Reviewerly/exHarmony huggingface dataset:
Access-oblivious and Privacy-Preserving K Nearest Neighbors Classification in dual clouds

Massive data and data services (such as online classification) can be outsourced to the cloud for better processing and less operation cost. However, if the sensitive information is contained in outsourced data, and user's requests and returned results are visible and controlled by the semi-trusted cloud server, this will lead to serious privacy leakage. To achieve secure and efficient outsourced classification services, both high security and low computation burden should be considered at the same time, but the existing works always focus on only one aspect of them. In this paper, a Privacy-Preserving K-Nearest-Neighbors Classification (PPKNNC) scheme based on two non-colluding servers is proposed. Specifically, two efficient privacy-preserving protocols and random permutation technique are used in the processing of user's requests, which greatly reduces the computational cost and protects the access pattern. Besides, an Elgamal cryptosystem technique called proxy re-encryption is adopted to ensure that every user has an individual secret key, so that the exact key for data decryption is known only to the data owner and not to other entities. Finally, the security analysis and the experiment results further illustrate that the proposed scheme is superior to the existing works in terms of security, computational and communication costs.
An Extensive Review of Web-Based Multi-Granularity Service Composition

The paper reviews the efforts to compose SOAP, non-SOAP and non-web services. Traditionally efforts were made for composite SOAP services, however, these efforts did not include the RESTful and non-web services. A SOAP service uses structured exchange methodology for dealing with web services while a non-SOAP follows different approach. The research paper reviews the invoking and composing a combination of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various devices. It also shows the systematic integration of the SOAP, non-SOAP and non-web services describing the composition of heterogeneous services than the ones conventionally used from the perspective of resource consumption. The paper further compares and reviews different layout model for the discovery of services, selection of services and composition of services in Cloud computing. Recent research trends in service composition are identified and then research about microservices are evaluated and shown in the form of table and graphs.
Multi‐user geometry and geography augmented reality applications for collaborative and gamified STEM learning in primary school

Abstract Augmented reality (AR) technology has been gaining a meaningful position in the education sector with multiple subjects’ application to support teaching and learning experiences. Thanks to the advancement of hardware and technological platforms it is becoming easier to adopt AR technology at schools. This study represents a systematic literature review of AR application in the area of science, technology, engineering and mathematics (STEM) education at primary school level with the focus on collaborative and gamified practices applied to mobile application for geometry and geography subjects. The main objective of this paper is to investigate and analyse the main characteristics and state of the art of the AR STEM gamified collaborative applications for geometry and geography in primary education and their efficiency on the teaching and learning process. This study performs qualitative analysis of the content of 97 articles that provide an overview of the relevant mobile applications for this systematic literature review, book chapters and conference articles published from 2010 to 2021. The main contribution of this article is in the area of in‐app augmented collaborative experiences in geometry and geography learning in primary grades for further research and technology development. In addition, an extensive analysis of the existing AR mobile applications from peer reviewed articles for the above‐mentioned subjects is provided in a comparative table.
Kuwaiti hospital patients’ continuance intention to use telemedical systems in the wake of the COVID19 pandemic

The objective of this study is to investigate factors influencing patients' continuance intention to use telemedicine after the COVID-19 pandemic in the medical sector of Kuwait. To reach this purpose, the updated Delone and Maclean (2003) model was utilized to investigate the aforementioned factors. As such, this research applied quantitative research methods with a sample of 290 participants from patients in Dar Al Shifa Hospital, a private hospital in Kuwait which utilizes telemedical services called 'Sehaty online'. The corresponding data was analyzed using SmartPLS. The findings of this study revealed that information quality and system quality have a positive and significant influence on patient's satisfaction, whereas service quality has an insignificant influence on patient's satisfaction. Also, patients' continuance intention to use telemedicine is found to be significantly impacted by their satisfaction. Furthermore, several limitations of the study, related future research, and recommendations have been discussed.
Identify What You are Doing: Smartphone Apps Fingerprinting on Cellular Network Traffic

Apps installed on smartphones may reveal users' privacy, which is often under malicious attacks. Most privacy attacks are based on network layer traffic. However, the encryption used in the cellular network makes it difficult for a passive adversary to obtain the traffic. In this paper, we leverage the data link layer metadata, such as PDCP packet size, distribution, and interarrival time, to create Apps fingerprints and then conduct a non-intrusive smartphone Apps privacy attack. We test three different smartphones on a 4G LTE laboratory network. On twenty popular Apps selected from the AppStore and Huawei AppGallary, we achieve an Fl-score ranging from 91.32% to 99.49%. Also, we investigate the effect of classification algorithms, time windows, monitoring duration and smartphone brands on Apps fingerprinting attack. Furthermore, we evaluate the performance of the attack using only downlink traffic, which is consistent with the actual attack scenario. Because the data link layer specifications of 4G LTE and 5G are similar, the method of Apps fingerprinting attack can be extended to the latest 5G networks.
Classification of Natural Disaster Using Satellite &amp; Drone Images with CNN Using Transfer Learning

