Reviewerly/exHarmony huggingface dataset:
ip2text: A Reasoning-Aware Dataset for Text Generation of Devices on the Internet

Nowadays, Internet of Things (IoT) search engines are more and more popular for users to explore devices on the Internet. Table-to-text generation of devices is helpful for users to understand search results from IoT search engines. However, it has yet to be available, and difficult to obtain a good text description of the devices because of lacking quality data for this task. Also, the content is hidden in multiple attributes of the devices, and it takes work to mine them well and directly. Thus, this paper introduces ip2text, a challenging dataset for reasoning-aware table-to-text generation of devices on the Internet. The input data in ip2text are tables, which contain many attributes of devices collected from the Internet. And the output data is their corresponding descriptions. Generating descriptions of devices is costly, time-consuming, and does not scale to Internet data. To tackle this problem, this paper designs an annotation method based on active learning according to the characteristics of devices and studies the performance of existing and typical state-of-the-art models for table-to-text generation on ip2text. The automatic evaluation shows that existing pre-trained baselines could be challenging to perform satisfactorily on ip2text, with BLEU almost all less than 1. Further, the human evaluation shows that BART and T5 are prone to produce hallucinations when reasoning, and results show that Hallucination is more than 0.10. Therefore, it is not easy to achieve satisfactory performance using the existing and mainstream seq2seq models based on the reasoning-aware ip2text. So, continuous improvement is urgently needed for the models and datasets for the table-to-text generation of devices on the Internet.
Towards a Cash-on-Delivery Management Solution Based on Blockchain, Smart Contracts, and NFT

The demand for transporting goods is increasingly diversified, it is associated with the economic development needs of a country. For domestic (eg, Cash-on-delivery - CoD) or international (eg, Letter-of-Credit) shipping models, they require trust with a third party between the seller and buyer, ie, carrier or bank. These two traditional models have a lot of holes that affect the interests of sellers and buyers, so there are many approaches to applying blockchain technology as well as smart contracts. However, some blockchain application models have revealed many shortcomings when removing the role of the shipper. Although many models have been implemented in practice and proven effective, some problems still require the intervention of a trusted third party to resolve the conflict. In this paper, we propose a model based on Blockchain technology, smart contracts to build a CoD system between seller - shipper - buyer, and NFT technology to create electronic invoices (i.e., proof-of-delivery) - dispute resolution if something goes wrong. Our research direction is towards eliminating the role of trusted third parties.
A Study of Measurement of Mentoring Activities Using Text Mining Technology

P-TECH is a pioneering education reform initiative to prepare young people with the academic, technical, and professional skills required for 21st-century jobs and ongoing education. Mentoring is one of the key activities of P-TECH for students to think about their pathways and build engineering attitudes. After each mentoring session, we take surveys to evaluate how the sessions affected the students and we achieved our objectives. In addition to conventional method, we executed systematic analysis of the responses to the open-ended questions using text mining technology. The subjects of this paper were 30 high school sophomores in 2019. We mentored them eight times in 2019 and 2020 and took surveys after each session. We gathered 1784 documents in total as the responses to open-ended questions and analyzed them with a text mining tool. The tool showed the top three frequent nouns were synonyms for “myself”, and the top three correlated nouns of “myself” were “future”, “thought”, and “way”. This indicates many students considered their pathways during the mentoring sessions and discussed them in their surveys. The tool also showed that the top correlated noun/verb phrases were “build… Plan”, “obtain… Advice”, and “have… Interest”. This indicates that students correctly received the messages intended by the mentoring. Our results showed that the mentoring activities helped students consider their future. Text mining was a useful technique to analyze answers to open-ended survey questions.
Data-Augmented Counterfactual Learning for Bundle Recommendation

Bundle Recommendation (BR) aims at recommending bundled items on online content or e-commerce platform, such as song lists or book lists. Several graph-based models have achieved state-of-the-art performance on BR task. But their performance is still sub-optimal, since the data sparsity problem tends to be more severe in real BR scenarios, which limits these models from more sufficient learning. In this paper, we propose a novel graph learning paradigm called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the impact of data sparsity problem and improve BR by introducing counterfactual thinking. Our paradigm consists of two main parts: counterfactual data augmentation and counterfactual constraint. In counterfactual data augmentation, we design a heuristic sampler to generate counterfactual graph views for graph-based models to alleviate the data sparsity. We further propose counterfactual loss to constrain model learning for mitigating the effects of noise in augmented data and achieving more sufficient model optimization. Further theoretical analysis demonstrates the rationality of our design. Extensive experiments of BR models applied with our paradigm on two real-world datasets are conducted to verify the effectiveness of the paradigm.
Causal MRC: Mitigating Position Bias Based on Causal Graph

