Reviewerly/exHarmony huggingface dataset:
Android Application on e-notifier for College Events Management using Firebase

Total number of smartphone users in the world is around 3.8 billion and average global user spending about 4.2 hours per day on a smartphone. This project proposes a mobile application on the Android stage, which will alarm understudies about most recent happenings and forthcoming occasions/events in the college continuously with the assistance of message pop-ups. It is profoundly difficult to reach every student through WhatsApp or different message platforms. As social media is restricted to Number of people, it is inconvenient to pass information in limited time. The mobile application with features of event information according to the category like technical, non-technical. students who can't check college web-site each time might utilize this application without any problem. This mobile application will act as a centralized platform for pushing notifications for all events in a college. The existing framework utilizes website for posting notices, or through message platforms. This is exceptionally tedious interaction. The android application improves on this cycle by giving notifications to the students or concerned staff. This Android mobile application is simpler, quicker, secure and user friendly.
Customised display of large mineralogical (XRD) data: Geological advantages and applications

Abstract X‐ray diffraction mineralogical analysis of geological sequences is a well‐established procedure in both academia and industry, rendering a large volume of data in short‐analytical time. Yet, standard data treatment and resulting interpretations present limitations related to the inherent complexities of natural geological materials (e.g. compositional variety, structural ordering), and are often time consuming and focussed on a very detailed inspection. Several alternatives were evaluated in terms of advantages and disadvantages to the main goal of generating a user‐friendly, fast and intuitive way of processing a large volume of X‐ray diffraction data. The potential of using raw X‐ray diffraction data to interpret mineralogical diversity and relative phase abundances along sedimentary successions is explored here. A Python based program was tailored to assist in raw data organisation. After this automated step, a 3D surface computation renders the final result within minutes. This single‐image representation can also be integrated with complementary information (sedimentary logs or other features of interest) for contrast and/or comparison in multi‐proxy studies. The proposed approach was tested on a set of 81 bulk and clay‐fraction diffractograms (intensity in counts per second—cps and respective angle—º2Ɵ) obtained from a Cenomanian mixed carbonate–siliciclastic stratigraphic succession, here explored by combining mineralogical (XY) and stratigraphic/geological information (Z). The main goal is to bypass preliminary data treatment, avoid time‐consuming interpretation and unintended, but common, user‐induced bias. Advantages of 3D modelling include fast processing and single‐image solutions for large volumes of XRD data, combining mineralogical and stratigraphic information. This representation adds value by incorporating field (stratigraphic/sedimentological) information that complements and contextualises obtained mineralogical data. Limitations of using raw intensity data were evaluated by comparison with the results obtained via other standard data interpretation methods (e.g. semi‐quantitative estimation). A visual and statistical contrast comparison confirmed a good equilibrium between computation speed and precision/utility of the final output.
Identifying Playstyles in Games with NEAT and Clustering

In the research of agents playing computer games, attempts have been made to create agents with particular playstyles. A typical approach adopted in previous studies is to define the reward functions so that the agent has the desired playstyle. However, based on this approach, it is difficult to identify other playstyles that have not been specified in advance. The purpose of this paper is to create agents with playstyles by combining a genetic algorithm called NeuroEvolution of Augmenting Topologies (NEAT) and clustering based on the logs of gameplay by agents. Here, individuals of NEAT are classified in some species by clustering according to the play log and crossed with the same species. We evaluated our method by an experiment with a roguelike game and observed that multiple playstyles were identified. Furthermore, by comparing the proposed method with the method using only NEAT, it was shown that the bias in the number of individuals among playstyles can be suppressed.
Concept Relative Attention Based Deep Knowledge Tracing

Most of the knowledge tracing models only output the student's next answer. This can only let us know whether a student answered questions correctly, rather than students' mastery of the knowledge concept. Such a fixed output cannot be utilized by the follow-up personalized recommendations. Furthermore, the previous model only considered the binary state of correct answer, ignoring the connection between the knowledge concepts in the question, resulting in over-fitting. In order to overcome these limitations, we proposed CRADKT, which integrates students' mastery of knowledge concepts, problem difficulty and problem utility into feature vectors in a new way. And considers the connections between knowledge concepts. While improving the performance of knowledge tracing, it also helps students' personalized recommendations. Experiments on real data sets show that CRADKT is more effective than SAKT, so CRADKT can be used as a new baseline for networks with Attention as the core.
Ascoli-Arzelà Theorem

