Reviewerly/exHarmony huggingface dataset:
End-To-End Evaluation of Deep Learning Architectures for Off-Line Handwriting Writer Identification: A Comparative Study

Identifying writers using their handwriting is particularly challenging for a machine, given that a personâ€™s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work.
A symbolic algorithm for the case-split rule in solving word constraints with extensions

Case split is a core proof rule in current decision procedures for the theory of string constraints. Its use is the primary cause of the state space explosion in string constraint solving, since it is the only rule that creates branches in the proof tree. Moreover, explicit handling of the case split rule may cause recomputation of the same tasks in multiple branches of the proof tree. In this paper, we propose a symbolic algorithm that significantly reduces such a redundancy. In particular, we encode a string constraint as a regular language and proof rules as rational transducers. This allows us to perform similar steps in the proof tree only once, alleviating the state space explosion. We also extend the encoding to handle arbitrary Boolean combinations of string constraints, length constraints, and regular constraints. In our experimental results, we validate that our technique works in many practical cases where other state-of-the-art solvers fail to provide an answer; our Python prototype implementation solved over 50% of string constraints that could not be solved by the other tools.
Skeleton-based shape similarity.

Many aspects of visual perception, including the classification of shapes into known categories and the induction of new shape categories from examples, are driven by shape similarity. But there is as yet no generally agreed, principled measure of the degree to which two shapes are "similar." Here, we derive a measure of shape similarity based on the Bayesian skeleton estimation framework of Feldman and Singh (2006). The new measure, called generative similarity, is based on the idea that shapes should be considered similar in proportion to the posterior probability that they were generated from a common skeletal model rather than from distinct skeletal models. We report a series of experiments in which subjects were shown a small number (1, 2, or 3) of 2D or 3D "nonsense" shapes (generated randomly in a manner designed to avoid known shape categories) and asked to select other members of the "same" shape class from a larger set of (random) alternatives. We then modeled subjects' choices using a variety of shape similarity measures drawn from the literature, including our new measure, skeletal cross-likelihood, a skeleton-based measure recently proposed by Ayzenberg and Lourenco (2019), a nonskeletal part-based similarity model proposed by Erdogan and Jacobs (2017), and a convolutional neural network model (Vedaldi & Lenc, 2015). We found that our new similarity measure generally predicted subjects' selections better than these competing proposals. These results help explain how the human visual system evaluates shape similarity and open the door to a broader view of the induction of shape categories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
<scp>Auto‐encoder</scp> neural network incorporating <scp>x‐ray</scp> fluorescence fundamental parameters with machine learning

Abstract We consider energy‐dispersive x‐ray fluorescence (XRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are constantly moving (leading to varying angles of incidence and distances) and there may be other factors not accounted for (like dust). Neural networks do not require instrument and fundamental parameters but training neural networks requires XRF spectra labeled with elemental composition, which is often limited because of its expense. We develop a neural network model that learns from limited labeled data and also benefits from domain knowledge by learning to invert a forward model. The forward model uses transition energies and probabilities of all elements and parameterized distributions to approximate other fundamental and instrument parameters. We evaluate the model and baseline models on a rock dataset from a lithium mineral exploration project. Our model works particularly well for some low‐Z elements (Li, Mg, Al, and K) as well as some high‐Z elements (Sn and Pb) despite these elements being outside the suitable range for common spectrometers to directly measure, likely owing to the ability of neural networks to learn correlations and nonlinear relationships.
Intelligent and Fair IoV Charging Service Based on Blockchain With Cross-Area Consensus

The emergence of electric vehicles promotes the development of Internet of Vehicle (IoV). However, there are a series of security problems to be solved in the IoV. Firstly, how to allow vehicle users to find the nearest non-queuing charging pile without detours is a challenge. Secondly, applications in the IoV are delay-sensitive, while high communication delays caused by security mechanisms would bring serious consequences to vehicles. Thirdly, the verifiability and fairness between charging and payment are difficult to be guaranteed. Aiming at the efficiency and security problems of the charging service in the IoV, this paper proposes a blockchain-based intelligent and fair IoV charging service system. According to the multi-factor constraints between vehicles and charging piles, a multi-factor IoV branch and bound algorithm is proposed to intelligently recommend charging piles for vehicles and maximize the overall energy saving. We propose the cross-area consensus protocol to achieve low latency in vehicle communication. In addition, we ensure the fairness between charging and payment through a payment channel protocol based on verifiable encrypted signatures. Finally, we implement the proposed scheme, and we put the project prototype on an open source platform is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chenruonan/blockchain-based-intelligent-and-fair-IoV-charging-service-protocol</uri> . The experimental results demonstrate the low latency and energy-saving advantages of our proposal. When the payment is executed 250 times, the delay of our proposal is only 1.54% of the normal on-chain payment time.
Towards an Automatic Speech-to-Text Transcription System: Amazigh Language

