Reviewerly/exHarmony huggingface dataset:
Comparative study of optimization algorithms on convolutional network for autonomous driving

&lt;p&gt;he last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications. Deep learning, and in particular convolutional networks have become a fundamental tool in the solution of problems related to environment identification, path planning, vehicle behavior, and motion control. In this paper, we perform a comparative study of the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the development of an intelligent sensor. This sensor, part of our research in reactive systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The training of the deep model is evaluated in terms of convergence, accuracy, recall, and F1-score metrics. Preliminary results show a better performance of the deep network when using the SGD function as an optimizer, while the Ftrl function presents the poorest performances.&lt;/p&gt;
The effect of maritime English vocabulary for beginners module on the vocabulary learning outcomes

This research started from the problem of the current covid-19 pandemic condition and situation, which makes cadets have to study online and most by themselves. In addition, it is based on the fact that there has been no specific module discussing basic terms or Maritime English vocabulary specifically for each department. The purpose of this study is to reveal whether there is an effect of using the maritime English vocabulary for beginners module on the vocabulary learning outcomes and how effective the use of the module is for cadets of Batch V Nautical Department, Shipping Polytechnic, West Sumatra. This type of research is experimental with the one-group pretest posttest design (pre-experimental design). This research was carried out in all classes of the nautical department Batch V, which consisted of 3 categories; Nautical A, Nautical B, and Nautical C. The sample used purposive sampling in this research, so the writer only took cadets in nautical majors, with a total sample of 60 persons. Based on the results, it was found that there was an effect of using the maritime English vocabulary for beginners module on the learning outcomes of maritime English vocabulary for Nautical Department cadets Batch V. Furthermore, from the results of questionnaire data processing, it can be concluded that the use of maritime English vocabulary for beginners module in maritime English vocabulary learning is effective for Nautical Department cadets Batch V in West Sumatra Merchant Marine Polytechnic in 2021.
Dual Interactive Attention Network for Joint Entity and Relation Extraction

The joint entity and relation extraction method establishes a bond between tasks, surpassing sequential extraction based on the pipeline method. Many joint works focus on learning a unified representation for both tasks to explore the correlations between Named Entity Recognition (NER) and Relation Extraction (RE). However, they suffer from the feature confusion that features extracted from one task may conflict with those from the other. To address this issue, we propose a novel Dual Interactive Attention Network to learn independent representations and meanwhile guarantee bidirectional and fine-grained interaction between NER and RE. Specifically, we propose a Fine-grained Attention Cross-Unit to model interaction at the token level, which fully explores the correlation between entity and relation. To obtain task-specific representation, we introduce a novel attention mechanism that can capture the correlations among multiple sequences from the specific task and performs better than the traditional self-attention network. We conduct extensive experiments on five standard benchmarks (ACE04, ACE05, ADE, CoNLL04, SciERC) and achieve state-of-the-art performance, demonstrating the effectiveness of our approach in joint entity and relation extraction.
A Median-based Resilient Distributed Optimization Algorithm Against Byzantine Attack

Distributed optimization algorithms are widely used for solving science and engineering problems in recent years. However, these algorithms become vulnerable in the face of external attacks, so it is urgent to develop novel resilient distributed optimization algorithms against attacks. In this paper, we design a novel median-based resilient distributed optimization algorithm to defend the Byzantine attack. The existence of transition matrix is proved, and the convergence and optimality of the proposed algorithm are analyzed. Simulation experiments are performed to verify the effectiveness of the proposed algorithm and the results show that it is more effective than the compared one and can get the best possible results against the attack.
Role of Presence, Memory and Spatial Ability in a Desktop Virtual Reality

This chapter presents some significant human factors that contribute to the virtual reality experience in desktop virtual reality. The relationship between human characteristics and virtual reality features is a decent example of the co-evolution of technology and human capabilities, which is an integral part of cognitive infocommunications. This chapter discusses the role of individual spatial abilities in desktop virtual reality. Furthermore, the presence profile of this virtual environment is shown, and a recent result of the different present experiences of novice and more experienced users. Finally, the characteristics of human memory in this virtual reality are discussed.
Geometry Calibration Control Method with 3D Sensors of Large Screen Interactive Projection Imaging System

