Reviewerly/exHarmony huggingface dataset:
Deep Learning em Sistemas Embarcados de Baixo Custo

In the topic of autonomous (electro) mobility and embedded vehicle electronics, object detection becomes inevitable.Yours importance is significant, because it covers other sectors such as manufacturing, agriculture and administrative processes.However, the image processing procedure suffers from the complexity of the training process, being necessary to pay attention to the hardware requirements.Given this, the paper is dedicated to presenting questions and manipulations of microprocessors, to make not only able to implement learning methods, but also effective, reaching higher levels of abstraction in low cost and high accessibility embedded systems.Thus, will be shown with the results generated, the best pre-trained deep learning algorithms available on the internet and effective processing techniques.To avoid using a graphics processing unit -GPU, a Raspberry Pi microprocessor will be used, due to the processing capacity, cost and other factors.That is, it will be possible to show the possibility or at least the limits, to work with elements responsible for vehicle autonomy, through easily accessible resources and reduced cost.
Leveraging the Potentials of Federated AI Ecosystems

Deep learning increasingly receives attention due to its ability to efficiently solve various complex prediction tasks in organizations. It is therefore not surprising that more and more business processes are supported by deep learning. With the proliferation of edge intelligence, this trend will continue and, in parallel, new forms of internal and external cooperation are provided through federated learning. Hence, companies must deal with the potentials and pitfalls of these technologies and decide whether to deploy them or not and how. However, there currently is no domain-spanning decision framework to guide the efficient adoption of these technologies. To this end, the present paper sheds light on this research gap and proposes a research agenda to foster the potentials of value co-creation within federated AI ecosystems.
AnnotSemRec: An RDF Based Semantic Framework for Personalized Web Image Search

With the increasing amounts of image and multimedia content on the World Wide Web, it is quite essential to retrieve images with maximum relevance and minimum deviations from the search query. The traditional content-based Image Retrieval algorithms are not compliant with the evolving Semantic Web. In this paper, a framework for Web Image Recommendation has been proposed that hybridizes Adaptive Normalized Compression Distance and Adaptive Pointwise Mutual Information strategies with varied and differential thresholds. The proposed framework facilitates ontology modeling for canonically synonymous terms that serve as an intelligent training strategy for web image retrieval. The proposed strategy for web image recommendation is annotations based that uses the context information of the image rather than the image features. The proposed web image retrieval is initiated by the query and driven by capturing user intentions dynamically incorporating the strategic query expansion technique. An overall F-Measure of 96.17% has been achieved.
Toward a Feminist Information Architecture

The need to focus on feminism in information architecture; the importance of defining “the user”; defining feminism in social and academic contexts; feminist studies and practices within information architecture and related disciplines such as HCI, information science, and interaction design; a feminist agenda for information architecture.
Fundamental Changes in Mathematics Education in the Context of Digital Transformation

The paper examines the relationship between the global process of digital transformation and technological solutions in teaching a modern mathematics course. We explore the possibilities of forming digital literacy through the active use of digital means, tools and resources (including general-use ones) in the school mathematics curriculum.
Effective Utterance Interpretation of Acronymic Letter Prosody through MFCC-HMM Algorithm

The era of fast and spontaneous communication do require effective utterance interpretation which in turn requires lexical, syntactic and semantic familiarity acquaintance. This acquaintance is required at acoustic, phonetic, linguistic and application, which are the speech grades. It is proven indeed that vocal mechanism of a particular speaker is unique but, still there is a utterance difference do exists with respect to speakers mood, his/her speaking style, the context of speech and individuals intentional utterance control, perturbations and frame of his/her mind. Acronymic letter prosody uses characteristics such as pattern, accentuation, pitch and tonality to convey information and meaning pertaining to uttered letter or word. Acronymic letter prosody recognition is a challenging task in all languages. A three letter approach, heuristic approach, logistical regression approach and SVM classifier and HMM are some of the previous works carried out on this aspect. In comparison with these developed approaches, this paper brings out an effective algorithm based on MFCC-HMM approach in order to arrest variability such as to get good accuracy. The work is implemented using MATLAB platform with feature extraction using MFCC, dimensional reduction through principle component analysis (PCA), Modelling and classification through HMM.
Motion Planning with Cartesian Workspace Information

