Reviewerly/exHarmony huggingface dataset:
Triplet-Uncertainty in Multi-Task Deep Learning for Improving Malignancy Characterization of Hepatocellular Carcinoma

Tumor segmentation and classification have important value in clinical diagnosis. Multi-task learning has been widely used for jointly tumor segmentation and classification in broad medical image scenarios. However, due to the presence of noise in medical image, the performance of multi-task learning model will be inevitably affected. Current research does not consider the impact of heteroscedastic(data) uncertainty and model uncertainty on multi-task model simultaneously. In this work, we propose a triplet-uncertainty in multi-task deep learning network (TU-MTL) for tumor segmentation and classification. Specifically, in addition to the uncertainty estimation of the subtask weight coefficient in multi-task learning, we also consider the heteroscedastic uncertainty estimation and model uncertainty estimation. Furthermore, we consider assigning the weight coefficient of the subtask of uncertainty estimation to the corresponding data uncertainty and model uncertainty of each task. The segmentation and malignancy classification of clinical hepatocellular carcinoma (HCC) show the effectiveness and advantages of the proposed method, especially the introduction of heteroscedastic uncertainty greatly improving the performance.
Prototype Governed Contrastive Learning for Robust Image Classification in Histopathology

When covariate and domain shifts are present, the performance of deep learning models suffers dramatically. In a safety-critical discipline like histopathology, the management of out-of-distribution (OOD) samples remains a significant obstacle. This work proposes a supervised training and prediction technique using the innovative Prototype-Governed Contrastive Loss (PGCL) to solve limitations of standard classification methods in dealing with OOD samples. We demonstrate that the proposed approach improves OOD detection without sacrificing in-distribution (ID) classification performance. Extensive tests are conducted on a dataset of human colorectal cancer cases utilizing different CNN and Transformer-based model architectures. Several OOD detection methods described in the scientific literature are also compared. The prediction confidence derived from the PGCL framework resulted in a considerable performance increase over methods using cross-entropy models and comparable performance to semi-supervised approaches.
Fast and Interpretable Unsupervised Domain Adaptation for FIB-SEM Cell Segmentation

Electron microscopy imaging techniques allow biologists to obtain nanoscale slice volumes of cells in which biological structures can be statistically analyzed under the condition that the tedious task of segmentation can be automated. If a learned segmentation has good results on images obtained under the same acquisition process, variations in this acquisition can drastically affect the final image’s features and can lead to a drop of segmentation performances. To tackle this problem, we propose an Unsupervised Domain Adaptation (UDA) framework based on Batch Normalization. Our method tested on mitochondria segmentation shows better results than state-of-the art methods and uses few computation resources, allowing a near real-time use. Furthermore, we propose a metric based on the Wasserstein distance to evaluate the effect of this normalization. We empirically show that this measure is log-linearly correlated with the drop of performances of the adapted segmentation and sheds light on the gain obtained by our UDA framework.
Analysis of Evil Twin, Deauthentication, and Disassociation Attacks on Wi-Fi Cameras

Millions of Wi-Fi cameras have been deployed in businesses and households in the last decade. Most of them are used to provide security surveillance services. It raises new security concerns because these cameras could become the target of various attacks. Among them, Evil Twin, Deauthentication, and Disassociation attacks are well-known, easy-to-launch, and dangerous ones. However, there is a lack of deep understanding and awareness of these attacks, as well as efficient mitigation mechanisms. In this paper, we design a set of experiments to demonstrate how easily and effectively these attacks can be launched from simple computing platforms like Raspberry Pi using publicly available, open-source, and easy-configurable tools toward a set of carefully selected, popular, and highly reputed Wi-Fi cameras. Based on our testing, we report our interesting observations and discuss the mitigation approaches. We believe these attacks are beyond cameras and we hope our work can bring serious attention to the security of Wi-Fi equipped devices.
MECBench: A Framework for Benchmarking Multi-Access Edge Computing Platforms

We present MECBench, an extensible benchmarking framework for multi-access edge computing. MECBench is configurable, and can emulate networks with different capabilities and conditions, can scale the generated workloads to mimic a large number of clients, and can generate a range of workload patterns. MECBench is extensible; it can be extended to change the generated workload, use new datasets, and integrate new applications. MECBench’s implementation includes machine learning and synthetic edge applications.We demonstrate MECBench’s capabilities through two scenarios: an object detection scheme for drone navigation and a natural language processing application. Our evaluation shows that MECBench can be used to answer complex what-if questions pertaining to design and deployment decisions of MEC platforms and applications. Our evaluation explores the impact of different combinations of applications, hardware, and network conditions, as well as the cost-benefit tradeoff of different designs and configurations.
Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques

Anomaly Detection in multivariate time series is a major problem in many fields. Due to their nature, anomalies sparsely occur in real data, thus making the task of anomaly detection a challenging problem for classification algorithms to solve. Methods that are based on Deep Neural Networks such as LSTM, Autoencoders, Convolutional-Autoencoders, etc., have shown positive results in such imbalanced data. However, the major challenge that algorithms face when applied to multivariate time series is that the anomaly can arise from a small subset of the feature set. To boost the performance of these base models, we propose a feature-bagging technique that considers only a subset of features at a time, and we further apply a transformation that is based on nested rotation computed from Principal Component Analysis (PCA) to improve the effectiveness and generalization of the approach. To further enhance the prediction performance, we propose an ensemble technique that combines multiple base models toward the final decision. In addition, a semi-supervised approach using a Logistic Regressor to combine the base models’ outputs is proposed. The proposed methodology is applied to the Skoltech Anomaly Benchmark (SKAB) dataset, which contains time series data related to the flow of water in a closed circuit, and the experimental results show that the proposed ensemble technique outperforms the basic algorithms. More specifically, the performance improvement in terms of anomaly detection accuracy reaches 2% for the unsupervised and at least 10% for the semi-supervised models.
MINE-based Geometric Constellation Shaping in AWGN Channel

The use of high-order constellation modulations is imperative to improve the spectral efficiency, for both radio frequency/laser-based satellite systems and optical wireless communications. The geometric shaping (GS) optimization as one typical constellation shaping method drives the improvement of communication capacity and system performance. This paper presents a novel mutual information neural estimation (MINE)based GS method to optimize the high-order constellations in pure additive white Gaussian noise (AWGN) channel, which uses the deep neural network (DNN) to estimate the mutual information (MI) value and maximize the MI to approach the AWGN capacity asymptotically. The proposed system trains both the encoder and MINE networks by back propagation, and does not need to train a decoder for optimization and thus can avoid the loss caused by the decoder. Simulation results show that the MINE-based shaping design outperforms the unshaped M-ary quadrature amplitude modulation (QAM) in terms of MI values. Note that the capacity gain increases slightly as the order M increases. Furthermore, the proposed scheme is promising for constellation design in various channel models, such as the phase noise and the fading channels, once the channel model used in MINE is matched, which can be a future research topic.
An OGC TrainingDML-AI approach for making EO training datasets ready in deep learning frameworks

Training data (TD) are vital for artificial intelligence deep learning or machine learning (AI DL/ML) in remote sensing image interpretation. With the proliferation of a large number of Earth Observation (EO) datasets, the availability of a wide range of datasets has introduced challenges in ensuring the FAIR (Findable, Accessible, Interoperable, Reusable) use of training datasets. This paper proposes an approach that leverages the OGC Training Data Markup Language for AI (TrainingDML-AI) standard to make training data ready to be consumable by existing DL frameworks. It presents a training data pipeline approach to integrate TD in DL. The approach enables the retrieval and transformation of training data for compatibility with existing deep learning frameworks.
IoT Sentinel: Correlation-based Attack Detection, Localization, and Authentication in IoT Networks

Security issues have become one of the major challenges for Internet-of-Things (IoT) networks. To overcome this challenge, the recent commonly-used approaches mainly focus on conducting encryption on IoT communication or performing continuous authentication for IoT devices by using pre-shared credentials (e.g., passcode and wireless channel signatures). However, these mechanisms are deemed insufficient, in part, due to the increasing number of data breaches and the recent proliferation of sensitive IoT devices and applications. We present IoT Sentinel - a novel security system that explores the correlation between IoT devices to effectively and efficiently secure IoT networks. Specifically, our system (i) detects potential attacks, (ii) localizes the attacker, and (iii) conducts dynamic implicit authentication at the same time. Moreover, instead of requiring full physical-layer access to IoT devices for finegrained measurement of the wireless signal, IoT Sentinel uses only coarse packet-level device correlation information to secure IoT networks with negligible overhead to the network. Thus, making our approach compatible with existing constrained IoT devices. We extensively evaluate the efficacy of IoT Sentinel in different scenarios and settings. The experiment results show that our approach achieves around 96% attack detection accuracy, more than 70% attacker localization accuracy, and around 100% device authentication accuracy.
mmLock: User Leaving Detection Against Data Theft via High-Quality mmWave Radar Imaging

