Reviewerly/exHarmony huggingface dataset:
Strange Images in Remote Sensing and Their Properties

Lossy image compression is used in many applications including remote sensing. Image size and number increase and this often leads to the necessity to apply image compression. In lossy compression, it is assumed that rate-distortion curves are monotonous functions and this assumption is put into basis of compression control. However, it has been shown recently that there are grayscale and color images called “strange” for which the rate-distortion curves are not monotonous. In this paper, we demonstrate that some remote sensing images can be strange as well and this takes place for JPEG and some other compression techniques. Analysis of properties for strange images using Spearman rank order correlation coefficient is carried out and it is shown that there several parameters characterizing image complexity that have a rather high correlation with probability that a given image is strange. For example, image entropy is one of such parameters.
Naturalistic Causal Probing for Morpho-Syntax

Abstract Probing has become a go-to methodology for interpreting and analyzing deep neural models in natural language processing. However, there is still a lack of understanding of the limitations and weaknesses of various types of probes. In this work, we suggest a strategy for input-level intervention on naturalistic sentences. Using our approach, we intervene on the morpho-syntactic features of a sentence, while keeping the rest of the sentence unchanged. Such an intervention allows us to causally probe pre-trained models. We apply our naturalistic causal probing framework to analyze the effects of grammatical gender and number on contextualized representations extracted from three pre-trained models in Spanish, the multilingual versions of BERT, RoBERTa, and GPT-2. Our experiments suggest that naturalistic interventions lead to stable estimates of the causal effects of various linguistic properties. Moreover, our experiments demonstrate the importance of naturalistic causal probing when analyzing pre-trained models. https://github.com/rycolab/naturalistic-causal-probing
Design and Effectiveness Evaluation of a Smart Greenhouse Virtual Reality Curriculum Based on STEAM Education

This study developed a smart greenhouse virtual reality (VR) curriculum based on STEAM learning and explored its effects on students’ satisfaction and learning outcomes. The objectives included evaluating STEAM capability indicators, the practicability of VR-assisted teaching, constructing the VR curriculum, discussing students’ satisfaction, and assessing the impact on learning effectiveness. The fuzzy Delphi method was used to evaluate the importance of STEAM capabilities and the practicability of VR-assisted teaching. Experimental teaching was carried out on 26 engineering students, and the case study method was adopted for hybrid analysis and discussion based on quality and quantity. The study found that “hands-on skills” and “problem-solving” were the most important capabilities, with the highest practicability in VR-assisted teaching. Based on this, an analysis was conducted on the integrated teaching design, and the smart greenhouse VR teaching materials based on STEAM learning were developed. After 18 weeks of experimental teaching, most students expressed significant positive affirmation of their satisfaction with the “STEAM smart greenhouse VR” curriculum. The study highlights the importance of hands-on skills and problem-solving in VR-assisted teaching. The study suggests that the practicability analysis of VR-assisted teaching should be reviewed according to the curriculum characteristics, and three phases of VR-assisted teaching modes, such as teacher operation, student exercises, and student testing, should be planned to guide students to learn step by step. The curriculum design and planning based on STEAM learning in this study could provide a reference for teachers and researchers to plan students’ STEAM capability training and interdisciplinary capability learning and development. The study highlights the importance of hands-on skills, problem-solving in VR-assisted teaching, and the positive impact of multi-sensory experiences on student learning outcomes. These findings can inform the development of future VR-assisted teaching materials and curricula.
Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models

Different social media platforms have become increasingly popular in the Arab world in recent years. The increasing use of social media, however, has also led to the emergence of a new challenge in the form of abusive content, including hate speech, offensive language, and abusive language. Existing research work focuses on automatic abusive content detection as a binary classification problem. In addition, the existing research work on the automatic detection task surrounding abusive Arabic content fails to tackle the dialect-specific phenomenon. Consequently, this has led to two important issues in the automatic abusive Arabic content detection task. In this study, we used a multi-aspect annotation schema to tackle the automatic abusive content detection problem in Arabic countries, based on the multi-class classification task and the dialectal Arabic (DA)-specific phenomenon. More precisely, the multi-aspect annotation schema includes five attributes: directness, hostility, target, group, and annotator. We specifically developed a framework to automatically detecting abusive content on Twitter using natural language processing (NLP) techniques. The developed framework used different models of machine learning (ML), deep learning (DL), and pretrained Arabic language models (LMs) using the multi-aspect annotation dataset. In addition, to investigate the impact of the other approaches, such as multi-task learning (MTL), we developed four MTL models built on top of a pretrained DA language model (called MARBERT) and trained on the multi-aspect annotation dataset. Our MTL models and pretrained Arabic LMs enhanced the performance compared to the existing DL model mentioned in the literature.
Quantum entanglement of photons on free electrons

