Reviewerly/exHarmony huggingface dataset:
Model Parallelism Optimization for CNN FPGA Accelerator

Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. Model parallelism is an effective way to reduce resource usage by distributing CNN inference among several devices. However, parallelizing a CNN model is not easy, because CNN models have an essentially tightly-coupled structure. In this work, we propose a novel model parallelism method to decouple the CNN structure with group convolution and a new channel shuffle procedure. Our method could eliminate inter-device synchronization while reducing the memory footprint of each device. Using the proposed model parallelism method, we designed a parallel FPGA accelerator for the classic CNN model ShuffleNet. This accelerator was further optimized with features such as aggregate read and kernel vectorization to fully exploit the hardware-level parallelism of the FPGA. We conducted experiments with ShuffleNet on two FPGA boards, each of which had an Intel Arria 10 GX1150 and 16GB DDR3 memory. The experimental results showed that when using two devices, ShuffleNet achieved a 1.42× speed increase and reduced its memory footprint by 34%, as compared to its non-parallel counterpart, while maintaining accuracy.
Knowledge graph embedding with the special orthogonal group in quaternion space for link prediction

Graph embedding is an important technique for improving the quality of link prediction models on knowledge graphs. Although embedding based on neural networks can capture latent features with high expressive power, geometric embedding has other advantages, such as intuitiveness, interpretability, and few parameters. Several recent works have used rotation in quaternion space to enhance performance, yet these rotations have been limited to 3D representations. This paper proposes Rotate4D, a novel model that performs 4D rotations in quaternion space using a special orthogonal group. Specifically, Rotate4D begins by embedding entities in quaternion space, then rotates the head embedding to the tail embedding in two different ways (isoclinic and double rotation). The group structure helps decompose the relation into unit vectors to perform 4D rotations in quaternion space. In addition, quaternion scaling is applied to solve hierarchical relations. With this approach, the proposed model improved link prediction performance compared with state-of-the-art models on standard datasets, especially for the Hits@1 metric (an improvement of 2.72% to 3.49%). In addition to these experiments, we also theoretically prove that Rotate4D can model all relational patterns, including symmetry, anti-symmetry, inversion, and composition.
DFFMD: A Deepfake Face Mask Dataset for Infectious Disease Era With Deepfake Detection Algorithms

Deepfake is a technology that creates fake images and videos with replaced or synthesized faces. Deepfakes are becoming a concerning social phenomenon, as they can be maliciously used to generate false political news, disseminate dangerous information, falsify electronic evidence, and commit digital harassment and fraud. The ease and accuracy of creating Deepfakes have been bolstered by the popularity of wearing face masks since the beginning of the infectious disease outbreak (2020). Because these masks obstruct defining facial features, fake videos are now even more challenging to identify, increasing the necessity for advanced Deepfake detection technology. The research also creates a real/fake video dataset with face masks because the field lacks the dataset required for detection-model training. The proposed research proposes a Deepfake Face Mask Dataset (DFFMD) based on a novel Inception-ResNet-v2 with preprocessing stages, feature-based, residual connection, and batch normalization. The combination of preprocessing stages, feature-based, residual connection, and batch normalization increases the detection accuracy of deepfake videos in the presence of facemasks, unlike the traditional methods. The study’s results compared with existing state-of-the-art methods detect face-mask-Deepfakes with 99.81% accuracy compared to the traditional InceptionResNetV2 and VGG19, whose accuracy is 77.48%, and 99.25%, respectively. Future work should evaluate the accuracy of developing a subsequent experimental work for increased detection of deepfake with facemasks.
The Lighter the Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning

Vision transformers have recently set off a new wave in the field of medical image analysis due to their remarkable performance on various computer vision tasks. However, recent hybrid-/transformer-based approaches mainly focus on the benefits of transformers in capturing long-range dependency while ignoring the issues of their daunting computational complexity, high training costs, and redundant dependency. In this paper, we propose to employ adaptive pruning to transformers for medical image segmentation and propose a lightweight and effective hybrid network APFormer. To our best knowledge, this is the first work on transformer pruning for medical image analysis tasks. The key features of APFormer are self-regularized self-attention (SSA) to improve the convergence of dependency establishment, Gaussian-prior relative position embedding (GRPE) to foster the learning of position information, and adaptive pruning to eliminate redundant computations and perception information. Specifically, SSA and GRPE consider the well-converged dependency distribution and the Gaussian heatmap distribution separately as the prior knowledge of self-attention and position embedding to ease the training of transformers and lay a solid foundation for the following pruning operation. Then, adaptive transformer pruning, both query-wise and dependency-wise, is performed by adjusting the gate control parameters for both complexity reduction and performance improvement. Extensive experiments on two widely-used datasets demonstrate the prominent segmentation performance of APFormer against the state-of-the-art methods with much fewer parameters and lower GFLOPs. More importantly, we prove, through ablation studies, that adaptive pruning can work as a plug-n-play module for performance improvement on other hybrid-/transformer-based methods. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xianlin7/APFormer</uri> .
Multi-path decoder U-Net: A weakly trained real-time segmentation network for object detection and localization in ultrasound scans

