Reviewerly/exHarmony huggingface dataset:
Intrinsic and Complete Structure Learning Based Incomplete Multiview Clustering

In the real-world, some views of samples are often missing for the collected multiview data. Faced with the incomplete multiview data, most of the existing clustering methods tended to learn a common graph from the available views, where the hidden information of the absent views was ignored. Furthermore, some methods filled the absent instances with the average vector of the available samples for each view, which could not reflect a real distribution of the data. To solve these problems, in this paper an intrinsic and complete structure learning based incomplete multiview clustering method (ICSL_IMC) is proposed. Firstly, we calculate the initial complete graphs for all views by exploring the available incomplete graphs, which are further taken as the constraints for the reconstruction of the absent data integrating the self-representation method. Afterwards, encouraged by the complete multiview data, a complete structure inferring strategy is proposed to learn the intrinsic and complete structures for all views, such that the real distribution of the absent instances can be reflected in the completed structure of each view. We integrate these three learning phases into a joint optimization model, which can promote each other in the iterative learning procedure, simultaneously. Comparing with the other state-of-the-art methods, the proposed ICSL_IMC can achieve the best performances on different databases.
A Steganalysis Classification Algorithm Based on Distinctive Texture Features

Steganography is the technique for secretly hiding messages in media such as text, audio, image, and video without being discovered. Image is one of the most essential media for concealing data, making it hard to identify hidden data not visible to the human eye. In general, the cover image and the encrypted image are symmetrical in terms of dimension size, resolution, and qualities. This makes the difference difficult to perceive with the human eye. As a result, distinguishing between the two symmetric images required the development of methods. Steganalysis is a technique for identifying hidden messages embedded in digital material without having to know the embedding algorithm or the “non-stego” image. Due to their enormous feature vector dimension, which requires more time to calculate, the performance of most existing image steganalysis classification (ISC) techniques is still restricted. Therefore, in this research, we present a steganalysis classification method based on one of the texture features chosen, such as segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). The classifiers employed include Gaussian discriminant analysis (GDA) and naïve Bayes (NB). We used a public database in our proposed method and applied it to IStego100K datasets to be able to assess its performance. The experimental results reveal that in all classifiers, the SFTA feature surpassed all of the texture features, making it a great texture feature for image steganalysis classification. In terms of feature dimension and classification accuracy (CA), a comparison was made between the suggested SFTA-based GDA approach and various current ISC methods. The outcomes of the comparison are obvious show that the proposed method surpasses current methods.
Machine Learning the Carbon Footprint of Bitcoin Mining

Building on an economic model of rational Bitcoin mining, we measured the carbon footprint of Bitcoin mining power consumption using feed-forward neural networks. We found associated carbon footprints of 2.77, 16.08 and 14.99 MtCO2e for 2017, 2018 and 2019 based on a novel bottom-up approach, which (i) conform with recent estimates, (ii) lie within the economic model bounds while (iii) delivering much narrower prediction intervals and yet (iv) raise alarming concerns, given recent evidence (e.g., from climate–weather integrated models). We demonstrate how machine learning methods can contribute to not-for-profit pressing societal issues, such as global warming, where data complexity and availability can be overcome.
Review on the Usage of Mixed Reality and Augmented Reality Assisted Learning Tool in Aircraft Maintenance

Mixed Reality (MR) and Augmented Reality (AR) are immersive experience of emerging the real and virtual worlds environment to produce integrated visual surrounding that can act as future problem solving or enhancing tools for various field such as in education, entertainment, automotive, and communication. In the last 5 years, VR and AR have attracted the interest of investors and the general public. Nowadays many researchers have done to implement mixed reality technology in discovering new way of having collaborative learning through the integration of modern technology in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. The aim of this study is to carry out a literature review is to examine current state-of-the-art of AR and VR technology and highlight its applications within the Aircraft Maintenance procedures. Based on the findings, mixed reality could bring many benefits such as to increase efficiency, encourage interactivity, breakdown complex information and innovate working culture to align to competitive industry.
An Efficient Lightweight Cryptographic Instructions Set Extension for IoT Device Security

