Reviewerly/exHarmony huggingface dataset:
A new neurocomputing approach for medium-range temperature prediction

To predict medium-range temperature with appreciable accuracy this study has been undertaken. We have tried to predict max-min temperature time series (signal) 5, 7 and 9 days ahead with the help of a new machine learning (neurocomputing) technique. Variational Mode Decomposition (VMD) has been used to decompose temperature time series and decomposed modes obtained from VMD have been individually used as input to Long Short Term Memory (LSTM). The LSTM predicted the modes individually and predicted modes are combined to generate the predicted signal. The signal predicted by this method closely matches with the actual test signal and minimizes the error in predicting the signal.
Museums and Crisis Management due to Covid -19. Effects of Pandemic and the Role of Digital and Social Networks Communication

The overall purpose of this study is the effects of lockdown within Italian museums caused by the Coronavirus disease-19 (Covid-19) emergency. During the pandemic, the museums are being closed, and the use of digital content and social networks seems to have been the solution to overcome the closure problem. The study analyzes the role of digital content and tools in communication to manage the shock due to Covid-19. The basic design of the research recurs to the crisis management model used to analyze three stages crossed by museums as preparedness, reaction, and recovery. The study involved several Italian museums grouped according to the art collection, governance, and funding sources. A semi-structured questionnaire was sent to them. The main findings show the presence of digital as a strategy already adopted by museums before the outbreak. However, the results reveal the absence of a preventive phase as requested by the model of crisis management. At the same time, it has emerged the need to adapt the digital content to guarantee the mission of fruition for the public.
Improving oral communication skills of English learners with voice messages and short videos

University students face the challenge of communicating in a foreign language while speaking. This is perceived as one of the most complex skills to develop. The lack of opportunities to interact in the target language, in and out of the English classrooms, prevents students from acquiring fluency and confidence. This study examines the development of students' oral communication skills of Level B1 (Pre-Intermediate level of the Common European Framework of Reference for Languages) of English as a Foreign Language (EFL) using the WhatsApp applications. Forty–five EFL learners aged 18-25 from a language center at a university in Ecuador performed exercises using voice messages and short videos as part of their course planned activities. The data collection method consisted of surveys and interviews with the participants and their teacher’s observations. Data were analyzed using descriptive statistics and cross-tabulation accordingly. Analysis of the data evidenced that short videos were far more effective than voice messaging in improving the participants' oral communication skills. Other benefits were the improvement of grammar and pronunciation observed in the learners' voice messages, with the consequent improvement of their speaking fluency. It can be said that the participants' overall oral communication skills developed with the use of the WhatsApp application in this study. Therefore, the authors recommend exploring the application of technological resources in the EFL classroom.
ESPOUSAL OF ONLINE SOCIAL NETWORKS (OSNS) IN TOURISM SUPPLY CHAIN NETWORK (TSCN) DURING COVID-19

The COVID-19 epidemic has broken the original business network structure and globally affected the tourism supply chains. Online social networks (OSNs) have become a necessary component of ICT-enabled services, affecting every area of human existence. The espousal of OSNs into tourism supply chain networks during a pandemic is crucial. However, scarce studies have been conducted on the espousal of OSNs in tourism supply chain networks. The study developed a research model based on the IS success model, uses and gratifications (U&amp;G) theory, institutional theory, and transaction cost theory to analyze the espousal of OSNs in the tourism supply chain network (TSCN) during COVID-19. The study employed structural equation modeling (SEM) to examine 99 data acquired via online surveys from supply chain specialists in the travel and hospitality sectors. The research model outperforms better and fulfills all model fitness requirements. The research found that tourism supply chain experts adopt online social networks to address supply and demand disruptions caused by COVID-19. Utilitarian features (e.g., supply chain perceptibility), technological features (e.g., system quality), and cost-benefit aspects substantially influence the espousal of OSNs in the tourism supply chain network. Espousals of OSNs improve tourism supply chain performance during Covid-19. As a result of this research, tourism supply chain management, MIS, and social media literature will be enriched by introducing OSNs in the tourism supply chain environment. The research will also provide adequate implications to supply chain managers and OSNs service provider to adopt OSNs in the tourism supply chain during COVID-19.
Improved Dual Attention for Anchor-Free Object Detection

