Reviewerly/exHarmony huggingface dataset:
A nonlinear diffusion method for semi-supervised learning on hypergraphs

Hypergraphs are a common model for multiway relationships in data, and hypergraph semi-supervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes. Diffusions and label spreading are classical techniques for semi-supervised learning in the graph setting, and there are some standard ways to extend them to hypergraphs. However, these methods are linear models, and do not offer an obvious way of incorporating node features for making predictions. Here, we develop a nonlinear diffusion process on hypergraphs that spreads both features and labels following the hypergraph structure, which can be interpreted as a hypergraph equilibrium network. Even though the process is nonlinear, we show global convergence to a unique limiting point for a broad class of nonlinearities, which is the global optimum of a interpretable, regularized semi-supervised learning loss function. The limiting point serves as a node embedding from which we make predictions with a linear model. Our approach is much more accurate than several hypergraph neural networks, and also takes less time to train.
Towards Semi-Automatic Model Specification

This paper presents a natural language understanding (NLU) approach to transition a description of a phenomenon towards a simulation specification. As multidisciplinary endeavors using simulations increase, the need for teams to better communicate and make non-modelers active participants on the process increases. We focus on semi-automating the model conceptualization process towards the creation of a specification as it is one of the most challenging steps in collaborations. The approach relies on NLU processing of narratives, create a model that captures concepts and relationships, and finally provide a specification of a simulation implementation. An initial definition set and grammatical rules are proposed to formalize this process. These are followed by a Design of Experiments (DoE) to test the NLU model accuracy and a test case that generates Agent-Based Model (ABM) conceptualizations and specifications. We provide a discussion on the advantages and limitations of using NLUs for model conceptualization and specification processes.
Data-Driven Exploration of Lentic Water Bodies with ASVs Guided by Gradient-Free Optimization/Contour Detection Algorithms

This paper presents a local-path planner for water quality monitoring involving an Autonomous Surface Vehicle (ASV). The planner determines new measuring waypoints based on the information collected so far, and on two gradient-free optimization and contour-detection algorithms. In particular, the optimization algorithm generates the locations where the variable/substance under study must be measured and use them as the waypoints of the external loop of the Guidance, Navigation and Control system of our ASV. Besides, the contour algorithm obtains useful waypoints to determine the water body locations where the variable/substance under study reaches a given value. The paper also analyzes how the approach works via progressive simulations over an ASV carefully modelled with a set of non-linear differential equations. Preliminary results suggest that the approach can be useful in real-world single-ASV water-quality monitoring missions where there is not previous knowledge of the state and location of the variable/substance under study.
DEVELOPMENT OF THE "ENGINEERING GRAPHICS" MODULE OF THE "TECHNOLOGY" SUBJECT AREA FOR THE STUDENTS OF HUMANITARIAN EDUCATIONAL INSTITUTIONS

Decrees of the President of the Russian Federation dated 07.05.2018 No. 204 “On national goals and strategic objectives of the development of the Russian Federation for the period up to 2024” and dated 21.07.2020 No. 474 “On National development goals of the Russian Federation for the period up to 2030” formulated a new most important strategic task – accelerated introduction of digital technologies in the economy and social sphere. One of the seven directions of the state program was the federal project “Personnel for the digital Economy”. Providing the digital economy with highly qualified specialists requires new state educational standards. Intended for implementation in schools as from September 1, 2022, the Federal State Educational Standard significantly expands the possibilities of using information systems for various purposes in the work programs of the subject “Technology”. Schoolchildren and college students actively apply the knowledge, skills and abilities acquired within the subject area “Mathematics and Informatics” for programming automated systems and automatic robots, modeling graphic objects and building on their basis product prototypes, for organizing project activities and preparing documentation. The authors of the article developed a working program for the “Computer Graphics” module of the subject “Technology”, which is based on the Russian programming environment PascalABC.Net, and recommend its implementation in the educational process of humanitarian educational institutions.
Digitized Land Registration Using Blockchain Technology

The land registration system is a property rights recording process, which provides evidence of the rights of property owners. It is a centralized system, using paper records, which may suffer from natural disasters. The next improvement of this system will be digitalization of land registration. But in this improvement, there is a lot of vulnerability, such as fraud intrusion, alteration and errors of records. This leads to integrity and reliability issues in digitalized land registration system records. These issues are overcome by introducing blockchain technology into the digitalized land registration system, because blockchain technology has decentralized, transparent and immutable features. In a digitalized land registration system using blockchain technology, data is distributed, and these distributed nodes are connected via the network; the record of transactions is transparent to the public and immutable in nature (not changeable). Blockchain technology has many features that lead to integrity, reliability and efficiency in a digitalized land registration system.
A Survey: The State-of-the-Art Development of B-Learning Services

