Reviewerly/exHarmony huggingface dataset:
Micronutrients prediction via pXRF spectrometry in Brazil: Influence of weathering degree

Management of micronutrient levels in soils must be done carefully to avoid their deficiency or toxicity to plants. The laboratory determination of micronutrient contents is time-consuming, expensive and generates chemical wastes, making it difficult for soil surveys required in precision agriculture, especially in tropical countries. While proximal sensors like portable X-ray fluorescence (pXRF) spectrometry have been successfully used to predict contents of soil available macronutrient, little effort has focused on micronutrients, especially involving a large dataset, soils weathering degree and a practical application of the predictions. This study aimed to use pXRF data for the prediction of available micronutrients in 1514 samples from variable soil classes (from Entisols to Oxisols) from seven Brazilian states using machine learning algorithms and to assess the influence of soil weathering degree on such prediction models. The soil samples were collected from both surface (A) and subsurface (B or C) horizons of various soil classes under several land uses, and with varying parent materials. Available B, Cu, Fe, Mn, and Zn were predicted via stepwise multiple linear regression (SMLR), support vector machine (SVM), extreme gradient boosting (XGB), and random forest (RF) algorithms and subsequently validated. The best prediction models were classified according to micronutrient availability classes (categorical validation). Adequate predictions were achieved for Cu: R2 = 0.80; RPD = 2.28; Mn: 0.68; 1.76; and Zn: 0.68; 1.70. Predictions of B, Cu, Fe, Mn, and Zn availability classes yielded overall accuracy of 0.90, 0.65, 0.67, 0.73, and 0.53, respectively. Summarily, pXRF data in conjunction with prediction models can be an effective and rapid method to determine available Cu, Mn, and Zn. Soil weathering degree must be considered on such predictions as they strongly influence model accuracy.
Blockchain: Secured Solution for Signature Transfer in Distributed Intrusion Detection System

Exchange of data in networks necessitates provision of security and confidentiality.Most networks compromised by intruders are those where the exchange of data is at high risk.The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network.Malicious activities are monitored and detected by the Intrusion Detection System (IDS) that operates with nodes connected to a distributed network.The IDS operates in two phases, where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest, Convolutional neural network, and XGBoost along with genetic algorithm to improve the performance of IDS.The novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second phase.This phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher level.The blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0, to create a prototype for secure signature transfer.It exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperledger Fabric.The performance of the proposed blockchain system is evaluated on UNSW NB15 dataset.Blockchain performance has been evaluated in terms of execution time, average latency, throughput and transaction processing time.Experimental evidence of the proposed IDS system demonstrates improved performance with accuracy, detection rate and false alarm rate (FAR) as key parameters used.Accuracy and detection rate increase by 2% and 3% respectively whereas FAR reduces by 1.7%.
Generative Adversarial Networks for Abnormal Event Detection in Videos Based on Self-Attention Mechanism

Unsupervised anomaly detection defines an abnormal event as an event that does not conform to expected behavior. In the field of unsupervised anomaly detection, it is a pioneering work that leverages the difference between a future frame predicted by a generative adversarial network and its ground truth to detect an abnormal event. Based on the work, we improve the ability of video prediction framework to detect abnormal events by enhancing the difference between prediction results for normal and abnormal events. We incorporate super-resolution and self-attention mechanism to design a generative adversarial network. We propose an auto-encoder as a generator, which incorporates dense residual networks and self-attention. Moreover, we propose a new discriminator, which introduces self-attention on the basis of a relativistic discriminator. To predict a future frame with higher quality for normal events, we impose a constraint on the motion in video prediction by fusing optical flow and gradient difference between frames. We also introduce a perception constraint in video prediction to enrich the texture details of a frame. The AUC of our method on CUHK Avenue and Shanghai Tech datasets reaches 89.2% and 75.7% respectively, which is better than most existing methods. In addition, we propose a processing flow that can realize real-time anomaly detection in videos. The average running time of our video prediction framework is 37 frames per second. Among all real-time methods for abnormal event detection in videos, our method is competitive with the state-of-the-art methods.
Infrared images enhancement using fuzzy dissimilarity histogram equalization

