Reviewerly/exHarmony huggingface dataset:
Analysis of outboard cross‐eye jamming using an unmanned platform

To address the problem and reduce the risk wherein a ship-based cross-eye jammer could set a protected ship as a target, an unmanned platform equipped with a jammer is proposed to be used. However, the jamming situation violates the original cross-eye jamming conditions and introduces some new elements. In this regard, the impact of outboard cross-eye jamming in the presence of ship echoes is derived in detail. The analysis shows that the outboard cross-eye jamming combines the characteristics of outboard active and traditional cross-eye jamming gains. It can reduce the standard cross-eye jamming requirement of the jamming-to-signal ratio (JSR) and improve the jamming effect. The phase difference of a cross-eye jammer can be controlled at 0° or 180° to obtain a larger angular positioning deviation based on the JSR requirement, thus providing a theoretical basis for applications of unmanned outboard cross-eye jamming.
DevOps Tools: Silver Bullet for Software Industry

DevOps is a development and operations practice where engineers collaborate throughout the full-service lifecycle, from design to development to production support. DevOps is a newly emerging field in the field of Software application. Most of the giant companies have now shifted towards DevOps practices as it channelizes the development and operation process for any software development. The present chapter aims to provide the DevOps information for the basic to reach out to maximum people working in or planning to shift towards DevOps. Starting from the definition, the phases, tools, and security are discussed in the present paper. Each phase of the DevOps life cycle is discussed with the tools used in that phase. The DevOps practices are powered with several tools to provide end-to-end automation in software development. The present paper presents the basic knowledge of prevalent tools available for DevOps practices. Along with the DevOps automation, the chapter also gives an overview of DevOps security, its need, and its tool. The chapter covers the software phases and the tools used to automate it. It also provides information regarding the tool platform, availability, and usage. To emphasize more on DevOps, the chapter has also summarized the industrial and academic opportunities in DevOps.
CNN-Based Models for Image Forgery Detection

Image forgery (IF) is a technique in which images are manipulated through several tampering software such as Photoshop, Adobe, Corel, etc., and it becomes difficult to discriminate between authentic and forged images. Conventional techniques suffered from the weakness that they can only extract specific kinds of features and can identify only one type of tampering. This chapter introduces deep learning methods especially convolutional neural network (CNN) models, ResNet-50, and MobileNetv2 for tampering detection. Two datasets are used—CASIA v1.0 and CASIA v2.0 for experiments. These datasets have been divided into 80% training set and 20% testing set and achieved an overall highest accuracy of 95%.
Robust and Secured Reversible Data Hiding Approach for Medical Image Transmission over Smart Healthcare Environment

With the rapid progress of cloud computing, there has been a marked improvement in the development of smart healthcare applications such as Internet of Medical Things (IoMT), Telemedicine, etc. Cloud-based healthcare systems can efficiently store and communicate patient electronic healthcare records (EHR) while allowing for quick growth and flexibility. Despite the potential benefits, identity violation, copyright infringement, illegal re-distribution, and unauthorized access have all been significant. To address all these breaches, in this paper, a reversible medical image watermarking scheme using interpolation is proposed. The medical image is partitioned into Border Region (BR), Region of Interest (ROI), and Region of Non-interest (RONI) regions. BR is used for embedding integrity checksum code generated from ROI for tamper detection. RONI is used for embedding watermark. To ensure complete recovery of ROI and high embedding capacity, ROI is compressed before embedding. To ensure high-security compressed ROI, hospital emblem and EHR merged and then encrypted using a random key generated from Polybius magic square to get higher security. The proposed scheme is proved to take less computational time as there are no complex functions used in the embedding. The experiments performed on the proposed scheme is proved to have high imperceptibility, robustness, embedding capacity, security, and less computational time. All these confirm that the proposed approach is a potential candidate for suitable in smart healthcare environment.
Social Impact Teaching and Learning

Use the techniques presented here to enhance students’ capacity to produce outcomes that enable future growth and to develop the critical foresight that permits learners to adapt to societal disruptions resiliently and empathetically.
Siamese hierarchical feature fusion transformer for efficient tracking

