Reviewerly/exHarmony huggingface dataset:
A Fusion Model for Saliency Detection Based on Semantic Soft Segmentation

With the rapid development of neural networks in recent years, saliency detection based on deep learning has made great breakthroughs. Most deep saliency detection algorithms are based on convolutional neural networks, which still have great room for improvement in the edge accuracy of salient objects recognition, which may lead to fuzzy results in practical applications such as image matting. In order to improve the accuracy of detection, a saliency detection model based on semantic soft segmentation is proposed in this paper. Firstly, the semantic segmentation module combines spectral extinction and residual network model to obtain low-level color features and high-level semantic features, which can clearly segment all kinds of objects in the image. Then, the saliency detection module locates the position and contour of the main body of the object, and the edge accurate results are obtained after the processing of the two modules. Finally, compared with the other 11 algorithms on the DUTS-TEST data set, the weighted F-measure value of the proposed algorithm ranked first, which was 5.8% higher than the original saliency detection algorithm, and the accuracy was significantly improved.
A comparative study of a front opening unified pod (FOUP) moisture removal efficiency with different purging gases

Airborne molecular contamination (AMC) is one of the main causes of chip products defects during manufacturing. The downward moist air flow originated from the fan filter unit (FFU) inside the mini-environment (ME) can penetrate into the front opening unified pod (FOUP) and cause wafer defects. Purging nitrogen or clean dry air (CDA) are two common techniques used to minimize the relative humidity (RH) level inside the FOUP. This computational fluid dynamics (CFD) study compares purge performance of CDA and nitrogen when the FOUP door is open. The RH contours and moist air stream line were simulated inside the FOUP during purge. The FFU velocity, purging devices flow rate and temperature are kept constant during the simulation. An innovative large eddy simulation (LES) model was performed with the assistance of a small mesh size to simulate accurately the flow behavior inside the FOUP and ME. The results show that purging the FOUP with either CDA or nitrogen can significantly decrease the RH level to below 5% (RH < 5%). However, the FOUP purge with nitrogen is preferred due to the higher moisture removal efficiency. The CFD simulation results are verified by RH measurements with SHT-35 sensor inside the FOUP under the same processing conditions.
Adaptive Problem Solving Dynamics in Gate-Model Quantum Computers

Gate-model quantum computer architectures represent an implementable model used to realize quantum computations. The mathematical description of the dynamical attributes of adaptive problem solving and iterative objective function evaluation in a gate-model quantum computer is currently a challenge. Here, a mathematical model of adaptive problem solving dynamics in a gate-model quantum computer is defined. We characterize a canonical equation of adaptive objective function evaluation of computational problems. We study the stability of adaptive problem solving in gate-model quantum computers.
Realizing an excellent solution for detecting and solving conflicts between viewpoints of designers in self-adaptive systems

Abstract Recently, given the rise of emerging demands on intelligent technological systems, increasing attention has been given to proliferating and enhancing the adaptation capacities of self-adaptive systems (SAS), which are capable of adapting their behavior to changes in the context or system resources contributing to overcome the complexity of today’s software-intensive systems. However, there are a significant number of challenges for developing adaptations on the SAS, such as simplifying the designer’s task, improving responsiveness, and reducing the conflicts between its adaptations. This study focuses on simplifying the designers’ tasks by utilizing independent designers’ viewpoints on context modeling. We proposed several solutions for solving the conflicts among different viewpoints of designers in the layer of context-aware management (CAM). The validation results are promising and show that our method effectively provides a solution that supports the problem of using independent viewpoints in the context modeling process and improves the SAS adaptation capacity.
ON THE SYNTAX OF VP DISLOCATION IN MANDARIN*

Abstract By drawing on data of two types of VP dislocation in Mandarin, this paper argues that these two types represent a syntactic incarnation of XP‐split constructions providing an interesting argument for the conception of information structure starting in the numeration and multiple Spell‐Out of copies regulated by the checking of strong features over the course of a syntactic derivation. Building on Fanselow and Ćavar’s (2002) distributed deletion analysis, I show that Type 1 involves two autonomous nominals that form a nominal predicate structure in a VP, and the VP is copied and internally merged to a topic position, followed by multiple Spell‐Out of the copies whose information structural features are checked. In contrast, Type 2 is derived via base‐generation. I argue that the two types are derived employing the same set of operations in the computational system without extra postulations. Issues involved in dealing with Mandarin topic constructions are addressed.
An Enhanced Steganography Network for Concealing and Protecting Secret Image Data

