Reviewerly/exHarmony huggingface dataset:
The ontology for agents, systems and integration of services: OASIS version 2$

Semantic representation is a key enabler for several application domains, and the multi-agent systems realm makes no exception. Among the methods for semantically representing agents, one has been essentially achieved by taking a behaviouristic vision, through which one can describe how they operate and engage with their peers. The approach essentially aims at defining the operational capabilities of agents through the mental states related with the achievement of tasks. The OASIS ontology — An Ontology for Agent, Systems, and Integration of Services, presented in 2019 — pursues the behaviouristic approach to deliver a semantic representation system and a communication protocol for agents and their commitments. This paper reports on the main modelling choices concerning the representation of agents in OASIS 2, the latest major upgrade of OASIS, and the achievement reached by the ontology since it was first introduced, in particular in the context of ontologies for blockchains.
Blockchain-Based Data Access Control and Key Agreement System in IoT Environment

Recently, with the increasing application of the Internet of Things (IoT), various IoT environments such as smart factories, smart homes, and smart grids are being generated. In the IoT environment, a lot of data are generated in real time, and the generated IoT data can be used as source data for various services such as artificial intelligence, remote medical care, and finance, and can also be used for purposes such as electricity bill generation. Therefore, data access control is required to grant access rights to various data users in the IoT environment who need such IoT data. In addition, IoT data contain sensitive information such as personal information, so privacy protection is also essential. Ciphertext-policy attribute-based encryption (CP-ABE) technology has been utilized to address these requirements. Furthermore, system structures applying blockchains with CP-ABE are being studied to prevent bottlenecks and single failures of cloud servers, as well as to support data auditing. However, these systems do not stipulate authentication and key agreement to ensure the security of the data transmission process and data outsourcing. Accordingly, we propose a data access control and key agreement scheme using CP-ABE to ensure data security in a blockchain-based system. In addition, we propose a system that can provide data nonrepudiation, data accountability, and data verification functions by utilizing blockchains. Both formal and informal security verifications are performed to demonstrate the security of the proposed system. We also compare the security, functional aspects, and computational and communication costs of previous systems. Furthermore, we perform cryptographic calculations to analyze the system in practical terms. As a result, our proposed protocol is safer against attacks such as guessing attacks and tracing attacks than other protocols, and can provide mutual authentication and key agreement functions. In addition, the proposed protocol is more efficient than other protocols, so it can be applied to practical IoT environments.
Kepler: Robust Learning for Parametric Query Optimization

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training anML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.
Vicinity Vision Transformer

Vision transformers have shown great success on numerous computer vision tasks. However, their central component, softmax attention, prohibits vision transformers from scaling up to high-resolution images, due to both the computational complexity and memory footprint being quadratic. Linear attention was introduced in natural language processing (NLP) which reorders the self-attention mechanism to mitigate a similar issue, but directly applying existing linear attention to vision may not lead to satisfactory results. We investigate this problem and point out that existing linear attention methods ignore an inductive bias in vision tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , 2D locality. In this paper, we propose Vicinity Attention, which is a type of linear attention that integrates 2D locality. Specifically, for each image patch, we adjust its attention weight based on its 2D Manhattan distance from its neighbouring patches. In this case, we achieve 2D locality in a linear complexity where the neighbouring image patches receive stronger attention than far away patches. In addition, we propose a novel Vicinity Attention Block that is comprised of Feature Reduction Attention (FRA) and Feature Preserving Connection (FPC) in order to address the computational bottleneck of linear attention approaches, including our Vicinity Attention, whose complexity grows quadratically with respect to the feature dimension. The Vicinity Attention Block computes attention in a compressed feature space with an extra skip connection to retrieve the original feature distribution. We experimentally validate that the block further reduces computation without degenerating the accuracy. Finally, to validate the proposed methods, we build a linear vision transformer backbone named Vicinity Vision Transformer (VVT). Targeting general vision tasks, we build VVT in a pyramid structure with progressively reduced sequence length. We perform extensive experiments on CIFAR-100, ImageNet-1 k, and ADE20 K datasets to validate the effectiveness of our method. Our method has a slower growth rate in terms of computational overhead than previous transformer-based and convolution-based networks when the input resolution increases. In particular, our approach achieves state-of-the-art image classification accuracy with 50% fewer parameters than previous approaches.
Harnessing Quantumness of States using Discrete Wigner Functions under (non)‐Markovian Quantum Channels

