bluuebunny/crossref_metadata_2025 huggingface dataset:
Asymmetric Multifractal Cross-Correlations Between Economic Policy Uncertainty and Agricultural Futures Prices
Authors: Feng You-Shuai , Li Yang , Cao Bao-Ming
Published: April 2022

This paper investigates the fluctuation characteristics and asymmetry of cross-correlations between economic policy uncertainty (EPU) and agricultural futures prices in China and the US by using the multifractal detrended cross-correlation analysis (MF-X-DFA) and multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA). We find that the multifractal cross-correlations exist between EPU and agricultural futures prices, and the cross-correlations are anti-persistent and asymmetric. The anti-persistent cross-correlations in China are all stronger than those in the US. The multifractal degree of cross-correlation between EPU and soybean futures price is lower in China than in the US, while the multifractal degree of cross-correlation between EPU and soybean meal, soybean oil or corn futures price is higher in China than in the US. Moreover, China’s soybean futures price is more susceptible to the upward and downward trends in China’s EPU, while the US soybean meal, soybean oil, and corn futures prices are more susceptible to them in the US EPU.
Shock Response of Porcine Meniscus along with Axial and Radial Directions
Authors: TRAN DAT TRONG , JUANG YUN-CHING , TSAI LIREN
Published: April 2022

The meniscus is a multifunctional fibrocartilage tissue in the knee joint which stables joint movement, bears load and absorbs impact. Improper collisions will cause damage to meniscus tissue and lose its original functionality. However, it is difficult to fully evaluate the mechanical properties of the meniscus based on static test results alone. In this study, Split Hopkinson Pressure Bar (SHPB) and hydraulic material testing system (MTS) were utilized to examine the quasi-static and dynamic properties of the porcine meniscus along with two different orientations. The results showed that the meniscus is a strain rate sensitive material and its mechanical properties mainly depend on the orientation of collagen fiber bundles in the peripheral direction. The meniscus tissue did not show obvious yield characteristics under quasi-static test conditions. However, the meniscus showed clear yield behavior under dynamic loading. When the strain rate increased, the elastic modulus of the radial meniscus remained around 35 MPa while the elastic modulus of the axial meniscus increased from 30 MPa to 80 MPa. This study demonstrates that the meniscus is sensitive to strain rate at both dynamic and quasi-static conditions, and the meniscus is an anisotropic biological tissue.
COMPARISON BETWEEN TWO NONINVASIVE METHODS USED TO ESTIMATE BIOMECHANICAL PROPERTIES OF THE INTERNAL CAROTID ARTERY
Authors: AYADI ASMA , SAHTOUT WASSILA , BALEDENT OLIVIER
Published: April 2022

Background: Many cardiovascular diseases modified the arterial wall stiffness. Objectives: This work focuses on the quantification of the elastic biomechanical properties of the internal carotid (ICA) wall by applying the cepstral analysis on healthy volunteers aged from 22 to 86 years old. The purpose of this study is to compare two methods of measurement of arterial compliance ([Formula: see text], arterial distensibility ([Formula: see text], arterial elastance (Eh), and Young’s modulus ([Formula: see text]. Material and methods: First, arterial compliance and arterial distensibility were measured in function of wave speed ([Formula: see text], which is measured in our previous works by using two methods. Second, elastance Eh was estimated through the ratio between diastolic radius ([Formula: see text] and [Formula: see text]. Finally, [Formula: see text] was estimated from a statistical study from the literature on h due to the difficulty of measuring wall thickness ([Formula: see text]. Results: The Student test demonstrated that there is a very significant difference between young and old subjects in terms of elastance, compliance, and Young’s modulus ([Formula: see text]). These findings are in agreement with the reference values reported in the literature. They are very satisfying for distinguishing a pathological change in parietal elasticity. Conclusion: The in vivo application of these methods presents their potential for clinical measurement of arterial stiffness.
Numerical Investigation of the Attenuation of Tsunami-like Waves by a Vegetated, Sloped Beach
Authors: Sun W. Y. , Qu K. , Kraatz S. , Lan G. Y. , Jiang C. B.
Published: April 2022

