Reviewerly/exHarmony huggingface dataset:
Personalized recommendation system of UGC (User Generated Content) video resources based on user interest graphs

The research object of this paper is a personalized recommendation system of UGC (User Generated Content) video resource based on user interest graph. This article takes the personalized recommendation system of UGC video resources under the interest graph of the whole network as the research object, and generates and integrates user interest graph, development evolution and feedback of user interest graph, analysis and recognition of video content based on deep learning. The key technology theories and implementation plan of the video recommendation system based on user interest graphs are studied deeply. The goal is to form a perfect user interest graph and UGC video resource personalized recommendation framework, and providing solutions for personalized recommendation of network video resources.
ANALYSIS OF ORGANIC AND ANORGANIC DEBRIS IN AIR MANIS BEACH, NIRWANA BEACH AND CAROLINA BEACH OF PADANG CITY WEST SUMATERA PROVINCE

This research was conducted in June 2019 to determine the kind of marine debris, source, production and the correlation with the number of tourists in Air Manis, Nirwana, and Carolina Beach of Padang City, West Sumatera. Three sampling stations were surveyed. The results showed that marine debris found in those beaches were categorized organic, such as: fruits, leaves, wood, bamboo, sea plants and else an organic especially: plastic, glass, rubber, metal, etc. Air Manis Beach had the highest production of marine debris (1340 pcs), followed by Nirwana Beach (538 pcs) and Carolina Beach (500pcs). The source of marine debris were assessed to come from visitors and from outside those beac hes carried by sea current. The total of marine debris production was 29.165 kg. Plastic was the most common type of debris found. The production of garbage in the number of visitors shows very strong relationships (Y = -94,505 + 2,018x ; r = 0,97 ). There is strong correlation between the number of visitors and marine debris production in those beaches.
A method of decreasing connectability of derived data, using local differential privacy

A lot of personal data in the company are processed into various formats for each purpose of use, such as aggregate tables, and are generally stored as derived data. After the enforcement of the GDPR, when the user exercises “right to the erasure of personal data”, the companies are obliged to delete any link, or copy of the data taking all reasonable measures. On the other hand, since the data necessary for companies to comply with legal obligations should be retained, risk assessment of data to be deleted and data to be left is necessary. However, many derived data can be combined and the original data may be restored, and it is difficult to determine whether the data should be deleted. In this paper, we propose a method to measure the connectability of each attribute between derived data and manage the relationship by a graph structure. Then, by searching as a route the connectivity between the derived data, we measure the risk of connecting and restoring personal data. Using this structure, we propose a method to reduce connectability by using local differential privacy to disturb only the attribute with the highest connectability among the searched routes. And we also propose a measurement method of privacy protection index necessary to process to the level that cannot distinguish the users when two people were extracted from a database and applied differential privacy, and the effect was verified by experiments.
A topological completeness theorem for transfinite provability logic

Abstract We prove a topological completeness theorem for the modal logic $$\textsf{GLP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>GLP</mml:mi> </mml:math> containing operators $$\{\langle \xi \rangle :\xi \in \textsf{Ord}\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>{</mml:mo> <mml:mo>⟨</mml:mo> <mml:mi>ξ</mml:mi> <mml:mo>⟩</mml:mo> <mml:mo>:</mml:mo> <mml:mi>ξ</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>Ord</mml:mi> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> intended to capture a wellordered sequence of consistency operators increasing in strength. More specifically, we prove that, given a tall-enough scattered space X , any sentence $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> consistent with $$\textsf{GLP}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>GLP</mml:mi> </mml:math> can be satisfied on a polytopological space based on finitely many Icard topologies constructed over X and corresponding to the finitely many modalities that occur in $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> .
Autonomous Driving on the Thin Trail of Great Opportunities and Dangerous Trust

Achieving fully autonomous driving cars is a considerable technological milestone that will have significant impact on many lives and the adaption of new technologies. The question of when this milestone will be achieved is currently being debated and contradictory forecasts are increasingly being made. In this chapter, the most important components of self-driving cars are presented and different approaches are discussed. We show what makes autonomous driving so challenging and what misjudgments have been made in the past. In particular, the role of artificial intelligence will be illuminated to give a clear picture of what progress is realistic in the coming years. Next, we discuss related challenges that need to be solved in the coming years. Based on our own research, we will also show how hard it is to interpret models, like neural networks, i.e., understanding why they make the decisions they make in the context of self-driving.
Which Are the Correct Membership Functions? Correct “And”- and “Or”- Operations? Correct Defuzzification Procedure?

