Reviewerly/exHarmony huggingface dataset:
Private and Verifiable Inter-bank Transactions and Settlements on Blockchain

Blockchain based inter-bank payments enhance accountability, transparency and ensure faster payments by recording transactions on a shared ledger, eliminating reconciliation needs. However, a blockchain network can only validate that a bank (or a user) transacts with another bank (or another user). It records only a portion of transactions and cannot verify debits/credits between sender/receiver bank as balances are private to the banks. In this paper, we propose a solution based on permissioned blockchain to address privacy concern of customer financial information such as balances, while providing cryptographic guarantees for transaction debits and credits without revealing customer balances and transaction amounts. Our solution offers fully auditable inter-bank payments as well as faster end-of-cycle settlements using cryptographic primitives such as bilinear pairings for enhancing customer privacy. Bilinear pairings such as BLS are used to create verifiable proofs, called the authenticators, for transaction amounts and customer balances, striking a balance between user privacy and transaction auditability. These BLS signatures (authenticators) are stored on blockchain act as global proof for enabling verifiable audits. Furthermore, blockchain ensures data transparency among banking entities and enables automation of settlement process through smart contracts. We demonstrate practical performance and security guarantees through evaluation.
Pupil vs. Eyelid: Evaluating the Accuracy of Blink Detection in Pupil-based Eye Tracking Devices

Spontaneous eye blinks as a means to tap into human cognitive and affective processes have been demonstrated to offer valuable insight in various fields of application, such as human-computer-interaction and driver drowsiness detection. Furthermore, studies focusing on gaze tracking and pupillometry have to deal with eye blinks as artifacts, leading to distortions in gaze and pupil data. Accurate detection of blinks and calculation of their duration is, consequently, essential for both areas of research and development. In the present study, the accuracy of standard blink detection approaches based on pupil-based eye trackers was investigated with respect to blink durations as well as blink occurrence detection for different eye closure thresholds. We compared the results from two different detection algorithms and two eye tracking devices against ground truth data, i.e. blink events that were labeled manually on a frame-by-frame basis from video recordings. The Eye Aspect Ratio was used for the estimation of blink amplitudes and eye closure. The results reveal significant differences in blink detection rate and blink duration depending on the method used. Blink detection by pupil-based eye tracking devices is demonstrated to be systematically influenced by the choice of device, detection algorithm, and blink definition. Our results highlight the importance of discussing different approaches to blink detection when comparing blink metrics across different devices and applications. In addition, we encourage dialogue within the community to transparently communicate blink detection methods and discuss their impact on study outcomes.
Virtual Hydrogen, a virtual reality education tool in physics and chemistry

In efforts to explore possibilities of virtual reality (VR) as education tools, we focus on one of the most important subjects taught in undergraduate physics and chemistry classes, namely, the quantum atomic orbitals or wave functions of the hydrogen atom. Our goal is to enable students to experience attractive and enjoyable scenarios which make it more amenable to master the concepts of quantum mechanics or quantum chemistry. Along this line, we have built a virtual environment of the hydrogen atom, named “Virtual Hydrogen”, enabling to explore the atomic orbitals in 3D space. The most fundamental 21 atomic orbitals of hydrogen have been successfully visualized and will be experienced through modern VR head-mounted displays.
Temporal relation identification in functional requirements

In this study, we propose a method for applying a temporal relation identification model to functional requirements. We discuss the limited availability of data in the requirements engineering domain compared to other fields when used for supervised learning, and therefore employ a corpus from the news domain for training. The experimental results demonstrate that the types of temporal relations present in functional requirements are limited, indicating that focusing on learning with a narrowed set of labels is effective. Additionally, We incorporate Dependency Path (DP) into the temporal relation identification model and report, through comparative experiments, that leveraging DP is effective, but minor modifications to DP do not lead to significant improvements in accuracy. By demonstrating specific application methods of temporal relation identification in requirements engineering, we anticipate contributing to the analysis of functional requirements in software development.
Automating Lexicon Generation: A Comprehensive Review of Alternative Approaches

