Reviewerly/exHarmony huggingface dataset:
A Novel Multi-Layer Sparse Regularizer based GRU Model for Consumer Reviews Summarization

Text Summarization is considered as potential study area in recent past for its well-established growing popularity in domain specific areas.The consumer dominated society approved the summarization process as a tool for communication in shortest time span with wide coverage of valued basic knowledge.The Auto Text Summarization is developed digitally with various sophisticated tools and techniques in compressed opinion building process delivering millions of consumer's verdict at one scratch.The newest methodology of deep learning neural network had been adopted to narrate the precise fact with proposed novel MSRGRU (Multilayer Stacked Regularizer GRU) model for consumer review summarization technique.The adaptation of the model by proper utilization of sparse regularization process enhances quality of predicted summaries.Eventually the accuracy level of the model is remarkable as compared to the existing state-of-the -art techniques.
Provision for Energy: A Resource Allocation Problem in Federated Learning for Edge Systems

Abstract The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction.
Flouting Relevance Maxim Benefits of Hillary Clinton's President Candidate Debate on 2016

This research has an objective to analyze and reveal the benefits of flouting the relevance maxim of Hillary Clinton's speech in the 2016 USA candidate presidential debate. The data consist of the debate transcript which was taken from the Washington Post website, and a video that could be watched on YouTube. They are analyzed by confirming the relevance of semantic answers to every question within the context of speech. In the end, there are nine findings of the benefits of flouting the relevance maxim, which consists of increasing positive self-impression, avoiding giving direct agreement to a sensitive issue, closing potential negative attacks, decreasing the rival's credibility, guessing truthfulness, beating the rival's credibility, increasing effort to show positive personal credibility, telling the rival's past negative history, and proposing indirect conclusion. The findings are the implication of the complex use of verbal speech that is combined with action. The formal situation of the debate supports the maximum benefit of every flouting relevance maxim. The main conclusion of this research is that flouting the relevance maxim only sometimes negatively impacts both a speaker and an interlocutor. The impact is influenced by complex factors such as the background of the occasion, past conditions, and passion for an interaction.
Politeness and Impoliteness Strategies in Lecturer-Student Communication Within Cyberpragmatic Chats

This qualitative research on Cyberpragmatic attempts to explore the application of politeness and impoliteness principles in student-lecturer internet-mediated communication in English and Indonesian languages at two faculties of Universitas Islam Negeri (UIN) Salatiga. The native speakers' perceptions of those strategies and principles as applied in the online chatroom were also elicited to confirm the descriptive analysis of the utterances. Pragmatic data were taken, categorized, and selected from Whatsapp conversation and email correspondence screenshots between lecturers and students in eight (8) online English Language classes and six (6) classes on Ushul Fiqh subjects at Universitas Islam Negeri (UIN) Salatiga. Additionally, as demonstrated in the screenshots, an online questionnaire was used to elicit data on students' and lecturers' perspectives on the cyberpragmatic activities. The descriptive analysis shows that the students considered the principles of politeness as a prominent aspect of their communicative actions and managed to appropriately applied it in their cyberpragmatic activities. Additional pragmatic features of religious expressions were also used to amplify the politeness effect. Nevertheless, there were some cases where impoliteness principles were used by the students, regardless of their awareness of their pragmatic consequences. The acceptability judgement questionnaire confirmed the level of politeness and impoliteness strategies previously described. Consequently, future research may benefit from this study by exploring other aspects of cyberpragmatics such as ethnicity, gender, and other socio-political aspects, from interdisciplinary perspectives.
Automatic brain extraction and brain tissues segmentation on multi-contrast animal MRI

For many neuroscience applications, brain extraction in MRI images is the first pre-processing step of a quantification pipeline. Once the brain is extracted, further post-processing calculations become faster, more specific and easier to implement and interpret. It is the case, for example, of functional MRI brain studies, or relaxation time mappings and brain tissues classifications to characterise brain pathologies. Existing brain extraction tools are mostly adapted to work on the human anatomy, this gives poor results when applied to animal brain images. We have developed an atlas-based Veterinary Images Brain Extraction (VIBE) algorithm that encompasses a pre-processing step to adapt the atlas to the patient's image, and a subsequent registration step. We show that the brain extraction is achieved with excellent results in terms of Dice and Jaccard metrics. The algorithm is automatic, with no need to adapt the parameters in a broad range of situations: we successfully tested multiple MRI contrasts (T1-weighted, T2-weighted, T2-weighted FLAIR), all the acquisition planes (sagittal, dorsal, transverse), different animal species (dogs and cats) and canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic). VIBE can be successfully extended to other animal species, provided that an atlas for that specific species exists. We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm.
High-Frequency Quantitative Trading of Digital Currencies Based on Deep Reinforcement Learning Models with Fusion Evolutionary Strategies

