Reviewerly/exHarmony huggingface dataset:
Scene Matching Method for Children’s Psychological Distress Based on Deep Learning Algorithm

In the process of children’s psychological development, various levels of psychological distress often occur, such as attention problems, emotional problems, adaptation problems, language problems, and motor coordination problems; these problems have seriously affected children’s healthy growth. Scene matching in the treatment of psychological distress can prompt children to change from a third-person perspective to a first-person perspective and shorten the distance between scene contents and child’s perceptual experience. As a part of machine learning, deep learning can perform mapping transformations in huge data, process huge data with the help of complex models, and extract multilayer features of scene information. Based on the summary and analysis of previous research works, this paper expounded the research status and significance of the scene matching method for children’s psychological distress, elaborated the development background, current status, and future challenges of deep learning algorithm, introduced the methods and principles of depth spatiotemporal feature extraction algorithm and dynamic scene understanding algorithm, constructed a scene matching model for children’s psychological distress based on deep learning algorithm, analyzed the scene feature extraction and matching function construction of children’s psychological distress, proposed a scene matching method for children’s psychological distress based on deep learning algorithm, performed scene feature matching and information processing of children’s psychological distress, and finally conduced a simulation experiment and analyzed its results. The results show that the deep learning algorithm can have a deep and abstract mining on the characteristics of children’s psychological distress scenes and obtain a large amount of more representative characteristic information through training on large-scale data, thereby improving the accuracy of classification and matching of children’s psychological distress scenes. The study results of this paper provide a reference for further researches on the scene matching method for children’s psychological distress based on deep learning algorithm.
Invoice Factoring Registration Based on a Public Blockchain

Invoice factoring is a very useful tool for developing businesses that face liquidity problems. The main property that a factoring system needs to fulfill is to prevent an invoice from being factored twice. In order to prevent double factoring, many factoring ecosystems use one or several centralized entities to register factoring agreements. However, this puts a lot of power in the hands of these centralized entities and makes it difficult for users to dispute situations in which factoring data is unavailable, wrongly recorded or manipulated by negligence or on purpose. In this article, we propose an architecture for invoice factoring registration based on a public blockchain. To solve the aforementioned drawbacks, we replace the trusted third parties for factoring registration with a smart contract. Using a smart contract, we record digital evidence of the terms and conditions of factoring agreements in explicit detail, allowing auditability and dispute resolution. Relevant information is highly available on the blockchain while its privacy is protected. The registration is optimal, since it needs only one blockchain transaction and one key-value storage per invoice factoring.
Efficient Software Implementation of the SIKE Protocol Using a New Data Representation

Thanks to relatively small public and secret keys, the Supersingular Isogeny Key Encapsulation (SIKE) protocol made it into the third evaluation round of the post-quantum standardization project of the National Institute of Standards and Technology (NIST). Even though a large body of research has been devoted to the efficient implementation of SIKE, its latency is still undesirably long for many real-world applications. Most existing implementations of the SIKE protocol use the Montgomery representation for the underlying field arithmetic since the corresponding reduction algorithm is considered the fastest method for performing multiple-precision modular reduction. In this paper, we propose a new data representation for supersingular isogeny-based Elliptic-Curve Cryptography (ECC), of which SIKE is a sub-class. This new representation enables significantly faster implementations of modular reduction than the Montgomery reduction, and also other finite-field arithmetic operations used in ECC can benefit from our data representation. We implemented all arithmetic operations in C using the proposed representation such that they have constant execution time and integrated them to the latest version of the SIKE software library. Using four different parameters sets, we benchmarked our design and the optimized generic implementation on a 2.6 GHz Intel Xeon E5-2690 processor. Our results show that, for the prime of SIKEp751, the proposed reduction algorithm is approximately 2.61 times faster than the currently best implementation of Montgomery reduction, and our representation also enables significantly better timings for other finite-field operations. Due to these improvements, we were able to achieve a speed-up by a factor of about 1.65, 2.03, 1.61, and 1.48 for SIKEp751, SIKEp610, SIKEp503, and SIKEp434, respectively, compared to state-of-the-art generic implementations.
An IoT Attribute-Based Security Framework for Topic-Based Publish/Subscribe Systems

