Reviewerly/exHarmony huggingface dataset:
Automatic ROI recognition and parameters selection for digital image correlation in measuring structures with complex shapes

Abstract For the digital image correlation (DIC) method, the measurement of specimens with complex shapes may encounter difficulties due to the time-consuming recognition of region of interest (ROI), and the indeterminate parameter selection caused by the non-uniform deformation. This paper proposes an automatic DIC for the measurement of structures with complex shapes. An automatic ROI segmentation is developed by combining a convolutional neural network and image morphology, so the boundary of the specimen can be acquired accurately and efficiently. In dealing with the non-uniform deformation, a strain-related automatic selection of DIC parameters is developed, in which the sampling intervals and the subset sizes at different areas can be automatically determined. Both results of the simulated experiment and real experiment show that, by combing the two approaches with segmentation-aided DIC, the proposed automatic DIC can characterize the complex deformation including the boundary of the structures effectively.
Analytics of User Behaviors on Twitter Using Machine Learning

Twitter is a leading social networking site when it comes down to topics such as politics, news, and trends around the globe. Another main reason for people to use Twitter is because they are able to share their emotions and feelings with others and form new relationships and views. With about 330 million users on Twitter (in 2020), it continues to rapidly grow, but at the same time, it is also losing users at a fast pace. In 2019, Twitter had 340 million users, but a year later, it lost 10 million of them. The goal of this chapter is to find the reasons of three questions. The first, to find the reason behind Twitter losing its users. The second, to see how a user changes behavior after usage of Twitter, and third, how a user's behavior changes when expanding his/her social circle on Twitter. For all of these questions, this chapter has designed a data set and executed experiments based on the authors' hypotheses. The results report the accuracies of each of these hypotheses.
Advanced Replicating Technology in Adventure Fiction

In the adventure fiction and Sci-Fi TV show The Orville (2017), a material synthesizer is a machine that can create food, drink, clothing, and so much more. While the device in the show has a small role, the implications of unrestricted material access have vast effects on the cast and real-world implications on our identities as well. The framework of this chapter focuses on a qualitative and case study analysis of literary and film-based narrative to examine the implications of material synthesis in The Orville. It examines the plot element and expounds upon the impact on unrestricted indulgence, potential corruption, and the ability to create more freedom both for the characters and the show itself. Upon this analysis, the chapter concludes that while the device is incredibly far from a practical achievement, it does lead us to analyze how current food generators and 3D printers have the potential for unrestricted powers that need to be examined and better understood in order to prevent misuse and exploitation.
Social Media Intelligence

This chapter aims at discussing how social media intelligence (SOCMINT) can be and has been applied to the field of criminal justice. SOCMINT is composed of a set of computer forensic techniques used for intelligence gathering on social media platforms. Through this chapter, readers will be able to better understand what SOCMINT is and how it may be helpful for criminal investigation and national security. Different aspects of SOCMINT are addressed, including application in criminal justice, intelligence gathering, monitoring, metadata, cyber profiling, social network analysis, tools, and privacy concerns. Further, the challenges and future research directions are discussed as well. This chapter is not meant to serve as a technical tutorial as the focus is on the concepts rather than the techniques.
A new image restoration model associated with special elliptic quaternionic least-squares solutions based on LabVIEW

In this paper, we take advantage of the elliptic complex matrix representation of elliptic quaternion matrices. Then we obtain the methods of the elliptic quaternionic least-squares solution, the pure elliptic quaternionic least-squares solution, and the real least-squares solution with the least norm of the elliptic quaternion matrix equation AX=B. We also apply the newly obtained method of the pure elliptic quaternionic least-squares solution with the least norm to the color image restoration based on the LabVIEW program. In this context, we propose a new image restoration model called “ELSI image restoration model” associated with elliptic quaternionic least-squares solutions.
Research on agricultural product quality traceability system based on blockchain technology

From the planting base to the consumer’s table, agricultural products must go through multiple links such as planting, processing, transportation, warehousing, and sales. The quality and safety of agricultural products have received extensive attention from all walks of life. Based on the block chain technology, this paper will build a traceability system for the quality and safety of agricultural products, refine the research objects, and design solutions from the aspects of overall structure, role authority, operating process, and functional modules according to the characteristics of planted agricultural products, so as to realize the whole process of agricultural product supply chain tracking, traceability to ensure the quality and safety of agricultural products.
Weighted distances in the Cairo pattern

