Reviewerly/exHarmony huggingface dataset:
Causal Graph Discovery from Self and Mutually Exciting Time Series

We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. Furthermore, we develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program to establish confidence intervals for a wide range of non-linear monotone link functions. We validate our theoretical results and show the competitive performance of our method via extensive numerical experiments. Most importantly, we demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs) while achieving comparable prediction performance to powerful ``black-box'' models such as XGBoost. Thus, the future adoption of our proposed method to conduct continuous surveillance of high-risk patients by clinicians is much more likely.
BiBench: Benchmarking and Analyzing Network Binarization

Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width. However, recent research has shown that applying existing binarization algorithms to diverse tasks, architectures, and hardware in realistic scenarios is still not straightforward. Common challenges of binarization, such as accuracy degradation and efficiency limitation, suggest that its attributes are not fully understood. To close this gap, we present BiBench, a rigorously designed benchmark with in-depth analysis for network binarization. We first carefully scrutinize the requirements of binarization in the actual production and define evaluation tracks and metrics for a comprehensive and fair investigation. Then, we evaluate and analyze a series of milestone binarization algorithms that function at the operator level and with extensive influence. Our benchmark reveals that 1) the binarized operator has a crucial impact on the performance and deployability of binarized networks; 2) the accuracy of binarization varies significantly across different learning tasks and neural architectures; 3) binarization has demonstrated promising efficiency potential on edge devices despite the limited hardware support. The results and analysis also lead to a promising paradigm for accurate and efficient binarization. We believe that BiBench will contribute to the broader adoption of binarization and serve as a foundation for future research. The code for our BiBench is released https://github.com/htqin/BiBench .
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates

In recent years, particle-based variational inference (ParVI) methods such as Stein variational gradient descent (SVGD) have grown in popularity as scalable methods for Bayesian inference. Unfortunately, the properties of such methods invariably depend on hyperparameters such as the learning rate, which must be carefully tuned by the practitioner in order to ensure convergence to the target measure at a suitable rate. In this paper, we introduce a suite of new particle-based methods for scalable Bayesian inference based on coin betting, which are entirely learning-rate free. We illustrate the performance of our approach on a range of numerical examples, including several high-dimensional models and datasets, demonstrating comparable performance to other ParVI algorithms with no need to tune a learning rate.
BayesSpeech: A Bayesian Transformer Network for Automatic Speech Recognition

Recent developments using End-to-End Deep Learning models have been shown to have near or better performance than state of the art Recurrent Neural Networks (RNNs) on Automatic Speech Recognition tasks. These models tend to be lighter weight and require less training time than traditional RNN-based approaches. However, these models take frequentist approach to weight training. In theory, network weights are drawn from a latent, intractable probability distribution. We introduce BayesSpeech for end-to-end Automatic Speech Recognition. BayesSpeech is a Bayesian Transformer Network where these intractable posteriors are learned through variational inference and the local reparameterization trick without recurrence. We show how the introduction of variance in the weights leads to faster training time and near state-of-the-art performance on LibriSpeech-960.
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph

Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as resolution. Most affine methods are agnostic to anatomy, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, an easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing, right off the MRI scanner. First, we leverage a strategy to train networks with wildly varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. We rigorously analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates consistent and improved accuracy. It is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain MRI.
Joint Training of Deep Ensembles Fails Due to Learner Collusion

Ensembles of machine learning models have been well established as a powerful method of improving performance over a single model. Traditionally, ensembling algorithms train their base learners independently or sequentially with the goal of optimizing their joint performance. In the case of deep ensembles of neural networks, we are provided with the opportunity to directly optimize the true objective: the joint performance of the ensemble as a whole. Surprisingly, however, directly minimizing the loss of the ensemble appears to rarely be applied in practice. Instead, most previous research trains individual models independently with ensembling performed post hoc. In this work, we show that this is for good reason - joint optimization of ensemble loss results in degenerate behavior. We approach this problem by decomposing the ensemble objective into the strength of the base learners and the diversity between them. We discover that joint optimization results in a phenomenon in which base learners collude to artificially inflate their apparent diversity. This pseudo-diversity fails to generalize beyond the training data, causing a larger generalization gap. We proceed to comprehensively demonstrate the practical implications of this effect on a range of standard machine learning tasks and architectures by smoothly interpolating between independent training and joint optimization.
A comparative study of deep learning and decision tree based ensemble learning algorithms for network traffic identification

