Reviewerly/exHarmony huggingface dataset:
Unified Energy-Based Generative Network for Supervised Image Hashing

Hashing methods often face critical efficiency challenges, such as generalization with limited labeled data, and robustness issues (such as changes in the data distribution and missing information in the input data) in real-world retrieval applications. However, it is non-trivial to learn a hash function in existing supervised hashing methods with both acceptable efficiency and robustness. In this paper, we explore a unified generative hashing model based on an explicit energy-based model (EBM) that exhibits a better generalization with limited labeled data, and better robustness against distributional changes and missing data. Unlike the previous implicit generative adversarial network (GAN) based hashing approaches, which suffer from several practical difficulties since they simultaneously train two networks (the generator and the discriminator), our approach only trains one single generative network with multiple objectives. Specifically, the proposed generative hashing model is a bottom-up multipurpose network that simultaneously represents the images from multiple perspectives, including explicit probability density, binary hash code, and category. Our model is easier to train than GAN-based approaches as it is based on finding the maximum likelihood of the density function. The proposed model also exhibits significant robustness toward out-of-distribution query data and is able to overcome missing data in both the training and testing phase with minimal retrieval performance degradation. Extensive experiments on several real-world datasets demonstrate superior results in which the proposed model achieves up to 5% improvement over the current state-of-the-art supervised hashing methods and exhibits a significant performance boost and robustness in both out-of-distribution retrieval and missing data scenarios.
Algorithmic Segmentation of Job Ads Using Textual Analysis

As job ads are getting more prevalent online, an automated analysis is becoming increasingly important, especially in the field of human resource management. In this paper, we propose an approach to automatically segment job ads by predefined categories like the description of a job or the offering company, which is needed to categorize and quantify different aspects of job ads. Using a manually annotated data set, textual features are extracted for each segment type in a first step and utilized to train state-of-the-art machine learning classification methods. Subsequently, these models are used by iterative algorithms to detect the individual segments. Using several optimization techniques like detecting typical segment start phrases, comprehensive evaluations show promising results.
Exploiting Metonymy from Available Knowledge Resources

Metomymy is challenging for advanced natural language processing applications because one word or expression is used to refer to another implicit related concept. So far, metonymy has been mainly explored in the context of its recognition and resolution in texts. In this work we focus on exploiting metonymy from existing lexical knowledge resources. In particular, we analyse how metomynic relations are implicitly encoded in WordNet and SUMO. By using an existing automated reasoning framework to test the new modelling acquired from WordNet and SUMO, we propose a practical way to deal with figurative expressions.
Recognizing Textual Entailment Using Weighted Dependency Relations

In this paper, we describe a hybrid approach for Recognizing Textual Entailment (RTE) that makes use of dependency parsing and semantic similarity measures. Dependency triplet matching is performed between dependency parsed Text (T) and Hypothesis (H). In case of dependency relation match, we also consider partial matching and semantic similarity between the associated words is calculated with the help of various semantic similarity measures. Importance of various dependency relations with respect to the TE task is computed in terms of their information gain and the dependency relations are weighted accordingly. This paper reports our experiments carried out on the RTE-1, RTE-2 and RTE-3 benchmark datasets using three approaches namely greedy approach, exhaustive search and greedy approach with weighted dependency relations. Experimental results show that weighted dependency relations significantly improve TE performance over the baseline.
Composing Word Vectors for Japanese Compound Words Using Dependency Relations

The use of distributed representations, e.g., via word2vec, has become popular in recent years. However, Japanese has many compound words and we often face the situation where meanings of a word and a compound word should be compared. Therefore, in the current study, we composed compound word vectors from those of constituent word vectors. We took into consideration the dependency relations of compound words to compose word vectors of them. The experiments revealed that, when we consider dependency relations, (1) we could obtain better representations for compound words when we separately learn models for each dependency relation, (2) each model could obtain good representations with fewer epochs, and (3) the learned weights for a model of compound words with one dependency relation could be used for fine-tuning for models for compound words of other dependency relations.
Initial Explorations on Chaotic Behaviors of Recurrent Neural Networks

