Reviewerly/exHarmony huggingface dataset:
ProActive DeepFake Detection using GAN-based Visible Watermarking

With the advances in generative adversarial networks (GAN), facial manipulations called DeepFakes have caused major security risks and raised severe societal concerns. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and fails in blocking the disinformation spread in advance. Alternatively, precautions such as adding perturbations to the real data for unnatural distorted DeepFake output easily spotted by the human eyes are introduced as proactive defenses. Recent studies suggest that these existing proactive defenses can be easily bypassed by employing simple image transformation and reconstruction techniques when applied to the perturbed real data and the distorted output, respectively. The aim of this paper is to propose a novel proactive DeepFake detection technique using GAN-based visible watermarking. To this front, we propose a reconstructive regularization added to the GAN’s loss function that embeds a unique watermark to the assigned location of the generated fake image. Thorough experiments on multiple datasets confirm the viability of the proposed approach as a proactive defense mechanism against DeepFakes from the perspective of detection by human eyes. Thus, our proposed watermark-based GANs prevent the abuse of the pretrained GANs and smartphone apps, available via online repositories, for DeepFake creation for malicious purposes. Further, the watermarked DeepFakes can also be detected by the SOTA DeepFake detectors. This is critical for applications where automatic DeepFake detectors are used for mass audits due to the huge cost associated with human observers examining a large amount of data manually.
Exploring unsupervised pre-training for echo state networks

Abstract Echo State Networks (ESNs) are a special type of Recurrent Neural Networks (RNNs), in which the input and recurrent connections are traditionally generated randomly, and only the output weights are trained. However, recent publications have addressed the problem that a purely random initialization may not be ideal. Instead, a completely deterministic or data-driven initialized ESN structure was proposed. In this work, an unsupervised training methodology for the hidden components of an ESN is proposed. Motivated by traditional Hidden Markov Models (HMMs), which have been widely used for speech recognition for decades, we present an unsupervised pre-training method for the recurrent weights and bias weights of ESNs. This approach allows for using unlabeled data during the training procedure and shows superior results for continuous spoken phoneme recognition, as well as for a large variety of time-series classification datasets.
Performance of the new MC‐80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600

Abstract Introduction Mindray MC‐80 is an automated system for digital imaging of white blood cells (WBCs) and their pre‐classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. Methods A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre‐classification values with the MC‐80 and DM9600 were compared with (1) the microscope, (2) Mindray BC‐6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post‐classification). Pearson's correlation, Lin's concordance, Passing‐Bablok regression, and Bland–Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC‐80 were calculated. Results The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC‐80, the microscope and the BC‐6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre‐ and post‐classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91–1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC‐80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. Conclusion We found that the MC‐80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.
Do RESTful API design rules have an impact on the understandability of Web APIs?

Abstract Context Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking. Objective We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this. Method We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a rule and one that was a violation of this rule. Participants answered comprehension questions and rated the perceived difficulty. Results For 11 of the 12 rules, we found that violation performed significantly worse than rule for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for violation . Conclusions Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators.
MFS enhanced SAM: Achieving superior performance in bimodal few-shot segmentation

Recently, Segment Anything Model (SAM) has become popular in computer vision field because of its powerful image segmentation ability and high interactivity of various prompts, which opens a new era of large vision foundation models. But is SAM really omnipotent? In this letter, we establish a comprehensive bimodal few-shot segmentation indoor dataset VT-840-5i, and compare SAM with eight state-of-the-art few-shot segmentation (FSS) methods on two benchmark datasets. Qualitative and quantitative experiment results show that although SAM is very effective in general object segmentation, it still has room for improvement in some challenging scenarios. Therefore, we introduce thermal infrared auxiliary information into the segmentation task and provide multiple fusion strategies (MFS) for readers to choose the most suitable approach for the specific task. Finally, we discuss several potential research trends about SAM in the future. Our test results are available at: https://github.com/VDT-2048/Bi-SAM.
Transactive Energy Systems in Decentralized Autonomous Renewable Energy Communities