Lexicon-based approaches to Document Classification are widely used, but the manual construction of lexicons can be time-consuming and resource-intensive. In this paper, we propose methods for automating the generation of lexicons later used for Document Classification. We explored diverse methods for generating lexicons, including semantic matches, frequency-based approaches, machine learning algorithms, and large language model techniques. We, later, used these lexicons to classify documents based on their content. By comparing our different lexicons results on a same task, based on criteria such as scalability and the F1 score, we determine optimized use-case for those methods. We show that our automated approaches are effective and efficient, producing accurate classifications with minimal human intervention. Some approaches have the potential to streamline the document classification process, reducing the time and resources required for manual lexicon generation, it also gives optimized use-case for the different methods. Thereafter, we discussed the obtained results.
Should supervised discretisation always be trusted unreservedly? On combining characteristics of supervised and unsupervised discretisation algorithms in two-step processing

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches.
An Approach of Interconnecting Romanian Lexical Resources

In this paper we will present an approach for interconnecting some of the most important lexical resources for the Romanian language, among which the Corpus for Contemporary Romanian Language (CoRoLa), Romanian WordNet, the Thesaurus Dictionary of Romanian Language in Electronic Format (eDTLR). We will show how they are indexed to each other in order to obtain a standard interconnected structure, and then, we will present a general architecture of a system dedicated to interconnecting lexical resources "on the fly".
Open versus Closed: A Comparative Empirical Assessment of Automated News Article Tagging Strategies

Automatically tagging news articles is a fundamental task for indexing and analyzing news streams. Most news portals a form of article tagging ontology, which is a predefined set of tags or keywords that are assigned to news articles to improve their discoverability and relevance. However, manually categorizing and tagging news articles using an ontology is a time-consuming and labor-intensive task, especially for news portals that publish a large volume of articles each day. The automation of the news article tagging process using machine learning techniques has emerged as a promising solution to address the challenge of manually categorizing and tagging a large volume of articles each day. In this paper, we explore the effectiveness of different machine learning approaches for automatically tagging news articles. Specifically, we train and compare several models using both closed-ontology and open-ontology approaches. We evaluate the performance of these models based on their ability to accurately categorize and tag news articles using comparative human evaluation. Our findings provide insights into the advantages and limitations of each approach and highlight the potential applications of automatic news article tagging.
Unsupervised text feature selection using NSGA II with Hill Climbing local search

This paper introduces a novel unsupervised text feature selection technique that combines the multi-objective evolutionary algorithm NSGA II with a local Hill Climbing based search. The objective functions in NSGA II are an adapted version of the Mean Absolute Difference criterion and the Holes projection pursuit index, both of which incorporate information regarding the cardinality of the selected feature subset. The local Hill Climbing procedure aims to further improve the solutions provided by NSGA II through elimination/replacement of redundant features. The redundancy level of a feature F in a chromosome is measured via the lexical and semantic similarities between F and the other features represented in the chromosome. Experiments conducted on three real-world textual subdatasets indicate that our proposed approach achieves very good results in comparison to four state-of-the-art evolutionary unsupervised feature selection techniques and to one classical feature extraction method with proven effectiveness (Principal Component Analysis).
Student modeling considering learning behavior history with deep factorization machines

To use an intelligent tutoring system (ITS) in an educational setting effectively, it is necessary to understand the skill status of students and recommend appropriate questions. Existing studies focused on improving the performance of ITS using student modeling to estimate the skill status of students. Knowledge tracing (KT) is the mainstream student modeling method, and deep learning approaches such as deep knowledge tracing and self-attentive knowledge tracing have been studied extensively in recent years. These models take only the questions solved by the student and the correct or incorrect answers to those questions as input; they do not assume the use of other features. In this study, we perform student modeling using DeepFM and FiBiNET that combines factorization machines and deep learning. Our results indicate that these models are more accurate than KT because of their ability to cope with sparse data and consider pairwise feature interactions, and more suited for real-world applications than KT.
What is supposed to help, actually hinders: about the impact of the GUI dynamics on the efficiency of gaze text entry

