Reviewerly/exHarmony huggingface dataset:
Identification of Subjects Wearing a Surgical Mask from Their Speech by Means of X-vectors and Fisher Vectors

This paper addresses the classification of speech recorded from subjects while wearing a surgical mask. Here, we employ two different types of feature extraction methods: the x-vectors embeddings, which is the current state-of-the-art approach for Speaker Recognition; and the Fisher Vector (FV), that is a method originally intended for Image Recognition, but here employed to discriminate utterances. These approaches make use of distinct frame-level representations: MFCC and PLP. Using Support Vector Machines (SVM) as the classifier, we perform a technical comparison between the performances of the FV encodings and the x-vector embeddings for this particular classification task. We find that the Fisher vector encodings provide better representations of the utterances than the x-vectors do for this specific dataset. Moreover, we show that a fusion of our best configurations outperforms all the original baseline scores.
Estimation of Prediction Error with Regression Trees

Prediction is a common task useful in data analysis. The goal is to predict the value of a variable in terms of the values of other variables. The most used technique for solving prediction task is regression analysis that approximates the data at hand by means of a poly- nomial function. Because data can be represented as a cloud of points, the approximation by means of a polynomial function has error. Most of authors try to search for techniques allowing a more accurate prediction than the one produced by the regression model. In this paper we propose a completely different approach. Our goal is, given a predictive model, use regression trees to establish the error of the measure proposed by the predictive model.
Bistochastic Privacy

We introduce a new privacy model relying on bistochastic matrices, that is, matrices whose components are nonnegative and sum to 1 both row-wise and column-wise. This class of matrices is used to both define privacy guarantees and a tool to apply protection on a data set. The bistochasticity assumption happens to connect several fields of the privacy literature, including the two most popular models, k-anonymity and differential privacy. Moreover, it establishes a bridge with information theory, which simplifies the thorny issue of evaluating the utility of a protected data set. Bistochastic privacy also clarifies the trade-off between protection and utility by using bits, which can be viewed as a natural currency to comprehend and operationalize this trade-off, in the same way than bits are used in information theory to capture uncertainty. A discussion on the suitable parameterization of bistochastic matrices to achieve the privacy guarantees of this new model is also provided.
Improvement of Estimate Distribution with Local Differential Privacy

Personalized high-quality services including route finding and detection of the nearest shops and restaurants are provided based on the current location of the owner of the smart device. However, location trace data are very sensitive data because of privacy concerns. They allow to estimate our home residence or office location to be estimated. Hence, privacy preservation is required for reporting current location traces from smart devices. This paper studies the privacy preservation of time-series location trace using the local differential privacy (LDP) algorithm Randomized Aggregable Privacy-Preserving Ordinal Response (RAPPOR). Location trace is independently randomized according to given procedures and then is sent to a service provider that aggregates data with noise. To discard noise and estimate true statistics, the maximum likelihood estimation is used in RAPPOR. However, maximum likelihood estimation (MLE) could fail if the data distribution is skewed or the data contain extraordinary-values. To address the problem, we propose the expectation maximization for estimating of true distributions. The proposed algorithm iteratively improves estimated posterior probabilities based on Bayes’ theorem until the difference converges for all elements. Our experiment using 6,528 individuals’ location traces in Tokyo provided from Nightley Inc. demonstrates that the proposed algorithm performs better than the original MLE used in RAPPOR for every special ward in Tokyo in one day. We found that the accuracy is improved as the privacy budget $$\epsilon $$ decreases and as many populations are provided.
Representation and Interpretability of IE Integral Neural Networks

While there has been a lot of research attention given to neural networks and other black-box machine learning methods, recent works on aggregation functions and fuzzy sets have highlighted the appeal of incorporating fuzzy integrals into network implementations in order to achieve interpretability. We present an application of the recently proposed inclusion-exclusion integral neural network to the Boston House-Price dataset to illustrate its potential and examine the settings leading to better performance.
An Analysis of Byzantine-Tolerant Aggregation Mechanisms on Model Poisoning in Federated Learning

