Reviewerly/exHarmony huggingface dataset:
A method of level of details control table for 3D point density scalability in video based point cloud compression

Recently, the emergence of 3D cameras, 3D scanners and various cameras including Lidar is expected to promote various 3D media applications such as AR, VR and autonomous mobile vehicles. The 3D media can be recently realized by not only 2D pixel and depth information but also points with texture and geometry information. In particular, 3D point cloud data is consisted of hundreds of thousands to millions of 3D points, and thus dramatically increases its data size compared to 2D media data, which brings up the development of an efficient encoding/decoding technology. Also, it is required to develop a scalability function such as Level of Detail (LoD) for both an effective service with different bandwidths, devices and Region of Interest (RoI). In this paper, we propose a new LoD quality parameter considering characteristics of 3D point cloud contents instead of bitrate change based on a video codec in MPEG Video-based Point Cloud Compression (V-PCC). Therefore, the use of LoD table proposed in this paper is confirmed to generate 3D point cloud contents with different point density.
How Artists Can Develop Their Artwork, Its Market and Deliver Their Creative Content for Virtual Reality Environments?

This research investigates how artists, from traditional fine art and applied art backgrounds, can create innovative immersive and virtual art and build upon their tangible skills, through a digital pipeline and tools?Findings indicated opportunities to co-create partnerships, develop transferable design skills; and exhibitions which enable new markets and audiences.
Structure verification of deep neural networks at compilation time using dependent types

This paper presents TensorSafe, a dependently typed Haskell library which makes possible the definition and structural validation of deep neural networks architectures. Nowadays, the development process of deep learning models has been notably simplified due to the availability of sophisticated tools in the industry. However, most of these tools do not provide any security controls at compilation time, making the developers deal with unexpected run-time errors and uncertainties. In particular, validating the structure of deep neural networks at compilation time is a complex subject which involves the mathematical validation of all operations that a deep learning model will perform. Moreover, this structural checking requires an advanced usage of types systems theories to manipulate abstract type definitions capable of modeling neural networks constructions. Many different programming techniques were involved in the specification of TensorSafe. Primarily, the application of the functional programming paradigm and the use of dependent types were of great importance for the development process and to probe the correctness of the neural network models. The experimental evaluation showed that by using TensorSafe it is possible to correctly create well known deep neural network architectures, such like MNIST or ResNet50.
Detection of conflicts and inconsistencies between architecture solutions

This paper presents a semi-automated approach for detecting conflicts and inconsistencies between architecture solutions. Inconsistencies occur when two or more architecture solutions rely on each other but cannot be satisfied, and conflicts when there are contradictions within one single decision specification, such as contradictory variable range and value. The proposed approach comprises a set of checks followed by transformations of architecture solutions specified according to a domain-specific-language, also created in the context of this work, into state machines. The semi-automated approach is implemented as a plugin for the MagicDraw modeling tool, and was evaluated in a project from the automotive domain.
Reciprocal Time Domain Disruption of Image for Secured Communication

Now a day, the transmission of digital files over the insecure medium such as the internet has been increasing tremendously. The cyber criminals can hack the informative data which are being transmitted through the open network. In such a scenario, security became a crucial factor in safeguarding the information in all sectors such as business, government etc. In this paper, a novel digital image encryption is proposed based on the key generated from chaos-based Lorentz attractor. At first, a confusion and diffusion operation is done in the low-frequency band of DWT transformed digital image to get a random cipher image. To enhance the security level, a 14-bit LFSR is designed on Cyclone IV E, EP4CE115F29C7 FPGA board to generate the synthetic image. As the final stage of the algorithm, diffusion is performed based on XOR operation between the random cipher image and a synthetic image to get a final encrypted image. The designed image encryption algorithm is validated by performing statistical test, error metrics and key space analyses. The developed algorithm has achieved entropy equals to 7.9965; PSNR is 8.9903 which prove the algorithm is secure from the exhaustive attacks.
Auto-psychological competence of future teacher as a component of professional training and development

