Reviewerly/exHarmony huggingface dataset:
Evaluating the Performance of Speaker Recognition Solutions in E-Commerce Applications

Two important tasks in many e-commerce applications are identity verification of the user accessing the system and determining the level of rights that the user has for accessing and manipulating system’s resources. The performance of these tasks is directly dependent on the certainty of establishing the identity of the user. The main research focus of this paper is user identity verification approach based on voice recognition techniques. The paper presents research results connected to the usage of open-source speaker recognition technologies in e-commerce applications with an emphasis on evaluating the performance of the algorithms they use. Four open-source speaker recognition solutions (SPEAR, MARF, ALIZE, and HTK) have been evaluated in cases of mismatched conditions during training and recognition phases. In practice, mismatched conditions are influenced by various lengths of spoken sentences, different types of recording devices, and the usage of different languages in training and recognition phases. All tests conducted in this research were performed in laboratory conditions using the specially designed framework for multimodal biometrics. The obtained results show consistency with the findings of recent research which proves that i-vectors and solutions based on probabilistic linear discriminant analysis (PLDA) continue to be the dominant speaker recognition approaches for text-independent tasks.
No-Reference Image Quality Assessment Based on Edge Pattern Feature in the Spatial Domain

This paper proposes a general-purpose no-reference image quality assessment (NR-IQA) method that investigates the image’s structure information from a new aspect, i.e., the characteristic of image edge profiles that depict the directional property of adjacent edge points in the spatial domain of the image. More specifically, we extracted the image’s edge map based on Laplacian of Gaussian (LoG) filtration and zero-crossing (ZC) detection and refined the edge map to be 1-pixel wide. We then explored the edge map by investigating edge profiles’ statistics in a local window with a 5×5-pixel size. Considering the consensus that natural images consist of directional structures, we found that the spatial distribution property of adjacent edge points can be represented through several edge profiles called edge patterns, which are selected from natural images with a proposed smooth criterion. With the proposed edge patterns and their statistical histogram for the image and the support vector regression technique, we proposed the NR-IQA model based on the edge patterns in the spatial domain, named EPISD. The proposed method has been extensively validated on the LIVE, CSIQ, TID2013, MDID2017, SIQAD, and SCID databases. The experimental results showed that EPISD has a competitive performance with state-of-the-art methods and works stably across different databases.
Permissioned Healthcare Blockchain System for Securing the EHRs with Privacy Preservation

Healthcare data is very sensitive as many healthcare organizations will be very reluctant to share health data. However, sharing the healthcare data is having many more uses for both the patients as well as the research institutions too. Moreover, the existing Electronic Healthcare Record (EHR) management system will be stored in the central database in the form of plaintext. Whenever the data needs to be accessed from the database, the users will be requesting the required EHRs. However, this mechanism possesses the several challenges such as single point of failure, takes more time for user identification, interoperability issues, data recoverability issues, lack of privacy and security. This paper mainly focuses on providing security for the healthcare data, which can be shared among the various health institutions. Authentication and authorization are provided by establishing multiple certification authorities on the permissioned healthcare blockchain network. In this proposed model data integrity is also achieved by the concept of hashing of the electronic health records rather than storing it directly onto the permissioned healthcare block chain network.
Using Compositional Embeddings for Fact Checking

Unsupervised fact checking approaches for knowledge graphs commonly combine path search and scoring to predict the likelihood of assertions being true. Current approaches search for said metapaths in the discrete search space spanned by the input knowledge graph and make no use of continuous representations of knowledge graphs. We hypothesize that augmenting existing approaches with information from continuous knowledge graph representations has the potential to improve their performance. Our approach Esther searches for metapaths in compositional embedding spaces instead of the graph itself. By being able to explore longer metapaths, it can detect supplementary evidence for assertions being true that can be exploited by existing fact checking approaches. We evaluate Esther by combining it with 10 other approaches in an ensemble learning setting. Our results agree with our hypothesis and suggest that all other approaches can benefit from being combined with Esther by 20.65% AUC-ROC on average. Our code is open-source and can be found at https://github.com/dice-group/esther.
Credit Card Fraud Detection using PSO Optimized Neural Network

