Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Decentralized Federated Learning Model

As a decentralized technology, blockchain has the characteristics of traceability and immutability. Using blockchain technology, each model update of federation information can be associated with each participant, and the traceability of transactions in the blockchain can be used to detect fraudulent transactions that attempt to tamper with data. At the same time, the blockchain is used to realize the decentralization of the system, which strengthens the fault tolerance and attack resistance of the system. Based on this, this chapter designs and implements a blockchain architecture to solve the problem of safe and trusted data sharing among participants in the decentralized federated learning model. A blockchain architecture is designed and implemented for decentralized federated learning, using the blockchain to store the model parameter information of participants in each round of training; in view of the shortcomings of existing consensus algorithms, combined with decentralized federated learning detection model, A new consensus algorithm - “High Performance Byzantine Fault Tolerance Based on Model Accuracy” consensus algorithm is proposed.
A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.
A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks

The emerging trend of deploying complex algorithms, such as Deep Neural networks (DNNs), increasingly poses strict memory and energy efficiency requirements on Internetof-Things (IoT) end-nodes.Mixed-precision quantization has been proposed as a technique to minimize a DNN's memory footprint and maximize its execution efficiency, with negligible end-to-end precision degradation.In this work, we present a novel hardware and software stack for energy-efficient inference of mixed-precision Quantized Neural Networks (QNNs).We introduce Flex-V, a processor based on the RISC-V Instruction Set Architecture (ISA) that features fused Mac&Load mixedprecision dot product instructions; to avoid the exponential growth of the encoding space due to mixed-precision variants, we encode formats into the Control-Status Registers (CSRs).Flex-V core is integrated into a tightly-coupled cluster of eight processors; in addition, we provide a full framework for the end-to-end deployment of DNNs including a compiler, optimized libraries, and a memory-aware deployment flow.Our results show up to 91.5 MAC/cycle and 3.26 TOPS/W on the cluster, implemented in a commercial 22nm FDX technology, with up to 8.5× speed-up, and an area overhead of only 5.6% with respect to the baseline.To demonstrate the capabilities of the architecture, we benchmark it with end-to-end real-life QNNs, improving performance by 2× -2.5× with respect to existing solutions using fully flexible programmable processors.
Situated socio-material assemblages: assemmethodology in the making

Abstract This article proposes the concept of assemmethodology, which combines assemblage theory and ethnomethodology. Relating to the ongoing studies in sociomateriality, this article advances our understanding of the details of social conduct and the consequentiality of materials. By explicating the role of the situation and its processual becoming, which is inherent in ethnomethodology, and by replacing the unit of analysis from the individual to the assemblage made up of heterogenous elements, this article contributes to communication research that seeks to advance our understanding of the ways in which humans are entangled with the material world. As a perspicuous case for the study of assemmethodology, this article explores situations in which screens are assembled with humans and other materials in social situations. The article uses these examples to highlight the necessity of maintaining a focus on the situated emergence of socio-material orders as a property of the activity.
A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

Ceramic tiles are one of the most indispensable materials for interior decoration. The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures. In this paper, we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network (GAN). The generated tile images can be tailored to meet the specific needs of the user for the tile textures. The proposed method consists of four steps. Firstly, a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN. Secondly, for each ceramic tile image in the dataset, the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches. Thirdly, the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed. Finally, we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of “one style with multiple faces”. We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset. To measure the generative diversity and quality, we use Frechet Inception Distance (FID) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) metrics. The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images, with FID of 32.47 and BRISQUE of 28.44.
Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation

Gustave Cortal, Alain Finkel, Patrick Paroubek, Lina Ye. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2023.
AraDiaWER: An Explainable Metric For Dialectical Arabic ASR

Linguistic variability poses a challenge to many modern ASR systems, particularly Dialectical Arabic (DA) ASR systems dealing with low-resource dialects and resulting morphological and orthographic variations in text and speech. Traditional evaluation metrics such as the word error rate (WER) inadequately capture these complexities, leading to an incomplete assessment of DA ASR performance. We propose AraDiaWER, an ASR evaluation metric for Dialectical Arabic (DA) speech recognition systems, focused on the Egyptian dialect. AraDiaWER uses language model embeddings for the syntactic and semantic aspects of ASR errors to identify their root cause, not captured by traditional WER. MiniLM generates the semantic score, capturing contextual differences between reference and predicted transcripts. CAMeLBERT-Mix assigns morphological and lexical tags using a fuzzy matching algorithm to calculate the syntactic score. Our experiments validate the effectiveness of AraDiaWER. By incorporating language model embeddings, AraDiaWER enables a more interpretable evaluation, allowing us to improve DA ASR systems. We position the proposed metric as a complementary tool to WER, capturing syntactic and semantic features not represented by WER. Additionally, we use UMAP analysis to observe the quality of ASR embeddings in the proposed evaluation framework.
Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images

