Reviewerly/exHarmony huggingface dataset:
Sarajevo War Tunnel - Mobile Virtual Cultural Heritage Application

This paper explores the new way of presenting one existing VR application, which was described in our previous work - Virtual Reality Experience of Sarajevo War Heritage. The goal of the application was to introduce more people with the Sarajevo siege and allow them to experience the Tunnel crossing at that time. Before this application, we made two versions, the first one for VR setup and the second for the web. In this paper, we introduce a mobile version with the same content. The challenge was to optimize the content for the mobile experience. The assets were optimized so a wider number of mobile phones with different hardware capabilities can run the application. The advantages and disadvantages of this approach are pointed out, and the limitations of the mobile application are emphasized. The memory usage and frame rate are measured for different Android devices with different operating system versions and hardware capabilities. The results show the optimized application can be run on different Android mobile devices. Nevertheless, for better user experience a higher number of frames per second is needed, which may include reducing the quality of the assets.
Stereo Matching of Urban Remote Sensing Image Based on Edge Block Coincidence Rate

With the continuous development of remote sensing technology, the acquisition of high-resolution remote sensing image information has become more simpler and faster. Stereo matching, as a basic part of the field of computer vision, stereo matching has a direct impact on post processing. Because the information of remote sensing images is more complicated and the interference is greater than that of ordinary images, it is more difficult to match real remote sensing images. Many stereo matching algorithms are not suitable for remote sensing images. In this paper, a new framework based on the edge block coincidence rate is proposed for the stereo matching problem of urban remote sensing images. The difficulty in stereo matching of remote sensing images lies in the fact that the epipolar lines of remote sensing images captured by linear CCD sensors carried by remote sensing satellites are quadratic curves rather than a straight line, so accurate epipolar line correction images cannot be obtained, thus limiting the matching search space to one dimension. This paper proposes a new stereo matching framework for urban remote sensing image, and experiment results show that the framework can achieve the desired matching effect. In order to solve this problem, this paper proposes to give up the use of epipolar constraint, choose the method of image registration, and adopt the stereo matching method based on mutual information to obtain the final accurate matching result. Experiments show that the framework can achieve ideal matching effect.
Correction to: Particulate Matter and Black Carbon in the Brahmaputra Valley of Northeast India: Observations and Model Simulation

Unfortunately, Fig. 5 has been published incorrect. Please find the corrected figure below.
On the Multiple Possible Adaptive Mechanisms of the Continuous Ant Colony Optimization

Among the existing techniques to improve the performance of metaheuristics in optimization problems, adaptive parameter control consists in varying one or more parameters of a given metaheuristic according to some indicator of the search conditions. This approach allows metaheuristics to change algorithmic behaviour during the search, and is particularly relevant for the optimization of dynamic problems. In this research we theoretically analyse in which ways the parameters of the ant colony optimization for continuous domains metaheuristic can be adapted, regarding how each parameter influences exploration and exploitation characteristics of the algorithm. Our experimental contributions include validating the colony success rate as a search condition estimator and choosing suitable maps from this estimator to the parameters q and \(\xi \) of the algorithm. Beyond that, we compare the performances of three proposed adaptive versions of the base metaheuristic and show the benefits of simultaneously adapting multiple parameters.
Parallel Programming Strategies for Computing Walsh Spectra of Boolean Functions

The utilization of all computational resources is significant to achieve efficient computation. In order to exploit the available computation resources, we combine two parallel programming models such as MPI and CUDA. Combining of these two programming models ensures usage of the whole computation resources available in one computer system (CPU and GPU). In this paper, we present a way to use the available parallel processing resources to their full potential utilizing different strategies and techniques regarding data transfer. We perform experimental computation of well know algorithm for computing Walsh spectra of Boolean functions by combining these two parallel programming models. Experiments are performed on two different class of parallel processing capability hardware. Randomly generated Boolean functions of fourteen, sixteen, eighteen and twenty variables represent the used data set for experiments evaluations. Performed experiments show how the growth of the data size results in gaining more parallelization and therefore accelerate the execution.
Wearable Technology as a Source of Data-Generation Tool for Artificial Intelligence

