Reviewerly/exHarmony huggingface dataset:
Enhanced Mining of High Utility Patterns from Streams of Dynamic Profit

Frequent pattern mining has been extended to the mining of other useful patterns. These include high-utility patterns. Many traditional high-utility mining algorithms focus on algorithmic efficiency when mining high-utility patterns from static databases. These algorithms rely on an assumption that the unit utility for a given item is a constant. However, as we are living in dynamic world where the unit utility (external unit profit) may change over time, such an assumption may not truly reflect reality in the real world. However, to the best of our knowledge, not a lot of works were done on mining dynamic profit from data streams yet. The emergence of big data has led to some performance challenges such that proper big data management techniques are needed for knowledge discovery from dynamic data streams. Traditional static data mining algorithms cannot directly apply to dynamic data. Furthermore, information in the data stream might not be uniformly distributed so it introduces extra challenges to process the data. Using big data stream processing platforms is necessary when mining real-world data stream. Leveraging the big data processing framework requires having scalable algorithms. In this paper, we present an enhanced high-utility data stream algorithm—called EHUI-Stream—to speed up the execution time and reduce memory usage. Utilizing our proposed algorithm, the data stream mining performance is expected to be further enhanced against both real-world datasets and synthetic datasets. Evaluation results on real-life data demonstrate the effectiveness of our platform in scalable high-utility pattern mining for dynamic profit from data streams for social and behavioral analytics.
Natural Language Inference by Integrating Deep and Shallow Representations with Knowledge Distillation

Natural language understanding models often make use of surface patterns or idiosyncratic biases in a given dataset to make predictions pertaining to natural language inference (NLI) tasks. Unfortunately, this renders the resulting model vulnerable to out-of-distribution datasets to which the identified features are inapplicable, thereby leading to erroneous results. Many of the methods developed for out-of-distribution datasets have proven effective; however, they also tend to impose a tradeoff in performance when applied to in-distribution datasets. In this paper, we use a teacher model providing knowledge for the student ensemble model as basic information for training. The student ensemble model then integrates information of deep and shallow representations to extend learning performance to a wide range of examples. The evaluation demonstrates that the proposed model outperformed state-of-the-art models when applied to in-distribution as well as out-of-distribution datasets.
ProPML: Probability Partial Multi-label Learning

Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In this paper, we introduce ProPML, a novel probabilistic approach to this problem that extends the binary cross entropy to the PML setup. In contrast to existing methods, it does not require suboptimal disambiguation and, as such, can be applied to any deep architecture. Furthermore, experiments conducted on artificial and real-world datasets indicate that ProPML outperforms existing approaches, especially for high noise in a candidate set.
Sliding Window Sampling over Data Stream – a Solution Based on Devil’s Staircases

The paper concerns sampling from a data stream {$S_{i}$}: at a moment t the sampler should hold a value $S_{t-j}$, where j$\in${0,$\ldots$,n-1} should be chosen according to an a priori specified probability distribution D on {0,$\ldots$,n-1}, where D as well as the window size n are fixed and do not depend on t. We assume that the sampler has a constant size memory, while n might be large, so the sampler cannot remember the last n values of the stream except for a few. The problem is that the window of the last n elements changes at each step and when we have to resample, then almost all values from which we have to choose are already forgotten. The case of uniform distribution D has been considered by Braverman, Ostrovsky, and Zaniolo in 2013. We present an alternative generic approach based on specific Markov chains called devil’s staircases. Unlike the previous solution, it is not limited to the uniform distribution: it generates a sample according to any admissible distribution in the window of size n and uses memory of size $\mathrm{O}(1)$. We provide sufficient conditions for the distribution D to be admissible. Although the class of such distributions is quite wide from the point of view of practical applications, we show some natural limitations for this class.
Non-Redundant Image Clustering of Early Medieval Glass Beads

Glass beads were among the most common grave goods in the Early Middle Ages, with an estimated number in the millions. The color, size, shape and decoration of the beads are diverse leading to many different archaeological classification systems that depend on the subjective decisions of individual experts. The lack of an agreed upon expert categorization leads to a pressing problem in archaeology, as the categorization of archaeological artifacts, like glass beads, is important to learn about cultural trends, manufacturing processes or economic relationships (e.g., trade routes) of historical times. An automated, objective and reproducible classification system is therefore highly desirable. We present a high-quality data set of images of Early Medieval beads and propose a clustering pipeline to learn a classification system in a data-driven way. The pipeline consists of a novel extension of deep embedded non-redundant clustering to identify multiple, meaningful clusterings of glass bead images. During the cluster analysis we address several challenges associated with the data and as a result identify high-quality clusterings that overlap with archaeological domain expertise. To the best of our knowledge this is the first application of non-redundant image clustering for archaeological data.
Towards Quality Measures for xAI algorithms: Explanation Stability