Summary. In this article we formalize the Ascoli-Arzelà theorem [5], [6], [8] in Mizar [1], [2]. First, we gave definitions of equicontinuousness and equiboundedness of a set of continuous functions [12], [7], [3], [9]. Next, we formalized the Ascoli-Arzelà theorem using those definitions, and proved this theorem.
Overview of Optimization Algorithms for Large-scale Support Vector Machines

Support vector machine (SVM) is one of the most classical machine learning algorithms, which performs well in many fields. However, the traditional training algorithms are not satisfactory in dealing with big data. Therefore, many algorithms for large-scale datasets have been proposed. Considering the classification problems, this paper introduces the main SVM training algorithms for large-scale datasets including the optimization methods of binary-class problems and multi-class problems.
Ring Trapdoor Redactable Signatures from Lattice

Redactable signature plays a significant role in real-life applications such as electronic health records, and has been studied extensively. Nevertheless, how to construct a redactable signature scheme with designated redactors is still unknown. In this paper, we affirmatively answer this problem by presenting a notion of ring trapdoor redactable signature (RTRS). RTRS is a variant of redactable signature where the redactors are specified. We first introduce the concept of ring trapdoor preimage sampleable functions (RPSFs), which inherits the merit of preimage sampleable functions and ring trapdoor functions, and then show an instantiation of RPSFs under the assumption of inhomogeneous small integer solution problem. We then present two concrete constructions of RTRS (a simplified version and a full version) from a family of RPSFs and a common signature scheme. It is proved that the unforgeability, privacy and restriction of proposed schemes relies on the security of underlying common signature schemes and ring one-way property of the RPSFs. Besides, we also prove that our schemes satisfy the indistinguishability.
An Effective, Robust and Fairness-aware Hate Speech Detection Framework

With the widespread online social networks, hate speeches are spreading faster and causing more damage than ever before. Existing hate speech detection methods have limitations in several aspects, such as handling data insufficiency, estimating model uncertainty, improving robustness against malicious attacks, and handling unintended bias (i.e., fairness). There is an urgent need for accurate, robust, and fair hate speech classification in online social networks. To bridge the gap, we design a data-augmented, fairness addressed, and uncertainty estimated novel framework. As parts of the framework, we propose Bidirectional Quaternion-Quasi-LSTM layers to balance effectiveness and efficiency. To build a generalized model, we combine five datasets collected from three platforms. Experiment results show that our model outperforms eight state-of-the-art methods under both no attack scenario and various attack scenarios, indicating the effectiveness and robustness of our model. We share our code along with combined dataset for better future research <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
No Size Fits All: Design Considerations for Networked Professional Development in Higher Education

This chapter develops a framework for design considerations that can be used to analyze, contrast, and design networked professional development (NPD) in higher education (HE) contexts. The framework was developed after reflecting on three professional development (PD) courses, each with facilitators who are academic developers across the African continent. Using a Collaborative Autoethnographic methodology, the three authors reflect on design considerations for different forms of blended and online PD courses, based on their experiences of designing and/or facilitating these interventions and with PD more broadly. We argue that course designs can be positioned along a range of dimensions, namely open/closed, structured/unstructured, facilitated/unfacilitated, certified/uncertified, with/without date commitments, homogenous versus autonomous learning path, content vs. process centric, serious vs. playful, and individual vs. collaborative. We discuss relationships between dimensions and learning theories (the more open dimensions speak to connectivist, while more structured courses follow social constructivist approaches). We also identify various tensions that arise in the design of NPD, such as between academic developers’ pedagogical advocacy vs. usefulness, need to maintain volunteerism without exploitation of affective labour, and struggle to create spaces for agency within institutional constraints.
Based on Optimization Research on the Evaluation System of English Teaching Quality Based on GA-BPNN Algorithm

