Reviewerly/exHarmony huggingface dataset:
Fuzziness Evaluation on Hybrid Context Based Clustering Methods with Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization Algorithm

Abstract This research contains to hybrid Context Based Clustering Method integrated with Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization (CFGWC-PSO). The computation time using CFGWC-PSO Algorithm is faster than other algorithms. CFGWC-PSO algorithm was applied on 11 variables from data factors causing the spread of dengue in East Java. One of the parameters used in this analysis is fuzziness (m), which is the parameter used to measure the level of obscurity from the clustering results. In this paper will use different fuzziness (m) values to evaluating best fuzziness value (m) which are appropriate used to clustering with CFGWC-PSO algorithm. CFGWC-PSO algorithm using fuzziness (m) = 1.5 and fuzziness (m) = 2, number of clusters = 2 then CFGWC-PSO will evaluated using IFV index. Based on IFV index found that the best clustering in this case with CFGWC-PSO algorithm is with using fuzziness value (m) = 2.
Accurate and Robust Atmospheric Light Estimation for Single Image Dehazing

Single image dehazing is performed by solving atmospheric scattering model(ASM), which depends on atmospheric light and transmission. Therefore, accurate estimation of transmission and atmospheric light is essential for quality de-hazing. The accuracy in transmission depends on accurate value of atmospheric light. Several methods have been proposed in literature to estimate atmospheric light. However, these methods inaccurately estimates atmospheric light in the presence of objects brighter than atmospheric light in input hazy image. Therefore, a robust method to accurately estimate the atmospheric light is proposed in this paper. The robustness and accuracy of the proposed method is evaluated using HazeRD dataset, which proves the accuracy and robustness of the proposed method.
What makes a smile? A Deep Neural Network Point of View

Artificial intelligence is the mainstream solution to various problems, and thanks to developments in hardware, it is possible to achieve performance like never before. Continuous data collection provides us with opportunities for the creation of various datasets, which are the basis for various challenges. One of those challenges is recognizing emotions from humans' facial expressions. Multiple deep learning models exist in the wild to solve such a task. They always yield high accuracy on their respective validation and test set. However, the performance of such a model tends to decrease when used on real-world images. This work gives insight into how such a deep learning model can predict facial expression from biases present in training data.
Improved Facial Recognition Algorithms Based on Dragonfly and Grasshopper Optimization

In this paper, we investigate two relatively new optimization algorithms in facial recognition, the grasshopper optimization algorithm (GOA) and binary dragonfly algorithm (BDA) which had the best performance out of 13 optimization algorithms that were compared. We investigate the effectiveness of both optimization algorithms alongside two classifiers, k-nearest neighbor (KNN) and support vector machine (SVM). Performance evaluation of the four combinations, BDA-KNN, BDA-SVM, GOA-KNN and GOA-SVM, indicate near-ideal recognition rates, with the GOA variants slightly outperforming their BDA counterparts. When compared to other recently proposed facial recognition approaches, the proposed algorithms depict improved accuracy.
Partial Contour Matching Based on Affine Curvature Scale Space Descriptors

In real applications, the same object may have been presented by different shapes due to the moment and the angles of image acquisition, which does not guarantee a complete contour extraction without being disturbed by the noise or the distortions. In this paper, we propose a new method to match partially occluded shape based on affine curvature scale space. Firstly, an affine curve re-parameterization is defined, inspired by the properties of affine curvature scale space (ACSS) shape descriptor. Then, the different parts will be matched in order to minimize the $$ L_{2} $$ distance by the calculation of the pseudo-inverse matrix to estimate the translation and the linear transformation based on the affine curve matching (ACM) algorithm. Finally, a matching curve algorithm is obtained according to any planar affine transformation and in any partial occluded case. Experiments are conducted on multi-view curve dataset.
On polynomials x − 1 over binary fields whose irreducible factors are binomials and trinomials

Let Fq be a finite field with q elements, where q is a power of 2. In this paper we study the positive integers n for which the irreducible factors of the polynomial xn−1 over Fq are all binomials and trinomials. In particular, we completely describe these integers for q=2,4.
Segmentation and detection of Human Face

