Reviewerly/exHarmony huggingface dataset:
Language relatedness evaluation for multilingual neural machine translation

Multilingual neural machine translation (MNMT) has attracted more and more attention in recent days because it can use a single neural machine translation (NMT) model to translate between multiple languages. As several languages are involved in MNMT, recent studies have shown that using part of these languages rather than all of them to train the model leads to comparable results. However, previous work on this topic mainly focuses on language clustering and features defined by linguists. The semantic relationship and language distance are not fully considered. How to select the most related language pairs to current low-resource pair to optimize the performance of MNMT is still an open question. In this paper, we propose to take language relatedness computation as a ranking problem, where features such as language distance, linguistic typological information and semantic relatedness features are incorporated into a random decision forest to improve the language relatedness evaluation (LRE) for MNMT. Since the model only focuses on monolingual LRE in general cross-lingual natural language processing tasks, we also propose two features related to machine translation (data size and bilingual relatedness) to predict the final language pairs. Experimental results on IWSLT and WMT datasets show that our proposed LRE method can achieve significant improvements compared with other models. We also conducted several groups of experiments on IWSLT and WMT datasets to further evaluate the effectiveness of the proposed method on MNMT. The results show that the MNMT model trained on language pairs predicted by the LRE method outperforms other language selection methods.
Economic feasibility of Gabal Um Takha leucogranitic intrusion, South Sinai, Egypt: Integrated remote sensing, geochemical, aeromagnetic, and geotechnical approach

Variable granitic rocks are widely used as decorative stones. The present work conducted accurate mapping and the utilization of a large leucogranitic intrusion (Um Takha area) as a decorative stones through physico-mechanical tests, mineralogical and geochemical compositions. These leucogranites are medium-grained, classified as syenogranites, peraluminous, calc-alkaline, and highly evolved granites developed during the post-collisional setting. This study utilized various remote sensing techniques to identify the Um Takha leucogranitic using ASTER and Landsat-9 data. A novel FCC band ratio of the ASTER image (RGB: b1+b3/b2, b2/b1, b7+b9/b8) was created, along with the Landsat-9 FCC band ratio of (4/7, 3/4, and 2/1) in the RGB, to effectively map the distribution of in the study area. These results, combined with field observation, led to the creation of a new modified geological map of the Um Takha area. The analysis of magnetic data reveals that the depth of magnetic sources under the study area ranges between 170 m and 2215 m, as calculated by source parameter image and Euler deconvolution techniques. The application of horizontal gradient, tilt derivatives and improved logistic techniques reveals that the major structural trend affecting the study area is in the N–S, NNE-SSW and NNW-SSE direction. The physico-mechanical properties of leucogranitic rocks (M13) fulfilled the requirements of dimension stone in terms of bulk density, compressive strength, and to some extent water absorption. Therefore, they could be acceptable for interior use and the light-duty purposes of exterior use, such as facades and cladding. This suggest that a large resources of leucogranitic can be used as a decorative stones.
A quantum-enhanced solution method for multi classification problems

With the increasing data size of multi classification problems, the running efficiency of classical algorithms is seriously affected. In the paper, in order to improve the implementation efficiency of the algorithm, we propose a quantum-enhanced solution method for multi classification problems. The method mainly introduces the quantum-enhanced technology into the classical algorithm. Aiming at the two steps of solving Euclid distance and kernel function in the classical algorithm, the paper relates the classical inner product principle with the amplitude evolution of quantum states. On the basis of quantizing the sample data, a general quantum circuit that can calculate the inner product is designed and constructed. The circuit can make full use of the advantages of quantum parallel computing to achieve exponential acceleration of computing efficiency. Aiming at solving linear equations in the classical algorithm, a quantum circuit based on the quantum singular value estimation is designed and constructed. The circuit makes use of the acceleration advantage of quantum computing in matrix computing to achieve polynomial acceleration of matrix computing. The experimental results show that the method can not only find the optimal solution for multi classification problems, but also greatly improve the operation efficiency of the algorithm. Compared with the classical methods, the method has at least polynomial improvement in time complexity and spatial complexity.
A visible-infrared clothes-changing dataset for person re-identification in natural scene

