Reviewerly/exHarmony huggingface dataset:
Impredicative Encodings of Inductive-Inductive Data in Cedille

Cedille is a dependently typed programming language known for expressive and efficient impredicative encodings. In this work, we show that encodings of induction-induction are also possible by employing a standard technique from other encodings in Cedille, where a type representing the shape of data is intersected with a predicate that further constrains. Thus, just as with indexed inductive data, Cedille can encode a notion that is often axiomatically postulated or directly implemented in other dependent type theories without sacrificing efficiency.
Advancements in Handwritten Digit Recognition: A Literature Review of Machine Learning and Deep Learning Approaches

Abstract: Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning. With the advent of digitized data and advanced algorithms, this area has experienced significant advancements over the years. This review paper presents a comprehensive analysis of various techniques and methodologies employed in handwritten digit recognition. We explore the historical context, state-of-the-art approaches, challenges, and real-world applications of this important field
Faster, Simpler Red-Black Trees

For more than two decades, functional programmers have refined the persistent red-black tree—a data structure of unrivaled elegance. This paper presents another step in its evolution. Using a monad to communicate balancing information yields a fast insertion procedure, without sacrificing clarity. Employing the same monad plus a new decomposition simplifies the deletion procedure, without sacrificing efficiency.
Attribution-Scores in Data Management and Explainable Machine Learning

We describe recent research on the use of actual causality in the definition of responsibility scores as explanations for query answers in databases, and for outcomes from classification models in machine learning. In the case of databases, useful connections with database repairs are illustrated and exploited. Repairs are also used to give a quantitative measure of the consistency of a database. For classification models, the responsibility score is properly extended and illustrated. The efficient computation of Shap-score is also analyzed and discussed. The emphasis is placed on work done by the author and collaborators.
Knowledge Engineering in the Era of Artificial Intelligence

Knowledge engineering with respect to knowledge graphs and graph data in general is becoming a more and more essential component of intelligent systems. Such systems benefit from the wealth of structured knowledge, which does not only include native graph data, such as social networks or Linked Data on the Web, but also general knowledge describing particular topics of interest. Furthermore, the flexibility of the graph model and its ability to store data relationships explicitly enables the integration and exploitation of data from very diverse sources. Hence, to truly exploit their potential, it becomes crucial to provide intelligent systems with verifiable knowledge, reliable facts, patterns, and a deeper understanding of the underlying domains. This paper will therefore chart a number of current challenges in knowledge engineering and discuss opportunities.
FARMUR: Fair Adversarial Retraining to Mitigate Unfairness in Robustness

Deep Neural Networks (DNNs) have been deployed in safety-critical real-world applications, including automated decision-making systems. There are often concerns about two aspects of these systems: the fairness of the predictions and their robustness against adversarial attacks. In recent years, extensive studies have been devoted to addressing these issues independently through adversarial training and unfairness mitigation techniques. To consider fairness and robustness simultaneously, the robustness-bias concept is introduced, which means an attacker can more easily target a particular sub-partition in the dataset. However, there is no unified and mathematical definition for measuring fairness in the robustness of DNNs independent of the type of adversarial attacks. In this paper, we first provide a unified, precise, and mathematical theory and measurement for fairness in robustness independent of adversarial attacks for a DNN model. Finally, we proposed a fair adversarial retraining method (FARMUR) to mitigate unfairness in robustness that retrains the DNN models based on vulnerable and robust sub-partitions. In particular, FARMUR leverages different objective functions for vulnerable and robust sub-partitions to retrain the DNN. Experimental results demonstrate the effectiveness of FARMUR in mitigating the unfairness in robustness during adversarial training without significantly degrading robustness. FARMUR improves fairness in robustness by $$19.18\%$$ with only $$2.22\%$$ reduction in robustness in comparison with adversarial training on the UTKFace dataset, which is partitioned based on race attributes.
Comparing and Improving Active Learning Uncertainty Measures for Transformer Models

Despite achieving state-of-the-art results in nearly all Natural Language Processing applications, fine-tuning Transformer-encoder based language models still requires a significant amount of labeled data to achieve satisfying work. A well known technique to reduce the amount of human effort in acquiring a labeled dataset is Active Learning (AL): an iterative process in which only the minimal amount of samples is labeled. AL strategies require access to a quantified confidence measure of the model predictions. A common choice is the softmax activation function for the final Neural Network layer. In this paper we compare eight alternatives on seven datasets and show that the softmax function provides misleading probabilities. Our finding is that most of the methods primarily identify hard-to-learn-from samples (outliers), resulting in worse than random performance, instead of samples, which reduce the uncertainty of the learned language model. As a solution this paper proposes a heuristic to systematically exclude samples, which results in improvements of various methods compared to the softmax function.
Nearly Macro-free microKanren

