Reviewerly/exHarmony huggingface dataset:
ConvFaceNeXt: Lightweight Networks for Face Recognition

The current lightweight face recognition models need improvement in terms of floating point operations (FLOPs), parameters, and model size. Motivated by ConvNeXt and MobileFaceNet, a family of lightweight face recognition models known as ConvFaceNeXt is introduced to overcome the shortcomings listed above. ConvFaceNeXt has three main parts, which are the stem, bottleneck, and embedding partitions. Unlike ConvNeXt, which applies the revamped inverted bottleneck dubbed the ConvNeXt block in a large ResNet-50 model, the ConvFaceNeXt family is designed as lightweight models. The enhanced ConvNeXt (ECN) block is proposed as the main building block for ConvFaceNeXt. The ECN block contributes significantly to lowering the FLOP count. In addition to the typical downsampling approach using convolution with a kernel size of three, a patchify strategy utilizing a kernel size of two is also implemented as an alternative for the ConvFaceNeXt family. The purpose of adopting the patchify strategy is to reduce the computational complexity further. Moreover, blocks with the same output dimension in the bottleneck partition are added together for better feature correlation. Based on the experimental results, the proposed ConvFaceNeXt model achieves competitive or even better results when compared with previous lightweight face recognition models, on top of a significantly lower FLOP count, parameters, and model size.
Distance learning technologies with blockchain elements in the system of continuous education

The relevance of the study was explained by the fact that the support of management of the distance learning information processes is associated with the problem of effective allocation of system resources used upon transferring and processing of educational information. The purpose of this study is a comprehensive analysis and development of the principles of developing an electronic portal for the system for continuous education. In the work, the authors used general methods of generalization and systematization, as well as the method of mathematical calculations. 150 teachers of secondary and higher primary schools took part in the calculations. The results of the calculations made it possible to confirm the need to use distance learning technologies with blockchain elements in educational institutions. The use of blockchain technologies for the implementation of an electronic portal can increase the motivation of students and teachers, facilitating work in the learning process. Keywords: continuous education, distance learning, blockchain, electronic portal, automation of learning.
Layout-Aware Information Extraction for Document-Grounded Dialogue

Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to comprehend and retrieve information from documents is a challenging research problem. Previous work ignores the visual property of documents and treats them as plain text, resulting in incomplete modality. In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems. LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents, becoming the largest VRD-based information extraction dataset to the best of our knowledge. We also develop benchmark methods that extend the token-based language model to consider layout features like humans. Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.
Attack is the Best Defense: Towards Preemptive-Protection Person Re-Identification

Person Re-IDentification (ReID) aims at retrieving images of the same person across multiple camera views. Despite its popularity in surveillance and public safety, the leakage of identity information is still at risk. For example, once obtaining the illegal access to ReID systems, malicious user can accurately retrieve the target person, leading to the exposure of private information. Recently, some pioneering works protect private images with adversarial examples by adding imperceptible perturbations to target images. However, in this paper, we argue that directly applying adversary-based methods to protect the ReID system is sub-optimal due to the 'overlap identity' issue. Specifically, merely pushing the adversarial image away from its original label would probably make it move into the vicinity of other identities. This leads to the potential risk of being retrieved when querying with all the other identities exhaustively. We thus propose a novel preemptive-Protection person Re-IDentification (PRIDE) method. By explicitly constraining the adversarial image to an isolated location, the target person is far away from neither the original identity nor any other identities, which protects him from being retrieved by illegal queries. Moreover, we further propose two crucial attack scenarios (Random Attack and Order Attack) and a novel Success Protection Rate (SPR) metric to quantify the protection ability. Experiments show consistent outperformance of our method over other baselines across different ReID models, datasets and attack scenarios.
Health worker perspectives of Smart Triage, a digital triaging platform for quality improvement at a referral hospital in Uganda: a qualitative analysis

Effective triage at hospitals can improve outcomes for children globally by helping identify and prioritize care for those most at-risk of death. Paper-based pediatric triage guidelines have been developed to support frontline health workers in low-resource settings, but these guidelines can be challenging to implement. Smart Triage is a digital triaging platform for quality improvement (QI) that aims to address this challenge. Smart Triage represents a major cultural and behavioural shift in terms of managing patients at health facilities in low-and middle-income countries. The purpose of this study is to understand user perspectives on the usability, feasibility, and acceptability of Smart Triage to inform ongoing and future implementation.This was a descriptive qualitative study comprising of face-to-face interviews with health workers (n = 15) at a regional referral hospital in Eastern Uganda, conducted as a sub-study of a larger clinical trial to evaluate Smart Triage (NCT04304235). Thematic analysis was used to assess the usability, feasibility, and acceptability of the platform, focusing on its use in stratifying and prioritizing patients according to their risk and informing QI initiatives implemented by health workers.With appropriate training and experience, health workers found most features of Smart Triage usable and feasible to implement, and reported the platform was acceptable due to its positive impact on reducing the time to treatment for emergency pediatric cases and its use in informing QI initiatives within the pediatric ward. Several factors that reduced the feasibility and acceptability were identified, including high staff turnover, a lack of medical supplies at the hospital, and challenges with staff attitudes.Health workers can use the Smart Triage digital triaging platform to identify and prioritize care for severely ill children and improve quality of care at health facilities in low-resource settings. Future innovation is needed to address identified feasibility and acceptability challenges; however, this platform could potentially address some of the challenges to implementing current paper-based systems.
Selecting the Best Health Care Systems: An Approach Based on Opinion Mining and Simplified Neutrosophic Sets

