Reviewerly/exHarmony huggingface dataset:
A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism

Cloud segmentation is a fundamental step in accurately acquiring cloud cover. However, due to the nonrigid structures of clouds, traditional cloud segmentation methods perform worse than expected. In this paper, a novel deep convolutional neural network (CNN) named MA-SegCloud is proposed for segmenting cloud images based on a multibranch asymmetric convolution module (MACM) and an attention mechanism. The MACM is composed of asymmetric convolution, depth-separable convolution, and a squeeze-and-excitation module (SEM). The MACM not only enables the network to capture more contextual information in a larger area but can also adaptively adjust the feature channel weights. The attention mechanisms SEM and convolutional block attention module (CBAM) in the network can strengthen useful features for cloud image segmentation. As a result, MA-SegCloud achieves a 96.9% accuracy, 97.0% precision, 97.0% recall, 97.0% F-score, 3.1% error rate, and 94.0% mean intersection-over-union (MIoU) on the Singapore Whole-sky Nychthemeron Image Segmentation (SWINySEG) dataset. Extensive evaluations demonstrate that MA-SegCloud performs favorably against state-of-the-art cloud image segmentation methods.
Student Writings with DeepL: Teacher Evaluations and Implications for Teaching

Technological changes have the power to disrupt standard educational practices. One recent advancement is neural machine translation (NMT) systems such as Google Translate and DeepL which due to their widespread use have already impacted foreign language education. To explore the effect of NMTs on student essay writing and teachers’ evaluation of it, a small-scale study was conducted in which students were divided into two groups, one group used the NMT DeepL and the other did not. English teachers assessed these essays by evaluating them using a standard rubric and then judging whether they believed NMT was used. Results from a Mann-Whitney U Test indicate that teachers tend to evaluate essays that used NMT higher than those that did not and they can accurately judge whether NMT was used. Implications of this study are discussed as well as possible ways to effectively use NMT in the writing classroom. As technology continues to improve, foreign language education also has to evolve with these changes. テクノロジーの変化は、標準的な教育の実践を混乱させる力を持っている。最近の進歩としては、ニューラル機械翻訳（NMT）システムの普及が外国語教育にも影響を与えている。学生と教師双方へのNMTの影響を理解するために、2つのグループの学生を対象に、一方のグループはNMTを使用し、もう一方のグループは使用しないでエッセイを書くという研究を行った。英語教師はこれらのエッセイを標準的なルーブリックで評価し、NMTが使用されているかどうかを判断した。Mann-Whitney U Testの結果から、教師はNMTを使ったエッセイをそうでないエッセイよりも高く評価する傾向があり、NMTを使って書いたかどうかを正確に判断できることが示唆された。本研究の意義は、ライティングの授業でNMTを効果的に使用する方法を議論することにある。テクノロジーが進化し続ける中、外国語教育もその変化に合わせて進化していかなければならない。
SMPP-CBIR: shorted and mixed aggregated image features for privacy-preserving content-based image retrieval

Thanks to recent breakthroughs in photographic and digital technology, enormous amounts of image data are generated daily. Many content-based image retrieval (CBIR) systems have been developed for searching image collections. However, these systems need more computer and storage resources that can be met by cloud servers, since they supply a lot of processing power at a reasonable price. The protection of users' personal information is a worry for image owners since cloud services are not exactly trustworthy. In this paper, we suggest and put into practice a CBIR (SMPP-CBIR) technique for searching and retrieving ciphertext information that protects security. Asymmetric scalar-product-preserving encryption process (ASPE) is used to preserve aggregated mixed feature vectors while still enabling computation between them to describe the related picture collection. The k-means clustering algorithm is used to recursively arrange all encrypted attributes into a tree index in order to speed up search times. The findings show that SMPP-CBIR is more scalable, more precise, and faster in indexing and retrieval than earlier systems.
20 Artificial Intelligence and Text and Data Mining: Future Rules for Libraries?