We describe changes to the microKanren implementation that make it more practical to use in a host language without macros. With some modest runtime features common to most languages, we show how an implementer lacking macros can come closer to the expressive power that macros usually provide—with varying degrees of success. The result is a still functional microKanren that invites slightly shorter programs, and is relevant even to implementers that enjoy macro support. For those without it, we address some pragmatic concerns that necessarily occur without macros so they can better weigh their options.
Designing Secure and Efficient Biometric Access Mechanism for Banking Systems

With the advancement of the application, numerous applications have been made that influence reality exercises. Arrangement of a client confirmation strategy to lay out approved clients is essential. e-Banking administration gives true monetary exchanges and induces worries from both monetary establishments and clients on secure confirmation strategies. Numerous confirmation techniques are utilized in e-Banking. In any case, many imperfections convey them as defenseless to intimidation. This paper dissects confirmation issues in e-Banking. Then, this research study proposes a protected finger impression-based client verification strategy. Numerous apps have evolved as a result of the growth of the Internet and influence daily activities. It is crucial to offer a user authentication technique so that authorized users can be found. The provision of actual financial transactions through e-Banking raises questions concerning safe authentication techniques for both financial institutions and customers. In e-banking, several authentication techniques are employed. But because of their numerous weaknesses, they are exposed to dangers. In this work, we examine e-banking authentication-related difficulties. Then, we provide a safe way of user authentication based on fingerprints.
From Uncontrolled Keywords to FAST? Attempting Metadata Reconciliation for a Canadian Research Data Aggregator

AbstractHow aggregators reconcile repositories’ user-supplied subject keywords is a growing challenge in the metadata profession. While aggregators allow users to search across multiple databases to find information, the search capability is only as good as the supplied metadata. This paper is a case study of a project to reconcile harvested metadata keywords within a research data discovery service. The Federated Research Data Repository (FRDR) Discovery Service is a national, bilingual platform for discovering Canadian research data that harvests metadata from over 90 repositories. This paper outlines the work of a cross-Canada, volunteer group of experts who attempted to develop a semi-automated workflow to map the FRDR subject keywords to Faceted Application of Subject Terminology (FAST) to improve discoverability. The authors, who were members of the working group, discuss why the project failed, the problems encountered, and their thoughts on the future of automated metadata reconciliation.Keywords: repositoriesaggregatorsmetadata reconciliation AcknowledgmentThe authors thank the FAST & FRDR working group for all the work they put in this project. A special thank you to Christine Bone, Cataloguing and Metadata Librarian at the University of Manitoba Libraries for her contribution to the brainstorming of this article and reviewing the draft.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available in the Federated Research Data Repository (FRDR) at https://doi.org/10.20383/102.0671.Notes1 At the time of writing, the components of FRDR have separated into two different services: the repository, which will continue to be known as FRDR, and the discovery platform, now called Lunaris. For this paper, we will refer to the original platform built of both and call it FRDR.2 Members usually worked directly within OpenRefine until faced with performance issues with the shared instance. However, since everyone was looking at specific letters, it was possible to export a selection and re-built the final spreadsheet with all the files maintained locally.
YouTube Video Spam Comment Detection Using Light Gradient Boosting Machine

The impact of allowing too many spam comments for a YouTube video is that the number of new views for the video goes down drastically. A technique for the identification and detection of spam comments on YouTube has been offered by this study. The current spam detecting techniques used by YouTube is not detecting the spam comments effectively. This study has used 20 different algorithms to train and test the data and have found LightGBM to be the best of all. As the performance of LightGBM is very impressive, this study has used lightGBM algorithm to detect the possible spam comments on a YouTube video.
Spectral Data Classification for Supply Chain Management