Measuring what hospital offers the best services is very difficult, for that reason, the opinions from previous patients have become an essential tool for the new possible clients to decide which services they must select. Many online platforms deal with opinions to analyze their services/products, primarily, by means of aspect-based sentiment analysis techniques. These techniques are mainly based on the detection of features from services/products to model the feelings toward them. Most models primarily cope with positiveness, negativeness and neutrality; nevertheless, these do not reflect other situations in which there are positive and negative aspects, but the overall sentiment is not neutral, but indeterminate. To face this issue, simplified neutrosophic sets can be a useful tool. Therefore, this study presents a novel application of the simplified neutrosophic sets to hospital ranking. The application, first, detects the hospital features, second, models the feelings toward them using simplified neutrosophic sets, and finally, ranks the hospitals according to the patients’ preferences. It has been tested using opinions from a real website and compared against other fuzzy logic-based approaches. The achieved results outperform the ones obtained by other proposals, revealing that simplified neutrosophic sets can be an interesting solution to model feelings.
Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.
From Known to Unknown

State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the large amount of data needed for training and the limited ability of a neural network in handling previously unknown data, it is hard for existing GNN-based methods to operate in an open set setting. To address this problem, we design a Quality-aware Self-improving Graph Neural Network (QSGNN) which extends the knowledge from known to unknown by leveraging the best of known samples and reliable knowledge transfer. Specifically, to fully exploit the labeled data, we propose a novel supervised pairwise loss with an additional orthogonal inter-class relation constraint to train the backbone GNN encoder. The learnt, already-known events further serve as strong reference bases for the unknown ones, which greatly prompts knowledge acquisition and transfer. When the model is generalized to unknown data, to ensure the effectiveness and reliability, we further leverage the reference similarity distribution vectors for pseudo pairwise label generation, selection and quality assessment. Following the diversity principle of active learning, our method selects diverse pair samples with the generated pseudo labels to fine-tune the GNN encoder. Besides, we propose a novel quality-guided optimization in which the contributions of pseudo labels are weighted based on consistency. Experimental results validate that our model achieves state-of-the-art results and extends well to unknown events.
SiamMBFAN: Siamese tracker with multi-branch feature aggregation network

Siamese trackers have attracted considerable attention in the field of object tracking because of their high precision and speed. However, one of the main disadvantages of Siamese trackers is that their feature extraction network is relatively single. They often use AlexNet or ResNet50 as the backbone network. AlexNet is shallow and thus cannot easily extract abundant semantic information, whereas ResNet50 has many convolutional layers, reducing the real-time performance of Siamese trackers. We propose a multi-branch feature aggregation network with different designs in the shallow and deep convolutional layers. We use the residual module to build the shallow convolutional layers to extract textural and edge features. The deep convolution layers, designed with two independent branches, are built with residual and parallel modules to extract different semantic features. The proposed network has a depth of only nine modules, and thus it is a simple and effective network. We then apply the network to a Siamese tracker to form SiamMBFAN. We design multi-layer classification and regression subnetworks in the Siamese tracker by aggregating the last three modules of the two branches, improving the localization ability of the tracker. Our tracker achieves a better balance between performance and speed. Finally, SiamMBFAN is tested on four challenging benchmarks, including OTB100, VOT2016, VOT2018, and UAV123. Compared with other trackers, our tracker improves by 7% (OTB100).
A Softmax-Free Loss Function Based on Predefined Optimal-Distribution of Latent Features for Deep Learning Classifier

In the field of pattern classification, the training of deep learning classifiers is mostly end-to-end learning, and the loss function is the constraint on the final output (posterior probability) of the network, so the existence of Softmax is essential. In the case of end-to-end learning, there is usually no effective loss function that completely relies on the features of the middle layer to restrict learning, resulting in the distribution of sample latent features is not optimal, so there is still room for improvement in classification accuracy. Based on the concept of Predefined Evenly-Distributed Class Centroids (PEDCC), this article proposes a Softmax-free loss function based on predefined optimal-distribution of latent features—POD Loss. The loss function only restricts the latent features of the samples, including the norm-adaptive Cosine distance between the latent feature vector of the sample and the center of the predefined evenly-distributed class, and the correlation between the latent features of the samples. Finally, Cosine distance is used for classification. Compared with the commonly used Softmax Loss, some typical Softmax related loss functions and PEDCC-Loss, experiments on several commonly used datasets on several typical deep learning classification networks show that the classification performance of POD Loss is always significant better and easier to converge. Code is available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TianYuZu/POD-Loss</uri> .
Qubit-efficient simulation of thermal states with quantum tensor networks

