Reviewerly/exHarmony huggingface dataset:
IoTAuth: IoT Sensor Data Analytics for User Authentication Using Discriminative Feature Analysis

The revolution of IoT highly impacts on different applications such as remote sensing, smart cities, and remote digital healthcare. People use IoT devices for performing business transactions, daily tasks, and healthcare monitoring. IoT devices generate huge amounts of data assets that have potential applications. Biometrics is a potential application of sensors data. The traditional biometric methods such as PINs, passwords are exposed to numerous attacks such as replication, repeated passwords, etc. Sensors&#x2019; data-based continuous authentication methods are suitable for maintaining users&#x2019; privacy and security in mobile IoT systems. Most of the existing authentication methods have applied motion-based sensors for building users&#x2019; identification profiles. The proposed method uses motion sensors and biomedical sensors for reliable and multi-factor user authentication. In this article, we have introduced an IoT sensors data analytics framework to construct user authentication models. We apply the fiducial points-based feature extraction method data for extracting discriminative features. These features act as unique user profiles for authentication purposes. We have performed a detailed analysis of the proposed approach using the publically available datasets. The experiments elaborate on the effectiveness of IoTauth for improved authentication results.
High-Order Social Graph Neural Network for Service Recommendation

Driven by proliferation of the Service-Oriented Architecture (SOA), the quantity of published software services and their users keeps increasing rapidly in the service ecosystem; thus, personalized service selection and recommendation has remained a hot topic. Recent studies have revealed that users’ social connections may help better model their potential behaviors. Therefore, in this paper, we study how users’ high-order social networks may help improve service recommendation as well as its explainability. Two observations are set forth. First, a user’s service preference may be influenced by his trusted users, whom in turn influenced by their social connections. Second, such chained influences will not remain static and equally-weighted, as a user’s confidence over his social relations may vary confronted with different targeted services. We thus introduce a novel High-order Social Graph Neural Network (HSGNN) to support social-aware service recommendation. The key idea of the model is a graph convolution-based, multi-hop propagation module devised to extract the high-order social similarity signals from users’ local social networks, and encode them into the users’ general representations. Afterwards, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users’ specific preference. Extensive experiments in a real-world service dataset show that our HSGNN makes service recommendation more accurately, i.e., by 4.71% in terms of normalized discounted cumulative gain (NDCG), than state-of-the-art baseline methods.
Software &amp; System Verification with KIV

This paper gives an overview of the KIV system, which in its long history has evolved from a prover for sequential programs using Dynamic Logic to a general purpose theorem prover. Today, KIV's main focus is the refinement-based development of sequential and concurrent software systems. In this paper we describe KIV's logic, highlighting recent developments such as support for polymorphism and for exceptions in programs. We show its proof engineering support that uses a graphical user interface and explicit proof trees, as well as KIV's support for the development of large-scale software systems using modular components and for the verification of concurrent algorithms using a rely-guarantee calculus. Finally, we give a short survey over the case studies that have been conducted with KIV.
Low-Light Image Enhancement Using Image-to-Frequency Filter Learning

Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning (DL) approaches. Yet most existing techniques adopt an image-to-image learning paradigm where DL model architectures are constrained due to latent image feature reconstruction. In this paper, we propose a new LLI enhancement solution titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning. It is designed independently from custom DL architectures and can be seamlessly coupled with existing feature extractors like ResNet50 and VGG16. We have conducted a large battery of experiments using SICE and Pascal VOC datasets to evaluate LLHFNet's enhancement quality. Our solution consistently ranks among the best existing image enhancement techniques and is able to robustly handle LLIs and normal-light images (NLIs).
Get Your Foes Fooled: Proximal Gradient Split Learning for Defense Against Model Inversion Attacks on IoMT Data

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0$\%$, 17.9$\%$, and 36.9$\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.
Unsupervised Domain Adaptation Semantic Segmentation for Remote-Sensing Images via Covariance Attention

