Reviewerly/exHarmony huggingface dataset:
SNLI Indo: A Recognizing Textual Entailment Dataset in Indonesian Derived from the Stanford Natural Language Inference Dataset

Authors:
raw_author_name: I Made Suwija Putra
raw_affiliation_string: Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
raw_author_name: Daniel Siahaan
raw_affiliation_string: Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
raw_author_name: Ahmad Saikhu
raw_affiliation_string: Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Abstract: Recognizing textual entailment (RTE) is an essential task in natural language processing (NLP). It is the task of determining the inference relationship between text fragments (premise and hypothesis), of which the inference relationship is either entailment (true), contradiction (false), or neutral (undetermined). The most popular approach for RTE is neural networks, which has resulted in the best RTE models. Neural network approaches, in particular deep learning, are data-driven and, consequently, the quantity and quality of the data significantly influences the performance of these approaches. Therefore, we introduce SNLI Indo, a large-scale RTE dataset in the Indonesian language, which was derived from the Stanford Natural Language Inference (SNLI) corpus by translating the original sentence pairs. SNLI is a large-scale dataset that contains premise-hypothesis pairs that were generated using a crowdsourcing framework. The SNLI dataset is comprised of a total of 569,027 sentence pairs with the distribution of sentence pairs as follows: 549,365 pairs for training, 9,840 pairs for model validation, and 9,822 pairs for testing. We translated the original sentence pairs of the SNLI dataset from English to Indonesian using the Google Cloud Translation API. The existence of SNLI Indo addresses the resource gap in the field of NLP for the Indonesian language. Even though large datasets are available in other languages, in particular English, the SNLI Indo dataset enables a more optimal development of deep learning models for RTE in the Indonesian language.
Machine-Learning-Assisted Soft Fiber Optic Glove System for Sign Language Recognition

Authors:
raw_author_name: Jiang Lin
raw_affiliation_string: Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Jian S Dai
raw_affiliation_string: Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems, Southern University of Science and Technology, Shenzhen, China

Abstract: Sign language recognition devices are effective approaches to breaking the communication barrier between signers and non-signers and exploring human-machine interactions. Wearable gloves have been developed for gesture recognition and virtual reality applications by employing flexible sensors for motion detection and machine learning for data analysis. However, most existing wearable devices present limited sign language translating capacity due to the sensors' design and distribution. Here, we propose a cost-effective dual-hand soft fiber optic glove system consisting of multimode soft liquid-core fiber optic sensors, gyroscopes, wireless printed circuit boards, and batteries for sign language translation. In combination with different deep learning techniques and recognition strategies, the glove system can recognize static gestures and dynamic gestures of American Sign Language, and deduce the meaning of sentences by the sequence of gestures. The soft glove system exhibits a broad sign language range (10 numbers, 26 alphabets, 18 words, and 5 sentences meaning prediction), and high recognition accuracy (98.6% for static gestures, 95% for dynamic gestures). The results also present the recognizing capacity for high-correlated gestures (e.g., “M” and “N”). Finally, we demonstrate its application for controlling the motion of a virtual character through 7 discrete commands in the VR interface.
Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold Transform-Based Embedding

Authors:
raw_author_name: G Suresh
raw_affiliation_string: Department of Computer Science and Engineering, Kings Engineering College, Sriperumbudur, Tamil Nadu, 602117, India
raw_author_name: G Manikandan
raw_affiliation_string: Department of Information Technology, Kings Engineering College, Sriperumbudur, Tamil Nadu, 602117, India
raw_author_name: G Bhuvaneswari
raw_affiliation_string: Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, 602105, India
raw_author_name: P Shanthakumar
raw_affiliation_string: Department of Computer Science and Engineering, Nehru Institute of Technology, Coimbatore, Tamil Nadu, 641105, India

Abstract: Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance. The Arnold transform used in this work provides security by encrypting the data. The use of SqueezeNet makes the proposed model a simple design and this reduces the computational time. Thus, the AT[Formula: see text]SqeezeNet_PWOA attained better correlation coefficient (CC), peak signal-to-noise ratio (PSNR) and mean square error (MSE) of 0.908, 48.66 and 0.001 dB with the Gaussian noise.
TaSPM: Targeted Sequential Pattern Mining

Authors:
raw_author_name: Gengsen Huang
raw_affiliation_string: Jinan University, Guangzhou, China
raw_author_name: Wensheng Gan
raw_affiliation_string: Jinan University, Guangzhou, China
raw_author_name: Philip S. Yu
raw_affiliation_string: University of Illinois Chicago, Chicago, USA