This article discusses auto-psychological competence as a system of characteristics that provide a high level of professional competence. Determination of the auto-psychological competence is connected with the fact that a person involved in professional activities should know his / her individual characteristics, abilities and inclinations and use them as means of realizing this activity. In addition, the auto-psychological competence acts as a factor contributing to the professional development of a person, where decisive importance is acquired to: need for self-organization in professional activities, independence and self-responsibility of the future teacher.&#x0D; The auto-psychological competence as a component of professional competence contributes to the conscious perception of the future teacher of their activities and, accordingly, allows them to manage the processes of self-development and self-education in professional activities, as well as provides a concentration of individual experience for solving professional problems.&#x0D;
Editorial for the Special Issue on “Natural Language Processing and Text Mining”

Natural language processing (NLP) and Text Mining (TM) are a set of overlapping strategies working on unstructured text [...]
The Italian Constitutional Court and the constitutionality of the criminalisation of assisted suicide of patients suffering from serious and incurable diseases

On 16 November 2018, the Italian Constitutional Court addressed for the first time the controversial issue of the constitutionality of the criminalisation of assisted suicide of patients suffering from serious and incurable diseases. In its judgment, the Court held that the criminalisation of assisted suicide is not contrary to the Constitution, rejecting the existence of a right to die, in line with the European Court of Human Rights case law. Nevertheless, the Constitutional Court recognised that in cases of patients suffering from serious and incurable diseases, an absolute prohibition on assisted suicide could run contrary to the freedom of self-determination and the constitutional principles of human dignity and equality. The present note describes and analyses the facts of the case and the reasoning of the Constitutional Court, especially the structure of the argument and the new ‘decision technique’ adopted.
An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key

The topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected layers on the effectiveness of classification was conducted. Based on the conclusions from the studies, an improved convolutional neural network was created, which is characterized by the state-of-art level of classification efficiency but containing 20 times less parameters to learn during the training process. Smaller number of learnable parameters results in faster network learning, easier convergence, and smaller memory and computing power requirements. The paper contains description of the current state of art, description of the experimental environment, structures of the studied networks and the results of classification accuracy.
Embedding Complexity of Learned Representations in Neural Networks

In classification tasks, the set of training examples for each class can be viewed as a limited sampling from an ideal infinite manifold of all sensible representants of this class. A layered artificial neural network model trained for such a task can then be interpreted as a stack of continuous transformations which gradually mold these complex manifolds from the original input space to simpler dissimilar internal representations on successive hidden layers – the so-called manifold disentaglement hypothesis. This, in turn, enables the final classification to be made in a linear fashion. We propose to assess the extent of this separation effect by introducing a class of measures based on the embedding complexity of the internal representations, with evaluation of the KL-divergence of t-distributed stochastic neighbour embedding (t-SNE) appearing as the most suitable method. Finally, we demonstrate the validity of the disentanglement hypothesis by measuring embedding complexity, classification accuracy and their relation on a sample of image classification datasets.
Evaluation Model of Teaching Quality Based on Cloud Computing

In the teaching quality evaluation, due to the influence of various human factors, the teaching quality evaluation system is presented with some grey characteristics. Traditional evaluation methods cannot solve the problem effectively. Through gray relational analysis, the grey system of partial information known and unknown can be analyzed. It is difficult for the qualitative description language evaluation index to use traditional mathematical model for accurate representation. In this paper, cloud model is combined with grey relational analysis, and an evaluation method is proposed based on the cloud-grey relational analysis, in order to realize the mutual transformation between the qualitative information and quantitative numerical. Three university teachers’ information was collected by questionnaire survey, and the teaching quality was evaluated through the evaluation model based on grey relational analysis built in MATLAB. The results were compared with those of fuzzy comprehensive evaluation and the BP neural network evaluation. The results show that the teaching quality evaluation model based on cloud grey relational analysis has certain rationality and feasibility.
A Method to Automatically Identify Diachronic Variation in Collocations.