Semantic segmentation for remote sensing is a crucial but challenging task. Many supervised semantic segmentation methods rely heavily on a large-scale pixel-wise annotated data set, but it is time-consuming and laborious to provide manual annotation. However, due to the common domain shift of remote sensing images, a direct transfer might not perform well. Therefore, many unsupervised domain adaptation methods have been proposed to solve the data distribution discrepancy in remote-sensing data sets, but these methods cannot completely utilize the features extracted in the training process. In addition, the correlations between feature map channels are crucial for the pixel-wise classification task. In this letter, a covariance-based channel attention module is proposed to capture correlations by covariance metric and weighting the feature map channels. To further improve the domain adaptation performance, we propose a three-stage unsupervised domain adaptation semantic segmentation method for remote-sensing images, we fine-tune the model which has been trained on the source domain on the target domain via self training and knowledge distillation. To test the effectiveness of the proposed method, experiments are conducted on the ISPRS 2-D Semantic Labeling data set and an urban drone data set. Our method shows a better performance advantage compared with other state-of-the-art methods.
Cross-Domain Multitask Model for Head Detection and Facial Attribute Estimation

Extracting specific attributes of a face within an image, such as emotion, age, or head pose has numerous applications. As one of the most widely used vision-based attribute extraction models, HPE (Head Pose Estimation) models have been extensively explored. In spite of the success of these models, the pre-processing step of cropping the region of interest from the image, before it is fed into the network, is still a challenge. Moreover, a significant portion of the existing models are problem-specific models developed specifically for HPE. In response to the wide application of HPE models and the limitations of existing techniques, we developed a multi-purpose, multi-task model to parallelize face detection and pose estimation (i.e., along both axes of yaw and pitch). This model is based on the Mask-RCNN object detection model, which computes a collection of mid-level shared features in conjunction with some independent neural networks, for the detection of faces and the estimation of poses. We evaluated the proposed model using two publicly available datasets, <i>Prima</i> and <i>BIWI</i>, and obtained MAEs (Mean Absolute Errors) of 8.0 &#x00B1; 8.6, and 8.2 &#x00B1; 8.1 for yaw and pitch detection on <i>Prima</i>, and 6.2 &#x00B1; 4.7, and 6.6 &#x00B1; 4.9 on <i>BIWI</i> dataset. The generalization capability of the model and its cross-domain effectiveness was assessed on the publicly available dataset of <i>UTKFace</i> for face detection and age estimation, resulting a MAE of 5.3 &#x00B1; 3.2. A comparison of the proposed model&#x2019;s performance on the domains it was tested on reveals that it compares favorably with the state-of-the-art models, as demonstrated by their published results. We provide the source code of our model for public use at: <uri>https://github.com/kahroba2000/MTL_MRCNN</uri>.
NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing

In this paper, we consider the problem of formally verifying a Neural Network (NN) based autonomous landing system. In such a system, a NN controller processes images from a camera to guide the aircraft while approaching the runway. A central challenge for the safety and liveness verification of vision-based closed-loop systems is the lack of mathematical models that captures the relation between the system states (e.g., position of the aircraft) and the images processed by the vision-based NN controller. Another challenge is the limited abilities of state-of-the-art NN model checkers. Such model checkers can reason only about simple input-output robustness properties of neural networks. This limitation creates a gap between the NN model checker abilities and the need to verify a closed-loop system while considering the aircraft dynamics, the perception components, and the NN controller. To this end, this paper presents NNLander-VeriF, a framework to verify vision-based NN controllers used for autonomous landing. NNLander-VeriF addresses the challenges above by exploiting geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs to the NN controller. By converting this model into a NN (with manually assigned weights) and composing it with the NN controller, one can capture the relation between aircraft states and control actions using one augmented NN. Such an augmented NN model leads to a natural encoding of the closed-loop verification into several NN robustness queries, which state-of-the-art NN model checkers can handle. Finally, we evaluate our framework to formally verify the properties of a trained NN and we show its efficiency.
Personalized Location Privacy With Road Network-Indistinguishability