Artificial Intelligence (AI) is not a general method but an umbrella that includes many digital tools that are changing the library environment. Chatbots, document classification, personalized services, text and data mining (TDM), intelligent education, and user discovery are some of the AI tools that offer new and broad possibilities for research, access, and use of vast amounts of data. But there is growing attention and discussion on how global and domestic copyright law should allow or impede access to the use of data. This chapter examines current AI trends for libraries, focuses on TDM, and presents relevant regional and domestic copyright legislative developments on the subject. The analysis indicates that norms passed to regulate TDM vary in content and objectives from country to country, yielding an ill-suited environment for libraries to work as a channel for international research based on AI tools. It concludes with a dialog centered on the evolving relationship between knowledge, AI, TDM, and the cre­ation of new limitations and exceptions (L&Es) for copyright law. This chapter informs the reader of impacts of recent AI, TDM, and copyright developments on libraries and highlights policy developments that could bring a more uniform international legislative body of law in which libraries could operate and support researchers.
Service-Oriented Modeling for Blockchain-Enabled Supply Chain Quality Information Systems

Quality management is one of the most critical issues in supply chain management. The rapid growth of information technologies, such as blockchain technology, has facilitated effective information systems development to support supply chain quality management. However, a significant challenge in developing blockchain-enabled supply chain quality information systems is how to deal with information asymmetry and the conflicting interests of supply chain partners. Taking a service-dominant view, this research proposes a Blockchain-Oriented Service Modeling (BOSM) approach for blockchain-enabled supply chain quality information systems. We provide a visual language for modeling the coordination and integration of business processes and domain knowledge at the knowledge level to facilitate the alignment of blockchain technology with supply chain quality management. The proposed approach bridges operational service computing with strategic service management in blockchain-enabled supply chain quality management and facilitates the communication between business people in supply chain management and software professionals in blockchain-based service computing. A case study on a dairy supply chain is presented to show advantages of the modeling framework under the service-dominant view, separating the cause of quality from the carrier of quality in the design of blockchain-enabled supply chain quality information systems.
Implementation Of Qr-Code Based Point Of Sales Application For Retail Store

Point of Sales is an application used to record transactions. This application can be used to help business owners including retail stores, self-service stores, and even supermarkets so that every transaction can be supervised and tracked. This kind of application gives convenience and easiness to avoid as many as possible from transaction fraud that may happen in running the business. This kind of application has features that may fulfill the direct and in-direct needs in business transactions. POS Applications can now be installed and operated in any kind of common electronic device in society including but not limited to laptops, smartphones, and personal computers. Because of the developments of technologies, the use of cameras in every device has become common nowadays. In the context of technology, this new feature can be utilized to search and find data and even read the data in the system. To store data in the real world, the use of QR-Code has been becoming common but rarely been implemented in POS Application. Because of this kind of capability, the three of these technologies can be integrated to ease the human’s works. Several problems in business need contributions like this. In order to prove that, this research had been conducted to a retail shop by implementing Point of Sales Application and doing interviews to collect data as proof of the hypothesis. In order to do that, the research will be started to ask several questions about the needs of the business and build the application using Rapid Application Development Methodology. Rapid Application Development Method will be using the data from the interview as the requirements of system development. The Rapid Application Development Methodology was used because it has effectiveness and efficiency to build applications without wasting time and efforts in building useless features. After the application is done, then the testing will be conducted by listing all main features of the application and checking whether the application works as expected. When the testing is done, the implementation of application will be conducted directly to the business. Implementation will be meeting several issues and revisions, in this section, another interview will be conducted in order to gather information about anything that needs to be fixed or changed. After the last interview was conducted, several facts were found. It proves that what has been stated in previous research was correct. Research has found that fraud and human error that spoil the business has occurred.
Satisfaction towards virtual courses: Development and validation of a short measure in COVID-19 times

This study was aimed to develop and validate a short scale to measure satisfaction with virtual courses (SVC-S) in a sample of higher education students during the covid-19 pandemic; specifically, in the year 2021. A total of 3080 students between 16 and 56 years of age participated (Mean = 25.71; SD = 8.83); 1836 were female (59.60 %) and 1244 male (40.40 %). The participants were students from three cities in Peru (77.90% from Lima, 12.70% from Trujillo and 9.42% from Cajamarca). Qualitative and quantitative procedures were followed for the construction of the SVC-S. Item response theory (IRT) considering Samejima's two-parameter Graded Response Model (GRM) (2PL) and the test-item information function was used to establish accuracy/reliability, and the relationship of the SVC-S with a similar measure was examined to demonstrate convergence and discrimination. The results reveal that the data present an optimal fit (M2 (2) = 3.62; RMSEA = .016; CFI = 1.00). Reliability is excellent (rxx = .93) and the information function suggests that the instrument is more accurate at low levels of the latent trait. Regarding convergence with an academic satisfaction scale, the SVC-S showed an appropriate correlation (r = .70) whose average variance extracted (AVE) reported good discrimination of the constructs; despite being conceptually similar. SVC-S is concluded to be a valid and reliable measure that can be used in future studies in higher education.
Variational Graph Autoencoder with Adversarial Mutual Information Learning for Network Representation Learning

