Reviewerly/exHarmony huggingface dataset:
Vulnerability of Package Dependency Networks

Software reuse by importing packages from centralised repositories is an efficient and increasingly widespread way to develop software. Given the transitivity of dependencies, defects introduced in the repository can have extensive effects on the software ecosystem. Drawing from complex network theory, we define a model of repository vulnerability based on the statistically expected damage that the repository sustains from the random introduction of software defects. We test the model in stylized networks derived from real repositories, PyPI, Maven and npm, and show that the existence of a giant strongly connected component (SCC) explains most of the vulnerability. Indeed, we found that theoretical protection (immunization) of this entire component would remove almost all vulnerability from the network. Since repositories and their communities have limited resources to mitigate issues, we further model the problem of how to best apply these resources, finding sets much smaller than the giant SCC whose protection is nearly as good. Furthermore, we prove that the optimal selection of sets of given size is NP-Hard but can be approached with heuristics, yielding respectable results. Our model contributes to a better understanding of software package repositories and could also be applied to other systems with a similar structure.
Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

The time-variant Lyapunov equation (TVLE) has played an important role in many fields due to its ubiquity and many neural dynamics models have been developed to obtain the online solution of the TVLE. In prevalent methods, the gradient neural dynamics (GND) models suffer from the large residual error due to the lack of predictive computing, while the zero neural dynamics (ZND) models have large computing complexity because of the inverse of the mass matrix in models. To mitigate these deficiencies, an adaptive parameter containing the time-derivative of time-variant parameters in the TVLE is added to the GND model to form the adaptive GND (AGND) model, which enables the AGND model predictive computing as ZND models and inherits the free of matrix inverse from the GND models. Moreover, two strategies are proposed to design the accelerated AGND (AAGND) models that enjoy a faster convergence rate. The accuracy and the convergence rate of AAGND models are theoretically analyzed, indicating that AAGND models achieve zero residual error and a faster convergence rate. In addition, numerical simulations and two applications are provided to verify the theoretical analyses and the efficiency of AAGND models. The experimental results demonstrate that the AAGND model can solve the TVLE with high accuracy and have great potentialities in applications.
Hierarchical Active Learning With Qualitative Feedback on Regions

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to data regions, which are subspaces of the input data space, representing subpopulations of data. Since labeling now is performed on a region level, 0/1 labeling becomes imprecise. Thus, we design the region label to be a qualitative assessment of the class proportion, which coarsely preserves the labeling precision but is also easy for humans to do. To identify informative regions for labeling and learning, we further devise a hierarchical active learning process that recursively constructs a region hierarchy. This process is semisupervised in the sense that it is driven by both active learning strategies and human expertise, where humans can provide discriminative features. To evaluate our framework, we conducted extensive experiments on nine datasets as well as a real user study on a survival analysis of colorectal cancer patients. The results have clearly demonstrated the superiority of our region-based active learning framework against many instance-based active learning methods.
Manifold Regularized Joint Transfer for Open Set Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to leverage knowledge of a well-labeled source domain to learn an effective classifier for an unlabeled target domain. However, a common scenario in real-world applications is that the target domain contains unknown categories that are not observed in the source domain. This setting is termed as open set domain adaptation (OSDA). Most existing approaches of OSDA can only classify known classes well but fail to recognize unknown samples effectively. In this paper, we propose an effective method, named manifold regularized joint transfer (MRJT), for OSDA. MRJT learns new feature representations by simultaneously reducing distribution discrepancy between domains, increasing compactness of within-class, discriminating different known classes, and distinguishing the unknown from the known. The learned new features are projected onto reproducing kernel Hilbert space. In this space, a weighted structural risk minimization method is integrated with manifold regularization to utilize geometric information sufficiently to learn an effective classifier. Extensive experimental results on four real-world datasets verify the superiority of our method. It can not only classify known samples into the right known classes but also recognize unknown samples effectively.
A Novel Image Cryptosystem Inspired by the Generation of Biological Protein Sequences

