Reviewerly/exHarmony huggingface dataset:
Concerns with using Machine Learning in Airworthiness Applications

Machine Learning is seeing accelerating growth in its use across a wide swath of applications ranging from wall mounted thermostats to automobiles. Even the historically conservative aviation industry is beginning to explore the use of machine learning in benign applications, e.g., predictive health monitoring, and beyond, including plans for flight critical applications such as navigation solutions. With the potential push of machine learning into flight critical applications, airworthiness practitioners concerned with software should ask: are there concerns with using machine learning in flight critical airworthiness applications? The goal of this paper is to answer this question with a resounding 'yes', by identifying some specific concerns. These concerns include data-driven development, uncertain (statistical/probabilistic) output, and extending to functional behavior, repeatability, non-rigorous development, and specific deployment environments. Beyond just identifying the concerns, this paper proposes approaches to build justified confidence in the use of machine learning in flight critical applications through combined evaluation of performance assurance, development assurance, and mitigations.
Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR’16

Autonomous or self-driving networks are expected to provide a solution to the myriad of extremely demanding new applications with minimal human supervision. For this purpose, the community relies on the development of new Machine Learning (ML) models and techniques. However, ML can only be as good as the data it is fitted with, and data quality is an elusive concept difficult to assess. In this paper, we show that relatively minor modifications on a benchmark dataset (UGR’16, a flow-based real-traffic dataset for anomaly detection) cause significantly more impact on model performance than the specific ML technique considered. We also show that the measured model performance is uncertain, as a result of labelling inaccuracies. Our findings illustrate that the widely adopted approach of comparing a set of models in terms of performance results (e.g., in terms of accuracy or ROC curves) may lead to incorrect conclusions when done without a proper understanding of dataset biases and sensitivity. We contribute a methodology to interpret a model response that can be useful for this understanding.
Research on the Security of Data Cross-border Circulation in Cyberspace

The arrival of the 5G era has promoted the innovation of people’s production and lifestyle and promoted the circulation of data among enterprises, networks, and countries. However, promoting data circulation security construction faces challenges such as lack of system, large scale of data, and escalating attacks. To ensure that data can circulation that securely and efficiently in cyberspace, the article analyzes the current situation of data circulation security, data circulation construction, data circulation security law, and data circulation key security technology. In this paper, we will make recommendations in the following three areas: legal rules, platform regulation, and technology, especially at the technical level; new technologies such as blockchain, privacy computing, and data covert transmission should be used to guarantee the security of data flow.
Modeling Design of Intelligent Agricultural Plant Protection UAV

In order to improve the autonomous operation ability and effectiveness of plant protection drones in obstacle environments, and meet the requirements of autonomous spraying, real-time obstacle avoidance, and precise operation of plant protection drones, this paper conducts relevant research from two aspects: obstacle avoidance and increasing the coverage of fog droplets around obstacles when avoiding obstacles. Aiming at obstacle avoidance of plant protection UAVs, it mainly involves research on field obstacle information recognition, obstacle avoidance algorithm improvement, obstacle avoidance system and flight platform design and construction; In order to increase the coverage of fog droplets around obstacles during obstacle avoidance and detour, the research on side spraying technology of plant protection drones is mainly carried out. The human-computer interaction interface is simplified and emotional designed to improve user comfort. The appearance of the plant protection UAV is designed with beautiful bionic seven-star ladybug shape, simple and easy-to-use human-computer interaction interface, solar endurance and retractable rotor, and it is equipped with high-definition camera and infrared detector to remotely collect field information. It has changed the shortcomings of the common plant protection UAV design, such as dull appearance, poor human-computer interaction, difficult operation, and single function. At the same time, the plant protection UAV designed by bionic appearance has not only powerful practical functions, but also more profound artistic spirit and value connotation through emotional communication with users on social, cultural and spiritual levels.
P3S: Pertinent Privacy-Preserving Scheme for Remotely Sensed Environmental Data in Smart Cities

