Reviewerly/exHarmony huggingface dataset:
A Comprehensive Dataset of Spelling Errors and Users’ Corrections in Croatian Language

This paper presents a unique and extensive dataset containing over 33 million entries with pairs in the form “spelling error → correction” from ispravi.me, the most popular Croatian online spellchecking service, collected since 2008. The dataset, compiled from the contribution of nearly 900,000 users, is a valuable resource for researchers and developers in the field of natural language processing (NLP), improving spellcheck accuracy, and language learning applications. The dataset may be used to accomplish several goals: (1) improving spellchecking accuracy by incorporating common user corrections and reducing false positives and negatives; (2) helping language learners identify common errors and learn correct spelling through targeted feedback; (3) analyzing data trends and patterns to uncover the most common spelling errors and their underlying causes; (4) identifying and evaluating factors that influence typing input; (5) improving NLP applications such as text recognition and machine translation. Tasks specific to the Croatian language include the creation of a letter-level confusion matrix and the refinement of word suggestions based on historical usage of the service. This comprehensive dataset provides researchers and practitioners with a wealth of information, opening the path for advancements in spellchecking, language learning, and NLP applications in the Croatian language.
A Method of Sentiment Analysis and Visualized Interaction Based on Ernie-Tiny and BiGRU

Sentiment analysis of subjective texts in social media is beneficial to help people adjust and intervene in a negative mental state in time, which is significant to mental health care. At present, limited by the accuracy of word segmentation, sentiment analysis of subjective text has difficulties in dealing with context, sentence patterns, and word co-occurrence. This paper aims to propose an efficient method of semantic feature representation and sentiment analysis, thereby providing a basis for sentiment visualization and interactive applications. Based on Ernie-Tiny and BiGRU, this paper proposes a sentiment analysis model ET_s_BG+p to solve problems in analyzing Chinese subjective texts’ complex semantics, diverse sentence patterns, and shortness. The model inputs the semantic features obtained via Ernie-Tiny into BiGRU and then splices the output with the sentence vectors of Ernie-Tiny to form final text features and perform sentiment classification. Experiments are performed on a dataset integrating text comments from Weibo, takeaway, and e-commerce platforms. The results show that the model proposed in this paper performs best in most of the evaluation indicators compared with baseline models such as CNN, BiLSTM, and GRU. The experiments show that the accuracy of the model on the dataset built in this research is 84.30%, the precision is 83.95%, the recall rate is 88.35%, and the F1 value is 85.98%. At the same time, based on ET_s_BG+p, this paper develops a prototype visual display platform that integrates functions such as text input, sentiment analysis, and agent interaction, which can provide support for daily emotion monitoring and adjustment.
Making existing software quantum safe: A case study on IBM Db2

The software engineering community is facing challenges from quantum computers (QCs). In the era of quantum computing, Shor's algorithm running on QCs can break asymmetric encryption algorithms that classical computers practically cannot. Though the exact date when QCs will become "dangerous" for practical problems is unknown, the consensus is that this future is near. Thus, the software engineering community needs to start making software ready for quantum attacks and ensure quantum safety proactively. We argue that the problem of evolving existing software to quantum-safe software is very similar to the Y2K bug. Thus, we leverage some best practices from the Y2K bug and propose our roadmap, called 7E, which gives developers a structured way to prepare for quantum attacks. It is intended to help developers start planning for the creation of new software and the evolution of cryptography in existing software. In this paper, we use a case study to validate the viability of 7E. Our software under study is the IBM Db2 database system. We upgrade the current cryptographic schemes to post-quantum cryptographic ones (using Kyber and Dilithium schemes) and report our findings and lessons learned. We show that the 7E roadmap effectively plans the evolution of existing software security features towards quantum safety, but it does require minor revisions. We incorporate our experience with IBM Db2 into the revised 7E roadmap. The U.S. Department of Commerce's National Institute of Standards and Technology is finalizing the post-quantum cryptographic standard. The software engineering community needs to start getting prepared for the quantum advantage era. We hope that our experiential study with IBM Db2 and the 7E roadmap will help the community prepare existing software for quantum attacks in a structured manner.
DROPO: Sim-to-real transfer with offline domain randomization

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult. In this paper, we introduce DROPO, a novel method for estimating domain randomization distributions for safe sim-to-real transfer. Unlike prior work, DROPO only requires a limited, precollected offline dataset of trajectories, and explicitly models parameter uncertainty to match real data using a likelihood-based approach. We demonstrate that DROPO is capable of recovering dynamic parameter distributions in simulation and finding a distribution capable of compensating for an unmodeled phenomenon. We also evaluate the method in two zero-shot sim-to-real transfer scenarios, showing successful domain transfer and improved performance over prior methods.
Multi-sentence video captioning using spatial saliency of video frames and content-oriented beam search algorithm

Video captioning algorithms aim at expressing the information and activities contained in a video clip in the form of lingual sentences. Most existing video captioning approaches have used only one sentence to describe the semantic content of a video. However, one sentence cannot transfer all the semantic information of a video, especially in videos with high informative content. Although a few studies have been conducted for multi-sentence video captioning, such as paragraph and dense captioning, they produce several sentences by focusing on different activities, objects, or temporal parts of a video. However, a video clip with a single object or activity may include a lot of information from different perspectives that can not be described by a single sentence, effectively. To counter the problem, we propose a multi-sentence video captioning algorithm using the spatial saliency of video frames as well as a content-oriented beam search algorithm. In the proposed algorithm, the spatial saliency of video frames is employed during the encoding stage to generate informative sentences by focusing on different parts of video frames. Furthermore, a content-oriented beam search algorithm is employed during the decoding stage to generate informative sentences. A multi-stage filter is also employed to remove the sentences with incorrect structure or sentences that are less relevant to the semantic content of the video. To evaluate the performance of the proposed algorithm, two well-known video description databases were used, and the results showed a significant improvement in the evaluation metrics, especially in the best-1 sentences. We also tested the proposed algorithm with several real-life movies.
Enabling blockchain-assisted certificateless public integrity checking for industrial cloud storage systems

Due to the sharp increase in industrial data, cloud computing has been integrated into intelligent Industrial Internet of Things systems to store and process massive data. Since core data are key factors in the industrial production process, any intercepted or altered data may cause severe consequences, and the integrity and confidentiality of outsourced industrial data have become extremely concerning security issues. In this paper, we focus on devising an enhanced blockchain-assisted certificateless public integrity checking (BA-CPIC) mechanism for industrial cloud storage systems. BA-CPIC contributes to checking outsourced industrial encrypted data, and exploits the smart contract in the Ethereum blockchain to provide assistance to users for checking malicious behaviors of auditors (e.g., colluding with industrial cloud servers or lazing checking). Besides, BA-CPIC preserves the identity privacy of users enjoying outsourced storage and blockchain-assisted integrity checking services. By contrast, some misbehaved users in industrial cloud storage systems could also be traced and revoked in due course. BA-CPIC avoids both inherent drawbacks of key escrow and complex certificate management, it is provided corresponding security analysis under the certificateless security model. The performance evaluation demonstrates the feasibility of BA-CPIC in the deployment of industrial cloud storage systems.
A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering

In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers. However, the performance of OpenQA systems is often hindered by issues such as short and semantically ambiguous queries, making it challenging for the retriever to find relevant passages quickly. This paper introduces Hybrid Text Generation-Based Query Expansion (HTGQE), an effective method to improve retrieval efficiency. HTGQE combines large language models with Pseudo-Relevance Feedback techniques to enhance the input for generative models, improving text generation speed and quality. Building on this foundation, HTGQE employs multiple query expansion generators, each trained to provide query expansion contexts from distinct perspectives. This enables the retriever to explore relevant passages from various angles for complementary retrieval results. As a result, under an extractive and generative QA setup, HTGQE achieves promising results on both Natural Questions (NQ) and TriviaQA (Trivia) datasets for passage retrieval and reading tasks.
On the construction of new toric quantum codes and quantum burst-error-correcting codes

A toric quantum error-correcting code construction procedure is presented in this work. A new class of an infinite family of toric quantum codes is provided by constructing a classical cyclic code on the square lattice $${\mathbb {Z}}_{q}\times {\mathbb {Z}}_{q}$$ for all odd integers $$q\ge 5$$ and, consequently, new toric quantum codes are constructed on such square lattices and from geometrically uniform codes regardless of whether q can be represented as a sum of two squares. Furthermore, this work supplies for each q the polyomino shapes that tessellate the corresponding square lattices and, consequently, tile the lattice $${\mathbb {Z}}^{2}$$ . The quantum channel without memory to be considered for these constructed toric quantum codes is symmetric, since the $${\mathbb {Z}}^{2}$$ -lattice is autodual. Moreover, we propose a quantum interleaving technique by using the constructed toric quantum codes, which shows that the code rate and the coding gain of the interleaved toric quantum codes are better than the code rate and the coding gain of Kitaev’s toric quantum codes for $$q=2n+1$$ , where $$n\ge 2$$ , and of an infinite class of Bombin and Martin-Delgado’s toric quantum codes. In addition to the proposed quantum interleaving technique that improves such parameters, it can be used for burst-error correction in errors which are located, quantum data stored and quantum channels with memory.
Multi-proxy based deep metric learning

Deep metric learning (DML) achieves excellent performance in many open-set scenarios. However, the current multi-proxy methods rely on a classification framework and the performances of these methods rely on large batch size due to the representation limitation of mini-batch for neighbor distribution of proxies. This study proposes a multi-proxy method aiming to enhance the generalization ability of DML on unseen classes by enhancing the data representation ability. As a sample may not share all fine-grained semantic information of a class, our model does not require full association between samples and proxies, and is updated only according to the associated pairs. Moreover, a mini-batch may not contain sufficient information for learning. So our model also considers the proxy-proxy relation in order to provide a global view of the data structure which improves the learning on the intra-class distance. This study also investigates how the positive and negative margins, i.e., the parameters of our model which control the required similarity for a class and different classes, affect our performance, and finds that less demanding on the negative margin avoids over-training and improves generalization ability to unseen classes. The outstanding performance of our model is confirmed by experimental results compared with the state-of-the-art DML methods in the benchmark image retrieval datasets.
Ranks, copulas, and permutons

Abstract We review a recent development at the interface between discrete mathematics on one hand and probability theory and statistics on the other, specifically the use of Markov chains and their boundary theory in connection with the asymptotics of randomly growing permutations. Permutations connect total orders on a finite set, which leads to the use of a pattern frequencies. This view is closely related to classical concepts of nonparametric statistics. We give several applications and discuss related topics and research areas, in particular the treatment of other combinatorial families, the cycle view of permutations, and an approach via exchangeability.
Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices

Modern education has undergone tremendous progress, and a large number of advanced devices and technologies have been introduced into the teaching process. We explore the application of artificial intelligence to education, using AI devices for classroom behavior analysis. Embedded systems are special-purpose computer systems tailored to an application. Embedded system hardware for wearable devices is often characterized by low computing power and small storage, and it cannot run complex models. We apply lightweight models to embedded devices to achieve real-time emotion recognition. When teachers teach in the classroom, embedded portable devices can collect images in real-time and identify and count students’ emotions. Teachers can adjust teaching methods and obtain better teaching results through feedback on students’ learning status. Our optimized lightweight model PIDM runs on low-computing embedded devices with fast response times and reliable accuracy, which can be effectively used in the classroom. Compared with traditional post-class analysis, our method is real-time and gives teachers timely feedback during teaching. The experiments in the control group showed that after using smart devices, the classroom teaching effect increased by 9.44%. Intelligent embedded devices can help teachers keep abreast of students’ learning status and promote the improvement of classroom teaching quality.
Agreement and Disagreement-Based Co-Learning with Dual Network for Hyperspectral Image Classification with Noisy Labels

Deep learning-based label noise learning methods provide promising solutions for hyperspectral image (HSI) classification with noisy labels. Currently, label noise learning methods based on deep learning improve their performance by modifying one aspect, such as designing a robust loss function, revamping the network structure, or adding a noise adaptation layer. However, these methods face difficulties in coping with relatively high noise situations. To address this issue, this paper proposes a unified label noise learning framework with a dual-network structure. The goal is to enhance the model’s robustness to label noise by utilizing two networks to guide each other. Specifically, to avoid the degeneration of the dual-network training into self-training, the “disagreement” strategy is incorporated with co-learning. Then, the “agreement” strategy is introduced into the model to ensure that the model iterates in the right direction under high noise conditions. To this end, an agreement and disagreement-based co-learning (ADCL) framework is proposed for HSI classification with noisy labels. In addition, a joint loss function consisting of a supervision loss of two networks and a relative loss between two networks is designed for the dual-network structure. Extensive experiments are conducted on three public HSI datasets to demonstrate the robustness of the proposed method to label noise. Specifically, our method obtains the highest overall accuracy of 98.62%, 90.89%, and 99.02% on the three datasets, respectively, which represents an improvement of 2.58%, 2.27%, and 0.86% compared to the second-best method. In future research, the authors suggest using more networks as backbones to implement the ADCL framework.
Experimental demonstration of optimal unambiguous two-out-of-four quantum state elimination

