Reviewerly/exHarmony huggingface dataset:
Using photometric redshift data to improve the detection of galactic filaments with the Bisous model

Filament finders are limited, among other things, by the abundance of spectroscopic redshift data. As there are proportionally more photometric redshift data than spectroscopic, we aim to use photometric data to improve and expand the areas where we can detect the large-scale structure of the Universe. We present a proof of concept, showing that the Bisous filament finder can improve the detected filamentary network with photometric redshift data. We created mock data from the MultiDark-Galaxies catalogue. Galaxies with spectroscopic redshifts were given exact positions from the simulation. Galaxies with photometric redshifts were given uncertainties along one coordinate. The errors were generated with different Gaussian distributions for different samples. There are three different types of samples: spectroscopic only, photometric only, and mixed samples of galaxies with photometric and spectroscopic redshifts. In photometric-only samples, the larger the uncertainty for photometric redshifts, the fewer filaments are detected, and the filaments strongly align along the line of sight. Using mixed samples improves the number of filaments detected and decreases the alignment bias of those filaments. The results are compared against the full spectroscopic sample. The recall for photometric-only samples depends heavily on the size of uncertainty and dropped close to 20%; for mixed samples, the recall stayed between 40% and 80%. The false discovery rate stayed below 5% in every sample tested in this work. Mixed samples showed better results than corresponding photometric-only or spectroscopic-only samples for every uncertainty size and number of spectroscopic galaxies in mixed samples. Mixed samples of galaxies with photometric and spectroscopic redshifts help us to improve and extend the large-scale structure further than possible with only spectroscopic samples.
The design of the least square method on sales of admission tickets to Madura tourism in forecasting cases

The prediction system that has been made is a system that can help provide forecasts of tourism ticket sales in the future. The system is web-based so that it can be easier to use. The importance of analysis and modeling in problems in the development of prediction systems, To provide references and instructions to developers in making the system. This research proposes the modeling stages in designing the system, including problem identification, data collection, literature study, analysis, design, development, and evaluation. It is done to collect information related to the problem with interview and observation techniques, then collect tourism data on all tourism in Bangkalan district and collect literature studies related to the least square method. The error test on the least fair method is carried out using the error test level method (error) using MAD and MAPE. Tests are carried out to find out the nominal error value, so that it can be used as a value recommendation in forecasting for the next period. The calculation result of MAD from this research is 402.16, while the calculation result from MAPE is 10.54. The result, the breakdown of system requirements at the analysis stage and the design system using a use case diagram. And finally, a web-based prediction system has been built based on the locations that have been passed.
Query expansion using pseudo relevance feedback based on the bahasa version of the wikipedia dataset

The work of finding documents that are relevant to a user's query on an information retrieval system (IRS) is a very interesting study. The relevance of the list of documents returned by the IRS is influenced by the accuracy of the method of calculating the similarity between documents and the determination of the keywords. Many users are difficult to describe their information needs in words. Sometimes the user enters only one or two words that do not reflect the domain of information required. This results in a list of documents were very less relevant to the user's needs. The approach to improve the list of words in the user's query to make it more representative is called Query Expansion. One technique that can be used to expand a query is Pseudo Relevance Feedback. This paper describes the results of research that has been carried out to expand Query using Pseudo Relevance Feedback on an IRS based on the Indonesian version of the Wikipedia dataset, totaling about 450 thousand documents. Calculation of the similarity between the query and the list of tourism news documents uses the cosine similarity, while the weighting scheme for each term uses TF-IDF. The test results show that the pseudo-relevance feedback decreases the precision of the IRS up to 30%. This is due to the failure of the chosen approach to finding the right words to expand the original query. The abstract of articles in Wikipedia is general and is not limited to the tourism domain. The selection of the expansion base dataset is greatly determined by the new query quality and datasets from the same domain are recommended. It is highly recommended that the QE reference dataset is domain specific and filtered before being used as a QE basis.
A SM2 based efficient and lightweight batch verification approach for IC cards

