Reviewerly/exHarmony huggingface dataset:
Geost Botnet. Operational Security Failures of a New Android Banking Threat

Effective operational security is difficult to maintain due to an increase in the costs of work and a decrease in the performance of actions. This is true both for security analysts and malicious attackers. It is tedious, and errors are easy to make. This paper describes the rare discovery of a new Android banking botnet, named Geost, from the operational security failures of its botmaster. They made many mistakes, including using the illegal proxy network of the HtBot malware, not encrypting their Command and Control servers, re-using security services, trusting other attackers with less operational security, and not encrypting chat sessions. The Geost botnet has hundreds of malicious domains, thirteen IP addresses for C&C servers, approximately 800,000 victims in Russia, and potential access to several million Euros in the bank accounts of the victims. More importantly, the operational security mistakes lead to the discovery of members of an underground group that develop and maintain the C&C of Geost. It is seldom possible to glimpse into the decisions taken by the attackers due to failures in their operational security. This research presents the finding of a new Android banking botnet from operational security mistakes, creates an overview of the botnet operation, analyses the victims, and study the relationships with the discovered groups of developers.
PUZZLING ENGINE: A DIGITAL PLATFORM TO AID THE REASSEMBLING OF FRACTURED FRAGMENTS

Abstract. The reassembling of fractured fragments is a paramount task in the fields of digital heritage documentation and reconstruction of archaeological artifacts and monuments. This process is typically carried out by manually puzzling matching clues such as decoration,shape, contour, etc. This labor poses a challenge for restorers as fractured fragments are fragile, deteriorated and in some cases bulky. In order to aid experts in this meticulous and time-consuming process, a puzzling engine is developed with the aim of providing the user with tools to facilitate the reassembling of 3D digital fractured fragments. The assisting tools that compose the puzzling engine include 3D manipulation, reference plane alignment, segmentation, and registration. Furthermore, a Virtual Reality (VR) environment is presented as an alternative matching tool. This allows the user to have an intuitive understanding of the fragments in terms of scale, texture, materials, etc., thus facilitating and speeding up the reassembling process. To show the potential of the proposed tool, the engine is tested by archaeologists not only to puzzle classical stone fragments but also to match deteriorated ancient Egyptian rock tomb blocks.
Identifying Causal Effects via Context-specific Independence Relations

Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces context-specific independence (CSI) relations, the existing identification procedures and criteria based on do-calculus are inherently incomplete. We show that deciding causal effect non-identifiability is NP-hard in the presence of CSIs. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs. The approach is provably sound and it includes standard do-calculus as a special case. With the approach we can obtain identifying formulas that were unobtainable previously, and demonstrate that a small number of CSI-relations may be sufficient to turn a previously non-identifiable instance to identifiable.
Text Genre and Training Data Size in Human-like Parsing

Domain-specific training typically makes NLP systems work better.We show that this extends to cognitive modeling as well by relating the states of a neural phrase-structure parser to electrophysiological measures from human participants.These measures were recorded as participants listened to a spoken recitation of the same literary text that was supplied as input to the neural parser.Given more training data, the system derives a better cognitive model -but only when the training examples come from the same textual genre.This finding is consistent with the idea that humans adapt syntactic expectations to particular genres during language compre-
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines

We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain.We describe the data collection process and the annotations performed by medical professionals, including illness and personal attributes (name, age, gender, family relationships) for the patient and their family members.An initial system that performs argument identification and relation extraction shows promising results -average F-score of 0.87 on complex sentences on the targeted relations.
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations

The main alternatives nowadays to deal with sequences are Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) architectures and the Transformer.In this context, RNN's, CNN's and Transformer have most commonly been used as an encoderdecoder architecture with multiple layers in each module.Far beyond this, these architectures are the basis for the contextual word embeddings which are revolutionizing most natural language downstream applications.However, intermediate layer representations in sequence-based architectures can be difficult to interpret.To make each layer representation within these architectures more accessible and meaningful, we introduce a web-based tool that visualizes them both at the sentence and token level.We present three use cases.The first analyses gender issues in contextual word embeddings.The second and third are showing multilingual intermediate representations for sentences and tokens and the evolution of these intermediate representations along the multiple layers of the decoder and in the context of multilingual machine translation.
Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays

We present a unique dataset of student sourcebased argument essays to facilitate research on the relations between content, argumentation skills, and assessment.Two classroom writing assignments were given to college students in a STEM major, accompanied by a carefully designed rubric.The paper presents a reliability study of the rubric, showing it to be highly reliable, and initial annotation on content and argumentation annotation of the essays.
A Contingency Plan Framework for Cyber-Attacks

Contingency plans administered in computer centers are necessary for business continuity since organizations not only store or process data to offer different service platforms. Organizations could experience different repercussions facing external or internal risks affecting their investments. The fact of having computer support for unwanted events that affect devices is important. Cyber-attacks in data networks lead to a serious problem due to economic damages that an unauthorized intrusion entails. A contingency plan for cyber-attacks can cover just technical issues. However, these are long term projects with continuous execution and improvement, since necessary tasks master for going from production to contingency, and then reversing the changes in the case whether an emergency or an unexpected event occurs. This paper aims to be a framework reference about how to cope with a situation of cyber-attack.
CityCompass VR - A Collaborative Virtual Language Learning Environment

CityCompass VR is a collaborative virtual language learning environment that utilizes interactive omnidirectional video (iODV) content. In the application, two remotely located users navigate through cityscapes in order to reach a common goal. One of the users takes the role of a tourist who needs to find a local attraction, and the other one acts as their guide. The users communicate in a foreign language with each other via headsets. The application is used with HMD device and uses a dwell-time, head-position based interaction.
Analysis of Data Reliability based on Importance Analysis

The quality and accuracy of many decision problems can be affected by the reliability of initial data. Nowadays, a different interpretation of the conception of data reliability exists. In this paper, the reliability of initial data is interpreted as the reliability of complex system according to conceptions of reliability analysis. This interpretation allows using the approach of reliability analysis for the examination of initial data. In particular, in this paper, the importance analysis of the system is used for data reliability evaluation. The importance analysis is part of reliability analysis and allows evaluate the influence of some system component to the system reliability modification. In case of the analysis of initial (input) data, the importance analysis permits to evaluate the importance (influence) of every data attribute. A new algorithm for the initial data analysis based on importance analysis is proposed in this paper. This algorithm is approbated for evaluation of data for the comparison of two methodologies in the education of pupils.
The Direct Path May Not Be The Best: Portuguese-Chinese Neural Machine Translation

Machine Translation (MT) has been one of the classic AI tasks from the early days of the field. Portuguese and Chinese are languages with a very large number of native speakers, though this does not carry through to the amount of literature on their processing, or to the amount of resources available to be used, in particular when compared with English. In this paper, we address the feasibility of creating a MT system for Portuguese-Chinese, using only freely available resources, by experimenting with various approaches to pairing source and target parallel data during training. These approaches are (i) using a model for each source-target language pair, (ii) using an intermediate pivot language, and (iii) using a single model that can translate from any language seen in the source side to any language seen on the target side. We find approaches whose performance is higher than that of the strong baseline consisting of an MT service provided by an IT industry giant for the pair Portuguese-Chinese.
An Empirical Study of Link Sharing in Review Comments

In the pull-based development, developers sometimes exchange review comments and share links, namely Uniform Resource Locators (URLs). Links are used to refer to related information from different websites, which may be beneficial to pull request evaluation. Nevertheless, little effort has been done on analyzing how links are shared and whether sharing links has any impacts on code review in GitHub. In this paper, we conduct a study of link sharing in review comments. We collect 114,810 pull requests and 251,487 review comments from 10 popular projects in GitHub. We find that 5.25% of pull requests have links in review comments on average. We divide links into two types: internal links which point to context in the same project, and external links which point to context outside of the project. We observe that 51.49% of links are internal, while 48.51% of links are external. The majority of internal links point to pull requests or blobs inside projects. We further study impacts of links. Results show that pull requests with links in review comments have more comments, more commenters and longer evaluation time than pull requests without links. These findings show that developers indeed share links and refer to related information in review comments. These results inspire future studies which enable more effective information sharing in the open source community, and improve information accessibility and navigability for software developers.
Extended Target Tracking and Shape Estimation via Random Finite Sets

