Reviewerly/exHarmony huggingface dataset:
BURST

We present BURST, a benchmarking platform for uniform random sampling techniques. With BURST, researchers have a flexible, controlled environment in which they can evaluate the scalability and uniformity of their sampling. BURST comes with an extensive --- and extensible --- benchmark dataset comprising 128 feature models, including challenging, real-world models of the Linux kernel. BURST takes as inputs a sampling tool, a set of feature models and a sampling budget. It automatically translates any feature model of the set in DIMACS and invokes the sampling tool to generate the budgeted number of samples. To evaluate the scalability of the sampling tool, BURST measures the time the tool needs to produce the requested sample. To evaluate the uniformity of the produced sample, BURST integrates the state-of-the-art and proven statistical test Barbarik. We envision BURST to become the starting point of a standardisation initiative of sampling tool evaluation. Given the huge interest of research for sampling algorithms and tools, this initiative would have the potential to reach and crosscut multiple research communities including AI, ML, SAT and SPL.
GridEx: An Algorithm for Knowledge Extraction from Black-Box Regressors

Knowledge-extraction methods are applied to ML-based predictors to attain explainable representations of their operation when the lack of interpretable results constitutes a problem. Several algorithms have been proposed for knowledge extraction, mostly focusing on the extraction of either lists or trees of rules. Yet, most of them only support supervised learning – and, in particular, classification – tasks. Iter is among the few rule-extraction methods capable of extracting symbolic rules out of sub-symbolic regressors. However, its performance – here intended as the interpretability of the rules it extracts – easily degrades as the complexity of the regression task at hand increases. In this paper we propose GridEx, an extension of the Iter algorithm, aimed at extracting symbolic knowledge – in the form of lists of if-then-else rules – from any sort of sub-symbolic regressor—there including neural networks of arbitrary depth. With respect to Iter, GridEx produces shorter rule lists retaining higher fidelity w.r.t. the original regressor. We report several experiments assessing GridEx performance against Iter and Cart (i.e., decision-tree regressors) used as benchmarks.
Medical code prediction via capsule networks and ICD knowledge

Abstract Background Clinical notes record the health status, clinical manifestations and other detailed information of each patient. The International Classification of Diseases (ICD) codes are important labels for electronic health records. Automatic medical codes assignment to clinical notes through the deep learning model can not only improve work efficiency and accelerate the development of medical informatization but also facilitate the resolution of many issues related to medical insurance. Recently, neural network-based methods have been proposed for the automatic medical code assignment. However, in the medical field, clinical notes are usually long documents and contain many complex sentences, most of the current methods cannot effective in learning the representation of potential features from document text. Methods In this paper, we propose a hybrid capsule network model. Specifically, we use bi-directional LSTM (Bi-LSTM) with forwarding and backward directions to merge the information from both sides of the sequence. The label embedding framework embeds the text and labels together to leverage the label information. We then use a dynamic routing algorithm in the capsule network to extract valuable features for medical code prediction task. Results We applied our model to the task of automatic medical codes assignment to clinical notes and conducted a series of experiments based on MIMIC-III data. The experimental results show that our method achieves a micro F1-score of 67.5% on MIMIC-III dataset, which outperforms the other state-of-the-art methods. Conclusions The proposed model employed the dynamic routing algorithm and label embedding framework can effectively capture the important features across sentences. Both Capsule networks and domain knowledge are helpful for medical code prediction task.
Collision system of West Pribaikalie: aerospace geological map of Olkhon Region (Baikal, Russia)

We announce the second edition of the Aerospace geological map of the Olkhon Region (Baikal, Russia), scale 1:40 000, which was published in 2017. The map has been considerably revised and updated, and its changes are critical for correct understanding of the regional geology, tectonics and geodynamics. Only a small number of its printed copies have been released, and therefore the map may not be available for all interested specialists. The electronic version of the map is available for studying and/or printing (see the link to its pdf file in the paper’s supplement). The pdf file is about 68 MB, i.e. small compared to the original map (more than 5 GB), but the quality is maintained. The map does not show the base layer due to the terms of the licenses owned by the companies and satellite owners.
Double degeneracy associated with hidden symmetries in the asymmetric two-photon Rabi model

