Reviewerly/exHarmony huggingface dataset:
CloudMach: Cloud Computing Application Performance Improvement through Machine Learning

Cloud computing is rapidly becoming the standard through which enterprises of all sizes fulfill their computing infrastructure demands. This work aims at exploring the impact that machine learning algorithms can have on Cloud application behavior profiling and prediction. Although classic machine learning algorithms have been used in Cloud Computing context before, cutting-edge algorithms like deep learning (DL) and reinforcement learning (RL) are yet to be convincingly exploited for this specific problem. Despite being a revelation with fields like image processing and speech recognition, these algorithms (deep neural networks for instance) face adoption challenges outside certain topics. There is a high demand for timely research work that dissects these algorithms and develops novel techniques to facilitate seamless adoption for Cloud providers and clients. In this work, we evaluate the efficiency of machine learning algorithms in the Cloud context by applying them to a large scale application resource utilization data set (TU Delft Bitbrains traces). The objective is to design a Cloud application behavior prediction technique based on machine learning predictors. Any improvement on prediction precision has direct impact on key performance indicators for both Cloud providers and Cloud tenants/clients. Experimental results show the potential of our approach to improve Cloud resource scheduling in a Cloud data center.
CARE: Context-awareness for elderly care

Abstract We present CARE, a context-aware tool for nurses in nursing homes. The system utilises a sensors infrastructure to quantify the behaviour and wellbeing ( e.g., activity, mood, social and nurse interactions) of elderly residents. The sensor data is offloaded, processed and analysed in the cloud, to generate daily and long-term summaries of residents’ health. These insights are then presented to nurses via an Android tablet application. We aim to create a tool that can assist nurses and increase their awareness to residents’ needs. We deployed CARE in a local nursing home for two months and evaluated the system through a post-hoc exploratory analysis and interviews with the nurses. The results indicate that CARE can reveal essential insights on the wellbeing of elderly residents and improve the care service. In the discussion, we reflect on our understanding and potential impact of future integrated technology in elderly care environments.
Image Dehazing Algorithm Based On Improved Guided Filtering

Abstract Aiming at the problem that the image defogging algorithm based on the dark channel prior will cause the edge halo effect in the restored image, an image defogging algorithm based on improved guided filtering is proposed. First, the calculation method of atmospheric light value based on the quadtree algorithm is adopted to obtain a more accurate atmospheric light value; then, the edge weight factor based on the Canny operator is added to the guided filtering algorithm to solve the local halo effect; finally, the image is restored based on the atmospheric physical model. Experiments show that this algorithm is better than other algorithms in subjective visual effects, and the image edge defogging is more natural; the comparison of objective performance indicators shows that the improved defogging algorithm has higher image quality and better image restoration.
Lessons from Teaching HCI for a Diverse Student Population

HCI education has been an active area in the past two decades. Many researchers have reported on challenges unique to teaching HCI and investigated different pedagogical approaches to improving the students' learning experience. We add to this body of research by describing our experience in teaching HCI as a mandatory undergraduate course, but with the added complexity of having non-majors and graduate students in it. We describe three pedagogical approaches to teaching this course and summarize the lessons learned. We outline our next steps and hope our discussion can help shape future HCI course developments.
Educational Service and Information Sharing Mechanism of University Library Based on Internet Social Network

The development of Internet social networks provides a new service channel for university library education in the Internet age. Social platform, as one of the tools of university library's external information service, is conducive to the extension of the educational service scope of university libraries to the society. This paper discusses the main characteristics of Internet social network communication and analyses the main information service content of social network in university libraries. And then we elaborate the construction ideas of university library information sharing platform, and propose some Measures for university library to develop educational services through social networks.
What Metadata Matters?: Correlation of Metadata Elements with Click-Through Rates for E-Books and Streaming Video in the Academic Library Catalog

The relationship between metadata and a user’s ability to effectively locate and select a given resource has been a primary interest of library and information professionals. Important questions about how metadata can best meet emerging search behaviors require continuous and engaged analysis. This paper describes such an analysis of click-through rates for e-book and streaming video resources, attempting to correlate use rates with the presence and character of metadata elements in their associated records. We found no strong correlation between the length of a metadata record, or the presence of individual fields, and higher use rates. A discussion of the implications of these findings for further research is presented.
SIFT-Enhanced CNN Based Objects Recognition for Satellite Image