This paper presents a geometric calibration control method based on large-screen interactive projection system. By establishing the mapping relationship between the captured image and the actual projected image captured by the 3D sensor Kinect camera, the checkerboard is selected using the geometric mapping model to realize the geometric calibration. The projective transformation method is utilized to reduce the nonlinear effect of the projector. Then the projective mapping matrix is obtained by the method of linear transformation. The projective transformation relation matrix is used to establish the precise mapping relationship between the projector and the camera. The self-developed large-screen human-computer interaction projection interactive system platform is employed to perform geometric calibration experiments. The experimental results show that the geometric calibration method of the large-screen interactive projection system based on 3D sensor proposed in this paper achieves a good precision positioning and can be widely applied to various human-computer interaction system.
Postphenomenological Dimensions of Digitally Mediated Domestic Heating

Designing interactive technology to support and reduce domestic energy consumption has occupied HCI researchers for decades. This paper extends this body of HCI research by using postphenomenology as an analytical lens to understand how the digital energy assistant' Watts' mediates domestic district heating. We base our inquiry on empirical insights from a two-year study. We conducted informal meetings, focus groups with seven households before Watts' distribution, two rounds of semi-structured interviews with seven household representatives using Watts, and 304 questionnaire respondents. We illustrate findings in three postphenomenological dimensions - practical, ontological, and epistemological - showing a two-sidedness of technology-mediated domestic district heating. For instance, Watts enables some to reduce heating by magnifying certain new aspects of consumption, but also constrains others from doing the same by concealing the "raw" consumption data. Finally, we discuss four tensions of Watts mediation and how postphenomenology can complement HCI studies on domestic energy consumption.
Viability of compensating employees in cryptocurrency – An exploratory study

Cryptocurrency is an emerging digital currency that revolutionized the transactional economy. It was valued at $2.2 trillion in April 2021, suggesting the widespread acceptance and usage of the currency. It penetrated numerous sectors including entertainment, infrastructure, healthcare, public sector, agriculture, financial services, travel, retail and information services. Recently these sectors started the practice of compensating employees in cryptocurrency (coins/tokens). This research explored the viability of compensating employees in crypto, across various components of compensation structure. This qualitative informative-exploratory research used the principles from the PRISMA model to screen 8,621 scholarly publications and reviewed 26 research publications and 23 companies using crypto for compensating employees. Content analysis and thematic analysis were conducted to report the findings. This research found that multinational organisations prefer to compensate global employees in crypto because of several advantages including ease of transfer, encrypted features, integration of blockchain technology, tax benefits, cost-saving and gain from crypto investments. Limiting factors such as the bartering nature of crypto, high volatility, tax regulations, limited investment avenues, resistance from employees and the market and concerns over its legality posed some doubts on its viability as a mode of payment. This study is of value to compensation and benefits experts, human resources professionals, finance professionals, scholars, and organisations who are interested in evolving technologies adding value to employees and businesses. This research added a new piece of knowledge concerning paying employees in crypto-currency that remained understudied until now.
A Real-World Industrial Application of Particle Swarm Optimization: Baghouse Designing

Due to the high ability and flexibility of meta-heuristic algorithms (MAs), they can widely be used in many applications to solve different problems. Recently, real-world engineering applications of these optimization algorithms have attracted researchers’ attention. This paper applies particle swarm optimization (PSO) as an effective population-based MAs to design the baghouse (BH). BH filters are among the most commonly used devices in air pollution control systems in mining and food manufacturers and power plants. Designing the BH depends on several parameters such as its capacity or airflow (Nm 3 /h), air-to-cloth ratio ([Formula: see text]), cam velocity, and installation limitations. Generally, industrial designers select the number and length of bags and their arrangement based on the experimental observations to meet the parameters mentioned above. The minimum cost or total weight of equipment is utilized for proposing a competitive price for suppliers. In this paper, a PSO algorithm is used to minimize the total cost by finding the best possible design (the number, length, and arrangement of bags). In addition, a real example of installed BH in a pelletizing plant is given and compared with PSO results to investigate the efficiency of the proposed algorithm. The results suggest that PSO can find a better design with minimum total cost than an installed BH filter, and therefore, PSO is applicable to industrial designers.
Mining News Articles Dealing with Food Security

Food security is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for food insecurity and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. To this end, we propose an original and dedicated pipeline that combines different textual analysis approaches (e.g., word embedding, sentiment analysis, and discrimination calculation) to obtain an explanatory model evaluated on real-world and large-scale data. The results of our analyses have proven how our approach provides significant results that offer distinct and complementary qualitative information on the food security theme and its spatial and temporal characteristics.
Effect of icon size, icon position and sex on clicking motion when operating smartphones with single hand