Abstract The negativity of the discrete Wigner functions (DWFs) is a measure of non‐classicality and is often used to quantify the degree of quantum coherence in a system. The study of Wigner negativity and its evolution under different quantum channels can provide insight into the stability and robustness of quantum states under their interaction with the environment, which is essential for developing practical quantum computing systems. The variation of DWF negativity of qubit, qutrit, and two‐qubit systems under the action of (non)‐Markovian random telegraph noise (RTN) and amplitude damping (AD) quantum channels is investigated. Different negative quantum states that can be used as a resource for quantum computation and quantum teleportation are constructed. The success of quantum computation and teleportation is estimated for these states under (non)‐Markovian evolutions.
Low-threshold all-optical nonlinear activation function based on injection locking in distributed feedback laser diodes

We experimentally demonstrate an all-optical nonlinear activation unit based on the injection-locking effect of distributed feedback laser diodes (DFB-LDs). The nonlinear carrier dynamics in the unit generates a low-threshold nonlinear activation function with optimized operating conditions. The unit can operate at a low threshold of -15.86 dBm and a high speed of 1 GHz, making it competitive among existing optical nonlinear activation approaches. We apply the unit to a neural network task of solving the second-order ordinary differential equation. The fitting error is as low as 0.0034, verifying the feasibility of our optical nonlinear activation approach. Given that the large-scale fan-out of optical neural networks (ONNs) will significantly reduce the optical power in one channel, our low-threshold scheme is suitable for the development of high-throughput ONNs.
Whole Slide Imaging-Based Prediction of <i>TP53</i> Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

Abstract In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated TP53 mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as TP53 mutations. Comparative expression and histologic cell type analyses identified a TP53-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers. Significance: Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. See related commentary by Bordeleau, p. 2809
Machine Learning Algorithms for Binary Classification of Breast Cancer

Breast cancer is one of the most serious diseases in women, and it is the second largest cause of cancer mortality. Breast cancer occurs when malignant, cancerous tumors form from breast cells. Self-tests and routine clinical examinations aid in early detection and, as a result, dramatically improve survival chances. Breast cancer classification is a medical strategy that researchers and scientists face great challenges. Neural networks have acquired appeal as a technique for cancer data classification. This research begins by looking into the use of machine learning algorithms to classify breast masses for various diagnostic, predictive, or prognostic tasks in a number of imaging modalities, including MRI, ultrasound, digital breast tomosynthesis, and mammography. For evaluation, we proposed various machine learning algorithms such as support vector machine, random forest, Naive Bayes, logistic regression, K-nearest neighbor, artificial neural network, and deep neural network. We have used publicly accessible Breast Cancer Wisconsin (Diagnostic) datasets from the UCI Repository. As demonstrated in the results sections, the DNN classifier has a high level of accuracy (99.42%), showing that it outperforms traditional models.
Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime

Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the success, generating data augmentations as hard positive examples that may increase their effectiveness is under-explored. This paper proposes an Adversarial Word Dilution (AWD) method that can generate hard positive examples as text data augmentations to train the low-resource text classification model efficiently. Our idea of augmenting the text data is to dilute the embedding of strong positive words by weighted mixing with unknown-word embedding, making the augmented inputs hard to be recognized as positive by the classification model. We adversarially learn the dilution weights through a constrained min-max optimization process with the guidance of the labels. Empirical studies on three benchmark datasets show that AWD can generate more effective data augmentations and outperform the state-of-the-art text data augmentation methods. The additional analysis demonstrates that the data augmentations generated by AWD are interpretable and can flexibly extend to new examples without further training.
AUC Maximization for Low-Resource Named Entity Recognition

