Reviewerly/exHarmony huggingface dataset:
How Composable is the Web? An Empirical Study on OpenAPI Data model Compatibility

Composing Web APIs is a widely adopted practice by developers to speed up the development process of complex Web applications, mashups, and data processing pipelines. However, since most publicly available APIs are built independently of each other, developers often need to invest their efforts in solving incompatibility issues by writing ad-hoc glue code, adapters and message translation mappings. How likely are Web APIs to be directly composable?The paper presents an empirical study to determine the potential composability of a large collection of 20,587 public Web APIs by verifying their schemas’ compatibility. We define three levels of data model elements compatibility – considering matches between property names and/or data types – which can be determined statically based on API descriptions conforming to the OpenAPI specification. The study research questions address: to which extent are Web APIs compatible; the average number of compatible endpoints within each API; the likelihood of finding two APIs with at least one pair of compatible endpoints.To perform the analysis we developed a compatibility checker tool which can statically determine API schema compatibility on the three levels and find matching pairs of API responses which can be directly forwarded as requests to the same or other APIs. We run the tool on a dataset of 751,390 request and response message schemas extracted from publicly available OpenAPI descriptions.The results indicate a relatively high number of compatible APIs when matching their data models only on the level of their elements’ data type. However, this number gets lower narrowing the scope to only the ones handling data objects having identical properties name. The average likelihood of finding two compatible APIs with both matching property names and data types reaches 21%. Also, the number of compatible endpoints within the same API is very low.
Landmarks in Case-Based Reasoning: From Theory to Data

Widespread application of uninterpretable machine learning systems for sensitive purposes has spurred research into elucidating the decision making process of these systems. These efforts have their background in many different disciplines, one of which is the field of AI &amp; law. In particular, recent works have observed that machine learning training data can be interpreted as legal cases. Under this interpretation the formalism developed to study case law, called the theory of precedential constraint, can be used to analyze the way in which machine learning systems draw on training data – or should draw on them – to make decisions. These works predominantly stay on the theoretical level, hence in the present work the formalism is evaluated on a real world dataset. Through this analysis we identify a significant new concept which we call landmark cases, and use it to characterize the types of datasets that are more or less suitable to be described by the theory.
Academic leadership in the context of the development of transprofessional identity in the conditions of digitalization and regionalization

The purpose of the article is to develop a theoretical concept and adequate methodological tools for the study of academic leadership as a factor in the development of transprofessional identity and the formation of a new economy market in the conditions of digitalization of higher education. Academic leadership as a factor of transprofessional identity and the formation of the labor market is in demand in the context of the dominant digitalization trends in higher education. This is expressed in the fact that within the framework of the formation of the labor market of the future, the request of specialists focused on transprofessional identity, a fixed attitude to change professional identity under the influence of the «supply-demand» scheme in the labor market becomes relevant. It is obvious that in the modern conditions of higher education development, changes in the organization, structure of training, dispositions of education actors are associated with a semantic reset of academic leadership. Outside of the analysis of this process, higher education does not realize the functions of training specialists for the market of the new economy and creates blocking effects on the way of introducing the latest technologies, digitalization of higher education. In this context, academic leadership is interpreted as a set of factors and conditions aimed at restructuring the higher education system according to the criteria of readiness for transprofessional identity and competitiveness in the market of the new economy.
Digital Competence of Higher Education Professors in the European Context

The aim of this article is to map the current state of knowledge on the digital competences of higher education professors in the European context through a scoping review. The initial search in resulted in 1,568 publications, which, after applying the selection criteria, resulted in the inclusion of 14 publications. The content analysis showed that the digital tools made available by the intuitions are widely known by the professors, although they have little use. A broad use of the LMS was evidenced, but more specifically to administrative activities than to activities connected with the teaching and learning process. There was also a positive relationship between the level of digital competences and the pedagogical approach adopted by professors (focus on the construction of knowledge by the students, development of open education resources) as well as the participation in research projects. Teachers’ training was pointed out as a relevant topic and the literature analysed referred that this should be preceded by a rigorous analysis of the professors’ digital competence levels so that this process can be carried out in the most personalized way possible.
Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels

Supervised training with deep learning has exhibited impressive performance in numerous medical image domains. However, previous successes rely on the availability of well-labeled data. In practice, it is a great challenge to obtain a large high-quality labeled dataset, especially for the medical image segmentation task, which generally needs pixel-wise labels, and the inaccurate label (noisy label) may significantly degrade the segmentation performance. In this paper, we propose a novel Meta Pixel Loss Correction (MPLC) based on a simple meta guided network for the medical segmentation that is robust to noisy labels. The core idea is to estimate a pixel transition confidence map by meta guided network to take full advantage of noisy labels for pixel-wise loss correction. To achieve this, we introduce a small size of meta dataset with the meta-learning method to train the whole model and help the meta guided network automatically learn the pixel transition confidence map in an alternative training manner. Experiments have been conducted on three medical image datasets, and the results demonstrate that our method is able to achieve superior segmentation with noisy labels compared to the existing state-of-the-art approaches.
Query Optimization Framework for Graph Database in Cloud Dew Environment

