Reviewerly/exHarmony huggingface dataset:
Bridging Unpaired Facial Photos and Sketches by Line-Drawings

In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. Our main idea is bridging the photo domain ${\mathcal{X}}$ and the sketch domain Y by using the line-drawing domain ${\mathcal{Z}}$. Specially, we map both photos and sketches to line-drawings by using a neural style transfer method, i.e. $F:{\mathcal{X}}/{\mathcal{Y}} \mapsto {\mathcal{Z}}$. Consequently, we obtain pseudo paired data $({\mathcal{Z}},{\mathcal{Y}})$, and can learn the mapping $G:{\mathcal{Z}} \mapsto {\mathcal{Y}}$ in a supervised learning manner. In the inference stage, given a facial photo, we can first transfer it to a line-drawing and then to a sketch by G ○ F. Additionally, we propose a novel stroke loss for generating different types of strokes. Our method, termed sRender, accords well with human artists’ rendering process. Experimental results demonstrate that sRender can generate multi-style sketches, and significantly outperforms existing unpaired image-to-image translation methods.
Learning Accurate Head Pose for Consumer Technology From 3D Synthetic Data

Accurate 3D head pose estimation from a 2D image frame is an essential component of modern consumer technology (CT). It enables a better determination of user attentiveness and engagement and can support immersive audio and AR experiences. While deep learning methods have improved the accuracy of head pose estimation models, these depend on the accurate annotation of training data. The acquisition of real-world head pose data with a large variation of yaw, pitch and roll is a very challenging task. Available head-pose datasets often have limitations in terms of the number of data samples, image resolution, annotation accuracy and sample diversity (gender, race, age). In this work, a rendering pipeline is proposed to generate pixel-perfect synthetic 2D headshot images from high-quality 3D facial models with accurate pose angle annotations. A diverse range of variations in age, race, and gender are provided. The resulting dataset includes more than 300k pairs of RGB images with the corresponding head pose annotations. For every hundred 3D models there are multiple variations in pose, illumination and background. The dataset is evaluated by training a state-of-the-art head pose estimation model and testing against the popular evaluation dataset BIWI. The results show training with purely synthetic data produced by the proposed methodology can achieve close to state-of-the-art results on the head pose estimation task and is better generalized for age, gender and racial diversity than solutions trained on `real-World' datasets.
Class-Selective Mini-Batching and Multitask Learning for Visual Relationship Recognition

An image can be described by the objects within it, and interactions between those objects. A pair of object labels together with an interaction label is known as a visual relationship, and is represented as a triplet of the form (subject, predicate, object). Recognising visual relationships in images is a challenging task, owing to the combinatorially large number of possible relationship triplets, which leads to an extreme multiclass classification problem. In addition, the distribution of visual relationships in a dataset tends to be long-tailed, i.e. most triplets occur rarely compared to a small number of dominating triplets. Three strategies to address these issues are investigated. Firstly, instead of predicting the full triplet, models can be trained to predict each of the three elements separately. Secondly a multitask learning strategy is investigated, where shared network parameters are used to perform the three separate predictions. Thirdly, a class-selective mini-batch construction strategy is used to expose the network to more of the rare classes during training. Experiments demonstrate that class-selective mini-batch construction can improve performance on classes in the long tail of the data distribution, possibly at the expense of accuracy on the small number of dominating classes. It is also found that a multitask model neither improves nor impedes performance in any significant way, but that its smaller size may be beneficial. In an effort to better understand the behaviour of the various models, a novel evaluation approach for visual relationship recognition is introduced. We conclude that the use of semantics can be helpful in the modelling and evaluation process.
A Simple RF-Based Solution for Gesture Recognition

In this paper, a novel radiofrequency (RF) based method for fine gesture recognition using mutual coupling (S21) between the two ports of a two-port orthogonally polarized antenna is proposed. The frequency-time variations of S21's phase or magnitude (so called phase and magnitude heatmaps), induced by the fingers/hand movement in the antenna's near-field region, when excited from one port, are used as gesture signature. The method was tested using a two-port linearly polarized patch antenna with a central frequency of 2.3 GHz for data acquisition and 2D-correlation for gesture classification. The experimental results on twelve pre-defined gestures showed true detection ratios of 98.33% and 80% using respectively the phase and magnitude heatmaps. The practical implementation of this sensing module is sufficiently small, cost-effective, and power-efficient so that it can be embedded in many Consumer Electronics (CE) devices.
Anti-Collision Car System using Fuzzy Logic Technique

