Reviewerly/exHarmony huggingface dataset:
Comic style transfer based on generative confrontation network

Style transfer refers to extracting styles from a specified style image template, and mapping the extracted style features to the content image without destroying the image content. At present, the use of deep learning methods for style transfer is one of the hotspots in the field of image research. This paper implements an image style transfer architecture based on cyclic consistent generative adversarial network, i. e., cyclic consistent confrontation network, and uses densely connected convolutional network to deepen the number of network layers. Test results show that stylized image with the proposed method is more obvious than that of the original cyclic consistent generative adversarial network, and the image quality is improved apparently.
Towards a framework of hansson's descriptor revision for conditionals

In descriptor revision as introduced by Hansson, it is possible to express different kinds of belief change operations like revision or contraction in a structured and combined way. In this paper, the descriptor framework is instantiated over a logic of qualitative conditionals. We propose to extend an approach employing the principle of conditional preservation, but covering only literal descriptors, to disjunctive descriptors. Such an extension would yield the possibility to express a much broader variety of belief change operators, for instance, "making up one's mind on a given formula in the underlying logic." Furthermore, we argue that the resulting conditional descriptor revision can be realized via constraint satisfaction problems.
Adversarial defense via self-orthogonal randomization super-network

Deep neural networks are demonstrated to be vulnerable to adversarial examples. In this paper, starting from the robustness analysis about the model ensemble, we propose a novel type of defense method named “Self-Orthogonal Randomization Super-network” (SORS). More specifically, we think the main robustness benefit from the model ensemble comes from two aspects: smaller adversarial subspace and gradient orthogonality. However, the naive model ensemble has two fundamental limitations: 1) Though ensembling more models will introduce more robustness, training too many models is infeasible and resource-consuming. 2) Since these models are usually trained independently, the gradient orthogonality among them is often partial and weak. Motivated by this, we propose to train one single super-network that consists of the exponential number of sub-networks, and explicitly constrain the gradient of different sub-networks with respect to the same input to be orthogonal. In the inference stage, at each forward pass, one sub-network will be randomly sampled. Through extensive experiments, we demonstrate that the proposed method can achieve significantly better robustness than the vanilla single model baseline and the naive model ensemble baseline. Moreover, this new type of defense strategy is also complementary to other types of defense methods and achieves state-of-the-art performance.
Short-term prediction model of photovoltaic power generation based on rough set-BP neural network

Abstract It is very important to predict the photovoltaic power generation because of the great challenges to the safe and stable operation of the power grid. Based on the analysis of traditional forecasting models, a power generation forecasting model based on rough set and neural network is proposed. Firstly, the Hampel filtering algorithm and rough set theory are used to process outliers and redundant data for the collected information. The above algorithms solve the problem of big data processing before forecasting. Finally, the fuzzy C-means clustering was used to divide the data set and combined with the neural network to predict. The key weather feature variables combined with the forecast time were taken as the input and the power was taken as the output variable. The results show that the prediction method proposed in this paper is more accurate and fast in predicting the power generation at each time.
Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches

Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images.
Link Activation Using Variational Graph Autoencoders

An unsupervised method is proposed for link activation in wireless networks by identifying clusters of interfering users. A k-nearest neighbors interference graph is first defined for the wireless network which is then mapped to a stochastic latent space. The users are then clustered in the latent space using a Gaussian mixture model, and one user from each interfering cluster is activated while the rest of the users in that cluster remain idle. The proposed framework is scalable, works across several network topologies such as device to device (D2D), and is close to the optimal solution in performance.
Akin: Generating UI Wireframes From UI Design Patterns Using Deep Learning

During the User interface (UI) design process, designers use UI design patterns for conceptualizing different UI wireframes for an application. This paper introduces Akin, a UI wireframe generator that allows designers to chose a UI design pattern and provides them with multiple UI wireframes for a given UI design pattern. Akin uses a fine-tuned Self-Attention Generative Adversarial Network trained with 500 UI wireframes of 5 android UI design patterns. Upon evaluation, Akin's generative model provides an Inception Score of 1.63 (SD=0.34) and Fréchet Inception Distance of 297.19. We further conducted user studies with 15 UI/UX designers to evaluate the quality of Akin-generated UI wireframes. The results show that UI/UX designers considered wireframes generated by Akin are as good as wireframes made by designers. Moreover, designers identified Akin-generated wireframes as designer-made 50% of the time. This paper provides a baseline for further research in UI wireframe generation by providing a baseline metric.
Perfect Sampling of the Posterior in the Hierarchical Pitman–Yor Process