The query optimizer uses cost-based optimization to create an execution plan with the least cost, which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem, which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory, and disk access speeds in recent decades, allowing data compression to be used-strategies for improving database performance systems.For performance enhancement, compression and query optimization are the two most factors.Compression reduces the volume of data, whereas query optimization minimizes execution time.Compressing the database reduces memory requirement, data takes less time to load into memory, fewer buffer missing occur, and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering, which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.
Post Quantum Design in SPDM for Device Authentication and Key Establishment

The Security Protocol and Data Model (SPDM) defines a set of flows whose purpose includes the authentication of a computing device’s hardware identity. SPDM also allows for the creation of a secure session wherein data communication between two devices has both confidentiality and integrity protection. The present version of SPDM, namely version 1.2, relies upon traditional asymmetric cryptographic algorithms, and these algorithms are known to be vulnerable to quantum attacks. This paper describes the means by which support for post-quantum (PQ) cryptography can be added to the SPDM protocol in order to prepare SPDM for the upcoming world of quantum computing. As part of this paper, we examine the SPDM 1.2 protocol and discuss various aspects of using PQC algorithms, including negotiation of the use of post-quantum cryptography (PQC) algorithms, support for device identity reporting, mechanisms for device authentication, and establishing a secure session. We consider so-called “hybrid modes” where both classical and PQC algorithms are used to achieve security properties, especially given the fact that these modes are important during the transition period from the classical to the quantum computing regime. We also share our experience with implementing a software embodiment of PQC in SPDM, namely “PQ-SPDM”, and we provide benchmarks that evaluate a subset of the winning NIST PQC algorithms.
Hepatic Vein and Arterial Vessel Segmentation in Liver Tumor Patients

Preoperative observation of liver status in patients with liver tumors by abdominal Computed Tomography (CT) imaging is one of the essential references for formulating surgical plans. Preoperative vessel segmentation in the patient's liver region has become an increasingly important and challenging problem. Almost all existing methods first segment arterial and venous vessels on CT in the arterial and venous phases, respectively. Then, the two are directly registered to complete the reconstruction of the vascular system, ignoring the displacement and deformation of blood vessels caused by changes in body position and respiration in the two phases. We propose an unsupervised domain-adaptive two-stage vessel segmentation framework for simultaneous fine segmentation of arterial and venous vessels on venous phase CT. Specifically, we first achieve domain adaptation for arterial and venous phase CT using a modified cycle-consistent adversarial network. The newly added discriminator can improve the ability to generate and discriminate tiny blood vessels, making the domain-adaptive network more robust. The second-stage supervised training of arterial vessels was then performed on the translated arterial phase CT. In this process, we propose an orthogonal depth projection loss function to enhance the representation ability of the 3D U-shape segmentation network for the geometric information of the vessel model. The segmented venous vessels were also performed on venous phase CT in the second stage. Finally, we invited professional doctors to annotate arterial and venous vessels on the venous phase CT of the test set. The experimental results show that the segmentation accuracy of arterial and venous vessels on venous phase CT is 0.8454 and 0.8087, respectively. Our proposed framework can simultaneously achieve supervised segmentation of venous vessels and unsupervised segmentation of arterial vessels on venous phase CT. Our approach can be extended to other fields of medical segmentation, such as unsupervised domain adaptive segmentation of liver tumors at different CT phases, to facilitate the development of the community.
Ecolinguistic Approaches in Development of English Teaching Materials for English for Business

This study aims at finding an English learning model using an ecolinguistic approach in the preparation of syllabus and teaching materials. Qualitative descriptive methods are applied and natural phenomena are used to develop syllabus. This phenomenon focuses on learning and teaching strategies that are applied. Data obtained through tests, questionnaires and direct observation for needs analysis. The syllabus was developed based on the results of material analysis and needs.The ecological approach in education has the aim to preserve nature and its contents by building an environmentally friendly character. From this rationale, the ecological approach in language education has a number of characters that are used to find new ways to be applied to language learning. The result of the study shows that teaching materials have met the characteristics of the ecolinguistic approach, namely: relations, context, patterns / systems, emergence, quality, value, critical, variability, diversity, and activity. These characteristics are used as a theoretical basis for this research.
Deep Feature Interactive Aggregation Network for Single Image Deraining

