Reviewerly/exHarmony huggingface dataset:
Central limit theorem and bootstrap approximation in high dimensions: Near 1/n rates via implicit smoothing

Nonasymptotic bounds for Gaussian and bootstrap approximation have recently attracted significant interest in high-dimensional statistics. This paper studies Berry–Esseen bounds for such approximations with respect to the multivariate Kolmogorov distance, in the context of a sum of n random vectors that are p-dimensional and i.i.d. Up to now, a growing line of work has established bounds with mild logarithmic dependence on p. However, the problem of developing corresponding bounds with near n−1/2 dependence on n has remained largely unresolved. Within the setting of random vectors that have sub-Gaussian or subexponential entries, this paper establishes bounds with near n−1/2 dependence, for both Gaussian and bootstrap approximation. In addition, the proofs are considerably distinct from other recent approaches, and make use of an “implicit smoothing” operation in the Lindeberg interpolation.
Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification

In this work, we present for the first time a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels. Annotation acquisition for semantic segmentation datasets is time-consuming and requires plenty of human labor. In particular, review processes are time consuming and label errors can easily be overlooked by humans. The consequences are biased benchmarks and in extreme cases also performance degradation of deep neural networks (DNNs) trained on such datasets. DNNs for semantic segmentation yield pixel-wise predictions, which makes detection of label errors via uncertainty quantification a complex task. Uncertainty is particularly pronounced at the transitions between connected components of the prediction. By lifting the consideration of uncertainty to the level of predicted components, we enable the usage of DNNs together with component-level uncertainty quantification for the detection of label errors. We present a principled approach to benchmark the task of label error detection by dropping labels from the Cityscapes dataset as well as from a dataset extracted from the CARLA driving simulator, where in the latter case we have the labels under control. Our experiments show that our approach is able to detect the vast majority of label errors while controlling the number of false label error detections. Furthermore, we apply our method to semantic segmentation datasets frequently used by the computer vision community and present a collection of label errors along with sample statistics.
Dropping Pixels for Adversarial Robustness

Deep neural networks are vulnerable against adversarial examples. In this paper, we propose to train and test the networks with randomly subsampled images with high drop rates. We show that this approach significantly improves robustness against adversarial examples in all cases of bounded L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> perturbations, while reducing the standard accuracy by a small value. We argue that subsampling pixels can be thought to provide a set of robust features for the input image and, thus, improves robustness without performing adversarial training.
Tuning of the membership functions of a fuzzy control system using pattern search optimization method

The fuzzy control uses qualitative knowledge with linguistics descriptions about the operation of any process. Furthermore, Fuzzy Controller (FC) has several parameters which can be adjusted to change the performance. If this adjust is done through a “trial and error” procedure, then it will be ver y time-consuming and difficult to reach a good performance. Frequently, the tuning of the Membership Functions (MFs) have the most influence to improve the performance of a FC. Therefore, in this study we tune the MFs of a FC using optimization by Direct Search (DS) method, specifically the pattern search. In this sense, our goal is to improve the performance of the system to satisfy certain conditions and propose optimization method to tune the MFs. The methodology of the optimization is simulation-based. The Objective Function (OF) is the squared error between the set point and the output of the system. The evaluation of the OF and the implementation of the fuzzy control system were performed by simulations. An explanation of the algorithmic method based on the pattern search algorithm is shown. The method proposed in this paper is illustrated with examples for the non-linear systems. The results are compared and discussed with other controllers.
Cloud Data Center Selection Using a Modified Differential Evolution

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-i... | Find, read and cite all the research you need on Tech Science Press
A Deep-Learning Classification Framework for Reducing Communication Errors in Dynamic Hand Signaling for Crane Operation