Various studies inside the domain of research and the development of automatic speech recognition (ASR) technologies for several languages have not yet been published and thoroughly investigated.Nevertheless, the unique acoustic features of the Amazigh language, for example, Amazigh's consonant emphasis, pose many obstacles to the development of automatic speech recognition systems.In this study, we examine Amazigh language voice recognition.We treat the problem by focusing on transitions in vowel and consonant sounds and formant frequencies of phonemes.We present a hybrid strategy for phoneme separation based on energy differences.This includes analysis of consonant and vowel features, and identification methods based on formant analysis.
A Fuzzy Logic based Solution for Network Traffic Problems in Migrating Parallel Crawlers

Search engines are the instruments for website navigation and search, because the Internet is big and has expanded greatly.By continuously downloading web pages for processing, search engines provide search facilities and maintain indices for web documents.Online crawling is the term for this process of downloading web pages.This paper proposes solution to network traffic problem in migrating parallel web crawler.The primary benefit of a parallel web crawler is that it does local analysis at the data's residence rather than inside the web search engine repository.As a result, network load and traffic are greatly reduced, which enhances the performance, efficacy, and efficiency of the crawling process.Another benefit of moving to a parallel crawler is that as the web gets bigger, it becomes important to parallelize crawling operations in order to retrieve web pages more quickly.A web crawler will produce pages of excellent quality.When the crawling process moves to a host or server with a specific domain, it begins downloading pages from that domain.Incremental crawling will maintain the quality of downloaded pages and keep the pages in the local database updated.Java is used to implement the crawler.The model that was put into practice supports all aspects of a three-tier, realtime architecture.An implementation of a parallel web crawler migration is shown in this paper.The method for efficient parallel web migration detects changes in the content and structure using neural network-based change detection techniques in parallel web migration.This will produce highquality pages and detection for changes will always download new pages.Either of the following strategies is used to carry out the crawling process: either crawlers are given generous permission to speak with one another, or they are not given permission to communicate with one another at all.Both strategies increase network traffic.Here, a fuzzy logic-based system that predicts the load at a specific node and the path of network traffic is presented and implemented in MATLAB using the fuzzy logic toolbox.
Usability Testing on Tracer Study System Using The Heuristic Evaluation Method

A tracer study is a survey of graduates tracks alumni activities after graduating from an educational institution, Lancang Kuning University is one of the educational institutions that have a tracer study system. A system can be said to be good if it has good usability. So the purpose of this research is to find out whether the tracer study system of Universitas Lancang Kuning has good usability. The heuristic evaluation method will be used to measure usability using 10 aspects of usability heuristics with a questionnaire media in collecting data. Before distributing the questionnaires, an instrument test such as the Gregory test will be carried out by involving system experts and linguists. Then a validity test will be carried out, a reliability test will be carried out after that a heuristic test is carried out by distributing questionnaires. Based on the calculation results that the lowest percentage is owned by the visibility of the system status variable by 75% and the highest percentage results in the match between the system and the real world and consistency and standard with the same calculation results obtained by 81%. The results of the analysis show that the tracer system of Universitas Lancang Kuning has high usability with an average percentage of 78%.
Analyzing and Tracking Student Educational Program Interests on Social Media with Chatbots Platform and Text Analytics