With the success of Graph Neural Network (GNN) in network data, some GNN-based representation learning methods for networks have emerged recently. Variational Graph Autoencoder (VGAE) is a basic GNN framework for network representation. Its purpose is to well preserve the topology and node attribute information of the network to learn node representation, but it only reconstructs network topology, and does not consider the reconstruction of node features. This strategy will make node representation can not well reserve node features information, impairing the ability of the VGAE method to learn higher quality representations. To solve this problem, we arise a new network representation method to improve the VGAE method for well retaining both node features and network structure information. The method utilizes adversarial mutual information learning to maximize the mutual information (MI) of node features and node representations during the encoding process of the variational autoencoder, which forces the variational encoder to get the representation containing the most informative node features. The method consists of three parts: a variational graph autoencoder includes a variational encoder (MI generator (G)) and a decoder, a positive MI sample module (maximizing MI module), and an MI discriminator (D). Furthermore, we explain why maximizing MI between node features and node representation can reconstruct node attributes. Finally, we conduct experiments on seven public representative datasets for nodes classification, nodes clustering, and graph visualization tasks. Experimental results demonstrate that the proposed algorithm significantly outperforms current popular network representation algorithms on these tasks. The best improvement is 17.13% than the VGAE method.
An Evaluation of Ubiquitous Service Discovery and Remote Management in Ad-Hoc Networks

Due to the rise of ubiquitous computing, issues around the discovery of new services have become a hot topic in the academic community. This is because futuristic ubiquitous computing frameworks as well as unsupervised Ad hoc systems will require service discovery. Automatically discovering network services and their characteristics, as well as dynamically advertising their persistence, are both possible with service discovery. Multiple service discovery technologies, including Service Discovery Protocols (SDP) runners, Universal Plug and Play (UPnP), Service Location Protocol (SLP), and Jini, have been suggested. In this paper, we detail the process of service discovery and control using Ubiquitous Remote Manager (URM), a system for remote and autonomous management of home gateways that allows for more system simplicity and finer grained command of the underlying network in a ubiquitous computing setting. Ubiquitous Service Discovery (USD) is a key component of the infrastructure of the smart homes of the future. USD's goal is to locate the most suitable service, given the user's preferences and needs, in a pervasive and extensive setting.
What Do We Mean When We Say “Presence”?

Abstract Characterizations of presence are many and various. The first part of this article applies philosophical and psychological lenses to three common descriptions: Presence as (or as following from) “the suspension of disbelief,” presence as the “illusion of nonmediation,” and presence as “(the feeling of) being there.” These construals of presence—the assumptions and commitments they make—are compared with one another, their plausibility and utility appraised. The second, shorter part of the article is not so much interested in definitions as distinctions that may help us ask better questions moving forward. We briefly consider the role of attention in presence, whether, when, or how presence is “binary,” and whether it is helpful to speak of presence in unmediated physical reality. A recurring theme throughout the article is whether a given understanding of presence (mis)construes the mind as monolithic: That is, as uniformly rational and consciously accessible. In closing, we suggest that researchers specify precisely what type of presence they're referring to whenever the concept is evoked; and that states such as involvement, absorption, and engagement be kept conceptually separate from presence, since they speak to “higher” cognitive functioning than simply feeling self-located in virtual environments, and indeed need not presuppose placeness or spatiality at all.
Learning to detect boundary information for brain image segmentation

Abstract MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module ( BIM ) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate ( BAG ) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to $$5.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> compared to the state-of-the-art models) in detecting and segmenting brain tissue images.
Summary of the 2022 Low-Power Deep Learning Semantic Segmentation Model Compression Competition for Traffic Scene In Asian Countries

