Reviewerly/exHarmony huggingface dataset:
Preacher: Network Policy Checker for Adversarial Environments

Private networks are typically assumed to be trusted as security mechanisms are usually deployed on hosts and the data plane is managed in-house. The increasing number of attacks on network devices, and recent reports on backdoors, forces us to revisit existing security assumptions and demands new approaches to detect malicious activity. This paper presents Preacher, a runtime network policy checker, which leverages a secure, redundant and adaptive sample distribution scheme that allows us to provably detect and localize adversarial switches or routers trying to reroute, mirror, drop, inject, or modify packets (i.e., header and/or payload) even under collusion. The analysis performed by Preacher is highly parallelizable. We show that emerging programmable networks provide an ideal vehicle to detect suspicious network activity. Furthermore, we analytically and empirically evaluate the effectiveness of our approach in different adversarial settings, report on a proof-of-concept implementation using ONOS, and provide insights into the resource and performance overheads of Preacher.
Works of A. F. Pisemsky in the libraries of Siberia and the Far East in the pre-revolutionary period

The study relevance is due to the anniversary of the writer (200th anniversary of his birth), the demand for his works among the residents of the Siberian-Far-Eastern region in the pre-revolutionary period, the importance of studying library funds to reconstruct the potential reading circle of the region's population. Due to the lack of comprehensive studies of library collections of pre-revolutionary libraries in Siberia and the Far East, this article first considers the presence of works by A. F. Pisemsky in the libraries of the region in the late XIX - early XX centuries. The article objective is to identify the writer’s works in the libraries of the Siberian-Far Eastern region during this period. Tasks are to: 1) reconstruct the repertoire of the works by A. F. Pisemsky in the libraries of the region in the pre-revolutionary period; 2) calculate quantitative indicators of their availability; 3) identify lifetime editions of the writer in library collections. The author applies the comparative analysis, statistical, chronological, source-based methods. Information is given from 34 catalogs of pre-revolutionary libraries of Siberia. They reflect the funds of 24 libraries located in 4 provincial, 5 regional and 4 county centers of the region. The libraries of municipal public and local commercial and public meetings prevail among them, chosen as the most open and, thereby, most accessible for readers. For the first time, quantitative indicators of the presence of the works of A. F. Pisemsky in Siberian and Far Eastern libraries in the pre-revolutionary period were calculated and analyzed. The study results show that many of them had works by the writer during the period under review. Their funds included most of the famous works of this author. The most frequently found publications have been identified, as well as publishers whose products were presented in the libraries of the region. The revealed data allow reconstructing partly library collections and the potential repertoire of reading of Siberian and Far Eastern residents in the late XIX – early XX centuries, clarifying and supplying the picture of library business in the region in the pre-revolutionary period.
Designing immersive virtual reality stories with rich characters and high interactivity to promote learning of complex immunology concepts

Immunology concepts taught at the college level often involve complex factual and process-based knowledge and can be difficult for students to visualize and relate to. To promote students' understanding of specific immunology concepts and increase their learning motivation and engagement, we leveraged the strengths of immersive storytelling and virtual reality (VR) interactivity and created a guided interactive and immersive VR storytelling experience with rich characters and engaging narratives. The experience builds on immunology concepts of a specific type of white blood cell called neutrophils, and it takes students on an exciting journey inside the human body by embodying the cell and fighting pathogens. In this demo paper, we will describe the design of a working prototype of the VR application.
Stability of the Tree of Shapes to Additive Noise

The tree of shapes (ToS) is a famous self-dual hierarchical structure in mathematical morphology, which represents the inclusion relationship of the shapes (i.e. the interior of the level lines with holes filled) in a grayscale image. The ToS has already found numerous applications in image processing tasks, such as grain filtering, contour extraction, image simplification, and so on. Its structure consistency is bound to the cleanliness of the level lines, which are themselves deeply affected by the presence of noise within the image. However, according to our knowledge, no one has measured before how resistant to (additive) noise this hierarchical structure is. In this paper, we propose and compare several measures to evaluate the stability of the ToS structure to noise.
Quantum search of matching on signed graphs

