Miracle-dz/UniFAR-FTUs huggingface dataset:
An Classification Model of Helicoverpa assulta and Helicoverpa armigera Combining Spatial Transformation Network and Deep Convolutional Neural Network

In order to effectively classify and identify the major pests such as Helicoverpa assulta and Helicoverpa armigera in tobacco, as well as to monitor and control them subsequently, a classification and recognition algorithm called Spatial Transformer Network combined with Deep Convolutional Neural Network (STN-DCNN) model is proposed for analyzing the images of these pests. Firstly, the images of two types of pests are preprocessed using STN to extract relevant features and obtain two different sub-images. These sub-images are then fed into a DCNN to obtain predictions for the images. Finally, the cross-entropy loss function is used to measure the difference between the prediction results and the corresponding labels. The results show that the model achieves effective classification and identification of the Helicoverpa assulta and Helicoverpa armigera with a recognition rate of 89.8%. This approach can provide valuable technical support for more accurate pest identification.
Measuring the Complexity of an Exchangeable Graph

In this paper, we study the complexity of a well-studied class of graphs, exchangeable graphs, and quantify a graph’s complexity via the graphon entropy. Instead of selecting a particular graph descriptor that is only appropriate for one graph-generating mechanism, we estimate the entropy of the generating mechanism of a given graph, assumed to be exchangeable. We naturally take into account a graph’s key graph-theoretic and topological properties. We develop estimators of graphon entropy as measures of complexity for real-world graphs under an increasingly complex range of generating processes. We provide the convergence rate of the entropy estimator for any exchangeable graph and conclude by discussing how to use them to describe evolving real-world graphs.
How the Brain Translates Money into Force: A Neuroimaging Study of Subliminal Motivation

Unconscious motivation in humans is often inferred but rarely demonstrated empirically. We imaged motivational processes, implemented in a paradigm that varied the amount and reportability of monetary rewards for which subjects exerted physical effort. We show that, even when subjects cannot report how much money is at stake, they nevertheless deploy more force for higher amounts. Such a motivational effect is underpinned by engagement of a specific basal forebrain region. Our findings thus reveal this region as a key node in brain circuitry that enables expected rewards to energize behavior, without the need for the subjects`awareness.
Effects of sodium heparin on δ 13 C, δ 15 N and δ 34 S values in avian whole blood

Anticoagulants are often added to avian blood samples immediately after collection to allow plasma to be extracted at a later time, but the effects of anticoagulants on stable isotope studies are not well understood. We use a Bayesian approach to assess the effects of sodium heparin, one of the most commonly used anticoagulants, on δ 13 C, δ 15 N and δ 34 S values in avian whole blood using Herring Gulls Larus argentatus and Great Black-backed Gulls Larus marinus as study species. We found that the addition of sodium heparin only had a signi ﬁ cant effect on δ 34 S values ( − 0.4 ‰ ) and that this effect did not meaningfully impact the estimated diet proportions of broad-scale stable isotope mixing models, and only slightly affected the output of ﬁ ner-scale stable isotope mixing models. Our results highlight that researchers should be aware of the possible impacts of sodium heparin on measured stable isotope values in avian blood, particularly for δ 34 S, and that this effect should be carefully considered relative to the scale and particulars of the study (e.g. generalist versus specialist species and broad-scale versus ﬁ ne-scale analyses).
Distracted Driver Detection with MobileVGG Network

The escalation of road traffic fatalities in recent years has highlighted the issue of distracted driving as a significant problem that warrants attention. This paper presents a CNN-based approach for identifying and categorizing distracted driving behavior, catering to the requirements of advanced driver assistance systems. The proposed algorithm demonstrates an optimal balance between accuracy and efficiency, with respect to memory consumption and processing speed. The architecture employed, termed mobile VGG, is founded on the principles of deeply separable convolution. The outcome of de-duplicating the American University in Cairo's (AUC) dataset for distracted driving detection reveals that the proposed mobile VGG architecture has just 2.2M parameters and achieves 95.50% accuracy on the AUC dataset with 38% less computing time than alternative methods.
Federated Learning with Local Fairness Constraints

