Reviewerly/exHarmony huggingface dataset:
With support, children can accurately sequence within‐event components

Summary Accurate event sequencing can add critical detail to a child's account. However, our knowledge of sequencing in childhood to date primarily centers on distinct events separated by time. Sequencing a single event's components is also important, perhaps particularly in a forensic context. In two experiments, we explored children's ability to recall the sequence of a past event using a variety of prompts. In Experiment 1, 124 children (6–8 years) and Experiment 2, 96 children (6–8 years) attended a 45‐min workshop with four (Exp. 1) or five (Exp. 2) distinct components. Children were asked to sequence the components using different retrieval strategies (Exp. 1 within‐subjects; Exp. 2 between‐subjects). Children had difficulty reporting within‐event sequential order in response to open‐ended prompts but with sufficient visual supports, children were able to provide accurate information about the sequencing of within‐event components.
A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble

A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble
“Umm you know…” Speaking or Writing?: Examining EFL Students’ Writing Style in Argumentative Essays

Writing in English has always been a difficult skill to be mastered by Indonesian students. In the English Department of Universitas Dian Nuswantoro, students often face challenges in writing concerning grammatical rules and using suitable style in various text types. This research is aimed at investigating whether the students have applied formal academic style in their academic argumentative essays. The method used to analyse the data was descriptive qualitative. The findings demonstrate that there are still some significant errors made by students in their writing. Of the nine types of academic writing style errors proposed by Yakhontova (2003), there are eight types found in the data. These include contraction, addressing the reader directly, direct question, adverb in initial or final position, interjection and hesitation filler, short form of the word or slang, inappropriate negative form, and figures at the beginning of the sentence. Furthermore, the data also reveal a novel finding that there are two other kinds of error that cannot be categorised under Yakhontova’s framework, namely the use of spoken form and emoticon. This research suggests that students’ need to be specifically taught the differences between formal and informal styles of language in speaking and writing classes. They also need to be exposed to English academic texts more so as to be accustomed to formal or academic style.
A Dialogue SystemMaking Humor by Pseudo Mishearing of a Part of Users’ Queries Based on Examples

Dialogue systems, which give users quick and easy access to required information interactively, have been widely used in various fields. Dialogue systems equipped with interfaces (e.g., humanoid robots and anthropomorphic agents) have been developed in order to enhance familiarity and dialogue continuity. Related studies, in which interactive agents generate humor expressions, have also been reported. Humor is indispensable for the formation of friendly relationships between people and systems, and humor expressions can be applied in situations that generate familiar responses and provide fun to users. In this study, in order to evoke humor through dialogue, a method to generate humorous expression by asking again due to pseudo mishearing of a part of users’ queries based on examples is proposed. Specifically, a conversion candidate dictionary for humor expressions, based on Wikipedia of Japanese edition and a classification vocabulary table in which words are classified semantically, is created by word completion using distributed representation. In addition, a word conversion method is designed by approximately 1,000 mishearing survey from Twitter, and the function based on the proposed method is implemented in a dialogue system introduced into a university as a model case. In the results of the comparative evaluation with other methods quantitatively, the proposed methods gave users the most humor by converting singular and multiple words. Thus, the effectiveness of the proposed method was clarified.
An effective energy-efficient virtual machine placement using clonal selection algorithm

Online resource sharing has gained much attention and popularity among Internet users as this platform offers greater mobility and flexibility access.Cloud computing concept adopts this philosophy with the connected resources where they are easily accessible based on demand without direct active management by the user [1].A central server is required to manage and disseminate resources as well as control the operations in virtual platform environment [2].These cloud activities and operations consume a lot of energy, increasing the electricity consumption for a data centre.Based on a study conducted by Amazon, utilization of electricity in data centres accounts for more than 42% of the total operating cost, thus contributing the biggest chunk of the operation expenditure.*Author for correspondence Besides the cost factor, the generation of electricity which is mostly done using conventional method increases carbon dioxide emissions.Therefore, it is desirable and necessary to find ways to minimize the consumption of energy, and in our case for data centre operation.The history of cloud computing started back in 1996 by Compaq which was implemented for internal document management.This concept has since expanded as it gives many benefits to organizations especially in terms of cost and space.Cloud computing works in an environment where all available resources will be shared among authorised users to accomplish specific activities.This will reduce the need for an organization to provide multiple IT infrastructures (i.e.physical servers in many locations).Other than that, the practicality of this concept allows interactions between personnel and units running faster as resources management
Cybersecurity regulation: cybersecurity certification of operational technologies

