Reviewerly/exHarmony huggingface dataset:
Quantifiable &amp; comparable evaluations of cyber defensive capabilities: A survey &amp; novel, unified approach

Abstract Metrics and frameworks to quantifiably assess security measures have arisen from needs of three distinct research communities—statistical measures from the intrusion detection and prevention literature, evaluation of cyber exercises, e.g., red-team and capture-the-flag competitions, and economic analyses addressing cost-versus-security tradeoffs. In this paper we provide two primary contributions to the security evaluation literature—a representative survey, and a novel framework for evaluating security that is flexible, applicable to all three use cases, and readily interpretable. In our survey of the literature we identify the distinct themes from each community’s evaluation procedures side by side and flesh out the drawbacks and benefits of each. The evaluation framework we propose includes comprehensively modeling the resource, labor, and attack costs in dollars incurred based on expected resource usage, accuracy metrics, and time. This framework provides a unified approach in that it incorporates the accuracy and performance metrics, which dominate intrusion detection evaluation, the time to detection and impact to data and resources of an attack, favored by educational competitions’ metrics, and the monetary cost of many essential security components used in financial analysis. Moreover, it is flexible enough to accommodate each use case, easily interpretable and comparable, and comprehensive in terms of costs considered. Finally, we provide two examples of the framework applied to real-world use cases. Overall, we provide a survey and a grounded, flexible framework with multiple concrete examples for evaluating security which can address the needs of three currently distinct communities.
Learning to Navigate Using Mid-Level Visual Priors.

How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)? What are the consequences of not utilizing such visual priors in learning? We study these questions by integrating a generic perceptual skill set (a distance estimator, an edge detector, etc.) within a reinforcement learning framework (see Fig. 1). This skill set (mid-level vision) provides the policy with a more processed state of the world compared to raw images. Our large-scale study demonstrates that using mid-level vision results in policies that learn faster, generalize better, and achieve higher final performance, when compared to learning from scratch and/or using state-of-the-art visual and non-visual representation learning methods. We show that conventional computer vision objectives are particularly effective in this regard and can be conveniently integrated into reinforcement learning frameworks. Finally, we found that no single visual representation was universally useful for all downstream tasks, hence we computationally derive a task-agnostic set of representations optimized to support arbitrary downstream tasks.
Economic and Organizational Aspects of University Digital Transformation

The article discusses the economic and organizational aspects of the university digital transformation in the transition to Smart University. It is proposed to understand digital transformation as one of the university digital maturity levels. Digital maturity levels such as initial digitalization and digital manageability are also highlighted. The university digital transformation objects (data, university processes, user interfaces and people) are defined. The management system importance in the process of university digital transformation is indicated. The article also offers conceptual models for managing the university digital transformation in the form of managed Markov chains that take into account the educational environment specifics. The transformation processes modeling is carried out by the iterative optimization method. It is also considered the technological process of managing the university transformation based on digital technologies. The developed and proposed models and methods of decision-making in the university digital transformation management system take into consideration the management process versatility, as well as the degree of its uncertainty. The proposed models can be applied in the project offices activities to manage the university digital transformation and the transition to Smart education concept. The experience of creating an organizational infrastructure for digital transformation is considered on the example of Russian universities.
Speech encryption by multiple chaotic map with fast fourier transform

There are various ways of social communication including writing (WhatsApp, Messenger, Facebook, Twitter, Skype, etc), calling (mobile phone) and voice recording (record your voice and then send it to the other party), but there are ways to eavesdropping the calls and voice messages, One way to solve this problem is via cryptographic approach. Chaos cryptography build on top of nonlinear dynamics chaotic system has gained some footstep in data security. It provides an alternative to conventional cryptography built on top of mathematical structures. This research focuses on the protection of speech recording by encrypting it with multiple encryption algorithms, including chaotic maps (Logistic Map and Sine Maps).
Embodied Multimodal Multitask Learning

Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the inability to transfer to new words and concepts not seen during training using only a few examples. We propose a multitask model which facilitates knowledge transfer across tasks by disentangling the knowledge of words and visual attributes in the intermediate representations. We create scenarios and datasets to quantify cross-task knowledge transfer and show that the proposed model outperforms a range of baselines in simulated 3D environments. We also show that this disentanglement of representations makes our model modular and interpretable which allows for transfer to instructions containing new concepts.
Analysis of rainfall trend, fluctuation and pattern over Port Harcourt, Niger Delta coastal environment of Nigeria

Rainfall affects man in all spheres of his life especially in a one city state like Rivers State The study of analysis of rainfall trend fluctuation and pattern over Port Harcourt shows among other things that rainfall data is available in the study area From the analysis it was discovered that there is no significant climatic variability in the pattern of rainfall or change in climatic pattern over Port Harcourt the study area The general RF condition has not change over the years The rainfall pattern shows double Maxima with an August break or dry spell in between the two peak months of July and September The harmattan dry season starts from late November to late February this situation has not changed Finally it was also discovered that there is hardly any month without rainfall However the study further shows that the trend in rainfall is negative Decreases over the years a gradual decrease over the years in the study area implies that the amount of rainfall and the intensity is decreasing However this has not change the pattern or characteristics of rainfall The fluctuation shows that the intensity of rainfall and amount is decreasing over the years Finally a projection into years shows a very gradual decrease in rainfall It was also discovered that variability are high during the dry months than the wet months The five lowest RF variability months accounted for of the entire RF over Port Harcourt during the study period Whereas the five high RF variability months accounted for just of the total RF over Port Harcourt for the years of study
Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity

In online debates, users express different levels of agreement/disagreement with one another's arguments and ideas.Often levels of agreement/disagreement are implicit in the text and must be predicted to analyze collective opinions.Existing stance detection methods predict the polarity of a post's stance toward a topic or post, but don't consider the stance's degree of intensity.We introduce a new research problem, stance polarity and intensity prediction in response relationships between posts.This problem is challenging because differences in stance intensity are often subtle and require nuanced language understanding.Cyber argumentation research has shown that incorporating both stance polarity and intensity data in online debates leads to better discussion analysis.We explore five different learning models: Ridge-M regression, Ridge-S regression, SVR-RF-R, pkudblab-PIP, and T-PAN-PIP for predicting stance polarity and intensity in argumentation.These models are evaluated using a new dataset for stance polarity and intensity prediction collected using a cyber argumentation platform.The SVR-RF-R model performs best for prediction of stance polarity with an accuracy of 70.43% and intensity with RMSE of 0.596.This work is the first to train models for predicting a post's stance polarity and intensity in one combined value in cyber argumentation with reasonably good accuracy.
Small-GAN: Speeding up GAN Training using Core-Sets

Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actually small. In this work, we propose a method to do this, inspired by the use of Coreset-selection in active learning. When training a GAN, we draw a large batch of samples from the prior and then compress that batch using Coreset-selection. To create effectively large batches of 'real' images, we create a cached dataset of Inception activations of each training image, randomly project them down to a smaller dimension, and then use Coreset-selection on those projected activations at training time. We conduct experiments showing that this technique substantially reduces training time and memory usage for modern GAN variants, that it reduces the fraction of dropped modes in a synthetic dataset, and that it allows GANs to reach a new state of the art in anomaly detection.
Coverless Image Steganography Based on Image Segmentation