The domain of Artificial Intelligence has become ubiquitous across a wide plethora of domains and is now an integral part of the daily life of the ordinary citizen. While the need for increased transparency of the highly accurate black-box model is an important and very active area of research, the produced explanations themselves might not always be accurate. The measures to assess the quality of explanations are an important research topic. In this paper, a set of extensive experiments is performed to evaluate the stability of SHAP explanations under conditions of different noise types and different noise intensities as an effort to build a formal way of assessing the quality of explanations provided by the SHAP algorithm. The experiments are performed on four different datasets, with three different noise types at four different strength levels. The impact of the scenarios on SHAP explanations is reported, the implications for the evaluation of explainability methods are elaborated upon, along with the significance of the results for the SHAP method of explanations. The future directions are laid out thereafter.
Instils Trust in Random Forest Predictions

This paper addresses the interpretability and transparency behind the random forest model predictions. Random forest is an ensemble of bootstrapped independent decision trees that are trained on subsets of input data to make predictions. Although random forest is a robust model that can overcome bias, its inherent complexity, and poor interpretability can make it challenging to apply in many application domains that require transparency and explainability in the model’s predictions. This lack of transparency in the decision-making process can prevent users from analyzing what makes the model arrive at a specific prediction. The paper presents a visual analytic application to overcome transparency challenges by providing a clear structure of individual decision trees and hierarchical relationships between features. This allows users to analyze latent information and instils trust in the random forest model. Additionally, statistical analysis of feature ranking agreement and prediction popularity reduces mental burden of the user. The paper includes two case studies to evaluate model’s uncertainty, bias, and variances in predictions with explainability at local and global scales of decision paths. The visual analytics application provides a coordinated multiple-view system to instil trust in random forest models.
Tackling Model Mismatch with Mixup Regulated Test-Time Training

Test-time training (TTT) is an emerging approach for addressing the problem of domain shift. In its framework, a test-time training phase is inserted between the training phase and the test phase. During the test-time training phase, the representation layers are adapted using an auxiliary task. Then the updated model will be used in the test phase. Although the idea is very intuitive, TTT does not demonstrate competitive performance compared with some other domain adaption methods. In this paper, we present both theoretical and empirical analyses to explain the subpar performance of TTT. In particular, we point out that TTT causes a new kind of problem, which we term as Model Mismatch. To address this problem of Model Mismatch, we analyse a simple yet effective method inspired by the idea of mixup in robust training. Such effectiveness is shown in the experimental results.
All Translation Tools Are Not Equal: Investigating the Quality of Language Translation for Forced Migration

As the volume and complexity of forced movement continues to grow, there is an urgent need to use new data sources to better understand emerging crises. Organic sources, like social media and newspapers, can offer insights in near real time when administrative data are unavailable for timely and detailed analysis. However, in order to flexibly switch to different contexts, we need the ability to contextualize the drivers of movement for different locations and languages. Recent advances in natural language processing and specifically, neural machine translation, have shown impressive results on standard benchmark datasets for well-studied language pairs. However, the effectiveness of these models in a real-world scenario remains less known. To advance our understanding of real-world, contextual translation, we systematically study the performance of multiple widely used off-the-shelf machine translation tools using words associated with drivers of forced movement in both high- and low-resource languages. Our empirical results suggest significant variation between the performance of these machine translation tools in terms of accuracy and efficiency, highlighting a problem that must be faced by those conducting migration research using multilingual contexts. We conclude by suggesting strategies for obtaining reasonable translations from off-the-shelf language tools.
A Contextualized Transformer-Based Method for Cyberbullying Detection

Automatic detection of Cyberbullying is a challenging task due to the availability of limited trained data, which is usually noisy and inherently multimodal. Transfer learning over pre-trained BERT-based language models has succeeded in various complex use cases like sequence-to-sequence translation and text classification. These methods mainly utilize transformer models to learn the word and sentence-level relationships. While they have demonstrated promising results, they only focus on textual features without taking contextual and structural information into account. Moreover, due to the data-heavy nature of BERT-based models, they may fail to model all the desired relationships if not adequate training data is provided to them during the fine-tuning process. In this paper, we propose a novel Session-level Contextualized Transformer-based architecture for Cyberbullying Detection (SECTR-CD), which can leverage transfer learning for modeling word-level attention while also being able to model sentence-level relationships in large bodies of text. The model is also capable of utilizing other contextual features from various modalities like images and social information. Our experimental results indicate remarkable improvement in the Cyberbullying detection task even in the presence of limited training samples.
Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data