The object of research is the system and schemes of conformity assessment (certification) of cybersecurity of operational technologies (OT), as a set of rules and procedures that describe the objects of certification, determine the specified requirements and provide a methodology for certification. The terminological base and conceptual apparatus of the study of cybersecurity certification of operational technologies are based on the international standard ISO 17000:2020 Conformity assessment – Vocabulary and general principles. Cybersecurity certification systems and schemes are based on assessment standards, the choice and application of which is not unambiguous and historically has many interpretations and application mechanisms. These standards consist of tools, policies, security concepts, security assurances, guidelines, risk management approaches, best practices, safeguards, and technologies. But they have, to one degree or another, a significant drawback – the complexity of transforming the results of information security assessment according to these standards into security guarantees with any wide international recognition. In the context of globalization, this significantly degrades the cybersecurity quality. The main hypothesis of research is that the cybersecurity quality can be improved by converging towards a common methodology that is based on agreed international standards and international best practice for certification. The question of the key role of cybersecurity for operational technologies, which become the basis for Economy 4.0 and are now considered as a new frontier of cybersecurity, is considered. The need to create a system and schemes for certification of OT cybersecurity based on international and European certification principles is shown. A hierarchical model of cybersecurity certification system assessment standards and a hierarchical model of agreements on mutual recognition of cybersecurity certificates have been developed, which will allow a systematic approach to the creation of a system and schemes for OT cybersecurity certification. This provides an opportunity for developers of systems and certification schemes to form OT cybersecurity certification systems based on the principles of wide cross-border recognition of OT cybersecurity certificates.
Improved Gray Wolf Algorithm Based On Dynamic Inertia Weight and Adaptive Search

Abstract An improved ISAGWO algorithm is proposed to solve the problem that the traditional grey Wolf algorithm is easy to fall into local optimum, which leads to low search accuracy. By introducing the weight factor before the vector coefficient, the position vector updating formula and the adaptive search strategy of the dynamic modulation algorithm, the search accuracy of the algorithm and the ability of jumping out of the local optimum are improved. The efficiency of the improved grey Wolf algorithm is evaluated by statistical analysis, convergence rate analysis and classical reference function. The results show that the improved grey Wolf algorithm has better global search ability and solution robustness, and the optimization precision and convergence speed are also improved compared with the standard grey Wolf algorithm.
Deep Learning Techniques for Breast Cancer Diagnosis: A Systematic Review

Breast cancer analysis is carried out by many computer aided diagnosis methodologies. Recently among all methodologies deep learning techniques provides better accuracy. The image segmentation, extraction of features and classification process are done automatically using deep learning techniques. Hence deep learning techniques plays an important role in analysing images. In this paper various models of deep learning used for the classification of different types of cancer such as Benign, Malignant, In-situ, Invasive Ductal Carcinoma is described. The image processing steps are explained for each and every model starting from input layer to output layer. Transfer learning techniques along with fine tuning process is also given. It also survey works which uses images from various modalities such as screen film mammography digital mammography, ultrasound, magnetic resonance imaging, digital breast tomosynthesis. This review provides an overview of all the deep learning models used for breast cancer classification of images from various modalities.
Robust Point Cloud Registration Framework Based on Deep Graph Matching

3D point cloud registration is a fundamental problem in computer vision and robotics. There has been extensive research in this area, but existing methods meet great challenges in situations with a large proportion of outliers and time constraints, but without good transformation initialization. Recently, a series of learning-based algorithms have been introduced and show advantages in speed. Many of them are based on correspondences between the two point clouds, so they do not rely on transformation initialization. However, these learning-based methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matchingbased framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by singular value decomposition. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on registering clean, noisy, partial-to-partial and unseen category point clouds show that the proposed method achieves state-of-the-art performance. The code will be made publicly available at https://github.com/fukexue/RGM.
Green Entrepreneurship and Employment Generation in a Developing Nation: The Nigerian Case

