Reviewerly/exHarmony huggingface dataset:
A New Harris Hawk Whale Optimization Algorithm for Enhancing Neural Networks

The learning process of artificial neural-networks is considered as one of the burdensome challenges to the researchers. The major dilemma of training the neural networks is the nonlinear nature and unknown controlling parameters like weights and biases. Slow convergence and trap in local optima are demerits of training neural network algorithms. To overcome these demerits, this work proposes a hybrid of Harris hawk optimization with a whale optimization algorithm to train the neural network. Harris hawk is a metaheuristic evolutionary algorithm and is used here to optimize the weights and bias of neural networks. The efficacy of the proposed algorithm is assessed by evaluating it on different kinds of cancer datasets and other datasets like fraud, banking note authentication. The experimental results demonstrate that the proposed algorithm performs better than its contemporary counterparts.
Research on the Security Sharing Model of Power Grid Data Based on Federated Learning

In order to meet the needs of data availability and practicality in the power grid system to achieve data security and efficient sharing, this paper proposes a power grid data security sharing model based on federated learning, and through the differential privacy, secure multi-party computing, homomorphic encryption, function encryption Four security sharing methods are implemented to ensure the security and feasibility of grid data sharing.
Limits to Perception by Quantum Monitoring with Finite Efficiency

We formulate limits to perception under continuous quantum measurements by comparing the quantum states assigned by agents that have partial access to measurement outcomes. To this end, we provide bounds on the trace distance and the relative entropy between the assigned state and the actual state of the system. These bounds are expressed solely in terms of the purity and von Neumann entropy of the state assigned by the agent, and are shown to characterize how an agent's perception of the system is altered by access to additional information. We apply our results to Gaussian states and to the dynamics of a system embedded in an environment illustrated on a quantum Ising chain.
Multiparticle quantum interference in Bogoliubov bosonic transformations

We explore the multiparticle transition probabilities in Gaussian unitaries effected by a two-mode Bogoliubov bosonic transformation on the mode annihilation and creation operators. We show that the transition probabilities can be characterized by remarkably simple, yet unsuspected recurrence equations involving a linear combination of probabilities. The recurrence exhibits an interferometric suppression term---a negative probability---which generalizes the seminal Hong-Ou-Mandel effect to more than two indistinguishable photons impinging on a beam splitter of rational transmittance. Unexpectedly, interferences thus originate in this description from the cancellation of probabilities instead of amplitudes. Our framework, which builds on the generating function of the non-Gaussian matrix elements of Gaussian unitaries in Fock basis, is illustrated here for the most common passive and active linear coupling between two optical modes driven by a beam splitter or a parametric amplifier. Hence, it also allows us to predict unsuspected multiphoton interference effects in an optical amplifier of rational gain. In particular, we confirm the newly found two-photon interferometric suppression effect in an amplifier of gain 2 originating from timelike indistinguishability [Proc. Natl. Acad. Sci. 117, 33107 (2020)]. Overall, going beyond standard two-mode optical components, we expect our method will prove valuable for describing general quantum circuits involving Bogoliubov bosonic transformations.
Demystifying regular expression bugs

Regular expressions cause string-related bugs and open security vulnerabilities for DOS attacks. However, beyond ReDoS (Regular expression Denial of Service), little is known about the extent to which regular expression issues affect software development and how these issues are addressed in practice. We conduct an empirical study of 356 regex-related bugs from merged pull requests in Apache, Mozilla, Facebook, and Google GitHub repositories. We identify and classify the nature of the regular expression problems, the fixes, and the related changes in the test code. The most important findings in this paper are as follows: 1) incorrect regular expression semantics is the dominant root cause of regular expression bugs (165/356, 46.3%). The remaining root causes are incorrect API usage (9.3%) and other code issues that require regular expression changes in the fix (29.5%), 2) fixing regular expression bugs is nontrivial as it takes more time and more lines of code to fix them compared to the general pull requests, 3) most (51%) of the regex-related pull requests do not contain test code changes. Certain regex bug types (e.g., compile error, performance issues, regex representation) are less likely to include test code changes than others, and 4) the dominant type of test code changes in regex-related pull requests is test case addition (75%). The results of this study contribute to a broader understanding of the practical problems faced by developers when using, fixing, and testing regular expressions.
Design and realisation of residential property management information system based on browser/server mode

