Reviewerly/exHarmony huggingface dataset:
An Architecture for Blockchain over Edge-enabled IoT for Smart Circular Cities

Circular Economy is a novel economic model, where every 'asset' is not wasted but reused and upscaled. The Internet of Things-IoT paradigm can underpin the transition to a Circular Economy by enabling fine-grained and continuous asset tracking. However, there are issues related to security and privacy of IoT devices that generate and handle sensitive and personal data. The use of Blockchain technology provides an answer to this issue, however, its application raises issues related to the highly-constrained nature of these networks. In this paper, Edge Computing is presented as a solution to this issue, providing a way in which Blockchain and Edge Computing can be used together to address the constrained nature of IoT. Furthermore, we present the challenges that this combination poses and the opportunities that it brings. We propose an architecture that decreases the IoT devices requirements for memory capacity and increases the overall performance. We also discuss the architecture design and the challenges that it has, comparing it to the traditional Blockchain architecture as well as an Edge Computing architecture for Mobile Blockchain. The paper closes with a discussion and future extensions of our work are presented, as well.
DAGBENCH: A Performance Evaluation Framework for DAG Distributed Ledgers

Directed Acyclic Graph (DAG) has been emerging as the so-called Blockchain 3.0 after Bitcoin (Blockchain 1.0) and Ethereum (Blockchain 2.0). This new distributed ledger technology is getting significant attention for its high performance and low transaction fee. There have already been several notable implementations, such as IOTA [1], Nano [2] and Byteball [3]. In this paper, we present DAGBENCH as a performance evaluation framework for DAG implementations. DAGBENCH provides a number of sample workloads and adaptors that make effective and easy evaluation of different DAG implementations. It allows any DAG implementation to be evaluated by adding an adaptor. DAGBENCH allows to measure the performance of DAG implementation in terms of throughput, latency, scalability, success indicator, resource consumption, transaction data size and transaction fee. We demonstrate the efficacy of DAGBENCH with different DAG implementations. In particular, we have conducted experiments, on Amazon EC2, with three popular DAG implementations: IOTA, Nano and Byteball. Our experimental results provide the performance comparison between these implementations that helps developers/users effectively evaluate different performance characteristics; and, this enables them to identify bottlenecks and accordingly to improve performance.
Low-Lightgan: Low-Light Enhancement Via Advanced Generative Adversarial Network With Task-Driven Training

We propose a low-light enhancement method using an advanced generative adversarial network (GAN) and a task-driven training set. Unlike traditional training sets that only synthesize global illumination, we apply local illumination to make the training images. Furthermore, we enhance traditional GANs with spectral normalization and advanced loss functions, making training stable and leading to accurate results. Experimental results show that our method outperforms state-of-the art methods qualitatively and quantitatively and alleviates saturation problems in bright areas, which typically occur after traditional low-light enhancements.
Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions

Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insufficiencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study.
Integration of Blockchain Technology into a Land Registration System for Immutable Traceability: A Casestudy of Georgia

Land as an immovable property represents an important asset for which such crucial aspects evolve as ownership rights, security of land records, possible disputes, corruption risks and sundry transparency matters of land registry processes. Critical issues are traceability of records, hazards of document forgery as well as vulnerability to various errors. Delivering accountable land registry systems and particularly increasing validity of land titles is vital for present-day governments in terms of suppressing corruption, eliminating red tape, enhancing transparency, improving speed of the stated public service and eradicating risks of possible disputes. Furthermore, integration of the blockchain technology into land registries leads to achieving a disruptive transformation of public-service provision systems. This Georgia focused casestudy-based research ascertains how blockchain technology resolves the issues above concerning contemporary land registry systems and examines determinants for a successful application of the digital novelty. The findings from semi-structured interviews and document studies we analyze and scrutinize the present blockchain model of the Georgian government. Additionally, we provide recommendations for administering the blockchain-based digital solutions present in the public land registry service-provision system.
ARTS, an AR Tourism System, for the Integration of 3D Scanning and Smartphone AR in Cultural Heritage Tourism and Pedagogy

