Reviewerly/exHarmony huggingface dataset:
New Gold Mine: Harvesting IoT Data Through DeFi in a Secure Manner

As a massive amount of Internet of things (IoT) devices are being deployed for data collection, it is desirable to have a marketplace where both IoT device owners and data consumers can trade collected data freely and effectively. While it is possible to build a centralized data trading platform similar to existing commodity markets, it faces several limitations such as lack of neutrality and dependence on external payment system. The development of blockchain technology sheds light on addressing these concerns, and several works have been done on developing blockchain-based IoT data trading systems. Introducing the blockchain into the IoT data market construction also brings new challenges including management and trustworthiness of off-chain information, and supporting rich and fair trading options in a decentralized environment. We propose DTIDM, a decentralized trusted IoT data marketplace, to mitigate these issues and stimulate the trading of IoT data to maximize its potential value. DTIDM utilizes a hardware-software hybrid approach to address the trust issue in data trading and supports various trading mechanisms like selling, direct exchange, and other user-defined trading types. The design of DTIDM is decoupled from the underlying blockchain, so both the framework and key components of DTIDM can be leveraged in an environment selected by the user. We also develop a prototype of the trusted IoT data provider based on the DTIDM design.
Design of a Catapulted Self-deployable UAV

Abstract Unmanned Aerial Vehicle (UAV) is playing a gradually enhanced role in fields of recon and information acquisition, however restricted departure condition of fixed wing aircrafts, take-off preparation time of multirotor aircrafts etc. have limited its further applications. This research aims to combine advantage of hovering and self-deployable departure to reconcile the shortcomings, meanwhile adapts to drone swarm trend. A catapulted self-deployable quadrotor is designed using 3D modelling software, and later a compatible self-deployable control algorithm is developed using STM32F103 microcontroller, along with its circuitry. Eventually, a prototype of the design is 3D printed, assembled and tested. This design shows merits of easy to carry, low requirements for take-off conditions and good hovering performance and is compatible for multi-UAVs cooperation tendency.
Study of Group Equivariant Convolutional Networks For Image Classification

Image Classification has grabbed a lot of attention in the Artificial Intelligence domain. Convolutional Neural Networks (CNNs) are commonly used for this purpose. A further advancement in CNNs, the Group Equivariant Convolutional Networks or G-CNNs, use G-convolutions, a revolutionary type of neural layer which can share weight more efficiently than conventional convolution layers. This novel approach can make the existing CNN architecture rotation as well as reflection equivariant. To begin, this paper reviews the existing CNN model and areas in which Group Equivariant Convolutional Networks modify this existing architecture. Moreover, this paper also shows the implementation of the G-CNN model on various datasets and conclusions are drawn regarding the efficacy of this architecture.
An Effective Blockchain Based Secure Searchable Encryption System

Security of Patient health records (PHR) is the most important aspect of cryptography over the Internet due to its value and importance preferably in the medical Internet of Things (IoT).Search keywords access mechanism is one of the common approaches which is used to access PHR from database, but it is susceptible to various security vulnerabilities.Although Blockchain-enabled healthcare systems provide security, but it may lead to some loopholes in the existing schemes.However, these methods primarily focused on data storage, and blockchain is used as a database.In this paper, Blockchain as a distributed database is proposed with homomorphic encryption technique to ensure a secure search and keywords-based access to the database.Moreover, the proposed approach provides a secure key revocation mechanism and update various policies accordingly.A secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data.Our proposed approach provides improved security, efficiency, and transparency with cost effectiveness.We performed our simulations based on the blockchain based tool termed as the Hyperledger fabric and Origionlab for analysis and evaluation.We have compared our proposed results with the benchmark models.Our comparative analysis justify that our proposed framework gives improvement in security and searchable mechanism for healthcare system.
Surface measurement of silicon wafer using harmonic phase-iterative analysis and wavelength-scanning Fizeau interferometer