In the near future, renewable energy communities will play a crucial role in the transition to a cleaner energy system and the reduction of carbon emissions in the electricity sector. In order to ensure a broad base of participation among consumer and producers (prosumers), it is necessary to develop new business models and governance tools for energy communities, and, in this context, blockchain technology may have several applications in the implementation and management of these communities. This paper proposes a decentralized autonomous organization model for renewable energy communities, which will enable a transactive energy scenario for the community governance process. In order to illustrate the usefulness of the methodology, an example of the proposed approach with a distributed organization process for an automated local energy market among community members is presented.
Engineering Microservice-Based Applications Using an Integration Platform as a Service

Today, frequent changes of technologies lead to delivery gaps in the IT landscape. A goal of Integration Platforms as a Service (iPaaS) is to reduce this gap by increasing the reusability of APIs and services by providing an integration architecture. An advantage of APIs is the aspect that they hide the implementation technologies. However, knowledge and a structured engineering approach are required to develop applications using an iPaaS. We present how such kind of applications can be engineered with the focus on the integration and orchestration of the different microservices and data sources. Furthermore, we compare the terminology and architectural concepts of iPaaS with the state of the art. We illustrate how the microservice-based application ConnectedCarServices is developed by applying the concepts to MuleSoft's integration platform.
Spatial-Temporal Dynamic Frequency Prediction Based on Integrating Model-Driven and Data-Driven

With the establishment of the network transmission pattern based on high-voltage direct current (HVDC) transmission and the massive grid connection of new energy, the risk of frequency instability or even frequency collapse in the system increases. The frequency after disturbance presents spatial-temporal dynamics. Any location in the grid with a frequency outside the allowed range may trigger frequency instability or even frequency collapse. Predicting the spatial-temporal dynamic frequency response is essentially a problem of solving complex differential algebraic equations that describe the system, which traditional methods are very time-consuming and not generalizable and accurate enough to solve when online applications. In this paper, a method integrating long short-term memory network (LSTM) and average system frequency (ASF) to predict the spatial-temporal dynamic frequency is presented. The method uses the power system-related data before and after the disturbance for a short period of time as input features to predict the dynamic frequency of each generator node. Tests in the 39-bus system and 500-bus system show the generalizability and accuracy.
GNOSIS: Proactive Image Placement Using Graph Neural Networks &amp; Deep Reinforcement Learning

The transition from Cloud Computing to a Cloud-Edge continuum brings many new exciting possibilities for interactive and data-intensive Next Generation applications, but as many challenges. Approaches and solutions that successfully worked in the Cloud space now need to be rethought for the Edge's distributed, heterogeneous and dynamic ecosystem. The placement of application images needs to be proactively devised to reduce as much as possible the image transfer time and comply with the dynamic nature and strict requirements of the applications. To this end, this paper proposes an approach based on the combination of Graph Neural Networks and actor-critic Reinforcement Learning. The approach is analyzed empirically and compared with a state-of-the-art solution. The results show that the proposed approach exhibits a larger execution times but generally better results in terms of application image placement.
Design-Time Analysis of Time-Critical and Fault-Tolerance Constraints in Cloud Services

This work presents a model for designing and deploying time-critical, cloud-native applications under fault conditions.Our model considers the interactions and interferences among service components, as well as the possible occurrence of faults.Given a set of to-be-deployed applications with precise temporal constraints and a pre-defined configuration of the service components, we devised an optimizer to verify at design time if the cloud services guarantee compliance with the timing constraints while minimizing the resources needed to achieve fault tolerance.
Enhancing Sparse Data Performance in E-Commerce Dynamic Pricing with Reinforcement Learning and Pre-Trained Learning