With the rapid development of the Internet of Things (IoT), IoT security has become more and more important. A large number of IoT tasks use digital signatures to ensure authenticity, integrity, and non-repudiation. However, verifying lots of signatures reduces the performance of the IoT system. Batch verification can verify multiple digital signatures at the same time, which reduces the computational overhead and verification time compared with consecutive respective verification. Thus it is suitable for IoT systems. SM2(Shangyong Mima 2) digital signature algorithm is a public key cryptographic algorithm based on elliptic curve cryptography(ECC), and is widely used in IoT applications. We propose SM2Verifi in this paper, a batch verification approach for SM2 digital signatures. SM2Verifi calculates fewer scalar multiplications than consecutive respective verification and uses the KGLP algorithm and the multi-parameter inversion algorithm to speed up scalar multiplications and inversions. Therefore, SM2Verifi greatly reduces the computational overhead and verification time. SM2Verifi generates a set of random integers through the addition chain seminumeric computation to resist possible forged signatures and determines whether to accept a batch of signatures by calculating congruence equations and resultants. Our extensive experiments on NXP smart cards show that SM2Verifi can significantly improve verification efficiency. Specifically, without randomization, batch verification of nine signatures belonging to one signer is 6.41 times faster than respective verification. Considering the verification performance, security, and hardware overhead, SM2Verifi achieves the comprehensive optimum when verifying seven signatures signed either by one signer or seven signers with 32-bit randomization.
Quantum kernel logistic regression based Newton method

Kernel logistic regression (KLR) is a powerful machine learning model for classification, which has wide applications in pattern recognition. However, classical KLR algorithm is computationally expensive when dealing with big data sets. Since quantum technique exhibits a computational advantages in tackling machine learning problems, we devise a quantum KLR algorithm. Specifically, our algorithm makes use of quantum inner product estimation to prepare the desired state and then performs quantum singular value transformation based on the block-encoding framework to obtain the optimal model parameters. It is theoretically demonstrated that our algorithm has an exponential speedup over its classical counterpart.
Generalized cuckoo hashing with a stash, revisited

Cuckoo hashing is a common hashing technique, guaranteeing constant-time lookups in the worst case. Adding a stash was proposed by Kirsch, Mitzenmacher, and Wieder at SICOMP 2010, as a way to reduce the probability of failure (i.e., the probability that a valid Cuckoo assignment fails to exist). It has since become a standard technique in areas such as cryptography, where a negligible probability of failure is often required. We focus on an extension of Cuckoo hashing that allows multiple items per bucket, which improves the load factor. That extension was also analyzed by Kirsch et al. in the presence of a stash. In particular, letting d be the number of items per bucket, and s be the stash size, Kirsch et al. showed that, for constant d and s, the failure probability is O(n(s+1)(1−d)). In this paper, we first report a bug in the analysis by Kirsch et al. by showing a counter-example leading to an asymptotically-larger probability of failure (Ω(n−d−s−1)). Then we provide a general analysis and upper bound of the failure probability for (almost) arbitrary d and s, instead of just constant, which is useful for applications in cryptography. We finally deduce from the general analysis a tight bound Θ(n−d−s) for the probability of failure, for constants d and s.
Improvement of face recognition performance using a new hybrid subspace classifier

Multiple classification systems play an important role in increasing recognition performance, especially when using heterogeneous classifiers that effectively improve performance. In this study, a new hybrid classifier was designed using heterogeneous Fisherface and discriminative common vector approach (DCVA) subspace recognition methods, which gave successful results in face recognition. While the classification process of DCVA is based on the common properties of signals belonging to the classes, the classification process of Fisherface is based on the different properties of signals. To create a hybrid classifier, called the Hybrid DCVA-Fisherface, the classifiers' decision rules were combined using the Minimum Proportional Score Algorithm and Recognition Update Algorithm. In addition to the proposed subspace classifiers, convolutional neural networks, Transform learning-Alexnet, Alexnet + SVM, and Alexnet + KNN were used for classification. Studies were conducted using the ORL, YALE, Extended YALE B and Face Research Lab London Set (FRLL). To better examine the efficiency of the algorithms, tests were also carried out by downsampling the images. When the experimental results were analysed, the proposed hybrid classifier gave higher recognition rates than all classifiers for ORL, YALE, and Extended YALE B. However, deep learning methods generally achieved better recognition performance than subspace classifiers for the FRLL database, which has more classes than other databases.
Improved deep depth estimation for environments with sparse visual cues

Abstract Most deep learning-based depth estimation models that learn scene structure self-supervised from monocular video base their estimation on visual cues such as vanishing points. In the established depth estimation benchmarks depicting, for example, street navigation or indoor offices, these cues can be found consistently, which enables neural networks to predict depth maps from single images. In this work, we are addressing the challenge of depth estimation from a real-world bird’s-eye perspective in an industry environment which contains, conditioned by its special geometry, a minimal amount of visual cues and, hence, requires incorporation of the temporal domain for structure from motion estimation. To enable the system to incorporate structure from motion from pixel translation when facing context-sparse, i.e., visual cue sparse, scenery, we propose a novel architecture built upon the structure from motion learner, which uses temporal pairs of jointly unrotated and stacked images for depth prediction. In order to increase the overall performance and to avoid blurred depth edges that lie in between the edges of the two input images, we integrate a geometric consistency loss into our pipeline. We assess the model’s ability to learn structure from motion by introducing a novel industry dataset whose perspective, orthogonal to the floor, contains only minimal visual cues. Through the evaluation with ground truth depth, we show that our proposed method outperforms the state of the art in difficult context-sparse environments.
A Not-so-radical Rejoinder: Habituate Systems Thinking and Data (Science) Confession for Quality Enhancement