In this paper, we uncover the elusive level crossings in a subspace of the asymmetric two-photon quantum Rabi model (tpQRM) when the bias parameter of qubit is an even multiple of the renormalized cavity frequency. Due to the absence of any explicit symmetry in the subspace, this double degeneracy at the crossing points implies the existence of the hidden symmetry. It is found that the number of the doubly degenerate crossing points in the asymmetric tpQRM is comparable to that in asymmetric one-photon QRM in terms of the same order of the constrained conditions. The bias parameter required for the occurrence of level crossings in the asymmetric tpQRM is characteristically different from that at a multiple of the cavity frequency in the asymmetric one-photon QRM, suggesting the different hidden symmetries in the two asymmetric QRMs.
Filipino Physical Education Teachers‘ Technological Pedagogical Content Knowledge on Remote Digital Teaching

This study examined the Filipino physical education (PE) teachers’ technological pedagogical content knowledge (TPACK) on remote digital teaching approach. More specifically, it looked into their preparedness on remote digital teaching using the TPACK model, the relationship between their demographics, and the seven dimensions of knowledge of TPACK model and its interrelationship using a descriptive correlational research design. The sample was composed of 1,402 PE teachers across the three major islands of the Philippines who, using online survey, answered a 19-item questionnaire with a five-point Likert scale about their TPACK on remote digital teaching. Using the Pearson Product Moment Correlation and Chi-Square test of Independence, the study yielded four major results: 1) PE teachers have an average level of preparedness to conduct remote digital teaching in all domains of knowledge of TPACK; 2) The preparedness level of the PE teachers to conduct remote digital teaching in all domains of knowledge of TPACK is dependent to their age, sex and teaching experience, except technological knowledge, as it is independent to their highest educational attainment; and 3). There is a significant interrelationship on the PE teachers’ preparedness to conduct remote digital teaching among all domains of knowledge of TPACK. Hence, in order to successfully navigate a paradigm in education that accentuates the utilization of technology and other digital platforms, teachers have to be equipped with the tri-relationship of knowledge – content, pedagogy, and technology – through capability-building activities that consider their demographics and background.
The Focus and Trend of STEM Education Research in China—Visual Analysis Based on CiteSpace

In this study, 1146 journal papers on Chinese STEM education research were visualized by using CiteSpace visualization software.It is found that the core group of authors of STEM education research in China has not been formed.Also, there is no universal cooperation among the research institutions.The core areas of research include STEM education abroad, analysis of STEM theory, STEM curriculum and teaching.China's STEM education research can be roughly divided into three periods: initial exploration period, rapid development period and deepening research period.The key areas of future research are science education, research on foreign STEM education and modern educational technology innovation.
IIITT at CASE 2021 Task 1: Leveraging Pretrained Language Models for Multilingual Protest Detection

In a world abounding in constant protests resulting from events like a global pandemic, climate change, religious or political conflicts, there has always been a need to detect events/protests before getting amplified by news media or social media.This paper demonstrates our work on the sentence classification subtask of multilingual protest detection in CASE@ACL-IJCNLP 2021.We approached this task by employing various multilingual pre-trained transformer models to classify if any sentence contains information about an event that has transpired or not.Furthermore, we performed soft voting over the models, achieving the best results among the models, accomplishing a macro F1-Score of 0.8291, 0.7578, and 0.7951 in English, Spanish, and Portuguese, respectively.The source codes for our systems are published 1 .
A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.
A novel fusion paradigm for multi-channel image denoising