Crane operators and signalmen play an integral role in the safe and efficient operation of cranes on a construction site. Operating a crane is a complex and demanding task that requires careful coordination between operator and signalmen in order to avoid errors that could have dire consequences, including serious injury or loss of life. Therefore, special considerations should be taken to mitigate communication errors that could occur between the two parties. Technology can play an important role in enhancing communication, and, with recent advancements in technology, human–computer interaction has emerged as an active area of research within the field of computer vision. This paper presents a framework that integrates the YOLOv4 model (for object detection) and the long short-term memory (LSTM) model (a recurrent neural network) for dynamic hand signal classification in real time. The first step is the creation of a crane signalman dynamic hand signal data set with 18 classes. The YOLOv4 model is then customized for this application by modifying the activation function. Three modified YOLOv4 models are then integrated with the LSTM model. The modified YOLOv4 integrated with LSTM is found to achieve a maximum overall accuracy of 94.8% with an inference time of 55.1 frames per second. The model is further validated with real-time dynamic hand signal classification, achieving an accuracy of 93.5% and an inference time of 44 frames per second. The proposed models show improved quality in classification accuracy as well as in processing speed in comparison to some of the most widely used models currently in use. The proposed novel framework can be used as another layer of communication to supplement current practice and reduce communication errors between crane signalmen and crane operators.
A Systematic Security Assessment and Review of Internet of Things in the Context of Authentication

The Internet of Things is emerging globally as an intriguing trend expected to connect 15 billion devices by the end of 2022. Its ability to bring intelligence and automation to various application domains provides a plethora of opportunities while posing severe security challenges. Lack of proper authentication has been attributed to data disclosure's perils over wireless communication channels. Therefore, authentication as an essential security tenet continues to be a highly researched area, especially in resource constraint networks like IoT and IIoT. This paper constructs a comprehensive systematic literature review to identify and synthesize security issues in IoT from the perspective of authentication mechanisms. Initially, the prevalent security and privacy issues are identified, followed by the explanation of security threats across various layers of the IoT architecture. Additionally, the countermeasures available for addressing security issues are also covered. The highlight of the review is to present a literature review of various authentication mechanisms and different formal security evaluations holistically required for IoT authentication. Moreover, a comparative analysis of some of the popular existing authentication mechanisms designed for the IoT in terms of various performance parameters like computational, communication overhead, and energy consumption has also been covered. Finally, the paper discusses the typical methods for assessing network security and network simulator tools used to evaluate the performance parameters of authentication schemes. This review paper attempts to assist researchers in identifying the existing research gaps in various forms of authentication employed in a typical resource constraint network like IoT that would lead them to develop new solutions. The protocol provided by Kitchenham and Charters has been used to perform this Systematic Literature Review.
RSHAN: Image super-resolution network based on residual separation hybrid attention module

Transformer has become one of the main architectures in deep learning, showing impressive performance in various vision tasks, especially for image super-resolution (SR). However, due to the usage of high-resolution input images, most current Transformer-based image super-resolution models have a large number of parameters and high computational complexity. Moreover, some components employed in the Transformer may be redundant for SR tasks, which may limit the SR performance. In this work, we propose an efficient and concise model for image super-resolution tasks termed Residual Separation Hybrid Attention Network (RSHAN), which aims to solve the problems of redundant components and insufficient ability to extract high-frequency information of Transformer. Specifically, we present the Residual Separation Hybrid Attention Module (RSHAM) which fuses the local features extracted by the convolutional neural network (CNN) branch and the long-range dependencies extracted by Transformers to improve the performance of RSHAN. Extensive experiments on numerous benchmark datasets show that the proposed method outperforms state-of-the-art SR methods by up to 0.11 dB in peak signal-to-noise ratio (PSNR) metric, while the computational complexity and the inference time is reduced by 5% and 10%, respectively.
Social media sentiment monitoring in smart cities

Smart cities utilize different devices not only to solve the increasingly serious urban resource shortage, environmental pollution, traffic congestion, security risks but also to identify concerns of citizens. Building a smart city is not free from using social networks that have changed citizen's daily life and becoming a new source of real-time information, so there is no doubt that sentiment analysis can contribute as important decision support. we take these challenges by presenting a set of features that have been used with machine learning techniques, sentiment analysis, text classification to extract the intelligence needed from social media feeds containing Moroccan dialects. A case scenario analyses the opinions of users concerning the traffic in three cities in Morocco is illustrated in the following.
Being the Center of Attention

