Reviewerly/exHarmony huggingface dataset:
Activation Density Driven Efficient Pruning in Training

Neural network pruning with suitable retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy. Typical pruning methods require large, fully trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining procedure to regain the original accuracy. We propose a novel pruning method that prunes a network real-time during training, reducing the overall training time to achieve an efficient compressed network. We introduce an activation density based analysis to identify the optimal relative sizing or compression for each layer of the network. Our method is architecture agnostic, allowing it to be employed on a wide variety of systems. For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200 x reduction in parameters and over 60 x reduction in inference compute operations in the best case) with accuracy comparable to the baseline network. By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods. Furthermore, training compressed networks at different epochs with our proposed method yields considerable reduction in training compute complexity (1.6 x to 3.2 x lower) at near iso-accuracy as compared to a baseline network trained entirely from scratch.
Orthographic Projection Linear Regression for Single Image 3D Human Pose Estimation

3D human pose estimation from a single 2D image in the wild is an important computer vision task but yet extremely challenging. Unlike images taken from indoor and well constrained environments, 2D outdoor images in the wild are extremely complex because of varying imaging conditions. Furthermore, 2D images usually do not have corresponding 3D pose ground truth making a supervised approach ill-constrained. Therefore, in this paper, we propose to associate the 3D human pose, the 2D human pose projection and the 2D image appearance through a new orthographic projection based linear regression module. Unlike existing reprojection based approaches, our orthographic projection and regression do not suffer from small angle problems, which usually lead to overfitting in the depth dimension. Hence, we propose a deep neural network which adopts the 2D pose, 3D pose regression and orthographic projection linear regression module. The proposed method shows state-of-the-art performance on the Human3.6M dataset and generalizes well to in-the-wild images.
An Adaptive Part-Based Model For Person Re-Identification

Existing part-based models for person Re-IDentification (Re-ID) usually suffer from part-misalignment problem caused by uniform partition of feature maps. The performances of part-based model are highly dependent on the semantically-aligned parts of the query and gallery images. However, misalignments occur very commonly in person Re-ID tasks due to the variations of viewpoints and object distances. To address the part-misalignment problem and learn a more discriminative embedding for person Re-ID, we propose a novel Adaptive Part-based Model (APM), which adaptively partition the extracted feature maps by a Partition-Aware module to learn an embedding. The proposed adaptive partition method is very robust to the variations of the pedestrian scale and effective in resolving the part-misalignment problem. Experimental results on three commonly used datasets, including Market-1501, DukeMTMC-reID and CUHK03, clearly demonstrate that the proposed method achieves the state-of-the-art performance.
Group-wise Feature Orthogonalization and Suppression for GAN based Facial Attribute Translation

Generative Adversarial Network (GAN) has been widely used for object attribute editing. However, the semantic correlation, resulted from the feature map interaction in the generative network of GAN, may impair the generalization ability of the generative network. In this work, semantic disentanglement is introduced in GAN to reduce the attribute correlation. The feature maps of the generative network are first grouped with an efficient clustering algorithm based on hash encoding, which are used to excavate hidden semantic attributes and calculate the group-wise orthogonality loss for the reduction of attribute entanglement. Meanwhile, the feature maps falling in the intersection regions of different groups are further suppressed to reduce the attribute-wise interaction. Extensive experiments reveal that the proposed GAN generated more genuine objects than the state of the arts. Quantitative results of classification accuracy, inception score and FID score further justify the effectiveness of the proposed GAN.
Synchronization of Acoustic Signals for Steganographic Transmission

Steganography is a technique that makes it possible to hide additional information (payload) in the original signal (cover work). This paper focuses on hiding information in a speech signal. One of the major problems with steganographic systems is ensuring synchronization. The paper presents four new and effective mechanisms that allow achievement of synchronization on the receiving side. Three of the developed methods of synchronization operate directly on the acoustic signal, while the fourth method works in the higher layer, analyzing the structure of the decoded steganographic data stream. The results of the research concerning both the evaluation of signal quality and the effectiveness of synchronization are presented. The signal quality was assessed based on both objective and subjective methods. The conducted research confirmed the effectiveness of the developed methods of synchronization during the transmission of steganographic data in the VHF radio link and in the VoIP channel.
Batik Clothes Auto-Fashion using Conditional Generative Adversarial Network and U-Net

