Reviewerly/exHarmony huggingface dataset:
Clay Mineral Alteration in Oil and Gas Fields: Integrated Analyses of Surface Expression, Soil Spectra, and X-Ray Diffraction Data

Subsurface hydrocarbon occurrences can be detected by clay mineral (CM) alteration at the surface as a consequence of hydrocarbon migration. This study analyzed CM alteration in an oil and gas (O&G) field in the West Tugu field, located in the northwest Java Basin of Indonesia. Landsat 8 OLI data acquired on 25 September 2015 and soil spectral reflectance (SSR) data recorded using analytical spectral devices (ASD) in March 2017 were processed using clay indices to analyze CM alteration. Soil samples from a field survey of the same area were also analyzed using X-ray diffraction (XRD) to identify their CM compositions. CM distribution was empirically modeled by integrating the SSR bands re-sampled to Landsat 8 OLI (SSRL) bands and the CM composition using the best subset regression method and regression analyses. The resulting model was applied to Landsat 8 OLI images to map the surface CM alteration. The results show that CM alteration observed in the O&G field is an indicator of hydrocarbon microseepage at the surface. These results can be used as a technique to identify prospective regions that contain hydrocarbons and differentiate from those that are barren, and could be useful for increasing the rate of drilling success.
Multifractal analysis of embryonic eye tissues from female mice with folic acid deficiency. Part II: Local Connected Fractal Dimension Analysis

The aim of this work is to study local changes of fractality in mouse embryonic lens. Embryos were removed from female mice that have been subjected to a diet deficient in folic acid for two and eight weeks. The local change of fractality is studied through the analysis of the Local Connected Fractal Dimension (LCFD) on binarised images of these lenses using FracLac plugin for ImageJ, after immunohistochemical analysis performed using anti-collagen IV or anti-laminin-1. The samples were analysed previously by the authors to locate under and overexpression lens zones of these proteins. The results show that there are variations of the LCFD with respect to control groups. These changes show significant positive correlations with changes in spatial patterns of both proteins expression.
Improving the Usability and UX of the Swiss Internet Voting Interface

Up to 20% of residential votes and up to 70% of absentee votes in Switzerland are cast online. The Swiss system aims to provide individual verifiability by different verification codes. The voters have to carry out verification on their own, making the usability and UX of the interface of great importance. To improve the usability, we first performed an evaluation with 12 human-computer interaction experts to uncover usability weaknesses of the Swiss Internet voting interface. Based on the experts' findings, related work, and an exploratory user study with 36 participants, we propose a redesign that we evaluated in a user study with 49 participants. Our study confirmed that the redesign indeed improves the detection of incorrect votes by 33% and increases the trust and understanding of the voters. Our studies furthermore contribute important lessons for designing verifiable e-voting systems in general.
Exploring Virtual Reality Mechanics in Puzzle Design

We explore practical implementations of various custom virtual reality mechanics, developed specifically for this study, in the context of puzzle game design with an experimental approach. These mechanics include swimming, crawling, climbing, and hiding objects in virtual spaces. Each mechanic has two different variations: realistic and game-like, and the main goal is to test which variation is more enjoyable to use. Convenience sampling is used in the study and the sample size is 22 volunteers. Both qualitative and quantitative data are collected. The data collection methods used are questionnaire and observation. The enjoyability of the mechanics is evaluated based on four different aspects: perceived realism, personal traits and abilities of the testing sample, the testing order, and perceived difficulty. A special interest is to observe whether real-life skills corresponding to the studied mechanics affect the enjoyment and performance levels in the respective mechanics. The more realistic mechanics turn out to be more enjoyable by a significant margin, suggesting that they should be utilised in the future. Additionally, the real-life diving and swimming skills, puzzle variation testing order, previous gaming experience, and testers’ familiarity with the testing supervisor are identified to have a clear impact on the enjoyment levels.
Efficient Locality-Sensitive Hashing Over High-Dimensional Data Streams

Approximate Nearest Neighbor (ANN) search in high-dimensional space is a fundamental task in many applications. Locality-Sensitive Hashing (LSH) is a well-known methodology to solve the ANN problem with theoretical guarantees and empirical performance. We observe that existing LSH-based approaches target at the problem of designing search optimized indexes, which require a number of separate indexes and high index maintenance overhead, and hence impractical for high-dimensional streaming data processing. In this paper, we present PDA-LSH, a novel and practical disk-based LSH index that can offer efficient support for both updates and searches. Experiments on real-world datasets show that our proposal outperforms the state-of-the-art schemes by up to 10× on update performance and up to 2× on search performance.
A Strategy for Mitigating Denial of Service Attacks on Nodes with Delegate Account of Lisk Blockchain

