Reviewerly/exHarmony huggingface dataset:
ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data

Anomaly detection techniques are applicable for recognizing excessive energy consumption and device failure, thereby contributing to the maintenance of operational and sustainable energy supply systems. In this context, human decision makers can benefit from receiving explanation attempts for detected anomalies as part of a semi-automated software solution. Therefore we introduce the framework ADEPT, which comprises interfaces for processing user-supplied time series data and interactively visualizing explanatory anomaly information. Our framework features several shallow and deep machine learning algorithms for anomaly detection and explanation. We demonstrate ADEPT using energy consumption data collected from our university campus.
Comparison of Methodologies Used in Cybersecurity Reports

New legislation in the field of cybersecurity makes public and private institutions legally responsible for keeping the nation's IT infrastructure and data safe under the threat of legal consequences. This sudden shift saw many companies and public institutions scrambling to meet the new legal demands but have often found a lack of professionals, institutions, systems, but mainly information. The laws do oblige most organizations to report incidents and breaches. How to turn this data into information is still up to discussion. It is this that has led us to our research, which consists of defining, comparing, and assessing the methodologies of four major cybersecurity reports, characterizing their interactions with cybersecurity legislation in the Slovak republic to create a base for developing a methodology for Slovakia. The reports we used are Verizon's Data Breach Investigation Report, IBM's X-Force Threat Intelligence Report, Ponemon Institute's Separating Truth from Myth in Cybersecurity, and e-Governance Academy's National Cybersecurity index. Despite the difference in their approaches, we were able to discover unifying best practices and issues that should not be repeated in the development of a new methodology. Three key best practices are sound statistical methods, timeliness of data, and transparency. Three key issues were lack of transparency in methodology, limitations, bias and reasoning, inconsistency, and loss of relevance due to late publication. Additionally, we have identified that reports of this nature are in line with cybersecurity legislature and national strategies. Such reports may be used as a part of preventative measures by creating broad awareness, increasing interest in the topic, warning organizations of any dangers, and finding ways of creating and improving cybersecurity strategies to build capacities and resilience.
Training Parameterized Quantum Circuits with Triplet Loss

Training parameterized quantum circuits (PQCs) is a growing research area that has received a boost from the emergence of new hybrid quantum classical algorithms and Quantum Machine Learning (QML) to leverage the power of today’s quantum computers. However, a universal pipeline that guarantees good learning behavior has not yet been found, due to several challenges. These include in particular the low number of qubits and their susceptibility to noise but also the vanishing of gradients during training. In this work, we apply and evaluate Triplet Loss in a QML training pipeline utilizing a PQC for the first time. We perform extensive experiments for the Triplet Loss based setup and training on two common datasets, the MNIST and moon dataset. Without significant fine-tuning of training parameters and circuit layout, our proposed approach achieves competitive results to a regular training. Additionally, the variance and the absolute values of gradients are significantly better compared to training a PQC without Triplet Loss. The usage of metric learning proves to be suitable for QML and its high dimensional space as it is not as restrictive as learning on hard labels. Our results indicate that metric learning provides benefits to mitigate the so-called barren plateaus.
Speculating on Higher Education in 2041—Earthworms and Liminalities

Abstract What if… this chapter asks, might higher education be twenty years from now? This chapter speculates a future that takes place 20 years from now, a future that acknowledges the challenges of the present, as discussed at greater length in the earlier chapters. We take up speculative inquiry as a method to consider a future where the teens of 2021 bring their experience of living and learning during this pandemic time to the shaping and leadership of universities in 2041. Beginning with a what-if scenario of a reconceived higher education, we create a speculative fiction text—a letter from the future—around which we perform a diffractive reading (Barad, 2014). What this diffraction brings about is a higher education imaginary of activisms and revolts that result from current tensions and challenges in education and research. The imaginary does not predict the future but offers a critical lens through which to make sense of this present and the possible futures tied to it. In so doing, we suggest potentialities of practices like elevating decolonised ways of knowing and engaging geographical, human and nonhuman diversities in campuses across urban and remote areas. Traversing twenty years from now, the chapter speculates on higher education, spanning virtual and physical spaces for re-connection of research, learning and assessment with, in and through assemblies of diverse beings, human and otherwise. The chapter concludes with a codetta, which leaves the reader with a brief account of a speculative encounter with Socrabots as they prepare to enter the teaching profession in the 2040s.
A Blockchain Oracle-Based API Service for Verifying Livestock DNA Fingerprinting