In this paper, we study training a classifier satisfying local fairness constraints via federated learning. We compare two schemes: local fair training (LFT) combined with the ensemble method (LFT+Ensemble), and LFT combined with federated averaging (LFT+FedAvg). Our theoretical analysis shows that (i) LFT+FedAvg outperforms LFT+Ensemble in terms of fairness at the cost of frequent communication, and (ii) LFT+FedAvg may not reach the optimal fairness achievable through centralized training. The findings explain the success of recently proposed federated learning algorithms that virtually compute global fairness constraints with additional communication rounds. Moreover, we also present numerical experiments that support our findings in more general settings.
Tell Me What Happened

About the Authors. Series Preface. Acknowledgements. 1 Interviewing Children About Abuse: An Overview and Introduction. 2 Factors Affecting the Capacities and Limitations of Young Witnesses. 3 How do Investigators Typically Interview Alleged Victims? 4 The NICHD Investigative Interview Protocols for Young Victims and Witnesses. 5 Does Use of the Protocol Affect theWay Investigators Interview Alleged Victims and Witnesses? 6 Interviewing Suspected Victims Under Six Years of Age. 7 The Effects of the Protocol on the Broader Investigative Process. 8 Interviewing Reluctant Suspected Victims and Suspects. 9 Interviewing Children with Intellectual and Communicative Difficulties. 10 Promoting and Maintaining Developmentally-Appropriate Interviewing by Training Interviewers. 11 What Has Been Achieved: What Else Needs to Be Done? Appendix 1: Investigative Interview Protocol. Appendix 2: Focused Questions about Tactile Contact [Touching]. Appendix 3: Interview Guide for Youthful Suspects. References. Index.
Quickly-Decodable Group Testing with Fewer Tests: Price–Scarlett’s Nonadaptive Splitting with Explicit Scalars

We modify Price and Scarlett’s fast binary splitting approach to nonadaptive group testing [1]. We show that, to identify a uniformly random subset of k infected persons among a population of n, it takes only ln(2−4ε)−2k ln n tests and decoding complexity O(ε−2k ln n), for any small ε > 0, with vanishing error probability. In works prior to ours, only two types of group testing schemes exist. Those that use ln(2)−2k ln n or fewer tests require linear-in-n complexity, sometimes even polynomial in n; those that enjoy sub-n complexity employ O(k ln n) tests, where the big-O scalar is implicit, presumably greater than ln(2)−2. We almost achieve the best of both worlds, namely, the almost-ln(2)−2 scalar and the sub-n decoding complexity. How much further one can reduce the scalar ln(2)−2remains an open problem.
Blahut-Arimoto Algorithm for Marton’s Inner Bound

The Blahut-Arimoto algorithm was recently extended to computing inner and outer bounds for broadcast channels through the exchange of max-min. However, the convergence analysis is still limited, especially for the minimization part. In this work, we first simplify the algorithm for the superposition coding region, then extend the algorithm to the general supporting hyperplanes of Marton’s inner bound, and finally provide a detailed treatment of the convergence analysis. Numerical experiments on the superposition coding region and Marton’s inner bound validate the effectiveness of our algorithms.
A Comparative Study on Different Transfer Learning Approaches for Identification of Plant Diseases

Plant diseases present a substantial threat to global food security, as they have the potential to significantly reduce crop yields. Detecting these plant diseases early and managing them effectively is crucial for ensuring sustainable agriculture. In recent years, image analysis, particularly transfer learning approaches, has emerged as a promising solution for identifying plant diseases from images. This paper comprehensively analyzes six transfer learning models, including Xception, InceptionV3, DenseNet121, VGG16, ResNet50, and MobileNetV2 for identifying plant diseases. The primary objective of this study is to evaluate the accuracy, and loss function values, both before and after fine-tuning and performance parameters of these models. The results of this study demonstrate that fine-tuning significantly improves the performance of all models. The study shows that the Xception model (98.41% testing accuracy and 0.079 loss value) achieves state-of-the-art performance, outperforming the other transfer learning models in terms of accuracy, loss function values, and performance parameters. By analyzing and comparing the various models, this research provides a comprehensive package for researchers and practitioners seeking to develop accurate and efficient techniques for identifying plant diseases.
Rate-Optimal Streaming Codes over Small Finite Fields for Burst/Random Erasure Channels