The proliferation of location-based services (LBS) leads to increasing concern about location privacy. Location obfuscation is a promising privacy-preserving technique but yet to be adequately tailored for vehicles in road networks. Existing obfuscation schemes are based primarily on the Euclidean distances and can lead to infeasible results, e.g., off-road locations. In this paper, we define Road Network-Indistinguishability (RN-I) to evaluate obfuscation-based location privacy-preserving schemes in road networks. To protect drivers’ location privacy in road networks, we propose a Personalized Location Privacy-Preserving (PLPP) scheme and prove it achieves RN-I. The PLPP scheme employs a dual-obfuscation algorithm, consisting of a connection perturbation and an interval perturbation, to obfuscate on-road locations. An efficient personalization algorithm is designed for the PLPP scheme to fine-tune location privacy budgets for capturing drivers’ sensitive locations and privacy requirements. Experiments upon two real-world datasets confirm the location privacy-preserving capability, data utility, and efficiency of the proposed PLPP scheme.
On Mixed-Initiative Content Creation for Video Games

In this article, we present a survey of mixed-initiative methods for the creation of content for video games. We also propose a definition of what mixed initiative implies, as the term lacks a clear specification. The survey includes works not directly aimed at video games but which create content that can potentially be used in games, such as art programs utilizing mixed initiative. Furthermore, we highlight research areas that overlap wholly or partly with mixed initiative, such as casual creators, explainable artificial intelligence, or interactive evolutionary computation. We examine these and several other topics in the context of mixed initiative. Finally, we provide a catalogue of typical techniques and challenges connected with mixed initiative before considering future directions.
HSIL: Hybrid Semantic Infused Learning Approach for Course Recommendation

Course recommendation is essential to enable learners to choose the best course among many available courses. So, as a result, a semantically driven course recommendation system is required as the data over the world wide web is increasing, and the web is also tending towards Semantic Web 3.0. In this approach, the initial term pool is created based on the existing user learner profile and the current clicks of the user to determine the exact needs of the user for the term enrichment is done by incorporating ontologies of courses and metadata generation approach is proposed where the metadata is initially classified using XGBoost algorithm where the top 25% is used. Then, the core dataset is also classified using the XGBoost algorithm based on the enriched user terms. And then, the semantic similarity is computed using concept similarity under the genetic algorithm and is recommended to the user. Overall, the accuracy of 96.3% is achieved.
Building Wikipedia N-grams with Apache Spark

With the rise of the natural language processing models over the last two decades, there is a need to construct large pre-processed datasets such as N-grams for language modeling. N-grams, which are defined as a continuous sequence of words of length N, are useful in numerous language models. In this work, we introduce a distributed data processing approach to build an N-gram corpus, which is many times faster when compared to previous single machine techniques.
Adaptive Vertical Federated Learning on Unbalanced Features

Most of the existing FL systems focus on a data-parallel architecture where training data are partitioned by samples among several parties. In some real-life applications, however, partitioning by features is also of practical relevance and the number of features is usually unbalanced among parties. The corresponding learning framework is referred to as Vertical Federated Learning (VFL). Though some pioneering work focused on VFL, the convergence properties of VFL on unbalanced features, especially when parties conduct different numbers of local updates concerning heterogeneous computational capabilities are still unknown. In this article, we propose a new learning framework to improve the training efficiency of VFL on unbalanced features. Given the number of features and the computational capability owned by each party, our thorough theoretical analysis exhibits that the number of local updates conducted by each party has a great effect on the convergence rate and the computational complexity, both of which jointly determine the overall training efficiency in an interrelated and sophisticated way. Based on our theoretical findings, we formulate an optimization problem and derive the optimal solution by selecting an adaptive number of local training rounds for each party. Extensive experiments on various datasets and models demonstrate that our approach significantly improves the training efficiency of VFL.
Attribute-Guided Multiple Instance Hashing Network for Cross-Modal Zero-Shot Hashing

