Reviewerly/exHarmony huggingface dataset:
Explore Paleoanthropology Fieldwork

This classroom exercise utilizes a free mobile virtual reality app to introduce students to (1) the process by which paleoanthropologists find and interpret fossils, (2) the importance of two recent hominin fossil finds in the Cradle of Humankind of South Africa, and (3) the variety of science careers that contribute to paleoanthropological research. Before their virtual field trip, learners are introduced to the Homo naledi and Australopithecus sediba discoveries and to some of the researchers from the team through a collection of online resources. In the app, learners explore and find fossils in the Dinaledi Chamber of Rising Star Cave in South Africa, where the first fossils of H. naledi were found. Postexploration analysis and reflection prompts encourage learners to consider how researchers decide how and where to excavate, which skills are needed to study our human origins, and why this research is important.
Innovation of Corporate Governance: A Discussion based on Blockchain Technology

Blockchain, as a newly-born application of computer technology, has unique advantages in data storage, point-to-point transmission, consensus mechanism and encryption algorithm, which could lead to innovation of corporate governance. The application of blockchain technology in corporate governance has attracted more and more attention as it provides new approaches to improve corporate innovation ability as well as operation efficiency. This paper will discuss three types of applications of blockchain technology in corporate governance, including operating based on DAO, creating employee performance appraisal and incentive mechanism and cooperating among Multi-departmental employees. There are three aspects that will be included in the discussion of each application, namely mechanism, preconditions and benefits, so as to show the potentials of blockchain technology comprehensively. Furthermore, this paper will provide detailed design of corporate governance model, including the structure of Blockchain-based platforms, design of operating system and design of appraisal and incentive system, which offers a guideline to corporations that intends to adopt blockchain technology in their corporate governance.
A Diachronic Parallel Corpus Study of Two-letter Words in Reverse Order in Early Modern Sino-Japanese/Korean

This paper examines the correspondence pattern between Korea and Japan and the process of change to modern language for Sino-Japanese/Korean with reversed letters extracted from the modern Korean-Japanese parallel corpora. The number of pairs corresponding to Sino-Japanese/Korean words with reversed letters such as “AB-BA” between Korea and Japan was 26, and it was found that there were many words that were newly used since the end of the 19th century as Korea and Japan accepted pairs of Sino-Japanese/Korean words with reversed letters. The correspondence pattern of Sino-Japanese/Korean words with reversed letters was examined through several classifications and examples. Among them, in the cases of “屯駐-駐 屯” and “長成-成長,” where the target words correspond to Sino-Japanese/Korean words with reversed letters, the process of transitioning to modern languages by dividing the meaning and usage as synonyms using the diachronic corpora after the end of the 19th century can be seen. In addition, this suggests the possibility of studying the differences between Korean and Japanese modern and contemporary vocabulary and word history.
Font Transfer Based on Parallel Auto-encoder for Glyph Perturbation via Strokes Moving

Glyph perturbation is an increasing subject in information embedding. It can be generated by moving strokes of Chinese characters to convey secret messages. However, the generation is limited by the large number and diverse fonts of Chinese characters. Several attempts have been made to generate Chinese characters in the font transfer based on deep learning, up to now no studies have investigated font transfer for glyph perturbation. We propose a font transfer method for glyph perturbation of Chinese characters named Glyph-Font, which focuses on the position of strokes while transferring fonts. More specifically, we first build an image dataset for glyph perturbation of Chinese characters through perturbing strokes. Secondly, the generator based on a parallel auto-encoder simultaneously generates four glyph perturbations for each character in target fonts. In addition, a discriminator is designed to optimize the network by calculating the difference between real and generated images of Chinese characters. Finally, perturbation loss and patch-pixel loss are defined to amend incorrectly generated pixels and distinguish position changes of strokes. Experimental results demonstrate that our proposed Glyph-Font has the potential to generate glyph perturbations of Chinese characters automatically in various fonts.
A Time-Series Econometric Framework for Modelling the Distribution of Intraday Rooftop Solar Generation