Abstract To address the emerging market of digital currencies where researchers have less applied deep reinforcement learning, this paper proposed deep evolutionary reinforcement learning (DERL) model, which is a deep reinforcement learning model based on an evolutionary strategy to extract trading information between users using Bitcoin K-line data as base data. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic viewpoint to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algorithm can successfully extract the significant hidden features in the factors when faced with large and complex high-frequency factors, this paper trains the agent in reinforcement learning using three different learning algorithms, including Q-learning, evolution strategy, and Policy Gradient. The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November 2022. According to the experimental results, the evolution strategy algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results also demonstrate that deep reinforcement learning based on the evolution strategy outperforms Q-learning and Policy Gradient concerning risk resistance and return capability.
Prurarilingualism and International Corporation for Enhancing the Image of Private School in Banyumas Region

School is a special social space in preparing the futur generation, which not only forms cognitive, psycho-motor, and affective aspects, but also a forum for socio-cultural development. As a social instrument, the diversity of situations contributes to theirs images which is clearly reflected in the generally image of schools in Indonesia, and particularity in Banyumas region, including the demarcation of public and private schools. This dichotomy directly implies a different quality for the image of private schools which are often considered a marginal areas, even though both public and private schools have their own uniqueness in preparing their students. Recently, there has been a strategy through prurarilingual teaching programs to deal with the phenomenon of globalization. In reality, the implementation of this strategy is still far from perfect in preparing young Indonesians who are dynamic academically and professionally. This is because the prurarilingualism aspects have not been integrated that support the language competence of the learners. As a result, prurarilingual teaching as a strategy for enhancing the image of a private school serves only as a slogan, and not as a milestone. Therefore, through the application of a prurarilingualism program that focuses on developing: (1) paralinguistic behavior: (i) body language, (ii) using anomatope, and using (iii) prosody, and (2) paratextual behavior: (i) illustration and ( ii) typography, is expected to be able to improve the image of private schools that make multilingual programs as an imaging strategy which is also supported by international corporation networks
Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN

This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previous methods suffer from the high variance between class-wise accuracy which leads to not being applicable for all types of anomalies. The proposed method named RCALAD tries to solve this problem by introducing a novel discriminator to the structure, which results in a more efficient training process. Additionally, RCALAD employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution, effectively separating anomalous samples from their reconstructions and facilitating more accurate anomaly detection. To further enhance the performance of the model, two novel anomaly scores are introduced. The proposed model has been thoroughly evaluated through extensive experiments on six various datasets, yielding results that demonstrate its superiority over existing state-of-the-art models. The code is readily available to the research community at https://github.com/zahraDehghanian97/RCALAD.
A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions

In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed significant growth. Incorporating the copy mechanism with traditional encoder-decoder architectures and using pre-trained encoder-decoders and large language models have set new performance benchmarks. This paper presents various experiments that replicate and expand upon recent NMT-based SPARQL generation studies, comparing pre-trained language models (PLMs), non-pre-trained language models (NPLMs), and large language models (LLMs), highlighting the impact of question annotation and the copy mechanism and testing various fine-tuning methods using LLMs. In particular, we provide a systematic error analysis of the models and test their generalization ability. Our study demonstrates that the copy mechanism yields significant performance enhancements for most PLMs and NPLMs. Annotating the data is pivotal to generating correct URIs, with the "tag-within" strategy emerging as the most effective approach. Additionally, our findings reveal that the primary source of errors stems from incorrect URIs in SPARQL queries that are sometimes replaced with hallucinated URIs when using base models. This does not happen using the copy mechanism, but it sometimes leads to selecting wrong URIs among candidates. Finally, the performance of the tested LLMs fell short of achieving the desired outcomes.
Assisting clinical practice with fuzzy probabilistic decision trees