Extractive Machine Reading Comprehension (MRC) requires models to obtain start and end positions of answers from a given passage. MRC models may tend to rely on position bias as a shortcut, and thus they fail to learn the multi-source knowledge from both passages and questions sufficiently. Recent debiasing methods proposed to exclude the position prior during inference. However, they cannot distinguish the “good” position context and “bad” position bias from the whole prior. In this paper, we propose a novel MRC framework CausalMRC based on causal graph to mitigate position bias. Motivated by causal inference, we design a causal graph for MRC to formulate the position bias as the direct causal effect of passages on answers. Specifically, we mitigate the position bias by subtracting the direct position effect from the total causal effect. Experiments demonstrate that our proposed CausalMRC achieves competitive performance on the biased SQuAD dataset while performing robustly on the original SQuAD.
Blood and Blood Products Management System Based on Blockchain, and NFT Technologies

Nowadays, the need to use blood and its products for the treatment and diagnosis of diseases is increasing. At present, no method can replace blood and its products in the treatment method - the only solution is to utilize the donors. On the one hand, the proposed processes only focus on solving the problem of preservation and transportation between medical facilities based on peer-to-peer and blockchain approaches. On the other hand, proposals to raise people’s awareness of blood donation have not yet been incorporated into current systems. One of those methods is encouraging blood donors to continue participating in the future (i.e., each donor needs some time to recover depending on the blood and blood products they donate). In this paper, we propose a supply chain management model for blood and its products based on Blockchain and smart contracts. In addition, we also review the process of managing electronic certificates after each blood donation to replace the current paper certificates based on NFT technology. Therefore, our work contributes to four aspects: (a) propose a mechanism to manage the supply chain of blood and its products; (b) propose a model to generate electronic blood donation certificates; (c) implement the proposed model; and (d) deploy proof-of-concept on four platforms (i.e., BNB Smart Chain, Fantom, Polygon, and Celo) to choose the most suitable.
MeFormer: Generating Radiology Reports via Memory Enhanced Pretraining Transformer

Writing a radiology image report is a very time-consuming and tedious task. Using AI to generate the report is an efficient approach, but there are still two significant challenges. First, the model requires to be fine-tuned regularly with the increasing number of patients; Secondly, the quality of text generation needs to be improved because medical observations are complex. In order to solve above challenges, we propose Memory Enhanced Pretraining Transformer (MeFormer). It uses the pretrained Vision Transformer, which efficiently reduces the number of training parameters and transfers fruitful knowledge for the downstream task. At the same time, memory module was introduced into Transformer. The salient pattern in radiology reports are memorized through this design, and they can serve as cross-references during text generation, moderately enhancing the quality of generated diagnostic reports. Extensive experiments on two datasets show that our method achieves comparable performance to other state-of-the-art methods.
Zero-Shot Entity Typing in Knowledge Graphs

Knowledge graphs are often highly incomplete due to their large sizes and one major task for knowledge graph completion is entity typing, that is to predict missing types of entities or vice versa. It is especially challenging to perform entity typing when the type is new, i.e., unseen during training, which is known as the zero-shot entity typing problem. Existing entity typing models cannot handle the zero-shot case as it requires the models to be retrained to embed the unseen types, and other zero-shot knowledge graph completion approaches cannot be applied to the entity typing task either. In this paper, we propose a novel zero-shot entity typing approach based on a generation architecture, and introduce a novel feature distribution and semantic encoding method that combines both ontological and textual knowledge. We also construct the first zero-shot entity typing datasets based on commonly used benchmarks. Our experiment evaluation shows the effectiveness of our approach.
Awareness Support with Mutual Stimulation Among People to Enrich Group Discussion in AIR-VAS

While the Internet has made it possible for people all over the world to be connected, they continue to face the barrier of value diversity. Although we often have to consider or respect other people’s values, it is not easy to sense them due to the limitations of our knowledge, experience, and imagination. We tackle this issue by developing AIR-VAS, a discussion support system that supports the mutual consideration of unfamiliar values and thus encourage synergistic communication. AIR-VAS is a group discussion system that supports awareness of other people’s values. AIR-VAS can recognize characteristic opinions raised in a session and share them among all discussion participants. Through the sharing of opinions, people can obtain different viewpoints on the issues currently being discussed so AIR-VAS can stimulate people to generate/evaluate/analyze ideas. AIR-VAS has the function of visualizing statements presented during a discussion in the form of a word co-occurrence network. We realized opinion sharing as the process of network re-construction, including the use of sub-networks to represent the opinions of other groups. Experiments show that the system successfully enhances the diversity of ideas.
Mobile Application Ranking with Transductive Transfer Learning

