Reviewerly/exHarmony huggingface dataset:
Self-supervised Image Hash Retrieval Based On Adversarial Distillation

Hash algorithms have become the mainstream of large-scale similarity image retrieval due to their high storage and search efficiency. The deep learning-based hashing greatly improves the retrieval performance with supervision, but it is difficult for the self-supervised deep hashing to achieve satisfactory performance when there is a lack of reliable supervised signals. In addition, to solve the problems of poor robustness and numerous parameters in traditional neural networks, a lightweight robust deep hash retrieval algorithm is proposed in this paper. The algorithm obtained a robust teacher network by self-supervised adversarial training, then trained the student network using optimized distillation loss and immune injection, and finally extracted image hash sequences using an attention mechanism based on convolution modules. Inspired by the fact that adversarial training is currently the most effective method to improve model robustness, and that knowledge distillation can compress the network while ensuring model performance, this paper proposes a self-supervised image hash retrieval algorithm based on adversarial distillation. The method was tested on three public datasets and compared with other hash algorithms, all of which showed satisfactory results. Specifically, the mAP of the adversarial distillation algorithm proposed in this paper is 3&#x0025; and 2&#x0025; higher than that of the next best SGH method at 64bits and 128bits hash lengths respectively. The experiments show that the hash retrieval model constructed in this paper has good performance while ensuring lightweight and robustness.
Semantic and Visual Enrichment Hierarchical Network for Medical Image Report Generation

This paper highlights a novel medical image report generator named SVEH-Net (Semantic and Visual Enrichment Hierarchical Network), which is based on the encoder-decoder framework and attention mechanism. With the consideration of semantic, visual, and tag features fusion, an image feature encoding (IFE) module is introduced to provide global image features for the decoder, and a hierarchical decoder (H-Decoder) which can fusion all semantic and visual features and generate two reports at one time is proposed. In the experiments, our proposed models are evaluated on the Indiana University Chest X-ray radiology report dataset (IU X-ray) and PEIR Gross dataset. On the both two datasets, our model outperforms the state-of-the-art method in BLEU-1/2/3/4, METEOR, and ROUGE-L scores.
Cancer metastasis fast location based on coarse-to-fine network

The Whole Slide Images(WSIs) play a very important role in breast cancer diagnosis and the pathologist need to locate the lymph node metastasis on the such the gigapixel pathology image. Recently, the deep convolutional neural network has show the promise of metastases localization, it is still a challenge to fast locate the metastases. Either divide WSIs into small patches and perform classification or scan the bigger image block on WSIs in inference. In either way, the algorithm needs to be performed at the finest magnification, which greatly limits inference time. In this paper, we propose a cascade coarse to fine network to expedite the speed of to locate metastases in WSIs, which contain the coarse network to handle the low magnification to find the rough metastases speedily and the fine network efficiently reclassifies the positive responses at high magnification. The experiment is performed on the Camelyon16 dataset demonstrated that the proposed method compared to the previous method is the fastest and also can achieve the localization average FROC score of 81.0 on the test set.
Fusion of Global and Local Features for Text Classification

Text classification is an important problem in natural language processing. In this work, we propose a feature fusion model for document classification, which has excellent text representation ability. We first build a shared graph from training data sets to bring global information. Then we build individual graph for each document which maintain the word order. Finally, we get the document representation vector through attention layer. Extensive experiments show that our method performs well on three standard datasets, which illustrates the effectiveness of our model.
Image Hashing by Pre-Trained Deep Neural Network

Image hashing can map a high-dimensional image to a low-dimensional binary code, so that it is widely used in image retrieval in big data. An image hashing method based on deep neural network is presented in this paper by expanding the usage of deep neural network. Firstly, a pre-trained deep neural network for image classification is used to extract the feature vector of the image. Secondly, the feature vector is transformed by discrete cosine transform to get low-frequency components. Finally, the low-frequency components are binarized to generate hash code of the image. Compared with commonly used perceptual hash, the proposed method keeps the generality of perceptual hash and is robust to image flipping and rotation. The experiments conducted on caltech-256 data set showed that the proposed method improved the accuracy of image retrieval and the quality of hash code.
A Synergic Neural Network For Medical Image Classification Based On Attention Mechanism