Streaming codes provide reliable low-latency communication over the packet erasure channels. In the sliding window channel model, the maximum number of erasures and the maximum length of the burst erasure are given for an arbitrary sliding window of a fixed length. This paper presents an explicit construction of a rate-optimal streaming code over small finite fields for the sliding window channels.
Robust Single Image Reflection Removal Against Adversarial Attacks

This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.
Homomorphic Encryption on Deep Learning in accurate prediction of Brain Tumour

Data breach and data theft has become a repulsive problem in the digital world and medical data is the most vulnerable in this case as it contains numerous information of patients which can hinder a patient's personal life. Moreover, due to patients' rights and confidentiality, hospital's are unwilling to share the medical data to third parties. Also, Brain tumour is one of the most occurring disease as everyday, around the globe vast amount of MRI image are taken. Many existing models have been introduced throughout the decades to detect brain tumors, mostly based on Neural Network models. To secure the MRI images and protect patient rights, we have worked on a technique based on Partial Homomorphic Encryption (PHE) which produces encrypted images while keeping most of the features intact. As a result, existing NN models can produce a higher accuracy by extracting features from the encrypted images and also reduces the time complexity of established Paillier cryptosystem. We were able to achieve $\max$ 82% accuracy using a VGG-19 model. Using our proposed model we can safely and securely read encrypted medical image data via our PHE method while being efficient enough to be used on a mass scale in the medical industry.
Lower Bounds for Multiclass Classification with Overparameterized Linear Models

Subramanian et al. [1] introduced an asymptotic Gaussian-features model for overparameterized multiclass classification in which the number of classes, training points, and parameters all go to infinity. They provided some achievable regions where min-norm interpolating classifiers successfully asymptotically generalize as well as conjecturing the full form of the region based on a heuristic analysis. Here, we introduce a converse for such min-norm interpolating classifiers in their model which fully matches their conjectured regions. The key technical tool is a variant of the Hanson-Wright concentration inequality that applies to the sparse bilinear forms that arise.
An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns; then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination; as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost; while all other possible combinations showed around 80–82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings; as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation.
Perceptions of high-involvement work practices and burnout: the mediating role of job demands

This study examined the impact of perceived high involvement work practices (HIWPs) on job demands (role conflict, role overload and role ambiguity) and burnout (emotional exhaustion and depersonalisation). The study was conducted in a Canadian general hospital. Findings from structural equation modelling (N = 545) revealed that perceived HIWPs were significantly and negatively related to job demands and burnout. Role conflict and role overload have a significant positive association with emotional exhaustion and depersonalisation. Finally, role conflict and role overload partially mediate the relationship between perceived HIWPs and burnout. We discuss the theoretical and managerial implications of these findings for our understanding of how HIWPs influence the job demands and burnout of employees.
PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation

The modern machine learning-based technologies have shown considerable potential in automatic radar scene understanding. Among these efforts, radar semantic segmentation (RSS) can provide more refined and detailed information including the moving objects and background clutters within the effective receptive field of the radar. Motivated by the success of convolutional networks in various visual computing tasks, these networks have also been introduced to solve RSS task. However, neither the regular convolution operation nor the modified ones are specific to interpret radar signals. The receptive fields of existing convolutions are defined by the object presentation in optical signals, but these two signals have different perception mechanisms. In classic radar signal processing, the object signature is detected according to a local peak response, i.e., CFAR detection. Inspired by this idea, we redefine the receptive field of the convolution operation as the peak receptive field (PRF) and propose the peak convolution operation (PeakConv) to learn the object signatures in an end-to-end network. By incorporating the proposed PeakConv layers into the encoders, our RSS network can achieve better segmentation results compared with other SoTA methods on a multi-view real-measured dataset collected from an FMCW radar. Our code for PeakConv is available at https://github.com/zlw9161/PKC.
Dispersion patterns of kin in young-of-the-year Atlantic salmon (Salmo salar L.) in Catamaran Brook, New Brunswick

