Original Form Research Articles

The Reality of Digital Rights Management Technologies: A Systematic Mapping Study

With the proliferation of internet applications and services, human life has become different from what it was before, as the speed and ease of information exchange have increased, alongside the potential number of parties that can exchange information. This also means that the concept of security is no longer limited to the computer that retains the original information but has extended to the security of digital information. Moreover, information security no longer applies only to access to it, but also to what the user can do with that information. Encryption is no longer sufficient, as the user can easily or unintentionally transfer unencrypted information to users who are not authorized to access and view it.

Full waveform inversion using frequency shift envelope-based global correlation norm for ultrasound computed tomography

Many studies have been carried out on ultrasound computed tomography (USCT) for its ability to offer quantitative measurements of tissue sound speed. Full waveform inversion (FWI) is a technique for reconstructing high-resolution sound speed images by iteratively minimizing the difference between the observed ultrasound data and the synthetic data based on the waveform equation. However, FWI suffers from cycle-skipping, which usually causes FWI convergence at a local minimum. Cycle-skipping occurs when the phase difference between the observed data and the synthetic data exceeds half a cycle. The simplest way to avoid cycle-skipping is to use low-frequency information for reconstruction. Nevertheless, in imaging systems, the response bandwidth of the probe is limited, and reliable low-frequency information often exceeds the response band. Therefore, it is a challenge to perform FWI imaging and avoid cycle-skipping problems without low-frequency information. In this paper, we propose a frequency shift envelope-based global correlation norm (FSEGCN), where an artificial source wavelet with a lower frequency is adopted to calculate synthetic data. FSEGCN compared with FWI, envelope inversion (EI), global correlation norm (GCN), envelope-based global correlation norm (EGCN) through concentric circle phantom without low-frequency information. The experimental results demonstrated the capability of the proposed method to recover the sound speed close to the exact model in the absence of low-frequency information, whereas FWI, EI, GCN, and EGCN cannot. Experiments on phantoms of the human head and calf show that artificial source wavelets can reduce image artifacts and enhance reconstruction robustness, when original low-frequency information is absent.

Weighted information entropy‐based Kalman filter for outliers and structural noise

AbstractStructural noise and outliers are widely present in real‐world state estimate scenarios, and they significantly degrade the performance of most filtering algorithms based on minimum mean square error (MMSE) criterion. To address this problem, this paper first models structural noise and outliers as independent and piecewise identical distribution (IPID). Then, a minimum error weighted entropy‐based Kalman filter (MEWE‐KF) is proposed, where a new cost function is constructed by introducing a weight function related to error location distances in an original information space into the minimum error entropy (MEE) criterion. Further, the iterative formulations of the proposed filter are derived, and the computational complexity and the convergence are also analyzed. Simulation results show that the proposed filter with adaptive weights has the superior performance for suppressing structural noise and outliers.

Modified Unit-Mediated Strand Displacement Reactions for Direct Detection of Single Nucleotide Variants in Active Double-Stranded DNA.

Accurate identification of single nucleotide variants (SNVs) in key driver genes holds a significant value for disease diagnosis and treatment. Fluorescent probes exhibit tremendous potential in specific, high-resolution, and rapid detection of SNVs. However, additional steps are required in most post-PCR assays to convert double-stranded DNA (dsDNA) products into single-stranded DNA (ssDNA), enabling them to possess hybridization activity to trigger subsequent reactions. This process not only prolongs the complexity of the experiment but also introduces the risk of losing target information. In this study, we proposed two strategies for enriching active double-stranded DNA, involving PCR based on obstructive groups and cleavable units. Building upon this, we explored the impact of modified units on the strand displacement reaction (SDR) and assessed their discriminatory efficacy for mutations. The results showed that detection of low variant allele frequencies (VAF) as low as 0.1% can be achieved. The proposed strategy allowed orthogonal identification of 45 clinical colorectal cancer tissue samples with 100% specificity, and the results were generally consistent with sequencing results. Compared to existing methods for enriching active targets, our approach offers a more diverse set of enrichment strategies, characterized by the advantage of being simple and fast and preserving original information to the maximum extent. The objective of this study is to offer an effective solution for the swift and facile acquisition of active double-stranded DNA. We anticipate that our work will facilitate the practical applications of SDR based on dsDNA.

