Reviewerly/exHarmony huggingface dataset:
Transformer-Based Neural Machine Translation for Post-OCR Error Correction in Cursive Text

This study investigates transformer-based Neural Machine Translation (NMT) for post-processing of noisy Optical Character Recognition (OCR) output. While recognition engines have matured significantly for most languages, the problem still remains challenging for text in cursive scripts reporting high character but relatively lower word recognition rates. This study targets post-processing of the noisy output of OCR for cursive text (using Urdu as a case study) and leverages a transformer-based NMT framework to correct the OCR errors. More specifically, we feed the noisy text as input and the correct transcription as target to the transformer. The model is trained and evaluated on such pairs of text collected from News tickers in videos and tweets from multiple News channels. A comprehensive experimental study shows significant performance improvement by introducing the proposed post-processing step.
A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet

In this study, we have presented an efficient procedure using two state-of-the-art approaches from the literature of handwritten text recognition as Vertical Attention Network and Word Beam Search. The attention module is responsible for internal line segmentation that consequently processes a page in a line-by-line manner. At the decoding step, we have added a connectionist temporal classification-based word beam search decoder as a post-processing step. In this study, an end-to-end paragraph recognition system is presented with a lexicon decoder as a post-processing step. Our procedure reports state-of-the-art results on standard datasets. The reported character error rate is 3.24% on the IAM dataset with 27.19% improvement, 1.13% on RIMES with 40.83% improvement and 2.43% on the READ-16 dataset with 32.31% improvement from existing literature and the word error rate is 8.29% on IAM dataset with 43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on READ-2016 dataset with 47.27% improvement from the existing results. The character error rate and word error rate reported in this work surpass the results reported in the literature.
Arxiv Tables: Document Understanding Challenge Linking Texts and Tables

We introduce Arxiv Tables, a novel challenge for Document Understanding focused on tables, but in relation to text passages. In order to build the data set, we leverage arXiv, a large open-access archive of scholarly papers. We use both source codes and graphical renderings of papers and combine tables with their references in the main text to create a quasi-Question Answering dataset by masking selected fragments available in the table. What distinguishes the dataset is that (1) the domain is science, (2) the input texts are longer than in typical Document Understanding Question Answering tasks, and (3) both the input and output contain non-standard characters used in scientific notation. For easier comparison for future research using this dataset, strong baselines are also given.
Text Line Detection and Recognition of Greek Polytonic Documents

In this work we highlight the significance of Text Line Detection in documents. By utilizing a well-known Deep Neural Network and by proposing some simple but efficient modifications applied during training of such models, we can achieve very accurate results in different datasets of high diversity. Moreover, such models can be robust even when trained with few data. Our focus is on Greek polytonic documents (typewritten and handwritten) and we provide a new dataset to the public (GTLD-small) for text line detection. We evaluate our method through scenarios applied to the detection and recognition tasks, while demonstrating promising results when compared to popular commercial or open-source systems.
Semantic portals from sociothecnical perspective of the actor-network theory

ABSTRACT Semantic portals, such as socio-technical artifacts, involve different actors that are aligned to offer communication and information search services more quickly and accurately. The objective of this study is to describe the network of actors involved in the construction of semantic portals and their interrelationships. The precepts of the Actor-Network Theory were used as a methodology to design a network of semantic portals from selected studies in the literature. The results indicated that the design of the network of semantic portals points to actions of human actors: portal administrators, portal developers and end users, but also of non-human actors, which are made up mainly of technologies. Each actor has his or her interest contemplated as the other actors fulfill their roles towards a common goal - information of high added value. It can be concluded that the study of the features of the portal actors' network can guide changes in current standards, focusing on the quality of research results and the informational needs of the user community.
Comparing Self-Supervised Pre-Training and Semi-Supervised Training for Speech Recognition in Languages with Weak Language Models

This paper investigates the potential of improving a hybrid automatic speech recognition model trained on 10 hours of transcribed data with 200 hours of untranscribed data in lowresource languages.First, we compare baseline methods of cross-lingual transfer with MFCC features and features extracted with the multilingual self-supervised model XLSR-53.Subsequently, we compare two approaches that can leverage the untranscribed data: semi-supervised training with LF-MMI and continued self-supervised pre-training of XLSR-53.Our results on well-resourced English broadcast data derived from MGB show that both methods achieve 18% and 27% relative improvements compared to the baseline, respectively.On the low-resource South African Soap Opera dataset, the relative improvement with semi-supervised training is only 3% due to the inherently weak language model.However, continued pretraining achieves 8.6% relative improvement because it does not rely on any external information.
Investigating the Perception Production Link through Perceptual Adaptation and Phonetic Convergence

