Reviewerly/exHarmony huggingface dataset:
Quantization Effects in a CNN-based Channel Estimator

In this paper, we study the impact of the convolutional neural networks (CNN) quantization for the channel estimation. In the wireless network edge, with the adoption of deep learning (DL) algorithms, the limited computational resources bottleneck needs to be considered. Thus, a study using a field-programmable gate array (FPGA) platform is carried out, where the resource utilization and the timing requirements are analyzed. A single-input single-output orthogonal frequency-division multiplexing (OFDM) end-to-end link is adopted in this work. The bit error rate (BER) measures the quantization impact of the CNN-based channel estimation on the global system. The obtained results show that an improvement in the maximum operating frequency and in the resource efficiency can be obtained without deteriorating the end-to-end performance.
The design of an experimental cloud resource provisioning language

As a cornerstone of cloud-native systems, Kubernetes uses YAML, a data description language, to configure resources. However, YAML does not meet the configuration requirements of complex scenarios and has three major problems. First, YAML has no type checking mechanism and therefore data type mismatches cannot be detected during compilation. Second, YAML does not have the ability to reuse data descriptions and there are duplicate code for largescale data. Third, YAML lacks a type merging algorithm that meets the needs of multi-team development in enterprises. This paper implements an experimental cloud resource provisioning language, Hermias, which is based on the functional programming language OCaml. Hermias is used to describe the resource configuration of cloud servers, which solves the above three problems in YAML. The novelty of this work is to propose a type merging algorithm that computes records with common labels by union types and subtyping.
Research on image captioning based on LSTM and YOLOv5 fusion attention mechanism

Humans can easily learn to recognize every object in life, every landscape, and describe the things around them in detail from the process of growing up, but computers cannot. How to make computers learn to describe things in pictures has become the research direction of many scholars. If this technology is mature, it will bring great boon to people with visual impairments. They can understand the things around them and the beautiful earth through hearing. Robots recognize objects and understand their surroundings. With the development of artificial intelligence, the power of convolutional neural networks is more and more comparable to that of the human brain. In recent years, many scholars have proposed different methods to seek better solutions to this problem, including generative adversarial networks. Based on the classic structure of Encoder-Decoder, this paper first compares the code implementation and results of ResNet101 as an Encoder on the COCO dataset, and then proposes a new solution that integrates YOLOv5 and LSTM, aiming to improve the model inference speed and inference accuracy.
On the reliability of the Panalesis (v.0) paleogeographic maps

Pal&amp;#230;ogeography is the definition of the geography of the Earth in the geological past. However, geography depends on topography (both on land and under the sea) and the sea level which defines the coastline. Topography is a multifactorial resultant whose heart is the geodynamic context that created it (V&amp;#233;rard, 2019). In other words, there are no pal&amp;#230;ogeographic reconstructions if there is no plate tectonic model underlying them.The pal&amp;#230;ogeographic maps presented here are derived from the Panalesis model (preliminary version or v.0), corresponding to pal&amp;#230;o-digital elevation models (pal&amp;#230;o-DEM) that cover the entire Phanerozoic on a global scale associated with sea level variations from Haq et al. (1987, 2008, 2018).The results show two main facts. First, the main shortcomings of the method for converting a plate tectonic map to a pal&amp;#230;ogeographic map (V&amp;#233;rard et al., 2015) are relatively well understood and should be improved with new versions of the plate tectonic model (Verard, 2021) and the conversion code. Secondly, lithofacies databases (fossils and pal&amp;#230;o-environments) on a global scale are needed to identify areas that are outside the &amp;#8220;standard mode&amp;#8221; defined by synthetic topographies and to understand the reasons for the discrepancies. Conversely, variations (global to regional) in lithofacies can only be understood if a quantified topographic model is proposed as a reference, which Panalesis is, to date, the only one to systematically offer.&amp;#160;REFERENCES1. V&amp;#233;rard, C., 2019. Panalesis: Towards global synthetic palaeogeographies using integration and coupling of manifold models. Geological Magazine, 156 (2), 320-330.2.1. Haq, B. U., Hardenbol, J., Vail, P. R., 1987. Chronology of fluctuating sea levels since the Triassic. Science, 235 (4793), 1156-1167.2.2. Haq, B. U., Schutter, S. R., 2008. A chronology of Paleozoic sea-level changes. Science, 322 (5898), 64-68.2.3. Haq, B. U., 2018. Triassic eustatic variations reexamined. The Geological Society of America (GSA Today), 28, 6 pages.3. V&amp;#233;rard, C., Hochard, C., Baumgartner, P. O., Stampfli, G. M., 2015. 3D palaeogeographic reconstructions of the Phanerozoic versus sea-level and Sr-ratio variations. Journal of Palaeogeography, 4 (2), 167-188.4. V&amp;#233;rard, C., 2021. 888 &amp;#8211; 444 Ma global plate tectonic reconstruction with emphasis on the formation of Gondwana. Frontiers in Earth Science, 9 (666153), 28 pages.
Geo-Hazardous of AlUla Ancient City, Saudi Arabia