Tsunamis, such as the 2004 Indian and 2011 Japan tsunamis, routinely cause severe damage along coasts. Coastal vegetation serves as an excellent, naturally occurring protection from tsunamis. However, prior studies focusing on the wave attenuation by vegetation assumed that solitary waves would adequately represent the major aspects of tsunamis. However, there are substantial differences between solitary tsunami waves with regards to their wave profiles and how it evolves with time. This study aims to improve our understanding of the wave-vegetation interactions by employing a more realistic wave profile (tsunami-like instead of solitary). This work uses measurements obtained during the 2011 Japan tsunami to parameterize the observed tsunami-like wave profile, which is then used to investigate the wave-vegetation interaction on a sloped beach, using a nonhydrostatic wave (NHWAVE) model. The work investigates the efficiency of vegetated sloped beach in mitigating the maximum run-up height and total wave energy as function of wave height, water depth, vegetation width, vegetation density and wave model (solitary vs. tsunami-like). Results show that a vegetated sloped beach is effective in reducing the wave energy of both kinds of waves. However, when a solitary wave is used, the vegetation patch is shown to be relatively better at attenuating wave energy and in reducing maximum run-up heights. The findings indicate that the solitary wave model overestimates protections afforded by coastal vegetation, and that it underestimates maximum run-up heights. The findings drawn from this study further broaden our understanding on the wave attenuation of tsunami surges and waves by a vegetated sloped beach.
Interactions between Dongsha Atoll and Mw 9 Tsunamis and their Impacts in South China Sea Region
Authors: Wang Jinghua , Liu Philip L.-F.
Published: April 2022

Tsunamis induced by megathrust in the Manila subduction zone impose alarming threats to the coastal cities in the northern South China Sea (SCS), and risk assessment of tsunami hazards in this region becomes demanding. One distinguishable geographic feature in this region is Dongsha Atoll, which is situated between the tsunami source zone and the China coastline. This study discusses the role of the Dongsha Atoll in modifying the tsunami impacts through numerical simulations of a group of synthetic tsunami events with [Formula: see text]. Three types of representations for the Atoll in the numerical simulations are employed, specifically (i) the real topo-bathymetry of the Atoll is fully resolved (Model-1), (ii) the Atoll is removed (Model-2), and (iii) the Atoll is artificially represented by a cylinder (Model-3). The results show that without the Dongsha Atoll presence (Model-2), the tsunamis can induce a substantial increment of water level behind the Atoll and in the vicinity of the Pearl River Delta (PRD) region in China. While the results obtained by realistically modeling the Atoll (Model-1) show that a large amount of tsunami wave energy/momentum can be entrapped by the lagoon and slowly radiated to the ocean. The interactions between the tsunamis and the Atoll will lead to severe flooding on the Dongsha Island on the west bank of the Atoll. However, the peak water levels behind the Atoll and at the selected mainland coastal sites are reduced, indicating that the Dongsha Atoll can offer some degree of protection for the region in its behind. Meanwhile, a slightly smaller reduction of tsunami heights is reported in the simulations with the Atoll being represented by a cylinder (Model-3), implying that the reduction effects are sensitive to the representations of the Atoll. Therefore, the full model of the topo-bathymetry of the Dongsha Atoll is recommended for future tsunami risk assessments for both Dongsha Atoll and the mainland in the SCS region.
Algorithmic aspects of domination number in nano topology induced by graph
Authors: Sutha Devi V. , Lellis Thivagar M. , Sundareswaran R.
Published: April 2022

The basic intention of this paper is to impart the importance of Nano topology induced by graph. Nano topology induced by graph paves us a method to find the well-known parameter of a graph, namely its domination number as well as all the possible dominating sets of a graph. In general, each paper, depending upon the author’s axe (or the pen), has its own point of emphasis. Accordingly, in this paper, the computational and algorithmic aspects of graph are emphasized. By experience, we know that to solve a problem using graph theory leads to a large graph virtually impossible to analyze without the aid of a computer. Hence, to simplify the problem we have used a Java program code that suits even for all graphs of finite number of vertices. We have made an attempt to find the proper location of guards and minimize the number of guards to observe all the warning lights in a nuclear power plant using such a Java program code.
Semi-Automatic Construction of a Readability Corpus for the Vietnamese Language
Authors: Luong An-Vinh , Dinh Dien
Published: April 2022

Text readability is a measure of how easy or difficult it is to read a text. This readability factor plays a crucial role in the processes of drafting and comprehending the texts, affecting the choice of proper texts for reading. While text readability has been a focus in research on computational linguistics for English and other resource-rich languages, there is still little work on this subject in understudied languages like Vietnamese. In this study, we propose a semi-automated method to build a corpus for text readability purposes for the Vietnamese language. This method performs the classification of new documents based on comparing the correlation readability with the documents that have been manually pre-assessed by language experts. The evaluation results of experts show that the classification accuracy of documents in the corpus is over 90%. The experimental results on other available corpus also show that our proposed method can not only address the lack of corpora that are graded on various difficulty levels in Vietnamese, but it can also be extended to the analysis of other resource-poor languages without requiring major adjustments.
Stress-strain curve determined from a non-uniformly deformed specimen: An application of autonomous finite element analysis
Authors: Wang Leyu , Kan Cing-Dao , Lo James , Lee James
Published: April 2022

