Reviewerly/exHarmony huggingface dataset:
An Efficient Hash-Selection-Based Blockchain Architecture for Industrial IoT (IIoT)

Internet of Things is transforming devices making them “smart”, thereby overlapping the digital and physical worlds. Interaction between IoT devices creates networks with unprecedented scalability which, however, creates many exploitable vulnerabilitiesVulnerabilities due to the lack of built-in security in IoT devices. Blockchain can resolve this particular limitation due to its immutableImmutable, decentralized nature. The industrial Internet of Things (IIoT) uses IoT devices to analyse and manage industrial data in real time for various purposes. Thus, protecting this data from different types of attacks is necessary, for which blockchain is viable. In this chapter, we have proposed a novel blockchain-based model that groups IIoT devices into tier-based clusters depending on computational capability by benchmarking. Each clusterCluster is individually more efficient in terms of both energy utilization and security for IIoT systems by using different computationally suitable hash algorithms. The higher efficiency of our model compared to current solutions is proven by experimental results.
A Cloud-based Edtech Framework for Augmenting 3D Objects to Assist Vocational Learning

This study proposes a dynamic framework packaged as a mobile app, for the easy creation of multi-user educational Augmented Reality (AR) experiences. The proposed system leverages various technologies to allow educators to create there own custom mobile AR experiences and share them with learners via a cloud infrastructure. Using readily-available mobile devices an educator can scan 3D objects, making them available to learners. Learners can label the object in 3D space and store their work to a cloud database. Quantitative data was collected and analysed using profiling tools and tests at different angles/distances. Object detection and alignment of content was found to be best with large or medium size 3D models, even at extreme angles. Qualitative data was collected from thirteen participants. A pilot-test during a live lesson at MCAST Agribusiness Centre was performed, with feedback gathered following the AR app experience. Qualitative data were analysed using Google’s NLP API and showed that participants preferred learning through the AR mobile application than the traditional way of learning, the main factors being ease of use, and better learning as theory is brought closer to real-life. The cloud storage feature facilitated deployment and usability.
Biogeography-Based Optimization

Biogeography can be broken down into “bio” and “Geography” which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology-inspired algorithm is inclined toward providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, some for the worse. This paper tries to mimic the natural influences in a habitat in a Python environment and try to come up with a minimal objective value after iterating through the given meta-heuristic algorithm.
A Randomized Distributed Kaczmarz Algorithm and Anomaly Detection

The Kaczmarz algorithm is an iterative method for solving systems of linear equations. We introduce a randomized Kaczmarz algorithm for solving systems of linear equations in a distributed environment, i.e., the equations within the system are distributed over multiple nodes within a network. The modification we introduce is designed for a network with a tree structure that allows for passage of solution estimates between the nodes in the network. We demonstrate that the algorithm converges to the solution, or the solution of minimal norm, when the system is consistent. We also prove convergence rates of the randomized algorithm that depend on the spectral data of the coefficient matrix and the random control probability distribution. In addition, we demonstrate that the randomized algorithm can be used to identify anomalies in the system of equations when the measurements are perturbed by large, sparse noise.
The Effects of the Applications of Blockchain Technology on the Logistics sector

Logistics is a combination of businesses involving various activities and processes which generate value by goods and services.Maintaining track of all transactions is a crucial task in logistics.Logistics 4.0 enables the optimum synchronization of activities inside corporate boundaries; if effective, logistical constraints resulting from industrial sender and receiver channels may be considerably alleviated.Blockchain paves the way for implementation of smart logistics.It served as storage for the transactions after distributed ledger technologies was implemented before the digital cryptocurrency a year ago.It is a decentralized system based on five essential principles: decentralization, P2P, transparency with privacy protection, and algorithmic logic.Some logistics companies are already employing block chain.This paper summarizes adoption of distributed ledger technology in logistics is being investigated.Furthermore, the strengths and weakness of blockchain in logistic industry were extracted from recent scientific literature for readers to overview the application of technology.It is reported that blockchain adoption provides immutability, data security, tracking, storage, dependability, and cost-effective alternatives in logistics industry.However, there are a few obstacles that prevent full-scale adaption by many logistics and transportation sectors, such as throughput, and latency constraints.Nodes aren't monitored by centralized entity to notify security breach, so data security may be compromised.Furthermore, the blockchain is still in its infancy; there's no single standard, theories are difficult to grasp, and even the most basic types of application need programmer assistance.
Sentiment Extraction from English-Telugu Code Mixed Tweets Using Lexicon Based and Machine Learning Approaches

