Reviewerly/exHarmony huggingface dataset:
Learnable dynamic margin in deep metric learning

With the deepening of deep neural network research, deep metric learning has been further developed and achieved good results in many computer vision tasks. Deep metric learning trains the deep neural network by designing appropriate loss functions, and the deep neural network projects the training samples into an embedding space, where similar samples are very close, while dissimilar samples are far away. In the past two years, the proxy-based loss achieves remarkable improvements, boosts the speed of convergence and is robust against noisy labels and outliers due to the introduction of proxies. In the previous proxy-based losses, fixed margins were used to achieve the goal of metric learning, but the intra-class variance of fine-grained images were not fully considered. In this paper, a new proxy-based loss is proposed, which aims to set a learnable margin for each class, so that the intra-class variance can be better maintained in the final embedding space. Moreover, we also add a loss between proxies, so as to improve the discrimination between classes and further maintain the intra-class distribution. Our method is evaluated on fine-grained image retrieval, person re-identification and remote sensing image retrieval common benchmarks. The standard network trained by our loss achieves state-of-the-art performance. Thus, the possibility of extending our method to different fields of pattern recognition is confirmed.
Privacy Preserving Biometric Authentication on the blockchain for smart healthcare

Privacy Preserving Biometric Authentication (PPBA) schemes are designed for anonymous authentication of patients to protect patient’s privacy in accessing healthcare services. Recently, blockchain technology in healthcare has emerged as a new research area to provide tamper-resistance and non-repudiation in e-health systems. One aspect of this research could lead to blockchain-based secure biometric identification for smart healthcare, which may face the paradox of anonymous biometric authentication on public blockchains. In this paper, we describe an efficient, fully anonymous and GDPR-compliant PPBA protocol built into the blockchain of any privacy coin such as Monero. The new protocol provides encrypted offline storage and processing in the encrypted domain. The infrastructure necessary for the online authentication is outsourced to the public blockchain that provides integrity of its data. In addition to auditing capabilities for misbehaving entities, the new system reduces the number of transactions necessary for authentication and enables revocation of biometric identities. We provide new PPBA schemes both for set difference/overlap and Euclidean distance metrics without using bilinear pairings, where the former leads to an efficient solution to the compatibility for organ transplant. We limit the generation of encrypted templates for public testing even if biometric/health data is of low min-entropy. Due to the anonymity of the cryptocurrency, we break the link between the stealth address of an authenticating user and its biometrics. We describe the user and identity privacy notions independent of the underlying privacy coin and guarantee the security of our proposal in the framework of those generic notions. Finally, we simulate the new proposal on Monero blockchain and analyze the transaction fees required for hill climbing attacks. The results show that our design leads to a natural hindrance against these attacks that could be successful even if the templates are stored as encrypted. To the best of our knowledge, this is the first efficient blockchain-based PPBA scheme that exhibits a punishment against hill climbing attacks through transaction fees.
Preference-based inconsistency-tolerant query answering under existential rules

Ontology-mediated query answering (OMQA) emerged as a paradigm to enhance querying of data sources with an ontology that encodes background knowledge. In applications involving large amounts of data from multiple data sources, it might well be the case that inconsistency arises, making standard query answering useless, since everything is entailed by an inconsistent knowledge base. Being able to provide meaningful query answers in the presence of inconsistency is thus a critical issue to make OMQA systems successful in practice. The problem of querying inconsistent knowledge has attracted a great deal of interest over the years. Different inconsistency-tolerant semantics of query answering have been proposed, that is, approaches to answer queries in a meaningful way despite the knowledge at hand being inconsistent. Most of the semantics in the literature are based on the notion of repair, that is, a “maximal” consistent subset of the database. In general, there can be several repairs, so it is often natural and desirable to express preferences among them. In this paper, we propose a framework for querying inconsistent knowledge bases under user preferences for existential rule languages. Specifically, we introduce preference rules, a declarative formalism which enable users to express (i) preferences over both the database and the knowledge that can be derived from it via an ontology, and (ii) preconditions for preferences to hold. We then define two notions of preferred repairs which take preference rules into account. This naturally leads us to introducing preference-aware counterparts of popular inconsistency-tolerant semantics, where only preferred repairs are considered for query answering. We provide a thorough analysis of the data and combined complexity of different relevant problems for a wide range of existential rule languages.
Fidelity-Guaranteed Entanglement Routing in Quantum Networks

Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (S-D) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Finally, we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and re-routing process for multiple routing requests. Simulation results show that the proposed algorithms not only can provide fidelity-guaranteed routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.
Graph-Guided Unsupervised Multiview Representation Learning

Without the valuable label information to guide the learning process, it is demanding to fully excavate and integrate the underlying information from different views to learn the unified multi-view representation. This paper focuses on this challenge and presents a novel method, termed Graph-guided Unsupervised Multi-view Representation Learning (GUMRL), taking full advantage of multi-view graph information during the learning process. To be specific, GUMRL jointly conducts the view-specific feature representation learning, which is under the guidance of graph information, and the unified feature representation learning, which fuses the underlying graph information of different views to learn the desired unified multi-view feature representation. Regarding downstream tasks, such as clustering and classification, the classic single-view algorithms can be directly performed on the learned unified multi-view representation. The designed objective function is effectively optimized based on an alternating direction minimization method, and experiments conducted on six real-world multi-view datasets show the effectiveness and competitiveness of our GUMRL, compared to several state-of-the-art methods.
Diminishing-feature attack: The adversarial infiltration on visual tracking

Deeply understanding machine learning is a challenging task because its backbone technology is still obscure. The adversarial attack is established toward the vulnerability of deep models and can be utilized for understanding deep neural networks and enhancing model robustness. Adversarial attacks on tracking models can generate imperceptible noise to an input and consequently increase the cumulative error of tracking displacement. In general, existing adversarial visual-attacking models are white-box attacks. Although it successfully deceives the baseline tracker, the training model’s attacking effect is insufficiently transferred to blind trackers. Here, we present a new approach, the diminishing-feature attack, that generates a subtle perturbation into the input frame. The malicious noise in the proposed model is generated to disturb the feature heatmap to distract the classification score and annihilate bounding-box prediction. The adversarial noise generator is solely trained from SiamRPN++ ResNet features of template frames without search frames or other parameters. Our method requires fewer computing resources than other visual tracking attackers when malfunctioning visual trackers. To validate the proposed model, we benchmark the adversarial input frame with the tracker on four tracking datasets: OTB100, VOT2018, LaSOT and UAV123. Our attacking method achieves a high performance drop that is comparable to the other existing adversarial attackers in visual tracking, yet our adversarial noise is lower than the aforementioned attackers. Furthermore, the transferability attacking of our algorithm is excellent on state-of-the-art visual tracking, for example, SiamRPN, DaSiam and DiMP.
A Multi-Criteria Recommender System For NFT Based IAP In RPG Game

The market value of the gaming industry in 2021 is said to be more than 198.40 billion USD. Market value is also supported by the number of gamers, with 4.75 billion in January 2022. In today’s In-App Purchase (IAP) income method has become a big trend in today’s modern era models of free-to-play games, where gamers can choose or buy many items during the game to speed up the progress of the game or enjoy the full content of the game. However, sometimes players are overwhelmed with the number of items on offer, making it difficult for players to choose because the game content is too diverse. In addition, players are also worried about the security of their digital assets because, in 2021, there will be 7.5 million digital assets lost due to hacking. We propose a recommendation system to make it easier for players to choose items that suit their playstyle to solve this problem. We use the multi-criteria recommender system (MCRS) method because this method can improve the accuracy of recommendations compared to conventional recommendations that only use one criterion. In this study, we used eight criteria to calculate the recommendations. The results of our recommendation test show the accuracy value = 0.71, precision = 0.76, recall = 0.71 and F1 score = 0.66. To address security issues, we propose the implementation of a Non-Fungible Token (NFT) for each item. NFT can increase security because it uses a decentralized blockchain architecture in which every transaction is encrypted. The system guarantees that the assets will remain online so that users do not risk losing ownership of their assets when the developer changes game data, or the game server closes.
Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE

At the end of 2019, the world was hit by the COVID-19 virus, which caused a pandemic. Indonesia has become one of the countries that are affected by this pandemic. To control the COVID-19 pandemic, the government has made various efforts, one of which is the use of the PeduliLindunig app. To access the PeduliLindungi app, the public can download it from Google Play. Google Play enables its users to write reviews on the apps that have been downloaded. This study aims to determine the sentiment analysis on the PeduliLindungi application on Google Play using the Random Forest Algorithm with SMOTE. Based on this study, public sentiment towards the PeduliLindungi app on Google Play tends to be negative. The Random Forest and SMOTE algorithms are used to classify sentiment in this study. The implementation of Random Forest and SMOTE resulted in 71% accuracy, 70% recall, and 70% precision.
The mechanism of value cocreation in robotic services: customer inspiration from robotic service novelty

Service robots provide operational benefits to restaurants, but the mechanism through which novel robotic services optimize customer experience given appropriate robots' dependence on human employees remains unclear. To fill this gap, this study adopts the transmission model of customer inspiration and identifies customer value cocreation processes in robotic services. Combined evidence from two online experiments and a field experiment demonstrates that robotic service novelty has a positive effect on customer cocreation intention via customer inspiration. Robotic task interdependence, which reflects the extent to which a robot relies on human employees to complete its tasks, attenuates the relationship between robotic service novelty and customer cocreation intention through decreased customer inspiration. These findings offer insights into marketing strategies for robotic services that provide enhanced customer experiences.
A knowledge graph and service for regional geologic time standards

Geologic time is an important dimension in geological research. Geologic time data are commonly collected from multiple sources in data-intensive studies of Earth’s history and raise an issue of data cleansing and integration. A knowledge graph of the international geological time scale has been established to harmonize heterogeneous data to facilitate effective and efficient data-driven discovery. Although many regional geologic time standards are also used in various databases and literature, there is limited discussion or development of knowledge graph for them. In this research, we construct a knowledge graph for the geologic time standards in 17 regions at the Epoch and Age levels. This regional geologic time knowledge graph is integrated with the international geologic time knowledge graph as a comprehensive deep-time knowledge base. A SPARQL endpoint has been established to provide open and free online service to the knowledge base. Several use cases are presented here to demonstrate the functionality of the knowledge graph we built as well as its application in open data exploration. Our work addresses the shortage of machine-readable knowledge graphs for regional geologic time standards and will help accelerate geologic data integration from multiple sources in data-intensive studies. All data and code in this paper are made open source and are accessible on GitHub and Zenodo.
Investment and economic growth: do institutions and economic freedom matter?

Purpose This paper analyzes the effect of institutional quality and economic freedom on investment and economic growth in sub-Saharan Africa (SSA). Design/methodology/approach Focusing on a panel of 27 countries, the study employed the panel fixed and random effect models to analyze data spanning from 2005 to 2018. The study also employed the Wu–Hausman test to determine if the endogeneity problem exists in the model. Findings The findings of the study show that individually, an improvement in economic freedom stimulates economic growth while the improvement in institutional quality is effective in spurring investment. However, the interaction effect of improvement in institutional quality and economic freedom is the stimulation of both investment and economic growth. The findings are robust to alternative model specifications. Practical implications The study implies that for SSA countries to effectively achieve higher investment and economic growth outcomes, there is the need to simultaneously strengthen institutional quality and improve economic freedom. Focusing on either of the factors without the other leads to less desirable growth and investment outcomes. Originality/value The study examined the combined influence of institutional quality and economic freedom on investment and growth in SSA. To the best of the authors’ knowledge, no study has investigated this in the context of SSA.
CLSQL: Improved Q-Learning Algorithm Based on Continuous Local Search Policy for Mobile Robot Path Planning

How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.
Lahjoita puhetta: a large-scale corpus of spoken Finnish with some benchmarks