Detection of natural disasters is a very crucial problem to mitigate the damage caused by them. Remote sensing technology can used in detection of certain disasters on an area. We have built a model to detect natural disasters like earthquake, cyclones, wildfires and floods using real time data from satellite and drones. The training data for the model consists of images classified into four classes namely earthquake, cyclones, wildfires and floods. The realtime video stream or a pre-recorded video is passed as the input for detection of calamities. Model takes each casing successively and measures it. For each frame, the probability of all the classes is calculated. Then class label with maximum probability is chosen. The class label is displayed on the top of the frame with a probability calculated by the model. If no disaster is predicted, it shows “normal” otherwise it will display the disaster predicted by the model.
Modeling personalized representation for within-basket recommendation based on deep learning

Within-basket recommendation, which predicts the related item to be added to the basket from the item corpus, is prevalent in grocery shopping and e-commerce. Besides user-item collaborative filtering information, the retail platform also needs to combine the items that the user currently owns to make a recommendation. Previous work solves the task by rule mining or incorporating various types of associations. However, the representation of the basket and the high-order feature interaction is hardly investigated previously. In this work, we propose a deep learning-based model named DBFM (Deep Basket-Sensitive Factorization Machine) to address the task. We first make a personalized representation for a basket based on its constituent items instead of ID by latent factor learning, which improves the generalization of the model to baskets. Then we combine both low-order and high-order feature patterns to capture the sophisticated structures from inputs. Finally, a linear function is used to integrate with the results of different components. Experiments on three real-world datasets demonstrate higher performance of our model over state-of-the-art methods.
Detection of Malaria Disease Using Image Processing and Machine Learning

Malaria is a contagious disease that claims millions of lives each year. A standard laboratory malaria diagnosis requires a careful study of both healthy and infected red blood cells. Malaria can be diagnosed by looking at a drop of the patient’s blood under a microscope and opening it on a slide as a blood smear. The quality of the blood smear also influences its accuracy and correctness in the classification and detection of malaria disease. This results in a large number of inevitable errors, which are not acceptable. The goal of this research is to create a computer-aided method for the automatic detection of malaria parasites using image processing and machine learning techniques. Uninfected or parasitized blood cells have been classified using handcrafted features extracted from red blood cell images. We have implemented Adaboost, K-Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine and Multinomial Naive Bayes machine learning models on a dataset of 27,558 cell images. Among these algorithms, Adaboost, Random Forest, Support Vector Machine, and Multinomial Naive Bayes achieved an accuracy of about 91%. Furthermore, the ROC curve demonstrates that the Random Forest classification model is the best. We hope that by decreasing the requirement for human intervention throughout the detection process, this approach can greatly improve the efficiency of malaria disease detection.
Mathematical Modeling of the Electrophysical Properties of a Layered Nanocomposite Based on Silicon with an Ordered Structure

The authors carried out modeling of the electrophysical properties of composite media. The frequency dependences of the dielectric constant on the type of inclusions in the composite are investigated. On the basis of the nanocomposite considered in the work, based on Si, B, and SiO2, the authors model a reflecting screen, the lattice elements of which have a layered hierarchically constructed structure similar to a fractal formation. The influence of the level of fractality on the optical properties of the object was also investigated, and it was found that the proposed structure makes it possible to increase the operating frequency range of the reflecting screen and the efficiency, in comparison with reflecting screens that have a lattice of traditional structure. The results obtained can be of practical interest for broadband and nonlinear radar devices, localization devices and mobile objects, microelectronics, as well as intelligent applications in the field of information security.
A Safety Vehicle Detection Mechanism Based on YOLOv5

Vehicle affect the safety of people's daily life, and it is difficult to detect and discriminate the surrounding conditions of the vehicle because of a variety of object types and different sizes. The paper aims to establish a digital assistant driving detection system, enhance the rapid detection of this multi-scale objects, and provide effective decision-making responses to ease the traffic accidents. The paper proposes a safety pre-warning detection method based on YOLOv5. Two mainly detection models YOLOv3 and YOLOv5 are taken to estimate the accuracy and efficiency of the detection mechanism. Experimental results show that YOLOv5 can obtain a higher average detection speed and be good for decision-making. Furthermore, in order to test the real driving data, a software system has been developed, and a real scenario application environment and some test experiments are conducted to prove the efficiency of the proposed mechanism.
Chromium (Cr) geochemical mapping based on fixed-values' method: Case studies in China

