Reviewerly/exHarmony huggingface dataset:
Network Infrastructure Development in Serang District

This study examines the development of network infrastructure in electronic-based government in Serang District by examining empirically the design of this infrastructure development. The data collection technique used is Library Research on secondary data. Literature study was conducted to obtain secondary data in examining in depth the object of study. Data analysis was carried out using the content analysis approach. The results of the study show that the Office of Communication, Informatics, Encryption and Statistics of Serang Regency must be ready to respond to any technological developments to strengthen the position of the Regional Government of Serang Regency. Based on the results of the study it can be concluded that the Information Technology Network Infrastructure Planning can be a direction, a guide in developing information technology in Serang Regency government agencies. This blueprint serves as a guide for unit/Regional Apparatus/Agency leaders in implementing and making the best use of information technology which will have an impact on increasing productivity and quality of service to the community.
Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.
Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques

Abstract This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra- and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline’s performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics. Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94.7% for IDCDB and 95.2% for BreaKHis, surpassing existing feature engineering-based models (92.1% for IDCDB and 87.7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed feature-engineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models. This pipeline has the potential to enhance breast cancer diagnosis and treatment and may be applicable to other diseases.
IMPACT OF GROWTH SATISFACTION, SUPERVISORY SUPPORT, AND SELF-EFFICACY ON JOB PERFORMANCE: A MEDIATING ROLE OF JOB SATISFACTION

This study analyzed JS as a mediator of the relationship among growth satisfaction, supervisory support, self-efficacy, and job performance. The social exchange theory is reviewed in this study as a hypothetical perspective, which shows the improvement of the hypothesis. A quantitative approach was utilized for this action; 350 questionnaires were distributed in the services sector in Karachi. A sample size of 287 was utilized for the test study. Smart PLS have been utilized as test analysis, and their outcome demonstrates the partial mediation of JS on the relationship among growth satisfaction, supervisory support, self-efficacy, and job performance. The study especially discovered that supervisory support is significantly linked with JS while significantly also linked with job performance. Additionally, self-efficacy is negatively insignificantly linked with JS but significant with job performance. Meanwhile, growth satisfaction is insignificantly linked with JS and significant with job performance. However, JS is significantly linked with job performance.
Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention

Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed image-based features are yet to be included despite correlating with risk. Complex information can be extracted from mammograms using deep-learning algorithms, however, this is a challenging area of research, partly due to the lack of data within the field, and partly due to the computational burden. We propose an attention-based Multiple Instance Learning (MIL) model that can make accurate, short-term risk predictions from mammograms taken prior to the detection of cancer at full resolution. Current screen-detected cancers are mixed in with priors during model development to promote the detection of features associated with risk specifically and features associated with cancer formation, in addition to alleviating data scarcity issues. MAI-risk achieves an AUC of 0.747 [0.711, 0.783] in cancer-free screening mammograms of women who went on to develop a screen-detected or interval cancer between 5 and 55 months, outperforming both IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]) alone when accounting for established clinical risk factors.
Toward Self-Learning End-to-End Task-oriented Dialog Systems

End-to-end task bots are typically learned over a static and usually limited-size corpus. However, when deployed in dynamic, changing, and open environments to interact with users, task bots tend to fail when confronted with data that deviate from the training corpus, i.e., out-of-distribution samples. In this paper, we study the problem of automatically adapting task bots to changing environments by learning from human-bot interactions with minimum or zero human annotations. We propose SL-Agent, a novel self-learning framework for building end-to-end task bots. SL-Agent consists of a dialog model and a pre-trained reward model to predict the quality of an agent response. It enables task bots to automatically adapt to changing environments by learning from the unlabeled human-bot dialog logs accumulated after deployment via reinforcement learning with the incorporated reward model. Experimental results on four well-studied dialog tasks show the effectiveness of SL-Agent to automatically adapt to changing environments, using both automatic and human evaluations. We will release code and data for further research.
Multitask Scheduling on Distributed Cloudlet System Built Using SoCs