Federated learning is a distributed setting where multiple participants jointly train a machine learning model without exchanging data. Recent work has found that federated learning is vulnerable to backdoor model poisoning attacks, where an attacker leverages the unique environment to submit malicious model updates. To address these malicious participants, several Byzantine-Tolerant aggregation methods have been applied to the federated learning setting, including Krum, Multi-Krum, RFA, and Norm-Difference Clipping. In this work, we analyze the effectiveness and limits of each aggregation method and provide a thorough analysis of their success in various fixed-frequency attack settings. Further, we analyze the fairness of such aggregation methods on the success of the model on its intended tasks. Our results indicate that only one defense can successfully mitigate attacks in all attack scenarios, but a significant fairness issue is observed, highlighting the issues with preventing malicious attacks in a federated setting.
Construction of Network Organization Structure of College Students’ Education Management Based on Distributed Network

To construct the network organizational structure of college students’ educational management and realize the informatization development of college students’ educational management, the construction method of network organizational structure of college students’ educational management is proposed based on the distributed network. MySQL and PostgreSQL are used as the bottom data structure models of the network-based organizational structure of college students’ education management, and the multisource information scheduling method, which is used to construct the transaction identifier (TID) labeling tuple model of college students’ education management network. The initial topological structure model of the network-based organizational structure nodes of college students’ education management network is constructed by the 4-tuple model, and the linear structure decomposition of the construction nodes of the network-based organizational structure of college students’ education management is carried out. The correlation characteristics of the transmission channel of the network organization structure of college students’ education management are extracted; the three-layer architecture system of data layer, network layer, and application layer is adopted; the architecture model of the network organization structure of college students’ education management is established; the multinode distributed network transmission technology is used to build a tree structure model, so as to realize the design of the network organization structure model of college students’ education management and the scheduling of multi-source information parameters; and the fuzzy multi-attribute decision-making method to realize the adaptive scheduling of the network organization structure of college students’ education management is adopted, so as to improve the storage management, fault tolerance, and safe access ability of the network organization structure of college students’ education management. The test results show that the network organizational structure of college students’ educational management is designed by this method, which improves the ability of information transmission and scheduling and improves the informatization level of college students’ educational management.
Goppa codes over the p-adic integers and integers modulo p

Goppa codes were defined by Valery D. Goppa in 1970. In 1978, Robert J. McEliece used this family of error-correcting codes in his cryptosystem, which has gained popularity in the last decade due to its resistance to attacks from quantum computers. In this paper, we present Goppa codes over the p-adic integers and integers modulo pe. This allows the creation of chains of Goppa codes over different rings. We show some of their properties, such as parity-check matrices and minimum distance, and suggest their cryptographic application, following McEliece's scheme.
Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals

A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient's hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.
Adaptive preference transfer for personalized IoT entity recommendation

• An IoT entity recommendation method based on adaptive preference transfer is proposed. • A lightweight user feedback feature embedding method is proposed to solve the problem of sparse user feedback. • The rigorous experiments are designed to verify the validity of the proposed method. Internet of Things (IoT) recommendation can effectively improve the convenience and intelligence of users in obtaining information about interested physical entities in a huge entity search space. However, existing transfer learning-based recommendation methods mainly focus on Internet resources and services, lack personalized transfer weight consideration for different user preferences, and fail to address the entity rating matrix sparsity problem, resulting in limited IoT entity recommendation performance. Thus, we design an adaptive preference transfer IoT entity (APTE) recommendation method in this paper. Firstly, considering various user preference characteristics, an adaptive dual-domain transfer model for item domain and social domain is designed to meet users’ personalized requirements. Then, a lightweight user feedback embedding method is proposed to mine the explicit and implicit features and to embed the entity rating matrix to solve the problem of sparse user feedback information. Simulation results demonstrate that APTE can effectively improve the recommendation performance compared with state-of-the-art baselines.
The LACRIMALit Ontology of Crisis: An Event-Centric Model for Digital History

