Reviewerly/exHarmony huggingface dataset:
Big data deployment in containerized infrastructures through the interconnection of network namespaces

Summary Big Data applications tackle the challenge of fast handling of large streams of data. Their performance is not only dependent on the data frameworks implementation and the underlying hardware but also on the deployment scheme and its potential for fast scaling. Consequently, several efforts have focused on the ease of deployment of Big Data applications, notably through the use of containerization. This technology was indeed raised to bring multitenancy and multiprocessing out of clusters, providing high deployment flexibility through lightweight container images. Recent studies have focused mostly on Docker containers. Notwithstanding, this article is actually interested in recent Singularity containers as they provide more security and support high‐performance computing (HPC) environments and, in this way, they can make Big Data applications benefit from the specialized hardware of HPC. Singularity 2.x, however, does not isolate network resources as required by most Big Data components. Singularity 3.x allows allocating each container with isolated network resources, but their interconnection requires a nontrivial amount of configuration effort. In this context, this article makes a functional contribution in the form of a deployment scheme based on the interconnection of network namespaces, through underlay and overlay networking approaches, to make Big Data applications easily deployable inside Singularity containers. We provide detailed account of our deployment scheme when using both interconnection approaches in the form of a “how‐to‐do‐it” report, and we evaluate it by comparing three Big Data applications based on Hadoop when performing on a bare‐metal infrastructure and on scenarios involving Singularity and Docker instances.
Strong Authenticity with Leakage Under Weak and Falsifiable Physical Assumptions

Authenticity can be compromised by information leaked via side-channels (e.g., power consumption). Examples of attacks include direct key recoveries and attacks against the tag verification which may lead to forgeries. At FSE 2018, Berti et al. described two authenticated encryption schemes which provide authenticity assuming a leak-free implementation of a Tweakable Block Cipher ($$\mathsf {TBC} $$). Precisely, security is guaranteed even if all the intermediate computations of the target implementation are leaked in full but the $$\mathsf {TBC} $$ long-term key. Yet, while a leak-free implementation reasonably models strongly protected implementations of a $$\mathsf {TBC} $$, it remains an idealized physical assumption that may be too demanding in many cases, in particular if hardware engineers mitigate the leakage to a good extent but (due to performance constraints) do not reach leak-freeness. In this paper, we get rid of this important limitation by introducing the notion of Strong Unpredictability with Leakage for $$\mathsf {BC} $$'s and $$\mathsf {TBC} $$'s. It captures the hardness for an adversary to provide a fresh and valid input/output pair for a $$(\mathsf {T})\mathsf {BC} $$, even having oracle access to the $$(\mathsf {T})\mathsf {BC} $$, its inverse and their leakages. This definition is game-based and may be verified/falsified by laboratories. Based on it, we then provide two Message Authentication Codes ($$\mathsf {MAC} $$) which are secure if the $$(\mathsf {T})\mathsf {BC} $$ on which they rely are implemented in a way that maintains a sufficient unpredictability. Thus, we improve the theoretical foundations of leakage-resilient $$\mathsf {MAC} $$ and extend them towards engineering constraints that are easier to achieve in practice. (The full version of this paper is available on ePrint [8].)
Line Detection Using Arranging Coordinate Point Method

Line detection system is a system line that can recognize the line mark painted on the road. This is one of the digital driver assistance tools that improve driving safety. The video streaming captured using a camera which is installed in front of the vehicle as the input system will detected the line by using the Arranging Coordinate Point Algorithm. The system will provide the correction value of the vehicle distance from the center of the road and guide the driver to stay on the track. Based on the experiment result, the system could detect a straight and curved line. The line is well detected by the system in good condition such as less of noise of the road, good weather, and clear lane line. The system has a computational process 0.0625 fps with average error calculation of position from the center of the road is 0.0992 m and standard deviation is 0.62448 m.
Question Classification for e-Learning Using Machine Learning Approach

Along with the use of e-learning, the collection of questions in the database is also increasing. The questions that have been uploaded to the e-learning system can certainly be used repeatedly. The use of repetitive questions will be easy to do with the process of reinvention if the questions have been grouped properly. Unfortunately, grouping questions based on subjects to details on grouping per topic is rarely done in e-learning. This makes the process of tracking the existing problems difficult. In addition, the state of the educational curriculum in Indonesia is often changing. Changes can be changes in whole or in part. This change can make the material content in the subject change order or be eliminated. When these changes occur, the previous problems that can still be used will be difficult to use when curriculum changes occur. Therefore, the large amount of question data and the difficulty in finding the questions again made the researcher to conduct this research. E-learning systems must have machine learning capabilities that are able to classify questions automatically based on topics to sub topics. Examined deeper, each question can have a classification in the form of subject categories, topics, sub topics to the level of difficulty of the questions. In this study, researchers focused on grouping questions based on subject categories and topics. The purpose of this study is to find the right method in building machine learning models in classifying questions according to topic categories and subtopics accurately and quickly. So, the questions in e-learning can be called back exactly as needed automatically.
DNSBL for Internet Content Filtering Utilizing pfSense as The Next Generation of Opensource Firewall

