Reviewerly/exHarmony huggingface dataset:
Internet of Things, Cyber Physical System, and Data Analytics: Open Questions, Future Perspectives, and Research Areas

Due to recent/rapid development in technology, i.e., from wired to wireless, the communication among human and devices/machines has been move/shifted to a new level. Some new technologies/concept has been created in current day-to-day life. Internet of things and cyber physical systems are one among them. Generally, Internet of things is about connecting “Things” (objects and machines) to the Internet and eventually to each other (also called Internet connected things) while cyber physical systems (CPS) is integration of computation, networking, and physical process. On another side, Internet connected things (ICTs) allow integration of service with connecting physical objects like fridge, air conditioner (AC), etc. IoT makes its data and services available more open. Internet of things (IoT) refers to the fundamental part of a CPS which is connection and communication of entities through Internet. IoT technology interconnects of all types of devices to the Internet that enables them to exchange data, optimize processes, and monitor devices. This interconnection of things is of great benefits for the industry/organizations, the economy of a nation, and the end user/consumer. It forms new systems and services by creating a network of sensors, actuators, and devices. At last, the efficient tools (like Apache Hadoop, Apache Spark, Apache MLib, MongoDB, R Programming, Neo4j) we used in big data analytics based on popularity, feature richness, and usefulness are being discussed. Hence, this work provides a detail discussion about the terms (discussed above) like Internet of things, cyber physical systems, its importance in various applications, importance to humanity/benefits to society, issues, challenges and research gaps, etc., in detail and also conclude this interested topic with several future research directions for future researchers (who are working in these computing environments).
Understanding Trust and Cloud Computing: An Integrated Framework for Assurance and Accountability in the Cloud

Abstract Trust is regularly cited as one the main barriers for increased adoption of cloud computing, however conceptualisations of trust in cloud computing literature can be simplistic. This chapter briefly introduces the trust literature including definitions and antecedents of trust. Following an overview of cloud computing, we discuss some of the cited barriers to trust in cloud computing, and proposed mechanisms for building trust in the cloud. We present a high-level framework for exploring assurance (trust building) and accountability (trust repair) in the cloud and call for a more integrated multi-stakeholder approach to trust research in this multi-faceted context.
Authenticated secret session key using elliptic curve digital signature algorithm

Abstract Elliptic curve Diffie‐Hellman (ECDH) scheme is the basic key exchange used in elliptic curve cryptography. ECDH is a public key cryptosystem based on the discrete logarithm problem, but is vulnerable to man‐in‐the‐middle attack because it does not authenticate the secret session key. The integration of the elliptic curve digital signature allows authenticating the secret session key of the ECDH scheme (A‐ECDH). However, it is a technique (A‐ECDH) that uses a single random variable and it has been shown in the literature that a single random variable does not make the system secure. Therefore, the use of two random variables has been suggested to secure an authentication scheme. In this paper, a new authentication scheme using the elliptic curve digital signature algorithm is proposed. The performance analysis of the proposed authentication protocol is performed. The comparative results from the perspective of security and computation time showed that the proposed authentication protocol is more efficient in terms of computation time compared to the related works and it provides all the security features.
3D Object Representation Learning: A Set-to-Set Matching Perspective

In this paper, we tackle the 3D object representation learning from the perspective of set-to-set matching. Given two 3D objects, calculating their similarity is formulated as the problem of set-to-set similarity measurement between two set of local patches. As local convolutional features from convolutional feature maps are natural representations of local patches, the set-to-set matching between sets of local patches is further converted into a local features pooling problem. To highlight good matchings and suppress the bad ones, we exploit two pooling methods: 1) bilinear pooling and 2) VLAD pooling. We analyze their effectiveness in enhancing the set-to-set matching and meanwhile establish their connection. Moreover, to balance different components inherent in a bilinear-pooled feature, we propose the harmonized bilinear pooling operation, which follows the spirits of intra-normalization used in VLAD pooling. To achieve an end-to-end trainable framework, we implement the proposed harmonized bilinear pooling and intra-normalized VLAD as two layers to construct two types of neural network, multi-view harmonized bilinear network (MHBN) and multi-view VLAD network (MVLADN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed MHBN and MVLADN in 3D object recognition.
Edge4Real