In this paper, we apply Deep Learning (DL) and decision-tree-based ensemble learning algorithms to classify network traffic by application. Various Deep Learning (DL) models for network traffic identification have been presented, implemented and compared, including 1D convolutional, stacked autoencoder, multi-layer perceptron, and combination of the aforementioned. Then the results of DL models have been compared to those obtained with two popular ensemble learning models based on decision trees-Random Forest and XGBoost. To train and test the classification models, a dataset containing both encrypted and unencrypted traffic has been collected in a real network, under normal operating conditions, and pre-processed in a way that ensures non-biased results. The classification uncertainties of the models have been also quantified on publicly available ISCX VPN-nonVPN dataset. The models have been compared in terms of precision, recall, F1 score and accuracy, for different levels of complexity and training dataset sizes. The evaluation results indicate that the decision-tree ensemble learning algorithms provide more accurate results and outperform the DL algorithms. The performance gap reduces with the dataset complexity.
Evidence of porphyry copper-gold mineralization in the Zardkooh prospect area, Southeastern Iranshahr

Evidence of porphyry copper-gold mineralization in the Zardkooh prospect area, Southeastern Iranshahr
Mineralization, geochemistry and microthermometry of Fluid inclusion in Siajak prospect area, south of Zahedan, Iran

Mineralization, geochemistry and microthermometry of Fluid inclusion in Siajak prospect area, south of Zahedan, Iran
Geothermobarometery–mineralogical and mineral chemistry studies of Dacite and trachydacite of Masahim volcano south and east of Shahr-e-Babak north

Geothermobarometery–mineralogical and mineral chemistry studies of Dacite and trachydacite of Masahim volcano south and east of Shahr-e-Babak north
Hematological Parameters of Alpine Goats in Bosnia and Herzegovina

The aim of our study was to assess the values of hematological profile of Alpine goats in Bosnia and Herzegovina in order to monitor health status of goats; moreover, to determine the effect of age on hematological parameters of goats. A total of 40 apparently healthy Alpine goats included in this study were divided into four groups according to age: &lt;1 year old (Group 1), 1 to 3 years old (Group 2), 3 to 5 years old (Group 3) and &gt;5 years old (Group 4). The following hematological parameters were measured: Red Blood Cell count (RBC), hematocrit (HCT), hemoglobin (HGB), Mean Cell Volume (MCV), Mean Cell Hemoglobin (MCH), Mean Cell Hemoglobin Concentration (MCHC), Reticulocyte count (RETIC), White Blood Cell (WBC), as well as white blood cells differentiation and determination of absolute values white blood cells; platelet count (PLT), and Mean Platelet Volume (MPV). All values investigated in our study were within reference range, except MCHC, percentage of neutrophils and percentage of monocytes. For investigated hematologic parameters, significant (P &lt; 0.05) age-related differences were found for HCT, MCV, MCHC, WBC, lymphocytes and platelet count. Recorded differences might be influenced by undetected infections, genetics, nutrition, fluctuations in the climate or management.
Federated Learning Vulnerabilities, Threats and Defenses: A Systematic Review and Future Directions

Federated learning (FL) is a new paradigm of machine learning (ML) training that depends on decentralized collaborative learning between various numbers of clients where the data are located on each client locally. FL has brought many advantages such as helping preserve privacy where the clientâ€™s local data are kept locally to train the model. FL is a trending approach in many intelligent applications that need to be studied to identify, evaluate and understand the full potential as well as the risks and vulnerabilities. Despite the advantages of FL, studies have shown that FL is vulnerable to various types of poisoning and inference attacks. This paper provides a comprehensive analysis of FL security and privacy concerns that can bridge the gap between the existing state of studies and a future one. We present various training approaches for ML and identify FL threat models along with their potential vulnerabilities. Then, we present poisoning attack studies and the privacy techniques for protecting against these attacks. We conclude our paper by identifying FL challenges and future research directions in the hopes of addressing current limitations.
Anchoring Trust. Order of Moves and Agents’ Behavior in Two Trust Games

