Reviewerly/exHarmony huggingface dataset:
An efficient multi-order cascade distillation model for the detection of small targets and occluded objects in transmission line inspection

Authors:
raw_author_name: Zhaoyang Qu
raw_affiliation_string: Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center, Jilin 132012, China; School of Computer, Northeast Electric Power University, Jilin 132012, China
raw_author_name: Yunchang Dong
raw_affiliation_string: State Grid Jilin Electric Power Research Institute, Changchun 130012, China
raw_author_name: Siqi Song
raw_affiliation_string: Northeast Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Changchun 130021, China
raw_author_name: Tao Jiang
raw_affiliation_string: School of Computer, Northeast Electric Power University, Jilin 132012, China
raw_author_name: Min Li
raw_affiliation_string: School of Computer, Northeast Electric Power University, Jilin 132012, China
raw_author_name: Feng Liang
raw_affiliation_string: School of Computer, Northeast Electric Power University, Jilin 132012, China

Abstract: The cascade distillation detection model is proposed to address the problems of low accuracy of multiple small target defect detection and lack of robustness under complex environments in transmission line UAV intelligent inspection. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. Secondly, a high-order aggregated feature extraction backbone network is proposed to enhance the feature extraction of small and medium-sized targets and obscured targets on transmission lines. Then, a spatially deformable attention mechanism is designed to optimize the selection of sampling points for transmission line targets and defects and the calculation of their attention weights by aggregating contextual information in the vicinity of the targets, enhancing the module's ability to refine and adjust the bounding boxes. Finally, a large dataset of transmission line defect targets is constructed and generated, and the validity of the model is verified by experiments.
Language relatedness evaluation for multilingual neural machine translation

Authors:
raw_author_name: Chenggang Mi
raw_affiliation_string: Foreign Language and Literature Institute, Xi’an International Studies University, Xi’an, Shaanxi, China

Abstract: 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

Authors:
raw_author_name: El Saeed R. Lasheen
raw_affiliation_string: Geology Department, Faculty of Science, Al-Azhar University, Cairo, P.O. Box 11884, Egypt
raw_author_name: Mahmoud H. Elyaseer
raw_affiliation_string: Geology Department, Faculty of Science, Al-Azhar University, Cairo, P.O. Box 11884, Egypt
raw_author_name: Waheed H. Mohamed
raw_affiliation_string: Geology Department, Faculty of Science, Al-Azhar University, Cairo, P.O. Box 11884, Egypt
raw_author_name: Mokhles K. Azer
raw_affiliation_string: Geological Sciences Department, National Research Centre, P.O Box 12622, Cairo, Egypt

Abstract: 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

Authors:
raw_author_name: Yijun Zhang
raw_affiliation_string: Experimental Training Base, National University of Defense Technology, Xi’an 710106, China
raw_author_name: Xiaodong Mu
raw_affiliation_string: Graduate School, Rocket Force University of Engineering, Xi’an 710025, China
raw_author_name: Peng Zhang
raw_affiliation_string: Graduate School, Rocket Force University of Engineering, Xi’an 710025, China; State Key Laboratory of Astronautics Dynamics, Xi’an satellite control center, Xi’an 710043, China
raw_author_name: Dao Zhao
raw_affiliation_string: Graduate School, Rocket Force University of Engineering, Xi’an 710025, China; State Key Laboratory of Astronautics Dynamics, Xi’an satellite control center, Xi’an 710043, China

Abstract: 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

Authors:
raw_author_name: Xianbin Wei
raw_affiliation_string: Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, China
raw_author_name: Kechen Song
raw_affiliation_string: Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, China
raw_author_name: Wenkang Yang
raw_affiliation_string: Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, China
raw_author_name: Yunhui Yan
raw_affiliation_string: Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, China
raw_author_name: Qinggang Meng
raw_affiliation_string: The Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK

Abstract: 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

Authors:
raw_author_name: Mengfei Ji
raw_affiliation_string: College of Electronic Science & Engineering, Jilin University, Jilin, China; The Department of Quantum & Computer Engineering, Delft University of Technology, Delft, Netherlands
raw_author_name: Zaid Al-Ars
raw_affiliation_string: The Department of Quantum & Computer Engineering, Delft University of Technology, Delft, Netherlands
raw_author_name: Yuchun Chang
raw_affiliation_string: School of Microelectronics, Dalian University of Technology, Dalian, China
raw_author_name: Baolin Zhang
raw_affiliation_string: College of Electronic Science & Engineering, Jilin University, Jilin, China

Abstract: 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

Authors:
raw_author_name: Weiwei Zhang
raw_affiliation_string: College of Engineering, Huaqiao University, Quanzhou, 362021, China
raw_author_name: Yufeng Guo
raw_affiliation_string: College of Engineering, Huaqiao University, Quanzhou, 362021, China
raw_author_name: Peng Liang
raw_affiliation_string: College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China
raw_author_name: Ming Ji
raw_affiliation_string: College of Engineering, Huaqiao University, Quanzhou, 362021, China
raw_author_name: Chenghui Zhen
raw_affiliation_string: College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China

Abstract: 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

Authors:
raw_author_name: Stefano Bannò
raw_affiliation_string: Department of Psychology and Cognitive Science, University of Trento, Trento, Italy; SpeechTek Lab, Fondazione Bruno Kessler, Trento, Italy
raw_author_name: Marco Matassoni
raw_affiliation_string: SpeechTek Lab, Fondazione Bruno Kessler, Trento, Italy

Abstract: 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

Authors:
raw_author_name: Yunqi Zhang
raw_affiliation_string: Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China; School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China
raw_author_name: Jidong Yuan
raw_affiliation_string: Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China; School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China
raw_author_name: Chixuan Wei
raw_affiliation_string: Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China; School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China
raw_author_name: Yifei Xie
raw_affiliation_string: College of Engineering, City University of Hong Kong, Hong Kong, China

Abstract: 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

Authors:
raw_author_name: Kristian Miok
raw_affiliation_string: ICAM – Advanced Environmental Research Institute, Unversity of Timisoara, Timisoara, Romania
raw_author_name: Encarnación Hidalgo Tenorio
raw_affiliation_string: Department of English and German Studies, Facultad de Filosofía y Letras, Universidad de Granada, Granada, Spain
raw_author_name: Petya Osenova
raw_affiliation_string: Faculty of Slavic Studies, Sofia University St. Kl. Ohridski, Sofia, Bulgaria; Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria
raw_author_name: Miguel-Ángel Benítez-Castro
raw_affiliation_string: Department of English and German Studies, Faculty of Social Sciences and Humanities, University of Zaragoza, Teruel, Spain
raw_author_name: Marko Robnik-Šikonja
raw_affiliation_string: Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

Abstract: 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

Authors:
raw_author_name: Cairong Yan
raw_affiliation_string: Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China; School of Computer Science and Technology, Donghua University, Shanghai, China
raw_author_name: Xiaoke Li
raw_affiliation_string: School of Computer Science and Technology, Donghua University, Shanghai, China
raw_author_name: Ran Tao
raw_affiliation_string: School of Computer Science and Technology, Donghua University, Shanghai, China
raw_author_name: Zhaohui Zhang
raw_affiliation_string: Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China; School of Computer Science and Technology, Donghua University, Shanghai, China
raw_author_name: Yongquan Wan
raw_affiliation_string: School of Information Technology, Shanghai Jian Qiao University, Shanghai, China

Abstract: 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).