Detecting and localizing an anatomical structure of interest within the field of view of an ultrasound scan is an essential step in many diagnostic and therapeutic procedures. However, ultrasound scans suffer from high levels of variabilities across sonographers and patients, making it challenging for sonographers to accurately identify and locate these structures without extensive experience. Segmentation-based convolutional neural networks (CNNs) have been proposed as a solution to assist sonographers in this task. Despite their accuracy, these networks require pixel-wise annotations for training; an expensive and labor-intensive operation that requires the expertise of an experienced practitioner to identify the precise outline of the structures of interest. This complicates, delays, and increases the cost of network training and deployment. To address this problem, we propose a multi-path decoder U-Net architecture that is trained on bounding box segmentation maps; not requiring pixel-wise annotations. We show that the network can be trained on small training sets, which is the case in medical imaging datasets; reducing the cost and time needed for deployment and use in clinical settings. The multi-path decoder design allows for better training of deeper layers and earlier attention to the target anatomical structures of interest. This architecture offers up to a 7% relative improvement compared to the U-Net architecture in localization and detection performance, with an increase of only 0.75% in the number of parameters. Its performance is on par with, or slightly better than, the more computationally expensive U-Net++, which has 20% more parameters; making the proposed architecture a more computationally efficient alternative for real-time object detection and localization in ultrasound scans.
Stochastic Weight Averaging Revisited

Averaging neural network weights sampled by a backbone stochastic gradient descent (SGD) is a simple-yet-effective approach to assist the backbone SGD in finding better optima, in terms of generalization. From a statistical perspective, weight-averaging contributes to variance reduction. Recently, a well-established stochastic weight-averaging (SWA) method was proposed, which featured the application of a cyclical or high-constant (CHC) learning-rate schedule for generating weight samples for weight-averaging. Then, a new insight on weight-averaging was introduced, which stated that weight average assisted in discovering a wider optima and resulted in better generalization. We conducted extensive experimental studies concerning SWA, involving 12 modern deep neural network model architectures and 12 open-source image, graph, and text datasets as benchmarks. We disentangled the contributions of the weight-averaging operation and the CHC learning-rate schedule for SWA, showing that the weight-averaging operation in SWA still contributed to variance reduction, and the CHC learning-rate schedule assisted in exploring the parameter space more widely than the backbone SGD, which could be be under-fitted due to a lack of training budget. We then presented an algorithm termed periodic SWA (PSWA) that comprised a series of weight-averaging operations to exploit such wide parameter space structures as explored by the CHC learning-rate schedule, and we empirically demonstrated that PSWA outperformed its backbone SGD remarkably.
Journalism in Democracy: A Discourse Analysis of Twitter Posts on the Ferrerasgate Scandal

This research analyses the discourse on Twitter surrounding the “Ferrerasgate” scandal involving the Spanish journalist Antonio García Ferreras, director and host of the television show &lt;em&gt;Al Rojo Vivo &lt;/em&gt;(La Sexta, Spanish TV channel). It examines the main object of criticism, the tone of the discourse, the argumentation made by users, as well as the existence of hate in their rhetoric. The tweets included in the study’s sample (&lt;em&gt;N &lt;/em&gt;= 2,846), posted between 5 and 15 July 2022 and extracted on 16 July 2022, were examined in two complementary phases. The first entailed a quantitative content analysis of the messages and the second analysed whether hate speech was found in the sample as a whole. The Sketch Engine tool was used to determine whether “crypto hate speech” existed in the sample as a whole, and to whom it was targeted. The results reveal that “Ferrerasgate” sparked a debate that spilt over into journalism across the board, calling into question the media’s role in a democracy. The most prominent arguments were the condemnation of misinformation, lack of independence, and absence of professionalism in the journalism sector. It should be noted that most of the messages were destructive in tone; hate was found in the tweets analysed, although these did not represent a high percentage in relation to the total sample.
A new 2D-ASC chaotic system and its image encryption applications