This research presents a chatbot application to provide educational information for university students. There are three objectives: 1) to study the problem of providing information to university students with chatbots, 2) to develop a model and construct a chatbot to predict the interest of university students, and 3) to assess the satisfaction of the information provided by the chatbot application. The research datasets were the conversations from the Messenger Facebook Page of the Faculty of Information Technology, Rajabhat Maha Sarakham University, during the academic year 2020-2021. In total, there were 1,094 transactions used in this research work. Furthermore, data mining and machine learning techniques, including CRISP-DM, Naïve Bayes, K-Nearest Neighbors, and Neural Network, were used as the research tools. The cross-validation and confusion matrix techniques were used to test the model performance. Moreover, a questionnaire was the application satisfaction assessment tool for 30 respondents. As a result, it showed that the developed model provided high-level results, which are 88.73% accuracy and an average of 3.97 for application satisfaction. In the future, the researchers plan to apply the results for the next academic year and expand into other academic programs.
Impediments of Using E-Learning Platforms for Teaching English: A Case Study in Jordan

E-learning platforms are essential tools used widely for teaching and learning English, especially since the COVID-19 pandemic. They are also used to communicate and interact with students, assess progress, evaluate assignments, and provide feedback. However, teachers of English face potential barriers when they use such platforms. This study examines the use of e-learning platforms in teaching English as a foreign language in Jordan. The study employed a quantitative research method. The findings revealed that using e-learning platforms for educational purposes is beneficial regarding accessibility when attending courses. E-learning enabled the students to practise more and to be more engaged in the learning process, which improved their language skills. Effective e-learning platform strategies significantly broaden students' perceptions and increase the opportunity to exchange information with their classmates. Nonetheless, several impediments may hinder the application of e-learning platforms, including teacher-related, technical, and technological factors. The study recommends that teachers use interactive methods, including images, sounds, videos, and multimedia, to engage learners with various needs and abilities. The study also suggests building codified standards when designing e-learning to develop students' skills at all levels and training teachers on using modern technological strategies in e-learning.
Managing the digital transformation of education as part of the Chinese national project of a “Great Unity” society

The article deals with the phenomenon of digital transformation of the Chinese educational space as the main component of knowledge management system in the society of “Great Unity”. Using the comparative, hermeneutic, historical and philosophical methods, the authors analyse three problem areas – social ideal, forecasting, and national education in China. The conclusion is made that the traditional project of a society of “Great Unity”, that is, the Confucian model of the ideal Datong society, can be transformed into a national foresight project. A well-developed educational system and equal access to education are the key components of this project, digitalization of education being the most important direction of the modern Chinese educational reform. Based on the findings of studies carried out during the COVID-19 pandemic and other materials, various trends in the current digitalization process are described. Historical features of and socio-cultural preconditions for the development of the educational system existing in China today are analysed. The authors conclude that the system rests on a solid socio-cultural foundation and is aimed towards the digital future of the Chinese society.
Variable Autonomy for Human-Robot Teaming (VAT)

As robots are introduced to various domains and applications, Human-Robot Teaming (HRT) capabilities are essential. Such capabilities involve teaming with humans in\ on\ out-the-loop at different levels of abstraction, leveraging the complementing capabilities of humans and robots. This requires robotic systems with the ability to dynamically vary their level or degree of autonomy to collaborate with the human(s) efficiently and overcome various challenging circumstances. Variable Autonomy (VA) is an umbrella term encompassing such research, including but not limited to shared control and shared autonomy, mixed-initiative, adjustable autonomy, and sliding autonomy.
HCTIRdeblur: A hybrid convolution-transformer network for single infrared image deblurring

Infrared images captured by mobile platforms often suffer image blurs such as defocus blur and motion blur, which seriously degrade the quality of infrared images. However, the existing image deblurring methods generally focused on visible image deblurring while failing to perform infrared image deblurring effectively, due to the infrared images with low resolution, lack of detailed textural information, and tend to fused objects with backgrounds when low-level temperature difference. To this end, this study proposed a novel end to end network for single infrared image blind deblurring. An encoder contains multiple hybrid convolution-transformer feature extraction blocks is designed to effectively extract inherent characteristics of infrared image. The bidirectional feature pyramid structured decoder with full scale connections is adopted to achieve fully reuse multi-stage features and reconstructed clear infrared images ideally. The multi-stage training strategy and a novel mixed loss function are introduced to speed up the convergence of training process and obtain better image deblurring performance. Moreover, a dataset dedicated to infrared images blind deblurring is constructed to facilitate the task of infrared image deblurring. Extensive ablation studies and comparison experiments have been conducted on the test set of the proposed infrared image deblurring dataset. The experimental results demonstrated the effectiveness of the proposed network structure and the superiority of the proposed network over other state of the arts deblurring methods. Finally, comparative experiment is conducted on real captured blurred infrared images and the results verified the superiority and robustness of the proposed network over other existing image deblurring methods.
Multilayer time delay reservoir with double feedback loops for time series forecasting task

