Reviewerly/exHarmony huggingface dataset:
Museum-Authorization of Digital Rights: A Sustainable and Traceable Cultural Relics Exhibition Mechanism

The digital rights management of museums is a mechanism that protects digital content from being abused by controlling and managing its usage rights. Traditional museums attach importance to the collection, display, research, and education functions of “objects”. In response to natural or man-made disasters, people are often caught off guard, destroying material, intangible assets, and spiritual symbolism. Therefore, with the advancement of digital technology, this research is based on the mechanism of blockchain, through the authorization of cryptographic proxy re-encryption, and proposes a new method for the preservation and authorization of digital content in museums, which can effectively display, store, and promote “important cultural relics and digital archives”. In this research, the Elliptic Curve Digital Signature Algorithm (ECDSA), blockchain, and smart contracts are used to design a sustainable and traceable cultural relic exhibition mechanism. The proposed scheme achieves publicly verifiable, transparency, unforgeability, traceability, non-repudiation, standardization of stored data, timeliness, etc., goals. It is the museum’s preservation and innovation approach for the unpredictable future. Through appropriate preservation and management mechanisms, it has extremely important practical significance for the protection of museum collections, the inheritance of historical and cultural heritage, and the expansion of social education.
BlockSLAP: Blockchain-Based Secure and Lightweight Authentication Protocol for Smart Grid

Due to intelligent electronic management, the smart grid has recently played a significant role in modern energy infrastructure. However, along with widespread deployment of the smart grid, many potential security threats (e.g., impersonation attack, replay attack, man-in-the-middle attack) rise to the surface. To defend against these possible attacks, numerous cryptography-based authentication schemes have been proposed for the smart grid. Most of the schemes investigate the secret key distribution problem, but the requirement of decentralized registration authority is neglected. In addition, over-complicated cryptographic primitives also strengthen the burden of authentication system. In contrast with previous researches, our proposed BlockSLAP utilizes cutting-edge blockchain technology as well as smart contract to decentralize the registration authority and reduce the interaction process to 2 steps. Moreover, our protocol is proved secure under computational hard assumption and informal security analysis. Finally, experimental results show that smart grid authentication performance in our protocol has been improved compared to the other existing ECC-related schemes.
Stochastic analysis of an adaptive cubic regularization method under inexact gradient evaluations and dynamic Hessian accuracy

We here adapt an extended version of the adaptive cubic regularization method with dynamic inexact Hessian information for nonconvex optimization in Bellavia et al. [Adaptive cubic regularization methods with dynamic inexact hessian information and applications to finite-sum minimization. IMA Journal of Numerical Analysis. 2021;41(1):764–799] to the stochastic optimization setting. While exact function evaluations are still considered, this novel variant inherits the innovative use of adaptive accuracy requirements for Hessian approximations introduced in the just quoted paper and additionally employs inexact computations of the gradient. Without restrictions on the variance of the errors, we assume that these approximations are available within a sufficiently large, but fixed, probability and we extend, in the spirit of Cartis and Scheinberg [Global convergence rate analysis of unconstrained optimization methods based on probabilistic models. Math Program Ser A. 2018;159(2):337–375], the deterministic analysis of the framework to its stochastic counterpart, showing that the expected number of iterations to reach a first-order stationary point matches the well-known worst-case optimal complexity. This is, in fact, still given by O(ϵ−3/2), with respect to the first-order ϵ tolerance. Finally, numerical tests on nonconvex finite-sum minimization confirm that using inexact first- and second-order derivatives can be beneficial in terms of the computational savings.
HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software

Software is playing the most important role in recent vehicle innovations, and consequently the amount of software has rapidly grown in recent decades. The safety-critical nature of ships, one sort of vehicle, makes software quality assurance (SQA) a fundamental prerequisite. Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in the maritime domain reported feasible prediction performance. However, we still consider that the prediction model has room for improvement since the parameters of the model are not optimized yet. Harmony search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond the baseline of a previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite the fact that we considered balance as a performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with a high class imbalance ratio. Finally, we expect that our research can be extended to improve the performance of JIT-SDP not only in maritime domain software but also in open source software.
EasyTalk: A Translator for Sri Lankan Sign Language using Machine Learning and Artificial Intelligence