AlUla, is a city in the Medina Region in north-western Saudi Arabia. The city lies within the governorate of Ula, one of seven in the Medina Region, covering an area of 29,261 square kilometres. The city is 110 km southwest of Tayma and 300 km north of Medina. The city (municipality) covers 2,391 square kilometres. The population of the city is 5,426.The walled city of AlUla was founded in the 6th century BC.It was located along "Incense Road", the network of routes that facilitated the trading luxury items through Arabia, Egypt and India. AlUla stands on the site of the Biblical city of Dedan but was founded with the ancient North Arabian Kingdom of Lihyan, which ruled from the 5th to 2nd century BC. The older history of the oasis has been divided into several phases. The Dedanite kingdom spans the seventh and sixth centuries BC. It is thought that around the turn of the fifth century BC the kingdom became hereditary. In the 20th century the new town centre was established beside the old town and eventually the people left the old buildings. The last family left in 1983. Both the ruins of the town and the site of the Liyhanite settlement now lie within the limits of the modern town. AlUla has the potential to be one of the most important archaeological destinations for tourists from around the world. AlUla&amp;#8217;s Hegra archaeological site (Al-Hijr / Mada&amp;#8217;in Salih) was the first world heritage property to be inscribed in Saudi Arabia. A major integrated archaeological survey of AlUla valley and beyond was launched by the royal commission for AlUla (RCU). AlUla is a unique and extraordinary landscape because of the distinct geological eras packed together in one region. The earliest of the geologic ages which formed the rocks during the Precambrian Arabian shield rocks, marked by different layers of sedimentary rock. Second, the area&amp;#8217;s underlying sandstone created during the Cambrian period and tectonic activity has helped further shape its dramatic geology, creating volcanic landscapes, basalt plateaus and sandstone massifs. These geological features enable the area to act as a water catchment basin allowing the valley to flow southwards towards AlUla&amp;#8217;s settlements. Trilobite trace fossils discovered in sandstone date to at least 252 million years ago. The soft and porous rock, perfect for carving, also acts as an aquifer that can store the vital fresh water resources required to sustain life in an arid environment. Following that the black basalt layer formed by volcanic eruptions creates the dramatic plateaus called the Harrat. The desert of AlUla is famous for its several geological rock formations, formed over millions of years by wind and water, to form natural shapes untouched by human hands. geological and geophysical studies show that Geohazards in the area may include weathering and erosion processes, rock falling or sliding, and collapse of the carved architectonic structures.
Special Issue: 25th International Conference on Developments in Language Theory (DLT 2021) — Preface

International Journal of Foundations of Computer ScienceVol. 34, No. 02n03, pp. 81-83 (2023) No AccessSpecial Issue: 25th International Conference on Developments in Language Theory (DLT 2021) — PrefaceNelma Moreira and Rogéxrio ReisNelma MoreiraCMUP & DCC, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal and Rogéxrio ReisCMUP & DCC, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugalhttps://doi.org/10.1142/S012905412302001XCited by:0 Next This article is part of the issue: Special Issue: International Conference on Developments in Language Theory (DLT) 2021Guest Editors: Nelma Moreira (University of Porto, Portugal) and Rogéxrio Reis (University of Porto, Portugal) AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out these Handbooks in Computer Science FiguresReferencesRelatedDetails Recommended Vol. 34, No. 02n03 Metrics History Published: 22 February 2023 PDF download
A Quantitative Plant Type Recognition algorithm based on Gene Expression Programming