Time Delay Reservoir (TDR) is a special single-node reservoir to process time dependent signals simply and efficiently. It has good performance on time series forecasting tasks. Meanwhile, the single-node structure can be implemented easily in hardware. In fact, its prediction performance is also limited by its own special structure. In this paper, we experimentally verified the low memory capacity of the single feedback loop and the low utilization of virtual nodes in the single layer structure. Therefore, we propose a multilayer time delay reservoir of the double feedback loops to improve the overall efficiency and performance of the reservoir computing. Firstly, we use the double feedback loops structure which has another feedback loop with longer cycle on the basis of the single feedback loop. In this way, the responses generated earlier will be re-injected into the reservoir to improve the memory capacity. Secondly, the single layer is extended to multiple layers. The utilization of virtual nodes in the reservoir was improved during the training process of the output weights. Finally, the nonlinear function of the original TDR is replaced with a nonlinear function of exponential decay to reduce parameters. The experimental results show that the model proposed has better prediction accuracy and anti-noise performance than that of standard TDR on the time series forecasting task and also has better performance compared with other five models.
Adaptive Filter Pruning via Sensitivity Feedback

Filter pruning is advocated for accelerating deep neural networks without dedicated hardware or libraries, while maintaining high prediction accuracy. Several works have cast pruning as a variant of l1 -regularized training, which entails two challenges: 1) the l1 -norm is not scaling-invariant (i.e., the regularization penalty depends on weight values) and 2) there is no rule for selecting the penalty coefficient to trade off high pruning ratio for low accuracy drop. To address these issues, we propose a lightweight pruning method termed adaptive sensitivity-based pruning (ASTER) which: 1) achieves scaling-invariance by refraining from modifying unpruned filter weights and 2) dynamically adjusts the pruning threshold concurrently with the training process. ASTER computes the sensitivity of the loss to the threshold on the fly (without retraining); this is carried efficiently by an application of L-BFGS solely on the batch normalization (BN) layers. It then proceeds to adapt the threshold so as to maintain a fine balance between pruning ratio and model capacity. We have conducted extensive experiments on a number of state-of-the-art CNN models on benchmark datasets to illustrate the merits of our approach in terms of both FLOPs reduction and accuracy. For example, on ILSVRC-2012 our method reduces more than 76% FLOPs for ResNet-50 with only 2.0% Top-1 accuracy degradation, while for the MobileNet v2 model it achieves 46.6% FLOPs Drop with a Top-1 Acc. Drop of only 2.77%. Even for a very lightweight classification model like MobileNet v3-small, ASTER saves 16.1% FLOPs with a negligible Top-1 accuracy drop of 0.03%.
A High-Precision Two-Stage Legal Judgment Summarization

Legal judgments are generally very long, and relevant information is often scattered throughout the text. To complete a legal judgment summarization, capturing important, relevant information comprehensively from a lengthy text is crucial. The existing abstractive-summarization models based on pre-trained language have restrictions on the length of an input text. Another concern is that the generated summaries have not been well integrated with the legal judgment’s technical terms and specific topics. In this paper, we used raw legal judgments as information of different granularities and proposed a two-stage text-summarization model to handle different granularities of information. Specifically, we treated the legal judgments as a sequence of sentences and selected key sentence sets from the full texts as an input corpus for summary generation. In addition, we extracted keywords related to technical terms and specific topics in the legal texts and introduced them into the summary-generation model as an attention mechanism. The experimental results on the CAIL2020 and the LCRD datasets showed that our model achieved an overall 0.19–0.41 improvement in its ROUGE score, as compared to the baseline models. Further analysis also showed that our method could comprehensively capture essential and relevant information from lengthy legal texts and generate better legal judgment summaries.