Reviewerly/exHarmony huggingface dataset:
Quantum dimensionality reduction by linear discriminant analysis

The dimensionality reduction is generally used as the data preprocessing stage of many machine learning tasks and has become an essential branch of information processing. Due to the exponential growth of data, how constructing reasonable and efficient dimensionality reduction algorithms has been an open challenge recently. In this paper, we propose a quantum linear discriminant analysis algorithm for dimensionality reduction. In the proposed algorithm, the idea of quantum solving linear equations is utilized to obtain the shadow principal components and an intermediate state. After that, the special unitary operations are designed to assist in completing the dimensionality reduction. The theoretical analysis shows the proposed algorithm has exponential acceleration on the number of vectors and a quadratic speedup on the dimensionality of the original data space. Since this algorithm solves the restriction that the between-class scatter matrix must be reversible, it makes the algorithm more applicable. Moreover, instead of getting the quantum state in the projection direction, the algorithm obtains the corresponding quantum state of dimensionality reduction data so that the outputs can be directly applied to other quantum machine learning algorithms. To illustrate it, a simple example, quantum K-nearest Neighbor classification, is provided.
Dual distance adaptive multiview clustering

Adaptively adjusting the graph, by taking the clustering capability into consideration, has become popular in graph-based clustering methods and extended to multiview clustering problem. Existing methods learn the graph from pairwise distances of the data in the original space or a linearly projected space, which requires that those representations can finely reflect the implicit data structure. However, the data structure in high-dimensional space may not always lie on a linear manifold, and this problem becomes more critical in multiview conditions. Aim at this, we propose a multiview clustering method based on adaptive graph and dual distance. Specifically, we fuse the distances computed from nonlinear embedding space and original space. An adaptive graph is then constructed based on the fused distance, and it is more reliable for multiview clustering. In our approach, the clustering result with exact number of clusters can be found without post-processing. We evaluate the proposed method on several multiview datasets, the experimental results show our approach is superior to the state-of-the-art multiview clustering methods.
The prediction of mortality influential variables in an intensive care unit: a case study

The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients’ demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.
A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.
Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods.
Application of an emotional classification model in e-commerce text based on an improved transformer model

With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification.
Improved probabilistic decision‐based trimmed median filter for detection and removal of high‐density impulsive noise

This study focuses on the detection and expulsion of noisy pixels from an image contaminated by impulsive noise. A noise detection approach is developed to avoid the misinterpretation of noise-free pixel as noisy. In order to design the noise removal algorithm, a probabilistic decision-based improved trimmed median filter (PDITMF) algorithm is proposed which is intended to work out the conflict related to the even number of noise-free pixels in the trimmed median filter. It deploys two new estimation techniques for de-noising, namely, improved trimmed median filter (ITMF) and patch else ITMF (PEITMF) as per noise density. At last, the noise detection approach is applied in the proposed PDITMF to build up a new technique called a probabilistic decision-based adaptive improved trimmed median filter (PDAITMF) algorithm. The proposed algorithms, PDITMF and PDAITMF experiment with many standard sample images. Simulation results show the proposed algorithms are capable of detecting and de-noising the contaminated image very efficiently and have a better visual representation. Under the authors' knowledge, the PDAITMF outperforms recently reported algorithms in context to peak signal-to-noise ratio as well as an image enhancement factor with the lower execution time at all noise densities.
Learning Fitness Functions for Machine Programming

The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genetic algorithms have been used successfully for many problems, one criticism is that hand-crafting its fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Our framework presents a novel approach to learn the fitness function using neural networks to predict values of ideal fitness functions. We also augment the evolutionary process with a minimally intrusive search heuristic. This heuristic improves the framework's ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach with several state-of-the-art program synthesis methods and demonstrate that it finds more correct programs with fewer candidate program generations.
The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study

In recent years, a variety of supervised manifold learning techniques have been proposed to outperform their unsupervised alternative versions in terms of classification accuracy and data structure capturing. Some dissimilarity measures have been used in these techniques to guide the dimensionality reduction process. Their good performance was empirically demonstrated; however, the relevant analysis is still missing. This paper contributes to a theoretical analysis on a) how dissimilarity measures affect maintaining manifold neighbourhood structure and b) how supervised manifold learning techniques could contribute to the reduction of classification error. This paper also provides a cross-comparison between supervised and unsupervised manifold learning approaches in terms of structure capturing using Kendall's Tau coefficients and co-ranking matrices. Four different metrics (including three dissimilarity measures and Euclidean distance) have been considered along with manifold learning methods such as Isomap, t-Stochastic Neighbour Embedding ( t-SNE), and Laplacian Eigenmaps (LE), in two datasets: Breast Cancer and Swiss-Roll. This paper concludes that although the dissimilarity measures used in the manifold learning techniques can reduce classification error, they do not learn well or preserve the structure of the hidden manifold in the high dimensional space, but instead, they destroy the structure of the data. Based on the findings of this paper, it is advisable to use supervised manifold learning techniques as a pre-processing step in classification. In addition, it is not advisable to apply supervised manifold learning for visualization purposes since the two-dimensional representation using supervised manifold learning does not improve the preservation of data structure.
Data Movement Is All You Need: A Case Study on Optimizing Transformers