Publisher: New England Statistical Society, Journal: The New England Journal of Statistics in Data Science, Title: A Not-so-radical Rejoinder - Habituate Systems Thinking and Data (Science) Confession for Quality Enhancement, Authors: Xiao-Li Meng
Enhanced Belief Function-Based Decision Blending for Detecting Fault in Wireless Sensor Networks

In wireless sensor networks (WSN), various software and hardware issues can lead to various fault types. The issue can be found using many forms of fault detection. The diverse obstacles determine the distinct fault kinds and need to find out effective fault detection and problem-solving are required. This chapter discusses four main types of faults: gain fault, offset fault, stuck-at fault. In this work, the authors use the notion of decision blending to categorize the blending outcomes and to assess the accuracy in order to save energy and make better use of the available bandwidth for data transmission. Three performances are assessed by the decision blending function: detection accuracy (DA), sensitivity, and rate of error. Different methods, such as k-nearest neighbor (KNN), enhanced extreme learning machine (EELM), enhanced support vector machine (ESVM), and enhanced recurrent extreme learning machine, are used in the belief function approach (ERELM). Here, the authors applied decision blending approaches in WSNs to emulate these techniques for improving belief function.
IoT Security Enhancement System

The internet of things (IoT) is one of the new technologies based on new requirements and innovations. It is a set of equipment that consists of several sensor nodes and devices to make efficient communication. It is an embedded system that contains several sensor nodes, software, and exchanging equipment. There are several applications of IoT such as smart homes, health monitoring systems, smart factories, wearable health monitoring systems, logistic tracking, etc. Although, it has several applications and advantages, but it has also some limitations regarding data privacy, data security, data volume and data complexity, etc. Hence, it requires innovating and modeling of security of IoT based on different techniques. Therefore, in this chapter, a details review is discussed based on the fusion of edge computing and blockchain.
Toward Fully Functional Encrypted Databases

After acquiring sufficient knowledge about searchable encryption, we are now able to delve into the world of fully functional encrypted databases. First of all, as a real-world application, there are many other dimensions beyond the key functionality “search” that we have to pay attention to. For instance, how to support concurrent queries (i.e., handling multiple queries at the same time), how to deal with the failure caused by various accidental reasons (e.g., power interruptions, unstable network, I/O failures), and how to enforce constraints to guarantee data comply with certain rules, etc.
Introduction

As more and more data are being generated, the dilemma mentioned above becomes more challenging to handle. When data gets bigger, it tends to be more privacy-sensitive. Thus, from the data ownership point of view, it seems more reasonable to host such sensitive data in-house at on-premise servers. But bigger data also demands more computing capacities to work with. For many individual and enterprise data owners, resorting to off-premise cloud-based solutions would seemingly be a more affordable choice. So, the remaining question is: would it be possible for data owners to enjoy the benefits of using cloud infrastructures while always retaining the ownership of their important and sensitive data, as if it were stored at on-premise servers? As we’re going to lay out in this book, we hope to convince our audiences that there will be a firmly positive answer to this question. The technical pathway is promising, but not without challenges ahead.
Recent Advancements on Functionality and Performance

In this chapter, we will continue our journey to introduce recent advancements of searchable encryption on functionality and performance. We will first show how to construct a dynamic searchable symmetric encryption, i.e., supporting data insertions, deletions, and updates in Sect. 4.1. After that, we will extend our scope to other common functionalities, i.e., multi-user supports in Sect. 4.2 and Boolean queries over encrypted data in Sect. 4.3. Next, Sect. 4.4 will discuss some practical optimizations to SSE schemes to an appropriate depth. Finally, some selected in-depth and distinguished readings will be given in Sect. 4.5.
Security Impact of Leakage Profiles: Threats and Countermeasures