With the emergence of IoT, new computing paradigms have also emerged. Initial IoT systems had all the computing happening on the cloud. With the emergence of Industry 4.0 and IoT being the major building block, clouds are not the only solution for data storage and analytics. Cloudlet, Fog Computing, Edge Computing, and Dew Computing models are now available, providing similar capabilities as the cloud. The term cloudlet was introduced first in 2011, but research in this area has picked up only over the past five years. Unlike clouds, which are built with powerful server-class machines and GPUs, cloudlets are usually made using simpler devices such as SoCs. In this paper, we propose a complete novel distributed architecture for cloudlets, and we are also proposing algorithms for data storage and task allocation across various nodes in the cloudlet. This cloudlet system was built using Qualcomm Snapdragon 410c. We have analyzed the architecture and the algorithm for varying workloads, bandwidth and data storage. The primary aim of the algorithm and the architecture is to ensure uniform processing and data loads across the nodes of the system.
Particle Swarm Optimization Algorithm Based on Levy Flight

On account of the shortcomings of particle swarm optimization algorithm(PSO), such as poor global search capability and low convergence accuracy, levy flight [1] and Gaussian mutation [2] are used to propose particle swarm optimization algorithm based on levy flight. In iteration, the particle aggregation degree [3] is calculated, and the corresponding probability is selected to carry out levy flight [4] according to the particle aggregation degree, it strengthen the global optimization ability. Gaussian variation is carried out on particle positions of each iteration, and particles with better fitness are selected for iteration, which enhances the local search capability. At the same time, adaptive perturbation is carried out to the global optimal position of each iteration to increase the optimization precision of the optimal value. The test shows that the convergence rate of the improved algorithm is better than that of PSO, and the convergence accuracy is higher.
AGAIN: A Multimodal Human-Centric Event Retrieval System using dual image-to-text representations

Event retrieval from visual data presents a formidable challenge, requiring the identification of specific events from a pool of similar occurrences based on concise textual descriptions or a sequence of consecutive frames. The AI Challenge HCMC 2023 - Event Retrieval from Visual Data was a competition to encourage scientists to create interactive retrieval systems tailored for extensive Vietnamese video news databases. Participating teams promptly submitted precise event identifications derived from visual data or natural language descriptions. Therefore, we propose a lightweight and user-friendly yet robust event retrieval system, incorporating functionalities such as image-text matching, object detection, automatic speech recognition, and optical character recognition. This system enables users to search for events through natural language descriptions and relevant images. Notably, our team, named "Again," secured the second position on the public leaderboard of the competition using this system.
Study of irrigation performance index in Saba irrigation area

Saba Irrigation Area is located in Gerokgak and Seririt District, Buleleng regency, Bali. The assessment of the irrigation performance index is required to maintain the value of irrigation asset function and condition. Some problems occur in the irrigation area from the standard area of 1548 ha, but the productive land is around 889 ha or about 57% of the standard area. The problems were found in the irrigation canal, which hampered the performance of irrigation downstream, where water cannot flow to rice fields. The wet cross-section of the channel is covered by sediment. Even at several locations, the sediment height has the same elevation as the surrounding surface. The purpose of this study is to obtain value in the form of an assessment index on the primary and secondary irrigation systems, which will be classified into their respective components. Furthermore, rehabilitation priority is determined using the decision-making method with the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). The assessment of irrigation performance index scored 62.94% consisting of physical infrastructure 29.28%; crop productivity 7.24%; supporting facilities 4.95%; personnel organization 10.75%; documentation 3.40%; and water user associations 7.33%. Determining the rehabilitation priority using the AHP method and ANP method showed the same results, i.e. physical infrastructure in rank 1, personnel organization in rank 2, crop productivity in rank 3, water user associations in rank 4, supporting facilities in rank 5, and documentation in rank 6. Determination of the selected rehabilitation in the physical infrastructure aspect is the sub-aspect of irrigation channels. Irrigation channels get the highest score with the worst damage conditions from all sub-aspects.
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, Jose Camacho-Collados. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models

