Reviewerly/exHarmony huggingface dataset:
An Image Processing-Based Method to Assess the Monthly Energetic Complementarity of Solar and Wind Energy in Colombia

Solar and wind energy systems, without storage, cannot satisfy variable load demands, but their combined use can help to solve the problem of the balance between generation and consumption. Energetic complementarity studies are useful to evaluate the viability of the use of two or more renewable energy sources with high variability in a specific interval of time in a determined region. In this paper, the monthly energetic complementarity study of solar and wind resources of Colombia is carried out. A novel approach to conduct the study is proposed. A dataset with the average monthly solar radiation and wind speed values is obtained from high-resolution images of renewable resources maps, using image processing algorithms. Then, the dataset is used to calculate the energetic complementarity of the sources employing the negative of the Pearson correlation coefficient. The obtained values are transformed to energetic complementarity maps, previously eliminating the protected areas. The obtained results show that there is a good energetic complementarity in the north and northeastern regions of the country throughout the year. The results indicate that projects related to the joint use of solar and wind generation systems could be developed in these regions.
Stackelberg Differential Game Based Resource Sharing in Hierarchical Fog-Cloud Computing

The tremendous increase of computation-heavy applications has posed great challenges in terms of enhanced service coverage and high-speed data processing in the Fifth Generation (5G) networks. As responding, the integrated fog and cloud computing (FCC) system has been expected as an efficient approach to support low-latency and on-demand computing services. This work considers the computing resource market in an FCC system operated by one cloud computing service provider (CCP) and multiple fog computing service providers (FCPs), in which the CCP shares its cloud computing resource among FCPs and itself to serve users with computational tasks. To facilitate the resource trading between the CCP and FCPs, a Stackelberg differential game based resource sharing mechanism is proposed. In this mechanism, performance discrepancy is introduced as a penalty factor to denote the mismatch between the resource supply and demand, which will encourage all computing providers (CPs) to make their trading decisions that can truthfully reflect their resource capacity and requirements. In addition, an evolutionary game based replicator dynamics is established to analyze the users' service selection among CPs. Based on the established hierarchical game framework, interactions between user selection and computing resource sharing are investigated. The performance of the designed resource sharing mechanism is validated in the simulations, which also reveal the convergence and equilibrium states of user selection, resource pricing and resource allocation.
Multi-View Based Multi-Model Learning for MCI Diagnosis

Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
Feature Space Reduction for Multimodal Human Activity Recognition

This work describes the implementation, optimization, and evaluation of a Human Activity Recognition (HAR) system using 21-channel biosignals. These biosignals capture multiple modalities, such as motion and muscle activity based on two 3D-inertial sensors, one 2D-goniometer, and four electromyographic sensors. We start with an early fusion, HMM-based recognition system which discriminates 18 human activities at 91% recognition accuracy. We then optimize preprocessing with a feature space reduction and feature vector stacking. For this purpose, a Linear Discriminant Analysis (LDA) was performed based on HMM state alignments. Our experimental results show that LDA feature space reduction improves recognition accuracy by four percentage points while stacking feature vectors currently does not show any positive effects. To the best of our knowledge, this is the first work on feature space reduction in a HAR system using various biosensors integrated into a knee bandage recognizing a dive rse set of activities.
Improved approximated median filter algorithm for real-time computer vision applications

Median filter is one of the predominant filters that are used to suppress impulse noise. Its simplicity and ability to maintain edges has led to an extensive application in the domain of image processing and computer vision. However, challenges such as moderate to high running time of the standard median filter algorithm and relatively poorer performance when the image is highly corrupted with impulse noise, have led to the design of several variations of the algorithm. One set of variation of the algorithm concentrates on generating quality outputs, while the other set focuses on reducing running time. Among the set targeting the reduction of the running time of the median filter is the DP approximated median filter. However, DP performs poorly when images are corrupted with moderate to high levels of noise. This paper therefore proposes an Improved Approximation Median Filtering Algorithms (IAMFA-I & IAMFA-II) based on DP to generate a better output. The introduction of Mid-Value-Decision-Median in DP reduces the chances of selecting corrupted pixel for denoised image. Experimental results indicate that the IAMFA-II has better running time and equivalent output compared with DP, while IAMFA-I generates better output and has equivalent running time when compared with DP.
Scalability in Perception for Autonomous Driving: An Open Dataset Benchmark