The Internet of Things is changing all sectors such as manufacturing, agriculture, city infrastructure, and the automotive industry. All these applications ask for secure processors that can be embedded in the IoT devices. Furthermore, these devices are restricted in terms of computing capabilities, memory, and power consumption. A major challenge is how to meet the need for security in such resource-constrained devices. This paper presents a customized version of LEON3, the ReonV RISCV (Reduced Instruction Set Computer-five) processor, dedicated for IoT applications that has strong effective security mechanisms built in at the design stage. Firstly, efficient lightweight cipher designs are elaborated and validated. Then, the proposed cryptographic instructions (PRESENT and PRINCE) are integrated into the default instruction set architecture of the ReonV processor core. The instruction set extensions (ISE) of lightweight cipher modules can be instantiated in software routines exactly as the instructions of the base architecture. A single instruction is needed to implement a full lightweight cryptographic instruction. The customized ReonV RISCV processor is implemented on a Xilinx FPGA platform and is evaluated for Slice LUTs plus FF-pairs, frequency, and throughput. Obtained results show that our proposed concepts not only can achieve good encryption results with high performance and reduced cost but also are secure enough to resist against the most common attacks.
Adversarial Reinforcement Learning With Object-Scene Relational Graph for Video Captioning

Existing video captioning methods usually ignore the important fine-grained semantic attributes, the video diversity, as well as the association and motion state between objects within and between frames. Thus, they cannot adapt to small sample data sets. To solve the above problems, this paper proposes a novel video captioning model and an adversarial reinforcement learning strategy. Firstly, an object-scene relational graph model is designed based on the object detector and scene segmenter to express the association features. The graph is encoded by the graph neural network to enrich the expression of visual features. Meanwhile, a trajectory-based feature representation model is designed to replace the previous data-driven method to extract motion and attribute information, so as to analyze the object motion in the time domain and establish the connection between the visual content and language under small data sets. Finally, an adversarial reinforcement learning strategy and a multi- branch discriminator are designed to learn the relationship between the visual content and corresponding words so that rich language knowledge is integrated into the model. Experimental results on three standard datasets and one small sample dataset indicate that our proposed method achieves state-of-the-art performance. Also, the ablation experiments and visualization results verify the effectiveness of proposed each strategy.
Lightweight algorithm of insulator identification applicable to electric power engineering

In order to reach actual-time viewing of insulator identification, this paper comes up with a lightweight model based on MobileNet-YOLOv4, which replaces the YOLOv4 backbone network CSPDarknet53, making the network structure lightweight enough for easier deployment on mobile devices Meanwhile, Gaussian filtering and K-Means++ clustering, Mosaic data augmentation are applied for pre-processing on the datasets to improve detection accuracy. This model is adopted to identify and test the insulators on the transmission line, and the results show that the average insulator identification precision of this model is 97.78%. Average detection speed of the model is 0.168s faster than YOLOv4, and model size is 20.91% lower than YOLOv4, which can meet the purpose of real-time monitoring.
Current Status and Security Trend of OSINT

Recently, users have used open-source intelligence (OSINT) to gather and obtain information regarding the data of interest. The advantage of using data gathered by OSINT is that security threats arising in cyberspace can be addressed. However, if a user uses data collected by OSINT for malicious purposes, information regarding the target of an attack can be gathered, which may lead to various cybercrimes, such as hacking, malware, and a denial-of-service attack. Therefore, from a cybersecurity point of view, it is important to positively use the data gathered by OSINT in a positive manner. If exploited in a negative manner, it is important to prepare countermeasures that can minimize the damage caused by cybercrimes. In this paper, the current status and security trends of OSINT will be explained. Specifically, we present security threats and cybercrimes that may occur if data gathered by OSINT are exploited by malicious users. Furthermore, to solve this problem, we propose security requirements that can be applied to the OSINT environment. The proposed security requirements are necessary for securely gathering and storing data in the OSINT environment and for securely accessing and using the data collected by OSINT. The goal of the proposed security requirements is to minimize the damage when cybercrimes occur in the OSINT environment.
A Comparative Study: Toward an Effective Convolutional Neural Network Architecture for Sensor-Based Human Activity Recognition

