Reviewerly/exHarmony huggingface dataset:
Lost in the middle – a pragmatic approach for ERP managers to prioritize known vulnerabilities by applying classification and regression trees (CART)

Purpose Cyber risk has significantly increased over the past twenty years. In many organizations, data and operations are managed through a complex technology stack underpinned by an Enterprise Resource Planning (ERP) system such as systemanalyse programmentwicklung (SAP). The ERP environment by itself can be overwhelming for a typical ERP Manager, coupled with increasing cybersecurity issues that arise creating periods of intense time pressure, stress and workload, increasing risk to the organization. This paper aims to identify a pragmatic approach to prioritize vulnerabilities for the ERP Manager. Design/methodology/approach Applying attention-based theory, a pragmatic approach is developed to prioritize an organization’s response to the National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) vulnerabilities using a Classification and Regression Tree (CART). Findings The application of classification and regression tree (CART) to the National Institute of Standards and Technology’s National Vulnerability Database identifies prioritization unavailable within the NIST’s categorization. Practical implications The ERP Manager is a role between technology, functionality, centralized control and organization data. Without CART, vulnerabilities are left to a reactive approach, subject to overwhelming situations due to intense time pressure, stress and workload. Originality/value To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. CART has previously not been applied to the prioritizing cybersecurity vulnerabilities.
AsyncGBP: Unleashing the Potential of Heterogeneous Computing for SSL/TLS with GPU-based Provider

The proliferation of IoT and 5G technologies has led to an explosion of data traffic that data centers must handle while ensuring secure transmission via SSL/TLS. The high volume of cryptographic operations required imposes performance bottlenecks. The GPU-based cryptographic accelerator is one of the competitive solutions. However, significant structural differences with practical applications confine their capacities to specific domains, such as offline cryptanalysis, undermining their potential for real-world cryptographic acceleration.
A computable biomedical knowledge object for calculating in‐hospital mortality for patients admitted with acute myocardial infarction

Quality indicators play an essential role in a learning health system. They help healthcare providers to monitor the quality and safety of care delivered and to identify areas for improvement. Clinical quality indicators, therefore, need to be based on real world data. Generating reliable and actionable data routinely is challenging. Healthcare data are often stored in different formats and use different terminologies and coding systems, making it difficult to generate and compare indicator reports from different sources.The Observational Health Sciences and Informatics community maintains the Observational Medical Outcomes Partnership Common Data Model (OMOP). This is an open data standard providing a computable and interoperable format for real world data. We implemented a Computable Biomedical Knowledge Object (CBK) in the Piano Platform based on OMOP. The CBK calculates an inpatient quality indicator and was illustrated using synthetic electronic health record (EHR) data in the open OMOP standard.The CBK reported the in-hospital mortality of patients admitted for acute myocardial infarction (AMI) for the synthetic EHR dataset and includes interactive visualizations and the results of calculations. Value sets composed of OMOP concept codes for AMI and comorbidities used in the indicator calculation were also created.Computable biomedical knowledge (CBK) objects that operate on OMOP data can be reused across datasets that conform to OMOP. With OMOP being a widely used interoperability standard, quality indicators embedded in CBKs can accelerate the generation of evidence for targeted quality and safety management, improving care to benefit larger populations.
Deep Learning untuk Identifikasi Daun Tanaman Obat Menggunakan Transfer Learning MobileNetV2

Medicinal plants are plants used as alternative medicines for healing or preventing various diseases due to their active substances. The utilization of medicinal plants in Indonesia has been widespread among the community since ancient times and is a heritage passed down from ancestors. Medicinal plants have leaf structures that are almost similar between one plant and another, which can lead to confusion for some people and require precision in identifying the leaves of medicinal plants. Incorrect identification can have negative consequences for the users. In recent years, deep learning has been used to identify objects because of its ability to interpret images. This study used a transfer learning method to identify medicinal plants. Transfer learning utilizes a pre-trained model to learn and perform new tasks, making it suitable for smaller datasets. The pre-trained model used in this study is MobileNetV2. MobileNetV2 has a lightweight architecture and high accuracy. Fine-tuning techniques were applied in this study to improve the model's performance. Several experiments were conducted with parameters such as epochs and fine-tuning layers to obtain the best results. The research yielded a training accuracy of 97%, validation accuracy of 96%, and testing accuracy of 93%.
Some Analytic Systems of Rules