Publish/subscribe is a widely used paradigm in the Internet of Things (IoT).It allows a loose coupling between data producers and data consumers using a network of interconnected brokers.However, sensitive data could be exposed if a broker is compromised or if the broker itself is curious about the information that is exchanged.In this paper, we present a complete security framework for topic-based publish/subscribe systems to ensure both security and privacy at the broker level, going beyond the naive encryption of information while keeping the loose coupling between publishers and subscribers.Furthermore, the proposed solution enables user revocation at the broker level; i.e. a revoked user can no longer subscribe to published data.To achieve that, we propose a unified solution relying on attribute-based cryptography with: (1) Attribute-Based Encryption (ABE) for data encryption; (2) a new construction of Attribute-Based Keyword Search (ABKS) to allow the broker to perform an encrypted matching that enforces privacy; and (3) an Attribute-Based Signature (ABS) to enforce the data authentication.
Efficient Frequency Domain Feature Extraction Model using EPS and LDA for Human Activity Recognition

Activity identification based on machine learning for human computing aims to understand or capture the state of human behavior, its environment, and integrate user by exploiting distinct types of sensors to give adjustment to the exogenous computing system. The ascent of universal computing systems requires our environment a solid requirement for novel methodologies of Human Computer Interaction (HCI). The recognition of human activities, commonly known as HAR can play a vital task in this regard. HAR has an appealing use in the health-care system and monitoring of Daily Living Activities (DLA) of elderly people by offering the input for the development of more interactive and cognitive environments. This paper is presenting a model for the recognition of Human Activities. In this proposed model, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal, and the Linear Discriminant Analysis (LDA) is used as a dimensionality reduction procedure to extract the discriminant features for human daily activity recognition. After completing EPS feature extraction techniques, LDA is performed on those extracted spectra for extracting features using the dimension reduction technique. Finally, the discriminant vocabulary vector is trained by the Multiclass Support Vector Machine (MCSVM) to classify human activities. For validating the proposed scheme, UCI-HAR datasets have been implemented which demonstrates higher recognition accuracy which has been acknowledged.
Feature Descriptors for Face Recognition

Human face has lot of information about identity as well as emotional status of the individual. Face recognition for authentication purpose is a challenging as well as an interesting problem. These problems have great impacts on crucial applications in several areas like identification, banking, law enforcement, security system access, and personal identification etc. This paper focuses on to provide information about different finding face descriptors for an image so that it can be used for classification purpose for various machines learning algorithm. Different types of finding descriptors are divided into two main types. Heuristic and Local Binary Pattern. PCA and LDA works on principal component and Fisher Discriminant Criterion for facial images.
Differential Privacy Stochastic Gradient Descent with Adaptive Privacy Budget Allocation

The Stochastic gradient descent algorithm (SGD) is a classical algorithm for model optimization in machine learning. Introducing a differential privacy model to avoid privacy leakages in the optimization iteration process can achieve the balance of training accuracy and data availability. In addition, a fixed number of iterations are chosen in a conventional implementation scheme. At each iteration, parameters are updated with a noisy gradient. However, the privacy budget is mostly split evenly to each iteration without taking into account the difference in the privacy leakage risk under optimal processing. In this paper, we improve the SGD-based algorithms by appropriately allocating the privacy budget for each iteration. Intuitively, the gradient value is inversely proportional to the number of iterations. The closer the parameter is to its optimal objective value, the smaller the gradient is, and hence the gradients need to be measured more accurately. We propose an adaptive “noise reduction” algorithm that can be applied to private SGD-based empirical risk minimization (ERM) algorithms, meets the accuracy constraint simultaneously. We apply our approach to the backpropagation (BP) neural network. In the experiment, we show and validate that the proposed noise parameter configuration method provides sufficient privacy protection and improves the accuracy of data utility.
Inter-Structure and Intra-Structure Similarity of Use Case Diagram using Greedy Graph Edit Distance

Use case diagram is one of the behaviour diagrams in UML diagrams. It models the basic needs of users and how users and systems interact to achieve them. Each use case diagram forms a structure which abides a set of standard notations and grammar. Therefore, we can measure the structural similarity between the two use case diagrams. Measuring the similarity of two use case diagram structures can help in the process of reusing diagrams, detecting clones, and assessing learning results. This study proposes an advanced method to measure the structural similarity between the two use case diagrams. Each use case diagram is translated into a graph. The graph represents the inter-structure and intra-structure of a diagram. The graph inter-structure models the interaction between the main use cases and their actors. In comparison, the graph intra-structure models the detailed structure descriptions of every main use case. The similarity between the two represented graph calculates using Greedy Graph Edit Distance. Our experimental result shows that the proposed graph can measure the similarity of the use case structural diagram. The Greedy Graph Edit Distance also can calculate the similarity other UML diagrams for the specific purpose.
State-of-the-Art Review of Deep Learning for Medical Image Analysis