The predictive probabilities of the hierarchical Pitman-Yor process are approximated through Monte Carlo algorithms that exploits the Chinese Restaurant Franchise (CRF) representation. However, in order to simulate the posterior distribution of the hierarchical Pitman-Yor process, a set of auxiliary variables representing the arrangement of customers in tables of the CRF must be sampled through Markov chain Monte Carlo. This paper develops a perfect sampler for these latent variables employing ideas from the Propp-Wilson algorithm and evaluates its average running time by extensive simulations. The simulations reveal a significant dependence of running time on the parameters of the model, which exhibits sharp transitions. The algorithm is compared to simpler Gibbs sampling procedures, as well as a procedure for unbiased Monte Carlo estimation proposed by Glynn and Rhee. We illustrate its use with an example in microbial genomics studies.
High‐sensitivity synchronous image encryption based on improved one‐dimensional compound sine map

An improved one-dimensional compound sine map is introduced. The evaluation of this chaotic system shows that it has high sensitivity and random chaotic behaviour. Based on this chaotic system, a new fast image encryption scheme is proposed. Through the cross-processing of multiple chaotic sequences, two high-sensitivity pseudo-random sequences are generated, and the generated high-sensitivity random sequence is used for synchronization cross-processing in four directions. Using the symmetry of the image, the image is divided into four directions, and the processing of each direction is different based on the high-sensitivity sequence and the chaotic value. Through one traversal, the entire image is processed four times differently. The encryption process is uncontrollable and invisible depending on the chaotic sequence. Simulation test results show that the algorithm has good encryption results, is sensitive to the initial secret key and provides satisfactory security capabilities compared with other algorithms.
Caption Generation of Robot Behaviors Based on Unsupervised Learning of Action Segments

Bridging robot action sequences and their natural language captions is an important task to increase explainability of human assisting robots in their recently evolving field. In this paper, we propose a system for generating natural language captions that describe behaviors of human assisting robots. The system describes robot actions by using robot observations; histories from actuator systems and cameras, toward end-to-end bridging between robot actions and natural language captions. Two reasons make it challenging to apply existing sequence-to-sequence models to this mapping: (1) it is hard to prepare a large-scale dataset for any kinds of robots and their environment, and (2) there is a gap between the number of samples obtained from robot action observations and generated word sequences of captions. We introduced unsupervised segmentation based on K-means clustering to unify typical robot observation patterns into a class. This method makes it possible for the network to learn the relationship from a small amount of data. Moreover, we utilized a chunking method based on byte-pair encoding (BPE) to fill in the gap between the number of samples of robot action observations and words in a caption. We also applied an attention mechanism to the segmentation task. Experimental results show that the proposed model based on unsupervised learning can generate better descriptions than other methods. We also show that the attention mechanism did not work well in our low-resource setting.
Development of laser-based system to determine fault surface morphology at the micron scale

Fault surface morphology plays an important role in the earthquake rupture process. However, determining the frictional surface morphology at the micron scale is challenging owing to the lack of reliable measurement instruments. To overcome the existing instruments' limitations, we developed a novel system. The system effectively measures the steep slope generated in the adjacent fault striations and fault steps. The single measurement area is large, with a value of 30 cm × 30 cm. Furthermore, the developed instrument has a horizontal (X and Y orientations) and vertical (Z orientation) positioning uncertainty of 3.5 μm and 4.5 μm (Z orientation), and a horizontal and vertical resolution of 0.5 μm and 0.076 μm, respectively, which are better than those of existing instruments. The proposed device can be used to practically evaluate the wear surface's shape to obtain high-accurate point cloud data, satisfying the engineering requirements for morphological analyses.
A Comparative Study to Analyze the Performance of Advanced Pattern Recognition Algorithms for Multi-Class Classification