As we grow more reliant on renewable generation sources to meet our energy requirements, forecasts of the generation output become more important. This paper proposes a time-series econometric framework for modelling and forecasting intraday rooftop solar generation, one that relies only on historical generation data. Some elements have been adapted from financial time series models to deal with the strongly diurnal, and seasonal nature of the data. Based on an application to regional wide aggregate output, it is shown that this framework captures the important features of the data and produces reliable forecasts of the level and volatility of output. This is achieved using traditional time-series methods, and without relying on large-scale weather information.
Retraction: Object detection YOLO-v4 based smart car system with an integrated Virtual Assistant (J. Phys.: Conf. Ser. 1916 012047)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
An Analysis of the Authenticity of Financial Data of Listed Companies Based on Vector Machines

Driven by the benefits of listing at a high premium, financial fraud in listed companies has become a widespread problem worldwide. In the face of economic interests, even developed countries with relatively complete laws and policies cannot completely eliminate financial fraud. The financial fraud incidents of listed companies have seriously disrupted the normal order of my country’s capital market. Therefore, the specific financial data of listed companies are analyzed to obtain the influence of financial indicators on the identification of financial fraud and to determine whether there is financial fraud in listed companies.
Sloterdijk's Love Letter on Humanism

Media philosopher Peter Sloterdijk’s controversial “Rules for the Human Zoo: A Response to the Letter on Humanism” exhumes the genesis of humanism from the literary practices of classical antiquity. According to Sloterdijk’s genealogy of the term, humanism emerged out of the reading and writing practices of the Greco-Roman liberal humanities, a media regime organized around the cultivation of a literate, malleable “circle of friends” dedicated to unfolding a “continuing process of justice and self-discovery.” Drawing on the work of Martin Heidegger, Jean-Paul Sartre, Friedrich Nietzsche, Cicero, Aristotle, and Plato, Sloterdijk articulates our profound indebtedness to humanism as a media-rhetorical strategy for cultivating ourselves while constraining our “bestializing” impulses. Rash dismissals of humanism, according to Sloterdijk, disavow the radical extent to which ineluctable techno-medial processes are empowering humanity to transform itself beyond its wildest cybernetic fantasies. The paramount question is not whether humanistic values should still play a role in influencing the trajectory of history but whether they are capable of doing so.
Image Encryption with Logistic Chaotic Model Using C-QUATRE Algorithm

With the continuous update and progress of the network, a large number of public and private images and other multimedia information are transmitted. When lots of images are transmitted, security is an important aspect. Image encryption is the principal problem to be solved. Among the image encryption methods, the chaotic logistic function is one of the simplest and common methods. In a general encryption system, the initial secret key directly generated by the one value and parameter of the chaotic map is easily cracked by the exhaustive attack. Before generating the initial secret key, this paper applies Competitive QUasi-Affine TRansformation Evolution (C-QUATRE) algorithm to optimize the initial key based on global optimization capability. This novel image chaotic encryption model dramatically improves the effect.
EXERCISE NEUROSCIENCE: A BIBLIOGRAPHIC REVIEW

The objective of the present study was to carry out a literature review on the subject of exercise neuroscience to identify which studies were produced on the exposed subject and how the objective of each study was conceived. To prepare this literature review, independent online searches for articles were carried out between January 2015 and December 2020, in the electronic databases Google Scholar, Scielo, Pubmed/Medline, Cochrane and Web of Science, using selected descriptors. randomly in line with the initial neuroscience theme of exercise. As a search strategy, the descriptors were organized according to two groups: neuroscience and exercise, exercise neuroscience, exercise neuroscience and exercise and neuroscience. The selection of articles was carried out by the researcher himself, independently and without the call of a second reviewer. Of the 98 articles found, only 03 articles fit the search, inclusion and exclusion criteria. It is concluded that although the articles address the neuroscience content involved in brain health, there is a lack of studies that address the aforementioned topic between neuroscience and exercise or exercise neuroscience. It is suggested that more robust and complex studies be carried out in order to start with filling the gaps between the brain and body in the neuroscience of exercise; associated with the benefits of neuroscience for exercise whether through neuroanatomy, neurophysiology or neuropsychology.
A Mathematical Description of Artificial Neural Networks