Sensing devices, high-performance networking, and privacy preservation algorithms have an important role to play in remotely sensed environmental data in smart cities. The data generated by these sensors are heterogeneous, vast, and sensitive. Therefore, it is imperative that adequate security mechanisms are put in place to protect environmental data from privacy breaches and malicious attacks. Prior to data analysis, remotely sensed environmental data, such as weather conditions (windy, cloudy, or rainy), soil types, and other similar data, must be protected. The biggest risks of remotely connected devices are that sensitive information could be leaked and devices could be compromised. Considering these security threats, this paper proposes a Pertinent Privacy Preserving Scheme (P3S). The presented scheme is reliable for sensitive geo-sensed data in thwarting the aforementioned security issues. The data is concealed using two-factor authentication from the transmitter end. In this authentication, the device and receiver's signature are overlapped for improved authentication. The failure in overlapping is identified by delayed signing time and non-coherent agreements. This identification is recurrently analyzed using federated learning. Therefore, the signing process is paused until the device verification is performed. Hence, if the device verification succeeds, then a new data privacy accumulation session is introduced. Contrarily, the accumulation is dropped, preventing compromised actual data from preserving accuracy. In two-factor authentication, lightweight digital signing cryptography is utilized. The proposed scheme maximizes the average authentication success rate and average overlapping factor by 8.86%, and 12.20% respectively. This scheme further reduces average authentication time, false data, and verification time by 10.14%, 9.70%, and 10.19% respectively.
Perspectives on International Image Rights over the Past Twenty Years

Abstract Image rights enable individuals to stop the unauthorized use of their publicly identifiable attributes, typically their name, likeness, voice, or other personal indicia (in sum, their ‘image’). Unlike widely recognized intellectual property rights like copyright, patents, and trade marks, image rights have developed in a piecemeal, organic fashion, with differences in their nature and scope in various jurisdictions. Nonetheless, we conclude from an evaluation of approaches in the US, Europe, China, and the UK that image rights protection is on a growth trajectory, having undergone an overall expansion in the past twenty years, especially with the advent of the digital era. Turning to the present and future of image rights, we discuss emerging applications of image rights in the context of deepfakes, influencer marketing, digital avatars, and non-fungible tokens (NFTs). Additionally, online platform guidelines and actions are coalescing into de facto protection of an individual’s persona in an increasingly borderless, digital world. In such an environment, the interrelationships between a person’s autonomy, dignity, privacy, and economic aspects of reputation will require a more joined-up approach which is starting to emerge. We discuss this harmonizing trend as well as the current practical difficulties in enforcing image rights online, and suggest some solutions to these issues.
State Sharding for Permissioned Blockchain

Blockchain has attracted lots of attention in recent years. However, the performance of blockchain cannot meet the requirement of massive IoT devices. One of the important bottlenecks of blockchain is the limited computing resources on a single server while executing transactions. To address this issue, we propose Aeolus blockchain to achieve distributed execution of blockchain transactions. There are two key challenges to achieving this for IoT blockchain: transaction structure and state consistency. Facing these challenges, we first propose a distributed blockchain transaction structure, which imports extra parameters to divide the transaction execution into different stages to enable distributed execution. Second, we propose distributed state update sharding, which equips each blockchain peer with its own master and shard servers. In this way, each blockchain peer can be considered as a cluster that distributes the transaction to shorten the processing time and reach the consensus finally. We implement Aeolus on Go-Ethereum to evaluate its feasibility, on a test-bed including 132 cloud servers. Our system runs stably for more than 8 hours under the workload of 190,000,000 real-world user transactions. Experimental results show the efficiency that Aeolus can achieve more than 100,000 TPS of blockchain transactions, which is 15.6 times the throughput of the original blockchain.
Stochastic Approach to the Quantum Noise of a Single-Emitter Nanolaser

It is shown that the intensity quantum noise of a single-emitter nanolaser can be accurately computed by adopting a stochastic interpretation of the standard rate equation model under the only assumption that the emitter excitation and photon number are stochastic variables with integer values. This extends the validity of rate equations beyond the mean-field limit and avoids using the standard Langevin approach, which is shown to fail for few emitters. The model is validated by comparison to full quantum simulations of the relative intensity noise and second-order intensity correlation function, g(2)({\tau} ). Surprisingly, even when the full quantum model displays vacuum Rabi oscillations, which are not accounted for by rate equations, the intensity quantum noise is correctly predicted by the stochastic approach. Adopting a simple discretization of the emitter and photon populations, thus, goes a long way in describing quantum noise in lasers. Besides providing a versatile and easy-to-use tool for modeling a new generation of nanolasers with many possible applications, these results provide insight into the fundamental nature of quantum noise in lasers.
The use of the interactive multifunctional cloud portfolio for constructing and implementing individual educational routes for students in the course of project activities