Abstract Mortality from breast cancer (BC) is among the top causes of cancer death in women. BC can be effectively treated when diagnosed early, improving the likelihood that a patient will survive. BC masses and calcification clusters must be identified by mammography in order to prevent disease effects and commence therapy at an early stage. A mammography misinterpretation may result in an unnecessary biopsy of the false-positive results, lowering the patient’s odds of survival. This study intends to improve breast mass detection and identification in order to provide better therapy and reduce mortality risk. A new deep-learning (DL) model based on a combination of transfer-learning (TL) and long short-term memory (LSTM) is proposed in this study to adequately facilitate the automatic detection and diagnosis of the BC suspicious region using the 80–20 method. Since DL designs are modelled to be problem-specific, TL applies the knowledge gained during the solution of one problem to another relevant problem. In the presented model, the learning features from the pre-trained networks such as the squeezeNet and DenseNet are extracted and transferred with the features that have been extracted from the INbreast dataset. To measure the proposed model performance, we selected accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) as our metrics of choice. The classification of mammographic data using the suggested model yielded overall accuracy, sensitivity, specificity, precision, and AUC values of 99.236%, 98.8%, 99.1%, 96%, and 0.998, respectively, demonstrating the model’s efficacy in detecting breast tumors.
Immersive VR-based instruction in vocational schools: effects on domain-specific knowledge and wellbeing of retail trainees

Abstract Immersive virtual reality (IVR) based simulation and training is gaining increasing importance in vocational education and training (VET). However, while IVR is primarily investigated and utilized in technical domains, its implementation and the resulting effects in commercial education remain largely unexplored. Moreover, the experience of motion sickness is a widely reported phenomenon while using IVR, which can interfere with cognitive processes and should therefore be considered more closely in terms of learning and instruction. This explorative study focuses on domain-specific knowledge acquisition in vocational apprenticeship for retailers and the accompanying side effects on students’ wellbeing in an IVR-based learning environment. For this purpose, an IVR-based scenario in a virtual supermarket was developed and tested with trainees in the field of retail at a German commercial vocational school. Using a quasi-experimental pretest-posttest design, we compared the effects of IVR-based and conventional instruction on domain-specific knowledge acquisition in a sample of first-year trainees ( N = 79). The findings indicate an advantage of IVR in the acquisition of domain-specific knowledge ( ɳ 2 = .261). Although moderate motion sickness symptoms were reported in the experimental group, no direct links between the experience of motion sickness and learning outcomes could be identified. These findings advance the current knowledge about learning-related effects of IVR-based instruction in the field of VET and provide further understanding about the special conditions of IVR scenarios conducive to learning.
Characterizing entanglement robustness of an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si84.svg" display="inline" id="d1e216"><mml:mi>N</mml:mi></mml:math>-qubit W superposition state against particle loss from quantum Fisher information

We investigate the entanglement robustness of an N-qubit W superposition state against particle loss with quantum Fisher information. In the noninteracting environment, the quantum Fisher information of reduced W superposition state is found same as it from W superposition state when the number of qubits is larger than 5, which shows very strong entanglement robustness compared to Twin-Fock state and Greenberger-Horne-Zeilinger state. Meanwhile, the re-particle-loss effect is also studied and it presents a rapid decay of quantum Fisher information with the increase of the number of losing qubits, indicating the weak entanglement robustness. In the Ising-type interacting environment, the results show that reduced W superposition state performs better than it in noninteracting environment. With the increasing qubits (N>5) the reduced W superposition state behaves much more robust than original W state. To simulate a real noisy environment, the effects of different decoherence channels are also considered.
Does Transliteration Help Multilingual Language Modeling?

Script diversity presents a challenge to Multilingual Language Models (MLLM) by reducing lexical overlap among closely related languages. Therefore, transliterating closely related languages that use different writing scripts to a common script may improve the downstream task performance of MLLMs. We empirically measure the effect of transliteration on MLLMs in this context. We specifically focus on the Indic languages, which have the highest script diversity in the world, and we evaluate our models on the IndicGLUE benchmark. We perform the Mann-Whitney U test to rigorously verify whether the effect of transliteration is significant or not. We find that transliteration benefits the low-resource languages without negatively affecting the comparatively high-resource languages. We also measure the cross-lingual representation similarity of the models using centered kernel alignment on parallel sentences from the FLORES-101 dataset. We find that for parallel sentences across different languages, the transliteration-based model learns sentence representations that are more similar.
Forward–forward training of an optical neural network

