Reviewerly/exHarmony huggingface dataset:
Type-2 fuzzy blended improved D-S evidence theory based decision fusion for face recognition

Data acquisition from (single/multiple) sensors can efficiently be combined by exploiting neural networks (NN) for accurate supervising of pattern recognition especially for biometric face recognition. In case where information obtained from the different sensor shows highly conflicting, the classical Dempster’s combination rule may give rise counter-intuitive result. This paper presents type-2 fuzzy blended improved Evidence D-S (T2FIE-DSDLF) combination rule for multi sensor data fusion which minimizes paradoxes of Dempster–Shafer (D-S) combination rule. Different textural facial image (using LBP and LTP) with genetically algorithm based G2DLDA methods are employed to generate feature vector corresponding to a face image. Multiple Basic Probability Assignments (BPA) corresponding to a class with respect to different feature vectors can be combined by using Vertical Slice Centroid Type reduction (VCTR) and the mass function using evidence theory is calculated to find the score for the class. The class having the maximum score is conceived as the class of input test face image. Our T2FIE-DSDLF method is evaluated with two popular classifiers — radial basis function (RBF) neural network, and support vector machines (SVM). The numerical examples presented shows the effectiveness of our method. Moreover, to validate the efficiency of our T2FIE-DSDLF method, we have compared our method with some state-of-the-art methods.
Multi-User Measurement-Device-Independent Quantum Key Distribution Based on GHZ Entangled State

As a multi-particle entangled state, the Greenberger-Horne-Zeilinger (GHZ) state plays an important role in quantum theory and applications. In this study, we propose a flexible multi-user measurement-device-independent quantum key distribution (MDI-QKD) scheme based on a GHZ entangled state. Our scheme can distribute quantum keys among multiple users while being resistant to detection attacks. Our simulation results show that the secure distance between each user and the measurement device can reach more than 280 km while reducing the complexity of the quantum network. Additionally, we propose a method to expand our scheme to a multi-node with multi-user network, which can further enhance the communication distance between the users at different nodes.
Secure and Anonymous Voting D-App with IoT Embedded Device Using Blockchain Technology

The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures complete voter anonymity and end-to-end security for all entities participating in the electronic voting process. The paper outlines the solution’s two layers. Implementation details are presented for the e-voting application, which was deployed inside of an IoT embedded device. The solution and presented protocols provide two major properties: privacy and verifiability. To ensure privacy, the proposed solution protects the secrecy of each electronic vote. As for implementing verifiability, the solution prevents a corrupt authority from faking in any way the process of counting the votes. Both properties are achieved in the presented solution e-VoteD-App.
A novel zero-watermarking algorithm based on robust statistical features for natural images

In this paper, we develop a robust image zero-watermarking algorithm based on multiple circular statistical features. First of all, we detect stable feature points from the host image, and construct circular region partitions from the identified feature points, which contain the critical information of the host image. Second, the statistics and differences of circular region partitions are calculated, which contain both the local differences and the global information of the host image. Third, we apply discrete cosine transform (DCT) and singular value decomposition (SVD) in statistics and differences in order to construct the feature image. The stability of DCT and SVD further warrants the robustness of the algorithm. Moreover, Arnold transform is used to scramble the watermark image to enhance the security of the algorithm. Finally, we obtain the zero watermark by performing the exclusive-or (XOR) operation between the feature image and the scrambled watermark image, and the zero watermark is stored in the copyright authentication database for copyright authentication. Moreover, to combat geometric attacks, we develop a blind image correction algorithm without using the original image to accurately correct the attacked image. Numerous experiments and comparisons with the state-of-the-art (SOTA) watermarking algorithms confirm that the proposed algorithm affords good robustness against various attacks, such as filtering attacks, noise attacks, geometric attacks, and even a random combination of the above-stated attacks.
Utilization aware and network I/O intensive virtual machine placement policies for cloud data center