Multi-channel and single-channel image denoising are on two important development fronts. Integrating multi-channel and single-channel image denoisers for further improvement is a valuable research direction. A natural assumption is that using more useful information is helpful to the output results. In this paper, a novel multi-channel and single-channel fusion paradigm (MSF) is proposed. The proposed MSF works by fusing the estimates of a multi-channel image denoiser and a single-channel image denoiser. The performance of recent multi-channel image denoising methods involved in the proposed MSF can be further improved at low additional time-consuming cost. Specifically, the validity principle of the proposed MSF is that the fused single-channel image denoiser can produce auxiliary estimate for the involved multi-channel image denoiser in a designed underdetermined transform domain. Based on the underdetermined transformation, we create a corresponding orthogonal transformation for fusion and better restore the multi-channel images. The quantitative and visual comparison results demonstrate that the proposed MSF can be effectively applied to several state-of-the-art multi-channel image denoising methods.
Yolov4FaceMask: COVID-19 Mask Detector

The rampant COVID-19 disease has brought global crisis with its deadly spread to most countries in the world, and about over 58 million confirmed cases along with 1.4 million deaths on April 2021. The absence of any active therapeutic agents and the lack of immunity against COVID-19 increase the population's vulnerability. Since there are efficient vaccines but it is not available to all worldwide, Especially the inhabitants of the third world, in addition the appearance of mutated copies (Indian version, Nigerian version ⋯), properly wearing masks in public areas is one of the major protection methods for people. Although it is important to wear the facemask correctly, there are not much research studies about facemask detection based on image processing. In this work, we propose Yolov4FaceMask, which is a model with high accuracy and can detect Masked/IncorrectMask/UnMasked faces. The main challenging point of this task is the absence of practical datasets. For this, we introduce our new practical dataset, which contains 14409 images. Experiments show that the Yolov4FaceMask achieves 88.82% mAP on our proposed dataset. This paper presents a Deep Learning (DL) based framework for automating face-masked/incorrect mask/unmasked people's detection using images and surveillance video and the library OpenCV for image processing. Our proposed model uses the object detection model named YOLOv4 to segregate human's faces from the background. The Yolo4FaceMask results are further compared with other popular state-of-the-art models of face mask detection, e.g. Face-mask InceptionV3 and SSDMNV2 and RetinaFaceMask in terms of mean Average Precision (mAP).
The use of mobile applications in higher education classes: a comparative pilot study of the students’ perceptions and real usage

Abstract This paper was developed within the scope of a PhD thesis that intends to characterize the use of mobile applications by the students of the University of Aveiro during class time. The main purpose of this paper is to present the results of an initial pilot study that aimed to fine-tune data collection methods in order to gather data that reflected the practices of the use of mobile applications by students in a higher education institution during classes. In this paper we present the context of the pilot, its technological settings, the analysed cases and the discussion and conclusions carried out to gather mobile applications usage data logs from students of an undergraduate degree on Communication Technologies. Our study gathered data from 77 participants, taking theoretical classes in the Department of Communication and Arts at the University of Aveiro. The research was based on the Grounded Theory method approach aiming to analyse the logs from the access points of the University. With the collected data, a profile of the use of mobile devices during classes was drawn. The preliminary findings suggest that the use of apps during the theoretical classes of the Department of Communication and Art is quite high and that the most used apps are Social networks like Facebook and Instagram. During this pilot the accesses during theoretical classes corresponded to approximately 11,177 accesses per student. We also concluded that the students agree that accessing applications can distract them during these classes and that they have a misperception about their use of online applications during classes, as the usage time is, in fact, more intensive than what participants reported.
THE MEDIATING ROLE OF JOB SATISFACTION BETWEEN HUMAN RESOURCE DEVELOPMENT AND PROJECT SUCCESS

ABSTRACT&#x0D; &#x0D; Purpose - Human resource development (HRD) is considered one of the most important components for the success of any project. The purpose of this study is to examine the impact of HRD activities (career development and team development) on project success by inquiring into job satisfaction.
Communication-efficient and Scalable Decentralized Federated Edge Learning

Federated Edge Learning (FEL) is a distributed Machine Learning (ML) framework for collaborative training on edge devices. FEL improves data privacy over traditional centralized ML model training by keeping data on the devices and only sending local model updates to a central coordinator for aggregation. However, challenges still remain in existing FEL architectures where there is high communication overhead between edge devices and the coordinator. In this paper, we present a working prototype of blockchain-empowered and communication-efficient FEL framework, which enhances the security and scalability towards large-scale implementation of FEL.
Composition with Knowledge Assumptions