Nowadays, increasingly more situations exist where smartphones are operated with one hand, requiring an in-depth understanding of human–computer interaction in single-hand scenarios. 104 volunteers (57 men, 47 women) participated in this study. We aimed to explore thumb movements with the right and left hand on smartphone touchscreens at different icon sizes (50, 80, 110 and 140 rpx) in different operation areas (a 4*7 icon matrix). The results partially conformed to Fitts’ Law. The movement time (MT) significantly increased as the icon size decreased, but this effect was not found over 110 rpx. The MT increased with distance in the vertical direction, but icons with the same horizontal distance had different MTs, indicating that one-handed operation restricted the click on the same side. Additionally, subjects rated 140 rpx better than other sizes, and men clicked faster than women. Suggestions regarding one-handed interface design for different hands of different sexes are provided. Practitioner summary: This study investigated how icon size, position and sex influenced one-thumb click usability on touch-screen mobile phones with different hands of different sexes. The results indicate single-hand operation partially conformed to Fitts’ Law. We suggested the most economical and comfortable size and the fast operation area in one-handed interface design.Abbreviations: MT: movement time; ID: index of difficulty; RPX: responsive pixel; NASA-TLX: task load index of National Aeronautics and Space Administration; ANOVA: analysis of variance.
NNCD-IQA: A new neural networks based compressed database for image quality assessment

Objective and subjective quality assessment is still a challenging problem in various image processing tasks. For instance, in the context of image compression, most of the conducted studies have focused on image datasets encoded using standard algorithms such as JPEG and JPEG2000. In this paper, we propose to further investigate the quality assessment issue in the presence of neural networks-based compressed images. More precisely, a new database of compressed images has been firstly built using JPEG2000 standard as well as four recent neural networks based coding schemes. Then, subjective experiments are performed to obtain the mean opinion scores of the generated distorted images. Finally, an extensive evaluation and analysis of objective image quality assessment metrics is achieved. For instance, in addition to conventional and machine learning metrics, we have considered different deep learning based models, which have been trained on our database. The new subjective database with its associated mean opinion scores as well as the learned models are publicly available at https://github.com/zakopz/NNCD-IQA-Database . The obtained results show the interest of deep learning based metrics in the context of neural networks-based compressed images.
The Menzerath-Altmann law on the clause level in English texts

Abstract Menzerath-Altmann law (MAL) describes the relationship between the size of the construct and of its constituents, where the larger the whole, the smaller its parts . Despite numerous investigations dedicated to MAL, few studies have observed the relationship syntactically, especially at the clause level. The present study investigates three units in which clauses in English can be measured, i.e., argument, phrase, and word, by fitting MAL to the relationship between the size of the clause and its constituents. Results show that 1) clause length in phrases can be well fitted by probability distributions, while the goodness-of-fit is less favorable for clause length in arguments and words. 2) MAL holds reasonably well between the size of the clause in phrases and of the phrase in words under some conditions, i.e., within a specific range of construct size and text genres. 3) To summarize, the phrase, a notion proposed by Mačutek, Ján, Radek Čech &amp; Jiří Milička. 2017. Menzerath-Altmann law in syntactic dependency structure. In Proceedings of the fourth international conference on dependency linguistics (Depling 2017) , 100–107 under the theoretical framework of dependency grammar, is the most appropriate one to be the neighboring unit of the clause among the three measurement units. These findings may shed light on the features of syntactic structures and lead to a better understanding of the human language system.
‘Hey, You, Can I Loan Your Yellow Pencil?’

This chapter presents a cross-sectional study exploring the development of young Norwegian EFL learners’ appraisal of requestive behaviour in English and their metapragmatic awareness of the linguistic and contextual features influencing request production and interpretation. The participants were in the third, fifth, and seventh grade of primary school, aged approximately 9, 11, and 13. Through an Emoticon task performed in groups, the learners appraised a selection of requests they themselves had produced, and were subsequently invited to explain their choices. Direct requests were appraised increasingly more positively with age, while the opposite was the case with conventionally indirect ones. Hints proved the most challenging to appraise and discuss due to the discrepancies between their linguistic form and speaker intentions. Metapragmatic discussions revealed a more frequent focus on the linguistic features of requesting in all age groups, with the marker ‘please’ consistently emerging as the origin of positive appraisals. Although contextual features, such as the age of and familiarity with the interlocutor, place and communicative situation, were discussed less commonly on the whole, they appeared more often with older learners, resulting in more nuanced appraisals and suggesting a developing awareness of the interplay between linguistic and contextual features.
Resonate-and-Fire Neurons for Radar Interference Detection

