Reviewerly/exHarmony huggingface dataset:
Counterterrorism for Cyber-Physical Spaces

Simulating terrorist scenarios in cyber-physical spaces---that is, urban open or (semi-) closed spaces combined with cyber-physical systems counterparts---is challenging given the context and variables therein. This paper addresses the aforementioned issue with ALTer a framework featuring computer vision and Generative Adversarial Neural Networks (GANs) over terrorist scenarios. We obtained the data for the terrorist scenarios by creating a synthetic dataset, exploiting the Grand Theft Auto V (GTAV) videogame, and the Unreal Game Engine behind it, in combination with OpenStreetMap data. The results of the proposed approach show its feasibility to predict criminal activities in cyber-physical spaces. Moreover, the usage of our synthetic scenarios elicited from GTAV is promising in building datasets for cybersecurity and Cyber-Threat Intelligence (CTI) featuring simulated video gaming platforms. We learned that local authorities can simulate terrorist scenarios for their cities based on previous or related reference and this helps them in 3 ways: (1) better determine the necessary security measures; (2) better use the expertise of the authorities; (3) refine preparedness scenarios and drills for sensitive areas.
Classification Via Simplicial Learning

Dictionary learning for sparse representations is generative in nature, hence discriminative modifications are commonly observed for classification problems. Classical dictionary learning bears a fundamental problem of not being capable of distinguishing two different classes lying on the same subspace, that cannot be resolved by any discriminative modification. This paper proposes an evolutionary simplicial learning method as a generative and compact sparse framework that solves the aforementioned problem for classification. Simplicial learning is an adaptation of conventional dictionary learning, in which subspaces designated by dictionary elements take the form of simplices through additional constraints on sparse codes. On top, an evolutionary approach is developed to determine the dimensionality and the number of simplices composing the simplicial. The proposed evolutionary learning is considered within multi-class classification tasks through synthetic and handwritten digit datasets and the superiority of it even as a generative-only approach is demonstrated. Simplicial learning loses its superiority over discriminative methods in high-dimensional real-world cases but can further be modified with discriminative elements to achieve state-of-the-art for classification.
Cross-Course and Multi-course Sentiment Classification of Student Posts

Affective Computing is one of the most active research topics in education. Increased interest in emotion recognition through text channels makes sentiment analysis (i.e., the Natural Language Processing task of determining the valence in texts) a state-of-the-practice tool. Considering the domain-dependent nature of sentiment analysis as well as the heterogeneity of the educational domain, development of robust sentiment classifiers requires an in-depth understanding of the effect of the teaching-learning context on model performance. This work investigates machine learning-based sentiment classification on datasets comprised of student posts in forums, pertaining to two different academic courses. Different dataset configurations were tested, aiming to compare performance: i) between single-course and multi-course classifiers, ii) between in-course and cross-course classification. A sentiment classifier was built for each course, exhibiting a fair performance. However, classification performance dramatically decreased, when the two models were transferred between courses. Additionally, classifiers trained on a mixture of courses underperformed single-course classifiers. Findings suggested that sentiment analysis is a course-dependent task and, as a rule of thumb, less but course-specific information results in more effective models than more but non-specialized information.
State of the Art in Semantic Organizational Knowledge

This chapter proposes a state-of-the-art survey on the emerging field of Semantic Organizational Knowledge. This concept refers to the technologies of the Semantic Web and Linked Data applied to the principles and procedures of organizational knowledge. Originally, organizational Knowledge is described as the ability of employees of an organization to exercise judgment based on the history and collective understanding of a particular context. Researchers have identified the existence of several types of knowledge in organized contexts including explicit knowledge, tacit knowledge, cultural knowledge, and embedded knowledge. Along these lines, a number of issues must be addressed in order to apply Semantic Web and Linked Data technologies. The main objective of this chapter is to demonstrate that there exists substantial research that supports the use of the Semantic Web or Linked Data technologies to effectively support all aspects of knowledge creation, sharing, distribution, and acquisition.
CCCNet: An Attention Based Deep Learning Framework for Categorized Counting of Crowd in Different Body States

