Reviewerly/exHarmony huggingface dataset:
Wiktionary matcher results for OAEI 2020.

This paper presents the results of the Wiktionary Matcher in the Ontology Alignment Evaluation Initiative(OAEI) 2020.Wiktionary Matcher is an ontology matching tool that exploits Wiktionary as external background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. This is the second OAEI participation of the matching system. Wiktionary Matcher has been improved and is the best performing system on the knowledge graph track this year.
The didactic potential of virtual information educational environment as a tool of geography students training.

The article clarifies the concept of information educational (VIEE) and examines the researchers' views on its meaning exposed in the scientific literature. The article determines the didactic potential of the virtual information educational environment for the geography students training based on the analysis of the authors' experience of blended learning by means of the Google Classroom. It also specifies the features (immersion, interactivity, and dynamism, sense of presence, continuity, and causality). The authors highlighted the advantages of virtual information educational environment implementation, such as: increase of the efficiency of the educational process by intensifying the process of cognition and interpersonal interactive communication; continuous access to multimedia content both in Google Classroom and beyond; saving student time due to the absence of necessity to work out the training material manually; availability of virtual pages of the virtual class; individualization of the educational process; formation of informational culture of the geography students ; and more productive learning of the educational material at the expense of IT educational facilities. Among the disadvantages the article mentions low level of computerization, insignificant quantity and low quality of software products, underestimation of the role of VIEE in the professional training of geography students, and the lack of economic stimuli, etc.
Using wavelet analysis to study floods in Bangkok

Abstract Floods in Bangkok are regular natural disasters that happen nearly every year during the monsoon season. Many streets of the city may turn into canals and seriously worsened rush-hour traffic causing many commuters to arrive late at work or school. Therefore, in this research, we will study floods in Bangkok area both seasonal flooding and occasional flooding due to the effect from La Nina cycles using the wavelet spectrum analysis and the cross wavelet method. The result from cross wavelet transform (XWT) shows that the standard precipitation index (SPI) (wet) and the oceanic index (ONI) (La Nina) have significant common power mainly in the 20-40 month band from 1967-1974 during the strong La Nina cycle. There is, however, a large area with common power outside the significance level around 32-80 month band from 1982-2002 but depicts a highly significant local correlation. The corresponding wavelet coherence spectra showed that SPI and ONI co-varied mostly in the 32-60 month band from 1966-1981 with the arrow pointing to the right and in the 64-80 month band from 1986-1994 with the arrows also pointing to the right. Although flooding is a common and annual occurrence in Bangkok, the La Nina climatic phenomenon can cause a higher than average rainfall. The intense extremely wet period during strong La Nina years was observed in 1955-1956, 1998-1999 and 2007-2008. In a La Nina years, Bangkok will receive more rain than normal years. In general, this work may contribute to the improvement of the understanding of how climate variability may impact flooding in Bangkok. Knowing of the La Nina years in advance will help the government in better planning and preparation to prevent flooding areas more than normal.
Speeding up generation of photon Fock state in a superconducting circuit via counterdiabatic driving*

Optimal creation of photon Fock states is of importance for quantum information processing and state engineering. Here an efficient strategy is presented for speeding up generation of photon Fock state in a superconducting circuit via counterdiabatic driving. A transmon qubit is dispersively coupled to a quantized electrical field. We address a Λ-configuration interaction between the composite system and classical drivings. Based on two Gaussian-shaped drivings, a single-photon Fock state can be generated adiabatically. Instead of adding an auxiliary counterdiabatic driving, our concern is to modify these two Rabi drivings in the framework of shortcut to adiabaticity. Thus an accelerated operation with high efficiency can be realized in a much shorter time. Compared with the adiabatic counterpart, the shortcut-based operation is significantly insusceptible to decoherence effects. The scheme could offer a promising way to deterministically prepare photon Fock states with superconducting quantum circuits.
Strengthen the Application Research of Computer Network Management Technology Innovation

Abstract With the computer network technology occupying an increasingly important position and role in our daily life, study and work, how to strengthen the computer network management technology and innovative application of computer network management technology has become an urgent problem in the development and application of computer network. In order to strengthen the application effect of computer network management technology innovation, this paper first introduces the application of computer network management technology, and then analyses the innovative application ways of computer network management technology.
Anti-immigration disinformation