Several machine learning approaches have been proposed to automatically derive clinical phenotypes from patient data. Nevertheless, methods leveraging similarity-based patient networks remain underexplored for temporal data. In this work, we propose a graph neural network (GNN) model that learns patient representation using different network configurations and feature modes. To explore the sequential nature of time series, features were extracted using a recurrent neural network (RNN) and embedded using information from the network structure via the GNN. Our method improves upon statistical and RNN baselines, with performance boosts up to 1% and 22% accuracy in the inductive and transductive settings, respectively. We also show that network configurations significantly impact performance in the transductive learning setting. Thus, automated phenotyping models based on GNNs could be used to support phenotype-based clinical research and ultimately for personalized clinical decision support.Data and Code Availability: This paper uses the MIMIC-III dataset [1], which is available on the PhysioNet repository [2]. The experiments are based on the public open source phenotyping benchmark of Harutyunyan et al. [3]. All our source code is publicly available at https://github.com/ds4dh/mimic3-benchmarks-GraDSCI23.
Privacy-aware Adaptive Collaborative Learning Approach for Distributed Edge Networks

To facilitate the Edge AI paradigm in distributed networks, we propose novel collaborative learning methodologies for a connected network of edge nodes. Our proposed methodologies tackle the challenges in distributed learning where there are constraints on data privacy and a low degree of overlap between the classes observed by the nodes. These approaches entail sharing class distribution information between nodes, computing nodes, and class weights, training local models on each node, then aggregating the models using the determined weights. It favors nodes that have encountered unique or less common classes in their local datasets. Through a series of experiments using an activity recognition dataset, we demonstrate the effectiveness and scalability of our proposed approaches. We show the adaptive nature of the proposed approach by achieving classification accuracy above the baseline, even with little overlap between the observed classes. This study serves as a foundation for future advancements in collaborative learning on edge networks, and encourages the development of scalable solutions.
ScaleFace: Uncertainty-aware Deep Metric Learning

The performance of modern deep learning-based systems dramatically depends on the quality of input objects. For example, face recognition quality is lower for blurry or corrupted inputs. Moreover, it is difficult to predict the influence of input quality on the resulting accuracy in more complex scenarios. We propose a deep metric learning framework that allows for direct estimation of the uncertainty with almost no additional computational cost. The developed ScaleFace algorithm uses trainable scale values that modify similarities in the space of embeddings. These input-dependent scale values represent a measure of confidence in the recognition result, thereby providing provably reasonable uncertainty estimation. We present results from comprehensive experiments on open-set classification tasks, including face recognition, which demonstrate the superior performance of ScaleFace compared to other uncertainty-aware face recognition approaches. We also extend our study to the task of text-to-image retrieval, showing that the proposed approach outperforms competitors by significant margins.
Price Prediction of Digital Currencies using Machine Learning

Cryptocurrencies have gained immense significance and popularity in recent times. With thousands of digital currencies available, selecting the right one can be challenging for users. In the financial sector, accurately predicting future prices is crucial for profitable investments in digital currencies. However, price prediction in this realm poses unique challenges, as it lacks physical goods or services as the basis, unlike stock prices. Machine learning emerges as a pivotal tool for addressing this challenge and plays a vital role in price prediction. This research analyzes five prominent currencies - Monero, Bitcoin, Ethereum, IOTA, and Zcash - employing five models: SVR, LRG, Huber, RANSAC, MLP, and AdaBoost. The experimental results demonstrate promising outcomes, showcasing the ability to predict digital currency prices with an impressive R2 score of 1.0 for specific machine learning algorithms. This advancement opens new avenues for informed decision-making and profitable ventures in the dynamic world of digital currencies
Enhancing Federated Learning by One-Shot Transferring of Intermediate Features from Clients