The internet at this time has become an important part of everyday life. From an early age, children already introduced to a digital environment and used to use internet connected devices for various activities such as learning, entertainment, have a chat with family and friends. Apart from convenience and benefits, the Internet also poses a threat to children and adolescents, from inappropriate content such as pornography, violence, narcotics culture, exposure to online pedophiles or dangerous behavior that can make children unsafe. The role of parents in monitoring the online activities of children and adolescents becomes very important. The market offers a variety of control systems for parents who can block or filter content, manage usage, monitor activities, set boundary lines, and quota. This research was conducted to collect basic information about several sites that are often accessed by children with the aim of implementing an internet content screening program utilizing DNSBL and pfSense to increase parental awareness of various technologies that can be used to protect children from the dangers of cyberspace, providing various information for parents of tools that can protect children, and as a form of socialization about the importance of children internet usage monitoring by parents. The study was conducted in the city of Sukabumi by using 30 respondents who were parents of children aged 3-11 years. The most accessible site for children in the city of Sukabumi is Youtube. Therefore, preventive measures are needed to reduce the negative impact caused by filtering content.
Conceptualizing BCI and AI in Video Games

Video Games are the most common and popular ways of entertainment mainly amongst the younger generation of world. Due to the advent of Virtual Reality (VR) and Brain Computer Interface (BCI), the gaming experience has increased exponentially amongst the users and the use of Artificial Intelligence (AI) in video games makes it less predictive and more tough and exciting to play. With Virtual Reality now looking distinctly normal, Brain Computer Interface is all set to become the futuristic technology in the gaming horizon. The researchers discover where the technology is at present and how it could transform the way users play in the future. Through this paper, we propose a method to make the video games more exciting and challenging for the users to experience ultimate gaming by combining the trending topics in the world of computer science i.e. BCI and AI.
A Semantic Map for Indoor Robot Navigation Based on Predicate Logic

Interacting with mobile robots through natural language is the main concern of this article, which focuses on the semantic meaning of concepts used in natural language instructions to navigate robots indoors. Assuming the building structure is the prior knowledge of the robot and the robot has the ability of navigating itself locally to avoid collision with the environment, the building structure is represented with predicate logic on SWI-Prolog as the database of the indoor environment, which is called semantic map in this paper, in which the basic predicate clauses are based on two kinds of entities, namely ‘area' and ‘node.' The area names (in natural language convention) of indoor environment are organized with an ontology and are defined in the semantic map which includes the geometric information of areas and connection relationships between areas. With the semantic map database, functions for robot navigation, like a topological map, path planning, and self-localization, are realized through reasoning by properly designed predicates based on constraint satisfaction problem (CSP). An example building is given to show the idea proposed in this article, the real data of which was used to establish the semantic map, and the predicates for navigation functions worked well on SWI-Prolog.
HITHCD-2018: Handwritten Chinese Character Database of 21K-Category

Current state of handwritten Chinese character recognition (HCCR) conducted on well-confined character set, far from meeting industrial requirements. The paper describes the creation of a large-scale handwritten Chinese character database. Constructing the database is an effort to scale up Chinese handwritten character classification task to cover the full list of GBK character set specification. It consists of 21-thousand Chinese character categories and 20-million character images, larger than previous databases both in scale and diversity. We present solutions to the challenges of collecting and annotating such large-scale handwritten character samples. We elaborately design the sampling strategy, extract salient signals in a systematic way, annotate the tremendous characters through three distinct stages. Experiments are conducted the generalization to other handwritten character databases and our database demonstrates great values. Surely, its scale opens unprecedented opportunities both in evaluation of character recognition algorithms and in developing new techniques.
Research on Image Detection and Application of Security Dangerous Goods Based on SSD

SSD (Single Shot MultiBox Detector) is a widely used target detection framework. Based on the Object Detection API based on the TensorFlow framework and the SSD algorithm, this paper uses SSD to realize the target detection of security dangerous goods images. The experimental results show that the SSD-based image neural network detection model can fully utilize the flexibility of TensorFlow and SSD, effectively improve the stability, speed effectiveness and accuracy of image recognition, and the average detection accuracy rate is over 90%.
On the Radical De- and Re-Construction of Today’s Enterprise Applications