The development of Internet technology has provided great convenience for data transmission and sharing, but it also brings serious security problems that are related to data protection. As is detailed in this paper, an enhanced steganography network was designed to protect secret image data that contains private or confidential information; this network consists of a concealing network and a revealing network in order to achieve image embedding and recovery separately. To reduce the system's computation complexity, we constructed the network's framework using a down-up structure in order to compress the intermediate feature maps. In order to mitigate the input's information loss caused by a sequence of convolution blocks, the long skip concatenation method was designed to pass the raw information to the top layer, thus synthesizing high-quality hidden images with fine texture details. In addition, we propose a novel strategy called non-activated feature fusion (NAFF), which is designed to provide stronger supervision for synthetizing higher-quality hidden images and recovered images. In order to further boost the hidden image's visual quality and enhance its imperceptibility, an attention mechanism-based enhanced module was designed to reconstruct and enhance the salient target, thus covering up and obscuring the embedded secret content. Furthermore, a hybrid loss function that is composed of pixel domain loss and structure domain loss was designed to boost the hidden image's structural quality and visual security. Our experimental results demonstrate that, due to the elaborate design of the network structure and loss function, our proposed method achieves high levels of imperceptibility and security.
Non-contact detection of degradation of in-service steel sheet piles due to buckling phenomena by using digital image analysis with Hough transform

Steel sheet pile materials are primarily used for canal structure construction for irrigation and drainage of agricultural fields in Japan. Recently, accelerated corrosion of steel materials and buckling phenomena in pitting corrosion parts have been detected in in-service steel sheet pile canals. In this study, we focus on the buckling phenomenon of in-service steel sheet pile canals, which is detected by non-contact monitoring using digital image analysis. The buckling phenomenon index is evaluated as the inclination angle of the steel sheet pile due to deformation. As a digital image analysis, Hough transform is applied, which is a figure detection method in digital images, to detect a straight line that describes the distance and angle parameters. The angle parameter corresponds to the inclination angle index of in-service steel sheet pile with a buckling phenomenon. As a result, the maximum difference between the analyzed angles by Hough transform and the measured angles is evaluated as 0.9° after the image rotation correction. The relation of the analyzed angles by Hough transform and the measured angles of the buckling phenomenon is correlated. Therefore, the degradation of steel sheet pile canals is quantitatively evaluated by using digital image analysis with unmanned aerial vehicle monitoring. This study’s significance is non-contact and simple measurement by using a digital image which can reduce a working time and perform quick diagnosis in extensive areas.
People Analytics Case Study: Performance Prediction Model and Effective Decision-making Timing

As digital transformation is applied to the business environment, decision-making through data began to become common in personnel management activities. One of the main interests in these activities, referred to as People Analytics, is to predict employees who are expected to perform well in advance. Nevertheless, existing studies on cases and methods for predictive analysis are insufficient. In addition, few discussions have been conducted on what criteria to look at data in making decisions for predictive analysis. Therefore, the purpose of this study is to provide the concept of predictive analysis and actual analysis cases together with R code based on the cases of South Korean companies. In addition, it emphasizes the importance of connecting data analysis and decision-making to academics and practitioners, along with discussions on when decision-making should be made.&#x0D;
Socio-Humanitarian Technologies of Education of the Future: Philosophical and Methodological Basis