For Indonesian people, batik is one of the cultural heritages they are trying to preserve. Batik represents many regions in Indonesia, and each region has many motifs and also its usage. In this research, we propose a new method to apply batik patterns into a piece of clothing word by a person in a photo. Our proposed method consists of segmenting pieces of clothing from the images and blend them with our generated batik. We propose a CGAN with U-Net architecture to generate semantic segmentation on the image's clothes. This research also offers a batik motif generation system on the image's clothes system using CGAN with U-Net architecture. Experimental results show the advantages of our Batik Generative Method. Our experiments show that our best segmentation network can achieve 80.20% in F1 Measure and 70.32% in Jaccard Index. Our texture synthesis can achieve 99.992% in cosine similarity and 5.35 in Euclidean distance metric.
Medical Image Retrieval Using Convolution Neural Networks

Abstract In this paper, medical image retrieval is done in an effective manner by using convolution neural network (CNN). This proposed system locates reference tags and classes the DICOM images using image processing techniques and retrieval of images are done by using GUI panel. First, the deep learning technique is used to extract powerful features of the image for tag description. Conversely, this technique performs tag matching directly by passing suitable parameters which recognizes the classes as queries. The comparison features are able to capture the general form of the input image and its class based on image tags. Here, we have used a collection of 22 classes of database to demonstrate the efficiency of our system. The experimental results show the classification of images by deep learning algorithm which is used to gain the rate of retrieval accuracy by using MATLAB.
Tensor Reordering for CNN Compression

We show how parameter redundancy in Convolutional Neural Network (CNN) filters can be effectively reduced by pruning in spectral domain. Specifically, the representation extracted via Discrete Co-sine Transform (DCT) is more conducive for pruning than the original space. By relying on a combination of weight tensor reshaping and reordering we achieve high levels of layer compression with just minor accuracy loss. Our approach is applied to compress pre-trained CNNs and we show that minor additional fine-tuning allows our method to recover the original model performance after a significant parameter reduction. We validate our approach on ResNet-50 and MobileNet-V2 architectures for ImageNet classification task.
Study on the Current Status of Smart Garden

Modern society is becoming more informed and intelligent with the development of digital technology, in which humans, objects, and networks relate with each other.In accordance with the changing times, a garden system has emerged that makes it easy to supply the ideal temperature, humidity, sunlight, and moisture conditions to grow plants.Therefore, this study attempted to grasp the concept, perception, and trends of smart gardens, a recent concept.To achieve the purpose of this study, previous studies and text mining were used, and the results are as follows.First, the core characteristics of smart gardens are new gardens in which IoT technology and gardening techniques are fused in indoor and outdoor spaces due to technological developments and changes in people's lifestyles.As technology advances and the importance of the environment increases, smart gardens are becoming a reality due to the need for living spaces where humans and nature can co-exist.With the advent of smart gardens, it will be possible to contribute to gardens' vitalization to deal with changes in garden-related industries and people's lifestyles.Second, in current research related to smart gardens and users' experiences, the technical aspects of smart gardens are the most interesting.People value smart garden functions and technical aspects that enable a safe, comfortable, and convenient life, and subjective uses are emerging depending on individual tastes and the comfort with digital devices.Third, looking at the usage behavior of smart gardens, they are mainly used in indoor spaces, with edible plants are being grown.Due to the growing importance of the environment and concerns about climate change and a possible food crisis, the tendency is to prefer the cultivation of plants related to food, but the expansion of garden functions can satisfying users' needs with various technologies that allow for the growing of flowers.In addition, as users feel the shapes of smart gardens are new and sophisticated, it can be seen that design is an essential factor that helps to satisfy users.Currently, smart gardens are developing in terms of technology.However, the main components of the smart garden are the combination of humans, nature, and technology rather than focusing on growing plants conveniently by simply connecting potted plants and smart devices.It strengthens connectivity with various city services and smart homes.Smart gardens interact with the landscape of the architect's ideas rather than reproducing nature through science and technology.Therefore, it is necessary to have a design that considers the functions of the garden and the needs of users.In addition, by providing citizens indoor and urban parks and public facilities, it is possible to share the functions of communication and gardening among generations targeting those who do not enjoy 'smart' services due to age and bridge the digital device and information gap.Smart gardens have potential as a new landscaping space.
Cyber Resiliency in the Context of Cloud Computing Through Cyber Risk Assessment