A core principle of quantum theory is that nonorthogonal quantum states cannot be perfectly distinguished with single-shot measurements. However, it is possible to exclude a subset of nonorthogonal states without error in certain circumstances. Here we implement a quantum state elimination measurement which unambiguously rules out two of four pure, nonorthogonal quantum states---ideally without error and with unit success probability. This is a generalized quantum measurement with six outcomes, where each outcome corresponds to excluding a pair of states. Our experimental realization uses single photons, with information encoded in a four-dimensional state using optical path and polarization degrees of freedom. The prepared state is incorrectly ruled out up to $3.3(2)%$ of the time.
Mega trend diffusion-siamese network oversampling for imbalanced datasets’ SVM classification

Imbalanced class distribution is a frequent and problematic issue in the domains of data engineering and machine learning. Traditional classification algorithms or machine learning models frequently fail, in this difficult situation, to provide accurate classification results for the minority class. To improve classification performance for the minority class, some research has advised creating artificial instances in the minority class using the oversampling technique. However, on the support vectors machines (SVM) model, as new minority class instances were created in an original data space, classification improvements made using these popular oversampling techniques may not hold true. In this paper, we develop a novel oversampling method termed DB-MTD-SN (for distance-based mega-trend-diffusion Siamese Network) for generating artificial minority class instances to increase the SVM model’s classification accuracy for imbalanced datasets. In the proposed method, we utilize distance-based mega-trend-diffusion (DB-MTD) technique to estimate data domain for tiny support vectors to generate synthetic examples with multi-model distributions of minority class. Further, we construct a novel Siamese network model based on membership function (MF-based SN) to find the most representative synthetic minority class examples. The proposed SN model is used to map the original data onto a high-dimensional space to easily learn complicated patterns between majority and minority classes. In our proposed SN model, the MF-based contrastive loss function is used to measure the similarity between a new synthetic example and original examples to avoid generating noise. To demonstrate efficacy of the proposed DB-MTD-SN approach, ten benchmark datasets are used in this study. On two types of SVM models, we compare the proposed method with three state-of-the-art oversampling methods. Further, three evaluation metrics: G-mean, F1, and index of balanced accuracy (IBA) are utilized to measure SVM classification performance on imbalanced datasets. The experimental datasets are set at different imbalanced ratios (IRs) as 15, 20, and 30 to test classification performance using five methods. For a high IR value of 30, on the two SVM models, the proposed method achieved the best average in terms of G-mean (0.865 and 0.865), F1 (0.785 and 0.772), and IBA (0.681 and 0.689) metrics, respectively. The paired Wilcoxon test is used to evaluate whether the suggested approach has statistically significant differences from the four other methods on the three evaluation metrics. The test results demonstrate that classification results using the proposed DB-MTD-SN method enjoys significant improvements (i.e. p-value <0.05) in terms of G-mean, F1, and IBA indicators when compared to the other four methods. Our experimental results indicate the suggested DB-MTD-SN method outperforms other oversampling methods for imbalanced datasets.
Digital self-care in the management of spine musculoskeletal disorders: A systematic review and meta-analysis

Objective: to analyze the effectiveness of digital self-care in the management of pain and functional disability among people with spine musculoskeletal disorders. Method: a systematic literature review, developed with the PRISMA checklist, of randomized clinical trials of people with spine musculoskeletal disorders and digital interventions accessed by means of computers, smartphones or other portable devices. Databases researched: National Library of Medicine, Excerpta Médica dataBASE, SciVerse Scopus, Literatura Latino-Americana e do Caribe em Ciências da Saúde, Science Citation Indexes, Cumulative Index to Nursing and Allied Health Literature and Physiotherapy Evidence Database. The descriptive synthesis of the results and by means of meta-analyses (fixed-effects model) was performed with the Review Manager software. The methodological quality was evaluated with the Physiotherapy Evidence Database scale. Results: a total of 25 trials were selected (5,142 participants), which showed statistically significant improvements (p &lt;0.05) in 54% (12/22) in the pain levels and 47% (10/21) in functional disability in the Intervention Group. The meta-analyses showed moderate effects on pain intensity and small effects on functional disability. There was a predominance of medium quality studies. Conclusion: the digital care interventions showed a beneficial result in pain intensity and in functional disability, mainly for chronic low back pain. Digital care emerges as promising to support self-management of the spine musculoskeletal conditions. PROSPERO registry number CRD42021282102.