We construct a quantum searching model of a signed edge driven by a quantum walk. The time evolution operator of this quantum walk provides a weighted adjacency matrix induced by the assignment of a sign to each edge. This sign can be regarded as so-called the edge coloring. Then as an application, under an arbitrary edge coloring which gives a matching on a complete graph on $$n+1$$ vertices we consider a quantum search of a colored edge from the edge set of the complete graph. We show that this quantum walk finds a colored edge within the time complexity of $$O(n^{\frac{2-\alpha }{2}})$$ with probability $$1-o(1)$$ , while the corresponding random walk on the line graph finds them within the time complexity of $$O(n^{2-\alpha })$$ if we set the number of the edges of the matching by $$t=O(n^{\alpha })$$ for $$0 \le \alpha \le 1$$ red with $$t \le \frac{n}{2}$$ .
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models

Growing concerns regarding the operational usage of AI models in the real-world has caused a surge of interest in explaining AI models’ decisions to humans. Reinforcement Learning is not an exception in this regard. In this work, we propose a method for offering local explanations on risk in reinforcement learning. Our method only requires a log of previous interactions between the agent and the environment to create a state-transition model. It is designed to work on RL environments with either continuous or discrete state and action spaces. After creating the model, actions of any agent can be explained in terms of the features most influential in increasing or decreasing risk or any other desirable objective function in the locality of the agent. Through experiments, we demonstrate the effectiveness of the proposed method in providing such explanations.
A Recognition Method For Radar Emitter Signals Based on EEMD and EfficientNet

Aiming at the weak representation ability under low signal noise ratio (SNR) in radar emitter signals recognition, a recognition method based on Ensemble Empirical Mode Decomposition (EEMD) noise reduction and EfficientNet is proposed. First, the appropriate EEMD parameters to denoise the time domain signals and Ambiguity Function Contour Map of Tangent Matrix (AFCMTM)' datasets are created. Then, a Deep Network based on EfficientNet-B0 model is established and Transfer Learning is used to complete the training of labeled data. Finally, the network is used to achieve radar emitter signals identification. The simulated experiments show that the average recognition accuracy rate of six kinds of complex modulated signals, i.e., BPSK, BFSK, FMCW, QPSK, LFM-BC and MSEQ, by proposed method keeps above 96.83% in fixed SNR environment above -5dB with good generalization ability and strong robustness.
NAS-EOD: an end-to-end Neural Architecture Search method for Efficient Object Detection

Model efficiency for object detection has become more and more important recently, especially when intelligent mobile devices get more and more popular. Current lightweight object detection model is either migrated from lightweight classification models, or pruned directly from complex object detection models. These pipelines can not match the performance requirements of edge devices. In this work, we propose a neural architecture search (NAS) method to build a detection model automatically that can perform well on edge devices. Specifically, the proposed method supports the search of not only multiscale feature network, but also backbone network. This enables us to search out a global optimal model. To this end, we've made a special design that the backbone and feature network can share the same search space. This method greatly reduces the search time while ensuring the search accuracy. It can find a good architecture in 14GPU days. Additionally, we add latency information into the main objective during performance estimation. Therefore, we can control between the speed and accuracy to better adapt to the edge environment. Experiments on the PASCAL VOC benchmark indicate that the searched architecture (named NAS-EOD) can get good accuracy even if training from scratch. When using a pre-training scheme, our model is superior to state-of-the-art small object detection models.
Flow Immersive: A Multiuser, Multidimensional, Multiplatform Interactive Covid-19 Data Visualization Tool

Covid-19 has prompted a surge of data visualizations that have been published for public consumption, yet, many have not had broad appeal or may have not been well-understood by laypeople. A data storytelling platform called Flow Immersive has been created to successfully engage both laypeople and experts in understanding complex information. This tool integrates emerging technologies [e.g., augmented reality (AR) and virtual reality (VR)] with a multiplatform, multiuser publishing approach. From October 2020 to December 2020, Flow’s Covid-19 AR videos captured 9 million (9,000,000) views, and have been used in multiple professional presentations. This paper documents the journey from development to deployment, and some user feedback which all led to breakthroughs in scalability and higher levels of engagement.
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game