In this article, a novel double-plateau limit histogram equalization (HE) method using the modification of the fuzzy dissimilarity histogram (FDH) is presented for the contrast improvement of Infrared (IR) images. Initially, FDH is formulated using fuzzy contextual information from the input IR image. Next, the average of the multi-peaks of the FDH is considered as the threshold limit of histogram sub-division. After that, sub-histograms are clipped based on their individual plateau limit values. Plateau-limit values of the two sub-histograms are based on the mean of individual histogram, and the remaining portion of each clipped sub-histogram is equally redistributed with the non-zero bins. Here, clipping of the histogram and redistribution process together can control over enhancement of image. Finally, the modified sub-histograms are independently equalized to form the desired output images. Simulation outcomes reveal that the proposed HE method effectively enhances the visual quality of the image. Compared with the other traditional state-of-the-art contrast enhancement methods, visual assessment and quantitative measurements like discrete entropy (DE), feature similarity index metric (FSIM), spectral residual similarity index measure (SR-SIM) and linear index of fuzziness, efficiently confirm the advancement of the proposed method. Moreover, a comparative result of the methods is demonstrated with a dataset of 100 IR images to show the superiority of the proposed method.
A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks

This paper aims to propose a framework for manifold regularization (MR) based distributed semi-supervised learning (DSSL) using single layer feed-forward neural network (SLFNN). The proposed framework, denoted as DSSL-SLFNN is based on the SLFNN, MR framework, and distributed optimization strategy. Then, a series of algorithms are derived to solve DSSL problems. In DSSL problems, data consisting of labeled and unlabeled samples are distributed over a communication network, where each node has only access to its own data and can only communicate with its neighbors. In some scenarios, DSSL problems cannot be solved by centralized algorithms. According to the DSSL-SLFNN framework, each node over the communication network exchanges the initial parameters of the SLFNN with the same basis functions for semi-supervised learning (SSL). All nodes calculate the global optimal coefficients of the SLFNN by using distributed datasets and local updates. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that DSSL-SLFNN based algorithms work in a fully distributed fashion and are privacy preserving methods. Finally, several simulations are presented to show the efficiency of the proposed framework and the derived algorithms.
Multi-level refinement enriched feature pyramid network for object detection

Class Imbalance and scales imbalance are common in object detection. A class imbalance occurs due to insufficient inequality between the number of instances with respect to different classes, while an imbalance in scale occurs when object have different scales and a different number of examples of different scales. In order to solve the problem of scale variance (scale imbalance) and class imbalance together, we propose a simple and effective feature enhancement scheme that explicitly uses all information of a multi-level structure to generate a multilevel contextual features pyramid with multiple scales. We also introduce a cascaded refinement scheme that incorporates multi-scale contextual features into the Single Shot Detector (SSD) predictive layers to improve their distinctiveness for multi-scale detection. A stack of multi-scale contextual feature modules is used in a feature enhancement scheme to merge the multi-level and multi-scale features. Then we collect the equivalent scale features over the Multi-layer Feature Fusion (MLFF) unit to construct a feature pyramid in which each feature map is made up of layers from multiple levels. More robustness and contextual information are integrated into the pyramid through chain parallel pooling operation. To improve classification and regression, a cascaded refinement scheme is proposed that effectively captures a large amount of contextual information and refines the anchors to solve the class imbalance problem. The experiments are carried out on two benchmarks datasets: MS COCO and PASCAL VOC 07/12. Our proposed approach achieves state-of-the-art accuracy with an AP of 40.6 in the case of multi-scale inference on MS COCO Test-dev (input size 320 × 320). For 512 × 512 input on the MS COCO Test-dev, our approach leads in an absolute gain in precision of 1.8% compared to the best reported results of single-stage detector (AP: 45.7).
A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results.
A Human-Computer Control System Based on Intelligent Recognition of Eye Movements and Its Application in Wheelchair Driving

This paper presents a practical human-computer interaction system for wheelchair motion through eye tracking and eye blink detection. In this system, the pupil in the eye image has been extracted after binarization, and the center of the pupil was localized to capture the trajectory of eye movement and determine the direction of eye gaze. Meanwhile, convolutional neural networks for feature extraction and classification of open-eye and closed-eye images have been built, and machine learning was performed by extracting features from multiple individual images of open-eye and closed-eye states for input to the system. As an application of this human-computer interaction control system, experimental validation was carried out on a modified wheelchair and the proposed method proved to be effective and reliable based on the experimental results.
The Animation Transformer: Visual Correspondence via Segment Matching

