Reviewerly/exHarmony huggingface dataset:
ODE4ViTRobustness: A tool for understanding adversarial robustness of Vision Transformers

Understanding the adversarial robustness of Vision Transformers (ViTs) has long been needed since the vulnerability of neural networks hinders their use of it. We present an approach that decomposes the network into submodules and calculates the maximal singular value for each module w.r.t. input, which is a good indication of adversarial robustness. To understand whether Multi-head Self-Attention (MSA) in ViTs contributes to its adversarial robustness, we replace the module with convolutional layers with our decomposing method and conclude that MSA has limited power to defend against adversarial attacks.
“I learned it on the job”

Abstract Graphs, charts, and infographics have long been a part of newsrooms. Nevertheless, in recent decades, new data processing and graphical representation findings have led to a boom in data and visual journalism. Both professionals and newsrooms have had to adapt quickly to the fast-developing field of Data Visualization while facing an unprecedented crisis. Through semi-structured interviews with worldwide graphic reporters and data journalists, this research delves into academic backgrounds, daily tasks, and design processes to answer the question of how someone comes to design graphics for a living in recent years. One common phenomenon was identified: self-teaching.
FIAT

Home IoT (Internet of Things) deployments are vulnerable to local adversaries, compromising a LAN, and remote adversaries, compromising either the accounts associated with IoT devices or third-party devices like mobile phones used to control the IoT. There is, however, a fundamental difference between an attacker and a legitimate IoT user: the physical interaction with the device (e.g., via a mobile app) used to operate the IoT. Such physical interactions can be used to build frictionless authentications. However, their integration with IoT requires each vendor to independently adopt them, which is both complex and expensive. We instead design and build FIAT, the first third-party mechanism to automatically authorize IoT traffic by learning recurring traffic and validating human actions behind unpredictable traffic. FIAT does not require modification of the IoT devices or apps, as it operates passively on network traffic. Our evaluation shows that FIAT achieves high accuracy with minimal impact on the user experience.
Research on Strategies of Synchronous Interaction Hybrid Basic Sports and University Education Interaction Model Based on SERVQUAL Model

Promoting the balanced development of regional basic education and finally realizing education equity is an important task for my country's current educational development. To this end, the balanced development of informatization and basic education has carried out experiments and practical explorations of informatization to promote the balanced development of regional basic education. Through synchronized interactive hybrid classrooms, the sharing of high-quality urban teachers to rural teaching sites has helped them to implement national regulations. Course. Using the SERVQUAL model for data analysis, synchronous interactive hybrid classrooms, as a new form of high-quality teacher sharing, are of great significance to promoting the balanced development of basic education in my country. It has attracted great attention from education researchers and is considered to be a solution area. Effective means for the problem of imbalanced sex education resources. Therefore, the VR teaching experience, the use of wearable sports equipment, the construction of a physical education network platform, and the four basic sports professional courses of physical education and university education interactive strategies are proposed, and the effects of the interactive strategies are analyzed. The experimental results show that the students' evaluation of the traditional basic physical education teaching mode is low, and the score is basically below 5 points. After the interaction between basic physical education and university education when applying the SERVQUAL model, the VR teaching experience strategy has a significant impact on basic physical education. VR teaching experience strategy basic physical education students achieve a score of 10, and their interest has increased by 34%. Sports performance under this interactive strategy increased by 51%. The strategy of interaction between basic physical education and university education has increased students' interest in basic physical education courses, improved students' performance in basic physical education courses, and promoted the development of physical education to a certain extent.
CDSM: <u>C</u> ascaded <u>D</u> eep <u>S</u> emantic <u>M</u> atching on Textual Graphs Leveraging Ad-hoc Neighbor Selection

Deep semantic matching aims at discriminating the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document features and the extrinsic neighbor features to derive discrimination. Most of the existing works mainly care about how to utilize the presented neighbors, whereas limited effort is made to filter appropriate neighbors. We argue that the neighbor features could be highly noisy and partially useful. Thus, a lack of effective neighbor selection will not only incur a great deal of unnecessary computation cost but also restrict the matching accuracy severely. In this work, we propose a novel framework, C ascaded D eep S emantic M atching ( CDSM ), for accurate and efficient semantic matching on textual graphs. CDSM is highlighted for its two-stage workflow. In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost. We design both one-step and multi-step selection methods. In the second stage, a high-capacity graph-based matching network is employed to compute fine-grained relevance scores based on the well-selected neighbors. It is worth noting that CDSM is a generic framework which accommodates most of the mainstream graph-based semantic matching networks. The major challenge is how the selector can learn to discriminate the neighbors’ usefulness which has no explicit labels. To cope with this problem, we design a weak-supervision strategy for optimization, where we train the graph-based matching network at first and then the ad-hoc neighbor selector is learned on top of the annotations from the matching network. We conduct extensive experiments with three large-scale datasets, showing that CDSM notably improves the semantic matching accuracy and efficiency thanks to the selection of high-quality neighbors. The source code is released at https://github.com/jingjyyao/CDSM.
Exploring the Potential Adoption of Metaverse in Government