In anchor-free object detection, the center regions of bounding boxes are often highly weighted to enhance detection quality. However, the central area may become less significant in some situations. In this paper, we propose a novel dual attention-based approach for the adaptive weight assignment within bounding boxes. The proposed improved dual attention mechanism allows us to thoroughly untie spatial and channel attention and resolve the confusion issue, thus it becomes easier to obtain the proper attention weights. Specifically, we build an end-to-end network consisting of backbone, feature pyramid, adaptive weight assignment based on dual attention, regression, and classification. In the adaptive weight assignment module based on dual attention, a parallel framework with the depthwise convolution for spatial attention and the 1D convolution for channel attention is applied. The depthwise convolution, instead of standard convolution, helps prevent the interference between spatial and channel attention. The 1D convolution, instead of fully connected layer, is experimentally proved to be both efficient and effective. With the adaptive and proper attention, the correctness of object detection can be further improved. On public MS-COCO dataset, our approach obtains an average precision of 52.7%, achieving a great increment compared with other anchor-free object detectors.
A Normalized Autoencoder for LHC Triggers

Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distribution anomaly searches based on the compressibility of features do not apply to the LHC, while existing density-based searches lack performance. We present the first autoencoder which identifies anomalous jets symmetrically in the directions of higher and lower complexity. The normalized autoencoder combines a standard bottleneck architecture with a well-defined probabilistic description. It works better than all available autoencoders for top vs QCD jets and reliably identifies different dark-jet signals.
Transformer induced enhanced feature engineering for contextual similarity detection in text

Availability of large data storage systems has resulted in digitization of information. Question and answering communities like Quora and stack overflow take advantage of such systems to provide information to users. However, as the amount of information stored gets larger, it becomes difficult to keep track of the existing information, especially information duplication. This work presents a similarity detection technique that can be used to identify similarity levels in textual data based on the context in which the information was provided. This work presents the transformer based contextual similarity detection (TCSD), which uses a combination of bidirectional encoder representations from transformers (BERT) and similarity metrics to derive features from the data. The derived features are used to train the ensemble model for similarity detection. Experiments were performed using the Quora question similarity data set. Results and comparisons indicate that the proposed model exhibits similarity detection with an accuracy of 92.5%, representing high efficiency.
MODELLING OF PEDAGOGICAL PATTERNS THROUGH E-LEARNING OBJECTS

Software platforms for e-learning support various options for presenting educational content. One of the ways to organize and structure it is via so-called pedagogical patterns. They are a method for describing and sharing knowledge and practical experience. Pedagogical patterns are used to describe pedagogical situations that occur repeatedly in the learning process. In the context of e-learning systems, there are various approaches to digitalization of pedagogical patterns. The purpose of the paper is to show how to build instances of pedagogical patterns using e-learning pedagogical objects, which can be easily and conveniently used as models in an adaptive e-learning environment. An e-learning pedagogical object is an abstract concept that can be presented in the concrete form of an e-learning object, an e-learning methodological object, an e-learning object for monitoring and diagnostics or an e-learning object with learning outcomes. These objects are building blocks for constructing instances of pedagogical patterns. This paper thoroughly discusses the issue of creating instances of pedagogical patterns of the four types of e-learning pedagogical objects. The instance of a pedagogical pattern is meant to serve to create subsections of educational topics. The instances of the patterns built of e-learning objects are learning units that are used depending on the context of a particular pedagogical situation. The e-learning pedagogical objects and the pedagogical pattern instances are intended to be applied in an adaptive e-learning environment as teaching aids. Their theoretical models are applied in Moodle LMS in line with the tendency for software to assist and replace some of the teacher functions, while the teacher’s role is raised to a higher organizational, pedagogical and methodological level. Three instances of pedagogical patterns have been created through e-learning pedagogical objects: “Early Feedback”, “Feedback Sandwich” and “Consistent Metaphor” in LMS Moodle, which have been tested in the training course “Modeling of training courses in Moodle” during the autumn trimester of the academic year 2021/2022 at the Faculty of Mathematics and Informatics of Paisii Hilendarski University of Plovdiv, Bulgaria.
Designing Integral Learning Outcomes in Higher Education Within the Frameworks of the Competency-Based Approach

Uptick of informatization and technologization of society, increased competition both in labor markets and in learning services have caused dramatic changes in higher education and have led to its reorientation from knowledge-centric to the competency-based learning paradigm. Developing and implementing flexible learning paradigm consider one of modern universities’ key priority within the European Higher Education Area. Since the main purpose of modern humanities is to form a conscious and responsible attitude of human being towards own learning and carrier in terms of dynamic changes, the issue of designing integral learning outcomes in higher education, technologization of competency acquisition and its further development in lifelong education becomes especially relevant. The study is aimed at a critical analysis of the modern experience in organizing the learning process on the competency-based approach, disclosing, exemplified by research competence, the end-to-end design technology of integral learning outcomes in the context of the triple system of learning professionals with academic degree.
ACGAN: Attribute controllable person image synthesis GAN for pose transfer