Sign language is used by the hearing-impaired and inarticulate community to communicate with each other. But not all Sri Lankans are aware of the sign language or verbal languages and a translation is required. The Sri Lankan Sign Language is tightly bound to the hearing-impaired and inarticulate. The paper presents EasyTalk, a sign language translator which can translate Sri Lankan Sign Language into text and audio formats as well as translate verbal language into Sri Lankan Sign Language which would benefit them to express their ideas. This is handled in four separate components. The first component, Hand Gesture Detector captures hand signs using pre-trained models. Image Classifier component classifies and translates the detected hand signs. The Text and Voice Generator component produces a text or an audio formatted output for identified hand signs. Finally, Text to Sign Converter works on converting an entered English text back into the sign language based animated images. By using these techniques, EasyTalk can detect, translate and produce relevant outputs with superior accuracy. This can result in effective and efficient communication between the community with differently-abled people and the community with normal people.
Fairness-Aware Recommendation in Multi-Sided Platforms

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be important to optimize utilities not just for the end user, but also for other entities such as item sellers or producers who desire a fair representation of their items. Existing solutions either lack the multi-sided nature of fairness in recommendations, or do not properly address various aspects of multi-sided fairness in recommendations. In this thesis, we aim at first investigating the impact of unfair recommendations on the system and how it can negatively affect major entities in the system. Then, we seek to propose a general graph-based solution that works as a post processing approach after recommendation generation to tackle the unfairness of recommendations. We plan to perform extensive experiments to evaluate the effectiveness of the proposed approach.
Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks

Rapid developments in urbanization and smart city environments have accelerated the need to deliver safe, sustainable, and effective resource utilization and service provision and have thereby enhanced the need for intelligent, real-time video surveillance. Recent advances in machine learning and deep learning have the capability to detect and localize salient objects in surveillance video streams; however, several practical issues remain unaddressed, such as diverse weather conditions, recording conditions, and motion blur. In this context, image de-raining is an important issue that has been investigated extensively in recent years to provide accurate and quality surveillance in the smart city domain. Existing deep convolutional neural networks have obtained great success in image translation and other computer vision tasks; however, image de-raining is ill posed and has not been addressed in real-time, intelligent video surveillance systems. In this work, we propose to utilize the generative capabilities of recently introduced conditional generative adversarial networks (cGANs) as an image de-raining approach. We utilize the adversarial loss in GANs that provides an additional component to the loss function, which in turn regulates the final output and helps to yield better results. Experiments on both real and synthetic data show that the proposed method outperforms most of the existing state-of-the-art models in terms of quantitative evaluations and visual appearance.
NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search

Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years. It could be time to take a step back and analyze the good and bad aspects in the field of NAS. A variety of algorithms search architectures under different search space. These searched architectures are trained using different setups, e.g., hyper-parameters, data augmentation, regularization. This raises a comparability problem when comparing the performance of various NAS algorithms. NAS-Bench-101 has shown success to alleviate this problem. In this work, we propose an extension to NAS-Bench-101: NAS-Bench-102 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-102 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. The design of our search space is inspired by the one used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph. Each edge here is associated with an operation selected from a predefined operation set. For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-102 includes all possible architectures generated by 4 nodes and 5 associated operation options, which results in 15,625 neural cell candidates in total. The training log using the same setup and the performance for each architecture candidate are provided for three datasets. This allows researchers to avoid unnecessary repetitive training for selected architecture and focus solely on the search algorithm itself. The training time saved for every architecture also largely improves the efficiency of most NAS algorithms and presents a more computational cost friendly NAS community for a broader range of researchers. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms. In further support of the proposed NAS-Bench-102, we have analyzed it from many aspects and benchmarked 10 recent NAS algorithms, which verify its applicability.
XII International Conference on Mathematics, Science and Technology Education