Vehicles operating close to the ground with Light Detection and Ranging (LiDAR) pose a distinct set of challenges compared to traditional sensors such as cameras or radars. The main issues are that each target can generate hundreds of returns depending on target proximity and size, and the perceived shape of the target can vary depending on its viewing angle relative to the LiDAR. In this paper, we introduce the Occupancy Grid (OG) Gaussian Mixture (GM) Probability Hypothesis Density (PHD) filter that leverages the extended target measurements for dynamic target association and tracking. The new filter extends the GM-PHD filter to track a modified occupancy grid map representation for each target. This allows the weights of the Gaussian mixture terms to be updated in a Bayesian manner based on the similarities between the propagated target representation and the new target measurements. This filter also reconstructs an occupancy grid map representation of the tracked targets in a Bayesian manner to estimate the target shapes. The proposed filter was implemented using LiDAR data obtained from a stationary mid-tier HDL-32E Velodyne LiDAR in an urban environment. In simulations, the OG-GM-PHD filter successfully reconstructed the shape of the three tracked targets. Further, the filter tracked targets resulting in a lower Optimal Sub-Pattern Assignment error metric with up to 20% improvement and a lower cardinality estimation error compared to the traditional GM-PHD filter.
Niche Genetic Algorithm for Solving Multiplicity Problems in Genetic Association Studies

This paper proposes a novel genetic algorithm (GA) that embeds a niche competition strategy (NCS) in the evolutionary flow to solve the combinational optimization problems that involve multiple loci in the search space. Unlike other niche-information based algorithms, the proposed NCSGA does not need prior knowledge to design niche parameters in the niching phase. To verify the solution capability of the new method, benchmark studies on both the travelling salesman problem (TSP) and the airline recovery scheduling problem were first made. Then, the proposed method was used to solve single nucleotide polymorphism (SNP) barcodes generation problems in a genetic association study. Experiments showed that the proposed NCS-based solver substantially improves solution quality by maintaining multiple optima.
Data-driven tool for monitoring of students performance

In today’s education, school success is defined as ensuring achievement for every student. To reach this goal, educators need tools to help them identify students who are at risk academically and adjust instructional strategies to better meet these students’ needs. Student progress monitoring is a practice that helps teachers use student performance data to continually evaluate the effectiveness of their teaching and make more informed instructional decisions. This paper reflects the main output of the SPEET project as an IT tool that implements specific algorithms developed to deal with the basic problems tackled in the project: Classification, Clustering and Drop-out Prediction.
Machine Learning Algorithms for Myanmar News Classification

Text classification is a very important research area in machine learning.Artificial Intelligence is reshaping text classification techniques to better acquire knowledge.In spite of the growth and spread of AI in text mining research for various languages such as English, Japanese, Chinese, etc., its role with respect to Myanmar text is not well understood yet.The aim of this paper is comparative study of machine learning algorithms such as Naïve Bayes (NB), k-nearest neighbours (KNN), support vector machine (SVM) algorithms for Myanmar Language News classification.There is no comparative study of machine learning algorithms in Myanmar News.The news is classified into one of four categories (political, Business, Entertainment and Sport).Dataset is collected from 12,000 documents belongs to 4 categories.Well-known algorithms are applied on collected Myanmar language News dataset from websites.The goal of text classification is to classify documents into a certain number of pre-defined categories.News corpus is used for training and testing purpose of the classifier.Feature selection method, chi square algorithm achieves comparable performance across a number of classifiers.In this paper, the experimental results also show support vector machine is better accuracy to other classification algorithms employed in this research.Due to Myanmar Language is complex, it is more important to study and understand the nature of data before proceeding into mining.
Mapping Entity Sets in News Archives Across Time