In this paper we analyzed the dynamics of Recurrent Neural Network architectures. We explored the chaotic nature of state-of-the-art Recurrent Neural Networks: Vanilla Recurrent Network and Recurrent Highway Networks. Our experiments showed that they exhibit chaotic behavior in the absence of input data. We also proposed a way of removing chaos from Recurrent Neural Networks. Our findings show that initialization of the weight matrices during the training plays an important role, as initialization with the matrices whose norm is smaller than one will lead to the non-chaotic behavior of the Recurrent Neural Networks. The advantage of the non-chaotic cells is stable dynamics. At the end, we tested our chaos-free version of the Recurrent Highway Networks (RHN) in a real-world application. In the language modeling task, chaos-free versions of RHN perform on par with the original version.
A Lightweight Local-Global Attention Network for Single Image Super-Resolution

For a given image, the self-attention mechanism aims to capture dependencies for each pixel. It has been proved that the performance of neural networks which employ self-attention is superior in various image processing tasks. However, the performance of self-attention has extensively correlated with the amount of computation. The vast majority of works tend to use local attention to capture local information to reduce the amount of calculation when using self-attention. The ability to capture information from the entire image is easily weakened on this occasion. In this paper, a local-global attention block (LGAB) is proposed to enhance both the local features and global features with low calculation complexity. To verify the performance of LGAB, a lightweight local-global attention network (LGAN) for single image super-resolution (SISR) is proposed and evaluated. Compared with other lightweight state-of-the-arts (SOTAs) of SISR, the superiority of our LGAN is demonstrated by extensive experimental results. The source code can be found at https://github.com/songzijiang/LGAN .
Three-stage Training Pipeline with Patch Random Drop for Few-shot Object Detection

Self-supervised learning (SSL) aims to design pretext tasks for exploiting the structural information of data without manual annotation, which has been widely used in few-shot image classification for improving the generalization of the model. However, few works explore the influence of SSL on Few-shot object detection (FSOD) which is a more challenging task. Besides, our experimental results demonstrate that using a weighted sum of different self-supervised losses causes performance degradation compared to using a single self-supervised task in FSOD. To solve these problems, firstly, we introduce SSL into FSOD by applying SSL tasks to the cropped positive samples. Secondly, we propose a novel self-supervised method: patch random drop, for predicting the location of the masked image patch. Finally, we design a three-stage training pipeline to associate two different self-supervised tasks. Extensive experiments on the few-shot object detection datasets, i.e., Pascal VOC, MS COCO, validate the effectiveness of our method.
Image Retrieval with Well-Separated Semantic Hash Centers

Recently, some point-wise hash learning methods such as CSQ and DPN adapted “hash centers” as the global similarity label for each category and force the hash codes of the images with the same category to get closed to their corresponding hash centers. Although they outperformed other pairwise/triplet hashing methods, they assign hash centers to each class randomly and result in a sub-optimal performance because of ignoring the semantic relationship between categories, which means that they ignore the fact that the Hamming distance between the hash centers corresponding to two semantically similar classes should be smaller than the Hamming distance between the hash centers corresponding to two semantically dissimilar classes. To solve the above problem and generate well-separated and semantic hash centers, in this paper, we propose an optimization approach which aims at generating hash centers not only with semantic category information but also distinguished from each other. Specifically, we adopt the weight of last fully-connected layer in ResNet-50 model as category features to help inject semantic information into the generation of hash centers and try to maximize the expectation of the Hamming distance between each two hash centers. With the hash centers corresponding to each image category, we propose two effective loss functions to learn deep hashing function. Importantly, extensive experiments show that our proposed hash centers and training method outperform the state-of-the-art hash models on three image retrieval datasets.
Hyperspectral Image Residual Denoising Network Based on Mixed-Domain Attention Mechanism

Hyperspectral images (HSIs) contain not only spatial information, but also detail information on spectrum that reflects the internal features of objects, which can be used to monitor crop growth, for example. It is noteworthy that noises are inevitably introduced in the obtained HSIs due to the imperfection of imaging equipment and data transmission process, which will probably lead to misjudging the species of objects. Currently, HSIs-denoising methods based on deep learning have received considerable amount of attention and achieved promising results. However, these methods did not consider the interdependence among the three domains of HSIs. Based on this, we present a mixed-domain attention-based residual denoising network (for short named MA-RDN), so as to better the noises suppression by taking all the three domains into consideration. Different from existing methods, we introduce a mixed-domain attention module, which consists of three branches, respectively modeling the correlation between two of the domains. In this way, the model is guided to simultaneously focus on all the cross-domain features that are influential in denoising tasks. We take the average value of the three branches as the module's output. Then, a sparse feature extraction subnetwork is designed to preserve spatial-spectral features of HSIs as many as possible, which contains several multiscale structures and channel attentions. In order to avoid the gradient disappearance and model degradation caused by the deepening of the network, we utilize two weighted skip connections in the output. Simulation experiments show that, in different noise conditions, the peak signal-to-noise ratio PSNR of our method is increased of about 1.6 that compared with the Cao et al's GRN [11] method, and the structural similarity SSIM is slightly better than it.
Multi-Carrier Information Hiding Algorithm Based on GHM Multiwavelet Transform and Singular Value Decomposition