Image encryption is essential to keep images secure either in personal devices or over the cloud drives. Various robust image cryptosystems have been implemented during the past decades to keep the encrypted images far from illegal decryption by eavesdroppers. Biological sequences contain high density information that introduce new challenges and opportunities to traditional cryptography. This article presents a novel image cryptosystem, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Image-to-Protein (I2P)</i> , that is based on successively encoding the image pixels into sequences of symbols analogous biological <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNA</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Protein</i> sequences, and then scrambling these sequences into frequent and non-frequent patterns which in turn are binary encoded into the encrypted file. The proposed cryptosystem has been applied on experimental grayscale and color images. The large key space and the complicated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Protein</i> encoding operations guaranteed resistance to many known cryptographic attacks. Furthermore, the biological encoding of the plain image pixels resulted in high similarity that achieved noticeable lossless compression when compressed using known text compression techniques.
Efficient Fine-Grained Object Recognition in High-Resolution Remote Sensing Images From Knowledge Distillation to Filter Grafting

With the development of high-resolution remote sensing images (HR-RSIs) and the escalating demand for intelligent analysis, fine-grained recognition of geospatial objects has become a more practical and challenging task. Although deep learning-based object recognition has achieved superior performance, it is inflexible to be directly utilized to the fine-grained object recognition tasks of HR-RSIs under the limitation of the size of geospatial objects. An efficient fine-grained object recognition method in HR-RSIs from knowledge distillation to filter grafting is proposed. Specifically, fine-grained object recognition consists of two stages: Stage 1 utilizes oriented region convolutional neural network (oriented R-CNN) to accurately locate and preliminarily classify geospatial objects. At the same time, it serves as a teacher network to guide students’ effective learning of fine-grained object recognition; in Stage 2, we design a coarse-to-fine object recognition network (CF-ORNet), as the second teacher network, which realizes fine-grained recognition through feature learning and category correction. After that, we propose a lightweight model from knowledge distillation to filter grafting on two teacher networks to achieve efficient fine-grained object recognition. The experimental results on VEDAI and HRSC2016 datasets achieve competitive performance.
A Comparative Analysis on the Model Adequacy of Four Transformation Techniques

ABSTRACT This study compared four different transformation techniques by applying a simple linear regression on raw and transformed data. The R2 of each model was obtained and a test on the significance of these R2 was carried out. Also, the rxy(coefficient of correlation) were also obtained. The data used is a secondary data consisting of 53years (1964-2016) of the infant mortality rate in Nigeria (https://www.ceicdata.com/en/nigeria/health-statistics/ng-mortality-rate-infant-per-1000-live-births). The rxy were also compared and the results, 95.8%, 95.8%, 96.2%, 93.0%, and 92.9% respectively. The R2 obtained for the raw data, logarithm, square-root, square and inverse are as follows: 91.8%, 91.7%, 92.5%, 86.6% and 86.4% respectively. However, the R2 obtained for the raw data, logarithm, square-root, square and inverse compete favourably but the performance of inverse transformation suits the data most in terms of model accuracy. Key Words: Transformation, Raw data, logarithm, square-root, square, inverse
Object Recognition with Class Conditional Gaussian Mixture Model - A Statistical Learning Approach

Object recognition is one of the key tasks in robot vision. In RoboCup SPL, the Nao Robot must identify objects of interest such as the ball, field features et al. These objects are critical for the robot players to successfully play soccer games. We propose a new statistical learning method, Class Conditional Gaussian Mixture Model (ccGMM), that can be used either as an object detector or a false positive discriminator. It is able to achieve a high recall rate and a low false positive rate. The proposed model has low computational cost on a mobile robot and the learning process takes a relatively short time, so that it is suitable for real robot competition play.
Tech United Eindhoven @Home 2022 Champions Paper

This paper provides an overview of the main developments of the Tech United Eindhoven RoboCup@Home team. Tech United uses an advanced world modeling system called the Environment Descriptor. It allows for straightforward implementation of localization, navigation, exploration, object detection & recognition, object manipulation and human-robot cooperation skills based on the most recent state of the world. Other important features include object and people detection via deep learning methods, a GUI, speech recognition, natural language interpretation and a chat interface combined with a conversation engine. Recent developments that aided with obtaining the victory during RoboCup 2022 include people and pose recognition, usage of HSR’s display and a new speech recognition system.
A Library and Web Platform for RoboCup Soccer Matches Data Analysis