This article proposes a novel study on personality recognition using video data from different scenarios. Our goal is to jointly model nonverbal behavioral cues with contextual information for a robust, multi-scenario, personality recognition system. Therefore, we build a novel multi-stream Convolutional Neural Network (CNN) framework, which considers multiple sources of information. From a given scenario, we extract spatio-temporal motion descriptors from every individual in the scene, spatio-temporal motion descriptors encoding social group dynamics, and proxemics descriptors to encode the interaction with the surrounding context. All the proposed descriptors are mapped to the same feature space facilitating the overall learning effort. Experiments on two public datasets demonstrate the effectiveness of jointly modeling the mutual Person-Context information, outperforming the state-of-the art-results for personality recognition in two different scenarios. Last, we present CNN class activation maps for each personality trait, shedding light on behavioral patterns linked with personality attributes.
PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data.

In natural language processing (NLP), there is a need for more resources in Portuguese, since much of the data used in the state-of-the-art research is in other languages. In this paper, we pretrain a T5 model on the BrWac corpus, an extensive collection of web pages in Portuguese, and evaluate its performance against other Portuguese pretrained models and multilingual models on three different tasks. We show that our Portuguese pretrained models have significantly better performance over the original T5 models. Moreover, we demonstrate the positive impact of using a Portuguese vocabulary. Our code and models are available at this https URL.
Action anticipation for collaborative environments: The impact of contextual information and uncertainty-based prediction

To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the observation of the human limb movements may not be sufficient to anticipate their actions unambiguously. In this work, we consider two additional sources of information (i.e., context) over time, gaze, movement and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e., deterministic) criterion. The main contributions of this paper are fourfold: (i) We propose a novel and effective decision-making criterion that can be used to anticipate actions even in situations of high ambiguity; (ii) we propose a deep architecture that outperforms previous results in the action anticipation task when using the Acticipate collaborative dataset; (iii) we show that contextual information is important to disambiguate the interpretation of similar actions; and (iv) we also provide a formal description of three existing performance metrics that can be easily used to evaluate action anticipation models. Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations. Also, considering that a good anticipation model should perform well in the action recognition task, we achieve an average accuracy of 100% in action recognition on the Acticipate dataset, when the entire observation set is used.
Bag of feature with discriminative module for non-rigid shape retrieval

Non-grid shape retrieval has been an important and challenging task that usually requires high-level and discriminative shape features. However, most of the existing descriptors are hand-crafted, which fail to distinguish and focus on important features from different regions and scales for given query shape. To tackle this issue, this paper presents a novel shape retrieval method, namely BoF-DM (Bag of feature with Discriminative Module), which is specially designed to highlight discriminative features from the multi-scale context structures. Specifically, a shape decomposing method is designed by combining the KPs (key points) and ring graph, result in local to global feature learning. To obtain high-level shape features, the spatial context of each contour fragment is learned by the stacked lightweight network in an unsupervised manner. In people's cognition, discriminative parts, such as heads or legs, play an essential role for identifying animal shape classes. Inspired by this phenomenon, we propose a TF-IDF (term frequency-inverse document frequency) with entropy-based Discriminative Module, which fully considers the attention issue of inter-class difference and intra-class similarity. Finally, decision-making is done by metric similarity. The experiments demonstrate that the proposed method achieves the high BER (Bull's Eye Retrieval) rates of 93.63%, 100%, 100%, and 99.00% on the considered MPEG-7, Kimia's 99, Kimia's 216, and Articulated shape datasets respectively. Furthermore, our method also obtains high performance under noisy and articulated conditions.
MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion

We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion. Our controllable approach enables variable-length generations customizable by action category, across more than 100 categories. To enable intra/inter-category diversity, we model the latent generative space using a Conditional Gaussian Mixture Variational Autoencoder. To enable realistic generation of actions involving locomotion, we decouple local pose and global trajectory components of the action sequence. We incorporate duration-aware feature representations to enable variable-length sequence generation. We use a hybrid pose sequence representation with 3D pose sequences sourced from videos and 3D Kinect-based sequences of NTU-RGBD-120. To enable principled comparison of generation quality, we employ suitably modified strong baselines during evaluation. Although smaller and simpler compared to baselines, MUGL provides better quality generations, paving the way for practical and controllable large-scale human action generation.
MEXIN: Multidialectal Ontology supporting NLP approach to improve government electronic communication with the Mexican Ethnic Groups