Learning how to adapt to complex and dynamic environments is one of the most important factors that contribute to our intelligence. Endowing artificial agents with this ability is not a simple task, particularly in competitive scenarios. In this paper, we present a broad study on how popular reinforcement learning algorithms can be adapted and implemented to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style. Finally, we pinpoint how the behavior of each agent derives from their learning style and create a baseline for future research on this scenario.
Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System

Numerous technologies exist for promoting a healthier lifestyle.These technologies collectively referred to as ''Behavior Change Support Systems''.However, the majority of existing apps use quantitative data representation.Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users' motivation and fail to promote behavior change.Therefore, an interpretation of quantitative data needs to be provided as a supplement.However, different descriptions of the same data may lead to different outcomes.In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper -a web-based app that provides interpretations of the users' daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking.Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data.
Effect of the Agglomerate Geometry on the Effective Electrical Conductivity of a Porous Electrode

The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).
Snapshot-Based Migration of ES6 JavaScript

Recently, researches have proposed application (app) migration approaches for JavaScript programs to enable a non-breaking user experience across different devices. To migrate a stateful JavaScript app’s runtime, past studies have proposed snapshot-based techniques in which the app’s runtime state is profiled and serialized into a text form that can be restored back later. A common limitation of existing literature, however, is that they are based on old JavaScript specifications. Since major updates introduced by ECMASCript2015 (a.k.a. ES6), JavaScript supports various features that cannot be migrated correctly with existing methods. Some of these features are in fact heavily used in today’s real-world apps and thus greatly reduces the scope of previous works.In this paper, we analyze ES6 features such as block scopes, modules, and class syntax that were previously uncovered in app migration. We present an algorithm that enables migration of apps implemented with these new features. Based on the standards adopted in modern JavaScript engines, our approach serializes a running program into a scope tree and reorganizes it for snapshot code generation. We implement our idea on the open source V8 engine and experiment with complex benchmark programs of modern JavaScript. Results show that our approach correctly migrates 5 target programs between mobile devices. Our framework could migrate the most complex app of source code size 213 KB in less than 200 ms in a X86 laptop and 800 ms in an embedded ARM board.
Multi-dimensional Resource Management System Based on Blockchain and Cybertwin

With the rapid development of 6G and the Internet of Things, a large number of terminal devices are connected to the network, and the cloud service scheduling problem in the edge computing scenario has become a difficult problem. This paper divides the computationally intensive tasks into delay-sensitive and delay-tolerant. For these two types of tasks, we proposed different offloading strategies, and analyzed the time cost and energy consumption cost of different strategies. As edge clouds are more vulnerable to malicious attacks. Therefore, we propose an architecture based on the combination of Cybertwin and blockchain to schedule and manage cloud service resources which can ensure the computing tasks are completed more credibly.
Object Detection Model Based on Scene-Level Region Proposal Self-Attention

In order to improve the performance of two-stage object detection and consider the importance of scene and semantic information for visual recognition, the neural network of object detection algorithm is studied and analyzed in this paper. The main research work of this paper includes: A scene level region proposal self-attention object detection model based on depth separable convolution is proposed. In order to obtain stronger semantic information and context information of the target scene, the scene-level region proposal self-attention module is reconstructed based on the process of region proposal recognition. The feature map of the output feature pyramid network is sent into three parallel branches: semantic segmentation module, candidate area network module and region proposal self-attention module. At the same time, for the overall performance of the model, a deep separable convolutional network module is constructed on the backbone network, which includes six stages. In the fifth to sixth stage of the network, the separable convolutional network module is integrated respectively. Finally, a object detection method based on border regression network enhancement is proposed to achieve accurate target location. In order to verify the effectiveness of each model, the experimental results of each model are analyzed.
Geology, Petrography and Mineralogy of Agbaja Formation: An insight to petrogenesis of Ironstone Deposits.

