Reviewerly/exHarmony huggingface dataset:
Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities

This paper addresses the problem of detecting multiple static and mobile targets by an autonomous mobile agent acting under uncertainty. It is assumed that the agent is able to detect targets at different distances and that the detection includes errors of the first and second types. The goal of the agent is to plan and follow a trajectory that results in the detection of the targets in a minimal time. The suggested solution implements the approach of deep Q-learning applied to maximize the cumulative information gain regarding the targets’ locations and minimize the trajectory length on the map with a predefined detection probability. The Q-learning process is based on a neural network that receives the agent location and current probability map and results in the preferred move of the agent. The presented procedure is compared with the previously developed techniques of sequential decision making, and it is demonstrated that the suggested novel algorithm strongly outperforms the existing methods.
Complementary characteristics fusion network for weakly supervised salient object detection

Salient object detection is a challenging and 1 fundamental research in computer vision and image pro-2 cessing.Although the fully convolutional network has 3 made a great progress in the saliency detection task, 4 most existing methods mainly rely on dense ground 5 truth as labels for training, which takes extensive ef-6 fort and is time-consuming.This paper proposes a novel 7 and e↵ective scribble-based weakly supervised approach 8 named complementary characteristics fusion network 9 (CCFNet), which learns from easily accessible scribbles 10 such as centerlines instead of fully pixel-wise ground 11 truth.To be more specific, in order to deal with the 12 fact that scribbles are always located inside the ob-13 jects with lacking annotations close to the semantic 14 boundaries, an edge fusion module is presented to equip 15 our model with the power of aggregating edge infor-16 mation, which would be beneficial to generate saliency 17 maps with more useful information.Alternatively, since 18 scribbles are too sparse to provide enough supervision 19 for the network, we design feature correlation modules 20 based on low-level, high-level global and edge informa-21 tion, which will complement each other to obtain rel-22 atively complete salient regions using features of dif-23 ferent ways.To further improve the results of saliency 24 maps in foreground and background, a self-supervised 25 saliency detection loss is designed to ensure the network 26
Entrepreneurial Sign and Click-Worthy Teachers in the Digital Social World

This paper presents an abridged version of a critical semiotic analysis of the language and sign in high-profile, teacher-centered Instagram® posts.A social media platform centered around visual images (e.g., photographs, videos, memes), Instagram enables users to curate specific social identities by sharing content and commentary.Our primary objective is to apply a multimodal social semiotic approach (Kress, 2010), incorporating critical frames of race, gender, and capital, to public Instagram teacher 'influencers' in order to theorize how visual representations of teaching interact with shifting conceptions of professional identity and community.By identifying mechanisms that reify and transform traditional teaching ideologies and archetypes, this research has social, professional, and ideological implications for the roles of educators in this 'post-truth era' of contemporary society. Theoretical framework: Social semiotics of race, gender, and capitalWe rely on Kress' (2010, 2011) multimodal social semiotic approach as a theory of language and sign in the social world that "is interested in meaning, in all its forms" (Kress, 2010, p. 54).Kress's theory looks beyond linguistic code as a unit of analysis, including embodied, textual, and visual cues.It takes seriously the interest of the sign-maker as an agentive and strategic communicator because it is the sign-maker who constructs modality (or reliability) through the deployment of particular semiotic resources.Moreover, by approaching the production of signs with what is akin to a "visual grammar" (Kress & van Leeuwen, 2006), we are able to view the composition of these Instagram posts as culturally-sourced and thus indicative of practices, beliefs, and ideologies within the community.Additionally, we draw from Meiners' (2002) synthesis of the 'white Lady Bountiful teacher' archetype and from Brown's (2015) treatment of neoliberal subjectivity to analyze the
Transforming Temporal Embeddings to Keypoint Heatmaps for Detection of Tiny Vehicles in Wide Area Motion Imagery (WAMI) Sequences

Nowadays, due to its many applications, objects detection in wide area motion imagery (WAMI) sequences has received a lot of attention. Unlike natural images, object detection in WAMI faces unique challenges. Lack of appearance information due to the small size of objects makes object detection difficult for conventional methods. In addition, pixel noise, registration errors, sparse or densely populated objects, brings on pronounced artifacts which amplifies the difficulty of detection. This paper aims to address object detection problem in the presence of these issues by considering objects as keypoints in the relevant background and proposes a spatiotemporal anchor-free detector for tiny vehicles in WAMI images. Instead of background subtraction, a region of interest network refines large search space of sequences to indicates object clusters. For further investigation, clusters are encoded by a codebook which is learned through an unsupervised encoder-decoder network. To accurately generate the detections, a Transformer network is trained on cluster embeddings using ground-truth heatmaps that are described by Gaussian distribution rather than hard label annotation. The network is trained with a redesigned version of Focal loss comprising a shape prior regularizer which help the generated heatmaps to conform to the shape of the keypoints. Extensive experiments on WPAFB dataset demonstrate the high capability of our method for the detection of small vehicles where it achieves competitive performance when compared to the state-of-the-art.
Bayesian Sensor Calibration

