Reviewerly/exHarmony huggingface dataset:
The Augmented Reality as an Instrument for the Representation/Visualization of Architecture

This paper deals with issues related to the representation/visualization of architecture and its narration through the augmented reality tools. Specifically, the possible scenarios and the different strategies to address the problems related to the application of AR platforms in the field of cultural heritage are presented. Some case studies will be analyzed and the use of augmented reality applied to museums and archaeological sites including the Museum of the Cité de l’architecture et du patrimoine in Paris, the Regional Archaeological Museum A. Salinas in Palermo and the archaeological site of Selinunte. Thanks to these exemplary cases, the potential and critical aspects of the latest AR technologies developed by Google applied to indoor and outdoor contexts will be analyzed.
Assessment of Test Maturity Model

Team and individual capability in the software testing depends heavily on the maturity level in the conducting a software testing activities. This paper is the result of qualitative research in comparing software testing maturity frameworks that has been proposed by some researchers. In the context of software process improvement, interesting results obtained when more in-depth evaluation between TMMi, TPI Next, and Test SPICE 3.0. This paper presents comparisons between these three frameworks that are equivalent to process reference models and have their own best practices. These three frameworks can be considered to be used as an improvement in the software testing process, with the trade off in the selecting of either one. In addition, a summary of the chosen frameworks implementation examples is also given. The results of this study are very important as a reference for the organization in preparing software capability roadmap in software testing.
Structural and Informational Diversity of Digital Filters Based on Multivariate Arithmetic of Finite Field

In modern computing systems, one of the most commonly used algorithms for digital signal processing is digital filtering. Digital filters are implemented by various hardware. One of the main properties of any hardware structure is its fault tolerance. The main way to increase fault tolerance is the introduction of redundancy, as a rule, hardware redundancy. In existing computational structures, the introduction of redundancy is associated with an increase in computation time and hardware costs, which in some cases is unacceptable. To eliminate the drawbacks of these methods and ensure the reliability of the results of calculations, it is proposed to investigate the possibility of using multivariate finite field arithmetic to ensure the structural and information reliability of digital filters.
A multilabel classification on topics of qur’anic verses in English translation using K-Nearest Neighbor method with Weighted TF-IDF

There are so many information contained in the Qur'an, it will be difficult to bring up the information manually, moreover if someone wants to know more about the Qur'an. Therefore, there is a need to find information with a certain topic that already classified in the Qur'an, especially in one verse of the Qur'an may have more than one topic (multilabel). This research examined how to build classifier to classify multilabel data which is topics of Qur'anic verses with k-Nearest Neighbor method. In this research, there is a comparison between feature extraction, Weighted TF-IDF and TF-IDF. The result of that comparison is that Weigthed TF-IDF has better performance compared to normal TF-IDF. The highest result by finding the most optimal k score is k=25 with the average score of hamming loss = 0.134875. There will be a test to measure the effect of stopword removal and lemmatization with optimal k value, for a case without stopword removal, the result is 0.136375, whereas without the lemmatization the result is 0.13537. For not using stopword removal and lemmatization the average hamming loss is 0.1373125.
Feature Map Transformation for Multi-sensor Fusion in Object Detection Networks for Autonomous Driving

We present a general framework for fusing pre-trained object detection networks for multiple sensor modalities in autonomous cars at an intermediate stage. The key innovation is an autoencoder-inspired Transformer module which transforms perspective as well as feature activation characteristics from one sensor modality to another. Transformed feature maps can be combined with those of a modality-native feature extractor to enhance performance and reliability through a simple fusion scheme. Our approach is not limited to specific object detection network types. Compared to other methods, our framework allows fusion of pre-trained object detection networks and fuses sensor modalities at a single stage, resulting in a modular and traceable architecture. We show effectiveness of the proposed scheme by fusing camera and Lidar information to detect objects using our own as well as the KITTI dataset.
Introduction to Steganography in RRNS based Communications

In this paper we introduce the idea of using Redundant Residue Number System (RRNS) codes in steganography, and we determine their embedding capacities. We take advantage of the redundancy parameter and the correction capacity of RRNS codes, i.e., we hide secret data in some residues in such way that the altered number of residues does not exceed half the redundancy of the code.
Representation Learning on Networks: Theories, Algorithms, and Applications