This paper presents a link between green entrepreneurship and how it can drive employment in a developing economy-being an indicator for sustainable development through green production and services.It starts by introducing the concept of green entrepreneurship, and explains how difficult it has been to find a universal definition of the term.The study moved further to create a background for green business through methods that do not only combat climate change, but also provide lasting finances in the process.By adopting analysis of variance, standard deviation and mean, analyzed questionnaire instrument which sought to know the relevance and existing green entrepreneurship employments revealed that green entrepreneurship will ultimately add value to a nation's economy, and also bring it closer to reaching its Nationally Determined Contributions.
Internet In Professional Formation Of Students

The article presents the results of an interdisciplinary study of students of the Department of Personnel Management of the State University of Management on the topic "The role of the Internet in the process of professional development of students".The respondents were asked the following issues: about the aspects of professional information that are most interesting for students, which they select on the Internet; about the resources for obtaining information; the purpose of discovering data about the HR manager's profession and practice-oriented actions that this material stimulates; about mental methods of evaluating the information received, about emotional reactions to the profession; on the role of information in the formation of a holistic image of the profession and a professional in the field of personnel management among students.In particular, it was revealed that of the whole variety of information about the profession of a personnel manager, students are most interested in salary issues, the demand for the profession and the activities of employing organizations.Investigation demonstrates the impact of professional information on the development of thinking of future professionals (in particular, criticality), their professional identity (the formation of a holistic image of the profession and the standard of a professional); professionally important qualities (creativity and cognitive interests).It is presented that information about the profession is not always critically perceived by students, is used for a greater extending within the framework of traditional forms of education (to consolidate lecture material) and in itself does not fully stimulate their practical actions.
Improved Filtering and Hole Filling Algorithm for the Point Cloud of Rotor Surface Based on PCL

The quality of rotor is key to ensuring the quality and stability of the blower. Point cloud-based detection utilizes three-dimensional depth information, making it time-saving, efficient, and particularly suitable for rotor quality inspection. In order to ensure efficient and accurate detection, the collected rotor point cloud needs to be preprocessed. The preprocessing includes filtering and hole filling. Therefore, to tackle the problem of outliers and isolated points in the point cloud on the rotor surface, this paper proposes a straight-through filtering algorithm based on region selection. Our method solves the problem of inadaptability for traditional straight-through filtering to curved point clouds, and ensures the speed and elimination effect. In addition, to address the problem of holes in the rotor point cloud, this paper proposes a hole-filling algorithm based on neighborhood statistics, which compensates for the misjudgment of hole positioning in the hole-filling algorithm based on cubic spline interpolation, and improves the effect of hole-filling. Experiments show that the combined application of the two proposed algorithms provides a relatively complete and smooth rotor point cloud for quality inspection, thereby guaranteeing the quality and robustness of the rotor.
Should Graph Neural Networks Use Features, Edges, Or Both?

Graph Neural Networks (GNNs) are the first choice for learning algorithms on graph data. GNNs promise to integrate (i) node features as well as (ii) edge information in an end-to-end learning algorithm. How does this promise work out practically? In this paper, we study to what extend GNNs are necessary to solve prominent graph classification problems. We find that for graph classification, a GNN is not more than the sum of its parts. We also find that, unlike features, predictions with an edge-only model do not always transfer to GNNs.
The Rise of Interactional Multimodality in Human-Computer Interaction

The field of human-computer interaction (HCI) investigates the intersection between the design of devices and users. From an early focus on interaction modeling based on psychological experiments, the field has since experienced a shift towards the study of how actual humans interact with autonomous devices. The field became conductive to ethnographic, observational and videographic studies of human-device interaction. Conversation-analytic HCI became possible. That said, this new wave of researchers was never truly able to dethrone the psychological common sense of the field. With recent developments in both the technical-sensorial capabilities and outward actuational range of embodied virtual agents, the field of HCI has once again returned to the question of the sequential unfolding of the interaction between users and intelligent agents, and the multimodal interactional repertoire that is deployed throughout. This review will highlight the situational orientation of high-impact research in the field, and relate it to the cotemporaneous development of ethnomethodological and conversation analytic frameworks. Acknowledgments. The article was prepared in the framework of a research grant funded by the Ministry of Science and Higher Education of the Russian Federation (grant ID: 075-15-2020-908). The article was prepared in cooperation with the Sber (ex. – Sberbank’s) Gamification Lab.
Advanced Study on Variability in Length of Rainy Season for Selected Crops Production in Coastal and Upland Areas of Akwa Ibom State, Nigeria

