Reviewerly/exHarmony huggingface dataset:
Interacting dreaming neural networks

Abstract We study the interaction of agents, where each one consists of an associative memory neural network trained with the same memory patterns and possibly different reinforcement-unlearning dreaming periods. Using replica methods, we obtain the rich equilibrium phase diagram of the coupled agents. It shows phases such as the student–professor phase, where only one network benefits from the interaction while the other is unaffected; a mutualism phase, where both benefit; an indifferent phase and an insufficient phase, where neither are benefited nor impaired; a phase of amensalism where one is unchanged and the other is damaged. In addition to the paramagnetic and spin glass phases, there is also one we call the reinforced delusion phase, where agents concur without having finite overlaps with memory patterns. For zero coupling constant, the model becomes the reinforcement and removal dreaming model, which without dreaming is the Hopfield model. For finite coupling and a single memory pattern, it becomes a Mattis version of the Ashkin–Teller model.
A reinforcement learning approach for VQA validation: An application to diabetic macular edema grading

Recent advances in machine learning models have greatly increased the performance of automated methods in medical image analysis. However, the internal functioning of such models is largely hidden, which hinders their integration in clinical practice. Explainability and trust are viewed as important aspects of modern methods, for the latter’s widespread use in clinical communities. As such, validation of machine learning models represents an important aspect and yet, most methods are only validated in a limited way. In this work, we focus on providing a richer and more appropriate validation approach for highly powerful Visual Question Answering (VQA) algorithms. To better understand the performance of these methods, which answer arbitrary questions related to images, this work focuses on an automatic visual Turing test (VTT). That is, we propose an automatic adaptive questioning method, that aims to expose the reasoning behavior of a VQA algorithm. Specifically, we introduce a reinforcement learning (RL) agent that observes the history of previously asked questions, and uses it to select the next question to pose. We demonstrate our approach in the context of evaluating algorithms that automatically answer questions related to diabetic macular edema (DME) grading. The experiments show that such an agent has similar behavior to a clinician, whereby asking questions that are relevant to key clinical concepts.
Evidential Deep Learning for Class-Incremental Semantic Segmentation

Class-Incremental Learning is a challenging problem in machine learning that aims to extend previously trained neural networks with new classes. This is especially useful if the system is able to classify new objects despite the original training data being unavailable. Although the semantic segmentation problem has received less attention than classification, it poses distinct problems and challenges, since previous and future target classes can be unlabeled in the images of a single increment. In this case, the background, past and future classes are correlated and there exists a background-shift. In this paper, we address the problem of how to model unlabeled classes while avoiding spurious feature clustering of future uncorrelated classes. We propose to use Evidential Deep Learning to model the evidence of the classes as a Dirichlet distribution. Our method factorizes the problem into a separate foreground class probability, calculated by the expected value of the Dirichlet distribution, and an unknown class (background) probability corresponding to the uncertainty of the estimate. In our novel formulation, the background probability is implicitly modeled, avoiding the feature space clustering that comes from forcing the model to output a high background score for pixels that are not labeled as objects. Experiments on the incremental Pascal VOC and ADE20k benchmarks show that our method is superior to the state of the art, especially when repeatedly learning new classes with increasing number of increments.
Wearing face mask detection using deep learning through COVID-19 pandemic

During the COVID-19 pandemic, wearing a face mask has been known to be an effective way to prevent the spread of COVID-19. In lots of monitoring tasks, humans have been replaced with computers thanks to the outstanding performance of the deep learning models. Monitoring the wearing of a face mask is another task that can be done by deep learning models with acceptable accuracy. The main challenge of this task is the limited amount of data because of the quarantine. In this paper, we did an investigation on the capability of three state-of-the-art object detection neural networks on face mask detection for real-time applications. As mentioned, here are three models used, Single Shot Detector (SSD), two versions of You Only Look Once (YOLO) i.e., YOLOv4-tiny, and YOLOv4-tiny-3l from which the best was selected. In the proposed method, according to the performance of different models, the best model that can be suitable for use in real-world and mobile device applications in comparison to other recent studies was the YOLOv4-tiny model, with 85.31% and 50.66 for mean Average Precision (mAP) and Frames Per Second (FPS), respectively. These acceptable values were achieved using two datasets with only 1531 images in three separate classes.
Macro determinants of sustainable financial development in the Middle East and North Africa (MENA) region countries