Satellite images usually contain multiple in randomly located objects whose size is minimal since the height of the satellite observation. Because SIFT feature points have the invariant ability the same as CNN feature, to increase the accuracy of satellite images in object recognition, this paper uses the Scale-Invariant feature transform (SIFT) algorithm to locate the objects. Image cropping is applied to get the object region from image features. These images are put into CNN object recognition system. As experiment results, the proposed algorithm can recognize specific objects such as aircraft and ships with accuracy is about 70%.
Improving time series forecasting using information fusion in local agricultural markets

This research explores the capacity of Information Fusion and Data Mining to extract knowledge about associations among agricultural products and achieve better predictions for future consumption in local markets in the Andean region of Ecuador. This commercial activity is performed using Alternative Marketing Circuits (CIALCO), seeking to establish a direct relationship between producer and consumer prices, and promote buying and selling among family groups. The time-series forecasting, presented as a machine learning formulation, is enhanced with multi-variate predictions based on association rules extracted from transactions data analysis. These transactional data are used to learn the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in forecasting accuracy by including these variables in multi-variate forecasting models. In the results we see that, from establishing best association rules valid in the original dataset, we can achieve a considerable improvement in prediction accuracy, validated with independent test subsequences of agricultural products using non-linear regression techniques including neural networks with a varying number of hidden layers.
A Comparative Analysis of Various Filters to Denoise Medical X-Ray Images

in recent years, processing of medical images and analysing them is an evolving field for researchers across the globe. Various medical imaging methods like, Computed Tomography scans (CT scans), and Magnetic Resonance Imaging (MRI), X-rays etc. allow medical practitioners to diagnose various abnormalities in the human body. X ray images play vital role in medical fields for detection of various diseases. But, due to the presence of various noises, important medical information can be corrupted and altered, which surely can lead to incorrect diagnosis. Though advanced medical imaging technologies are being used, still the effect of noise could not be eradicated fully. Moreover, with various medical image processing technologies, medical X-Ray images are being used for automatic detection of various diseases, in such cases also, noises present in the image can affect the accuracy of the learning models. In this research artificial noise modeling was applied to medical X-Ray images, and a comparative analysis was done to find which filtering scheme works better for which type of noises, on the basis of various parameters like MSE, PSNR, SNR, and SSIM. It was found that, for both kind of noises, HMF Filtering is quite effective. Anisotropic Diffusion Filtering also performed satisfactory. Though, no filter could be declared as most effective on both type of noises. But, their performance varied according to type of noise present and evaluation parameters.
Learning Complete 3D Morphable Face Models from Images and Videos

Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity and expression geometry, and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expressions. We present the first approach to learn complete 3D models of face identity and expression geometry, and reflectance, just from images and videos. The virtually endless collection of such data, in combination with our self-supervised learning-based approach allows for learning face models that generalize beyond the span of existing approaches. Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis. Our method also allows for in-the-wild monocular reconstruction at test time. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches. We show that the learned model can also be personalized to a video, for a better capture of the geometry and albedo.
Track Obstacle Detection Algorithm Based on YOLOv3

In this paper, YOLOv3 algorithm is applied to track obstacle detection to achieve the robustness of detection. Firstly, the network structure and principle of YOLOv3 algorithm are introduced. Then, the boundary box prediction function and category prediction of multi-scale target detection of YOLOv3 are analyzed. Finally, the implementation steps of YOLOv3 are given. Experimental results show that, compared with YOLOv2 algorithm, YOLOv3 algorithm has better robustness, more targets can be detected and positioning is more accurate in the case of complex background with good lighting and low illumination contrast. In the case of more pedestrians in front of the track in a good light environment, the number of detected targets increased from 4 to 8, and the average prediction probability of the target category increased from 0.673 to 0.978. For the complex background with human-vehicle mixture, the average prediction probability increased from 0.72 to 0.94. In the case of poor illumination environment in rainy dusk, the detection target increased from 2 cars to 4 cars and 1 pedestrian, and the average forecast probability increased from 0.51 to 0.77. For the small target image of two pedestrians in the distance in front of the track, YOLOv2 algorithm cannot detect the target, while YOLOv3 algorithm can accurately detect the target.
A Multi-Label Feature Selection Based on Mutual Information and Ant Colony Optimization