The need for fully human-understandable models is increasingly being recognised as a central theme in AI research. The acceptance of AI models to assist in decision making in sensitive domains will grow when these models are interpretable, and this trend towards interpretable models will be amplified by upcoming regulations. One of the killer applications of interpretable AI is medical practice, which can benefit from accurate decision support methodologies that inherently generate trust. In this work, we propose FPT, (MedFP), a novel method that combines probabilistic trees and fuzzy logic to assist clinical practice. This approach is fully interpretable as it allows clinicians to generate, control and verify the entire diagnosis procedure; one of the methodology's strength is the capability to decrease the frequency of misdiagnoses by providing an estimate of uncertainties and counterfactuals. Our approach is applied as a proof-of-concept to two real medical scenarios: classifying malignant thyroid nodules and predicting the risk of progression in chronic kidney disease patients. Our results show that probabilistic fuzzy decision trees can provide interpretable support to clinicians, furthermore, introducing fuzzy variables into the probabilistic model brings significant nuances that are lost when using the crisp thresholds set by traditional probabilistic decision trees. We show that FPT and its predictions can assist clinical practice in an intuitive manner, with the use of a user-friendly interface specifically designed for this purpose. Moreover, we discuss the interpretability of the FPT model.
XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages

Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are summarized to generate English Wikipedia pages. But, for low-resource languages, the scarcity of reference articles makes monolingual summarization ineffective in solving this problem. Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference articles, written in various languages, to generate Wikipedia-style text. Accordingly, we contribute a benchmark dataset, XWikiRef, spanning ~69K Wikipedia articles covering five domains and eight languages. We harness this dataset to train a two-stage system where the input is a set of citations and a section title and the output is a section-specific LR summary. The proposed system is based on a novel idea of neural unsupervised extractive summarization to coarsely identify salient information followed by a neural abstractive model to generate the section-specific text. Extensive experiments show that multi-domain training is better than the multi-lingual setup on average.
How Tiny Can Analog Filterbank Features Be Made for Ultra-low-power On-device Keyword Spotting?

Analog feature extraction is a power-efficient and re-emerging signal processing paradigm for implementing the front-end feature extractor in on device keyword-spotting systems. Despite its power efficiency and re-emergence, there is little consensus on what values the architectural parameters of its critical block, the analog filterbank, should be set to, even though they strongly influence power consumption. Towards building consensus and approaching fundamental power consumption limits, we find via simulation that through careful selection of its architectural parameters, the power of a typical state-of-the-art analog filterbank could be reduced by 33.6x, while sacrificing only 1.8% in downstream 10-word keyword spotting accuracy through a back-end neural network.
Training Quantum Boltzmann Machines with the $β$-Variational Quantum Eigensolver

The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. The training of the QBM consists of minimizing the relative entropy from the model to the target state. This requires QBM expectation values which are computationally intractable for large models in general. It is therefore important to develop heuristic training methods that work well in practice. In this work, we study a heuristic method characterized by a nested loop: the inner loop trains the $\beta$-variational quantum eigensolver ($\beta$-VQE) by Liu et al. (2021) to approximate the QBM expectation values; the outer loop trains the QBM to minimize the relative entropy to the target. We show that low-rank representations obtained by $\beta$-VQE provide an efficient way to learn low-rank target states, such as classical data and low-temperature quantum tomography. We test the method on both classical and quantum target data with numerical simulations of up to 10 qubits. For the cases considered here, the obtained QBMs can model the target to high fidelity. The approach offers a valuable route towards variationally training QBMs on near-term quantum devices.
pgmpy: A Python Toolkit for Bayesian Networks

Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These implementations focus on modularity and easy extensibility to allow users to quickly modify/add to existing algorithms, or to implement new algorithms for different use cases. pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org.
ProPanDL: A Modular Architecture for Uncertainty-Aware Panoptic Segmentation

