Reviewerly/exHarmony huggingface dataset:
Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative Hypergraphs

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users’ positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the cold-start item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, normalized discounted cumulative gain, and hit rate verify the effectiveness of the proposed method.
Diagnosis of Leukemia Disease through Deep Learning using Microscopic Images

The abnormal production of WBC (white blood cell) in the bone marrow is known as leukemia. Leukemia is one of the most affecting diseases around the globe. Several types of ML (machine learning) and DL (deep learning) classification models have been presented in the literature to detect this disease, but they still possess some drawbacks. This study proposes a framework for detecting five classes of leukemia: ALL, AML, CLL, CML, and normal cell. In this research, a pre-trained DCNN (deep convolutional neural network) has been proposed for the detection of leukemia through microscopic images. Pre-processing of microscopic images improves the contrast and removes the noise by enhancing and filtering images. Segmentation of microscopic images is used to highlight the area of the disease. Alex-Net and ResNet-34 architecture are used for classification purposes. After comparing these two models through statistical parameters, ResNet-34 attained the most accurate result than Alex-Net using the publicly available ALL-IDB dataset, the evaluation through the statistical parameters revealed that ResNet-34 attained a classification accuracy of 98.4% on ALL, 98.4% on AML, 98.13% over CLL, 98.14 over CML. AlexNet attained 96.1% classification accuracy on ALL, 95.5% on AML, 95.7% on CLL, and 96.8% on CML. The proposed framework significantly outperforms existing technologies and can be used in clinical applications.
Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning

We introduce Scalar2Vec, a new deep learning solution that translates scalar fields to velocity vector fields for scientific visualization. Given multivariate or ensemble scalar field volumes and their velocity vector field counterparts, Scalar2Vec first identifies suitable variables for scalar-to-vector translation. It then leverages a k-complete bipartite translation network (kCBT-Net) to complete the translation task. kCBT-Net takes a set of sampled scalar volumes of the same variable as input, extracts their multi -scale information, and learns to synthesize the corresponding vector volumes. Ground-truth vector fields and their derived quantities are utilized for loss computation and network training. After training, Scalar2Vec can infer unseen velocity vector fields of the same data set directly from their scalar field counterparts. We demonstrate the effectiveness of Scalar2Vec with quantitative and qualitative results on multiple data sets and compare it with three other state-of-the-art deep learning methods.
Evaluating Features for Machine Learning Detection of Order- and Non-Order-Dependent Flaky Tests

Flaky tests are test cases that can pass or fail without code changes. They often waste the time of software developers and obstruct the use of continuous integration. Previous work has presented several automated techniques for detecting flaky tests, though many involve repeated test executions and a lot of source code instrumentation and thus may be both intrusive and expensive. While this motivates researchers to evaluate machine learning models for detecting flaky tests, prior work on the features used to encode a test case is limited. Without further study of this topic, machine learning models cannot perform to their full potential in this domain. Previous studies also exclude a specific, yet prevalent and problematic, category of flaky tests: order-dependent (OD) flaky tests. This means that prior research only addresses part of the challenge of detecting flaky tests with machine learning. Closing this knowledge gap, this paper presents a new feature set for encoding tests, called Flake16. Using 54 distinct pipelines of data preprocessing, data balancing, and machine learning models for detecting both non-order-dependent (NOD) and OD flaky tests, this paper compares Flake16 to another well-established feature set. To assess the new feature set's effectiveness, this paper's experiments use the test suites of 26 Python projects, consisting of over 67,000 tests. Along with identifying the most impactful metrics for using machine learning to detect both types of flaky test, the empirical study shows how Flake16 is better than prior work, including (1) a 13% increase in overall F1 score when detecting NOD flaky tests and (2) a 17% increase in overall F1 score when detecting OD flaky tests.
KubeSC‐RTP: Smart scheduler for Kubernetes platform on CPU‐GPU heterogeneous systems

