Reviewerly/exHarmony huggingface dataset:
A Programming Model for Portable Fault Detection and Diagnosis

Portable applications support the write once, deploy everywhere paradigm. This paradigm is particularly attractive in building applications, where current practice involves the manual deployment and configuration of such applications, requiring significant engineering effort and concomitant costs. This is a tedious and error-prone process which does not scale well. Notwithstanding recent advances in semantic data modelling that allow a unified representation of buildings, we still miss a paradigm for deploying portable building applications at scale. This paper introduces a portable programming model for such applications, which we examine in the context of Fault-Detection and Diagnosis (FDD). In particular, we look at the separation of the FDD logic and the configuration with specific data inputs. We architect a software system that enables their self-configuration and execution across various building configurations, expressed in terms of Brick metadata models. Our initial results from authoring and executing APAR (AHU Performance Assessment Rules) on multiple AHUs of two museums demonstrate the potential of our model to reduce repetitive tasks and deployment costs of FDD applications.
Residual Vector Product Quantization for approximate nearest neighbor search

Vector quantization is one of the most popular techniques for approximate nearest neighbor (ANN) search. Over the past decade, many vector quantization methods have been proposed for ANN search. However, these methods do not strike a satisfactory balance between accuracy and efficiency because of their defects in quantization structures. To overcome this problem, a quantization method, named as Residual Vector Product Quantization (RVPQ), is proposed in this study. Under this method, the data space is decomposed into several subspaces, and a residual structure consisting of several ordered residual codebooks is constructed for each subspace. Learned by using an effective joint training algorithm, the quantization structure of RVPQ is much better than other methods, and it greatly enhances the performance of ANN search. Except that, an efficient residual quantization encoding method H-Variable Beam Search is also proposed to achieve higher encoding efficiency with negligible loss of accuracy. Furthermore, Inverted Multi-Index based on RVPQ is also designed to effectively solve the ANN search for a very large-scale database. Experimental results and theoretical evaluations show that RVPQ outperforms the-state-of-the-art methods on retrieval accuracy while retaining a comparable computational complexity.
An efficient privacy-preserving authentication scheme with enhanced security for IoMT applications

The Internet of Medical Things (IoMT), an essential application of Internet of Things (IoT) technology in the health sector, is of great practical importance in improving healthcare quality and reducing healthcare costs and errors. For years, researchers have designed many cryptographic schemes for securing IoMT applications. However, in this work, we reveal that the state-of-the-art cloud-centric IoMT-based smart healthcare system cannot be deployed in real-world scenarios by proposing a concrete signature forgery attack on their underlying pairing-based homomorphic certificateless signature (CLS) scheme. Then we present a new CLS scheme without pairings and formally prove its security under the standard cryptographic assumption. Based on our design, we put forward a new secure and efficient IoMT-based smart healthcare system. The performance comparison results illustrate the practicality of our construction.
Cyber-physical attack graphs (CPAGs): Composable and scalable attack graphs for cyber-physical systems

Attack graphs are a fundamental security tool focused on depicting how multi-stage attacks can be carried out through a network to compromise specific assets and systems. While attack graphs have been widely utilised in the IT cyber domain, their use in Operational Technology (OT) environments requires new approaches able to properly model and analyse Cyber-Physical Systems (CPS). In this paper, we introduce Cyber-Physical Attack Graphs (CPAGs) as a class of attack graphs able to cover both cyber and physical aspects. CPAGs aim at extending the reach of standard attack graphs to cyber-physical networks typically observed in industrial environments and critical infrastructure systems, analyse how an attacker can move within the network, and understand the impact that these actions may have on the system. We propose a constructive methodology to design CPAGs backed up by a formal rule-based approach that specifies how integral parts of the model can be generated and later composed to build more complex CPAGs. We then explore the semantics of CPAGs associated to cyber and physical attack actions as well as their impact on CPS environments. We also discuss potential CPAG-based analysis techniques and focus on risk analysis using Bayesian CPAGs. Finally, we show the application of the proposed model over a realistic scenario on smart farming using our open source tool T-CITY.
A Novel Technique for Implementing Equidistant Sinusoidal Membership Functions for Fuzzy Sets

This paper outlines a novel technique to implement naturally equidistant and smooth sinusoidal fuzzy sets used in modelling and controlling both linear and nonlinear systems. This study describes the requirements and processes for designing equidistant sinusoidal membership functions in order to accomplish generalization, smoothing, and the use of fuzzy logic in modelling, simulating, and controlling both linear and nonlinear systems. The centroid defuzzification method is assessed using both traction and scaling techniques. MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ⓒ</sup> software package is used to validate the mathematical equations of the model.
An Optimized Fuzzy-based Load Balancing in Cloud Computing