To resist the risk of the stego-image being maliciously altered during transmission, we propose a coverless image steganography method based on image segmentation. Most existing coverless steganography methods are based on whole feature mapping, which has poor robustness when facing geometric attacks, because the contents in the image are easy to lost. To solve this problem, we use ResNet to extract semantic features, and segment the object areas from the image through Mask RCNN for information hiding. These selected object areas have ethical structural integrity and are not located in the visual center of the image, reducing the information loss of malicious attacks. Then, these object areas will be binarized to generate hash sequences for information mapping. In transmission, only a set of stego-images unrelated to the secret information are transmitted, so it can fundamentally resist steganalysis. At the same time, since both Mask RCNN and ResNet have excellent robustness, pre-training the model through supervised learning can achieve good performance. The robust hash algorithm can also resist attacks during transmission. Although image segmentation will reduce the capacity, multiple object areas can be extracted from an image to ensure the capacity to a certain extent. Experimental results show that compared with other coverless image steganography methods, our method is more robust when facing geometric attacks.
FCN-Based Carrier Signal Detection in Broadband Power Spectrum

Carrier signal detection has been a problem for a long time, which is the first step for blind signal processing. In this paper, we propose a new method for carrier signal detection in the broadband power spectrum based on the fully convolutional network (FCN). FCN is a deep learning method and used in semantic image segmentation tasks. By regarding the broadband power spectrum sequence as a one-dimensional (1D) image and each subcarrier on the broadband as the target object, we can transform the carrier signal detection problem on the broadband into a semantic segmentation problem on a 1D image without prior knowledge. We design a 1D deep convolutional neural network (CNN) based on FCN to classify each point on broadband power spectrum array into two classes: subcarrier or noise, and then we can easily locate the subcarrier signals' position on the broadband power spectrum. We train the deep CNN on a simulation dataset and validate it on a real satellite broadband power spectrum dataset. The experimental results show that our method can effectively detect the subcarrier signal in the broadband power spectrum and achieve higher accuracy than the slope tracing method.
OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2019-2011-1-00783) supervised by the IITP(Institute for Information & communications Technology Promotion).
Smart City Case Study: City of Coral Gables Leverages the Internet of Things to Improve Quality of Life

The City of Coral Gables, Florida, has implemented a smart city engineering framework that features a diverse array of cyber-physical systems (CPS), Internet of Things (IOT) platforms, and smart connected devices for numerous applications. The City's IoT network relies on a transport layer of high-speed resilient communications with fiber optics and wireless infrastructure that covers the City's most critical arteries, facilities, smart districts in Downtown as well as university campus and residential areas. The City's IoT systems rely on distributed cloud platforms and datacenters where data is aggregated, integrated, analyzed and correlated in a business intelligence and analytics backend with computer vision, artificial intelligence (AI) and machine learning (ML) capabilities. Realtime IoT data is visualized and presented to the public and all stakeholders for consumption and collaboration via the Coral Gables Smart City Hub public platform.
Research Challenges and Opportunities in Multi-Agent Path Finding and Multi-Agent Pickup and Delivery Problems

Recent years have shown a large increase in applications and research of problems that include moving a fleet of physical robots. One particular application that is currently a multi-billion industry led by tech giants such as Amazon robotics and Alibaba is warehouse robots. In this application, a large number of robots operate autonomously in the warehouse picking up, carrying, and putting down the inventory pods. In this paper, we outline several key research challenges and opportunities that manifest in this warehouse application. The first challenge, known as the Multi-Agent Path Finding (MAPF) problem, is the problem of finding a non-colliding path for each agent. While this problem has been well-studied in recent years, we outline several open questions, including understanding the actual hardness of the problem, learning the warehouse to improve online computations, and distributing the problem. The second challenge is known as the Multi-Agent Package and Delivery (MAPD) problem, which is the problem of moving packages in the warehouse. This problem has received some attention, but with limited theoretical understanding or exploitation of the incoming stream of requests. Finally, we highlight a third, often overlooked challenge, which is the challenge of designing the warehouse in a way that will allow efficient solutions of the two above challenges.
An Autonomic Resource Allocation Framework for Service-Based Cloud Applications: A Proactive Approach