In this paper, a new 2D Arnold-sin-coupling (2D-ASC) hyperchaotic map is established and a new image encryption algorithm based on bit-level operations and the ASC hyperchaotic system is proposed. The algorithm first decomposes a normal image into bit-level images by rows, incorporates a sequence of bits generated by a chaotic system to the algorithm to increase randomness and security, and then performs a circular shift of the rows and columns of the bit-level image to obtain a confused intermediate image. Finally, the intermediate image is diffused by our constructed S-box according to the size of each pixel value to obtain a stable and robust encrypted image. Moreover, the initial condition of the chaotic mapping used is calculated from the SHA-256 hash value of the original image, enhancing the correlation of the encryption process with the plaintext image. Through simulation experiments, the image encryption scheme is analyzed in terms of key space, histogram, information entropy, robustness and key sensitivity. The theoretical analysis and experimental findings demonstrate that the algorithm has good performance and sufficient level of security. The information entropy of the encrypted image is above 7.9993, the average NPCR is 99.6440 and the average UACI is 33.4996.
Lightweight certificateless privacy-preserving integrity verification with conditional anonymity for cloud-assisted medical cyber–physical systems

Cloud-assisted medical cyber–physical systems (MCPSs) leverage the power of cloud computing for scalable storage and management of outsourced medical data. Based on the crucial outsourced medical data, guardians could know patient’s health condition timely, doctors can make accurate diagnoses of patients, and medical research centers could also conduct medical big data analysis. However, medical data is highly sensitive and demands strong protection. Meanwhile, the integrity of outsourced medical data is vulnerable to hardware failures, software bugs or human errors. To date, a large number of data privacy-preserving integrity auditing schemes have been presented, but most of them suffer from the burden of complex public key certificate management or the key escrow problem of identity-based cryptography. In this paper, we propose a lightweight certificateless privacy-preserving integrity verification scheme (LCPPIV) with conditional anonymity for cloud-assisted MCPSs by developing an elliptic-curve digital signature and a key exchange mechanism, which can achieve the functionalities of medical data privacy protection, integrity verification of outsourced medical data, conditional anonymity, batch auditing, and secure compensation mechanism, simultaneously. Theoretical security analysis demonstrates that our scheme is provably secure in the random oracle model, with resistance to the public key replacement attack, malicious-but-passive-KGC attack, and response auditing proofs forgery. The performance evaluations indicate that LCPPIV is much more practical for cloud-assisted MCPSs compared with state-of-art data auditing schemes.
Comparative Study of Machine Learning and Deep Learning Classifiers on Handwritten Numeral Recognition

Handwriting digit recognition is a computer technology that allows it to accept and decipher sound transcribed input from various sources, including paper reports, contact screens, and pictures. This paper presents the machine and Deep Learning approach for handwritten digit recognition from image input. This approach uses bench marked dataset MNIST English handwritten numeral digit dataset of size 70,000. Four machine learning and deep learning algorithms are explored, and a pattern recognition approach is used. We have explored the pattern matching approach and achieved 86% accuracy for the Decision Tree classifier, 91% accuracy for the Support Vector Machine classifier, 97% for Artificial Neural Network and 98.84% for Convolutional Neural Network. In the Deep Learning approach, the Convolutional Neural Network algorithm with the Vgg16 network is implemented to train the MNIST digit dataset and achieved an accuracy of 98.84%.
Chaos Robustness and Computation Complexity of Piecewise Linear and Smooth Chaotic Chua’s System

Chaotic systems are often considered to be a basis for various cryptographic methods due to their properties, which correspond to cryptographic properties like confusion, diffusion and algorithm (attack) complexity. In these kinds of applications, chaos robustness is desired. It can be defined by the absence of periodic windows and coexisting attractors in some neighborhoods of the parameter space. On the other hand, when used as a basis for neuromorphic modeling, chaos robustness is to be avoided, and the edge-of-chaos regime is needed. This paper analyses the robustness and edge-of-chaos for Chua’s systems, comprising either a piecewise linear or a smooth function nonlinearity, using a novel figure of merit based on correlation coefficient and Lyapunov exponent. Calculation complexity, which is important when a chaotic system is implemented, is evaluated for double and decimal data types, where needed calculation time varies by a factor of about 1500, depending on the nonlinearity function and the data type. On the other hand, different data types result in different number precision, which has some practical advantages and drawbacks presented in the paper.
Experimental Implementation of Short-Path Nonadiabatic Geometric Gates in a Superconducting Circuit