For current research in using the deep learning method analyzing and processing medical image in CT or MRI format, identifying the category is challenging because the pathology of the medical images can only be distinguished by tiny difference since the medical image of human organ or tissue is highly homogeneous. The purpose of this paper is to design a convolutional neural network model to extract medical image features and establish a model to classify the medical images. We designed a synergic network based on attention mechanism, capturing the intra-class differences and similarities between classes. The whole pipeline consists of three parts: the input layer, attention based convolutional block and synergic network. By combining these three components, we trained an end-to-end model to classify the medical image. Moreover, through a carefully designed attention model, the network can adaptively pay attention to the region of interest of medical images, avoiding using expensive annotations like bounding boxes or part information labeling. We compare our model on the ADNI dataset(part of) and MRN et dataset with the current SOTA method; the results show our method achieves SOTA performance.
Dust Image Enhancement Algorithm Based on Feature Transformation

In recent years, image enhancement work has been well developed to improve the quality of images, which is beneficial to subsequent task such as object detection, license plate recognition and anomaly detection. But the selection of methods to solve the problems in different scenes is still the main work of image enhancement. In the dust weather environment, due to the absorption and scattering of light by the dust particles suspended in the air, the image obtained by the image acquisition device has the characteristics of yellowish reddish and blurred image, which seriously affects the visual perception of the human eye. And the lack of datasets for dust image enhancement further increases the difficulty of the related task. Thus, based on the color and contour features of the real captured dust images, this paper proposes a synthetic dust image dataset for training deep learning networks. Also, based on the feature transformation idea of Recurrent Generative Adversarial Networks (CycleGAN), we presents a dust image enhancement algorithm which uses an end - to-end deep learning network and avoids the dependence on physical imaging models. Comparison of state-of-the-art approaches available in the literature, our proposed approach obtains better subjective and objective evaluation results on the test set of our proposed database.
Semi-supervised Crowd Counting based on Patch Crowds Statistics

Crowd counting has been widely applied in various fields including social security, urban planning, and intelligent monitoring. A series of excellent fully-supervised crowd counting methods spring up and achieve great performance. Nevertheless, all of the fully-supervised methods deeply depend on large quantities of annotated crowd density maps. Collecting and annotating crowd images is time-consuming and expensive especially for highly dense crowds. In contrast, unlabeled crowd images can be acquired without having to make a great effort. However, it is challenging to effectively exploit unlabeled data for crowd counting. To this end, we propose a semi-supervised crowd counting method that aims to optimize the crowd counting models via exploiting large amounts of unlabeled crowd images. Firstly, we design an effective proxy task based on image patch counts statistics. Then, we present an end-to-end iterative learning strategy to train our semi-supervised framework. To prove the effectiveness of our semi-supervised method, we conducted various experiments on three benchmark crowd counting datasets. Experimental results demonstrate that our semi-supervised algorithm achieves competitive performance compared with the the-state-of-art semi-supervised crowd counting approaches. Furthermore, experimental results show that our method performs well on cross-dataset.
Learning 3D Face Representation with Vision Transformer for Masked Face Recognition

Masked face recognition, a non-contact biometric technology, has attracted much attention and developed rapidly during the coronavirus disease 2019 (COVID-19) outbreak. The existing work trains the masked face recognition model based on a large number of 2D masked face images. However, in practical application scenarios, it is difficult to obtain a large number of masked face images in a short period of time. Therefore, combined with 3D face recognition technology, this paper proposes a masked face recognition model trained with non-masked face images. In this paper, we locate and segment the complete face region and the face region not occluded by masks from the face point clouds. The geometric features of the 3D face surface, namely depth, azimuth, and elevation, are extracted from the above two regions to generate training data. The proposed masked face recognition model based on vision Transformer divides the complete faces and part of the faces into sequence images, and then captures the relationship between the image slices to compensate for the impact caused by the lack of face information, thereby improving the recognition performance. Comparative experiments with the state-of-the-art masked face recognition work are carried out on four databases. The experimental results show that the recognition accuracy of the proposed model is improved by 9.86&#x0025; on Bosphorus database, 16.77<sup>&#x0025;</sup> on CASIA-3D FaceV1 database, 2.32&#x0025; on StirlingESRC database, and 34.81&#x0025; on Ajmal main database, respectively, which verifies the effectiveness and stability of the proposed model.
Cell nuclei segmentation using distance map regression and inverted Huber loss