In cloud computing, many resources are pooled together to help users operating in a distributed environment collaborate. A load balancer distributes Virtual Machines (VMs) to users in compliance with their required resources and tasks. Existing load balancing algorithms are insufficient for obtaining fast response times and better optimisation of cloud services and their resources when the load increases. Rule-based fuzzy inferences enable optimal resource utilisation by assigning user requests in the most efficient manner. This paper presents an Optimal Fuzzy-based Load Balancing (OFLB) model for efficient resource distribution. The proposed model employs memory, bandwidth, and disc space needs as fuzzy variables and implements categorization-based fuzzy constraints to improve performance. The tasks are assigned to virtual devices based on defined threshold values for membership functions. In the experiments, the OFLB is compared to other extant load-balancing algorithms in terms of memory, bandwidth and disc space utilisation. The analysis of the results shows that the OFLB-based modal improves the efficacy of the cloud system in terms of resource utilization by approximately 18% as compared to existing algorithms that distribute VMs.
Constraints to Debt Sustainability in Egypt: Structural Response and Fiscal Fatigue

Public debt sustainability is a multidimensional process.Egypt maintains a sustainable general path in conducting its fiscal policy.However, the rising path of accumulated debt to GDP ratio implies critical restrictions on fiscal stance and fiscal space.The research tests the structural endogenous long-term response of the Egyptian government towards the accumulated stock of public debt.in addition to examining the fiscal fatigue hypothesis when debt jumps to high extreme levels.The research adopts the widely accepted approach proposed by Bohn to estimates a fiscal reaction function.the Autoregressive Distributed Lag ARDL technique is applied for the time period from 1981 to 2021.Empirical wise, two critical specified regressions are estimated.The first measures the response of the structural long-term primary balance as a percentage of GDP to the stock of public debt to GDP ratio.This provides a general judgement whether the fiscal policy is expansionary, neutral or restrictive.Second, a non-linear estimation is implemented to test for the possibility of fiscal fatigue.the existence of an inverse U-shaped response can be used to endogenously determine a debt threshold above which the government is unable to keep its fiscal position within the sustainability condition.Findings of the first regression indicate that the government is unable to adjust its structural primary balance positively with the increase in the stock of previous debt to GDP ratio during the examined time period.Thus, fiscal policy of the Egyptian government tends to be restrictive during the examined time period.Furthermore, the fiscal fatigue hypothesis applies as the magnitude of government fiscal response is conditioned by the level of accumulated public debt.At low levels of debt to GDP ratio the government adjustment of its primary balance is not structurally planned.Government show weak response.When the level of accumulated debt to GDP ratio goes high, government undertakes serious measures and effectively adjust the primary balance to GDP with the increased public debt.however, when the level of the previous debt to GDP ratio jump to extreme high levels, government is unable to maintain the positive response indicating the possibility of fiscal fatigue and fiscal default.The research emphasizes the necessity of a planned serious fiscal actions to correct the long-term response.Cyclical automatic response of the government should be revised.Moreover, Non-interest expenditures should be structurally adjusted to maintain the required level of government reasonable primary balance.
YOLOV3 based Real Time Drone Detection for Counter Drone System

Nowadays, surveillance across the borders is a major issue since the dropping and supply of ammunition, explosives, and weapons is done using drones. In this paper, an implementation of YOLO based drone detection and recognition was implemented for surveillance applications. Drones are unmanned aircraft that may be remotely piloted (ground control) and come in a variety of sizes and configurations for use in a variety of applications. Lawbreakers employ drones for unlawful operations despite restrictions on their use in sensitive areas being in place because they are inexpensive and easy to use. To detect drones in real time and with high accuracy, a deep learning-based object detection model (YOLOv3) is trained on a specific dataset in this study.
Transformative course design practices to develop inclusive online world language teacher education environments from a critical digital pedagogy perspective

A fast digitization process has characterized higher education in the last few years. To plan digitally enhanced inclusive learning experiences, this essay outlines a course design pedagogy targeted at fostering the development of inclusive online world language teacher education environments. The course design pedagogy is the result of the combination of Design Thinking (DT) and Design Justice. A problem-based approach, namely DT, is combined with Design Justice, focusing on diversity, to develop course design practices challenging ableism and fostering inclusion. The course design pedagogy developed is suited to devising online world language teacher education courses from a critical digital pedagogy perspective. The new course design pedagogy can be used to design online courses fostering access, inclusion, social justice, and didactic innovation that are grounding values of the United Nations Sustainable Development Goals 4 (SDG4) for education, the UNESCO recommendation on open educational resources (OERs), and OER-enabled pedagogy.
Demonstrating Multiple Prolog Programming Techniques Through a Single Operation