Radar devices sense the environment and detect range, velocity, and angel of arrival by applying multiple Fourier transformations. However, these calculations are expensive and assume that the data are in memory. Frequency-Modulated-Continuous-Wave sensors are primarily used in the automotive industry but are affected by signal superposition with other sensors. It can introduce ghost targets or increase the noise such that low reflective targets are lost. Inspired by the energy efficiency of Spiking Neural Networks, we show that Resonate-and-Fire neurons are able to encode the temporal radar signal into spikes and use a population of Leaky Integrate-and-Fire neurons to distinguish between the normality and patterns such as interference or saturation. We use simulations to prove the concept and achieve in this preliminary study an average accuracy of 85% by utilizing Back-Propagation Through Time with surrogate gradient learning.
The Digital Paradox in the Middle East and North Africa and the Upside of Digital Technologies

No AccessMay 2022The Digital Paradox in the Middle East and North Africa and the Upside of Digital TechnologiesAuthors/Editors: Ana Paula Cusolito, Clément Gévaudan, Daniel Lederman, Christina A. WoodAna Paula CusolitoSearch for more papers by this author, Clément GévaudanSearch for more papers by this author, Daniel LedermanSearch for more papers by this author, Christina A. WoodSearch for more papers by this authorhttps://doi.org/10.1596/978-1-4648-1663-5_ch2AboutView ChaptersPDF (0.5 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Presents the evidence of the “digital paradox” in the Middle East and North Africa (MENA) and explains why general-purpose technologies such as digital tools should have economywide benefits. Whatever the reasons for the slow growth of the region’s digital economy, it clearly does not reflect insufficient coverage of information and communication technology (ICT) infrastructure, slow internet speeds, or insufficient access to the internet. Notably, while the use of social media per capita in countries in the region outperforms that in comparator countries, the use of digital payments underperforms that of comparator countries. Yet without wider diffusion of digital payments, the region’s digital economy will remain nascent. For society to adopt the widespread use of digital technologies to conduct economic transactions, however, users have to trust the regulatory environment, the financial or banking institutions, and the government itself. In this light, the region’s digital paradox might rest on gaps in societal trust. Previous chapterNext chapter FiguresreferencesRecommendeddetails View Published: May 2022ISBN: 978-1-4648-1663-5e-ISBN: 978-1-4648-1664-2 Copyright & Permissions Related RegionsMiddle East and North AfricaRelated TopicsFinance and Financial Sector DevelopmentInformation and Communication TechnologiesPrivate Sector Development KeywordsDIGITAL DIVIDEDIGITAL DEVELOPMENTDIGITAL ECONOMYFINANCIAL SECTORTELECOMMUNICATIONS INDUSTRYFINANCIAL SERVICESINFORMATION AND COMMUNICATIONS TECHNOLOGYICTSOCIAL MEDIAINTERNET PDF DownloadLoading ...
Decision-Making Model for Medical Diagnosis Based on Some New Interval Neutrosophic Hamacher Power Choquet Integral Operators

A multicriteria decision support scheme can assist in the medical diagnosis of diseases. Especially during a pandemic situation when resources are limited, a rapid decision needs to be taken considering the severity of the patient's health condition. An appropriate decision-making tool can help to identify the most serious patients who require immediate medical attention. This chapter presents the Hamacher operation rules on interval neutrosophic numbers, and a medical diagnosis model is proposed based on some novel interval neutrosophic Hamacher power Choquet integral (CI) operators. To determine the severity of a disease based on the patient's health condition, the interval neutrosophic membership degrees are assigned to the symptoms of the disease. The proposed interval neutrosophic Hamacher power CI operators are utilized to determine overall evaluation from the available interval neutrosophic fuzzy information about the disease symptoms. A decision-making algorithm for detecting the most serious patient using the proposed interval neutrosophic Hamacher power CI and interval neutrosophic Hamacher power geometric CI operators is developed. The proposed algorithm can be useful in a pandemic situation where the number of health workers is generally lower compared to the number of patients, using the medical diagnosis result of the big data of the mass population. Finally, an illustrative example follows.
Predicting Molecular Properties Using Photonic Chip-Based Machine Learning Approach

The intensive neural network architecture for molecules resulted in exponential growth in computation cost. Photonic chip technology offers an alternative platform with faster processing. We apply an optical neural chip to predict multiple quantum mechanical properties of molecules.