Two-uniform quantum information masking in multipartite systems based on orthogonal arrays

Quantum information masking constitutes a methodological framework wherein the intrinsic quantum data is obfuscated from the view of local subsystems. In this paper, we explore the connection between 2-uniform quantum information masking and orthogonal arrays. Through constructing an orthogonal array with the Hadamard matrix, we can obtain a 2-uniform state. Furthermore, applying an X-gate to each qubit of this state yields another 2-uniform state. Consequently, the original information can be 2-uniformly masked in multipartite systems. In addition, when the range of quantum bits for the 2-uniform state is 2n−1+3≤N≤2n−1, the original information can also be achieved 2-uniform masking. At last, we provide a specific example for n=4 and demonstrate a specific application based on this example.

Concise Review on Scientific Approaches to Burns and Scars.

Burns are large open surgical lesions bathed in virulent pus that result in rupturing of the cutaneous membrane, which has serious consequences such as an extensive loss of proteins, and body fluids, increased chances of infections, and sometimes death. These can be classified based on their penetration levels, i.e., first-degree burns penetrating the epidermis, second-degree burns including both epidermis and dermis, third-degree burns to both layers including the hair follicular cells, sweat glands and various core tissues, fourth-degree burns to adipose tissue, fifth stage burns to muscles, and sixth stage burns to bones. Wound healing/wound repair is a very perplexing process in which the tissues of the affected/burnt area repairs themselves to attain their original form and functionality but develop a scar at the wound site. This article mainly focuses on the algorithms to differentiate various degrees of burns, general first aid approaches to burns and scars, the rationale of treatment of burns, basic mechanisms highlighting the healing processes in humans in terms of free from scar formation as well as with scar formation at their elementary levels including cellular as well as biochemical levels, utility, and progression of pre-clinical data to humans and finally approaches for the improvement of scar formation in man.

Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding

Grounding temporal video segments described in natural language queries effectively and efficiently is a crucial capability needed in vision-and-language fields. In this paper, we deal with the fast video temporal grounding (FVTG) task, aiming at localizing the target segment with high speed and favorable accuracy. Most existing approaches adopt elaborately designed cross-modal interaction modules to improve the grounding performance, which suffer from the test-time bottleneck. Although several common space-based methods enjoy the high-speed merit during inference, they can hardly capture the comprehensive and explicit relations between visual and textual modalities. In this paper, to tackle the dilemma of speed-accuracy tradeoff, we propose a commonsense-aware cross-modal alignment network (C 2 AN), which incorporates commonsense-guided visual and text representations into a complementary common space for fast video temporal grounding. Specifically, the commonsense concepts are explored and exploited by extracting the structural semantic information from a language corpus. Then, a commonsense-aware interaction module is designed to obtain bridged visual and text features by utilizing the learned commonsense concepts. Finally, to maintain the original semantic information of textual queries, a cross-modal complementary common space is optimized to obtain matching scores for performing FVTG. Extensive results on two challenging benchmarks show that our C 2 AN method performs favorably against state-of-the-arts while running at high speed. Our code is available at https://github.com/ZiyueWu59/CCA.

Selective metasomatism of ultramafic cumulates within Archean supracrustal sequences

The Neoarchean Storø Supracrustal Belt in SW Greenland comprises a sequence of mature quartzite, metapelite, amphibolite, and ultramafic rocks that underwent amphibolite facies metamorphism during the amalgamation of the Mesoarchean Akia Terrane and the Eoarchean Færingehavn Terrane. In this belt, tourmaline is found in a transition zone between ultramafic and metapelitic rocks, but also occurs as orbicules within the ultramafic rocks. These tourmaline orbicules hosted by ultramafic rocks are reported for the first time in the North Atlantic craton, thus indicating a unique formation mechanism. We conducted a comprehensive examination of the petrology, whole-rock and mineral chemistry, and oxygen isotope compositions from borehole samples in the Storø Supracrustal Belt, to elucidate the metasomatic events associated with the formation of the orbicular tourmalines. The Storø ultramafic rocks have high MgO, Cr, and Ni contents, with low abundances of REE and HFSE, and preserve a typical cumulate texture. These features are similar to those of ultramafic cumulates found in other Archean supracrustal belts, suggesting a cumulate origin for the Storø ultramafic rocks. Furthermore, the morphology and composition of the tourmaline orbicules within these cumulates indicate that they originated from melts with high boron and high water concentrations that infiltrated the ultramafic rocks. The main factor influencing the morphology of the tourmaline orbicules is the silicification of the ultramafic rocks, driven by their lower chemical potential of SiO2 compared to the surrounding rocks. This silicification process, in combination with compositional variations of cumulates during fractional crystallization, has contributed to the geochemical diversity observed in Archean ultramafic rocks. Thus, it is crucial to understand the effects of such selective metasomatism on Archean ultramafic rocks, as this will facilitate the extraction of original information preserved in the early rock record.