Object tracking is a fundamental task in computer vision. Recent years, most of the tracking algorithms are based on deep networks. Trackers with deeper backbones are computationally expensive and can hardly meet the real-time requirements on edge platforms. Lightweight networks are widely used to tackle this issue, but the features extracted by a lightweight backbone are inadequate for discriminating the object from the background in complex scenarios, especially for small objects tracking task. In this paper, we adopted a lightweight backbone and extracted features from multiple levels. A hierarchical feature fusion transformer (HFFT) was designed to mine the interdependencies of multi-level features in a novel model-SiamHFFT. Therefore, our tracker can exploit comprehensive feature representations in an end-to-end manner, and the proposed model is capable of handling small target tracking in complex scenarios on a CPU at a rate of 29 FPS. Comprehensive experimental results on UAV123, UAV123@10fps, LaSOT, VOT2020, and GOT-10k benchmarks with multiple trackers demonstrate the effectiveness and efficiency of SiamHFFT. In particular, our SiamHFFT achieves good performance both in accuracy and speed, which has practical implications in terms of improving small object tracking performance in the real world.
Research on Fuzzy Comprehensive Evaluation Method in Wireless Sensor Network Course Evaluation

Wireless Sensor network (WSN) is a Capstone course in engineering education certification, because the implementation of this course should reflect implementation, complexity and teamwork. It is difficult for teachers to use traditional and linear assessment criteria to evaluate students' learning effects. Aiming at the above problems, an evaluation method of wireless sensor network course based on fuzzy theory and multi-level evaluation index factor set is proposed in this paper. The method imitates the evaluation experience and intelligent behavior of education experts, and obtains an optimal course evaluation model. Through the fuzzy comprehensive evaluation of multi-level index system, students' learning effect can be evaluated accurately, objectively, fairly and scientifically.
Building a knowledge society during Japan’s coronavirus pandemic

This article will attempt to explain Japan’s policy adoption regarding education during the coronavirus pandemic from the perspective of technology adoption and with the intention of adhere to the ideals of a knowledge society, while at the same time, exploring the probable causes and consequences of these decisions and the possible repercussions in the Japanese youth.
Still Free to Have a <i>Wh</i>-Phrase: A Reply to Donati, Foppolo, Konrad, and Cecchetto 2022

Donati et al. (2022) aim at supporting the theoretical and empirical claims in Donati and Cecchetto 2011 about the grammar and free relative clauses by responding to the new data and criticism presented in Caponigro 2019. I critically examine the data and arguments in Donati et al. 2022, provide new data and arguments against the core theoretical proposal and the analyses advanced in Donati and Cecchetto 2011 and endorsed in Donati et al. 2022, and suggest a different approach, based on Citko 2008, Ott 2011, and Chomsky 2013, 2015. My overall conclusion is that there are theoretical reasons and rich crosslinguistic evidence supporting the view that the grammar allows for free relative clauses that are introduced by varieties of wh-phrases, rather than just wh-words, pace Donati and Cecchetto 2011 and Donati et al. 2022.
Finding Multidimensional Simpson's Paradox

Finding and analyzing Simpson's paradox, a well known statistical phenomenon, has found many applications. While the existing literature focuses on only analyzing the causes of identi ed Simpson's paradox, there is no systematic analysis on Simpson's paradox in multidimensional spaces. In this paper, we develop a simple yet practical approach to automatically identify all Simpson's paradox instances formed by various sub-populations and separator attributes in a multidimensional data set. Moreover, we analyze the distribution of the multidimensional Simpson's paradox instances on three real data sets with respect to dimensionality, size of sub-populations, participation of individual records, redundancy, and more. We obtain a series of interesting observations about a few questions that have never been asked before. The results open doors to a few interesting directions for future study. Moreover, this paper is an outcome from a high-school student summer research internship. It re ects our on-going e ort in promoting data science research to youth and high school students.
Introduction to the Special Section on AI in Manufacturing

On 19 September 2022, the first workshop on AI for Manufacturing (AI4M Workshop) took place at ECML-PKDD, the European Conference on Machine Learning and Principles and Practice for Knowledge Discovery in Databases. The workshop brought together researchers and practitioners, from academia and industry, contributing their perspectives. This special section includes five articles in which Artificial Intelligence methods are used to address real problems in the manufacturing industry, ranging from the supply chain, to production, to quality insurance, and predictive maintenance. In this introduction, we present a high-level overview of the current state of the area: observed trends and the main open challenges. This overview is based on these papers, the keynote presentation, the panel discussion, and the discussion that emerged during the workshop.
Efficient and Provably Secure Data Selective Sharing and Acquisition in Cloud-Based Systems