This paper introduces a reinforcement learning-based framework designed to tackle dynamic pricing challenges in e-commerce. Prior research has predominantly concentrated on algorithm selection to enhance performance in dense data scenarios. However, many of these models fail to robustly address sparse data structures, such as low-traffic products, leading to the ‘cold-start’ problem [4]. Through numerical analysis, our framework offers innovative insights derived from the design of the reward function and integrates product clustering with pre-trained learning to mitigate this issue. As a result of this optimization, the performance of predictive models on sparse data is expected to see substantial improvement.
Making Your Program Oblivious: A Comparative Study for Side-channel-Safe Confidential Computing

Trusted Execution Environments (TEEs) are gradually adopted by major cloud providers, offering a practical option of confidential computing for users who do not fully trust public clouds. TEEs use CPU -enabled hardware features to eliminate direct breaches from compromised operating systems or hypervisors. However, recent studies have shown that side-channel attacks are still effective on TEEs. An appealing solution is to convert applications to be data oblivious to deter many side-channel attacks. While a few research prototypes on TEEs have adopted specific data oblivious operations, the general conversion approaches have never been thoroughly compared against and tested on benchmark TEE applications. These limitations make it difficult for researchers and practitioners to choose and adopt a suitable data oblivious approach. To address these issues, we conduct a comprehensive analysis of representative conversion approaches and perform an extensive empirical study to provide insights into their performance and ease of use with benchmark TEE applications.
Physics-Constrained Backdoor Attacks on Power System Fault Localization

The advances in deep learning (DL) techniques have the potential to deliver transformative technological breakthroughs to numerous complex tasks in modern power systems that suffer from increasing uncertainty and nonlinearity. However, the vulnerability of DL has yet to be thoroughly explored in power system tasks under various physical constraints. This work, for the first time, proposes a novel physics-constrained backdoor poisoning attack, which embeds the undetectable attack pattern into the learned model and only performs the attack when it encounters the corresponding trigger. The paper illustrates the proposed attack on the real-time fault line localization application. Furthermore, the simulation results on the 68-bus power system demonstrate that DL-based fault line localization methods are not robust to our proposed attack, indicating that backdoor poisoning attacks pose real threats to DL implementations in power systems.
Improving Medicare Fraud Detection through Big Data Size Reduction Techniques

Classification models serve as effective tools for Medicare fraud detection, but their performance can be influenced by a number of factors. This paper focuses on addressing two common challenges using the Medicare Part D Big Data: high dimensionality and class imbalance. To tackle these issues, we explore the use of feature selection, Random Undersampling (RUS), and a combination of our novel feature selection technique followed by RUS. RUS is employed to create five different class ratios, while six supervised feature selection methods are utilized within ensemble feature selection techniques. The performance of six machine learning classifiers is evaluated for Medicare fraud detection, using Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision-Recall Curve (AUPRC). Additionally, we compare the performance of models built with the original dataset to establish the superiority of our technique over a baseline approach. The results clearly demonstrate that reducing the training datasets significantly using RUS with a minority:majority class ratio of 1:81 and a feature subset size of ten leads to similar or improved model performance compared to models built with the original dataset in terms of AUPRC metric. Additionally, the data reduction allows for faster model training times. Therefore, details on how the results were obtained are a contribution to Big Data research.
Application and Infrastructure-Aware Orchestration in the Cloud-to-Edge Continuum

Defining a scheduling and orchestration strategy for modern distributed microservices-based applications is a complex problem to deal with, especially if they are deployed on geo-distributed Cloud-to-Edge environments. Kubernetes is today the de-facto standard for container orchestration on Cloud data centers. However, its static container scheduling strategy is not suitable for the placement of complex and distributed microservices-based applications on Edge environments. Current infrastructure network conditions and resource availability neither run time application state are taken into account when scheduling microservices. To deal with these limitations in this work we present an extension of the Kubernetes platform in order to implement an effective application and infrastructure-aware container scheduling and orchestration strategy. In particular, we propose an extension of the default Kubernetes scheduler that considers application and infrastructure telemetry data when taking scheduling decisions. Furthermore, a descheduler component is also proposed that continuously tunes the application placement based on the ever changing application and infrastructure states. An evaluation of the proposed approach is presented by comparing it with the default Kubernetes scheduling strategy.
LexiFuse+: A Unified One-Class Solution for Imbalanced Short-Text Classification