Blended learning in recent years has received intensive attentions in education, which combines the traditional offline teaching and emerging online teaching to improve the learning effects. For blended learning, there are many hot topics which are required to explore. In this paper, we comprehensively survey the state-of-the-art development of blended learning, specifically, we review the connotation and types of blended learning and further six patterns of blended learning are clearly discussed in detail, they are face-to-face driver, rotation, online lab, flex, self-blend and online driver. The applications of these patterns are fully summarized according to existing works. Also we compare the blended learning with traditional learning and online learning in locations, learning approaches, technology use, flexibility, sense of participation, interaction and evaluation. Finally, to improve the effects of blended learning, we present two strategies for optimizing blended learning, one is using intelligent technologies augment teacher preparations, another is building customized learning experience.
An Investigation on the Teaching Effect of College Oral English Course in the Application-oriented University – with the Aid of Moso App

This study integrated the Moso App into the oral English teaching design in the application-oriented university, and used this app to sign in before class, discuss in class, share teaching resources and submit homework after class.When the course was completed, a questionnaire survey was used to investigate the teaching effect of this course design, and the conclusions are as follows: (1) With the use of Moso App, the teaching design of oral English course in application-oriented colleges has a positive teaching effect; (2) The positive teaching effect is mainly due to the increase of classroom interaction between teachers and students, and the function of collecting homework in electronic form.
An Efficient and Accurate Mixed Dynamic Data Race Detection Method

research-article An Efficient and Accurate Mixed Dynamic Data Race Detection Method Share on Authors: Jiaze Sun Xi'an University of Posts&Telecommunications, China Xi'an University of Posts&Telecommunications, ChinaSearch about this author , Yanman Yang Xi'an University of Posts&Telecommunications, China Xi'an University of Posts&Telecommunications, ChinaSearch about this author , Xinfeng Shu Xi'an University of Posts&Telecommunications, China Xi'an University of Posts&Telecommunications, ChinaSearch about this author Authors Info & Claims AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern RecognitionSeptember 2021 Pages 531–535https://doi.org/10.1145/3488933.3488998Online:25 February 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Earth’s Energy Balance

The incoming energy to the Earth system is in the form of solar radiation and roughly corresponds to that of a black body at the temperature of the Sun of about 6000 K. The Sun’s emissions peak at a wavelength of about 0.6 μm and much of this energy is in the visible part of the electromagnetic spectrum although some extends beyond the red into the infrared and some extends beyond the violet into the ultraviolet (Fig. 4.5). As noted earlier, because of the very nearly spherical shape of Earth, at any time half of Earth is in night (Fig. 1.2) and the average amount of energy incident on a level surface outside the atmosphere is one-quarter of the total solar irradiance, or 340 W m−2. About 30% of this energy is scattered or reflected back to space by air molecules, tiny airborne particles (known as aerosols), clouds in the atmosphere, or by Earth’s surface, which leaves about 240 W m−2 on average to warm Earth’s surface and atmosphere (Fig. 3.1).
Procedural Impact of Negotiations and Its Variations on Utilization of Resources in Cloud Environment

Monitoring Cloud Computing enabled devices for services produces heterogeneous types of tools which will fit for several applications and outputs the performance characteristics for that specified software. Cloud Computing attributes like elasticity, authentication and cloning within the framework exhibit the back-up storage space, service grabbing, writing technologies and harmful attacks. We undertake the problem of negotiation in between the stack holders of the SAAS resource utilization and constitue an optimized algorithm.
Exploring a Textile-based Shadow Lamp Display Incorporating Shape Memory Alloys

This paper explores a novel concept of textile-based shadow displays that incorporates shape memory alloys (SMA) on fabrics to cast dynamic shadows as an ambient information display. To explore this, we implemented a lamp prototype, capable of presenting a set of dynamic shadows. These shadows exemplified iconic vs. abstract shadow figures, as well as demonstrating changes in shadow sharpness, size and/or position on the lamp’s shade. We conducted a user study (n =8) to investigate the perception toward our concept. Our findings report that the concept does not only provide the potential for a variety of use cases, dynamic alterations of the shadows feel like a decorative addition to the living spaces, as well as creating relaxing channels of information display.
Equipping European Higher Education Teachers for Successful and Sustainable e-Learning with Home Remote Work