Abstract Efficient and accurate plant type identification is an important foundation for biological research. The existing traditional machine learning classification algorithms have the defects of low classification accuracy in solving plant type recognition and cannot quantitatively give the corresponding class recognition model. In this paper, we propose a Quantitative Plant Type Recognition algorithm based on Gene Expression Programming (QPTR-GEP). Firstly, the plant type data set to be processed is preprocessed; then, the GEP population corresponding to plant type identification is constructed, and the corresponding quantitative identification function expressions are obtained after genetic operations such as mutation, recombination and transposition; finally, the classification expression with the highest fitness is used as the classifier corresponding to a single plant type, and the classifier model integrating the data set containing all plant type is put into the data set for plant type identification. Experimental results on the open source Iris demonstrate that our proposed algorithm outperforms SVM and Decision Tree algorithms for plant type identification.
On Optimizing the Structure of Neural Networks Through a Compact Codification of Their Architecture

Neural networks stand out in Artificial Intelligence for their capacity of being applied to multiple challenging tasks such as image classification. However, designing a neural network to address a particular problem is also a demanding task that requires expertise and time-consuming trial-and-error stages. The design of methods to automate the designing of neural networks define a research field that generally relies on different optimization algorithms, such as population meta-heuristics. This work studies utilizing Teaching-Learning-based Optimization (TLBO), which had not been used before for this purpose up to the authors’ knowledge. It is widespread and does not have specific parameters. Besides, it would be compatible with deep neural network design, i.e., architectures with many layers, due to its conception as a large-scale optimizer. A new encoding scheme has been proposed to make this continuous optimizer compatible with neural network design. This method, which is of general application, i.e., not linked to TLBO, can represent different network architectures with a plain vector of real values. A compatible objective function that links the optimizer and the representation of solutions has also been developed. The performance of this framework has been studied by addressing the design of an image classification neural network based on the CIFAR-10 dataset. The achieved result outperforms the initial solutions designed by humans after letting them evolve.
A Conversation with Mary E. Thompson

Mary E. Thompson (née Beattie) was born September 9, 1944, in Winnipeg, Manitoba, Canada. She obtained a B.Sc. in Mathematics from the University of Toronto in 1965, and earned M.Sc. (1966) and Ph.D. (1969) degrees in Mathematics from the University of Illinois at Urbana-Champaign. She joined the Department of Statistics at the University of Waterloo as a Lecturer in 1969 and became an Assistant Professor in 1971. In 2004, she was awarded the honour of University Professor and in 2011 became Distinguished Professor Emerita at the University of Waterloo. She has served in many leadership roles including Chair of the Department of Statistics and Actuarial Science, Acting Dean of the Faculty of Mathematics, President of the Statistical Society of Canada (SSC) and Chair of the COPSS Presidents' Award Committee. She chaired the Development Committee for the Canadian Statistical Sciences Institute (CANSSI) and was its founding Scientific Director. Thompson has received numerous honours and awards including the SSC's Gold Medal, the Elizabeth L. Scott Award, the Waksberg Award of Survey Methodology and the Governor General's Innovation Award. She is an elected member of the International Statistical Institute, an Honorary Member of the SSC and is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the Royal Society of Canada and the Fields Institute. Thompson has made fundamental contributions to several areas in statistics including sampling theory and the analysis of surveys. She is the author of two books in these areas: Theory of Sample Surveys (1997) and Sampling Theory and Practices (2020 with C. Wu). She has also made key contributions in estimation theory and stochastic processes. As the author of over 150 published, refereed papers, Thompson has influenced the theory and practice of statistics. The following conversation took place virtually in September 2022 with interviewer Rhonda J. Rosychuk of the University of Alberta.
Construction of Precision Teaching Mode and Teaching Practice Application Based on Big Data Analysis