Without doubt Prolog, as the most prominent member of the logic programming (LP) approach, presents significant differences from the mainstream programming paradigms. However, demonstrating its flexibility to larger audiences can indeed be a challenging task, since the declarative style of LP lies outside the mainstream programming languages most are familiar with. In this paper, we demonstrate how alternative implementations of a single list operation can prove to be a rather helpful tool for demonstrating a plethora of Prolog programming aspects and techniques, and some issues associated with these, such as efficiency, readability and writability of code.
Reflections on Automation, Learnability and Expressiveness in Logic-Based Programming Languages

This position paper sketches an analysis of the essential features that logic-based programming languages will need to embrace to compete in a quickly evolving field where learnability and expressiveness of language constructs, seen as aspects of a learner’s user experience, have become dominant decision factors for choosing a programming language or paradigm. Our analysis centers on the main driving force in the evolution of programming languages: automation of coding tasks, a recurring promise of declarative languages, instrumental for developing software artifacts competitively. In this context we will focus on taking advantage of the close correspondence between logic-based language constructs and their natural language equivalents, the adoption of language constructs enhancing the expressiveness and learnability of logic-based programming languages and their synergistic uses in interacting declaratively with deep learning frameworks.
Shape-Guided Dual Consistency Semi-Supervised Learning Framework for 3-D Medical Image Segmentation

Popular semi-supervised 3-D medical image segmentation networks commonly suffer from two limitations: First, the geometry shape constraint of targets is frequently disregarded, leading to coarse segmentation results. Second, semi-supervision is only performed on the last layer of the decoder, resulting in the insufficient representation learning of 3-D convolution neural network. To address these issues, we propose a shape-guided dual consistency semi-supervised learning (SDC-SSL) framework for 3-D medical image segmentation. Indeed, the proposed framework has two dominating advantages. Initially, a geometry-aware shape constraint is presented and used to learn the shape representation, which converts the differences between two networks into an unsupervised loss and lets the framework learn the boundary distance information of targets in unlabeled challenging regions. Additionally, a deep-supervised knowledge transfer strategy is developed and employed by the proposed framework, which can upgrade the generalization ability of our framework without increasing any extra parameters and computation costs in the inference phase. Experimental results demonstrate that the proposed framework outperforms state-of-the-art methods on two challenging 3-D medical image segmentation tasks due to effective geometry-aware shape constraint on unlabeled data and the strong ability of knowledge mining on labeled data. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SUST-reynole/SDC-SSL</uri> .
Fault Detection Exploiting Artificial Intelligence in Satellite Systems

Mission control and fault management are fundamental in safety-critical scenarios such as space applications. To this extent, fault detection techniques are crucial to meet the desired safety and integrity level. This work proposes a fault detection system exploiting an autoregressive model, which is based on a Deep Neural Network (DNN). We trained the aforementioned model on a dataset composed of telemetries acquired from Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). The training process has been designed as a sequence-to-sequence task, varying the length of input and output time series. Several DNN architectures were proposed, using both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) as basic building blocks. Lastly, we performed fault injection modeling faults of different nature. The results obtained show that the proposed solution detects up to 90% of injected faults. We found that GRU-based models outperform LSTM-based ones in this task. Furthermore, we demonstrated that we can predict signal evolution without any knowledge of the underlying physics of the system, substituting a DNN to the traditional differential equations, reducing expertise and time-to-market concerning existing solutions.
Imbalanced Data Handling for Deep Learning-Based Autonomous Crater Detection Algorithms in Terrain Relative Navigation

This paper investigates the effects of an imbalanced distribution of crater mask labels in datasets used for image segmentation. It can be regarded as an addendum to existing works delving with autonomous Crater Detection tasks neglecting non uniform pixel distribution. In fact, the craters edge pixels represent approximately only 3% of each single mask. The training was performed with the U-Net using the Focal Tversky Loss, which is more adaptable to similar real-world data imbalance problems with respect to the binary cross-entropy loss. Contextually, the Intersection over Union is computed in the pixel domain for the two cases to assess which approach achieves better segmentation performances. Results of the models trained with Focal Tversky Loss and binary cross entropy are post-processed and compared for performance benchmarking in terms of precision, recall and $$F_1$$ in the crater domain. Despite being explicitly targeted at performing Terrain Relative Navigation, these results can prove to be useful both in absolute and relative navigation phases. In fact, these scores give insight about the percentage of image-to-catalogue matched craters and represent the reliability of an artificial intelligence based navigation algorithm.
Computational Simulation of the Effects of Different Culture Types and Leader Qualities on Mistake Handling and Organisational Learning