Network representation learning offers a revolutionary paradigm for mining and learning with network data. In this tutorial, we will give a systematic introduction for representation learning on networks. We will start the tutorial with industry examples from Alibaba, AMiner, Microsoft Academic, WeChat, and XueTangX to explain how network analysis and graph mining on the Web are benefiting from representation learning. Then we will comprehensively introduce both the history and recent advances on network representation learning, such as network embedding and graph neural networks. Uniquely, this tutorial aims to provide the audience with the underlying theories in network representation learning, as well as our experience in translating this line of research into real-world applications on the Web. Finally, we will release public datasets and benchmarks for open and reproducible network representation learning research. The tutorial accompanying page is at https://aminer.org/nrl_www2019.
Granularity-Based Assessment of Similarity Between Short Text Strings

The capacity to discover the similarity between two textual bases, or inside one textual base, has much utilization including plagiarism detection and in the area of reused text (strings) in a database manageable to the removal of duplication. Past structure-metric methodologies have used either suffix trees or variance of longest common subsequence algorithms to recognize duplicate text. In this paper, different string distance metrics have been investigated: Levenshtein Distance (L. Dist.), Cosine Similarity (C.S.), and Hamming Distance (H. Dist) and also Hashes (ASCII-based hashing) on token sequences to detect matching of strings were used. Similarity index techniques vary on the basis of granularity: some techniques work on character level, word level, and some work on corpus-based granularity. The benefit of the approaches evaluated is to handle multiples patterns for similarity at a time. The work has been carried out on strings. From the simulation, it has been observed that ASCII-based hashing performs better than other techniques in terms of running time and accuracy. All techniques face one issue of increase in similarity searching time linearly with database size, whereas hashing handles this issue efficiently. ASCII-based hashing handles the issue of scalability very well.
Path planning for unmanned vehicles with localization constraints

This article addresses a path planning problem for an unmanned vehicle in the presence of localization constraints. Landmarks are used for localizing the position of the vehicle at any time. The localization constraints require that at least two landmarks must be present in the sensing range of the vehicle at any time instant. This problem is formulated as a Mixed Integer Linear Program (MILP) which aims to compute an optimal path for the vehicle and the optimal locations where landmarks must be placed. The facial structure of the polytope of feasible solutions to the MILP is then analyzed, and a branch-and-cut algorithm is developed to find an optimal solution. Extensive computational results that corroborate the effectiveness of the proposed approach are also presented.
Extension of the BiDO Ontology to Represent Scientific Production

The SPAR Ontology Network is a suite of complementary ontology modules to describe the scholarly publishing domain. BiDO Standard Bibliometric Measures is part of its set of ontologies. It allows describing of numerical and categorical bibliometric data such as h-index, author citation count, journal impact factor. These measures may be used to evaluate scientific production of researchers. However, they are not enough. In a previous study, we determined the lack of some terms to provide a more complete representation of scientific production. Hence, we have built an extension using the NeOn Methodology to restructure the BiDO ontology. With this extension, it is possible to represent and measure the number of documents from research, the number of citations from a paper and the number of publications in high impact journals according to its area and discipline.
A Bayesian Nonparametric Mixture Measurement Error Model With Application to Spatial Density Estimation Using Mobile Positioning Data With Multi-Accuracy and Multi-Coverage

The development of mobile network technologies has made it possible to collect location data of mobile devices through various positioning technologies. The location data can be used to estimate the spatial density of mobile devices, which in turn can be used by mobile service providers to plan for network capacity improvements. The two most prevalent positioning technologies are the assisted global positioning system (AGPS) and cell tower triangulation (CTT) methods. AGPS data provide more accurate location information than CTT data but can cover only a fraction of mobile devices, while CTT data can cover all mobile devices. Motivated by this problem, we propose a Bayesian nonparametric mixture measurement error model to estimate the spatial density function by integrating both noise-free data (i.e., AGPS data) and data contaminated with measurement errors (i.e., CTT data). The proposed model estimates the true latent locations from contaminated data, and the estimated latent locations, combined with noise-free data, are used to infer the model parameters. We model the true density function using a Dirichlet process (DP) mixture model with a bivariate beta distribution for the mixture kernel and a DP prior for the mixing distribution. The use of bivariate beta distributions for the mixture kernel allows the density function to have various shapes with a bounded support. Moreover, the use of a DP prior for the mixing distribution allows the number of mixture components to be determined automatically without being specified in advance. Therefore, the proposed model is very flexible. We demonstrate the effective performance of the proposed model via simulated and real-data examples.
Recommendation Algorithm for Mobile E-commerce Based on Reputation