Telugu is a regional language in both Andhra Pradesh and Telangana states in India. People of this region are using mostly Telugu, English, and English-Telugu code mixed data to share their opinions and responses on social networks. Extraction of sentiments from English-Telugu code mixed text is the most emerging research area and strenuous task, because of its low resource language. Extracting the sentiments from English-Telugu code mixed tweets can be done in two steps. The first step is a transliteration of the roman script into Telugu, pre-processing of data, and annotation with sentiment labels. The second step is sentiment classification which has two approaches. One is extraction using TeluguSentiWordNet, and the second is the machine learning approach. In this research work, Andhra Pradesh political tweets were collected and 39% of tweets are in English-Telugu code mixed type. The accuracy of the machine learning approach by using the SVM algorithm is 86%, and the accuracy of a lexicon-based approach using Sentiment Extraction from Telugu Code Mixed Tweets (STCMT) algorithm is 74%.
Video Super Resolution Based on Motion Compensation

An image super-resolution method and device provided by acquiring the captured video of the power equipment. The method according to the time sequence of the image frames in the video of the power equipment, extracts the appearance image of the power equipment, selects the reference image, obtains the target image, and obtains the motion vector of any pixel moving from the position coordinates in the target image to the position coordinates in the reference image. The reference images are input into the spatiotemporal sub-pixel convolution network, and then the two-dimensional super-resolution image is output, which improves the image clarity, and can image the moving power equipment with high resolution. In this paper We use time fusion strategy in the model of video SR. Our model constructs a motion compensation mechanism based on spatial transformation network. This mechanism can be trained jointly for video SR. The results obtained by our method are proved to be superior in peak signal to noise ratio (PSNR).
Research on the Development of Personalized Recommendation

With the rapid development of Internet technology and big data technology, the problem of information overload has become more and more serious, especially in the field of e-commerce.The personalized recommendation system can judge the user’s preferences based on the user’s browsing history, purchase history, webpage stay time, etc., so as to push the most suitable product for the user, meet the user’s product demand while saving business time, and achieve a win-win situation. This article will comprehensively analyze the development profile, main technologies, application status and future trends of the recommendation system, and further elaborate the problems and development directions of the personalized recommendation system.
Cross Domain Embedding Transfer for Cold-start Users in Click-through Rate Prediction

Click-through Rate (CTR) prediction is one of the core tasks in recommender systems. Despite many successful CTR models, they suffer from poor performance on cold-start users who have no interactions for ID embedding learning. One promising cross-domain solution is to map user interactions from another source domain to the target embedding space. However, existing methods only train mapping functions with individual information on the overlapping user subset, which causes gaps with other non-overlapping active users. We propose to explicitly bridge the gap with a Linearly Neighbor Assembly (LNA) module. LNA interprets a cold-start user as a linear combination of well learned active users with similar interests, thus generating more stable and comprehensive user embeddings even if the individual information is not sufficient. The full Individual Transfer with Neighbor Assembly (ITNA) network combines individual transfer and neighbor support adaptively to produce the final embeddings. Experiments on several cross-domain scenarios show the effectiveness and compatibility of our proposed method.
Multi-view Discriminative Feature Selection

Linear discriminant analysis (LDA) is a popular feature extraction method. In the discriminative feature selection (DFS) algorithm, LDA can be transformed into a feature selection method by adding the row sparse regularization of the transformation matrix. However, this algorithm is only suitable for single view data. To address the problem of multiple views, in this paper, we propose a multi-view discriminative feature selection (MvDFS) method. In this framework, we introduce two redefined between-class and within-class scatter matrices for multi-view data into DFS algorithm. We also present an iterative algorithm for MvDFS. To show the effectiveness of MvDFS, we compare it with several related single-view feature selection methods on some real-world data sets.
Application of Odd-Even Congruence Graph Labeling in Secured Cyber Physical Systems