The calibration of multisensor systems can cause significant costs in terms of time and resources, in particular when cross-sensitivities to parasitic influences are to be compensated. Successful calibration ensures the trustworthy subsequent operation of a sensor system, guaranteeing that one or several measurands of interest can be inferred from its output signals with specified uncertainty. As shown in the present study, this goal can be reached by reduced calibration procedures with fewer calibration conditions than parameters that are needed to model the device response. This is achieved using Bayesian inference by combining the calibration data of a sensor system with statistical prior information about the ensemble to which it belongs. Optimal reduced sets of calibration conditions are identified by the method of Bayesian experimental design. The method is demonstrated on a Hall–temperature sensor system whose nonlinear response model requires seven parameters in the temperature range between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {-}30$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$150 ^{\circ} \text{C}$ </tex-math></inline-formula> and for magnetic field values <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> between −25 and 25 mT. For the prior, a multivariate normal distribution of the model parameters is acquired using 14 specimens of the sensor ensemble. I-optimal calibration at one, two, and three temperatures reduces the root-mean-square (rms) standard deviation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> inferred from sensor output signals from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$203 \boldsymbol {\mu } \text{T}$ </tex-math></inline-formula> before calibration down to 78, 41, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$34 \boldsymbol {\mu }\text{T}$ </tex-math></inline-formula> . Similar conclusions apply to G-optimal calibration. This article describes how to implement the Bayesian prior acquisition, inference, and experimental design. The proposed approach can help save resources and cut costs in sensor calibration.
Unsupervised Severely Deformed Mesh Reconstruction (DMR) From A Single-View Image for Longline Fishing

Much progress has been made in the supervised learning of 3D reconstruction of rigid objects from multi-view images or a video. However, it is more challenging to reconstruct severely deformed objects from a single-view RGB image in an unsupervised manner. Training-based methods, such as specific category-level training, have been shown to successfully reconstruct rigid objects and slightly deformed objects like birds from a single-view image. However, they cannot effectively handle severely deformed objects and neither can be applied to some downstream tasks in the real world due to the inconsistent semantic meaning of vertices, which are crucial in defining the adopted 3D templates of objects to be reconstructed. In this work, we introduce a template-based method to infer 3D shapes from a single-view image and apply the reconstructed mesh to a downstream task, i.e., absolute length measurement. Without using 3D ground truth, our method faithfully reconstructs 3D meshes and achieves state-of-the-art accuracy in a length measurement task on a severely deformed fish dataset.
AL-PA

In this paper, we focus on the portability issue in profiled side-channel attacks (SCAs) that arises due to significant device-to-device variations. Device discrepancy is inevitable in realistic attacks, but it is often neglected in research works. In this paper, we identify such device variations and take a further step towards leveraging the transferability of neural networks. We propose a novel adversarial learning-based profiled attack (AL-PA), which enables our neural network to learn device-invariant features. We evaluated our strategy on eight XMEGA microcontrollers. Without the need for target-specific preprocessing and multiple profiling devices, our approach has outperformed the state-of-the-art methods.
Wavelet descriptor network for arbitrary-shaped text detection

Arbitrary-shaped scene text detection is still a challenging task in computer vision, due to its high versatility and complexity. Most methods directly describe text instances by rectangles, quadrangles, or curves, probably leading to insufficient representations. We introduce wavelet descriptor (WD) for arbitrary-shaped text representation that decomposes text contours to wavelet coefficients and extracts feature sequences by discrete wavelet transform (DWT). Based on WD, we propose WD network, which predicts wavelet coefficients then reconstructs text instances accurately and smoothly via inverse DWT and nonmaximum suppression. We further study the restoration capability of various wavelet functions for text contour, including sym5, bior3.1, bior4.4, coif3, db5, dmey, and rbio2.8. To evaluate the proposed method, we conduct experiments on available public datasets. The F-measure of our method achieves 85.8% on challenging curved dataset CTW1500 and higher than 85% on Total-Text and ICDAR2015, demonstrating the excellent performance of our method.
Lightweight Digital Twin and Federated Learning with Distributed Incentive in Air-Ground 6G Networks

