Reviewerly/exHarmony huggingface dataset:
Aerial Physical Human Robot Interaction for Payload Transportation

Recent human-robot interaction paradigms on aerial robots unfold many potential applications, and efforts are further being made to explore this field. Physical interaction with aerial robots can provide an intuitive way of delivering high-level commands and allowing humans to perform collaborative tasks. The presented work demonstrates the feasibility of deploying the aerial robot to physically work with the human operator to transport the payload collaboratively in outdoor settings. A system comprised of a rigid object lifted by a human and a quadcopter from its end is considered. Custom build sensor systems, namely Human Handle Device and Cable Attitude Device, have been designed to estimate human commands and state feedback reliably. A control strategy for the quadcopter is designed to interact naturally with the operator for safer and smooth collaborative payload transportation. Successful outdoor experiments with five novice subjects are presented that demonstrate the feasibility and potential application of the proposed modality.
Blockchain-based implementation of Tradable Green Certificates

Blockchain networks, given that they are not based on power-hungry proof-of-work methods, can be used in smart grid applications, in particular in the tradable green certificates use case. Thanks to the Blockchain and the smart contracts implemented, certificates can be tracked and exchanged between entities without the intervention of third parties, as well as keeping an immutable and reliable record of these certificates. This work proposes a hardware implementation of a green certificate trading system based on Blockchain, in which prosumers use hardware secure elements to implement the cryptographic tools used to interact with the Blockchain. Smart contracts help to automate these processes, deleting intermediaries, saving costs and avoiding bureaucracy.
Exploratory hypothesis tests can be more compelling than confirmatory hypothesis tests

Researchers often distinguish between:(1) Exploratory hypothesis tests — unplanned tests of post hoc hypotheses that may be based on the current results, and(2) Confirmatory hypothesis tests — planned tests of a priori hypotheses that are independent from the current resultsThis distinction is supposed to be useful because exploratory results are assumed to be more “tentative” and “open to bias” than confirmatory results. In this paper, we challenge this assumption and argue that exploratory...
Echo State Network Optimization: A Systematic Literature Review

In the recent years, numerous studies have demonstrated the importance and efficiency of reservoir computing (RC) approaches. The choice of parameters and architecture in reservoir computing, on the other hand, frequently leads to an optimization task. This paper attempts to present an overview of the related work on echo state network (ESN) and deep echo state network (DeepESN) optimization and to collect research papers through a systematic literature review (SLR). This review covers 129 items published from 2004 to 2022 that are concerned with the issue of our focus. The collected papers are selected, analysed and discussed. The results indicate that there are two techniques of parameters optimization (bio-inspired and non-bio-inspired methods) have been extensively used for various reasons. But Different models employ bio-inspired methods for optimizing in a variety of fields. The potential use of particle swarm optimization (PSO) has also been noted. A significant portion of the research done in this field focuses on the study of reservoirs and how they behave in relation to their unique qualities. In order to test reservoirs with varied parameters, topologies, or training techniques, NARMA, the Mackey glass, and Lorenz time-series prediction dataset are the most commonly employed in the literature. This review debate diverse point of view about ESN's hyper-parameter optimization, metrics, time series benchmarks, real word applications, evaluation measures, and bio-inspired and non-bio-inspired techniques, this paper identifies and explores a number of research gaps.
Evolving Role of Instagram as a Digital Marketing Tool in Libraries

The role of social media in libraries has been gaining attention in recent years as libraries look for new ways to connect with users and promote their services. This chapter assesses the use of Instagram, a popular photo and video-sharing platform, by libraries as a means of outreach and engagement. Through a literature review and case studies of library Instagram accounts, the chapter explores how libraries use Instagram to showcase their collections, promote events and services, and create a sense of community. It also examines the benefits and challenges of using Instagram as a digital marketing tool and the implications for library practice. The chapter explains that Instagram can effectively reach and engage with users, particularly younger generations.
Digital and ICT Literacy in Distance Education