In this paper, I evaluate a type of denial of service attack, bandwidth depletion, that difficult the block propagation in blockchain networks. Towards the end, I study the attack on Lisk blockchain and explore its effects in the Delegated Proof of Stake consensus. I also propose a methodology joint with two tools I've created as countermeasures against such type of attack. The methodology is composed of the configuration of the same delegate account in more than one node joint with the use of created tools capable to detect the percentage of consensus on each monitored node and activate block forging status in a single node dynamically. Therefore, allowing a block to be forged even when the delegate account is under attack on another node and reducing the chance of forks creation on the blockchain with the same delegate account configured and activated on two or more nodes in the same forging time slot.
MDCStream

The establishment of modern technological paradigms like ubiquitous computing, big data, cyber-physical systems, or communication networks has strongly increased the need for efficient, effective data stream analysis. MDCStream is a MATLAB tool for generating temporal-dependent numerical datasets in order to stress-test stream data classification, clustering, and outlier detection algorithms. MDCStream is built on MDCGen, therefore showing a high flexibility for creating a wide diversity of data scenarios. To show an example of the potential of MDCStream, we tested a stream data clustering algorithm recently proposed in the literature with datasets generated with MDCStream. Datasets were designed to draw challenges related to space geometries and concept drift.
Graph Embeddings for One-pass Processing of Heterogeneous Queries

Effective information retrieval (IR) relies on the ability to comprehensively capture a user's information needs. Traditional IR systems are limited to homogeneous queries that define the information to retrieve by a single modality. Support for heterogeneous queries that combine different modalities has been proposed recently. Yet, existing approaches for heterogeneous querying are computationally expensive, as they require several passes over the data to construct a query answer.In this paper, we propose an IR system that overcomes the computational challenges imposed by heterogeneous queries by adopting graph embeddings. Specifically, we propose graph-based models in which both, data and queries, incorporate information of different modalities. Then, we show how either representation is transformed into a graph embedding in the same space, capturing relations between information of different modalities. By grounding query processing in graph embeddings, we enable processing of heterogeneous queries with a single pass over the data representation. Our experiments on several real-world and synthetic datasets illustrate that our technique is able to return twice the amount of relevant information in comparison with several baselines, while being scalable to large-scale data.
C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.
SRN: Side-Output Residual Network for Object Reflection Symmetry Detection and Beyond

This article establishes a baseline for object reflection symmetry detection in natural images by releasing a new benchmark named Sym-PASCAL and proposing an end-to-end deep learning approach for reflection symmetry. Sym-PASCAL spans challenges of multiobjects, object diversity, part invisibility, and clustered backgrounds, which is far beyond those in existing data sets. The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the symmetry ground truth and the side outputs of multiple stages of a trunk network. By cascading RUs from deep to shallow, SRN exploits the “flow” of errors along multiple stages to effectively matching object symmetry at different scales and suppress the clustered backgrounds. SRN is interpreted as a boosting-like algorithm, which assembles features using RUs during network forward and backward propagations. SRN is further upgraded to a multitask SRN (MT-SRN) for joint symmetry and edge detection, demonstrating its generality to image-to-mask learning tasks. Experimental results verify that the Sym-PASCAL benchmark is challenging related to real-world images, SRN achieves state-of-the-art performance, and MT-SRN has the capability to simultaneously predict edge and symmetry mask without loss of performance.
Formation and dissolution of conductive channels in an Ag<sub>2</sub>S-islands network

A network that shows dynamical change in response to each input pattern works as a reservoir. In this study, we propose to use a network of Ag2S islands for reservoir computing. Input of a small number of electrons to an Ag2S island from a neighbored island causes Ag nanowire growth, which eventually connects the two islands. Shrinkage of an Ag nanowire also occurs depending on the distribution of a local electric potential. Thus, growth and shrinkage of Ag nanowires among Ag2S islands shows dynamical change in conductive channels responding to input bias, which is demonstrated by an Ag2S-island network surrounded by input/output electrodes. The Ag2S islands that are electrically connected to a surrounding electrode are identified using a conductive-atomic force microscope, confirming the operating mechanism.
Aggression Identification in English, Hindi and Bangla Text using BERT, RoBERTa and SVM.