Geochemical map is a kind of hierarchical color contour map that can be expressed visually. The commonly used mapping methods are limited to a fixed area with fixed quantity of data and unfavorable for comparative study in different areas. A new method with fixed values to contour Cr geochemical map is presented here with case studies in China. The fixed-values' method of Cr geochemical map is colored hierarchically with 26 levels on 25 values of Cr concentration and six classes are classified on these 26 levels with increasing Cr concentrations. The cumulative-frequency method used commonly and the new presented method are compared in Lixian town of Daxing district in Beijing, China as a case study on the geochemical survey scale of 1:20 000. The results on the fixed-values' method show that Cr concentrations in this area belong to the high-background area of Cr concentration. Based on the China Geochemical Baselines (CGB) project data of Cr concentrations on the geochemical survey scale of 1:8 000 000, the Cr geochemical map in Chinese mainland is contoured on the fixed-values' method. Two northeast lines of Lhasa-Changchun and Qinzhou-Hangzhou can be used to separate the whole mainland into three areas as the low-background area in the northwest, the high-background area in the middle, and the low-background area in the southeast. The results in Daxing district including the Lixian town in Beijing on the scale of 1:8 000 000 are consistent with the results from the Lixian town map on the scale of 1:20 000. This indicates that the fixed-values' method of Cr geochemical map provides a methodological technique for Cr geochemical mapping universally and a reference for other elements' geochemical mapping.
Information Technology of Data Processing and Analysis on Automated Measuring Complex of Water Systems Stability

The article proposes is to solve the problem of measuring and controlling the state of water resources, in particular circulating water in water supply and sewerage systems. Existing hardware and software do not provide sufficient accuracy and reliability of the results, as there is a problem in the obsolescence of hardware, imperfection of the set of measuring instruments, as well as in the absence of high quality and modern software capable of processing large volumes of data in real time. The structure of the measuring complex for the automated quality control of circulating waters is offered in the article, the structural-functional scheme of the complex is developed, the sensors are selected and integrated. A separate item of novelty is the developed software package based on the neural network and studies that show a significant acceleration of data processing and increased measurement accuracy.
Divide-and-conquer based all spanning tree generation algorithm of a simple connected graph

All spanning tree generation of a simple connected graph is a well-approached problem in graph theory. In this paper, an algorithm based on a new technique, namely divide-and-conquer, has been proposed. The performance of the proposed algorithm has also been benchmarked against several existing algorithms, including one algorithm implemented in parallel, in this domain. The basis of comparison is on the number of circuits generated and CPU time taken by each of the algorithms compared, using a set of randomly generated graph instances on a common platform.
From Work to Proof of Work: Meaning and Value after Blockchain

The price of Bitcoin is once more soaring. From early October 2020 to early January 2021, the price of a single Bitcoin token went from roughly $10,000 to nearly $65,000, reinspiring the hopes of the crypto-faithful in the inevitability of a future beyond centralized banking and leaving the rest to dread the jargon of computational libertarianism. The speculative betting driving this recent price action, however, belies a more rudimentary and overlooked shift in the digital economy signaled by cryptocurrencies and Bitcoin in particular. Unlike an earlier industrial logic that sought to reduce heat loss and improve efficiency to maximize surplus value, Bitcoin's proof-of-work system shifts the basis of value production from efficiency to inefficiency. Moreover, it does so by using a cryptographic algorithm whose purpose is to destroy the meaning of its inputs. Through an exploration of Bitcoin's proof-of-work technics and its inversion of traditional models of value extraction, the text argues that Bitcoin reveals a profound transformation in the nature of surplus represented by computational capitalism.
Multi-scale Selection and Multi-channel Fusion Model for Pancreas Segmentation Using Adversarial Deep Convolutional Nets