In Chap. 3 , we discussed the security definitions of searchable encryption. In short, an SE scheme is said to achieve adaptively semantic security with a leakage function $$\mathcal {L}(\cdot )$$ if the information revealed during its operation is bounded by $$\mathcal {L}(\cdot )$$ . Typically, allowed leakages include the size of encrypted data collection, whether a search token (trapdoor) has been repeated, which encrypted documents have been accessed, etc. In the extreme case, we can claim that a trivial plaintext solution is secure with $$\mathcal {L}(\mathrm {DB}, w_1, \dots , w_t)=(\mathrm {DB}, w_1, \dots , w_t)$$ , i.e., it leaks all plaintext data, though this leakage function is meaningless since it obviously reveals confidential data to the untrusted server.
Fundamental Cryptographic Algorithms and Technologies

In this chapter, we plan to introduce several fundamental cryptographic primitives that could be used to establish an encrypted search system. By briefly discussing these techniques’ properties and design intuitions, we hope to build the necessary background to introduce the book’s main focus on searchable/structured symmetric encryption schemes, their modern constructions, and the roadmap toward full-fledged encrypted databases in the following chapters. Moreover, we will cover the major limitations of these technologies from both security and efficiency perspectives.
Computer Vision and Mathematical Methods Used to Analyse Children’s Drawings of God(s)

Abstract In contrast to mainstream research methods in psychology, the project Children’s Drawings of Gods encompasses computer vision and mathematical methods to analyse the data (drawings and drawing annotations). The first part of the present work describes a set of methods designed to extract measures, namely features, directly from the drawings and from annotations of the images. Then, the dissimilarities between the drawings are computed based on particular features (such as the gravity centre of the smallest image unit, namely pixel, or the annotated position of god) and combined in order to measure numerically the differences between the drawings. In the second part, we conduct an exploratory data analysis based on these dissimilarities, including multidimensional scaling and clustering, in order to determine whether the chosen features permit us to distinguish the different strategies that the children used to draw god.
Theoretical Basis of Matched Signal Filtration

This chapter discusses the basic concepts and mathematical forms of describing the problem of matched filtering of signals used in radiolocation and in radionavigation.
Ground, Hiperbolic Radionavigation Systems

As mentioned before, considering hyperbolic radionavigation systems, the position lines can be described as the hyperbolaHyperbola branches, which are the geometric locus of the points.
Learning Data-Driven Propagation Mechanism for Graph Neural Network

A graph is a relational data structure suitable for representing non-Euclidean structured data. In recent years, graph neural networks (GNN) and their subsequent variants, which utilize deep neural networks to complete graph analysis and representation, have shown excellent performance in various application fields. However, the propagation mechanism of existing methods relies on hand-designed GNN layer connection architecture, which is prone to information redundancy and over-smoothing problems. To alleviate this problem, we propose a data-driven propagation mechanism to adaptively propagate information between layers. Specifically, we construct a bi-level optimization objective and use the gradient descent algorithm to learn the forward propagation architecture, which improves the efficiency of learning different layer combinations in multilayer networks. The experimental results of the model on seven benchmark datasets demonstrate the effectiveness of the proposed method. Furthermore, combining this data-driven propagation mechanism with models, such as Graph Attention Networks, can consistently improve the performance of these models.
Freedom of Expression in Interpersonal Interactions

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Who Gets Flagged? An Experiment on Censorship and Bias in Social Media Reporting

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Entanglement measures based on the complete information of reduced states

Quantum entanglement has been verified experimentally and applied in quantum computing, quantum sensing, and quantum networks. It is of great significance to find measures to characterize the quantum entanglement faithfully. In this work, by exploiting the Schmidt decomposition of bipartite states, we first establish a one-to-one correspondence between the characteristic polynomial of the reduced state of a bipartite pure state and the trace of the reduced state. We introduce a family of entanglement measures based on the eigenvalues of the reduced density matrices. Specific measures called informationally complete entanglement measures (ICEMs) are presented. It is demonstrated that such ICEMs can characterize better the entanglement than the existing well-known entanglement measures. The ICEMs also give rise to criteria of state transformations under local operation and classical communication. Moreover, it is shown that the ICEMs can be efficiently estimated on a quantum computer. The full separability, entanglement, and genuine multipartite entanglement can be detected faithfully on quantum devices.
Fictional Names and Co-Identification

This paper provides an account of co-identification with fictional names, the way in which a fictional name can be used to talk about the same fictional character on disparate occasions. I develop a version of the view that fictional characters are roles constituted by sets of properties that is couched within a dynamic understanding of fictional discourse. I argue that this view captures what is right about both so-called name-centric and information-centric approaches to co-identification with fictional names. I show how the dynamic view in addition accounts for a number of uses of fictional names, including fictional uses, assertoric uses, metafictional uses.