Reviewerly/exHarmony huggingface dataset:
AuSR3: A new block mapping technique for image authentication and self-recovery to avoid the tamper coincidence problem

This paper proposes a new block mapping technique for image authentication and self-recovery designed to avoid the tamper coincidence problem called the AuSR3. The tamper coincidence problem can arise when modifications to an image affect the original block and its recovery data, resulting in the inability to recover the tampered region of the image. The new block mapping technique ensures that the recovery data of a block is embedded into the most distant location possible, minimizing the tamper coincidence problem. In addition, the improved LSB shifting algorithm is employed to embed the watermark data consisting of authentication and recovery data. The experimental result shows that the AuSR3 can produce high-quality watermarked images across various datasets with average PSNR values of 46.2 dB, which improved by 2.1 dB compared to the LSB replacement technique. The new block mapping technique avoids the tamper coincidence problem by up to 25% tampering rates. It contributes to the high-quality recovered image with a PSNR and SSIM value of 39.10 dB and 0.9944, respectively, on a 10% tampering rate on the USC-SIPI dataset.
High Speed and Efficient Reconfigurable Histogram Equalisation Architecture for Image Contrast Enhancement

Histogram equalisation is a point processing technique used to improve image quality in wide variety of applications including real-time video surveillance, medical imaging, industrial automation, intelligent self-navigation systems, and oceanography. This research paper proposes a unique, simple high-speed reconfigurable architecture for the implementation of histogram equalisation on field programmable gate arrays (FPGA). The proposed histogram equalisation architecture comprises of a pixel mapping unit and a histogram computing unit. The histogram computation unit makes use of counters and comparators to compute the cumulative histogram of images. The pixel mapping unit comprise of a delay control unit with a register array to map the output pixel intensity value for each input pixel grey level by adopting a straightforward and effective pixel transformation method. The proposed architecture has been developed using register transfer level (RTL) - compliant Verilog HDL code, simulated using Xilinx's integrated simulator (ISim), synthesized and implemented on a Kintex7 family of Field Programmable Gate Array (FPGA) device. Simulation and synthesis results demonstrate that the proposed architecture can be implemented with a processing time of 2.353 ns and a maximum frequency of operation of 425 MHz. Experimental results shows that the proposed method outperforms other existing methods.
Homomorphic Encryption Schemes Using Nested Matrices

With the increasing necessity to secure data stored in different cloud platforms and transmitted through the internet, many encryption algorithms are developed to protect data from attacks. Homomorphic encryption (HE) which is used to allow performing arithmetic appertains on encrypted data without the need to decrypt is widely used to encrypt data in the cloud platform and privacy preservation for machine learning training. A recently proposed HE scheme uses nested matrices to encrypt and decrypt any field numbers of data so it can be used with machine learning and artificial intelligence applications. In this work, we propose an enhancement to the model which will increase the security and the randomness of the model and make it more reliable to be used with machine learning models. We also present a comparative analysis showing the performance of the originally suggested scheme and the improved scheme proposed in this work.
Using BERT for Swiss German Sentence Prediction

Natural Language Processing builds the foundation for a wide range of language-based applications. While widely spread languages benefit from a wide selection of sophisticated models, lesser known and spoken languages are less supported by specific tools. In this work, a BERT model was set up to process Swiss German sentences. Besides the fact, that there is no pre-trained model for Swiss German, the diversity of the language leads to significantly worse results than is normally the case with BERT models. Missing processing power, and therefore limited processing time were contributing factors for the unsuccessful model. However, the main reason can be identified in the language itself. Missing grammatical structure as well as a good number of different dialects made it almost impossible for the model to succeed in the language tasks. To improve the created BERT model, the language database needs to be categorized by dialect. Some efforts are already being made to gather language data in dialects by involving the Swiss people. But since participation is voluntary it may take some time to build a strong database. Additionally, the model needs to be pre-trained separately for each dialect to improve its accuracy and reliability.
Federated Unsupervised Cluster-Contrastive learning for person Re-identification: A coarse-to-fine approach

