Reviewerly/exHarmony huggingface dataset:
Benchmarking Cloud Providers on Serverless IoT Back-End Infrastructures

Internet of Things (IoT) is one of the trending topics in the technological revolution of the last decade. The huge amount of sensors composing IoT systems implies the need for powerful back-end infrastructures that find a perfect habitat in cloud services. Nowadays, many players offer cloud services and it is thus essential for the user to consciously learn which one mostly fits his needs. Among cloud providers, three conquered a leader position in the sector: 1) Amazon Web services; 2) Google cloud platform; and 3) Microsoft azure. In this article, we thoroughly test these providers to highlight their strengths and weaknesses. To produce relevant results, we stress a back-end infrastructure designed to handle a national-sized network of IoT nodes. Our analysis is not limited to the cloud provider performance as a whole, while it also investigates and compares several cloud components separately. As part of the contribution, we also test different time-series databases and we discuss the advantages of such kind of technologies. Finally, an in-depth pricing analysis is conducted to better understand the differences between each platform from an economic perspective.
Integrating contextual information into multi-class classification to improve the context-aware recommendation

Researchers and practitioners in various fields, including e-commerce customization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management, have realized the value of contextual information. Context-aware recommender systems assist users in finding their chosen material in a reasonable amount of time by utilizing information that describes the scenario in which the items will be consumed. For better personalized user recommendation, recommender systems leverage the contextual information in their process of recommendation called context-aware recommendation. Classification is used for context-prediction which represents the prediction of future context based on recorded previous context. The context prediction algorithm’s goal is to recognize typical behavior patterns that have been seen in the past and then offer the most likely continuation of a presently observed collection of context components based on this knowledge. In this article we study the correlation between the multi-class classification and the context-aware recommendation.With this correlation we conclude that the linkage between contextual information and classification enhance and improve the recommendation results.
Plastic: a passengerial marketplace icon

We provide a critical reading of plastic in consumer culture highlighting its furtive omnipresence and supporting role in enabling the consumption of countless products, services, and brands, including many previously identified marketplace icons. We introduce the term “passengerial icon” to explore how the iconicity of plastic is often characterised by its unobtrusive and inconspicuous presence in consumers’ lives. Like a passenger, plastic most typically accompanies consumers on various experiential journeys rather than drives them. Drawing upon Leder’s concept of dys-appearance, we discuss the “absent presence” of passengerial icons as they tend to fade from consumers’ awareness, remaining present but unseen and unthought about until something about them appears to dysfunction. We discuss the dysfunctional appearance of plastic as catalysed most dramatically by environmental and health consequences. Though plastic’s dys-appearance affects society broadly, it is often hermeneutically and fetishistically handled by individuals through precautionary consumption adjustments rather than collective political action.
Flood video segmentation on remotely sensed UAV using improved Efficient Neural Network

Semantic segmentation can be used to analyze the video data taken by UAV in the flood monitoring system. An accurate analysis can help rescue teams to assess and mitigate flood disasters. This paper proposed an improved Efficient Neural Network architecture to segment the UAV video of flood disaster. The proposed method consists of atrous separable convolution as the encoder and depth-wise separable convolution as the decoder. The experimental results reveal that the proposed method outperforms Efficient Neural Networks’ other architecture and gives the highest frame per second.
Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures

Medical data includes clinical trials and clinical data such as patient-generated health data, laboratory results, medical imaging, and different signals coming from continuous health monitoring. Some commonly used data analysis techniques are text mining, big data analytics, and data mining. These techniques can be used for classification, clustering, and machine learning tasks. Machine learning could be described as an automatic learning process derived from concepts and knowledge without deliberate system coding. However, finding a suitable machine learning architecture for a specific task is still an open problem. In this work, we propose a machine learning model for the multi-class classification of medical data. This model is comprised of two components-a restricted Boltzmann machine and a classifier system. It uses a discriminant pruning method to select the most salient neurons in the hidden layer of the neural network, which implicitly leads to a selection of features for the input patterns that feed the classifier system. This study aims to investigate whether information-entropy measures may provide evidence for guiding discriminative pruning in a neural network for medical data processing, particularly cancer research, by using three cancer databases: Breast Cancer, Cervical Cancer, and Primary Tumour. Our proposal aimed to investigate the post-training neuronal pruning methodology using dissimilarity measures inspired by the information-entropy theory; the results obtained after pruning the neural network were favourable. Specifically, for the Breast Cancer dataset, the reported results indicate a 10.68% error rate, while our error rates range from 10% to 15%; for the Cervical Cancer dataset, the reported best error rate is 31%, while our proposal error rates are in the range of 4% to 6%; lastly, for the Primary Tumour dataset, the reported error rate is 20.35%, and our best error rate is 31%.
Simulation methods for squared Euclidean and Mahalanobis type distances for multivariate data and their application in assessing the uncertainty in hierarchical clustering