A Heterogeneous Group CNN for Image Super-Resolution.

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR) under complex scenes. In this article, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance the internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent the appearance of obtained redundant features, a refinement block (RB) with signal enhancements in a serial way is designed to filter useless information. To prevent the loss of original information, a multilevel enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel upsampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://github.com/hellloxiaotian/HGSRCNN.

Rate 5/6 TCM Code Having 64 States with 64 QAM for Fading Channel

Trellis Coded Modulation (TCM) is powerful technique employed in digital communication systems to improve the reliability and efficiency of data transmission. TCM combines error control coding and modulation schemes to achieve superior performance in challenging channel conditions. In TCM, information bits are encoded using a trellis encoder, which generates a sequence of encoded symbols. These symbols are then mapped onto a modulation scheme, such as Quadrature Amplitude Modulation (QAM) or Phase Shift Keying (PSK), to create the modulated signal. At the receiver, the received signal is demodulated and decoded using a trellis decoder, which employs maximum likelihood decoding to recover the original information bits. The trellis structure allows for efficient error correction and makes TCM particularly suitable for channels with fading, noise, and other impairments. TCM has been widely used in various communication standards and applications to enhance data transmission reliability and spectral efficiency. TCM is a coding technique that merges coding and modulation to attain substantial coding gain without compromising bandwidth efficiency. The emergence of TCM occurred in the late 1970s, and ever since, it has found extensive utilization in various contemporary information transmission systems. In this paper, a new methodology is introduced for the design of a 64-state rate 5/6 TCM code optimized for fading channels. The outcomes obtained from the study are remarkably encouraging, demonstrating coding gain of around 10 dB in comparison to uncoded 32 PSK. This substantial gain plays a pivotal role in boosting data rates, especially in situations where bandwidth-limited channels are involved.

GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies-Sea Ice Thickness Inversion in Beaufort Sea.

Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.

Indigenous Heritage Hub

In India, a wide variety of monuments and literal spots are dispersed throughout the nation, artistic heritage protection has lately gained significance. This study introduces a web operation for monument discovery powered by SUPPORT VECTOR MACHINE (SVM) that provides a variety of rudiments that will prop in guarding India’s artistic heritage. SVM are used by the website as the deep literacy technology to precisely fete and classify monuments grounded on photos that stoner’s input. It also provides a number of helpful features, similar as original information like tabernacle, culture and other information for druggies at the monument point. When visiting literal places, these aspects help to give guests a more thorough and immersive experience. The issues demonstrate how effective the SVM- grounded web operation is at classifying monuments. A sizable collection of images of notorious spots in India were used to test the proposed system. The system may be used for artistic preservation, tourism, and education.

Residual neural network with spatiotemporal attention integrated with temporal self-attention based on long short-term memory network for air pollutant concentration prediction

Accurate prediction of air pollutant concentrations can be effective in controlling and preventing air pollution, which is crucial for both the government's policy response and the public's reasonable ability to avoid air pollution. In this study, we propose a model based on the principle of big data correlation and deep learning techniques for predicting the concentration of air pollutant concentrations. The proposed model, named STARes-SaLSTM, consists of a residual neural network with spatiotemporal attention (STAResNet) and a temporal self-attention based on long short-term memory network (SaLSTM). The spatiotemporal attention module in STAResNet enhances the original input information by weighting it in both spatial and temporal dimensions, meanwhile the features of the spatial distribution of pollutant and meteorological information across many cities are deeply extracted using ResNet. A novel self-attention method known as temporal self-attention is developed to extract the temporal dependency of air pollutants. LSTM is used as a sequence encoder in this temporal attention method to calculate queries, keys, and values to acquire a more comprehensive temporal dependence than is possible with normal self-attention. The STARes-SaLSTM model successfully predicts air pollution concentrations in the target city for the future by extracting spatiotemporal correlation of feature sequence. In comparison to previous neural network models and conventional methods, the model increases the accuracy of pollutant concentration prediction. The suggested prediction model works well for the one to 3 h prediction job, with a root mean square error (RMSE) ranging from 6.716 to 11.648. Furthermore, even for the 1–24 h prediction assignment, we executed multi-scale prediction in the target city and obtained a reasonable performance, with an average RMSE value of 20.576.