Digital pathology gives opportunity for automatic analysis of tissue sample images aiming to produce quantitative profiles that could be exploited for diagnosis and treatment decisions. One of the most important steps in the tissue analysis is segmentation of cell nuclei. This task is challenging because of large variability of nuclear morphological features and wide presence of nuclear clusters that leads to merged instances. In this paper we propose cell nuclei segmentation method utilizing distance map regression to address the problem of touching nuclei. Our main contribution is a novel loss function created by modification of Huber loss. The proposed loss demonstrates better performance compared to other commonly used loss functions, while the proposed method outperforms other approaches that have similar complexity of neural network architecture.
A software system for the assessment of the sleep-wake rhythm using Axivity AX3

Mobility limitations are a major public health concern along with physical inactivity because they limit an individual's independent life, and, worse, have shown to be key risk factor for several morbidity, including cardiovascular. It is important to investigate modifiable factors that might help preserve mobility, especially in later life. In this work we present the development of the software system Axivity, that supports specialists in conducting in-depth research on patient activity, taking into account data recorded by an actigraph (a simple and non-invasive device used to diagnose sleep) and data provided by the patient or care provider, so as to provide also relationships between activity energy expenditure estimated from wrist-worn accelerometers and the intensity of physical activity. It will be shared as open source.
Design of Two-Axis Velocity Tracking Earthquake Shaking Table Using Stepper Motor

Earthquakes can cause buildings to collapse. Therefore, prevention of such calamity is essential. One way to prevent building damage is to design buildings that are resistant to earthquake movements. A simulation of the movement of buildings when an earthquake occurs can be carried out using an earthquake shaking table, which can determine the response of the structure to the earthquake. This research examines the design of an earthquake shaking table with two-axis horizontal movements. The earthquake shaking table used ball screws and stepper motors as actuators, whereas Teensy 3.2 board was utilized as controller. The earthquake shaking table used the displacement and velocity of actual earthquakes as input to create movements similar to the real earthquake.
Multi-Context Recommendation Systems (CARS) in Autonomous Driving and Other Applications

Recommendation systems (RS) are important for their instantaneous ability to suggest to users their desired items and for ensuring a smooth and worthwhile user experience. To optimize the user's experience with the RS, it can be programmed to be aware of the various contexts the user has while interacting with the system. Context-aware recommendation systems (CARS) aim to add more nuance to the process of predicting items by considering different factors such as location and time. This paper presents the foundations of recommendation systems, such as categories, evaluation metrics, datasets, and challenges. In this paper, we show the effectiveness of multi-contexts in autonomous driving. Three CARS models are developed and tested. Experimental results show that adding multi-contexts provides more personalized options for drivers, allowing intelligent decisions.
Fast Light Field Reconstruction Using Convolutional Neural Network on Lenslet Images

Light field (LF) imaging is superior to traditional photography in terms of the quantity of information it can capture. It collects angular and spatial data from all directions. Despite this, its poor resolution makes it impossible to take advantage of this immense potential. We attempted to mitigate the effect of this disadvantage in our research by using a deep learning technique. To facilitate our task, we have converted multiple images of the same scene from a 4D variant into a single 2D image (lenslet image). Thus, using very deep models to recover lost details and reconstruct LF images is no longer necessary. We trained our model for one reconstruction task, but the same model can be retrained for other reconstruction tasks. Our results show that the proposed method reconstructs high-quality images faster than other approaches.
A Comparative Study Of Sentiment Analysis For Big Data On Hadoop

Nowadays, people express almost all their feelings and views about everything in the surrounding world on social media applications. If these posts are analyzed accurately, they will be a huge opportunity for various organizations to increase their market value by using that information in decision-making. Opinion mining from social media, also known as sentiment analysis, provides up-to-date information; the reason is the proliferation of social media at various social levels. Sentiment analysis can be extracted from social posts using machine learning algorithms or lexicon-based approaches. The increasing of people who use social media led to more data being produced continuously. These huge amounts of data require an efficient framework to handle them. Hadoop provides a software framework for distributed storage and processing of big data using the MapReduce programming model and Hadoop Distributed File System (HDFS). This paper will discuss different approaches for sentiment analysis of big data, especially on Hadoop, and display their strengths and weaknesses through a comparative study.
KIETA: Key-insight extraction from scientific tables

Abstract An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.
When Will it Get Done? Answering Agile’s Hardest Question