The purpose—This chapter is devoted to the problem of integration of value-purpose orientations of education and human-subject orientation in the context of transition from a technogenic to a socio-humanitarian civilization. Design/methodology/approach—The study aims to investigate the philosophical and methodological foundations of socio-humanitarian technologies in education in the twenty-first century. The chapter considers objective, subjective and meta-subjective approaches to the organization of education in the information society. The problem of ethical systems in the context of future education is investigated. The concept of the study is based on modern philosophical and methodological approaches, which focus on self-developing poly-subjective environments. Post-non-classical ontologies of educational processes are considered and an interdisciplinary approach is used as a system of knowledge integration on the research topic. Findings—The conclusions of the research form the initial basis for the development and implementation of socio- humanitarian technologies of advanced education. It is assumed that it is possible to successfully solve some challenges of education through the development of socio-humanitarian technologies, consolidation of society in the establishment of certain multicultural ethical values. The influences of modern technologies and digital platforms on education are taken into account. The chapter substantiates the necessity of socio-humanitarian training of educational specialists. Research limitations—The main limitations of this study are connected to the subjective nature of many of the used concepts and with a low level of evidence.
Insights about Reading Competences of Students in Public Schools in Central Sulawesi, Indonesia

This article was aimed to investigate the insights about reading competences of students in public schools in Central Sulawesi, Indonesia. In particular, it was aimed to explain what and how reading competences of students looked like especially when they were related to their learning achievements at school. The article employed a library-based research design as there was little chance to do a case study to gain in-depth analysis related to the issues being investigated due to time constraint. As this was a library- based research, the main data collection technique was based on evidences derived from extensive literatures related to reading competences of high school students in this province. The analysis was mainly based on the content analytical point of views. The findings suggest that a number of factors strongly affected high school students’ reading competences. Despite the case, it was identified that learning and reading cultures of Indonesian students predominantly influenced the ways they approached and made use of reading materials at their hands which eventually led to their reading achievements. The results of the research suggest that as students now live in the era of advanced communication and information technologies, provisions of these technological-based learning devices are inevitable in high schools. Moreover, the local education authorities urgently need to provide adequate reading text books and other interesting reading materials to students. In addition, it is to inform teachers and other educational stakeholders to create learning atmospheres in which students were interested in reading activities from which they could develop their reading cultures for the future
Hybrid Intelligence. Main Concepts and Application Scenarios

The purpose—Hybrid Intelligence is a cooperative collaboration between Human and Artificial Intelligence in solving intelligence tasks. The concept of Hybrid Intelligence originates from the ideas of William Ross Ashby, Joseph Carl Robnett Licklider, and Douglas Carl Engelbart. Fuzzy set theory, introduced by Lofti Zadeh, is a natural tool for describing and modelling Hybrid Intelligence. Fundamental problems of Hybrid Intelligence—modelling human perception and operating with perception-based information have been discussed in the report. Two application scenarios—personalization of human interaction with the digital world and evaluation and monitoring of complex processes have been discussed and illustrated, too. Design/methodology/approach—The results are based on system analysis, fuzzy logic, some mathematical and psychology theories, and facts. Findings—Hybrid Intelligence is a pragmatic aspect of intelligence technologies, and this concept could reply to the crisis of modern Artificial Intelligence. Originality/value—Hybrid Intelligence is a new formal model which could be a theoretical base for the implementation of the concepts like “automation of knowledge work” (McKinney), “augmenting human performance” (NSF), “human–machine symbiosis” (DARPA), etc., which have broad areas of applications. Research/Practical/Social/Environment implications—Hybrid Intelligence can help people comfortable and effectively interact with the digital world. It can be a base for developing systems for evaluating and monitoring complex societal and environmental processes for their better understanding, management, and optimization. Research limitations—The Hybrid Intelligence approach can augment a regular human performance in solving routine intelligence tasks but cannot substitute a genius.
Intelligent Carom Billiards Assistive System for automatic solution path generation and actual path prediction with principal component analysis-based ball motion detection

This article proposes an Intelligent Carom Billiards Assistive System for a novice at Carom Billiards. Since a novice player is very difficult to find the solution paths of the cue ball, the ICBAS automatically calculates the solution paths for given ball configuration which is detected by a monocular vision and displays them in a guidance monitor. For rapid and systematically calculating the solution paths, the five and half system that is the most representative method in the field of billiard is implemented. In addition, the paths of the cue ball changed by the direction of the cue stick are predicted in real time and projected onto the pool table bed. By comparing the solution paths of the cue ball with the projected paths, a player can hit the cue ball easily and successfully. For automatically providing the solution paths and the predicted paths during billiard game, the ball movement is detected by the principal component analysis under linear motion constraints robustly against image noise, occlusion, and so on. Whenever all of the balls stop, both paths are newly calculated and provided for a player. For verifying the feasibility of the proposed Intelligent Carom Billiards Assistive System, the experiments are conducted with the real pool table.
Secure Virtual Machine Live Migration using Advanced Metric Encryption