The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user's 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios.
Performance Analysis of Real-Time Video Surveillance Application Leveraging Edge and Cloud

With the advent of the Edge and Cloud server in the 5G system, an application needs to be designed to have multiple components, where a part of it (Data Intensive Component (DIC)) is executed on the Edge server while the other part (Computation Intensive Component (CIC)) is executed on the Cloud server. Such deployment of the applications’ components into the Edge and Cloud server opens up opportunities for managing the Edge, Cloud, and network resources. In this work, performance aspects of the simultaneous deployment of a video surveillance application on the Edge and Cloud server are explored. Furthermore, application placement approach at the Edge and Cloud server based on the service time requirement of an application is demonstrated. In addition, an adaptive data transmission mechanism at the Edge server is presented, where the components that run at the Edge server use a scaled-down version of the video based on Initial Analysis, reducing the bandwidth consumption between the Edge server and UE. As a use-case, a surveillance application to identify traffic violations (jumping signal) is deployed. The performance of the simultaneous deployment of video surveillance application (Edge-cloud approach) is evaluated by demonstrating bandwidth preserved and end-to-end bandwidth requirement in comparison with the different Cloud only approaches. To simulate actual deployments, the surveillance application is deployed on an ETSI-compliant 5G MEC testbed with the Edge and Cloud server.
Neural network representations for the inter- and intra-class common vector classifiers

Common Vector Approach (CVA) is a known linear regression-based classifier, which also enables an extension to inter-class discrimination, known as the Discriminative Common Vector Approach (DCVA). The characteristics of linear regression classifiers (LRCs) enable the possibility of a schematic implementation that is similar to the neuron model of artificial neural networks (ANNs). In this work, we explore this schematic similarity to come up with an ANN representation for both CVA and DCVA. The new representation eliminates the need for projection matrices in its implementation, hence significantly reduces the memory requirements and computational complexities of the processes. Furthermore, since the new representation is in a neural style, it is expected to provide a solid and intriguing extension of CVA (and DCVA) by further incorporating adaptation or activation processes to the already successful CVA-based classifiers.
2D-supervised fast neural fluid reconstruction technique for time-resolved volumetric flame reconstruction

The volumetric tomography reconstruction technique (VTRT) can calculate and restore the three-dimensional physical field based on the multi-view projection information of the flame and similar flow fields, which plays an important role in the research of volumetric targets. However, the reconstruction of three-dimensional scalar/vector fields representing the physical information of the flow field (temperature, density, chemical composition, etc.) requires a significant amount of computational resources and time, especially in the turbulent flame flow field, which requires high spatial and temporal resolutions. Based on the said background, in this work, we proposed a Fast Neural Fluid Reconstruction Technique (Fast-NFRT) based on deep learning. In this investigation, we first test the reconstruction accuracy, speed, and noise robustness of Fast-NFRT using numerically simulated flames. Then Fast-NFRT is used to reconstruct the experimental turbulent jet flame under two different conditions. Finally, with the camera settings preserved, the Fast-NFRT model was tested for transfer learning between the numerical simulation flame and the two experimental jet flames to examine the generalization performance of the reconstruction. It is found that the proposed Fast-NFRT model can achieve a temporal resolution of 50-500 fps with a similar reconstruction fidelity to traditional algebraic reconstruction methods, which demonstrates the capacity of the Fast-NFRT model and its potential in real-time reconstruction applications and dynamic analysis for complex flow dynamics.
A Security Method for Cloud Storage Using Data Classification

Cloud computing is an information technology model that provides computing and storage resources as a service. Data storage security remains the main challenge in adapting this new model. The common solution to secure data in the cloud is data encryption. However, handling all the data with the same security policy does not appear to be good practice, because they do not have the same sensibility for the data owner. The present research proposes a new method to improve the security of data in cloud storage. It combines two domains represented by machine learning and multi criteria decision making, in order to provide a new classification method, that classifies data before being introduced into a suitable encryption system according to their category. A Cloudsim simulation has been used to demonstrate the effectiveness of the proposed method. The results of the simulation exhibit that our method is more efficient and accurate and takes less processing time, while ensuring data confidentiality and integrity.
Analysis of the modern school in times of pandemic in Ecuador