The problem and the aim of the study. One of the priorities of the modern school is informatization and individualization of education. Its implementation can be facilitated by the e-portfolio as a tool for evaluating students’ individual educational results, optimizing work with information, and supporting the design of a non-linear structure for presenting materials. The purpose of the research is to study the possibilities of using the interactive multifunctional cloud portfolio for implementing individual educational routes for students in the course of project activities. Research methods. The methodology is based on the analysis of the didactic potential of the e-portfolio technology, support for UNESCO initiatives, state education development programs. The analysis and synthesis of literature on the problem of using interactive tools in the digital school, processing of test results and electronic content of the portfolio are applied. The experiment involved 52 students of classes 5-10, who study at gymnasium in Kirovo-Chepetsk (Russian Federation). The designed portfolio was implemented in the environment of the Unified Regional Information System for Education of the Kirov Region. Pearson's chi-squared test (χ2 ) was used for statistical data processing. Results. The model of the interactive multifunctional cloud portfolio was developed, which determines its structure, including the sections "My individual educational route", "My lessons", "My studios", "My affairs". The implementation of the model in the unified regional information system made it possible to change the nature of the interaction between participants of educational relations in the experimental group; improve the quality of the provision of services in electronic form; determine priority areas for development for each student, etc. Statistically significant differences in qualitative changes in the pedagogical system were revealed (χ2 = 9.302; p &lt; 0.05). In conclusion, the features of using the proposed portfolio model are described: the principles of the unity of education and upbringing, individualization, variability, reflection and cooperation, etc. The difficulties of the proposed innovations are formulated: the need to clearly define the didactic functions of the projected portfolio; the problem of choosing digital tools; accuracy of task formulation, etc. Keywords: digitalization of education, individualization of education, e-portfolio, cloud technology, project, unified regional information system
Computational approaches to detect experts in distributed online communities: a case study on Reddit

Abstract The irreplaceable key to the triumph of Question &amp; Answer (Q &amp; A) platforms is their users providing high-quality answers to the challenging questions posted across various topics of interest. From more than a decade, the expert finding problem attracted much attention in information retrieval research. Based on the encountered gaps in the expert identification across several Q &amp; A portals, we inspect the feasibility of identifying data science experts in Reddit. Our method is based on the manual coding results where two data science experts labelled not only expert and non-expert comments, but also out-of-scope comments, which is a novel contribution to the literature, enabling the identification of more groups of comments across web portals. We present a semi-supervised approach which combines 1113 labelled comments with 100,226 unlabelled comments during training. We proved that it is possible to develop models that can identify expert, non-expert and out-of-scope comments peaking the AUC score at 0.93, accuracy at 0.83, MAE at 0.15 degrees and R2 score at 0.69. The proposed model uses the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user feature sets. We conclude that the NLP and user feature sets contribute the most to the better identification of these three classes. It means that this method can generalise well within the domain. Finally, we make a novel contribution by presenting different types of users in Reddit, which opens many future research directions.
Implementation of Chaos-Based Image Encryption on FPGA

Information security is becoming more crucial to data transmission and storage in this era of digital communication. In numerous processes, images are frequently used. As a result, protecting the image data from misuse is equally crucial. Image encryption is a solution to protect visual data from hackers. The chaotic image encryption algorithm is a unique choice, but software algorithms can be hacked from anywhere. The paper proposes the encryption scheme along with a standalone device using FPGA. Even if the software is compromised, the FPGA encryption keys are more secure. Because the encryption scheme is developed on core reconfigurable hardware, which can increase the security level and speed since multiple operations can be performed simultaneously as also FPGA, it is generally true that without physical access to a standalone FPGA device, it is difficult for a non-intended user to hack the key stored on the device. FPGAs typically store their configuration information (including the key) in non-volatile memory, such as flash memory, which is not easily accessible to an attacker without physical access to the device. Furthermore, FPGA is more flexible in altering its internal architecture based on the proposed encryption scheme, which adapts to different encryption schemes. Chaos-based image encryption using FPGA can overcome several issues like security, key management, robustness, and scalability. The proposed encryption scheme is validated by performing entropy, correlation, and histogram test analyses.
Hiding Secret Data Using AES Encryption and DFS Graph Traversal in 3D Images