Medical image analysis involves solving medical issues by the analysis of images fetched by medical practice. Main objective is to fetch the effective and efficient data in order to perform diagnosis in a better way. Improvements in the area of deep learning especially convolutional neural networks have become the choice of researchers for the medical image analysis field. Deep convolutional neural networks are mostly applied as compared to other available deep learning methods for the medical image analysis tasks. Deep learning and pattern recognition tools are utilized broadly in medical image analysis. In this paper a concise discussion of different techniques that are being deployed using deep learning for medical imaging such as classification, detection, & segmentation is investigated.
Robustness Contracts for Scalable Verification of Neural Network-Enabled Cyber-Physical Systems

The proliferation of artificial intelligence based systems in all walks of life raises concerns about their safety and robustness, especially for cyber-physical systems including multiple machine learning components. In this paper, we introduce robustness contracts as a framework for compositional specification and reasoning about the robustness of cyber-physical systems based on neural network (NN) components. Robustness contracts can encompass and generalize a variety of notions of robustness which were previously proposed in the literature. They can seamlessly apply to NN-based perception as well as deep reinforcement learning (RL)-enabled control applications. We present a sound and complete algorithm that can efficiently verify the satisfaction of a class of robustness contracts on NNs by leveraging notions from Lagrangian duality to identify system configurations that violate the contracts. We illustrate the effectiveness of our approach on the verification of NN-based perception systems and deep RL-based control systems.
Usability Analysis of the Concordia Tool Applying Novel Concordance Searching

This paper describes a novel tool for concordance searching, named Concordia. It combines the capabilities of standard concordance searchers with the usability of a translation memory. The tool is described in detail with regard to main applied methods and differences when compared to already existing CAT tools. Concordia uses three data structures, i.e. hashed index, markers array and suffix array, which are loaded into memory to enable fast lookups according to fragments that cover a search pattern. In this new concordancing system, sentences are stored in the index and marked with additional information, such as unique ids, which are then retrieved by the Concordia search algorithm. The usability of the new tool is analysed in an experiment involving two English-Croatian human translation tasks. The paper presents a detailed scheme and methodology of the conducted experiment. Furthermore, an analysis of the experiment results is presented, with special emphasis on the users’ attitudes towards the usefulness and functionalities of Concordia.
Multi-line laser projection 3D measurement simulation system technology

Based on the research of stripe projection measurement technology, this paper presents a 3D simulation system of divergent multi-line laser projection. Firstly, calculate the coordinates of the reference plane points, backlight tracing method combined with variable step length iteration method was used to calculate the intersection point of line and surface. Then the intersection coordinates are converted from the world coordinate system to the laser coordinate system. Finally, light intensity calculation and light intensity modulation calculation are carried out to obtain the simulation image of divergent multi-line laser stripe projection. The characteristic of this system is that the simulated laser is a divergent multi-line laser and the stripe generation system is highly integrated. The number of light stripe of multi-line laser can be adjusted arbitrarily, and the light intensity distribution of each light stripe is calculated according to the Gaussian distribution model. In the simulation system, there is no need to ensure the intersection point between the optical axis of the camera and the optical axis of the laser. The system simulation method can provide convenience for the further error analysis of the later measurement system and the optimization of the system structure. Experiments show that the system can simulate the 3d measurement system of divergent multi-line laser projection under computer environment. Multi-line laser projection simulation can be carried out for various shapes of objects under different parameters. The accuracy analysis show that the simulation image has high accuracy. The distribution of light intensity accords with the actual situation.
Convergence Analysis of Whale Optimization Algorithm