Abstract In a world of complexity, work is unpredictable. Stakeholders want predictability and want to know when it will be done. Jahanger Hussain explores metrics and practices to answer this question in an agile context.
The Digital Skills Network Launch

Abstract At a recent event held in its London office and attended by over 70 high-profile guests, BCS announced the Digital Skills Network – a new community founded to meet tomorrow’s societal challenges through filling today’s digital skills gap.
A Tale of Four Disciplines for All Ages and All Languages

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, namely Lambek's pregroup semantics.A key observation is that the monoidal category of (finite dimensional) vector spaces, linear maps and the tensor product, as well as any pregroup, are examples of compact closed categories.Since, by definition, a pregroup is a compact closed category with trivial morphisms, its compositional content is reflected within the compositional structure of any non-degenerate compact closed category.The (slightly refined) category of vector spaces enables us to compute the meaning of a compound well-typed sentence from the meaning of its constituents, by 'lifting' the type reduction mechanisms of pregroup semantics to the whole category.These sentence meanings live in a single space, independent of the grammatical structure of the sentence.Hence we can use the inner-product to compare meanings of arbitrary sentences.A variation of this procedure which involves constraining the scalars of the vector spaces to the semiring of Booleans results in the well-known Montague semantics.
Meta-Context Ontology for Self-Adaptive Mobile Web Service Discovery in Smart Systems

Self-adaptive mobile web service (MWS) discovery in Smart Systems depends on heterogeneous context-sensitive attributes that are composed dynamically to satisfy the user’s MWS request despite the constantly changing environment. Several ontologies are developed to deal with the issues of heterogeneous knowledge representation in smart systems. Unfortunately, these ontologies are mostly inexpressive, unextendible, constricted, and slightly fragmented due to a lack of socially focused ontology development procedures. In this paper, a context ontology for self-adaptive MWS discovery in smart systems is proposed to provide a better representation of the heterogeneous context for smart systems and facilitate the delivery of MWS. The lightweight unified process for ontology building (UPON Lite) is adopted for ontology development. The ontology is experimentally evaluated in protégé using real-world smart systems from the healthcare and agriculture domain. Consequently, the ontology was found to be more expressive, extensible, and support self-adaptive MWS discovery in Smart Systems.
A Learning Vector Particle Swarm Algorithm Incorporating Sparrow for UAV Path Planning

UAV path planning has become a research hotspot in the current era. In order to make UAV plan the route reasonably in the real environment, this paper proposes a learning vector particle swarm optimization algorithm (slpso) based on sparrow, which uses vector decomposition of individual position to control the safety in the path; Firstly, the elite secondary reverse learning strategy is used to increase the distribution of the population; Then, the discoverer phase of sparrow search algorithm is introduced to update the optimal location of particle swarm optimization algorithm and enhance the population diversity. When the algorithm comes to a standstill, a one-dimensional learning strategy is used to improve the subsequent optimization means to help the algorithm jump out of the local optimization. Through the path planning experiments of the two models and Wilcoxon rank sum test, it can be seen that slpso has better effect than other algorithms in terms of path planning and convergence speed, and the route planned in complex environment is more secure and stable.
MRFDCNet: Multireceptive Field Dense Connection Network for Real-Time Semantic Segmentation

Semantic segmentation is widely used in automatic driving systems. To quickly and accurately classify objects in emergency situations, a large number of images need to be processed per second. To make a semantic segmentation model run on hardware with low memory and limited computing capacity, this paper proposes a real-time semantic segmentation network called MRFDCNet. This architecture is based on our proposed multireceptive field dense connection (MRFDC) module. The module uses one depthwise separable convolution branch and two depthwise dilated separable convolution branches with a proposed symmetric sequence of dilation rates to obtain local and contextual information under multiple receptive fields. In addition, we utilize a dense connection to allow local and contextual information to complement each other. We design a guided attention (GA) module to effectively utilize deep and shallow features. The GA module uses high-level semantic context to guide low-level spatial details and fuse both types of feature representations. MRFDCNet has only 1.07 M parameters, and it can achieve 72.8% mIoU on the Cityscapes test set with 74 FPS on one NVIDIA GeForce GTX 1080 Ti GPU. Experiments on the Cityscapes and CamVid test sets show that MRFDCNet achieves a balance between accuracy and inference speed. Code is available at https://github.com/Wsky1836/MRFDCNet.