Reviewerly/exHarmony huggingface dataset:
Examination and Comparison of Countermeasures Against Web Tracking Technologies

Web tracking technologies, such as supercookies and browser fingerprinting, continue to be developed, and the deletion of HTTP cookies and the use of private browsing may be insufficient to prevent web tracking. Several countermeasures exist against web tracking technologies, including browser history deletion, browser extensions that prevent web tracking, private browsing, and the Tor browser. However, the effectiveness of these approaches has not been determined. In this study, we investigated the effectiveness of these countermeasures, and the results indicate that different measures have different degrees of effectiveness.
Reproducible and Reliable Distributed Classification of Text Streams

Large-scale classification of text streams is an essential problem that is hard to solve. Batch processing systems are scalable and proved their effectiveness for machine learning but do not provide low latency. On the other hand, state-of-the-art distributed stream processing systems are able to achieve low latency but do not support the same level of fault tolerance and determinism. In this work, we discuss how the distributed streaming computational model and fault tolerance mechanisms can affect the correctness of text classification data flow. We also propose solutions that can mitigate the revealed pitfalls.
Implementation of a Digital Signature in Backward-Compatible QR Codes Using Subcell Division and Double Encoding

The current version of a quick response (QR) code can be easily created by anyone and then used to perform an impersonation or forgery. As one countermeasure against this, methods that implement a digital signature algorithm for QR codes have been proposed. However, many of these methods store application data and the digital signature in the same area. From the viewpoint of compatibility, it is necessary for a digital signature to be stored in an area that is independent of the application data. Therefore, we propose a method of dividing a black cell into four subcells and implementing an elliptic curve digital signature algorithm using double encoding.
Research on Initial Alignment Method of SINS with Improved CKF

The initial alignment model of Strapdown Inertial Navigation System (SINS) with large misalignment angle is a linear and nonlinear mixed system model. Aiming at the model, an improved Cubature Kalman Filter(CKF) algorithm is adopted. Firstly, the initial alignment model of SINS is established and analyzed. Then, singular value decomposition (SVD) is used to assist Cholesky decomposition to ensure numerical stability in the time update phase of the algorithm, while adjusting the modified covariance, and performing Kalman in the measurement update phase. Finally, the experimental results of initial alignment show that the improved CKF can deal with the SINS initial alignment problem well. The filtering accuracy is comparable to the CKF estimation accuracy, and the solving speed is better than the CKF.
Training Feed-forward Neural Networks using Asexual Reproduction Optimization (ARO) Algorithm

Artificial neural networks have been increasingly used in many problems of data classification because of their learning capacity, robustness and extendibility. Training in the neural networks accomplished by identifying the weight of neurons which is one of the main issues addressed in this field. The process of network learning by back-propagation algorithm which is based on gradient, commonly fall into a local optimum. Due to the importance of weights and neural network structure, evolutionary neural networks have been emerged to obtain suitable weight set. This paper will concentrate on training a feed-forward networks by a modified evolutionary algorithm based on asexual reproduction optimization (ARO) in order to data classification problems. The idea is to use real representation (rather the binary) for adjusting weights of the network. Experimental results show a better result in terms of speed and accuracy compared with other evolutionary algorithms including genetic algorithms, simulated annealing and particle swarm optimization.
Keddah

As a distributed system, Hadoop heavily relies on the network to complete data-processing jobs. While the traffic generated by Hadoop jobs is critical for job execution performance, the actual behaviour of Hadoop network traffic is still poorly understood. This lack of understanding greatly complicates research relying on Hadoop workloads. In this article, we explore Hadoop traffic through empirical traces. We analyse the generated traffic of multiple types of MapReduce jobs, with varying input sizes, and cluster configuration parameters. We present Keddah, a toolchain for capturing, modelling, and reproducing Hadoop traffic, for use with network simulators to better capture the behaviour of Hadoop. By imitating the Hadoop traffic generation process and considering the YARN resource allocation, Keddah can be used to create Hadoop traffic workloads, enabling reproducible Hadoop research in more realistic scenarios.
Collaboration Detection that Preserves Privacy of Students’ Speech