The research aimed to test the influence on text entry efficiency of supporting features in gaze text entry systems (aka gaze keyboards), leading to dynamic changes in key layout and size. We examined three gaze-controlled on-screen keyboards, that differ in the degree of support, which translates into the amount of change that occurs in the arrangement of the keys and their size - three degrees of dynamism of keyboards' graphical user interface. We also attempted to examine the importance of user attention while using dynamic interface and its impact on text input performance.
Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images

Pathological anomalies exhibit diverse appearances in medical imaging, making it difficult to collect and annotate a representative amount of data required to train deep learning models in a supervised setting. Therefore, in this work, we tackle anomaly detection in medical images training our framework using only healthy samples. We propose to use the Masked Autoencoder model to learn the structure of the normal samples, then train an anomaly classifier on top of the difference between the original image and the reconstruction provided by the masked autoencoder. We train the anomaly classifier in a supervised manner using as negative samples the reconstruction of the healthy scans, while as positive samples, we use pseudo-abnormal scans obtained via our novel pseudo-abnormal module. The pseudo-abnormal module alters the reconstruction of the normal samples by changing the intensity of several regions. We conduct experiments on two medical image data sets, namely BRATS2020 and LUNA16 and compare our method with four state-of-the-art anomaly detection frameworks, namely AST, RD4AD, AnoVAEGAN and f-AnoGAN.
Multi-Source Domain Adaptation with Incomplete Source Label Spaces

Domain adaptation is a practicable tool in real world application where there exists data scarcity. Multi-source domain adaptation attracts increasing attention due to its ability to enrich transfer knowledge by combining information from multiple domains. However, knowledge transfer can trigger privacy concerns by accessing source data. In addition, multiple domains can have label heterogeneity problem. In this paper, to solve the mentioned problems, we conduct an incomplete multi-source domain adaptation (IMSDA) method which can address transfer learning with and without the access to source data. As far as we are aware, this is the first work handling source-free incomplete domain adaptation. To take the benefits of multiple sources, multi-task learning is adopted to learn a general source model which can perform on multiple domains. A data matching strategy with and without source data forcing target sample to source latent feature space is developed to combine with self-supervision to adapt source model to the target domain. Experiments on real-world datasets indicate the superiority of the proposed method.
New Approaches for String Site-Removal Problem

The string site-removal problem (SSRP) originates from the field of computational biology and is known to be NP-hard. The problem is defined on the set of strings of equal length and an integer k, and its goal is to find the largest subset of the strings that become identical after removing at most k sites (columns). This paper proposes two new approaches to SSRP. The first is an exact algorithm that uses the quadratically constrained program (QCP). The second approach combines QCP and genetic algorithm (GA). In a series of experiments, we showed that our exact program performs faster on average than the state-of-the-art method (Integer Linear Programming, ILP). Regarding heuristic approaches, our new proposal gives the identical results as the state-of-the-art reference method (Large Neighborhood Search, LNS) with the same CPU time budget. All source codes and benchmark instances we made publicly available via GitHub.
Joint Decision Making in Ant Colony Systems for Solving the Multiple Traveling Salesman Problem

The Multiple Traveling Salesman Problem (multiple-TSP) is a straightforward extension of the well-known Traveling Salesman Problem (TSP), in which more salesmen must visit a set of interconnected cities. Ant Colony Optimization (ACO) algorithms are designed to build sequentially the solutions, aspect which on multiple-TSP imposes new challenges. Compared to TSP which deals with one sample space - the set of cities (locations), multiple-TSP involves two sample spaces: the set of salesmen (agents) and the set of cities. Existing ACO algorithms addressing multiple-TSP are two-phase sampling procedures which, firstly, independently sample from the first set (the set of salesmen), and then conditionally sample from the second set. Our claim is that a joint sampling mechanism, which will exploit a joint probability space, is likely to lead to superior results. We validate our hypothesis by implementing five ACO-based algorithms to solve multiple-TSP: three of them are two-phase algorithms exploring various methods to sample from the salesmen space, while two of them implement the joint sampling scheme. The results are analyzed both in a single-objective manner that considers the minimization of the longest tour, and also from a bi-objective perspective that considers two conflicting objectives: 1) minimization of the total traveled distance and 2) work balancing – which amounts to minimizing the amplitude of the costs of individual tours.
Linear Discriminant Analysis Tumour Classification for Unsupervised Segmented Mammographies