Abstract: Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.
Hardware Accelerator for High Accuracy Sign Language Recognition with Residual Network Based on FPGAs

Authors:
raw_author_name: Dong Yang
raw_affiliation_string: Zhengzhou Key Laboratory of Electronic Information Functional Materials and Devices, Huanghe Science and Technology College
raw_author_name: Jianwu Li
raw_affiliation_string: Advanced Technology Research Institute, Beijing Institute of Technology; Zhengzhou Key Laboratory of Electronic Information Functional Materials and Devices, Huanghe Science and Technology College
raw_author_name: Lei Zhang
raw_affiliation_string: School of Integrated Circuits and Electronics, Beijing Institute of Technology
raw_author_name: Xiaoran Li
raw_affiliation_string: Advanced Technology Research Institute, Beijing Institute of Technology; School of Integrated Circuits and Electronics, Beijing Institute of Technology

Abstract: The ResNet series of networks has demonstrated powerful capabilities in the fields of object detection and image classification, garnering increasing attention from researchers. However, due to their deep network architectures, accelerator development based on FPGA faces challenges associated with limited on-chip resources and lengthy design cycles. This paper presents a versatile hardware acceleration system based on FPGA, achieving optimization through both hardware implementation and software inference architecture. The system reduces network complexity by employing techniques such as inter-layer fusion and dynamic quantization, while enhancing hardware resource utilization through channel parallelism and tightly-pipelined hardware design principles. By configuring and reusing computation units, the forward inference process of ResNet series networks can be rapidly deployed on FPGA, shortening the development and validation cycles. The proposed system is validated using the ResNet-18 model on a PYNQ-Z2 development board within a gesture recognition application scenario. The overall power consumption of the system is 2.136W, with hardware inference accuracy reaching 98.87%.
Hilbert’s and Takeda’s single-shot interferometry with a linear-carrier: a comparison

Authors:
raw_author_name: Jos A Ferrari
raw_affiliation_string: Facultad de Ingeniera, Universidad de la Republica Instituto de Fisica, J.Herrera y Reissig 565, 11300 Montevideo, Montevideo, Montevideo, 11400, URUGUAY
raw_author_name: Jorge L. Flores
raw_affiliation_string: Departamento de Ingenieria en Electro, Universidad de Guadalajara, Blvd. Marcelino García Barragán #1421, esq Calzada Olímpica,, Guadalajara, JAL, 44430, MEXICO
raw_author_name: Gastón A. Ayubi
raw_affiliation_string: Universidad de la República Uruguay, J. Herrera y Reissig 565, Montevideo, Montevideo, 11200, URUGUAY

Abstract: Abstract The purpose of the present work is to compare single-shot phase-retrieval methods based on Hilbert’s transform with Takeda’s spatial linear-carrier method. In addition, we briefly discuss the so-called slightly-off-axis method that involves two interferograms as an efficient way to remove background illumination bias. After carefully analyzing the assumptions implicit in both approaches, we demonstrate that the Hilbert transform based method is completely identical to the 30 years old Takeda’s interferometry with a spatial linear-carrier. Validation experiments are presented.
Correlation-aware Graph Data Augmentation with Implicit and Explicit Neighbors

Authors:
raw_author_name: Bo-Yu Chen
raw_affiliation_string: Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
raw_author_name: Wen-Chih Peng
raw_affiliation_string: Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
raw_author_name: Chih-Chieh Hung
raw_affiliation_string: Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan

Abstract: In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local neighbors within the topological structure. To address this issue, we identify two types of neighbors in a citation graph: explicit neighbors based on the topological structure and implicit neighbors based on node features. Our primary motivation is to clearly define and visualize these neighbors, emphasizing their importance in enhancing graph neural network performance. We propose a Correlation-aware Network (CNet) to re-organize the citation graph and learn more valuable informative representations by leveraging these implicit and explicit neighbors. Our approach aims to improve graph data augmentation and classification performance, with the majority of our focus on stating the importance of using these neighbors, while also introducing a new graph data augmentation method. We compare CNet with state-of-the-art (SOTA) GNNs and other graph data augmentation approaches acting on GNNs. Extensive experiments demonstrate that CNet effectively extracts more valuable informative representations from the citation graph, significantly outperforming baselines. The code is available on public GitHub. 1
Attacking Click-through Rate Predictors via Generating Realistic Fake Samples