This paper introduces a novel method to track collocational variations in diachronic corpora that can identify several changes undergone by these phraseological combinations and to propose alternative solutions found in later periods.The strategy consists of extracting syntactically-related candidates of collocations and ranking them using statistical association measures.Then, starting from the first period of the corpus, the system tracks each combination over time, verifying different types of historical variation such as the loss of one or both lemmas, the disappearance of the collocation, or its diachronic frequency trend.Using a distributional semantics strategy, it also suggests linguistic structures that convey meanings similar to those of extinct collocations.A case study on historical corpora of Portuguese and Spanish shows that the system speeds up and facilitates the finding of some diachronic changes and phraseological shifts that are harder to identify without using automated methods.
REVEAL 2019

The REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and where each decision to recommend something will have an impact on future steps and long-term rewards. This framing creates a number of challenges we will discuss at the workshop. How can recommender systems be evaluated offline in such a context? How can we learn recommendation policies that are aware of these delayed consequences and outcomes?
Spatio-Temporal Discriminative Correlation Filter Based Object Tracking

In this paper, a object tracking method based on spatio-temporal discriminative correlation filter is proposed. Firstly, a correlation filter layer is added into the the Siamese fully convolutional network to achieve end-to-end learning representation; secondly, the semantic feature is combined with the appearance feature to further enhance the discriminative ability of Siamese fully convolutional network; finally, the spatio-temporal regularized correlation filter is utilized to reduce the training time and improve the tracking performance. Extensive experiments conducted on VOT2017 dataset demonstrate the superior performance of the proposed approach over the examined state-of-the-art approaches.
Application-Level Traceroute: Adopting Mimetic Mechanisms to Increase Discovery Capabilities

Traceroute is a popular network diagnostic tool used for discovering the Internet path towards a target host. Besides network diagnostic, in the last years traceroute has been used by researchers to discover the topology of the Internet. Some network administrators, however, configure their networks to not reply to traceroute probes or to block them (e.g. by using firewalls), preventing traceroute from providing details about the internal structure of their networks. In this paper we present camouflage traceroute (camotrace), a traceroute-like tool aimed at discovering Internet paths even when standard traceroute is blocked. To this purpose, camotrace mimics the behavior of a popular TCP-based application-level protocol. We show preliminary results that confirm that camotrace is able to obtain additional information compared to standard traceroute.
Ontology-based analysis of the large collection of historical Hebrew manuscripts

Abstract In this paper, we present a new semi-automatic methodology for construction of event-based ontology from the library catalogue of the largest collection in the world of metadata records of historical Hebrew manuscripts. Based on the constructed ontology, we developed and implemented a new framework for catalogue data enrichment, correction, and its systematic quantitative analysis. Finally, we demonstrate the results of the proposed large-scale analysis of three most prominent event types in the corpus, as well as a few cross-event relations and trends.
Bandwidth Embeddings for Mixed-Bandwidth Speech Recognition

In this paper, we tackle the problem of handling narrowband and wideband speech by building a single acoustic model (AM), also called mixed bandwidth AM. In the proposed approach, an auxiliary input feature is used to provide the bandwidth information to the model, and bandwidth embeddings are jointly learned as part of acoustic model training. Experimental evaluations show that using bandwidth embeddings helps the model to handle the variability of the narrow and wideband speech, and makes it possible to train a mixed-bandwidth AM. Furthermore, we propose to use parallel convolutional layers to handle the mismatch between the narrow and wideband speech better, where separate convolution layers are used for each type of input speech signal. Our best system achieves 13% relative improvement on narrowband speech, while not degrading on wideband speech.
The Next Millennium Document Format

Most of today's leading document formats have their roots in the eighties. Their design was built upon requirements of these days: to represent the document state on one single machine or to exchange a document by floppy disc or modem. Often designed for a single purpose far narrower than their current usage. New features were often accomplished by workarounds. For example, change-tracking of any office format does not track a defined interoperable change. Only the earlier state of the changed area is stored, to be swapped back in case of rejection. Nowadays, with the rise of mobile devices, online collaboration is ubiquitous and creates challenges when dealing with documents designed for an environment from the eighties.
MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING

Abstract. Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.
Feasible computation based on quantum logic