Multi-label learning aims at assigning more than a class label to each instance. Due to the development of digital technologies, in many real-world applications that are high-dimensional. Feature selection methods are widely used in multi-label learning to reduce the dimensionality of the data. In this paper, multivariate and filter feature selection method based on the ant colony optimization and mutual information is proposed. The proposed method employs the ant colony optimization to rank the features based on their importance. To this end, first the search space is mapped to a graph and then each ant moves through the graph to select a predefined number of features. Moreover, a novel information-theoretic measure is proposed to evaluate the features selected by each ant. This measure uses the concept of mutual information to calculate the relevancy of each feature with a set of labels. This measure is also employed to assess the redundancy between selected features. The pheromones of features are assigned based on the quality of solutions founded by the ants. Finally, the features are sorted based on their assigned pheromones, and then those of top features are chosen as the final feature set. The proposed method is performed on some real-world datasets and the results show the superiority of the proposed method in comparison with some well-known and state-of-the-art methods.
A visualization of family relations inspired by the London metro map

We propose a new visualization of family relations, called family metro map, which is inspired by the famous London Underground Map. A family is represented by a metro line, where the parents are the end nodes and the children the intermediate nodes. We introduce family trees as rooted binary trees and consider the problem of automatically drawing a family metro map for a given family tree without crossing line segments, where the successive nodes of a metro line are placed at equal distances. The inter-node distance may be chosen to depend on the generation of the family, such that metro lines of earlier generations are shown more prominently than metro lines of later generations.
Multi-View Deep Clustering based on AutoEncoder

Abstract In recent years, with the development of deep learning, replacing traditional clustering methods with subspaces extracted by deep neural networks will help better clustering performance. However, due to the instability of unsupervised learning, the features extracted each time are different even if the same data is processed. In order to improve the stability and performance of clustering, we propose a novel unsupervised deep embedding clustering multi-view method, which treats multiple different subspaces as different views through some data expansion methods for the same data. Specifically, our method uses a variety of different deep autoencoders to learn the latent representation of the original data and constrain them to learn different features. Our experimental evaluations on several natural image datasets show that this method has a significant improvement compared to existing methods.
Experimental evaluation of algorithms for computing quasiperiods

Quasiperiodicity is a generalization of periodicity that was introduced in the early 1990s. Since then, dozens of algorithms for computing various types of quasiperiodicity were proposed. Our work is a step towards answering the general question: “Which algorithm for computing quasiperiods to choose?”. The central notions of quasiperiodicity are covers and seeds. We implement algorithms for computing covers and seeds in the original and in new simplified versions and compare their efficiency on various types of data. We also discuss other known types of quasiperiodicity, distinguish partial covers as currently the most promising for large real-world data, and check their effectiveness using real-world data.
Sentiment Analysis For Product Reviews Based on Deep Learning

Abstract With the popularity of the Internet and e-commerce, the sentiment analysis of text can help users to quickly and accurately obtain effective information they are interested in from massive product reviews to purchase satisfactory products. In this paper, a sentiment analysis system for product reviews was designed based on deep learning, and the digital and electronic products on Jingdong Mall with at least 100,000 reviews were crawled as the training data set. After data pre-processing operations such as word segmentation and removal of stop words for product reviews to remove useless features, the feature vectors were constructed based on the bag of words model and word2vec method, and then three classification algorithms, namely LSTM, Naive Bayes and logistic regression were used to model reviews. The LSTM algorithm is significantly superior to Naive Bayes and logistic regression algorithm in the training stage, and provides a reliable reference for the analysis of product reviews.
Detection of social media platform insults using Natural language processing and comparative study of machine learning algorithms

The rise of the digital era has been the most ingenious aspect of the millennium. It brought the world closer, facilitating growth in communication, ideas and thereby trade. The flare of various social media platforms such as Facebook, YouTube, Twitter etcetera precipitated a virtual world which with its many goodness also gave rise to perpetrators who behind their computer screen gained gratification by posting abusive comments in the social media platform. Curbing such activities is the need of the hour in order to protect the vulnerable victims from mental anguish. In this paper, we have proposed approaches to identify and classify social media insults. A comparative analysis between Support vector machines and Random forest has been elaborated in this paper.
Scheduling of Operations in Quantum Compiler