Technological advancement in the recent decades enhanced the calibre of human life. Contemporary research in machine learning (ML) exhibits a mock-up to make decisions on its own and is applied in various fields including medical diagnosis, email filtering, banking, computer vision, financial marketing, image processing, cyber security. The systems inter-connected across the world via internet are attacked by hackers, and it is prevented by cyber security. The optimum solution for cyber-attacks is attained by collaborating ML techniques with cyber security and envisioned issues are designed by cyber machine learning models. In this chapter, an algorithm is proposed to defend data by encoding the text to an unintelligent text and decoding it to original text by applying graph labelling in cryptography. Symmetric key is designed based on the edge label of an odd-even congruence graph to achieve secured communication in CPS. In addition, a program is suggested using Python programming to attain cipher text and its converse.
An Authentication Scheme for Smart Charging of Electric Vehicles

The charging and discharging of electric vehicles can be incorporated into the dispatching and control system of power grid. Vehicle to Grid (V2G) technology can alleviate the negative impact of the large-scale application of electric vehicles on the grid. However, V2G technology cannot be separated from the support of information and communication network technology. Aiming at the engineering application of electric vehicles participating in power grid dispatching and control through load aggregation technology, this paper studies the safety certification method of electric vehicle charging and discharging dispatching control, and proposes corresponding information security solutions. The article uses proxy short signature technology to propose a security authentication mechanism between the communication entities of electric vehicles, aggregation units, and control centers, and verifies the effectiveness of the proposed scheme through security analysis and simulation.
Novel Input Interactions for a Möbius Shaped Flexible Handheld Device

Flexible devices open opportunities to explore new shapes and interactions for future digital devices. In this paper, we introduce a novel concept of Möbius shaped flexible handheld device. The Möbius shape is proposed due to (a) its ability to double the display view with smaller device structure and (b) propose natural input interactions. We also present input interactions using the free form structure of Möbius shape for commonly performed digital tasks such as scrolling, multi-tasking, multiple selection, writing and an expanded view. Further, we present how Möbius can be converted into a wearable device and a triangular device that supports the user in different usage contexts. Finally, we conclude the paper by providing our views on the contribution of this paper and why such work in important in expanding the horizon of mobile interaction and HCI research on flexible displays.
Fuzzy Model-Based Robust Clustering with Multivariate t-Mixture Distributions

In general, multivariate Gaussian distribution (MGD) is the most used probability model. However, MGD is not a good probability model for clustering under the circumstance with outliers or noisy points. In this case, multivariate t distribution (MtD) should be a better choice than MGD because MtD has more heavy tail than MGD. In this article, we propose a fuzzy model-based clustering on multivariate t-mixture distributions, called fuzzy model-based t-distribution (F-MB-T) clustering, which is a feasible choice for robustness in clustering data sets with outliers or noisy points. To show the novelty of the proposed F-MB-T algorithm, we use numerical as well as real data to compare the proposed F-MB-T with some existing methods, such as fuzzy model-based Gaussian mixture, EM with t-mixture (EM-T) and fuzzy classification maximum likelihood with t-mixture (FCML-T). The experimental results and comparisons demonstrate that the F-MB-T gives good aspects. This presents the proposed F-MB-T as a good model-based clustering algorithm.
Latent Space Conditional Adversarial Learning for Intelligent Dialogue Generation

Generating reasonable and relevant responses remains a challenge for dialogue generation tasks. Currently, latent variable models in deep learning have been applied in text generation, but these latent variables are often full of randomness, leading to uncontrollable generated responses. In this paper, we propose a two-stage conditional-response generation framework based on latent space adversarial learning. Our model first performs representation learning of latent sentence encoding through an autoencoder, and then adds the latent variables of the context to the latent space adversarial learning so that the latent representations of replies generated by our encoder can be highly correlated with our context and further, we decode the latent encoding representations of replies into sentences of replies. The related experimental results show that our model can generate more contextually relevant, fluent, and rich replies than the baseline model.
Discovery Practices of Early Career Life Sciences and Engineering Faculty: A Qualitative Approach