Some studies have shown that the definitions of digital and ICT literacies need clarification, as they are sometimes considered the same thing. Digital literacy refers to the ability to use software or operate a digital device, while ICT literacy involves using digital technology, communication tools, and/or networks to access, manage, integrate, evaluate, create, and transmit information in order to function in an information society. The Covid-19 pandemic has made these two concepts even more critical, as all educational activities are now carried out using technological tools. This chapter aims to define digital and ICT literacies and highlight the differences between these two concepts. Additionally, it will discuss the importance and definitions of these literacies in the context of distance education in the 21st century.
A Study on Disseminating Scholarly Information Through Online Resources in Universities

The chapter analyzes the dissemination of scholarly information through online resource use by the respondents in university. The study found that, out of 460 respondents, 234 (50.86%) belong to engineering /architecture—this is the top one category among the categories of disciplines of the study. The majority 435 (94.6%) of the respondents are aware of e-resources. 413 (89.78%), they prefer to make use of e-journals. E-Books are widely used by 426 (92.60%) respondents, followed by the use of e-magazines by 212 (46.08%) respondents. The result shows 185 (40.21%) of the respondents preferred general books. The majority 197 (42.82%) of the respondents preferred to use electronic resources for publishing papers in journals. The majority 142(30.86%) of them have preferred the electronic resources for preparing articles to seminars and conference; the respondents have opined as satisfied.
A Machine Learning Approach for Polymer Classification Based on the Thermal Response under Data Scarcity─Tested on PMMA

An important application of machine learning techniques is the intelligent nondestructive testing of polymers. However, data scarcity and class imbalance (for real applications) shape some of the involved challenges. In order to tackle these challenges, an intelligent screening framework for poly(methyl methacrylate) (PMMA) samples is studied here. An efficient thermal and experimental test is designed and coupled with several machine learning tools for quick classification with four features. Furthermore, a set of sampling techniques are employed/compared; these techniques are Random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling (ADASYN). Then, Linear Discriminant Analysis, K-Nearest Neighbor, Naive Bayes, decision tree, random forest, pattern recognition network, support vector machine, and ensemble learning are employed for classification. For assessing these algorithms, their performances are evaluated using a collection of metrics (i.e., Geometric-mean, F1 score, Matthews correlation coefficient, accuracy, true positive rate, true negative rate, positive predictive value, and negative predictive value). Among others, the average G-mean measures, which is a paramount measure for assessing the imbalance data, are increased from 75.97% (original data) to 94.24% (ROS), 93.49% (SMOTE) and 91.27% (ADASYN). That is a clear proof of successful oversampling. The final results show that ROS oversampling coupled with Ensemble classification methods can significantly improve all performance metrics for PMMA classification.
A Visual Sensitivity Aware ABR Algorithm for DASH via Deep Reinforcement Learning

In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8%, respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.
Accurate and fast extraction of adhesive cells based on concave points detection and matching

Abstract Precise image segmentation of adhesive cells is a challenge during the cell detection process for biomedical image analyses. In this article, a new segmentation and extraction method for adhesive cells based on concave points detection and matching is proposed. First, image preprocessing is used to reduce interference information, which mainly includes sequential gray scale transformation, binarization, hole filling, adhesive cells recognition, and median filtering process. Second, an improved concave detection algorithm is presented which can reduce the interfering feature points and detect the concave points on the adhesive cells accurately. Third, precise extraction of adhesive cell contour is realized by concave matching based on ellipse fitting. Experiments of blood cell sample detection show that the average accuracy is 97.18% with a costing time of 3.36 s. The proposed method can effectively reduce the interference of multiple concave points and extract the cell contour from adhesive regions with higher accuracy and less time, which shows great application potential in the field of biological image detection and analyses.
Knowledge Base, Hot Areas and Development Context of Mobile Learning Research in China

Through CiteSpace, this paper analyzes the literature on the theme of "mobile learning" from CSSCI journals from the knowledge base, research hotspots and development trends of mobile learning.The period of literature collection is 2002-2021.The conclusion is that the research on mobile learning has achieved rapid development, but the research field needs to be further broadened and deepened.The research institutions are mainly teachers' colleges and universities, which can encourage more colleges and universities to participate in the research.More and more attention has been paid to the application effects of mobile learning, especially in the field of English language learning.With the development of artificial intelligence technology, intelligent research on mobile learning has become a research frontier and development trend in recent years.It can expand the application research of mobile learning in teaching practice of different levels and different objects.
Research on Classroom Teaching Quality Evaluation in Colleges and Universities Based on Cloud Model