Blockchain, a type of distributed ledger technology, has revolutionized the digital economy such as cryptocurrencies and supply chain management with its transparency, immutability, and decentralization properties. In addition, smart contracts are introduced to the blockchain to provide programmability removing third parties for administration. Although promising, blockchains and smart contracts are closed technologies meaning they have no interaction with the external world where real-world data and events exist, i.e., off-chain data. It becomes more challenging when the off-chain data is unstorable onto the blockchain due to data volume and privately maintained by third parties for security and confidentiality. In this paper, we address the problem of enabling a private blockchain platform to access privately owned sensitive off-chain data (i.e., DNA fingerprinting). This off-chain data is used for the traceability of products (i.e., products’ origin) along the supply chain with a real-world livestock use case. To this end, we present a livestock blockchain oracle (LBO) as a service to mitigate the accessibility issue and automate the process of verifying purchasable products for livestock DNA fingerprinting verification. We have conducted an evaluation study using real-world livestock data from third-party service providers. Results based on the livestock product information and registered DNA service providers show that LBO is a reliable and responsive decentralized oracle blockchain for verification.
Mixed Integer Linear Programming for Optimizing a Hopfield Network

This work presents an approach to optimize the weights of a discrete Hopfield network as mixed integer linear program (MILP). As the original formulation involves a sign-function, it is not differentiable, but parameter optimization using a (mixed integer) LP is possible. As autoassociative memory, a key question is the amount of patterns which can be stored in such a Hopfield network. In this work it is shown, that the traditional storage description models are far inferior to a globally optimized solution which can be obtained with a MILP. In contrast to a gradient descent based optimization is the proposed approach nearly parameter free and independent from seeding and other factors which are crucial for differentiable programming. Additionally it is possible to enforce sparsity constraints on the weights. Such additional constraints improve the generalization of such a model and make the Hopfield network more stable for the case of outliers or missing values. Several experiments demonstrate the effectiveness of the model.
Computationally Collected and Curated ‘What If’ Questions to Spark a SoTL Collaboration

Abstract As the project team began this journey at Melbourne Graduate School of Education, The University of Melbourne at the start of the pandemic in early 2020, we asked, what collaborations are needed for next practice collaborative thinking to emerge in the academy in the current socio-political climate, and what conditions do we need in place for these collaborations to develop into new and agile educational research partnerships? Our next practices project was co-designed across 2020–2021 through a process of speculative thinking that “thrives on imagination and aims to open up new perspectives on what are sometimes called wicked problems, to create spaces for discussion and debate about alternative ways of being, and to inspire and encourage people’s imaginations to flow freely” (Dunne &amp; Raby, 2013, p.2).
Cage: A Hybrid Framework for Closed-Domain Conversational Agents

Current conversational agents are primarily designed to answer user queries based on structured pre-defined utterance-response pairs. While question-answering (QA) systems extracts potential answers, to queries, from unstructured texts. However, in domain-specific settings, manual creation of query-response pairs is expensive, and domain adaptation of QA platforms is crucial. To this end, we propose Cage, a “hybrid” conversational framework seamlessly integrating structured and unstructured data to obtain precise answers for user queries – improving user experience and quality-of-service. We describe the different components combining query matching and extractive question answering, and demonstrate the multi-lingual chatbot interface provided to a user.
Explorations in Active Learning Applied to Image Classification

While development of very large models is the core of today’s artificial intelligence, very often the cost of model training is being raised. In this context, active learning is pointed to as a method to maximize model quality, while minimizing the amount of resources needed to train it. The aim of this contribution is to systematically compare performance of active learning applied to the image classification task for three datasets.
An Evaluation on the Performance of Privacy Preserving Split Neural Networks Using EMNIST Dataset

Preserving the Privacy of user data is a raising concern in the current scenario, since the usage of client’s data from edge devices play a vital role in the field of artificial intelligence and deep learning. Distributed learning is presented as a solution to the scenario, where there are restrictions in exposing the data to gain meaningful insights. Split Learning is a distributed collaborative learning technique in which the neural network is split into multiple sections for training. Each of these sections are trained on a different collaborating client and the training of the network is done only by transferring the weights between the layers of the sections without any raw data transfer. In this study, the focus is given on the application of EMNIST as a challenging dataset for the evaluation of performance of Split Neural Networks (SNNs) and Federated Learning (FL). The FL model gives an accuracy of 97.49% and 96.8% for MNIST and EMNIST dataset respectively. Also, SNNs implemented using a vanilla architecture over multiple clients provide better results in comparison with simple CNN models with an accuracy of 97.83% and 88.05% for MNIST and EMNIST dataset respectively. The experimental results prove that EMNIST can be used apart from popularly used MNIST and CIFAR 10 datasets and SplitNNs can be used to overcome the drawbacks of FL.
Neuro-Symbolic Regression with Applications