In this paper, we propose a system that classify Human faces, which are issued from video sequences captures. Segmentation of moving objects from video sequences plays an important role in many fields of computer science. We present some background subtraction approaches, then we compare their results. The DeepSphere" is employed in this paper to perform foreground objects detection and segmentation in video sequences. We employ the Deeplearning approach and the viola& Jones algorithms to segment faces from images. Finally, we classify the obtained faces using fractal dimensions.
An End-to-End Context Aware Anomaly Detection System

Anomaly detection (AD) is very important across several real-world problems in the heavy industries and Internet-of-Things (IoT) domains. Traditional methods so far have categorized anomaly detection into (a) unsupervised, (b) semi-supervised and (c) supervised techniques. A relatively unexplored direction is the development of context aware anomaly detection systems which can build on top of any of these three techniques by using side information. Context can be captured from a different modality such as semantic graphs encoding grouping of sensors governed by the physics of the asset. Process flow diagrams of an operational plant depicting causal relationships between sensors can also provide useful context for ML algorithms. Capturing such semantics by itself can be pretty challenging, however, our paper mainly focuses on, (a) designing and implementing effective anomaly detection pipelines using sparse Gaussian Graphical Models with various statistical distance metrics, and (b) differentiating these pipelines by embedding contextual semantics inferred from graphs so as to obtain better KPIs in practice. The motivation for the latter of these two has been explained above, and the former in particular is well motivated by the relatively mediocre performance of highly parametric deep learning methods for small tabular datasets (compared to images) such as IoT sensor data. In contrast to such traditional automated deep learning (AutoAI) techniques, our anomaly detection system is based on developing semantics-driven industry specific ML pipelines which perform scalable computation evaluating several models to identify the best model. We benchmark our AD method against state-of-the-art AD techniques on publicly available UCI datasets. We also conduct a case study on IoT sensor and semantic data procured from a large thermal energy asset to evaluate the importance of semantics in enhancing our pipelines. In addition, we also provide explainable insights for our model which provide a complete perspective to a reliability engineer.
Formal Analysis and Estimation of Chance in Datasets Based on Their Properties

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation.
The Question of Transformation of Higher Education in the Conditions of Accelerated Technological Progress

This article is devoted to the transformation of Russian education system in accordance with the requests of the new technological scheme of the economy. The global economy has changed dramatically during an extremely short period of time due to the COVID-19 pandemic. All levels of the economy and people’s behavior have been modified, and according to experts, these changes are almost irreversible. In this situation, there is a need not only to change the management system of all levels of economic systems, but also to transform education, especially Russian higher education system, due to the fact that now Russian education has two simultaneous tasks: to adapt to the changes that have occurred under the influence of the pandemic and to increase the prestige of Russian universities in the international educational space. Universal scientific methods were used in this research process, including comparative and factor analysis methods, synthesis, and generalization. Achievements: 1. In the article the current trends in the development of the Russian economy in the context of the pandemic and the entry of innovative technologies in all areas of economic relations were analyzed, which have an impact on the formation of new labor market demands. 2. The specific features and problematic aspects of the current system of Russian education, which are slowing down the processes of its transformation, are identified. 3. It is justified, that the higher education system itself should correspond to the smart development model, focused on the adoption of a new paradigm of economic decision-making, on the application of progressive organizational and managerial, informational, environmental, social and other technologies to achieve this goal. 4. The positive practices, which demonstrate advanced domestic higher education institutions, are described. They fit into emerging new markets and correspond to consumer’s sentiment and expectations, taking into account the emerging professions of the future and the increasing complexity of professional tasks. 5. The main trajectories of restructuring Russian education to meet the needs in the innovative economy are made, in general, and to ensure the current basic training of graduates, in particular, to achieve their competitiveness in the labor market.
A New Application-Aware No-Reference Quality Assessment Method for IP Voice Services

Abstract The increasing number of Voice over LTE deployments and IP-based voice services raise the demand for their user-centric service quality monitoring. This domain’s leading challenge is measuring user experience quality reliably without performing subjective assessments or applying the standard full-reference objective models. While the former is time- and resource-consuming and primarily executed ad-hoc, the latter depends upon a reference source and processes the voice payload that may offend user privacy. This paper presents a packet-level measurement method (introducing a novel metric set) to objectively assess network and service quality online. It is accomplished without inspecting the voice payload and needing the reference voice sample. The proposal has three contributions: (i) our method focuses on the timeliness of the media traffic. It introduces new performance metrics that describe and measure the service’s time-domain behavior from the voice application viewpoint. (ii) Based on the proposed metrics, we also present a no-reference Quality of Experience (QoE) estimation model. (iii) Additionally, we propose a new method to identify the pace of the speech (slow or dynamic) as long as voice activity detection (VAD) is present between the endpoints. This identification supports the introduced quality model to estimate the perceived quality with higher accuracy. The performance of the proposed model is validated against a full-reference voice quality estimation model called AQuA, using real VoIP traffic (originated in assorted voice samples) in controlled transmission scenarios.
Online education in new period of sustainable development after the pandemic