Person Re-identification (ReID) has attracted considerable interests in recent years, largely driven by the escalating demand for public safety measures. However, the acquisition and handling of sensitive personal data can trigger significant privacy concerns. Federated learning has been introduced as a potential solution to this problem, with the goal of limiting the exposure of sensitive data across different participating entities (clients). Existing methods often depend on labor-intensive data annotations and face difficulties in maintaining cross-domain uniformity. To tackle these challenges, we propose a Federated Unsupervised Cluster-Contrastive (FedUCC) method based on deep learning for Person ReID that follows a generic-to-specific learning strategy. First, FedUCC procures generic knowledge from a conventional federated learning scheme to aggregate and distribute parameters across local clients. Second, specialized knowledge is explored to facilitate client personalization by disentangling client-specific knowledge from generic knowledge through parameter localization. Third, to further enhance effective fine-grained patterns instead of overfitting on specialized client knowledge, we investigate two key aspects: patch-level feature alignment and camera-invariant learning. Comprehensive experiments on eight public benchmark datasets demonstrate the state-of-the-art performance of our proposed method.
Cooperative acoustic emission locating with velocity tomography in true triaxial experiment

During true triaxial experiments, it is impractical to directly affix sensors to the rock sample, which consequently gives rise to a heterogeneous velocity structure extending from the source of acoustic emissions to the sensors. In pursuit of ascertaining the travel path of acoustic emission waves under anisotropic conditions and comprehending the evolution of wave velocity during triaxial loading, we conducted cooperative event localization in conjunction with velocity tomography. Initially, a numerical test was executed to validate the precision and stability of the employed fast-marching tomography method. Subsequently, true triaxial loading was applied to a granite rock specimen. The algorithm's accuracy was corroborated by the congruence between the final locating results and the observable crack positions. The tomography inversion results indicated that the wave velocity field remained relatively stable when the maximum principal stress was below 16 MPa, despite the occurrence of numerous acoustic emissions during this stage. The P-wave velocity exhibited a discernible upward trend with increasing maximum principal stress, particularly evident after reaching the 40 MPa threshold. Nevertheless, it's important to note that stress escalation does not invariably correspond to fracturing during the elastic deformation phase under confining pressure. The inversion method employed here enables the visualization of variable velocities and provides insight into the rock's deformation physics.
Interoperability of the revolutionary blockchain architectures and Islamic and conventional technology markets: Case of Metaverse, HPB, and Bloknet

This study examines how Islamic and conventional technology stock indices interact with blockchain technology assets including Metaverse, High-Performance Blockchain, and Blocknet, throughout different market conditions and horizons. A three-pronged approach is employed, namely the quantile cross-spectral coherency approach, the time-varying parameter vector autoregressions (TVP-VAR), and the Causality in quantiles methodology. The quantile coherency results show that connectivity between new generation blockchains and conventional and Islamic markets varies. It was found that the connectedness of the network of variables rises in the medium run and reaches its peak in the long run as a result of the TVP-VAR approach. It is evident from the results that the application of sharia screening only impacts the relationship between the technology stock index and digital assets significantly, but does not affect the connectedness and causality between the two. Technology managers, policymakers, and blockchain designers are able to gain new insights from these research findings.
Hyperledger Fabric-Based Tea Supply Chain Production Data Traceable Scheme

With the advent of Industry 4.0, blockchain is one of the emerging technologies that can be used in multiple fields, especially in supply chain networks, where it is considered a sustainable concept technology. Because of its decentralization, reliability, transparency, consistency, and traceability, blockchain can challenge existing conventional models in supply chain scenarios. Therefore, this study proposes a framework that integrates the technology of Blockchain 3.0 (Hyperledger Fabric) into the tea supply chain. The concept of data traceability driven by blockchain technology consists of documenting the tea supply chain system, in which the cultivation, processing, and retailing of tea leaves are documented, and optimizing the existing anti-counterfeiting mechanisms. The ECDSA algorithm is used to confirm the identity among the entities located in the Hyperledger Fabric channel, and IPFS technology is introduced to store tea traceability data to solve the problem of data increment in the tea production process. Only the hash value and traceability code of the traceability data are stored in the block. We analyze the relationship between the number of transactions and latency, and we can see the latency time of both write and read transactions. The system tested write transactions and read transactions with a minimum write speed of 0.97 s and a maximum of 6.26 s and a minimum read speed of 0.1 s and a maximum of 4.62 s via Caliper analysis. The experimental results show that the performance analysis sounds good. This study uses distributed theory and service-oriented thinking in its specific application to the tea supply chain system model and takes each entity through a B2B connection to achieve anti-counterfeit traceability in the tea supply chain of a regulatory nature.
Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone

Soil organic matter (SOM) is one of the most important indicators of soil quality. Hyperspectral remote sensing technology has been recognized as an effective method to rapidly estimate SOM content. In this study, 173 samples (0–20 cm) were collected from farmland soils in the northwestern arid zones of China. Partial least squares regression (PLSR), support vector machine regression (SVMR), and random forests regression (RFR), based on 15 types of mathematical transformations of the original spectral data of soil, were applied for identifying the optimal estimation method. Distribution of SOM content was mapped using both ground-measured values and predicted values estimated based on the optimum models. Obtained results indicated that the important spectral wavebands with the highest correlation were identified as 421 nm, 441 nm, 1014 nm, 1045 nm, and 2351 nm for SOM in the soil. Spectral transformations had obvious effects on the spectral characteristics of SOM. The optimal estimation was obtained when RFR was combined with the reciprocal logarithmic first-order differential (RLFD) (R2 = 0.884, RMSE = 2.817%, MAE = 2.222) for SOM contents. Finally, the RFR-RLFD method had much better performance compared with the PLSR and SVMR models. Results of this study can provide an alternative to the application of the hyperspectral estimation of SOM in farmland soils in arid zones.
Examination of distraction and discomfort caused by using glare monitors: a simultaneous electroencephalography and eye-tracking study

Since the COVID-19 pandemic started, remote work and education and digital display use have become more prevalent. However, compared with printed material, digital displays cause more eye fatigue and may decrease task performance. For instance, the reflections on the monitor can cause discomfort or distraction, particularly when glare monitors are used with black backgrounds.This study simultaneously uses electroencephalography (EEG) and an eye-tracker to measure the possible negative effects of using a glare monitor on the illegibility of sentences.The experiment results showed no difference in reading time and subjective illegibility rating between glare and non-glare monitors. However, with glare monitors, eye fixation when reading lasted longer. Further, EEG beta (15-20 Hz) power variations suggested that the participants were less engaged in the reading task when a glare monitor was used with a black background.These results indicate that the negative effects of using a glare monitor are subtle but certainly present. They also show that physiological measures such as EEG and eye tracking can assess the subtle effects in an objective manner, even if behavioral measures such as subjective illegibility ratings or reading time may not show the differences.
Secure Ring Signature Scheme for Privacy-Preserving Blockchain

Blockchain integrates peer-to-peer networks, distributed consensus, smart contracts, cryptography, etc. It has the unique advantages of weak centralization, anti-tampering, traceability, openness, transparency, etc., and is widely used in various fields, e.g., finance and healthcare. However, due to its open and transparent nature, attackers can analyze the ledger information through clustering techniques to correlate the identities between anonymous and real users in the blockchain system, posing a serious risk of privacy leakage. The ring signature is one of the digital signatures that achieves the unconditional anonymity of the signer. Therefore, by leveraging Distributed Key Generation (DKG) and Elliptic Curve Cryptography (ECC), a blockchain-enabled secure ring signature scheme is proposed. Under the same security parameters, the signature constructed on ECC has higher security in comparison to the schemes using bilinear pairing. In addition, the system master key is generated by using the distributed key agreement, which avoids the traditional method of relying on a trusted third authorizer (TA) to distribute the key and prevents the key leakage when the TA is not authentic or suffers from malicious attacks. Moreover, the performance analysis showed the feasibility of the proposed scheme while the security was ensured.
BECIA: a behaviour engineering-based approach for change impact analysis

Abstract This paper introduces Behaviour Engineering-based Change Impact Analysis (BECIA), a novel approach to Change Impact Analysis (CIA). BECIA enables visualization of change impacts from modified requirements on architecture design. It also introduces the Change Impact Indicator (CII) metric, quantifying impact by calculating the Kolmogorov Complexity (KC) of design models. Our contributions include: an algorithm to convert behavior trees to composition trees, a requirements components dependency network, and a metric for quantifying change impacts.
Inferring and estimating

This chapter examines general issues relating to inferential statistics. It begins by explaining the need for, and the role of, probability in inferential statistics. Inferential statistics are so named because they allow us to infer something about populations from samples. Knowing something about populations allows us to assess whether patterns apparent in samples are just a product of sample error or not. There are two main areas of inferential statistics: estimation and null hypothesis significance testing (NHST). In estimation, scientists use descriptive statistics (which describe samples) to estimate parameters (which describe populations). Meanwhile, scientists use NHST, such as a <italic>t</italic>-test, to decide whether the results have been obtained by chance alone. The chapter then describes the use, calculation, and reporting of standard error and confidence intervals of the mean before offering guidance on using errorplots to compare samples.
Reducing noise-triplets via differentiable sampling for knowledge-enhanced recommendation with collaborative signal guidance