The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at this http URL.
Deep Dilated V-Net for 3D Volume Segmentation of Pancreas in CT images

Pancreas segmentation is essential in the medical diagnosis of cancer, pancreatitis, and pancreatic surgeries. Computed tomography (CT) abdominal scans help in the detection of tumors, infections, and other injuries in the pancreas. The treatment of pancreatic tumors begins with surgery, followed by neoadjuvant therapy. However, it is challenging to detect boundaries, due to variable shape, small anatomical structures, and low contrast images of the pancreas in the abdominal CT scans. Recently, Convolutional Neural Networks (CNNs) based deep learning models show significant performance on medical imaging related tasks. However, most of the data available for the evaluation of diseases consists of 3D CT scan volumes. Hence, learning from volumetric data is essential in biomedical applications. V-Net achieves extraordinary performance in various medical datasets consists of 3D scan. In this paper, we have proposed a multi-rate Deep-Dilation Network (DDN) in V-Net for the segmentation of pancreas in CT-82 abdominal dataset. To overcome data-imbalance between bright and dark pixels, we propose a Weighted Fusion Loss (WFL) using Balanced Binary Cross-entropy (BBCE) loss and Smooth Dice Coefficient (SDC) loss. The proposed model attains a state-of-the-art performance for pancreas segmentation. The achieved dice score, sensitivity and precision are 83.31%, 87.70% and 97.07% respectively.
Avoiding Unintended Bias in Toxicity Classification with Neural Networks

The growing popularity of online platforms that allow users to communicate with each other, exchange opinions about various events and leave comments, has contributed to the development of natural language processing algorithms. Tens of millions of messages per day published by users of a certain social network must be analyzed in real time for moderation to prevent the spread of various illegal or offensive information, threats and other types of toxic comments. Of course, such a large amount of information can be processed quite quickly only automatically. That is why it is necessary to find a way to teach a computer to “understand” a text written by a man. It is a non-trivial task, even if the word “understand” here means only to detect or classify. The rapid development of machine learning technologies has led to the widespread adoption of new algorithms. Many tasks that for years were considered almost impossible to solve using computer now can be successfully solved with deep learning technologies. In this article, the author presents modern approaches to solving the problem of toxic comments detection using deep learning technologies and neural networks. The author introduces two state-of-theart neural network architectures and also demonstrates how to use a contextual language representation model to detect toxicity. Furthermore, in this article will be presented the results of the developed algorithms, as well as the results of their ensemble, tested on a large training set, gathered and marked up by Google and Jigsaw.
Kollaps

The performance and behavior of large-scale distributed applications is highly influenced by network properties such as latency, bandwidth, packet loss, and jitter. For instance, an engineer might need to answer questions such as: What is the impact of an increase in network latency in application response time? How does moving a cluster between geographical regions affect application throughput? What is the impact of network dynamics on application stability? Currently, answering these questions in a systematic and reproducible way is very hard due to the variability and lack of control over the underlying network. Unfortunately, state-of-the-art network emulation or testbed environments do not scale beyond a single machine or small cluster (i.e., MiniNet), are focused exclusively on the control-plane (i.e., CrystalNet) or lack support for network dynamics (i.e., EmuLab).
Quantum Secure Direct Communication Protocol with Mutual Authentication Based on Single Photons and Bell States