This chapter investigates computationally the following research hypotheses: (1) Higher flexibilityFlexibility and discretion in organisational cultureOrganisational culture results in better mistake managementMistake management and thus better organisational learningOrganisational learning, (2) Effective organisational learningOrganisational learning requires a transformational leaderTransformational leader to have both high socialSocial and formal status and consistencyConsistency, and (3) Company culture and leaderLeader’s behavior must align for the best learningLearning effects. Computational simulationsComputational simulation of the introduced adaptive networkAdaptive network were analyzed in different contextsContext varying in organisationOrganisation culture and leaderLeader characteristics. Statistical analysisStatistical analysis results proved to be significant and supported the research hypotheses. Ultimately, this chapter provides insight into how organisationsOrganisation that foster a mistakeMistake-tolerant attitude in alignment with the leaderLeader can result in significantly better organisational learningOrganisational learning on a teamTeam and individualIndividual level.
An XAI Integrated Identification System of White Blood Cell Type Using Variants of Vision Transformer

White Blood Cells (WBCs) serve as one of the primary defense mechanisms against various diseases. Therefore, in order to detect blood cancer as well as many other disorders, routine WBC monitoring may be necessary. Numerous studies have proposed automated 4 types of WBC detection through Machine Learning and Deep Learning based solutions. However, transformers based applications, which primarily originated from the field of Natural Language Processing, are very scarce. Our proposed study showcases the applications of Vision Transformers (VTs) for WBC type identification. Firstly, a pre-augmented dataset of nearly 12,500 images was taken. Afterward, two variants of VTs were trained and evaluated on the dataset. Our analysis revealed that the accuracy for all the models ranged from 83% to 85%, making the performance of the VTs equivalent to that of the standard Deep Learning models. Meanwhile, VTs have demonstrated significantly faster learning symptoms during the training phase, which can be useful when one wants to maximize learning through fewer epochs, for example, in a federated learning environment. Finally, the application of Explainable AI (XAI) was visualized on the VTs using Gradient-weighted Class Activation Mapping (GradCam).
Dynamic Logic Programming

Dynamic Logic Programming (DLP) is an extension to logic programming designed to support the representation of knowledge about dynamic worlds. It combines the strengths of safe, stratified, side-effect-free logic programming in defining relations with the power of simultaneous transition rules for defining dynamic operations. Because relation definitions in DLP are safe and stratified and side-effect-free, dynamic logic programs are simpler than general Prolog programs and they allow for efficient implementation. At the same time, defining operations using simultaneous transition rules adds expressive power without compromising the conceptual simplicity of logic programming. DLP is the basis for the logic programming language Epilog (aka Dynamic Prolog) [10].
A Robust NFT Assisted Knowledge Distillation Framework for Edge Computing

With the development and improvement in chip manufacturing and network communication, Internet of Things (IoT) have been addressing more and more popularity around these days. Due to the fact that the end devices in an IoT system can perform higher computational tasks, there are more and more IoT applications requiring on-device local training procedures. Hence, the concept of Knowledge Distillation is introduced to solve the on-device machine learning problem–each end device will receive a distilled light-weight student model from the comprehensive central teaching model. However, several security concerns need to be resolved before KD being put into industrial environments, including data integrity and robustness over external attacks. In this paper, we propose an NFT assisted KD framework, aiming at leveraging the blockchain features on data security to solve the intrinsic robustness defects in a naive KD architecture. Our major contributions can be concluded as following 1) the first NFT assisted KD framework (KD-NFT) which initializes the chance of NFT usages in scientific fields; 2) providing a two-dimension (vertical and horizontal) security over KD data vulnerability under attacks; and 3) a fail-over scheme when external poisoning happened, to recovering KD-NFT training process back to last-best status, by using NFT history full-traceable feature and providing automatic system robustness.
Efficient Privacy-Preserving Inference Outsourcing for Convolutional Neural Networks

Inference outsourcing enables model owners to deploy their machine learning models on cloud servers to serve users. In this paradigm, the privacy of model owners and users should be considered. Existing solutions focus on Convolutional Neural Networks (CNNs) but their efficiency is much lower than GALA, which is a solution that only protects user privacy. Furthermore, these solutions adopt approximations that reduce the model accuracy and thus require model owners to retrain the models. In this paper, we present an efficient CNN inference outsourcing solution that protects the privacy of both model owners and users. Specifically, we design secure two-party computation protocols based on two non-colluding cloud servers, which calculate with additive secret shares of the model and the user’s input. Our protocols avoid the expensive permutation operations in linear calculations and approximations in non-linear calculations. We implement our solution on realistic CNNs and experimental results show that our solution is even 2–4 times faster than GALA.