The concept of Metaverse and its applicability in the public sector as a tool to change the delivery of governmental services are discussed in this article. This study’s contribution is that it explores the opportunities and challenges of the Metaverse’s adoption, so that decision-makers will be prepared before using the virtual world of the Metaverse to deliver public services. The quality of public services is a topic that many governments are attempting to address. Metaverse is a virtual environment that enhances the experience of obtaining government services by incorporating them into a virtual world. The purpose of this article is to explain how Metaverse works and why the public sector should use it. The benefits and drawbacks of Metaverse are discussed in detail in order to provide decision-makers with a clear picture of the technology’s worth.
Edge detection from X‐ray tomographic data for geometric image registration

In this paper, we propose new variational model for image registration from tomographic data. First, we employ the topological gradient approach for a tomographic reconstruction that uses the first‐ and the second‐order discontinuities in order to detect important objects of a given observed X‐ray tomographic data (sinograms). Second, we use this geometric information furnished by a high‐order operator in order to define an appropriate fidelity measure for the image registration process. A theoretical study of the proposed model is provided; Gauss–Newton method and multilevel technique are used for its numerical implementation. The performed numerical experiments show the efficiency and effectiveness of our model.
Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem

Class imbalance problem occurs when the training dataset contains significantly fewer samples of one class in contrast to another class. Conventional extreme learning machine (ELM) gives the same importance to all the samples leading to the results, which favor the majority class. To solve this intrinsic deficiency, modifications of ELM have been developed such as weighted ELM (WELM) and WELM based on the overall distribution (ODW-ELM). Recently class-specific ELM (CS-ELM) was designed for class imbalance learning. It has been shown in this work that the derivation of the output weights, $$\varvec{\beta }$$ , is more efficient compared to class-specific cost regulation ELM (CCRELM) for handling the class imbalance problem. Motivated by CCRELM, X. Luo et al. have proposed the classifier ODW-ELM, which is also not efficient for imbalance learning. In this work, a novel class-specific ELM based on overall distribution (OD-CSELM) and the kernelized version of OD-CSELM (OD-CSKELM) is proposed to address the binary class imbalance problem more effectively. OD-CSELM and OD-CSKELM are motivated by CS-ELM. In addition, the computational complexity of OD-CSELM and OD-CSKELM is significantly lower than WELM and kernelized WELM, respectively. The proposed work is evaluated by using the benchmark real-world imbalanced datasets. The experimental results demonstrate that the proposed work gives good generalization performance in contrast to the rest of the classifiers for class imbalance learning.
Quantifying calcium carbonate and organic carbon content in marine sediments from XRF-scanning spectra with a machine learning approach

Geochemical variations of sedimentary records contain vital information for understanding paleoenvironment and paleoclimate. However, to obtain quantitative data in the laboratory is laborious, which ultimately restricts the temporal and spatial resolution. Quantification based on fast-acquisition and high-resolution provides a potential solution but is restricted to qualitative X-ray fluorescence (XRF) core scanning data. Here, we apply machine learning (ML) to advance the quantification progress and target calcium carbonate (CaCO3) and total organic carbon (TOC) for quantification to test the potential of such an XRF-ML approach. Raw XRF spectra are used as input data instead of software-based extraction of elemental intensities to avoid bias and increase information. Our dataset comprises Pacific and Southern Ocean marine sediment cores from high- to mid-latitudes to extend the applicability of quantification models from a site-specific to a multi-regional scale. ML-built models are carefully evaluated with a training set, a test set and a case study. The acquired ML-models provide better results with R2 of 0.96 for CaCO3 and 0.78 for TOC than conventional methods. In our case study, the ML-performance for TOC is comparably lower but still provides potential for future optimization. Altogether, this study allows to conveniently generate high-resolution bulk chemistry records without losing accuracy.
ADPP: A Novel Anomaly Detection and Privacy-Preserving Framework using Blockchain and Neural Networks in Tokenomics