Recognition of human behaviours including body motions and facial expressions plays a significant role in human-centric software engineering. However, due to the data and computation intensive nature of human behaviour recognition through video analytics, expensive powerful machines are often required, which could hinder the research and application in human-centric software engineering. To address such an issue, this paper proposes a cost-effective human behaviour recognition system named Edge4Real which can be easily deployed in an edge computing environment with commodity machines. Compared with existing centralised solutions, Edge4Real has three major advantages including cost-effectiveness, easy-to-use, and real-time. Specifically, Edge4Real adopts a distributed architecture where components such as motion capturing, human behaviour recognition, data decoding and extraction, and the application of the recognition result, can be deployed on separated end devices and edge nodes in an edge computing environment. Using a virtual reality application which can capture a user's motion and translate into the motion of a 3D avatar in real time, we successfully validate the effectiveness of the system and demonstrate its promising value to the research and application of human-centric software engineering. The demo video can be found at https://youtu.be/tnEshD8j-kA.
Social Media Research and the Methodological Problem of Harassment: Foregrounding Researcher Safety

As interest in online harassment rises in writing studies, so too does the need for new methodologies that account for the unique challenges that online harassment poses to social media researchers. Drawing from a research experience that left her vulnerable to harassment, the author presents three methodological concerns that harassment gives rise to: researcher safety, trauma and emotional fatigue, and publishing on online harassment. Throughout, the author provides actions social media researchers can take to prepare for potential harassment experiences during a research process. Ultimately, the author argues that researcher safety is a necessary prerequisite to other research concerns, such as participant and data safety, and should thus be foregrounded in research designs.
Short-term solar irradiance forecasting using convolutional neural networks and cloud imagery

Abstract Access to accurate, generalizable and scalable solar irradiance prediction is critical for smooth solar-grid integration, especially in the light of the accelerated global adoption of solar energy production. Both physical and statistical prediction models of solar irradiance have been proposed in the literature. Physical models require meteorological forecasts—generated by computationally expensive models—to predict solar irradiance, with limited accuracy in sub-daily predictions. Statistical models leverage in-situ measurements which require expensive equipment and do not account for meso-scale atmospheric dynamics. We address these fundamental gaps by developing a convolutional global horizontal irradiance prediction model, using convolutional neural networks and publicly accessible satellite cloud images. Our proposed model predicts solar irradiance in 12 different locations in the US for various prediction time horizons. Our model yields up to 24% improvement in an hour-ahead predictions and 26% in a day-ahead predictions compared to a persistence forecast. Moreover, using saliency maps and target-location-focused cropping, we demonstrate the benefits of incorporating meso-scale atmospheric dynamics for prediction performance. Our results are critical for energy systems planners, utility managers and electricity market participants to ensure efficient harvesting of the solar energy and reliable operation of the grid.
Decentralising the patent system

Modern patent systems are slow, inefficient, expensive, and may result in outcomes that actively harm technological progress. This paper proposes a substantive re-think of these systems and lays a foundation upon which practical solutions can be built. Many solutions proposed in the past, such as prior-art bounties, outsourced examination, and dynamic fee setting, have gone unheeded due to the cost of administering them and the rigidity of the patent system. We explore how distributed ledger technologies (DLTs) enable these major changes by altering the way stakeholders are able to interact with the patent records system. We find that transitioning to a DLT-based patent records system can enable many previously suggested improvements to current patent systems in a flexible, scalable, and transparent manner. The case for such a transition is strengthened when jointly considering the complex but common roots of problems facing modern patent systems, rather than a balkanised set of technical solutions to address each issue independently. Noting that a DLT-based system is not a panacea, we also provide comment on the political, legal, and organisational challenges that must be overcome for such changes to be implemented at scale.
A Survey ofNFC Mobile Payment: Challenges and Solutions using Blockchain and Cryptocurrencies