In the new technical background, more and more goods appear in front of the user. Unfortunately, users are increasingly easy to get lost in the massive commodity information. In order to improve the users experience of mobile e-commerce, combined with the advanced technology of large data age, there have been many recommendation algorithms for mobile e-commerce. All of this recommendation algorithms based on customer interests, sales and other different considerations, in order to achieve a purposeful and efficient screening recommendation information. In many ways of thinking, the user reputation as a new era of the spirit of the contract reflects the important value. In this paper, we focus on the current technical situation, designing a set of scoring algorithm through the analysis of the previous behavioral data modeling. After this algorithm, from different customer categories of different needs to start using the mainstream for the score of the business recommendation algorithm algorithm to achieve. And the results are compared with the expected analysis in order to obtain the current algorithm in the credibility of the algorithm under the improvement and adjustment. Finally, we will design a set of recommendation algorithm based on the reputation standards.
Smart Contracts and Void Declarations of Intent

Among the characteristics of blockchain-based Smart Contracts, the aspect of irreversibility of blockchain transactions is one of the most controversially commented features from a legal perspective – taking into account that “traditional” contracts can be void or voidable for various reasons. But while there are indeed cases where existing private law concepts struggle to provide sound and predictable solutions to Smart Contract-induced problems, many of these controversies derive either from the use of disambiguous terminology in interdisciplinary communication, the diversity of the instrument of voidness and avoidance from a comparative perspective (and thus not the Smart Contract as such) or a general “misconception” of the irreversability of traditional forms of performance. This contribution intends to clarify where the irreversability of blockchain-based transactions does – against all odds – not make a difference compared to traditional contract law solutions, but also determines where legal conflicts indeed may arise from Smart Contracts and mistake.
A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks

Machine learning based convolutional neural networks (CNN) are becoming increasingly popular for identification tasks like image classification or speech recognition. However, CNNs have high memory and computational demands which makes it challenging to implement them on cost-efficient and energy-autonomous hardware. To cope with this challenge we present a heterogeneous and reconfigurable embedded architecture implemented on an inexpensive and widely available entry-level system on chip (SoC). Our architecture combines an ARM CPU and a coarse-grained reconfigurable architecture (CGRA) which execute a CNN in parallel to reach a higher energy-efficiency. Our results show up to 130% higher performance and 78% better energy-efficiency compared with an embedded Nvidia GPU.
Automatic Text Summarization of News Articles in Serbian Language

Text Summarization is a technique of creating short, accurate, and fluent summaries of longer text documents or sets of documents. With growing amount of textual data circulating in the digital space, there is a need to develop machine learning algorithms that can automatically shorten longer texts and deliver accurate summaries. First, the generated summaries should fluently pass the intended messages. Second, the generated summaries should reduce reading time and speed up the process of researching for relevant information. By large majority, most of research in text summarization has been done for English texts. In this paper, we experiment with a variation on a graph-based method called TextRank for extractive text summarization. The dataset used in this study is collected from one of the online news sources in Bosnia and Herzegovina.
A Study on Blockchain Applications in Healthcare

Blockchain or the distributed ledger technology has the power to transform various industries including healthcare. It reduce or abolish the friction and costs of current intermediaries present in network. Decentralized and programmable nature of blockchain applications can be used for the betterment of public and private healthcare system. It has the ability to transform health care, placing the patient at the center of the healthcare ecosystem and increasing the security, privacy, and interoperability of information related to health. It creates various opportunities to reduce complexity, enable trustless collaboration, and create secure and immutable information. Management of electronic health record, conserving patient identification, insurance claiming process, frauds in public programs like medicaid and medicare and prevention of counterfeit drugs are the areas where the use of this new technology is particularly beneficial. Many organizations and research groups are working on resolving issues like interoperability, privacy and security of data, frauds in medicaid program, counterfeit drugs, that plague health IT and have come up with various blockchain solutions to it. We present some of the blockchain solutions, ongoing projects and case studies in this paper.
Quantized and Regularized Optimization for Coding Images Using Steered Mixtures-of-Experts

