Reviewerly/exHarmony huggingface dataset:
Securing Messages Using AES Algorithm and Blockchain Technology on Mobile Devices

In recent years, there has been a rapid increase in the use of mobile devices and messaging applications for communication, leading to a growing concern about the security of text messages exchanged through these platforms. This study proposes a novel method that uses the AES algorithm and Blockchain to secure text messages in messaging applications on mobile devices. The AES algorithm is selected due to its faster encryption and decryption processes, which are superior to asymmetric cryptography algorithms. On the other hand, Blockchain is chosen for its inherent security properties that only allow data addition and cannot be altered. This study aims to achieve both speed and security to prevent cybercrime in text messages. The Avalanche Effect calculation and Processing Time measurement are used as the analysis methods to evaluate the proposed approach. The results show that the computation time of the message delivery process using Blockchain and AES algorithm has an average total process time of 33.59 milliseconds. Additionally, the testing results of the Avalanche Effect value show that the AES algorithm has a value of 50% for character lengths up to 16 characters and below 50% for character lengths greater than 16 characters. Based on these testing results, the proposed combination of the AES algorithm and Blockchain is an effective method for securing text messages in messaging applications. This method can offer a secure and efficient way of exchanging text messages on mobile devices and can adopt as a standard approach for messaging applications.
Anomaly detection in Fourier transform infrared spectroscopy of geological specimens using variational autoencoders

Fourier Transform infrared spectroscopy (FTIR) is an emerging cost effective and rapid mineralogical characterization technique being applied in the geosciences. Detecting anomalous FTIR spectra is especially relevant to the geoscience domain, as it may indicate abrupt changes in geology or mineralogical composition of the rock sample being examined. Given a large volume of data, detecting anomalies that exhibit significant and abrupt spatial and compositional variability is a time-consuming and challenging task. This paper explores the use of an unsupervised variational autoencoder (VAE) for determining anomalies that may exist within a set of FTIR spectra collected from reverse circulation (RC) drill chip samples spanning several iron ore deposits from the Pilbara region in Western Australia. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) were measured from 1,579 two-metre composite samples. Our results showed that the VAE was effective in separating anomalous spectra from spectra typical of unmineralized banded iron formation by leveraging the probabilistic latent representation of the spectra in as few as two latent dimensions. To validate our results, detected anomalous samples were compared with their respective geochemical assays to analyse their mineralogical differences, which may have led to the anomalous spectra. In the iron ore sample data used in this study, the observed spectral anomalies were shown to have elevated concentrations of Al2O3 and TiO2 wt.% while being several standard deviations below the mean Fe2O3 wt.% indicating mineralogies rich in shale as opposed to iron oxide rich mineralogies. While the paper demonstrates the efficacy of the VAE in anomaly detection, it can also be effective in assuring the quality of the FTIR data as a pre-processing step, which is critically important for machine learning applications.
Meta-learning with topic-agnostic representations for zero-shot stance detection

Zero-shot stance detection (ZSSD) aims to classify stances on unseen topic data with the ability of a model to generalize in the range of topics in the real world. The key point of ZSSD is to prevent the model from overfitting on seen topic data and show robust stance recognition performance on unseen topic samples. In this paper, we propose a new ZSSD framework to generalize stance detection performance by alleviating seen data information from the encoder and focusing on improving stance classification ability in zero-shot conditions. The proposed framework involves two learning stages contributing to ZSSD: topic-agnostic text encoder learning and zero-shot meta-learning. Our framework achieves notable improvements on the three benchmark zero-shot stance detection datasets and zero-shot aspect target sentiment classification dataset showing the effectiveness of our method in the zero-shot settings.
Research on iterative decoupling algorithm in color fringe projection profilometry

An iterative decoupling algorithm (IDA) is proposed to improve the performance of a color fringe projection measurement system that suffers from the complicated color coupling effect. Firstly, we analyzed the effect of projection intensity and the measured object on color coupling, and the mechanism of coupling; then, get the coupling coefficients by three red, green, and blue uniform intensity images; finally, use the proposed IDA method to correct the captured color fringe pattern for phase recovery. The advantage of the IDA method is that only three uniform color images are needed to pre-calibrate the coupling coefficients and are therefore easy to implement, highly robust, and widely used. Simulation and experimental results present that the proposed algorithm can significantly reduce the interference of color coupling and then improve the measurement accuracy of the color-channel-based phase-shifting profilometry.
A dual-projector three-dimensional measurement model for shading problem of micro-scale object complex surface