Abstract The data filling, mathematical calculations and statistical operations for property management are realised by a system development tool that adopts the browser/server (B/S) architecture, Java, as the development language and a framework that adopts Spring MVC mode; additionally, the MySQL database is used, the source code and database interaction process use the Mybatis framework and the front-end display uses the VUE.js framework.. Functional modules include the following elements: owner information, real estate information, engineering equipment, personnel management and lease management. The system has passed the functional test, if it has been in trial operation for 1 year in multiple residential communities and has the characteristics of simple operation, stable operation and strong scalability, among others.
Path Planning for Robot in Multi-dimensional Environment Based on Dynamic PRM Blended Potential Field

Probabilistic roadmap (PRM) can effectively solve the path planning problem in environment with high-dimension and complex constraints, but has limitations of low quality and efficiency of path planning in narrow channel and dynamic environment. In order to improve the applicability and dynamic characteristics of PRM, a path planning for mobile robot in Multi-dimensional environment based on dynamic PRM blended potential field is proposed in this paper. It contains three main parts: Firstly, the potential field is set in the workspace, then the number of adaptive sampling points and area-division are carried out according to the strength of the potential field. Secondly, the variable radius sampling strategy is designed for each area to make the distribution of sampling points more reasonable, after which the roadmap is built. Lastly, the changing potential field strategy is constructed to meet the change of the environment. Under the changing potential field force, the sampling point moves. The local reconstruction of the roadmap is carried out according to the change of the moving sampling points driven by the potential field force. By these strategies above, the connectivity and responsiveness of PRM in multi-dimensional environment is improved.
On the Multi-user Security of Short Schnorr Signatures with Preprocessing

The Schnorr signature scheme is an efficient digital signature scheme with short signature lengths, i.e., 4k-bit signatures for k bits of security. A Schnorr signature $$\sigma $$ over a group of size $$p\approx 2^{2k}$$ consists of a tuple (s, e), where $$e \in \{0,1\}^{2k}$$ is a hash output and $$s\in \mathbb {Z}_p$$ must be computed using the secret key. While the hash output e requires 2k bits to encode, Schnorr proposed that it might be possible to truncate the hash value without adversely impacting security. In this paper, we prove that short Schnorr signatures of length 3k bits provide k bits of multi-user security in the (Shoup's) generic group model and the programmable random oracle model. We further analyze the multi-user security of key-prefixed short Schnorr signatures against preprocessing attacks, showing that it is possible to obtain secure signatures of length $$3k + \log S + \log N$$ bits. Here, N denotes the number of users and S denotes the size of the hint generated by our preprocessing attacker, e.g., if $$S=2^{k/2}$$ , then we would obtain secure 3.75k-bit signatures for groups of up to $$N \le 2^{k/4}$$ users. Our techniques easily generalize to several other Fiat-Shamir-based signature schemes, allowing us to establish analogous results for Chaum-Pedersen signatures and Katz-Wang signatures. As a building block, we also analyze the 1-out-of-N discrete-log problem in the generic group model, with and without preprocessing.
Face Recognition Algorithm Comparison based on Backpropagation Neural Network

Abstract Face recognition is a hot research direction in the field of computer in recent years. Due to the constant update and progress of computer hardware technology and software algorithm, face recognition technology is becoming more and more perfect, and face recognition technology starts to appear in different fields. Because of the high security of face recognition, we can design a face recognition access control system based on backpropagation neural network. This paper mainly compare three different face recognition algorithms based on matlab and test the accuracy of these algorithms.
ResMem-Net: memory based deep CNN for image memorability estimation

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: "What makes an image memorable?". The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production.
Towards Graph Machine Learning for Smart Grid Knowledge Graphs in Industrial Scenarios

Knowledge Graphs (KGs) demonstrated promising application perspective in different scenarios, especially when combined with Graph Machine Learning (GML) techniques able to interpret and infer over facts. Given the natural network structures of Smart Grid equipment and the exponential growth of electric power data, Smart Grid Knowledge Graphs (SGKGs) provides unprecedented opportunities to manage massive power resources and provide intelligent applications. However, a single representation of the SGKGs is never sufficient to properly exploit GML techniques that leverage different aspects of the KG for various objectives. In this work, we provide a methodology to extract various significant views of the SGKG by iteratively applying a series of transformation to the description of the power network in the IEC CIM standard. Our implementation is based on a declarative approach to guarantee easier portability, and we deploy the transformations as a stateless microservice, facilitating modular integration with the rest of the Smart Grid Semantic Platform. Experimental evaluation on two real power distribution networks demonstrates the efficacy of our approach in highlighting important topological information, without discarding precious additional knowledge present in the SGKG.
Improved Census Transform Method for Semi-Global Matching Algorithm