English teaching is an important part of basic teaching in our country, which has been deeply concerned by all aspects. Its teaching quality not only is related to the purpose of English teaching, but also has a far-reaching impact on students' English learning. Therefore, the construction of English teaching quality evaluation system has become the focus of research. However, the traditional English teaching quality evaluation method has some problems; for example, the subjectivity of teaching evaluation is strong, the evaluation index is not comprehensive, and the evaluation results are distorted. Therefore, this paper studies the English teaching quality evaluation system based on optimized GA-BP neural network algorithm. On the basis of BP neural network algorithm evaluation simulation, GA algorithm is introduced for optimizing, and GA-BP neural network algorithm model is further optimized by GA adaptive degree variation and entropy method. The experimental results show that the optimized GA-BP neural network algorithm has faster convergence speed and smaller error. At the same time, the optimized GA-BP neural network algorithm evaluation model has better adaptability and stability, and its expected results are more in line with the ideal value. The results of English teaching quality evaluation are more scientific, showing higher value in the application of English teaching quality evaluation.
Improved Identification of Portraiture of the Julio-Claudian Period with Mobile Apps

We present the results of our image analysis of portrait art from the Roman Empire&#x2019;s Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization&#x2019;s ancient past and increase appreciation of our diverse cultural heritage.
Deep-learning-based Extraction of Electronic Component Parameters from Datasheets

In this paper, we propose an automatic extraction process of the dimension parameters shown in three-view drawings. It is divided into two stages. In the first stage, we detect three-view drawings in datasheets and find out the text regions containing the parameters in the drawings by deep learning. We then recognize the values in these regions. In the second stage, we design two algorithms, based on k-nearest neighbors (k-NN) and statistical evaluation, respectively, to match the digitized parameters with the values. We also conduct experiments to show the high accuracy in the two stages.
Noise effects on time delay reservoir computing using silicon microring resonators

Time-delay Reservoir Computing (TRC) are neural networks which thanks to their hardware simplicity are suited for photonic implementation. Here, we numerically investigated and experimentally tested a TRC based on a nonlinear single silicon microring resonator (MRR) coupled to an optical fibre loop which provides an external tuneable optical feedback. The work is inspired by the key role that MRRs demonstrate in integrated optical devices, together with the significant advantage they provide due to their fully passive nonlinearity. Insights about the computational properties of the system are provided as a result of the performance on multiple benchmark tasks.
Design Pattern Detection by Using Cosine Similarity Technique

Design pattern detection (DPD) is a reengineering task that is used in the object-oriented software development process. It is helpful to software developers to easily understand, maintain, and redevelop software. In this paper, we have applied a new technique for design pattern detection by using the cosine similarity technique (CST). Usually, the cosine similarity technique is used to find the similarity between 2 matrices. However, in this paper, we apply the cosine similarity technique for finding the similarity between the design pattern graph to system model graph.
Monte Carlo Tree Search With Reversibility Compression

Monte Carlo Tree Search (MCTS) has been shown to be an effective algorithm for solving search problems in the absence of heuristics. MCTS performs best in spaces where every action has a significant impact on the outcome of the search. In many domains, however, particularly single-player games, many actions have little impact on the outcome, which makes MCTS perform poorly without heuristic support. To address this deficiency in such sparse-impact search problems, we introduce MCTS with Reversibility Compression, or MCTS-R, which uses the notion of action reversibility to compress MCTS trees as they are constructed, without loss of information. This not only reduces the memory footprint of the search tree, but also accelerates search by preventing the duplication of already-explored states, and increasing the attention paid to significant actions. We show that our approach outperforms several comparable algorithms for solving sparse-impact search problems.
An Anomaly Detection Model for ADS-B Systems Based on Improved GAN and LSTM Networks

Automatic Dependent Surveillance-Broadcast (ADS-B) is an important part of the next generation air transportation systems, but its protocol does not provide relevant authentication and data encryption, so it is extremely vulnerable to various spoofing attacks. Therefore, it is necessary to develop a set of ADS-B data anomaly detection models. According to the strong temporal dependence and fast update rate of ADS-B data, we initiatively introduce a novel anomaly detection model based on Improved Generative Adversarial Network and long short-term memory networks (IGAN-LSTM). In the model, IGAN mainly improves the generator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> architecture of GAN through upgrading the commonly used encoder architecture to encoder-decoder-encoder architecture, which captures the training data distribution within both data space and latent space. IGAN then utilizes encoding losses based on latent spatial features to determine whether a data sample is anomalous or not. At the same time, to model ADS-B data with the temporal dependence, we use the LSTM networks as the main architecture of the model, which is able to make use of long-term temporal dependence and avoid the vanishing gradient problem during training. Experimental results show that the proposed model has a higher area under the receiver operating characteristic curve and the precision recall curve than the other baseline methods from the literature, and reveal that the model has better performance of anomaly detection of ADS-B data.
Optimizing Person Re-Identification Using Generated Attention Masks