The quantum entanglement of photons is one of the interesting and important manifestations of the quantum world, which is already being used in quantum technologies. The study of new methods of quantum entanglement of photons is one of the promising areas of quantum optics. In this work, it is shown that photons, in a two-mode electromagnetic field, can be quantum entangled when they interact with free electrons. It is shown that the quantum entanglement of such photons can be very large and such photons can be a good source of high quantum entanglement. In addition, it is shown that free electrons can be a beam splitter for a two-mode electromagnetic field.
PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

Abstract Motivation Alzheimer’s disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal state and AD. Not all people who have MCI convert to AD. The diagnosis of AD is made after significant symptoms of dementia such as short-term memory loss are already present. Since AD is currently an irreversible disease, diagnosis at the onset of the disease brings a huge burden on patients, their caregivers, and the healthcare sector. Thus, there is a crucial need to develop methods for the early prediction AD for patients who have MCI. Recurrent neural networks (RNN) have been successfully used to handle electronic health records (EHR) for predicting conversion from MCI to AD. However, RNN ignores irregular time intervals between successive events which occurs common in electronic health record data. In this study, we propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer’s Disease (PPAD) and PPAD-Autoencoder. PPAD and PPAD-Autoencoder are designed for early predicting conversion from MCI to AD at the next visit and multiple visits ahead for patients, respectively. To minimize the effect of the irregular time intervals between visits, we propose using age in each visit as an indicator of time change between successive visits. Results Our experimental results conducted on Alzheimer’s Disease Neuroimaging Initiative and National Alzheimer’s Coordinating Center datasets showed that our proposed models outperformed all baseline models for most prediction scenarios in terms of F2 and sensitivity. We also observed that the age feature was one of top features and was able to address irregular time interval problem. Availability and implementation https://github.com/bozdaglab/PPAD.
Touch events and human activities for continuous authentication via smartphone

The security of modern smartphones is related to the combination of Continuous Authentication approaches, Touch events, and Human Activities. The approaches of Continuous Authentication, Touch Events, and Human Activities are silent to the user but are a great source of data for Machine Learning Algorithms. This work aims to develop a method for continuous authentication while the user is sitting and scrolling documents on the smartphone. Touch Events and Smartphone Sensor Features (from the well-known H-MOG Dataset) were used with the addition, for each sensor, of the feature called Signal Vector Magnitude. Several Machine Learning Models have been considered with different experiment setups, 1-class, and 2-class, for evaluation. The results show that the 1-class SVM achieves an accuracy of 98.9% and an F1-score of 99.4%, considering the selected features and the feature Signal Vector Magnitude very significant.
Unsupervised Outlier Detection based on Random Projection Outlyingness with Local Score Weighting

This paper proposes an enhanced model of Random Projection Outlyingness (RPO) for unsupervised outlier detection. When datasets have multiple modalities, the RPOs have frequent detection errors. The proposed model deals with this problem via unsupervised clustering and a local score weighting. The experimental results demonstrate that the proposed model outperforms RPO and is comparable with other existing unsupervised models on benchmark datasets, in terms of in terms of Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).
Variability-Inducing Requirements for Programs: Increasing Solution Variability for Similarity Checking

Similarity checking is a common approach for detecting cheating in programming courses. A known limitation is high rates of similar pairs for programs lacking variability in possible solutions, especially for small programs. We experienced this issue in our CS1 course, where similarity checking in early weeks yielded many highly-similar pairs, many of which were not likely due to copying. Yet, we wish to catch copying students early, so that we can intervene and help those students avoid developing copying habits that may cause them trouble later. Our approach is to modify the program specifications to include variability-inducing requirements, namely places in the specifications where students make choices in their solutions, where different choices reduce the similarity scores. Those variability-inducing requirements are intentionally designed to avoid making the problem much harder for students. Examples of variability-inducing requirements include adding requirements to check for invalid input, or counting items. Such requirements have many different possible ways of implementing each. Essentially, variability-inducing requirements decrease the odds that two students would submit programs scored as highly-similar by a similarity checker, even for small programs. For 5 programs in our CS1 course, we added some variability-inducing requirements. Compared to an earlier term, the similarity checker's highly-similar-pairs rate dropped from 52% to 20% on average. Students' scores stayed the same from 98% to 96%, though time did increase from 18 min to 31 min on average. Adding such requirements helps instructors to do similarity detection and perform early interventions if desired.
Quantum-like correlation of two-qubit open system in the Markovian regime

