Reviewerly/exHarmony huggingface dataset:
Knowledge-Based Scene Text Recognition for Industrial Applications

Scene text recognition (STR) methods combined with semantic information have made great progress to recognize texts in natural scenes, most of which are daily words. However, research on mining semantic information in industrial texts attracts less attention. Since industrial texts follow a different semantic pattern defined by industry standards, it challenges many existing methods to conduct accurate semantic reasoning. In this paper, we abstract the industry standards into two aspects of prior knowledge: the grouping property and the prior lexicon. Correspondingly, a knowledge-based language model is proposed with several group-wise correlation modules and a lexicon-based reasoning module to learn semantic rules from both data and the prior knowledge. Besides, we transfer the prior knowledge into data by generating synthetic pure text datasets according to the industry standards’ rules, which introduces more prior knowledge to the language model. Furthermore, a novel STR framework is presented by combining the knowledge-based language model and an attention-based vision model. For evaluation, two industrial text datasets called CIN and SB are collected from real-world industrial field surveillance. Experiments indicate that our method’s word-level accuracy outperforms state-of-the-art methods with 15% and 10.61% on CIN and SB datasets respectively.
A Flexible and Hybrid Feature Mapping Technique Based on multivariate Beta Distribution Kernel Applied to Medical Applications

In this work, we propose two novel feature mapping techniques based on multivariate Beta distribution and its mixture models. Support vector machine (SVM) is one of the most famous and powerful discriminative classifiers which has been applied in various domains. We improved its discrimination power by considering the nature of data and integrating discriminative approach with generative method. Such a novel hybrid method could improve the accuracy of the model compared to SVM with traditional kernels. To evaluate our model performance, we applied it to medical applications including Barrett’s oesophagus detection and colonoscopy image analysis. The outputs indicate that our proposed model could be considered as a promising alternative.
Scaling Smart Contracts via Layer-2 Technologies: Some Empirical Evidence

Blockchain-based smart contracts can potentially replace traditional contracts through decentralized enforcement and reduced costs. However, scalability is a key bottleneck hindering their broader application and adoption, often leading to concentrated or exclusive networks, contrary to the original promise of the technology. Consequently, firms actively explore "layer-2'' technologies for scaling, including moving information aggregation, which produces data feeds for smart contracts, from on the blockchain to off-chain peer-to-peer networks. We use a natural experiment to study the effect of such a scaling solution. We find that this scaling solution reduces operating costs by 76% without reducing data accuracy. Importantly, the scaling solution led to decentralization with lower market concentration and greater network participation, which in turn improves data accuracy and security. These findings constitute informative initial evidence of how blockchain and smart contracting technologies evolve to achieve accurate, decentralized, and scalable trust and service provision.
Are You Ready for the Latest Data Integrity Guidance? Part 2: Computerized Systems, Outsourcing, Quality, and Regulatory Oversight

The new PIC/S data guidance document PI-041 was published in July 2021. This column is the second part of a review looking at the expectations for computerized systems, outsourcing, regulatory actions, and remediation of data integrity failures. Spoiler alert: You won’t like this column if you use hybrid systems.
Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network

Summary There are two main difficulties for identification of dual‐rate sampled nonlinear systems; one is the unknown nonlinear properties and the other is the dual‐rate sampled data. Considering the above two points, this paper proposes a cycle reservoir with regular jumps (CRJ) network based recursive identification algorithm. Unlike a traditional method, the proposed one does not require a priori knowledge of the nonlinearity and can handle dual‐rate data; therefore, its applicability can be guaranteed. Firstly, the CRJ network is used to describe the nonlinear characteristics of the target systems. However, the update of the CRJ network requires the missing outputs, thus the auxiliary model identification theory is adopted and the missing outputs are replaced by the estimated outputs of the network. In this process, the output weights of the CRJ network are updated at a slow rate, while the estimates of the missing outputs are updated quickly, resulting in an interactive estimation computation process. Then, to improve the identification accuracy, the hyper‐parameters of the CRJ network are optimized by means of the particle swarm optimization algorithm. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.
E2AlertNet: An explainable, efficient, and lightweight model for emergency alert from aerial imagery