Stress-strain curve cannot be directly converted from force-displacement curve if the deformation is not uniform in test specimen. An automated inverse analysis is developed to select the candidate stress-strain curve whose force-displacement result in simulation has the best goodness of fit (GOF) to the test measurement. The candidate stress-strain curves are randomly generated by a Random Stress Strain Curve Generator (RSSCG) under several physical constrains without using test data or analytical formula. The process of finite element analysis (FEA) is packed into a white box function with stress-strain curve as input and its corresponding GOF as output. A Leveled Random Search (LRS) algorithm optimizes the shape of stress-strain curve for a better GOF value. LRS preforms several random probes at each search level. At next level, the size of search zone is reduced while the center of the search zone is repositioned to the best solution found so far. This process continues until a satisfactory result is found. LRS is designed to work with discontinuous response surface and overcome local minimal problem. As an optional feature, Human in the Loop (HITL) can be used to adjust the search zone for fast convergence. Examples in tension and compression test are provided to demonstrate the process. The concept of the automated inverse analysis is summarized as Autonomous Finite Element Analysis (AFEA). The fully automated computer program has been successfully used in material modeling of automobile crashworthiness, producing 40 beyond-necking point stress-strain curves in two weeks.
Super-optimization of Smart Contracts
Authors: Albert Elvira , Gordillo Pablo , Hernández-Cerezo Alejandro , Rubio Albert , Schett Maria A.
Published: April 2022

Smart contracts are programs deployed on a blockchain. They are executed for a monetary fee paid in
gas
—a clear optimization target for smart contract compilers. Because smart contracts are a young, fast-moving field without (manually) fine-tuned compilers, they highly benefit from automated and adaptable approaches, especially as smart contracts are effectively immutable, and as such need a high level of assurance. This makes them an ideal domain for applying formal methods. Super-optimization is a technique to find the best translation of a block of instructions by trying all possible sequences of instructions that produce the same result. We present a framework for super-optimizing smart contracts based on Max-SMT with two main ingredients: (1) a
stack functional specification
extracted from the basic blocks of a smart contract, which is simplified using rules capturing the semantics of arithmetic, bit-wise, and relational operations, and (2) the
synthesis of optimized blocks
, which finds—by means of an efficient SMT encoding—basic blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one.

We implemented our framework in the tool
syrup 2.0
. Through large-scale experiments on real-world smart contracts, we analyze performance improvements for different SMT encodings, as well as tradeoffs between quality of optimizations and required optimization time.
Cross-User Similarities in Viewing Behavior for 360° Video and Caching Implications
Authors: Carlsson Niklas , Eager Derek
Published: April 2022

The demand and usage of 360° video services are expected to increase. However, despite these services being highly bandwidth intensive, not much is known about the potential value that basic bandwidth saving techniques such as server or edge-network on-demand caching (e.g., in a CDN) could have when used for delivery of such services. This problem is both important and complicated as client-side solutions have been developed that split the full 360° view into multiple tiles, and adapt the quality of the downloaded tiles based on the user’s expected viewing direction and bandwidth conditions. This article presents new trace-based analysis methods that incorporate users’ viewports (the area of the full 360° view the user actually sees), a first characterization of the cross-user similarities of the users’ viewports, and a trace-based analysis of the potential bandwidth savings that caching-based techniques may offer under different conditions. Our analysis takes into account differences in the time granularity over which viewport overlaps can be beneficial for resource saving techniques, compares and contrasts differences between video categories, and accounts for uncertainties in the network conditions and the prediction of the future viewing direction when prefetching. The results provide substantial insight into the conditions under which overlap can be considerable and caching effective, and inform the design of new caching system policies tailored for 360° video.
Women’s Participation in Open Source Software: A Survey of the Literature
Authors: Trinkenreich Bianca , Wiese Igor , Sarma Anita , Gerosa Marco , Steinmacher Igor
Published: April 2022

