in recommendation systems for cross-domain scenarios.
Machine Vision for Driver Safety: YOLOv5-powered Real-time Drowsiness Detection

Drowsiness significantly threatens road safety, contributing to numerous accidents and fatalities worldwide. Many incidents could be prevented by alerting drivers when they exhibit drowsiness. The peril of drowsy driving extends beyond merely falling asleep at the wheel; it encompasses instances where drivers are not fully engaged with the road, potentially leading to momentary lapses of consciousness. Video data, captured by a dashboard-installed camera, is the basis for extracting relevant features. Unlike traditional methods that often rely on behavioral indicators, some of which can be intrusive and distracting, or necessitate costly sensor setups, our approach addresses these challenges. This paper presents a lightweight, real-time driver drowsiness detection system implemented as a web application. The system utilizes facial landmarks, computes metrics such as Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR), and employs adaptive thresholding for drowsiness detection. Machine learning algorithms, specifically the YOLOv5 classifier, are integrated to assess the effectiveness of the proposed methodology. Empirical findings highlight the system’s capability to achieve an accuracy of 91%, showcasing the potential of YOLOv5 in enhancing driver safety through efficient drowsiness detection.
CNN-FD:deep Convolutional Neural Network model for Fire Detection

Fire is one of the earliest discoveries made by mankind and has become a turning point in human evolution. Unfortunately, it is a major global disaster. Therefore, a fire system must detect a fire quickly in the shortest possible time to report it to the concerned authorities. This causes unlimited damage that is visible across social and economic levels. For the image classification task, cutting-edge approaches VGG16 [7], VGG19 [7], Inception v3 [8], and Xception [9] are extensively utilized standards. In this paper, we present an efficient deep convolutional neural network model for image fire detection that implements multipath convolutional layers of varying filter sizes (1$\times$1, 1$\times$3, 3$\times$1, and 3$\times$3) to acquire and learn features locally and globally from images. The suggested model is trained and tested using a custom dataset. The performance of the proposed model is superior to that of cutting-edge algorithms [7] –[9].
A Comprehensive Study of the New Audit Report with Key Audit Matters: What have We Learnt from Our First Implementation?

: This paper provides comprehensive evidence on the impacts of the first implementation of the new audit report with key audit matters (KAMs) in Thailand in 2016. Evidence was derived from the analyses of survey questionnaires and archival data. It was found that users pay little attention to the audit reports and have little understanding of audit functions, which has resulted in a continuous presence of a reasonableness gap. Although standard setters and regulators in Thailand have succeeded in narrowing the deficiency standard gap and the deficiency performance gap, further large steps remain to close the deficient standards gap. Weak evidence was also found that the new audit report improves audit quality with an increase in audit fees and audit delays; however, no impact of KAMs on the market reaction was found.
Exploring the Impact of Generative Artiﬁcial Intelligence on the Design Process: Opportunities, Challenges, and Insights

Generative artiﬁcial intelligence (GAI) created a whirlwind in late 2022 and emerged as a transformative technology with the potential to revolutionize various industries, including design. Many design practitioners have also begun utilizing GAI as a design tool, stimulating creativity, integrating data more rapidly, and facilitating iterative design processes. However, while enjoying the convenience of generative artiﬁcial intelligence, many underlying negative impacts can also exist. This study comprehensively explore the potential applications and limitations of generative AI in the design process, this study employs the Double Diamond model as a framework to examine the impact of GAI on the design process. The ﬁndings of this study demonstrate that generative AI holds signiﬁcant potential for optimizing the design process, sig-niﬁcantly increasing the divergent part’s breadth and the convergent part’s efﬁciency. However, existing generative AI has a signiﬁcant limitation in the deﬁne phase. Furthermore, interpreting the authenticity and bias issues in the content generated by generative AI tools depends on the ethical awareness and judgment of the designers using these tools.
flexible power system operations simulation model for assessing wind integration

--With the advent of wind power generation on worldwide power systems, many operators and researchers are analyzing the impacts that higher future amounts may have on system operations. Many of the tools are analyzing longer term impacts on the steady-state operations of power systems and are primarily using cost as a metric. They are also using tools that are often inflexible to accommodating different market designs or operational structures. In this paper a model was developed to mimic operator behavior using a combination of security-constrained unit commitment, security-constrained economic dispatch, and automatic generation control programs. New metrics are used to compare reliability in terms of energy imbalance for different systems or different market and operational structures at very high time resolution. Finally an example application of the tool and results for a test system are shown.
Design of a Generic Dynamically Recon ﬁ gurable Convolutional Neural Network Accelerator with Optimal Balance