The feature extraction of human activity recognition (HAR) based on sensor data has been studied as a hand-crafted method.The significant feature extraction ability is a key factor in improving the accuracy of HAR.Recently, deep learning methods have been employed for feature extraction.In this paper, we review previous studies on deep learning methods in HAR and discuss suitable models for feature extraction.First, we applied various convolutional neural networks to clarify the effective architecture for HAR.Afterward, we developed advanced models by embedding submodules, such as self-attention and recurrent neural networks, often adopted in recent studies.Comparative experiments on HASC, UCI, and WISDM public datasets showed that Inception-V3, which used cross-channel multi-size convolution transformation, outperformed other backbones.Through comparative experiments after embedding submodules, submodules do not always have a positive effect on accuracy.Compared with other submodules, SENet has a positive effect.We conclude that it is essential to select an appropriate backbone model before applying the submodules, and submodules are unnecessary in some cases.
Evaluation of Natural Language Processing for the Identification of Crohn Disease–Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project

The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs.The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD.We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables.The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively.This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish.
Resilient Interactive Sensor-Independent-Update Fusion Navigation Method

To improve the robustness and reliability of multi-sensor navigation and reduce the uncertainties and complexity of sensor management in challenging environment, a resilient interactive sensor-independent-update (ISIU) method is proposed. Inspired by the interactive cooperation theory, the contributions can be divided into two aspects. Firstly the priority of trust of navigation sensors is introduced into the information fusion in the form of transition probability matrix defined by Markov chain. Secondly every observable sensor is integrated with the propagated system in an elemental filter with sensor-independent-update structure. The multi-sensor integration is implemented in state estimation domain enhanced by interactive information fusion rather than in measurement domain implemented in traditional filter method. The overall estimation is determined by the weighted sum of average from every filter estimate. This weight of every model is dynamic updated by the prior transition information and posterior model likelihood. The same independent structure is also applied to adopt new available sensor to realize plug-and-play navigation. The kinematic vehicle experiment in sub-urban and urban canyon environment verified the superiority of the proposed method. The ISIU method shows better accuracy and reliability compared to classical Kalman filters. The introduction of priority of sensors and decoupled measurement update process make it robust and insensitive to sensor measurement noise and outliers. The interactive sensor-independent-update structure has the natural function of fault detection and exclusion without additional operations. The effect of dynamic sensor selection is achieved in this processing. The proposed ISIU method is pretty suitable for resilient navigation in challenging environments.
Industrial IoT-Enabled Prediction Interval Estimation of Mechanical Performances for Hot-Rolling Steel

The prediction of mechanical properties holds the key to ensuring product quality and developing new materials in the steel industry. In this study, an industrial Internet of Things platform is developed to obtain the property-related parameters, and a data-driven approach for estimating the prediction intervals (PIs) of these properties is proposed to address the drawbacks of point prediction methods. By combining an optimized extreme learning machine (ELM) and the delta method, the proposed approach specifically applies the regularization mechanism to improve the generalization and stability of the model; it also uses the artificial bee colony algorithm to optimize the initial weights and bias of the input layer in regularized ELM (RELM). An actual steel coil dataset consisting of 27 input dimensions and 2120 samples was used to validate the proposed method, in comparison with the backpropagation neural network, standard ELM, RELM optimized using the genetic algorithm, and lower upper bound estimation method. Experiment results confirm that the proposed method can obtain PIs with a higher coverage probability, narrower interval width, and smaller calculation error.
The Inclusion of Students With Disabilities: Challenges for Italian Teachers During the Covid‐19 Pandemic