Discovering symbolic models is growing in popularity with the increasing interest in interpretable machine learning. Symbolic regression is the task of learning an analytical form of underlying models in data. Two machine learning techniques have proven their effectiveness: reinforce trick and transformer neural network. This paper discusses in detail the two techniques and presents the application of symbolic regression on a simulated data set that describes a high-energy physics process.
Incremental SVD-Based Hybrid Movie Recommendation to Improve Content Delivery Over CDN

With the tremendous growth in the number of users watching on-demand movies over the Internet, Content Delivery Network (CDN) is used to provide a more efficient network and improve user experiences. CDN accelerates the response to end users after identifying the popularity of movie content. Nowadays, massive content has created difficulties in accurately predicting each movie's popularity. A recommendation system uses filtering tools that recommend content according to the popularity among the users. Currently, recommendation systems play a vital role in identifying the relevant content from massive data sources and providing options to users to select content according to interest. To deal with related content, we analyze different algorithms utilized in popular recommendation systems and develop a hybrid system. We are using a hybrid approach by merging basic content based with Incremental SVD-based filtering to enhance the effect of the recommendation system for movie content to the end users over CDN.
Future of E-commerce by Implementing Blockchain Payments System

Blockchain technology is presently widening into different sections of the Information Technology, Banking and Finance, Currency, Healthcare, Records of Property, Voting, Communications Technology community and especially the main field which plays an important part in E-commerce. Our proposed idea provides a solution to 4 new problems: faster processing of funds, ensuring user privacy and user control, providing smooth flow of various different modules and optimized database modules to manage and easily process the data to the front-end. It also keeps all the records of what is shared with whom when and why without involving any 3rd party. When paired with Data encryption keys, blockchain offers transparent, tamper-proof, and secure platforms that can allow creative solutions. In this paper we will show how e-commerce can be enabled using blockchain with all the drawbacks that a customer faces while ordering online currently. This helps the small and medium enterprise businesses to run with the confidentiality that no data or any private details will be retrieved or leaked.
Ordinal Quantification Through Regularization

Quantification, i.e., the task of training predictors of the class prevalence values in sets of unlabelled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for binary and multiclass problems in which the classes are not ordered. We here study the ordinal case, i.e., the case in which a total order is defined on the set of $$n>2$$ classes. We give three main contributions to this field. First, we create and make available two datasets for ordinal quantification (OQ) research that overcome the inadequacies of the previously available ones. Second, we experimentally compare the most important OQ algorithms proposed in the literature so far. To this end, we bring together algorithms that are proposed by authors from very different research fields, who were unaware of each other’s developments. Third, we propose three OQ algorithms, based on the idea of preventing ordinally implausible estimates through regularization. Our experiments show that these algorithms outperform the existing ones if the ordinal plausibility assumption holds.
Towards Real-Time Monitoring of Blockchain Networks Through a Low-Code Tool

Blockchain is a secure and distributed technology which is growing in popularity since it enables the traceability, immutability and transparency of data. However, monitoring blockchain networks requires experts who have vast experience in this technology. To address this challenge, in this paper we present a low-code tool, which allows inexperienced blockchain developers to define graphical flows that specify inputs, outputs and the logic necessary to monitor in real time the elements of a blockchain network. This tool has been successfully applied to a vaccine delivery scenario, facilitating the monitoring of a smart contract that stores temperature measurements of a certain vaccine. As a result, when a new transaction is mined in the blockchain network, it will be promptly notified and sent to the different data sinks specified in the flow modeled by a non-expert in blockchain.
Use of a Communication Robot—Chatbot in Order to Reduce the Administrative Burden and Support the Digitization of Services in the University Environment