COVID-19 consequences in the shape of restrictions and lockdowns caused a sudden need of transition from traditional higher education (HE) teaching scenario to e-learning with teachers working remotely from home. HE entered in emergency mode and was hardly capable of keeping education service. Sustainability of HE during possible future crisis depends on a good analysis of what has happened during the contingency period. A combined study based on literature review and a specific survey to HE teachers in Europe has collected consistent results to suggest which should be the contents for equipping teachers for the new teaching paradigm. Conclusions show that ensuring HE education service sustainability requires giving teachers more support and training in several areas: distance learning pedagogy, technical troubleshooting, cybersecurity, data privacy, IPR and inclusion through digital accessibility and cultural and gender considerations in e-learning. These are essential factors to enable HE teachers to strength their digital education readiness through e-learning.
Flower pollination based dental image segmentation

Flower pollination-based algorithm (FPA), inspired from the pollination process of flowers, is a population-based metaheuristic optimisation algorithm for solving real-world problems. This article introduces the feature of insectivorous plants in catching the pollinators (preys) with the solution procedure of FPA for avoiding sub-optimal traps and models the dental image segmentation problem as an optimisation problem. This enhanced FPA (EFPA) is then applied in solving the formulated multilevel segmentation problem of dental images. The proposed EFPA-based method optimises the threshold values for dental images by effectively exploring the problem space. The segmented images along with PSNR values of the proposed method are compared with FPA-based and OTSU-basedsegmentation methods for exhibiting its superior performance.
Cybersecurity User Requirements Analysis: The ECHO Approach

Cyber defense requires research and investment in advanced technological solution as well as in the development of effective methods and tools for identifying cyber threats and risks. This implies a need for a well-defined process for user requirements elicitation. The paper presents a structured approach for the identification of cybersecurity knowledge and elicitation of user needs, based on the development of specific use cases. Employing use cases is an effective way to identify the cyber security gaps. Example use case descriptions of the attacks on a general computer network are given. The proposed use cases are analyzed within CAIRIS platform. The modelling process confirms that CAIRIS is a powerful tool to enrich the context of threat models and UML class diagrams. Also, the modelling with CAIRIS could support using security-by-design principles. The research is conducted under the activities of “The European network of Cybersecurity centres and competence Hub for innovation and Operations” (ECHO) project.
Towards a Free Text Dataset for Hiding Quasi-Identifiers

Personal information protection is becoming so important for individuals. Besides personal identifier information (PII), quasi-identifier information (QII) also needs protection, as the community argues, and the solution methods have attracted many research. Many existing methods for protecting QII focus on structured text data which is organized by tables of records. However, free text data that contains QII, is very common in application domains, such as data lakes of a company. The protection of QII in free text data thus need new methods. Supervised machine learning based solutions are promising while usually require a large scale dataset to train the model. Here we propose a novel method towards building such a desired dataset. Our method exploits an existing structured text dataset, a table to sentence generation deep learning model, and incorporated the idea of Piecewise Convolution Neural Network (PCNN). The resulted dataset contains more than 120,000 free text sentences, and many of them contains QII data.
Automating Software Engineering with Machine Learning

Software plays a crucial role in our everyday lives. The scarcity of skilled software engineers has become a bottleneck in delivering better software at scale. Can we automate software engineering to help improve developer productivity and software quality? Can we take advantage of massive codebases to learn about building correct and scalable software?
How to induce the forgetting of pictures

Visual long-term memory presents us with a paradox. It appears to be both limitless in capacity, and yet we can document failures to remember visual information in the laboratory and our daily lives. Here we discuss how laboratory experiments show that a major source of our visual long-term memory failures appears to be due to the competition between categorically related visual representations stored in memory. In this chapter, we test three proposals regarding the nature of this competition. First we asked whether induced forgetting operates over objects that are devoid of semantic information. We find that subjects do not forget objects that lack clear semantic associations. Next, does induced forgetting operate over emotionally arousing stimuli? We find that emotionally charged images are forgotten at rates similar to emotionally neutral stimuli. Finally, does induced forgetting depend on interference generated by objects from other categories? We show that inter-category interference does not play a part in our forgetting of objects represented in visual long-term memory. These results help to constrain models that seek to account for forgetting from visual long-term memory, and demonstrate the necessary features of inter-object competition that result in this inability to remember where I put my pants. (If you were living under a rock in 2014, this is a joke. In The LEGO Movie, Lord Business uses a TV show called “Where are my pants?” to distract the characters from his evil plan of gluing the world together (Lord et al., 2014).)
FIGSI—Facial Image Generation for Suspect Identification