Deep learning-based algorithms have shown significant state-of-the-art accuracy in aerial image classification. Besides, the nature of these algorithms is a black box, which puts the question of why a particular output is produced. Therefore explainability is one kind of solution to improve the transparency of DNN network's decision. In this paper, we have designed a lightweight and explainable convolutional neural network (CNN) architecture for emergency monitoring from aerial imagery. We interpret the outcomes of the proposed architecture with a newly designed explainable algorithm which is the improved version of the model-agnostic methods such as Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). We have used a dedicated dataset named as Aerial Image Database for Emergency Response (AIDER) for the experiments and explained the decisions of the proposed CNN classifier to ensure reliability. The proposed classifier achieves 96% accuracy with minimal memory requirements on a benchmark set with known ground truth and explains their outcomes with the newly proposed explainable algorithm.
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies addressing an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It performs iterations where the (unlabeled) real-world training images are labeled by intermediate deep models trained with both the (labeled) synthetic images and the real-world ones labeled in previous iterations. More specifically, a self-training stage provides two domain-adapted models and a model collaboration loop allows the mutual improvement of these two models. The final semantic segmentation labels (pseudo-labels) for the real-world images are provided by these two models. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for onboard semantic segmentation. Our procedure shows improvements ranging from approximately 13 to 31 mIoU points over baselines.
-ADIC QUOTIENT SETS: LINEAR RECURRENCE SEQUENCES

Abstract Let $(x_n)_{n\geq 0}$ be a linear recurrence of order $k\geq 2$ satisfying $x_n=a_1x_{n-1}+a_2x_{n-2}+\cdots +a_kx_{n-k}$ for all integers $n\geq k$ , where $a_1,\ldots ,a_k,x_0,\ldots , x_{k-1}\in \mathbb {Z},$ with $a_k\neq 0$ . Sanna [‘The quotient set of k -generalised Fibonacci numbers is dense in $\mathbb {Q}_p$ ’, Bull. Aust. Math. Soc. 96 (1) (2017), 24–29] posed the question of classifying primes p for which the quotient set of $(x_n)_{n\geq 0}$ is dense in $\mathbb {Q}_p$ . We find a sufficient condition for denseness of the quotient set of the k th-order linear recurrence $(x_n)_{n\geq 0}$ satisfying $ x_{n}=a_1x_{n-1}+a_2x_{n-2}+\cdots +a_kx_{n-k}$ for all integers $n\geq k$ with initial values $x_0=\cdots =x_{k-2}=0,x_{k-1}=1$ , where $a_1,\ldots ,a_k\in \mathbb {Z}$ and $a_k=1$ . We show that, given a prime p , there are infinitely many recurrence sequences of order $k\geq 2$ whose quotient sets are not dense in $\mathbb {Q}_p$ . We also study the quotient sets of linear recurrence sequences with coefficients in certain arithmetic and geometric progressions.
MDNN: Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

Prediction of students' engagement in a Collaborative Learning setting is essential to improve the quality of learning.Collaborative learning is a strategy of learning through groups or teams.When cooperative learning behavior occurs, each student in the group should participate in teaching activities.Researchers showed that students who are actively involved in a class gain more.Gaze behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning environments.Previous studies require the wearing of sensor devices or eye tracker devices, which have cost barriers and technical interference for daily teaching practice.In this paper, student engagement is automatically analyzed based on computer vision.We tackle the problem of engagement in collaborative learning using a multi-modal deep neural network (MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning environments.Our multi-modal solution was evaluated in a real collaborative environment.The results show that the model can accurately predict students' performance in the collaborative learning environment.
Topic Controlled Steganography via Graph-to-Text Generation

Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement in concealment.In this paper, we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs, and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods, we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.
A self‐distillation object segmentation method via frequency domain knowledge augmentation