Women are underrepresented in Open Source Software (OSS) projects, as a result of which, not only do women lose career and skill development opportunities, but the projects themselves suffer from a lack of diversity of perspectives. Practitioners and researchers need to understand more about the phenomenon; however, studies about women in open source are spread across multiple fields, including information systems, software engineering, and social science. This article systematically maps, aggregates, and synthesizes the state-of-the-art on women’s participation in OSS. It focuses on women contributors’ representation and demographics, how they contribute, their motivations and challenges, and strategies employed by communities to attract and retain women. We identified 51 articles (published between 2000 and 2021) that investigated women’s participation in OSS. We found evidence in these papers about who are the women who contribute, what motivates them to contribute, what types of contributions they make, challenges they face, and strategies proposed to support their participation. According to these studies, only about 5% of projects were reported to have women as core developers, and women authored less than 5% of pull-requests, but had similar or even higher rates of pull-request acceptances than men. Women make both code and non-code contributions, and their motivations to contribute include learning new skills, altruism, reciprocity, and kinship. Challenges that women face in OSS are mainly social, including lack of peer parity and non-inclusive communication from a toxic culture. We found 10 strategies reported in the literature, which we mapped to the reported challenges. Based on these results, we provide guidelines for future research and practice.
The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering
Authors: Chen Tao , Li Miqing
Published: April 2022

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem’s Pareto-optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights that reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice, since it simplifies the search via converting the original multi-objective problem into a single-objective one and enables the search to focus on what only the stakeholders are interested in.

This article questions such a “
weighted search first
” belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large-scale empirical study that consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is significantly better than its weighted counterpart the majority of the time (up to 77% of the cases), as long as we allow a sufficient, but not unrealistic search budget. This is a beneficial result, as it discovers a potentially new “rule-of-thumb” for the SBSE community: Even when clear preferences are available, it is recommended to always consider Pareto search by default for multi-objective SBSE problems, provided that solution quality is more important. Weighted search, in contrast, should only be preferred when the resource/search budget is limited, especially for expensive SBSE problems. This, together with other findings and actionable suggestions in the article, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at
https://github.com/ideas-labo/pareto-vs-weight-for-sbse
.
Scanner++: Enhanced Vulnerability Detection of Web Applications with Attack Intent Synchronization
Authors: Yin Zijing , Xu Yiwen , Ma Fuchen , Gao Haohao , Qiao Lei , Jiang Yu
Published: April 2022

Scanners are commonly applied for detecting vulnerabilities in web applications. Various scanners with different strategies are widely in use, but their performance is challenged by the increasing diversity of target applications that have more complex attack surfaces (i.e., website paths) and covert vulnerabilities that can only be exploited by more sophisticated attack vectors (i.e., payloads). In this paper, we propose Scanner++, a framework that improves web vulnerability detection of existing scanners through combining their capabilities with attack intent synchronization. We design Scanner++ as a proxy-based architecture while using a package-based intent synchronization approach. Scanner++ first uses a purification mechanism to aggregate and refine attack intents, consisting of attack surfaces and attack vectors extracted from the base scanners’ request packets. Then, Scanner++ uses a runtime intent synchronization mechanism to select relevant attack intents according to the scanners’ detection spots to guide their scanning process. Consequently, base scanners can expand their attack surfaces, generate more diverse attack vectors and achieve better vulnerability detection performance.

For evaluation, we implemented and integrated Scanner++ together with four widely used scanners, BurpSuite, AWVS, Arachni, and ZAP, testing it on ten benchmark web applications and three well-tested real-world web applications of a critical financial platform from our industry partner. Working under the Scanner++ framework helps BurpSuite, AWVS, Arachni, and ZAP cover 15.26%, 37.14%, 59.21%, 68.54% more pages, construct 12.95×, 1.13×, 15.03×, 52.66× more attack packets, and discover 77, 55, 77, 176 more bugs, respectively. Furthermore, Scanner++ detected eight serious previously unknown vulnerabilities on real-world applications, while the base scanners only found three of them.
Bash in the Wild: Language Usage, Code Smells, and Bugs
Authors: Dong Yiwen , Li Zheyang , Tian Yongqiang , Sun Chengnian , Godfrey Michael W. , Nagappan Meiyappan
Published: April 2022

The Bourne-again shell (Bash) is a prevalent scripting language for orchestrating shell commands and managing resources in Unix-like environments. It is one of the mainstream shell dialects that is available on most GNU Linux systems. However, the unique syntax and semantics of Bash could easily lead to unintended behaviors if carelessly used. Prior studies primarily focused on improving the reliability of Bash scripts or facilitating writing Bash scripts; there is yet no empirical study on the characteristics of Bash programs written in reality, e.g., frequently used language features, common code smells, and bugs.