Person re-identification (Re-ID) has been widely used in intelligent surveillance systems, aiming at retrieving specific pedestrian images across different cameras. Although existing person Re-ID methods have achieved inspiring success, there are still limitations in practical monitoring system applications. To narrow the gap with the practical application, we introduce the cross-modality person Re-ID problem in the clothes-changing scene. Meanwhile, we construct the first Visible-Infrared Clothes-Changing (NEU-VICC) dataset, which contained 16632 RGB images and 8374 infrared images of 107 pedestrians. The critical challenge of the cross-modality person Re-ID problem in the clothes-changing scene lies in the vast modality discrepancy and the intra-class discrepancy caused by changing clothes. So, we propose a novel Semantic-Constraint Clothes-Changing Augmentation Network (SC3ANet) based on current cross-modality person Re-ID methods to solve this problem. Specifically, we design a semantic-constraint clothes-changing module that guides the model to learn clothes-irrelevant features by randomly changing pedestrians' clothes. In addition, we devise a dual-granularity constraint loss module to mitigate inter-modality and intra-class differences. Experiments on our NEU-VICC dataset show that the SC3ANet achieves the best results. The dataset and code are available at: https://github.com/VDT-2048/NEU-VICC.
Fully Pipelined FPGA Acceleration of Binary Convolutional Neural Networks with Neural Architecture Search

In this paper, we present a fully pipelined and semi-parallel channel convolutional neural network hardware accelerator structure. This structure can trade off the compute time and the hardware utilization, allowing the accelerator to be layer pipelined without the need for fully parallelizing the input and output channels. A parallel strategy is applied to reduce the time gap in transferring the output results between different layers. The parallelism can be decided based on the hardware resources on the target FPGA. We use this structure to implement a binary ResNet18 based on the neural architecture search strategy, which can increase the accuracy of manually designed binary convolutional neural networks. Our optimized binary ResNet18 can achieve a Top-1 accuracy of 60.5% on the ImageNet dataset. We deploy this ResNet18 hardware implementation on an Alphadata 9H7 FPGA, connected with an OpenCAPI interface, to demonstrate the hardware capabilities. Depending on the amount of parallelism used, the latency can range from 1.12 to 6.33[Formula: see text]ms, with a corresponding throughput of 4.56 to 0.71 TOPS for different hardware utilization, with a 200[Formula: see text]MHz clock frequency. Our best latency is [Formula: see text] lower and our best throughput is [Formula: see text] higher compared to the best previous works. The code for our implementation is open-source and publicly available on GitHub at https://github.com/MFJI/NASBRESNET .
Global key knowledge distillation framework

The performance of deep neural networks is typically associated with their depth and width. As the performance of a model improves, it demands greater computational resources and memory. However, self-distillation offers a pathway to augment this performance by internally transferring knowledge. Traditional self-distillation methods lack guidance from teacher networks, thus lacking the ability to obtain correct and key information. In this paper, we propose a new self-distillation framework—Global key knowledge distillation framework (GK-KD). We implement error correction mechanisms to network logit outputs and assess the importance of feature maps through rank to obtain correct and important knowledge. Experiments demonstrate the effectiveness of this framework: on the CIFAR100 dataset, ResNet18 achieved a 4.56% accuracy improvement over the baseline, ResNet152 also achieved a 5.26% accuracy improvement, and for the VGG series, the average accuracy increased by 5.82%, outperforming previous research methods without adding additional computational costs. Further experiments show that this framework can effectively enhance the robustness and calibration of the network.
Back to grammar: Using grammatical error correction to automatically assess L2 speaking proficiency

In an interconnected world where English has become the lingua franca of culture, entertainment, business, and academia, the growing demand for learning English as a second language (L2) has led to an increasing interest in automatic approaches for assessing spoken language proficiency. In this regard, mastering grammar is one of the key elements of L2 proficiency. In this paper, we illustrate an approach to L2 proficiency assessment and feedback based on grammatical features using only publicly available data for training and a small proprietary dataset for testing. Specifically, we implement it in a cascaded fashion, starting from learners’ utterances, investigating disfluency detection, exploring spoken grammatical error correction (GEC), and finally using grammatical features extracted with the spoken GEC module for proficiency assessment. We compare this grading system to a BERT-based grader and find that the two systems have similar performances when using manual transcriptions, but their combinations bring significant improvements to the assessment performance and enhance validity and explainability. Instead, when using automatic transcriptions, the GEC-based grader obtains better results than the BERT-based grader. The results obtained are discussed and evaluated with appropriate metrics across the proposed pipeline.
Aggregating knowledge and collaborative information for sequential recommendation

