Reviewerly/exHarmony huggingface dataset:
Using VDMS to index and search 100M images

Data scientists spend most of their time dealing with data preparation, rather than doing what they know best: build machine learning models and algorithms to solve previously unsolvable problems. In this paper, we describe the Visual Data Management System (VDMS), and demonstrate how it can be used to simplify the data preparation process and consequently gain in efficiency simply because we are using a system designed for the job. To demonstrate this, we use one of the largest available public datasets (YFCC100M), with 100 million images and videos, plus additional data including machine-generated tags, for a total of about ~12TB of data. VDMS differs from existing data management systems due to its focus on supporting machine learning and data analytics pipelines that rely on images, videos, and feature vectors, treating these as first class citizens. We demonstrate how VDMS outperforms well-known and widely used systems for data management by up to ~364x, with an average improvement of about 85x for our use-cases, and particularly at scale, for a image search engine implementation. At the same time, VDMS simplifies the process of data preparation and data access, and provides functionalities non-existent in alternative options.
Multi-Initialization Graph Meta-Learning for Node Classification

Meta-learning aims to acquire common knowledge from a large amount of similar tasks and then adapts to unseen tasks within few gradient updates. Existing graph meta-learning algorithms show appealing performance in a variety of domains such as node classification and link prediction. These methods find a single common initialization for entire tasks and ignore the diversity of task distributions, which might be insufficient for multi-modal tasks. Recent approaches adopt modulation network to generate task-specific parameters for further achieving multiple initializations, which shows excellent performance for multi-modal image classification. However, different from image classification, how to design an effective modulation network to handle graph-structure dataset is still challenging. In this paper, we propose a Multi-Initialization Graph Meta-Learning (MI-GML) network for graph node classification, mainly consisting of local and global modulation neworks and meta learner. In terms of modulation network, we exploit local and global graph structure information to extract task-specific modulation parameters. On this basis, the meta learner is further modulated by the corresponding modulation parameter to produce task-specific representation for node classification. Experimental results on three graph-structure datasets demonstrate the effectiveness of MI-GML in few-shot node classification tasks.
Incremental construction of modal implication graphs for evolving feature models

A feature model represents a set of variants as configurable features and dependencies between them. During variant configuration, (de)selection of a feature may entail that other features must or cannot be selected. A Modal Implication Graph (MIG) enables efficient decision propagation to perform automatic (de)selection of subsequent features. In addition, it facilitates other configuration-related activities such as t-wise sampling. Evolution of a feature model may change its configuration logic, thereby invalidating an existing MIG and forcing a full recomputation. However, repeated recomputation of a MIG is expensive, and thus hampers the overall usefulness of MIGs for frequently evolving feature models. In this paper, we devise a method to incrementally compute updated MIGs after feature model evolution. We identify expensive steps in the MIG construction algorithm, enable them for incremental computation, and measure performance compared to a full rebuild of a complete MIG within the evolution histories of four real-world feature models. Results show that our incremental method can increase the speed of MIG construction by orders of magnitude, depending on the given scenario and extent of evolutionary changes.
A Single-Node Classifier Implementation on Chua Oscillator within a Physical Reservoir Computing Framework

The study lies in the field of physical reservoir computing and aims to develop a classifier using Fisher Iris dataset for benchmark tasks. Single Chua chaotic oscillator acts as a physical reservoir. The study was performed using computer simulation. The features of Iris flowers are represented as the consequence of short pulses at a constant level of a control parameter, which is fed to the oscillator, changing its dynamics. During the classification of flowers, the oscillator works without being reset, so each pulse on the input changes the phase trajectory and makes it unique for each Iris flower. Finally, the estimation of the symmetry of an attractor makes it possible to connect each species of Iris with the properties of the attractor. The resulting architecture of the classifier includes a single-node externally-driven Chua oscillator with time-delayed input. The classifier shows two mistakes in classifying the dataset with 75 samples working in chaotic mode.
Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network

Since high quality realistic media are widely used in various computer vision applications, image compression is one of the essential technologies to enable real-time applications. Image compression generally causes undesired compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a densely cascading image restoration network (DCRN), which consists of an input layer, a densely cascading feature extractor, a channel attention block, and an output layer. The densely cascading feature extractor has three densely cascading (DC) blocks, and each DC block contains two convolutional layers, five dense layers, and a bottleneck layer. To optimize the proposed network architectures, we investigated the trade-off between quality enhancement and network complexity. Experimental results revealed that the proposed DCRN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed joint photographic experts group (JPEG) images compared to the previous methods.
Projection of future daily global horizontal irradiance under four RCP scenarios: An assessment through newly developed temperature and rainfall-based empirical model