Aiming at the problem that the single-carrier information hiding algorithm is limited by the number of carriers, and its capacity and security cannot be further broken through, a digital image multi-carrier information hiding algorithm based on Geronimo Hardin Massopus (GHM) and singular value decomposition (SVD) is proposed. Firstly, the digital image is classified based on the color histogram and LDP fusion characteristics of the digital image. Secondly, the GHM multiwavelet transform is applied and the information hiding area is selected according to the energy characteristics. Finally, while ensuring the correlation between images, the carrier image is slightly modified in combination with the stability of singular value to embed secret information. By applying GHM multiwavelet transform to the carrier image, the data is embedded in the subgraph with relatively low energy weight, which effectively improves the concealment and anti-analysis of the algorithm. Using the unique stability and rotation invariance of image singular value, it is slightly modified to embed secret information in the carrier image, reduce the embedding distortion and improve the robustness of the proposed algorithm. Experimental analysis shows that compared with the comparison algorithm, the advantage of the algorithm is that the invisibility PSNR value is increased by 27.05% and 9.46% on average. When facing the high-intensity composite attack of 30% shear, JPEG2000 compression and 33° counterclockwise rotation at the same time, the PSNR can reach 39.3274dB, and its invisibility and robustness have been significantly improved.
A Lightweight 3D Segmentation Network for Abdominal Liver in CT Image

Abnormal liver function is linked to a variety of disorders. Precise and quick automatic liver segmentation can help clinicians make better diagnosis and treatment decisions. With the development of computer vision and deep learning approaches, there are more solutions for biomedical image segmentation tasks. In recent years, the U-Net architecture is by far the most widely used backbone architecture for biomedical image segmentation. Deep convolutional neural networks-based semantic segmentation has achieved sufficient accuracy. However, the scale of high-precision networks is growing, requiring an increasing amount of storage and computational resources. Furthermore, the deep neural network's operating time is lengthy, making it difficult to satisfy practical needs. As a result, the lightweight convolutional neural network design is used to the semantic segmentation task. As a consequence, in this article, a lightweight convolutional neural network is proposed to solve the aforementioned problems in the task of biomedical image segmentation. 3D U-Net is used as the backbone architecture and a modification of the Ghost module from GhostNet is introduced to boost up the effectiveness and the learning efficiency. The experimental results demonstrate that the proposed network improved the segmentation performance with fewer network parameters and requiring less floating-point computation.
Deep learning for image segmentation

The purpose of this article is to investigate the value of deep learning algorithms in the application of ultrasound images of thyroid nodules. Using a dataset of 7288 ultrasound images of thyroid nodules provided by the MICCAI 2020 Challenge, based on the U_Net framework, incorporating a multiscale input mechanism and improving loss optimization function, through continuous training to find the optimal model, so that the computer can autonomously segment the thyroid nodules. The segmentation accuracy reaches 0.955, and the network has good segmentation performance.
SAR Image Segmentation with Superpixel Constraint and Fuzzy Clustering

Image segmentation is a very important task in the application of synthetic aperture radar (SAR) images, especially to feature extraction of SAR images. Because of speckle noise in SAR images, it is easy to produce many isolated points if fuzzy clustering is performed directly on SAR images. Aiming at this, a SAR image segmentation method based on superpixel constraints and fuzzy clustering is proposed in this paper, which is named FCM_SS. The FCM_SS algorithm firstly introduces the improved SNIC algorithm to produce uniform superpixels, and then averages the pixels in the superpixels, which are used as the input for subsequent fuzzy clustering. The experimental results suggest that the FCM_SS algorithm has high segmentation accuracy and strong robustness to noise.
The One-stop Intelligent Environmental Protection Community based on Image Processing Techniques 