Compression algorithms that employ Mixtures-of-Experts depart drastically from standard hybrid block-based transform domain approaches as in JPEG and MPEG coders. In previous works we introduced the concept of Steered Mixtures-of-Experts (SMoEs) to arrive at sparse representations of signals. SMoEs are gating networks trained in a machine learning approach that allow individual experts to explain and harvest directional long-range correlation in the N-dimensional signal space. Previous results showed excellent potential for compression of images and videos but the reconstruction quality was mainly limited to low and medium image quality. In this paper we provide evidence that SMoEs can compete with JPEG2000 at mid-and high-range bit-rates. To this end we introduce a SMoE approach for compression of color images with specialized gates and steering experts. A novel machine learning approach is introduced that optimizes RD-performance of quantized SMoEs towards SSIM using fake quantization. We drastically improve our previous results and outperform JPEG by up to 42%.
Test-Driven Anonymization for Artificial Intelligence

In recent years, data published and shared with third parties to develop artificial intelligence (AI) tools and services has significantly increased. When there are regulatory or internal requirements regarding privacy of data, anonymization techniques are used to maintain privacy by transforming the data. The side-effect is that the anonymization may lead to useless data to train and test the AI because it is highly dependent on the quality of the data. To overcome this problem, we propose a test-driven anonymization approach for artificial intelligence tools. The approach tests different anonymization efforts to achieve a trade-off in terms of privacy (non-functional quality) and functional suitability of the artificial intelligence technique (functional quality). The approach has been validated by means of two real-life datasets in the domains of healthcare and health insurance. Each of these datasets is anonymized with several privacy protections and then used to train classification AIs. The results show how we can anonymize the data to achieve an adequate functional suitability in the AI context while maintaining the privacy of the anonymized data as high as possible.
On Construction of a Power Data Lake Platform Using Spark

Currently, the traditional architecture of data storage and analysis has become not suitable enough. With rapid flow of information, there is no doubt that big data technology brings significant benefits such as efficiency and productivity. However, a successful approach to big data migration requires efficient architecture. In this paper, we proposed an architecture to import existing power data storage system of our campus into big data platform with Data Lake. We use Apache sqoop to transfer historical data to Apache Hive for data storage. Kafka is used for making sure the integrity of streaming data and as the input source for Spark streaming that writing data to HBase. To integrate the data we use the concept of data lake which based on Hive and HBase. Impala and Apache Phoenix are individually used as search engines for Hive and HBase. Apache Spark can quickly analyze and compute the data from Data Lake, and we choose Apache Superset as the solution for visualization.
Trust and Context-based Rating Prediction using Collaborative Filtering

In order to tackle the problem of information overload and effective recommendation based on users' preference, need, and interest a number of research contributions has been made for the development of recommender systems. However, certain challenges, such as data sparsity, profiling attack, and black-box recommendation still exist and hamper their prediction accuracy. In this paper, we propose a hybrid approach to predict user ratings by incorporating both trust and context of the users in traditional recommender systems using collaborative filtering method. The similarity between two users is computed using both trust value and context-based similarity. The trust value is based on three trust statements -- rating deviation, emotions, and reviews helpfulness. On the other hand, context-based similarity is based on four contextual features -- companion, place, day, and priority. The performance of the proposed trust- and context-based hybrid approach is analyzed using mean absolute error and root mean square error on a real dataset generated from two movie data sources (IMDB and Rotten Tomatoes), and it performs significantly better in comparison to some of the standard baseline methods. The rating prediction using only trust statements gives better results in comparison to other collaborative filtering approaches, such as user-based and item-based filtering approaches. Similarly, context-based collaborative filtering approach also outperforms standard collaborative filtering approaches. In addition, rating prediction using both trust- and context-based features performs better in comparison to only trust-based or context-based approaches.
Towards a Decentralized, Trusted, Intelligent and Linked Public Sector: A Report from the Greek Trenches

This paper is a progress report on our recent work on two applications that use Linked Data and Distributed Ledger technologies and aim to transform the Greek public sector into a decentralized, trusted, intelligent and linked organization. The first application is a re-engineering of Diavgeia, the Greek government portal for open and transparent public administration. The second application is Nomothesia, a new portal that we have built, which makes Greek legislation available on the Web as linked data to enable its effective use by citizens, legal professionals and software developers who would like to build new applications that utilize Greek legislation. The presented applications have been implemented without funding from any source and are available for free to any part of the Greek public sector that may want to use them. An important goal of this paper is to present the lessons learned from this effort.