Sequential recommendation aims to predict users’ future activities based on their historical interaction sequences. Various neural network architectures, such as Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and self-attention mechanisms, have been employed in the tasks, exploring multiple aspects of user preferences, including general interests, short-term interests, long-term interests, and item co-occurrence patterns. Despite achieving good performance, there are still limitations in capturing complex user preferences. Specifically, the current structures of RNN, GNN, etc., only capture item-level transition relations while neglecting attribute-level transition relations. Additionally, the explicit item relations are studied using item co-occurrence modules, but they cannot capture the implicit item-item relations. To address these issues, we propose a knowledge-augmented Gated Recurrent Unit (GRU) to improve the short-term user interest module and adopt a collaborative item aggregation method to enhance the item co-occurrence module. Additionally, our long-term interest module utilizes a bitwise gating mechanism to select historical item features significant to users’ current preferences. We extensively evaluate our model on three real-world datasets alongside competitive methods, demonstrating its effectiveness in top K sequential recommendation.
Multi-aspect multilingual and cross-lingual parliamentary speech analysis

Parliamentary and legislative debate transcripts provide an informative insight into elected politicians’ opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While exiting research studied individual parliaments, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020. We analyze emotions and sentiment in the transcripts from the ParlaMint dataset collection, and assess if the age, gender, and political orientation of speakers can be detected from their speeches. The results show some commonalities and many surprising differences among the analyzed countries.
MeFiNet: Modeling multi-semantic convolution-based feature interactions for CTR prediction

Extracting more information from feature interactions is essential to improve click-through rate (CTR) prediction accuracy. Although deep learning technology can help capture high-order feature interactions, the combination of features lacks interpretability. In this paper, we propose a multi-semantic feature interaction learning network (MeFiNet), which utilizes convolution operations to map feature interactions to multi-semantic spaces to improve their expressive ability and uses an improved Squeeze &amp; Excitation method based on SENet to learn the importance of these interactions in different semantic spaces. The Squeeze operation helps to obtain the global importance distribution of semantic spaces, and the Excitation operation helps to dynamically re-assign the weights of semantic features so that both semantic diversity and feature diversity are considered in the model. The generated multi-semantic feature interactions are concatenated with the original feature embeddings and input into a deep learning network. Experiments on three public datasets demonstrate the effectiveness of the proposed model. Compared with state-of-the-art methods, the model achieves excellent performance (+0.18% in AUC and -0.34% in LogLoss VS DeepFM; +0.19% in AUC and -0.33% in LogLoss VS FiBiNet).
Dynamic optimization based on quantum computation-A comprehensive review

Solving dynamic optimization problems (DOPs) induced by time-varying optimization objective functions and constraints is challenging. Quantum computation has received considerable attention to accelerate the solution of such optimization problems in recent years, and quantum optimization algorithms have been developed. Yet, there are no comprehensive review papers on quantum optimization algorithms for DOPs. This paper presents a critical review of dynamic optimization based on quantum computing. A brief overview of dynamic optimization problems is first given. The existing quantum optimization algorithms are introduced in detail, including QPSO, QEA, QAA, QGA, QNN, etc. The principles of different quantum algorithms and their improved forms are presented and discussed. The optimization results obtained by different quantum optimization algorithms prove the superiority of quantum algorithms over traditional optimization algorithms for DOPs. In the near future, quantum computing applied to traditional intelligent algorithms can still be the platform for innovating new algorithms. The dynamic optimization based on quantum learning algorithms needs further research. The ultimate combination of quantum algorithms and quantum computers is still lacking. Developments in this area may show powerful capabilities to solve dynamic optimization problems.
HSB based reversible data hiding using sorting and pairwise expansion

Reversible data hiding (RDH) continues to advance in its applicability across diverse fields, including medical imaging, military applications, and cloud computing. In recent times, there has been a proliferation of RDH methods that focus on embedding in the higher significant bit (HSB) plane, as opposed to the least significant bit (LSB), in order to leverage the greater correlation, thereby achieving a larger embedding capacity (EC). This paper introduces a novel and efficient RDH approach that utilizes sorting and pairwise expansion. The proposed method initiates by partitioning the original image into HSB and LSB planes and then converting the HSB plane into a chessboard pattern. Subsequently, pixels are organized in ascending order based on their predicted values, forming groups of 1 × 3 pixels. The embedding process occurs in two passes: the first employs a difference expansion-based RDH, while the second pass employs pairwise expansion to minimize distortion. This method accomplishes an expanded EC while preserving the quality of the resulting image. Experimental results also demonstrate that the proposed approach maintains a significant lead of approximately 0.5 dB over the best-performing existing methods, underscoring its superior embedding efficiency.
Investigating FAIR data principles compliance in horizon 2020 funded Agri-food and rural development multi-actor projects