Summary Heterogeneous systems composed of multiple CPUs and GPUs are progressively attractive as platforms for high performance computing because of their higher performance. Especially with the use of containers which are rapidly replacing virtual machines as the compute instance of choice in cloud‐based deployments such in Kubernetes clusters. The task scheduling in a heterogeneous environment became one of the most important issues considered by the platform providers. The ability to choose the appropriate device, CPU or GPU, has a direct impact on the performance of a particular system. It reduces total processing time and increases customer satisfaction. In heterogeneous systems, optimizing resource consumption is a critical aspect for cloud service providers. Adequate scheduling of an application implies optimization of its execution time, which results in resource consumption for the service provider. The development of algorithms for scheduling applications in heterogeneous computing systems has received a significant amount of attention in recent years. A variety of efforts are dedicated to the design of such scheduling algorithms. This article is one of those efforts. We present in this work, KubeSC‐RTP, a scheduler for Kubernetes environment using machine learning based on runtime prediction of the applications in order to better select the appropriate device, CPU or GPU.
Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis

Mixed reality (MR) devices provide real-time environments for physical-digital interactions across many domains. Owing to the unprecedented COVID-19 pandemic, MR technologies have supported many new use cases in the health care industry, enabling social distancing practices to minimize the risk of contact and transmission. Despite their novelty and increasing popularity, public evaluations are sparse and often rely on social interactions among users, developers, researchers, and potential buyers.The purpose of this study is to use aspect-based sentiment analysis to explore changes in sentiment during the onset of the COVID-19 pandemic as new use cases emerged in the health care industry; to characterize net insights for MR developers, researchers, and users; and to analyze the features of HoloLens 2 (Microsoft Corporation) that are helpful for certain fields and purposes.To investigate the user sentiment, we collected 8492 tweets on a wearable MR headset, HoloLens 2, during the initial 10 months since its release in late 2019, coinciding with the onset of the pandemic. Human annotators rated the individual tweets as positive, negative, neutral, or inconclusive. Furthermore, by hiring an interannotator to ensure agreements between the annotators, we used various word vector representations to measure the impact of specific words on sentiment ratings. Following the sentiment classification for each tweet, we trained a model for sentiment analysis via supervised learning.The results of our sentiment analysis showed that the bag-of-words tokenizing method using a random forest supervised learning approach produced the highest accuracy of the test set at 81.29%. Furthermore, the results showed an apparent change in sentiment during the COVID-19 pandemic period. During the onset of the pandemic, consumer goods were severely affected, which aligns with a drop in both positive and negative sentiment. Following this, there is a sudden spike in positive sentiment, hypothesized to be caused by the new use cases of the device in health care education and training. This pandemic also aligns with drastic changes in the increased number of practical insights for MR developers, researchers, and users and positive net sentiments toward the HoloLens 2 characteristics.Our approach suggests a simple yet effective way to survey public opinion about new hardware devices quickly. The findings of this study contribute to a holistic understanding of public perception and acceptance of MR technologies during the COVID-19 pandemic and highlight several new implementations of HoloLens 2 in health care. We hope that these findings will inspire new use cases and technological features.
Comparison of text preprocessing methods

Abstract Text preprocessing is not only an essential step to prepare the corpus for modeling but also a key area that directly affects the natural language processing (NLP) application results. For instance, precise tokenization increases the accuracy of part-of-speech (POS) tagging, and retaining multiword expressions improves reasoning and machine translation. The text corpus needs to be appropriately preprocessed before it is ready to serve as the input to computer models. The preprocessing requirements depend on both the nature of the corpus and the NLP application itself, that is, what researchers would like to achieve from analyzing the data. Conventional text preprocessing practices generally suffice, but there exist situations where the text preprocessing needs to be customized for better analysis results. Hence, we discuss the pros and cons of several common text preprocessing methods: removing formatting, tokenization, text normalization, handling punctuation, removing stopwords, stemming and lemmatization, n-gramming, and identifying multiword expressions. Then, we provide examples of text datasets which require special preprocessing and how previous researchers handled the challenge. We expect this article to be a starting guideline on how to select and fine-tune text preprocessing methods.
RANet: A Reliability-Guided Aggregation Network for Hyperspectral and RGB Fusion Tracking