The 2022 low-power deep learning semantic segmentation model compression competition for traffic scene in Asian countries held in IEEE ICME2022 Grand Challenges focuses on the semantic segmentation technologies in autonomous driving scenarios. The competition aims to semantically segment objects in traffic with low power and high mean intersection over union (mIOU) in the Asia countries (e.g., Taiwan), which contain several harsh driving environments. The target segmented objects include dashed white line, dashed yellow line, single white line, single yellow line, double dashed white line, double white line, double yellow line, main lane, and alter lane. There are 35,500 annotated images provided for model training revised from Berkeley Deep Drive 100K and 130 annotated images provided for example from Asian road conditions. Additional 2,012 testing images are used in the contest evaluation process, in which 1,200 of them are used in the qualification stage competition, and the rest are used in the final stage competition. There are in total 203 registered teams joining this competition, and the top 15 teams with the highest mIOU entered the final stage competition, from which 8 teams submitted the final results. The overall best model belongs to team “okt2077”, followed by team “asdggg” and team “AVCLab.” A special award for the best INT8 model development award is absent.
Press, Swipe, and Read: Do Interactive Features Facilitate Engagement and Learning With e-Books?

To date, many children's e-books contain hotspotsinteractive areas children can press to receive immediate auditory or visual feedback.This study assesses whether children's (N = 76, age 3-5) interactions with hotspots elicit their engagement with reading and how such interactions are related to their comprehension of the story when they use an e-book independently.In terms of reading engagement, our results suggest that when children engage with reading through interacting with hotspots, they demonstrate enhanced emotional engagement and more sustained visual attention.In terms of reading comprehension, hotspot interactions can enhance children's memorization of story content relevant to the hotspot.These findings inform several implications for the design and use of touchscreen media in early childhood.
Security Countermeasures Selection Using the Meta Attack Language and Probabilistic Attack Graphs

Connecting critical infrastructure assets to the network is absolutely essential for modern industries. In contrast to the apparent advantages, network connectivity exposes other infrastructure vulnerabilities that can be exploited by attackers. To protect the infrastructure, precise countermeasure identification is necessary. In this regard, the objective for the security officers is to identify the optimal set of countermeasures under a variety of budgetary restrictions. Our approach is based on the Meta Attack Language framework, which allows for convenient modelling of said infrastructures, as well as for automatic generation of attack graphs describing attacks against them. We formalize the problem of the selection of countermeasures in this context. The formalization makes it possible to deal with an arbitrary number of budgets, expressing available resources of both monetary and time-like nature, and to model numerous dependencies between countermeasures, including order dependencies, mutual exclusivity, and interdependent implementation costs. We propose a flexible and scalable algorithm for the problem. The whole methodology is validated in practice on realistic models.
The HAAR-the JPEG based image compression technique using singular values decomposition

A combinational approach is devised in our paper for grayscale image compression. The suggested method exploits the features of Singular Value Decomposition (SVD), Discrete HAAR Wavelet Transform (DHWT) and Joint Photographic Experts group (JPEG). Firstly, the Image is compressed by discarding some singular values from the input image. In the second step, decomposing the image up to the first level using discrete HAAR wavelet transform and the approximation coefficients is considered only. Lastly, the JPEG algorithm is applied to get further compression. The newly devised method gives a better compression ratio with good image quality than other methods. Our proposed method produces a compression ratio of 24.74 and a PSNR value of 35.1574, which is higher than other existing methods.
Toward high accuracy measurements in structured light systems

In structured-light systems, the lens distortions of the camera and the projector reduce the measurement accuracy when calibrated as a standard stereo-vision system. The conventional compensation via distortion coefficients reduces the error, but still leaves a significant residual. Recently, we proposed a hybrid calibration procedure that leverages the standard calibration approach to improve measurement accuracy. This hybrid procedure consisted of building a pixel-wise phase-to-coordinate mapping based on adjusted 3D data obtained from the standard stereo-vision method. Here, we show experimentally that the measurement accuracy can be significantly improved, even using the linear pinhole model and linear mapping functions. We then move to consider the nonlinear model to improve the measurement accuracy further. Encouraging results show that this new calibration method increases the measurement accuracy without requiring elaborate calibration procedures or sophisticated ancillary equipment.