Considering the inherent defects of the traditional triangulation method, always a shadow area is produced in the image collected by the camera. In this work, based on the traditional monocular vision measurement system, an auxiliary light source was added to alleviate the shadow problem. Two symmetrical digital light processing (DLP) projectors were also employed to project the modulated monochrome red fringe patterns and the monochrome blue fringe patterns onto the object, whereas the distorted fringe patterns were captured by the colour camera. The image collected by the colour camera was separated, whereas the deformation fringe information of the measured object was obtained in the red channel and blue channel, respectively. Fusion of red and blue channels information to eliminate measurement shadows. From the experimental results, the root mean square error (RMSE) of the system was about 6µm, and this method can effectively solve the shadow problem in small object measurement.
Blockchain technology in e-business value chains

Abstract In electronic business (e-business), innovative technologies such as blockchain technology (BCT) have a fundamental impact on activities along the value chain. The perspective of the value chain in the context of blockchain technology has been explored, but contributions are mainly focused on supply chain management and tangible goods. Hence, the e-business domain lacks research, even though many beneficial features for service value chains and the transaction of intangible goods exist. Therefore, the current paper focuses on (1) how and why value chain activities are supported by using BCT and (2) how the stakeholder’s responsibilities change for value chain activities that are affected by BCT. A multiple case analysis of four e-business cases, i.e., Theta, OpenBazaar, Presearch, and Crypviser, is conducted. Based on four ideal value chains by Wirtz (2019) (cf. 4C-Net Model), steps that depend on BCT or that are supported by BCT are outlined. By conducting a cross-case analysis, we derive eight blockchain technology propositions that enlarge the existing knowledge base.
Migrating federated learning to centralized learning with the leverage of unlabeled data

Federated learning carries out cooperative training without local data sharing; the obtained global model performs generally better than independent local models. Benefiting from the free data sharing, federated learning preserves the privacy of local users. However, the performance of the global model might be degraded if diverse clients hold non-IID training data. This is because the different distributions of local data lead to weight divergence of local models. In this paper, we introduce a novel teacher–student framework to alleviate the negative impact of non-IID data. On the one hand, we maintain the advantage of the federated learning on the privacy-preserving, and on the other hand, we take the advantage of the centralized learning on the accuracy. We use unlabeled data and global models as teachers to generate a pseudo-labeled dataset, which can significantly improve the performance of the global model. At the same time, the global model as a teacher provides more accurate pseudo-labels. In addition, we perform a model rollback to mitigate the impact of latent noise labels and data imbalance in the pseudo-labeled dataset. Extensive experiments have verified that our teacher ensemble performs a more robust training. The empirical study verifies that the reliance on the centralized pseudo-labeled data enables the global model almost immune to non-IID data.
Fourteen years of cube attacks

Abstract Algebraic Cryptanalysis is a widely used technique that tackles the problem of breaking ciphers mainly relying on the ability to express a cryptosystem as a solvable polynomial system. Each output bit/word can be expressed as a polynomial equation in the cipher’s inputs—namely the key and the plaintext or the initialisation vector bits/words. A part of research in this area consists in finding suitable algebraic structures where polynomial systems can be effectively solved, e.g., by computing Gröbner bases. In 2009, Dinur and Shamir proposed the cube attack , a chosen plaintext algebraic cryptanalysis technique for the offline acquisition of an equivalent system by means of monomial reduction; interpolation on cubes in the space of variables enables retrieving a linear polynomial system, hence making it exploitable in the online phase to recover the secret key. Since its introduction, this attack has received both many criticisms and endorsements from the crypto community; this work aims at providing, under a unified notation, a complete state-of-the-art review of recent developments by categorising contributions in five classes. We conclude the work with an in-depth description of the kite attack framework , a cipher-independent tool that implements cube attacks on GPUs. Mickey2.0 is adopted as a showcase.
FPGA implementation of two multilayer perceptron neural network in cascade for efficient real time hand gestures tracking

