Reviewerly/exHarmony huggingface dataset:
A Novel Blockchain-Based Encryption Model to Protect Fog Nodes from Behaviors of Malicious Nodes

The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance.
Augmenting Emerging Hospitality Services: A Playful Immersive Experience to Foster Interactions among Locals and Visitors

As the world becomes increasingly interconnected, digital technologies impact the way humans experience places and spaces in (smart) cities. To some extent, these smart technologies are intertwined with the urban living enabling the augmentation of the city’s space and creating new opportunities for playful urban engagement. Moreover, touristic cities are becoming the arena of novel hospitality services, emerging from the widespread use of digital technologies and internet-based platforms. At the same time, visitors look for increasingly “authentic” travel experiences that provide meaningful interactions with locals. New business models, such as those emerging from the sharing economy, flourished in response to these fresh needs and opportunities. However, establishing points of dialogue that bring benefits to both residents and tourists is a complex task that still needs inquiry. Here we extend on previous efforts by proposing a playful immersive experience that focuses on creating an esthetically engaging environment for direct information exchange between locals and visitors. We investigate how technology can mediate and augment such information sharing, engaging tourists in a playful and immersive experience. Results of an experiment with 15 users are analyzed and presented to provide new insights to the HCI community.
A Framework for Constructing and Augmenting Knowledge Graphs using Virtual Space: Towards Analysis of Daily Activities

Daily living studies typically necessitate the use of a physical environment such as cameras, sensor networks, or experimental space. Moreover, it is difficult to collect data by flexibly changing the conditions. In the future, data from a physical space that can acquire real data and a virtual space that can easily change conditions and perform many experiments will need to be combined for analysis of daily life. This study proposes a framework for constructing and augmenting knowledge graphs (KGs) based on simulation results of daily living activities, using virtual space to enable various analyses of daily living activities. First, we design an ontology to represent virtual space activities and situational changes. Then, we construct KGs for the everyday living simulation. Second, we propose a method for KG augmentation that employs Markov chain to combine multiple activities KGs. Furthermore, we present several use cases using SPARQL queries and a KG embedding method. We also discuss the KG generation method, the proposed ontology, and the potential for expansion.
Encoding Two-Qubit Logical States and Quantum Operations Using the Energy States of a Physical System

In this paper, we introduce a novel coding scheme, which allows single quantum systems to encode multi-qubit registers. This allows for more efficient use of resources and the economy in designing quantum systems. The scheme is based on the notion of encoding logical quantum states using the charge degree of freedom of the discrete energy spectrum that is formed by introducing impurities in a semiconductor material. We propose a mechanism of performing single qubit operations and controlled two-qubit operations, providing a mechanism for achieving these operations using appropriate pulses generated by Rabi oscillations. The above architecture is simulated using the Armonk single qubit quantum computer of IBM to encode two logical quantum states into the energy states of Armonk’s qubit and using custom pulses to perform one and two-qubit quantum operations.
URL-based Phishing Websites Detection via Machine Learning

Phishing is a cybersecurity attack that is used to trick victim users to provide sensitive information or deploy malicious software on their infrastructure. Depending on the target system and users, these attacks can inflict severe negative impacts on the system. Therefore, researchers have been working on developing phishing detection and prevention techniques to thwart these attacks. In this paper, we present an efficient phishing websites detection system that analyzes the phishing websites URL addresses to learn data patterns that can identify authentic and phishing websites. Our system employs machine learning techniques such as neural networks and decision trees to learn data patterns in websites URLs. We evaluate our system on a recent phishing websites dataset using classification accuracy as a performance indicator. Our best result shows that decision trees models provide 97.40&#x0025; classification accuracy on the almost balanced-class dataset.
You Don’t Need Labeled Data for Open-Book Question Answering

Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). We present a two-step, retriever–extractor architecture in which a retriever finds the right documents and an extractor finds the answers in the retrieved documents. To test our solution, we are introducing a new dataset for open-book QA based on real customer questions on AWS technical documentation. In this paper, we conducted experiments on several information retrieval systems and extractive language models, attempting to find the yes–no–none answers and text answers in the same pass. Our custom-built extractor model is created from a pretrained language model and fine-tuned on the the Stanford Question Answering Dataset—SQuAD and Natural Questions datasets. We were able to achieve 42% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.
Automated Threat Analysis and Management in a Continuous Integration Pipeline