Abstract Most self‐distillation methods need complex auxiliary teacher structures and require lots of training samples in object segmentation task. To solve this challenging, a self‐distillation object segmentation method via frequency domain knowledge augmentation is proposed. Firstly, an object segmentation network which efficiently integrates multi‐level features is constructed. Secondly, a pixel‐wise virtual teacher generation model is proposed to drive the transferring of pixel‐wise knowledge to the object segmentation network through self‐distillation learning, so as to improve its generalisation ability. Finally, a frequency domain knowledge adaptive generation method is proposed to augment data, which utilise differentiable quantisation operator to adjust the learnable pixel‐wise quantisation table dynamically. What's more, we reveal convolutional neural network is more inclined to learn low‐frequency information during the train. Experiments on five object segmentation datasets show that the proposed method can enhance the performance of the object segmentation network effectively. The boosting performance of our method is better than recent self‐distillation methods, and the average F β and mIoU are increased by about 1.5% and 3.6% compared with typical feature refinement self‐distillation method.
Practical and Dynamic Attribute-Based Keyword Search Supporting Numeric Comparisons Over Encrypted Cloud Data

The attribute-based keyword search (ABKS), which simultaneously achieves searching and fine-grained access control over encrypted data, is frequently applied in cloud computing environments characterized by data storage and sharing. Recently, inspired by attribute-based encryption (ABE) and searchable encryption (SE) primitives, several ABKS schemes have been presented. However, almost all existing ABKS schemes actually only provide an attribute-based keyword equality match function and do not have a structural index to support practical search efficiency and dynamic data updates in real-world applications. To the best of our knowledge, this study is the first to realize an attribute-based keyword search construction supporting numerical comparison expressions with the practical search efficient and dynamic data update capacity (ABKS-NICEST), based on our proposed attribute-based keyword secure search scheme supporting numerical comparison expressions (ABKS-NICE) and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exclusive OR-chain</i> -based inverted index structure. To the best of our knowledge, ABKS-NICEST is the first attributed-based keyword search scheme with practical search efficiency and dynamic data update capacity. In addition, numerical values are an important and common attribute, so providing comparison expressions among numerical values can greatly enhance the expressivity of access policy. Therefore, we use the prefix membership verification technique to design a method to support any numeric comparison expression in a flexible and uniform manner. Through theoretical and experimental evaluations, we determine that ABKS-NICEST is the most efficient ABKS scheme.
Counting inversion sequences by parity successions and runs

Let In denote the set of inversion sequences of length n. In this paper, we enumerate members of In represented using positive integers according to parameters which track the number of runs and successions of letters of a given parity. To this end, we consider a four-parameter joint distribution on In and derive several general results for this distribution, providing further treatment of some particular cases. In order to prove our results, we make use of a non-standard generating function which allows for the system of recurrences satisfied by various refinements of the aforementioned joint distribution to be translated into a system of linear differential equations that can be solved explicitly. Finally, the more general problem of enumerating the members of In according to adjacencies involving two elements that are equivalent modulo m is considered.
Optimized fuzzy clustering‐based k‐nearest neighbors imputation for mixed missing data in software development effort estimation

Abstract Context Software development effort estimation (SDEE) is one of the most challenging aspects in project management. The presence of missing data (MD) in software attributes makes SDEE even more complex. K‐nearest neighbors imputation (KNNI) has been widely used in SDEE to deal with the MD issue. However, KNNI, in its classical process, has low tolerance to imprecision and uncertainty especially when dealing with categorical features. When dealing with categorical attributes, KNNI uses a classical approach, employing mainly numbers or classical intervals to represent software attributes and similarity measures originally designed for numerical attributes. Objectives This paper evaluates the use of an optimized fuzzy clustering‐based KNNI (FC‐KNNI) and compares it with classical KNN when dealing with mixed data in the context of SDEE. Methods We investigate the effect of two imputation techniques (FC‐KNNI and KNNI) on five SDEE techniques: case‐based reasoning, fuzzy case‐based reasoning, support vector regression, multilayer perceptron, and reduced‐error pruning tree. The evaluation is carried out using six publicly available datasets for SDEE using two performance measures, standardized accuracy (SA), and Pred (0.25). The Wilcoxon statistical test is also performed to assess the significance of results. Results The results are promising in the sense that using an imputation technique designed for mixed data is better than reusing methods originally designed for numerical data. We found that FC‐KNNI significantly outperforms KNNI regardless of the SDEE technique and dataset used. Another important finding is that F‐CBR improved the analogy process compared to CBR. Conclusion The introduction of fuzzy sets and fuzzy clustering in the analogy process improves its performances in terms of SA and Pred (0.25).
Hyperspectral image restoration via hybrid smoothness regularized auto-weighted low-rank tensor ring factorization