In this paper, we report the findings of a randomized experiment designed to test whether the order of play in two Trust Games influences the observed level of trust displayed by Trustors (as measured by the share of endowment sent to Trustees). Playing Trustor in the second game increases the average share sent to the Trustee. We suggest a role for anchoring due to the different order in which subjects play the Trustor role.
PROFESSIONAL TRAINING OF A SPECIALIST IN SOCIAL WORK FOR THE APPLICATION OF DIFFERENT METHODS

The article considers the peculiarities of preparing students in higher education to apply different methods of social work.The competencies that need to be formed in applicants for bachelor's and master's degrees in social work are described in accordance with the requirements of the Standard of Higher Education, field of knowledge 23 Social work.The analysis of the requirements to the professional training of the specialist, to the level of his knowledge, skills and abilities necessary for the effective solution of the existing social problems, his qualities and values is carried out.The functions of a social work specialist in the context of readiness to apply different methods of social work are defined: analytical and diagnostical, predictive, organizational and communicative, protective, psychotherapeutic, rehabilitative.We consider the improvement of professional's training and their expertise, as an important direction for optimization of social work and implementation of mentioned functions.Among the key professional competences, we emphasize: the ability to cooperate in international environment and to identify intercultural problems in professional practice; the use of modern experimental methods of work with social objects in field and laboratory conditions; the ability to evaluate social problems, needs, peculiarities and resources of the clients, etc. Approaches to optimizing the process of training students are considered: systemic, informational, personality-oriented, contextual, reflective, competence, activity, integrative.13It is concluded that the professional training of students to use different methods of social work should be optimized by motivating students to achieve high learning outcomes, the formation of active positive attitude to professional activities, to study the disciplines provided by the Standard, the use of educational technologies, methods, due to the intensification of project activities of students.
Predicting Physiological Response in Heart Failure Management: A Graph Representation Learning Approach using Electronic Health Records

Abstract Heart failure management is challenging due to the complex and heterogenous nature of its pathophysiology which makes the conventional treatments based on the “one size fits all” ideology not suitable. Coupling the longitudinal medical data with novel deep learning and network-based analytics will enable identifying the distinct patient phenotypic characteristics to help individualize the treatment regimen through the accurate prediction of the physiological response. In this study, we develop a graph representation learning framework that integrates the heterogeneous clinical events in the electronic health records (EHR) as graph format data, in which the patient-specific patterns and features are naturally infused for personalized predictions of lab test response. The framework includes a novel Graph Transformer Network that is equipped with a self-attention mechanism to model the underlying spatial interdependencies among the clinical events characterizing the cardiac physiological interactions in the heart failure treatment and a graph neural network (GNN) layer to incorporate the explicit temporality of each clinical event, that would help summarize the therapeutic effects induced on the physiological variables, and subsequently on the patient’s health status as the heart failure condition progresses over time. We introduce a global attention mask that is computed based on event co-occurrences and is aggregated across all patient records to enhance the guidance of neighbor selection in graph representation learning. We test the feasibility of our model through detailed quantitative and qualitative evaluations on observational EHR data.
DEVELOPING MORE EFFICIENT ALGORITHMS FOR SORTING AND SEARCHING LARGE DATA SETS

As data sets continue to grow in size and complexity, the efficiency of sorting and searching algorithms becomes increasingly important.In this paper, we propose new approaches to improve the efficiency of these algorithms for large data sets.For sorting, we propose the use of adaptive algorithms that adjust their behavior based on the characteristics of the data set, and distributed algorithms that leverage the power of multiple machines to sort the data in parallel.For searching, we propose the use of approximate algorithms that allow for fast search times with a trade-off of reduced accuracy.Through experimentation, we demonstrate the effectiveness of these proposed solutions and discuss their potential for improving the efficiency of sorting and searching large data sets.
USE OF VISUAL CRYPTOGRAPHY TO PREVENT PHISHING ATTACKS IN ONLINE VOTING SYSTEMS

According to a poll, due to their busy schedules, only a small number of individuals vote.There are several reasons for this, including the fact that everyone must go to the polling place, where they must stand in a lengthy line and will be exhausted due to their hectic schedule.As a result, we recommended an online voting method that makes use of the internet, as the probability of cheating/threats is rising by the day.One such issue is phishing attacks, which might cause authentication issues.As a result, we have built a secure online voting system based on Visual Cryptography (VC) to allow employees to vote on vital and private internal business decisions.
ANALISIS SENTIMEN MASYARAKAT TERHADAP PAYLATER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