Nonadiabatic geometric quantum computation (NGQC) has attracted a lot of attention for noise-resilient quantum control. However, previous implementations of NGQC require long evolution paths that make them more vulnerable to incoherent errors than their dynamical counterparts. In this work, we experimentally realize a universal short-path nonadiabatic geometric gate set (SP NGQC) with a 2-times shorter evolution path on a superconducting quantum processor. Characterizing with both quantum process tomography and randomized benchmarking methods, we report an average single-qubit gate fidelity of $99.86\mathrm{%}$ and a two-qubit gate fidelity of $97.9\mathrm{%}$. Additionally, we demonstrate superior robustness of single-qubit SP NGQC gate to Rabi frequency error in some certain parameter space by comparing their performance to those of the dynamical gates and the former NGQC gates.
ChatGPT, an Opportunity to Understand More About Language Models

ChatGPT, a leading large language model, has achieved some success beyond previous language models and caught the world’s attention since its release in late 2022. Businesses and healthcare professional fields have raised strong interests in investing in large language models to assist various kinds of information searching in their domain of expertise. Under the influence of ChatGPT, searched information may be received in a new personalized chat format, in contrast to the traditional search engines with pages of results for users to evaluate and open. Large language models and generative AI present new opportunities for librarians to understand more about language models’ development as well as the future directions of the language models that are developed behind the user interfaces. Being aware of how language models impact the communication of information will enrich librarians’ abilities to examine the quality of AI outputs and awareness of users’ rights and data curation policies, to better assist patrons’ research activities that involve using language models in the foreseeable future.
Understanding the acceptability of block-chain technology in the supply chain; case of a developing country

Purpose Technological development has been a cornerstone of any emerging economy in the past few years. Blockchain has emerged as a promising technology in the past few years, revolutionizing business dynamics. There is always a concern or hesitation during such novel technological innovation. This paper aims to investigate the blockchain technology (BCT) implementation and acceptance in the supply chain function domain. Design/methodology/approach The proposed model is based on the Technology Readiness Index (TRI) and extended theory of planned behavior (TPB). The responses were collected from information technology (IT) professionals working at management positions in various manufacturing industries. A total of 147 usable responses were collected for analyzing hypotheses using structural equation modeling. Findings As per the findings, perceived ease of use significantly impacts perceived usefulness and attitude toward technology acceptability. Perceived usefulness is significant to attitude toward use. Trust in technology has a significant impact on building up the attitude to use the technology. Originality/value The novelty of this work lies in gauging the acceptability of new ways and means of transacting among supply chain professionals and decision-makers. This study provides a broader perspective regarding reluctance and acceptance of the BCT in the developing country that may help the technologist to elucidate better for smooth adoption.
Supervised Contrastive Learning for RFF Identification with Limited Samples

Radio frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary dataset and the target dataset often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary datasets, in this paper, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN”. First, we analyze the causes of RFF, and model the RFF identification problem with augmented dataset. A non-auxiliary data augmentation method is proposed to acquire an extended dataset, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long-short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.
The Path of the Righteous: Using Trace Data to Understand Fraud Decisions in Real Time

Trace data—users’ digital records when interacting with technology—can reveal their cognitive dynamics when making decisions on websites in real time. Here, we present a trace­data method, analyzing movements captured via a computer mouse, to assess potential fraud when filling out an online form. In contrast to existing fraud­detection methods, which analyze information after submission, mouse­movement traces can capture the cognitive deliberations as possible indicators of fraud as it is happening. We report two controlled studies using different tasks, where participants could freely commit fraud to benefit themselves financially. As they performed the tasks, we captured mouse­cursor movement data and found that participants who entered fraudulent responses moved their mouse significantly more slowly and with greater deviation. We show that the extent of fraud matters such that more extensive fraud increases movement deviation and decreases movement speed. These results demonstrate the efficacy of analyzing mouse­movement traces to detect fraud during online transactions in real time, enabling organizations to confront fraud proactively as it is happening at internet scale. Our method of analyzing actual user behaviors in real time can complement other behavioral methods in the context of fraud and a variety of other contexts and settings.