Restoration of hyperspectral images (HSI) is a crucial step in many potential applications as a preprocessing step. Recently, low-rank tensor ring factorization was applied for HSI reconstruction, which has high-order tensors’ powerful and generalized representation ability. Although low-rank TR-based approaches with nuclear norm regularization achieved successful results for restoring hyperspectral images, there is still room for improved tensor low-rank approximation. In this article, we propose a novel Auto-weighted low-rank Tensor Ring Factorization with Hybrid Smoothness regularization (ATRFHS) for mixed noise removal in HSI. Nonlocal Cuboid Tensorization (NCT) is leveraged to transform HSI data into high-order tensors. TR factorization using latent factors rank minimization removes the mixed noise in HSI data. To highlight nuclear norms of factor tensors differently effective, an auto-weighted strategy is employed to reduce the more prominent factors while shrinking the smaller ones. A hybrid regularization combining total variation (TV) and phase congruency (PC) is incorporated into a low-rank tensor ring factorization model for the HSI noise removal problem. This efficient combination yields sharper edge preservation and resolves this weakness of existing pure TV regularization. Moreover, we develop an efficient algorithm for solving the resulting optimization problem using the framework of alternating minimization. Extensive experimental results demonstrate that our proposed method can significantly outperform existing approaches for mixed noise removal in HSI. The proposed algorithm is validated on synthetic and natural HSI data.
An Analysis of Implicature in SMP Negeri 4 Soromandi

The purpose of this study is to determine the types of implicatures and the function of implicatures used by teachers when interacting with students in the classroom during the learning process. The reason for this research is because it wants to provide sources of information or references regarding the types of implicatures and the functions of implicatures that are expected to be useful for the institution where the researcher conducts the study, for teachers, for other students, other researchers or readers. This study used descriptive qualitative method. There are two videos that are used as a source of research data. The videos show the activities of the learning process in the classroom and the interactions between teachers and students. Collecting research data, the researcher transcribes the videos accurately in written form, and then classifies the transcription data based on the types of implicatures and implicature functions so that the data can be easily identified and analyzed. After all the data has been collected, the researcher analyzes the data and draws conclusions. The results of this study indicate that: (1) There are 13 types of implicatures and there are 96 implicature functions used by teachers in the videos used as research data. Implicature types are divided into 2 conventional types and 11 unconventional types. Meanwhile, the implicature functions are divided into 36 assertive functions, 29 directive functions, 28 expressive functions, and 3 commissive functions.
SOCIOGRAM OF INTERPERSONAL RELATIONS IN A GROUP OF COLLEGE STUDENTS

The aim of the study is to study interpersonal relationships in a group of college students and build a sociogram. The study was conducted in a group of college students consisting of 29 people, including 15 girls and 14 boys aged 17-18 years. In order to clearly see the picture of the degree of cohesion of the group, a study was conducted in the form of group testing, for this a sociometric technique developed by J. Moreno was chosen. This technique involves the diagnosis of emotional ties, likes and dislikes within the group, the identification of a leader, the detection of intra-group cohesive formations. Based on the sociogram presented in the article, we see that there are no isolated guys in the group, which is an indicator of a good and full-fledged group. Forecasting the general prospects for the development of the educational team is actively moving in a positive direction, since none of the members of the group regrets that they entered this particular specialty, the forecast can be as follows: after training, at least 70% of students will graduate, they will already be mature personalities, find a job in their area of specialization.
YOLO v5 and Faster R-CNN Performance Evaluation of Solid Waste in Object Detection Application

Object detection is a booming technology that is on par with computer vision and image processing in which an object of a specific type is detected in an image or video. Object detection consists of several approaches like Retina-Net, Single Shot MultiBox Detector (SSD), and Faster R-CNN. These approaches are used in object detection with limited data, but these approaches either run in two algorithms or has high execution time; to overcome these limitations, the authors have used the latest version of Yolo with the custom dataset of solid waste. In this algorithm, an image in the solid waste dataset, which was annotated, labelled, pre-processed, and segmented and a build version is created with the yolo model; this version can either be used directly in the code for online execution or downloaded in the local system for offline execution.