3D models have taken centre stage in a variety of applications across numerous industries as a outcome of the enormous advancements in multimedia communications. As a result, these images are used in steganography as safe and trustworthy cover media for hiding secret data. The confidential material in this document is encrypted using the AES technique for increased protection. Next, using depth first search, a geometric domain oriented steganography technique is used, in which triangular meshes' each vertex of several 3D images are subtly altered to hold the hidden data. A geometrical attack on a 3D model, such as translation, uniform scaling, or rotation, is found to be resistant to our proposed method. Also, it is capable of blindly disclosing the confidential information. Our proposed algorithm's 9 bpv embedding performance is evaluated and contrasted with compare to existing literature. The numerical results oblige as an excellent benchmark for higher payload data security solution.
AN OVERVIEW OF COMPREHENSIVE TRAINING IN HIGHER EDUCATION BETWEEN 2015-2021

Today's society has faced unexpected situations as a result of unforeseen events such as the COVID-19 pandemic, which brought about one of the transitions never thought of, all academic and administrative procedures that were carried out in person, changed to make them through the benefits of technologies and education was no stranger to this situation and lived this change from a classroom education to a teaching model mediated by technology and currently to a model based on hybrid education; that is to say, it is possible to combine face-to-face and technology. Undoubtedly, this affected all social sectors, one of them higher education, in which it became necessary to establish a research whose general objective was framed in: to reflect on some theoretical-epistemological arguments linked to comprehensive training in higher education between 2015 - 2021, with the intention of having an approach to the contributions given for a quality education for and social benefit. The methodology used is framed in a qualitative research, through a documentary method supported by the technique of content analysis, which led to a systematic review of conceptual and theoretical elements that converge in revealing a cumulus of aspects that define and surround the integral formation in higher education in recent years. The review resulted in the management of bibliometric studies in search engines such as Scopus, Web of Science and Google Scholar. This allows us to conclude that multiple researches have been carried out, some of them with a very high level of scientific rigor that seek to rethink the pedagogical action in order to adjust it to the demands of society in general that demand changes for a new teaching model.
Level Sets Guided by SoDEF-Fitting Energy for River Channel Detection in SAR Images

To achieve river channel detection in SAR (synthetic aperture radar) images, we developed a level-set-based model (LSBM) guided by a designed data-fitting energy which is called the SoDEF (sum of dual exponential functions)-fitting energy. Firstly, we designed a function by computing the sum of dual exponential functions to substitute for the quadratic function, and used it to construct the data-fitting energy. Secondly, the adaptive area-fitting centers (AFCs) were computed based on two kinds of grayscale characteristics, which are more accurate and more stable. Thirdly, the Dirac function in gradient descent flow was displaced by an edge indicator function to help the evolving level sets stop at the target edges. Moreover, some regularized terms were incorporated into the objective function to guarantee the model’s stability. The river channel detection experiments conducted with real SAR images indicated that the developed model is superior to the related state-of-the-art methods in its detection accuracy and efficiency.
DeAR: A Deep-Learning-Based Audio Re-recording Resilient Watermarking

Audio watermarking is widely used for leaking source tracing. The robustness of the watermark determines the traceability of the algorithm. With the development of digital technology, audio re-recording (AR) has become an efficient and covert means to steal secrets. AR process could drastically destroy the watermark signal while preserving the original information. This puts forward a new requirement for audio watermarking at this stage, that is, to be robust to AR distortions. Unfortunately, none of the existing algorithms can effectively resist AR attacks due to the complexity of the AR process. To address this limitation, this paper proposes DeAR, a deep-learning-based audio re-recording resistant watermarking. Inspired by DNN-based image watermarking, we pioneer a deep learning framework for audio carriers, based on which the watermark signal can be effectively embedded and extracted. Meanwhile, in order to resist the AR attack, we delicately analyze the distortions that occurred in the AR process and design the corresponding distortion layer to cooperate with the proposed watermarking framework. Extensive experiments show that the proposed algorithm can resist not only common electronic channel distortions but also AR distortions. Under the premise of high-quality embedding (SNR=25.86dB), in the case of a common re-recording distance (20cm), the algorithm can effectively achieve an average bit recovery accuracy of 98.55%.
Rephrasing the Reference for Non-autoregressive Machine Translation

Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the reference sentence may be inappropriate for the training when the NAT output is closer to other translations. In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output. As we train NAT based on the rephraser output rather than the reference sentence, the rephraser output should fit well with the NAT output and not deviate too far from the reference, which can be quantified as reward functions and optimized by reinforcement learning. Experiments on major WMT benchmarks and NAT baselines show that our approach consistently improves the translation quality of NAT. Specifically, our best variant achieves comparable performance to the autoregressive Transformer, while being 14.7 times more efficient in inference.