After a short introduction to neural networks generally, a more detailed presentation of the structure of a feed forward neural network is done, using mathematical language, functions, matrices and vectors. Further, emphasis has been made on perceptrons and linear regression done by using ANN. Central concepts like learning, including weight updates, error minimization with gradient descent are introduced and studied using these simple networks. Finally, multilayer perceptrons are defined with their error functions and finally backpropagation are described precisely using composite functions and the concept of error signals.
Concluding Remarks

Summary As metaknowledge has a central role in many approaches discussed in this book, we address the issue of what kind of metaknowledge is used in different metalearning/AutoML tasks, such as algorithm selection, hypeparameter optimization, and workflow generation. We draw attention to the fact that some metaknowledge is acquired (learned) by the systems, while other is given (e.g., different aspects of the given configuration space). This chapter continues by discussing future challenges, such as how to achieve better integration of metalearning and AutoML approaches, and what kind of guidance could be provided by the system when configuring metalearning/AutoML systems to new settings. This task may involve (semi-)automatic reduction of configuration spaces to make the search more effective. The last part of this chapter discusses various challenges encountered when trying to automate different steps of data science.
A Divisive Hierarchical Clustering Algorithm to Find Clusters with Smaller Diameter to Cardinality Ratio

Given a point set S of n points on a d-dimensional space and a positive integer k, we are asked to split S into k clusters such that the maximum diameter to cardinality ratio among all clusters is minimized. In this paper, we give a divisive hierarchical clustering algorithm for finding such clusters which uses two different greedy heuristics at each iteration. Our proposed optimization criterion not only takes account of the diameter of the clusters like the popular diameter criterion but also the density of points within each cluster. We compare the performance of our algorithm with that of some well-known clustering algorithms including the widely used k-means clustering algorithm using three different similarity metrics and find some cases where our algorithm performs better than k-means clustering. We test our algorithm on different large benchmark datasets where the “ground truth” labels are known and show that our algorithm outperforms other clustering algorithms in almost every case. We then do the same on some small benchmark datasets and show that our algorithm has better accuracy than other algorithms in all cases. On average, we experimentally show that our algorithm with the Euclidean distance metric has the best accuracy among all. We also perform experiments with increasing value of k on another benchmark dataset and show that our algorithm performs better than other clustering algorithms.
Retraction: Triangular fuzzy numbers model with cost parameters (J. Phys.: Conf. Ser. 1916 012124)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Retraction: Internet of Things enabled Parking System In Smart Cities (J. Phys.: Conf. Ser. 1916 012061)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Enhanced Cuckoo Intelligence Search Algorithm

Cuckoo Search (CS) algorithm is a meta-heuristic technique that displays several merits.For example, it is easier to apply and less tuning parameters also, it is suitable for solving optimization problems.However, easily fall into local optimum has been established and has a slow convergence rate as a result of the cuckoo search parameters being kept constant.Therefore to handle this issue, an Enhanced Cuckoo Intelligence Search (ECIS) algorithm was developed which is an upgraded CS algorithm.The efficiency of ECIS was tested by some benchmark constrained optimization test functions and it was shown that ECIS gives a better optimal value than CS.
Small-scale pedestrian detection based on multi-level feature fusion

Pedestrian detection is a particular issue in both academia and industry. However, most existing pedestrian detection methods usually fail to detect small-scale pedestrians due to the introduction of feeble contrast and motion blur in images and videos. In this paper, we propose a multi-level feature fusion strategy to detect multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. We propose a multi-level feature fusion strategy to make the shallow feature maps encode more semantic and global information to detect small-scale pedestrians. In addition, we redesign the aspect ratio of anchors to make it more robust for pedestrian detection task. The extensive experiments on both Caltech and CityPersons datasets demonstrate that our method outperforms the state-of-the-art pedestrian detection algorithms. Our proposed approach achieves a MR<sup>−2</sup> of 0.84%, 23.91% and 62.19% under the “Near”, Medium” and “Far” settings respectively on Caltech dataset, and also leads a better speed-accuracy trade-off with 0.28 second per image of 1024×2048 pixel compared with others on CityPersons dataset.
Conclusion