The drastic increasing of mobile apps make users feel tough when finding Apps they need. An effective ranking service can filter many unrelated apps and provide users with a high quality result list that users need. Many existed work formed ranking service by considering semantics and built topic models and achieved good results, whereas many of them manually assigned weights when cogitating multiple features, which makes ranking result has biases. On the other hand, some of these work merely aimed at specific store of a country, therefore the algorithm cost and effectiveness when these methods are used in other countries or regions is uncertain. In this paper, we put forward an app ranking framework based on transfer learning (ARFT) which is used to rank apps for a given query and ponder relevance and quality simultaneously. ARFT as well avoided the shortage that classical ranking methods need to assign weights manually when facing multiple features and can pore over all features automatically so that ranking results become more natural. Furthermore, leveraging transfer learning, our framework fit app stores of different countries quickly and initially reflected that there exists inner attribute consistency between apps. Such consistency can largely simplify the complexity of future work in researching app stores. Experiments in real App Store dataset show that our framework can have $$50\%$$ of precision and 0.96 of NDCG in top 20 Apps reflecting to the query, better than other comparison methods.
Threat Action Extraction Based on Coreference Resolution

The knowledge of attacks contained in Cyber Threat Intelligence (CTI) reports is very important to effectively identify and quickly respond to cyber threats. However, CTI reports are usually described in natural language, and the existence of the phenomenon of coreference affects threat action extraction, which leads to the absence of some threat actions. In order to further investigate this phenomenon, in this paper the original CTI text is fed into the threat action extraction model after coreference resolution. The experimental results show that reference resolution can effectively improve the performance of models that use NLP technology, especially those that rely on POS tagging technology.
A Method for Generating Radar System Logical Architecture Models Based on Domain Ontology

This paper presents a method for generating a domain ontology-based model of the logical architecture of a radar system in the context of MBSE. The method consists of constructing a domain ontology for the logical architecture of the system and using it as a framework for a Model Knowledge Base, and then devising a method based on this to automatically generate a radar system logical architecture model from an existing radar system functional architecture model. The method aims to store and reuse domain knowledge of the logical architecture model of a radar system in the form of an ontology and to use radar domain expert knowledge to assist the user in the decision making process during the logical architecture model generation. This approach could improve the efficiency of modeling, speed up the design process of the radar system logical architecture model and assists the designer in trade-off exploration during the model generation process to find a better solution.
A Survey on Novel Hybrid Metaheuristic Algorithms for Image Segmentation

In computer vision, a serious issue is image segmentation. The reason is the increasing use of digital cameras. There are problems in segmenting the image into different sections, so numerous algorithms are being proposed that promise to cope with the problem to a greater extent. A novel and interesting approach is the use of hybridized metaheuristic algorithms to resolve the image segmentation problem. This paper focuses on the different hybrid metaheuristic algorithms used in recent years to resolve issues in different categories of images. The performance of hybridization is demonstrated using a few popular metaheuristic algorithms such as Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Spider Monkey Optimization (SMO), Harris Hawk Optimization (HHO), Butterfly Optimization Algorithm (BOA), and Sailfish Optimization (SO) in conjunction with other algorithms. The newly developed hybrid Chaotic Tent Map Function Sailfish Optimization (CTMFSO) is presented to keep pace with the world of hybridized metaheuristics algorithms for multilevel thresholding.
A Semi-supervised Learning Based Method for Identifying Idle Virtual Machines in Managed Cloud: Application and Practice

Due to unreasonable virtual machine (VM) resource planning and complex load variation, the waste of VM resource has become a significant issue for many enterprises. Although existing technical solutions have proven to have certain ability to identify idle VMs, most of them are researched in private cloud or public cloud scenarios. And it lacks an effective method customized for managed clouds, where the previous work still suffers from the challenges of fewer labels, poor data quality and large scale of VMs in production environments. For this reason, we first investigate the resource usage data of thousands of VMs from a real managed cloud. Based on the analysis results, we propose an innovative and practical method to identify idle VMs. Through elaborate data processing, feature engineering, and model training, the proposed method enables to achieve excellent performance. Sufficient experiments based on real data from the managed cloud of Sangfor company also prove its practicality and effectiveness in the production environment. Up to now, this service has been deployed in Sangfor cloud for more than 5 months, continuously detecting over 10K VMs, and helping to save at least 1K vCPU cores, 2.5 TB memory and 100 TB disk space.
MyKSC: Disaggregated Containerized Supercomputer Platform