Crowd counting problem that counts the number of people in an image has been extensively studied in recent years. In this paper, we introduce a new variant of crowd counting problem, namely categorized crowd counting, that counts the number of people sitting and standing in a given image. Categorized crowd counting has many real-world applications such as crowd monitoring, customer service, and resource management. The major challenges in categorized crowd counting come from high occlusion, perspective distortion and the seemingly identical upper body posture of sitting and standing persons. Existing density map based approaches perform well to approximate a large crowd, but lose important local information necessary for categorization. On the other hand, traditional detection-based approaches perform poorly in occluded environments, especially when the crowd size gets bigger. Hence, to solve the categorized crowd counting problem, we develop a novel attention-based deep learning framework that addresses the above limitations. In particular, our approach works in three phases: i) We first generate basic detection based sitting and standing density maps to capture the local information; ii) Then, we generate a crowd counting based density map as global counting feature; iii) Finally, we have a cross-branch segregating refinement phase that splits the crowd density map into final sitting and standing density maps using attention mechanism. Extensive experiments show the efficacy of our approach in solving the categorized crowd counting problem.
Inquiry into Anti-Vilification Protections

In this policy submission to Inquiry into Victorian Anti-Vilification Laws, researchers of the Allens Hub for Technology, Law and Innovation discuss the effectiveness of the Racial and Religious Tolerance Act 2001 Act; interaction between the Act and other state and Commonwealth legislation; the role of the state in relation to anti-vilification laws and the importance of retaining and expanding the scope of such laws; and the legal issues that may affect law enforcement approaches to detecting and prosecuting online offending.
A historical investigation into Persian prefixal verbs based on <i>Siyâsat-nâmeh</i> and a contemporary corpus

Abstract Siyâsat-nâmeh ‘letter of politics’ as a famous Persian book carries verbal forms which seem different from those found in contemporary corpora. In this paper, we focus on a comparison between prefixal verbs in Siyâsat-nâmeh and those in contemporary corpora. Our corpus is taken randomly from political section of a popular contemporary Iranian newspaper called Hamshahri. Since Siyâsat-nâmeh contains almost 100000 words, we take the same number of words from Hamshahri . We focus on verbs containing one of the five frequent prefixes. First, we extracted verbs carrying such prefixes from both sources under study, and then classified them in terms of types and tokens in AntConc (computer software). Afterward, we compared and contrasted verbs of the two sources. A number of verbal prefixes became completely obsolete; a number of them changed into prefixal nouns/adjectives to be used as non-verbal elements of new verbs.
Finite State-Spaces and Hidden Markov Models

SummaryThis short chapter considers the special case where the state-space of the considered model is finite; \(\mathcal {X}=\{1,\ldots ,K\}\). In that case the integrals of the forward and backward recursions become sums over K terms, which can be computed exactly at a cost that is shown to be \(\mathcal {O}(TK^2)\). State-space models with a finite state-space model are usually called hidden Markov models. Applying the generic algorithm to their “bootstrap” Feynman-Kac formalisation yields an exact algorithm known as the forward-backward algorithm.
Harnessing Adversarial Distances to Discover High-Confidence Errors

Given a deep neural network image classification model that we treat as a black box, and an unlabeled evaluation dataset, we develop an efficient strategy by which the classifier can be evaluated. Randomly sampling and labeling instances from an unlabeled evaluation dataset allows traditional performance measures like accuracy, precision, and recall to be estimated. However, random sampling may miss rare errors for which the model is highly confident in its prediction, but wrong. These high-confidence errors can represent costly mistakes, and therefore should be explicitly searched for. Past works have developed search techniques to find classification errors above a specified confidence threshold, but ignore the fact that errors should be expected at confidence levels anywhere below 100%. In this work, we investigate the problem of finding errors at rates greater than expected given model confidence. Additionally, we propose a query-efficient and novel search technique that is guided by adversarial perturbations to find these mistakes in black box models. Through rigorous empirical experimentation, we demonstrate that our Adversarial Distance search discovers high-confidence errors at a rate greater than expected given model confidence.
Development of Smart Workshop Mobile Application As Information

In Indonesia, as many as 146,858,759 vehicle units were collected by BPS (Statistics Indonesia), the COVID-19 pandemic, which hurt the automotive business in Indonesia. The focus of this research is to utilize technology that provides mobile applications that can be used for companies in Indonesia. This interactive mobile application provides information and maps, specifically for automotive business people in Indonesia. The study uses Android Studio and agrees to develop a prototype mobile application, balsamiq, starUML, to create interface designs. The target of developing this online workshop application is automotive business people and car workshop owners. The proposed mobile app is expected to be an alternative for supporters of normal business during the COVID-19 pandemic in Indonesia.
Opportunistic Knowledge Adaption in Self-Learning Systems

In the context of Autonomous Learning, the question arises how an online learning system adapts its knowledge according to a changing environment, i.e. arrival of new classes or changing noise functions, to maintain a robust level of performance. As a solution, we suggest an architectural design inspired by a variant of the Observer/Controller framework. We present a scenario, in which the presented architecture is assumed to improve the performance, because the system is aware of currently available knowledge and can opportunistically exploit this knowledge.
T<sup>2</sup>DNS: A Third-Party DNS Service with Privacy Preservation and Trustworthiness

We design a third-party DNS service named T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNS. T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNS serves client DNS queries with the following features: protecting clients from channel and server attackers, providing trustworthiness proof to clients, being compatible with the existing Internet infrastructure, and introducing bounded overhead. T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNS’s privacy preservation is achieved by a hybrid protocol of encryption and obfuscation, and its service proxy is implemented on Intel SGX. We overcome the challenges of scaling the initialization process, bounding the obfuscation overhead, and tuning practical system parameters. We prototype T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNS, and experiment results show that T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNS is fully functional, has acceptable overhead in comparison with other solutions, and is scalable to the number of clients.
Remote sensing data fusion and machine learning techniques for mineral exploration

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Remote sensing data fusion and machine learning techniques for mineral explorationAuthors: Priscilla AddisonStephen AlwonAlex JanevskiKristopher PurensClyde WheelerPriscilla AddisonDescartes LabsSearch for more papers by this author, Stephen AlwonDescartes LabsSearch for more papers by this author, Alex JanevskiDescartes LabsSearch for more papers by this author, Kristopher PurensDescartes LabsSearch for more papers by this author, and Clyde WheelerDescartes LabsSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428251.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWith the ability to continuously collect multispectral data over the whole Earth, satellite remote sensing provides a rich opportunity for mineral exploration at large scale. However, nuances of the available instruments require careful processing of the data before exploration work can begin. In this work, we explore how best to process the large amount of multispectral imagery available from both the Sentinel-2 and ASTER instruments, and apply both supervised and unsupervised machine learning techniques to the resultant images to enable continent-scale exploration.Presentation Date: Wednesday, October 14, 2020Session Start Time: 8:30 AMPresentation Time: 10:10 AMLocation: 351DPresentation Type: OralKeywords: mining, remote sensing, machine learningPermalink: https://doi.org/10.1190/segam2020-3428251.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Priscilla Addison, Stephen Alwon, Alex Janevski, Kristopher Purens, and Clyde Wheeler, (2020), "Remote sensing data fusion and machine learning techniques for mineral exploration," SEG Technical Program Expanded Abstracts : 1384-1388. https://doi.org/10.1190/segam2020-3428251.1 Plain-Language Summary Keywordsminingremote sensingmachine learningPDF DownloadLoading ...
Joint Energy Optimization of Cooling Systems and Virtual Machine Consolidation in Data Centers

Minimizing energy consumption of data centers is important to reduce carbon emissions. Virtual machines (VMs) consolidation is a typical technique to utilize the available data center resources and thus improve energy efficiency. The cooling systems consume up to 50% of the total data center electricity. In this work, we investigate the joint energy optimization of cooling systems and VM consolidations in cloud data centers. We propose a cooling-aware VM consolidation (CAVC for short) algorithm to the problem. The CAVC algorithm is a two-stage solution: 1) we first relax the constraints of the problem and determine an optimal number of physical machines (PMs) and an optimal CPU utilization of the PMs that yields the minimum cooling power; and 2) based on the initial solution of the first stage, we consolidate the VMs into the predetermined PMs with the predetermined CPU utilization ratio as much as possible. To the best of authors’ knowledge, this is the first work that jointly considers the VM consolidation and the cooling systems in minimizing energy consumption of cloud data centers. We derive an approximation ratio of CAVC over the optimal solution. The real-world data set (i.e., Google cluster data) is adopted in the simulations and the results show that the CAVC algorithm yields very close energy consumption to the theoretical lower bound.
Binarized Attributed Network Embedding via Neural Networks

Traditional attributed network embedding methods are designed to map structural and attribute information of networks jointly into a continuous Euclidean space, while recently a novel branch of them named binarized attributed network embedding has emerged to learn binary codes in Hamming space, aiming to save time and memory costs and to naturally fit node retrieval task. However, current binarized attributed network embedding methods are scarce and mostly ignore the local attribute similarity between each pair of nodes. Besides, none of them attempt to control the independency of each dimension(bit) of the learned binary representation vectors. As existing methods still need improving, we propose an unsupervised Neural-based Binarized Attributed Network Embedding (NBANE) approach. Firstly, we inherit the Weisfeiler-Lehman proximity matrix from predecessors to aggregate high-order features for each node. Secondly, we feed the aggregated features into an autoencoder with the attribute similarity penalizing term and the orthogonality term to make further dimension reduction. To solve the problem of integer optimization we adopt the relaxation-quantization method during the process of training neural networks. Empirically, we evaluate the performance of NBANE through node classification and clustering tasks on three real-world datasets and study a case on fast retrieval in academic networks. Our method achieves better performance over state- of-the-art baselines methods of various types.
Semantic comparisons of Alloy models

