Reviewerly/exHarmony huggingface dataset:
Analysis of the Lingering Effects of Covid-19 on Distance Education

Education has been severely impacted by the spread of the COVID-19 virus. In order to prevent the spread of the COVID-19 virus and maintain education in the current climate, governments have compelled the public to adopt online platforms. Consequently, this decision has affected numerous lives in various ways. To investigate the impact of COVID-19 on students’ education, we amassed a dataset consisting of 10,000 tweets. The motivations of the study are; (i) to analyze the positive, negative, and neutral effects of COVID-19 on education; (ii) to analyze the opinions of stakeholders in their tweets about the transition from formal education to e-learning; (iii) to analyze people’s feelings and reactions to these changes; and (iv) to analyze the effects of different training methods on different groups. We constructed emotion recognition models utilizing shallow and deep techniques, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-short Term Memory (LSTM), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Logical Regression (LR). RF algorithms with a bag-of-words model outperformed with over 80% accuracy in recognizing emotions.
Energy Efficiency of Deep Learning Compression Techniques in Wearable Human Activity Recognition

Deploying deep learning (DL) models onto low-power devices for Human Activity Recognition (HAR) purposes is gaining momentum because of the pervasive adoption of wearable sensor devices. However, the outcome of such deployment needs exploration not only because the topic is still in its infancy, but also because of the wide combination between low-power devices, deep models, and available deployment strategies. We have investigated the outcome of the application of three compression techniques, namely lite conversion, dynamic quantization, and full-integer quantization, that allow the deployment of deep models on low-power devices. This paper describes how those three compression techniques impact accuracy and energy consumption on an ESP32 device. In terms of accuracy, the full-integer technique incurs an accuracy drop between 2% and 3%, whereas the dynamic quantization and the lite conversion result in a negligible accuracy drop. In terms of power efficiency, dynamic and full-integer quantization allow for saving almost 30% of energy. The adoption of one of those two quantization techniques is recommended to obtain an executable network model, and we advise the adoption of the dynamic quantization given the negligible accuracy drop (Chiara Contoli is a researcher co-funded by the European Union - PON Research and Innovation 2014-2020.).
Towards Historical Map Analysis Using Deep Learning Techniques

This paper presents methods for automatic analysis of historical cadastral maps. The methods are developed as a part of a complex system for map digitisation, analysis and processing. Our goal is to detect important features in individual map sheets to allow their further processing and connecting the sheets into one seamless map that can be better presented online. We concentrate on detection of the map frame, which defines the important segment of the map sheet. Other crucial features are so-called inches that define the measuring scale of the map. We also detect the actual map area. We assume that standard computer vision methods can improve results of deep learning methods. Therefore, we propose novel segmentation approaches that combine standard computer vision techniques with neural nets (NNs). For all the above-mentioned tasks, we evaluate and compare our so-called “Combined methods” with state-of-the-art methods based solely on neural networks. We have shown that combining the standard computer vision techniques with NNs can outperform the state-of-the-art approaches in the scenario when only little training data is available. We have also created a novel annotated dataset that is used for network training and evaluation. This corpus is freely available for research purposes which represents another contribution of this paper.
Measuring the State-Observation-Gap in POMDPs: An Exploration of Observation Confidence and Weighting Algorithms

The objective of this study is to measure the discrepancy between states and observations within the context of the Partially Observable Markov Decision Process (POMDP). The gap between states and observations is formulated as a State-Observation-Gap (SOG) problem, represented by the symbol $$\varDelta $$ , where states and observations are treated as sets. The study also introduces the concept of Observation Confidence (OC) which serves as an indicator of the reliability of the observation, and it is established that there is a positive correlation between OC and $$\varDelta $$ . To calculate the cumulative entropy $$\lambda $$ of rewards in $$\langle o, a, \cdot \rangle $$ , we propose two weighting algorithms, namely Universal Weighting and Specific Weighting. Empirical and theoretical assessments carried out in the Cliff Walking environment attest to the effectiveness of both algorithms in determining $$\varDelta $$ and OC.
Mind the Gap: Addressing Incompleteness Challenge in Case-Based Reasoning Applications