Wavelength-scanning interferometry has been widely used for surface measurements of silicon wafers with a high reflection factor owing to its non-contact and high-resolution measurement. However, the measurement results are degraded by the second-order harmonics of the signal and nonlinearity of phase modulation in wavelength scanning. In this study, a harmonic phase-iterative technique using five-frame experimental interferograms was developed for the surface measurement of a silicon wafer, while simultaneously compensating for the second-order harmonics and nonlinear phase modulation. Considering the reflection factor of the silicon wafer and the harmonics of the signal, a harmonic phase-iterative technique was derived. Furthermore, by combining the proposed iterative technique with the harmonic convergence condition and the selected pixel technique, the harmonic phase-iterative technique was insensitive to divergence errors and considerably reduced the computational time of phase extraction. The surface of the silicon wafer was measured using the harmonic phase-iterative technique and five-frame interferograms obtained using a wavelength-scanning Fizeau interferometer. The repeatability error of the silicon wafer surface measurement was 2.617 nm. Finally, it was confirmed that the harmonic phase-iterative technique had superior phase-extraction ability in terms of residual ripples and repeatability errors compared to the conventional iterative technique and phase-modulation technique. • Development of harmonic phase-iterative technique using five interferograms. • Wafer surface measurement using developed harmonic phase-iterative technique. • Comparison of developed harmonic phase-iterative technique with other techniques. • Validity confirmation of developed phase-iterative technique.
Heterogeneous Information Network enhanced Academic Paper Recommendation

Academic paper recommender (APR) systems that assist researchers in solving the information overload problem have attracted lots of attention. Recently, many works have been done to improve APR with heterogeneous information network (HIN). However, these works plainly depend on graph embedding to generate recommendations and achieve unsatisfactory performance due to the neglect of high-order paper connectivity in the HIN and complex interactions between the user and academic papers. This paper proposes a new algorithm named Heterogeneous Information Network enhanced Academic Paper Recommendation (HIN-APR) to address the above problems. Firstly, based on the message-passing architecture of GNN, designing a novel heterogeneous graph neural network including dual-level attention is to learn the paper’s high-order feature in HIN. Then, the high-order feature was integrated into a new recommendation framework based on convolution neural network (CNN) to model the complex interactions and predict matching score between users and papers. Experimental results on citeulike-a and citeulike-t show that our proposed approach outperforms compared with baseline methods.
Emotion recognition in election day tweets using optimised kernel extreme learning machine classifier

Today, microblogging has turned into a very well-known specialised device among web clients. A huge number of clients share ideas on various aspects of daily life. In this manner these sites are rich sources of information utilised with the goal of sentiment investigation. This investigation is essentially valuable as well as challenging since everybody consistently need to know the views of existing clients about an item or service. In this article, the most prominent microblogging platform ‘Twitter’ is selected for the investigation of sentiment classification and mining opinions. This is helpful for shoppers who need to enquire about items before purchase, or organisations that need to screen the open assumption of their brands. A unique approach is presented for classifying the feeling of Political Twitter messages into Happy, Extremely Happy, Sad, Extremely Sad or else Neutral. There are numerous devices that give computerised opinion investigation. The dataset used here is the Twitter corpus Dataset named 2016 Political Election day tweets gathered using the Twitty and Simulation apparatus utilised in this examination is PYTHON. Utilising the corpus, sentiment classifier named Kernel Extreme Learning Machine (KELM) optimised by Salp Swarm Algorithm (SSA) determines the class of political tweets as Happy, Extremely Happy, Sad, Extremely Sad and Neutral Sentiments. This work mainly focus on English tweets. The proposed KELM classifier performance is compared with existing classifier approaches and noted with highest accuracy which defines the effective nature of the classifier. Moreover, test evaluation demonstrate that the proposed procedure is effective and performs superior to recently proposed works.
A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions.First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images.Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network.After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy.Furthermore, the best BU features are merged to increase the classification accuracy at the cost of high computational time.Finally, the feature dimension reduction ReliefF algorithm is applied to address the computational complexity issue.An online publicly available dataset of 780 BU images is utilized to validate the proposed approach.The dataset was further divided into 80 and 20 percent ratios for training and testing the models.After extensive testing and comprehensive analysis, it is found that the DenseNet-201 and Mobile-Net-v2 trained SVM has an accuracy of 90.39% and 94.57% for the original and augmented BU images online dataset, respectively.This study concluded that the proposed framework is efficient and can easily be implemented to help and reduce the workload of radiologists/doctors to diagnose breast cancer in female patients.
Blockchain based holistic trust management protocol for ubiquitous and pervasive IoT network