This paper describes findings of a research project conducted at the University of Pittsburgh Library System to uncover processes used by early career researchers in the life sciences and engineering to locate information during the discovery phase and the role that social media and library-subscribed A&I databases play.
Optimal Power Scheduling of Data Centers with Deferrable Computation Requests

Data centers, as huge power consumers in distribution power networks, are believed to have significant operational flexibility with dispatchable computation requests and indoor thermal inertia. This flexibility can be utilized to arbitrage predictable time-varying electricity price or provide regulation services in power market. This paper studies the optimal power scheduling of a data center with deferrable computation requests and controllable air conditioning. The objective is to utilize the data center's flexibility to reduce its total operational costs (including both the electricity costs and server degradation) with time-varying electricity prices in a wholesale power market. A queue model is designed to describe the constraints of computation requests and a linear model is utilized to approximate the operation of air conditioning and thermal dynamics of the data center. The proposed model is a mixed integer quadratic programming that can be efficiently solved by the branch-and-bound algorithm in off-the-shelf solvers. Numerical experiments based on operation data of a real-world data center are conducted to validate the proposed method.
Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization

Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are “easy to use”: theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances.
Understanding How Pair Programming Actually Works in Industry: Mechanisms, Patterns, and Dynamics

Abstract During pair programming (PP), two software developers work closely together on a technical task on one computer. Practitioners expect a number of benefits, such as faster progress, higher quality, and knowledge transfer. Much of prior research focused on directly measurable effects from laboratory settings, but could not explain the large variations observed. My research follows the Grounded Theory Methodology and is aimed at understanding how PP actually works by uncovering the underlying mechanisms to ultimately formulate practical advice for developers. The main findings from my qualitative analysis of recordings of 27 industrial PP sessions are: Task-specific knowledge about the software system is crucial for pair programming. Pairs first make sure they have a shared understanding of the relevant parts before they acquire lacking knowledge together. The transfer of general software development knowledge plays a rather small role and only occurs after the pair dealt with its need for system knowledge. Pairs who maintain a shared understanding may have short, but highly-productive Focus Phases ; if their Togetherness gets too low, however, a Breakdown of the pair process may occur.
Vision-Based Obstacle Avoidance in Drone Navigation using Deep Reinforcement Learning

Drones are soon getting into action in many commercial and non-commercial missions because of their low cost and easy deployment. Nonetheless, this so-called easy deployment requires a human operator to be safe and reliable. With the use of drone platforms as means of package delivery or search and rescue in dangerous sites, full autonomy is now attracting the robotic community. Specifically, using drones in urban areas requires some degrees of autonomy because of potential dynamic obstacles. This paper provides an architecture inspired by natural livings such as insects to mitigate the navigation challenge autonomously. We conduct our study in a standard simulation environment, and results will be compared to a human expert. Using deep reinforcement learning, we manage to generalize the learned policy in this specific domain. Finally, our simulated results imply that reasonable collision avoidance in urban environments is achieved compared to a human pilot.
STRIVE: Scene Text Replacement In Videos

We propose replacing scene text in videos using deep style transfer and learned photometric transformations. Building on recent progress on still image text replacement, we present extensions that alter text while preserving the appearance and motion characteristics of the original video. Compared to the problem of still image text replacement, our method addresses additional challenges introduced by video, namely effects induced by changing lighting, motion blur, diverse variations in camera-object pose over time, and preservation of temporal consistency. We parse the problem into three steps. First, the text in all frames is normalized to a frontal pose using a spatio-temporal transformer network. Second, the text is replaced in a single reference frame using a state-of-art still-image text replacement method. Finally, the new text is transferred from the reference to remaining frames using a novel learned image transformation network that captures lighting and blur effects in a temporally consistent manner. Results on synthetic and challenging real videos show realistic text transfer, competitive quantitative and qualitative performance, and superior inference speed relative to alternatives. We introduce new synthetic and real-world datasets with paired text objects. To the best of our knowledge this is the first attempt at deep video text replacement.