The article presents the building of an event-centric model for the computational representation of crisis events using an ontology encoded in the Web Ontology Language (OWL). The work presented here is done in collaboration with the Leaders and Crisis Management in Ancient Literature. A Comparative Approach (LACRIMALit) project, (2022–2025) hosted at the Institute for Mediterranean Studies/Foundation for Research and Technology (IMS-FORTH). A key outcome of the project is the LACRIMALit ontology that aims principally at the semantic annotation of ancient Greek historiographical texts in open access via Perseus Digital Library. The ontology will facilitate reasoning on and across these documents and enable their semantic querying. The tagset of annotations, concepts, relations, and terms of the ontology will be both human and machine readable, extensible and reusable. The annotated corpus of texts to be produced will be available for sophisticated queries based on the concepts and relations, defined by the ontologies. This will considerably improve the string-based querying of the texts in their present digital format. This article presents the principles of conceptualization of the domain in the three dimensions: domain knowledge (mainly classes illustrated with some individuals), linguistic dimension (terms, proper names, definite descriptions), and references.
Tandem Outlier Detectors for Decentralized Data

Today, the collection of decentralized data is a common scenario: smartphones store users’ messages locally, smart meters collect energy consumption data, and modern power tools monitor operator behavior. We identify different types of outliers in such data: local, global, and partition outliers. They contain valuable information, for example, about mistakes in operation. However, existing outlier detection approaches cannot distinguish between those types. Thus, we propose a “tandem” technique to join “local” and “federated” outlier detectors. Our core idea is to combine outlier detection on a single device with latent information about devices’ data to discriminate between different outlier types. To the best of our knowledge, our method is the first to achieve this. We evaluate our approach on publicly available synthetic and real-world data that we collect in a study with 15 participants operating power tools.
News topic detection based on the principle of minimum entropy

Current topic detection methods generally use different algorithms to aggregate the extracted features to obtain corresponding topics, but do not fully utilize the features of news texts and key elements of news. For this, a news topic detection model based on the principle of minimum entropy (ME-NTD) is proposed. First, we extract news keywords through TextRank, and extract news entity elements with the help of text lexical analysis tools. Then, we map news texts into text vectors through word embedding, calculate text association weights by combining news entity elements, and construct text association graphs through text vectors and text association weights. Finally, we use the idea of hierarchical coding based on the principle of minimum entropy to randomly walk the nodes on the text association graph to achieve hierarchical coding of topics and objects within topics. Experimental results on three news datasets show that the ME-NTD model has better performance and efficiency than the comparative methods.
Building Natural Language Interfaces for Databases in Practice

Natural language interfaces to databases have recently made substantial progress due to advances in machine learning. Users no longer need technical knowledge to search for insights in their database. However, research is largely focused on increasing the one-shot accuracy, instead of building systems that interact with and guide a user’s search. In this demo, we present Veezoo, an AI-powered data analytics platform that enables users to directly talk to their databases.
PM-Rtree: A Highly-Efficient Crash-Consistent R-tree for Persistent Memory

Persistent R-trees are important data structures for indexing large-scale spatial datasets using persistent memory (e.g., Intel Optane DIMMs). Existing persistent R-trees (e.g., FBR-tree) suffer from four major issues. (1) Node updates cause unnecessary writes to persistent memory, leading to high latency. (2) The locking overhead is high under high thread concurrency. (3) They support a limited number of maximum bounding rectangles on each node. (4) The persistent overhead of managing its bitmaps in persistent memory is high for repeatedly cache line reflushing.
Facilitating DoS Attack Detection using Unsupervised Anomaly Detection

Modern techniques in intrusion and DoS (Denial of Service) detection tend to be either supervised or semi-supervised, i.e., they require training and labelled data. In this work, we study the problem of correlating security attacks with anomalies reported at runtime by a fully unsupervised outlier detection module, i.e., a component that does not require any training at all. Through a concrete proof-of-concept case study, we demonstrate that unsupervised anomaly detection is both efficient and effective, but still, it needs to be combined with additional mechanisms to yield a complete intrusion detection and prevention solution.
Application Overview of Quantum Computing for Gas Turbine Design and Optimization