Classroom teaching quality evaluation (CTQE) has always played an important role in college's teaching systems and teaching quality management.Firstly, the evaluation criteria of college's CTQE is given by analyzing the university classroom teaching from teaching ideology and politics, teaching content and organization, and teaching methods and means; Then the weight of each evaluation criterion is obtained through the entropy weight method.Finally, after using the cloud model to convert the qualitative description into quantitative expression, the comprehensive evaluation method based on the normal cloud model is given, which can better solve the ambiguity and randomness of the teaching evaluation.An example calculated by MATLAB software is presented to illustrate the validity and feasibility of the evaluation method.
Information Technology Education and Impact in U.S.A.

The United States has the strongest information industry in the world.It is the world leader in the number of invention patents in information technology filed.This is closely related with its Information Technology Education through its entire educational process.This also led to the rising income inequality in the country.This paper establishes a general equilibrium model based on the framework of skill-biased technical change to illustrate that the rapid development and application of IT industry is the main source of rising income inequality in the United States.
Finite Element Software Analysis of Engineering Structure Test and Teaching Reform of Integration of Production and Teaching

The reform of information teaching in civil engineering specialty is an inevitable result under the background of information age.The course of architectural structure is a very important course of civil engineering, and civil engineering is a very practical major.Most of the civil structure test work needs to be completed with the help of computer software analysis, the industry to civil engineering students software application ability requirements are increasingly high.How to carry out the finite element software analysis teaching of structural test has become the key to cultivate applied civil engineering professionals.It not only enables students to master the basic theory of building structure test, but also cultivates students' ability of test design operation and SAP2000 software to establish finite element model.In the teaching, sap2000 software was used to analyze the finite element model of concrete frame and string beam structure.In order to better complete the finite element software information teaching of this course, reform the teaching method, adopt the combination of online and offline teaching, and better apply the teaching method of the integration of production and teaching, put forward some targeted solutions.
Dynamics-Aware Gated Graph Attention Neural Network for Student Program Classification and Knowledge Tracing

Faced with a large number of questions on the programming OJ (Online Judge) system, students are usually mindless when choosing questions, which is not conducive to helping students quickly improve their programming ability.Programming Knowledge Tracing (PKT) is a technology that dynamically traces students' programming knowledge states using their historical learning data including submitted programs.Relying on PKT, OJ can find students' unmastered knowledge points, and recommend questions examining these knowledge points to students, so as to help students overcome their weakness.However, existing program analysis modules in PKT models ignore dynamic information of program.Therefore, this paper proposes Dynamics-Aware Gated Graph Attention Neural Network (DGGANN), which inputs test cases of questions into program, obtains call frequency coefficients of every node in Abstract Syntax Tree (AST) through code coverage statistical tool, and introduces such call frequency information into process of program analysis.This paper applies DGGANN to two tasks in our experiments: classifying programs by functionalities and PKT.Experimental results show that our approach can achieve higher performance than the state-ofthe-art models in both tasks on datasets of two well-known OJ systems named CodeForces and Libre.
Research and Design of SPOC Teaching Service Platform Based on Big Data Technology and AHP

Higher education has been transformed and reformatted as a result of the new generation of information technology, which is typified by mobile Internet, artificial intelligence, and big data.Higher education will inevitably undergo a digital transition as it moves from the traditional period to the digital era and even the coming exponential era.Given the current issues with online teaching platforms in higher education institutions, this paper conducts research and designs a teaching platform based on SPOC in an effort to enhance and improve the teaching service system of higher education institutions.This study developed a teaching service platform with three main characteristics to address the primary issues of inadequate personalisation, subpar real-time supervision, and subpar thinking growth.It also implemented teaching evaluation based on Hadoop computing architecture and AHP approach.It aids in the development of a teaching service platform that has a perfect structure, a thorough procedure, and a high level of generalization.
ERODE: Error Resilient Object DetEction by Recovering Bounding Box and Class Information