Abstract We define two simple systems of rules, i.e. calculi with a global condition on the order of rule instances in a proof, for the modal logics of shift-reflexive and Euclidean frames respectively. Cut-elimination, and therefore the subformula property, can be derived directly from the cut-elimination property of adjacent logics. We compare our system to the calculus of grafted hypersequents, which has previously been used to capture both logics. We then discuss an attempt to obtain similar ‘modular’ cut-elimination proofs in other systems of rules. This general attempt is carried out for two more logics, namely the modal logic of serial frames and the intermediate logic axiomatised by the law of the weak excluded middle.
THE STUDY OF THE LEVEL OF SUBJECTIVE CONTROL OF STUDENTS WITH DIFFERENT TYPES OF CULTURAL AND VALUE ORIENTATIONS

The article is devoted to the study of the level of subjective control of students with different types of cultural and value orientations. The relevance of the research is due to the fact that the phenomenon of the level of subjective control determines the responsibility of an individual, the direction of his actions, which is especially significant for modern society during the period of mastering the profession. The formation of the level of subjective control of a personality is influenced by its individual typological features, upbringing, social environment and culture. Therefore, in order to build strategies to optimize the professional training of modern students, it is important to know their belonging to a particular culture.&#x0D; The purpose of the article is to consider the specifics of the relationship between the level of subjective control of students with different types of cultural and value orientations.&#x0D; Methods: questionnaire of USK J. Rotter’s adaptation by E.F. Bazhin, S.A. Golynkina, A.M. Etkind; test of cultural and value orientations by J.Townsend’s adaptation by L.G. Pochebut.&#x0D; As a result of the research, students belong to three types of cultures: traditional, modern and dynamically developing.The features of the indicators of internality of students of each type of culture in different spheres (achievements, failures, family, industrial and family relations) are identified and analyzed. It is proved that belonging to the type of culture has a statistically significant effect on the level of subjective control of the individual.
PENINGKATAN AKTIVITAS BELAJAR MATEMATIKA SECARA DARING DENGAN MENGGUNAKAN APLIKASI WHATSAPP DI SMA NEGERI 2 IV KOTO

The purpose of this study was to increase students' mathematics learning activities at SMAN 2 IV Koto. The pandemic situation greatly affected teaching and learning activities. By using social media, face-to-face substitute learning can be carried out in class. Several social media applications have been tried, and with the WhatsApp application 80% of students can take part in learning by integrating between sending photos of material summaries, sample questions, explanations with audio messages and questions and answers before giving assignments. With the recitation method which is integrated by sending subject matter and photos, followed by questions and answers and giving independent assignments through the WhatsApp application, students' mathematics learning activity increases to 85% ABSTRAKTujuan penelitian ini adalah untuk meningkatkan aktivitas belajar matematika siswa di SMAN 2 IV Koto. Situasi pandemi sangat mempengaruhi kegiatan belajar mengajar. Dengan menggunakan sosial media, dapat dilaksanakan pembelajaran pengganti tatap muka di kelas. Beberapa aplikasi sosial media sudah dicoba, dan dengan aplikasi whatsapp 80% siswa dapat mengikuti pembelajaran dengan cara mengitegrasikan antara pengiriman foto ringkasan materi, contoh soal, penjelasan dengan pesan audio dan Tanya jawab sebelum pemberian tugas. Dengan metode resitasi yang diintegrasikan dengan cara pengiriman materi pelajaran dan foto, dilanjutkan dengan tanya jawab dan pemberian tugas mandiri melalaui aplikasi whatsap, aktivitas belajar matematika siswa meningkat menjadi 85%.
Controlling stability through the rate of decay of the delay feedback kernels

&lt;abstract&gt;&lt;p&gt;Of concern is the Hopfield neural network system comprising discrete as well as distributed delays in the form of a convolution. For a desired convergence rate of the solution to the equilibrium state, we establish sufficient conditions on the delay kernels ensuring this matter. Our result improves an existing one in the literature. The adopted approach is completely different. It relies on a judicious choice of a Lyapunov-like function and careful manipulations.&lt;/p&gt;&lt;/abstract&gt;
Geometry of Deep Learning

Since the groundbreaking performance improvement by AlexNet at the ImageNet challenge, deep learning has provided significant gains over classical approaches in various fields of data science including imaging reconstruction. The availability of large-scale training datasets and advances in neural network research have resulted in the unprecedented success of deep learning in various applications. Nonetheless, the success of deep learning appears very mysterious. The basic building blocks of deep neural networks are convolution, pooling, and nonlinearity, which are primitive tools of mathematics. Interestingly, the cascaded connection of these primitive tools results in superior performance over traditional approaches. To understand this mystery, one can go back to the basic ideas of the classical approaches to understand the similarities and differences from modern deep-neural-network methods. In this chapter, we explain the limitations of the classical machine learning approaches, and provide a review of mathematical foundations to understand why deep neural networks have successfully overcome their limitations.
Personalized Beverage Blending System Using Interactive Genetic Algorithm and Its Search Analysis