Interactions between cultural heritage, tourism, and pedagogy deserve investigation in an as-built environment under a macro- or micro-perspective of urban fabric. The heritage site of Shih Yih Hall, Lukang, was explored. An Augmented Reality Tourism System (ARTS) was developed on a smartphone-based platform for a novel application scenario using 3D scans converted from a point cloud to a portable interaction size. ARTS comprises a real-time environment viewing module, a space-switching module, and an Augmented Reality (AR) guide graphic module. The system facilitates scenario initiations, projection and superimposition, annotation, and interface customization, with software tools developed using ARKit® on the iPhone XS Max®. The three-way interaction between urban fabric, cultural heritage tourism, and pedagogy was made possible through background block-outs and an additive or selective display. The illustration of the full-scale experience of the smartphone app was made feasible for co-relating the cultural dependence of urban fabric on tourism. The great fidelity of 3D scans and AR scenes act as a pedagogical aid for students or tourists. A Post-Study System Usability Questionnaire (PSSUQ) evaluation verified the usefulness of ARTS.
Mobile Learning via Educational Apps

This research explains the rationale behind the utilisation of mobile learning technologies. It involves a qualitative study among children to better understand their opinions and perceptions toward the use of educational applications (apps) that are available on their mobile devices, including smartphones and tablets. The researchers organized semi-structured, face-to-face interview sessions with primary school students who were using mobile technologies at their institution. The students reported that their engagement with the educational apps has improved their competencies. They acquired relational and communicative skills as they collaborated in teams. On the other hand, there were a few students who were not perceiving the usefulness and the ease of use of the educational apps on their mobile device. This study indicates that the research participants had different skill-sets as they exhibited different learning abilities. In conclusion, this contribution opens-up avenues for future research in this promising field of study.
Dynamic Virtual Machine Placement Considering CPU and Memory Resource Requirements

In cloud data centers, cloud providers can offer computing infrastructure as a service in the form of virtual machines (VMs).With the help of virtualization technology, cloud data centers can consolidate VMs on physical machines to minimize costs.VM placement is the process of assigning VMs to the appropriate physical machines.An efficient VM placement solution will result in better VM consolidation ratios which ensures better resource utilization and hence more energy savings.The VM placement process consists of both the initial as well as the dynamic placement of VMs.In this paper, we are experimenting with a dynamic VM placement solution that considers different resource types (namely, CPU and memory).The proposed solution makes use of a genetic algorithm for the dynamic reallocation of the VMs based on the actual demand of the individual VMs aiming to minimize under-utilization and over-utilization scenarios in the cloud data center.Empirical evaluation using CloudSim highlights the importance of considering multiple resource types.In addition, it demonstrates that the genetic algorithm outperforms the well-known best-fit decreasing algorithm for dynamic VM placement.
Coupling Two-Stream RGB-D Semantic Segmentation Network by Idempotent Mappings

In RGB-D semantic segmentation tasks, it has been shown that HHA embeddings effectively encode rich depth features and using HHA together with RGB images can improve segmentation performance. In this paper, we propose a novel method to effectively integrate RGB and HHA features. By replacing identity mappings in ResNet-based two-stream network with idempotent mappings, we can couple the originally separated two branches to mix features from two modalities, while still keep the good information flow nature of ResNet. Moreover, our method does not bring any additional network blocks or parameters, and only needs very small modification on basic two-stream networks. We conduct experiments on two challenging RGB-D semantic segmentation datasets NYUDv2 and SUN-RGBD. The experiment results show that our method can significantly improve segmentation performance and our method achieves the state-of-the-art on these two datasets.
Efficient knowledge graph accuracy evaluation

Estimation of the accuracy of a large-scale knowledge graph (KG) often requires humans to annotate samples from the graph. How to obtain statistically meaningful estimates for accuracy evaluation while keeping human annotation costs low is a problem critical to the development cycle of a KG and its practical applications. Surprisingly, this challenging problem has largely been ignored in prior research. To address the problem, this paper proposes an efficient sampling and evaluation framework, which aims to provide quality accuracy evaluation with strong statistical guarantee while minimizing human efforts. Motivated by the properties of the annotation cost function observed in practice, we propose the use of cluster sampling to reduce the overall cost. We further apply weighted and two-stage sampling as well as stratification for better sampling designs. We also extend our framework to enable efficient incremental evaluation on evolving KG, introducing two solutions based on stratified sampling and a weighted variant of reservoir sampling. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of our proposed solution. Compared to baseline approaches, our best solutions can provide up to 60% cost reduction on static KG evaluation and up to 80% cost reduction on evolving KG evaluation, without loss of evaluation quality.
Blockchain and Sustainability: A Systematic Mapping Study