In this article, we perform a large-scale empirical study of Bash usage, based on analyses over one million open source Bash scripts found in Github repositories. We identify and discuss which features and utilities of Bash are most often used. Using static analysis, we find that Bash scripts are often error-prone, and the error-proneness has a moderately positive correlation with the size of the scripts. We also find that the most common problem areas concern quoting, resource management, command options, permissions, and error handling. We envision that these findings can be beneficial for learning Bash and future research that aims to improve shell and command-line productivity and reliability.
Distance and Direction Based Deep Discriminant Metric Learning for Kinship Verification
Authors: Zhu Xiaoke , Li Changlong , Chen Xiaopan , Zhang Xinyu , Jing Xiao-Yuan
Published: April 2022

Image-based kinship verification is an important task in computer vision and has many applications in practice, such as missing children search and family album construction, among others. Due to the differences in age, gender, expression and appearance, there usually exists a large discrepancy between the facial images of parent and child. This makes kinship verification a challenging task. In this article, we propose a Distance and Direction Based Deep Discriminant Metric Learning (D
4
ML) approach for kinship verification. The basic idea of D
4
ML is to make full use of the discriminant information contained in the facial images of parent and child such that the network can learn more a discriminating distance metric. Specifically, D
4
ML learns the metric by utilizing the discriminant information from two perspectives: distance-based perspective and direction-based perspective. From the distance-based perspective, the designed loss function is used to minimize the distance between images having kinship and maximize the distance between images without kinship. In practice, the gender difference and large age gap may significantly increase the distance between facial images of parent and child. Therefore, learning the metric only from a distance-based perspective is insufficient. Considering that two vectors with a large distance may appear with high similarity in direction, D
4
ML also employs the direction-based loss function in the training process. Both kinds of loss function work together to improve the discriminability of the learned metric. Experimental results on four small size publicly available datasets demonstrate the effectiveness of our approach. Source code of our approach can be found at
https://github.com/lclhenu/D4ML
.
Coordinated Path Planning through Local Search and Simulated Annealing
Authors: Yang Hyeyun , Vigneron Antoine
Published: April 2022

The third computational geometry challenge was on a coordinated path planning problem in which a collection of square robots need to move on the integer grid, from their given starting points to their target points, and without collision between robots, or between robots and a set of input obstacles. We designed and implemented three algorithms for this problem. First, we computed a feasible solution by placing middle-points outside of the minimum bounding box of the starting positions, the target positions and the obstacles, and moving each robot from its starting point to its target point through a middle-point. Second, we applied a simple local search approach where we repeatedly delete and insert again a random robot through an optimal path. It improves the quality of the solution, as the robots no longer need to go through the middle-points. Finally, we used simulated annealing to further improve this feasible solution. We used two different types of moves: We either tightened the whole trajectory of a robot, or we stretched it between two points by making the robot move through a third intermediate point generated at random.
Numerical Analysis of Horizontal Impact Resistance of Rock Socketed Pile with Steel Casing
Authors: Zhang Xiaolong , Duan Lunliang , Wang Duoyin
Published: April 2022

Based on a Wharf project in Chongqing, this paper carries out experimental research and nonlinear finite element analysis on the impact resistance of steel casing rock socketed pile of inland river frame Wharf under horizontal impact load. The results show that the impact force and its growth rate, the bending moment of concrete pile core, and the maximum lateral displacement of pile top increase significantly with the increase of impact energy, and the linear increase of the maximum displacement of pile top is better. Under the same impact energy, with the increase of impact mass, the maximum impact force decreases, the maximum bending moment of concrete pile core and its vibration center value (platform value) increase, and the maximum displacement of pile top and its vibration center value (platform value) increase. Based on the impact mass m threshold, the criterion of different dynamic response forms of pile body is proposed. The impact mass threshold corresponds to the development of plastic hinge at the root of pile body. Through the parametric analysis method, it is obtained that the impact mass, impact velocity, and the maximum displacement of pile top meet the parabolic spherical relationship.
Research on University Innovation and Entrepreneurship Resource Database System Based on SSH2
Authors: Wu Libo , Feng Lili , Yan Jianna
Published: April 2022