Supply chain management plays a pivotal role in business operations, specifically with regard to the distribution network and ensuring the pristine condition of delivered products. Damages that may occur during shipping pose significant concerns, encompassing accidents, improper cargo handling, and related factors. To address this challenge, the implementation of machine learning algorithms, particularly through motion detection, emerges as a promising solution. By meticulously analyzing spectral features and accelerometric properties, the algorithm proficiently classifies motions across three-dimensional axes. Leveraging a K-Nearest Neighbor classifier, the system accurately categorizes these motions based on a carefully curated dataset of labeled non-damaging motions. Through the identification of outlier motions, the system effectively detects potential risks, thus facilitating the implementation of streamlined and optimized transportation mechanisms. This refined approach harnesses the power of machine learning to mitigate damages and elevate overall transportation practices within the supply chain. By enhancing the accuracy of motion classification and identifying deviations, proactive measures can be taken to prevent damage during transportation. This ultimately leads to improved efficiency and customer satisfaction throughout the supply chain.
YOLO-Based Video Processing for CCTV Surveillance

A visual surveillance system is primarily employed for the analysis and interpretation of object behaviours within a given video scene. Surveillance system is important because it acts as a deterrent to crime, helping prevent theft, vandalism, assault and enhances public safety by monitoring public spaces and allowing for a timely response to emergencies. The performance of a surveillance system using traditional cameras is vastly superior during daytime as compared to night time, this is primarily due to the availability of natural light during the day whereas at night in the absence of ambient light it's difficult for detecting objects. The developed system that detects anomalous activity during the day as well as night and alert the authorized user. The proposed system captures the video using CCTV camera and converts the video into frames, these frames undergo pre-processing to detect objects using YOLO algorithm. In the event of any anomalies or suspicious activities detected within the surveillance system, an alert message is promptly sent to the concerned user or designated authority. The results of the system demonstrate that the accuracy of the proposed methods reaches 98.3%.
Qerberos: A Protocol for Secure Distribution of QRNG Keys

A communication protocol for the distribution of cryptographic keys generated by a quantum random number generator (QRNG) has been developed by introducing a minor modification to Kerberos and using SRP as the authentication mechanism. The protocol, named Qerberos, allows two users to acquire the same symmetric key generated by a trusted third party with access to a QRNG, whose keys have higher entropy than a classical generator. An implementation that employs two different Q RN Gs has been tested for different parameters achieving good performance and short request times.
A NoSQL DBMS Transparent Data Encryption Approach for Cloud/Edge Continuum

Edge systems are increasingly popular for data collection and processing. Typically, due to their limited storage capacity, pieces of data are continuously exchanged with Cloud systems which store them in distributed DataBase Management System (DBMS). This scenario, known as Cloud/Edge Continuum, is critical from a data security point of view as it is exposed to many risks. Transparent Data Encryption (TDE) is proposed as a possible solution for encrypting database files. However, current solutions do not suit the Cloud/Edge continuum requirements. In this paper, we aim at fulfilling this gap by proposing a solution to encrypt the data locally at the Edge and transfer them to a distributed database over the Cloud. Our approach allows us to perform queries directly on encrypted data over the Cloud and to retrieve them on the Edge for decryption. Experiments performed on different NoSQL DBMS solutions demonstrate the feasibility of our approach.
RoG§: A Pipeline for Automated Sensitive Data Identification and Anonymisation

Nowadays, the amount of data available online is constantly increasing. This data may contain sensitive or private information that can expose the person behind the data or be misused by malicious actors for identity theft, stalking, and other nefarious purposes. There is thus, a growing need to protect individuals' privacy and prevent data breaches in several application domains. Protecting data privacy though, is a complex and multifaceted issue that involves a range of legal, ethical, and technical considerations. In this paper, we discuss the challenges associated with data protection, the role of automated tools, and the effectiveness of identifying and anonymising sensitive data. We then, propose a fully-automated process for sensitive data identification and anonymisation, based on Natural Language Processing (NLP) techniques, that can be applied both in big diverse datasets and to a wide range of domains.
Multi-Client Searchable Symmetric Encryption in Redactable Blockchain for Conjunctive Queries

Sharing and searching encrypted data securely in outsourced environments poses a challenge due to possible cooperation between compromised users and untrusted servers. This paper studies the problem of multi-client dynamic searchable symmetric encryption, where a data owner stores encrypted documents on an untrusted remote server and selectively allows multiple users to access them through keyword search queries. The paper proposes a practical multi-client conjunctive searchable symmetric encryption scheme in a redactable blockchain to address this challenge. This scheme achieves multi-client sublinear conjunctive keyword search, and the data owner can authorize clients to access the documents. The scheme combines encryption primitives with novel access control techniques and constructs a redactable blockchain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\zeta$</tex> -oblivious group cross tags for sublinear search. The system's security is proven in a simulation-based security model. A prototype implementation using a blockchain-based approach is developed and evaluated on a real-world database containing millions of documents to demonstrate its practicality.
MFG-R: Chinese Text Matching with Multi-Information Fusion Graph Embedding and Residual Connections