At present, pose transfer and attribute control tasks are still the challenges for image synthesis network. At the same time, there are often artifacts in the images generated by the image synthesis network when the above two tasks are completed. The existence of artifacts causes the loss of the generated image details or introduces some wrong image information, which leads to the decline of the overall performance of the existing work. In this paper, a generative adversarial network (GAN) named ACGAN is proposed to accomplish the above two tasks and effectively eliminate artifacts in generated images. The proposed network was compared quantitatively and qualitatively with previous works on the DeepFashion dataset and better results are obtained. Moreover, the overall network has advantages over the previous works in speed and number of parameters.
THE USE OF MOBILE ASSISTED LANGUAGE LEARNING (MALL) IN TEACHING STUDENTS’ LISTENING AND SPEAKING SKILLS

This research is aimed to investigate is there any significant effect of using Mobile Assisted Language Learning (MALL) towards students’ listening and speaking skill on the eleventh grade of SMAN 1 Tirtayasa in academic year 2020-2021. The population is the second grade students of SMAN 1 Tirtayasa. The sample of the research is two classes. They are XI IPA 1 as the controlled class and XI IPA 3 as experimental class. It was conducted by using experimental research design. The experimental group was taught by using Mobile Assisted Language Learning (MALL) through Talk English Application (TEA) which is connected to the smartphone, while the controlled group was taught by using conventional technique. The instrument was used to collect the data by answering the questions of listening and make conversation video in pair for speaking. After analyzing the data, it was found that there was significant different effect between the class which taught by using MALL and the class taught by conventional with paired sample t-test. The result of the test shows a significance value of 2 tailed 0.000 &lt;0.05. It means that alternative hypothesis (Ha) was accepted and null hypothesis (Ho) was rejected. The score of the result that teaching by using Mobile Assisted Language Learning (MALL) was higher than taught conventional. Then, the researcher confirmed the findings of the learning outcomes by using MALL besides being able to develop students' listening and speaking skills, Students have easier access to material about daily conversation. Students are assisted by an application that is integrated into their smartphones, an application that guides learning English interactively. Students also can hone their listening and speaking skills at the same time because in learning using Talk English Application (TEA) students are trained to understand the speaker's expressions and respond to what the speaker says.
Quantum dynamics for energetic advantage in a charge-based classical  full-adder

We present a proposal for a one-bit full-adder to process classical information based on the quantum reversible dynamics of a triple quantum dot system. The device works via the repeated execution of a Fredkin gate implemented through the dynamics of a single time-independent Hamiltonian. Our proposal uses realistic parameter values and could be implemented on currently available quantum dot architectures. We compare the estimated energy requirements for operating our full-adder with those of well-known fully classical devices, and argue that our proposal may provide a consistently better energy efficiency. Our work serves as a proof of principle for the development of energy-efficient information technologies operating through coherent quantum dynamics.
Virtual Communication Pattern Of Twitter Autobase Management (Study Of Sharing Real Life Things Media On @bertanyarl Account)

Autobase raises the phenomenon of sending messages anonymously by using the help of Bots to send messages automatically via DM (direct messages) on Twitter. One of them is the @bertanyarl autobase, wihich allows its users to ask questions, discuss, or share things in real life which in its utilization and use cannot be separated from the help of the admin who manages and monitors the activities that occur in the @bertanyarl autobase. The purpose of this study was to determine the virtual communication pattern in the @bertanyarl autobase management as well as the supporting and inhibiting factors of the @bertanyarl Twitter autobasae management. This research uses a qualitative descriptive method, and the source of data obtained from five informants uses a purposive sampling technique. By using data collection techniques in the form of observation interviews and documentation, the results obtained are the managements virtual communication patterns carried out by the admin in the form of activities consisting of questions and answer about real life, open followback, MIXLR, and paid promotion, as well as efforts to maintain the security and comfort of the @bertanyarl autobase by creating rules or regulations and imposing sanctions. From each virtual communication, two different communication network pattern are found, known as the pattern of all channels and the pattern of the wheel. As well as the supporting factors found, namely the active Bot and sender features, the impediment is that Twitter frequently locks and suspends accounts.
Escaping Local Optima with Local Search: A Theory-Driven Discussion