Data quality is a crucial aspect of case-based reasoning (CBR), and incomplete data is a ubiquitous challenge that can significantly affect the accuracy and effectiveness of CBR systems. Incompleteness arises when a case lacks relevant information needed to solve a problem. Existing CBR systems often struggle to handle such cases, leading to sub-optimal solutions, and making it challenging to apply CBR in real-world settings. This paper highlights the importance of data quality in CBR and emphasizes the need for systems to handle incomplete data effectively. The authors provide for the first time a framework for addressing the issue of incompleteness under the open-world assumption. The proposed approach leverages a combination of data-driven and knowledge-based techniques to detect incompleteness. The approach offers a promising solution to the incompleteness dimension of data quality in CBR and has the potential to improve the practical utility of CBR systems in various domains as illustrated by the results of a real data-based evaluation.
The Importance of the Current Input in Sequence Modeling

The last advances in sequence modeling are mainly based on deep learning approaches. The current state of the art involves the use of variations of the standard LSTM architecture, combined with several adjustments that improve the final prediction rates of the trained neural networks. However, in some cases, these adaptations might be too much tuned to the particular problems being addressed. In this article, we show that a very simple idea, to add a direct connection between the input and the output, skipping the recurrent module, leads to an increase of the prediction accuracy in sequence modeling problems related to natural language processing. Experiments carried out on different problems show that the addition of this kind of connection to a recurrent network always improves the results, regardless of the architecture and training-specific details. When this idea is introduced into the models that lead the field, the resulting networks achieve a new state-of-the-art perplexity in language modeling problems.
GEORGIAN HIGHER EDUCATION POLICY AND E-LEARNING CHALLENGES

Rapid development of science and technology, the acceleration of the process of internationalization and globalization in higher education in the 21st century, increased the demand, perspective and popularity of the wide use of distance and electronic learning model/method. Nowadays it is clear that in many developed countries, distance and electronic learning, taking into account a number of advantages, has radically changed the teaching process and effectively met the educational activities. Based on Georgian example and comparative legal analysis, this article presents the modern challenges and main problems of e-learning in the higher education system, offers relevant conclusions and recommendations.
Geolocation to Identify Online Study-Eligible Gay, Bisexual, and Men who have Sex with Men in Philadelphia, Pennsylvania

Background: Data collection and cleaning procedures to exclude bot-generated responses are used to maintain the data integrity of samples from online surveys. However, these procedures may be time-consuming and difficult to implement. Thus, we aim to evaluate the validity of a single-step geolocation algorithm for recruiting eligible gay, bisexual, and men who have sex with men in Philadelphia for an online study. Methods: We used a 4-step approach, based on common practices for evaluating bot-generated and fraudulent responses, to assess the validity of participants’ Qualtrics survey data as our referent standard. We then compared it to Qualtrics’ single-step geolocation algorithm that used the MaxMind commercial database to map participants’ Internet protocol address to their approximate location. We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the single-step geolocation approach relative to the 4-step approach. Results: There were 826 respondents who completed the survey and 440 (53%) were eligible for enrollment based on the 4-step approach. The single-step geolocation approach yielded a sensitivity of 91% (95% CI = 88%, 93%), specificity of 79% (95% CI = 74%, 83%), PPV of 83% (95% CI = 80%, 86%), and NPV of 88% (95% CI = 85%, 91%). Conclusions: Geolocation alone provided a moderately high level of agreement with the 4-step approach for identifying geographically eligible participants in the online sample, but both approaches may be subject to additional misclassification. Researchers may want to consider multiple procedures to ensure data integrity in online samples.
The Impact of Individuals’ Social Environments on Contact Tracing App Use: Survey Study