We introduce ProPanDL, a family of networks capable of uncertainty-aware panoptic segmentation. Unlike existing segmentation methods, ProPanDL is capable of estimating full probability distributions for both the semantic and spatial aspects of panoptic segmentation. We implement and evaluate ProPanDL variants capable of estimating both parametric (Variance Network) and parameter-free (SampleNet) distributions quantifying pixel-wise spatial uncertainty. We couple these approaches with two methods (Temperature Scaling and Evidential Deep Learning) for semantic uncertainty estimation. To evaluate the uncertainty-aware panoptic segmentation task, we address limitations with existing approaches by proposing new metrics that enable separate evaluation of spatial and semantic uncertainty. We additionally propose the use of the energy score, a proper scoring rule, for more robust evaluation of spatial output distributions. Using these metrics, we conduct an extensive evaluation of ProPanDL variants. Our results demonstrate that ProPanDL is capable of estimating well-calibrated and meaningful output distributions while still retaining strong performance on the base panoptic segmentation task.
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study

Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign high confidence to low-quality predictions, leading to compromised reliability and trustworthiness in real-world applications. Probabilistic deep learning methods are common solutions to the miscalibration problem. However, their relative effectiveness in complex autoregressive summarization tasks are not well-understood. In this work, we thoroughly investigate different state-of-the-art probabilistic methods' effectiveness in improving the uncertainty quality of the neural summarization models, across three large-scale benchmarks with varying difficulty. We show that the probabilistic methods consistently improve the model's generation and uncertainty quality, leading to improved selective generation performance (i.e., abstaining from low-quality summaries) in practice. We also reveal notable failure patterns of probabilistic methods widely-adopted in NLP community (e.g., Deep Ensemble and Monte Carlo Dropout), cautioning the importance of choosing appropriate method for the data setting.
On some congruences and exponential sums

Let $\varepsilon>0$ be a fixed small constant, ${\mathbb F}_p$ be the finite field of $p$ elements for prime $p$. We consider additive and multiplicative problems in ${\mathbb F}_p$ that involve intervals and arbitrary sets. Representative examples of our results are as follows. Let ${\mathcal M}$ be an arbitrary subset of ${\mathbb F}_p$. If $\#{\mathcal M} >p^{1/3+\varepsilon}$ and $H\ge p^{2/3}$ or if $\#{\mathcal M} >p^{3/5+\varepsilon}$ and $H\ge p^{3/5+\varepsilon}$ then all, but $O(p^{1-\delta})$ elements of ${\mathbb F}_p$ can be represented in the form $hm$ with $h\in [1, H]$ and $m\in {\mathcal M}$, where $\delta> 0$ depends only on $\varepsilon$. Furthermore, let $ {\mathcal X}$ be an arbitrary interval of length $H$ and $s$ be a fixed positive integer. If $$ H> p^{17/35+\varepsilon}, \quad \#{\mathcal M} > p^{17/35+\varepsilon}. $$ then the number $T_6(\lambda)$ of solutions of the congruence $$ \frac{m_1}{x_1^s}+ \frac{m_2}{x_2^s}+ \frac{m_3}{x_3^s}+\frac{m_4}{x_4^s}+ \frac{m_5}{x_5^s}+\frac{m_6}{x_6^s} \equiv \lambda\mod p, \qquad m_i\in {\mathcal M}, \quad \ x_i \in {\mathcal X}, \quad i =1, \ldots, 6, $$ satisfies $$ T_6(\lambda)=\frac{H^6(\#{\mathcal M})^6}{p}\left(1+O(p^{-\delta})\right), $$ where $\delta> 0$ depends only on $s$ and $\varepsilon$.
Towards the Transferable Audio Adversarial Attack via Ensemble Methods

In recent years, deep learning (DL) models have achieved significant progress in many domains, such as autonomous driving, facial recognition, and speech recognition. However, the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufficient robustness and generalization. Also, transferable attacks have become a prominent method for black-box attacks. In this work, we explore the potential factors that impact adversarial examples (AEs) transferability in DL-based speech recognition. We also discuss the vulnerability of different DL systems and the irregular nature of decision boundaries. Our results show a remarkable difference in the transferability of AEs between speech and images, with the data relevance being low in images but opposite in speech recognition. Motivated by dropout-based ensemble approaches, we propose random gradient ensembles and dynamic gradient-weighted ensembles, and we evaluate the impact of ensembles on the transferability of AEs. The results show that the AEs created by both approaches are valid for transfer to the black box API.