The increasing popularity of crypto assets has resulted in greater cryptocurrency investor interest and more exposure in both industry and academia. Despite the substantial socioeconomic benefits, the anonymous character of cryptocurrency trading makes it prone to abuse and a magnet for illicit purposes, which cause monetary losses for individual traders and erosion in the standing of the tokenomics industry. To regulate the illicit behavior and secure users' privacy for cryptocurrency trading, we present an Anomaly Detection and Privacy-Preserving (ADPP) Framework integrating blockchain and deep learning technologies. Specifically, ADPP leverages blockchain technologies to build a user management platform that ensures anonymity and enhances the privacy-preservation of user information. Atop the user management system, an Anomaly Detection System adapts neural networks and imbalanced learning on topological cryptocurrency flow among users to identify anomalous addresses and maintain a sanction list repository. The experiments on the real-world dataset demonstrate the effectiveness and superior performance of ADPP. The flexible framework can be easily generalized to the crypto assets with public real-time transaction (e.g., Non-fungible Token), which takes up a significant proportion of market capitalization in the domain of tokenomics.
Intelligent planning for large‐scale multi‐agent systems

Abstract This article summarizes the New Faculty Highlights talk with the same title at AAAI 2021. Intelligent agents such as different types of robots will soon become an integral part of our daily lives. In real‐world multi‐agent systems, the most fundamental challenges are assigning tasks to multiple agents (task‐level coordination problems) and planning collision‐free paths for the agents to task locations (motion‐level coordination problems). This article surveys four directions of our research on using intelligent planning techniques for the above multi‐agent coordination problems. Link to video abstract: https://youtu.be/HDAFcatq9_I
InBiodiv-O

To present the biodiversity information, a semantic model is required that connects all kinds of data about living creatures and their habitats. The model must be able to encode human knowledge for machines to be understood. Ontology offers the richest machine-interpretable semantics that are being extensively used in the biodiversity domain. Various ontologies are developed for the biodiversity domain; however, these ontologies are not capable to define the Indian biodiversity information though India is one of the megadiverse countries. To semantically analyze the Indian biodiversity information, it is crucial to build an ontology that describes all the terms of this domain. Since the curation of the ontology depends on the domain where these are used, there is no ideal methodology defined yet. The aim of this article is to develop an ontology that semantically encodes all the terms of Indian biodiversity information in all its dimensions based on the proposed methodology. The evaluation of the proposed ontology depicts that ontology is well built in the specified domain.
Augmented Reality

Abstract: Imagine a world with a technology that creates the 3 dimensional images of a virtual object around you with which you can interact, see, hear, smell, and even touch it. Technologies such as computer graphics, virtual reality, and augmented reality together can be used to implement this in real world. Augmented reality actually superimposes virtual objects into the real environment with the real objects for enriching the viewer’s experience Augmented reality with virtual reality in virtual space, also enhances the audience perception by displaying additional information. In this survey we present the different technologies that are involved in the implementation of augmented reality. These technologies are displays which are used for displaying or combining the virtual object by the real environment, tracking or gesture recognition helps in real time interaction part while the modelling is used to register the objects into 3D for enhancing the quality and perception of the viewer.
Tracing the Innovation – Decision Process for Adopting Microchip Implants: Reflections and Experiences of Estonian Employees

Relying on the diffusion of innovations theory by Everett Rogers (1962 [2003]), the current paper traces the five steps of the innovation-decision process for adopting microchip implants in a workplace. Semi-structured individual interviews with microchipped employees (n=14) from six different organisations in Estonia were carried out to explore their opinions and experiences with adopting the use of microchip implants in their workplaces. Our analysis indicates that social reinforcement from their colleagues played an important role in the formation of the attitudes and beliefs of our interviewees about microchip implants. In fact, a strong element of homophily existed within the social system both on the organisational and interpersonal levels. Our findings suggest that the employees who decided to get microchip implants were considered to be more loyal and dedicated, as well as more in sync with the overall goals and values of the organisation; the employees who rejected the innovation were viewed as less motivated and not as invested in their organisations. All our interviewees stressed that they were voluntary adopters of the innovation and wholeheartedly believed in the value of trade-offs between convenience and privacy.
Il ricettario della Santissima Annunziata di Firenze