The article is devoted to the problem of assessing the effectiveness of the online distance learning format, based on the results of work during the first wave of the COVID-19 pandemic. The purpose of the article is to identify specific problems faced by students of various education profiles: natural science and humanitarian. The objectives of the article are to analyze the scientific literature on the research topic, as well as present the results of the empirical research conducted by the authors among two groups of respondents. The methodology of the article is based on a systematic approach and includes a group of general scientific methods (analysis, synthesis, deduction, induction), as well as special methods: content analysis of scientific literature on the research topic, the method of sociological research (questionnaire survey), as well as the method of statistical analysis. Based on the results of the study, the authors came to the following conclusions: the online format of distance learning is not compatible with a number of natural science disciplines, which leads to a sharp decrease in the quality of education, especially in the field of applied sciences.
Robust Adversarial Active Learning with a Novel Diversity Constraint

Active learning adopts an iterative process that prioritizes the labeling of the most informative samples. However, in many real-world applications, the training data usually contains Out-of-Distribution (OoD) samples that affect the robustness of the trained models. Unfortunately, most existing active learning approaches focus on the uncertainty measure, thus have a bias towards selecting OoD samples. In this paper, we propose a robust adversarial active learning method that performs well on datasets with OoD samples. First, we incorporate recent advances in adversarial networks into an active learning framework to select the samples that are most dissimilar to the labeled pool. Secondly, we design a novel loss function based on Earth-Mover (EM) distance, which makes the model training more stable. Moreover, we propose a novel diversity constraint learned from feature space that penalizes the OoD samples. Experimental evaluation results on the datasets of varying size demonstrate the effectiveness of our approach.
Multi-label Classification of Chinese Judicial Documents based on BERT

Judicial decisions are an important part of modern democratic societies. In this paper, I present results of multi-label classification of Chinese judicial documents. The experiments employ the same corpus that was used in Chinese AI & Law Challenge(CAIL) 2018.
The Generative Adversarial Network Based on Attention Mechanism for Image Defogging

The image collected under the haze weather conditions has some obvious problems such as reduced contrast, reduced clarity and color distortion, which seriously reduces the image quality. Combining with deep learning, this paper proposes a new end-to-end generative adversarial defogging network based on attention mechanism, which transforms the problem of image defogging into the generation of fog-free images based on foggy images. On the basis of generative adversarial network, the attention mechanism was introduced into the generating network to enhance the features of the foggy region and generate the attention map. Then the attention map and the foggy image were input into the auto-encoder for encoding and decoding the image and re-decode the foggy. Finally, the fogless image was output. The attention map was introduced into the discriminate network to discriminate the true and false of the fogless image (the prediction fogless image and the real fogless image). A lot of experiments were carried out on the foggy images in different scenes, and the various comparative analysis were carried out with the evaluation index of image quality. The result shows that the defogging network model proposed in this paper can not only be better applied to all kinds of foggy scenes, but also generate fog-free images that are more suitable for real scenes, and effectively improve the average gradient value, information entropy and NRSS of the image, and can better restore edge information and color information of foggy images.
Stacked Hourglass Network with Additional Skip Connection for Human Pose Estimation

The human pose estimation is a problem of localizing human joints in a single image, and that is still a challenge in the field of computer vision. The hourglass network has been used in many researches to achieve good performance in human pose estimation problems. For human pose estimation problem, not only high-level features but also low-level features are important for understanding the whole human body. However, the vanilla hourglass network has the problem of passing only high-level features to the next stack. Therefore, we propose a network structure that can solve the problems of the vanilla hourglass by using an additional skip connection. The proposed skip connection improves network performance by passing relative low-level features to the next stack. In addition, the skip connection is a simple element-wise Sum operation, so there is no increase in the number of parameters. In this work, we use the well-known human pose estimation data set, MPII, to evaluate the proposed method. We conducted experiments to evaluate the objective performance of the proposed method, and it was confirmed through this evaluation that the proposed method improves the performance of human pose estimation of the vanilla hourglass network.
Learning Embeddings of Directed Networks with Text-Associated Nodes—with Application in Software Package Dependency Networks