Purpose This research aims to examine the macro determinants that significantly affect financial development in the Middle East and North Africa (MENA) region, which could be used furtherly to play a major role in economic sustainability since one of the major driving forces for economic development is the financial development. Design/methodology/approach The significant determinants of financial development should be efficiently used by the MENA region countries for creating huge financial sector development and innovation, stimulating economic development in turn and leading to the completion of the cycle of development and sustainability. To achieve this study's objective, the researcher employed a quantitative method to develop an econometric model. Findings This model consisted of two Panel EGLS Cross-Section Random Effects Models (REMs) in which Domestic credit to the private sector as a percentage of GDP (?PCGDP?_it) and stock market capitalization ratio (?SMC?_it) were taken as the dependent variables. In addition, the independent variables included the corruption perception index, financial freedom (FF), political stability (PS) and trade openness (TO). The researcher extracted the data for the analysis from different databases including the World Bank, the Organization for Economic Cooperation and Development and the International Monetary Fund. Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance. Originality/value Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.
Distributed non-negative RESCAL with automatic model selection for exascale data

With the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been exponential growth in the volume of data produced worldwide. Among these data, relational datasets are growing in popularity as they provide unique insights regarding the evolution of communities and their interactions. Relational datasets are naturally non-negative, sparse, and extra-large. Relational data usually contain triples (subject, relation, object) and are represented as graphs/multigraphs, called knowledge graphs, which need to be embedded into a low-dimensional dense vector space. Among various embedding models, RESCAL allows the learning of relational data to extract the posterior distributions over the latent variables and to make predictions of missing relations. However, RESCAL is computationally demanding and requires a fast and distributed implementation to analyze extra-large real-world datasets. Here we introduce a distributed non-negative RESCAL algorithm for heterogeneous CPU/GPU architectures with automatic selection of the number of latent communities (model selection), called pyDRESCALk. We demonstrate the correctness of pyDRESCALk with real-world and large synthetic tensors and the efficacy showing near-linear scaling that concurs with the theoretical complexities. Finally, pyDRESCALk determines the number of latent communities in an 11-terabyte dense and 9-exabyte sparse synthetic tensor.
Blockchain implications for the accounting realm: A critique of extant studies

The current paper critically explores current research on the topic of accounting and blockchain. It draws from the commonly accepted categorization of account-ing studies into financial accounting, managerial accounting, taxation, and other accounting-related subjects. Borrowing this categorization and assuming a more holistic posture to observe blockchain implications for accounting, the study dis-cusses how blockchain can contribute to each building block of accounting studies and helps expand the boundaries of accounting research. The piece has relevance for the academic debate and, above all, for practitioners and policymakers, raising several issues that need to be dealt with in the foreseeable future
High-precision tomography of ion qubits based on registration of fluorescent photons

Abstract We develop a new method for high-precision tomography of ion qubit registers under conditions of limited distinguishability of logical states. It is not always possible to achieve low error rates during the readout of the quantum states of ion qubits due to the finite lifetime of excited levels, photon scattering, detector dark counts, low numerical aperture, etc. However, the model of fuzzy quantum measurements makes it possible to ensure precise tomography of quantum states. To do this, we developed a fuzzy measurement model based on counting the number of fluorescent photons. A statistically adequate algorithm for the reconstruction of quantum states of ion qubit registers based on fuzzy measurement operators is proposed. The algorithm uses the complete information available in the experiment and makes it possible to account for systematic measurement errors associated with the limited distinguishability of the logical states of ion qubits. We show that the developed model, although computationally more complex, contains significantly more information about the state of the qubit and provides a higher accuracy of state reconstruction compared to the model based on the threshold algorithm.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Graph anomaly detection (GAD) has wide applications in real-world networked systems.In many scenarios, people need to identify anomalies on new (sub)graphs, but they may lack labels to train an effective detection model.Since recent semi-supervised GAD methods, which can leverage the available labels as prior knowledge, have achieved superior performance than unsupervised methods, one natural idea is to directly adopt a trained semi-supervised GAD model to the new (sub)graphs for testing.However, we find that existing semi-supervised GAD methods suffer from poor generalization issues, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the graph.Motivated by this, we formally define the problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph(s) and the unseen test graph(s).Nevertheless, it is a challenging task since only limited labels are available, and the normal data distribution may differ between training and testing data.Accordingly, we propose a data augmentation method named AugAN (Augmentation for Anomaly and Normal distributions) to enrich training data and adopt a customized episodic training strategy for learning with the augmented data.Extensive experiments verify the effectiveness of AugAN in improving model generalizability.
Resolution Invariant Face Recognition