Near-field communication (NFC) is one of the essential technologies in the Internet of Things (IoT) that has facilitated mobile payment across different services. The technology has become increasingly popular, as cryptocurrencies like Bitcoin have revolutionized how payment systems can be designed. However, this technology is subject to security problems, such as man-in-the-middle attacks, doublespending, and replay attacks, raising the need to incorporate other solutions such as blockchain technology. Concerns about the security and privacy of payments using NFC technology raise the need to adopt blockchain-based cryptocurrency payment. For instance, NFC payment has been criticized for a lack of measures to counter potential attacks, such as brute force or double-spending. Thus, incorporating blockchain technology is expected to improve the security features of the NFC mobile payment protocol and improve user experience. Blockchain technology has been praised for enabling fair payment, as it permits direct transactions without engaging a third party. Therefore, integrating blockchain cryptocurrency in IoT devices will revolutionize the NFC payment method and provide value transfer using IoT devices. Combining NFC with blockchain technology and cryptocurrencies is necessary to address security and privacy problems. The purpose of this paper is to explore the potential behind incorporating blockchain technology and cryptocurrencies like Bitcoin in the NFC payment protocol.
Management of cloud resources and social change in a multi-tier environment: A novel finite automata using ant colony optimization with spanning tree

The enormous demand for computational resources due to rapid Cloud growth has led to the creation of large-scale data centers (DC). Consequently, a tremendous amount of energy is consumed with high carbon dioxide emissions, which necessitates that cloud service providers (CSP) develop high quality of service (QoS) strategies to address such challenges. Proper management and utilization of resources ensure a well-balanced load distribution that makes energy consumption sufficient. Developing new algorithms and exploring efficient methods and techniques is highly desired for the management of virtualized DCs. This research proposes Finite Automata using Ant Colony Optimization with Spanning Tree (FAACOST), a machine-learning concept for managing cloud resources in a multi-tier environment. The overall objective is to minimize energy consumption through data placement leverage and virtual machines (VM) consolidation. The proposed technique's efficiency was benchmarked on four performance metrics (machine learning, dynamic nature, scalability, and QoS). Based on the extensive experiments conducted, FAACOST recorded an energy consumption of 132 w with 20 tasks compared to the benchmarked techniques that consumed 363 w. The experimental results show that FAACOST achieves the optimal number of physical machines (PMs) and is more energy-efficient.
Multiscale Dilated UNet for Segmentation of Multi-Organ Nuclei in Digital Histology Images

Millions of deaths occurs every year due to various kinds of cancer. Late diagnosis and no proper treatment planning are the main contributing factors of these deaths. Tissue slides are commonly used for tumor assessment by extracting bio-markers from the biopsies. These bio-markers are then further used for cancer diagnosis. Digitized tissue slides contain multi gigapixels which is why automatic tumor segmentation methods have been developed. However, these methods fail to delineate accurate boundaries as well as are unable to detect objects at multiple scales. Therefore to eradicate this problem we have proposed Multi-scale Dilated U-Net (MD-UNet) which performs feature extraction at multiple scales and delineate accurate boundaries. MD-UNet is trained on 5 Nuclei Segmentation datasets each belonging to different organ of human body. The proposed model outperforms DeepLab v3+, SegNet, U-Net and U-Net++ on all the 5 Nuclei Segmentation datasets.
Election Result Prediction Using Twitter Sentiments Analysis

Democracy is the form of government where people have the right to choose their leaders. In our country, India, elections are held periodically to choose their political leader. Nowadays, people like to know beforehand that who is going to win the elections. They predict results based on news, their discussions and online platforms. Online sites such as Facebook, Twitter and WhatsApp have become a trend these days. Anyone with internet connection has access to these social sites. The views, news and discussions available on these platforms help in predicting the results beforehand. These are the platforms to share ones views' on an online junction. Twitter is one such platform which becomes popular during any event in our country. Anything that is news becomes a trend on twitter. People share their views, hatred, campaigns towards any political party, figure or leader. It is very useful to predict electoral results before the exit poll results. Therefore, this work analyzes tweets collected from twitter and predicts election results by performing sentimental analysis on them.
A Comparison of Various Data Mining Algorithms to Distinguish Mammogram Calcification Using Computer-Aided Testing Tools