This study aims to implement the following four advanced pattern recognition algorithms, such as “optimal Bayesian classifier,” “anti-Bayesian classifier,” “decision trees (DTs),” and “dependence trees (DepTs)” on both artificial and real datasets for multi-class classification. Then, we calculated the performance of individual algorithms on both real and artificial data for comparison. In Sect. 1, a brief introduction is given about the study. In the second section, the different types of datasets used in this study are discussed. In the third section, we compared the classification accuracies of Bayesian and anti-Bayesian methods for both the artificial and real-life datasets. In the fourth section, a comparison between the classification accuracy of DT and DepT classification methods for both the artificial and real-life datasets is discussed. In the fifth section, a comparison between the classification accuracy of the four algorithms, such as (a) Bayes, (b) anti-Bayes, (c) DTs, and (d) DepTs for both the artificial and real datasets is explained. We used 5-fold cross-validation to determine the classification accuracy of individual, machine learning-based, advanced pattern recognition (PR) models.
Improving CNN performance on FPGA clusters through topology exploration

Field Programmable Gate Array (FPGA) platform has been a popular choice for deploying Convolution Neural Networks (CNNs) as a result of its high parallelism and low energy consumption. Due to the limited on-chip computation and storage resources, FPGA clusters are becoming promising candidates to improve CNN throughputs. In this paper, we first put forward strategies to optimize the inter-board resource allocation in FPGA clusters. Then we model the multi-board cluster problem based on dynamic programming to get the optimal topology of the FPGA clusters. Experimental results show that typical well-known CNNs with our proposed FPGA cluster topology obtains an average throughput 4.33X than single-board solutions and 1.87X than other state-of-the-art multi-board solutions.
Similar scene retrieval in soccer videos with weak annotations by multimodal use of bidirectional LSTM

This paper presents a novel method to retrieve similar scenes in soccer videos with weak annotations via multimodal use of bidirectional long short-term memory (BiLSTM). The significant increase in the number of different types of soccer videos with the development of technology brings valid assets for effective coaching, but it also increases the work of players and training staff. We tackle this problem with a nontraditional combination of pre-trained models for feature extraction and BiLSTMs for feature transformation. By using the pre-trained models, no training data is required for feature extraction. Then effective feature transformation for similarity calculation is performed by applying BiLSTM trained with weak annotations. This transformation allows for highly accurate capture of soccer video context from less annotation work. In this paper, we achieve an accurate retrieval of similar scenes by multimodal use of this BiLSTM-based transformer trainable with less human effort. The effectiveness of our method was verified by comparative experiments with state-of-the-art using actual soccer video dataset.
Representation learning for clustering via building consensus

Abstract In this paper, we focus on unsupervised representation learning for clustering of images. Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be close in the representation space (exemplar consistency), and/or similar images must have similar cluster assignments (population consistency). We define an additional notion of consistency, consensus consistency , which ensures that representations are learned to induce similar partitions for variations in the representation space, different clustering algorithms or different initializations of a single clustering algorithm. We define a clustering loss by executing variations in the representation space and seamlessly integrate all three consistencies (consensus, exemplar and population) into an end-to-end learning framework. The proposed algorithm, consensus clustering using unsupervised representation learning (ConCURL), improves upon the clustering performance of state-of-the-art methods on four out of five image datasets. Furthermore, we extend the evaluation procedure for clustering to reflect the challenges encountered in real-world clustering tasks, such as maintaining clustering performance in cases with distribution shifts. We also perform a detailed ablation study for a deeper understanding of the proposed algorithm. The code and the trained models are available at https://github.com/JayanthRR/ConCURL_NCE .
Augmented Reality Shopping Framework