Transformers have become widely used for language modeling and sequence learning tasks, and are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and significant attention has been given to optimizing transformers. Despite this, existing implementations do not efficiently utilize GPUs. We find that data movement is the key bottleneck when training. Due to Amdahl's Law and massive improvements in compute performance, training has now become memory-bound. Further, existing frameworks use suboptimal data layouts. Using these insights, we present a recipe for globally optimizing data movement in transformers. We reduce data movement by up to 22.91% and overall achieve a 1.30x performance improvement over state-of-the-art frameworks when training BERT. Our approach is applicable more broadly to optimizing deep neural networks, and offers insight into how to tackle emerging performance bottlenecks.
Analysis of length of finger segments with different hand postures to enhance glove design

It is important to understand how the hand and fingers elongate and contract with hand posture for optimally fitting and comfortable gloves. Nevertheless, the measurement and analysis of the finger segments for glove designs remain largely neglected. Here, the length and proportion of the finger segments when splayed and during gripping, and between the dorsal and palm sides of 30 participants are 3D scanned and analysed. The full digit lengths change by 7.6–11.9% with hand posture, but the finger segment changes are not proportional. The ratios of the fingertip to distal interphalangeal joint/full digit, and fingertip to the proximal interphalangeal joint/full digit, are important variables. The results are validated with 10 more subjects based on ratings of a ready-to-wear sports glove. Inaccurate proportioning of the finger regions causes shifting which results in displacement and discomfort. This research contributes to glove pattern engineering, with a focus on the finger segments.
Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.
Towards evidence‐based decision‐making for identification and usage of assets in composite software: A research roadmap

Abstract Software engineering is decision intensive. Evidence‐based software engineering is suggested for decision‐making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open‐source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision‐making for composite software. This paper presents a roadmap for research required to support evidence‐based decision‐making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5‐year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence‐based decision‐making and those related to supporting the decision‐making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence‐based decision‐making for composite software.
The Evolution and Approaches of Information Analysis Service of University Libraries in China

The role of reference and information service has been redefined in some Chinese university libraries. In these libraries the focus of reference and information service has transformed into information analysis. University libraries have been a participant in the management and evaluation in scientific research, technological innovation, and discipline construction. The information analysis service of Zhejiang University Library is a typical case. Zhejiang University library established an information analysis group in 2009 in reference and information service division. To further guarantee the professionalism of service, the information analysis service center was set up in 2018 as a separate division. Over the past decade, information analysis service has played the potential of library in supporting decision-making, scientific research and innovation, discipline construction, research performance evaluation, etc. Information analysis service makes the popularity and status of the university library improve continuously. The evolution of information analysis service of Zhejiang University Library is reviewed in this paper, from the initial stage to the expansion stage, and then to the establishment of professional division. Approaches to promote the progress of information analysis service are summarized. The future goals are also discussed from the perspectives of strengthening the innovation mind-set, librarian development, and information analysis literacy.
Generalized Sorting with Predictions

Generalized sorting problem, also known as sorting with forbidden comparisons, was first introduced by Huang et al. [HKK11] together with a randomized algorithm which requires Õ(n3/2) probes. We study this problem with additional predictions for all pairs of allowed comparisons as input. We propose a randomized algorithm which uses O(n log n + w) probes with high probability and a deterministic algorithm which uses O(nw) probes, where w is the number of mistakes made by prediction.
Scene Based Classification of Aerial Images using Convolution Neural Networks

The advent of computer vision and evolution of high-end computing in remote sensing images have embellish various researchers for unprecedented development in remotely sensed aerial images. The requirement to extract essential information stimulated anatomization of aerial images for its usefulness. Deep learning provides state of the art solutions for widely explored visual recognition system and has emerged as an evolutionary area, being applicable to large scale image processing applications. Convolutional Neural Networks (CNNs), an essential component of deep learning algorithms consists of increasing the depth and connections in the processing layers to learn various features of data at different abstract levels. . In this paper, we present an outlook for classifying and extracting the features of aerial images using CNN. We propose a CNN architecture based on various parameters and layers for classification. CNN has been evaluated on two publicly available aerial data sets: UCMerced Land Use and RSSCN7. Experimental results show that the proposed CNN architecture is competent and efficient in terms of accuracy as performance evaluation parameter in comparison with conventional classifiers like Bag of Visual Words (BOVW).
Blended learning based on ebook integrated Youtube in learning mathematics

This study aims to develop an blended learning based on E-book integrated Youtube in mathematics learning geometry material that is valid, practical and effective and to determine students' readiness for the use of blended learning at the E-learning stage in mathematics learning. This research is a development research design development model used is the Analyze, Design, Development, Implementation and Evaluation model. And to see the readiness of students towards the use of Blended Learning at the E-learning stage the researchers used a type of qualitative research using descriptive research methods. This research was conducted for high school students in Negeri 1 Langsa City. The instruments used in this study were (1) Plans for implementing learning and E-books integrated with Youtube; (2) expert validation sheets, (3) teacher and student observation sheets; (4) tests; and (5) questionnaire. The results of the research show that developing a blended learning based on E-book integrated Youtube in mathematics learning geometry materia meets valid, practical and effective criteria. The results of the students' readiness questionnaire towards the use of blended learning in the E-learning stage in mathematics learning showed results on an average indicator with a value of 72.69% with a good category.