With the wide application of the Internet, the entrepreneurial resources of colleges and universities have grown at an exponential rate. With the rapid accumulation of this information, it is difficult for students to find what they are interested in from a large amount of information. To accurately recommend innovation and entrepreneurship resources, this paper proposes a recommendation algorithm based on user trust and a probability matrix. After obtaining the user trust data, the PMD (probability matrix decomposition) algorithm is used to complete the trust matrix and normalize it to get the similarity matrix. At the same time, the trust factor between users is added to the calculation process of the posterior probability, and the prediction score is obtained by maximizing the posterior probability. On this basis, the weights of users in the group are normalized, and the weighting strategy based on user interaction is used to integrate the preferences of group members to obtain the final recommendation results. When designing the recommendation system, the web system of the mainstream SSH2 framework is used to design, and the B/S structure of the entrepreneurial resource recommendation system platform is realized. Experimental results show that the proposed system has a higher recommendation quality compared with other recommended algorithms.
Aesthetic Index Analysis of Fusion of Folk Martial Arts and Dance Based on Deep Learning Algorithm
Authors: Wei Yaqing , Huang Zongfeng
Published: April 2022

In this paper, we propose a computable method to evaluate the aesthetic value of fusion images of folk martial arts and dance based on human visual and aesthetic habits, extract features and use them as evaluation indexes from three aspects: technical features, perceptual features, and social features, establish an aesthetic evaluation model by fusing each index, and determine the influence factors of each index by using online research. The aesthetic evaluation of fusion images of folk martial arts and dance is carried out automatically. The results of the experimental tests on the evaluation dataset of the aesthetic quality of fusion images of folk martial arts and dance at the Communication University of China show that the accuracy of the proposed deep learning algorithm-based method for analyzing the aesthetic index of fusion of folk martial arts and dance can reach 98.08% with certain validity, and the evaluation results of each index are clear and intuitive, which can play a guiding role in improving the aesthetic quality of fusion of folk martial arts and dance from various angles. It can be used as a guide to improving the aesthetics of the fusion of folk martial arts and dance from various perspectives. By integrating the model into the mobile application, it is possible to evaluate and score the aesthetics of multiple portrait photos uploaded by users and select photos to be kept or deleted based on the scoring results, thus simplifying user operations.
Separable Reversible Data Hiding in Encrypted VQ-Encoded Images
Authors: Cao Fang , Fu Yujie , Yao Heng , Zou Mian , Li Jian , Qin Chuan
Published: April 2022

In this paper, a reversible data-hiding scheme in encrypted, vector quantization (VQ) encoded images is proposed. During image encryption, VQ-encoded image, including codebook and index table, is encrypted by content owner with stream-cipher and permutation to protect the privacy of image contents. As for additional-data embedding, a baseline method is first proposed and its corresponding optimized method is then given. By grouping one high-occurrence index with one or multiple low-occurrence indices, a series of index groups are constructed. Thus, by modifying the high-occurrence index to the corresponding index within the same group according to the current to-be-embedded bits, data embedding can be realized. The optimal hiding capacity is obtained by optimizing the coefficient vector for different types of index groups. Separable operations of data extraction, image decryption, and recovery can be achieved on the receiver side based on the availability of the encryption and data-hiding keys. Experimental results show that our scheme can achieve high hiding capacity and satisfactory directly decrypted image quality and guarantee security and reversibility simultaneously.
Research on Open Oral English Scoring System Based on Neural Network
Authors: Wang Xin
Published: April 2022

This study designs and implements a scoring system for open-spoken English using NN technology. The system scores the oral recording from the phonetic level and the text level, respectively, and can comprehensively evaluate its oral level. The system will separately score the spoken speech and the spoken content through different scoring models and add the scoring results as the final score, in which the spoken content is obtained by text transcription of the recording by an external speech recognition engine. An acoustic sensor is adopted to collect pronunciation signals of spoken English. Modern signal processing and automatic pattern recognition technology are used to distinguish the quality of spoken pronunciation. Similar semantic units are marked between acoustic feature sequences, which make use of the parallel algorithm processing mode of multi-computing cores of modern GPU and allow multiple units to independently execute the comparison algorithm at the same time. Experiments show that the model in this study achieves better comprehensive scoring performance. The scoring model is of great significance to the development of educational informatization and intelligence, and it also provides a reference for the construction of intelligent oral scoring system.
Impact Response Spectrum for Vessel-Bridge Collision Design
Authors: Fu Tao , Hou Jianpu , Wang Kai , Ren Xiaoqian , Zhu Zhixin , Meng Lingxiao
Published: April 2022