On the timing of the opening of Western Pacific's back-arc seas: Data from Sr, O and C isotopes in Miocene molluscs of Sakhalin and Hokkaido

This study presents Sr-isotope data to determine the stage affiliation of different parts of the Miocene Sertunaiskaya and Kurasiyskaya formations in Sakhalin, as well as the upper part of the Togeshita Formation in Hokkaido. Additionally, it confirms a diachronous boundary between the Sertunaiskaya and Kurasiyskaya formations. The data received will be useful for globally correlating the Miocene formations of the Sakhalin-Hokkaido area. The study indicates that the Japan and Kuril back-arc basins in the Western Pacific underwent their final opening stages during the Middle Miocene (18.1–17.7 and 15.9–14.2 Ma, respectively), as determined by the SIS method. This process was likely influenced by both global and local factors. One of the local influences on the formation of back-arc basins in the Western Pacific appears to be the influx of cool boreal waters, as indicated by the δ18O signals. The Miocene data from the Sakhalin-Hokkaido area, including evidence from Sr, O, and C isotopes, combined with corresponding paleontological, stratigraphic, palaeomagnetic, and palaeobiogeographic data from the Paleogene and Neogene, provide a four-stage chronological sequence of events related to the opening of the Western Pacific's back-arc seas. This paper presents original information on the Miocene C-isotope excursion in the Western Pacific, which are correlated with the Monterey Event. Recent data on this topic may have enhanced our comprehension of the conditions required for the formation of hydrocarbons, which are found in substantial amounts in the Miocene of northern Sakhalin and Hokkaido.

A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction

The development of accurate and reliable gas load forecasting models is critical to residential safety and gas company economics. However, since the seq2seq neural network conveys information by sequentially updating the hidden states, when processing gas load data, the information needs to be passed several times before it can affect the prediction results at subsequent time steps. This can lead to a lack of ability of the model to capture long-term dependencies. To address the above issues, we propose a novel compound framework. Time-enhanced perception transformer (TEPT) in the framework achieves parallel computation of historical data and stronger location awareness through convolutional self-attention and improved attention scores. This results in better capture of local patterns and long-term dependencies in gas loads. In addition, the framework proposes two-stage feature extraction (TSFE) for decomposing the important parts of the original information and extracting more refined features based on them. We constructed a real gas load data set from Wuxi, China to verify the effectiveness. The results show that MAE, MAPE, and RMSE of the proposed method are 3.45, 1.07%, and 4.68, which provide higher accuracy compared to other prediction methods.

GAN-based self-supervised message passing graph representation learning

Graph representation learning aims to learn embedding representations of nodes in graph-structured data through supervised or unsupervised methods. However, for unlabeled or sparsely labeled graph datasets, conventional supervised approaches fail to perform adequately, and existing unsupervised methods have difficulty achieving high accuracy on multiple downstream tasks such as node classification and link prediction at lower embedding dimensions. To address this issue, we propose a novel self-supervised graph representation learning approach called GAN-based Message Passing Graph Representation Learning. This approach significantly improves the accuracy of node embeddings without relying on label information. Specifically, GMP-GL has developed a generator based on neighbor-weighted random walk to obtain positive and negative node sample pairs, as well as a discriminator that distinguishes whether a real connection exists between node pairs. Through adversarial learning, GMP-GL automatically learns node embeddings. Furthermore, GMP-GL optimizes the training outcomes of the generative adversarial model through self-supervised embedding aggregation learning, thus introducing rich structural information from the graph for the subsequent rounds of generative adversarial training. In addition, GMP-GL employs multiple autoencoders to generate and filter original embeddings, ensuring that the pre-embeddings preserve a significant amount of original feature information for formal learning. The study selects six real-world datasets and conducts comparative experiments with ten SOTA baseline methods. The experimental results demonstrate that the proposed model not only surpasses previous unsupervised methods but also matches or even exceeds the performance of supervised methods. This indicates the great potential of the proposed approach for representation learning tasks on unlabeled graph datasets and its effective applicability to real-world problems.