Abstract The whale optimization algorithm (WOA) has been widely used in different applications. It has simple control parameters and novel population updating mechanism. However, there is few theoretical analysis of WOA and the convergence property of WOA is ambiguity. This paper analyzes the convergence property of WOA by using the Markov chain of the stochastic process theory. The Markov chain model of the WOA algorithm is established. The one step transition probabilities and convergence properties of different population updating mechanisms in WOA are given. It’s proved that the convergence property of WOA is determined by its shrinking encircling mechanism. Finally, three algorithms with different population updating mechanisms are tested with thirteen benchmark functions on accuracy and convergence speed. The simulation results on benchmark functions verify the validity of the theoretical analysis of WOA.
The Data Protection of Intelligent Connected Vehicles Cloud Control Framework Using Fully Homomorphic Encryption

With the development of Intelligent Connected Vehicles (ICVs), Cloud Control Platform is becoming an important part to compute driving strategies. However, when strategies are put to cloud, some of vehicle manufacturers' private data must be sent to the cloud, like Engine Map, which are core data for vehicle manufactures. How to protect these data has been the largest obstacle for the ICVs. Thus, this paper proposes a new framework in which Fully Homomorphic Encryption (FHE) and Blockchain technology are combined to compute encryption data on the cloud and record trails of cloud request. In this framework, private data can be protected, the scope of data usage will be limited, and at the same time, the execution of specific type of computations with encryption data on the cloud are fulfilled. In the end, with the help of Simple Encrypted Arithmetic Library (SEAL) developed by Microsoft Research, and IBM blockchain framework Hyperledger Fabric, this framework is verified to be feasible to build a trustworthy ICVs cloud computing system.
Artificial Intelligence applications in renewable energy systems

In this chapter, the basic principles of Artificial Neural Network (ANN) and Fuzzy Logic (FL) are presented. Procedures to use Particle Swarm Algorithm, Salp Swarm Algorithm, Grey Wolf Algorithm, Genetic Algorithm and Simulated Annealing are demonstrated and presented. Numerical examples about these methods are given in detail.
Emergence of Blockchain Technology

The Internet of Things (IoT) is dominating every aspect of life. Smart homes, security systems, industrial automation, smart and intelligent power grids, etc. are important implementations of the IoT. It is an impactful addition to information and communication technology. As the IoT rises to the top, several issues emerge. Security, privacy, and vulnerability issues need to be addressed, to secure future IoT advancements. Blockchain technology is another tremendous addition to modern technology. Its decentralized architecture, distributed ledger, and cryptographic protection make this technology a potential integration element with other technologies, such as the IoT. Blockchain-enabled solutions for various IoT issues are the main aspect of this work. This chapter presents blockchain fundamentals and principles in detail, including blockchain-based IoT security solutions, with a comprehensive overview of the IoT architecture and its potential categorized security issues. The main motivation is to integrate blockchain in leveraging the issues of the IoT, as well as point out future research trends.
Multi-typed Objects Multi-view Multi-instance Multi-label Learning

Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels. M4L is more general and powerful than the typical Multi-view Multi-instance Multi-label Learning (M3L), which only accommodates single-typed bags and lacks the power to jointly model the naturally interconnected multi-typed objects in the physical world. To combat with this novel and challenging learning task, we develop a joint matrix factorization based solution (M4L-JMF). Particularly, M4L-JMF firstly encodes the diverse attributes and multiple inter(intra)-associations among multi-typed bags into respective data matrices, and then jointly factorizes these matrices into low-rank ones to explore the composite latent representation of each bag and its instances (if any). In addition, it incorporates a dispatch and aggregation term to distribute the labels of bags to individual instances and reversely aggregate the labels of instances to their affiliated bags in a coherent manner. Experimental results on benchmark datasets show that M4L-JMF achieves significantly better results than simple adaptions of existing M3L solutions on this novel problem.
Detecting and mitigating cyberattacks using software defined networks for integrated clinical environments

Abstract The evolution of integrated clinical environments (ICE) and the future generations of mobile networks brings to reality the hospitals of the future and their innovative clinical scenarios. The mobile edge computing paradigm together with network function virtualization techniques and the software-defined networking paradigm enable self-management, adaptability, and security of medical devices and data management processes making up clinical environments. However, the logical centralized approach of the SDN control plane and its protocols introduce new vulnerabilities which affect the security of the network infrastructure and the patients’ safety. The paper at hand proposes an SDN/NFV-based architecture for the mobile edge computing infrastructure to detect and mitigate cybersecurity attacks exploiting SDN vulnerabilities of ICE in real time and on-demand. A motivating example and experiments presented in this paper demonstrate the feasibility of of the proposed architecture in a realistic clinical scenario.