An equivalent static method is employed in calculating the impact response of vessel-bridge collisions, in which the dynamic characteristics of vessel-bridge collisions are ignored. An impact response spectrum for vessel-bridge design is proposed according to the principle of the earthquake response spectrum. The impact force time-histories are obtained through a numerical simulation method using finite element software. The relationship between the displacement amplification factor of the single-degree-of-freedom, period, and damping ratio under the excitation of impact force-time histories is obtained. The regularity of the displacement amplification is summarized. The characteristics of the vessel-bridge collision are given. The three-stage expression and specific application steps of the impact response spectrum for the design of vessel-bridge collision are provided.
Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images
Authors: Ramezani Kousar , Tofangchiha Maryam
Published: April 2022

Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.
A Machine Learning-Based Approach to Railway Logistics Transport Path Optimization
Authors: Cao Ke
Published: April 2022

With the rapid development of high-speed railways, the continuous improvement of the road network, and the leapfrog increase in demand for cross-line passenger travel, the connectivity between cities is also increasing. The network structure has gradually evolved from a single line structure to a complex network structure, and the routes connecting city nodes are no longer unique. At this stage, there are multiple effective routes between destinations on the network, and the choice of train routes has diversified. Different options for interline trains have different degrees of impact on line and station operations. Therefore, this study constructed a deep learning CNN-GRU combined model to predict the traffic speed of railway logistics. The model used a convolutional neural network (CNN) to extract the spatial characteristics of speed data and GRU to extract the time characteristics of speed data. The experiment proved that the prediction accuracy of the combined model was better than that of the single GRU and CNN models. Finally, a multi-objective optimization model with the shortest total train running distance, the shortest total train running time, and the least influence on road sections is constructed using the prediction results.
Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai
Authors: Sushmi N. B. , Subbulekshmi D.
Published: April 2022

Sun is the sustainable and abundantly available alternative resource on the planet Earth. The uncertain nature of the source caused due to various environmental factors increases the need to quantify the irradiation potential at the targeted location, especially for power smoothing processes, solar-fed electrical applications, and the utility grid. The irradiation measuring devices lead to appropriate maintenance and more expenses and are ineffective under dynamically varying irradiation conditions. Hence, a robust ML-based forecasting technique is sufficient to predict GHI under dynamically varying irradiation profiles caused by various environmental factors. Therefore, this study focuses on short-term (hourly) GHI forecasting using the FFBP-LM-ANN approach. In most of the related articles, the selection of independent variables and designing the network is being a challenge and obstacle in attaining fast and efficient prediction. Such a scenario increases the overall computation complexity and understanding. Hence, this study was intended to use a simple process to crucially select 5 environmental factors and to optimally choose the number of neurons for designing the network for better irradiation prediction. Thus, the network is designed with an input layer of 5 selected environmental variables and one hidden layer with 20 neurons, and hourly GHI in the output layer is performed. The approach is trained using the Levenberg-Marquardt BP algorithm in MATLAB toolbox, with the help of a 4-year dataset received for the rooftop panels of VIT University, Chennai, from NREL. The performance of the model during training and testing was validated and analyzed using 9 performance matrices. As a result, FFBP-LM-ANN satisfactorily predicts hourly GHI for the targeted location based on rRMSE of 7.21%, MAE of 0.042, MBE of 0.000492, R of 0.96, MAPE of 44.4%, MRE of 9.5%, and NSE of 94% obtained under testing process. Moreover, the model has performed much better when compared with 9 related models that exist in literature based on the input variables used, network design, epoch, and RMSE. Subsequently, such a predictor will be adaptable and more suitable for the explicit prediction of hourly GHI for different regions across the world having varying climatic conditions, since the study model is designed for locations facing robust climatic nature. More importantly, the designed model is superior with only environmental variables, which are rarely found in the article, rather than geographical variables, which are predominantly used in most of the related literature.
Contraction Mappings in Intuitionistic Fuzzy Rectangular Extended B-Metric Spaces
Authors: Kattan Doha , Alzanbaqi Amjad Owaidh , Islam Sahidul
Published: April 2022

In this study, we present the notion of intuitionistic fuzzy rectangular extended b-metric spaces as a generalization of intuitionistic fuzzy metric spaces and intuitionistic fuzzy rectangular b-metric spaces. Some well-known fixed-point results in metric fixed-point theory are generalized in the sense of intuitionistic fuzzy rectangular extended b-metric spaces. Several nontrivial examples and an application to nonlinear fractional differential equations are also imparted in this work to examine the validity of given results.
Bifurcations and Structures of the Parameter Space of a Discrete‐Time SIS Epidemic Model
Authors: Liu Yun , Liu Xijuan
Published: April 2022