The agri-food and rural development sector in Europe is undergoing digitalization, and therefore becoming increasingly data-dependent. However, limited experience in implementing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles has restricted knowledge sharing and reusability. This study aims to investigate the existing FAIR Data Management practices in the agri-food and rural development sector by assessing the FAIRness of project outputs from recent H2020-funded Multi-Actor Projects (MAPs). We conducted a FAIRness assessment of project outputs using both semi-automatic and manual framework, and we also carried out a comprehensive review of the data sharing practices among selected MAPs. Of the investigated MAPs, <10% have achieved FAIR compliance and applied FAIR data management practices. The measured FAIRness of project products, including journal articles, conference papers, and books, is higher than that of other product types such as videos, audios, and presentations. The study highlights the critical need for standardizing the adoption of FAIR data principles and data management practices across both the agri-food and rural development sector but also across bureaucratic data management throughout Europe more widely. Such a change would facilitate broader utilization and re-use of MAPs outputs and results, enhancing decision-making and agri-food and rural development practices.
GST-based polarization approach to picking P-and S-waves arrival time for high-speed rail early warning system

The first arrival time of seismic P- and S- waves affects the determination of the beginning of an earthquake by the Earthquake Early Warning System for High-speed Rail (HSREEWs). Accordingly, we propose an approach to picking P- and S-waves arrival time for HSREEWs. This approach can treat the continuous seismic signals in real-time without prior knowledge of whether an earthquake occurs. Firstly, we replace the fixed time window of the S transform (ST) with the dynamic window of the generalized S transform (GST) to make the necessary time of signal processing more flexible. Meanwhile, the GST extends the sequential signal to the time-frequency domain (TF), and combines the TF polarization to construct four three-component polarization filters. Accordingly, the P-wave and S-wave can be separated from the seismic waveform by those filters, which effectively avoids the phenomenon that the S-wave phase is difficult to determine because of the interference of S-wave overlapping in the coda region. Secondly, based on the instantaneous frequency of seismic signal, we construct a new STA/LTA (GSPA-STA/LTA) with the characteristic function (CF) of constrained TF spectrum to pick the first arrivals of the P- and S-waves, and provide the corresponding automatic picking algorithm. Finally, we analyze 504 seismic events larger than Ms40 near the Japanese Tohoku Shinkansen to comprehensively verify the effectiveness of our approach. Since the P-wave and S-wave are first separated, the subsequent process just has to identify the first arrival times of their respective components. The approach has a good practical value in maintaining the safe operation of high-speed rail under earthquake, which meets the industry demands for speed, scale and accuracy in HSREEWs.
Secure Summation: Capacity Region, Groupwise Key, and Feasibility

The secure summation problem is considered, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> users, each holds an input, wish to compute the sum of their inputs at a server securely, i.e., without revealing any information beyond the sum even if the server may collude with any set of up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> users. First, we prove a folklore result for secure summation - to compute 1 bit of the sum securely, each user needs to send at least 1 bit to the server, each user needs to hold a key of at least 1 bit, and all users need to hold collectively some key variables of at least <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> − 1 bits. Next, we allow any arbitrary group of users to share an independent key and any arbitrary group of users to collude with the server. For such a general groupwise key and colluding user setting, we show that secure summation is feasible if and only if the hypergraph, where each node is a user and each edge is a group of users sharing the same key, is connected after removing the nodes corresponding to any colluding set of users and their incident edges. Finally, we focus on the symmetric groupwise key setting, where every group of G users share an independent key. We show that for symmetric groupwise keys with group size <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> , if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> = 1 or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> > <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> , the secure summation problem is not feasible; else 1 < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> , to compute 1 bit of the sum securely, each user needs to send at least 1 bit to the server and the size of each groupwise key is at least ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> − <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> − 1)/( <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>K</i>−<i>T</i></sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>G</i></sub> ) bits.