Machine learning has been the driving force of many real-world applications. Many machine learning models rely on gradient optimization algorithms that fit the model parameters over the training data. As the data volume becomes larger and larger, extending gradient optimization algorithms to distributed environments is indispensable. This book thereby studies gradient optimization in the setting of distributed machine learning.
Pranking in children's helpline calls

© 2016 The Australian Linguistics Society. Pranking can be understood as challenging a normative social order. One environment where pranking occurs is in institutional interaction. The present study examines a sample of pranking calls to telephone helplines for children and young people. Some cases had been posted on YouTube by the person doing the pranking; others were from a subcollection of possible pranks, extracted from a larger corpus of Australian children's counselling helpline calls. Drawing on ethnomethodology and conversation analysis we aim to understand the inferential and sequential resources involved in pranking within telephone-mediated counselling services for children and youth. Our analysis shows pranksters know the norms of counselling helplines by their practices employed for subverting them. YouTube pranksters exploit next turns of talk to retrospectively cast what the counsellor has just said as a possible challenge to the perception of the call as a normal counselling one. One practice evident in both sources was the setting up of provocative traps to break a linguistic taboo. This detailed study of pranking in interaction provides documentary evidence of its idiosyncratic yet patterned local accomplishment in telephone-mediated counselling services aimed at children and youth.
A Tech Hybrid-Recommendation Engine and Personalized Notification: An integrated tool to assist users through Recommendations (Project ATHENA)

Purpose – Project ATHENA aims to develop an application to address information overload, primarily focused on Recommendation Systems (RSs) with the personalization and user experience design of a modern system. Method – Two machine learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering (CBF); (2) Classification with Matrix Factorization- Singular Value Decomposition (SVD) applied with Collaborative filtering (CF) and mean (normalization) for prediction accuracy of the CF. Data sampling in academic Research and Development (R&amp;D) of Philippine Council for Agriculture, Aquatic, and Natural Resources Research and Development (PCAARRD) e-Library and Project SARAI publications plus simulated data used as training sets to generate a recommendation of items that uses the three RS filtering (CF, CBF, and personalized version of item recommendations). Series of Testing and TAM performed and discussed. Results – Findings allow users to engage in online information and quickly evaluate retrieved items produced by the application. Compatibility-testing (CoT) shows the application is compatible with all major browsers and mobile-friendly. Performance-testing (PT) recommended v-parameter specs and TAM evaluations results indicate strongly associated with overall positive feedback, thoroughly enough to address the information-overload problem as the core of the paper. Conclusion – A modular architecture presented addressing the information overload, primarily focused on RSs with the personalization and design of modern systems. Developers utilized Two ML algorithms and prototyped a simplified version of the architecture. Series of testing (CoT and PT) and evaluations with TAM were performed and discussed. Project ATHENA added a UX feature design of a modern system. Recommendations – High-end hardware specs of v-Parameter are recommended, at least 8-cores of vCPU with 16GiB of memory and sufficient driver-type size to run the model and End-to-end jobs execution for continuously maintaining the application. Research Implications – Future Developers must use/integrate a large dataset. Other ML approaches can expand Hybrid-RS better. Implicit data and additional filtering methods may enhance the application in the future.
Analyzing CNN Models’ Sensitivity to the Ordering of Non-natural Data

Convolutional Neural Networks (CNN) have revolutionized image recognition technology, and has found uses in various non-image related fields. When dealing with non-natural data, where the ordering of various parts of a data sample is not dictated by nature, it is known that a model trained on certain orderings of the data performs better than models trained on other orderings. Understanding how to best order the training data for improving CNN performance is not well-studied. In this paper, we investigate this problem by examining several different CNN models. We define a functional algorithm for ordering, show that order importance in CNNs is model dependent and that depending on the model, statistical relationships are an important tool in establishing order with better performance.