The world is fascinated to see how technology evolves each passing day. All too soon, there's an emerging technology that is trending around us, and it is no other technology than smart wearable technology. Less attention is paid to the data that our bodies are radiating and communicating to us, but with the timely arrival of wearable sensors, we now have numerous devices that can be tracking and collecting the data that our bodies are radiating. Apart from numerous benefits that we derive from the functions provided by wearable technology such as monitoring of our fitness levels, etc., one other critical importance of wearable technology is helping the advancement of artificial intelligence (AI) and machine learning (ML). Machine learning thrives on the availability of massive data and wearable technology which forms part of the internet of things (IoT) generates megabytes of data every single day. The data generated by these wearable devices are used as a dataset for the training and learning of machine learning models. Through the analysis of the outcome of these machine learning models, scientific conclusions are made.
An attempt to convert BPMN models to SDL

Models of complex systems and systems of systems are described with NAF (NATO Architecture Framework) or DoDAF (DoD Architecture Framework). Business Process Model and Notation (BPMN) is part of NAF and allows describing the behaviour of the different participants in the model. This notation is used by the French Army as well as its main suppliers to describe the interactions between participants involved in a mission. It is therefore important the models are correct. The VeriMoB project was a research project financed by the DGA (Direction Générale de l'Armement) which aimed at developing a tool that will help users to validate their BPMN models among the stakeholders. The tool had to cover three main aspects: a static verification, an interactive execution, and an automatic exploration of the possible scenarios. This paper will present the work done trying to convert BPMN to SDL in order to fulfil this objective with an existing toolset.
Efficient Segmentation Using Gamma Correction with Complement Image of Chinese Rubbing Image

For Chinese rubbing image under the complex background, to against its characteristic of low contrast and large noise, we develop a gamma correction enhancement algorithm in which the grayscale space is being conducted before the Otsu’s binarization, experiments on multiple pictures show the superiority of the algorithm. At first, the global contrast is enhanced based on the gamma correct algorithm for the complement image of the Chinese rubbing image. After that, we have implemented optimum global thresholding using Otsu’s method for image segmentation. The experimental results show that our algorithm could correct the background noise of the image and enhance the stroke in the low contrast Chinese rubbing image, and there is no need to denoise in advance. The performance of the algorithm is simple, fast, and produces very good segmentation.
Practical Control Flow Integrity using Multi-Variant execution

The popularity of computers and networks brings many conveniences to our daily life but also brings a variety of security threats. The proliferation of attacks and the rapid spread of viruses make security researchers gradually shift their perspective from passive defense to active defense. Among many active defenses, the technique of software multi-variant execution (MVX) framework has been widely concerned. It can detect and defend most known or even 0-day attacks without depending on the feature of specific threats. However, there is a big performance bottleneck in this technique. Control flow integrity (CFI) is another ideal security solution, but it is not widely used in practice because of performance loss and compatibility problems. In this paper, we propose a multi-variant execution framework called MVX-CFI. MVX-CFI improves execution efficiency without losing its original security. MVX-CFI is a dynamic and transparent CFI implementation based on the MVX framework. It can effectively capture the control flow of the target software and find illegal path transfer caused by malicious acts such as attacks. MVX-CFI extends the general MVX with a feedback assisted-detection module, which reduces a lot of repetitive voting work of traditional MVX and improves the performance. It also provides a probability to detect preset back door in a software.
Study of Content Based Image Retrieval Using Data Mining Techniques

&lt;p&gt;The field of image retrieval has been an active research area for several decades and has been paid more and more attention in recent years as a result of the dramatic and fast increase in the volume of digital images. Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and un annotated image databases. In recent years, a variety of techniques have been developed to improve the performance of CBIR. In reaction to the needs of users, who feel problems connected with traditional methods of image searching and indexing, researchers focus their interest on techniques for retrieving images on the basis of automatically-derived features, often denoted as Content-Based Image Retrieval (CBIR). CBIR systems index the media documents using salient features extracted from the actual media rather than by textual annotations. Query by content is nowadays a very active research field, with many systems being developed by industrial and academic teams. Results performed by these teams are really promising. The situation gets diametrically different when we move our attention from the usual CBIR task, i.e. the retrieval of images which are similar (as a whole) to the query image, to the task “find all images that contain the query image”. The proposed CBIR technique uses more than one clustering techniques to improve the performance of CBIR. This optimized method makes use of K-means and Hierarchical clustering technique to improve the execution time and performance of image retrieval systems in high dimensional sets. In this similarity measure is totally based on colors. In this paper more focus area is the way of combination of clustering technique in order to get faster output of images. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divides and conquers K-means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.&lt;/p&gt;
Slovakia/Austria: Official opening of the TRAM cultural project on 19 February 2018 in Vienna by the presidents of Slovakia and Austria – UIC Communications