We present a holographic quantum simulation algorithm to variationally prepare thermal states of $d$-dimensional interacting quantum many-body systems, using only enough hardware qubits to represent a $(d\ensuremath{-}1)$-dimensional cross section. This technique implements the thermal state by approximately unraveling the quantum matrix-product density operator (qMPDO) into a stochastic mixture of quantum matrix-product states (sto-qMPS). The parameters of the quantum circuits generating the qMPS and of the probability distribution generating the stochastic mixture are determined through a variational optimization procedure. We demonstrate a small-scale proof-of-principle demonstration of this technique on Quantinuum's trapped-ion quantum processor to simulate thermal properties of correlated spin chains over a wide temperature range using only a single pair of hardware qubits. Then, through classical simulations, we explore the representational power of two versions of sto-qMPS ansatzes for larger and deeper circuits and establish empirical relationships between the circuit resources and the accuracy of the variational free energy.
Core conceptual features of successful blended learning in higher education: Policy implications 

COVID-19 has “taught” universities worldwide that using digital technologies to support purely online or blended learning is a survival strategy. This lesson plus the inclusion of technology in continental, national, and university policies and strategic plans implicate significant technology integration, especially blended learning, in higher education in the post-pandemic era. However, there lacks sound theoretical frameworks to adequately explain success indicators and success factors in blended learning. Existing frameworks provided particulars about the impacts of blended learning within certain contexts; none provided a comprehensive analysis of the significant factors that transcend specific application contexts. Moreover, the frameworks did not offer clear conceptions of knowledge, teaching, learning, and technology and its role in learning. To better inform successful blended learning adoption, this study problematizes success indicators and success factors based on a configurative review of existing frameworks and emerging theoretical perspectives in higher education. A holistic conceptual framework that transcends context specificity is proposed to better inform policy making, instructional design, and teaching and learning. Conceptions of adaptive policy, policy as learning design, and policy as practice are found relevant for blended learning policy making and analysis in higher education.
Dynamic congestion management system for cloud service broker

&lt;span lang="EN-US"&gt;The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients’ requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.&lt;/span&gt;
A hybrid model of complexity estimation: Evidence from Russian legal texts

This article proposes a hybrid model for the estimation of the complexity of legal documents in Russian. The model consists of two main modules: linguistic feature extractor and a transformer-based neural encoder. The set of linguistic metrics includes both non-specific metrics traditionally used to predict complexity, as well as style-specific metrics developed in order to deal with the peculiarities of official texts. The model was trained on a dataset constructed from text sequences from Russian textbooks. Training data were collected on either subjects related to the topic of legal documents such as Jurisprudence, Economics, Social Sciences, or subjects characterized by the use of general languages such as Literature, History, and Culturology. The final set of materials used contain 48 thousand selected text blocks having various subjects and level-of-complexity identifiers. We have tested the baseline fine-tuned BERT model, models trained on linguistic features, and models trained on features in combination with BERT predictions. The scores show that a hybrid approach to complexity estimation can provide high-quality results in terms of different metrics. The model has been tested on three sets of legal documents.
Intrinsically-typed definitional interpreters à la carte

Specifying and mechanically verifying type safe programming languages requires significant effort. This effort can in theory be reduced by defining and reusing pre-verified, modular components. In practice, however, existing approaches to modular mechanical verification require many times as much specification code as plain, monolithic definitions. This makes it hard to develop new reusable components, and makes existing component specifications hard to grasp. We present an alternative approach based on intrinsically-typed interpreters, which reduces the size and complexity of modular specifications as compared to existing approaches. Furthermore, we introduce a new abstraction for safe-by-construction specification and composition of pre-verified type safe language components: language fragments . Language fragments are about as concise and easy to develop as plain, monolithic intrinsically-typed interpreters, but require about 10 times less code than previous approaches to modular mechanical verification of type safety.
The Playful Potential of Digital Commensality: Learning from Spontaneous Playful Remote Dining Practices

With one-person households being increasingly common and Covid-19 lockdown policies forcing people to stay home, remote dining has become common practice for many, who take it as an opportunity to connect with others in times of loneliness. Sharing meals online, also known as digital commensality, is a rich form of interaction, where people leverage technology to achieve a sense of connectedness and belonging while eating. In this paper, we look at digital commensality and we explore its inherent playful potential with the aim to inspire the design of engaging technologies that can support, enhance and augment this form of interaction. For this, we used a situated play design approach to document and analyze the behavior of 36 people (including pairs of friends and strangers) sharing meals online. Our analysis surfaced a set of play potentials of remote dining -- i.e., playful things people already do and enjoy spontaneously while sharing meals online. We present those play potentials as inspirational material: they can motivate and enrich the design of future digital commensality technologies by responding to people's desire for playful and social interaction with, through, and around food.
Model-Based Security Analysis in Additive Manufacturing Systems

Additive manufacturing (AM) is expected to revolutionize industrial manufacturing processes by providing access to readily available, lower cost, high-performance parts, including those with complex designs and diverse materials, not attainable in conventional subtractive machining processes. These benefits are distinctly advantageous for low-volume rapid prototyping. They also reduce the build time of complex, safety-critical components that traditionally require assembly.