This study assessed variability of rainfall in coastal and upland areas of Akwa Ibom State, as a guide for cultivation of maize, tomato, yam, cassava, oil palm and cocoa in the areas. Rainfall variability is the degree to which onset, length of growing season, cessation, amount and distribution of rainfall varies across an area due to climatic influence. The study was done in four different locations – coastal areas (Eket and Oron) and upland areas (Uyo and Ikot Ekpene). Daily rainfall data were obtained from Nigeria Meteorological Agency (NiMet), Abuja, for a period of 30 years (1989 – 2018). The amount of annual rainfall was determined using descriptive statistics; percentage of rainfall distribution was determined using precipitation concentration index equation; and onset, cessation and length of growing season were determined using Instat+ 3.36 software. The annual rainfall shows high variability between coastal and upland areas. Coastal areas experienced high amount of rainfall with average annual rainfall of 3282.50 mm (Eket) and 3094.87 mm (Oron); while the average rainfall for upland areas were 2272.24 mm (Uyo) and 2007.49 mm (Ikot Ekpene). All the areas experienced moderate precipitation distribution with coastal areas – Eket (11.31%) and Oron (11.67%); upland areas – Uyo (12.67%) and Ikot Ekpene (13.70%). Coastal areas (Eket and Oron) recorded early onset, late cessation and long length of growing season than upland areas (Uyo and Ikot Ekpene). The mean onset for Eket and Oron were 19th and 21st February; cessation 4th and 1st December and length of growing season 289.0 and 283.5 days respectively. The mean onset for Uyo and Ikot Ekpene were 8th and 17th March; cessation 16th November and 12th November and length of growing season 265.2 and 255.3 days respectively. The study shows that only upland areas can support sustainable production of yam and cassava, while both upland and coastal areas can support sustainable production of oil palm and cocoa. Also, sustainable production of tomato and maize production may not be achieved in both areas. Climate variability is a serious environmental issue that possess great threat to sustainable development and food security. Therefore, to avert the risks of crops failure, poor yield and economic loss, the use of seasonal climate outlook is recommended for adjusting farm operations and farming system decisions.
Control of the vocal tract when experienced saxophonists perform normal notes and overtones

Acoustic measurement of the vocal tract proved that experienced saxophonists tune their vocal tract during advanced performances to effectively influence the vibration frequency of the reed (Scavone et al., J. Acoust. Soc. Am., 123, 2391–2400 (2008); Chen et al., J. Acoust. Soc. Am., 129, 415–426 (2011)). To understand how the shape of the vocal tract is altered, the vocal tracts of experienced saxophonists were scanned in three dimensions with magnetic resonance imaging while they played the instrument using different pitches with normal and overtone techniques. The scanned images demonstrated that the tongue was located posteriorly in the vocal tract for low notes; however, it moved forward when the participants produced overtones. The input impedance was then calculated for the players' air columns, including both the supra- and sub-glottal tracts, using an acoustic tube model. When the tongue moved forward to produce overtones, both the frequency and amplitude of the second impedance peak increased, suggesting an effective acoustic influence of the vocal behavior on the vibrating reed. The first impedance peak was less variable, regardless of the significant change in the vocal-tract shape for different notes.
A flexible framework for web-based virtual reality presentation of cultural heritage

The Virtual Reality (VR) experiences are great for an engaging presentation creating wide public awareness of cultural heritage, especially if the experience is built on a web-based technology. One of the still posing challenges is that once an application has been developed, for example for a virtual museum or a virtual tour, it usually remains fixed to an embedded in the application model. Adding new functionalities or interaction paradigms needs additional development and new deployment. In this paper, we propose a framework, which achieves the decoupling of an experience from the used models and interaction paradigms through scene and input templates and configuration files. This allows an easy way for generation of a variety of experiences even by people with no or limited programming skills.