Cyber resiliency in Cloud computing is one of the most important capability of an enterprise network that provides continues ability to withstand and quick recovery from the adversary conditions. This capability can be measured through cybersecurity risk assessment techniques. However, cybersecurity risk management studies in cloud computing resiliency approaches are deficient. This paper proposes resilient cloud cybersecurity risk assessment tailored specifically to Dropbox with two methods: technical-based solution motivated by a cybersecurity risk assessment of cloud services, and a target personnel-based solution guided by cybersecurity-related survey among employees to identify their knowledge that qualifies them withstand to any cyberattack. The proposed work attempts to identify cloud vulnerabilities, assess threats and detect high risk components, to finally propose appropriate safeguards such as failure predicting and removing, redundancy or load balancing techniques for quick recovery and return to pre-attack state if failure happens.
Skip Attention GAN for Remote Sensing Image Synthesis

High-quality remote sensing images are difficult to obtain due to limited conditions and high cost for data acquisition. With the development of machine vision and deep learning, some image generation methods (e.g., GANs) are introduced into this field, but it&#x2019;s still hard to generate images with good texture details and structural dependencies. We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. With this method, long-range dependencies learned from low-resolution are used for generating remote sensing images with more structural details. We name this method as Skip Attention GAN, which is the first method applying cross-scale attention mechanism for unsupervised remote sensing image generation. Experiments show that our method outperforms previous methods under several metrics. Visual and ablation results of attention layers show that Skip Attention has learned long-distance structural dependencies between similar targets.
American Sign Language Detection Using Instance-Based Segmentation

Deaf & mute people communicate usually in Sign Language with each other. Although the sign-language is very well known among these people it's quite unknown by other people. An attempt is made in this project to bridge the communication gap for the people who don't know Sign Language. The sign language used is the American Sign Language Lexicon Video dataset for this project which can be further extended to different languages. The dataset was in video format so a dominant frame extraction algorithm that would extract the dominant images from the video was used to create an as-per-requirement dataset. We found a lot of work had been already done on finger-spellings recognition but since finger-spellings make it much more complicated in actual time we thought of moving ahead with rather identifying words. The work was completed on a Computer Vision's State-of-the-art model based on Deep Learning that helped in achieving the completion of the task at instance level classification making the recognition task to a better identification at pixel-level which in-turn helps in achieving better results. An attempt has been made here to employ the latest method to make the computer learn sign languages that are independent of a person's complexion, lighting conditions, and orientations.
Using a 6 Degrees of Freedom Virtual Reality Input Device With An Augmented Reality Headset In A Collaborative Environment

Augmented reality headsets have become increasingly consumer-available. Often gesture and speech are the main input modalities provided by these headsets. For some tasks, users may need a more precise input method. Tracked controllers can be added by using image tracking; however, this is not always the most accurate solution. This work outlines how to use off-the-shelf products to create a collaborative cross-device mixed reality experience. In that experience, the positionally tracked inputs from one headset can be used by another headset that may not natively support them.
A literature survey of sentiment analysis based on E-commerce reviews

Electronic commerce lately has become an easy place for individuals to purchase and consume. People have become keener to display their ideas on the web concerning the different products. The emerging social platforms have also played a crucial role in contributing to these practices, providing an open forum for exchanging views across the globe. Consumer reviews on e-commerce sites can form the basis for performing sentiment analysis, which in turn can assist in effectively improving satisfaction among the users. This paper presents a comprehensive way of sentiment analysis which provides the viewpoints highlighted in various articles regarding the process, required tasks and strategies of sentiment analysis. It also discusses various challenges associated with the sentiment classification process. The paper also discusses further work that can be done in the field.
Diversify or Not: Dynamic Diversification for Personalized Recommendation

Diversity is believed to be an essential factor in improving user satisfaction in recommender systems, while how to take advantage of it has long been a problem worth exploring. Existing work either ignores the influence of diversity or overlooks users’ different diversity demands in recommendations. In this study, we analyze users’ behaviors on a real-world dataset collected from an e-commerce website and find that the demand for diversity changes among different users, even the same user’s demand varies among different shopping scenarios. There is also evidence that users’ behaviors are affected by the diversity of impressions, which has been often ignored by traditional session-based recommendation models. Then, we propose a Dynamic Diversification Recommendation Model (DDRM) with the integration of both click and impression diversities to better make use of diversity for recommendations. Extensive experimental results demonstrate that DDRM outperforms all baseline methods significantly. Further studies show our model can provide more precise and reasonable recommendations.
Activity Recognition Using First-Person-View Cameras Based on Sparse Optical Flows