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples. But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions. Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. We give evidence that by simply combining two binary-classifiers that maximize the AUC score, significant performance improvement over traditional loss functions is achieved under low-resource NER settings. We also conduct extensive experiments to demonstrate the advantages of our method under the low-resource and highly-imbalanced data distribution settings. To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting. Furthermore, we show that our method is agnostic to different types of NER embeddings, models and domains. The code of this work is available at https://github.com/dngu0061/NER-AUC-2T.
Interactive Concept Bottleneck Models

Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate that a simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms static approaches as well as active feature acquisition methods proposed in the literature. We show that the interactive CBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech-UCSD Birds, CheXpert and OAI datasets.
Solving Explainability Queries with Quantification: The Case of Feature Relevancy

Trustable explanations of machine learning (ML) models are vital in high-risk uses of artificial intelligence (AI). Apart from the computation of trustable explanations, a number of explainability queries have been identified and studied in recent work. Some of these queries involve solving quantification problems, either in propositional or in more expressive logics. This paper investigates one of these quantification problems, namely the feature relevancy problem (FRP), i.e.\ to decide whether a (possibly sensitive) feature can occur in some explanation of a prediction. In contrast with earlier work, that studied FRP for specific classifiers, this paper proposes a novel algorithm for the \fprob quantification problem which is applicable to any ML classifier that meets minor requirements. Furthermore, the paper shows that the novel algorithm is efficient in practice. The experimental results, obtained using random forests (RFs) induced from well-known publicly available datasets, demonstrate that the proposed solution outperforms existing state-of-the-art solvers for Quantified Boolean Formulas (QBF) by orders of magnitude. Finally, the paper also identifies a novel family of formulas that are challenging for currently state-of-the-art QBF solvers.
A Model of a Multi-sensor System for Detection and Tracking of Vehicles and Drones

This paper proposes a model of a multi-sensor system for detection and tracking of road vehicles and drones, based on developed original methods and algorithms for signal and data processing, which is a fundamental scientific task. The proposed model uses the polar Hough transform to combine the heterogeneous data in a multi-sensor system.
Preparation of multiphoton high-dimensional GHZ states

Multipartite high-dimensional entanglement presents different physics from multipartite two-dimensional entanglement. However, how to prepare multipartite high-dimensional entanglement is still a challenge with linear optics. In this paper, a multiphoton GHZ state with arbitrary dimensions preparation protocol is proposed in optical systems. In this protocol, we use auxiliary entanglements to realize a high-dimensional entanglement gate, so that high-dimensional entangled pairs can be connected into a multipartite high-dimensional GHZ state. Specifically, we give an example of using photons' path degree of freedom to prepare a 4-particle 3-dimensional GHZ state. Our method can be extended to other degrees of freedom and can generate arbitrary GHZ entanglement in any dimension.
Intelligent Recommendations Based on COVID-19 Related Twitter Sentiment Analysis and Fake Tweet Detection in Apache Spark Environment

