Reviewerly/exHarmony huggingface dataset:
An Image Encryption Scheme based on Adaptive Bit-Plane Division

Abstract With the application and popularization of digital images, the security of images has been paid more and more attention. The bit-level image encryption method has become one of the commonly used methods. It has the characteristic of changing the pixel value while disturbing the position of the pixel point, but at the same time it is accompanied by a higher computational complexity. In order to improve the efficiency of the encryption algorithm, according to the characteristic that the higher 4 of the 8 bit-planes of the grayscale image contain 94.125% of the information of the entire image, the encryption structure of higher 4 bit-planes and lower 4 bit-planes is widely used. But for different images, this characteristic is not accurately applicable. In response to this problem, we design a method of adaptive bit-plane division, which uses mutual information and Jensen-Shannon divergence to measure the amount of information of bit-planes and then divides bit-planes into two parts. The results of bit-plane division are different for different images. Based on this, we propose an image encryption scheme based on adaptive bit-plane division. Compared with the traditional higher 4-bits and lower 4-bits encryption method, this scheme reduces the encryption cost and improves the encryption efficiency. Experimental results show that the proposed scheme can effectively resist the chosen plain-image attacks and has sufficient security.
Inter-Operability of Compression Techniques for Efficient Deployment of CNNs on Microcontrollers

Machine Learning (ML) has become state of the art for various tasks, including classification of accelerometer data. In the world of Internet of Things (IoT), the available hardware with low-power consumption is often microcontrollers. However, one of the challenges for embedding machine learning on microcontrollers is that the available memory space is very limited, and this memory is also occupied by the rest of the software elements needed in the IoT device. The problem is then to design ML architectures that have a very low memory footprint, while maintaining a low error rate. In this paper, a methodology is proposed towards the deployment of efficient machine learning on microcontrollers. Then, such methodology is used to investigate the effect of using compression techniques mainly pruning, quantization, and coding on the memory budget. Indeed, we know that these techniques reduce the model size, but not how these techniques interoperate to reach the best accuracy to memory trade-off. A Convolutional Neural Network (CNN) and a Human Activity Recognition (HAR) application has been adopted for the validation of the study .
Human Recognition for Resource-Constrained Mobile Robot Applied to Covid-19 Disinfection

The global COVID-19 pandemic has stimulated the use of disinfection robots: in September 2021, following a European Commission’s action, 200 disinfection robots were delivered to European Hospitals. UV-C light is a common disinfection method, however, direct exposure to UV-C radiation is harmful and disinfection can be operated only in areas strictly forbidden to human personnel. We believe more advanced safety mechanisms are needed to increase the operational flexibility and safety level. We propose a safety mechanism based on vision and artificial intelligence, optimised for execution on mobile robot platforms. It analyses in real-time four video streaming and disables UV-C lamps when needed. Concerning other detection methods, it has a relatively wider and deeper range, and the capability to operate in a dynamic environment. We present the development of the method with a performance comparison of different implementation solutions, and an on-field evaluation through integration on a mobile disinfection robot.
Multi-camera Metrology System for Shape and Position Correction of Large Fuselage Components in Aircraft Assembly

The assembly of large-volume fuselage components made of composite materials are particularly suitable for metrology-based full automation. So far, mobile coordinate measurement machines have performed the task of measuring large fuselage components for most production processes. This requires the manual fixing of several reflector targets in specifically designed pre-fitting stations, which not only is time consuming but also prone to human error. Additionally, the measurement times tend to be very high due to the sequential measurement of targets. To address this challenge, this paper proposes a multi-camera system for marker-less measurement of the shape and position of large fuselage panels through the detection of its own features. This approach aims to improve the current fuselage section assembly by reducing its runtime, hardware costs and improving flexibility. Moreover, the integration of such a system is analyzed by utilizing it in shape adjustment experiments.
Integrative assessment of the effectiveness of Russian library corporations

The article substantiates the need for the development of Russian library corporations as the main direction of library automation. The main objectives of the development of library corporations are determined on the basis of the history of the development of corporate relations. Based on the theoretical substantiations already developed, the relevance of assessing the effectiveness of Russian library corporations in the conditions of digitalization of modern society is shown. The results of the practice of evaluating the effectiveness of the OPAC-Global AIS based on the developed indicators are proposed.
Features and specifics of the formation of cadets ' readiness for interaction in the multicultural environment of the region