Speech perception and production may be linked.In this pilot study, we aim to investigate the nature of this link by examining how perception and production of VOT adjust in social interaction.In an online experiment with 2x2 conditions participants were asked to categorize nine stimuli on a VOT continuum between /tin/ and /din/ and produce the words din and tin before and after playing a game with a bot.During the game participants were trained on a sound to word correspondence with a bias either towards /din/ or /tin/.In two conditions participants alternated categorizing and producing the stimuli, while in the other two participants only categorized the stimuli.Significant differences in categorization between conditions occurred only during the game.Whereas significant changes in VOT can be observed between pre and post-test for three conditions.This could mean that perception and production do not adjust symmetrically over the course of an interaction.
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Let p≠3 be any prime. The algebraic structures of all λ-constacyclic codes of length 3ps over the finite commutative chain ring R=Fpm+uFpm are provided by classifying all ideals of the local ring R[x]〈x3ps−λ〉. Using these structures, in this paper, the exact values of Hamming distances of all such λ-constacyclic codes of length 3ps are established. As an application, we identify all maximal distance separable λ-constacyclic codes of length 3ps.
An end-to-end method for palm-leaf manuscript segmentation based on U-Net

As a virtual restoration tool, digital imaging is widely used to share the patrimony and preserve the original material of ancient documents. However, the captured raw images usually consist of multiple backgrounds, influencing subsequent image processing, especially the damage investigation process. In this study, an end-to-end method named PLM-SegNet was proposed for the palm-leaf manuscript (PLM) segmentation based on U-Net. Two cameras (Nikon and Sony) were used to capture 83 palm-leaf manuscript images. The images were labeled by the software Labelme and were then cropped into patches with a specific size to train, validate, and test the PLM-SegNet model. The patch was fed into PLM-SegNet, and the foreground distribution map of this patch was obtained. The foreground distribution map of each patch in an image was predicted and stitched together into one global foreground distribution map. With assistance from the distribution map, the PLM can be segmented from the image easily. The results on two independent test sets showed pixel accuracy of 99.73% and 98.36%, intersection over union (IoU) of 99.42% and 98.31%, Recall of 99.68% and 99.95%, and F1-Score of 99.70% and 99.15% could be achieved, respectively. Additionally, damage detection was adopted as a case study to show the significance of PLM-SegNet. Compared with the raw PLM images, the performance of damage detection on segmented PLM images was improved by 15.00% and 19.33% on F1-Score and IoU, respectively. The results show that PLM-SegNet is a precise and automated method for palm-leaf manuscript segmentation when the labeled training data is limited. The source code is available at https://github.com/Ryan21wy/PLM-SegNet.
A class of quadratic forms and the associated few-weight codes

In this paper, we first study the (non)-degeneracy of a class of quadratic forms. Then with a proper defining set determined by the given quadratic form, we construct a class of linear codes, decide their weight enumerators and show that they are two- or three-weight codes by using the Weil sums. Some of the punctured codes of the discussed linear codes are optimal or distance optimal to the Griesmer bound.
Motion and geometry-related information fusion through a framework for object identification from a moving camera in urban driving scenarios

Accurate and robust semantic scene understanding for urban driving is challenging due to the complex object types, object motion, and as well as ego-motion. Typical approaches to this problem use the fusion of multiple sensors such as camera, IMU, LiDAR, and radar to identify the state of the surrounding objects, including distance, direction, position, and velocity. However, such sensor modalities are very much complex and costly. This paper proposes a new framework for object identification (FOI) from a moving camera in a complex driving environment utilizing only camera sensor data. The framework is capable of detecting objects, extracting their behavioral features in terms of motion, position, velocity, and distance. All of this information (referred to as object-wise semantic information) are fused in order to acquire a better understanding of the driving scenario. The work addresses ego-motion compensation and extraction of accurate motion information of moving objects from a moving camera using image registration and optical flow estimation. A moving object detection model is designed within the framework by integrating an encoder–decoder network with a semantic segmentation network. The approach involves two mutual tasks: semantic segmentation of objects into specific classes and binary pixel classification to predict whether the detected object is moving or static based on temporal information. The work also contributes a novel dataset for moving object detection that covers all types of dynamic objects. The evaluation of FOI has been performed on different sequences of KITTI, EU-life long, and the proposed datasets. The experimental results show that the proposed framework provides accurate object-wise semantic information.
Revisiting the Software-Efficient Stream Ciphers RCR-64 and RCR-32