Security and privacy threat modeling is commonly applied to systematically identify and address design-level security and privacy concerns in the early stages of architecture and design. Identifying and resolving these threats should remain a continuous concern during the development lifecycle. Especially with contemporary agile development practices, a single-shot upfront analysis becomes quickly outdated. Despite it being explicitly recommended by experts, existing threat modeling approaches focus largely on early development phases and provide limited support during later implementation phases.In this paper, we present an integrated threat analysis toolchain to support automated, continuous threat elicitation, assessment, and mitigation as part of a continuous integration pipeline in the GitLab DevOps platform. This type of automation allows for continuous attention to security and privacy threats during development at the level of individual commits, supports monitoring and managing the progress in addressing security and privacy threats over time, and enables more advanced and fine-grained analyses such as assessing the impact of proposed changes in different code branches or merge/pull requests by analyzing the changes to the threat model.
Modelling quasi-three-dimensional distribution of solar irradiance on complex terrain

Solar radiation, driving land-atmosphere interactions and terrestrial carbon assimilation, varies both temporally and spatially due to surface gradient, slope aspect and vegetation shading. Here we present a double-shading transposition model (DST) for simulating temporal and spatial distribution of downwelling shortwave solar radiation. The DST model considers influences of both topographic and canopy structure on radiation transmission. It simulates sub-hourly radiation distribution on and beneath vegetation canopy for any weather conditions. The model is tested against observations at two opposite-facing slopes. The simulated above- and under-canopy radiation compares well with the observed data. The model provides a convenient and effective tool for simulating spatial and temporal variation of solar radiation on mountainous areas with vegetation cover and has great potential in ecohydrological modelling and catchment management.
Analysis of Lung Cancer by Using Deep Neural Network

Lung cancer is one of the world’s deadliest cancers and one of the highest mortality rates. There has been a recent increase in the prevalence of lung cancer. The key aim of this research was to create a computer-aided diagnostic (CAD) method for classifying histopathological photographs of lung tissues. For the creation and validation of CAD, we used a publicly available dataset (15,000 samples) of histopathological photographs of lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue from three different types. In order to extract image features, multi-scale processing was used. Finally, the comparison study has been made based upon seven pre-trained convolution neural network (CNN) models, including MobileNet, VGG-19, ResNet 101, DenseNet 121, DenseNet 169, Inception V3, InceptionResNet V2, and MobileNetV2 for classification of lung cancer. All pre-trained models are hyper-tuned by considering several factors such as batch size, learning rate, number of epochs, and model accuracy. Among all, ResNet 101 had the best accuracy of these CNN version, at 98.67%. This research will aid researchers in the development of more successful CNN-based lung cancer detection models.
Path planning of intelligent mobile robot based on Dijkstra algorithm

Abstract Under the premise of grid environment modeling method, a relaxed Dijkstra algorithm is proposed to solve the problem of real-time path planning for mobile robot in large-scale and obstacle intensive working environment. Firstly, four neighborhood search is used to construct the Manhattan distance potential field from the source point to the global point in linear time, and then eight neighborhood search is performed from the target point to the source point to return a collision free and approximately optimal path. Matlab simulation results show that the algorithm is 10 times faster than dijksta algorithm and A-star algorithm, and the error of path length is within a reasonable range compared with the shortest path.
Effects of smartphone numeric keypad designs on performance and satisfaction of elderly users