Cross-Modal Zero-Shot Hashing (CMZSH) is an important image retrieval technique, e.g., Text Based Image Retrieval. Most of existing CMZSH methods mainly use semantic attributes as guidance to generate hash codes for both the images and texts of seen and unseen categories. However, existing CMZSH methods only focus on learning global attribute vectors and hash codes for images, which mixes up information of complex semantics and background clutters, and thus impedes the retrieval performance. To solve this issue, we propose an Attribute-Guided Multiple Instance Hashing (AG-MIH) network for CMZSH, where each instance represents one image region. Instead of generating global image hash codes, AG-MIH can effectively learn instance-level hash codes based on instance attributes. To improve the attribute learning for instances, AG-MIH can exploia novel 2-D Category-Attribute Relation (CAR) layer, which uses different matching templates to model the relationships between each instance and the attributes for different categories. Under the guidance of semantic attributes, AG-MIH can effectively learn hash codes for each visual instance and texts by a Multi-stream Instance Hashing Refinement (MIHR) procedure. In the MIHR, the pseudo supervisions for the instance-level attributes and hash codes in each stream are from its proceeding stream. Empirical studies on benchmark datasets show that AG-MIH achieves state-of-the-art performance on both cross-modal and single-modal zero-shot image retrieval tasks.
Towards Blockchain-Enabled ESG Reporting and Assurance: From the Perspective of P2P Energy Trading

Corporate disclosure of Environmental, Social, and Governance (ESG) has dramatically increased and become an essential resource for worldwide investors. Both researchers and practitioners have devoted significant efforts to creating methodologies to achieve accurate, verifiable, and real-time ESG reports. The characteristics of blockchain, such as decentralization, transparency, and self-execution, could greatly improve the quality and timing of ESG reporting and assurance. Therefore, this paper describes a novel system to integrate ESG with financial information and report in real time via blockchain and automate assurance service via smart contracts. Armed with those technologies, the credibility, transparency, and traceability of ESG information could be largely improved, and automated and tamper-resistant continuous assurance could be enabled. The system is further implemented for a real P2P energy trading company as proof-of-concept. This system provides an example of leveraging emerging technologies to create novel disclosure and assurance methodologies, illuminating the future of ESG reporting and assurance.
Blockchain Revolution from 1.0 to 5.0: Technological Perspective

Since the inception of bitcoin application in 2008, several trust-based global solutions have been developed to overcome the malicious intent that occurs in critical applications. Adopting blockchain technology resolves the issues related to frauds in various sectors like banks, finance, and government. It was a massive concern as fraudulent activities were carried out on a large scale to maneuver the business operation. A blockchain network's capabilities are sufficient to address these aforementioned issues. With blockchain, the third-party services were brought to a halt, and digital signature was associated with every task, thus, making blockchain a secure network. The chapter presents different forms of blockchain that evolved over the years and the methods it has adopted to transform into a system that conducts transactions in a trusted, safe, and secure environment. Further, this chapter first introduces the origin of blockchain. Then it discusses how blockchain generations varied with time as demands grew and technological innovations that keep evolving at a rapid pace. Finally, we discuss the elements of blockchain, its working, benefits, and limitations of blockchain.
Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP’s computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today’s noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices–which appears to be decades away– hinder practical applications of current QEMTP methods. This paper devises a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) design of shallow-depth QEMTP quantum circuits for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTP’s performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.
Enhancing the competitive advantage via Blockchain: an olive oil case study

In the Olive Oil Supply Chain (OOSC), olive oil is priced according to its origin and quality, yet it periodically suffers from scandals concerning quality or fraud issues. Blockchain Technology (BT) has been put forward as a solution that should bring visibility to the food supply chain. BT is proposed to guarantee the veracity of the product’s information at the different stages of its production. Despite BT’s extraordinary interest in operations and supply chain management, no research supports how its use will generate visibility and avoid fraud in the olive oil quality. We present here how BT combined with Internet of Things (IoT) devices and smart contracts will improve visibility and avoid fraud in the olive oil quality so providing a competitive advantage. A theoretical use case based on real costs of the financial gain from using BT in a typical OOSC is presented. We propose that all food supply chains where quality, origin, and production process have to be verified should deploy BT to maximize visibility and competitive advantage for the operators in the chain.