The task of person re-identification has important applications in security and surveillance systems. It is a challenging problem since there can be a lot of differences between pictures belonging to the same person, such as lighting, camera position, variation in poses and occlusions. The use of Deep Learning has contributed greatly towards more effective and accurate systems. Many works use attention mechanisms to force the models to focus on less distinctive areas, in order to improve performance in situations where important information may be missing. This paper proposes a new, more flexible method for calculating these masks, using a U-Net which receives a picture and outputs a mask representing the most distinctive areas of the picture. Results show that the method achieves an accuracy comparable or superior to those in state-of-the-art methods.
3D coronary artery elastic registration based on differential invariant signatures

In this paper, a point-set similarity model (PSM) is introduced, which provides a new elastic point set registration frame by using similarity invariant signature curves. According to the potential correspondences obtained by PSM, the similarity transformations between the point sets could be estimated. Experiments on synthetic data and real data of coronary artery show that our method offers certain advantages comparing with typical methods.
Empirical Investigation of IoT Traffic in Smart Environments: Characteristics, Research Gaps and Recommendations

Adopting the Internet of Things (IoT) in multi-functional domains has led to management, operational, and security challenges. As a foundation stone, researchers have invested sincere efforts towards classifying traffic, thereby categorizing the devices. However, the classification solutions are missing the vital attributes of the state-of-the-art high-performance real-time framework. This paper provides the taxonomy of the techno-functional application areas of the IoT characterization. The article also inferences empirically investigated IoT traffic attributes leveraging an Australian dataset collected from 28 IoT devices over six months. Based on the forensics of IoT traffic, the characteristics of IoT-based traffic are listed, which paves the grounds of the security, operational, and management solutions for IoT devices. The paper also details the research gaps in the implemented solutions by exploring additional research dimensions of a trailblazing real-time classification solution, which the researchers often ignore. Lastly, the paper offers recommendations and prospects.
Investigating COVID-19 Tracing Apps as e-Government Service in European Context

research-article Share on Investigating COVID-19 Tracing Apps as e-Government Service in European Context Authors: Marlon Freire University of Porto, Portugal University of Porto, PortugalView Profile , Jordana Casarin University of Porto, Portugal University of Porto, PortugalView Profile Authors Info & Claims ICEGOV 2021: 14th International Conference on Theory and Practice of Electronic GovernanceOctober 2021 Pages 444–449https://doi.org/10.1145/3494193.3494253Online:12 January 2022Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Assessment of Excavated Volume and Labor Investment at the Novotemirsky Copper Ore Mining Site

In this work, we attempt to estimate the volume of excavated material at the prehistoric mining site of Novotemirsky. We gather field data and employ computer-aided modeling to reconstruct the unknown three-dimensional geometry of the copper ore quarry and calculate the volume of a vertical shaft located near the quarry. To achieve this, we create a three-dimensional model of the dump around the quarry using the photogrammetric data and the cross-sections; a three-dimensional model of the quarry using the excavation data; a three-dimensional model of the shaft. Each model allows calculating the object's volume and then estimating the amount of excavated rock. Finally, we apply the ethnographic and experimental data to calculate the labor investment and compare this estimation with the archaeological period's length. We conclude that the work could be done in a relatively short period of 2–7 years by one full-time worker. As the work most likely was collective and was stretched during some 300 years or more, the actual annual labor investments were relatively small. Perhaps, the mining work was carried out on a seasonal basis by small groups of miners.
Blockchain-driven Supply Chain Finance - A Structured Review

The use of blockchain technology in supply chain finance promises to solve existing inefficiencies of conventional supply chain finance methods. With the help of a systematic literature review, these issues are uncovered, showing how blockchain could solve them. The main problems include the non-transparency of complex networks, insufficient risk control, information asymmetries, and inefficient processes. Blockchain-based SCF platforms can solve these problems with key features such as decentralization, transparency, traceability, and automation capability.