When scheduling quantum operations, a shorter overall execution time of the resulting schedule yields a better throughput and higher fidelity output. In this paper, we demonstrate that quantum operation scheduling can be interpreted as a special type of job-shop problem. On this basis, we provide its formulation as Constraint Programming while taking into account commutation between quantum operations. We show that this formulation improves the overall execution time of the resulting schedules in practice through experiments with a real quantum compiler and quantum circuits from two common benchmark sets.
Quantum Computing for High-School Students An Experience Report

Quantum computing is an emerging field that can revolutionize our ability to solve problems and enable breakthroughs in many areas including optimization, machine learning, chemistry, and drug design. With the increasing computational power of quantum computers and the proliferation of quantum development kits, the demand for a skilled workforce in quantum computing increases significantly. The theory of quantum computing lies at the crossroads of quantum physics, mathematics, and computer science. The field of quantum computing has matured and can now be explored by all students. While today, quantum computers and simulators are readily accessible and programmable over the internet, quantum computing education is just ramping up. This paper describes our experiences in organizing and delivering quantum computing workshops for high-school students with little or no experience in the abovementioned fields. We introduce students to the world of quantum computing in innovative ways, such as newly designed “unplugged” activities for teaching basic quantum computing concepts. Overall, we take a programmatic approach and introduce students to the IBM Q Experience using Qiskit and Jupyter notebooks. Our experiences and findings suggest that basic quantum computing concepts are palatable for high-school students, and-due to significant differences between classical and quantum computing-early exposure to quantum computing is a valuable addition to the set of problem-solving and computing skills that high-schoolers obtain before entering university.
CPI for Runtime Performance Measurement: The Good, the Bad, and the Ugly

Originally used for micro-architectural performance characterization, the metric of cycles per instruction (CPI) is now emerging as a proxy for workload performance measurement in runtime cloud environments. It has been used to evaluate the performance per workload before and after applying a system configuration change and to detect contentions on the micro-architectural resources in workload colocation. In this paper, we re-examine the use of CPI on two representative cloud computing workloads. An alternative metric, reference cycles per instruction (RCPI), is defined for comparison. We show that CPI is more sensitive than RCPI in identifying micro-architectural performance change in some cases. However, in the other cases with a different frequency scaling, we observe a better CPI value given a worse performance. We conjecture that both the observations are due to the bias of CPI towards scenarios with a low core frequency. We next demonstrate that a significant change in either CPI or RCPI does not necessarily indicate a boost or loss in performance, since both CPI and RCPI are dependent on workload intensities. It implies that the use of CPI without referring to the workload intensity is probably inappropriate. This provokes the discussion of the right way to use CPI, e.g., modeling CPI as a dependent variable given other relevant factors as the independent variables.
Path-Planning Dynamic 3D Space Using Modified A* Algorithm

Abstract Path planning is critical to the development of any robotic system or any autonomous moving vehicle in the modern world. The traditional planning algorithms such as the A*, D*, APF algorithms and more have scored great deal of efficiency along the way of path planning and navigating though some like the A* and D* lacks the ability to adopt the three-dimensional environments, since they have been intended to be light and fast in execution. This paper shows a modified version of the A* algorithm that works on 3D sphere space in both simple and cluttered environments. Giving ability to the algorithm to operate with float values and navigate around sphere obstacles safely, which is a closer scenario to the real world environments. The modified algorithm has the ability to avoid dynamic as well as static obstacles, generating semi-smooth and completely safe path from start point to the goal.
Hybrid Multi-population Evolution based on Genetic Algorithm and Regularized Evolution for Neural Architecture Search

This paper presents a hybrid multi-population evolution algorithm based on genetic algorithms and regularized evolution for neural architecture search. The proposed algorithm improves the search quality by maintaining population diversity. The total population is divided into multiple tribes. In each tribe, there are multiple subpopulations with different crossover and mutation strategies. The best individuals in subpopulations are migrated between subpopulations and tribes to guide search direction toward optimum solutions. The experimental results show that the proposed algorithm performs better than traditional multi-population genetic algorithms and comparably compete with regularized evolution and other state of the art NAS algorithm on CIFAR-10 dataset.