Nowadays, big data has brought great changes to all industries with the rapid development of information technology, including teaching field. The 19th CPC National Congress and the second and third Plenary Sessions of the 19th CPC Central Committee pointed out that it was vital to deepen comprehensive reform in the field of education, adhere to the core concept of the deep integration of information technology and education and teaching, improve the quality of education, and develo the innovative spirit of students. Big data can not only enhance students' learning ability, but also improve teachers' efficiency in teaching. Teachers can make use of big data to make real-time evaluation, comprehensively, efficiently and accurately obtain students' feedback data for analysis, which helps to understand the situation of learning before class and determine teaching based on learning, timely adjust teaching based on learning in class, and review practice after class to promote learning, so as to truly achieve “learning-centered”, promote learning and make teaching accurate and efficient. In this paper, the SPOC mixed teaching mode is constructed based on the teaching status of Fanya platform and the course practice of Front Room Management as the carrier.
Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness

Background Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented. Methods We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications. Results The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321). Conclusion This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.
Stereo matching method of distribution box hole image based on improved AD-Census algorithm

Abstract In mountain tunnels’ mechanical and electrical engineering, it is necessary to carry out cable distribution plates. To ensure the accuracy of the cable distribution plate, it is essential to measure the distance between the distribution box hole and tunnel mouth and the size of the distribution box hole. Stereo matching is a crucial step in using binocular vision for measurement, and the quality of the matching effect directly affects the measurement accuracy of the following steps. This study focuses on the measurement of tunnel distribution box hole size and studies the stereo-matching algorithm of distribution box hole images. AD-Census, a semi-global stereo-matching algorithm, is selected as the main stereo-matching algorithm in this paper, and the stereo-matching experiment is carried out. According to the effect of stereo matching, the AD-Census algorithm is improved, and the MSD-Census algorithm fused with edge information is proposed to obtain better disparity information, which lays a foundation for the subsequent measurement of distribution box holes.
Spaces IV

Abstract Several of the most important OPTIC spaces are studied in detail, in as many as four voices and therefore as many as four dimensions. Topologically, many OPTIC spaces are orbifolds, a kind of space that may include singularities (points with exceptional topological characteristics, such as mirror boundaries) and other sometimes counterintuitive features. As equivalence relations are added, the successively smaller spaces that arise can be identified with fundamental regions or normal regions within previously constructed larger spaces, often bounded by axes or planes of symmetry. OP-space takes the form of a Möbius strip in two voices, and analogous but more complex forms in higher dimensions as the number of voices increases. T-, PT-, OPT-, and OPTI-spaces are also studied in two, three, and four voices; OPTI-space is compared with maps of set-class space. Paths traced by musical voice leadings highlight distinctive features of each space. Spaces accommodating cardinality equivalence, mathematically more intractable than the other OPTIC relations, are discussed only briefly.
Spaces II

Abstract This chapter expands the repertoire of musical spaces by defining spaces whose elements are not single notes but higher-level entities such as chords, pitch-class sets, key areas, or twelve-tone rows. Double-circle spaces are introduced to accommodate major and minor triads in the same space; some related constructions are employed to model twelve-tone row spaces. Other important chordal spaces include Weber space and neo-Riemannian triad space, which is the dual graph of the note-based tonnetz. Distances between triads in several different spaces may be measured using appropriately defined distance functions. Other models examined include diatonic and generic chordal spaces, chordal-regional space, a three-dimensional tonnetz of seventh chords, and the “spiral array.” Several short analyses trace the movement of musical passages through various spaces. Examples studied in this way include diatonic sequences; works by Webern, Adams, and Britten; and three examples by Schubert.
Scales II

Abstract In this chapter the focus broadens from the diatonic to scales of other kinds. Spelled heptachords, certain sets of seven spelled pitch classes, facilitate the study of some chromaticism as inflected diatonicism and recapture some aspects of signature transformations in non-diatonic contexts. The property of maximal evenness, often noted of the diatonic scale, is of interest in other cases as well; the geometric perspective provided by the OPTIC spaces proves fruitful in this connection, and filtered point-symmetry models produce maximally even sets through a quantization process. Logical relationships among maximal evenness and several other scalar properties are described. Many scale systems of interest consist of a generic scale in a generalized sense, not necessarily of seven notes, within a larger specific scale, not necessarily of twelve; the question of which such systems are most closely analogous to the usual diatonic and chromatic scales may be addressed in many ways. Enharmonic systems formalize systems in which the generic cardinality remains 7 but the specific cardinality is variable; a system with a 19-note specific scale is examined in some detail.