Introducing LexiFuse+: a semi-supervised model merging lexicon features, BERT transfer learning, and one-class classifiers to detect anomalous content in short texts. It tackles challenges of informal text and imbalanced datasets, excelling in hate speech detection. By leveraging one-class classifiers in a fully deep fine-tuned network trained with unlabeled data, LexiFuse+ surpasses base models.
AI-Enabled Solutions, Explainability and Ethical Concerns for Predicting Sepsis in ICUs: A Systematic Review

Artificial Intelligence (AI) advances are pushing the boundaries across research domains with AI-driven solutions in healthcare claiming a significant share. A key objective of these studies concerns the timely prediction of various pathological conditions. Sepsis is a life-threatening syndrome and one of the main causes of death in intensive care unit (ICU) patients. As it becomes a major health problem worldwide, sepsis early prediction could assist healthcare professionals towards making informed clinical decisions, and thereby, significantly reducing the sepsis' morbidity and mortality. A notable body of literature involving the use of AI for sepsis prediction exists. However, to the best of our knowledge, only a handful of studies focus on performing a systematic review of the AI enabled solutions for sepsis prediction in ICUs. In this context, the present paper aims to identify knowledge gaps, stimulate interest and yield motivations for future research. Moreover, to discuss ethical and explainability aspects and associated challenges. The literature search was conducted between February 2023 and April 2023 and considered eligible articles published within the last five years.
Assessing the Effectiveness of Siamese Neural Networks to Mitigate Frequent Retraining in IoT Device Identification Models

The rapid proliferation of the Internet of Things (IoT) requires the development of effective methods to identify devices over a network. Although traditional machine learning and deep learning approaches have been successful in this task, they require continuous retraining when new devices join the IoT network. To address this challenge, Siamese Neural Networks (SNNs) have been proposed, demonstrating strong performance without the need for model retraining when a new device is added to the network. In this work, we further assess the reliability of SNNs in reducing the necessity for model retraining when multiple devices join the network, as well as their generalization capabilities. Our findings indicate that while performance slightly declines with an increasing number of newly added devices, it remains satisfactory overall. Furthermore, we demonstrate the generalization of SNNs by training the model on one dataset and evaluating its performance when incorporating devices from a different dataset without retraining. The results highlight the robustness of SNNs and their ability to recognize devices on a different IoT network than the one they were initially trained on, and most importantly, without retraining. Finally, we show how the quality of the initial training set can affect the generalization capabilities of SNNs.
Selective Preemption of Distributed Deep Learning Training

As more distributed deep learning (DDL) jobs run in public clouds, their effective scheduling becomes a major challenge. Current studies prioritize the execution of jobs with less remaining time, which is known to be the best in reducing average job completion time (JCT). However, we observe that this approach does not work when the preemption for pausing and loading jobs weighs in; sometimes, the preemption overheads of DDL jobs take up to hundreds of seconds. This results in very ineffective scheduling, so in some cases, the first-in-first-out policy performs much better. This paper proposes a new scheduling framework called Xion that takes into account the preemption overheads and only preempts DDL jobs when it is beneficial. Our evaluation results demonstrate that Xion effectively reduces the average JCT by 19% and improves the waiting time by 1.64×.
Declarative and Linear Programming Approaches to Service Placement, Reconciled

This article proposes an approach to the data-aware multi-service application placement problem in Cloud-Edge settings. We propose both declarative programming and a Mixed-Integer Linear Programming (MILP) approach to determine eligible placements that minimise operational costs and reduce the number of used nodes to contain the amount of data transfers. After assessing the performance of both approaches, we reconcile them into a methodology that combines the best of the two worlds by exploiting a declarative pre-processing step to boost the MILP solver while determining optimal solutions. We open-sourced the methodology into a prototype that is l0x faster than pure MILP, determines optimal results, and easily accommodates non-numerical constraints on application placements.