We propose three extensions to the known sampling-based Exploring/Exploiting Tree (EET) Robot Motion Planner with following considerations: a) robot joint motion bounds, b) additional constraints on robot end-effector pose and c) parallelization of planning procedures to get alternative solutions. We also tackle the gap between global and local motion planning by combining sampling-based motion planning and reactive control approaches. These modifications complement the EET algorithm, which enables our planners to be more beneficial for practical applications. The experimental results demonstrate that our extended EET planners outperform other state-of-the-art sampling-based motion planners for some planning problems according to criteria such as planning time and path length.
Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs

A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar optimum. In this work, we shed light on such gradient conflict challenges and suggest a solution named Cost-Out, which randomly drops the sub-costs for each iteration. We provide the theoretical and empirical evidence of the existence of escaping pressure induced by the Cost-Out mechanism. While simple, the empirical results indicate that the proposed method can enhance the performance of multi-task learning problems, including two-digit image classification sampled from MNIST dataset and machine translation tasks for English from and to French, Spanish, and German WMT14 datasets.
The Design of Mine Encapsulated and Intrinsically Safe Explosion-proof Computers

With the continuous improvement of the level of intelligence in coal mines, construction sites have put forward new requirements for the performance and compatibility of mining computers. Aiming at the field conditions of intelligent underground drilling in coal mines, this paper designs a mine-used encapsulated and intrinsically safe computer as a data processing terminal, with the characteristics of a large display screen, a moderate overall weight, powerful operating performance and multiple compatible interfaces, which can replace the explosion-proof and intrinsically safe computers in current field applications.
Regularized Regression on Compositional Trees with Application to MRI Analysis

A compositional tree refers to a tree structure on a set of random variables where each random variable is a node and composition occurs at each non-leaf node of the tree. As a generalization of compositional data, compositional trees handle more complex relationships among random variables and appear in many disciplines, such as brain imaging, genomics and finance. We consider the problem of sparse regression on data that are associated with a compositional tree and propose a transformation-free tree-based regularized regression method for component selection. The regularization penalty is designed based on the tree structure and encourages a sparse tree representation. We prove that our proposed estimator for regression coefficients is both consistent and model selection consistent. In the simulation study, our method shows higher accuracy than competing methods under different scenarios. By analyzing a brain imaging data set from studies of Alzheimer's disease, our method identifies meaningful associations between memory decline and volume of brain regions that are consistent with current understanding.
A Brief Study on Approaches for Extractive Summarization

Moved by the cutting edge mechanical advancements, information is to this century what oil was to the past one. Today, our reality is dropped by the gettogether and spread of gigantic measures of data. With a particularly enormous measure of information flowing in the advanced space, there is a need to create Artificial Intelligence calculations that can naturally abbreviate longer messages and convey exact outlines that can fluidly pass the proposed messages. This paper puts forth a brief survey of five major extractive methods of text summarization- the TFIDF, clustering, neural network, fuzzy logic, and graph-based approaches. A comparison of the five approaches is also presented.
Semantic Similarity Based on Word Recurrence Ratio Focusing on WordNet

Some texts can convey the same meaning without consisting of common words because of the distinct ramification of sentences. Thence traditional string overlapping is not enough for measuring the semantic similarity. For the acquirement of semantic textual similarity, the word semantics and their senses should be considered. In this paper, we proposed a ratio considering words overlapping. The ratio is calculated in terms of common words and the uncommon words of two texts. We employed WordNet components to estimate semantic similarity. Various existing WordNet measures emphasize different relationships and information of words in a taxonomy. We used three prominent WordNet measures. With the procedure we applied on WordNet measures, they are individually not susceptible enough to the criterion of sentence similarity approximation. Therefore, based on the ratio, we dynamically choose several combinations of WordNet measures to capture the semantic similarity. To validate our proposed method, we conduct several experiments on the SemEval STS dataset. The experimental result revealed that our proposed method shows an efficient p erformance and outperformed some known related methods in terms of different evaluation metrics.
Public Sector Corruption Analysis with Modified K-means Algorithm Using Perception Data

Corruption in public sectors has received increased attention over the last few decades due to its widespread presence all over the globe. We assert that public sector corruption must be properly analyzed and detected as soon as possible so that preventive and corrective measures can be taken. Thus, we developed a model that uses a modified K-means approach, based on K-means algorithm, on perception data, to analyze corruption within the public sector. The model was applied to analyze the corruption within public sectors of Bangladesh. The public sector organizations were segmented based on their different levels of corruption and resultant clustering showed an accuracy of 87.5% with official corruption data as reference. This paper also demonstrates the architecture of a cloud based web-application, developed based on this model that adds robustness and autonomy to the intelligent system.
On bases and the dimensions of twisted centralizer codes