Authors:
raw_author_name: Mingxing Duan
raw_affiliation_string: Hunan University, Changsha, China
raw_author_name: Kenli Li
raw_affiliation_string: Hunan University, Changsha, China
raw_author_name: Weinan Zhang
raw_affiliation_string: Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Jiarui Qin
raw_affiliation_string: Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Bin Xiao
raw_affiliation_string: Hong Kong Polytechnic University, Hong Kong, China

Abstract: How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy.
Dynamic Attentive Convolution for Facial Beauty Prediction

Authors:
raw_author_name: Zhishu SUN
raw_affiliation_string: College of Computer and Data Science, Fuzhou University
raw_author_name: Zilong XIAO
raw_affiliation_string: College of Computer and Data Science, Fuzhou University
raw_author_name: Yuanlong YU
raw_affiliation_string: College of Computer and Data Science, Fuzhou University
raw_author_name: Luojun LIN
raw_affiliation_string: College of Computer and Data Science, Fuzhou University

Abstract: Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
Content-Adaptive Optimization Framework for Universal Deep Image Compression

Authors:
raw_author_name: Koki TSUBOTA
raw_affiliation_string: Dept. of Information and Communication Engineering, The University of Tokyo
raw_author_name: Kiyoharu AIZAWA
raw_affiliation_string: Dept. of Information and Communication Engineering, The University of Tokyo

Abstract: While deep image compression performs better than traditional codecs like JPEG on natural images, it faces a challenge as a learning-based approach: compression performance drastically decreases for out-of-domain images. To investigate this problem, we introduce a novel task that we call universal deep image compression, which involves compressing images in arbitrary domains, such as natural images, line drawings, and comics. Furthermore, we propose a content-adaptive optimization framework to tackle this task. This framework adapts a pre-trained compression model to each target image during testing for addressing the domain gap between pre-training and testing. For each input image, we insert adapters into the decoder of the model and optimize the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion, with the adapter parameters transmitted per image. To achieve the evaluation of the proposed universal deep compression, we constructed a benchmark dataset containing uncompressed images of four domains: natural images, line drawings, comics, and vector arts. We compare our proposed method with non-adaptive and existing adaptive compression methods, and the results show that our method outperforms them. Our code and dataset are publicly available at https://github.com/kktsubota/universal-dic.
Rotation-Invariant Convolution Networks with Hexagon-Based Kernels

Authors:
raw_author_name: Yiping TANG
raw_affiliation_string: Faculty of Information Science and Electrical Engineering, Kyushu University
raw_author_name: Kohei HATANO
raw_affiliation_string: Faculty of Information Science and Electrical Engineering, Kyushu University; Riken AIP
raw_author_name: Eiji TAKIMOTO
raw_affiliation_string: Faculty of Information Science and Electrical Engineering, Kyushu University

Abstract: We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.
High-Accuracy People Counting in Large Spaces Using Overhead Fisheye Cameras

Authors:
raw_author_name: Janusz Konrad
raw_affiliation_string: Boston University, Department of Electrical and Computer Engineering, 8 Saint Mary's Street, MA 02215, Boston, USA
raw_author_name: Mertcan Cokbas
raw_affiliation_string: Boston University, Department of Electrical and Computer Engineering, 8 Saint Mary's Street, MA 02215, Boston, USA
raw_author_name: Prakash Ishwar
raw_affiliation_string: Boston University, Department of Electrical and Computer Engineering, 8 Saint Mary's Street, MA 02215, Boston, USA

Abstract: Quantifying the number of people in various spaces of a commercial building is important for saving energy, optimizing space usage, and assisting with public safety. To accomplish these goals requires obtaining accurate, fine-grained counts in real time. However, existing methodologies are ineffective for covering large areas with high occupancy. We propose an occupancy-sensing system that uses multiple overhead fisheye cameras and state-of-the-art deep-learning algorithms to cover large spaces with high counting accuracy. Tested in a 2,000 ft2 space, our system shows 54% to 83% reduction in commonly-used error metrics compared to recent people-counting methods proposed for large-space scenarios. Our system is scalable to arbitrarily-large spaces; additional cameras can be integrated with minimal commissioning. We also introduce two new performance metrics for assessing counting accuracy that, unlike common metrics used to date, are independent of occupancy level and can be easily compared across different occupancy scenarios.