Organ segmentation from existing imaging is vital to the medical image analysis and disease diagnosis. However, the boundary shapes and area sizes of the target region tend to be diverse and flexible. And the frequent applications of pooling operations in traditional segmentor result in the loss of spatial information which is advantageous to segmentation. All these issues pose challenges and difficulties for accurate organ segmentation from medical imaging, particularly for organs with small volumes and variable shapes such as the pancreas. To offset aforesaid information loss, we propose a deep convolutional neural network (DCNN) named multi-scale selection and multi-channel fusion segmentation model (MSC-DUnet) for pancreas segmentation. This proposed model contains three stages to collect detailed cues for accurate segmentation: (1) increasing the consistency between the distributions of the output probability maps from the segmentor and the original samples by involving the adversarial mechanism that can capture spatial distributions, (2) gathering global spatial features from several receptive fields via multi-scale field selection (MSFS), and (3) integrating multi-level features located in varying network positions through the multi-channel fusion module (MCFM). Experimental results on the NIH Pancreas-CT dataset show that our proposed MSC-DUnet obtains superior performance to the baseline network by achieving an improvement of 5.1% in index dice similarity coefficient (DSC), which adequately indicates that MSC-DUnet has great potential for pancreas segmentation.
Specification and adaptive verification of access control policy for cyber-physical-social spaces

Cyber-Physical-Social Space (CPSS) is a promising paradigm to provide people with an intelligent environment by emphasizing the deep fusion of cyberspace, physical space, and social space. The interdependence of these spaces makes that CPSS is more likely to be attacked. The attack consequences may directly affect the state of the physical world and even endanger the people's life. Thus, the most challenging issue for CPSS is to ensure the space security. However, existing security analysis methods focus on the static analysis at the design phase. They do not consider the open and dynamic characteristics which are the core features of CPSS. In this paper, we propose an adaptive security analysis framework for CPSS to prevent the unauthorized flow of information. Firstly, the access control model of CPSS is proposed. It controls the access behaviors by considering the space information covering the social, cyber, and physical spaces. Secondly, Labelled Transition System (LTS) is established to describe the future evolutions of CPSS. The space states in the LTS which violate the security requirements are reasoned by the model checking technology. Thirdly, a policy adjustment method is proposed to prevent the system from entering the violated states or mitigate the bad results caused by the violations. In the end, the effectiveness of our approach is evaluated by a smart building case, and the necessity of our approach is analyzed by the performance evaluation.
Sustainable Learning Micro-Credential using Blockchain for Student Achievement Records

This research objective is to present assessment and management to ensure the confidentiality of documents that are permanent, transparent, and sustainable for students by utilizing Blockchain technology and can be accessed directly by students. Maintained confidentiality of documents allows students to personalize the value of education and produce permanent documentation in formal and informal learning and determine the lifelong learning path of each individual. The method in this research using the Literature Review Study to learn problems from research with the theme of blockchain-based student achievement credentials in the world of education. The implication of this research is to provide an analysis of the use of blockchain micro-credentials on student achievement records in universities using the Literature Review Study Method. The results show that blockchain guarantees a foundation for student achievement record credentials that are durable, secure, and offer solid administration in managing student credentials. They can control them on an ongoing basis. Universities are also given the advantage of increasing student file security and reducing administrative and bureaucratic costs. Further research can be carried out by combining blockchain with AI innovation and private key and decentralized cloud to address issues concerning productivity, flexibility, capacity, and tighter security. With future updates, it is hoped that the use of blockchain in student achievement credentials can become more mature and can be applied to 1000 universities in Indonesia.
Deep-Learning-Based Adaptive Advertising with Augmented Reality

In this work we describe a system composed of deep neural networks that analyzes characteristics of customers based on their face (age, gender, and personality), as well as the ambient temperature, with the purpose of generating a personalized signal to potential buyers who pass in front of a beverage establishment; faces are automatically detected, displaying a recommendation using deep learning methods. In order to present suitable digital posters for each person, several technologies were used: Augmented reality, estimation of age, gender, and estimation of personality through the Big Five test applied to an image. The accuracy of each one of these deep neural networks is measured separately to ensure an appropriate precision over 80%. The system has been implemented into a portable solution, and is able to generate a recommendation to one or more people at the same time.
User experience with conversational agent: a systematic review of assessment methods

Conversational agents are becoming popular for providing a more natural and realistic user experience. New studies have become significant to understand how to assess this experience, mainly because of the increase in applications of this nature. We systematically reviewed the literature to identify how the user experience is assessed when interacting with conversational agents. A total of 443 studies were identified in the ACM, IEEE, Springer, and Scopus databases. Of these, 27 studies met the eligibility criteria. Most studies used their own evaluation methods, without adopting questionnaires validated for UX evaluation. Few studies used assessment tools before participants interacted with agents, and only two carried out assessments before, during, and after use. The results of the assessments can be better if specific instruments for UX are adopted. Furthermore, it is necessary to assess the experience at different times and use combined methods, to understand aspects related to the participants’ feelings and behaviours.