Anti-immigrant disinformation is strongly associated with the ideology of exclusion and nativist supremacy that underpin right-wing populism and far-right extremism. In many countries, high-profile political actors have normalised anti-immigrant disinformation, often in compliance with sympathetic media outlets. The infrastructure of platforms facilitates anti-immigrant disinformation in many ways. As with disinformation generally, automated bots and fake accounts are frequently used to inflate the popularity of anti-immigrant disinformation. Anti-immigrant attitudes and the appeal of anti-immigrant disinformation have been contextualised in relation to patterns of economic and social change and the decline of traditional party systems. In many respects, anti-immigrant disinformation is part of a culture war in which an ecosystem of actors (far right, alt-right, populist, and conservative) reinforces a common opposition to a pluralist worldview. Finally, the proliferation of anti-immigrant disinformation requires attention from mainstream media and politicians in terms of how immigration is discussed and reported.
The Semantic Retrieval Model and Divergent Thinking as Critical to Understanding Logical Reasoning in Children

Logical reasoning is the ability to distinguish between possible and necessary conclusions. It is a vital component of mathematical and scientific reasoning, and it is also important for navigating the complexities of social and political life. This makes understanding how logical reasoning develops critically important both for theoretical and educational contexts. In the following, we examine some of the main approaches to the development of logical reasoning. We then present a semantic memory theory and argue that the development of reasoning in children is underpinned by the form of divergent thinking. Educational implications are then considered.
Low Cost Markerless Motion Capture Systems: A Comparison Between RGB Cameras and RGB-D Sensors

Abstract In the last years, the advent of low-cost markerless motion capture systems fostered their use in several research fields, such as healthcare and sport. Any system presents benefits and drawbacks that have to be considered to design a Mocap solution providing a proper motion acquisition for a specific context. In order to evaluate low-cost technology, this research work focuses on the evaluation of the accuracy of two categories of devices: the RGB active cameras and the RGB-D, or depth sensors devices. In particular, GoPro Hero 6 active cameras and Microsoft Kinect v2 devices have been selected as representative of the two categories. In particular, this work evaluates and compares the performances of the two systems used to track the position of human articulations. The two devices have been chosen among those available on the market after a state of the art has been completed. Before starting with the campaign of acquisition, the number of sensors and their layout have been designed to optimize the acquisition with both mark-less Mocap systems. Their comparison is based on a list of specific movements of upper and lower limbs. Each movement has been acquired simultaneously, to guarantee the same test conditions. The results have been organized, compared and discussed by evaluating performances and limitations of both solutions related to specific context of use. Conclusions highlight the best candidate technology.
Data deduplication concepts

Abstract This chapter gives the history, concept, and background of various data deduplication approaches. The significance of data deduplication is shown by calling attention to data blast and an enormous measure of redundancies. We explain the issues of existing solutions regarding data redundancies removal. We present data optimization from client to server through the network. We also explore the concept of various types of storage along with their differentiation viz. redundant arrays of independent disks, direct attached storage, storage area network, and network attached storage. We categorize different deduplication approaches based on techniques identified with granularity and explained file level, block-level (fixed and variable), and content aware deduplication. In view of the deduplication location, source, and target deduplication are clarified. We have presented inline and postprocess methods of deduplication which are based on the deduplication process. We have focused on comparison of compression with data deduplication. Also we discussed about challenges in data deduplication.
JAW: Studying Client-side CSRF with Hybrid Property Graphs and Declarative Traversals

Client-side CSRF is a new type of CSRF vulnerability where the adversary can trick the client-side JavaScript program to send a forged HTTP request to a vulnerable target site by modifying the program’s input parameters. We have little to-no knowledge of this new vulnerability, and exploratory security evaluations of JavaScript-based web applications are impeded by the scarcity of reliable and scalable testing techniques. This paper presents JAW, a framework that enables the analysis of modern web applications against client-side CSRF leveraging declarative traversals on hybrid property graphs, a canonical, hybrid model for JavaScript programs. We use JAW to evaluate the prevalence of client-side CSRF vulnerabilities among all (ie, 106) web applications from the Bitnami catalog, covering over 228M lines of JavaScript code. Our approach uncovers 12,701 forgeable client-side requests affecting 87 web applications in total. For 203 forgeable requests, we successfully created client-side CSRF exploits against seven web applications that can execute arbitrary server-side state-changing operations or enable cross-site scripting and SQL injection, that are not reachable via the classical attack vectors. Finally, we analyzed the forgeable requests and identified 25 request templates, highlighting the fields that can be manipulated and the type of manipulation.
Heterogeneous Model Transfer between Different Neural Networks