High-performance computing in the cloud environment uses ample virtual resources, which in turn require physical infrastructures provided by cloud service providers (CSPs). Naturally, CSPs would like to place virtual machines (VM) in a cost-effective way satisfying the resource requirement of the VMs and the capacity constraints of the hosts. Such consideration has led to the well known virtual machine placement (VMP) problem. In this paper, we consider a variant of the VMP, taking into account the communication among the VMs. We introduce the concept of Virtual Cluster, made up of VMs communicating among themselves. It is desirable to place the VMs of a cluster close to each other and thus reduce the communication cost. The objective for the Virtual Cluster Placement (VCP) problem is to reduce the communication cost. For this, we have to consider the network topology of the data center. In this paper, we have proposed some heuristics for both kinds of placement problems (VMP as well as VCP) and given Integer Linear Programming (ILP) formulations. We have also presented algorithms based on Semidefinite Programming (SDP) and meta-heuristic based on Genetic Algorithms. We have compared the performance of proposed heuristics with other existing works and the optimal solutions obtained from ILPs.
Design of fuzzy rule-based models with fuzzy relational factorization

This study opens an original avenue of designing interpretable fuzzy rule-based models realized in the presence of data characterized by a large number of attributes (input variables). In the presence of such high-dimensional data, the development of rule-based models faces serious challenges both at the design and interpretability level. To address these two problems, we develop a new architecture of the rules and establish a two-step design process. First, the dimensionality reduction is realized with the aid of a fuzzy relational matrix factorization which transforms original fuzzy set-based encoded variables and yields a logic-oriented level of activation of individual rules. The results of relational factorization are logic expressions built over the encoded input variables forming condition parts of the rules. Second, the conclusions of the rules are built by optimizing local functions (both constant and linear functions are considered). A number of experiments are reported along with comparative studies involving a standard Takagi-Sugeno (TS) model.
A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation.
GEMvis: a visual analysis method for the comparison and refinement of graph embedding models

Graph embedding, which constructs vector representation of nodes in a network, has shown effectiveness in many graph analysis tasks, such as node classification, node clustering, and link prediction. However, due to the complexity of graph embedding models (GEMs) and their nontransparency of hyperparameters, evaluation and comparison of embedding results in retaining the original graph features, and consequently, the selection of suitable GEMs according to graph analysis tasks are challenging for people. In this paper, we present a visual analysis method, GEMvis, to support the evaluation and comparison of GEMs from the original graph, node metric, and embedding result spaces. The method also supports the online refining of GEM by tuning the parameters in its three components (graph sampling method, neural network structure, and loss function). A series of metrics, R_node metrics, for measuring GEMs’ ability to preserve specific node metrics, such as R_degree and R_closeness, is also proposed to support quantitative evaluation and comparison of GEMs’ ability to preserve original graph features. Finally, three case studies and expert feedback illustrate the effectiveness of GEMvis.
Privacy Aware Internet of Medical Things Data Certification Framework on Healthcare Blockchain of 5G Edge

The 5G communication technology and edge computing jointly form 5G Edge and eliminate the constraints of Internet-of-Medical Things (IoMT) devices to facilitate several real-time healthcare services, including patient monitoring and diagnoses from anywhere. Nevertheless, the 5G Edge platform introduces risks of internal attacks to the data integrity and privacy of IoMT data. Hence, end users cannot trust data retrieved from the 5G Edge. This paper proposes a privacy-preserving search result certification framework for IoMT data on 5G and edge computing-assisted blockchain networks. The novelty of this paper is twofold. First, the proposed framework introduces a blockchain platform involving 5G Edge servers to ensure tamperproof data storage. In addition, an encrypted data storage model and a symmetric-key cryptography-based privacy-preserving search mechanism are developed for the 5G Edge blockchain. Second, the framework leverages an efficient multi-signature scheme to design a tamperproof search result certification mechanism for IoMT data on the blockchain of 5G Edge. Several experiments are conducted to evaluate the performance of the proposed framework.
Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment

Virtual Reality (VR) has been adopted as a leading technology for the metaverse, yet most previous VR systems provide one-size-fits-all experiences to users. Context-awareness in VR enables personalized experiences in the metaverse, such as improved embodiment and deeper integration of the real world and virtual worlds. Personalization requires context data from diverse sources. We proposed a reusable and extensible context data collection framework, ManySense VR, which unifies data collection from diverse sources for VR applications. ManySense VR was implemented in Unity based on extensible context data managers collecting data from data sources such as an eye tracker, electroencephalogram, pulse, respiration, galvanic skin response, facial tracker, and Open Weather Map. We used ManySense VR to build a context-aware embodiment VR scene where the user's avatar is synchronized with their bodily actions. The performance evaluation of ManySense VR showed good performance in processor usage, frame rate, and memory footprint. Additionally, we conducted a qualitative formative evaluation by interviewing five developers (two males and three females; mean age: 22) after they used and extended ManySense VR. The participants expressed advantages (e.g., ease-of-use, learnability, familiarity, quickness, and extensibility), disadvantages (e.g., inconvenient/error-prone data query method and lack of diversity in callback methods), future application ideas, and improvement suggestions that indicate potential and can guide future development. In conclusion, ManySense VR is an efficient tool for researchers and developers to easily integrate context data into their Unity-based VR applications for the metaverse.
Challenges in platforming and digitizing decentralized energy services

Abstract This paper aims to identify and classify the challenges and issues faced by the energy sector in digitizing distributed energy operation and services using digital platforms. It contributes to two fields: information systems (IS) in the domain of platforms ecosystems and digital services innovation and the field of energy informatics in the domain of digital business models and service innovation. Through a systematic literature review, we investigate current research on digitalization and digital platforms in the context of electrical energy services and identify challenges and areas for future research. The key challenges are then classified into two categories, each with three subcategories: Architectural challenges, including aspects of design, ecosystem management, and agility and openness; and business and regulatory challenges, including contracts and relationship management, business models, and standards. The main limitation of this study is that it does not focus on a specific geographic domain, which means that the results are somewhat general and may not be applicable to certain countries or regions. The paper concludes with recommendations for future research directions in the digitalization and platformization of the energy sector in all six-sub-areas.
The Idea Machine: LLM-based Expansion, Rewriting, Combination, and Suggestion of Ideas

We introduce the Idea Machine, a creativity support tool that leverages large language models (LLMs) to empower people engaged in idea generation tasks. The tool includes a number of affordances that can be used to enable various levels of automation and intelligent support. Each idea entered into the system can be expanded, rewritten, or combined with other ideas or concepts. An idea suggestion mode can also be enabled to make the system proactively suggest ideas.
Toward Industrial Private AI: A Two-Tier Framework for Data and Model Security

With the advances in 5G and IoT devices, industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding data privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques, but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a federated learning and encryption-based private (FLEP) AI framework that provides two-tier security for data and model parameters in an Industrial IoT environment. We propose a three-layer encryption method for data security and provided a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlight several open issues and challenges regarding the FLEP AI framework's realization.
S-Store: A Scalable Data Store towards Permissioned Blockchain Sharding

Sharding technique, which divides the whole network into multiple disjoint groups or committees, has been recognized as a revolutionary solution to enhance the scalability of blockchains. For account-based model, state data are partitioned over all committees and organized as Merkle trees to ensure data consistency and immutability. However, existing techniques on Merkle tree-based state storage fail to scale out due to a large amount of network and compute overheads incurred by data migration and Merkle tree reconstruction, respectively. In this paper, we propose $\mathcal{S}$-Store, a scalable data storage technique towards permissioned blockchain sharding based on Aggregate Merkle B+ tree (AMB-tree). $\mathcal{S}$-Store utilizes consistent hashing to reduce data migration among committees and uses split and merge on AMB-tree to decrease Merkle tree reconstruction overheads. $\mathcal{S}$-Store also employs a novel committee addition protocol that guarantees the system service availability during data migration. Extensive experiments show that $\mathcal{S}$-Sotre outperforms existing techniques by one order of magnitude in terms of transaction execution, data transmission, and committee addition.
Blockchain and Zero-Sum Game-based Dynamic Pricing Scheme for Electric Vehicle Charging