Abstract The synchronous stream ciphers RCR-64 and RCR-32 designed by Sekar, Paul and Preneel are strengthened variants of the ciphers TPy and TPypy (designed by Biham and Seberry), respectively. The RCR ciphers have remained unbroken since they were published in 2007. In this paper, we present arguments that not only support the designers’ security claims but suggest, in general, that the ciphers are secure against several classes of cryptanalytic attacks. We find that the ciphers are best used with 256-bit keys and 384-bit IVs. We also suggest ways to protect software implementations of the RCR ciphers against (cache-)timing and processor flag attacks. Our performance evaluation suggests that the protected implementation of the RCR-64 encrypts long messages at speeds comparable to some of the fastest stream ciphers available today. Consequently, we find that the RCR ciphers may be well suited for PC-based applications in general and streaming audio / video applications in particular. This is the first paper to present a detailed study on the security and performance of the RCR ciphers.
Classes of Hard Formulas for QBF Resolution

To date, we know only a few handcrafted quantified Boolean formulas (QBFs) that are hard for central QBF resolution systems such as Q-Res and QU-Res, and only one specific QBF family to separate Q-Res and QU-Res. Here we provide a general method to construct hard formulas for Q-Res and QU-Res. The construction uses simple propositional formulas (e.g. minimally unsatisfiable formulas) in combination with easy QBF gadgets (Σb2 formulas without constant winning strategies). This leads to a host of new hard formulas, including new classes of hard random QBFs. We further present generic constructions for formulas separating Q-Res and QU-Res, and for separating Q-Res and LD-Q-Res.
A high‐speed and low‐latency hardware implementation of RC4 cryptographic algorithm

Abstract In this letter, a high‐speed and low‐latency Ron Rivest‐4 (RC4) encryption algorithm is designed based on SMIC 14 nm process. Since the key issue of limiting the throughput of RC4 encryption algorithm is the swapping operation of S‐box, this proposed design is capable of achieving four swapping operations of S‐box within a clock cycle by utilizing fully combinational logic designs and advanced computations. All 256 swapping operations of key scheduling algorithm (KSA) can be done in 64 clock cycles. Moreover, the novel design greatly improves the encryption efficiency of RC4 since each clock cycle offers 4 bytes of key stream. The whole area can be further optimized by reusing the existing modules, and a lightweight countermeasure is embedded into the RC4 design to break the correlation between the processed data and power dissipation against differential power analysis (DPA) attacks. Finally, the whole design is assessed by the SMIC 14 nm process design kits (PDK). The corresponding clock frequency, throughput, area, and latency, respectively, are achieved as 1 GHz, 32 Gbps, 51,596 m 2 , and 74 clock cycles.
Stock market development and capital formation in selected African economies

Purpose This study aims to explore the possibility that securities markets in selected African countries of Egypt, Kenya, Nigeria and South Africa play a significant role in capital accumulation using panel data analysis. This is done by exploring the relationship between gross fixed capital formation on the one hand and financial market development indicators on the other hand. Thus, the study aims to examine if stock market size and liquidity are determinants of capital accumulation. Design/methodology/approach The analysis is based on annual times series from 1991 through 2017 spanning four African stock markets. The analysis utilizes the fixed-effect and random-effect econometric models. The Durbin–Wu–Hausman test is used to choose between the two models. Findings The key results indicate that stock market capitalization is a positive determinant of gross fixed capital formation. The market value traded and turnover have no relationship with capital formation. Therefore, the role of stock African stock markets in promoting capital accumulation and, subsequently, industrial growth in Africa is seriously questioned. Originality/value Only a handful of studies have examined the role of the African securities market in promoting capital accumulation. This study is unique in which it focuses on the leading stock markets in the four corners of Africa. The markets are from Egypt in the north, South Africa from the south, Nigeria from the west and Kenya from the east. These four markets account for a significant segment of all African markets.
Contamination, Precision and Accuracy of pXRF Geochemistry: Case Study-Polymetals Resources Ltd., Mansala Gold Project, Siguiri Basin, Northeast Guinea