We propose an effective heterogeneous model transfer (HMT) method that can transfer the knowledge from one pretrained neural network to another neural network. Most of the existing deep learning methods depend much on a pretraining-finetuning strategy, i.e., pretraining a deep model on a large task-related (source) dataset and finetuning it on a small target dataset. Pretraining provides a universal feature representation for the target learning task and thus reduces the overfitting on a small target dataset. However, it is often assumed that the pretrained model and the target model share an identical backbone, which significantly limits the scalability of pretrained deep models. This paper relaxes this limitation and generalizes to heterogeneous model transfer between two different neural networks. Specifically, we select the longest chain from the source model and transfer it to the longest chain of the target model. Motivated by one-shot neural architecture search methods, the longest chain inherits merits from the source model and also serves as a weight-sharing path of the target model, thus provides a good initialization. With the longest chains, the layer-to-layer weight transfer is then transformed by bilinear interpolation and cyclic stack. HMT opens a new window for the pretraining-finetuning strategy and significantly improves the reuse efficiency of pretrained models without re-pretraining on the large source dataset. Experiments on several datasets show the effectiveness of HMT. Anonymous code is at: https://anonymous.4open.science/r/6ab184dc-3c64-4fdd-ba6d-1e5097623dfd/
Agglomerative Clustering of Network Traffic Based on Various Approaches to Determining the Distance Matrix

We are presenting a real-time traffic flow classification model for maintaining QoS in dynamic networks such as Software Defined Networks (SDN). In previous works, we managed to achieve high accuracy (90-95%) on the database of flows known for the model using Machine Learning (Supervised Learning) methods but in a dynamic SDN new network applications and flows appear more often than usual. For detection of new flows it is proposed to use the Agglomerative clustering method, which has never been used to solve the problem of network flow classification, because early approaches to traffic clustering gave insufficient results and the speed of its operation was too low. This paper offers a combination of different Machine Learning methods in such a way that Agglomerative clustering is responsible only for updating the class database, and Supervised Learning methods are responsible for quickly classifying known flows, which solves the problem of model speed. Clustering accuracy is improved by automatically controlling the cluster construction process by determining the distances between flows using the Random Forest and Extra Trees methods. In the experimental part of the study, three more most promising ways of determining distances are given for comparison: Random Trees Embedding, Euclidean and Manhattan distance. Results of clustering of TCP and UDP applications for different number of clusters and different size of the initial sample are presented. Experimental studies confirm the effectiveness of using hierarchical clustering in traffic clustering tasks under the condition of controlled cluster construction.
Improving the denoising of WNNM-based imagery: three different strategies

Weighted nuclear norm minimization (WNNM) has produced remarkable denoising results; however, it still has some limitations, including only being able to measure the similarity of noisy patches by ...
REAL TIME VOICE CLONING

Recent progress in deep learning has shown impressive results in the area of speech-to-text. For this reason, a deep neural network is usually trained from a single speaker using a corpus of several hours of voice recorded professionally. Giving such a model a new voice is highly expensive, as it needs a new dataset to be collected and the model retrained. A recent research has developed a three-stage pipeline that allows you to clone an unseen voice from just a few seconds of reference speech during practice and without retraining the template. The researchers share strikingly natural-sounding findings. The plan is to replicate this model and open source it to the public. With a new vocoder model, the aim is to adapt the framework to make it run in real time. The aim is to develop a three-stage deep learning system that will perform real-time voice cloning. This framework is the result of Google's 2018 paper, for which only one public implementation exists before ours. The system could capture a realistic representation of the voice spoken in a digital format from a speech utterance of only 5 seconds. Because of a text prompt, it can use any voice extracted from this process to perform text-to-speech. With our own implementations or open-source ones then plan is to replicate each of the three stages of the model. The plan is to implement successful models of deep learning and appropriate pipelines for pre-processing information. The next step is training these models from several thousand speakers for weeks or months on large datasets of tens of thousands of hours of speech. Instead examine their strengths and their drawbacks. The main focus on making this system function in real time, that is, to allow a voice to be captured and speech to be generated in less time than the duration of the speech produced. The framework will be able to clone voices it has never heard during training, and to generate speech from text it has never seen.
Automated identification of vulnerable devices in networks using traffic data and deep learning