Credit card fraud is one of the most important problems that financial institutions are currently facing. Although the technology has allowed to increase the security in the credit cards with the use of PIN keys, the introduction of chips in the cards, the use of additional keys such as tokens and improvements in the regulation of its use is also a necessity for banks, to act preventively against this crime. To act preventively, it is necessary to monitor in real time the operations that are carried out and have the ability to react in a timely manner against any doubtful operation that is performed. This paper presents an implementation of automatic credit card fraud detection system using Particle Swarm Optimized Neural Network classifier on Kaggle dataset. The selection of proper attributes for reducing the training overhead and claiming higher accuracy for the fraud detection using soft computing. Performance evaluation is achieved using confusion matrix plot with accuracy, sensitivity and precision values.
Secure Authentication for Banking Using Face Recognition

With the increasing demand for online banking lack of security in the system has been felt due to a tremendous increase in fraudulent activities. Facial recognition is one of the numerous ways that banks can increase security and accessibility. This paper proposes to inspect the use of facial recognition for login and for banking purposes. The potency of our system is that it provides strong security, username and password verification, face recognition and pin for a successful transaction. Multilevel Security of this system will reduce problems of cybercrime and maintain the safety of the internet banking system. The end result is a strengthened authentication system that will escalate the confidence of customers in the banking sector.
Time, Privacy, Robustness, Accuracy: Trade-Offs for the Open Vote Network Protocol

Abstract The open vote network ( [10]) is a secure two-round multi-party protocol facilitating the computation of a sum of integer votes without revealing their individual values. This is done without a central authority trusted for privacy, and thus allows decentralised and anonymous decision-making efficiently. As such, it has also been implemented in other settings such as financial applications, see e.g. [15, 17]. An inherent limitation of is its lack of robustness against denial-of-service attacks, which occur when at least one of the voters participates in the first round of the protocol but (maliciously or accidentally) not in the second. Unfortunately, such a situation is likely to occur in any real-world implementation of the protocol with many participants. This could incur serious time delays from either waiting for the failing parties and perhaps having to perform extra protocol rounds with the remaining participants. This paper provides a solution to this problem by extending with mechanisms tolerating a number of unresponsive participants, the basic idea being to run several sub-elections in parallel. The price to pay is a carefully controlled privacy loss, an increase in computation, and a statistical loss in accuracy, which we demonstrate how to measure precisely.
Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Abstract Pattern mining is well established in data mining research, especially for mining binary datasets. Surprisingly, there is much less work about numerical pattern mining and this research area remains under-explored. In this paper we propose Mint , an efficient MDL-based algorithm for mining numerical datasets. The MDL principle is a robust and reliable framework widely used in pattern mining, and as well in subgroup discovery. In Mint we reuse MDL for discovering useful patterns and returning a set of non-redundant overlapping patterns with well-defined boundaries and covering meaningful groups of objects. Mint is not alone in the category of numerical pattern miners based on MDL. In the experiments presented in the paper we show that Mint outperforms competitors among which IPD, RealKrimp , and Slim .
Framework for imbalanced data classification

Classifying imbalanced data remains a challenging task. The paper presents a framework for imbalanced datasets classification which makes use of different methods of oversampling and methods of dynamical selection of classifiers. The framework allows to perform extensive experiments to determine best possible configuration for the examined dataset in terms of geometric mean metric (g-mean). The results on benchmark datasets are presented.
Which RESTful API Design Rules Are Important and How Do They Improve Software Quality? A Delphi Study with Industry Experts

Several studies analyzed existing Web APIs against the constraints of REST to estimate the degree of REST compliance among state-of-the-art APIs. These studies revealed that only a small number of Web APIs are truly RESTful. Moreover, identified mismatches between theoretical REST concepts and practical implementations lead us to believe that practitioners perceive many rules and best practices aligned with these REST concepts differently in terms of their importance and impact on software quality. We therefore conducted a Delphi study in which we confronted eight Web API experts from industry with a catalog of 82 REST API design rules. For each rule, we let them rate its importance and software quality impact. As consensus, our experts rated 28 rules with high, 17 with medium, and 37 with low importance. Moreover, they perceived usability, maintainability, and compatibility as the most impacted quality attributes. The detailed analysis revealed that the experts saw rules for reaching Richardson maturity level 2 as critical, while reaching level 3 was less important. As the acquired consensus data may serve as valuable input for designing a tool-supported approach for the automatic quality evaluation of RESTful APIs, we briefly discuss requirements for such an approach and comment on the applicability of the most important rules.
Morphological analysis of mosaic shapes with directed relationships based on attribute and relational model representations