Single image deraining task aims at removing rain streaks from a degraded input and reconstructing the high-quality image. In recent years, image processing tasks mostly apply the U-shaped architecture to capture rich contextual information. However, it is difficult to achieve long-range pixel dependencies due to the local receptive field of the convolution operation. In this paper, we propose a deep feature interactive aggregation network for single image deraining to enhance the long-range dependencies among features and realize the interaction of information. To fully utilize the high-level semantic features, we design a long-range dependency feature aggregation module to greatly improve the representational ability of the original U-shaped architecture. It aggregates multi-scale features and calculates the interactive attention of non-overlapping patches among feature maps. In addition, we adopt group normalization to retain the independence of each given image. It interacts with the information among features in an individual image and normalizes the channels of each group to weaken the correlation between batch data processing. Experimental results on widely acknowledged datasets also demonstrate the superiority of our proposed network over previous state-of-the-art methods.
MD3D: Mixture-Density-Based 3D Object Detection in Point Clouds

The design factors of anchor boxes, such as shape, placement, and target assignment policy, greatly influence the performance and latency of the 3D object detectors. Unlike image-based 2D anchors, 3D anchors must be placed in a 3D space and determined differently for each class of different sizes. This imposes a significant burden on the design complexity. To tackle this issue, various studies have been conducted on how to set the anchor form. However, for practical reasons, anchor-based methods select the anchor design by compromising between performance and latency. Consequently, only objects that are similar in shape and size to an anchor can obtain high accuracy. In this paper, we propose a Mixture-Density-based 3D Object Detection (MD3D) in point clouds to predict the distribution of 3D bounding boxes using a Gaussian Mixture Model (GMM). With an anchor-free detection head, MD3D requires few hand-crafted design factors and eliminates the inefficiency of separating the regression channel for each class, and thus offering both latency and memory benefits. MD3D is designed to utilize various types of feature encoding; therfore, it can be applied flexibly by replacing only the detection head of the existing detectors. Experimental results on the KITTI and Waymo open datasets show that the proposed method outperforms its counterparts that are based on the conventional anchor-based detection head in its overall performance, latency, and memory. The code is publicly available at https://github.com/sky77764/MD3D.
Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot hTetro

Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. However, frequent cleaning tasks adversely impact the robot's performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. In this scheme, the robot will clean only dirty areas instead of the whole region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption.
Deep learning techniques for detecting and recognizing face masks: A survey

The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.
Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot

This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.
Calculating the Moore–Penrose Generalized Inverse on Massively Parallel Systems

In this work, we consider the problem of calculating the generalized Moore–Penrose inverse, which is essential in many applications of graph theory. We propose an algorithm for the massively parallel systems based on the recursive algorithm for the generalized Moore–Penrose inverse, the generalized Cholesky factorization, and Strassen’s matrix inversion algorithm. Computational experiments with our new algorithm based on a parallel computing architecture known as the Compute Unified Device Architecture (CUDA) on a graphic processing unit (GPU) show the significant advantages of using GPU for large matrices (with millions of elements) in comparison with the CPU implementation from the OpenCV library (Intel, Santa Clara, CA, USA).
Bearing Vibration Signal Fault Diagnosis Based on LSTM-Cascade CatBoost

&lt;p&gt;Bearing is one of the most concerned parts in the field of fault diagnosis. At present, there are numerous excellent algorithms applied to bearing fault detection. This paper proposes a new fault bearings diagnosis model named LSTM-Cascade CatBoost, which can directly classify bearing vibration signals in the case of multiple granularity and high dimensions without signal processing. The model is based on gcForest, whose complexity can be adjusted automatically to the size of data set and it uses LSTM to extract features of time series signals instead of multi-grained scanning for improving the model&amp;rsquo;s feature extraction ability. CatBoost is used as the base classifier of cascade forest to improve the classification accuracy of the model. Experimental results show the fact that this model is highly accurate in CWRU and XJTU-SY datasets. Besides, it not only proves that the feature extraction ability of LSTM is significantly better than that of multi-grained scanning, but CatBoost as a base classifier can further improve the accuracy of cascade forest.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A Framework for Modeling and Detecting Security Vulnerabilities in Human-Machine Pair Programming

&lt;p&gt;To detect and mitigate security vulnerabilities early in the coding phase is an important strategy for secure software development. Existing solutions typically focus on finding certain vulnerabilities in certain computer systems without giving a systematic way of handling different types of vulnerabilities. In this paper, we present a framework for systematically modeling and detecting potential security vulnerabilities during the construction of programs using a particular programming paradigm known as Human-Machine Pair Programming. The framework provides designers with a general way of modeling a class of attacks in detail, and shows how programmers can discover and fix a vulnerability in a timely manner. Specifically, our framework advocates three primary steps: (1) generating an attack tree to model a given security threat, (2) constructing vulnerability-matching patterns based on the result of the attack tree analysis, and (3) detecting corresponding vulnerabilities based on the patterns during the program construction. We also present a case study to demonstrate how it works in practice.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;