In order to solve the problem that the original Census transform algorithm is easily affected by the gray value of the central pixel, and further improvement of the matching accuracy of SGM algorithm, this study proposes an improved Census transform algorithm based on the sequence template. It divides the pixel values in the Census window into sequential sequences from the outside to inside, and then constructs Census encoding templates within and between sequences according to predefined rules in this study, such that the Census value of the center pixel can contain more information within the window, and can further improve the matching accuracy of SGM. Experimental results show that the improved sequence template-based Census transform method can enhance average 16.26% matching accuracy of SGM algorithm, while preserving its efficiency.
Generation and grading of arduous MCQs using NLP and OMR detection using OpenCV

During the 21st century pandemic, it has become difficult for students and teachers to engage physically in the aftermath of the epidemic, the process of assessing and grading students has become cumbersome. Multiple Choice Questions (MCQs) have increasingly become a more popular method of assessing a person's knowledge, but the questions which are generated directly from the chapter provided are readily available on the internet. Thus évaluer, the proposed approach solves this problem by paraphrasing the text, and thus the questions, so as to make it difficult to look it up on the internet. Usage of OMR (Optical Mark Recognition) sheets or manual correction had been prominent in the pre-pandemics situation. The évaluer automates the process of generation of MCQs which are difficult to search on the internet, and speeds up the task of grading a candidate's OMR answer sheet.
Near-Infrared Colorization using Neural Networks for In-Cabin Enhanced Video Conferencing

Classical color pictures can not be captured in every scenario, due to the light condition limitations, especially when driving during the night. In those situations, cameras that capture the Near-Infrared spectrum of frequencies prevail. However, pictures from this domain have numerous differences to the ones in the visible spectrum. Therefore, processing Near-Infrared images is a task that requires extensive consideration. In this paper, a method for Near-Infrared image segmentation using artificial neural networks is proposed, together with a colorization algorithm and a novel pipeline for obtaining real-time realistic RGB images, with virtual background for in-cabin enhanced video conferencing.
Adversarial Example Attack on Electric Power Network Security Situation Awareness

More and more security situation awareness strategies in power network use deep learning as the data processing method. However, the endogenous security problem of deep learning poses a challenge to the robustness required for power network security situation awareness, and gives attackers an opportunity to add small perturbation to the network and fail the security situation awareness strategy. In this paper, we build the deep neural networks for the power network dataset. We verify a variety of adversarial example attack methods against deep neural networks, and tested the success rate of attacks on different types of networks to compare the difference of attack methods and target model. We can see from the experiment results that adversarial example attacks can have a great impact on the power network security situation awareness and pose a threat to the power network.
Operational Effectiveness Evaluation of Space-based Information System based on Improved AHP-BPNN Model

In view of the characteristics of complex influencing factors and coupling interaction of various combat capabilities in the effectiveness evaluation of space- based information system (SIS), the effectiveness evaluation method of SIS based on improved AHP-BP neural network was proposed, which was mapped to multi-attribute decision making problem. The weight of experts was determined from the objective perspective to improve the traditional AHP and calculate the relevant influencing factors among experts. The comprehensive evaluation value is obtained according to the evaluation matrix, and the comprehensive weight of the index is obtained by the weighted average of the expert weight and the index weight. The evaluation data obtained from improved AHP and combat simulation data are used as prior samples to train BPNN to obtain the effectiveness evaluation model. Finally, sample data were obtained through MATLAB and STK co-simulation to evaluate the operational effectiveness of SIS and verify the effectiveness of the model.
Analyzing Sequences of Airspace States to Detect Anomalous Traffic Conditions