Visual correspondence is a fundamental building block on the way to building assistive tools for hand-drawn animation. However, while a large body of work has focused on learning visual correspondences at the pixel-level, few approaches have emerged to learn correspondence at the level of line enclosures (segments) that naturally occur in hand-drawn animation. Exploiting this structure in animation has numerous benefits: it avoids the memory complexity of pixel attention over high resolution images and enables the use of real-world animation datasets that contain correspondence information at the level of per-segment colors. To that end, we propose the Animation Transformer (AnT) which uses a Transformer-based architecture to learn the spatial and visual relationships between segments across a sequence of images. By leveraging a forward match loss and a cycle consistency loss our approach attains excellent results compared to state-of-the-art pixel approaches on challenging datasets from real animation productions that lack ground-truth correspondence labels.
Context-aware seq2seq translation model for sequential recommendation

Context information, such as product category, plays a vital role in sequential recommendations. Recently, there has been a growing interest in context-aware sequential recommender systems. However, in previous studies, contexts have often been treated as auxiliary information without the consideration of the inter-sequence dependency between the item sequence and the context sequence. Such a dependency provides valuable details for predicting a user’s future behavior. For example, a user may buy electronic accessories after buying an electronic product. In this paper, we propose a context-aware seq2seq translation model to capture the inter-sequence dependency for sequential recommendations. The key component in our model is a tripled seq2seq translation architecture with an injected variational autoencoder (VAE). The tripled architecture, consisting of forward and backward translation, naturally encodes bi-directional inter-sequence dependency. Moreover, the injected VAE enables the translation process to redress the semantic imbalance between context and item. We conduct extensive experiments on four real-world datasets. The results show the superior performance of our model over the state-of-the-art baselines. The code and datasets are available at https://github.com/NLPWM-WHU/CAST.
Early Introduction of AI in Spanish Middle Schools. A Motivational Study

Abstract This paper describes the practical initiative to include Artificial Intelligence (AI) in the Spanish educational system’s curriculum at an early age. This proposal is in line with the current trend of introducing AI in school curricula all over the world. To this end, we propose an Artificial Intelligence workshop for middle schools within the existing subject, Technology, Programming and Robotics. In order to test the suitability of introducing AI at an early age, we conducted the activities at a bilingual middle school in Madrid. As evaluation tools, a quiz and motivational study of the students concerning AI was carried out using Situational Motivational Scale (SIMS) before and after introducing the activities. Responses of 84 students were analysed and the conclusion was reached that it is slightly better to introduce AI at an early age.
Design of Intelligent Human-Computer Interaction System for Hard of Hearing and Non-Disabled People

Since the hard of hearing cannot communicate effectively with the non-disabled, which may cause various inconveniences. As an essential member of a harmonious society, it is particularly urgent to solve their communication problems with non-disabled people. Effective communication between the hard of hearing and the non-disabled has become possible with the continuous development of artificial intelligence. In this paper, an intelligent human-computer interaction system is designed to solve communication inconvenience between the hard of hearing and the non-disabled. This system combines artificial intelligence with wearable devices and classifies gestures with BP neural network, effectively solving the communication problem between the hard of hearing and the non-disabled.
On predictability of solar irradiance

Fair forecast comparisons are exceedingly rare in the literature of solar forecasting. Since many published works have been operating under the condition “the proposed methods outperform the benchmarks,” it is unlikely that the actual advancement in solar forecasting science is indeed successive in that the latter works supersede the former. It follows that one must hold fast to skepticism on those model superiority claims until they can be truly justified. In order to quantify the real progress, one needs to not only employ formal verification methods but also make inquiries on predictability, which, even in the field of statistics, is a controversial topic. Although predictability on its own is hard to define, let alone to quantify, one logically attractive proxy is to examine the performance of a short-range forecasting method relative to that of an optimal long-range forecasting method. This strategy is reasonable on the account that a climatological forecast in the short-range horizon marks the worst-case scenario, by which the relative improvement due to an alternative forecasting method can be gauged: situations with high predictability correspond to large relative improvements, and that with low predictability correspond to low relative improvements. If this argument can be considered admissible, the remaining task is to examine what properties ought the short-range forecasting method possess. This paper proposes, in this regard, a new measure of predictability for solar irradiance.
Efficient Fine-Grained Website Fingerprinting via Encrypted Traffic Analysis with Deep Learning