In this study, we improved the dispensing accuracy of the automatic juice blending system in the previous study to optimize the mixing ratio of juices that match the preference of each user using an interactive genetic algorithm (IGA). We verify the properties of the solution obtained by IGA by conducting subject experiments under an improved experimental environment that allows adjustment of the discharging of each juice with an accuracy of 100 ms. In addition, we analyze the difference in convergence tendency from the experimental results of the subject experiments when the design variable space is expanded.
Analysis of blood cell image recognition methods based on improved CNN and Vision Transformer

Leukemia is a common and highly dangerous blood disease that requires early detection and treatment. Currently, the diagnosis of leukemia types mainly relies on the pathologist's morphological examination of blood cell images, which is a tedious and time-consuming process, and the diagnosis results are highly subjective and prone to misdiagnosis and missed diagnosis. This research suggests a blood cell image recognition technique based on an enhanced Vision Transformer to address these problems. Firstly, this paper incorporate convolutions with token embedding to replace the positional encoding which represent coarse spatial information. Then based on the Transformer's self-attention mechanism, this paper proposes a sparse attention module that can select identifying regions in the image, further enhancing the model's fine-grained feature expression capability. Finally, this paper uses a contrastive loss function to further increase the intra-class consistency and inter-class difference of classification features. According to experimental results, The model in this study has an identification accuracy of 92.49% on the Munich single-cell morphological dataset, which is an improvement of 1.41% over the baseline. And comparing with sota Swin transformer, this method still get greater performance. So our method has the potential to provide reference for clinical diagnosis by physicians.
Demo: Decision Support System Quality Assessment Tool

This demo showcases the DSS Quality Assessment Tool, an online service developed at the MUDI Lab (University of Milano-Bicocca, Milan, Italy) for the holistic assessment of the quality of AI-based decision support systems (DSS), along four quality dimensions: data similarity, calibration, robustness, and human interaction.
Alleviating the Long-Tail Problem in Conversational Recommender Systems

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, i.e., a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored.
Demo: VR4Green - Walk through the (Visual) Effects of Climate Change

Climate crisis has become one of the main challenges of the 21st century. However, there appears to be a lack of awareness among individuals regarding the impact of our daily routines on the environment. Virtual Reality has shown to be an effective technology to virtually bring people to emotional scenarios, such as, in this case, a dystopian future. This work introduces a futuristic depiction of the city of Torino, Italy, in Virtual Reality, showcasing the alarming consequences of climate change on nature, individuals, and urban infrastructure. It includes a representation of the city center, and a virtual house with household appliances, where users can run their daily activities and see the consequences of energy consumption. The aim is to raise consciousness and understanding of the potential personal impact on climate change.
How Do Users Perceive Deepfake Personas? Investigating the Deepfake User Perception and Its Implications for Human-Computer Interaction

Although deepfakes have a negative connotation in human-computer interaction (HCI) due to their risks, they also involve many opportunities, such as communicating user needs in the form of a “living, talking” deepfake persona. To scope and better understand these opportunities, we present a qualitative analysis of 46 participants’ think-aloud transcripts based on interacting with deepfake personas and human personas, representing a potentially beneficial application of deepfakes for HCI. Our qualitative analysis of 92 think-aloud records indicates five central user deepfake themes, including (1) Realism, (2) User Needs, (3) Distracting Properties, (4) Added Value, and (5) Rapport. The results indicate various challenges in deepfake user perception that technology developers need to address before the potential of deepfake applications can be realized for HCI.
Deep Situation-Aware Interaction Network for Click-Through Rate Prediction

User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain rich interaction information, such as the behavior type, time, location, etc. However, so far, the information related to user behaviors has not yet been fully exploited. In the paper, we propose the concept of a situation and situational features for distinguishing interaction behaviors and then design a CTR model named Deep Situation-Aware Interaction Network (DSAIN). DSAIN first adopts the reparameterization trick to reduce noise in the original user behavior sequences. Then it learns the embeddings of situational features by feature embedding parameterization and tri-directional correlation fusion. Finally, it obtains the embedding of behavior sequence via heterogeneous situation aggregation. We conduct extensive offline experiments on three real-world datasets. Experimental results demonstrate the superiority of the proposed DSAIN model. More importantly, DSAIN has increased the CTR by 2.70%, the CPM by 2.62%, and the GMV by 2.16% in the online A/B test. Now, DSAIN has been deployed on the Meituan food delivery platform and serves the main traffic of the Meituan takeout app. Our source code is available at https://github.com/W-void/DSAIN.
Retracted: Dilemma and Solution of Copyright Justice System for Health Short Video under Network Big Data Environment Monitoring