The Presidents of Austria and Slovakia officially opened the common Austrian-Slovak cultural project TRAM. The opening ceremony took place at the Vienna Main Station and the CEOs of both OBB and…
Performance Analysis of Computer Vision with Machine Learning Algorithms on Raspberry Pi 3

Computer vision is a dynamic field, with many applications in a wide variety of industries. Choosing a platform to deploy computer vision algorithms is a complex task, with a massive amount of choice, varying in operating system, computing power, and physical size. This paper aims to measure common computer vision algorithms on a Raspberry Pi 3, helping to clarify some performance measurements and provide a clearer image of the Raspberry Pi’s viability for common computer vision operations, as well as recommend some platforms for specific algorithms.
Decentralizing Dispute Resolution in Two-Sided Platforms: The Case of Review Blackmail

We study the relative merits of different dispute resolution mechanisms in two-sided platforms, in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. In our model, the seller takes into account the impact of consumer reviews on his future earnings; recognizing this, a malicious consumer may attempt to blackmail the seller by purchasing the product, posting a negative review, and demanding ransom to remove it. Without a dispute resolution mechanism in place, the presence of malicious consumers in the market can lead to a significant decrease in seller profit, especially in settings characterized by high uncertainty about product quality. The introduction of a standard centralized dispute resolution mechanism (whereby the seller can report allegedly malicious reviews to the host platform, which then judges whether to remove the review) can restore efficiency to some extent, but requires the platform's judgments to be both very quick and highly accurate. We demonstrate that a more decentralized mechanism (whereby the firm is allowed to remove reviews without consulting the platform, subject to ex post penalties for wrongdoing) can be much more effective, while simultaneously alleviating -- almost entirely -- the need for the platform's judgments to be quick. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution.
Research and Application of a Data Processing Method on Outliers in Unmanned Aerial Vehicle (UAV) Tracking Measurement

In unmanned aerial vehicle (UAV) flight test, some complex outliers often appear in tracking measurements due to various factors stemming from environment, instrumentations, and even operators. This seriously affects the reliability of applications and analysis. This paper aims at the problem of outliers elimination to measurement data and proposes a joint data processing method comprising grouped data extraction, data trend modeling, and outliers detection. In the context of accuracy assessment test for measurement and control equipment (MCE), an approach using the measurement parameter for UAV flight is designed to estimate the time system deviation between MCEs, as well as to validate the effect of the proposed joint method. Finally, a simulation is implemented and results show the effectiveness and feasibility of the joint method to outliers elimination and time deviation estimation.
SRCBT: Secure Regeneration of Corrupted Blocks by TPA in Cloud

To conserve the deployed information in cloud repository contrary to adulterations, including fault toleration to cloud repository together with information integrity verification and failure restoration becomes important. Belatedly, reconstructing codes acquire recognition because of their reduced reparation bandwidth while ensuring fault toleration. Prevailing distant auditing schemes for reconstructing-coded information authorizes an intermediary to reconstruct authenticators and information blocks on the suspended servers in the course of the reparation process. To address this issue, we propose Secure Regeneration of Corrupted Blocks by TPA in Cloud (SRCBT). We eliminate the semi-trusted proxy and allow the TPA to reconstruct authenticators and information chunks on the suspended servers in the course of the reparation process. Thus, our mechanism can totally relinquish information proprietors from online burden. The proposed scheme preserves privacy, TPA efficiently reconstructs authenticators and information chunks on the suspended servers in the course of the reparation process and also effectively performs batch auditing. The performance analysis shows that our mechanism is extremely effective and can be conceivably blended into the reconstructing-code-based distributed repository.
Parallel Defect Detection Model on Uncertain Data for GPUs Computing by a Novel Ensemble Learning