This work proposed to design anti-collision car system that could predict the collision using fuzzy logic, controlled by PIC16F877A with pulse width modulation (PWM) technique. The ultrasonic sensor and humidity sensor are used to measure the distance and detect the relative humidity (RH) of the road condition. PWM duty ratio of a moving and stop condition of the vehicle are indicated with a value of > 70% and <; 40%, respectively. For a dangerous situation, the DC motor is turned off and the LED display is turned on as a warning to the driver.
TSMSAN: A Three-Stream Multi-Scale Attentive Network for Video Saliency Detection

Video saliency detection is an important low-level task that has been used in a large range of high-level applications. In this paper, we proposed a three-stream multi-scale attentive network (TSMSAN) for saliency detection in dynamic scenes. TSMSAN integrates motion vector (MV) representation, static saliency map, and RGB information in multi-scales together into one framework on the basis of Fully Convolutional Network (FCN) and spatial attention mechanism. On the one hand, the respective motion features, spatial features, as well as the scene features can provide abundant information for video saliency detection. On the other hand, spatial attention mechanism can combine features with multi-scales to focus on key information in dynamic scenes. In this manner, the proposed TSMSAN can encode the spatiotemporal features of the dynamic scene comprehensively. We evaluate the proposed approach on two public dynamic saliency datasets. The experimental results demonstrate TSMSAN is able to achieve the state-of-the-art performance as well as the excellent generalization ability. Furthermore, the proposed TSMSAN can provide more convincing video saliency information, in line with human perception.
Perancangan Dan Penyusunan Sistem Akuntansi Berbasis Microsoft Access Pada Toko Batam Filterindo

Toko Batam Filterindo is a business that is engaged in trading of sparepart for drinking water. Toko Batam Filterindo still does not have an accurate accounting recording system. The purpose of this work is hope that a system that has been designed by the author based on microsoft access can produce accurate financial reports and help make it easier for shop owners to record daily transactions. Therefore, the first thing the author did was to find the problems faced by shop owners and provide the right solutions to the shop owners.            The method of implementation applied by the author in this work is training. The author conducts training for owners and employees on how to access the system that has been designed from the main page to how to monitor the financial reports available in the system. The output achieved in this work is designing accounting recording system based on microsoft access, in this system it can produce financial position reports, income statements, changes in equity reports, cash flow reports, inventory reports, general ledgers, and general journals. In addition to designing an accounting recording system, the author arrange guidebook to make it easier for owners in the process of using the system. The accounting recording system designed by the author hopes to improve the recording system at the Toko Batam Filteridno. Recommendations for future are designing a system that contains accounts payable reports, asset reports, tax reports and other reports that have not been designed in this system.
AEFETA: Encrypted traffic classification framework based on self-learning of feature

This paper proposes an end-to-end framework based on feature self-learning, AEFETA, to handle the task of encrypted network traffic classification. In the AEFETA, we propose a data preprocessing method based on the structure information of the network traffic, and at the same time, a lightweight deep learning model combined with an attention mechanism also be proposed in this framework. We can convert the raw pcap file into a file with the data type that can be input to the deep neural network through data preprocessing, and then, automatically extract the spatial-temporal features perform classification tasks. AEFETA framework achieved a recall of 0.98 and precision of 0.97 in the encrypted traffic classification task, meanwhile, we find the best data format through experimental comparison.
Multiple Auxiliary Networks for Single Blind Image Deblurring

Single blind image deblurring caused by a combination of multiple factors has been one of the most challenging visual tasks. Recently, many essential methods of this task are based on deep learning networks and have achieved high performance. However, most of them only apply norm pixel-wise L1-loss function as the guide of training, which is not suitable or effective enough. In this paper, we propose Multiple Auxiliary Networks (MANet) for single blind image deblurring to assist norm L1-loss function and enhance the quality of the deblurring image. The main branch of our MANet is an encoder-decoder structure made up of residual blocks, and the three auxiliary branches are the edge prediction branch, the multi-scale refinement branch, and the perceptual loss branch. The experimental results demonstrate that the proposed MANet can obtain better deblurring performance with more details than state-of-the-art methods. The code is released at github.com/ZERO2ER0/MANet.
Attribute-Image Similarity Measure for Multimodal Attention Mechanism

Multimodal attention mechanisms in computer vision applications enable rich feature extraction by attending to specific image regions, highlighted through a second mode of data regarded as auxiliary information. The correspondence between image regions and auxiliary data can be defined as the similarity between parts of the two modes. In this paper, we propose a similarity measure that maximizes the posterior for matching high-level object attributes with image regions. In contrast to previous methods, we rely on attribute space rather than textual descriptions. We evaluate our results on the CUB dataset. The results show that the proposed method better minimizes the similarity loss function compared to the text-image similarity measurement.
A Visual System for Exploring Digital Cultural Relics and Generating Narrations