The new emerging blockchain (BC) technology integrated with the IoT ecosystem revolutionised the IoT world. Classic BC with bitcoin method was realised as very expensive operations difficult to adopt for smart IoT applications; therefore, we integrated IoT network with overlay BC with distributed ledger capability to provide a secure trust management system, which can address access control issues of devices on resources. Further, the R-LEACH protocol followed by the same group urges additional cluster head requirement to establish trust between nodes is not considered in our proposed approach. The main advantage of this method is utilising ledgers for holding the trust and IoT information ensuring tamper-proof data. The miners of blockchain layer perform the trust value calculations based on trust evidence and achieved fast trust convergence, accuracy, and resilience against adversary attacks. Our proposed approach enhances privacy, reliability, availability, and more importantly, sharing and storage of trust information and also followed the consensus mechanism Proof-of-Authority (PoA) to approve the synthesis trust value of related transactions by the pre-authenticated miners/validators, from which we can take more accurate trust-based decisions. Performance results of our blockchain-based trust management approach outperformed literature review trust mechanisms for protecting trust data manipulation against the malicious nodes.
Multi-level interactions for RGB-D object detection

Abstract In order to efficiently utilize high-level information and depth information in RGB-D saliency object detection, multi-level fusion is studied. Different from existing methods which ignore feature dilution in the process of feature downward transmission, a multi-level interactive fusion method is designed and compared with five advanced models through four evaluation indexes. The experimental results show that the model in this paper is advanced.
RGBD salient object detection based on depth feature enhancement

Abstract The depth map contains spatial location information, which has been proven to be beneficial for RGBD salient object detection. However, the quality of the depth map will directly affect the accuracy of RGBD saliency detection. In order to solve the problem of different quality of depth maps, a detection model based on depth enhancement is designed to enhance the available depth information and filter low-quality depth maps. By comparing four evaluation indicators with five advanced models, the experimental results show that the model in this paper is advanced.
Securing Machine Learning: A Red vs Blue Approach

Increases in multidisciplinary research performed with deep learning has exposed compelling security vulnerabilities in computer vision applications through the exploitation of digital perturbations. Due to this budding threat of duplicity, we present research which focuses on defending adversarial attacks and increasing our algorithms robustness. In order to effectively defend our deep networks against white-box attacks, we utilized a red vs. blue teaming style approach. The red team explored adversarial attack methods while the blue team explored network robustification methods and adversarial defenses. To effectively evaluate our defenses we explored binary and multi-class classification problems on visual data. This paper describes the red vs. blue teaming process and the lessons learned during our first competition style demonstration.
The Impact of an Audience on the Appeal of Virtual Reality

Virtual reality in a public place is enticing for some yet daunting for others. Social Impact theory proposes that performing in front of larger (vs. smaller) audiences is typically seen as more anxiety provoking and less desirable. Having peers perform with you can offset this, however. Our goal was to test whether Social Impact theory extends to the context of trying virtual reality in a busy public setting, and whether any such effects are influenced by extroversion and trait anxiety. In Experiment 1, we ran an online study with 100 participants and found that images of people trying virtual reality in front of others were indeed rated as more anxiety provoking than images with no audiences. Images with ( vs . without) audiences were also rated as scenarios in which people would be less willing to try virtual reality. There was no impact of extroversion levels on people’s reported Willingness to Try; however extroverted individuals were less affected by audience size compared to introverts in terms of how anxiety-provoking they considered the scenario. Experiment 1 also found that the presence of a monitor showing one’s virtual reality “performance” made Extroverts keener to try the experience, yet Introverts less keen. Experiment 2 tested whether the main findings of the first study extended to a real-world scenario. 69 participants observed 0–3 individuals trying a virtual-reality experience in the foyer of a busy library and were then questioned on expected anxiety levels and Willingness to Try. Whilst anxiety levels were again influenced by the audience size (number of people in the foyer at the start of each test), there was no impact of audience size on Willingness to Try virtual reality. Note that relative inattention of the audience on those trying VR in Experiment 2 (compared to Experiment 1), as well as a small sample size, may have made it hard to detect effects here. Extroverts were again less anxious about trying VR in-front of others compared to introverts. These findings offer some ways to make public space virtual reality experiences more accessible, whilst suggesting future steps to properly assess some exploratory findings presented here.
Speech Recognition: A Concise Significance

