Reviewerly/exHarmony huggingface dataset:
Comparing election algorithm and Election Campaign Optimization Algorithm

This paper attempts to compare the two socio-inspired based metaheuristics algorithms which were introduced recently. These metaheuristics optimizations and search techniques “Election algorithm, and Election Campaign Optimization Algorithm” were inspired by socio-political processes of human ideologies. This paper gives an overview of these algorithms with respect to their control parameters, components, intensification and diversification as well as their applications. It also makes a simple comparison between the two metaheuristics algorithms in terms of quality of solution, ease of finding correct control parameters and further explored their strengths and limitations as well as their superiority over other metaheuristics algorithms.
G-I transfer in multicue judgment tasks: Discussion improves group members’ knowledge about target relations.

One benefit of working in groups is that group members can learn from each other how to perform the task, a phenomenon called group-to-individual transfer (G-I transfer). In the context of quantitative judgments, G-I transfer means that group members improve their individual accuracy as a consequence of exchanging task-relevant information. This improved individual accuracy allows groups to outperform the average of a comparable number of individuals, that is, G-I transfer leads to synergy. While there is mounting evidence that group members benefit from G-I transfer in quantitative judgment tasks, we still know rather little about what exactly group members learn from each other during this transfer. Here, we build on the distinction between metric knowledge (knowing what constitutes a plausible range of values) and mapping knowledge (knowing the relative magnitude of the targets) to gain further insights into the nature of G-I transfer. Whereas previous research found evidence that G-I transfer improves group members' metric knowledge, there is, so far, no evidence that group discussion also improves mapping knowledge. Using a multicue judgment task, we tested whether group members would benefit from G-I-transfer and, if so, whether this G-I transfer would manifest in the form of improved mapping knowledge. The results of two experiments suggest that this is the case. Participants who worked in real interacting groups outperformed participants who worked individually, and this increase in accuracy was accompanied not only by improved metric but also by increased mapping knowledge. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Qualitative Analysis of Commercial Services in MEC as Phased-Mission Systems

Currently, mobile edge computing (MEC) is one of the most popular techniques used to respond to real-time services from a wide range of mobile terminals. Compared with single-phase systems, commercial services in MEC can be modeled as phased-mission systems (PMS) and are much more complex, because of the dependencies across the phases. Over the past decade, researchers have proposed a set of new algorithms based on BDD for fault tree analysis of a wide range of PMS with various mission requirements and failure behaviors. The analysis to be performed on a fault tree can be either qualitative or quantitative. For the quantitative fault tree analysis of PMS by means of BDD, much work has been conducted. However, for the qualitative fault tree analysis of PMS by means of BDD, no much related work can be found. In this paper, we have presented some efficient methods to calculate the MCS encoding by a PMS BDD. Firstly, three kinds of redundancy relations-inclusive relation, internal-implication relation, and external-implication relation-within the cut set are identified, which prevent the cut set from being minimal cut set. Then, three BDD operations, IncRed, InImpRed, and ExImpRed, are developed, respectively, for the elimination of these redundancy relations. Using some proper combinations of these operations, MCS can be calculated correctly. As an illustration, some experimental results on a benchmark MEC system are given.
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling

In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.
Detect All Abuse! Toward Universal Abusive Language Detection Models

Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years. Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain. In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains. Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user's linguistic behaviour. Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language. Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.
Augmented reality

Augmented reality (AR) involves computer-generated information being used to supplement elements of the real world, often through direct digital overlay onto physical objects or even whole landscapes via a mobile device screen or visor. There have been many uses of this term in an educational context and a broad range of techniques and technologies could be considered to be part of AR. Something common to all approaches, however, is that the integration of digital media with physical spaces offers some exciting opportunities to create engaging and kinaesthetic learning experiences. This chapter presents a structured summary of the technological approaches that are available and some of the opportunities for use in geography education, reflecting on the research challenges that remain in developing and deploying effective AR techniques. It draws upon experiences of using a range of techniques for augmenting the landscape on fieldtrips, as well as of using projection augmentation on landscape models in the classroom.
Multiverse: Exploring Human Machine Learning Interaction Through Cybertextual Generative Literature