AbstractThis paper extends the Suzuki and Shimodora method [Suzuki R, Shimodora H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics. 2006;22:1540–1542] for assessing uncertainty in hierarchical cluster analysis to multivariate datasets and examines the reliability of the simulated cluster probabilities in relation to the simulation method adopted. The extension is applied to squared Euclidean and Mahalanobis-type distances and employs three simulation methods, the Monte-Carlo and bootstrap methods, and a new proposed method, the distance distribution. The distance distribution method is very fast and gives satisfactory predictions for the distance, its standard deviation, skewness, and kurtosis. The performance of the Monte-Carlo method is equally satisfactory, whereas the bootstrap method usually gives acceptable predictions only for the distance. The distance distribution and Monte-Carlo methods give similar cluster probabilities. The bootstrap probabilities are not very different; thus this method can be used in datasets of unknown distance distribution.KEYWORDS: Biodistance estimationcluster analysisEuclidean distanceMahalanobis distance AcknowledgementsThe authors would like to thank two anonymous reviewers for their valuable input.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingEN’s contribution to this work was co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation [Project: EXCELLENCE/1216/0023]. This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 811068.
Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources

Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution-combining data from multiple sources-faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus(T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average(WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making.
Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

In order to improve the accuracy and efficiency of library information recommendation, this paper proposes a fast recommendation algorithm for library personalized information based on density clustering. According to the analysis of the clustering principle, the algorithm achieves the clustering of library information by designing density interval function. Then, the collection priority of library personalized information is judged, and the library personalized information is recommended quickly by designing tags according to the library users’ preferences. Experimental results show that the recommendation accuracy and F value of the proposed algorithm are higher than those of the two traditional algorithms, and its coverage rate is higher and the mean absolute error is lower, indicating that the proposed algorithm effectively achieves the design expectation.
Weapon Detection Using Faster R-CNN Inception-V2 for a CCTV Surveillance System

Thailand has faced unrest in recent years, as have other countries around the world. The continuation of present trends means a tendency for an increase in both crimes against people and property. Nowadays, CCTV technology is widely used as surveillance and monitoring tools to help keep people safe. However, most of them still rely primarily on police personnel to inspect the displays. A weapon detection system can reduce the screen-reading workload of police officers with a limited workforce. The integration of weapon detection with CCTV cameras has a role to play in solving the problem. To develop the weapon detection system, the datasets used in this research were collected from 2 public datasets: ARMAS Weapon detection dataset and IMFDB Weapon detection system. The object detection method was used from TensorFlow Object Detection API using 1) SSD MobileNet-V1, 2) EfficientDet-D0 and 3) Faster R-CNN Inception Resnet-V2. For all experimental results, the object detection model is the Faster R-CNN Inception V2 using Dataset 1, ARMAS Weapon detection dataset, with the highest mAP of 0.540 with the Average Precision with 0.5 IoU and 0.75 IoU at 0.793 and 0.627, respectively.
FAIR Data Infrastructure

In this chapter the concept of research data management is highlighted in the context of the data publication and data infrastructures. One focus of this contribution lies on the topics of metadata and the FAIR data principles associated with data sharing and data infrastructures such as data repositories. The challenges for researchers and research communities towards open science are discussed and the first steps towards FAIR data infrastructures are illustrated.
Hybrid Network with Multi-Level Global-Local Statistics Pooling for Robust Text-Independent Speaker Recognition

In this paper, we propose a new hybrid system for extracting a speaker embedding vector. More specifically, the proposed system employs a multi-level global-local statistics pooling method in order to aggregate the speaker information within short time-span and utterance-level context. In order to evaluate the proposed system, a set of experiments on the NIST SRE 2016, Short-duration speaker verification (SdSV) Challenge 2021, and VoxCeleb datasets were conducted, and the proposed hybrid network was able to outperform the conventional approaches trained on the same dataset. Moreover, our experiments showed that the proposed system is able to achieve stable performance even when using a relatively smaller dataset, which highlights the efficiency of the proposed system in extracting the speaker-dependent information.
Tripartite Evolutionary Game Analysis of Trust Relationship between Enterprises in a Cloud Manufacturing Environment: A Service Composition Perspective

Due to the existence of limited rationality and information asymmetry, trust between enterprises in the cloud manufacturing environment is characterized by significant complexity. In this paper, to promote the formation of a better trust relationship between enterprises and to ensure the efficient service and stable operation of the manufacturing process to a greater extent, we take into account the subjective cognition of the game parties from the perspective of service combination, introduce the prospect theory, construct an evolutionary game model of “service demander-service provider-platform supervisor,” analyze the evolutionary stabilization strategy, and explore the influence of different factors on the interenterprise trust relationship in the cloud manufacturing environment through numerical simulation. The results show that enhancing the synergy between multiple service providers has a significant impact on improving trust between cloud manufacturing enterprises. The rewards and punishments of the cloud manufacturing platform are conducive to improving interenterprise trust. There exists a critical value dividing opportunistic behavior into explicit and implicit behaviors. When it is less than the critical value, opportunistic behavior is implicit and the service demander trusts the service provider and the transaction continues; when it is greater than the critical value, opportunistic behavior is explicit and the service demander no longer trusts until the service provider is fully trustworthy. The inhibitory effect of good transaction history on opportunistic behavior is evident. Finally, the reasons and effects of the above factors and the subjective perceptions of the game parties in cloud manufacturing are discussed, as well as their concrete manifestations in the CASICloud (China Aerospace Science&amp;Industry Cloud) and relevant suggestions are put forward.
On how neural networks enhance quantum state tomography with limited resources