This paper presents the results of the classifiers we developed for the shared tasks in aggression identification and misogynistic aggression identification. These two shared tasks were held as part of the second workshop on Trolling, Aggression and Cyberbullying (TRAC). Both the subtasks were held for English, Hindi and Bangla language. In our study, we used English BERT (En-BERT), RoBERTa, DistilRoBERTa, and SVM based classifiers for English language. For Hindi and Bangla language, multilingual BERT (M-BERT), XLM-RoBERTa and SVM classifiers were used. Our best performing models are EN-BERT for English Subtask A (Weighted F1 score of 0.73, Rank 5/16), SVM for English Subtask B (Weighted F1 score of 0.87, Rank 2/15), SVM for Hindi Subtask A (Weighted F1 score of 0.79, Rank 2/10), XLMRoBERTa for Hindi Subtask B (Weighted F1 score of 0.87, Rank 2/10), SVM for Bangla Subtask A (Weighted F1 score of 0.81, Rank 2/10), and SVM for Bangla Subtask B (Weighted F1 score of 0.93, Rank 4/8). It is seen that the superior performance of the SVM classifier was achieved mainly because of its better prediction of the majority class. BERT based classifiers were found to predict the minority classes better.
Decentralized operation of interdependent power and energy networks: Blockchain and security

Emerging wireless sensor networks enable wide-area sensing and monitoring of interdependent networks and critical infrastructures. Ubiquitous sensing plays a pivotal role in optimal, secure, and efficient operation of smart city infrastructures, including interdependent power and energy networks. It also leads to a transition from central operation of these infrastructures toward decentralized resources and Internet of things, such as distributed energy resources, microgrids, and smart mobility sensing devices (e.g., road-side units and intelligent traffic cameras). In this study, we first investigate the power grid modernization and the transition toward intelligent power systems. Smart grids introduce more reliable, secure, efficient, and sustainable energy delivery to the consumers by engaging demand side in load-generation balance. Further, we introduce blockchain-based energy trading as a means to expedite the decentralization of interdependent power and energy networks by enabling decentralized transactions and smart contracts. To this end, we overview the prior work on blockchain-based smart grids. We then explain different frameworks for implementing decentralized energy exchange among power and energy system agents in a decentralized and secure fashion.
Comparison on Feature Selection Methods for Text Classification

The high-dimensional text data always contains a large quantity of noisy terms which bring negative effects on the performance of text classification. Feature selection is the common solution for dimension reduction in text classification. The choices of feature selection methods for text classification have significant impacts on classification accuracy. According to our literature review, few recent studies of feature selection focus on performance comparisons on feature selection methods. To fill this gap, this paper conducts discussions to compare performances of typical feature selection methods which are commonly involved in previous studies for text classification. Firstly, we introduce and discuss a series of typical feature selection methods in previous studies for text classification in details. Secondly, we conduct comparison experiments on four benchmark datasets to compare the effectiveness of twenty typical feature selection methods in text classification. Finally, we give conclusions on performance of the typical feature selection methods. The result of this paper gives a guideline for selecting appropriate feature selection methods for text classification academic analysis or real-world text classification applications.
Civic Empowerment through Digitalisation: The Case of Greenlandic Women

This paper explores the disruptive and transformative effects of digital technology on gendered security asymmetries in Greenland. Through ethnographic fieldwork conducted in Greenland and Denmark, research findings emerged through in-depth interviews, collaborative mappings and field observations with 51 participants. Employing a critical feminist lens, the paper identifies how Greenlandic women develop digital security practices to respond to Greenland's ecologically, politically and socially induced transformation processes. By connecting individual security concerns of Greenlandic women with the broader regional context, the findings highlight how digital technology has created transitory spaces in which collective security is cultivated, shaped and challenged. The contribution to HCI scholarship is therefore threefold: (1) identification and acknowledgement of gendered effects of increased usage of digital technology in remote and hard-to-reach communities, (2) a broader conceptualisation of digital security and (3) a recommendation for more contextualised, pluralistic digitalisation policies and design.
Named-Entity Recognition for Legal Documents

The law has language at its core, so it is not surprising that software operating on natural language has played a role in certain areas of the legal industry for a long time. The last few years have seen a significant upsurge of interest in this area, including an increasing number of start-ups applying deep learning techniques in the context of specific legal applications. In this paper, we present a simple yet powerful method that is applied to legal documents from different legal bodies to correctly recognize a numerous entity to find relevant information for some specific matter at hand. To the best of our knowledge, no attempt has been made in this direction so far and as such our work opens a new direction of research, which will be very much useful for the society.
Supporting Online Video Learning with Concept Map-based Recommendation of Learning Path