The sixth-generation (6G) wireless network is conceptualized to provide ubiquitous and reliable network access through effective inter-networking among space, air, and terrestrial networks, while posing considerable pressure on dynamic network orchestration. Digital twin (DT) provides an alternative approach to proactively make real-time resource allocation by mapping and learning the complex network topology. However, the dual challenges of limited energy capacity and insufficient computing power of unmanned aerial vehicles make it difficult to establish digital twin on aerial networks. In light of this, in this paper, we propose a lightweight DT empowered air-ground network architecture, where the DT modelling task is distributed to diverse ground devices based on federated learning. To improve the efficiency of DT modelling, we design a distributed incentive mechanism that incentivizes high-performance ground devices to take part in federated learning. Considering the computing burden and possible private disclosure caused by the incentive, we further solve the incentive scheme with a new distributed algorithm. Simulation results show the effectiveness of the proposed lightweight DT scheme in energy consumption and model accuracy.
Quantum correlation and entropic uncertainty in a quantum-dot system

We explore the dynamical behaviors of the measurement uncertainty and quantum correlation for a vertical quantum-dot system in the presence of magnetic field, including electron-electron interaction and Coulomb-blocked systems. Stemming from the quantum-memory-assisted entropic uncertainty relation, the uncertainty of interest is associated with temperature and parameters related to the magnetic field. Interestingly, the temperature has two kinds of influences on the variation of measurement uncertainty with respect to the magnetic-field-related parameters. We also discuss the relation between the lower bound of Berta et al. and the quantum discord. It is found that there is a natural competition between the quantum discord and the entropy <?CDATA ${\min }_{{\Pi }_{i}^{{\rm{B}}}}{S}_{{\Pi }_{i}^{{\rm{B}}}}({\rho }_{{\rm{A}}|{\rm{B}}})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>min</mml:mi> </mml:mrow> <mml:mrow> <mml:msubsup> <mml:mi mathvariant="normal">Π</mml:mi> <mml:mi>i</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> </mml:msubsup> </mml:mrow> </mml:msub> <mml:msub> <mml:mi>S</mml:mi> <mml:mrow> <mml:msubsup> <mml:mi mathvariant="normal">Π</mml:mi> <mml:mi>i</mml:mi> <mml:mi mathvariant="normal">B</mml:mi> </mml:msubsup> </mml:mrow> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>ρ</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">A</mml:mi> <mml:mrow> <mml:mo>|</mml:mo> <mml:mi mathvariant="normal">B</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> . Finally, we bring in two improved bounds to offer a more precise limit to the entropic uncertainty.
Person Tracking with Re-Identification in Multi-Camera Setup: A Distributed Approach

Person monitoring and tracking in surveillance applications are challenging tasks. Nowadays, we find video surveillance system everywhere that captures the data 24/7. Manual monitoring is a tedious task. A single-camera captures the data within its field of view, limited to a specific small area. Hence, we need multiple cameras at different locations for monitoring the significant areas. Therefore, person tracking across all the cameras becomes still a more challenging task. This work proposes a framework to track the person(s) in a multi-camera environment by re-identifying the person(s) captured across the cameras. We use a deep sort algorithm with a Kalman filter for person tracking that enables us to consider distance and appearance as critical factors for tracking. We demonstrate our method on EPFL and MARS datasets and achieve an accuracy of 79%.
Пирит зон сульфидизации терригенных комплексов Яно-Колымского орогенного пояса (Северо-Восток России): генерации, типохимизм, минеральные ассоциации

Finely disseminated sulfide mineralization in terrigenous carbon-bearing strata is known to have a certain metal-bearing potential and to be an additional source of ores for deposits. In the zones of finely disseminated sulfide mineralization in the Debinsky, Chay-Yuryinsky and Orotukan-Srednekansky faults of the Yana-Kolyma orogenic belt, three generations of pyrite have been identified and studied: sedimentogenic, hydrothermal-metamorphogenic, and tentatively ore-hydrothermal with different sets of mineral associations. The studies were carried out in the Late Triassic and Early Jurassic sediments adjacent to the faults in the basins of the Spokoyny-Matrosova Creek, the Chay-Yurya River, the Ozyorny Creek, the Khara-Yuryakh Creek, the Nera River, and the Pravy Srednekan River. It was found that the sulfide mineralization had been formed in several stages. Morphological and genetic varieties of pyrite permit to distinguish sedimentary and metamorphogenic pyrite from ore pyrite. According to the quantitative analysis performed on a Camebax microanalyzer with an EDS detector X-Max Oxford, the main trace impurities in pyrite are As, Co, and Ni.
THE CAUSAL RELATIONSHIP BETWEEN BITCOIN ENERGY CONSUMPTION AND CRYPTOCURRENCY UNCERTAINTY