The article presents the peculiarities and specifics of the formation of cadets' readiness for interaction in multicultural environment of the region. Theoretical and methodological approaches to the mentioned problem are analyzed. The relevance of the work lies in the fact that in our changing world of the 21st century, cadets face a new problem of communication in multicultural environment. The aim of the work is to develop the skills of communication and cooperation with people of different nationalities, races and religions in an international and multicultural environment, strengthening mutual understanding and promoting peace and respect in society. The analysis of the topic carried out in this article allows us to define the learning environment of Russian cadets as a transnational society at the stage of educational development. The introduction of multicultural knowledge in the textbook is based on an analysis of the universality and uniqueness of society. It is a special part of interpersonal communication training.
Autohighlight: Highlight detection in League of Legends esports broadcasts via crowd-sourced data

Every minute, 500 h of footage is uploaded to Youtube.com, and ∼ 1900 h of footage is livestreamed on Twitch.tv. It can therefore be challenging for viewers to find the content they are most likely to enjoy. Highlight videos can entertain users who did not watch a broadcast, e.g. due to a lack of awareness, availability, or willingness. Furthermore, livestream content creators can grow their audiences by using highlights as advertisement, while also engaging casual followers who do not watch full broadcasts. However, hand-generating these videos is laborious, thus automatic highlight detection is an active research challenge. We examine automatic highlight detection by focusing on esports broadcasts. Esports are an emerging genre of sport played using a video games. We focus on League of Legends , a popular title with multiple professional leagues. Esports broadcasts are high-quality and professionally produced, mirroring traditional sports. We tackle the problem in a weakly supervised manner, utilising two datasets, one of ‘crowd-sourced’ highlight videos and one of unedited broadcasts. These datasets allow us to leverage massive data while hugely reducing the human cost of data curation and annotation. We propose two novel extensions to state-of-the-art rank-based highlight detection architectures. Firstly, a multimodal hybrid–fusion architecture that enables audio-visual highlight detection, and secondly, a smoothing step to incorporate context into decision making. Both extensions show significant improvement over state-of-the-art ranking models, in places performing nearly twice as well as competing architectures. Additionally, we examine the effectiveness of each modality and compare ranking models with classification based systems.
LiNGAM with Hidden Common Causes

This chapter discusses methods for estimating the causal structure of observed variables given hidden or unobserved common causes. Specifically, it introduces the LiNGAM model with hidden common causes and its estimation methods. It also discusses latent or unobserved variables in LiNGAM other than hidden common causes; specifically, it considers latent factors and classes.
Introduction

In recent years, causal inference frameworks, such as the potential outcome and structural causal models, have increasingly been used to describe assumptions and concepts in machine learning. This chapter introduces basic concepts of statistical causal inference. It first introduces a mathematical framework (structural causal model) to represent causality and then describes how to estimate the causation magnitude when the causal graph is known from prior knowledge. Finally, when unknown, it presents a discussion of causal discovery methods for inferring causal graphs from data. Accordingly, regarding machine learning prediction, the performance of constructed prediction models can be evaluated based on validation data using cross-validation methods. Nonetheless, it is essential to continue advancing statistical causal inference and seek novel ideas to build better causal models based on domain knowledge and data.
How the Quality of Maintenance Tasks is Affected by Criteria for Selecting Engineers for Collaboration

In industry, software projects might span over decades, with many engineers joining or leaving the company over time. In these circumstances, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution advanced by one engineer might be enhanced with contributions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.
Adomian Decomposition Method

The Adomian decomposition method, also known as the inverse operator method.
Frequency-Domain Approximation Method

At present, the frequency-domain approximation method proposed by Sun et al. is widely used in engineering to solve fractional-order chaotic systems. This method is the theoretical basis for the analog circuit realization of fractional-order chaotic systems.
Model‐Based Explanation of Feedback Effects in Syllogistic Reasoning