Quantum state tomography is defined as a process of reconstructing the density matrix of a quantum state and is an important task for various emerging quantum technologies. In this work, we propose a general quantum state tomography framework that employs deep neural networks to reconstruct quantum states from a set of measurements with high efficiency. In particular, we apply it to two cases, including few measurement copies and incomplete measurement. Numerical results demonstrate that the proposed method exhibits a significant potential to achieve high fidelity for quantum state tomography when measurement resources are limited.
Batch feature standardization network with triplet loss for weakly-supervised video anomaly detection

Video anomaly detection refers to detecting anomalies automatically without manual labor, which is of great significance to intelligent security. With the emergence of weakly-supervised learning, the performance of video anomaly detection has been greatly advanced. However, the abnormal frames and their adjacent normal frames often make slight differences, increasing the difficulty and complexity of video anomaly detection. To address this problem, we propose a batch feature standardization module using a special standardization approach to facilitate the identification of obscure abnormal events. Meanwhile, we propose a novel strategy to refine the anomaly degree to classify the anomalous videos into two categories, i.e., weak anomalies and strong anomalies. Then the triplet loss is utilized to further improve the discriminative power of the model. Extensive experiments results demonstrate that our method works well on two benchmark datasets, and obtains a frame-level AUC 97.65% on ShanghaiTech and 84.29% on UCF-Crime, achieving comparable performance with the recent state-of-the-art methods.
An Area-Efficient FPGA Implementation of a Real-Time Multi-Class Classifier for Binary Images

Developing image classification modules in embedded systems is a complex task due to the limited resources available. In this brief, a multi-class image classifier using HOG feature extractor and SVM classifier is proposed for binary images. The novelty of the proposed system is applying two steps of binarization to the HOG technique to improve processing speed and area efficiency. First, HOG features are extracted from binary images to simplify the feature extraction process. Second, block normalization of the HOG is replaced with binarization to reduce hardware resource utilization. Compared to a similar existing work, our system speeds up the classification process while utilizing fewer hardware resources, with an 11.4% higher classification accuracy using the same setting.
A New Integrated Teaching Mode for Labor Education Course Based on STEAM Education

In the current market full of the competition of talents, the current education has focused on how to cultivate the talents needed by the market, and STEAM education concept has also attracted widespread attention. STEAM education is a problem-oriented approach that can effectively improves students' independent thinking and problem-solving ability through multidisciplinary education. Since its proposal, it has been applied to many disciplines, including the labor education course. As a typical interdisciplinary course integration approach based on real scenario theme has provided new ideas for transboundary integration of labor education course. On this basis, the application of STEAM education concept in labor education curriculum was analyzed in detail in this paper to establish a new-type teaching method integrating science, technology, engineering, art, and mathematics while combining the problem-oriented thinking process. Correspondingly, according to the characteristics of the Bayesian network, a college student labor education course online self-study model was established. The Bayesian network mode and non-directive teaching mode were introduced to effectively improve the teaching effect of labor education courses. The study has found that after applying STEAM-based integrated teaching mode in labor education course, students' labor education course grade, labor skills, self-learning capacity, excitation exploration ability, analytical competence, team spirit, and sense of participation have significantly enhanced, indicating that this teaching mode is significantly superior to traditional teaching methods.
Estimating Human Weight from A Single Image

Body weight, as one of the biometric traits, has been studied in both the forensic and medical domains. However, estimating weight directly from 2D images is particularly challenging since visual inspection is rather sensitive to the distance between the subject and camera, even for frontal view images. In this case, the widely used body mass index (BMI), which is associated with body height and weight, can be employed as a measure of weight to indicate health conditions. Previous works on the estimation of BMI have predominantly focused on using multiple 2D images, 3D images, or facial images; however, these cues are not always available. To address this issue, we explore the feasibility of obtaining BMI from a single 2D body image with the dual-branch regression framework proposed in this work. More specifically, the framework comprises an anthropometric feature computation branch and a deep learning-based feature extraction branch. One aggregation layer maps all the features to an estimated BMI value. In addition, a new public 2D image-to-BMI dataset, which contains 4189 images (1477 males and 2712 females) from approximately 3000 subjects with attributes including gender, age, height, and weight, was collected and released to facilitate the study. Extensive experiments confirm that the proposed framework combining anthropometric features and deep features outperforms the single-type feature approaches to BMI estimation in most cases.
Exploring the effectiveness of word embedding based deep learning model for improving email classification

Purpose Classifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model. Design/methodology/approach In this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation. Findings In the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets. Originality/value The experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.