The development of digital humanities provides the entrance and the support for exploring cultural relic data. As the manager of exhibitions, curators would like to create such a tool to help them carrying out a reasonably content curation. However, a challenge is how to connect the separate cultural relics from the perspective of narration. Knowledge map could connect cultural relics as entities and make them associated. In this paper, we combine the cultural knowledge map and visualization, and realize a visual system to assist curators. The system supports experts conducting query and theme-associated exploration on cultural relics. By calculating the imagery similarity, a group of clustered cultural relics will be recommended. Besides, we design flexible interactions to generate the cultural relic network with narrative ideas from multiple perspectives. Thematic words are dynamically extracted from the narrative network for intuitive semantic support. We used the data from the Hunan museum and the Shanxi museum to verify the feasibility and effectiveness of our system.
A Security Analysis of the Danish Deposit Return System

The process that allows one to get rewarded for returning a container through reverse vending machines (RVM) involves people and technology. In fact, it typically sees a set of human parties (e.g. customers, cashiers) and technical parties (e.g., RVMs, databases, scanners) to collaborate in order to enable effective recycling. In this paper, we advance a formal treatment of the Danish Deposit Return System (DRS). We investigate the security of the ceremony that people are expected to perform in the context of DRS using field observation and automated reasoning tools. We give a particular focus to the security threats due to people interacting with the technology behind DRS. The findings of our investigation enable novel considerations of the ceremony weaknesses and make it possible to delineate potential mitigations.
Sketchable Interaction: Drawing User Interfaces with Interactive Regions

Sketchable Interaction (SI) describes a concept and environment where end-users create regions by drawing on a canvas. These regions apply effects to each other on collision. Attributes of regions, e.g. position, can be linked to each other so that they change together once modified, e.g. moved on the canvas. Within Sketchable Interaction, all entities - mouse pointer, desktop icons, or windows - are implemented as interactive regions. End-users customize this environment by drawing new regions that apply certain actions e.g. tagging files, deleting other regions or automating processes.
Gap Loss for Production Status Detection

In order to classify the monitoring model of the production state system more accurately and effectively, and accelerate the convergence speed of the model, a new loss function Gap Loss function is proposed. The image data and sensor data collected by the system in real time are used as feature data, the five self-defined states are the learning targets, and Gap Loss is used instead of the cross-entropy loss function for model training. Experimental verification shows that this method can more effectively classify production states compared with the cross entropy Loss function.
Interface Design for Users with Spinal Cord Injuries and Disorders: An Interdisciplinary Research Program with the US Department of Veterans Affairs

Assistive technologies such as environmental control units (ECUs) enhance veterans’ quality of life by enabling them to perform necessary tasks independently. Previous research has identified usability problems regarding the ECU’s multimodal interface. This paper outlines an interdisciplinary team’s program of research across academia, government agencies, and the private sector to evaluate and re-design the ECU interface for improved usability/user experience. The ECU was evaluated in two phases based on established Interface Design principles. First, heuristic analyses were performed to identify significant design issues. Second, modifications were made as the result of these initial analyses, and interactive digital prototypes of both the current and the enhanced ECU versions were developed and implemented to facilitate remote usability testing during the pandemic. The research program is currently in the A/B testing data collection phase to evaluate both interfaces for usability and user experience outcomes across defined tasks. This paper consequently provides an overview of each multidisciplinary partner’s contribution to the program of research, complete with challenges, strategies, and recommendations for practitioners interested in launching their own interdisciplinary projects.
Attacking and Defending Behind A Psychoacoustics-Based Captcha

This paper proposes a novel audio CAPTCHA system that requires a user to respond immediately after hearing a short and easy-to-remember cue in its mixture with background music. Potential attacking paths based on cross correlation (CC) and sound event detection (SED) are implemented to test the security of the system. Then, two defending measures based on phase-modification of the cue and audio watermarking are established against CC and SED-based attacks, respectively. Human subjects were recruited to test the system and the results indicated that the subjects can pass the proposed audio CAPTCHA >90% of the times. In contrast, the proposed defending measures suppress the passing rate of both attacks to 8.2% and 0.4%, respectively.
Research on Landscape Design of Country Parks in Cold Regions Based on Ecological Restoration