First-person-view (FPV) cameras are finding wide use in daily life to record activities and sports. In this paper, we propose a succinct and robust 3D convolutional neural network (CNN) architecture accompanied with an ensemble-learning network for activity recognition with FPV videos. The proposed 3D CNN is trained on low-resolution (32 × 32) sparse optical flows using FPV video datasets consisting of daily activities. According to the experimental results, our network achieves an average accuracy of 90%.
Unmanned Aerial Vehicle Classification and Detection Based on Deep Transfer Learning

Currently, major security incidents caused by the “unlicensed flying” of Unmanned Aerial Vehicle (UAV) have emerged one after another, which poses a grave threat to the security issues of public facilities and sensitive areas. Whether it can timely detect and prevent “unlicensed flying” of UAV has become a social concern. In response to this demand, the transfer learning method is adopted in this paper to conduct twoclassification and detection on UAV images. Image recognition technology based on transfer learning is an effective method to improve recognition accuracy by applying deep learning models to small samples. Different from the large number of training samples required by deep learning, transfer learning transfers the weights of the pre-trained deep neural network, and uses only small sample data to obtain good results in UAV image recognition. First of all, this paper proposes to construct a UAV data set according to different types of UAV shape structures, to perfect the classification and detection effect and the generalization ability of the model. Then, based on the transfer learning method, experimental comparison is made between three classic deep convolutional neural network classification models (Inception V3, ResNet 101 and VGG16) and two classic deep convolutional neural network detection models (Faster RCNN and SSD). Finally, an experimental evaluation is conducted on the collected UAV test data set. Compared with the traditional recognition model, the image classification model based on transfer learning employed in this paper has achieved important improvements in accuracy, recall and precision. Especially in the InceptionV3 model of transfer training, the recall reaches 96.98%. In addition, the image detection model based on transfer learning has achieved good detection results in accuracy, recall and F1-score.
Path Following and Obstacle Avoidance for Unmanned Aerial Vehicles Using a Virtual-Force-Based Guidance Law

This paper presents a virtual-force-based guidance law (VFGL) for path following and obstacle avoidance in unmanned aerial vehicles. First, a virtual spring force and a virtual drag force are designed for straight-line following; then, the dynamic of the cross-track-error is equivalent to a spring mass system, which is easy to tune to acquire stability and non-overshoot convergence. Secondly, an additional virtual centripetal force is designed to counteract the influence of the curvature of the planned path so that the guidance law can accurately track a curve with a time-varying curvature. Thirdly, an extra virtual repulsive force is designed directly according to the sensor inputs; the virtual repulsive force pushes the vehicle away to move around obstacles. The use of artificial physics means the guidance law is founded on solid physical theory and is computationally simple. The physical meanings of the parameters are definite, and the VFGL has a large parameter adaptation. These make the guidance law easy to tune in application. Both the numerical and hardware-in-the-loop simulation results demonstrated the effectiveness of the proposed guidance law for path following and obstacle avoidance in unmanned aerial vehicles.
City-Stories: Combining Entity Linking, Multimedia Retrieval, and Crowdsourcing to Make Historical Data Accessible

Digitized historical image collections as provided by individuals or memory institutions often suffer from limited or a complete lack of metadata In this paper, we present the City-Stories system that combines entity linking, multimedia retrieval, and crowdsourcing to make historical images searchable even across collections.
RGB-T Tracking via Multi-expert Correlation Filters using Spatial-temporal Robustness

RGB-T tracking has made impressive progress due to the complementarity of RGB images and TIR images. However, most CF based trackers use the cross-modal information through simple feature concatenation, which does not take into account the cross-modality discrepancy. In this paper, we propose a multi-expert tracking framework based on discriminative correlation filter. Specifically, a simple robustness evaluation strategy is used to select the most proper tracking result from multiple experts as the tracking result. Besides, we use principal component analysis to reduce redundancy and improve tracking speed, with little sacrifice on the tracking accuracy. Comprehensive experiments on RGBT210 dataset has proved the effectiveness of the proposed tracker.
A Supporting System Design for Basketball Offense Tactics

In terms of basketball, a player always decides an option of the next movement what leads to get score. In this research, we propose an environment focusing on a ball holder, who has to select the next action. As a support, our environment visualizes options for the ball holder’s next actions. Furthermore, the positional relationship among players is measured from the coordinate obtained from the game data, and the system caliculates and provides information about how easy to get score. We design such a system with these functions so that the user can learn how to select the next movement.