The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of today, it has been downloaded approximately 45 million times.This study aims to investigate the influence of (non)users' social environments on the usage of the CWA during 2 periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.We conducted a longitudinal survey study in Germany with 833 participants in 2 waves to investigate how participants perceive their peer groups' opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We analyzed these questions with generalized estimating equations. Further, 2 related sample tests were performed to test for differences between users of the CWA and nonusers and between the 2 points in time (wave 1 with the highest death rates observable during the pandemic in Germany versus wave 2 with significantly lower death rates).Participants perceived that peer groups have a positive opinion toward using the CWA, with more positive opinions by the media, family doctors, politicians, and virologists/Robert Koch Institute and a lower, only slightly negative opinion originating from social media. Users of the CWA perceived their peer groups' opinions about using the app as more positive than nonusers do. Furthermore, the perceived positive opinion of the media (P=.001) and politicians (P<.001) was significantly lower in wave 2 compared with that in wave 1. The perceived opinion of friends and family (P<.001) as well as their perceived influence (P=.02) among nonusers toward using the CWA was significantly higher in the latter period compared with that in wave 1. The influence of virologists (in Germany primarily communicated via the Robert Koch Institute) had the highest positive effect on using the CWA (B=0.363, P<.001). We only found 1 decreasing effect of the influence of politicians (B=-0.098, P=.04).Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that the influence of virologists/Robert Koch Institute and family/friends exerts the strongest effect on participants' decisions to use the CWA while politicians had a slightly negative influence. Our results also indicate that it is crucial to accompany the introduction of such a contact tracing app with explanations and a media campaign to support its adoption that is backed up by political decision makers and subject matter experts.
Geochemical and isotopic tracers to define the aquifer’s vulnerability: the case study of the alluvial multi-aquifer system of the Friulian plain

The Friuli-Venezia Giulia Region (north of Italy) is characterized by the presence of high-quality freshwater resources which benefit local citizens, animals, environmental habitats, and also agriculture and production activities. Waters from wells, canal, and wastewater selected in the Fiume Veneto area, through a detailed lithological modeling, were sampled and analyzed to characterize them from a geochemical point of view. The chemical and isotopic characterization made it possible to establish provenance, and the average age of water used, making available the estimation of the relationships between recharge capacity and water use in the Fiume Vento area. The focus of this study is to define the average age of the resources based on the time required for the recharge contributions to compensate the losses induced by exploitation. The results made it possible to support the plans for a water balance using the provenance and average age of water sources for the protection of water reserves formed by the multi-aquifer system of the high and medium Friuli plain. The methodology applied has followed the legislation of the water directive considering the overexploitation due to unauthorized withdrawals of the sampling area.
Local Orthogonal Moments for Local Features

By introducing parameters with local information, several types of orthogonal moments have recently been developed for the extraction of local features in an image. But with the existing orthogonal moments, local features cannot be well-controlled with these parameters. The reason lies in that zeros distribution of these moments' basis function cannot be well-adjusted by the introduced parameters. To overcome this obstacle, a new framework, transformed orthogonal moment (TOM), is set up. Most existing continuous orthogonal moments, such as Zernike moments, fractional-order orthogonal moments (FOOMs), etc. are all special cases of TOM. To control the basis function's zeros distribution, a novel local constructor is designed, and local orthogonal moment (LOM) is proposed. Zeros distribution of LOM's basis function can be adjusted with parameters introduced by the designed local constructor. Consequently, locations, where local features extracted from by LOM, are more accurate than those by FOOMs. In comparison with Krawtchouk moments and Hahn moments etc., the range, where local features are extracted from by LOM, is order insensitive. Experimental results demonstrate that LOM can be utilized to extract local features in an image.
3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs

3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on diverse training data or sequentially conducting image translation and segmentation with two relatively independent networks. In this work, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cyclic Segmentation</i> Generative Adversarial Network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CySGAN</b> ) that conducts image translation and instance segmentation simultaneously using a unified network with weight sharing. Since the image translation layer can be removed at inference time, our proposed model does not introduce additional computational cost upon a standard segmentation model. For optimizing CySGAN, besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we also utilize self-supervised and segmentation-based adversarial objectives to enhance the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neuronal nuclei</i> segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. The proposed CySGAN outperforms pre-trained generalist models, feature-level domain adaptation models, and the baselines that conduct image translation and segmentation sequentially. Our implementation and the newly collected, densely annotated ExM zebrafish brain nuclei dataset, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NucExM</i> , are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://connectomics-bazaar.github.io/proj/CySGAN/index.html</uri> .
Robust detection of seam carving with low ratio via pixel adjacency subtraction and CNN-based transfer learning