As cognitive, motor, and sensory skills decline with age, the interface needs of elderly users differ from that of young adults, especially when entering important information in healthcare applications (e.g. blood glucose values) via a numeric keypad on a smartphone touchscreen. The aim of this study is to propose an optimal numeric keypad design for elderly users. A total of 51 participants greater than or equal to 65 years old completed a 6-digit numeric entry task on a smartphone touchscreen. The Java-based experimental program allows participants to operate 45 different keypad designs consisting of three button shapes (circle, vertical rectangle, and square), five button sizes with 2.5-mm increments (7.5 mm, 10 mm, 12.5 mm, 15 mm, and 17.5 mm), and three button spacings (0 mm, 1 mm, and 3 mm). The usability of the keypad design was compared based on task completion time, error rate, and subjective satisfaction, while the data was analyzed using repeated measures ANOVA. The results show that circle and square buttons outperform the vertical rectangle button in completion time, accuracy, and satisfaction. Square and circle buttons in 15 mm size led to improved efficiency and decreased error rate. Moreover, task completion time for 3 mm is significantly shorter than 0 mm spacing. Given sufficient space on the touchscreen, the 3 mm spacing can be considered optimal when designing a numeric keypad interface. This study presents a comparison of different numeric keypad designs and the impact on the performance and satisfaction of elderly users. The result can be applied to smartphone numeric keypad design to increase input accuracy and speed. In addition, both circle and square buttons can be adopted in user interfaces for elderly users without the decline of usability. The proposed configuration of button shape, size, and spacing can be applied to the numeric keypad user interface (UI) of smartphones targeting elderly users, especially in healthcare applications.
Transfer-based taxonomy induction over concept labels

Given a domain-specific set of concepts, taxonomy induction is the problem of inducing a taxonomy from the set of concepts. The problem, despite having practical importance, has not received as much research attention, in contrast with related problems such as link prediction, due to its difficulty and lack of domain-specific benchmarks. In this paper, we present a principled approach for taxonomy induction in the e-commerce domain over a set of concept-labels, given background resources such as a pre-trained language representation learning model and examples of other taxonomies, induced over other concept-sets, but no example links for the target concept-set. Our approach, developed as an academic-industrial collaboration, is significantly more competitive than seven different baselines, including the transformer-based RoBERTa model, on three real-world and widely used e-commerce concept-sets.
An Analysis of Ethereum Smart Contract Vulnerabilities

New vulnerabilities are constantly being discovered in Ethereum and the found vulnerabilities are fixed. This situation causes many vulnerabilities to be effective periodically. This article analyzes the smart contracts deployed on the Ethereum blockchain in 2020 and examines the ten most common vulnerabilities. By doing so, we aimed to determine a threat landscape for 2020. During this analysis, we studied how the vulnerabilities emerged, critical attacks that exploit the vulnerabilities, and what can be done to avoid them.
Symmetric positive definite manifold learning and its application in fault diagnosis

Locally linear embedding (LLE) is an effective tool to extract the significant features from a dataset. However, most of the relevant existing algorithms assume that the original dataset resides on a Euclidean space, unfortunately nearly all the original data space is non-Euclidean. In addition, the original LLE does not use the discriminant information of the dataset, which will degrade its performance in feature extraction. To address these problems raised in the conventional LLE, we first employ the original dataset to construct a symmetric positive definite manifold, and then estimate the tangent space of this manifold. Furthermore, the local and global discriminant information are integrated into the LLE, and the improved LLE is operated in the tangent space to extract the important features. We introduce Iris dataset to analyze the capability of the proposed method to extract features. Finally, several experiments are performed on five machinery datasets, and experimental results indicate that our proposed method can extract the excellent low-dimensional representations of the original dataset. Compared with the state-of-the-art methods, the proposed algorithm shows a strong capability for fault diagnosis.
Enhancement of measurement accuracy of discontinuous specular objects with stereo vision deflectometer

Phase measurement deflectometry (PMD) is widely used for specular surface measurement. However, when the measured surface is discontinuous, its measurement accuracy will decrease due to the slope integration process in PMD model. This paper proposes a method to realize high-precision measurement of discontinuous specular object using stereo vision deflectometer (SVD). The discontinuous surface is separated into continuous areas by region segmentation method to avoid the integration error of PMD. Then, slope integration is conducted in each continuous surface region to achieve high measurement accuracy. The absolute position of a height reference point (HRP) of each continuous surface is calculated to evaluate the relative positions between different surfaces. The performance of the proposed method is verified by measuring two sample specular objects with discontinuous surfaces. The experimental results show that the continuous surfaces can achieve nanometer level form measurement accuracy, while the absolute height measurement accuracy is in micrometer level.
ROBY: Evaluating the adversarial robustness of a deep model by its decision boundaries