Collaboration is a 21st Century skill as well as an effective method for learning, so detection of collaboration is important for both assessment and instruction. Speech-based collaboration detection can be quite accurate but collecting the speech of students in classrooms can raise privacy issues. An alternative is to send only whether or not the student is speaking. That is, the speech signal is processed at the microphone by a voice activity detector before being transmitted to the collaboration detector. Because the transmitted signal is binary (1 = speaking, 0 = silence), this method mitigates privacy issues. However, it may harm the accuracy of collaboration detection. To find out how much harm is done, this study compared the relative effectiveness of collaboration detectors based either on the binary signal or high-quality audio. Pairs of students were asked to work together on solving complex math problems. Three qualitative levels of interactivity was distinguished: Interaction, Cooperation and Other. Human coders used richer data (several audio and video streams) to choose the code for each episode. Machine learning was used to induce a detector to assign a code for every episode based on the features. The binary-based collaboration detectors delivered only slightly less accuracy than collaboration detectors based on the high quality audio signal.
A Convolutional Neural Network Model for Superresolution Enhancement of UAV Images

In recent years, the use of unmanned air vehicles (UAVs) in various fields has become widespread. These UAVs have a set of sensors that allow obtaining information of the scenarios by which they fly, in order to monitor them or to be used in their navigation tasks. The camera is one of the most relevant elements. UAVs have limitations regarding their autonomy time. If you want to monitor a large geographical area in a short time, you need to make flights at a higher altitude. This implies loss of spatial resolution, since the cameras themselves have their limitations in terms of their optics and the size of the pixels. In recent years, super-resolution techniques based on deep Convolutional Neural Network (CNN) have been developed, which are able to learn the correspondence between low resolution images with their high-resolution counterparts. The problem of these models lies in the computation requirements that they need for their execution, not being viable to be executed in the embedded hardware of a UAV. In this work we propose a super-resolution method based on deep CNNs capable of being executed in the on-board equipment of an UAV.
Research on Radar/Infrared Fusion Algorithm Based on IMM/MS-MDPDAF

Abstract In the clutter environment, radar/infrared sensors are used to track the background of maneuvering targets. Aiming at the shortcomings of traditional probabilistic data association theory in solving multiple target echoes and numbers in measurement, it is presented by combining interactive multiple model (IMM) and multiple detection probabilistic data association filter (MDPDAF) in a multi-sensor (radar and infrared) scenario known as IMM/MS-MDPDAF algorithm. The IMM algorithm has the ability to adapt to the target high maneuver and clutter environment. The MS-MDPDAF algorithm can detect multiple effective target echoes, considering various uncertainties in the clutter environment. According to the multi-detection mode of radar sensor, the target is effectively measured and the state vector is updated. Then the data fusion and probability data association theory are used to calculate the corresponding probability and state prediction, estimation and update under the Bayesian framework. The simulation results show that the IMM/MS-MDPDAF algorithm can improve the effectiveness and tracking accuracy of target, and has better tracking performance than IMM/MSPDAF.
New Cetificateless Aggregate Signature Algorithm for Vehicular Ad hoc Networks

Certificateless public cryptography can solve the problem of certificates management of the public key infrastructure and overcome the key escrow problem of the identity-based public cryptography. Aggregate signature, which can compress many signatures of messages generated by different users into single signature on different messages, was applied to reduce the cost of computation and communication in the resource constrained-environments. Recently, Wang et al. presented a probably secure cetificateless aggregate signature algorithm for vehicular ad hoc networks, they claimed that their schemes are secure against the adversaries. Nevertheless, in this paper, we show that their schemes are insecure against normal Type II adversary. The malicious key generation center (KGC) can get the user’s partial private key to realize the passive attack. We describe the processes of forging attacks in detail and analyze the main reasons aiming at two different attacks. Moreover, we proposed a novel scheme to improve the security of Wang et al.’s schemes.
Towards an Optimized, Cloud-Agnostic Deployment of Hybrid Applications

Serverless computing is currently taking a momentum due to the main benefits it introduces which include zero administration and reduced operation cost for applications. However, not all application components can be made serverless in sight also of certain limitations with respect to the deployment of such components in corresponding serverless platforms. In this respect, there is currently a great need for managing hybrid applications, i.e., applications comprising both normal and serverless components. Such a need is covered in this paper through extending the Melodic platform in order to support the deployment and adaptive provisioning of hybrid, cross-cloud applications. Apart from analysing the architecture of the extended platform, we also explain what are the relevant challenges for supporting the management of serverless components and how we intend to confront them. One use case is also utilised in order to showcase the main benefits of the proposed platform.
Healthcare Design Based on Virtual Reality Technology: Enhance the User’s Engagement in the Rehabilitation Process