Face images recorded from security cameras and other similar sources are generally of low-resolution and bad quality. There are many recent face recognition models which extract face features/encodings using deep neural networks (DNNs) and give very good results when tested against images with higher resolution (HR). Moreover, the performance of these types of algorithms deteriorates to a great extent for images with low-resolution (LR). To reduce the shortcoming, we used convolution neural network (CNN) architecture along with the combination of super-resolution (SR) technique during the pre-processing steps to achieve the comparable results on the state-of-the-art techniques. The proposed method can be outlined in 4 steps: Face retrieval, image pre-processing and super-resolution, training the model, and face detection/classification. The dataset used for this study is a publicly available with name Face Scrub Dataset, subset of this dataset is used containing 20050 images of 229 people for the experiments.
Metaheuristic planner for cooperative multi-agent wall construction with UAVs

This paper introduces a wall construction planner for Unmanned Aerial Vehicles (UAVs), which uses a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to generate near-time-optimal building plans for even large walls within seconds. This approach addresses one of the most time-consuming and labor-intensive tasks, while also minimizing workers’ safety risks. To achieve this, the wall-building problem is modeled as a variant of the Team Orienteering Problem and is formulated as Mixed-Integer Linear Programming (MILP), with added precedence and concurrence constraints that ensure bricks are built in the correct order and without collision between cooperating agents. The GRASP planner is validated in a realistic simulation and demonstrated to find solutions with similar quality as the optimal MILP, but much faster. Moreover, it outperforms all other state-of-the-art planning approaches in the majority of test cases. This paper presents a significant advancement in the field of automated wall construction, demonstrating the potential of UAVs and optimization algorithms in improving the efficiency and safety of construction projects.
A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis

More and more individuals are paying attention to the research on the emotional information found in micro-blog comments. TEXTCNN is growing rapidly in the short text space. However, because the training model of TEXTCNN model itself is not very extensible and interpretable, it is difficult to quantify and evaluate the relative importance of features and themselves. At the same time, word embedding can't solve the problem of polysemy at one time. This research suggests a microblog sentiment analysis method based on TEXTCNN and Bayes that addresses this flaw. First, the word embedding vector is obtained by word2vec tool, and based on the word vector, the ELMo word vector integrating contextual features and different semantic features is generated by ELMo model. Second, the local features of ELMo word vector are extracted from multiple angles by using the convolution layer and pooling layer of TEXTCNN model. Finally, the training task of emotion data classification is completed by combining Bayes classifier. On the Stanford Sentiment Classification Corpus data set SST (Stanford Sentiment Classification Corpus Data bank), the experimental findings demonstrate that the model in this paper is compared with TEXTCNN, LSTM, and LSTM–TEXTCNN models. The Accuracy, Precision, Recall, and F1-score of the experimental results of this research have all greatly increased. Their values are respectively 0.9813, 0.9821, 0.9804 and 0.9812, which are superior to other comparison models and can be effectively used for emotional accurate analysis and identification of events in microblog emotion analysis.
Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification

Code classification is a difficult issue in program understanding and automatic coding. Due to the elusive syntax and complicated semantics in programs, most existing studies use techniques based on abstract syntax tree (AST) and graph neural network (GNN) to create code representations for code classification. These techniques utilize the structure and semantic information of the code, but they only take into account pairwise associations and neglect the high-order correlations that already exist between nodes in the AST, which may result in the loss of code structural information. On the other hand, while a general hypergraph can encode high-order data correlations, it is homogeneous and undirected which will result in a lack of semantic and structural information such as node types, edge types, and directions between child nodes and parent nodes when modeling AST. In this study, we propose to represent AST as a heterogeneous directed hypergraph (HDHG) and process the graph by heterogeneous directed hypergraph neural network (HDHGN) for code classification. Our method improves code understanding and can represent high-order data correlations beyond paired interactions. We assess heterogeneous directed hypergraph neural network (HDHGN) on public datasets of Python and Java programs. Our method outperforms previous AST-based and GNN-based methods, which demonstrates the capability of our model.
Achieving Timestamp Prediction While Recognizing with Non-autoregressive End-to-End ASR Model

Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment (FA) and provide timestamps, while end-to-end ASR systems especially AED based ones are short of such ability. This paper proposes to perform timestamp prediction (TP) while recognizing by utilizing continuous integrate-and-fire (CIF) mechanism in non-autoregressive ASR model - Paraformer. Foucing on the fire place bias issue of CIF, we conduct post-processing strategies including fire-delay and silence insertion. Besides, we propose to use scaled-CIF to smooth the weights of CIF output, which is proved beneficial for both ASR and TP task. Accumulated averaging shift (AAS) and diarization error rate (DER) are adopted to measure the quality of timestamps and we compare these metrics of proposed system and conventional hybrid force-alignment system. The experiment results over manually-marked timestamps testset show that the proposed optimization methods significantly improve the accuracy of CIF timestamps, reducing 66.7% and 82.1% of AAS and DER respectively. Comparing to Kaldi force-alignment trained with the same data, optimized CIF timestamps achieved 12.3% relative AAS reduction.
Three-Types-of-Graph-Relational Guided Domain Adaptation Approach for Fault Diagnosis of Nuclear Power Circulating Water Pump

Existing domain adaptation methods strive to align all domains equally under a single domain shift dimension, which poses two problems. On the one hand, multi-aspect domain transferring factors and homogenous alignment may lead to sub-optimal results in more distant domains. On the other hand, such a global alignment ignores local discriminatory information, making class boundary samples susceptible to misclassification. Hence, the three-types-of-graph-relational guided domain adaptation (TGGDA) is proposed. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain graph</i> is formed based on condition-dependent slow variables. The domain discriminator is redesigned to reconstruct the domain graph. Second, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intrinsic</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">penalty graphs</i> are integrated to draw the same class but different domains sample closer and vice versa. The TGGDA is a system-assisted cross-domain diagnosis method that enables multidimensional domain information measurable, and the adjacency alignment allows for more accurate diagnostic results. Finally, experiments on gearbox fault diagnosis in circulating water pumps show that TGGDA can improve diagnosis accuracy.
Using Secret Sharing to Store Cryptocurrency

ABSTRACTWe describe Shamir's secret sharing scheme and explain how it can be used for secure and redundant cryptocurrency storage. We include samples of individual and group assignments that can be used in an upper-division cryptology class for students who are familiar with modular arithmetic. It takes about one class to cover Shamir's secret sharing, but additional time can be spent on the described coding project about splitting mnemonic Bitcoin seeds into shares. We also provide references for topics of further study that can you use for student research projects.KEYWORDS: Cryptologysecret sharingcryptocurrency DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Notes1 The Electrum software avoids this problem by using a variable digit representation, where each word in a phrase depends on the previous word.Additional informationNotes on contributorsMarkus ReitenbachMarkus Reitenbach received his Ph.D. from Syracuse University. His classes bridge the gap between theory and applications. He has recently become interested in neural networks and he has taught an interdisciplinary class on Bitcoin. His papers have appeared in the Journal of Algebra, Mathematics Magazine and the International Journal of Mathematical Education in Science and Technology.
Extended Reality Over 5G—Standards Evolution

High performance XR systems can offload intensive computation tasks such as graphic rendering to the edge server - enabling a richer experience while reducing the power consumption on the XR end-user device. This paper provides an overview of the requirements for such split-compute XR systems over 5G and the resulting traffic characteristics. The design of 5G system, from the very first release, provides support for such emerging use cases with low power requirement and traffic requirements of low latency, high bandwidth, and high reliability. As split XR systems scale in terms of the number of users, evolve to support more immersive experience with consumer-friendly device form-factors, and become a key enabler for metaverse experiences, 5G system will continue to evolve to meet the more demanding requirements as part of “5G-Advanced” phase of 5G standards development, starting with 3GPP Release 18. This paper provides an overview of the advances in 5G with 3GPP Release 18 across RAN and SA2 working groups to address the emerging XR requirements.