Abstract This paper presents the preliminary results of a study on the untitled and unpublished recipe book transmitted by the codex conv.soppr.F.VIII.1834 (Biblioteca Nazionale Centrale di Firenze), previously conserved in the library of the convent of the Most Holy Annunciation (Florence); the codex contains both culinary recipes and «secrets». We argue that the composition of the manuscript took place in the second half of the seventeenth century (§§2‒3) and was carried out by multiple authors (§4). Subsequently, we analyse its main linguistic features, with particular regard to orthographical instability and morphological variation (§5). Finally, we investigate the main sources of the cookbook (§6), examining in detail some recipes taken from Bartolomeo Scappi’s Opera .
A Random Sampling-Based Method via Gaussian Process for Motion Planning in Dynamic Environments

Motion planning is widely applied to industrial robots, medical robots, bionic robots, and smart vehicles. Most work environments of robots are not static, which leads to difficulties for robot motion planning. We present a dynamic Gaussian local planner (DGLP) method to solve motion planning problems in dynamic environments. In a dynamic environment, dynamic obstacles sometimes make part of the global path invalid, so the local invalid path needs to be local re-planned online. Compared with the node sampling-based methods building large-scale random trees or roadmaps, the Gaussian random path sampling (GRPS) module integrated in the DGLP directly samples smooth random paths discretized into sparse nodes to improve the local path re-planning efficiency. We also provide the path end orientation constraint (PEOC) method for the local re-planning paths in order to merge them smoothly into the global paths. In the robot experiments, the average planning time of the DGLP is 0.04s, which is at least 92.31% faster than the test methods, and its comprehensive evaluation scores, which consider the consuming time, path quality, and success rate of local re-planning, are at least 44.92% higher than the test methods. The results demonstrate that the proposed DGLP method is able to efficiently provide high-quality local re-planning paths in dynamic environments.
Network artificial intelligence, fast and slow

As networks have historically been built around connectivity, architectural features concerning quality of service, mobility, security and privacy have been added as afterthoughts - with consequent well known architectural headaches for their later integration. Despite Artificial Intelligence (AI) is more a means to an end, that an architectural feature itself, this is not completely different from what concerns its integration: in particular, while Cloud and Edge computing paradigms made it possible to use AI techniques to relieve part of network operation, however AI is currently little more than an additional tool. This paper describes a vision of future networks, where AI becomes a first class commodity: its founding principle lays around the concept of "fast and slow" type of AI reasoning, each of which offers different types of AI capabilities to process network data. We next outline how these building blocks naturally maps to different network segments, and discuss emerging AI-to-AI communication patterns as we move to more intelligent networks.
Benchmarking graph neural networks for internet routing data

The Internet is composed of networks, called Autonomous Systems (or, ASes), interconnected to each other, thus forming a large graph. While both the AS-graph is known and there is a multitude of data available for the ASes (i.e., node attributes), the research on applying graph machine learning (ML) methods on Internet data has not attracted a lot of attention. In this work, we provide a benchmarking framework aiming to facilitate research on Internet data using graph-ML and graph neural network (GNN) methods. Specifically, we compile a dataset with heterogeneous node/AS attributes by collecting data from multiple online sources, and preprocessing them so that they can be easily used as input in GNN architectures. Then, we create a framework/pipeline for applying GNNs on the compiled data. For a set of tasks, we perform a benchmarking of different GNN models (as well as, non-GNN ML models) to test their efficiency; our results can serve as a common baseline for future research and provide initial insights for the application of GNNs on Internet data.
SOM-SIS approach to auto summary of clustering results on university academic performance

The analysis of the performance of higher education academic quality in terms of student achievement, study period, and drop out rates is still an intensive study among researchers. Several clustering methods are often used to understand student and graduate groups, in influencing college performance. However, the conventional method has only arrived at the results of clustering, so it is difficult to interpret it as a support for academic decisions, especially in mapping the position of universities against other universities nationally. This article introduces a combination of techniques from self organizing map and technique for oorder preference by similarity to an ideal solution (SOM-SIS), an auto-summarizing technique from clustering results as well as mapping university academic performance. First, the academic performance indicators are grouped using the self organizing map (SOM) method and the results are concluded using the technique for order preference by similarity to an ideal solution (TOPSIS) approach. The SOM-SIS technique was tested using data from one of the universities in Indonesia. As a result, the SOM-SIS technique has a 100% compatibility rate with the higher education quality assurance system, through recommendations from three university experts.