This study on VR technology in healthcare design aims to enhance the engagement and provide an interactive way for the whole rehabilitation process. By presenting the real-time data to the users and collecting their feedback, users’ participation and achievements increase. From the study and analysis on the existing postoperative knee rehabilitation training, we establish typical user profiles, analyze their pain points and specific needs, test it by employing the users’ data we collected, build the data modeling and then propose corresponding solutions. A working prototype of the device is used to test and illustrate the device’s availability in the generation and calculation of motion data. Based on this, in the research, the further application of VR in the field of medical rehabilitation is promising, and a variety of possibilities concerning the combination of VR as a technology and a kind of future life style from the essence of things are put forward.
USING OPEN SOURCE SOFTWARE AND OPEN STANDARDS FOR OPERATING ROBOTIC TELESCOPES

The University of G¨ottingen, the McDonald Observatory of the University of Texas at Austin, and the South African Astronomical Observatory (SAAO) operate two robotic telescopes called MONET at McDonald Observatory in Fort Davis, Texas (MONET/North), and at the SAAO in Sutherland, South Africa (MONET/South). After problems with our original observation control system and some difficulties with another one, we decided to build our own system, initially providing only the minimally required functionality, but allowing for easy extensibility. A decision was made to build on open standards and open source software only, so that we can use existing and well-tested technologies. In this paper we will describe our efforts to implement such an open observation control system using HTTP and XMPP. Furthermore, we will discuss possibilities for connecting multiple telescopes via VOEvents and RTML.
A Semantic Web Solution for Enhancing the Interoperability of E-Learning Systems by Using Next Generation of SCORM Specifications

E-learning is a very dynamic field, constantly growing, which refers to an educational content or learning experiences provided or made by means of digital technologies. The development of this area has a direct impact on the quality of education and cost reduction. E-learning is now dominated by learning management systems such as Moodle, to improve the interoperability of these systems; the use of data/resources is a major requirement, this need to push the e-Learning community to seek new approaches to improve this interoperability of systems. On the other hand, semantic Web technologies offers standards like RDF allows to describe Web resources with their metadata. We propose in this work an approach based on Semantic Web technology to improve the interoperability of e-Learning systems like Moodle.
Partition of graphs and quantum walk based search algorithms

In this paper, we show reduction methods for search algorithms on graphs using quantum walks. By using a graph partitioning method called equitable partition for the the given graph, we determine “effective subspace” for the search algorithm to reduce the size of the problem. We introduce the equitable partition for quantum walk based search algorithms and show how to determine “effective subspace” and reduced operator.
Educational Policies of Writing and Reading (A commentary on D.G. Ryndin’s article “Merab Mamardashvili: event of thought and pedagogic practice”)

The paper contains an “asymmetric commentary” on a philosophical text by means of its subsequent placing in the educational contexts. The first of them is related to an explication of the cultural mission of a pedagogical project suggested by the article. The second one is aimed at the analysis of the pedagogical action of the text determined by the writing/reading samples placed in it. The third one performs the function of a counter-text problematizing a pedagogical program of the article being commented. The exhaustion of orientation to symbolic identification with invariant symbols of culture is substantiated. Educational transformation is connected with overcoming the methodological individualism. Education is transformed into a subject of public collaboration. The emphasis in collective work has to be placed not on establishment of the coordinated definition situation, but on the conflict of its interpretations, collision of practical positions and detection of qualitatively various educational prospects. The commentary by means of the trial-and-error method also researches a possibility of building a demonstrative description able to cause dereification effects in the writing/reading processes. It is supposed that deconstructive writing can become a major element of students’ academic competence in the process of university training.
Macroeconomic Model of Banking Digitization Process

The widespread use of digitization in the field of credit and banking activities is based on the fact that it accelerates and reduces the cost of providing relevant services and also increases the security of operations for both banks and their customers. Among the most obvious advantages of banking digitization customers highlight the possibility of remote account management. However, this advantage is associated with serious problems. First of all, it is various embezzlement of funds from bank accounts also with the help of digital technologies, conducting fraudulent operations. The aim of this research work is to analyze implications of credit and banking sector digitization through macroeconomic modeling. A significant increase in the number of crimes committed with the help of digital devices and a multiple increase in the amount of damage from them were revealed in the article based on an empirical study. The cumulative negative impact of crimes committed with the help of digital technologies and digital devices was revealed not only on individual banks and the banking system as a whole, but also on the possibilities of the country’s social and economic development.
Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Abstract Many real world problems have big data, including recorded fields and/or attributes. In such cases, data mining requires dimension reduction techniques because there are serious challenges facing conventional clustering methods in dealing with big data. The subspace selection method is one of the most important dimension reduction techniques. In such methods, a selected set of subspaces is substituted for the general dataset of the problem and clustering is done using this set. This article introduces the Shared Subscribe Hyper Simulation Optimization (SUBHSO) algorithm to introduce the optimized cluster centres to a set of subspaces. SUBHSO uses an optimization loop for modifying and optimizing the coordinates of the cluster centres with the particle swarm optimization (PSO) and the fitness function calculation using the Monte Carlo simulation. The case study on the big data of Iran electricity market (IEM) has shown the improvement of the defined fitness function, which represents the cluster cohesion and separation relative to other dimension reduction algorithms.
System Architecture for Progressive Augmented Reality (poster)