Demand for solar energy is increasing due to its clean nature in contrast to conventional fossil fuel sources and lowering of the cost harvesting solar energy. Consequently, it’s important to assess the spatio-temporal distribution and future projection of global horizontal irradiance (GHI) under future climate scenarios. However, due to the absence of high-quality ground observations, whether related to the measuring site accessibility or the associated high cost of equipment, it’s generally difficult to get GHI at the desired spatial interval. Empirical calibrated models may be used for estimating GHI using other local meteorological parameters available at finer spatial interval. The present study compared the performance of four popular empirical models including a newly proposed MND model with an intention to reduce model complexity. The models are calibrated and validated over nineteen locations spread over four climatic zones of India. The results reveal that the MND model performs more efficiently than the other three models. Statistical parameters also confirm the accuracy level of MND. Four sets of common coefficients are generated for projection of daily GHI in any location of the respective four zones. Finally, temperature and rainfall data sets projected by the CMIP5 models, corresponding to 23 locations across the West Bengal State, are used to produce GHI for four future periods under four Representative Concentration Pathway scenarios. Simulated results support the view of having less GHI availability in the near future. In general, the whole State might experience a deficit in annual mean daily GHI by ≥1 MJ m-2day−1.
A State-of-the-art Review for Gastric Histopathology Feature Extraction Methods

Gastric cancer is the fourth most common cancer in the world and the second most common cause of cancer-related deaths. Accurate diagnosis of gastric cancer is very important. In recent years, with the development of computer-aided diagnosis system, pathologists can make diagnosis decisions quickly and improve their efficiency. In the computer aided diagnosis system, feature extraction is a very important step, the quality of feature extraction will affect the accuracy of gastric cancer pathological image segmentation or classification. In this paper, We summarized all the present methods of feature extraction of gastric cancer histopathological images.
A novel and universal GAN-based countermeasure to recover adversarial examples to benign examples

Some recent studies have demonstrated that the deep neural network (DNN) is vulnerable to adversarial examples, which contain some subtle and human-imperceptible perturbations. Although numerous countermeasures have been proposed and play a significant role, most of them all have some flaws and are only effective for certain types of adversarial examples. In the paper, we present a novel and universal countermeasure to recover multiple types of adversarial examples to benign examples before they are fed into the deep neural network. The idea is to model the mapping between adversarial examples and benign examples using a generative adversarial network (GAN). Its GAN architecture consists of a generator based on UNET, a discriminator based on ACGAN, and a newly added third-party classifier. The UNET can enhance the capacity of the generator to recover adversarial examples to benign examples. The loss function makes full use of the advantages of ACGAN and WGAN-GP to ensure the stability of the training process and accelerate its convergence. Besides, a classification loss and a perceptual loss, all from the third-party classifier, are employed to improve further the generator's capacity to eliminate adversarial perturbations. Experiments are conducted on the MNIST, CIFAR10, and IMAGENET datasets. First, we perform ablation experiments to prove the proposed countermeasure's validity. Then, we defend against seven types of state-of-the-art adversarial examples on four deep neural networks and compare them with six existing countermeasures. Finally, the experimental results demonstrate that the proposed countermeasure is universal and has a more excellent performance than other countermeasures. The experimental code is available at https://github.com/Afreadyang/IAED-GAN.
Interactive Media Information Security Immersive Experience System Based on Virtual Reality Technology

With the rapid development of information technology, mobile in recent years, with the rapid development of information technology, mobile 5G, virtual reality, augmented reality, big data, cloud computing, artificial intelligence, etc. are gradually integrated into people's lives, work and Learning brings infinite possibilities for the future. From a learning perspective, in order to allow learners to gain a deep sense of immersive experience under virtual reality technology, and to enhance learners' good spatial learning experience, this article introduces the research on the interactive media information security immersion system based on virtual reality technology. The teaching platform in the media conducts research and design, uses dynamic screen image transmission algorithms to design models and confirmation frameworks, and also presents the effect satisfaction of the immersive system through a comparative experiment method, and finally tests the completeness of the immersive system function And completeness. The test accuracy of the immersive system is as high as 90%, and the effect satisfaction is as high as 80%. It reflects the effectiveness of the system, enhances the learner's experience and spatial perception ability, and has a very high impact on the student's learning process and learning effect.
Extraction Security of Sequential Aggregate Signatures