Recently, with the emergence of cloud-based machine learning application services such as ChatGPT and big data analytics, which require high-volume data learning, demand for large-scale high-performance computing (HPC) resources has significantly increased. However, there are many difficulties in providing cloud-based services owing to the differences in existing HPC resource operation and execution environments. To address these challenges, there are many ongoing research efforts to support cloud application services on HPC systems using container technology. However, there are fundamental differences between traditional HPC and cloud applications that make administrators hesitant to adopt them in actual operating environments. To solve these problems, this paper introduces MyKSC, which provides a unified user interface based on a loosely-coupled architecture. MyKSC is a system applied to Nurion, a supercomputer operated by the Korean National Supercomputing Center (KISTI). Nurion users can use new cloud application services such as Jupyter and RStudio along with existing HPC application services like MPI through MyKSC. To achieve this, MyKSC divides the entire system into Kubernetes and HPC clusters and selects suitable cluster resources to run the application based on user preferences. This paper proposes the loosely-coupled containerized supercomputer platform and introduces its current implementation.
Facemask Detection Using Convolutional Neural Networks

COVID-19 Worldwide health crises have escalated due to pandemic. It is a highly transmissible virus that spreads through inhaling droplets released while a person is talking, sneezing, or coughing. The virus can spread rapidly over close connection with a diseased person or by touching a polluted surface or object. Wear a protective facemask to protect yourself from the Corona virus transmission. This is the finest protection mechanism for survival. Two extra safety measures that must be followed in public places to stop the virus from spreading are wearing a facemask and maintaining a safe social distance. It is a challenging chore to manually check to see if people are correctly wearing face masks and to alert the victim in crowded and public places. In order to make a safe atmosphere that contributes to public safety, we offer an effective facemask detection method that detects the existence of a facemask on people faces in Live Streaming via Webcam and on photos. We employ Convolutional Neural Networks (CNN) to develop a model for real-time facemask identification, it is a type of Deep Neural Network (DNN) that is generally used in picture recognition and categorization. The proposed system is trained and tested using Kaggle datasets. Real-time detection of facemask use is performed by the system using OpenCV and MobileNet. If not, the individual is identified and reported to the appropriate authority.
Analyzing Machine Learning Algorithm for Breast Cancer Diagnosis

The most common cause of mortality for women globally is breast cancer. Malignant and benign tumors can be distinguished in order to diagnose this condition. Because of this, clinicians need a reliable diagnostic technique to distinguish between malignant and benign tumors. In order to identify malignancies, this process must be automated. Many studies have attempted to categorize breast cancer using machine learning algorithms, and it has been shown by many researchers that these algorithms perform better during the diagnosis process. In order to determine which classifier performs better in the classification of breast cancer, machine learning techniques have been utilized in this study. Additionally, this study made use of the Wisconsin breast cancer (Diagnostic) dataset. The primary goal of this work is to compare various classifiers and choose the best classifier that provides the best accuracy. According to the study’s findings, a support vector machine has the highest accuracy (98%) and the fewest false discoveries. The experiments for this study were conducted in Jupyter Notebook, which provides a unique toolbox for machine learning methods, and were handled there.
Sensor-Based Personal Activity Recognition Using Mixed 5-Layer CNN-LSTM and Hyperparameter Tunning

Inertial motion data known as personal activity recognition (PAR) has seen significant growth in the recent years, with applications in health care and research alike. From an abstract perspective, this is due to an increase in the construction of intelligent and smart devices and systems that cover all aspects of human life, including health care, elder care, sports, industry, business, and human sustainability. Activity recognition is a requirement for these environments and systems, which aims to identify the actions, characteristics, and goal line of one or more people based on a chronological and temporal series of observations from one or more sensors. Current research suggests that deep learning techniques are better suited for computerised feature extraction from raw sensor data because conventional deep learning (DL) methods rely on handcrafted features in the extraction process. Based on long short-term memory (LSTM) for time-series areas, the common PAR framework for smart device sensor data is proposed in this proposal. Quatern LSTM grids are the subject of some moderate research into the effects of utilising various kinds of smart device sensor data. A mixture LSTM grid known as Quatern-layer 5-layer CNN-LSTM is proposed to improve personal activity recognition performance in accumulation. The standard technique is assessed on a communal cell phone-based data set from UCI-HAR through different mixes of test age cycles, gender, and approval methods. This learning also makes use of Bayesian optimisation methods because they are useful for fine-tuning the hyperparameters of each LSTM grid. The proposed grid CNN-LSTM grid performs well in activity recognition, as demonstrated by the results of the experiments.