The dynamics of a discrete‐time SIS epidemic model are reported in this paper. Three types of codimension one bifurcation, namely, transcritical, flip, Neimark–Sacker (N‐S) bifurcations, and their intersection codimension two bifurcations including 1 : 2, 1 : 3, and 1 : 4 resonances are discussed. The necessary and sufficient conditions for detecting these types of bifurcation are derived using algebraic criterion methods. Numerical simulations are conducted not only to illustrate analytical results but also to exhibit complex behaviors which include period‐doubling bifurcation in period −2, −4, −8, −16 orbits, invariant closed cycles, and attracting chaotic sets. Especially, here we investigate the parameter space of the discrete model. We also investigate the organization of typical periodic structures embedded in a quasiperiodic region. We identify period‐adding, Farey sequence of periodic structures embedded in this quasiperiodic region.
Potential Small Molecules for Therapy of Lupus Nephritis Based on Genetic Effect and Immune Infiltration
Authors: Qing Jianbo , Song Wenzhu , Tian Lingling , Samuel Sonia Biju , Li Yafeng
Published: April 2022

Lupus nephritis (LN) is the most common and significant complication of systemic lupus erythematosus (SLE) due to its poor prognosis and mortality rates in SLE patients. There is a critical need for new drugs as the pathogenesis of LN remains to be elucidated and immunosuppressive therapy comes with many deficiencies. In this study, 23 hub genes (IFI6, PLSCR1, XAF1, IFI16, IFI44, MX1, IFI44L, IFIT3, IFIT2, IFI27, DDX58, EIF2AK2, IFITM1, RTP4, IFITM3, TRIM22, PARP12, IFIH1, OAS1, HERC6, RSAD2, DDX60, and MX2) were identified through bioinformatics and network analysis and are closely related to interferon production and function. Interestingly, immune cell infiltration analysis and correlation analysis demonstrate a positive correlation between the expression of 23 hub genes and monocyte infiltration in glomeruli and M2 macrophage infiltration in the tubulointerstitium of LN patients. Additionally, the CTD database, DsigDB database, and DREIMT database were used to explore the bridging role of genes in chemicals and LN as well as the potential influence of these chemicals on immune cells. After comparison and discussion, six small molecules (Acetohexamide, Suloctidil, Terfenadine, Prochlorperazine, Mefloquine, and Triprolidine) were selected for their potential ability in treating lupus nephritis.
Transportation of Payload Using Multiple Quadrotors via Rigid Connection
Authors: Chen Ti , Shan Jinjun , Liu Hugh H. T.
Published: April 2022

Due to the limited payload capability of an aerial robot, multiple quadrotors can be used to manipulate payloads in aerial transportation, construction, and assembly tasks. This paper focuses on the cooperative transportation of a payload rigidly attached to multiple quadrotor bodies. These quadrotors may have different orientations. The dynamics equation of a rigid body in 3-D space is derived to describe the motion of such a transportation system. Robust position and attitude controllers are designed to drive the system to the desired pose. To assign control signals for each quadrotor, the control command allocation method compatible with the case that partial or all quadrotors are in parallel planes is developed. Finally, experimental results are presented to validate the effectiveness of the proposed controllers and control command allocation methods. Different from classical works in this field, this paper can solve the dynamics modeling, controller design, and control command allocation problems for the transportation of a rigidly connected payload using a team of quadrotors with different orientations.
Neural Network-Based Ultra-High-Definition Video Live Streaming Optimization Algorithm
Authors: Feng Yunning , Hu Nan , Yu Xiaosheng
Published: April 2022

Online live streaming has been widely used in distant teaching, online live shopping, and so on. Particularly, online teaching live streaming breaks the time and space boundary of teaching and has better interactivity, which is a new distant education mode. As a new online sales model, online live shopping promotes the rapid development of Internet economy. However, the quality of live video affects the user experience. This paper studies the optimization algorithm of ultra-high-definition live streaming, focusing on superresolution technology. Convolutional neural network (CNN) is a multilayer artificial neural network designed to process two-dimensional input data. It takes advantage of CNN in image processing. This paper proposes an image superresolution algorithm based on hybrid dilated convolution and Laplacian pyramid. By mixing the dilated convolution module, the receptive field of the network can be improved more effectively to obtain more context information so that the high-frequency features of the image can be extracted more effectively. Experiment was running on Set5, Set14, Urban100, and BSD100 datasets, and the results reveal that the proposed algorithm outperforms baselines with respect to peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), and image quality.