Abstract As a special form of urban green space, country parks can provide precious resources, leisure space and a good natural environment for the region. As the suburbs of cities are areas with fuzzy boundaries in the urban-rural integration zone, urban planning often only pays attention to the construction of the central area and neglects the maintenance of the urban edges, forcing the suburbs to suffer from agricultural pollution, domestic waste pollution and industrial pollution, and the ecological environment continues to deteriorate. As a result, the suburban ecological security system has become fragile, and the city has lost its green protection circle. There are many cold cities in China, and each cold city has different ecological problems due to human activities and different geographical conditions. This article is based on the existing theoretical research in cold cities, and comprehensively analyzes the ecological problems faced by cold cities. Based on the ecological restoration technology, this article explores the landscape design of the ecological restoration of country parks in cold regions, and provides references for restoring the ecological barriers of cold cities and promoting the sustainable development of the cold city ecosystem.
Fast and Efficient Neural Network for Light Field Disparity Estimation

As with many imaging tasks, disparity estimation for light fields seems to be well-matched to machine learning approaches. Neural network-based methods can achieve an overall bad pixel rate as low as four percent on the 4D light field benchmark dataset, continued effort to improve accuracy is resulting in diminishing returns. On the other hand, due to the growing importance of mobile and embedded devices, improving the efficiency is emerging as an important problem. In this paper, we improve the efficiency of existing neural net approaches for light field disparity estimation by introducing efficient network blocks, pruning redundant sections of the network and downsampling the resolution of feature vector. To improve performance, we also propose densely sampled epipolar image plane volumes as input. Experiment results show that our approach can achieve similar results compared with state-of-the-art methods while using only one-tenth runtime.
Real Image Super-Resolution Using Token Based Contextual Attention

Current state-of-the-art (SOTA) image super-resolution (SR) methods rely heavily on deep neural network (DNN), and many of them use attentions to regulate feature channels. While these models perform well on benchmark datasets where low-resolution (LR) images are constructed from high-resolution (HR) references with known blur kernel, real image SR is more challenging when the LR-HR pair are both collected from real cameras with complex blur kernel and noise statistics. Besides, current methods are trained in small image patches where channel attentions are calculated based on statistics of the full patch. This leads to impressive performance when the test image is small or chopped to small patches, but performs poorly when tested on full size real images. To alleviate these issues, we propose a new token based attention module with innovative contextual encoding to enable SR models to be robust to image patch sizes at testing. The dot-product attention between different tokens can efficiently describe the affinity relationship for different regions in an image. Together with the proximity relationship considered by contextual encoding, it leads to better global SR effects for full size images. Comprehensive experiments illustrate the superior performance of the proposed scheme.
National Innovation Ecosystems in the G-20 Countries: Institutions, Knowledge Infrastructure, and Firm Capabilities

Based on an ecosystem perspective, this study develops a conceptual model that identifies institutions, knowledge infrastructure, and firm capabilities as core complementary contributing factors to a nation’s aggregate innovative performance. It then tests the model’s basic propositions across nineteen of the G20 countries during the years 2006-2014. Preliminary results provide strong support for the propositions. The findings have important research, theory, and public policy implications, particularly for developing countries seeking to spur economic growth through enhanced innovation and entrepreneurial activities.
Automated Dimensional Analysis and Defect Recognition Using X-ray Images of Machinery Parts

In the modern world, due to contemporaneous globalization and industrial revolutions many industries got nurtured into huge economies, thereby being a crucial source of income for countries around the globe. In that framework, the role of hardware and manufacturing industries, heavy machinery, and electrical companies became important and is still relevant as yet. This opportunity presents great scope for engineering fields to discover various techniques to make the process easier and efficient in terms of time, cost of manufacturing, and maintenance. While recognizing efficiency in terms of production quality and low maintenance it's crucial to consider errors which in heavy machinery parts are subjected to defects like Voids, Cracks, inclusions, notches, rough lines and any other metallurgical changes, etc. This paper highlights our key findings on automated methods of recognition of such defects and its dimensional analysis using Xray images. Now further these defects are classified into External or Surface-level defects and Internal or Non-surface level defects, external defects are easier to find on a machinery part but internal defects are a very serious concern.
A Survey on Performing E-Voting through Facial Recognition

The election procedure is one of the most essential processes to take place in a democracy. Even though there have been immense technological advancements, the process of election has been highly limited. Most of the election procedures have been performed using ballot boxes which is an old process and needs to be updated. The security of such practices is also a concern as the identification of the voters is being done manually by the election officers. This process also needs an improvement to increase accuracy and reduce human errors by automating the process. Therefore, for this purpose, this research article analyzes the previous researches on this paradigm. This allows an effective understanding of the machine learning algorithms that are used for automatic facial recognition in the E-voting systems. This paper comes to the conclusion that the Recurrent Neural Networks are best suited for such an application for facial recognition. The future editions of this research will elaborate more on the proposed system in detail.