Seam carving is an outstanding content-aware image resizing technique. When the resizing ratio is relatively lower, seam carving can achieve image resizing without leaving any visual obvious artifacts. However, it may also be used for malicious purposes, such as changing image semantic content by object removal. Seam caving changes the relationship between neighboring pixels, thus seam carved images should be different from natural ones. Inspired by this, an end-to-end seam carving detection network, namely SCDNet, is proposed to detect seam carved images. To highlight seam carving artifacts, a pre-processing layer, namely the subtractive pixel adjacency module, is designed as the first layer of SCDNet. Then, 16 residual blocks with shortcut learn features from the input, and a spatial pyramid pooling module is embedded at the end of the residual block to generate fixed-length features, which can eliminate the requirement of fixed-resolution input images for existing deep models. Finally, the features are fed into the fully-connected layer for final classification. In addition, to expose seam carved images with a small carving ratio, a transfer learning strategy is exploited to gradually clarify the artifacts. Extensive experiments demonstrate that SCDNet obtains better detection accuracy than state-of-the-art methods, especially for seam carving with small scaling ratios.
Computational Approaches

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An Introduction to Psychologically Plausible Sampling Schemes for Approximating Bayesian Inference

The brain must make inferences about, and decisions concerning, a highly complex and unpredictable world, based on sparse evidence. An “ideal” normative approach to such challenges is often modeled in terms of Bayesian probabilistic inference. But for real-world problems of perception, motor control, categorization, language understanding, or commonsense reasoning, exact probabilistic calculations are computationally intractable. Instead, we suggest that the brain solves these hard probability problems approximately, by considering one, or a few, samples from the relevant distributions. Here we provide a gentle introduction to the various sampling algorithms that have been considered as the approximation used by the brain. We broadly summarize these algorithms according to their level of knowledge and their assumptions regarding the target distribution, noting their strengths and weaknesses, their previous applications to behavioural phenomena, as well as their psychological plausibility.
Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing

Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference. Contemporary computing platforms such as CPUs, GPUs, and TPUs are struggling to keep up with the demands of these AI applications. Non-coherent optical computing represents a promising approach for light-speed acceleration of AI workloads. In this paper, we show how cross-layer design can overcome challenges in non-coherent optical computing platforms. We describe approaches for optical device engineering, tuning circuit enhancements, and architectural innovations to adapt optical computing to a variety of AI workloads. We also discuss techniques for hardware/ software co-design that can intelligently map and adapt AI software to improve performance on non-coherent platforms.
Inter-Layer Hybrid Quantization Scheme for Hardware Friendly Implementation of Embedded Deep Neural Networks

Compression techniques have been widely deployed to amortize the model size and inference computations of Deep Neural Networks (DNNs), particularly for embedded systems. In this work, we propose an inter-layer approach that deploys a weight distribution aware quantization scheme (a hybrid of fixed-point and power-of-two) and multi-precision (3-bit and 4-bit) to better use heterogeneity in FPGA resources. Based on our evaluation, with similar hardware logic and memory resource usage, our proposed approach improved the throughput of the ResNet-18 network by 37% with negligible accuracy loss compared to the state-of-the-art on an embedded FPGA.
Stochastically Pruning Large Language Models Using Sparsity Regularization and Compressive Sensing

Deep learning models have achieved state-of-the-art performance in many natural language processing tasks. However, their large size and computational complexity make them difficult to deploy in resource-constrained environments. In this paper, we propose a novel approach for reducing the complexity of deep learning models for NLP by combining compressive sensing and Bayesian learning. In our approach, we use compressive sensing with Bayesian learning to identify the most important weights in a deep learning model and represent them in a compressed form. We then prune the non-critical weights and ensure that the model's accuracy is preserved. We evaluate our approach on several NLP tasks, including sentiment analysis and text classification, and compare its performance to that of other compression methods, such as weight pruning and knowledge distillation. Our results show that our approach can significantly reduce the complexity of deep learning models (90% compression) while maintaining high accuracy (<1% drop). In conclusion, our novel approach offers a promising solution for reducing the complexity of deep learning models for NLP and making them more feasible for deployment in resource-constrained environments. By combining compressive sensing and Bayesian learning, we achieve a trade-off between model size and accuracy that is superior to other methods.
SCRAMBLE-CFI: Mitigating Fault-Induced Control-Flow Attacks on OpenTitan

Secure elements physically exposed to adversaries are frequently targeted by fault attacks. These attacks can be utilized to hijack the control-flow of software allowing the attacker to bypass security measures, extract sensitive data, or gain full code execution.