Abstract We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.
Using Grover's search quantum algorithm to solve Boolean satisfiability problems: Part I

column Using Grover's search quantum algorithm to solve Boolean satisfiability problems: Part I Share on Authors: Diogo Fernandes University of Porto, Portugal University of Porto, PortugalView Profile , Inês Dutra University of Porto, Portugal University of Porto, PortugalView Profile Authors Info & Affiliations XRDS: Crossroads, The ACM Magazine for StudentsVolume 26Issue 1Fall 2019 pp 64–66https://doi.org/10.1145/3358251Published:17 September 2019 0citation289DownloadsMetricsTotal Citations0Total Downloads289Last 12 Months67Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Apache Spark usage and deployment models for scientific computing

This talk is about sharing our recent experiences in providing data analytics platform based on Apache Spark for High Energy Physics, CERN accelerator logging system and infrastructure monitoring. The Hadoop Service has started to expand its user base for researchers who want to perform analysis with big data technologies. Among many frameworks, Apache Spark is currently getting the most traction from various user communities and new ways to deploy Spark such as Apache Mesos or Spark on Kubernetes have started to evolve rapidly. Meanwhile, notebook web applications such as Jupyter offer the ability to perform interactive data analytics and visualizations without the need to install additional software. CERN already provides a web platform, called SWAN (Service for Web-based ANalysis), where users can write and run their analyses in the form of notebooks, seamlessly accessing the data and software they need. The first part of the presentation talks about several recent integrations and optimizations to the Apache Spark computing platform to enable HEP data processing and CERN accelerator logging system analytics. The optimizations and integrations, include, but not limited to, access of kerberized resources, xrootd connector enabling remote access to EOS storage and integration with SWAN for interactive data analysis, thus forming a truly Unified Analytics Platform. The second part of the talk touches upon the evolution of the Apache Spark data analytics platform, particularly sharing the recent work done to run Spark on Kubernetes on the virtualized and container-based infrastructure in Openstack. This deployment model allows for elastic scaling of data analytics workloads enabling efficient, on-demand utilization of resources in private or public clouds.
Energy-Efficient Embedded Inference of SVMs on FPGA

We propose an energy-efficient embedded binarized Support Vector Machine (eBSVM) architecture and present its implementation on low-power FPGA accelerator. With binarized input activations and output weights, the dot product operation (float-point multiplications and additions) can be replaced by bitwise XNOR and popcount operations, respectively. The proposed accelerator computes the two binarized vectors using hamming weights, resulting in reduced execution time and energy consumption. Evaluation results show that eBSVM demonstrates performance and performance-per-Watt on MNIST and CIFAR-10 datasets compared to its fixed point (FP) counterpart implemented in CPU and GPU with small accuracy degradation.
Risk Factor and Cluster Analysis to Identify Malaria Hot Spot for Control Strategy in Samigaluh Sub-District, Kulon Progo, Indonesia

Background: In 2015, Indonesia government targeted to eliminate malaria in Java Island. Nevertheless, until now malaria still occurs, including in Samigaluh, Kulon Progo District although many malaria programs has been run. Complexity and dynamic of the population also limited budget may become the reason of malaria combat difficulties. Subsequently, a method to direct the policymaker on how to provide program effectively and efficiently was needed. We examined malaria risk factor using statistical and cluster analysis. Methods: A quantitative study with case-control approach was conducted during Spring 2017 in Samigaluh II Public Health Centre, Indonesia. The structured questioner was used to collect the information from both of case and control which were people who had blood examination regarding malaria diagnosed during JanuaryDecember 2016. Global Positioning System was used to record the geographical position of house participant which was used in cluster analysis. Results: Occupation was recognized as the significant risk factor to malaria. One most likely cluster was detected and translated as the source of transmission because of its fall in malaria hotspot. Conclusion: Satscan be able to detect a spatial cluster of malaria case and a promising method for supporting malaria control.
Implementation of Classification and Recognition Algorithm for Text Information Based on Support Vector Machine

At present, Internet data is the world’s largest data resource database. In order to realize fast and automatic intelligent classification, it is of great significance to develop automatic classification of public security intelligent data systems. This paper studies the actual needs of public security information text classification, analyzes the text automatic classification technology support vector machine (SVM) theory and designs and implements SVM-based public security information, and also realizes the classification system of public security information. Automatic classification provides support for subsequent text mining systems and text searches and designs the performance of the system. After optimization and testing, the system was found to have good practical application results.