Neural networks (NNs) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple programmable layers together with these physical systems poses challenges, as they are difficult to fully characterize and describe with differentiable functions, hindering the use of error backpropagation algorithm. The recently introduced forward-forward algorithm (FFA) eliminates the need for perfect characterization of the physical learning system and shows promise for efficient training with large numbers of programmable parameters. The FFA does not require backpropagating an error signal to update the weights, rather the weights are updated by only sending information in one direction. The local loss function for each set of trainable weights enables low-power analog hardware implementations without resorting to metaheuristic algorithms or reinforcement learning. In this paper, we present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system. The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA can lead to performance improvements, even with a relatively small number of trainable weights. The proposed method offers a new path to the challenge of training optical NNs and provides insights into leveraging physical transformations for enhancing the NN performance.
LLDE: Enhancing Low-Light Images with Diffusion Model

Limited generalization capability has been an unsolved issue in the domain of low-light image enhancement. Many models find enhancing out-of-distribution underexposed images challenging. In this work, we offer a fresh point of view on this issue. Our approach involves dividing the enhancement process into many small steps and performing them gradually. This method allows the model to acquire a more robust understanding of the data. To put this concept into practice, we proposed to adopt a diffusion model for low-light image enhancement, as its way of encoding the mapping between the source and target distributions fits our idea. Empirically, we show that our proposed model (LLDE) can outperform recent SOTAs quantitatively and visually. The code is publicly available at https://github.com/OoiXinPeng/LLDE.
Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.
The Effectiveness of Gallery Walk and Numbered-Heads Together in Teaching Reading Comprehension

This study aimed to investigate the effectiveness of gallery walk and numbered-heads together for teaching reading comprehension to students with distinct personalities. The objectives of the study were, to investigate extrovert, and introvert students' scores of reading comprehension to analyze the effectiveness of gallery walk, to investigate extrovert and introvert students' score of reading to analyze the effectiveness of numbered-heads together, to explore the significant differences between extroverted students after learning to use gallery walk and numbered-heads together to teach reading comprehension, to explore the significant differences between introverted students after learning to use gallery walk and numbered-heads together to teach reading comprehension, to analyze how teaching, the personality of students and the reading style interact. This was an experimental study with a 2x2 factorial design. Observations, questionnaires, and tests were used to get the research data. Data from reading tests and questionnaires were analyzed quantitatively using a T-test and ANOVA. The results show that the mean score of the extroverts is 78.40 and introverts is 78.60 after taught using gallery walk technique. While the mean score of the extroverts is 82.70 and introverts is 81.10 after learning using numbered-heads together. These techniques were effective to teach reading. The students’ score of reading comprehension after taught using numbered-heads together technique was higher than those taught by gallery walk. Besides, there was no significant interaction between the use of techniques, student personality, and student reading comprehension.
Liquid Democracy in DPoS Blockchains

Voting mechanisms play a crucial role in decentralized governance of blockchain systems. Liquid democracy, also known as delegative voting, allows voters to vote directly or delegate their voting power to others, thereby contributing to the resolution of problems such as low voter turnout. In recent years, liquid democracy has been widely adopted by Delegated-Proof-of-Stake (DPoS) blockchains and implemented successfully on platforms with millions of users. However, little is known regarding the characteristics and actual effectiveness of liquid democracy in decentralized governance. This paper explored for the first time the practical implementation of liquid democracy in DPoS blockchain systems. Using actual data collected from two major DPoS blockchains, EOS and Steem, our study compared and evaluated the participation of different types of users of DPoS blockchain systems in liquid democracy, as well as extracting and analyzing the delegation chains and networks formed during the process of liquid democracy within the systems. We believe that the findings of this paper will contribute to further studies on the design and implementation of liquid democracy and other voting mechanisms in decentralized governance.
Qualitative Research of Conference Online Learning for Web Design Students

The topic of the present research is analysis of conference online learning using qualitative methods. The following methods are used: participating monitoring, questionnaire cards, interviewing and comparative analysis. The learning is carried out in the subject of „Web Design“. Teaching includes an online conference platform, web sources, digital cloud services, etc. Several key differences between the distant learning and conference online learning, used in regular education are pointed out. In the paper are discussed some basic characteristics of qualitative methods and conference online learning.
Spark SQL: Foundation

As Spark evolves and matures as a unified data processing engine with more features in each new release, its programming abstraction also evolves. The resilient distributed dataset (RDD) was the initial core programming abstraction when Spark was introduced to the world in 2012. In Spark version 1.6, a new programming abstraction, called Structured APIs, was introduced. This is the new and preferred way for the data engineering tasks, such as performing data processing or building data pipelines. The Structured APIs were designed to enhance developer productivity with easy-to-use, intuitive and expressive APIs. The new programming abstract requires the data available in a structured format, and the data computation logic needs to follow a certain structure. Armed with these two pieces of information, Spark can perform the necessary and sophisticated optimizations to speed up data processing applications.