The technology of chatbots has come a long way in the last decade thanks to the growing popularity of artificial intelligence and machine learning. Chatbots are established as a beneficial tool in many scenarios of our daily lives. This paper aims to discuss the design, architecture, and potential application of a chatbot in a university environment. Our work is divided into 3 chapters. The first chapter consists of a literature review of chatbot systems. The second chapter focuses on the methodology of the work and the stated objectives. The third chapter describes the current state of the ongoing processes at the Faculty of Management at Comenius University. On the selected process a time and financial analysis were performed. The output of this chapter consists of the identified critical path in the processes as an area for possible implementation of chatbots in the academic department to reduce the administrative burden. The result is the architecture and development of the proposed solution. The paper concludes with opportunities for improvement of the proposed solution and recommendations that we recommend to be applied in practice.
BEDSpell: Spelling Error Correction Using BERT-Based Masked Language Model and Edit Distance

The spelling correction problem, the task of automatically correcting misspellings in a text, is critical in natural language processing (NLP). Although it can be considered a standalone task, in most cases, it is an integral component of various NLP tasks as a preprocessing step since a dataset with typos can lead to erroneous results. Many previous automatic spelling correctors use a dictionary, independently search the word in a predefined list of words, and recommend the most similar one without considering the context. Even though these models’ output may be a correctly spelled word, it could be semantically incorrect. Therefore, some correctors consider the context when correcting typos based on language models. However, only employing the language model is insufficient, and the corrected word should be similar to the misspelled word. In our approach, we select a candidate for the typo based on masked language model output, character-level similarities, and edit distance. Exploiting the combination of the masked language model, character-level similarities, and edit distance assists us in recommending similar context-related candidates. We have used recall (correction rate) as our evaluation metric, and the results demonstrate a considerable improvement compared with previous studies.
Structure-Preserving Gaussian Process Dynamics

Most physical processes possess structural properties such as constant energies, volumes, and other invariants over time. When learning models of such dynamical systems, it is critical to respect these invariants to ensure accurate predictions and physically meaningful behavior. Strikingly, state-of-the-art methods in Gaussian process (GP) dynamics model learning are not addressing this issue. On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time. We propose to combine the advantages of GPs as function approximators with structure-preserving numerical integrators for dynamical systems, such as Runge-Kutta methods. These integrators assume access to the ground truth dynamics and require evaluations of intermediate and future time steps that are unknown in a learning-based scenario. This makes direct inference of the GP dynamics, with embedded numerical scheme, intractable. As our key technical contribution, we enable inference through the implicitly defined Runge-Kutta transition probability. In a nutshell, we introduce an implicit layer for GP regression, which is embedded into a variational inference model learning scheme.
Circular BlockEntropies and Fast Legendre Moments Based Content Preserving Image Authentication

Digital Images plays widespread in multimedia information, image authentication applications have a great impact. In this research article, we propose a model using global and local entropies based on the content preserving image authentication. The images are sub-divided into equal-sized circular blocks and each block entropies are measured. These measured entropies locally define the information contained in the image on a circular basis. Moments acts as a global descriptor of the image, from which the global features of the image are extracted using Fast Legendre Moments and local entropies are used to generate the hash values. These global fast Legendre moments and generated hash function values are used to authenticate the image. The experimental results show that our proposed system outperforms against attacks for rotations in large scale is robust against content preserving manipulations along with a greater tolerance to the rotation of large angles up to 90°. The proposed system is 100% sensitive to content change in image and is also able to locate the region of change, however image recovery can only be achieve by compromising lengthy hash code.
Distributed Secure Data Aggregation for Smart Grid Using Partial Homomorphic Encryption

Today, smart grid advanced metering infrastructure (AMI) is a critical cyber-physical system where energy providers generate and distribute energy to different clients depending on their changing demands. Suppliers collect and aggregate energy usage data online for billing, control and statistical analysis. However, performing such operations over an insecure channel exposes the AMI network to various cyberattacks. In this paper, we propose fault-tolerant privacy-enhanced data aggregation scheme for computing total energy consumption at different levels in the smart grid AMI network. First, a trusted authority (TA) initiates the operation by constructing a client-server tree and assigning pseudonyms to the existing members. Here, the client refers to the data generator who introduce random noise into their power consumption readings. Then, they use Paillier homomorphic encryption to facilitate the additive computability and secrecy of their reading data. They sign their ciphertexts to enable its integrity verification. The successfully verified ciphers are progressively aggregated by their parent servers. Finally, the root server delegates the final aggregate decryption to TA. The distributed architecture of our scheme supports pseudonymized data processing from multiple participants. We present the experimental results and security proofs to demonstrate the usability of our scheme.