We present a prototype of a system for machine learning (ML) powered interactive generative literature called Multiverse. The system employs a set of neural networks models to dynamically generate a literary space from an initial writing prompt provided by its user-reader. The user-reader is able to choose the model used to generate the text as a kind of interactive machine learning (IML). The research explores how interaction design and HCI researchers can engage directly with ML by leveraging the powerful, yet accessible, models afforded by new developments in the field. User-readers testing the prototype found the imperfect aesthetics of the ML-generated texts to be entertaining and engaging but struggled to conceptualize the generated work as a navigable interactive literary space.
YOUTH IN THE DIGITAL AGE: ANTECEDENTS AND CONSEQUENCES OF DIGITAL SKILLS

What actors and factors shape children and young people’s digital skills? And how do their digital skills impact the rest of their lives? These are the two research questions addressed in this paper, along with an analysis of how the research literature to date has measured digital skills. The findings reported here come from a systematic evidence review of the antecedents and consequences of digital skills (Haddon, Cino, Doyle, Livingstone, Mascheroni and Stoilova, forthcoming) as part of the ySKILLS project funded by the EU’s Horizon 2020 programme.
Connecting Blockchain Technology with Libraries: Opportunities and Risks

This study describes a systematic investigation of blockchain technology and its applications across various domains with main focus on libraries. The concept and working of blockchain technology were discussed with respect to multiple domains to emphasize how the peculiar attribute of this technology can revolutionize “library practices”. Opportunities and Risks of blockchain are also discussed to guide the users of blockchain technology. Based on this study, we tried to aware the library professionals to understand this emerging technology and take some initiation for its application in libraries of their countries.
A Permissioned Blockchain Based Feature Management System for Assembly Devices

With the increasing spread and adoption of electronics and software as integral parts of all kinds of physical devices, such devices are becoming controlled by their embedded software. Correspondingly, the manufacturing business has started the transition from selling hardware to selling features (e.g. “insane mode” and “ludicrous mode” in Tesla Model S). Consequently, a trustworthy system to automate such a process becomes essential. This article introduces a permissioned blockchain-based feature management system for assembly devices. Firstly, it leverages software licensing technology to control assembly devices' features. Secondly, by recording the license ownership transaction data in a permissioned blockchain, the approach (1) takes advantage of blockchain's trust mechanism and its distributed nature to improve the trustworthiness of the feature management system, and (2) adopts the permissioned blockchain technology to ensure that the license transactions are only visible and applicable to authenticated actors. We further describe an implementation, a proof-of-concept evaluation focusing on functionality and performance, as well as a security analysis.
Remote work: development of employee digital competence

Remote work in the context of global computerization of all work processes is an integral part of the functioning of companies. With the growing number of employees working remotely, it is important to conduct correctly the adaptation period for the transition to a new communication format for both employees and the candidates for a vacancy. The aim of this article is to survey a number of classifications of employee digital competencies proposed by scientists. The study was carried out on the use of tools by employees in a stressed work format: it was revealed that when employees possess the skills to use digital tools for remote work, company managers do not use these skills when building communications and working in new conditions. The analysis of the resume base is given in order to determine the formation of digital competence among the candidates. A toolkit has been developed to determine the formation of digital competence and assess the effectiveness of an employee at remote work.
Measuring camera Shannon Information Capacity with a Siemens Star Image

Shannon information capacity, which can be expressed as bits per pixel or megabits per image, is an excellent figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems. Its strength is that is combines the effects of sharpness (MTF) and noise, but it has not been widely adopted because it has been difficult to measure and has never been standardized. We have developed a method for conveniently measuring information capacity from images of the familiar sinusoidal Siemens Star chart. The key is that noise is measured in the presence of the image signal, rather than in a separate location where image processing may be different&#x2014;a commonplace occurrence with bilateral filters. The method also enables measurement of SNRI, which is a key performance metric for object detection. Information capacity is strongly affected by sensor noise, lens quality, ISO speed (Exposure Index), and the demosaicing algorithm, which affects aliasing. Information capacity of in-camera JPEG images differs from corresponding TIFF images from raw files because of different demosaicing algorithms and nonuniform sharpening and noise reduction.
A Comparison of Rhetorical Moves in Students’ Undergraduate Thesis Abstracts