This paper presents implementation of a hand gestures tracking system with a fully connected multilayer perceptron and a supervised sequential learning algorithm on low cost FPGAs (Field Programmable Gate Array). Two networks are used in the study. The first network is trained to detect skins and the second identifies face and hand gestures. Also, a binary image is made from the first network, which is fed into the second network as input. Skin network has two output classes, and hand network 6. The first network recognizes skin and non-skin images and the other distinguishes hands from faces while hand gestures are tracked and classified. Both networks are trained in Matlab before each was implemented on a separate FPGA and the two FPGAs are then connected to each other. Finally, the overall result is compared. Both networks are implemented on Xilinx Spartan XC3S1000 4fg456. Two hardware-implemented networks are applied to a video sequence frame by frame. Features of both hands are extracted and the network is trained using a constructed vector to track left and right hands. Results show that the implemented system enjoys a suitable trade-off between accuracy and speed, reaching 208 fps with the least memory usage. This method has been compared to other methods, as well as methods in which only one threshold is used to detect skin color.
Fuzzy linear regression based on a hybrid of fuzzy C-means and the fuzzy inference system for predicting serum iron levels in patients with chronic kidney disease

Multiple regression has been proven to be a reliable method for solving prediction problems that require many independent variables. The biggest challenge in multiple regression is that the number of samples given must be sufficient based on the number of independent variables. In some cases, it may not be possible to eliminate the independent variables via feature selection. This study aims to build a multiple linear regression problem-solving model using Sugeno's fuzzy inference system (FIS) approach. The main contribution of this study is to provide an alternative model for performing linear regression, which has quite a lot of independent variables but not too many datasets. The case that was resolved was the prediction of serum iron (SI) based on nine independent variables of hematology measurement results. The proposed FIS model uses Fuzzy C-Means (FCM) clustering to produce fuzzy sets and fuzzy rules. The Gauss membership function is used as the membership function in each fuzzy set. The output of the fuzzy rules is a linear equation based on the Sugeno Order 1 fuzzy inference method. To test the performance of the model, a comparison of the mean square error (MSE) output of the system with classical multiple linear regression (CMLR) and backpropagation neural networks (BNNs) was performed. In nine scenarios, comparisons were made. The results showed that FIS had the best performance in each scenario. In training data, FIS had the best performance with an MSE of 0.0148, followed by CMLR and BNN with MSEs of 0.0180 and 0.0285, respectively. Whereas in data testing, FIS had the best performance with an MSE of 0.0239, followed by BNN and CMLR with MSEs of 0.0246 and 0.0255, respectively. Even in scenarios where there is little training data, FIS still shows good performance, with MSEs of 0.0219 and 0.0402 on training and testing data, respectively.
Semi-Supervised Node Classification via Semi-Global Graph Transformer Based on Homogeneity Augmentation

As a kind of generalization of Transformers in the graph domain, Global Graph Transformers are good at learning distant knowledge by directly doing information interactions on complete graphs, which differs from Local Graph Transformers interacting on the original structures. However, we find that most prior works focus only on graph-level tasks (e.g., graph classification) and few Graph Transformer models can effectively solve node-level tasks, especially semi-supervised node classification, which obviously has important practical significance due to the limitation and expensiveness of these node labels. In order to fill this gap, this paper first summarizes the theoretical advantages of Graph Transformers. And based on some exploring experiments, we give some discussions on the main cause of their poor practical performance in semi-supervised node classifications. Secondly, based on this analysis, we design a three-stage homogeneity augmentation framework and propose a Semi-Global Graph Transformer. Considering both global and local perspectives, the proposed model combines various technologies including self-distillation, pseudo-label filtering, pre-training and fine-tuning, and metric learning. Furthermore, it simultaneously enhances the structure and the optimization, improving its effectiveness, scalability, and generalizability. Finally, extensive experiments on seven public homogeneous and heterophilous graph benchmarks show that the proposed method can achieve competitive or much better results compared to many baseline models including state-of-the-arts.
Using Data Augmentation and Bidirectional Encoder Representations from Transformers for Improving Punjabi Named Entity Recognition