Knowledge Graph (KG) is widely used for recommendation tasks due to its rich semantic information and external structure. Current knowledge graph recommendation models are insufficient for the learning of user–item interactive behaviors as well as external knowledge, and ignore the noise information in KG. To alleviate these limitations, we present a Collaborative Relation-aware Attention Network with Differentiable sampling (DCRAN) that can fully learn user–item interactions and knowledge graphs, and accurately select recommendation-related entities in KG. Specifically, DCRAN explicitly learns the embeddings of users and items and encodes them into collaborative interaction signals, which are used as guidance signals to extract knowledge. Furthermore, DCRAN emphasizes the importance of KG relations and constructs a relation-aware attention network to learn the representation of items in KG. Most importantly, DCRAN applies a differentiable sampling strategy on entities, which can reduce triplets that have a negative effect on recommendation. Experimental results on three public datasets manifest the effectiveness of DCRAN.
Hardware-aware approach to deep neural network optimization

Deep neural networks (DNNs) have been a pivotal technology in a myriad of fields, boasting remarkable achievements. Nevertheless, their substantial workload and inherent redundancies pose ongoing challenges for both practitioners and academia. While numerous researchers endeavor to optimize DNNs, the inherent parallelism features of hardware are generally underutilized, resulting in inefficient use of hardware resources. To address this deficit, the paper unveils a hardware-aware mechanism, IHSOpti, which incorporates hardware characteristics with software algorithms for DNN optimization. IHSOpti endeavors to exploit the full potential of modern hardware parallelism, with significant emphasis on pipelining mechanisms. Specifically, IHSOpti formulates an advanced sparse training algorithm Polar_HSPG which incorporates the newly-proposed layer-wise refined polarization regularizer (LWPolar), grounded on the half-space project gradient (HSPG). Subsequently, IHSOpti pioneeringly introduces the residual strategy for optimizing the layer-level redundancies of neural networks, capitalizing on the pipelining attributes inherent in current hardware. Experimental findings demonstrate that IHSOpti attains outstanding pruning ratios in both parameters and FLOPs. Specifically, IHSOpti achieves up to 96.90% and 82.73% pruning ratios with the accuracy of 93.34% for VGGBN, 97.69% and 95.24% pruning ratios with the accuracy of 94.69% for ResNet, 98.07% and 97.80% pruning ratios with the accuracy of 95.73% for the cutting-edge network RegNet, respectively. Notably, the running efficiency exhibits remarkable improvements with accelerations ranging from 3.63× to 8.20× for CPUs and 1.22× to 2.25× for GPUs, respectively. These outcomes surpass the latest advances in the field. Through the incorporation of specific hardware characteristics, IHSOpti provides a comprehensive and effective approach to harness the intrinsic parallelism of contemporary hardware platforms for DNNs.
A robust twin support vector machine based on fuzzy systems

Purpose Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM). Design/methodology/approach This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets. Findings The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise. Originality/value This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.
Topologies for error-detecting variable-length codes

Given a finite alphabet A and a quasi-metric d over A⁎, we introduce the relation τd,k⊆A⁎×A⁎ such that (x,y)∈τd,k holds whenever d(x,y)≤k. The error detection capability of variable-length codes is expressed in term of conditions over τd,k. With respect to the prefix metric, the factor one, and any quasi-metric associated with some free monoid (anti-)automorphism, we prove that one can decide whether a given regular variable-length code satisfies any of those error detection constraints.
Reasoning and learning in the setting of possibility theory - Overview and perspectives

Possibility theory stands halfway between logical and probabilistic representation frameworks. Possibility theory, as a setting for handling epistemic uncertainty, may have a qualitative or a quantitative flavor depending on the way conditioning is defined. In particular, qualitative possibility theory is totally compatible with classical logic, while quantitative possibility theory is related to statistics. This feature suggests the possibility theory setting as an interesting candidate for interfacing reasoning and learning. The potential of the possibilistic representation framework for reasoning, explanation and learning tasks is particularly highlighted.