Abstract This paper represents a preface to the Proceedings of the XII International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2020) held at the Kryvyi Rih State Pedagogical University, Ukraine, 15–17 October 2020. Background information and the organizational structure of the meeting, and acknowledgements of the contributions of the many people who made the conference a success are presented.
A Novel Hybrid Textual-Graphical Authentication Scheme With Better Security, Memorability, and Usability

Despite numerous efforts, developing an authentication scheme that offers strong security while offering memorability and usability remains a grand challenge. In this paper, we propose a textual-graphical hybrid authentication scheme that improves the security, memorability and usability inadequacies of existing authentication schemes. This has been achieved by combining a range of mechanisms together, in a novel manner, to address weaknesses of the existing security schemes. Firstly, two dynamically selectable modes of password entry (Easy Login, and Secure Login) provide a trade-off between usability and security, allowing the user to dynamically switch to any of these methods in real-time based on the security of the surrounding environment (e.g., secure home environment versus insecure public places) or the criticality of the user account (e.g., a bank account). The other mechanisms included a novel use of the drawmetric mechanism for setting the password to improve memorability, multistep authentication, a novel adaptation of one-time password (OTP) concept using a random selection of password elements, random placement of password elements in different steps, assigning random numbers to the password elements to increase security, and use of simple addition to improve security. We have implemented and analysed the proposed scheme for its security against brute-force attacks, dictionary, shoulder surfing, random guessing, phishing or forming, keystroke/mouse logger, and multiple recording attacks. We have also investigated its usability and memorability, reporting various trends of password elements used and the respective authentication times. Moreover, we have compared the proposed scheme with eight other well-known authentication schemes in terms of its resilience and authentication time. The results and analyses demonstrate the effectiveness of the proposed scheme. We believe that a range of novel methods introduced in this proposed scheme opens several doors for innovation in security techniques.
Coreference Resolution in Research Papers from Multiple Domains

Coreference resolution is essential for automatic text understanding to facilitate high-level information retrieval tasks such as text summarisation or question answering. Previous work indicates that the performance of state-of-the-art approaches (e.g. based on BERT) noticeably declines when applied to scientific papers. In this paper, we investigate the task of coreference resolution in research papers and subsequent knowledge graph population. We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55,485 papers in 10 STM domains. Comprehensive experiments show the usefulness of the proposed approach. Our transfer learning approach considerably outperforms state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0), while the evaluation against a gold standard KG shows that coreference resolution improves the quality of the populated KG significantly with an F1 score of 63.5 (+21.8).
Early Childhood and Lockdown: The Challenge of Building a Virtual Mutual Support Network between Children, Families and School for Sustainable Education and Increasing Their Well-Being

The COVID-19 pandemic is questioning the achievement of main challenges we face as a society, for instance, to ensure a free, equitable, and good quality compulsory education for all children or to reduce social inequality. During the spring lockdown, particularly in Spain, schools were closed for six months and a process of virtualization of teaching was total; that context generated important educational challenges. This paper presents and analyses forms of digital reciprocity and solidarity among pre-primary education children, families, and teachers, by presenting a case study of the parents’ WhatsApp class groups and a collaborative YouTube channel. The procedure developed was netnography and the data analysis followed the model of grounded theory. Both digital spaces created by parents have become a network of mutual support. It has had multiple positive impacts: (i) providing and receiving social support; (ii) generating dynamics of reciprocity and empowerment; and (iii) activating values that generate a sense of community (feeling of belonging, trust, etc.). The case study shows how virtual networks increase the subjective well-being of participants in a difficult context and also invites reflection about the key role of cultural capital of the parents as a key element in the conditions of educability of children, especially in e-learning of pre-primary education.
Initializing 2<sup>14</sup> Pure 14-Qubit Entangled Nuclear Spin States in a Hyperpolarized Molecular Solid