In March 2020 all schools in Italy were closed due to the Covid‐19 pandemic, and the novelty of distance learning was introduced. During the 2020–2021 school year, pre‐primary and primary schooling was carried out in situ, while secondary education was re‐organized into a mixed system, with students spending 50% of their time attending classes from home, in distance learning. This reconfiguration was a challenge to students, teachers, and parents, affecting the learning experience of the most vulnerable students and students with disabilities, particularly. It necessarily brought into question Italy’s “progressive” legal framework for “school inclusion.” The scope of the present article is to analyze the teaching activities carried out with students with disabilities in Italy during the first wave of the emergency lockdown and their consequent challenges for school inclusiveness. An overview of the Italian inclusive model in education and the national measures adopted to guarantee the right to education during times of school closure/restriction is outlined. We have sought to test the hypothesis that distance learning may introduce many risks for inclusion (resulting in a “downgrading inclusion,” that is, a decline of the level of inclusion already reached for students with disabilities), but it may also present an improvement in how teachers address these students and their needs. To this end, after reporting data from the available studies on this target, we provide insights from a web questionnaire submitted to a non‐probabilistic sample of nearly 150 primary and (lower and upper) secondary school teachers. Results showcase that, though with a general worsening of school inclusion, in some cases, teachers were actually able to support students with disabilities and their families in a new, customized, empathetic, and more attentive manner.
Object Detection for Autonomous Vehicles with Sensor-based Technology Using YOLO

The year 2020 has been a tough year with the global pandemic situation, and the utmost priority is to live in a clean, green, and safe environment.One of the areas that the governments are emphasizing for the readiness of our ecosystem is autonomous and contactless environments in adapting to the new norm.Thus, Autonomous Vehicle (AV) is a promising technology to bring forward.One of the critical aspects of Autonomous Navigation is object detection.Most AV use multiple sensors to detect objects, such as cameras, radar and Light Detection and Ranging sensor (LiDAR).Nowadays, the LiDAR sensor is widely implemented due to the ability to detect objects in the form of pulsed lasers, benefiting in low-light object detection.However, even with advanced technology, poor programming can affect the performance of object detection system.Thus, the study explores the state-of-the-art of You Only Look Once (YOLO) algorithms namely Tiny-YOLO and Complex-YOLO for object detection on KITTI dataset.Their performances were compared based on accuracy, precision, and recall metrics.The results showed that the Complex-YOLO has better performance as the mean average precision is higher than the Tiny-YOLO model when tested with equal parameters.
VerifyThis 2019: a program verification competition

Abstract VerifyThis is a series of program verification competitions that emphasize the human aspect: participants tackle the verification of detailed behavioral properties—something that lies beyond the capabilities of fully automatic verification and requires instead human expertise to suitably encode programs, specifications, and invariants. This paper describes the 8th edition of VerifyThis, which took place at ETAPS 2019 in Prague. Thirteen teams entered the competition, which consisted of three verification challenges and spanned 2 days of work. This report analyzes how the participating teams fared on these challenges, reflects on what makes a verification challenge more or less suitable for the typical VerifyThis participants, and outlines the difficulties of comparing the work of teams using wildly different verification approaches in a competition focused on the human aspect.
Pix2Pix-Based Monocular Depth Estimation for Drones with Optical Flow on AirSim

In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work.
Implementation of the RSA algorithm and its cryptanalysis

In this paper the implementation of the Rivest-Shamir-Adleman (RSA) encryption algorithm is presented.The secret key consists of two large prime numbers p and q, and a part of the public key is their product, n = pq.The RSA cryptosystem security is investigated.It is shown that if the secret exponent length is approximately one quarter as many bits as the modulus(n), there is a way to attack the RSA algorithm.The cryptanalysis of the RSA algorithm is also discussed.