Object tracking based on RGB images may fail when the color of the tracked object is similar to that of the background. Hyperspectral images with rich spectral features can provide more information for RGB-based trackers. However, there is no fusion tracking algorithm based on hyperspectral and RGB images. In this paper, we propose a reliability-guided aggregation network (RANet) for hyperspectral and RGB tracking, which guides the combination of hyperspectral information and RGB information through modality reliability to improve tracking performance. Specifically, a dual branch based on the Transformer Tracking (TransT) structure is constructed to obtain the information of hyperspectral and RGB modalities. Then, a classification response aggregation module is designed to combine the different modality information by fusing the response predicted through the classification head. Finally, the reliability of different modalities is also considered in the aggregation module to guide the aggregation of the different modality information. Massive experimental results on the public dataset composed of hyperspectral and RGB image sequences show that the performance of the tracker based on our fusion method is better than that of the corresponding single-modality tracker, which fully proves the effectiveness of the fusion method. Among them, the RANet tracker based on the TransT tracker achieves the best performance accuracy of 0.709, indicating the effectiveness and superiority of the RANet tracker.
Dynamically induced clustering ant colony algorithm based on a coevolutionary chain

To improve the accuracy and stability of the ant colony algorithm, a dynamically induced clustering ant colony algorithm based on a coevolutionary chain is proposed. First, the pheromone distribution left by ants in small data clusters are divided based on density is used to induce subsequent ants to make selections, thus balancing the convergence speed and solution accuracy. Second, when the algorithm stalls, the coevolutionary chain increases the diversity and stability of the algorithm through population coevolution and dimensionality reduction on chain loops, assisting helping the algorithm get rid of the local optimum. Simulation experiments and rank-sum test analysis showed that the improved ant colony algorithm can effectively balance convergence speed and solution accuracy, and it has better stability.
A Multilingual Neural Coaching Model with Enhanced Long-term Dialogue Structure

In this work we develop a fully data driven conversational agent capable of carrying out motivational coaching sessions in Spanish, French, Norwegian, and English. Unlike the majority of coaching, and in general well-being related conversational agents that can be found in the literature, ours is not designed by hand-crafted rules. Instead, we directly model the coaching strategy of professionals with end users. To this end, we gather a set of virtual coaching sessions through a Wizard of Oz platform, and apply state of the art Natural Language Processing techniques. We employ a transfer learning approach, pretraining GPT2 neural language models and fine-tuning them on our corpus. However, since these only take as input a local dialogue history, a simple fine-tuning procedure is not capable of modeling the long-term dialogue strategies that appear in coaching sessions. To alleviate this issue, we first propose to learn dialogue phase and scenario embeddings in the fine-tuning stage. These indicate to the model at which part of the dialogue it is and which kind of coaching session it is carrying out. Second, we develop a global deep learning system which controls the long-term structure of the dialogue. We also show that this global module can be used to visualize and interpret the decisions taken by the the conversational agent, and that the learnt representations are comparable to dialogue acts. Automatic and human evaluation show that our proposals serve to improve the baseline models. Finally, interaction experiments with coaching experts indicate that the system is usable and gives rise to positive emotions in Spanish, French and English, while the results in Norwegian point out that there is still work to be done in fully data driven approaches with very low resource languages.
Application of Blockchain Technology in Agricultural Water Rights Trade Management

Water is a basic and essential natural resource, and its rational allocation plays a key role in environmental and economic sustainable development. Agriculture consumes a large share of water resources, but the allocation of water rights often deviates from water use in reality. Therefore, an appropriate management method for agricultural water rights trading is needed. In this paper, blockchain technology is applied to address the agricultural water rights trading issue. Firstly, an alliance chain and the practical Byzantine fault tolerance (PBFT) consensus mechanism are adopted to support a smart contract and application. Then, a trading platform based on blockchain for agricultural water rights trading is proposed. Finally, the role and function of a decentralized autonomous organization (DAO) in a self-financing irrigation drainage district (SIDD) are clarified. This study provides a secure and stable platform which can reduce the trading confirmation time and support numerous users. The trading process of agricultural water rights is updated to minimize the cost of water rights’ transactions and improve the system’s efficiency.
Histopathological image recognition of breast cancer based on three-channel reconstructed color slice feature fusion