The ADS-B system serves as a replacement for the current radar-based air traffic control systems. Although much effort and resources have been invested in designing and developing the ADS-B system, it is well known for its lack of security mechanisms. Previously suggested approaches for securing the ADS-B system are considered impractical because of the cost and time involved in modifying the system, and given the fact that it is already deployed in most aircraft and ground stations worldwide. In this article, we propose a software-based security solution for detecting anomalous traffic conditions, which does not require any modification of the current system architecture or the addition of external sensors. In order to identify nonlegitimate ADS-B messages, our approach utilizes a stacked-LSTM encoder–decoder model that learns the flight patterns of aircraft in a monitored aerial region. We evaluated our model against common attack patterns injected into six datasets containing real ADS-B data, and we compared our proposed model with commonly used online, unsupervised models. The results of the experiments showed that our method is able to accurately and efficiently detect all of the injected attacks. Moreover, we examined our model’s performance on a real flight that deviated from its planned route and confirmed that our method was capable of accurately detecting the anomaly.
A Comparative Analysis of Speckle Noise Removal in SAR Images

The synthetic aperture radar images have a granular disturbance noise know as Speckle. The speckle is also called as a multiplicative noise. Over last few decades, various filters like lee, frost, wiener, mean and median filters are used and claimed to be efficient to reduce this granular noise present in SAR images. This process of removing speckles is also known as despeckling. The aim of this paper is to make a comparative review of despeckling methods. These methods will be used to highlight the trends and many approaches which changed over the years. This paper has discussed the technical aspects of the different filters and summarized it to use to remove the speckle in SAR images. Quantitative and qualitative parameters like mean, variance, edge saving index in horizontal & vertical, target to clutter ratio, equivalent number of looks have been analyzed and it concludes a method which uses different window sizes to reduce speckle in SAR images, which has efficient noise removal capabilities as compared with traditional methods like adaptive and non-adaptive filtering. It can extends to use of various machine learning algorithms to optimize the result towards betterment of different performance parameters.
Real-time personnel counting of indoor area division based on improved YOLOV4-Tiny

With the growth of building energy consumption, determining how to effectively reduce energy waste has become a matter of concern. We believe that by acquiring the basic data for intelligent regulation such as the location and number of personnel, we can achieve the intelligent adjustment of indoor energy-using equipment and ultimately reduce energy consumption. First, this study describes the division of an indoor scene by space coordinate transformation. Second, considering the complex indoor scenes, the main module of the backbone feature network is improved based on YOLOV4-Tiny, making the algorithm more suitable for people detection in complex indoor environments. Finally, the improved algorithm adopted in this study is verified in the indoor real-time monitoring scene. The experimental results show that the improved network's ability to detect people in complex indoor scenes is improved. The overall average accuracy is increased by 18.15%, and the detection speed reaches about 73 Frames Per Second (FPS).
Human Performance Modeling with Deep Learning

Predicting human performance in interaction tasks allows designers or developers to understand the expected performance of a target interface without actually testing it with real users. In this chapter, we are going to discuss how deep learning methods can be used to aid human performance prediction in the context of HCI. Particularly, we are going to look at three case studies. In the first case study, we discuss deep models for goal-driven human visual search on arbitrary web pages. In the second study, we show that deep learning models could successfully capture human learning effects from repetitive interaction with vertical menus. In the third case study, we describe how deep models can be combined with analytical understanding to capture high-level interaction strategies and low-level behaviors in touchscreen grid interfaces on mobile devices. In all these studies, we show that deep learning provides great capacity for modeling complex interaction behaviors, which would be extremely difficult for traditional heuristic-based models. Furthermore, we showcase different ways to analyze a learned deep model to obtain better model interpretability, and understanding of human behaviors to advance the science.
BlockCert Higher Education with Public Key Infrastructure in Indonesia

Certificates of education and the issuance of validation of academic degrees in the preparatory structure in Indonesia are currently occurring in a semi or completely non-electronic manner. Proper digitization of the system can make it safer and reduce relationships with respect to document authentication, saving endlessly and working. Due to new developments in cheating and losing records, it is heard that this cycle has finally become clearer and stronger for each detailed assembly. This article presents advice and execution for digitization of degree support and academic credit for high-level training in the Indonesia guidance system. A clear model is subject to the proposed Blockchain, in which high-level training associations register their understudies and educational credits in the record chain using Indonesia's Key Public Infrastructure to describe tribal chiefs. This information, related to prudent arrangements, enables the issuance of reliable and decentralized degree support through chronic database authentication and trading arrangements using intelligent settings. At the heart of the convergence of this article is a demonstration of the investigation that supports this suggestion.