Alahmadi et al. (2017) [2] introduced the notion of twisted centralizer codes, CFq(A,γ), defined asCFq(A,γ)={X∈Fqn×n:AX=γXA}, for A∈Fqn×n, and γ∈Fq. Moreover, Alahmadi et al. (2017) [3] also investigated the dimension of such codes and obtained upper and lower bounds for the dimension, and the exact value of the dimension only for cyclic or diagonalizable matrices A. Generalizing and sharpening Alahmadi et al.'s results, in this paper, we determine the exact value of the dimension as well as provide an algorithm to construct an explicit basis of the codes for any given matrix A.
Bridging the Gap between Memory and Communication Efficiency on Distributed Deep Learning Systems

Large-scale distributed deep learning is of great importance in various applications. For data-parallel distributed training systems, limited hardware resources (e.g., GPU memory and interconnection bandwidth) often become a performance bottleneck, and it is necessary to consider the full utilization of multiple resources simultaneously, especially for extreme-scale deep neural networks. Although two different types of strategies, based on memory management and sparse communication, have been proposed to reduce the usage of resources, a naïve combination of these two optimizations is impractical, since they cannot successfully coexist with each other. We therefore consider the idea of collaborative optimization in terms of both system memory and bandwidth resources, and propose a layer-centric memory-efficient distributed sparse communication mechanism called LaySA. Firstly, to tackle the memory ballooning issue caused by sparse communication, the existing memory reuse strategy is refined, and the data object of the memory optimization is augmented and redefined. Secondly, a mirror weight update mechanism is proposed to address the contradiction between memory management and sparse communication optimization for weight gradients. Our scheme, which involves the deep integration and collaborative execution of these two types of strategies, can fill the gap in relation to multiple resource optimization in distributed GPU-based training systems. Our experimental results show that the proposed collaborative optimization can significantly alleviate the memory pressure on the computing nodes, and improve both the resource utilization and the throughput of distributed training systems. Compared with baseline systems using only a single strategy, LaySA can help to reduce the system memory usage by up to 80.5%, and the overall training time of the neural network models on a single GPU is reduced by about 12.25%. Furthermore, LaySA can scale up the batch size of the datasets by an extremely large factor during distributed training, and the overall throughput is increased by more than 150%, meaning that our approach outperforms current systems that use memory or communication optimization mechanisms alone.
Application of Nature-Inspired Swarm Optimization Algorithms in Artificial Neural Networks

Over the past decade, Neural Networks have been dominating in terms of versatility and technological success. As are result it has aided numerous breakthroughs and has consequently found its application in almost every field of modern science. The Deep ANNs are constructed through a wide range of steps and processes. Optimization is an important part of the Civil Engineering, Financial Modelling et cetra. There are a lot of such algorithms that have been proposed. Swarm optimization techniques are modelled after organisms belonging to the animal kingdom and have proven to be very successful when-paired with neural networks. Periods of scarcity force organisms to develop instinctive optimization techniques that are simulated in a way that it helps solve non-linear optimization problems. This paper reviews Bacterial Foraging, Ant Colony and Grey Wolf optimization algorithms and discusses the application of these algorithms in Neural Networks and Deep learning.
“Say no to coding”: Designing of mobile-app-based learning media using Glide apps

Abstract Designing mobile-app-based learning media is challenging for educators with minimal programming skills. This paper aims to introduce a conceptual model as a framework for designing learning media. In this paper, we discuss steps by step a mobile-app-based learning media designer by using glide apps. Descriptive qualitative with literature review design was used in this article. Based on the literature review, it can be known that glide app can design a mobile app which easy to use, build without code, applicable for the m-learning era, and used for free. However, the creation of a mobile app in education should be careful, because it needs to be adjusted to the age level and psychological characteristics of students. Therefore, educators can use the glide app as an alternative to m-learning media designers.
Emergency Communication Resource Prediction and Scheduling System Based on Deep Learning