In view of the spread of COVID-19 epidemic and many problems existing in the community, such as potential safety hazards, diluted interpersonal relationships, and out-of-place management, a system of one-stop intelligent environmental protection communities based on the Internet was proposed. It not only improves the ability of community staff, provides great convenience for residents and community workers, cares for vulnerable groups, and promotes a happy and harmonious neighborhood life, but also scores residents and staff while monitoring the community environment for safety with the technology application of thermal imaging recognition, PaddleHub-based face and mask recognition. This new system design is easy to implement at low cost and has a simple structure with many functions. The technologies for face and mask recognition proposed in this paper are based on PaddleHub. Experiments on MaskedFace-Net provided by Haute-Alsace University and the pretrained parameters loaded by PaddleHub showed that the accuracy rate with mask recognition was 94.3290 percent using this method.
An Attention Based Network for Two-dimensional Hand Pose Estimation

Accurate visual hand pose estimation at the joint level has been used in vision-based Human-Computer interaction (HCI) applications in a number of areas. However, current 2D hand pose estimation tends to focus on high accuracy prediction or fast speed prediction, which does not allow detectors to achieve both fast and accurate pose estimation. In this paper, we combine RepVGG with a self-attention mechanism proposed an improved network we called ARepNet. ArepNet doubled the speed of the network model by re-parameterized network and capturing long-range dependencies by connecting information from different places, thereby achieving an accuracy rate of 86.8%. We add a 2D hand pose dataset in low-light contexts and propose a simple contrast enhancement method to make 2D hand pose estimation robust to picture input in different environments. We have successfully deployed ARepNet to embedded devices, which FPS with 139 frames per second, meeting real-time requirements.
Enhanced Multi-Attention Network for Single Image Super-resolution

Recent research on single image super-resolution(SISR) shows that deep convolutional neural networks(DCNNs) with attention mechanism present a better improvement. Each different attention mechanism has its distinct focus. Specifically, channel attention mechanism has the capacity to enhance the influence of critical channels by focusing on the expression of characteristics at different channel levels, and pixel attention mechanism has the ability to improve the quality of reconstructed images by paying attention to the expression of spatial pixel features. We believe that the combination of these two mechanisms is a way to further improve the quality of super-resolution image. In this paper, an enhanced multi-attention network(EMAN) is proposed, which contains advantages of two attention mechanisms. Besides, to improve the utilization of high-frequency information, a novel edge-based loss function is added to boost the learning of the edge region. Plenty of experiments show that the proposed multi-attention network achieves better accuracy and visual effect against single-attention methods.
Improving the Detection Performance of Sparse R-CNN with Different Necks

Sparse R-CNN uses a purely sparse method to detect objects and achieves good results. However, it does not make full use of the features extracted from the image, so its detection performance needs to be further improved. And we propose Sparse R-CNNv1 and Sparse R-CNNv2. In these algorithms, we use VOVNet with attention mechanism to replace ResNet of the original Sparse R-CNN as our backbone. In addition, we also use two different improved neck networks, Augpan and FPNencoder, to further improve the detection performance of the algorithm from the perspective of feature fusion and increasing the receptive field of each layer, respectively. Our algorithms are trained and verified on COCO2017, and the experimental results show that Sparser-CNNv1 achieves 45.0 AP and Sparser-CNNV2 achieves 45.3 AP, higher than the original SparseR-CNN's 43.0 AP in standard 3× training schedule.
Higher Secondary Female Students’ Awareness Towards Social Media Safety Usage

The prime objective of the this study is to find out the Higher Secondary Female Students’ Awareness towards Social Media safety Usage. The present investigation has been undertaken by using Normative Survey Method. Awareness about Social Media safety Usage Inventory constructed and validated by Preet Monk (2017) has been used for this study. The present study was conducted with 200 Higher Secondary Female Students studying in Nagappattinam District of Tamilnadu State. The sample was selected by using Simple Random Sampling Technique. The findings shows that the Higher Secondary Female Students’ Awareness about Social Media safety Usage is moderate. This trend was found among all the sub samples too.
A Genetic Approach to the Formulation of Tetris Engine

The game Tetris is a great and famous topic for research in artificial intelligence and machine learning. Many investigations have already existed. However, we believe more things can be learned from this topic, and there is still space to improve. This paper will tackle the Tetris game using three different agents, the handcrafted, local search and reinforcement learning agents. We will implement, compare and analyze all three agents to understand their advantages and disadvantages. In brief, the main result is that the local search agent turns out to be the most successful agent, which performs ten times better than the handcrafted agent and five times better than the reinforcement learning agent. The main result implies two take-away messages. Firstly, sometimes the simple model is the optimal model. Secondly, we should be cautious when using a Convolutional Neural Network (CNN) to encode game state because of its spatial invariance property.