Named entity recognition (NER) is a task of proper noun identification from natural language text and classification into various types such as location, person, and organization. Due to NER's applications in different natural language processing (NLP) tasks, numerous NER approaches and benchmark datasets have been proposed. However, developing NER techniques for low-resource languages is still limited due to the absence of substantial training datasets. Punjabi is a classic example of low resource language. Although various researchers have worked on Punjabi, they focused on the Gurmukhi script. To overcome the challenges in developing NER for the Shahmukhi script, we present an improved technique for Punjabi NER for the Shahmukhi script in this paper. We firstly extend the existing dataset by adding new NER classes by leveraging a novel Pool of Words data augmentation strategy. Our extended dataset has 11,31,509 tokens and 1,25,789 labeled entities with more named entities (NEs) than the older dataset. In the next step, we fine-tuned a transformer model known as Bidirectional Encoder Representations from Transformers (BERT) for the NER task. We performed experiments using the proposed approach on a new and older dataset version, showing that our method achieved competitive results.
Measurement-device-independent multi-party quantum key agreement

Quantum key agreement (QKA) is an important quantum cryptography primitive. In a QKA protocol, two or more untrusted parties can agree on an identical key in such a way that they equally influence the key and no subset can decide it alone. However, in practical QKA, the imperfections of the participant’s detectors can be exploited to compromise the security and fairness of QKA. To remove all the detector-side-channel loopholes, a measurement-device-independent multi-party QKA protocol is proposed. The protocol exploits the post-selected GHZ states to generate a secure agreement key between legitimate participants, while ensuring the fairness of key agreement. Our protocol provides a new clue for the design of practical QKA protocols.
Enhancing Arabic‐text feature extraction utilizing label‐semantic augmentation in few/zero‐shot learning

Abstract A growing amount of research use pre‐trained language models to address few/zero‐shot text classification problems. Most of these studies neglect the semantic information hidden implicitly beneath the natural language names of class labels and develop a meta learner from the input texts solely. In this work, we demonstrate how label information can be utilized to extract enhanced feature representation of the input text from a Transformer‐based pre‐trained language model such as AraBERT. In addition, how this approach can improve performance when the data resources are scarce like in the Arabic language and the input text is short with little semantic information as is the case using tweets. The work also applies zero‐shot text classification to predict new classes with no training examples across different domains including sarcasm detection and sentiment analysis using the information in the last layer of a trained classifier in a transfer learning setting. Experiments show that our approach has a better performance for the few‐shot sentiment classification compared to baseline models and models trained without augmenting label information. Moreover, the zero‐shot implementation achieved an accuracy up to 0.874 in Arabic sarcasm detection from a model trained on a sentiment analysis task.
Unlocking a multimodal archive of Southern Chinese martial arts through embodied cues

Purpose Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials. Design/methodology/approach This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts. Findings Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability. Originality/value This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.
Demonstration of a photonic router via quantum walks

Abstract Motivated by the need for quantum computers to communicate between multiple, well separated qubits, we introduce the task of routing the quantum state from one input mode to a superposition of several output modes coherently. We report an experimental demonstration of a deterministic photonic routing protocol applied to an entangled state. We show in a quantum walk architecture, quantum networks perfectly route entangled states from an initial input mode to an arbitrary output mode coherently and deterministically. Our results demonstrate the key principle of a perfect router, opening a route toward data routing and transferring for quantum computing systems. The routing algorithm in our work can be applied to a wide range of physical systems, which provides a way for effective design of efficient routing protocols on practical quantum networks.
Efficient and privacy-preserving group signature for federated learning

Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users' devices, called clients. Only the training results, called gradients, are sent to the server to be aggregated which is used to generate an updated model. However, we cannot assume that the server can be trusted with the sensitive information, such as metadata related to the owner or source of the data. So, hiding client information from the server helps in reducing privacy-related attacks. Therefore, the privacy of the client's identity, along with the privacy of the client's data, is necessary to prevent such attacks. This paper proposes an efficient and privacy-preserving protocol for FL based on group signatures. A new group signature for federated learning, called GSFL, is designed to not only protect the privacy of the client's data and identity but also significantly reduce the computation and communication costs considering the iterative process of federated learning. We show that GSFL outperforms existing approaches in terms of computation, communication, and signaling costs. Also, we show that the proposed protocol can handle various security attacks in the federated learning environment. Moreover, we provide security proof of our protocol using a formal security verification tool, Automated Validation of Internet Security Protocols and Applications (AVISPA).