The Donate Speech campaign has so far succeeded in gathering approximately 3600 h of ordinary, colloquial Finnish speech into the Lahjoita puhetta (Donate Speech) corpus. The corpus includes over twenty thousand speakers from all the regions of Finland and from all age brackets. The primary goals of the collection were to create a representative, large-scale resource to study spontaneous spoken Finnish and to accelerate the development of language technology and speech-based services. In this paper, we present the collection process and the collected corpus, and showcase its versatility through multiple use cases. The evaluated use cases include: automatic speech recognition of spontaneous speech, detection of age, gender, dialect and topic and metadata analysis. We provide benchmarks for the use cases, as well downloadable, trained baseline systems with open-source code for reproducibility. One further use case is to verify the metadata and transcripts given in this corpus itself, and to suggest artificial metadata and transcripts for the part of the corpus where it is missing.
PageNet: Towards End-to-End Weakly Supervised Page-Level Handwritten Chinese Text Recognition

Handwritten Chinese text recognition (HCTR) has been an active research topic for decades. However, most previous studies solely focus on the recognition of cropped text line images, ignoring the error caused by text line detection in real-world applications. Although some approaches aimed at page-level text recognition have been proposed in recent years, they either are limited to simple layouts or require very detailed annotations including expensive line-level and even character-level bounding boxes. To this end, we propose PageNet for end-to-end weakly supervised page-level HCTR. PageNet detects and recognizes characters and predicts the reading order between them, which is more robust and flexible when dealing with complex layouts including multi-directional and curved text lines. Utilizing the proposed weakly supervised learning framework, PageNet requires only transcripts to be annotated for real data; however, it can still output detection and recognition results at both the character and line levels, avoiding the labor and cost of labeling bounding boxes of characters and text lines. Extensive experiments conducted on five datasets demonstrate the superiority of PageNet over existing weakly supervised and fully supervised page-level methods. These experimental results may spark further research beyond the realms of existing methods based on connectionist temporal classification or attention. The source code is available at https://github.com/shannanyinxiang/PageNet .
Features of using Moodle tools in the training of future social workers

In the article the formation of information and communication competence of future social workers is carried out. The sequence of topics studied by future social workers in the content part of the discipline of the information cycle "Modern information technology" is presented. The stages of ICT competence formation of future social workers are described. Authors provide a clear description of the sequence of learning and acquiring skills of practical work with a personal computer, cloud technology and its applications, free use of e-mail and additional features of Gmail, creating and formatting files of various formats, creating electronic educational resources by cloud technology, developing online courses and personal pages, understanding the capabilities and awareness of software for creating and processing various content by future social workers. They also analyze the benefits of the Moodle platform for its active use during the training of social professionals in the distance learning situation. These include: ease of use, intuitive interface for students and teachers, free, compliance with the requirements of higher education institutions for educational platforms, availability of methodological and reference materials, the ability to perform tests, planning and organizing the work of teachers and students and so on. In the article the need for skills and abilities to work with the above programs, services and technologies is emphasized too. The expediency of placing the discipline "Modern information technology" on the basis of the distance learning platform Moodle for future social workers is substantiated. The preconditions for the use of Moodle by the participants of the educational process are determined, the list of advantages and possibilities of this platform is given. The results (initial and final) of the study of the discipline "Modern information technology" are presented.
Evaluation of digital competence level among educators in UAE Higher Education Institutions using Digital Competence of Educators (DigComEdu) framework

Digital competence has become an important issue in academia owing to the advent of various communication tools. This study used a standardized questionnaire based on the Digital Competence of Educators (DigCompEdu) framework, which is a validated comprehensive framework, and its associated assessment tool. This tool was designed to assess professional skills of faculties in higher education institutes in United Arab Emirates (UAE) in terms of their abilities to use information and communication technology (ICT) and current digital competences in their teaching and educational practices. We conducted an online survey to help participants reflect on their strengths and weaknesses while using digital technologies in education to find correlation between them, thus helping teachers determine their relative strengths and identify areas that need more attention.