To detect defect is an important concept in machine leaning techniques and ambiguous dataset which develops into a challenging issue, as the software product expands in terms of size and its complexity. This chapter reveals an applied novel multi-learner model which is ensembled to predict software metrics using classification algorithms and propose algorithm applied in parallel method for detection on ambiguous data using density sampling and develop an implementation running on both GPUs and multi-core CPUs. The defect on the NASA PROMISE defect dataset is adequately predicted and classified using these models and implementing GPU computing. The performance compared to the traditional learning models improved algorithm and parallel implementation on GPUs shows less processing time in ensemble model compared to decision tree algorithm and effectively optimizes the true positive rate.
Reasoning Engine for Support Maintenance

This paper presents a reasoning system deployed for supporting the maintenance of IT devices in use by a leading broadcasting and cable television company in North America. We describe a reasoning engine pipeline relying on semantic data representation and some machine learning approaches such as clustering. The engine derives problems on a telecommunication network from a textual description and uses structured historical data of problems, error codes and proposed solutions to prescribe potential solutions. The engine is capable of proposing solutions to unseen problems by using analogical reasoning on structured representations. When a problem happens on the network or more precisely on one of the devices, these devices generate error codes. We addressed two scenarios; (i) we assumed that the list of error codes that we captured is complete, (ii) we assumed, more realistically, that this list is incomplete. In the first case, we suggested solutions for seen and new problems and reported results on real data. In the second case, we proposed a method to infer the complete list of errors, tested that method on synthetic data and showed results with high accuracy. Although both scenarios are in-use, the first scenario is more usual than the second one, but both need to be considered.
MARTINI

Hardware architectural vulnerabilities, such as Spectre and Meltdown, are difficult or inefficient to mitigate in software. Although revised hardware designs may address some architectural vulnerabilities going forward, most current remedies increase execution time significantly. Techniques are needed to rapidly and efficiently detect these and other emerging threats. We present an anomaly detector, MARTINI, that analyzes traces of memory accesses in real time to detect attacks. Our experimental evaluation shows that anomalies in these traces are strongly correlated with unauthorized program execution, including architectural side-channel attacks of multiple types. MARTINI consists of a finite automaton that models normal program behavior in terms of memory addresses that are read from, and written to, at runtime. The model uses a compact representation of n-grams, i.e., short sequences of memory accesses, which can be stored and processed efficiently. Once the system is trained on authorized behavior, it rapidly detects a variety of low-level anomalous behaviors and attacks not otherwise easily discernible at the software level. MARTINI's implementation leverages recent advances in in-cache and in-memory automata for computation, and we present a hardware unit that repurposes a small portion of a last-level cache slice to monitor memory addresses. Our detector directly inspects the addresses of memory accesses, using the pre-constructed automaton to identify anomalies with high accuracy, negligible runtime overhead, and trivial increase in CPU chip area. We present analyses of expected hardware properties based on indicative cache and memory hierarchy simulations and empirical evaluations.
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction

Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language.It is of great importance in many low-resource scenarios where cross-lingual training data is not available.To perform BDI, bilingual word embeddings (BWEs) are often used due to their low bilingual training signal requirements.They achieve high performance, but problematic cases still remain, such as the translation of rare words or named entities, which often need to be transliterated.In this paper, we enrich BWE-based BDI with transliteration information by using Bilingual Orthography Embeddings (BOEs).BOEs represent source and target language transliteration word pairs with similar vectors.A key problem in our BDI setup is to decide which information source -BWEs (or semantics) vs. BOEs (or orthography) -is more reliable for a particular word pair.We propose a novel classification-based BDI system that uses BWEs, BOEs and a number of other features to make this decision.We test our system on English-Russian BDI and show improved performance.In addition, we show the effectiveness of our BOEs by successfully using them for transliteration mining based on cosine similarity.
Application of Difficult Sample Mining based on Cosine Loss in Face Recognition

Due to the development of deep convolutional neural networks, face recognition has made great progress, and its main goal is how to improve feature recognition capabilities. In this regard, several loss functions based on angular boundaries have been proposed to increase the feature margin between different classes. Although very good results have been achieved in this direction, there are still some problems. These loss functions only expand the feature margin from the perspective of real classification in training, and do not provide distinguishability for misclassified samples. In order to solve this problem, this paper improves on the original cosine loss function, and implements feature learning in the direction of difficult samples based on misclassified feature vectors.