We introduce attribute and relational representations of mosaic image models with directed relationships between regions. Attribute representations of asymmetric relational models based on stacking, ranking and integral descriptions are considered. We propose some morphological shape similarity measures based on relational models. We show that using the same oriented relational model, various morphological operators can be constructed, in particular, of Serra- or Pyt’ev type. Some constructive methods for the design of such morphological operators in an attribute and relational domains are proposed. From this consideration we also extract a new morophlogical scheme for two-stage mutual adaptive image-and-shape joint filtering: at the first step, the shape is simplified (projected) with regard to the image to be projected, and at the second step, the image is simplified (projected) with regard to the simplified (projected) shape.
An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.
Frequentist parameter estimation with supervised learning

Recently, there has been a great deal of interest surrounding the calibration of quantum sensors using machine learning techniques. This work explores the use of regression to infer a machine-learned point estimate of an unknown parameter. Although the analysis is necessarily frequentist—relying on repeated estimates to build up statistics—the authors clarify that this machine-learned estimator converges to the Bayesian maximum a posteriori estimator (subject to some regularity conditions). When the number of training measurements is large, this is identical to the well-known maximum-likelihood estimator (MLE), and using this fact, the authors argue that the Cramér–Rao sensitivity bound applies to the mean-square error cost function and can therefore be used to select optimal model and training parameters. The machine-learned estimator inherits the desirable asymptotic properties of the MLE, up to a limit imposed by the resolution of the training grid. Furthermore, the authors investigate the role of quantum noise in the training process and show that this noise imposes a fundamental limit on the number of grid points. This manuscript paves the way for machine-learning to assist the calibration of quantum sensors, thereby allowing maximum-likelihood inference to play a more prominent role in the design and operation of the next generation of ultra-precise sensors.
A GNN-based Few-shot learning model on the Credit Card Fraud detection

In the era of big data, large-scale data can be very effective in improving model performance. However, in the real world, high-quality data is usually difficult to acquire due to privacy or cost. Especially when it comes to credit card fraud, the fraud samples are quite rare. Detecting card fraud with few samples is a meaningful task. Graph neural network (GNN) is a good way to deal with few samples because an advantage of GNN is that information can be disseminated through connections between nodes. However, the data structure of credit cards cannot be applied by the GNN-based method directly. In this paper, we proposed a GNN-based few-shot learning method which can detect credit card fraud with few samples effectively. We constructed a learnable parametric adjacency matrix method relying on the similarity of features to pass messages and utilized the GCN layer to extract node features. We compared our method with classical machine learning algorithms and other graph neural networks on the real-world data set. Our experimental results show that our proposed model can perform better extremely with fewer training samples than baselines.
A combination of least significant bit and deflate compression for image steganography

Steganography is one of the cryptography techniques where secret information can be hidden through multimedia files such as images and videos. Steganography can offer a way of exchanging secret and encrypted information in an untypical mechanism where communicating parties can only interpret the secret message. The literature has shown a great interest in the least significant bit (LSB) technique which aims at embedding the secret message bits into the most insignificant bits of the image pixels. Although LSB showed a stable performance of image steganography yet, many works should be done on the message part. This paper aims to propose a combination of LSB and Deflate compression algorithm for image steganography. The proposed Deflate algorithm utilized both LZ77 and Huffman coding. After compressing the message text, LSB has been applied to embed the text within the cover image. Using benchmark images, the proposed method demonstrated an outperformance over the state of the art. This can proof the efficacy of using Deflate as a data compression prior to the LSB embedding.
Physical Exercise Quality Assessment Using Wearable Sensors