Sustainability is a topic of increasing interest. The United Nations has released a list of 17 goals for sustainable development for the global community. Blockchain is a recent technological innovation that shows great promise in changing industries. In this paper, we look specifically at smart grids and supply chain management systems as areas where sustainable technological innovation can happen. To identify software engineering aspects of blockchain in smart grids and supply chain management, we start upon online libraries focusing on engineering and information technology, and we opted for the methodology of systematic mapping studies in software engineering. The search strategy identified 535 papers, of which 60 were identified as main studies for our mapping. To the best of the authors' knowledge, no previous similar studies exist. Results of the study show that the research connecting blockchain technology to smart grids and supply chain management systems is still young. None of the techniques or systems have yet been implemented in a real life setting. As such, more work has to be done before we can look at the actual implications of putting such technologies into use. Software engineering practices could prove to be very useful in the process of development. We propose that future studies can focus on bringing the technologies closer to real life implementations, as well as how to involve the end users in the development of the blockchain-based systems.
Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimation

The field of self-supervised monocular depth estimation has seen huge advancements in recent years. Most methods assume stereo data is available during training but usually under-utilize it and only treat it as a reference signal. We propose a novel self-supervised approach which uses both left and right images equally during training, but can still be used with a single input image at test time, for monocular depth estimation. Our Siamese network architecture consists of two, twin networks, each learns to predict a disparity map from a single image. At test time, however, only one of these networks is used in order to infer depth. We show state-of-the-art results on the standard KITTI Eigen split benchmark as well as being the highest scoring self-supervised method on the new KITTI single view benchmark. To demonstrate the ability of our method to generalize to new data sets, we further provide results on the Make3D benchmark, which was not used during training.
The skills that employers look for in software testers

Software testing is an integral part of software development that provides better-quality products and user experiences and helps build the reputation of software companies. Though software testers perform a role that requires specific tasks and skills, in-depth studies of software testers lag behind research studies of other roles within software development teams. In this paper, we aim to create a profile of testers by presenting an empirical analysis of the skills the industry currently needs. We analysed data from 400 job adverts in 33 countries. We mapped the skills on a taxonomy comprising test-related, technical, and domain-specific skills. In addition, we looked at the demand for educational attainment, relevant certifications, and previous experience requirements. Our findings show that employers are mostly interested in skills related to test planning and design, test automation, functional testing, performance testing, and progress reporting. One third of the job advertisers were interested in people with the skills to operate test execution tools. Selenium was the testing tool most in demand. The testers must have strong technical abilities, including programming skills in Java, C#, and SQL. Also, they must handle project management tasks such as estimation, risk management, and quality assurance. Employers do not emphasise domain-specific knowledge, which indicates that they consider testing skills portable across industries. One in seven job adverts asks for a software testing certification. Our study helps clarify the complexity of the testing job and outlines the capabilities one needs to fulfil a software tester's responsibilities.
Face and Gender Recognition System Based on Convolutional Neural networks

In the existing research, face features and gender attributes are separated, resulting in face recognition errors and gender recognition errors in complex backgrounds. In this work, we propose the Face and Gender Recognition System that uses convolutional neural networks (CNN). The system consists of two components: one is face recognition module and two is gender recognition module. Both face recognition module and gender recognition module use pre-trained CNN to extract face and gender features in the image. Specifically, in the face recognition module, we use the public datasets Labeled Faces in the Wild (LFW), YouTube Face (YTF) and VGGFace2 to train CNN, which improves the precision. In the gender recognition module, we use the public dataset Adience to train CNN and improve the best recognition accuracy from 91.80% to 93.22%.
A Comparative Study on different Keyword Extraction Algorithms

Growth in the number of research documents getting published is increasing. Finding a research document under interested domain by referring the whole paper has become a tedious task. Keywords, Keyphrases gives the summary of the text. Keywords and keyphrases help in understanding the information described in the research document. The domain of a research document can be determined based on the keywords and keyphrases extracted. Extracting keywords and keyphrases manually is a tedious task. Automatic keyphrase extraction techniques help in overcoming this challenging task. This paper is a comparative study of unsupervised keyphrase extraction algorithms without using corpus. It compares the performance of PositionRank which considers the position of the all words occurrences in the document with TextRank and RAKE (Rapid Automatic Keyword Extraction).
Decentralizing IoT Management Systems Using Blockchain for Censorship Resistance

Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address limitations faced by centralized systems. While many existing efforts are successful in decentralization with multiple servers (i.e., full nodes) to handle faulty nodes, an important issue has arisen that external clients have to rely on a relay node to communicate with the full nodes in the blockchain. Compromization of such relay nodes may result in a security breach and even a blockage of IoT sensors from the network. In this article, we propose blockchain-based decentralized IoT management systems for censorship resistance, which include a “diffusion” function to deliver all messages from sensors to all full nodes and an augmented consensus protocol to check data losses, replicate processing outcome, and facilitate opportunistic outcome delivery. We also leverage public key aggregation to reduce communication complexity and signature verification. The experimental results from proof-of-concept implementation and deployment in a real distributed environment show the feasibility and effectiveness in achieving censorship resistance.
Performance analysis of machine learning and deep learning architectures for malaria detection on cell images

Plasmodium malaria is a parasitic protozoan that causes malaria in humans. Computer aided detection of Plasmodium is a research area attracting great interest. In this paper, we study the performance of various machine learning and deep learning approaches for the detection of Plasmodium on cell images from digital microscopy. We make use of a publicly available dataset composed of 27,558 cell images with equal instances of parasitized (contains Plasmodium) and uninfected (no Plasmodium) cells. We randomly split the dataset into groups of 80% and 20% for training and testing purposes, respectively. We apply color constancy and spatially resample all images to a particular size depending on the classification architecture implemented. We propose a fast Convolutional Neural Network (CNN) architecture for the classification of cell images. We also study and compare the performance of transfer learning algorithms developed based on well-established network architectures such as AlexNet, ResNet, VGG-16 and DenseNet. In addition, we study the performance of the bag-of-features model with Support Vector Machine for classification. The overall probability of a cell image comprising Plasmodium is determined based on the average of probabilities provided by all the CNN architectures implemented in this paper. Our proposed algorithm provided an overall accuracy of 96.7% on the testing dataset and area under the Receiver Operating Characteristic (ROC) curve value of 0.994 for 2756 parasitized cell images. This type of automated classification of cell images would enhance the workflow of microscopists and provide a valuable second opinion.
Predicting Hospital Readmission: A Joint Ensemble-Learning Model

Hospital readmission is among the most critical issues in the healthcare system due to its high prevalence and cost. The improvement effort necessitates reliable prediction models which can identify high-risk patients effectively and enable healthcare practitioners to take a strategic approach. Using predictive analytics based on electronic health record (EHR) for hospital readmission is faced with multiple challenges such as high dimensionality and event sparsity of medical codes and the class imbalance. To response to these challenges, an analytical framework is proposed by data-driven approaches using hospital inpatient administrative data from a nationwide healthcare dataset. A joint ensemble-learning model, which combines the modified weight boosting algorithm with stacking algorithm, is developed and validated. Our study first explores the effects of different feature engineering methods, which effectively handles the challenge of medical vector representation and medical vector sparsity. Secondly, ensemble learning with the proposed modified weight boosting algorithm is used to tackle the class imbalance problem and improve predictability. Finally, we provide various misclassification costs by setting different weights for each class during model training. Using the framework with the proposed modified weight boosting algorithm improves overall model performance by 22.7% and recall from 0.726 to the highest of 0.891 comparing to the benchmark models. Hospital practitioners can also utilize the prediction results of different cost weight to select the most suitable readmission intervention for patients according to the penalty policy of Centers for Medicare and Medicaid Services (CMS) and the cost trade-off of their hospitals.
Quantum-inspired algorithms for multivariate analysis: from interpolation to partial differential equations

In this work we study the encoding of smooth, differentiable multivariate functions in quantum registers, using quantum computers or tensor-network representations. We show that a large family of distributions can be encoded as low-entanglement states of the quantum register. These states can be efficiently created in a quantum computer, but they are also efficiently stored, manipulated and probed using Matrix-Product States techniques. Inspired by this idea, we present eight quantum-inspired numerical analysis algorithms, that include Fourier sampling, interpolation, differentiation and integration of partial derivative equations. These algorithms combine classical ideas -- finite-differences, spectral methods -- with the efficient encoding of quantum registers, and well known algorithms, such as the Quantum Fourier Transform. {When these heuristic methods work}, they provide an exponential speed-up over other classical algorithms, such as Monte Carlo integration, finite-difference and fast Fourier transforms (FFT). But even when they don't, some of these algorithms can be translated back to a quantum computer to implement a similar task.