Twitter has become the main platform during COVID-19, in which people from different geographical location share their opinion. The tweets reflect the mentality of the public during COVID-19 which initiates the researchers to analyze the sentiments of the public; however, existing research works on sentiment analysis acquires limited accuracy and reliability due to a lack of contextual information and the presence of sarcastic tweets. To overcome these challenges, in this paper an Intelligent Recommendations-based Twitter Sentiment Analysis (IR-TSA) is proposed. Initially, all tweets from the apache spark environment are collected for fake tweet detection using the Light Gradient Boosting Machine (Light GBM) algorithm. After that, the location-aware topic modeling is carried out in which frequently discussed topics from different locations are modeled by constructing a Directed Acyclic Graph (DAG) structure. Then, pre-processing of tweets is done through several processes in which the context information about the tweets is preserved. The pre-processed tweets are fed into the Attention-Based Bi-directional Gated Recurrent Unit (Bi-GRU) CapsNet model for context-based sentiment classification. The CapsNet layer classifies the sentiments into five classes, namely, positive, strongly positive, neutral, negative, and strongly negative based on the scores. Finally, the generation of recommendations is carried out based on the sentiments by using the Twin delayed Deep Deterministic Policy Gradient (TD 3) algorithm and recommendations are disseminated selecting the optimal influencers using the Corona Virus Optimization Algorithm (CVOA). The proposed IR-TSA is validated in the Python 3.8.3 tool and compared with validation metrics which shows that our proposed work outperforms other existing works.
MEFNET: Multi-expert fusion network for RGB-Thermal semantic segmentation

Semantic segmentation using RGB and thermal images is crucial in a variety of applications, including autonomous driving and video surveillance. However, the validity of information differs between modalities, which is typically addressed by weighting image features using complex and inefficient networks. To address this issue, we propose a Multi-Expert Fusion Network (MEFNet) that decouples the three-dimensional attention matrix of image features into a two-dimensional modal weight matrix and a channel attention vector. This approach focuses more on modal and channel differences while excluding interferences from other factors. Specifically, MEFNet multiplies RGB and thermal features by their respective modal weights, and then uses channel attention to select important feature channels. Comprehensive experiments demonstrate that MEFNet is competitive with state-of-the-art methods, achieving 62.6% mIoU on the IR SEG dataset.
Virtual Reality, Augmented Reality and Metaverse: Customer Experience Approach and User Experience Evaluation Methods. Literature Review

Currently, the technologies associated with the web and telecommunications have an essential role in different areas, such as education, medicine, tourism, commerce, among others. The coronavirus pandemic (COVID-19) accelerated the process of adopting these technologies. The announcement of the metaverse and its large number of opportunities, immersive experiences, scenarios, contexts, and possibilities have generated much expectation. This has increased interest in technologies such as Virtual Reality (VR), Augmented Reality (AR), and the mixture of both, which are not new technologies, but now taking advantage of the opportunities for better performance offered by communication networks such as 5G. Thus, user and/or customer experiences can be changed. This paper presents a literature review related to two concepts which are the user experience (UX) and customer experience (CX) in VR, AR, and metaverse, and their evaluation methods. We follow a review protocol and consult five representative databases. We found studies related to the user experience in virtual reality and augmented reality but not related to the metaverse. Regarding the evaluation methods of the user experience found, it is possible to identify heuristic evaluations and user tests that include data collection through questionnaires and the use of brain and body signals. In future work, we pretend to extend this literature review in a systematic review and intend to create guides to evaluate virtual environments applied to VR, AR, or metaverse.
“I Love You, My Dear Friend”: Analyzing the Role of Emotions in the Building of Friendships in Online Fanfiction Communities

As people continue to develop friendships over the Internet in greater numbers than in-person, the complex factors behind them become important to study. One such factor is emotional expression, and we are motivated to better understand how it plays a role in both continuing existing and building new friendships. In this study, we examined the role of emotions in the formation of different degrees of bonds between members on Fanfiction.net, an online community where members post fanfiction and receive reviews from readers. We developed an emotional taxonomy and used it to qualitatively code 11,292 reviews from Fanfiction.net. We introduce a novel metric of counting characters in reviews, an adjusted character count (ACC). We found that both positive and negative reviews have implications on friendship building, such as through in-depth mentorship and co-creation. Through a mixed-methods analysis of different degrees of emotional expression and review length, we observe users going from shallow connections based on short reviews with low emotional expression to stronger relationships through repeated demonstrations of high emotional investment to tight friendships which transcend the fictional content being exchanged.