The office applications (spreadsheets, text processing, presentation programs) used in today’s enterprises are silos. So are the ERP (enterprise resource planning) systems. Tons of time is wasted to manually pump around data between office applications, ERP systems and the many further enterprise applications (CRM, DMS, BI etc. etc.) [1,2]. The whole situation is fragile (error prone), blocks business process re-engineering [3, 4] and prohibits (potentially massive) enterprise data analytics. Enterprise content management systems [5] help a little but do not fundamentally improve the situation: they do not crack the silos. The same is with the many enterprise application integration approaches (ad-hoc glue code [6], SOA platforms [7, 8, 9, 10, 11, 12], BPM suites [13, 14, 15, 16, 17, 18, 19, 20, 21]): nice-to-have, but no game changers. Enterprise wikis [22] could help somehow, but their potential is not well understood by today’s CIOs [1]; and again: they are silos. In this keynote, the presenter takes a different approach and fundamentally rethinks today’s enterprise application landscape. He explains the vision of a so-called massive backbone application (or system-2 application in terms of Stafford Beer’s Viable System Model [23, 24, 25]). He deconstructs today’s enterprise applications into their essential particles and recombines them coherently into the new approach: (i) collaborative editing of semi-structured data (wikis) [26], (ii) data types (data+operations) [27, 28], (iii) access rights mechanisms [29], (iv) version control [30, 31, 32], (v) expression-level transclusion [33, 34] of typed data [35] and (vi) an integrated querying language [35]. With such a backbone application, today’s enterprise applications become deeply standardized views (or perspectives) [36, 37] on a huge, single underlying information structure. Furthermore, (web) weaving [38, 39] of features becomes a first class citizen with such a backbone application. The presenter explains, how such a backbone application can radically improve the way we work.
A High Throughput And Speed Architecture of Lightweight Cipher LEA

A high-speed cryptography algorithm is the necessity of today's world to communicate between two resource-constrained tools. We have proposed a 128-bit block size and 256-bit key size LEA cipher for high speed and throughput purpose. High speed is an essential requirement with the secure transmission of information. Pipelining is the best way to achieve these requirements with higher throughput. The implementation results have shown that this method has a higher capability of speed and throughput as compared to previous ciphers. LEA has security against all existing cipher attacks and compatible with both hardware and software. Proposed work is 73.7% and 62.85% improved version of compared paper for respectively throughput and speed.
Hybrid Featured based Pyramid Structured CNN for Texture Classification

Texture is always considered as the preconscious for human vision. Texture also remains the same significance in computer vision field that can be used to help in detection, segmentation and classification tasks. Since texture is a global feature inherent in an image, containing essential surface information, which can be described in detail and hardly affected by image noises. We propose a novel end-to-end structure to make use of hybrid features by a mixture network and improve the classification accuracy, mainly combining Gray Level Co-occurrence Matrix (GLCM) statistical features together with pyramid structured deep convolutional neural networks (Pyramid CNNs) features in a paralleling network structure. Considering GLCM is a remarkable descriptor for texture statistical features, it can compensate the missing information in the convolution and pooling process of CNN and decline overfitting problems. Meanwhile, multi-resolution image pyramid structured CNN helps to capture both global features and local features. Quantitively, we carry out experiments on widely used datasets and results show that the GLCM and Pyramid CNN features merged structure obtains maximum 6.8% improvement comparing to the basic CNN methods.
X-STC

In this paper, we propose a simple extension to the spanning tree based coverage (STC) algorithm for a mobile robot. The proposed extended spanning tree based coverage (X-STC) algorithm attempts to cover even the partially occupied cells, unlike the spiral-STC, the basic STC algorithm. The properties of the X-STC algorithms are discussed and illustrated with examples. We compare the coverage performance of the proposed X-STC algorithm with two existing STC based algorithms, namely spiral-STC and full-STC. The results show that the proposed algorithm substantially improves the amount of coverage compared to that with the existing STC based algorithms.
Integrated Camera and Radar Tracking using Multi-Model Cubature Kalman Filter

In this paper, a multiple camera and radar based tracking method is proposed using an interactive multi-model (IMM) based cubature Kalman filter (CKF). In real world scenarios, object movement cannot be characterized with a single motion model. Furthermore, measurements from a single camera/radar may not contain relevant information to achieve desired tracking accuracy for critical applications. The approach proposed in this paper utilizes multiple motion models and the measurements from multiple camera and radar sensors to perform tracking. The proposed method operates a CKF with information weighted sensor fusion for each motion model. Furthermore, the likelihood of each motion model at a given time is calculated using the measurement residuals from all the camera and radar sensors. In the considered simulation set up, the proposed method achieves better tracking accuracy than an IMM based on the extended Kalman filter method and better switching capability among different motion models compared to the single sensor scenario.
Using Attack Pattern for Cyber Attack Attribution