An important part of ensuring continuous development and betterment of performance of a robot soccer team is the process of analyzing past matches and generating insights about possible causes for good or bad outcomes the team has in the field. RoboCup robot soccer matches from leagues such as the 2D Simulation League generate log files that can help create insights using data analysis, but since there is currently no active open collaboration between teams for building an ecosystem of analysis tools, this area is underdeveloped - as a community - and could be improved. We propose an open-source data analysis library that contains all of the basic structures needed for implementing any analysis, as well as a collection of ready-to-use, quasi-agnostic analysis that can be used to analyze matches from any soccer league with little or none adaptation. We believe this can be a common ground for developers from any team to work together in the advancement of technology and lower the barrier of entry into the data analysis realm for teams that are not yet involved in the area. We also demonstrate how this library can be leveraged as a software component for other projects, by building a custom web platform that utilizes it.
Web Soccer Monitor: An Open-Source 2D Soccer Simulation Monitor for the Web and the Foundation for a New Ecosystem

The 2D Simulation League (SIM2D) is one of the most accessible RoboCup leagues, since there are no hardware costs included, and codebases from which you can build a new team are readily available for free. The league, however, still has a notable barrier of entry: its setup. The setup process for the SIM2D environment can be daunting for newcomers, especially for people new to programming or that don’t have access to a linux based distro. In this sense, if there is interest in lowering the barrier of entry of the SIM2D league, and with it, of robotics in general, it would be helpful to have tools with minimal setup, or that don’t require any setup at all. This article reports on an Open-Source monitor for SIM2D games, “Web Soccer Monitor”, that runs entirely on the browser and doesn’t require a setup to function. It is useful in itself as it simplifies the experience of utilizing a monitor from the user’s perspective, while also providing developers with a more modern and agile framework in which to implement new features, but it also serves as the foundation in which the RoboCup community can start building an entire ecosystem of SIM2D web tools, which would lower even more the barrier of entry and would, among other things, facilitate the creation of new categories, such as a fully-fledged SIM2D Junior League.
HELIOS2022: RoboCup 2022 Soccer Simulation 2D Competition Champion

The RoboCup Soccer Simulation 2D Competition is the oldest of the RoboCup competitions. The 2D soccer simulator enables two teams of simulated autonomous agents to play a game of soccer with realistic rules and sophisticated game play. This paper introduces the RoboCup 2022 Soccer Simulation 2D Competition champion team, HELIOS2022, a united team from Okayama University of Science and Osaka Metropolitan University. The overview of the team’s two recent approaches is also described. The first one is the method of online search of cooperative behavior for the setplay planning. The second is a performance evaluation system for efficient team development.
Target guidelines for the formation of project activity of students in the conditions of specialized training

In the presented study, the need for the development of project activity of students in a modern professionally oriented high school is determined, and its targets are identified, which make it possible to track the effectiveness of pedagogical activity and personal development of the student. Among the scientific approaches, the practice-oriented approach of D. Dewey and the concept of the “open” school of T.S. Shatsky, which have found application and development in modern scientific psychological and pedagogical discourse, are predominant. The criteria for the formation of project activity: motivational, cognitive, rational, personal, promising allowed us to determine a list of indicators that can be monitored through diagnostic tools, which allows us to develop the conclusions obtained experimentally. The theoretical conclusions made were confirmed by the calculation of the expediency index, as well as by an expert survey of teachers of educational organizations in the number of twenty-eight people. In general, the conducted research demonstrates the need to develop the project activity of students in the educational process, as well as to design the educational process in such a way that the personal development of students becomes more ambitious, professionally oriented and aimed at the personal development of students. The presented results can be applied in the process of research and educational work in higher educational institutions.
Multiple-Criteria Rating Recommendation with Ordered Weighted Averaging Aggregation Operators

In recent years, the Fourth Industrial Revolution in Industry 4.0 has exploded, along with the increasing development of websites, social networks, and other Internet services, leading to tremendous growth in collected data resources. Therefore, it is becoming more and more challenging to select useful information to make decisions. The recommendation systems are considered a great solution to assist humans in finding helpful information effectively and speedily. Such systems can automatically analyze, classify, select, and provide valuable information to users. Furthermore, they can explore reviews on products and services using artificial intelligence techniques to provide valuable recommendations. Users sometimes give reviews and ratings multiple times on the same products, but they differ depending on the user’s mood, context, behavior, etc. Thus, the problem is accurately determining the user’s rating when exploring such reviews. This work has proposed a solution for multiple-criteria rating analysis. This study has explored reviews on different criteria and integrated them into one aggregate rating by considering the similar relationship between the ratings, users, or products based on criteria in the collaborative filtering-based recommendation approach. The proposed method has performed better than traditional collaborative filtering-based methods on more than 5000 film reviews from the DePaulMovie dataset.
Blockchain Model in Industrial Pangasius Farming