Alloy is a textual modeling language for structures and behaviors of software designs. The Alloy Analyzer provides various analyses making it a popular light-weight formal methods tool. While Alloy models can be analyzed, explored, and tested, there is little support for comparing different versions of Alloy models. We believe that these comparisons are crucial when trying to refactor, refine, or extend models. In this work we present an approach for the semantic comparisons of Alloy models. Our pair-wise comparisons include semantic model differencing and the checking of refactoring, refinement, and extension. We enable semantic comparisons of Alloy models by a translation of two versions into a single model that is able to simulate instances of either one or of the versions. Semantic differencing and instance computation require only a single analysis of the combined model in the Alloy Analyzer. We implemented our work and evaluated it using 654 Alloy models from different sources including version histories. Our evaluation examines the cost of semantic comparisons using the Alloy Analyzer in terms of running times and variable numbers over individual models.
An Efficient Binary Feature Based on Hierarchical Structure Histogram

An efficient binary feature is proposed in this paper to compromise the efficiency of simple hash feature and the discrimination of float-point feature. The proposed feature extraction method integrates a hierarchical feature descriptor and a simple but effective hash algorithm. Firstly, the local patch or image area is extracted from the test image. Then, the hierarchical feature descriptor is utilized to embed the structure information as well as the color information of pixels. Finally, a simple binary hash algorithm is proposed to change the hierarchical feature descriptor to a compact binary feature. The proposed binary feature is evaluated based on image retrieval on COREL-1K dataset. The experimental results show that the proposed binary feature is efficient while owns a comparable discrimination to the related works.
Higher-Order Heterogeneous Graph Convolutional Network Based on Meta-Paths

Graph convolution networks are potent methods in graph representation learning. Meta-paths, which connect different types of nodes, are extensively used to represent various se-mantic meanings in heterogeneous graphs. Inspired by the above, we design a higher-order heterogeneous graph convolutional network based on meta-paths. It not only chooses a few meta-paths but also captures higher-order meta-paths with important higher-order relations (such as communal relation). Besides, it contains a calculation method of higher-order meta-path-based adjacency matrices and a novel heterogeneous graph convolution network for generating node embeddings. At every message passing step, it linearly aggregates information from higher-order meta-path-based neighbors. The computational complexity analysis shows that our proposed model is of high efficiency and applies to large-scale heterogeneous graphs. Our proposed model outperforms the state-of-the-art results in three real-world heterogeneous graphs: DBLP, IMDB, and Amazon Kindle Review. The classification experiments show that the calculation of higher-order meta-path-based adjacency matrices brings 2.23% average accuracy improvement in DBLP and IMDB.
An Online Document Comparison Method Based on the Super Ledger Fabric Blockchain

This paper presents an online document comparison method based on blockchain technology. This method calls PBFT algorithm, and compares the content of the manuscript after hash matching with the records in the state database, and finally gives the results of the similarity comparison, as well as the source information of the document.
Ink Flow Patterns In Multi Color Inkjet Images And Their Impact On Graininess Noise

Graininess noise is a common artifact in inkjet printing. While current inkjet printing technologies attempt to control graininess in single color images, the results are often less than optimal for multi-color images. This is due to fluidic interactions between inks of different colors. This paper will describe a color decomposition methodology that can be used to study ink flow patterns in multi-color inkjet printed images at a microscopic scale. This technique is used to decompose multi-color images into several independent color components. The ink patterns in these components are analyzed to relate them to visually perceptible graininess noise.
Investigating the Relation Between Sense of Presence, Attention and Performance: Virtual Reality Versus Web

The sense of presence is an important construct of virtual experiences, as it relates to the user’s attention. This pilot study investigates the relations among sense of presence, attention levels and performance, comparing the outcomes across two different media formats (virtual reality versus web). Fourteen participants performed knowledge tests in the area of Financial Mathematics. Sense of presence was evaluated through questionnaire and attention levels were measured using a EEG biosensor. The results show that, although very prominent to the sense of presence and its tangential benefits, the virtual reality technology was not successful as the web in translating the allocation of attentional resources into better performance.