The objective of this paper is to propose a feasible computational framework based on quantum logic. The usual quantum logic-based computational processes easily get stuck. The difficulty with the computation is ascribed to the difference in the inference rules for quantum and classical negations. It is shown that the new inference rule for negation which is akin to the classical one is partially applicable even to the quantum case. The resulting framework makes it possible to go beyond what the usual quantum logic-based computation could do.
FPGA Compression Process for Number Theoretic CW Radar With Increased Unambiguous Detection Range

A complete compression-to-detection FPGA design process for a number theoretic CW radar architecture is presented. The architecture uses a unique RSNS (Robust Symmetrical Number System) transform of a P4 polyphase modulation (RSNS-P4) that significantly extends the maximum unambiguous detection range and has an integer Gray code property to minimize detection errors. The FPGA design is both scalable (additional moduli) and reconfigurable and includes a range compression, Doppler signal processor followed by a coherent integration of the range-Doppler maps to provide the correct amount of processing gain required for detection at the unambiguous range. Comparison of the HDL model with the Matlab simulation results with several Swerling-III targets embedded in sea clutter (sea state 2) show excellent agreement.
Clustering Synonym Sets in English WordNet

A lot of lexical research conducted by experts use existing words in the English Thesaurus. However, the English Thesaurus only gives synonyms of the words searched for and does not provide similarities between words that can be called WordNet. So in this study, a WordNet can be made that can help research that uses databases for English. The similarity between words makes many researchers today are still looking for word relations by manual method, or still use the English Thesaurus. The making of WordNet is expected to be very useful for researchers who want a lexical database for their research, which is currently still relatively small. Therefore it is better to make an English WordNet which will later accommodate words that have the same meaning or Synonym Sets and this WordNet focuses on grouping those words. So that researchers can do lexical research more broadly and unlimitedly with the existence of words that are still unclear in their similarities. Calculation with clustering gets an F1 Score Result at 10.68%, Recall at 8.53% and Precision at 14.28%.
Filters in Convolutional Neural Networks as Independent Detectors of Visual Concepts

With the development of Convolutional neural networks (CNN), a myth was formed in recent years that only the last convolutional layers, including the fully connected layers, provide the most essential visual features (visual concepts) of the images at the input. In this paper, we experimentally disprove this prejudice through demonstrations showing that all the convolution layers of a CNN contain essential information that influences the classification. In connection with the experiments conducted, we look at each filter from different convolution layers as an independent detector of a particular visual structure (visual concept), and on this basis we introduce a new notion called the "vector of visual concepts". This concept summarizes the information, what visual constellation detectors are activated by an input image, and how strong they are activated. The classification error obtained by the vector of visual concepts differs by only 0.12% from the results of the last (softmax) layer of the network on the training data domain, which undoubtedly suggests the information saturation of all layers in the network. Additionally, we show by experiments that in input data from a domain other than the training one, the features of the initial and intermediate convolution layers provide a more accurate classification than those of the last convolution layers.
Algorithms for Q-ary Error-Correcting Codes with Partial Feedback and Limited Magnitude

Berlekamp and Zigangirov completely determined the capacity error function for binary error correcting codes with noiseless feedback. It is still an unsolved problem if the upper bound for the capacity error function in the non-binary case of Ahlswede, Lebedev, and Deppe is sharp. We consider channels with limited magnitude and feedback. For several classes of these channels we completely determine the capacity error function. All our algorithms do not use all the feedback immediately. Furthermore, a special case of the problem is equivalent to Shannons zero-error problem.
Deep Learning Optimization Tasks and Metaheuristic Methods

In this paper we identify and formulate two optimization tasks solved in connection with training DL models and constructing adversarial examples. This guides our review of optimization methods commonly used within the DL community. Simultaneously, we present findings from the literature concerning metaheuristics and black-box optimization. We focus on well-known optimizers suitable for solving ℝN tasks, which achieve good results on benchmarks and in competitions. Finally, we look into the research connected with utilizing metaheuristic optimization methods in combination with deep learning models.
More Symmetric Crypto: Authenticated Encryption and Kerberos

In this chapter we’ll be covering some advanced symmetric cryptography, and we’ll get deeper into authenticated encryption.