Federated learning (FL) is an emerging paradigm using a parameter server (PS) to coordinate multiple decentralized clients for training a common model without exposing their raw data. Despite its amazing capability in preserving data privacy, FL confronts two significant challenges that have not been sufficiently addressed by existing works, which are: 1) heterogeneous data distributed on clients given that the PS cannot alter data locations. 2) limited computation resources of FL clients who may conduct model training with mobile devices. To tackle these challenges, we propose a novel federated one-shot transferring of intermediate features (FedOTF) algorithm. More specifically, FedOTF consists of two stages: feature extraction and model reconstruction. To overcome challenge 1, clients in stage 1 only collaboratively train a small model in a federated manner, with the objective to extract features. In stage 2, intermediate features (generated by the small model trained by stage 1) are transferred from clients to the PS so that a large model can be trained to overcome challenge 2. In addition, the design of FedOTF is robust, which can flexibly diminish the number of communication rounds of stage 1 when network capacity is limited, and reduce the amount of exposed features when privacy is concerned. To verify the superiority of FedOTF, we conduct comprehensive experiments with real datasets. The experiment results demonstrate that FedOTF can significantly improve the model utility of FL because a better model can be finally obtained by the PS without incurring heavy computational load on clients. Besides, we conduct robustness evaluation of FedOTF which can achieve stable performance when varying network capacity and privacy requirement.
ECC: Enhancing Smart Grid Communication with Ethereum Blockchain, Asymmetric Cryptography, and Cloud Services

Smart grids are suscceptible to security vulnerabilities of cyber-physical systems due to the heterogeneity of their interconnected components. There are high risks associated with potential attacks targeting the two-way communication between the smart meters and the utility servers. It is vital to ensure that data communicated between consumers and the utility is not tampered with and is authentic, private, and available. Conventional security measures in traditional communication and network systems fail to secure the data communication aspect in the complex network that composes the advanced metering infrastructure (AMI). In this work, we propose ECC: a novel prevention approach based on Ethereum smart contracts, asymmetric cryptographic functions, and cloud services for securing the two-way communication between smart meters and utility servers. Ethereum blockchain is utilized as a building block where communicated data is treated as transactions encrypted and stored in a distributed fashion to ensure data availability, confidentiality, and privacy. We also augment the Ethereum architecture with cloud services to extend the number of allowable transactions, ensure the availability of the electricity data, and reduce the cost associated with Ethereum transactions. The conducted experiments illustrate the efficacy of ECC in terms of the achieved security properties. This paper shows that the Ethereum Blockchain coupled with Cloud services can improve the efficiency of a system solely based on the Ethereum Blockchain.
Entity Recognition Based on Heterogeneous Graph Reasoning of Visual Region and Text Candidate

While significant progress has been made in recognizing entities from plain text, the exploration of entity recognition from multimodal data remains limited due to disparities in semantic representation. In light of this challenge, given the supportive nature of visual and text data, we propose a novel entity recognition model called Heterogeneous Graph Reasoning(HGR), leveraging the synergistic nature of visual and textual data. This is achieved through the utilization of the Vision Refine and Graph Cross Inference modules. In the Vision Refine module, semantically relevant objects hidden in the image are selected to aid in the text entity extraction. In the Graph Cross Inference module, cross-association inference between visual regions and textual entities is constructed through graph construction, heterogeneous graph fusion, visual region refinement and cross inference. Extensive experiments on four multimodal datasets are demonstrate the superiority of our model, when compared to the second-best state-of-the-art model.
Features of modern popular science discourse

The article is devoted to the study of modern popular science literature in order to identify some lexical and stylistic features, as well as to figure out the specificity of the methodology of nowadays scientific and popular information presentation. The authors pay special attention to studying of undeclared implicit discursive elements of popular science texts. Printed versions of books with a total volume of 196,6 conventional printing sheets serve as a material of the study. The analysis of the material showed that the authors of popular science books try to increase the degree of trust of the audience in a variety of ways, for example, by making the process of communication with the reader more intimate, by demonstrating the proximity to official academic community and by positioning the produced text as scientifically significant. One of the most typical phenomena that has been noted in the popular science texts is making some lexical units (including terms) hypersubjective in relation to the context (and their future use in a variety of situations) together with the creation of implicit ideological pressure, as well as with the persistent demonstration of the universality of these lexical units. In all the texts that have been analysed, the unobvious discourse-forming telelogy is noted, which is a tool for soft promotion of meaning-forming and partly ideologically conditioned ways of understanding various facts. Typical of the analysed popular science literature is the presence of reduced argumentation with unjustified usage of the third-party axiomatics and the extension of the argumentation base to the interdisciplinary junction. The use of primitivized argumentation targeted for a dilettante philistine level is noted. It is not uncommon to omit the existing scientific and relevant links and patterns, or to interpret them in a way that does not contradict the information certainty projected in the text. It is also possible to note a distracting informational redundancy, which the authors of the article consider as one of the ways of splitting the text, deliberately created by the addressee of the text.