A rigorous relationship between local quantum uncertainty and local quantum Fisher information as recent quantifiers of nonclassical correlations is investigated. It consists of analysing the quantum correlation rate ingrained in a bipartite quantum system interacting with its surrounding environment under the Markovian regime. Indeed, we quantify the separability between two qubits where each qubit interacts with its own environment. Furthermore, a common reservoir is also taken into consideration, which allows us to solve exactly the Markovian master equation of this system. Pointing out that the degrees of freedom that belong to the environment, act only implicitly. We study the local quantum uncertainty and local quantum Fisher information quantifiers of the open system. By controlling several parameters encoded in the reduced density operator of the open system, it is shown that the nonclassical measures fluctuate similarly between their maximum and minimum amplitudes. In particular, the high values of the damping rates related to each reservoir and some special values of the initial phase parameter allow for robust values of local quantum uncertainty and local quantum Fisher information. In particular, it is shown that in the non-resonance case, it is possible to enhance the quantum correlation of the proposed system.
Hits and Misses: Digital Contact Tracing in a Pandemic

Traditional contact tracing is one of the most powerful weapons people have in the battle against a pandemic, especially when vaccines do not yet exist or do not afford complete protection from infection. But the effectiveness of contact tracing hinges on its ability to find infected people quickly and obtain accurate information from them. Therefore, contact tracing inherits the challenges associated with the fallibilities of memory. Against this backdrop, digital contact tracing is the "dream scenario"-an unobtrusive, vigilant, and accurate recorder of danger that should outperform manual contact tracing on every dimension. There is reason to celebrate the success of digital contact tracing. Indeed, epidemiologists report that digital contact tracing probably reduced the incidence of COVID-19 cases by at least 25% in many countries, a feat that would have been hard to match with its manual counterpart. Yet there is also reason to speculate that digital contact tracing delivered on only a fraction of its potential because it almost completely ignored the relevant psychological science. We discuss the strengths and weaknesses of digital contact tracing, its hits and misses in the COVID-19 pandemic, and its need to be integrated with the science of human behavior.
HolisticDFD: Infusing spatiotemporal transformer embeddings for deepfake detection

Deepfakes, or synthetic audiovisual media developed with the intent to deceive, are growing increasingly prevalent. Existing methods, employed independently as images/patches or jointly as tubelets, have, up to this point, typically focused on spatial or spatiotemporal inconsistencies. However, the evolving nature of deepfakes demands a holistic approach. Inspection of a given multimedia sample with the intent to validate its authenticity, without adding significant computational overhead has, to date, not been fully explored in the literature. In addition, no work has been done on the impact of different inconsistency dimensions in a single framework. This paper tackles the deepfake detection problem holistically. HolisticDFD, a novel, transformer-based, deepfake detection method which is both lightweight and compact, intelligently combines embeddings from the spatial, temporal and spatiotemporal dimensions to separate deepfakes from bonafide videos. The proposed framework achieves 0.926 AUC on the DFDC dataset using just 3% of the parameters used by state-of-the-art detectors. An evaluation against other datasets shows the efficacy of the proposed framework, and an ablation study shows that the performance of the system gradually improves as embeddings with different data representations are combined. An implementation of the proposed framework is available at: https://github.com/smileslab/deepfake-detection/.
Using cylindric algebra to support local variables in rely/guarantee concurrency

Local variable blocks are a simple but effective program structuring technique. In the context of concurrency, local variables have the added advantage of avoiding interference from threads running concurrently with the thread declaring the local variable. To provide mechanised support for verifying concurrent programs in Isabelle/HOL, we have found making use of algebraic properties of programs simplifies the development of refinement laws. The algebraic properties of local variable blocks are similar to those of existential quantifiers in predicate calculus, the algebra of which is Tarski’s cylindric algebra. Hence to support local variables, we make use of a variant of cylindric algebra that allows quantification of a variable over a command, rather than a predicate. The approach allows a local variable to become a shared variable of parallel threads that are local to the block that introduced the variable.
Continuous Entity Authentication in the Internet of Things Scenario