Local search is the most basic strategy in optimization settings when no specific problem knowledge is employed. While this strategy finds good solutions for certain optimization problems, it generally suffers from getting stuck in local optima. This stagnation can be avoided if local search is modified. Depending on the optimization landscape, different modifications vary in their success. We discuss several features of optimization landscapes and give analyses as examples for how they affect the performance of modifications of local search. We consider modifying random local search by restarting it and by considering larger search radii. The landscape features we analyze include the number of local optima, the distance between different optima, as well as the local landscape around a local optimum. For each feature, we show which modifications of local search handle them well and which do not.
Revisiting QUIC attacks: A comprehensive review on QUIC security and a hands-on study

Abstract Built on top of UDP, the recently standardized QUIC protocol primarily aims to gradually replace the TCP plus TLS plus HTTP/2 model. For instance, HTTP/3 is designed to exploit QUIC’s features, including reduced connection establishment time, multiplexing without head of line blocking, always-encrypted end-to-end security, and others. This work serves two key objectives. Initially, it offers the first to our knowledge full-fledged review on QUIC security as seen through the lens of the relevant literature so far. Second and more importantly, through extensive fuzz testing, we conduct a hands-on security evaluation against the six most popular QUIC-enabled production-grade servers. This assessment identified several effective and practical zero-day vulnerabilities, which, if exploited, can quickly overwhelm the server resources. This finding is a clear indication that the fragmented production-level implementations of this contemporary protocol are not yet mature enough. Overall, the work at hand provides the first wholemeal appraisal of QUIC security from both a literature review and empirical standpoint, and it is therefore foreseen to serve as a reference for future research in this timely area.
Multi-Granularity Regularized Re-Balancing for Class Incremental Learning

Deep learning models suffer from catastrophic forgetting when learning new tasks incrementally.Incremental learning has been proposed to retain the knowledge of old classes while learning to identify new classes.A typical approach is to use a few exemplars to avoid forgetting old knowledge.In such a scenario, data imbalance between old and new classes is a key issue that leads to performance degradation of the model.Several strategies have been designed to rectify the bias towards the new classes due to data imbalance.However, they heavily rely on the assumptions of the bias relation between old and new classes.Therefore, they are not suitable for complex real-world applications.In this study, we propose an assumption-agnostic method, Multi-Granularity Regularized re-Balancing (MGRB), to address this problem.Re-balancing methods are used to alleviate the influence of data imbalance; however, we empirically discover that they would under-fit new classes.To this end, we further design a novel multi-granularity regularization term that enables the model to consider the correlations of classes in addition to re-balancing the data.A class hierarchy is first constructed by grouping the semantically or visually similar classes.The multi-granularity regularization then transforms the one-hot label vector into a continuous label distribution, which reflects the relations between the target class and other classes based on the constructed class hierarchy.Thus, the model can learn the inter-class relational information, which helps enhance the learning of both old and new classes.Experimental results on both public datasets and a real-world fault diagnosis dataset verify the effectiveness of the proposed method.
The excluded minors for 2- and 3-regular matroids

The class of 2-regular matroids is a natural generalisation of regular and near-regular matroids. We prove an excluded-minor characterisation for the class of 2-regular matroids. The class of 3-regular matroids coincides with the class of matroids representable over the Hydra-5 partial field, and the 3-connected matroids in the class with a $U_{2,5}$- or $U_{3,5}$-minor are precisely those with six inequivalent representations over GF(5). We also prove that an excluded minor for this class has at most 15 elements.
Embodied Multi-Agent Task Planning from Ambiguous Instruction

In human-robots collaboration scenarios, a human would give robots an instruction that is intuitive for the human himself to accomplish.However, the instruction given to robots is likely ambiguous for them to understand as some information is implicit in the instruction.Therefore, it is necessary for the robots to jointly reason the operation details and perform the embodied multi-agent task planning given the ambiguous instruction.This problem exhibits significant challenges in both language understanding and dynamic task planning with the perception information.In this work, an embodied multi-agent task planning framework is proposed to utilize external knowledge sources and dynamically perceived visual information to resolve the high-level instructions, and dynamically allocate the decomposed tasks to multiple agents.Furthermore, we utilize the semantic information to perform environment perception and generate sub-goals to achieve the navigation motion.This model effectively bridges the difference between the simulation environment and the physical environment, thus it can be simultaneously applied in both simulation and physical scenarios and avoid the notorious sim2real problem.Finally, we build a benchmark dataset to validate the embodied multi-agent task planning problem, which includes three types of high-level instructions in which some target objects are implicit in instructions.We perform the evaluation experiments on the simulation platform and in physical scenarios, demonstrating that the proposed model can achieve promising results for multi-agent collaborative tasks.