Towards the large amount of data generated everyday, data selective sharing and acquisition is one of the most significant data services in cloud-based systems, which enables data owners to selectively share their data to some particular users, and users to selectively acquire some interested data. However, it is challenging to protect data security and user privacy during data selective sharing and selective acquisition, because cloud servers are curious about the data or user’s interests, and even send data to some unauthorized users or some uninterested users. In this paper, we propose an efficient and provably secure Data selective Sharing and Acquisition ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf DSA}$ </tex-math></inline-formula> ) scheme for cloud-based systems. Specifically, we first formulate a generic data selective sharing and acquisition problem in cloud-based systems by identifying several design goals in terms of correctness, soundness, security and efficiency. Then, we propose the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf DSA}$ </tex-math></inline-formula> scheme to enable data owners to control the access of their data in a fine-grained manner, and enable users to refine the data acquisition without revealing their interests. Technically, a brand new cryptographic framework is developed to integrate attribute-based encryption with searchable encryption. Finally, we prove that the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\sf DSA}$ </tex-math></inline-formula> scheme is correct, sound, secure in the random oracle model, and efficient in practice.
Applying Recurrent Neural Networks for Short-Term Surface Temperature Forecasts Making

Today neural networks (NN) are omnipresent and its using is continuously growing in different areas like automation and optimization, researching, health care, engineering etc. This is due to NN possibility to recognize difficult patterns and structures. There are feed-forward and recurrent neural networks (RNN) among various architectures. Former implies only one-directional neuron’s relations, later permits bidirectional neuron’s interactions which mean possible cycles occurring, similar with cyclic graph. Nowadays RNN are within the best NN architectures for time series predictions. This paper we applied one of RNN architectures, namely long short-term memory (LSTM) for surface temperature time series prediction purposes, producing only short-term forecasts, not more than three hours lead time. Data was sourced from automatic weather station (AWS) archive, located in the Saint-Petersburg. The results obtained from conducted experiments showed LSTM fairly good performance. We also induced an algorithm to train LSTM and tackle with required predictions deadlines.
Exploring an Alternative Approach to the Assessment of Collision Risk

This paper explores the construction of a collision assessment method exploiting an alternative to probability theory for the representation and quantification of uncertainty of the states for two anthropogenic space objects (ASOs). Propagated uncertainty in the position and velocity of an ASO depends on, for example, seemingly random measurement errors, systematic errors in the selection of and assumptions in the dynamics and measurement models, and their combined influence on an orbit determination solution. Using outer probability measures allows for a holistic treatment of random and systematic uncertainty, and to establish if the available information on the ASOs’ states is sufficient to gauge the risk collision against an operational threshold through an upper and a lower bound on the probability of collision. The proposed method is illustrated in a context with similar hypotheses as Coppola’s direct method: the upper probability, i.e., credibility, of a collision is derived and compared to Coppola’s original probability of collision. It is shown that exploiting the credibility of collision is sufficient to derive a counterpart to Coppola’s risk assessment that does not suffer from the probability dilution effect when the available information on the ASOs is scarce. The resulting approach is conservative (favors false alarms over missed detections) and potential alternatives are discussed.
Detecting structural anomalies of quadcopter UAVs based on LSTM autoencoder

Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting the flight pattern or the external environment, such as wind conditions. In this paper, we propose a method for detecting structural anomalies in quadcopter UAVs, using only regular data and specifically considering flight patterns and runtime flight conditions. To this end, we employ a long short-term memory (LSTM) autoencoder model to learn complex features of regular flight data. While flying the UAV, the trained model estimates the degree of outlierness of the incoming data and assesses abnormal behavior of UAV by adaptively considering its movement. This way, the proposed method accurately detects structural anomalies in UAVs regardless of the runtime environment or flight mission. Our experiment results with an off-the-shelf UAV show that the proposed approach detects diverse structural anomalies by an average of 98.6% specificity and 90.3% sensitivity.
APRS: Automatic pruning ratio search using Siamese network with layer-level rewards

Structured pruning is still a mainstream model compression technique, for its merit of easy to implement and no reliance on specific hardware supporting library. In most previous works, the layer-wise channel pruning ratios were determined empirically. In this paper, we propose an Automatic Pruning Ratio Search (APRS) algorithm that can find the layer-wise optimal pruning ratio within the deep reinforcement learning framework. To solve the coarse-granularity reward problem existing in some previous works like AMC and CACP, a novel layer-level reward function is designed based on the Siamese network architecture for the fine-granularity agent-environment interaction purpose. We use a computationally efficient way to evaluate the effect of pruning action on each single layer. The incurred “backwardness disadvantage” problem has also been analyzed and addressed. The experiments are performed using the VGG-16, and MobileNet-v1 on the CIFAR10/100 and UC Merced Land-use datasets. The results verified that our method can better reveal the underlying sparse sensitivities of different layers in both high redundancy networks and compact networks, so that resulting a higher network accuracy after pruning compared to the traditional methods.
Real-time prediction of earthquake potential damage: A case study for the January 8, 2022 MS 6.9 Menyuan earthquake in Qinghai, China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs, which is a challenge in earthquake disaster reduction. Here, we propose a real-time Earthquake Potential Damage predictor (EPDor) based on predicting peak ground velocities (PGVs) of sites. The EPDor is composed of three parts: (1) predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models; (2) predicting the PGVs at distant sites based on the empirical ground motion prediction equation; (3) generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in (1) and (2). We apply the EPDor to the 2022 MS 6.9 Menyuan earthquake in Qinghai Province, China to predict its potential damage. Within the initial few seconds after the first station is triggered, the EPDor can determine directly whether there is potential damage for some sites to a certain degree. Hence, we infer that the EPDor has potential application for future earthquakes. Meanwhile, it also has potential in Chinese earthquake early warning system.
A baseline survey of the geochemical characteristics of the Arctic soils of Alexandra Land within the Franz Josef Land archipelago (Russia)