Abstract: Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is expected to be with the aim of having migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and identified research gaps to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated.
(De)facing the face of lecturing with Deleuze and Guattari

This paper articulates the separate accounts of facial education and lecturing found in A Thousand Plateaus in order to theorize a new concept of lecturing for a post-digital university. Many accounts of Deleuze and Guattari in educational theory turn away from lecturing as hierarchical and striated, yet this approach denies Deleuze and Guattari’s deterritorialization of the practice through their description of a lecture by the character Professor Challenger. When read alongside their plateau on facialization, Challenger’s unusual performance can be interpreted as an interruption of the abstract machine of facialization that animates traditional accounts of lecturing. The article concludes with implications for a de-faced form of lecturing for a post-digital university.
Study on Intelligent Analysis and Processing Technology of Computer Big Data Based on Clustering Algorithm

Aim: Clustering belongs to unsupervised learning, which divides the data objects into the data set into multiple clusters or classes, so that the objects in the same cluster have high similarity Background: The clustering of spatial data objects can be solved by optimization based on the clustering objective function. Objective: Study on intelligent analysis and processing technology of computer big data based on clustering algorithm. Methods: First, a new dynamic self-organizing feature mapping model is proposed, and the training algorithm of the model is given. Then, the spectral clustering technology and related concepts are introduced. The spectral clustering algorithm is studied and analyzed, and a spectral clustering algorithm that automatically determines the number of clusters is proposed. Furthermore, an algorithm for constructing a discrete Morse function to find the optimal solution is proposed, proving that the constructed function is the optimal discrete Morse function. At the same time, two optimization models based on the discrete Morse theory are constructed. Finally, the optimization model based on discrete Morse theory is applied to cluster analysis, and a density clustering algorithm based on the discrete Morse optimization model is proposed. Results: This study is focused on designing and implementing a partitional-based clustering algorithm based on big data, that is suitable for clustering huge datasets to meet low computational requirements. The experiments are conducted in terms of time and space complexity and it is observed that the measure of clustering quality and the run time is capable of running in very less time without negotiating the quality of clustering. The results show that the experiments are carried out on the artificial data set and the UCI data set. Conclusion: The efficiency and superiority of the new model, are verified by comparing it with the clustering results of the DBSCAN algorithm.
Local Adaptiveness of Mixed Higher Order Partial Differential Equations and Its Application in Image Denoising

Background: Image denoising methods based on partial differential equations have attracted much attention due to their "infinite" local adaptation capabilities, high flexibility, and strong mathematical theoretical support. Methods: This paper proposes a mixed higher order partial differential equation denoising model for the step effect caused by the second-order denoising model and the edge blur caused by the fourth-order denoising model. The model combines the second-order and fourth-order terms based on the relationship between the variational energy minimization and the partial differential equations. The fourth-order term is used to remove noise in the uniform area of the image to avoid the step effect, and the second-order term is used at the edge to avoid boundary blur. Results: Theoretical analysis and numerical experiment results show that the proposed model has weak solutions and can effectively avoid the step effect and maintain the edge. Conclusion: The image denoising results of the model are better than those of other improved denoising models in subjective effect, and objective evaluation indicators, such as SNR, PSNR, and MSSIM.
Benchmarking human face similarity using identical twins

The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. Due to the high facial similarity of both identical twins and look-alikes, these face pairs represent the hardest cases presented to facial recognition tools. This work presents an application of one of the largest twin data sets compiled to date to address two FR challenges: (1) determining a baseline measure of facial similarity between identical twins and (2) applying this similarity measure to determine the impact of doppelgangers, or look-alikes, on FR performance for large face data sets. The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face data sets to identify similar face pairs. An additional analysis that correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed.