Reviewerly/exHarmony huggingface dataset:
Language universal font watermarking with multiple cross-media robustness

The rapid development of digital devices and the increasing demand for copyright protection and leakage tracing of documents result in new demands for robust text watermarking. It becomes more important for a utility text watermarking scheme to have robustness in cross-media channels since the camera shooting operation to papers or screens has become very convenient. To achieve the corresponding cross-media robustness, this paper proposes a font-based text watermarking scheme, which is applicable to most commonly used languages while the previous works were usually designed for specific languages. To generate fonts with high efficiency, we propose a novel glyph centroid modification-based font generation algorithm, which can automatically create target-similar fonts. And we design an effective watermarking scheme that utilizes the relative centroid position (RCP) of glyphs to represent watermark signals. Compared with existing font-based text watermarking schemes that artificially design fonts, the proposed scheme ensures higher efficiency by generating the font codebook automatically. In addition, the proposed RCP-based watermarking scheme can achieve the robustness against lossy compression and several kinds of cross-media distortions. The watermark message can be extracted without the need to recognize the semantic information of glyphs by optical character recognition (OCR).
Film-penetrating transducers applicable to on-chip reservoir computing with spin waves

We have proposed a spin-wave transducer structure named film-penetrating transducers (FPTs). FPTs penetrate an on-chip magnetic film for a spin-wave transmission medium and allow flexible spatial arrangements of many exciters/detectors due to their zero-dimensional feature. We constructed four device models with different spatial arrangements of FPT/conventional exciters using a 10-nm-thick ferrimagnetic garnet film with a central FPT detector. We performed numerical experiments that combine electromagnetics with micromagnetics including thermal noise at 300 K. We evaluated important device features of FPTs, such as the signal-to-noise ratios (SNRs), input/output signal transmission efficiencies, and nonlinear phenomena of spin waves. We applied in-phase sinusoidal input currents with various amplitudes and frequencies and altered the damping strengths near the film boundaries. We obtained sufficient SNRs for the practical use of FPTs and revealed that FPTs have both higher transmission efficiencies and nonlinear strengths than conventional antennas, as the input frequency approaches the ferromagnetic resonance frequency of the film. Moreover, we observed and analyzed various nonlinear phenomena of spin waves, including beats in the time-domain waveform, components of integer harmonic frequencies, wide-range scatterings of inter-harmonic frequencies, and frequency doubling in spin precession. These characteristics probably originate from various device effects: FPTs effectively excite dipolar spin waves with large-angle precession, propagating spin waves reflect from the film boundaries, and spin waves dynamically and nonlinearly interfere with each other. This study demonstrated that FPTs have promising features for both their applications to reservoir computing and the studies on the physics of nonlinear and space-varying spin waves.
MIRROR: Mining Implicit Relationships via Structure-Enhanced Graph Convolutional Networks

Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a clear and generic definition of implicit relationships. Then, we formalize the problem and propose an efficient solution, namely MIRROR, a graph convolutional network (GCN) model to infer implicit ties under explicit connections. MIRROR captures rich information in learning node-level representations by incorporating attributes from heterogeneous neighbors. Furthermore, MIRROR is tolerant of missing node attribute information because it is able to utilize network structure. We empirically evaluate MIRROR on four different genres of networks, achieving state-of-the-art performance for target relations mining. The underlying information revealed by MIRROR contributes to enriching existing knowledge and leading to novel domain insights.
Self-organizing broad network using information evaluation method

Broad network (BN) is commonly used as an efficient and computationally inexpensive machine learning method. The flat structure of BN ensures the efficiency of model learning, but in the mean time the structural characteristics also affect the network performances, such as generalization. However, it is challenging to improve the BN’s generalization performance via adjusting its structure based on certain specific criteria. In this paper, to get the required network structure, a self-organizing BN based on the information evaluation method (IE-SOBN) is designed. First, a capability evaluation metric (CEM) is introduced into BN, which can evaluate the ability of hidden neurons to express and transmit data information. Then, the evaluation metric obtained by CEM is used as the basis for structure adjustment. Second, a structure adjustment mechanism (SAM) based on CEM is proposed to dynamically optimize the BN structure, leading to an improved generalization performance. Third, the convergence of the suggested IE-SOBN is theoretically analyzed to ensure its effectiveness. Finally, the advantages of IE-SOBN are verified by three real applications. The generalization performance and test accuracy of the proposed IE-SOBN are satisfactory in the experiments.
Realization of the Hadamard gate based on superposition of the composite solitons

In this article, we propose the implementation of quantum Hadamard gate through the superposition of composite solitons. Single qubit of the quantum state is defined by the components of the composite soliton which are the exact solution of the Manakov system. Through a rotation operation with an appropriate rotation angle, the Hadamard operation is realized on the single qubit, leading to the superposition state in which the measurement probability of individual qubits is half. Subsequently, repeating the similar Hadamard operation on the superposition state leads to the restoration of the original states of the qubit, as expected. Additionally, the measure of quantum fidelity of the qubit is expressed through the density operator matrix using Uhlmann-Jozsa fidelity. The versatility in the application of composite solitons to accomplish the quantum Hadamard operation through diverse physical systems is also demonstrated through suitable examples.
GTUNE: An Assembled Global Seismic Dataset of Underground Nuclear Test Blasts

Abstract From catalogs of available declassified underground nuclear explosions, we compiled a comprehensive seismic waveform and event catalog termed GTUNE (Georgia Tech Underground Nuclear Explosions). Nuclear blast seismic records are sourced from previously prepared published datasets and openly available waveforms from online sources. All seismic traces were assembled into a user-friendly format compatible with most python-based machine learning (ML) packages. The GTUNE dataset includes the raw seismogram time series, event coordinates and origin time, sampling rate, station metadata, channel, epicentral distance, and P-wave arrival time from the origin dataset when available and otherwise identified using a tuned automated picker. This is the first openly available comprehensive global underground nuclear blast seismic dataset and consists of 28,123 vertical-component waveforms from 774 nuclear test blasts between 1961 and 2017 recorded between 0 and 90 epicentral degrees. For stations where data are not directly included due to data-sharing restrictions, the mechanisms to acquire and process these data are included. In this article, we describe various steps involved in data collection and quality control to ensure accurate labels, and present summary properties of the catalog and data set. The catalog was initially developed for applications with ML methods but can be used for a wide range of studies such as source physics, earth structure, and event detection methodological development.
A Hybrid Incentive Mechanism for Decentralized Federated Learning

Federated Learning (FL) presents a privacy-compliant approach by sharing model parameters instead of raw data. However, how to motivate data owners to participate in and stay within an FL ecosystem by continuously contributing their data to the FL model remains a challenge. In this article, we propose a hybrid incentive mechanism based on blockchain to address the above challenge. The proposed mechanism comprises two primary smart contract-based modules, namely the reputation module and the reverse auction module. The former is used to dynamically calculate the reputation score of each FL participant. It employs a trust-jointed reputation scheme to balance the weights between trust values of parameters and bid prices. The latter is responsible for initiating FL auction tasks, calculating price rankings, and assigning corresponding token rewards. Experiments are conducted to evaluate the feasibility and performance of the proposed mechanism against the three typical threats. Experimental results indicate that our mechanism can successfully reduce incentive costs while preventing participants from colluding and over-bidding in the data sharing auction.
Incentivizing Data Quality in Blockchain-Based Systems—The Case of the Digital Cardossier

Inspired by an industry initiative to address the celebrated market for lemons (poor-quality used cars), we investigate how incentives for a permissioned blockchain-based system in the automobile ecosystem can be designed to ensure high-quality data storage and use by different stakeholders. The peer-to-peer distributed ledger platform connects organizations and car owners with disparate interests and hidden intentions. While previous literature has chiefly examined incentives for permissionless platforms, we leverage studies about crowdsensing applications to stimulate research on incentives in permissioned blockchains. This article uses the action design research approach to create an incentive system featuring a rating mechanism influenced by data correction measures. Furthermore, we propose relying on certain institutions capable of assessing data generated within the system. This combined approach of a decentralized data correction and an institutionalized data assessment is distinct from similar incentive systems suggested by literature. By using an agent-based model with strategy evolution, we evaluate the proposed incentive system. Our findings indicate that a rating-based revenue distribution leads to markedly higher data quality in the system. Additionally, the incentive system reveals hidden information of the agents and alleviates agency problems, contributing to an understanding of incentive design in inter-organizational blockchain-based data platforms. Furthermore, we explore incentive design in permissioned blockchains and discuss its latest implications.
Freezing of quantum and classical correlations via trace and Hellinger distances

Geometric measures of quantum and classical correlations are being proposed and characterized in qubit systems. In this work, the dynamics of quantum and classical correlations via trace and Hellinger distance is comparatively investigated for various initial states in a two-qutrit model under decoherence. It is shown that quantum correlations exhibit freezing in Markovian regime and multiple freezing with multiple sudden changes in non-Markovian regime, which is dependent on initial states and model parameters. Moreover, both measures evolve similar behaviors, but they are difference in the decaying or increasing rate, the revival amplitude, and the time interval of freezing. Remarkably, the time interval of freezing of trace quantum correlations is longer than that of Hellinger ones. However, the freezing of quantum correlations does not occur for the general Bell state, whereas classical ones are forever freezing. The dynamical hierarchy of both measures of correlations is discussed. Those are helpful to understand the rich phenomena of correlation dynamics in open qutrits. • Trace and Hellinger-distance correlations are studied for various initial states in two open qutrits. • Quantum correlations can exhibit freezing in Markovian regime and multiple freezing in non-Markovian regime. • The time interval of freezing for trace quantum correlations is longer than that for Hellinger ones. • Classical correlations are forever frozen for the general Bell state while quantum correlations do not. • The dynamical hierarchy of both measures is discussed.
State-selective electromagnetically induced transparency for quantum error correction in neutral atom quantum computers

We propose a way to measure the qubit state of an arbitrary subensemble of atoms in an array without significantly disturbing the quantum information in the unmeasured atoms. The idea is to first site-selectively transfer atoms out of the qubit basis so that one of the two states at a time is put into an auxiliary state. Electromagnetically induced transparency (EIT) light will then protect most states while detection light is scattered from atoms in the auxiliary state, which is made immune to the EIT protection by angular momentum selection rules and carefully chosen light polarization. The two states will be measured in turn, after which it is possible to recool and return the atoms to a qubit state. These measurements can be the basis of quantum error correction.
A Generic Approach towards Amharic Sign Language Recognition

In the day-to-day life of communities, good communication channels are crucial for mutual understanding. The hearing-impaired community uses sign language, which is a visual and gestural language. In terms of orientation and expression, it is separate from written and spoken languages. Despite the fact that sign language is an excellent platform for communication among hearing-impaired persons, it has created a communication barrier between hearing-impaired and non-disabled people. To address this issue, researchers have proposed sign language to text translation systems for English and other European languages as a solution. The goal of this research is to design and develop an Amharic digital text converter system using Ethiopian sign language. The proposed system was created with the help of two key deep learning algorithms: a pretrained deep learning model and a Long Short-Term Memory (LSTM). The LSTM was used to extract sequence information from a sequence of image frames of a specific sign language, while the pretrained deep learning model was used to extract features from single frame images. The dataset used to train the algorithms was gathered in video format from Addis Ababa University. Prior to feeding the obtained dataset to the deep learning models, data preprocessing activities such as cleaning and video to image frame segmentation were conducted. The system was trained, validated, and tested using 80%, 10%, and 10% of the 2475 images created during the preprocessing step. Two pretrained deep learning models, EfficientNetB0 and ResNet50, were used in this investigation, and they attained an accuracy of 72.79%. In terms of precision and f1-score, ResNet50 outperformed EfficientNetB0. For the proposed system, a graphical user interface prototype was created, and the best performing model was chosen and implemented. The proposed system can be utilized as a starting point for other researchers to improve upon, based on the outcomes of the experiment. More high-quality training datasets and high-performance training machines, such as GPU-enabled computers, can be added to the system to improve it.
A multi-methodological approach for mineral exploration and predictive metallurgy: the case of the Pilar gold deposit at the Quadrilátero Ferrífero, Brazil

Gold exploration is generally based on methods such as soil, stream and drill core geochemistry and geophysical techniques, but X-ray diffraction (XRD) and the Rietveld method-based mineral quantification are seldomly used. Here, a multi-methodological approach coupling traditional drill core macroscopic description and multi-element analysis, scanning electron microscopy/energy-dispersive X-ray fluorescence spectrometry (SEM/EDS), and electron probe microanalysis (EPMA) with XRD/Rietveld method and statistical techniques is presented as a useful tool for deposit characterization, mineral exploration and predictive metallurgy purposes. The Pilar gold deposit, situated in the Quadrilátero Ferrífero province, Brazil, was used as a case study for the proposed multi-methodological approach. Carbonate-facies banded iron formations (BIFs) and schist sequences showing an intense quartz and sulfide hydrothermal alteration represent the host rocks of Pilar mineralized intervals, which may be identified by three signatures: Au-As-W-S-quartz-chlorite-pyrrhotite-arsenopyrite-calcite for schists, and Au-As-quartz-chlorite-arsenopyrite-pyrrhotite and Au-Ag-S-Te-quartz-chlorite-pyrrhotite for BIFs. Within the mineralized BIFs, the expected average Au grades and gold textures vary according to the signature. In As-poor BIFs, gold is only observed within the structure of sulfide and telluride phases. On the other hand, native gold is present in As-rich BIF samples, which usually contain relatively higher Au grades, compared to the As-poor BIFs. Native gold is also identified in mineralized schists, whose average Au grades are similar to those of As-poor BIFs. Therefore, the proposed multi-methodological approach is useful in providing the following information: geochemical-mineralogical signatures and the expected average Au grades and mode of occurrence of gold for each one of these signatures. At Pilar, the geochemical-mineralogical behavior of mineralized zones is directly linked to Au grades and textures. In terms of a practical workflow, this study illustrates that, after the multi-methodological approach is applied and the representativeness of the ore-petrography is tested, the general described behavior of gold within each group of ores can be used for mineral exploration and metallurgical purposes without the need of repeated SEM/EDS and EPMA analyses. Instead, the average Au grades and the gold textures may be predicted solely by the signature definition based on the drill core geochemical-mineralogical analyses.
A novel solution for improved performance of Time-frequency concentration