The Cairo Pattern is an interesting tiling of the plane. It consists of pentagons which completely cover the plane without overlapping. The Cairo Pattern is the dual of a semi-regular grid. It is assumed that one can walk on the pentagons such that it is possible to step only two neighboring pentagons. Different types of steps have different weights. The distance of two pentagons is the length of the minimal path between them. All distances are based on a linear programming model. The main tool of the analysis is the simplex method of linear programming. The analysis also uses the special properties of the model of minimal path. All minimal paths are given explicitly. Therefore, the complexity of our final result is O(1) inspite of the fact that the general simplex method is thought to be exponential.
Collision avoidance and path planning for mobile robots based on state estimation approach

Planning a collision-free path while preserving processing time and minimizing cost function has been considered a significant challenge in developing an Autonomous Mobile Robot (AMR). Various optimization techniques for avoiding obstacles and path planning problems have been proposed recently. But, the computation time for executing these techniques is comparatively higher and has lesser accuracy. In this paper, the State Estimation Obstacle Avoidance (SEOA) algorithm has been proposed for estimating the position and velocity of both of the wheels of the AMR. Moreover, this algorithm has been also applied in path planning for reaching the destination point in minimum computational time. Five different positions of static obstacle are demonstrated in a real time static environment where the proposed SEOA algorithm has been compared with state-of-the-art path planning algorithms such as A* and VFH. The simulation results demonstrate that the proposed algorithm takes lesser computational time to generate the collision free path when compared to other mentioned algorithms.
Blockchain innovation and firm’s financial performance: patent analysis based on firm-level information

This research makes an initial attempt to analyse the effects of blockchain patents on a firm’s financial performance. Blockchain technology has received increased attention since bitcoin prices skyrocketed in 2016, and applications for blockchain patents rapidly increased thereafter. Prior studies have documented that blockchain innovation could add economic value to firms, but without robust empirical analyses. Therefore, this study provides the first empirical approach to determine the effect of blockchain patents on a firm’s financial performance. We find that a firm’s blockchain patents are positively associated with firm value and firm performance. Moreover, the association between a firm’s blockchain patents and firm value or performance is more pronounced in markets with higher competition. This study provides theoretical and practical implications. The empirical results of this study lend credence to the argument that blockchain patent contributes to real value creation. We also add to blockchain literature and extend the literature discussing environmental uncertainty. Moreover, this study suggests useful insights for firms that consider developing blockchain patents and implies the signalling effect of blockchain patent in the capital market.
Introduction to the special issue Experimental and Corpus-based Approaches to Ellipsis

Ellipsis has been, and continues to be, of both theoretical and empirical interest. It affects the syntax of phrases or clauses by stranding their various constituents but keeps the semantics of the stranded constituents identical to that of their non-elliptical counterparts. The theoretical value of ellipsis lies, therefore, in the relationship between meaning and form that it encodes, such that a complete propositional meaning is paired with what appears to be a syntactically incomplete form. This property of ellipsis has inspired researchers to probe, in particular, the syntax of ellipsis and the role the surrounding context plays in helping resolve ellipsis, as stranded constituents depend on the surrounding context for their interpretation. Among the constructions that have attracted considerable attention over the years are clausal ellipsis (e.g. sluicing (Example 1), sprouting (Example 2), stripping (Example 3), and fragments (Example 4)), pseudogapping (Example 5), gapping (Example 6), and Right Node Raising (RNR) (Example 7), all of which are discussed in the contributions to this special issue.
Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions

The problem of causal discovery is especially challenging when the variables of interest cannot be directly measured. In measurement models, the measured variables were generated by latent causal variables that are causally related to each other, and by estimating the measurement model from measured data, one is able to recover the latent variables and their causal relations. In this paper, we provide precise sufficient identifiability conditions for the linear pure measurement model, and show what information of the causal structure can be recovered from observed data without prior knowledge of data distributions. In particular, we first show that, based on second-order statistics, although the pure measurement model is in general not fully identifiable under the assumption of two pure measurement variables, we can identify the set of all candidate measurement models. We then further prove that the pure measurement model can be identified uniquely based on higher-order statistics. Next, to address more general situations, we offer the identifiability conditions of linear measurement models with arbitrary noise distributions. We finally develop a unified method to learn pure measurement models from data. Experimental results on both synthetic and real-world data demonstrate the usefulness of our theory and the effectiveness of our approach.
Efficient Associate Rules Mining Based on Topology for Items of Transactional Data