This paper proposes a zero-sum game theory and blockchain-based secure and decentralized dynamic pricing scheme for electric vehicle charging. It aims to secure data sharing between electric vehicles and charging stations. We integrate the sixth-generation (6G) communication network to enable data transactions between electric vehicles and charging stations with low latency and high reliability. We employ a zerosum game theory approach to maximize the payoff of electric vehicles and charging stations. The performance of the proposed system with 6G is evaluated by comparing it with 5G and 4G traditional networks. The performance evaluation of the proposed system has been analyzed with various parameters latency, profit for electric vehicles, profit for charging station, and optimal payoff of the system. The results show that the proposed system is highly secure and reliable than traditional systems.
Learning class-imbalanced data with region-impurity synthetic minority oversampling technique

Learning from class-imbalanced data is a tough task, which often leads classifiers to fail on identifying the minority class. To balance the class ratio, synthetic minority oversampling technique (SMOTE) has shown its improvement in classifying minority class by generating synthetic minority instances. However, in some scenarios, SMOTE and its extensions will generate noise instances and thus causing the performance degradation. This is because of that they were developed based on kNN (k nearest neighbors), which cannot identify the class distributions between pairs of two minority instances. Furthermore, the number of synthetic instances is left to be discussed in this field of study. To conquer these issues, we propose a new algorithm here named Region-Impurity Synthetic Minority Oversampling Technique (RIOT). Specifically, a region radius, we locate neighbors for minority instances and whereby to identify the relatively hard-to-learn minority instances, by the class ratio within the region and selecting building the base of sample generation. Then, generating synthetic instances until the region is approximately balanced. In the experiment, the results revealed that RIOT can perform better than some SMOTE extensions with less synthetic instances in terms of several model performance indicators for twelve real-world datasets.
Pooling Operations in Deep Learning: From “Invariable” to “Variable”

Deep learning has become a research hotspot in multimedia, especially in the field of image processing. Pooling operation is an important operation in deep learning. Pooling operation can reduce the feature dimension, the number of parameters, the complexity of computation, and the complexity of time. With the development of deep learning models, pooling operation has made great progress. The main contributions of this paper on pooling operation are as follows: firstly, the steps of the pooling operation are summarized as the pooling domain, pooling kernel, step size, activation value, and response value. Secondly, the expression form of pooling operation is standardized. From the perspective of "invariable" to "variable," this paper analyzes the pooling domain and pooling kernel in the pooling operation. Pooling operation can be classified into four categories: invariable of pooling domain, variable of pooling domain, variable of pooling kernel, and the pooling of invariable "+" variable. Finally, the four types of pooling operation are summarized and discussed with their advantages and disadvantages. There is great significance to the research of pooling operations and the iterative updating of deep learning models.
FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining

Frequent pattern mining is an important class of knowledge discovery problems. It aims at finding out high-frequency items or structures (e.g., itemset, sequence) in a database, and plays an essential role in deriving other interesting patterns, like association rules. The traditional approach of gathering data to a central server and analyze is no longer viable due to the increasing awareness of user privacy and newly established laws on data protection. Previous privacy-preserving frequent pattern mining approaches only target a particular problem with great utility loss when handling complex structures. In this paper, we take the first initiative to propose a unified federated analytics framework (FedFPM) for a variety of frequent pattern mining problems, including item, itemset, and sequence mining. FedFPM achieves high data utility and guarantees local differential privacy without uploading raw data. Specifically, FedFPM adopts an interactive query-response approach between clients and a server. The server meticulously employs the Apriori property and the Hoeffding’s inequality to generates informed queries. The clients randomize their responses in the reduced space to realize local differential privacy. Experiments on three different frequent pattern mining tasks demonstrate that FedFPM achieves better performances than the state-of-the-art specialized benchmarks, with a much smaller computation overhead.