Today, the problem of counterfeit goods, counterfeit goods, circulating goods, they have no origin, products such as seafood containing banned substances, toxic substances are negatively affecting consumers’ health, the solution of the traceability of manufactured products acts as an essential quality management tool to ensure the safety of providing complete information for products. However, current solutions data is stored centrally on a group of servers, this information can be edited and deleted by the administrator and by hackers; leading to low reliability of the data. In this article, we propose a blockchain model in industrial pangasius farming, one of the current solutions is to switch to a distributed model, data will be distributed to different physical servers, they are not data can be added, removed, edited or deleted without the consent of the parties involved. The proposed solution focuses on controlling all interactions and transactions between all involved participants in the production process, all transactions are recorded and stored in the ledger (immutability) of the blockchain. The experimental results are the transactions, all transactions are recorded and stored in the ledger of the blockchain. In addition, the data can be retrieved as needed.
Context-Awareness in Internet of Mobile Things

A new cognitive paradigm in internet of mobile things (IOMT) is currently on spot: Context-Awareness (CA), which is inspired by the activity of the human autonomous nervous system. The comprehensive target of CA is to realize IOMT, that can self-govern without direct human interventions. To solve this enormous challenge of CA requires a basic solution for CA concept. For this purpose, in this paper, the categorical approach is used to establish a strong formal basis for modeling CA in order to achieve the formal aspects of CA.
Extending Blended Learning and the Roles of Technology to Meet Teacher-Training Needs in the New Normal

The COVID-19 pandemic caused significant disruption to the traditional delivery of education at all levels. The forced shift to Emergency Remote Teaching (ERT) exposed different levels of preparedness and willingness for online teaching. This study first describes that tumultuous, and ongoing, period in Irish third-level education and, through a mixed-methods approach, goes on to identify, in both technological and pedagogical terms, the challenges raised by and the lessons learned from the enforced switch to ERT. Based on the results of this research project, the authors make a number of recommendations. Firstly, that there is a need to adequately equip language teachers with the technological and pedagogical competencies to re-evaluate online programme delivery to better cater to the affordances and constraints of online learning on different technological devices. Secondly, that teacher-training curricula should emphasise a more permanent blended learning approach which would include online presence across modules as a matter of course. Overall, the development of Critical Digital Literacy is central to current and future teaching needs.
The Performance of a Kernel-Based Variable Dimension Reduction Method

Building forecast models, especially nowcast models, on large data sets of time series variables is a topic of great interest. The most popular method used to build such models is the dynamic factor model, in which factors are extracted from input data sets using the principal component analysis (PCA) or sparse PCA (SPCA) methods. Many studies have shown that the forecast accuracy of models built under such an approach is higher than that of other benchmark models. But the PCA and SPCA methods are only effective when input data sets approximate a hyperplane, while real-world data sets are not always so. The purpose of this article is to briefly introduce a kernel-based variable dimension reduction method called the KTPCA, and a process of trial and error of this method called the KTPCA#. Experimenting on real-world data sets at the same sampling frequency as well as mixed sampling frequencies shows that the KTPCA# method is superior to the PCA, SPCA, Randomized SPCA (RSPCA), and robust SPCA (ROBSPCA) methods.
Collaborative Recommendation with Energy Distance Correlation

The recommendation systems are applied to many fields of the social life. In which, the measure of the similarity, and the measure of the distance are the core problems of the recommender systems, there are many proposals with the different approaches, it shows the characteristics of each recommendation system, commonly used measures such as: the measure Cosine, the measure Pearson, the measure Jaccard, etc. However, there have not been many studies on the energy dependence to determine the correlation of the objects in the process of building a recommendation system. In this article, we mainly focus on determining the correlation/compatibility of the energy-based objects in building a recommendation model. The experimental results are evaluated on two datasets, that are MSWeb datasets and Learning from Sets of Items 2019 datasets, the results show that the proposed model has higher accuracy than the traditional model.