A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases. In this paper, we propose an efficient method based on the topology space of the itemset for mining associate rules from transaction databases. To do so, we deduce a binary relation on itemset, and construct a topology space of itemset based on the binary relation and the quotient lattice of the topology according to transactions of itemsets. Furthermore, we prove that all closed itemsets are included in the quotient lattice of the topology, and generators or minimal generators of every closed itemset can be easily obtained from an element of the quotient lattice. Formally, the topology on itemset represents more general associative relationship among items of transaction databases, the quotient lattice of the topology displays the hierarchical structures on all itemsets, and provide us a method to approximate any template of the itemset. Accordingly, we provide efficient algorithms to generate Min-Max association rules or reduce generalized association rules based on the lower approximation and the upper approximation of a template, respectively. The experiment results demonstrate that the proposed method is an alternative and efficient method to generate or reduce association rules from transaction databases.
Measuring Academic Representative Papers Based on Graph Autoencoder Framework

Objectively evaluating representative papers in a specific scientific research field is of great significance to the development of academia and scientific research institutions. Representative papers on achievements in scientific research can reflect the academic level and research characteristics of researchers and research institutions. The existing research methods are mainly based on external feature indicators and citation analysis methods, and the method of combining artificial intelligence is in its infancy. From the perspective of scientific research institutions, this paper proposes a graph autoencoder framework based on heterogeneous networks for the measurement of paper impact, named GAEPIM. Specifically, we propose two versions of GAEPIM based on a graph convolutional network and graph transformer network. The models rank papers in a specific research field and find the most representative papers and their scientific institutions. The proposed framework constructs a heterogeneous network of papers, institutions, and venues and simultaneously analyzes the semantic information of papers and the heterogeneous network structural information. Finally, based on the complex network information diffusion model, the proposed method performs better than several widely used baseline methods.
A preliminary research regarding the role of price and promotion on customers’ restaurant selections in Muscat/Oman

This study aimed to examine the customer selection of restaurants in Muscat based on the effectiveness of the prices and promotions. Besides, this study focused on the factors affecting the customer's selection of restaurants. The survey questionnaire was applied to a sample of customers living in Muscat. A convenient sampling technique was adopted for data collection, and the collected data was processed and analysed using Mini-tab software. The findings indicate that the menu price affects restaurant customers’ selection, and restaurants with a high price have better service. As for promotion, the most effective method to advertise is through social media. In addition, the most important factors for Omani people that affect their restaurant selections are tasty food, food quality, cleanliness, quick service, price and value, and sales promotion and discounts, respectively. The current study is preliminary in nature but an unstudied one from the price and promotion approach in Muscat, so it makes an addition to the hospitality literature and sheds light on future studies. The study also provides recommendations for practitioners.
Post-Quantum Cryptographic Access Control Based on Hierarchical RBAC Model

The paper considers the isogeny-based cryptographically enforced data access control scheme CSIDH-HRBAC for untrusted cloud. CSIDH-HRBAC is based on a role-based access control model with support for a role hierarchy system. The proposed scheme implies the presence of a trusted party that manages cryptographic keys associated with users, roles, files. The basic procedures for gaining access to data, revoking access rights, adding new entities and updating parameters are given. Typical scenarios of attacks on the proposed scheme are considered, including role substitution, collusion by participants to compute the parent role key, attempt to access data after role revocation from user. To evaluate the performance of cryptographic operations, the simulation of the basic procedures was performed. The advantages and limitations of the CSIDH-HRBAC scheme are discussed. In particular, the need for protection against threats from the administrator, the prospect of using lattice-based post-quantum cryptographic primitives is noted.
Unsupervised query reduction for efficient yet effective news background linking

In this article, we study efficient techniques to tackle the news background linking problem, in which an online reader seeks background knowledge about a given article to better understand its context. Recently, this problem attracted many researchers, especially in the Text Retrieval Conference (TREC) community. Surprisingly, the most effective method to date uses the entire input news article as a search query in an ad-hoc retrieval approach to retrieve the background links. In a scenario where the lookup for background links is performed online, this method becomes inefficient, especially if the search scope is big such as the Web, due to the relatively long generated query, which results in a long response time. In this work, we evaluate different unsupervised approaches for reducing the input news article to a much shorter, hence efficient, search query, while maintaining the retrieval effectiveness. We conducted several experiments using the Washington Post dataset, released specifically for the news background linking problem. Our results show that a simple statistical analysis of the article using a recent keyword extraction technique reaches an average of 6.2× speedup in query response time over the full article approach, with no significant difference in effectiveness. Moreover, we found that further reduction of the search terms can be achieved by eliminating relatively low TF-IDF values from the search queries, yielding even more efficient retrieval of 13.3× speedup, while still maintaining the retrieval effectiveness. This makes our approach more suitable for practical online scenarios. Our study is the first to address the efficiency of news background linking systems. We, therefore, release our source code to promote research in that direction.
Decision Support Methodology Based on Evaluation of Customer Experience and Telecommunications Operator Efficiency Indicators