Fine-grained website fingerprinting (WF) enables potential attackers to infer individual webpages on a monitored website that victims are visiting, by analyzing the resulting traffic protected by security protocols such as TLS. Most existing studies focus on WF at the granularity of website, which takes website homepages as their representatives for fingerprinting. Fine-grained WF can reveal more user privacy, such as online purchasing habits and video-viewing interests, and can also be employed for web censorship. Due to striking similarly of webpages on a same website, it is still an open problem to conduct fine-grained WF in an accurate and time-efficient way.In this paper, we propose BurNet, a fine-grained WF method using Convolutional Neural Networks (CNNs). To extract differences of similar webpages, we propose a new concept named unidirectional burst, which is a sequence of packets corresponding to a piece of HTTP message. BurNet takes as input unidirectional burst sequences, instead of bidirectional packet sequences, which makes it applicable to local and remote attack scenarios. BurNet employs CNNs to build a powerful classifier, where sophisticated architecture is designed to improve classification accuracy while reducing time complexity in training. We collect real-world datasets from two well-known websites and conduct extensive experiments to evaluate the performance of BurNet. The closed-world evaluation results show that BurNet outperforms the state-of-the-art methods in both attack scenarios. In the more realistic open-world setting, BurNet can achieve 0.99 precision and 0.99 recall. BurNet is also superior to its CNN-based counterparts in terms of training efficiency.
<i>Pasadena:</i> Perceptually Aware and Stealthy Adversarial Denoise Attack

Image denoising can remove natural noise that widely exists in images captured by multimedia devices due to low-quality imaging sensors, unstable image transmission processes, or low light conditions. Recent works also find that image denoising benefits the high-level vision tasks, <i>e.g</i>., image classification. In this work, we try to challenge this common sense and explore a totally new problem, <i>i.e</i>., whether the image denoising can be given the capability of fooling the state-of-the-art deep neural networks (DNNs) while enhancing the image quality. To this end, we initiate the very first attempt to study this problem from the perspective of adversarial attack and propose the <i>adversarial denoise attack</i>. More specifically, our main contributions are three-fold: <i>First</i>, we identify a new task that stealthily embeds attacks inside the image denoising module widely deployed in multimedia devices as an image post-processing operation to simultaneously enhance the visual image quality and fool DNNs. <i>Second</i>, we formulate this new task as a kernel prediction problem for image filtering and propose the <i>adversarial-denoising kernel prediction</i> that can produce adversarial-noiseless kernels for effective denoising and adversarial attacking simultaneously. <i>Third</i>, we implement an adaptive <i>perceptual region localization</i> to identify semantic-related vulnerability regions with which the attack can be more effective while not doing too much harm to the denoising. We name the proposed method as <i>Pasadena</i> (Perceptually Aware and Stealthy Adversarial DENoise Attack) and validate our method on the NeurIPS&#x2019;17 adversarial competition dataset, CVPR2021-AIC-VI: unrestricted adversarial attacks on ImageNet, and Tiny-ImageNet-C dataset. The comprehensive evaluation and analysis demonstrate that our method not only realizes denoising but also achieves a significantly higher success rate and transferability over state-of-the-art attacks.
Geometrical and deep learning approaches for instance segmentation of CFRP fiber bundles in textile composites

Segmenting micro-Computed Tomography (µCT) images of textile composites is a necessary step before modeling the material at the mesoscale. However, the accurate segmentation of fiber bundles (or tows) remains a challenge in carbon fiber reinforced textile composites. Segmentation approaches based on local fiber orientation perform well in recognizing individual tows only under ideal conditions, namely when the local fiber orientation bordering two tows’ interface is different, or when the touching area is small relative to the thickness of a tow. Unfortunately, in many textile composite laminates used in the industry, these ideal conditions are not found. Such materials often consist of multiple plies, where each fiber is aligned in one of the two orthogonal directions, and where the touching area between similar-orientation tows is often much larger than the tow thickness. Therefore, we propose two new methodologies for splitting tow instances. One is based on the geometrical analysis of the material structure using conventional image analysis; the other is based on the deep learning prediction of ideal inputs for segmentation based on the watershed transform. The deep learning-based method is trained using randomly generated synthetic images of a woven composite material, which avoids an expensive human annotation step.