The user’s request changes dynamically in service-based cloud applications, which requires optimal amount of computing resources to meet service-level agreements (SLAs). The existing server-side resource allocation mechanisms have limitations in provisioning the required resources to handle the incoming load on the basis of user’s requests. To overcome the aforementioned situation, cloud computing provides ample amount of computing resources to meet the SLAs. There are possibilities that cloud resources might not be properly utilized and might suffer over and under utilization. In this paper, the authors have proposed an autonomic resource allocation framework, that automatically provisions (allocate and de-allocate) the required computing resources as per the load. In this study, the proposed model leverages the linear regression model to predict the resources future usage and further leverage fuzzy logic controller to optimize the resource allocation process. The primary goal of this study is to improve the virtual resource utilization and response time with respect to the existing methods. Finally, the proposed model have been evaluated under real workload traces and results have shown that the proposed model minimize the SLA violation by at least 79% and cost by at least 28% as compared with other approaches.
Correcting anisotropic intensity in light sheet images using dehazing and image morphology

Light-sheet fluorescence microscopy (LSFM) provides access to multi-dimensional and multi-scale in vivo imaging of animal models with highly coherent volumetric reconstruction of the tissue morphology, via a focused laser light sheet. The orthogonal illumination and detection LSFM pathways account for minimal photobleaching and deep tissue optical sectioning through different perspective views. Although rotation of the sample and deep tissue scanning constitutes major advantages of LSFM, images may suffer from intrinsic problems within the modality, such as light mismatch of refractive indices between the sample and mounting media and varying quantum efficiency across different depths. To overcome these challenges, we hereby introduce an illumination correction technique integrated with depth detail amelioration to achieve symmetric contrast in large field-of-view images acquired using a low power objective lens. Due to an increase in angular dispersion of emitted light flux with the depth, we combined the dehazing algorithm with morphological operations to enhance poorly separated overlapping structures with subdued intensity. The proposed method was tested on different LSFM modalities to illustrate its applicability on correcting anisotropic illumination affecting the volumetric reconstruction of the fluorescently tagged region of interest.
Open Domain Chinese Triples Hierarchical Extraction Method

Open domain relation prediction is an important task in triples extraction. When faced with the task of constructing large-scale knowledge graph systems, with the exception of structured data, it is necessary to automatically extract triples from a large amount of unstructured text to expand entities and relations. Although a large number of English open relation prediction methods have achieved good performance, the high-performance system for open domain Chinese triples extraction remains undeveloped due to the lack of large-scale Chinese annotation corpora and the difficulty of Chinese language processing. In this paper, we propose an integrated open domain Chinese triples hierarchical extraction method (CTHE) to solve this problem, considering the advantages of Bi-LSTM-CRF and Att-Bi-GRU models based on the pre-trained BERT encoding model. This method can recognize the named entities from Chinese sentences to establish entity pairs, and implement hierarchical extraction of specific and open relations based on the user-defined schema library and attention mechanism. The experimental results demonstrate the effectiveness of this method, which achieved stable performance on the test dataset, and better precision and F1-score in comparison with state-of-the-art Chinese open domain triples extraction methods. Furthermore, a large-scale annotated dataset for a Chinese named entity recognition (NER) task is established, which provides support for research on Chinese NER tasks.
Blockchain Based Big Data Security Protection Scheme

As the key platform to deal with big data, Hadoop cannot fully protect data security of users by relying on a single Kerberos authentication mechanism. In addition, the single Namenode has disadvantages such as single point failure, performance bottleneck and poor scalability. To solve these problems, a big data security protection scheme is proposed. In this scheme, blockchain technology is adopted to deploy distributed Namenode server cluster to take joint efforts to safeguard the metadata and to allocate access tasks of users. We also improved the heartbeat model to collect user behavior so as to make a faster response to Datanode failure. The smart contract conducts reasonable allocation of user role through the judgment of user tag and risk value. It also establishes a tracking chain of risk value to monitor user behavior in real time. Experiments show that this scheme can better protect data security in Hadoop. It has the advantage of metadata decentralization and the data is hard to be tampered.
<scp>A2Cloud‐RF</scp>: A random forest based statistical framework to guide resource selection for<scp>high‐performance</scp>scientific computing on the cloud

Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.