Aggregate signature schemes enable anyone to compress many signatures into one. Besides the unforgeability of aggregate signature, another property called aggregate extraction sometimes is useful. In sequential aggregate signature schemes, the aggregate signature is computed incrementally by the signers. We introduce extraction security in the sequential context which can be seen as an analogy of aggregate extraction introduced by Boneh et al. in CRYPTO 2003. In addition to the practical meaning, it also has benefits in the construction of hierarchical identity-based signatures as we will point out. We study several well known aggregate signature schemes and prove some of them satisfy extraction security.
Infrared Thermography-Based Facial Classification Using Machine Learning

Intelligent human-computer interaction (HCI) utilizes facial information, artificial intelligence, adaptive computation, and emotional awareness to improve human interaction with machines. Hence, analysis of face expression has been one of the important mediums for understanding actions and modeling emotions. This paper has introduced an Infrared Thermography (IT) based approach on MATLAB which uses 2D Discrete Wavelet Transform (2D-DWT) for decomposition of the captured thermal images of faces of different persons followed by feature extraction. Principle Component Analysis (PCA) has been used in respect to decrease the dimension of the extracted feature vector and thereafter the selected features have been rated in order to achieve the most relevant feature vector. Lastly, all the selected features in a single feature vector have been put into the Support Vector Machine (SVM) and Artificial Neural Network (ANN) for person identification and further classification of the information about person.
Multi-Objective Genetic Algorithm for Optimizing an ELM-Based Driver Distraction Detection System

Driver Assistance Systems (DAS) have been progressively incorporated into commercial vehicles in recent years. All these systems are paving the way for the forthcoming autonomous vehicle which will become a reality in the near future. Existing systems are based on numerous electronic systems with advanced skills, high performances, and high degrees of adaptability and intelligence. As is to be expected, these cutting-edge features require, in most cases, the use of powerful computing platforms. However, the deployment of such platforms is not an easy task, since they have to be integrated in the vehicle where there exist important restrictions regarding size, power consumption and cost. In this sense, every smart proposal aimed at reducing the complexity of these systems without degrading performance, is always a valuable contribution in the field. In this work, we propose a methodology to reduce the dimensionality of a driver distraction recognition system. The methodology is based on a multi-objective genetic algorithm that looks for the minimum set of useful features collected during the driving task and also for the simplest recognition system. The recognition algorithm is an Extreme Learning Machine (ELM) whose simplicity and fast learning procedure make it especially suitable to be used by a Genetic Algorithm which needs to evaluate thousands of candidate solutions. The proposed methodology has been tested with a real-world database collected from different drivers performing an itinerary with an instrumented car. The results obtained validate the proposal as a method to reduce the complexity of a driver distraction recognition system.
Increasingly Specialized Generative Adversarial Network for fine-grained visual categorization

Fine-grained visual categorization is challenging because the subordinate categories within an entry-level category can only be distinguished by subtle discriminations. This necessitates to localize key (most discriminative) regions and extract domain-specific features alternately, since implicit to fine-grained specialization is the existence of an entry-category visual shared among all classes. Existing methods predominantly implement fine-grained categorization independently, while neglecting that patch proposal and discrimination extraction are mutually correlated and can reinforce each other in an increasingly specialized manner. In this work, we concretize the above pipeline as an Increasing Specialized Generative Adversarial Network (IS-GAN), which recursively shapes a coarse-to-fine representation. It is a three-scale framework consisting of two highlights: a three-player expert GAN at each scale for feature extraction, and a Patch Proposal Network (PPN) between two adjacent scales for target positioning. To better anatomize pixel-to-pixel correlations at various octaves, the Gaussian pyramid and Laplacian pyramid descriptions are also integrated in each GAN. The PPN zooms the areas to shift the focus on the most representative regions by taking previous prediction of classifier as a reference, whilst a finer scale network receives an amplified attended region from previous scale. Overall, IS-GAN is driven by three focal losses from GANs and a converged object-level loss. Experiments demonstrate that IS-GAN can simultaneously (1) deliver competitive categorization performance among state-of-the-arts, i.e., validation accuracy achieves 92.23% and testing accuracy achieves 90.27%, and (2) recover fine-grained textures with high Peak Signal-to-Noise Ratios (PSNRs) (32.937) and Structural Similarities (SSIMs) (0.8607) from hand-crafted and public benchmarks.
The phantom alignment strength conjecture: practical use of graph matching alignment strength to indicate a meaningful graph match