The of Agbaja Ironstone deposits was investigated with a view of providing comprehensive data on the geology, petrographic characteristics and mineralogical composition. The investigation involved field description, thin section analysis and X-ray diffraction for phase identification. The Agbaja Ironstones comprises of oolitic, pisolitic and argillaceous (concretionary) materials which ranges in colour from red to brown and black with yellowish to red, brown and dark grey matrix and some with whitish materials in some of the rocks. The pisoliths occur mostly close to the surface overlying the ooliths and the rocks entirely lack sedimentary structures.Petrographically, the ironstone is well-sorted, characterized by loosely packed spherical ooids within a kaolinitic matrix with the major mineral assemblage of the concentric laminae being kaolinite, kaolinite-goethite and hematite ooids. While the pisolitic ironstones occur as poorly sorted, loosely packed and fragmented sub spherical pisoids and peloids of goethite and hematite within dominantly geotithe and kaolinite matrix. XRD diffraction pattern for the samples portrays remarkable reflection for the principal components identified under the microscope. These includes; Kaolinite , goethite, hematite and as major phases while minor phases such as quartz, illite and micas, illmenite, rutile, zircon, tourmaline and psilomelane siderite and sepiolite occurs as detrital inclusions and as secondary or alteration derivatives. The result of these studies supports a continental origin for the ironstones since benthic foraminifera, skeletal algae and fossils in general are absent. Also, the absence of any calcareous ooids and oncoids from the surrounding rocks indicates that the ferruginous ooids and oncoids are plausibly primary depositional grains forming kaolinite (at the expense of detrital feldspars) as the precursor for the ooids and pisoids.
Designing a collaborative virtual reality system to assess social inclusion among neurodiverse students

The following article presents a collaborative VR system designed to assess social inclusion among neurodiverse students. Students can join virtual environments to communicate and collaborate in a virtual supermarket shopping trip. A virtual classroom is developed in which students can be introduced to their task and the basic concepts of shopping. An overview of related literature in inclusion research is presented and used to discuss how the VR system can be used in future studies to measure social inclusion.
Train Your Classifier First: Cascade Neural Networks Training from Upper Layers to Lower Layers

Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset. That is, in general, freezing the trained feature extractor (the lower layers) and retraining the classifier (the upper layers) on the same dataset leads to worse performance. In this paper, for the first time, we show that the frozen classifier is transferable within the same dataset. We develop a novel top-down training method which can be viewed as an algorithm for searching for high-quality classifiers. We tested this method on automatic speech recognition (ASR) tasks and language modelling tasks. The proposed method consistently improves recurrent neural network ASR models on Wall Street Journal, self-attention ASR models on Switchboard, and AWD-LSTM language models on WikiText-2.
Earthquake early warning system in Liaoning, China based on PRESTo*

Liaoning is located in northeast China with a high level of seismic activity, and earthquake early warning is important for the mitigation of seismic hazard. In this work, we implement PRESTo, an open-source software platform for earthquake early warning based on regional seismic records, to the Liaoning seismic network. For the early warning of earthquakes in Liaoning, a travel-time table is created for event detection and location using an average crustal model, and the empirical relation is established between the earthquake magnitude and the initial P-wave amplitudes. Using archived seismic records of past earthquakes, we determine the optimal values for Liaoning using the core algorithms of PRESTo. Based on the optimal parameters, the uncertainty in event location is generally less than 5 km, and the lead time of the early warning is ~15 s at 100-km epicentral distance. The implemented system can be directly put into routine earthquake early warning operation by linking it with the real-time data stream from the Liaoning seismic network.
Location Extraction from Twitter Messages Using a Bidirectional Long Short-Term Memory Neural Network with Conditional Random Field Model

In the context of disaster management, location information is crucial in disaster scenarios to infer the incident location and facilitate disaster relief. In recent years the advent of social media has brought not only great opportunity to enhance disaster management in a crowdsourced perspective, but also a major challenge to interpret the noisy information. A conventional approach to location extraction from texts is Named Entity Recognition (NER), however it shows unsatisfactory performance on informal and colloquial texts such as social media messages, especially for the uncommon place names. To address this issue, we proposed a Bidirectional Long Short-Term Memory (LSTM) Neural Network with Conditional Random Field (CRF) layer to identify geo-entities especially the rarely known local places in social media messages, and the use of orthographic, semantic and syntactic features was explored to achieve best performance. The proposed model was tested on a dataset collected from Twitter, showing promising performance in detecting location information when compared with off-the-shelf NER tools.