Even in the 1990s, when many successful examples of fuzzy control appeared all the time, many users were somewhat reluctant to use fuzzy control. One of the main reasons for this reluctance was the perceived subjective character of fuzzy techniques—for the same natural-language rules, different experts may select somewhat different membership functions and thus get somewhat different control/recommendation strategies. In this paper, we promote the idea that this selection does not have to be subjective. We can always select the “correct” membership functions, i.e., functions for which, on previously tested case, we got the best possible control. Similarly, we can select the “correct” and- and or-operations, the correct defuzzification procedure, etc.
A Research on Teacher-Education-Related Standards Mobilities from Foreign Countries to Chinese Context

Standard mobility is explored to show process of standards knowledge from foreign countries to China. With content analysis and comparative studies, it is found that standard mobility takes place within IAM model and meanwhile robustness, and appropriateness and consistence should be paid attention. It is suggested that teacher-education-related standards should go deeper and higher, go from inside to outside, and go beyond standards. Teacher agency and competences should be initiated in the future.
Gaussian Processes for Efficient Plane-Based Camera Calibration

Camera calibration is a crucial pre-processing for 3D related computer vision applications. When non-expert calibration operators capture calibration images, they strongly require guidance what kind of images must be taken. In the literature, several types of supports have been proposed. Focusing on the plane-based calibration method proposed by Zhang, this paper proposed to such non-expert calibration operators. The proposed method asks such operators to take calibration images with variety of position and orientation and the method takes a subset of good quality images. Thanks to Gaussian Process modeling, the proposed method can select a near optimum subset with some accuracy guarantee in the sense of submodularity. To enable this, we propose to use 4-point parameterized homography as a global image feature. We conduct a small experiment with both synthesized and a public dataset and validate the proposed method works.
Synthesis and Evaluation of Improved Reference Matrix Models for High Capacity Image Steganography

This paper proposes two new reference matrix based steganographic methods called Grid Window Method (GWM) and Exploited Grid Window Method (EGWM). The goal of image steganography is to embed secret information within ingenuous cover images to protect the former from third-party adversarial attacks. The primary aim of this paper is to increase the size of the location map corresponding to the reference matrix which consequently increases the number of secrets digits that can be hidden within a single cover image pixel. Reference matrices are often constructed using turtle shells (hexagons) whereas the proposed grid windows are square-shaped. The number of secret digits hidden per cover-pixel pair is four in GWM and five in EGWM. GWM offers similar embedding capacity when compared to existing turtle shell methods (2 bpp) and EGWM surpasses these results by 25% (2.5 bpp). The parameters PSNR and SSIM for these models are also compared and evaluated.
Sound as Material for Eco-technogenesis

This paper delineates the conceptual outcomes from a two-week intensive cross-disciplinary conversation between an art historian, an interaction designer, and an artist/engineer.With the aim of applying the concept of technogenesis to an exploration of sound as material for art and design, we consider sound as a material force within an ecosystem.Through this lens, sound produced by either life-or technological-forms allows us to consider the ecological impact and potential meanings of generated sound.Drawing on biosemiotics, we propose that the co-evolution of sound, technology, and environments, what we call eco-technogenesis, demands relational, and thus ethical, thinking.The rowdy krause, an autonomous sonic agent, designed by Kadish to identify and inhabit an acoustic niche within an ecosystem, serves as a case study for thinking through ecotechnogenesis.
Supervised Phrase-Boundary Embeddings

We propose a new word embedding model, called SPhrase, that incorporates supervised phrase information. Our method modifies traditional word embeddings by ensuring that all target words in a phrase have exactly the same context. We demonstrate that including this information within a context window produces superior embeddings for both intrinsic evaluation tasks and downstream extrinsic tasks.
Sybil Account Detection in Online Social Networks Using Statistical Feature Selection Techniques