For decades, a significant number of models explaining human syllogistic inference processes were developed. There is profound work fitting the models' parameters and analyzing each model's ability to account for the data in order to support or reject the underlying theories. However, the model parameters are rarely used to extract explanations and hypotheses for phenomena that go beyond the original scope of the models. In this work, we apply three state-of-the-art models, the probability heuristics model (PHM), mReasoner, and TransSet, to data from reasoning experiments where participants received feedback for their conclusions. We derived hypotheses based on the models' explanations for the feedback effect and put these to the test by conducting an experiment targeting the hypotheses. The work contributes to the field in three ways: (a) the feedback effect could be replicated and was shown to be a robust effect; (b) we demonstrate the use of the model parameters in order to derive new hypotheses; (c) we present possible explanations for the feedback effect based on existing theories.
Access to ICT at Argentine elementary school children’s homes and its impact on school achievements

This article analyzes the relationship between access to Information and Communication Technology (ICT) and school performance. It contributes to the empirical literature in the area since there is no consensus yet. Moreover, the context associated with COVID-19 pandemic also considers the analysis as the most relevant. The goal of this article is to study the impact of ICT on school performance at elementary level. The hypothesis set forth is that having both a computer and connection to Internet at the students’ homes, improves their school related achievements. To contrast it, we propose an econometric model using the Propensity Matching Score (PSM) methodology with data from the Learning 2018 (Aprender 2018) campaign of students at the last year of elementary school in Argentina and in each of the regions that conform it. Finally, there is evidence in favor of the hypothesis.
A comprehensive analysis of lightweight 8-bit sboxes from iterative structures

Massive data needs to be cryptographically handled in emerging IoT applications. Therefore the design and analysis of lightweight symmetric-key primitives become a research hotspot. Theoretically, symmetric-key cryptograms should be secure against differential, linear and other proposed structural cryptanalysis. In addition to these cryptographic properties, software and hardware costs are also pivotal for resource constrained devices. Traditionally, 4-bit sboxes are widely used in lightweight cryptography due to their efficiency. In this paper, we investigate the iterative constructions of lightweight 8-bit sboxes. Our proposals contain 8-bit sboxes constructed with the Feistel, Lai–Massey and SPN structures, which are iterated with the published lightweight 4-bit sboxes or non-bijective functions. Moreover, we propose 8-bit sboxes under the generalized Feistel network (GFN), which are suitable for software and hardware implementations. Based on the bitslicing method, software and hardware performances are analyzed with their CPU cycles and Gate Equivalent (GE). Compare to the published lightweight 8-bit sboxes with iterative structures, our proposals achieve similar software performance, whilst enjoying better algebraic degrees and less GE costs in hardware implementation.
Equal probability embedded cubature particle PHD filter algorithm in multi-target tracking

In target tracking, multi-target tracking is the focus of research. It is primarily concerned with the issue of collaboratively estimating the number, state, or trajectory of targets based on sensor measurements in the presence of data association uncertainty, detection uncertainty, false observation, and noise. The current research hotspot in multi-target tracking is nonlinear multi-target tracking based on the probability hypothesis density algorithm. Furthermore, there are several considerations with this algorithm’s multi-target tracking, such as low estimate accuracy, filter divergence, and poor real-time performance. Based on equivalent probability sampling and the embedded volume criterion, this article demonstrates an equal probability embedded cubature particle probability hypothesis density filter algorithm. In the sampling stage, the algorithm implements the equal probability sampling method, divides the entire sampling area into several areas with equal probability, extracts particles from each region using the established criteria, generates limited integral points using the third-order embedded volume criterion, filters each sampling particle, fits the important density function, and predicts and updates the probability hypothesis density of multi-target state. The simulation results demonstrate that the equal probability sampling strategy outperforms other multi-target position and number estimation methods. Simultaneously, it demonstrates that the equal probability embedded cubature particle probability hypothesis density filter algorithm can effectively track multiple targets. The equal probability embedded cubature particle probability hypothesis density filter algorithm performs better in real-time and has a more accurate target number and state estimate than other algorithms.
Using dual evolutionary search to construct decision tree based ensemble classifier