: In many scenarios, edge devices perform computations for applications such as target detection and tracking, multimodal sensor fusion, low-light image enhancement, and image segmentation. There is an increasing trend of deploying and running multiple di ﬀ erent network models on one hardware platform, but there is a lack of generic acceleration architectures that support standard convolution (CONV), depthwise separable CONV, and deconvolution (DeCONV) layers in such complex scenarios. In response, this paper proposes a more versatile dynamically recon ﬁ g-urable CNN accelerator with a highly uni ﬁ ed computing scheme. The proposed design, which is compatible with standard CNNs, lightweight CNNs, and CNNs with DeCONV layers, further improves the resource utilization and reduces the gap of e ﬃ ciency when deploying di ﬀ erent models. Thus, the hardware balance during the alternating execution of multiple models is enhanced. Compared to a state-of-the-art CNN accelerator, Xilinx DPU B4096, our optimized architecture achieves resource utilization improvements of 1.08× for VGG16 and 1.77× for MobileNetV1 in inference tasks on the Xilinx ZCU102 platform. The resource utilization and e ﬃ ciency degradation between these two models are reduced to 59.6% and 63.7%, respectively. Furthermore, the proposed architecture can properly run DeCONV layers and demonstrates good performance.
Chapter 4 Mapping Austerity: Geographical Text Analysis of UK Place-Names in The Guardian and The Daily Telegraph

By analysing corpora of newspaper articles from The Guardian and The Daily Telegraph this chapter uses a modified form of geographical text analysis (GTA) to investigate whether (and in what form) mass media texts express a relationship between austerity and UK places. Concordance geoparsing is used to detect the use of place-names occurring within a set span of key terms, such as austerity , policy, crisis, measures and spending . The geoparsing process generates Place-Name Co-occurrences (PNCs), which are comprised of a search term, a place-name, its coordinates, and its surrounding co-text. The coordinates of PNCs can be plotted on maps using GIS (Geographical Information Systems) software, to show patterns in place-name mentions, while the co-text is well-suited to linguistic analysis. Ultimately, GTA facilitates the visualisation of corpora and adds the consideration of physical space to language analysis. The results of GTA show that coverage of UK austerity focuses overwhelmingly on England and that both newspapers are London-centric.
International Communication Strategies of Fujian Folk Dance under the "Belt and Road" Initiative

Within the framework of the 'Belt and Road' initiative, this study probes into the opportunities and challenges, alongside the viability and imperative, pertaining to the global propagation of traditional Fujian folk dance, and analyzes the path of international dissemination and innovative means of dissemination of Fujian folk dance. The study finds that Fujian folk dance, as a traditional Chinese dance art form with distinctive regional characteristics, should pay attention to cultural differences and barriers in the process of dissemination, and adopt innovative means for international dissemination. In order to realize the successful dissemination of Fujian folk dance on the international stage, it is necessary to seek a balance between inheritance and innovation, fully demonstrate its unique artistic value, and continue to integrate cultural differences in order to adapt to the needs of international cultural exchanges.
Evolving Project Based Learning Methodology at the Higher Education Level: A Need for More Guidance and Accountability

Project based learning instructional strategies have been used in the K-12 and college setting to activate learning and engagement. Problem Based Learning allows students to learn by taking on real world problems. Research and literature shows that Project Based Learning can increase collaboration, problem-solving skills, communication, self-direction, creativity, time-management, and work-ethic (Wurdinger & Qureshi, 2015). Moreover, literature reveals that 21st century teaching strategies such as flipped learning can enhance Project Based Learning Strategies (Sams & Bergman, 2013). Recent research and professional discussion reveal that more guidelines and accountability lead to better outcomes when using Project Based Learning (Cooper et al., 2017; Ferren & Anderson, 2016; & Klyoster et al., 2018). This meta-analysis explores the most recent scholarly research in Project Based Learning and addresses the need for guidance and accountability.
De ﬁ ning competencies for training non-native Korean speaking teachers: a Q methodology approach

: Learners of Korean are increasing globally, but this growth can only be sustained by training local non-native teachers. To address this issue, this paper identi ﬁ es the subjective perspectives of Korean language teaching professionals about the main competencies needed by non-native Korean teachers. Following a Q methodology protocol, 35 teachers sorted 42 statements about non-native Korean teacher competencies. The analysis identi ﬁ ed four perspectives. Factor 1 stresses the ability to adapt teaching to the learning context and Korean language pro ﬁ ciency. Factor 2 stresses the importance of pursuing continuous professional development without deeming a near-native language pro ﬁ ciency essential. Factor 3 stresses the need to love the language and the ability to maintain advanced language skills. Factor 4 again stresses the need to love the Korean language, the profession, and the importance of a strong sense of responsibility as an educator. Results are discussed with reference to teacher training program design.