Fault resilience in computer vision algorithms is paramount in critical applications such as autonomous driving or surveillance. Convolutional neural networks (CNNs) are usually used in these tasks to identify objects of interest, which are passed to other decisional algorithms and used to take specific actions. However, incorrect detections due to computation errors could pose a safety risk. In this work, we present ERODE (Error Resilient Object DetEction), a framework that can be paired with a CNN to filter the detections by identifying possible errors and restoring the correct predictions, improving the fault-resilience of the system. The proposed framework leverages the temporal correlation among consecutive images and CNN outputs, using motion estimation and tracking techniques to infer whether computation errors have occurred and, in that case, produce a new set of outputs. In order to evaluate the performance, precision and recall of the CNN with and without ERODE support have been computed and compared using the MOT17DET dataset, and EfficientDet DO quantized to 16-bit, with errors injected in the activations computed during the inference. The experimental results show significantly reduced task accuracy degradation induced by bit-flips, proving that ERODE can increase the system’s fault resilience.
A hardware-aware neural architecture search algorithm targeting low-end microcontrollers

Hardware-aware neural architecture search (HW NAS), the process of automating the design of neural architectures taking into consideration hardware constraints, has already outperformed the best human designs on many tasks. However, it is known to be highly demanding in terms of hardware, thus limiting access to non-habitual neural network users. Fostering its adoption for the next-generation IoT and wearable devices design, we propose an HW NAS that can be run on laptops, even if not mounting a GPU. The proposed technique, designed to have both a low search cost and resource usage, produces tiny convolutional neural networks (CNNs) targeting low-end microcontrollers. It achieves state-of-the-art results in the human-recognition tasks, on the Visual Wake Word dataset a standard TinyML benchmark, in just 3:37:0 hours on a laptop mounting an 11th Gen Intel(R) Core(TM) i7-11370H CPU @ 3. 30GHz equipped with 16 GB of RAM and 512 GB of SSD, without using a GPU.
Correlation Electromagnetic Analysis on an FPGA Implementation of CRYSTALS-Kyber

Post-quantum cryptography represents a category of cryptosystems resistant to quantum algorithms. Such schemes are under the scrutiny of their mathematical security in the context of the NIST standardization process, but they are not side-channel secure at the algorithm level. That is why their side-channel vulnerabilities must be assessed by the research community. In this paper, we present a non-profiled correlation electromagnetic analysis against an FPGA implementation of the standard key-encapsulation mechanism, CRYSTALS-Kyber. The attack correlates an electromagnetic radiation model of the polynomial multiplication execution with the captured traces. With 166,620 traces, this attack correctly recovers 100% of the subkeys. Furthermore, a countermeasure is presented for securing the target implementation against the presented attack.
The Potential of English for Social Purposes and Cooperation for the Development of Digital Literacy

Modern foreign languages and digital literacy are essential in today's education systems to ensure the development of fully prepared citizens. Among the languages learned in schools, English is certainly the most studied in Europe. Consequently, it is not surprising that educational initiatives are being developed to enhance the learning of this language. Such is the case of ‘English for Social Purposes and Cooperation', which aims to foster English language learning through relevant social and cultural topics and the use of educational technology so that learners develop their competences. Although relatively new, the potential of this approach for language learning, raising awareness of social and cultural issues and developing digital skills is undeniable. This chapter explores the potential of ‘English for Social Purposes and Cooperation' for developing teachers' digital literacy, empowering students, and fostering educational technology integration. Moreover, it presents a proposal of technology-based tasks that can help develop learners' digital literacy, equity, and inclusion.
Smart Pedagogy and Advanced Technology Support in Higher Education

Smart technologies, modern equipment, hardware, and software systems have created unprecedented opportunities for education and training institutions to take new approaches to teaching and the provision of educational services. The concept of smart university and related concepts such as smart learning environment, smart education, smart pedagogy, and smart learners are becoming buzzwords and widely adopted by universities. This chapter presents a comprehensive description of the smart university concept and its components. It presents the findings of an assessment of a smart university model at Hanoi University of Science and Technology against the established components of a smart university. The study utilizes quantitative and qualitative surveys of 535 students from the Hanoi University of Science and Technology in Vietnam. Investigation of these factors provided an assessment of the current initiative and paved a path for the future development of technological infrastructure and support.