Online Social Networks (OSNs) are fast becoming an essential media for social interactions among its users. With the rapid growth of these OSNs, the malicious and illegal activities are also on the rise posing potential threats such as disruption of communication, influencing decision making process of the gullible users, unauthorized control of resources etc. Sybil accounts pose such kind of potential threats in the OSNs in addition to wireless ad-hoc networks. Twitter is an OSN which we have used in this research work to identify such Sybil accounts with the help of Machine Learning (ML). ML helps in building models which are capable of learning from the existing datasets so as to be able to then apply it to solve Real-time or futuristic problems. The supervised ML techniques train the model with the labelled data which can be used to predict the discrete values based on its learning. In this paper, a classification model is trained using two classifiers namely, Random Forest (RF) and Support Vector Machine (SVM). To make the classification model more effective, Univariate and Correlation Matrix with Heatmap, these two Feature Selection (FS) techniques have also been used. The ML model then used the selected features to identify Sybil accounts. This study also explores the effect of biasing of data of real accounts with that of illegitimate Sybil accounts during the process of classification and Feature Selection. The results of this study show that the RF outperforms SVM by shade as far as the accuracy of prediction models is concerned under the given experimental setup.
Image Splicing Detection Based on the Inter-Block Correlation of Local Gabor Phase Quantization Features

The designing of high-quality features is of great significance for image splicing detection. For better splicing detection, this paper presents a novel approach to quantify the image representation, global-local gabor phase quantization feature (Global-LGPQ). Compared to existing features, our Global-LGPQ feature captures not only the intra-block but also the inter-block correlation among pixels, which makes the splicing detection more effective. Besides, we use a Multi-Layer Perception (MLP) as the classifier in our model for its strong capacity. A wide range of experiments on three public datasets demonstrate Global-LGPQ feature is more suitable for the image splicing detection task, and our model significantly outperforms several state-of-the-art models.
An Efficient Set-Based Algorithm for Variable Streaming Clustering

In this paper, a new algorithm for Data Streaming clustering is proposed, namely the SetClust algorithm. The Data Streaming clustering model focuses on making clustering of the data while it arrives, being useful in many practical applications. The proposed algorithm, unlike other streaming clustering algorithms, is designed to handle cases when there is no available a priori information about the number of clusters to be formed, having as a second objective to discover the best number of clusters needed to represent the points. The SetClust algorithm is based on structures for disjoint-set operations, making the concept of a cluster to be the union of multiple well-formed sets to allow the algorithm to recognize non-spherical patterns even in high dimensional points. This yields to quadratic running time on the number of formed sets. The algorithm itself can be interpreted as an efficient data structure for streaming clustering. Results of the experiments show that the proposed algorithm is highly suitable for clustering quality on well-spread data points.
Argumentative relation identification in academic texts

The argumentation in academic writings is necessary to clearly communicate the ideas of the students. The relations between argumentative components are an essential part since this shows the contrast or support of the presented ideas. In this paper, we present two approaches to relation identification between pairs of components. In the first, we detect initially which components are related, to later classify them in support or attack relation. In the second approach, we identify directly which components have a support relation. For these approaches, we employed machine learning techniques with representations of several lexical, syntactic, semantic, structural and indicator features. Experiments in argumentative sections of academic theses showed that the models achieve encouraging results solving the task, and revealing the argumentative structures prevailing in student writings.
Some New Hermitian Self-orthogonal Codes Constructed on Quaternary Filed

The self-orthogonal codes are linear codes, which are the basis for constructing quantum codes. In this paper, Hermitian self-orthogonal codes are studied with dual distance of 3 over the field $$F_{4}$$. For two types of codes length n, by using the method of recursion and combination, quaternary self-orthogonal codes are constructed and dual distance is 3. Quaternary quantum codes are obtained by CSS structure method, which have good parameters.
Obtaining Binary Perfect Codes Out of Tilings

A tiling of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> (considered as a graph) gives rise to a perfect code (according to a given metric) if the basic tile is a metric ball. We are concerned with metrics on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> that are determined by a weight which respects the support of vectors (TS-metrics) and in this case, a perfect code is called TS-perfect. We consider all the tilings of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> by tiles with up to 8 elements (which are classified in the literature) and determine which of them give rise to a TS-perfect code. In the sequence, for those tilings that give rise to a perfect code for some TS-metric, we classify the TS-metrics (up to equivalence) that turn it into a perfect code. Also, we give a general construction to obtain TS-perfect codes out of given TS-perfect codes, by proving that concatenation of two TS-perfect codes is by itself a TS-perfect code.
The Quick UDP Internet Connection (QUIC) and Transport Layer Security 1.3 Standards