Movement allows organisms to respond to heterogeneity in physical and biological conditions both at the individual and population level. Studying the movement of young stream fishes has proven to be problematic because of the difficulty of tagging small fishes using traditional techniques. In this study, recently developed microsatellite markers were used as a method of tagging individual young-of-the-year (YOY) Atlantic salmon ( Salmo salar L.). First, the association between the local-scale distribution patterns of 81 territorial YOY salmon and their degree of relatedness in a natural stream was tested. No significant association between relatedness and distance at the local scale was found. Focal fish were not more related to their nearest-neighbour (mean r x,y = -0.004) than to randomly selected fish (mean r x,y = 0.005), nor were they more related to their four nearest-neighbours (mean r x,y = -0.01) than to non-neighbours (mean r x,y = -0.007). Second, 97 anadromous adults moving into the stream to spawn, four redd locations, as well as 313 of their offspring were sampled in a 1.6 km reach of Catamaran Brook. The initial dispersal of offspring from the four redd locations and resulting long-range dispersion patterns of families were mapped. The average dispersal distance of 69 YOY salmon from the sampled redd locations ranged from 50--955m downstream, and 9--154m upstream. Five maternal and nine paternal half-sibling families were recaptured over an average dispersion distance (linear distance between the most upstream and most downstream sites of capture) of 1340m and 1018m, respectively. Four full-sibling families were identified with an average dispersion distance of 945m. The redd location (for sampled redds) and the most upstream location of a family (linear distance of the most upstream location of capture to the mouth of the brook) were combined and analysed as one variable. Dispersion distance was significantly, and positively correlated with redd or upstream location, with most families dispersing to the mouth of the brook. Third, alternative mating strategies take place in Atlantic salmon males, whereby both anadromous adults and precocious parr compete for fertilization opportunities with anadromous females. The present study suggests that precocious male parr may have fertilized up to 53% of the 313 sampled YOY salmon. This intensive field sampling allowed for the detection of the initial dispersal from redds and the large-scale dispersion of YOY salmon, which would not have been possible using alternative tagging methods. The results of the present study contributes to the understanding of dispersal and dispersion patterns of YOY salmon in freshwater habitats, and are applicable to the conservation and management of salmonid populations.
Deep Learning-Powered ECG-Based Biometric Authentication

A system for biometric authentication utilizes behavioral or biological data to identify individuals. However, some biometric recognition systems, such as which that rely on fingerprints or iris scans, can be circumvented, making them less reliable. To be effective, a biometric system must be both robust and reliable in the face of potential attacks. The electrocardiogram (ECG) is a biological biometric measure that relies on the specific anatomical and geometrical properties of the myocardium in person's heart. It is a unique characteristic that cannot be easily imitated, resulting in highly reliable ECG-based identification that is resistant to fraud and imitation attacks. In fact, ECG-based identification has been shown to be effective in detecting liveness, ensuring permanence, and achieving universality. In this paper, we employ a multiple deep learning based approaches to identify a person using an ECG signal, such as a transformer-based model, long short-term memory(LSTM) based model and Convolutional neural networks(CNNs) based model. We also implement an CNN-based Siamese network for verification of ECG-based biometric dataset. We conducted experiments to assess the effectiveness of our approach using two datasets: the MIT-BIH Arrhythmia and ECG-ID. Our most successful method achieved 98.99% accuracy for identifying individuals in the MIT-BIH dataset and 99.54% accuracy for the ECG-ID dataset. Additionally, for verification purposes, our 2D CNN-based Siamese model obtained an accuracy rate of 86% when applied to the ECG-ID dataset.
A Physical Fingerprint-Based Intrusion Detection and Localization in Fieldbus Network

The security of fieldbus networks is of utmost importance for industrial control systems. Within fieldbus networks, masquerade attacks and illegal device intrusions are two prevalent forms of attacks. The detection of these attacks is particularly challenging due to the sophisticated masquerading and deception techniques employed by attackers. To address the challenges of masquerade attacks and illegal device intrusions in fieldbus networks, this paper presents an intrusion detection and localization method based on physical fingerprints. The method involves constructing a physical fingerprint model for each device by collecting voltage signals transmitted in the fieldbus network and extracting relevant time-domain and frequency-domain features from these signals. Additionally, a predictive score detection mechanism is proposed, incorporating a multi-label SVM classification model to accurately identify masquerade attacks and illegal device intrusions within the network. Furthermore, the method utilizes differential delay features to estimate the location of the illegal intrusion device. To validate the effectiveness of the proposed method, it has been implemented on a CAN bus prototype, providing empirical evidence of its validity.