The compositions of soils and their parent materials were studied within one of the most northern land areas of the world — the island of Alexandra Land of the Franz Josef Land archipelago. Contents of 65 trace and major elements were determined using atomic emission spectrometry (ICP-AES) and inductively coupled plasma spectrometry (ICP-MS). Other analyzed characteristics included soil pH, particle-size distribution and contents of carbon and nitrogen. The bedrock had an alkaline pH, whereas the soil pH ranged from weakly acid to alkaline. The textural class of the soils predominantly corresponded to sandy loam. The contents of clay and silt increased with depth due to the migration of these fractions with groundwater. The studied soils were formed on basalts with high contents of MgO, Fe2O3, TiO2, Cu, Co, V, Ni, Cr, Zn, and low contents of Pb and Hg. The present study confirms that the FJL basalts are similar to the Siberian Platform basalts in composition and belong to the continental basalt series. The composition of soils was generally similar to that of the bedrock. Compared to other Arctic archipelagos (i.e., Svalbard, Severnaya Zemlya), the soils of Alexandra Land are characterized by increased contents of Cu, Mn, Co, and Fe and reduced contents of Hg, Pb, and Cd. The median concentrations (mg kg−1) of trace elements in the soils were as follows: Cu—142, Zn—100, Ni—72, Pb—2.4, Cd—0.1, and Hg—0.0052. The low contents of Hg, Pb, and Cd in the soils are indicative of low inputs of these elements from both long-range transport and local sources of pollutants.
Efficient combination graph model based on conditional random field for online multi-object tracking

Abstract The joint detection and re-identification (re-ID) strategy shares network features of detection and re-ID, sacrifices the complex probability graph model pairing strategy, and consolidates a two-stage video tracking process into a one-stage, making the multi-object tracking process simple, fast, and accurate. In dense scenes, identified transfer is a major challenge for joint detection and re-ID. To this end, a probability graph model suitable for joint detection and re-ID is presented. The proposed model abandons the idea of matching candidate detections with historical detections in a classical probability graph, uses a scheme to calculate the degree of matching between candidate detections and historical trajectories, and transforms task of ID matching in re-ID process into an energy minimization problem of a conditional random field (CRF). However, the solution space of general CRF is complex and requires an iterative search. To achieve efficient online tracking, the original CRF problem is approximately transformed into a combination of multiple CRF problems with closed-form solutions. Moreover, the proposed algorithm has been applied in practical applications using an edge-cloud model that maintains the balance between performance and efficiency. Extensive experiments on the well-known MOTchallenge benchmark demonstrate the superior performance of the proposed algorithm.
Measuring Machine Intelligence Using Black-Box-Based Universal Intelligence Metrics

Measuring the machine intelligence quotient (MIQ) of intelligent agent-based systems (IABSs) is very important based on the increasing number of intelligent systems applied to real-life problem solving. The most important property of an intelligence metric must be its universality. Developing universal intelligence metrics is difficult based on the very large diversity of intelligent systems. A feasible approach for ensuring the universality of measuring machine intelligence consists in using black-box-based methods able to measure the central intelligence tendency in problem solving. This paper represents a guide for choosing the most appropriate black-box-based intelligence metric for measuring the intelligence of developed IABSs, classification of IABSs in intelligence classes and detection of the IABSs with statistical low and high outlier intelligence. In research where the performance of heuristic and metaheuristic algorithms is studied, the performance indicator is frequently calculated as the mean or the median of experimental evaluation results. There is no consensus agreement regarding which of them is more appropriate. In some cases, both of them are reported. The manner in which it should be decided which of them to be used is scientifically grounded in this paper.