Although large language models (LLMs) have achieved excellent performance in a variety of evaluation benchmarks, they still struggle in complex reasoning tasks which require specific knowledge and multi-hop reasoning. To improve the reasoning abilities, we propose ChatCoT, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs (e.g., ChatGPT). In ChatCoT, we model the chain-of-thought (CoT) reasoning as multi-turn conversations, to utilize tools in a more natural way through chatting. At each turn, LLMs can either interact with tools or perform the reasoning. Our approach can effectively leverage the multi-turn conversation ability of chat-based LLMs, and integrate the thought chain following and tools manipulation in a unified way. Specially, we initialize the early turns of the conversation by the knowledge about tools, tasks, and reasoning format, and propose an iterative tool-augmented reasoning step to perform step-by-step tool-augmented reasoning. The experiment results on two complex reasoning datasets (MATH and HotpotQA) have shown the effectiveness of ChatCoT on complex reasoning tasks, achieving a 7.9% relative improvement over the state-of-the-art baseline.
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources. To study the problem, we construct a personalized knowledge-grounded dialogue dataset Knowledge Behind Persona (KBP), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that the SAFARI framework can effectively produce persona-consistent and knowledge-enhanced responses.
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning

Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs with symbolic solvers to improve logical problem-solving. Our method first utilizes LLMs to translate a natural language problem into a symbolic formulation. Afterward, a deterministic symbolic solver performs inference on the formulated problem. We also introduce a self-refinement module, which utilizes the symbolic solver’s error messages to revise symbolic formalizations. We demonstrate Logic-LM’s effectiveness on five logical reasoning datasets: ProofWriter, PrOntoQA, FOLIO, LogicalDeduction, and AR-LSAT. On average, Logic-LM achieves a significant performance boost of 39.2% over using LLM alone with standard prompting and 18.4% over LLM with chain-of-thought prompting. Our findings suggest that Logic-LM, by combining LLMs with symbolic logic, offers a promising avenue for faithful logical reasoning.
CQE: A Comprehensive Quantity Extractor

Quantities are essential in documents to describe factual information. They are ubiquitous in application domains such as finance, business, medicine, and science in general. Compared to other information extraction approaches, interestingly only a few works exist that describe methods for a proper extraction and representation of quantities in text. In this paper, we present such a comprehensive quantity extraction framework from text data. It efficiently detects combinations of values and units, the behavior of a quantity (e.g., rising or falling), and the concept a quantity is associated with. Our framework makes use of dependency parsing and a dictionary of units, and it provides for a proper normalization and standardization of detected quantities. Using a novel dataset for evaluation, we show that our open source framework outperforms other systems and – to the best of our knowledge – is the first to detect concepts associated with identified quantities. The code and data underlying our framework are available at https://github.com/vivkaz/CQE.
Comparative analysis of deep learning methods for lesion detection on full screening mammography

Breast cancer is the most prevalent type of cancer in women. Although mammography is used as the main imaging modality for the diagnosis, robust lesion detection in mammography images is a challenging task, due to the poor contrast of the lesion boundaries and the widely diverse sizes and shapes of the lesions. Deep Learning techniques have been explored to facilitate automatic diagnosis and have produced outstanding outcomes when used for different medical challenges. This study provides a benchmark for breast lesion detection in mammography images. Five state-of-art methods were evaluated on 1592 mammograms from a publicly available dataset (CBIS-DDSM) and compared considering the following seven metrics: i) mean Average Precision (mAP); ii) intersection over union; iii) precision; iv) recall; v) True Positive Rate (TPR); and vi) false positive per image. The CenterNet, YOLOv5, Faster-R-CNN, EfficientDet, and RetinaNet architectures were trained with a combination of the L1 localization loss and L2 localization loss. Despite all evaluated networks having mAP ratings greater than 60%, two managed to stand out among the evaluated networks. In general, the results demonstrate the efficiency of the model CenterNet with Hourglass-104 as its backbone and the model YOLOv5, achieving mAP scores of 70.71% and 69.36%, and TPR scores of 96.10% and 92.19%, respectively, outperforming the state-of-the-art models.Clinical Relevance — This study demonstrates the effectiveness of deep learning algorithms for breast lesion detection in mammography, potentially improving the accuracy and efficiency of breast cancer diagnosis.
Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance— This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.
The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

In brain imaging research, it is becoming standard practice to remove the face from the individual's 3D structural MRI scan to ensure data privacy standards are met. Face removal - or 'defacing' - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on 'brain age' prediction - a common benchmarking task of predicting a subject's chronological age from their 3D T1-weighted brain MRI. We compared brain-age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per-subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.