Currently, there is no such application used by the Forensic departments which generates faces automatically and quickly. We propose a system to generate a 2-D image of a face by using a novel technology called Conditional Generative Adversarial Networks (C-GANs). The system considers a few basic inputs like gender, hair color, face shape, nose size, eye size, and other detailed attributes. The user can enter this information through a form-like user interface and a face preview is displayed in real time. The face preview corresponds to the attributes selected by the user. Moreover, a face matching feature with the criminal database is also integrated so as to check whether the suspect already exists. The model has been trained on over 100 k images of the CelebaFaces Attributes dataset after preprocessing it. The model performance was evaluated by using Histogram Euclidean Distance and Frechet Inception Distance. Use of C-GANs for generating facial images will help in creating realistic images.
Spatial statistics in perception, learning, and navigation

Humans are exceptionally proficient at extracting and using statistics embedded in the visual world. In a single exposure, we can immediately perceive a summary of the general properties in a group of objects. Across multiple exposures, we can learn hidden regularities in how objects co-occur. This chapter reviews and synthesizes different forms of such statistical perception and statistical learning, respectively, as they operate over spatial information. We then explore how these foundational processes support more complex spatial behaviors, namely our ability to navigate through the world. Current research is converging on the realization that statistical learning and spatial navigation share cognitive and neural mechanisms. By integrating experiences and enabling prediction, this interdependence may make real-world behavior more efficient and rewarding.
Morphable Detector for Object Detection on Demand

Many emerging applications of intelligent robots need to explore and understand new environments, where it is desirable to detect objects of novel classes on the fly with minimum online efforts. This is an object detection on demand (ODOD) task. It is challenging, because it is impossible to annotate a large number of data on the fly, and the embedded systems are usually unable to perform back-propagation which is essential for training. Most existing few-shot detection methods are confronted here as they need extra training. We propose a novel morphable detector (MD), that simply "morphs" some of its changeable parameters online estimated from the few samples, so as to detect novel classes without any extra training. The MD has two sets of parameters, one for the feature embedding and the other for class representation (called "prototypes"). Each class is associated with a hidden prototype to be learned by integrating the visual and semantic embeddings. The learning of the MD is based on the alternate learning of the feature embedding and the prototypes in an EM-like approach which allows the recovery of an unknown prototype from a few samples of a novel class. Once an MD is learned, it is able to use a few samples of a novel class to directly compute its prototype to fulfill the online morphing process. We have shown the superiority of the MD in Pascal [12], COCO [27] and FSOD [13] datasets.
Universal Principles

One of the primary goals when creating an original composition is to create something new. Pushing boundaries and trying new things are a natural part of the creative process. There are some principles, though, that apply almost universally. Repetition can be a tool that unites a piece, but too much can bore the audience. Predictability can be satisfying in certain contexts, but surprises are what make music interesting. Too much variety without unifying elements can become confusing to the ear. The balance between unity and variety, and other types of universal principles help give a piece shape, context, flow, and meaning.
Self-supervised Medical Out-of-Distribution Using U-Net Vision Transformers

Mitigating out-of-distribution inaccuracies due to insufficient labeled data is a widespread problem in the medical AI field. To tackle this medical out-of-distribution problem, through the medical out-of-distribution challenge [], our team utilized self-supervised learning with UNETR []. UNETR is a 3D UNET model where the encoder incorporates Vision Transformers []. Abnormal samples were generated from normal samples using a 3D extension to the cutout method described by [], allowing for self-supervised learning. The input size was chosen to be one eighth of the original size due to the large computational cost associated with 3D image segmentation. This results in 3D images that are \(128 \times 128 \times 128\) pixels for brain samples and \(256 \times 256 \times 256\) pixels for abdominal samples. Abdominal samples were further divided into 8 different patches, with all 8 patches being used for training. The model produces a voxel-wise abnormality prediction ranging between 0 and 1, where 1 represents an abnormal voxel. We then further extrapolate this prediction to generate a sample-wise prediction by taking the maximum value.