Chinese text matching is an important task in natural language processing research, but the current techniques have problems in text feature extraction, such as insufficient word information extraction and lack of deep information in graph convolution networks. In this paper, we propose a model MFG-R for Chinese text matching with multi-information fusion graph embedding and residual connection. The model fuses the word embedding representation of the text obtained by graph convolution network with character-level information and word weight information to extract text features. At the same time, in order to perform deep interaction matching, we construct a word-level similarity interaction matrix between text pairs, and build a text interaction and feature extraction model based on residual network on this basis. Experiments show that MFG-R has excellent performance on two common Chinese datasets, Ant Financial Question Matching Corpus(AFQMC) and Large-scale Chinese Question Matching Corpus(LCQMC).
Class Imbalance of Bio-Medical Data by Using PCA-Near Miss for Classification

Class imbalance is a significant problem in many real-time domains dealing with healthcare data, which leads to poor classification performance and may have severe consequences. This work addresses the class imbalance problem in healthcare data, which often leads to poor classification performance and potentially serious consequences. This study proposes a hybrid PNM model that integrates the Near-Miss sampling technique and Principal Component Analysis to overcome this challenge. This study aims to investigate the effectiveness of the PNM model in improving classifier performance and compare it with several baseline classifiers. Further, the experiments are conducted on a real-world healthcare dataset containing various healthcare records. The results showed that the PNM model outperformed the baseline classifiers regarding the precision, recall, F1 score, and area under the curve (AUC). Specifically, the PNM model achieved a precision of - 0.85, recall of - 0.82, F1 score - 0.83, and AUC - 0.91, while the best-performing baseline classifier achieved a precision of 0.72, recall of 0.64, F1 score of 0.68, and AUC of 0.83. Our study demonstrates that the PNM model offers a promising approach to addressing the class imbalance in healthcare data and improving classifier performance. Integrating the Near-Miss sampling technique and Principal Component Analysis enables the model to achieve a better balance among the majority and minority classes, resulting in more accurate classification. The PNM model has the potential to be applied to various healthcare domains, such as disease diagnosis, patient risk stratification, and treatment prediction.
Cognitive Search: A Free Information Retrieval Web Service to Coronavirus Scientific Papers

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. With millions of people affected by the COVID-19 pandemic, many questions arise concerning transmission, diagnosis, treatment, development of vaccines, and viral pathogens. Bearing that in mind, the dangers of wrong and inaccurate information represent a socio-economic could do more damage than the disease itself. To help fight this ongoing outbreak, we present Cognitive Search - a friendly deployed service application IR exploring the latest language processing advance. The service provides access to CORD-19, a resource of scholarly articles about COVID-19 and related coronaviruses. The system allows rending documents retrieval by Term-Frequency and Semantic Neural Search and the Hybrid Term-Neural. The retrieval performance can often be significantly improved by using several different retrieval algorithms and allowing the user to combine the results instead of just one. Additionally, the Hybrid Term-Neural approach supports the exploitation of temporal information in documents and the usage of such information to anchor search results along a well-defined timeline. So it can generate insights through an intuitive and easy-to-use interface.
A Survey of Attacks and Defenses for Deep Neural Networks

This survey provides an overview of adversarial attacks and defenses for deep neural networks. We discuss the taxonomies of attacks on Machine learning systems and common algorithms for generating attacks. We also present a taxonomy of defense techniques for adversarial machine learning. Using the information in this paper, researchers can make an informed decision on creating secure models in machine learning. Based on the reviewed literature, we foresee promising paths for future research.
Feature Relevance in NAT Detection Using Explainable AI

Network Address Translation (NAT) was developed to overcome the IPv4 address exhaustion problem. NAT allows multiple devices on a local network to share a single public IP address. However, NAT can result in communication concerns, security flaws, network administration issues, and difficulties locating network faults. This paper addresses the limitations of existing NAT detection techniques through adoption of an innovative machine learning-based approach. The NAT detection model achieves promising results by utilizing novel traffic features. The current techniques used in NAT detection face challenges in terms of explainability and transparency. To address this issue, our proposed method integrates explainable artificial intelligence (XAI) techniques to increase the transparency and interpretability of NAT detection models, thereby improving their efficiency and effectiveness. The results obtained from explain ability highlight the significance of incorporating new features in NAT detection. Furthermore, explainable results indicate that the presence of NAT is closely correlated with specific features.