In the context of the Internet of Things (IoT), the proliferation of identity spoofing threats has led to the need for the constant entity verification of devices. Recently, a formal framework has been proposed to study resistance to impersonation attacks for One-Message Unilateral Entity Authentication (OM-UEA) schemes, in which the prover continuously authenticates itself through the use of a sequence of authentication messages. Given the limited computing power of the parties (particularly the prover) and the often limited bandwidth channel, in the IoT scenario it is desirable to design unilateral entity authentication schemes that require the use of a single message per session and light computations. In this paper, we first show that OM-UEA schemes can be implemented through digital signatures and that a weak form of unforgeability is sufficient to achieve security against active adversaries. We then apply the signature scheme proposed by Yang et al. in ASIACCS 2020 to our framework, resulting in an OM-UEA scheme that requires minimal computational effort and low storage requirements for the prover. Inspired by this last construction, we propose an OM-UEA scheme based on the hardness of the discrete logarithm problem, which further improves the computational performance for the prover. Our findings offer feasible options for implementing secure continuous entity authentication in IoT applications.
Robust Output-Feedback $\mathcal {H}_{\infty }$ Online Optimization Control for T–S Fuzzy Systems via Differential Evolution Algorithm

This article is concerned with robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance online optimization problem for a group of Takagi–Sugeno (T–S) fuzzy systems in the framework of differential evolution (DE) algorithm. First, by virtue of nonparallel distribution compensation (non-PDC) technique, sufficient conditions that can stabilize the fuzzy systems and guarantee the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance are derived. Whereafter, in the premise of ensuring the stability of systems, based on the allowable intervals of controller membership functions (MFs), a new MFs online optimization strategy utilizing DE technique is first proposed to seek the optimal values in real time so as to acquire a better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance. In contrast with the conventional control where the controller MFs are built based on experts' subjective knowledge or intuition, by this advanced optimal control method, not only is the influence of MFs modeling errors robustly tolerated, but also the practical disturbance resistance capability can be efficaciously enhanced. Additionally, the proposed MFs online optimization scheme is also able to sustain the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance when the interference channels suffer from modeling errors. Lastly, simulation examples are presented to ascertain the effectiveness and superiorities of the proposed theoretical results.
EdgeMedNet: Lightweight and Accurate U-Net for Implementing Efficient Medical Image Segmentation on Edge Devices

Convolutional neural networks have gained tremendous success in computer vision and medical imaging applications. To make these models truly portable and compatible for prototyping, their efficient implementations on low-power devices are vitally imperative. Thus, in this brief, we propose EdgeMedNet, which is one lightweight and accurate U-Net model to enable the efficient medical image segmentation on Intel/Movidius Neural Compute Stick 2 (NCS-2). Firstly, we optimize the feature maps of the baseline U-Net model into the optimum point for striking a balance between parameters reductions and segmentation performance. Then, we propose the enhanced activation block to provide comprehensive multi-scale analysis, for facilitating avoiding the representational bottleneck. By utilizing these elementary units, we further introduce the cascaded attention module and customize the layers to construct our EdgeMedNet. Extensive experiments are conducted on three medical imaging datasets, including BraTs dataset of brain MRI, heart MRI dataset, and COVID-19 CT lung and infection segmentation dataset. The experimental results demonstrate that we can gain up to 6.1× reductions of the model parameters. Compared with the state-of-the-art works, our EdgeMedNet is capable of achieving as high as 2.0× inference speedup on NCS-2, while maintaining the segmentation performance.
Implementing Convolutional Neural Networks on FPGA: A Survey and Research

The implementation of CNN FPGA is of increasing importance due to the growing demand for low-power and high-performance edge AI applications. This paper presents a comprehensive survey and research on the topic, with a focus on comparing and evaluating the performance of two main FPGA architectures, streaming and single unit computing. The study includes a detailed evaluation of the state-of-the-art CNNs, LeNet-5 and YOLOv2, on both FPGA architectures. The results provide useful insights into the trade-offs involved, limitations, challenges, and the complexity of implementing CNNs on FPGAs. The paper highlights the difficulties and intricacies involved in implementing CNNs on FPGAs and provides potential solutions for improving performance and efficiency.