To ensure health benefits and prevent injuries, the correct execution of fitness exercises is essential, particularly when vulnerable individuals are involved, such as during rehabilitation. As it is difficult for a person to assess the execution quality for themselves and most people cannot afford a personal trainer at all times, an automated assessment of execution quality is desirable. Whereas human activity recognition with modern sensor technologies has become a fundamental topic in scientific research and industry over the past decade, the execution quality of exercises is rarely addressed. In this paper, we assess the applicability of machine learning-based classification to differentiate not just between different fitness exercises, but also their execution quality. For this purpose, we propose three different system variants to recognize three different fitness exercises and at least three typical execution errors each based on acceleration and gyroscope data from up to four body-worn sensors. In our evaluation, we utilize data we recorded from 16 different participants to determine our systems’ recognition performance for different application and implementation scenarios.
Digital Competences of Prospective Engineers and Science Teachers: A Latent Profile and Correspondence Analysis

In the light of ubiquitous information and communication technologies (ICT) it is essential for everyone to be digitally competent. This is particularly true for prospective engineers and teachers since their jobs play an important role in shaping our common future. We assessed engineering and science teacher students’ (n=180) digital competences at two higher education institutions in Germany using the DigComp framework. Applying a group comparison using t-Test analysis we found no differences in the level of digital competences. However, the subsequent latent profile analysis followed by correspondence analysis revealed that high digital competences correspond with a frequent use of various ICT, supporting the theory of the importance of implicit learning. Secondly, the data points towards a reproduction of the patterns of the digital divide showing the influence of socio-economic background and gender on the expression of digital competences. Recommendations for agents (student advisory services, companies, etc.) that wish to support and improve students’ digital competences are incorporated in the conclusions.
The Timbre Explorer: A Synthesizer Interface for Educational Purposes and Perceptual Studies

When two sounds are played at the same loudness, pitch, and duration, what sets them apart are their timbres.This study documents the design and implementation of the Timbre Explorer, a synthesizer interface based on efforts to dimensionalize this perceptual concept.The resulting prototype controls four perceptually salient dimensions of timbre in real-time: attack time, brightness, spectral flux, and spectral density.A graphical user interface supports user understanding with live visualizations of the effects of each dimension.The applications of this interface are three-fold; further perceptual timbre studies, usage as a practical shortcut for synthesizers, and educating users about the frequency domain, sound synthesis, and the concept of timbre.The project has since been expanded to a standalone version independent of a computer and a purely online web-audio version.
Implementasi Deep Learning untuk Entity Matching pada Dataset Obat (Studi Kasus K24 dan Farmaku)

Data processing speed in companies is important to speed up their analysis. Entity matching is a computational process that companies can perform in data processing. In conducting data processing, entity matching plays a role in determining two different data but referring to the same entity. Entity matching problems arise when the dataset used in the comparison is large. The deep learning concept is one of the solutions in dealing with entity matching problems. DeepMatcher is a python package based on a deep learning model architecture that can solve entity matching problems. The purpose of this study was to determine the matching between the two datasets with the application of DeepMatcher in entity matching using drug data from farmaku.com and k24klik.com. The comparison model used is the Hybrid model. Based on the test results, the Hybrid model produces accurate numbers, so that the entity matching used in this study runs well. The best accuracy value of the 10th training with an F1 value of 30.30, a precision value of 17.86, and a recall value of 100.
Uhlmann phase in composite systems with entanglement

We study the geometric Uhlmann phase of entangled mixed states in a composite system made of two coupled spin-$\frac{1}{2}$ particles with a magnetic field acting on one of them. Within a uniform noisy quantum channel setup, an exact analytical expression for this geometric phase in each subsystem is derived. We find an explicit connection to the concurrence of the density matrix of such a channel, which allows us to characterize the features of the Uhlmann phase in terms of the degree of entanglement in the system. In the space of field direction and coupling parameter, it exhibits a phase singularity revealing a topological transition between orders characterized by different winding numbers. The transition occurs for fields lying in the equator of the sphere of directions and at critical values of the coupling which can be controlled by tuning the depolarization strength. Notably, under these conditions the concurrence of the composite system is bounded to the range $[0,1/2]$. We also compare the calculated Uhlmann phase to an interferometric phase, which has been formulated as an alternative for density matrices. The latter does not present a phase vortex, although they coincide in the weak entanglement regime for vanishing depolarization (pure states). Otherwise, they behave clearly different in the strong-entanglement regime.