Augmented Reality (AR) is becoming an important computing platform with the potential of enhancing the experiences of everyday users. One of those experiences is shopping. When customers buy any product, it is very discouraging to locate vital information such as the expiration date. This paper discusses an AR solution for shoppers that want to elevate their shopping experience in general by an enhanced information search. The solution can be also expanded in the future to assist shoppers who are suffering from a reduced visual sense. A proof of concept application is developed to test the solution, where we simulate a medicine shopping scenario. The application we developed on the HoloLens uses the Vuforia SDK to tag products and display information about these products. In addition, it contains elements that can alter the text size thus providing assistive support option for the visually impaired in the future. We predict that the convenience of having information displayed next to products as well as the ability to control the text size will make shopping more pleasant. Our architecture shows promise for a full assistive technology for the visually impaired that could be vital for the community, and there are future plans to port it to a different platform such as Android or iOS. Moreover, the architecture harnesses the ability to customize the information that the user can select to view, such as instructions, ingredients, and/or users' review. We conclude the paper with our look into the future.
Effective Universal Unrestricted Adversarial Attacks Using a MOE Approach

Recent studies have shown that Deep Leaning models are susceptible to adversarial examples, which are data, in general images, intentionally modified to fool a machine learning classifier. In this paper, we present a multi-objective nested evolutionary algorithm to generate universal unrestricted adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known image filters that are available in several image processing libraries, modern cameras, and mobile applications. The multi-objective optimization takes into account not only the attack success rate but also the detection rate. Experimental results showed that this approach is able to create a sequence of filters capable of generating very effective and undetectable attacks.
A Blockchain-based Approach to Secure Cloud Connected IoT Devices

With progress in science and technology and the rapid development of the Internet of Things (IoT) which are connected with the cloud-based network, need to communicate between themselves. In addition, a user needs several types of authentication process so that the user can interact with the devices. However, these devices can be under attack, such as a single-point-failure at any time, and some private data can be tampered and stolen by the attackers. In this paper, we propose a blockchain-based approach that assigns a unique device ID and using smart contracts to store the data transaction records in the blockchain for each IoT device as well as cloud and user access. Blockchain-based smart contracts provide distributed digital records that can endure data tampering and data snitching attacks. This method secures the user authenticity and maintains the privacy of IoT data on the cloud using Merkle Tree data structure and Practical Byzantine Fault Tolerance (pBFT). Finally, we implement an open-source blockchain platform that verifies the prototype of the proposed system.
Minimal Cardinality Diagnosis in Problems with Multiple Observations

Model-Based Diagnosis (MBD) is a well-known approach to diagnosis in medical domains. In this approach, the behavior of a system is modeled and used to identify faulty components, i.e., once a symptom of abnormal behavior is observed, an inference algorithm is run on the system model and returns possible explanations. Such explanations are referred to as diagnoses. A diagnosis is an assumption about which set of components are faulty and have caused the abnormal behavior. In this work, we focus on the case where multiple observations are available to the diagnoser, collected at different times, such that some of these observations exhibit symptoms of abnormal behavior. MBD with multiple observations is challenging because some components may fail intermittently, i.e., behave abnormally in one observation and behave normally in another, while other components may fail all the time (non-intermittently). Inspired by recent success in solving classical diagnosis problems using Boolean satisfiability (SAT) solvers, we describe two SAT-based approaches to solve this MBD with multiple observations problem. The first approach compiles the problem to a single SAT formula, and the second approach solves each observation independently and then merges them together. We compare these two approaches experimentally on a standard diagnosis benchmark and analyze their pros and cons.
Comparative Analysis of Active Contour Models for Brain Tumor segmentation from T1w MRI Images

Early detection of brain tumors helps the doctors to treat the patients, also decrease the risk and improve the chance of survival. In the early days, brain tumor identification is being done manually, which need a lot of expertise and it's a time-consuming task. Magnetic resonance images (MRI) scans support getting brain tumors at an early stage, and it has been turned into a hot research topic in medical image processing. Hence, presented the comparative analysis of chan-vese (C-V) model and level set (LS) method intended to detect the tumor from human brain MRI images. This article's major objective is to compare the effectiveness of active contour models (ACM) intended to segment the brain tumor in terms of accuracy and computation time. To detect tumors, T1weighted abnormal images by the size of 256 × 256 were segmented after suppressing the noise by applying the pre-processing step. Both segmentation model's performance is assessed using the BRATS 2017 and Harvard university datasets. From all the responses, the C-V model is detecting the tumor effectively with a high speed as compared to the LS method. Finally, this work reveals that the C-V segmentation technique outer performs to detect the brain tumor with high accuracy and minimal processing time over the LS method.