Several computer-aided testing tools have been developed more than ever in breast cancer research to minimize misdiagnosis. In this paper, a data mining method has been discussed that could help oncologists identify and detect breast cancer. Including microcalcifications, masses, and even regular findings from tissue was used as a stable database of 410 images. Two extraction techniques have been applied, particularly the unit for gray standard and length of the gray level unit. Many data mining classifications were also used for classification purposes. The findings were shown to be very favorable (roughly 70%) in terms of mammogram separation and BI-RADS® scale (>75%) with acceptable reliability and functional precision. The classification of random forest was the best predictive method to distinguish microcalcification with excellent performance.
Automatic Interior Design in Augmented Reality Based on Hierarchical Tree of Procedural Rules

Augmented reality has a high potential in interior design due to its capability of visualizing numerous prospective designs directly in a target room. In this paper, we present our research on utilization of augmented reality for interactive and personalized furnishing. We propose a new algorithm for automated interior design which generates sensible and personalized furniture configurations. This algorithm is combined with mobile augmented reality system to provide a user with an interactive interior design try-out tool. Personalized design is achieved via a recommender system which uses user preferences and room data as input. We conducted three user studies to explore different aspects of our research. The first study investigated the user preference between augmented reality and on-screen visualization for interactive interior design. In the second user study, we studied the user preference between our algorithm for automated interior design and optimization-based algorithm. Finally, the third study evaluated the probability of sensible design generation by the compared algorithms. The main outcome of our research suggests that augmented reality is viable technology for interactive home furnishing.
Inference Under Information Constraints III: Local Privacy Constraints

We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy of their data while enabling a central server to perform the tests. Under the notion of local differential privacy, we propose simple, sample-optimal, and communication-efficient protocols for these two questions in the noninteractive setting, where in addition users may or may not share a common random seed. In particular, we show that the availability of shared (public) randomness greatly reduces the sample complexity. Underlying our public-coin protocols are privacy-preserving mappings which, when applied to the samples, minimally contract the distance between their respective probability distributions.
A Hybrid Grey Wolf-Whale Optimization Algorithm for Optimizing SVM in Breast Cancer Diagnosis

Breast cancer is one of the major causes of death among women in many parts of the world but, it is confirmed that early detection and accurate diagnosis of this disease ensures the long survival of the patient. In our research, a hybrid meta-heuristic swarm intelligence based Support Vector Machine Classifier called Grey Wolf-Whale Optimization Algorithm with Support Vector Machine (GWWOA-SVM) is proposed for early-stage detection of the disease. Anamalgamation of Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO) is used for tuning the hyper-parameters of SVM. The effectiveness of GWWOA-SVM is evaluated against the Wisconsin Breast Cancer Dataset (WDBC). The performance of the proposed model is evaluated on various metrics such as accuracy, precision, recall, specificity, and F1 Score. Our model achieves a classification accuracy of 97.721 % for WDBC dataset. The GWWOA algorithm is compared with GWO and WOA based on their extent of optimization of the fitness function. The comparison of breast cancer diagnosis corroborates that our model evaluates more nuanced performance metrics.
A tree-based multiclassification of breast tumor histopathology images through deep learning

Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.
Automatic stroke generation for style-oriented robotic Chinese calligraphy

Intelligent robots, as an important type of Cyber–Physical systems, have promising potential to take the central stage in the development of the next-generation of efficient smart systems. Robotic calligraphy is such an attempt, and the current research focuses on the control algorithms of the robotic arms, which usually suffers from significant human inputs and limited writing styles. This paper presents an autonomous robotic writing system for Chinese calligraphy empowered by the proposed automatic stroke matching and generation mechanisms. Thanks to these mechanisms, the robot is able to effectively learn to write any Chinese characters in a style that is sampled by a small amount of handwritten Chinese characters with a certain target writing style. This is achieved by firstly disassembling each given Chinese character into individual strokes using the proposed character disassemble method; then, the writing style of the dissembled strokes is learned by a stroke generation module, which is built upon a generative adversarial learning model. From this, the robot can apply the learned writing style to any Chinese character from a given database, by dissembling the character and then generating the stroke trajectories based on the learned writing style. The experiments confirm the effectiveness of the proposed system in learning writing a certain style of characters based on a small style dataset, as evidenced by the high similarity between the robotic writing results and the handwritten ones according to the Fréchet Inception Distance.