Natural disasters and man-made safety accidents will cause heavy casualties and economic losses. The establishment of a reasonable and effective disaster response system is the development focus of disaster prevention and relief work. Based on the investigation of the existing emergency communication technology and emergency communication scheduling algorithm, this paper designs emergency communication resource prediction and scheduling algorithm based on deep learning. The algorithm can combine the basic situation of the disaster area with the real-time on-site situation, and reasonably dispatch multiple emergency communication resources according to the composition of the people in the disaster area, so as to ensure the smooth and reliable command and rescue of the disaster area and emergency communication.
In Search Of: Masterworks of Information Architecture

In order to identify and enumerate some of the normative criteria for critique (or appreciation) of a given info-architectural structure, system, or solution, two candidate “masterworks” of information architecture are provided for evaluation and discussion: the 1st edition of James Joyce’s first novel, Ulysses, and the only edition of Richard Saul Wurman’s second book, The City: Form and Intent.
Application of Computer Virtual Reality Technology in University Volleyball Teaching

Volleyball is a very popular sport, and many college students have taken volleyball courses. Using virtual reality (VR) technology for college volleyball teaching and training can effectively improve the teaching effect, and physical education teachers should pay attention to this. This article analyzes the advantages of VR technology in university volleyball teaching, explores the current problems in university volleyball teaching, and finally explores the effective use of VR technology in university volleyball teaching, hoping to use VR technology to further improve the level of college volleyball teaching.
Exploring Subword Segmentation Methods in English-Vietnamese Neural Machine Translation

Neural machine translation (NMT) is a popular paradigm used in the task of automatically translating text from a language to another. One of the challenges of NMT is to handle out-of-vocabulary (OOV) and low frequency words. Recent work shows that splitting words into subword units outperforms the classic word-level practice. However, many subword units have been proposed and it is difficult to find the one that is best suited for a language pair. In this paper, we conduct experiments using different subword segmentation methods including Byte Pair Encoding, Character Embedding, and so on in English-Vietnamese translation tasks. Experimental results on our dataset show that subword encoding yields better performance to regular encoding. We also give analyses on the results to suggest which subword method is best suited for English-Vietnamese NMT tasks.
Parametric Design of Underwater Optical Systems

The design of optical systems for underwater vehicles is a complex process where the selection of cameras, lenses, housings, and operational parameters greatly influence the performance of the complete system. Determining the correct combination of components and parameters for a given set of operational requirements is currently a process based on trial and error as well as the specialized knowledge and experience of the designer. In this paper, we introduce an open-source tool for the parametric exploration of the design space of underwater optical systems and review the most significant underwater light effects with the corresponding models to estimate the response and performance of the complete imaging system.
Cold-Start Active Sampling Via <i>γ</i>-Tube

Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its performance in a cold-start hypothesis. In this article, we first show that typical AL sampling can be equivalently formulated as geometric sampling over minimum enclosing ballsMEB of this article denotes a conceptual geometry over the cluster in generalization analysis. In the SVM community, it is related to hard-margin support vector data description.(MEBs) of clusters. Following the γ -tube structure in geometric clustering, we then divide one MEB covering a cluster into two parts: 1) a γ -tube and 2) a γ -ball. By estimating the error disagreement between sampling in MEB and γ -ball, our theoretical insight reveals that γ -tube can effectively measure the disagreement of hypotheses in original space over MEB and sampling space over γ -ball. To tighten our insight, we present generalization analysis, and the results show that sampling in γ -tube can derive higher probability bound to achieve a nearly zero generalization error. With these analyses, we finally apply the informative sampling policy of AL over γ -tube to present a tube AL (TAL) algorithm against the cold-start sampling issue. As a result, the dependency between the querying process and the evaluation policy of active sampling can be alleviated. Experimental results show that by using the γ -tube structure to deal with cold-start sampling, TAL achieves the superior performance than standard AL evaluation baselines by presenting substantial accuracy improvements. Image edge recognition extends our theoretical results.
Search Engine Database Systems Tools for Building Literature

This article discusses research information from search engines. A search engine is a static and centralized database that allows users to access the WWW on the Internet to search a query-based database of information. Justification is considered as obtaining information. This is a method for analyzing the design, software, and implementation of technical tools. Special attention is paid to organizations that search for information via the Internet. In this article, you develop a database procedure for storing information and implement it in Microsoft SQL Server using ASP NET. In fact, the book search information system was implemented. Today, there are many different search engines with features and functions on the Internet. In this article, it is decided that the search engine is an effective tool for creating a database of literature search.