[This retracts the article DOI: 10.1155/2022/9640574.].
Retracted: System Design for Online Foreign Language Education Based on Blockchain Technology

[This retracts the article DOI: 10.1155/2022/5180307.].
Retracted: Intelligent Physical Training Data Processing Based on Wearable Devices

[This retracts the article DOI: 10.1155/2022/1207457.].
Retracted: Exploring Wavelet Transform-Based Image Enhancement Algorithm for Image Restoration of Long March National Cultural Park

[This retracts the article DOI: 10.1155/2022/1434763.].
Retracted: Evaluation of College Students’ IPE Environment Based on User Rating Sparsity and Big Data Technology

[This retracts the article DOI: 10.1155/2022/3183195.].
Retracted: Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

[This retracts the article DOI: 10.1155/2022/4545125.].
Retracted: An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach

[This retracts the article DOI: 10.1155/2022/5489084.].
Retracted: An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

[This retracts the article DOI: 10.1155/2022/7276028.].
Retracted: Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

[This retracts the article DOI: 10.1155/2022/1779131.].
Retracted: Application of Computer-Aided Instruction (CAI) in the Physical Education: Survey Analysis of Chinese Universities

[This retracts the article DOI: 10.1155/2021/1328982.].
Retracted: Monitoring and Model Analysis of Vocal Performance Teaching Environment Using Cluster Analysis from the Perspective of Core Literacy

[This retracts the article DOI: 10.1155/2022/1477309.].
Retracted: Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

[This retracts the article DOI: 10.1155/2022/8552358.].
Hubungan Antara Perhatian Orang Tua dan Fasilitas Belajar dengan Prestasi Belajar Siswa

This study aims to determine the relationship between the variables of parental attention (X1) and learning facilities (X2) on student achievement (Y). This research is a type of descriptive and expost facto research. The population in this study were all first grade students of SMA Negeri 11 Semarang, South Semarang, Semarang City, Central Java, which consisted of 15 classes with a total of 430 students. The research sample was taken by random sampling, namely two classes were taken with a total of 70 students. The data collection method is a questionnaire to obtain data on parental attention and learning facilities, and documentation for learning achievement data. The data analysis technique used a regression analysis equation of two independent variables. The results of the analysis obtained the multiple regression equation Yˆ = 4.698 + 0.01X + 0.1X, and the coefficient of determination was 0.163 or 16.3%. This means that the contribution of the variables of parental attention and learning facilities together on student achievement at SMA Negeri 11 Semarang, South Semarang, Semarang City, Central Java is 16.3%, through the regression equation. Furthermore, the significance was tested using the F test. It turned out that FCount was 6.505 greater than FTable with dk = 67 and a significant level of 5% was 3.140. This means that the multiple regression model obtained can be used to predict learning achievement. Based on the data analysis, it can be explained that there is a positive and significant relationship between parental attention and learning facilities together with the learning achievement of first grade students at SMA Negeri 11 Semarang, South Semarang, Semarang City, Central Java.
Roman Jakobson and the Aleut materials collected by Waldemar Jochelson

This paper presents the text of a 1941 work by Roman Jakobson, which has never been fully published, — his description of Vladimir (Waldemar) Jochelson’s Aleut materials (the materials, as well as Jakobson’s description, are stored at the New York Public Library). The publication is provided with a detailed commentary.
Cryptography

Bitcoin gave rise to the specific type of virtual currency known as cryptocurrency. The reason for this name is the extended use of cryptography. A fundamental purpose of cryptography is to maintain confidentiality and integrity in communication, which is done through encryption. Many of the core technologies of Bitcoin were originally developed in the field of cryptography. A review of the most important cryptographic techniques is therefore necessary in order to understand Bitcoin.
Beyond

We saw in the introduction how Bitcoin might be described as a hyperobject that transcends local time and space. But Bitcoin does not seem reactive and inert like an object. It rather resembles a dynamic purposefull entity that could be called a hyper organism. The question is then whether Bitcoin might have more in common with living organisms.
Social Organization

Bitcoin cannot be compared to any other currency in our contemporary world with respect to the social engagement it elicits. Nobody is tweeting incessantly about the Moroccan dirham or engaging in passionate defenses of the Vietnamese dong. Nobody goes to conferences where the theme is the Swedish krona, and nobody lifts a finger to divine the future of the Brunei dollar. Bitcoin is a product of and produces a very particular social organization. In this chapter, we will focus on the different social groups and movements that played a significant part in developing a cryptographic virtual currency that would result in Bitcoin.