This research examines how the COVID-19 pandemic has impacted education in Ecuador. The challenges in the implementation of online education are analyzed, and the solutions adopted to guarantee educational continuity amid the health crisis are explored. In addition, the pandemic has revealed gaps in access to technology and connectivity in the country, making equitable access to online education difficult for all students. Likewise, the emotional and psychological impact that the pandemic has had is addressed, and the importance of providing psychosocial support during these difficult times is highlighted. The main results show different factors associated with school dropout during the pandemic, including the lack of families' financial resources and the need for technological equipment to sustain connectivity between the student and the teacher.
The Case Study of the Serbian Copyist Ioan. On the Development of a Scientific Method for South Slavonic Palaeography

The present research aims to address the case study about the Serbian copyist anagnost Ioan through the use of the web-based repository. The same scribe's signature is observed in the manuscripts Dečani No 127 and No 119, which are also connected by the typology and a similar dating. The goal is to compare the distinctive features of both manuscripts and the handwriting of the 14th century scribes who contributed to the copying. In conducting this research, we will use a new descriptive scientific model, created and tested by the research project team.
Citizens’ awareness of electoral campaign pledges

The theory of promissory representation (Mansbridge, 2003) proposes that voters select parties based on the pledges they made during the campaign. The elected parties then fulfill their promises and at the next election, voters reward or sanction the parties based on their pledge-fulfillment record. However, a fundamental assumption of promissory representation remains to be tested. If voters use party pledges to decide which party to vote for, they need to know which party made which pledges. To test the degree of awareness of citizens to party pledges, (a factor we dub pledge awareness), we included a module in the 2019 Canadian Election Study (CES) that tasks citizens to associate correctly six pledges found in the different electoral platforms with their respective parties. We find that while citizens may not know all six pledges included in our study, nonetheless, the most frequently selected answers to our pledge awareness questions are the correct ones. We also find that party identification and the information resources at the disposal of citizens play a large role in the citizen’s capacity to succeed at this matching task. Our study indicates that respondents tend to be more aware of the pledges made by the party they identify with, and well-informed respondents are more aware of pledges made by the other parties.
Analisis Penerapan Model Self Directed Learning Terhadap Kemandirian Dan Hasil Belajar Peserta Didik Kelas V Di SDN 01 Wonorejo Kecamatan Gondangrejo Tahun Pelajaran 2022/2023

The purposes of this research are: 1) To analyze the learning achievement of the teacher providing material from videos and textbooks through the messages of their parents, which of course students learn in one direction by applying the Self Directed Learning model. 2) To analyze the increase in learning independence of fifth grade students at SDN 01 Wonorejo by applying the Self Directed Learning model. 3) To analyze the increase in learning outcomes of class V students at SDN 01 Wonorejo by applying the Self Directed Learning model. The research method used is descriptive qualitative research. The subjects used were 42 students, 2 parents of students, and the class teacher. In selecting research subjects using purposive sampling technique. The data source for this research was carried out at SDN 01 Wonorejo, Gondangrejo District. Data collection techniques used are observation, interviews, and documentation. The validity of this research data uses a triagulation technique. Analysis techniques using: 1) data collection, 2) data reduction, 3) data presentation, 4) drawing conclusions. The results of this study are: 1) The application of Self Directed Learning carried out by Class V teachers at SDN 01 Wonorejo is that the teacher gives learning problems with healthy material, it is important that the sub-material explains that my blood circulation is healthy by showing students videos based on the Android application Tik-Tok. After that the teacher gives students the opportunity to ask questions and formulate problems. Students become more active and creative in completing each lesson 2) The application of the Self Directed Learning model can increase the learning independence of fifth grade students at SDN 01 Wonorejo, namely being able to have a positive influence on student motivation to be more independent on time doing exercises and be more active in participating in learning compared to learning using the 2013 curriculum method. 3) The application of the Self Directed Learning model can improve the learning outcomes of fifth grade students at SDN 01 Wonorejo by making students brave and confident in expressing opinions and the results of their own assignments.