Online shopping is so popular with the public because it is easy and convenient to do. The convenience of online shopping is supported by the payment method via paylater. However, paylater also results in bad behavior such as impulse buying. Various responses from the community made researchers conduct research to find out the public's view of paylater. In this study, researchers tried to do sentiment analysis using the Naive Bayes Classifier and TextBlob methods from the TetxBlob library with the Python programming language. From the dataset collected via Twitter, it produces 405 data. Sentiment analysis using the Naive Bayes Classifier method produces a negative sentiment of 70.62% or 286 data, positive sentiment is 22.72% or 92 data, neutral sentiment is 6.67% or 27 data. Meanwhile, using the TextBlob method also produced more negative sentiment, namely 55.8% or 226 data, positive sentiment collected 33.09% or 134 data, neutral sentiment amounted to 11.11% or 45 data. Thus, it can be concluded that the community feels unfavorable towards the use of paylater. In testing the model with the confussion matrix, it can be seen that the Naive Bayes Classifier algorithm is more accurate by 91% compared to TextBlob which is only 61%.
Bessarabian piano art at the confl uence of the XIX-XX centuries

Since the end of the XIX century — beginning of the XX century, the musical life in Bessarabia is being intensified due to the artistic activity of some professional musicians with studies at Russian and Western European conservatories, thus creating standards of musical education and interpretive mastery with a significant impact on the professional training of pianists. The first musical institutions appear: M. Voloshinovskaya, who had studied in Vienna, opened a private school in 1891 in Chisinau. Previously, in 1890, a music school, accessible to all social classes, was opened at the initiative of Vasile Gutor, born in Chișinău and having graduated from the Saint Petersburg Conservatory in Professor K. Davydov’s cello class. At the same time, premises of the national school of composition are manifested; the first works for piano being made in the style and forms of Western schools adapted to the native oral sound stratum. As a result of the adaptation and the valorization of musical folklore, professional music is being developed in the Bessarabian area.
Dropout of Young Teachers from the Education System: Motivations, Risk Factors and Possible Solutions

Abstract: This article discusses social, pedagogical, and managerial aspects of young teachers’ integration into the education system with a research focus on the issue of “young teachers dropping out” of the school system in the first years of their careers. This article further analyses the motives and risk factors which influence the process of teachers’ integration into the school environment. Possible solutions are sought to mitigate the risk of teachers permanently leaving the educational system. The need for developing a comprehensive system to support the successful professional adaptation and development of novice teachers - from their academic preparation and teaching practice, to their active period of professional consolidation and career development - is outlined. This article is based on up-to-date data of the implications of the issue within the education system of Israel. A mixed methods approach, combining quantitative and qualitative research approaches, is used for the purpose of this research. The quantitative data aim to provide a macro-level picture of the process of attrition in the teaching profession in terms of teacher and educational institution characteristics. The qualitative part of the research includes an analysis of a number of documents on the policy of the Ministry of Education of Israel on teachers leaving the profession. Keywords: Teacher preparation and qualification; Teacher's career development; Adaptation of novice teachers, Teacher-mentor, Mentor.
Multi-relational Matrix Factorization Approach for Educational Items Clustering

Pedagogical models development requires several steps, one of which is the mapping of tasks and skills, also known as the educational items clustering. This activity of clustering educational items usually requires the participation of domain experts. However, discovering the exact skills involved in performing the tasks is a complex activity for them. This paper aims at solving the task and skill-mapping problem by proposing an approach based on the Weighted Multi-Relational Matrix Factoring technique to help experts in this task. This approach relies on two types of relationship, the “ student does task” relationship and the “student has skills” relationship through a latent factor model to reconstruct the “ task requires skill” relationship, the latter being the mapping between tasks and skills. An evaluation conducted on a group of two hundred (200) students in lower 6th class in a general secondary school (Côte d'Ivoire), showed that this approach brought an improvement rate of about 82.8% of the skill-task mapping proposed by the experts in the field. This result confirms that our approach not only allows us to map tasks and skills but also to significantly improve the updating of curricula.