The article considers the methodology for dynamic analysis of the cognitive model for assessing customer experience in the context of communication providers. The relevance of the study is due to the need for telecom operators to have a decision support system that allows analyzing dependence of customer experience on the efficiency of the company's operating environment, as well as emulating customer experience management scenarios in the context of the main sales and service processes. The objective of the study is to formalize the methodology of dynamic analysis of the model for assessing the integral customer experience that is based on fuzzy cognitive maps of the hierarchical structure. In particular, the mechanics of changing target factors (i.e., customer experience indicators) when perturbing control factors (i.e., operational performance indicators that affect customer experience) are investigated.
RF-Alphabet: Cross Domain Alphabet Recognition System Based on RFID Differential Threshold Similarity Calculation Model

Gesture recognition can help people with a speech impairment to communicate and promote the development of Human-Computer Interaction (HCI) technology. With the development of wireless technology, passive gesture recognition based on RFID has become a research hotspot. In this paper, we propose a low-cost, non-invasive and scalable gesture recognition technology, and successfully implement the RF-alphabet, a gesture recognition system for complex, fine-grained, domain-independent 26 English letters; the RF-alphabet has three major advantages: first, this paper achieves complete capture of complex, fine-grained gesture data by designing a dual-tag, dual-antenna layout. Secondly, to overcome the disadvantages of the large training sets and long training times of traditional deep learning. We design and combine the Difference threshold similarity calculation prediction model to extract digital signal features to achieve real-time feature analysis of gesture signals. Finally, the RF alphabet solves the problem of confusing the signal characteristics of letters. Confused letters are distinguished by comparing the phase values of feature points. The RF-alphabet ends up with an average accuracy of 90.28% and 89.7% in different domains for new users and new environments, respectively, by performing feature analysis on similar signals. The real-time, robustness, and scalability of the RF-alphabet are proven.
Privacy-preserving association rule mining via multi-key fully homomorphic encryption

Association rule mining is an efficient method to mine the association relationships between different items from large transaction databases, but is vulnerable to privacy leakage as operates over users’ sensitive data directly. Privacy-preserving association rule mining has emerged to protect users’ privacy during rule mining. Unfortunately, existing privacy-preserving association rule mining schemes suffer from high overhead, fail to support multiple users, and are challenging to prevent collusion attacks between twin-server. To this end, in this paper, we propose a privacy-preserving association rule mining solution via multi-key fully homomorphic encryption over the torus (MKTFHE), which efficiently supports multiple users through a single server only. Specifically, we first construct some multi-key homomorphic gates based on MKTFHE. Then, we designed a series of privacy-preserving computational protocols based on multi-key homomorphic gates. Finally, we build a privacy-preserving association rule mining system with a single cloud server to support multiple users. Moreover, privacy analysis and performance evaluation demonstrate our proposal is efficient and feasible. In contrast to existing solutions, the proposed scheme outperforms encryption and communication, saving approximately 8.5% running time.
(β,γ)-Skew QC Codes with Derivation over a Semi-Local Ring

In this article, we consider a semi-local ring S=Fq+uFq, where u2=u, q=ps and p is a prime number. We define a multiplication yb=β(b)y+γ(b), where β is an automorphism and γ is a β-derivation on S so that S[y;β,γ] becomes a non-commutative ring which is known as skew polynomial ring. We give the characterization of S[y;β,γ] and obtain the most striking results that are better than previous findings. We also determine the structural properties of 1-generator skew cyclic and skew-quasi cyclic codes. Further, We demonstrate remarkable results of the above-mentioned codes over S. Finally, we find the duality of skew cyclic and skew-quasi cyclic codes using a symmetric inner product. These codes are further generalized to double skew cyclic and skew quasi cyclic codes and a table of optimal codes is calculated by MAGMA software.