The government services usually target all citizens, but sometimes physical services nor technology-based services do not cover all people. This research aims to tackle services given to underrepresented citizens in Mexico (Indigenous people) and apply NLP techniques supported by ontologies to achieve accurate translation to most dialects spoken in Mexico. The scope of this paper only tests with Mayan dialect spoken primarily in the Mexican peninsula.
Bucketed common vector scaling for authorship attribution in heterogeneous web collections: A scaling approach for authorship attribution

Domain, genre and topic influences on author style adversely affect the performance of authorship attribution (AA) in multi-genre and multi-domain data sets. Although recent approaches to AA tasks focus on suggesting new feature sets and sampling techniques to improve the robustness of a classification system, they do not incorporate domain-specific properties to reduce the negative impact of irrelevant features on AA. This study presents a novel scaling approach, namely, bucketed common vector scaling, to efficiently reduce negative domain influence without reducing the dimensionality of existing features; therefore, this approach is easily transferable and applicable in a classification system. Classification performances on English-language competition data sets consisting of emails and articles and Turkish-language web documents consisting of blogs, articles and tweets indicate that our approach is very competitive to top-performing approaches in English competition data sets and is significantly improving the top classification performance in mixed-domain experiments on blogs, articles and tweets.
Construction of Conditional Probability Tables of Bayesian Networks using Ontologies and Wikipedia

Building Bayesian Networks automatically serves to reduce time and effort by defining variables and the quantitative relation between variables. However, the quantitative part is the most complicated to solve because of it is statistical information.This research proposes a method to construct the quantitative part of a Bayesian Network based on text mining and ontologies for Intelligent Tutoring Systems. The network structure is built based on the variables and relations of an ontology. Conditional Probability Tables (CPT) are created from Wikipedia information.The constructed CPT reach a correlation of 0.895 against the experts' opinion. This correlation is good due to the subjectivity in the evaluations. We conclude that using the text mining in Wikipedia and ontologies, it is possible to construct CPT that adequately represents knowledge in an educative environment.
An on-line Framework for Experimenting with Concept Maps

A Concept Map provides a graphical, hierarchical, representation of a domain of knowledge, and can be used by teachers to define and organize the content of a course. The use of existing concept maps, possibly implemented by means of tested and suitable learning material, can be of great help for the teacher, while defining her/his own concept map. In this context, being able to establish the similarity between two concept maps is a significant asset, allowing to limit the analysis of other maps, and focus on those that can be of actual help for the teacher. This is true in a special way if the measure is computed “according to one among many possible pedagogical criteria of similarity”. We are working on the definition of measures entailing similarity based on different pedagogical criteria. In this paper, we meet the problem of experimenting such with measures, and comparing them with baseline measures, while investigating the effectiveness of a new measure. To support this kind of analysis we propose here a web-based system. We show the characteristics of the system, and use it to compare the behaviour of three measures of similarity: the pre-requisite measure (also presented) computes the similarity of two maps with respect to a particular pedagogical aspect. The other two measures are used as a baseline. We conclude that the system has shown its usefulness and can be used for more extensive analysis of other measures.
Multi-Patch Aggregation Models for Resampling Detection

Images captured nowadays are of varying dimensions with smartphones and DSLR's allowing users to choose from a list of available image resolutions. It is therefore imperative for forensic algorithms such as resampling detection to scale well for images of varying dimensions. However, in our experiments we observed that many state-of-the-art forensic algorithms are sensitive to image size and their performance quickly degenerates when operated on images of diverse dimensions despite re-training them using multiple image sizes. To handle this issue, we propose two novel deep neural networks - Iterative Pooling Network (IPN), which does not assume any prior information about the original image size, and Branched Network (BN), which uses this prior knowledge to produce better results. IPN adopts a novel iterative pooling strategy that converts tensors of multiple sizes to tensors of a fixed size, as required by deep learning models with fully connected layers. BN alternatively adopts a branched architecture with dedicated pathways for images of different sizes. The effectiveness of the proposed solution is demonstrated on two problems, resampling detection and photorealism detection, which are generally solved as independent problems with different deep learning models. The code is available at https://github.com/MohitLamba94/Iterative-Pooling.