Quantum secure direct communication (QSDC) can transmit secret messages directly from one user to another without first establishing a shared secret key, which is different from quantum key distribution. In this paper, we propose a novel quantum secure direct communication protocol based on signal photons and Bell states. Before the execution of the proposed protocol, two participants Alice and Bob exchange their corresponding identity IDA and IDB through quantum key distribution and keep them secret, respectively. Then the message sender, Alice, encodes each secret message bit into two single photons (| 01〉or|10〉) or a Bell state , and composes an ordered secret message sequence. To insure the security of communication, Alice also prepares the decoy photons and inserts them into secret message sequence on the basis of the values of IDA and IDB. By the secret identity IDA and IDB, both sides of the communication can check eavesdropping and identify each other. The proposed protocol not only completes secure direct communication, but also realizes the mutual authentication. The security analysis of the proposed protocol is presented in the paper. The analysis results show that this protocol is secure against some common attacks, and no secret message leaks even if the messages are broken. Compared with the two-way QSDC protocols, the presented protocol is a one-way quantum communication protocol which has the immunity to Trojan horse attack. Furthermore, our proposed protocol can be realized without quantum memory.
Students’ Perceptions toward Using YouTube in EFL Classrooms

The using of videos from YouTube channel in the EFL classroom is imperative to fill the educational needs of the younger generation. Although resources from textbook have remained the standard for centuries, currently, with the fast pace of the development of technology, the usage of YouTube videos in teaching and learning has become a trend. There are a few studies conducted on students’ perceptions of using YouTube to learn the English language. The objective of this research was to investigate the students’ perceptions on the use of YouTube in learning English process at English Education Department, Universitas Negeri Makassar Indonesia. Data were collected using a questionnaire that collected background information of participants and a five-point Likert scale to gauge the students’ perception use of the YouTube in EFL classroom. Findings from this research indicate that the majority of students showed a positive perception toward and a willingness to use YouTube in the EFL classroom. It was also discovered that the students have used YouTube to help them completed their course assignments and study tasks. Hence, it can be concluded that the students preferred to use English YouTube videos to help them enhance their English language level proficiency.
Texture-Aware Deblurring for Remote Sensing Images Using $ \ell _0$-Based Deblurring and $ \ell _2$-Based Fusion

This article presents an image deblurring method using ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm-based deblurring and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm-based texture-aware image fusion for remote sensing images. To restore the details of blurred texture, the proposed method first performs texture restoration by fusing the restored results using Richardson-Lucy deconvolution and unsharp masking. Next, we analyzed the intensity and dark channel properties of remote sensing images and perform the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm-based deblurring using the intensity and dark channel priors. Although the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm-based deblurring can provide a significantly restored result, it cannot overcome the loss of the texture region. On the other hand, the proposed ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm-based image fusion method can preserve both sharp edges and texture details. In the experiments, we demonstrate that the proposed method can provide better restored results than existing state-of-the-art deblurring methods without oversmoothing and undesired artifact.
Adversarial Neural Pruning with Latent Vulnerability Suppression

Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challenging to deploy them in real-world safety-critical applications. In this paper, we conjecture that the leading cause of adversarial vulnerability is the distortion in the latent feature space, and provide methods to suppress them effectively. Explicitly, we define \emph{vulnerability} for each latent feature and then propose a new loss for adversarial learning, \emph{Vulnerability Suppression (VS)} loss, that aims to minimize the feature-level vulnerability during training. We further propose a Bayesian framework to prune features with high vulnerability to reduce both vulnerability and loss on adversarial samples. We validate our \emph{Adversarial Neural Pruning with Vulnerability Suppression (ANP-VS)} method on multiple benchmark datasets, on which it not only obtains state-of-the-art adversarial robustness but also improves the performance on clean examples, using only a fraction of the parameters used by the full network. Further qualitative analysis suggests that the improvements come from the suppression of feature-level vulnerability.
VFlow: More Expressive Generative Flows with Variational Data Augmentation

Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architectural constraints on generative flows, making them less expressive than other types of generative models. In this work, we study a previously overlooked constraint that all the intermediate representations must have the same dimensionality with the original data due to invertibility, limiting the width of the network. We tackle this constraint by augmenting the data with some extra dimensions and jointly learning a generative flow for augmented data as well as the distribution of augmented dimensions under a variational inference framework. Our approach, VFlow, is a generalization of generative flows and therefore always performs better. Combining with existing generative flows, VFlow achieves a new state-of-the-art 2.98 bits per dimension on the CIFAR-10 dataset and is more compact than previous models to reach similar modeling quality.
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers

Disentangled generative models map a latent code vector to a target space, while enforcing that a subset of the learned latent codes are interpretable and associated with distinct properties of the target distribution. Recent advances have been dominated by Variational AutoEncoder (VAE)-based methods, while training disentangled generative adversarial networks (GANs) remains challenging. In this work, we show that the dominant challenges facing disentangled GANs can be mitigated through the use of self-supervision. We make two main contributions: first, we design a novel approach for training disentangled GANs with self-supervision. We propose contrastive regularizer, which is inspired by a natural notion of disentanglement: latent traversal. This achieves higher disentanglement scores than state-of-the-art VAE- and GAN-based approaches. Second, we propose an unsupervised model selection scheme called ModelCentrality, which uses generated synthetic samples to compute the medoid (multi-dimensional generalization of median) of a collection of models. The current common practice of hyper-parameter tuning requires using ground-truths samples, each labelled with known perfect disentangled latent codes. As real datasets are not equipped with such labels, we propose an unsupervised model selection scheme and show that it finds a model close to the best one, for both VAEs and GANs. Combining contrastive regularization with ModelCentrality, we improve upon the state-of-the-art disentanglement scores significantly, without accessing the supervised data.
An Efficient Method for Mining Top-<i>K</i> Closed Sequential Patterns

The problem of exploiting Closed Sequential Patterns (CSPs) is an essential task in data mining, with many different applications. It is used to resolve the situations of huge databases or low minimum support (minsup) thresholds in mining sequential patterns. However, it is challenging and needs a lot of time to customize the minsup values for generating appropriate numbers of CSPs desired by users. To conquer this issue, the TSP algorithm for mining top-k CSPs was previously proposed, with k being a given parameter. The algorithm would return the k CSPs which have the highest support values in a database. However, its execution time and memory usage were high. In this paper, an algorithm named TKCS (Top-K Closed Sequences) is proposed to mine the top-k CSPs efficiently. To improve the execution time and memory usage, it uses a vertical bitmap database to represent data. Besides, it adopts some useful strategies in the process of exploiting the top-k CSPs such as: always choosing the sequential patterns with the greatest support values for generating candidate patterns and storing top-k CSPs in an ascending order of the support values to increase the minsup value more quickly. The empirical results show that TKCS has better performance than TSP for discovering the top-k CSPs in terms of both runtime and memory usage.
Designing alternatives in decision problems

Abstract Generating alternatives for decision problems is a critical activity regularly underestimated and underdeveloped in the decision analysis literature. We present a survey showing how little this topic has been studied in the last 50 years. We then introduce a general framework under which formalize the design of alternatives. Two examples help to understand our point of view: alternatives are generated through separation of attributes describing the value space of the client/decision maker.
Testing Os Staining Approach for Visualizing Soil Organic Matter Patterns in Intact Samples via X-ray Dual-Energy Tomography Scanning

Challenges with in situ visualization of nonparticulate organics in porous materials limit understanding and modeling processes of transport, decomposition, and storage of organic compounds. In particular, it impedes deciphering the mechanisms driving accumulation and protection of soil organic matter (SOM), processes crucial for sustaining soil fertility and mitigating effects of global climate change. A recently proposed method of staining soil organics by OsO4 vapors with subsequent dual-energy X-ray computed microtomography scanning (μCT) offers new opportunities to visualize SOM within intact soil matrix. Our objective was to test the method’s performance in staining different organic materials located in media with contrasting pore characteristics: (1) roots of switchgrass (Panicum virgatum L.), either placed within fine and coarse sands or grown within soil microcores, (2) biochar fragments, and (3) soils with relatively low and high C contents. We found that the method was effective in staining organic materials of root origin and the organics associated with fine soil particles, but not the biochar. The estimated percent of total C that reacted with OsO4 vapors ranged from 0.7% in plant roots to 3.2% in sand-free fraction of the high C soil and was only 0.2% in the studied biochar. Total soil C and Os concentrations were strongly linearly related, suggesting a potential for future method development. However, we would recommend caution when interpreting the results in cases when gas diffusion through the soil matrix is limited.