Between 2015 and 2020, 7.8 million women were diagnosed with breast cancer. If the cancer is discovered early, it can be completely cured. Computer-aided detection and diagnosis systems are a helpful tool. We propose such a system: after pre-processing the mammography, the region of interest is identified using an unsupervised manner. Textural features are extracted from the Gray-Level Co-occurrence Matrix and used with the Linear Discriminant Analysis classifier, obtaining a diagnosis: benign or malignant. The proposed system is tested on the Mini-MIAS dataset, reaching an accuracy score of 95% and a precision and specificity of 100%.
Expert-adapted language models improve the fit to reading times

The concept of surprisal refers to the predictability of a word based on its context. Surprisal is known to be predictive of human processing difficulty and is usually estimated by language models. However, because humans differ in their linguistic experience, they also differ in the actual processing difficulty they experience with a given word or sentence. We investigate whether models that are similar to the linguistic experience and background knowledge of a specific group of humans are better at predicting their reading times than a generic language model. We analyze reading times from the PoTeC corpus [15,27] of eye movements from biology and physics experts reading biology and physics texts. We find experts read in-domain texts faster than novices, especially domain-specific terms. Next, we train language models adapted to the biology and physics domains and show that surprisal obtained from these specialized models improves the fit to expert reading times above and beyond a generic language model.
Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.
Object Detection with YOLOv5 in Indoor Equirectangular Panoramas

With growing interest in indoor environment digitization and virtual tour creation, the acquisition of spherical images is gaining importance. Today, these images can come from a wide variety of devices, from dedicated industrial scanners through semi-professional sports cameras to smartphones equipped with the proper software. They also constitute a crucial input for smart indoor systems, such as automatic vacuum cleaners or surveillance cameras, and are essential for the construction of a building's digital twin. One of the most popular data representations used for spherical pictures is an equirectangular panorama (ERP). Although popular, processing it is demanding - ERP is heavily distorted, typically of high resolution, and made with discontinued lighting conditions. Transferring it to the cloud is challenging and the in-place analysis on the edge device is complicated. Nevertheless, ERPs store massive amounts of data, especially in the context of the number of objects captured in a single image. Many techniques dedicated to aberration-resilient object detection were presented. However, they all required proper preprocessing of the data or changed the detection approach so much that it made them unfit for any other task. In this paper, we present research on the processing of an indoor equirectangular panorama (I-ERP) using a family of object detectors called YOLOv5 (You Only Look Once, fifth generation). We discuss the process of adjusting an already available dataset to a newer annotation representation and test the usefulness of these detectors on such input without any changes to the detector models themselves. The results achieved indicate that the newer generation of YOLO detectors can be used with satisfactory results on the panoramic input and that the performance gap in I-ERP processing between dedicated and out-of-the-shelf solutions is closing.
How Normalization Strategies Affect the Quality of Rank Aggregation Methods in Recommendation Systems

Many recommendation algorithms have been proposed in the literature that generates personalized recommendations. These recommendations are often presented to the user as an ordered list of suggested items (so-called top-N recommendation). However, despite years of research, no algorithm has been proposed to generate high-quality recommendations for all users in the system. To improve the quality of the final recommendation, aggregation techniques may be employed to combine the results returned by several recommendation algorithms. However, prior to the aggregation process, various normalization techniques are often utilized. This paper will present the results of experiments designed to investigate how different normalization strategies affect the quality of the created aggregation. The research was conducted using four such strategies and ten unsupervised aggregation methods on the publicly available MovieLens 100k dataset. Results were validated by statistical tests.