In spite of the evolution of Augmented Reality~(AR) technology, it is not wide spread in everyday life. There may be many reasons, but one of the reasons is that it has been developed for very specific users, such as researchers and professionals. To overcome this problem, Grubert et al. proposed the pervasive AR. It is not limited to a specific situation, but is usable in various instances and providing continuous and flexible AR experience. The AR browser is the example of utilizing pervasive AR. The AR browser understands the context of the user and provides corresponding information. However, if the corresponding information to the context of user cannot reach the user in time due to massive data transmission, unexpected network congestion, poor service quality or signal strength, it cannot be guaranteed to be continuous. This leads to a degradation of the user experience, and it cannot support pervasive AR. This paper presents the Progressive Augmented Reality, the way which quickly send incomplete, yet informative, response about the user's current context rather than wait for sending complete information to the user. The concept of Progressive Augmented Reality comes from Progressive Data Science. Our system is aware of network quality by collecting various network health parameters. According to the network status quality, it divides the chunk information to the optimal number and transmits the one that have the highest priority among the divided information. By conducting the above process iteratively, all the divided information is updated. Our client-side system utilizes Android ARCore and has been tested on Google Pixel 2XL.
Design Fiction for Real-World Connected Wearables

Wearable IoT technology has too much potential to be limited to a wristband. How can we design wearables with more variety while still providing value to the user? We describe a work in progress to develop a novel method of Participatory Design Fiction to inspire a real-world, everyday wearable IoT system. We show how this has led to a greater understanding of our users' needs, resulting in a technology probe for an everyday wearable IoT system that works towards meeting those needs.
meChat

In this demo, we demonstrate an in-device conversational photo sharing service, termed meChat, which helps users share in-device photos easily in messaging applications by searching conversation-related photos automatically. In particular, meChat understands the semantics of on-going conversation and in-device photos by projecting both of them into a single semantic space. Subsequently, it retrieves in-device photos related to the conversation context. Through this process, meChat makes it easy for the users to share the photos while communicating in messaging applications. In addition, it is worth noting that meChat works in a stand-alone, privacy-protecting manner without sending out any in-device photos and conversations to the external servers. This is different from existing photo services (e.g., Google Photos) which resort on the cloud server.
Ground-Based Emitter Location in the Presence of Multipath

We address the problem of ground-based stationary emitter geolocalization in the presence of multi-path in which an emitter's signal reaches the receiver through reflection from a surface. A particle filtering algorithm is applied to estimate the location of emitting sources in which a source could be the emitter itself or a mirror image of the emitter across a reflective boundary. The receiver measures the angle-of-arrival (AOA) to each image and possibly to the actual emitter, depending on the environment. Time-difference-of-arrival (TDOA) signals are also processed by the estimation algorithm to further constrain the locations of the images and emitter. We consider only single-bounce reflections of the emitter signal and do not assume that the emitter signal itself is necessarily received. The algorithm does not assume any knowledge of the reflective surfaces. We derive an approach to incorporating both the AOA and TDOA measurements into the measurement-to-source association problem. The algorithm is evaluated within a simulated environment on a variety of different scenarios, comparing results obtained with AOA measurements only as well as with both AOA and TDOA. Both single and multiple receiver scenarios are considered. Results indicate that the algorithm performs well and handles the large uncertainty in emitter locations early in the estimation, which is challenging when using an estimator based on a single Gaussian representation of the emitter state probability density function.
New Cyber Word Discovery Using Chinese Word Segmentation

Increasing new cyber words, if they could not be effectively identified, will seriously affect the accuracy of word segmentation, and bring great difficulties to the related work. In order to solve the problem that the Web text is not specific and the content is short, this paper proposes a new word discovery method combined with statistics and rules, which can be used to optimize the original word segmentation result by two new words extraction and rule filtering. The experimental results show that the method can effectively find new cyber words, and the accuracy of word segmentation has also been improved.