People increasingly use online video platforms, e.g., YouTube, to locate educational videos to acquire knowledge or skills to meet personal learning needs. However, most of existing video platforms display video search results in generic ranked lists based on relevance to queries. These relevance-based information display does not take into account the inner structure of the knowledge domain, and may not suit the need of online learners. In this paper, we present ConceptGuide, a prototype system for learning orientations to support ad hoc online learning from unorganized video materials. ConceptGuide features a computational pipeline that performs content analysis on the transcripts of YouTube videos queried by the user and generates concept-map-based visual recommendations of conceptual and content links between videos, forming learning pathways to provide structures feasible and usable for learners to consume.
Interactive visual analytics tool for multidimensional quantitative and categorical data analysis

With the advances in science and technology, a rapid growth of multidimensional (multivariate) datasets is observed in different fields. Projection and visualization of such data to a lower dimensional space without losing the data structure is a challenging task. We propose an interactive visual analytics tool that is applied for the combined analysis of multidimensional numerical and categorical data. The tool helps the analyst not only to find the clusters of similar objects but also to identify the important features specific to these clusters. The efficacy of the various functionalities of the tool is examined analyzing epidemiological data to understand the pathogenesis of obstructive sleep apnea. Our approach helps the user to visually analyze the data and get a better understanding of the data. The tool would be a valuable resource for analysts working on numerical and categorical data.
Smart City Recommendations Using the TOPSIS Method

Abstract This study aims to recommend a city that is suitable as a Smart City on the island of Sumatera. This study uses a Decision Support System with the TOPSIS (Technique For Others Reference by Similarity to Ideal Solution) Method. The TOPSIS method is one method that is often used for ranking problems. The research data used are data from 10 major cities on the island of Sumatera. with the largest population in 2019 obtained from the Wikipedia Website (https://www.wikipedia.org/wiki/Sumatra) and the Central Statistics Agency website (https://www.bps.go.id/) as a reference in determining the assessment of each Alternative later. The 10 major cities are Medan, Palembang, Bandar Lampung, Pekanbaru, Batam, Padang, Jambi, Bengkulu, Banda Aceh, and Pematangsiantar. To be able to determine and recommend cities in the islands of Sumatera that are eligible to become Smart Cities, there are 4 criteria as an assessment of each Alternative, namely, Infrastructure, Population, Area, and Economic Level. Based on calculations using the TOPSIS method, the results obtained that the city of Medan has the highest value in the eligibility to become a Smart City.
Triangle and Four Cycle Counting in the Data Stream Model

The problem of estimating the number of cycles in a graph is one of the most widely studied graph problems in the data stream model. Three relevant variants of the data stream model include: the arbitrary order model in which the stream consists of the edges of the graph in arbitrary order, the random order model in which the edges are randomly permuted, and the adjacency list order model in which all edges incident to the same vertex appear consecutively. In this paper, we focus on the problem of triangle and four-cycle counting in these models. We improve over the state-of-the-art results as follows, where n is the number of vertices, m is the number of edges and T is the number of triangles/four-cycles in the graph (i.e., the quantity being estimated): Random Order Model: We present a single-pass algorithm that (1+ε)-approximates the number of triangles using ~O(ε-2 m/√T) space and prove that this is optimal in the range T ≤ √m. The best previous result, a (3+ε)-approximation using ~O(ε-4.5 m/√T) space, was presented by Cormode and Jowhari~(Theor. Comput. Sci. 2017). Adjacency List Model: We present an algorithm that returns a (1+ε)-approximation of the number of 4-cycles using two passes and ~O(ε-4 m/√T) space. The best previous result, a constant approximation using ~O(m/T3/8) space, was presented by Kallaugher et al. (PODS~2019). We also show that (1+ε)-approximation in a single pass is possible in a) polylog(n) space if T=Ω(n2) and b) ~O(n) space if T=Ω(n). Arbitrary Order Model: We present a three-pass algorithm that (1+ε)-approximates the number of 4-cycles using ~O(ε-2 m/T1/4) space and a one-pass algorithm that uses ~O(ε-2 n) space when T=Ω(n2). The best existing result, a (1+ε)-approximation using ~O(ε-2 m2/T) space, was presented by Bera and Chakrabarti (STACS~2017). We also show a multi-pass lower bound and another algorithm for distinguishing graphs with no four cycles and graphs with many 4-cycles.