Quality Assurance and Quality Control (QA/QC) is a critical component of all pXRF geochemistry processes.A properly constructed pXRF QA/QC programme identifies possible instrumental errors and provides a means of securing fit for purpose data from the pXRF programme.pXRF QA/QC programmes involve daily contamination, precision, and accuracy checks to ensure the generation of fit for purpose data.In the exploration field or mine-site, pXRF is capable of producing extremely valuable data that is fit for purpose if calibrated properly.However, it should not be used as a replacement for acquiring data from an accredited laboratory using established analytical techniques that produce high quality data.Contamination is the checking of the cleanliness of the analyser window or the presence of dust in the measuring environment.At Polymetals, using Olympus Vanta C-Series pXRF analyser with silver anode, contamination is assessed by measuring an instrumental blank (SiO 2 ), to identify any foreign matter on the analyser window.Assuming that the window film is new, and the fused silica disc is dust free, only Si should be detected.If any other significant element is detected, the film is replaced, and the test is re-run.Accuracy is a measure of how close the measured value is to the true value and is assessed by measuring the abundance of selected elements contained within a Certified Reference Material (CRM) or the NIST check standard sample supplied with the pXRF analyser.Elements of interest must report within ±20% of the standard value.Precision is a measure of how close repeat measurements are to one another and is assessed by taking multiple readings on a particular sample to determine the stability of the analyser.The Relative Standard Deviation (RSD) of the replicate measurements is then calculated.The RSD values should be less than 20% for
Application research of RNN in text intelligent prediction scenario

Since deep learning has been continuously developing, research on language models utilizing deep learning has increased, and certain results have been obtained. There are many research results, but RNN has unique advantages regarding the construction of language models. In addition, due to its weight-sharing cyclic network form, the language model can have the information memory function described above, which facilitates the prediction of content in the future. Text generation is one of the most common applications in this area. However, RNN may have some limitations, including its inability to remember the above for a prolonged period of time, making the language model formed by RNN relatively weak in terms of intelligence and ability to connect the languages under study. Therefore, this paper proposes to build a language prediction model based on the RNN network model LSTM. This will allow the language model to capture the previous characteristics better, resulting in improved intelligent text prediction.
Handwritten digit recognition based on improved convolution neural network

An improved convolution neural network model is proposed, which has higher recognition rate for handwritten digits. Based on the AlexNet network model, the algorithm improves the feature extraction ability of the model by introducing residual module to modify the third and fourth volume layers in the model. The batch normalization (BN) method is used to prevent over-fitting after each convolution. In order to reduce the amount of computation, a full connection layer is reduced. The algorithm has a good effect on handwritten digit recognition by training and testing on MNIST dataset. Compared with AlexNet network model, the improved model has higher detection accuracy.
An image denoising method based on improved wavelet threshold and threshold function is presented

In order to remove noise effectively, a new wavelet threshold denoising algorithm is constructed on the basis of traditional wavelet theory. First of all, select appropriate wavelet basis function and and decomposition level, and then appropriate threshold function and threshold value are selected for threshold processing. At last, the denoised image is derived by reassembling both the low-frequency and high-frequency components. The adjustment parameter is introduced into the threshold function to make it flexible in denoising. Through Matlab simulation, the evaluation index results of several denoising algorithms are compared. The experimental results show that the subjective visual effect and objective evaluation index of this algorithm is obviously better than other algorithms, and it has a certain level of practicality.
Adapting multi-task perception in autonomous driving to bird’s-eye-view representation via multi-level guidance

Perception models using multi-camera images show promise in autonomous driving. This paper proposes a multi-task network leveraging these images and bird's eye view (BEV) perspective for efficient perception. While Transformers in BEV tasks help focus attention, they often require high computational cost. To address this, we introduce multi-level guidance in the Encoder and Decoder, using richer semantic information to initialize the Transformer for faster convergence and improved performance.We guide the initialization of 3D BEV features using 2D image features in the Encoder, and apply perspective supervision in the Decoder for optimized detection and segmentation. The detection head output guides the segmentation head to focus on regions of interest (ROI). Our method, validated on the nuScenes dataset, yields excellent results (54.8% NDS, 45.2% mAP, 49.7% mIoU). Code release is forthcoming.
A hybrid approach for developer recommendation based on social network

Bug fixing requires collaboration among developers, however, most of current studies recommend developers based on the textual content and similarity of bug reports, ignoring the implicit social relationships generated by developers when they collaborate on fixing tasks. This paper proposes a hybrid approach for bug developer recommendation to address this problem. The method constructs a developer social network, extracts developer social relationships implicitly modeled in bug report history records and comment, and uses Graph Convolutional Neural Network (GCN) to learn the closeness between developers. In addition, Convolutional Neural Networks (CNN) are used to learn the bug report text content, suitable developers are recommended by fusing the collaborative relationships between developers and the textual content of bug reports. According to experimental findings, the method's accuracy is significantly better than that of other methods.