A cyber attack is a malicious and deliberate attempt by an individual or organization to breach the integrity, confidentiality, and/or availability of data or services of an information system of another individual or organization. Being able to attribute a cyber attack is a crucial question for security but this question is also known to be a difficult problem. The main reason why there is currently no solution that automatically identifies the initiator of an attack is that attackers usually use proxies, i.e. an intermediate node that relays a host over the network. In this paper, we propose to formalize the problem of identifying the initiator of a cyber attack. We show that if the attack scenario used by the attacker is known, then we are able to resolve the cyber attribution problem. Indeed, we propose a model to formalize these attack scenarios, that we call attack patterns, and give an efficient algorithm to search for attack pattern on a communication history. Finally, we experimentally show the relevance of our approach.
Constrained Matrix Factorization for Course Score Prediction

Recommender system is widely used in e-commercial platforms to recommend users suitable items according to users's preferences. In recent years, an increasing amount of attention has been paid to the application of recommender system in education. There are many online learning systems that can recommend students suitable courses according to students' learning performances. However, there are few universities using recommender system to recommend students suitable elective courses. It is generally known that students in higher grade take the courses earlier than those in lower grade. Therefore, the elective course scores of sophomores can be predicted by using the course score information from students of higher grades. However, the unbalanced distribution of course-enrollment data makes it hard to predict the scores of the courses that are in a low selection rate. Therefore, we propose a Constrained Matrix Factorization (ConMF) algorithm to predict sophomores' elective course scores, which integrates the course average score into the objective function so as to make up the prediction deviation caused by the unbalanced course selection rate and make more accurate prediction than the traditional Matrix Factorization (MF) approach. The experimental results show that our proposed model outperforms the state-of-the-art methods in the task of university students' course score prediction.
Learning Free Line Detection in Manuscripts using Distance Transform Graph

We present a fully automated learning free method, for line detection in manuscripts. We begin by separating components that span over multiple lines, then we remove noise, and small connected components such as diacritics. We apply a distance transform on the image to create the image skeleton. The skeleton is pruned, its vertexes and edges are detected, in order to generate the initial document graph. We calculate the vertex v-score using its t-score and l-score quantifying its distance from being an absolute link in a line. In a greedy manner we classify each edge in the graph either a link, a bridge or a conflict edge. We merge every two edges classified as link together, then we merge the conflict edges next. Finally we remove the bridge edges from the graph generating the final form of the graph. Each edge in the graph equals to one extracted line. We applied the method on the DIVA-hisDB dataset on both public and private sections. The public section participated in the recently conducted Layout Analysis for Challenging Medieval Manuscripts Competition, and we have achieved results surpassing the vast majority of these systems.
The Markets of Manipulation: The Trading of Social Bots on Clearnet and Darknet Markets

Since the Brexit vote and the 2016 U.S. election, much has been speculated about the use of so-called social bots, (semi-)automatized pseudo-users in online media, as political manipulation tools. Accumulating global evidence shows that pseudo-users are used for different purposes, such as the amplification of political topics or the simulation of large numbers of followers. Social bots, as a (semi-)automated pseudo-user type, are part of a larger infrastructure, among others, entailing network access, fake accounts, and hosting services. Users and providers of social bots and their infrastructure can differ. Thus, it is plausible that a digital goods market has emerged for the exchange of social bots and infrastructure components. The present study used an ethnographic approach to study the accessibility, availability, and prices for pseudo-users and social bots on markets in the (German- and English-language) Clearnet and Darknet. The results show that an infrastructure for digital manipulation is widely available online, and that the tools for artificial content or connectedness amplification are easily accessible for lay users and are cheap on Clearnet and Darknet markets.
Object tracking using virtual particles driven by optical flow and Kalman filter

In the field of robot vision, object tracking plays an important role. We propose a method of tracking object, where Kalman filter and optical flow are used to track moving object. To apply the Kalman filter to object tracking, virtual particles which consist of pairs of velocity and position are introduced. The spatio-temporal differentiation method is used to derive the optical flow. And, to increase particles which contribute tracking, weighting and resampling processes are used. We used sythesized image and captured image to test the proposed method. Experimental results show that using Kalman filter makes object tracking accurate and using weighting and resampling enables particles to concentrate on the moving object.