A network embedding consists of a vector representation for each node in the network. Its usefulness has been shown in many real-world application domains, such as social networks and web networks. Directed networks with text associated with each node, such as software package dependency networks, are commonplace. However, to the best of our knowledge, their embeddings have hitherto not been specifically studied. In this paper, we propose PCTADW-1 and PCTADW-2, two algorithms based on neural networks that learn embeddings of directed networks with text associated with each node. We create two new node-labeled such networks: The package dependency networks in two popular GNU/Linux distributions, Debian and Fedora. We experimentally demonstrate that the embeddings produced by our algorithms resulted in node classification with better quality than those of various baselines on these two networks. We observe that there exist systematic presence of analogies (similar to those in word embeddings) in the network embeddings of software package dependency networks. To the best of our knowledge, this is the first time that such systematic presence of analogies is observed in network and document embeddings. We further demonstrate that these network embeddings can be novelly used for better understanding software attributes, such as the development process and user interface of software, etc.
Hierarchical Multipath Blockchain Based IoT Information Management Techniques for Efficient Distributed Processing of Intelligent IoT Information

As cloud technology advances, devices such as IoT (Internet of Things) are being utilized in various areas ranging from transportation, manufacturing, energy, automation, space, defense, and healthcare. As the number of IoT devices increases, the safety of IoT information, which is vulnerable to cyber attacks, is emerging as an important area of interest in distributed cloud environments. However, integrity techniques are not guaranteed to easily identify the integrity threats and attacks on IoT information operating in the distributed cloud associated with IoT systems and CPS (Cyber-Physical System). In this paper, we propose a blockchain-based integrity verification technique in which large amounts of IoT information processed in distributed cloud environments can be guaranteed integrity in security threats related to IoT systems and CPS. The proposed technique aims to ensure the integrity of IoT information by linking information from IoT devices belonging to subgroups in distributed cloud environments to information from specific non-adjacent IoT devices and blockchain. This is because existing techniques rely on third-party organizations that the data owner can trust to verify the integrity of the data. The proposed technique identifies IoT information by connecting the paths of IoT pre- and subsequent blocks into block chains so that synchronization can be achieved between subgroups in distributed cloud environments. Furthermore, the proposed technique uses probabilistic similarity information between IoT information blocks to react flexibly to subgroups that constitute distributed clouds so that IoT information blocks are not exploited maliciously by third parties. As a result of performance evaluation, the proposed technique averaged 12.3% improvement in integrity processing time over existing techniques depending on blockchain size. Furthermore, the proposed technique has to hash the IoT information that constitutes a subgroup with probability-linked information, validating the integrity of large-capacity IoT information, resulting in an average of 8.8% lower overhead than existing techniques. In addition, the proposed technique has an average improvement of 14.3% in blockchain-based integrity verification accuracy over existing techniques, depending on the hash chain length.
A Simple, Effective and Extendible Approach to Deep Multi-task Learning

Existing solutions to multi-task learning typically rely on manually enumerating multiple network architectures to find the optimal structure, which incurs a heavy design workload. In addition, extending these models to new tasks is difficult in many cases, since it often requires a network redesign for achieving the best performance. To overcome these limitations, in this paper, we propose a novel principle for multitask learning, which focuses on the learning process itself. For each task, we consider its feature as a state, and treat the learning problem as a state transformation process, which is driven by a task-specific gradient. Specifically, we introduce a Gradient Modification Unit (GMU), which consists of a Gradient Estimation Network (GEN) (shared among all tasks) and multiple tiny task-specific gradient correctors (one for each task). At each iteration, for any task T, the corresponding gradient corrector modifies the gradient estimated by the GEN to produce the desired task-specific gradient tensor that is applied to update the state of task T. Our solution has several benefits including automatic end-to-end learning of the optimal transformation process, simplification of network design and high expansibility to new tasks. We demonstrate the superiority of our approach over existing solutions on a variety of datasets, across both image classification and image retrieval tasks.