Abstract A typical ensemble learning process typically uses a forward integration mechanism to construct the ensemble classifier with a large number of base classifiers. Based on this mechanism, it is difficult to adjust the diversity among base classifiers and optimize the structure inside ensemble since the generation process has a certain amount of randomness, which makes the performance of ensemble classifiers heavily dependent on the human design decisions. To address this issue, we proposed an automatic ensemble classifier construction method based on a dual-layer evolutionary search mechanism, which includes a tree coding-based base classifier population and a binary coding-based ensemble classifier population. Through a collaborative searching process between the two populations, the proposed method can be driven by training data to update the base classifier population and optimize the ensemble classifiers globally. To verify the effectiveness of the dual evolutionary ensemble learning method (DEEL), we tested it on 22 classification tasks from 4 data repositories. The results show that the proposed method can generate a diverse decision tree population on the training data while searching and constructing ensemble classifiers from them. Compared with 9 competitor algorithms, the proposed method achieved the best performance on 17 of 22 test tasks and improved the average accuracies by 0.97–7.65% over the second place. In particular, the generated ensemble classifiers show excellent structure, which involve small number and diverse decision trees. That increases the transparency of ensembles and helps to perform interpretability analysis on them.
A security hybrid mechanism using fuzzy with adaptive ElGamal approach in cloud

Summary Due to the increasing number of people accessing their personal medical information online, it has raised concerns about the security of their data. Unauthorized access to patient's records can lead to disclosure of their privacy. There are various types of frauds that can affect the integrity of a patient's records such as theft of identity and tax fraud. It is therefore necessary to implement a secure data access and retrieval system in order to prevent these types of crimes. Security passwords are often the only barrier between hackers and users. Many of us often use the same ones for multiple accounts. Biometric passwords are more secure than traditional ones due to their ability to be created by observing a person's eyes. They can also be created with a secret key that's stored in an encrypted format. Proposed method combines a secure key with an optimized fuzzy extraction algorithm to provide high‐level security. The file is then encrypted using adaptive ElGamal and its digital signature is uploaded in cloud. Data recovery services are also introduced to help users keep their data secure using seed block algorithm (SBA). These services can be utilized to recover files once they get lost or get destroyed. The proposed security approach for back‐up files (SBA) can be used to minimize the time it takes for the recovery process. It also addresses the issue of unauthorized access to the data stored on remote servers.
Multidimensional adaptive P‐splines with application to neurons' activity studies

Abstract The receptive field (RF) of a visual neuron is the region of the space that elicits neuronal responses. It can be mapped using different techniques that allow inferring its spatial and temporal properties. Raw RF maps (RFmaps) are usually noisy, making it difficult to obtain and study important features of the RF. A possible solution is to smooth them using P-splines. Yet, raw RFmaps are characterized by sharp transitions in both space and time. Their analysis thus asks for spatiotemporal adaptive P-spline models, where smoothness can be locally adapted to the data. However, the literature lacks proposals for adaptive P-splines in more than two dimensions. Furthermore, the extra flexibility afforded by adaptive P-spline models is obtained at the cost of a high computational burden, especially in a multidimensional setting. To fill these gaps, this work presents a novel anisotropic locally adaptive P-spline model in two (e.g., space) and three (space and time) dimensions. Estimation is based on the recently proposed SOP (Separation of Overlapping Precision matrices) method, which provides the speed we look for. Besides the spatiotemporal analysis of the neuronal activity data that motivated this work, the practical performance of the proposal is evaluated through simulations, and comparisons with alternative methods are reported.
Foreground–background decoupling matting

Image matting aims to extract specific objects, deployed in many applications. Generally, the automatic matting methods need an extra before overcome the intricate details and the diverse appearances. Recently, the matting community has paid more attentions to the investigation of trimap-free matting direction to address the dependency of priors. Most trimap-free approaches divide the matting task into global segmentation and detail matting subtasks. Unfortunately, these methods suffer from stagewise modeling, uncorrectable errors, or subtasks bottleneck problems. To address these issues, we propose a new set of matting subtasks, including foreground segmentation, background segmentation, and disambiguation. And we present a novel Foreground–Background Decoupling Matting (FBDM) network motivated by the new subtasks. Specifically, we first design a nested attention mechanism to decouple the backbone features. Then, we utilize two independent progressive semantic decoders by the decoupling features to complete the foreground and background segmentation subtasks. Finally, we utilize multiple of the proposed frequency division local disambiguation modules to achieve the disambiguation subtask. Besides, we establish a challenging potted plant (PPT) benchmark which contains 100 potted plants images in the real world for the matting community. Extensive experiments on several public benchmarks and the PPTs benchmark demonstrate that the proposed FBDM generates the best results compared with the state-of-the-art trimap-free methods.