With the successful applications of DNNs in many real-world tasks, model's robustness has raised public concern. Recently the robustness of deep models is often evaluated by purposely generated adversarial samples, which is time-consuming and usually dependent on the specific attacks and model structures. Addressing the problem, we propose a generic evaluation metric ROBY, a novel attack-independent robustness measurement based on the model's feature distribution. Without prior knowledge of adversarial samples, ROBY uses inter-class and intra-class statistics to capture the features in the latent space. Models with stronger robustness always have larger distances between classes and smaller distances in the same class. Comprehensive experiments have been conducted on ten state-of-the-art deep models and different datasets to verify ROBY's effectiveness and efficiency. Compared with other evaluation metrics, ROBY better matches the robustness golden standard attack success rate (ASR), with significantly less computation cost. To the best of our knowledge, ROBY is the first light-weighted attack-independent robustness evaluation metric general to a wide range of deep models. The code of it can be downloaded at https://github.com/Allen-piexl/ROBY.
Traffic Sign Detection using Deep Learning Techniques in Autonomous Vehicles

Autonomous vehicle is an emerging topic for both researchers and the automobile industry as companies are still struggling to make fully functional autonomous vehicles. Driving a safe vehicle in a real world depends on different conditions, such as distance from other vehicles, pedestrians, animals, speed-breakers, traffic signals and other unpredictable dynamic environments. Autonomous vehicle can decrease vehicle crashes because software installed in the vehicle instructs the control system of the autonomous vehicle rather than human, and Software makes less error compare to human beings. Automated Traffic Sign Detection and Recognition (ATSDR) is an important task for a safe driving by an autonomous vehicle. Many researchers have used various deep learning-based models for in real-time ATSDR. Here in the present review, we have studied various deep learning models used for in real-time ATSDR. Our study suggested that YOLO and SSD can detect the traffic sign in real time and are superior models for ATSDR as compared to other deep learning methods as CNN, R-CNN, Fast R-CNN and Faster RCNN.
Modeling self-adaptative IoT architectures

An Internet of Things (IoT) architecture describes a network of (physical) objects exchanging data over the Internet. In complex IoT deployments, as those typical of Industry 4.0 systems, this architecture comprises four different layers: device, edge, fog, and cloud. Moreover, the composition and computation tasks assigned to each layer may dynamically evolve due to environmental changes.As such, modeling IoT architectures is a complex process that must cover as well the specification of self-adaptation rules to ensure the optimized execution of the IoT system. To facilitate this task, we propose a new IoT Domain-Specific Language (DSL) covering both the static and dynamic aspects of an IoT deployment. The definition of the DSL, its projectional-based editor and a first prototype of a Kubernetes manifest generator are available as open source software.
Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings

In recent years, clustering methods based on deep generative models have received great attention in various unsupervised applications, due to their capabilities for learning promising latent embeddings from original data. This article proposes a novel clustering method based on variational autoencoder (VAE) with spherical latent embeddings. The merits of our clustering method can be summarized as follows. First, instead of considering the Gaussian mixture model (GMM) as the prior over latent space as in a variety of existing VAE-based deep clustering methods, the von Mises-Fisher mixture model prior is deployed in our method, leading to spherical latent embeddings that can explicitly control the balance between the capacity of decoder and the utilization of latent embedding in a principled way. Second, a dual VAE structure is leveraged to impose the reconstruction constraint for the latent embedding and its corresponding noise counterpart, which embeds the input data into a hyperspherical latent space for clustering. Third, an augmented loss function is proposed to enhance the robustness of our model, which results in a self-supervised manner through the mutual guidance between the original data and the augmented ones. The effectiveness of the proposed deep generative clustering method is validated through comparisons with state-of-the-art deep clustering methods on benchmark datasets. The source code of the proposed model is available at https://github.com/fwt-team/DSVAE.