Many IoT devices are vulnerable to attacks due to flawed security designs and lacking mechanisms for firmware updates or patches to eliminate the security vulnerabilities. Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network and can be used to constrain the communications of vulnerable devices for preventing damage. In this contribution, we present and evaluate two deep learning approaches to the reliable IoT device-type identification, namely a recurrent and a convolutional network architecture. Both deep learning approaches show accuracies of 97% and 98%, respectively, and thereby outperform an up-to-date IoT device-type identification approach using hand-crafted fingerprint features obtaining an accuracy of 82%. The runtime performance for the IoT identification of both deep learning approaches outperforms the hand-crafted approach by three magnitudes. Finally, importance metrics explain the results of both deep learning approaches in terms of the utilization of the analyzed traffic data flow.
Automatic segmentation of the prostate on MR images based on anatomy and deep learning

Accurate segmentation of the prostate has many applications in the detection, diagnosis and treatment of prostate cancer. Automatic segmentation can be a challenging task because of the inhomogeneous intensity distributions on MR images. In this paper, we propose an automatic segmentation method for the prostate on MR images based on anatomy. We use the 3D U-Net guided by anatomy knowledge, including the location and shape prior knowledge of the prostate on MR images, to constrain the segmentation of the gland. The proposed method has been evaluated on the public dataset PROMISE2012. Experimental results show that the proposed method achieves a mean Dice similarity coefficient of 91.6% as compared to the manual segmentation. The experimental results indicate that the proposed method based on anatomy knowledge can achieve satisfactory segmentation performance for prostate MRI.
Digital Governance: Theory, Policy and Practice

The emergence of the information age, rising challenges in global governance and data privacy issues has led to the search for a new way to govern a nation that’s transitioning into the digital world. Blockchain technology has been emerging as a disruptive innovation in a wide range of applications, undoubtedly redesigning our perspective of business, politics and society at large. This paper aims to discuss key points of blockchain based decentralized governance and its role in sustainable economic growth of a nation. In particular, The paper will discuss current and past issues in global governance and verify to which extent digital governance can enhance scalability and interoperability in the future. The analysis expansively discusses concepts such as digital identity, central bank digital currency, digital voting system, governance oracles, scalability, interoperability, frameworks and its role in the future of governance.
Sun-path model as a simple helping tool for architecture students in understanding saving energy building design

Abstract Understanding the sun-path in designing an energy-saving building is essential. Saving energy is approached by daylight provision, which needs access for lighting the room during the day. In some latitudes, the building also needs the thermal mass to warm up the building during the cold time, requiring the sun’s heat radiation. The sun path on the regions creates various ways to design buildings. There is multiple architecture software embedding the sun-path feature. However, the software is not easily installed for free, and some other constraints become the challenges for students to learn it. Therefore, this article figures out the project-based learning in utilizing sun path in 3D models for architecture students. The methods comprise the small project designing sun path in 3D models in various latitudes supported by Andrew sun-path software. This study aims to understand the position of the sun throughout the year, which in building design is useful for saving energy in ventilation and lighting applies. The result is the students’ knowledge achievement for understanding the sun position and the site and building analysis toward the sun for obtaining the saving energy building design.
Parameter-based Value Functions

Traditional off-policy actor-critic Reinforcement Learning (RL) algorithms learn value functions of a single target policy. However, when value functions are updated to track the learned policy, they forget potentially useful information about old policies. We introduce a class of value functions called Parameter-based Value Functions (PVFs) whose inputs include the policy parameters. They can generalize across different policies. PVFs can evaluate the performance of any policy given a state, a state-action pair, or a distribution over the RL agent's initial states. First we show how PVFs yield novel off-policy policy gradient theorems. Then we derive off-policy actor-critic algorithms based on PVFs trained by Monte Carlo or Temporal Difference methods. We show how learned PVFs can zero-shot learn new policies that outperform any policy seen during training. Finally our algorithms are evaluated on a selection of discrete and continuous control tasks using shallow policies and deep neural networks. Their performance is comparable to state-of-the-art methods.
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows

We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.