Quantum entanglement has been realized on a variety of physical platforms such as quantum dots, trapped atomic ions, and superconductors. Here we introduce specific molecular solids as promising alternative platforms. Our model system is triplet pentacene in a host single crystal at level anticrossing (LAC) conditions. First, a laser pulse generates the triplet state and initiates entanglement between an electron spin and 14 hyperfine coupled proton spins (quantum bits or qubits). This gives rise to large nuclear spin polarization. Subsequently, a resonant high-power microwave (mw) pulse disentangles the electron spin from the nuclear spins. Simultaneously, high-dimensional multiqubit entanglement is formed among the proton spins. We verified the initialization of 214 pure 14-qubit entangled nuclear spin states with an average degree of entanglement of Eav = 0.77 ± 0.03. These results pave the way for large-scale quantum information processing with more than 10 000 multiqubit entangled states corresponding to computational (Hilbert) space dimensions of dim >1053.
Improving Video Retrieval by Adaptive Margin

Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representations by pushing the distance between the similarity of positive pairs and that of negative pairs apart from a fixed margin. However, negative pairs used for training are sampled randomly, which indicates that the semantics between negative pairs may be related or even equivalent, while most methods still enforce dissimilar representations to decrease their similarity. This phenomenon leads to inaccurate supervision and poor performance in learning video-text representations. While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue. First, we design the calculation framework of the adaptive margin, including the method of distance measurement and the function between the distance and the margin. Then, we explore a novel implementation called "Cross-Modal Generalized Self-Distillation" (CMGSD), which can be built on the top of most video retrieval models with few modifications. Notably, CMGSD adds few computational overheads at train time and adds no computational overhead at test time. Experimental results on three widely used datasets demonstrate that the proposed method can yield significantly better performance than the corresponding backbone model, and it outperforms state-of-the-art methods by a large margin.
Toward replication study types for design science research

In design science research, two important challenges exist to achieve greater influence in research and practice: (1) foster frequent reuse of artifacts and design theories and (2) increase knowledge accumulation in the field. In this article, we argue that replication studies could support the accumulation and development of design theories to reach a state that encourages reuse of artifacts and design theories. However, it is unclear precisely how replication relates to design science research—that is, what outcomes replication produces and how researchers should apply it within design science research. This study proposes three overarching research questions ( Does the artifact provide utility? Is the design theory complete? What design theory components fit a larger context?) and eight categories for replication studies in design science research (Test, Redesign, Justification, Adaptation, Explanation, Update, Recreation, and Meta-Replication). We offer guidance to researchers, editors, and reviewers on how to conduct replication studies in design science research and why such studies are so critical. Our goal is to provide “food for thought” on the significance of design science research replication studies and, in turn, help facilitate their widespread implementation and publication. We conclude our study by highlighting areas for further discussion and investigation, such as defining replication procedures and conceptualizing genuine replication goals within design science research.
To the Problems of Complete Assimilation of Educational Materials at Schools

The presented article discusses the issues of developing criteria for complete assimilation of educational material designed for secondary school classes. It also reveals social, mental and biological factors impeding the process of learning in the course of conducting activity
Sentiment Analysis of Bangla Language Using Deep Learning Approaches

Emotion is the most important gear for human textual communication with each other via social media. Nowadays, people use text for reviewing or recommending things, sharing opinions, rating their choices or unlikeness, providing feedback for different services, and so on. Bangladeshi people use Bangla to express their emotions. Current research based on sentiment analysis has got low-performance output by using several approaches on detecting sentiment polarity and emotion from Bangla texts. In this study, we have developed four models with the hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) with various Word Embeddings including Embedding Layer, Word2Vec, Global Vectors (Glove), and Continuous Bag of Words (CBOW) to detect emotion from Bangla texts (words, sentences). Our models can define the basic three emotions; happiness, anger, and sadness. It will make interaction lively and interesting. Our comparisons are bestowed against CNN, LSTM with different Word Embeddings, and also against some previous researches with the same dataset based on classical Machine Learning techniques such as Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors (K-NN). In our proposed study, we have used Facebook Bangla comments for a suitable dataset. In our study, we have tried to detect the exact emotion from the text. And in result, the best model integrating Word2Vec embedding layer with a hybrid of CNN-LSTM detected emotions from raw textual data with an accuracy of 90.49% and F1 score of 92.83%.