Abstract Chapter 4 examines the effect of Snowden on security protocols. For twenty years, Secure Sockets Layer (SSL) and its successor Transport Layer Security (TLS) provided security for Internet traffic. However, the TLS 1.2 protocol developed in 2008 suffered from a series of implementation and security issues. The 2013 Snowden revelations sent shock waves through the engineering community. The extent of the targeting of protocol vulnerabilities by security agencies had been greatly underestimated by the IETF. By 2016, Cisco, Fortinet, and Juniper revealed that the National Security Agency (NSA) had successfully targeted its firewalls for years. However, stasis within the IETF barred upgrade to TLS 1.3. The chapter analyses the emergence and contestation of potential solutions to TLS and how the parallel development of the QUIC protocol by Google opened a window of opportunity to enhance security. The agreement on TLS 1.3 in March 2018 was supported by digital rights groups.
Copy-Move Forgery Detection Based on Interest Point and Local Search Algorithm

Copy-move forgery detection mainly includes block-based and interest point-based methods, but both of them have shortcomings, such as weak resistance to affine transformations, weak ability to locate the forged region. To solve these problems, a novel algorithm is proposed in this paper, which involves two-stage search algorithm. The first stage takes image blocks as the search unit, and in the second stage, the forger region is searched at the pixel level. Experimental results demonstrate that the proposed method performants better compared to recent state-of-the-art methods.
Data Integrity in Policy-Based Attribute Access Control in Social Network Cloud

The integrity of files stored on cloud is crucial for many applications, specifically applications that manage online social networks. Now-a-days social networking sites have become primary locations for sharing documents. Due to security lapses at these sites, shared documents could be stolen or changed by cyber attackers. We have developed a model to protect shared data and resources from unauthorized accesses. This method, which is called policy-based attribute access control (PBAAC) [20], enables resource owners to define policies to manage their resources from unmanaged accesses. However, policies are saved as plain text in a file and could be compromised by unauthorized users, violating the integrity of those policies. In this paper, we proposed a method to protect policies saved in text files. We developed an algorithm to extract critical information from each policy and create a hash value, by executing a hash cryptography algorithm.
Pedagoģiskās kompetences refleksija profesionālajā izaugsmē

Formation of professional identity is an important point of reference for awareness of professional and personal experience. Moments of doubt and worry are both typical in the process of the acquisition of teacher’s profession, when going through these stages step by step, the belief in one’s own strength is achieved. The basis of teachers’ professional activity is their own self-development motivation and development together with their students, actualizing the didactic competence in the context of educational reforms. The self-educational competence can be characterised as an integrative quality of a personality and his/her professional activity, which is inseparable from the personality’s ability to engage in a systematic, independently organized cognitive activity directed towards the follow-up of education in general – cultural and professional aspects, and it is formed, organized and mentioned by the very subject of the cognitive activity. The data of the empirical research confirm that reflection on teachers’ involvement in the research and pedagogical work is a constant assessment process of their own activity and a requirement of modern education, as well as a deliberate professional necessity helping the practising teachers to develop their own theoretical and practical awareness of the issues of pedagogical competence enhancement.
More technologized is not more educated

This paper focuses mainly on the dangers of new communication technologies for the intellectual and cultural development of young people. Although these technologies also have some advantages for information and communication, however, the disadvantages are much greater. As such, we will analyze their effects structurally (neuro-physically), psychosocially and morally, and in the end we will try to find out what can be done to limit these negative effects.
Preliminary considerations regarding the cryptographic strength of the Pixel Sieve cryptographic primitive

Based on the theoretical model of the Pixel Sieve cryptographic primitive a simple application was written to test in practice the proposed method. In this paper, some preliminary test results are presented regarding the cryptographic strength of the method. For the appointed weaknesses conclusions and proposals are made to enhance the method.
Fine-Grained Explanations Using Markov Logic

Explaining the results of Machine learning algorithms is crucial given the rapid growth and potential applicability of these methods in critical domains including healthcare, defense, autonomous driving, etc. In this paper, we address this problem in the context of Markov Logic Networks (MLNs) which are highly expressive statistical relational models that combine first-order logic with probabilistic graphical models. MLNs in general are known to be interpretable models, i.e., MLNs can be understood more easily by humans as compared to models learned by approaches such as deep learning. However, at the same time, it is not straightforward to obtain human-understandable explanations specific to an observed inference result (e.g. marginal probability estimate). This is because, the MLN provides a lifted interpretation, one that generalizes to all possible worlds/instantiations, which are not query/evidence specific. In this paper, we extract grounded-explanations, i.e., explanations defined w.r.t specific inference queries and observed evidence. We extract these explanations from importance weights defined over the MLN formulas that encode the contribution of formulas towards the final inference results. We validate our approach in real world problems related to analyzing reviews from Yelp, and show through user-studies that our explanations are richer than state-of-the-art non-relational explainers such as LIME .