SFL-MDrone: Synchronous federated learning enabled multi drones

UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy.

The concept of Minimal Spanning Tree (MST) for Symmetric Encryption

As the digital communication has become a vital part of the human life nowadays, cybercrimes are ever expanding. One major tool to protect the message from hacking and tampering is concealing the original information. Modern cryptography not only concerns the encryption of the message but also focuses on the protection of the encrypted information from cyber-attacks.Apart from secret communication modern cryptography focuses on authentication and integrity of the message. The present paper explains a symmetric block cipher basing on graph structures. In this technique the message is encrypted twice. The first stage encryption using adjacency matrix of undirected graph; the second stage using exclusive OR (XOR) operation on the output of first stage. Encryption/Decryption Keys are distinct for distinct blocks and distinct rounds; derived from Minimum Span Tree (MST) technique of an undirected graph. Here multiple peers can communicate messages simultaneously. One of the entries selects some random ASCII characters whose number is same as the block length and uploads those characters in the cloud. All the members of the group establish block keys using key generation technique which is secret among them.

Robust deep fuzzy [formula omitted]-means clustering for image data

Image clustering is a difficult task with important application value in computer vision. The key to this task is the quality of images features. Most of current clustering methods encounter the challenge. That is, the process of feature learning and clustering operates independently. To address this problem, several researchers have been dedicated to performing feature learning and deep clustering together. However, the obtained features lack discriminability to address high-dimensional data successfully. To deal with this issue, we propose a novel model named as robust deep fuzzy K-means clustering (RD-FKC), which efficiently projects image samples into a representative embedding space and precisely learns membership degrees into a combined framework. Specifically, RD-FKC introduces Laplacian regularization technique to preserve locality properties of data. Moreover, by using an adaptive loss function, the model becomes more robust to diverse types of outliers. Furthermore, to avoid the latent space being distorted and make the extracted features retain the original information as much as possible, the model introduces reconstruction error and adds regularization to network parameters. Finally, an effective algorithm is derived to solve the optimization model. Numerous experiments have been conducted, illustrating the advantages and superiority of RD-FKC over existing clustering approaches.

A novel improved total variation algorithm for the elimination of scratch-type defects in high-voltage cable cross-sections.

In the quality inspection process of high-voltage cables, several commonly used indicators include cable length, insulation thickness, and the number of conductors within the core. Among these factors, the count of conductors holds particular significance as a key determinant of cable quality. Machine vision technology has found extensive application in automatically detecting the number of conductors in cross-sectional images of high-voltage cables. However, the presence of scratch-type defects in cut high-voltage cable cross-sections can significantly compromise the precision of conductor count detection. To address this problem, this paper introduces a novel improved total variation (TV) algorithm, marking the first-ever application of the TV algorithm in this domain. Considering the staircase effect, the direct use of the TV algorithm is prone to cause serious loss of image edge information. The proposed algorithm firstly introduces multimodal features to effectively mitigate the staircase effect. While eliminating scratch-type defects, the algorithm endeavors to preserve the original image's edge information, consequently yielding a noteworthy enhancement in detection accuracy. Furthermore, a dataset was curated, comprising images of cross-sections of high-voltage cables of varying sizes, each displaying an assortment of scratch-type defects. Experimental findings conclusively demonstrate the algorithm's exceptional efficiency in eradicating diverse scratch-type defects within high-voltage cable cross-sections. The average scratch elimination rate surpasses 90%, with an impressive 96.15% achieved on cable sample 4. A series of conducted ablation experiments in this paper substantiate a significant enhancement in cable image quality. Notably, the Edge Preservation Index (EPI) exhibits an improvement of approximately 20%, resulting in a substantial boost to conductor count detection accuracy, thus effectively enhancing the quality of high-voltage cable production.