Purpose- Investigating the relationship between the energy consumption for Bitcoin and the price and policy uncertainties in the cryptocurrency markets. Methodology- It was preferred for unit root tests of series the Zivot-Andrews Unit Root Test, which takes into account structural breaks. Depending on the stagnation of the variables at different levels, the Toda-Yamamoto (1995) causality test was applied by using weekly data in period 19.02.2017 and 07.02.2021. Findings- One-way causality was found on the indices of cryptocurrency price uncertainty and cryptocurrency policy uncertainty from bitcoin energy consumption. In addition, it is understood from the Chi-Square Test Statistic (13.16980) coefficient that the change in bitcoin energy consumption is more dominant on the crypto money policy uncertainty. It was reached that changes in bitcoin energy consumption have an effect on both price and crypto money policies in all crypto markets. Conclusion- In line with these results, it is concluded that the uncertainties in the crypto markets are under the influence of many external political factors. This study investigated the effect of price and political uncertainty on bitcoin energy consumption in the entire cryptocurrency market, but it was concluded that bitcoin energy consumption is not only linked to crypto markets, but also under the influence of government interventions, bans, ill-recognition, and developments and movements in other financial markets. Keywords: Bitcoin energy consumption, bitcoin mining, cryptocurrencies, crypto money price uncertainty index. JEL Codes: G00, G19, P43
General Countermeasures of Anti-Forensics Categories

Digital forensics methods and techniques have turned into a significant element in the investigation of digital crimes and gathering evidence about these crimes. The investigators of digital forensics regularly obey standard ways and use recognized methods and techniques throughout investigating. On the other hand, cybercriminals recognize which methods and tools are utilized in an examination and the way that digital forensic tools work. Therefore, they began to discover and perform another techniques assigned to anti-forensics. It foils the forensics process and tools by destroying, hiding, or modifying digital evidence and increases the difficulty of detecting the effects of cybercrimes. This paper tries to reach a standard approach for defining the term of anti-forensics, classifying the anti-forensics tools, and finally, determining general countermeasures to preserve forensics integrity.
Land Registry Using a Distributed Ledger

Land registration is a system that provides landowners with land titles secured by the government. Zimbabwe has no electronic system for keeping land registry records. Developing a system that automates, and keeps the land registry records immutable, is possible by creating a blockchain system that stores all the transactions made during the process. The immutability nature of blockchain offers plenty of security features that will protect the system from hacking and other malpractices from intruders. It uses strong cryptographic protocols and standards to secure the records stored in a block of data thus making it more reliable for the land registry to transfer the land ownership from a seller to a new buyer. This paper presents a system based on a Hyperledger Fabric distributed ledger that will record, store real-time transactions made during the process of land registration, thus decreasing the cases of fraud as immutable transactions are being stored in the permissioned distributed ledger network.
Mimicking of thermal spin dynamics by controlling sparsity of interactions in Ising spin computing with digital logic circuits

Abstract We introduce extraction-type majority voting logic (E-MVL) that explores the ground state of an Ising spin model. The spin dynamics of E-MVL approximately follow that of thermal spins based on the Boltzmann distribution, even though E-MVL intentionally reduces the information of the problem graph by controlling the sparsity of problem graphs to realize high-speed calculation. We solved the Sherrington-Kirkpatrick spin-glass model using E-MVL and simulated annealing (SA). The probability of obtaining a solution with a 99% E-MVL accuracy is maintained at approximately 85% over the problem size, and E-MVL achieves a high accuracy compared to that of SA.
Lethal autonomous weapons systems &amp; artificial intelligence: Trends, challenges, and policies

We see and expect increased global proliferation of lethal autonomous weapons. Global coordination is needed to control and regulate these weapons.
Publishing China satellite data on the GEOSS Platform

This paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS); it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems. The paper analyses the China Satellite datasets and describes the data publishing process from China GEOSS Data Provider to the GEOSS Platform considering both administrative registration as well as the technical registration. The China Satellite datasets are considered as one of the most important satellite data shared by the GEOSS Platform. The analysis provides some insights as well about GEOSS user searches for China Satellite datasets.