Although computer-aided diagnosis (CAD) have shown excellent performance in Breast cancer (BC) histopathological image, it commonly requires a high-level network, and the recognition efficiency is frequently unsatisfied due to the complex structure of histopathological image. In this study, a ten-layers convolutional neural network (CNN) model called "ColorDeep" is used to extract color features corresponding to the different tissue parts in cell, pure color image slices obtained by a three-channel separation and reconstruction method are used as model input. Two models are tested on the BreaKHis dataset show that images under four magnifications achieved the recognition accuracy of 96.89%-99.67% at the image level, which is better than many state-of-the-art methods. The characteristics contained by the B channel have the largest effect on BC recognition, and compared to other research results, the proposed model improves the recognition speed on a single image by about 0.1s. More importantly, instead of using large histopathological images to input into the model for BC diagnosis, and instead of segmenting the nuclei, only the reconstructed B-channel features containing the nuclei region in the stained BC image need to be input into the model to enable accurate diagnosis of BC.
Is GitHub Copilot a Substitute for Human Pair-programming? An Empirical Study

This empirical study investigates the effectiveness of pair programming with GitHub Copilot in comparison to human pair-programming. Through an experiment with 21 participants we focus on code productivity and code quality. For experimental design, a participant was given a project to code, under three conditions presented in a randomized order. The conditions are pair-programming with Copilot, human pair-programming as a driver, and as a navigator. The codes generated from the three trials were analyzed to determine how many lines of code on average were added in each condition and how many lines of code on average were removed in the subsequent stage. The former measures the productivity of each condition while the latter measures the quality of the produced code. The results suggest that although Copilot increases productivity as measured by lines of code added, the quality of code produced is inferior by having more lines of code deleted in the subsequent trial.
Design and implementation of environmental design based on new energy technology

In recent years, China’s new energy industry has developed rapidly. The role of new energy in promoting the economy is also becoming more and more obvious. Over time, the world’s energy structure will undergo earth-shaking changes. This paper aims to study how to conduct research on environmental design based on new energy technologies. This paper presents the basic concepts of new energy technology and environmental design. The experimental results of this paper show that although the energy consumption of traditional environmental art design is not increasing all the time, the general trend is rising. From about 65% at the beginning to about 90% at the end, it shows that the energy consumption generated by traditional environmental art design is very huge. This also leads to increased environmental design costs and damage to the environment. The overall energy consumption of environmental art design based on new energy technology is only between 20% and 30%. Whether it is viewed from the perspective of the sustainable development of the economy and society and the protection of the earth’s ecological environment on which human beings depend, or the practical energy supply for some special purposes, the development of new energy is of great strategic significance. This shows that the new energy technology saves a lot of cost for environmental art design, and has little damage to the environment. It can be seen that it is very meaningful to apply new energy technology to environmental design.
Performance Analysis of Machine Learning Algorithms for Erythemato-Squamous Diseases Classification

Now a days Erythemato-squamous diseases is one of the most common and dangerous skin disease peoples across worldwide are suffering from disease. This is a popular class of dermatology. In this paper various machine learning classifiers has been used for Erythemato-squamous diseases (ESDs) classification and their performance has been compared and analyzed. Features plays an important role in accuracy of classifiers, for this purpose a random forest classifier has been used as feature selection algorithm. These features are compared and best 15 features are selected among available 34 features for classification. Supervised machine learning models are trained and their accuracy, f1-score and time taken for classification has been compared. Logistic regression, support vector machine and K-Nearest neighbor classifier achieves 99% accuracy. Time taken by ensemble learning approaches such as AdaBoost, random forest, light GBM, XGBoost, and extra trees classifiers are relatively higher.