Zero-knowledge succinct non-interactive arguments (zk-SNARKs) rely on knowledge assumptions for their security. Meanwhile, as the complexity and scale of cryptographic systems continues to grow, the composition of secure protocols is of vital importance. The current gold standards of composable security, the Universal Composability and Constructive Cryptography frameworks cannot capture knowledge assumptions, as their core proofs of composition prohibit white-box extraction. In this paper, we present a formal model allowing the composition of knowledge assumptions. Despite showing impossibility for the general case, we demonstrate the model's usefulness when limiting knowledge assumptions to few instances of protocols at a time. We finish by providing the first instance of a simultaneously succinct and composable zk-SNARK, by using existing results within our framework.
A Cuckoo Search Algorithm Using Improved Beta Distributing and Its Application in the Process of EDM

Lévy flights random walk is one of key parts in the cuckoo search (CS) algorithm to update individuals. The standard CS algorithm adopts the constant scale factor for this random walk. This paper proposed an improved beta distribution cuckoo search (IBCS) for this factor in the CS algorithm. In terms of local characteristics, the proposed algorithm makes the scale factor of the step size in Lévy flights showing beta distribution in the evolutionary process. In terms of the overall situation, the scale factor shows the exponential decay trend in the process. The proposed algorithm makes full use of the advantages of the two improvement strategies. The test results show that the proposed strategy is better than the standard CS algorithm or others improved by a single improvement strategy, such as improved CS (ICS) and beta distribution CS (BCS). For the six benchmark test functions of 30 dimensions, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 3.67, 2.67, 1.5, and 1.17, respectively. For the six benchmark test functions of 50 dimensions, moreover, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 2.83, 2.5, 1.67, and 1.0, respectively. Confirmed by our case study, the performance of the ABCS algorithm was better than that of standard CS, ICS or BCS algorithms in the process of EDM. For example, under the single-objective optimization convergence of MRR, the iteration number (13 iterations) of the CS algorithm for the input process parameters, such as discharge current, pulse-on time, pulse-off time, and servo voltage, was twice that (6 iterations) of the IBCS algorithm. Similar, the iteration number (17 iterations) of BCS algorithm for these parameters was twice that (8 iterations) of the IBCS algorithm under the single-objective optimization convergence of Ra. Therefore, it strengthens the CS algorithm’s accuracy and convergence speed.
Heterogeneous self-tracked health and fitness data integration and sharing according to a linked open data approach

Abstract The huge volume of data gathered from wearable fitness devices and wellness appliances, if effectively analysed and integrated, can be exploited to improve clinical decision making and to stimulate promising applications, as they can provide good measures of everyday patient behaviour and lifestyle. However, several obstacles currently limit the true exploitation of these opportunities. In particular, the healthcare landscape is characterised by a pervasive presence of data silos which prevent users and healthcare professionals from obtaining an overall view of the knowledge, mainly due to the lack of device interoperability and data representation format heterogeneity. This work focuses on current, important needs in self-tracked health data modelling, and summarises challenges and opportunities that will characterise the community in the upcoming years. The paper describes a virtually integrated approach using standard Web Semantic technologies and Linked Open Data to cope with heterogeneous health data integration. The proposed approach is verified using data collected from several IoT fitness vendors to form a standard context-aware resource graph, and linking other health ontologies and open projects. We developed a web portal for integrating, sharing and analysing through a customisable dashboard heterogeneous IoT health and fitness data. In this way, we are able to map information onto an integrated domain model by providing support for logical reasoning.
A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, the researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in confirming the information-theoretical optimality of the coding procedure to the stochastic generative model. Hence, in this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. That is based on the quadtree so that our model effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. In general, the computational cost to calculate the posterior distribution required in the Bayes code increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of the optimality. Some experiments are performed to confirm the flexibility of the proposed stochastic model and the efficiency of the introduced algorithm.