Abstract The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matching. Unfortunately, when a graph matching algorithm fails to find the truth because of weak signal, there may be “phantom alignment strength” from meaningless matchings that, by random noise, have fewer disagreements than average (sometimes substantially fewer); this alignment strength may give the misleading appearance of significance. A practitioner needs to know what level of alignment strength may be phantom alignment strength and what level indicates that the graph matching algorithm obtained the true matching and is a meaningful measure of the graph correlation. The Phantom Alignment Strength Conjecture introduced here provides a principled and practical means to approach this issue. We provide empirical evidence for the conjecture, and explore its consequences.
Mixer

Visual contents, including images and videos, are dominant on the Internet today. The conventional search engine is mainly designed for textual documents, which must be extended to process and manage increasingly high volumes of visual data objects. In this paper, we present Mixer, an effective system to identify and analyze visual contents and to extract their features for data retrievals, aiming at addressing two critical issues: (1) efficiently and timely understanding visual contents, (2) retrieving them at high precision and recall rates without impairing the performance. In Mixer, the visual objects are categorized into different classes, each of which has representative visual features. Subsystems for model production and model execution are developed. Two retrieval layers are designed and implemented for images and videos, respectively. In this way, we are able to perform aggregation retrievals of the two types in efficient ways. The experiments with Baidu's production workloads and systems show that Mixer halves the model production time and raises the feature production throughput by 9.14x. Mixer also achieves the precision and recall of video retrievals at 95% and 97%, respectively. Mixer has been in its daily operations, which makes the search engine highly scalable for visual contents at a low cost. Having observed productivity improvement of upper-level applications in the search engine, we believe our system framework would generally benefit other data processing applications.
Optimization of Multi-UAV Path Planning for Radiation Source Localization

In this paper, we propose two optimization schemes in terms of flight angle constraint and sampling interval distribution in the multi-UAV path planning for radiation source localization, taking the time difference of arrival localization (TDOA) as an example, then conduct theoretical derivation and simulation experiments. The experimental results show that, compared with the traditional multi-UAV passive positioning path planning algorithm, the optimization schemes will effectively shorten the UAV flight path, reduce the computational effort and improve the system efficiency while achieving the same positioning accuracy.
Binary fringe pattern in infrared phase measuring deflectometry

Infrared phase measuring deflectometry (IR PMD) is a superior PMD technique, which builds the direct relationship between phase and depth to directly calculate 3D shape of the non-continuous specular object from the phase data, instead of integrating the local slope data. IR PMD has been extensively studied because of its advantages of non-contact operation, high-precision measurement, and reducing influence of ambient light. However, there are certain requirements for the depth of field (DOF) in IR PMD system because many specular surfaces have large depth range. To tackle this issue, this paper proposes a novel method based on defocusing binary fringes. This method combines binary fringe defocusing technique and IR PMD technique. This paper discusses the measurement principle and binary defocusing technique, and conducts simulated and practical experiments. The experimental results validate the effectiveness and accuracy of the proposed method.
Lightweight blockchain network architecture for IoT devices

In this work, we elaborate a survey on the different implementations of the blockchain designed for IoT; we highlight the constraints of the IoT environment and the challenges of the blockchain to secure and facilitate the interconnection of heterogeneous devices. We propose a blockchain framework adapted specially for the IoT, a new lightweight architecture that meets the constraints of heterogeneous devices as well as their variances and low computational power in IoT layer. Our work consists of developing a new lightweight cloud-based framework adapted to the IoT environment that minimizes the energy consumption of the devices and secures communication and access to the private blockchain network.
An overlapping sequence tagging mechanism for symptoms and details extraction on Chinese medical records

The description of the patient's symptoms and details in the Chinese electronic medical record is an important basis for assisting clinicians to make diagnosis and treatment decisions in the clinical decision system. Therefore, it is greatly significant to extract symptoms and details. We propose an end-to-end overlapping joint extraction method to complete the entity identification and relationship extraction of symptoms and details in the medical record. By overlapping annotation mechanism, this method uses word vector features, word segmentation features and medical dictionary features in the embedding layer of BiLSTM-CRF to implement the overlapped relationship extraction between entity identification and entity of symptom and detail in one step. The experimental results show that our method achieves 80% in F1 score on the real outpatient medical record test set, which outperforms the existed state-of-the-art methods.