Conceptual designs require optimization methods to identify the best fit in the system. The article investigates the application of quantum computation in gas turbine design and simulation problems with current technologies, approaches and potential capabilities. Quantum optimization algorithms and quantum annealers help in predicting overall efficiency and optimizing various operating parameters of the gas turbine. A comparison of both classical and quantum computers has been discussed briefly. The classical model challenges are mitigated with the use of quantum computation. A novel hybrid model for simulating gas turbines has been proposed, which consists of a combination of both physics and machine learning to eliminate few of the critical problems faced. This review elaborates application of quantum computing based machine learning for design and optimization of a gas turbine. The overall states of the gas paths of gas turbines could be analyzed using the quantum computing model in the future.
Blockchain in Trust with Reputation Management for Financial Stock Market Using Distributed Ledger Technology and Bayesian Theory Based on Fault Tolerance Model

Blockchain, which is at the heart of bitcoin and other digital currencies, is a game-changing technology, particularly in the stock market and financial technology. These characteristics make blockchain adoption appealing for enhancing information security, privacy and reliability in a variety of scenarios. This article develops the trust with reputation-based management for the financial stock market. Here distributed ledger technology (DLT) has been integrated for implementing the ledger in which data is stored across a network of decentralized nodes. By this, proof-based ledger has been maintained with transparency, traceability and security. DLT here used is based on trust design in mitigating the malicious activities of the financial stock marketing technology. Then using Bayesian theory based on the fault tolerance (BDLT_FT) model has been used in improving the efficiency of DLT and financial technology. This acts as the protocol which reduces energy consumption and enhance network scalability and security in digital transactions like cryptocurrency The financial data related to blockchain cryptocurrency were collected between the time period 2000 and 2020. The dataset of the stock market is evaluated under the fault tolerance model with statistical analysis. The statistical evaluation of financial data with developed BDLT_FT expressed that there is no association between the fault tolerance and the classification error.
Relationship of Reward Systems with Creativity of Officials and Pars Abad Health Network Staff

Background & objectives: Considering the rewards, intrinsic motivation and trusting the goodwill can provide a situation for employee creativity that is vital to the survival of the organization. Therefore, considering the important role of creative employees in organizations, the goal of present research was to investigate the relationship between reward systems and staff creativity of Pars Abad health network
NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups

Real-world network data consisting of social interactions can be incomplete due to deliberately erased or unsuccessful data collection, which cause the misleading of social interaction analysis for many various time-aware applications. Naturally, the link prediction task has drawn much research interest to predict the missing edges in the incomplete social network. However, existing studies of link prediction cannot effectively capture the entangling topological and temporal dynamics already residing in the social network, thus cannot effectively reasoning the missing interactions in dynamic networks. In this paper, we propose the NEAWalk, a novel model to infer the missing social interaction based on topological-temporal features of patterns in the social group. NEAWalk samples the query-relevant walks containing both the historical and evolving information by focusing on the temporal constraint and designs a dual-view anonymization procedure for extracting both topological and temporal features from the collected walks to conduct the inference. Two-track experiments on several well-known network datasets demonstrate that the NEAWalk stably achieves superior performance against several state-of-the-art baseline methods.
Clone detection for business process models

Models are key in software engineering, especially with the rise of model-driven software engineering. One such use of modeling is in business process modeling, where models are used to represent processes in enterprises. As the number of these process models grow in repositories, it leads to an increasing management and maintenance cost. Clone detection is a means that may provide various benefits such as repository management, data prepossessing, filtering, refactoring, and process family detection. In model clone detection, highly similar model fragments are mined from larger model repositories. In this study, we have extended SAMOS (Statistical Analysis of Models) framework for clone detection of business process models. The framework has been developed to support different types of analytics on models, including clone detection. We present the underlying techniques utilized in the framework, as well as our approach in extending the framework. We perform three experimental evaluations to demonstrate the effectiveness of our approach. We first compare our tool against the Apromore toolset for a pairwise model similarity using a synthetic model mutation dataset. As indicated by the results, SAMOS seems to outperform Apromore in the coverage of the metrics in pairwise similarity of models. Later, we do a comparative analysis of the tools on model clone detection using a dataset derived from the SAP Reference Model Collection. In this case, the results show a better precision for Apromore, while a higher recall measure for SAMOS. Finally, we show the additional capabilities of our approach for different model scoping styles through another set of experimental evaluations.