Research on rhetorical move in abstracts has received attention in recent years.Different disciplines employ different strategies in manifesting rhetorical move in their writing.However, cross-disciplinary studies on rhetorical move in abstract still need to be done to examine the similarities and differences of the realization of rhetorical moves between different disciplines.This study aims to identify the manifestation of rhetorical move used in the undergraduate thesis abstracts written by Natural Science and Social Science students in a university in Bandung, Indonesia.A total of 120 abstracts were taken from six disciplines (20 abstracts each) i.e.Mathematics, Chemistry, Physics, Geography, Sociology, and History.Hyland's ( 2004) model was used as the guideline for the analysis and the AntMover software was used to assist the analysis.The result showed that Natural Science and Social Science employed different strategies in realizing the moves.Introduction, Methodology, and Findings were the most manifested move in Natural Science, while Introduction, Purpose, Methodology, and Findings were the most manifested move in Social Science.The findings also showed that both Natural Science and Social Science employ different strategies in realizing some steps in a certain move.In conclusion, the nature of the disciplines does affect the manifestation of rhetorical moves in undergraduate thesis abstracts.This study provides an insight into the trend of rhetorical move patterns in both Natural Science and Social Science undergraduate thesis.Some recommendations have been provided as an insight concerning the trend of rhetorical move both Natural Science and Social Science undergraduate theses.
Delta Omnidirectional Wheeled Table Tennis Automatic Pickup Robot Based on Vision Servo

The survey found that during table tennis training there are often many scattered balls that need to be picked up manually, affecting the efficiency of the players’ training. Currently, table tennis is picked up manually using a table tennis picker. In this paper, delta omnidirectional wheeled table tennis automatic pickup robot based on the vision servo achieves fully automatic table tennis pickup by combining the characteristics and requirements of table tennis sports. In this paper, we design a vision algorithm for a table tennis automatic pickup robot system, give a kinematic inverse solution for the delta omnidirectional wheel, complete the design of the robot control system, and build the platform for the experiment. A pickup experiment is conducted on scattered ping-pong balls of different colors. Experiments have shown that the robot can safely clean up scattered table tennis balls on the table tennis field with high positioning accuracy and top pick up rate. It is proved that the robot designed in this paper has extensive application value and prospects.
Application of ISO Standard Methods to Optical Design for Image Capture

The ISO 12233 standard for digital camera resolution includes two methods for the evaluation of camera performance in terms of a Spatial Frequency Response (SFR). In many cases, the measured SFR can be taken as a measurement of the camera-system Modulation Transfer Function (MTF), used in optical design. In this paper, we investigate how the ISO 12233 method for slantededge analysis can be applied to such an optical design. Recent improvements to the ISO method aid in the computing of both sagittal and tangential MTF, as commonly specified for optical systems. From computed optical simulations of actual designs, we apply the slanted-edge analysis over the image field. The simulations include the influence of optical aberrations, and these can present challenges to the ISO methods. We find, however, that when the slanted-edge methods are applied with care, consistent results can be obtained.
Statistical Analysis of the Pre- and Post-Surgery in the Healthcare Sector Using High Dimension Segmentation

This chapter presents the achievements of the set objectives and the comparative performance evaluations and simulation of experimental results. The researcher discusses the damaged cells of the mind due to the abnormalities of the mobile. The sampling collections are used for the have a look at consists of clinical mind snap shots for human being. The researcher will almost implement and simulate the experiments of to the testing on human being and their mind tissues. The purpose of using a set of tools to work with light field images in MATLAB is to decode, calibrate correct, correct color, filter basic images, and visualize light field images. The research closes with a discussion of the challenges of the deep learning method in relation to medical imaging and the research problem. The researcher describes the statistical results of brain cancer through an interface of Magnetic Resonance Imaging-four-dimensional photo segmentation.
Fairness-aware Agnostic Federated Learning

Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about federated learning focus on the privacy protection and communication cost reduction. However, how to achieve fairness in federated learning is under-explored and challenging especially when testing data distribution is different from training distribution or even unknown. Introducing simple fairness constraints on the centralized model cannot achieve model fairness on unknown testing data. In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution. We use kernel reweighing functions to assign a reweighing value on each training sample in both loss function and fairness constraint. Therefore, the centralized model built from AgnosticFair can achieve high accuracy and fairness guarantee on unknown testing data. Moreover, the built model can be directly applied to local sites as it guarantees fairness on local data distributions. To our best knowledge, this is the first work to achieve fairness in federated learning. Experimental results on two real datasets demonstrate the effectiveness in terms of both utility and fairness under data shift scenarios.
Video Action Understanding: A Tutorial.

Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, the span of video action problems and the set of proposed deep learning solutions is arguably wider and more diverse than those of their 2D image siblings. Finding, identifying, and predicting actions are a few of the most salient tasks in video action understanding. This tutorial clarifies a taxonomy of video action problems, highlights datasets and metrics used to baseline each problem, describes common data preparation methods, and presents the building blocks of state-of-the-art deep learning model architectures.
Research on path planning of robot based on adaptive ACS fused with SHAA neural network

In this paper, we proposed an adaptive ACS algorithm by introducing an adaptive pheromone volatility coefficient and the algorithm diversity dynamically varying in different iterations of the algorithm. It incorporates a shunting hierarchical hybrid neural network application algorithm (Shunting HHNN Application Algorithm, SHAA) to overcome the drawbacks of global optimization capabilities of ant colony system (ACS) in solving robot path and easily being trapped into the local optimal solution. Considering the influence of the activation value size on the selection of the grid in the SHAA neural network algorithm, the distance factor and the activation value are combined to improve the heuristic function. This will not only ensure the convergence speed, but also avoid the premature stagnation and being trapped into a local optimal path. Simulation results show that the algorithm discussed in this paper outperforms better in both the global optimization ability and the robustness.
Structural and Functional Approach to the Assessment of the Educational Programs Quality

The article is devoted to the use of social diagnostics based on structural and functional methodology to assess the quality of bachelor’s educational programs. The main components of this technology are situational analysis of programs, which more accurately determines the purpose of diagnostics and criteria for assessing, and a structural-functional model that allows to determine dysfunctional zones in the structure of education and their causes. The approbation of this diagnosis was carried out on the educational program of bachelors in Social Management at the Moscow Aviation Institute. Focus groups and questionnaires of graduate students were the main methods of data collection. The diagnostic results allowed us to determine that the vector of students’ needs in the field of education coincides with the competence model of the program. The decline in the quality of the program occurs due to the low level of development of practical skills and analytical competencies. The subjective perception of students about the structural factors of the decline in the quality of education was also determined. These are the factors of organization and the type of interaction between professors and students. Structural and functional diagnostics showed the absence of uniform institutional requirements for the educational program and the problems of its organization. The data obtained can be used in the management of the educational program. From a scientific point of view, the results are a contribution to the development of sociological diagnostic tools based on a situational approach and understanding of modern values of youth in the field of education.
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms

Meta-learning or few-shot learning, has been successfully applied in a wide range of domains from computer vision to reinforcement learning. Among the many frameworks proposed for meta-learning, bayesian methods are particularly favoured when accurate and calibrated uncertainty estimate is required. In this paper, we investigate the similarities and disparities among two recently published bayesian meta-learning methods: ALPaCA (Harrison et al. [2018]) and PACOH (Rothfuss et al. [2020]). We provide theoretical analysis as well as empirical benchmarks across synthetic and real-world dataset. While ALPaCA holds advantage in computation time by the usage of a linear kernel, general GP-based methods provide much more flexibility and achieves better result across datasets when using a common kernel such as SE (Squared Exponential) kernel. The influence of different loss function choice is also discussed.