To improve the time–frequency (TF) concentration performance of the short-time fractional Fourier transform (STFrFT), and solve the multi-order matching problem of multi-component signals, this paper introduces a new algorithm that is referred to as the Variational mode decomposition-short-time fractional Fourier transform- Synchroextracting transform (VSSTFrFT). This work employs the Variational mode decomposition (VMD) algorithm to decompose multi-component signals into single-component sets, then the STFrFT algorithm matches the optimal rotation order of each component separately to solve the multi-order matching problem. Finally, this paper utilizes the Synchroextracting transform (SET) to extract the TF coefficient of ridgeline position in STFrFT distribution, improving the concentration performance. For different types of signals, the VSSTFrFT algorithm can get a higher TF concentration than the traditional TFA methods. In the application of engineering measured data, the VSSTFrFT algorithm can extract the modalities of the signals and display the frequency curve clearly, which can be used for structural complexity and time-varying characteristics analysis in practical engineering applications.
UniInst: Unique representation for end-to-end instance segmentation

Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions. Thus, mainstream methods usually rely on a hand-designed non-maximum suppression (NMS) post-processing step to select the optimal prediction result, consequently hindering end-to-end training. To address this issue, we propose a box-free and NMS-free end-to-end instance segmentation framework, dubbed UniInst, which yields only one unique representation for each instance. Specifically, we design an instance-aware one-to-one assignment scheme, named Only Yield One Representation (OYOR). It dynamically assigns one unique representation to each instance according to the matching quality between predictions and ground truths. Then, a novel prediction re-ranking strategy is elegantly integrated into the framework to address the misalignment between the classification score and mask quality, enabling the learned representation to be more discriminative. With these techniques, our UniInst, the first FCN-based box-free and NMS-free end-to-end instance segmentation framework, achieves competitive performance, e.g., 39.0 mask AP using ResNet-50-FPN and 40.2 mask AP using ResNet-101-FPN on COCO test-dev. Moreover, the proposed instance-aware method is robust to occlusion scenes because of non-dependent on box and NMS. It outperforms common baselines by remarkable mask AP on the heavily-occluded OCHuman benchmark. Code is available at https://github.com/b03505036/UniInst.
Disaster Analysis on Government Data

Firstly, (Sai Prasad and Pasupathy in J Mech Cont Math Sci 13(5):164–176, 2018) big data is the latest approach to analyze and can handle a large amount of data. Disaster analysis which contains the dataset related to the accidents which are caused due to natural calamities like tsunami, earthquakes, and manmade. This project disaster analysis on government data contains attributes which is representing the accidents which are caused in different sectors like emergencies medical service calls, traffic accident calls, fire calls, etc. So, through dataset visualizing the accidents that took place often. This helps in providing a nimble analysis that which kind of incident may occur. Through this improving of emergency services and also useful for other research like which reduces the occurrences of this disasters. Using big data, we can analyze quickly by applying machine learning techniques on the dataset on daily basis.
Image Security Based on Rotational Visual Cryptography

Visual cryptography (VC) is a procedure to hide original information of image, text, etc. In VC, the basic principle is that an image is split up into sections known as shares. Shares are assigned for the authorized persons, and these persons are mandatory for the recreation of the image. It depends on several details like size, quality, pixel extension, and nature of the picture. At first, VC can only performed on binary pictures, and now, we can able to perform on color pictures by the help of latest algorithms. Image is moreover withdrawn into shares in the form of squares. The earlier declared procedure has few of difficulties, related to the small size of key. For improvement of encryption, an alternate procedure is appended where this uses key of size 8 bit. One key is utilized for encryption in which it involves ten turns.