Speech Recognition and communication between humans and computers have made tremendous progress over the last three decades. Speech recognition technologies allow the machine to respond correctly to a human voice. Nowadays a lot of Automatic Speech Recognition Systems are developed which are more resistant to environmental, speaker, and language variability. The voice-based application provides valuable and useful services to the user. Deep learning is an emerging area, in the last few years research has focused on using it for speech-related different applications. Feature extraction, speech classifiers, speech representation, speech database, and performance are some important issues that should be considered while designing a speech recognition system. The challenges that exist in ASR, as well as the different methods developed by various researchers, have been described in sequence. This paper explores the significant advances in speech communication research over the years, also helps to identify a different tool along with its merits and demerits. The primary aim of the article is to conduct a comparison between various speech recognition methods. This paper shed light on the trends in speech recognition system and also bring focus to new research topics
Blockchain-Based Manufacturing Supply Chain Management Using HyperLedger Fabric

Production of goods has reached record numbers in the last decades as it became more efficient and effective than ever before due to the automation and digitalization of the production process. This gave customers more choice and the delivery process became faster and more satisfying. However, supply chain management remains a bottleneck due to the limitations of the supply chain management systems which suffer from poor traceability, product tampering, lack of timely information sharing, and delays. These issues can be addressed by the blockchain as a digital platform with features like immutability, transparency, and decentralization of information. In this paper, we propose a case study for improving the manufacturing supply chain management through a blockchain-based solution. The proposed system would provide a more transparent supply chain with improved product traceability because of the tamper-proof and decentralized nature of blockchains.
Denoising Adversarial Examples Using CNN Models

Abstract It has always been a complicated problem to resolve adversarial attacks because figures with adversarial attacks look similar to the original figures so that models can be fooled. With deceptive data, adversarial attacks can be a threat to neural networks. There are various ways to generate adversarial attacks. For instance, they are using one-step perturbation and using multi-step perturbation. In both methods, noise is added to the images. Therefore, a question pops up: are adversarial attacks similar to normal random noise? This paper aims to find if there is anything in common between random noise and adversarial attacks. A normal denoising CNN model is trained with random noise. Then groups of adversarial examples are collected by training on LeNet. Next, the denoising CNN model has been used to denoise those adversarial examples. Finally, after denoising the adversarial examples with the CNN model trained on normal random noise, the classification accuracy increases. Thus, it is reasonable to conclude that normal random noise and adversarial tracks have some common patterns.
Open-Source Software in the Classroom: Empowering Students to Self-Select Projects to Contribute

Contribution: Instructors are leveraging open-source software (OSS) as a way to experience authentic examples of software problems with their students. Recommender engines might assist students in selecting the right project based on metrics mined from project repositories (e.g., GitHub). This vision is realized through GitMate, a GitHub-based recommender for supporting students in their OSS selection. Background: Contributing to OSS is a valuable way to immerse students into the realities of software development. When it comes to OSS selection, self-selection seems to be the most engaging alternative. Yet, students lack the time (and skills) to analyze project facets and draw comparisons among OSS projects. Research Questions: How can students be assisted to select a good OSS project to contribute to? Specifically, how would a recommender system might help? The envisioned intervention should be useful not only in finding the right project but also challenging students’ initial selections with other alternatives, spurring reflection. Methodology: The aim is to act upon a dependent variable (mind changing in project selection) through an independent variable (project comparison). This is achieved through GitMate, a recommender system on top of GitHub. Its search facilities are used for students to locate three projects at their wish. Next, GitMate recommends similar projects based on the project facets (e.g., number of committers, commits, and stars), mined from GitHub. Pondering the importance of distinct facets, students can now tradeoff different projects. The experiment checks whether students change their first choice. Findings: The results indicate that GitMate helps students compare GitHub projects to the extent of making them change their first choice. Nearly, 80% of the students changed at least one project as a result of using GitMate. This seems to suggest GitMate being effective on its goal: facet-based comparison thinking during OSS selection.
A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving

This paper addresses the problem of detecting pedestrians using an enhanced object detection method. In particular, the paper considers the occluded pedestrian detection problem in autonomous driving scenarios where the balance of performance between accuracy and speed is crucial. Existing works focus on learning representations of unique persons independent of body parts semantics. To achieve a real-time performance along with robust detection, we introduce a body parts based pedestrian detection architecture where body parts are fused through a computationally effective constraint optimization technique. We demonstrate that our method significantly improves detection accuracy while adding negligible runtime overhead. We evaluate our method using a real-world dataset. Experimental results show that the proposed method outperforms existing pedestrian detection methods.