Reviewerly/exHarmony huggingface dataset:
Convolutional Adaptive Network for Link Prediction in Knowledge Bases

Knowledge bases (KBs) have become an integral element in digitalization strategies for intelligent engineering and manufacturing. Existing KBs consist of entities and relations and deal with issues of newly added knowledge and completeness. To predict missing information, we introduce an expressive multi-layer network link prediction framework—namely, the convolutional adaptive network (CANet)—which facilitates adaptive feature recalibration by networks to improve the method’s representational power. In CANet, each entity and relation is encoded into a low-dimensional continuous embedding space, and an interaction operation is adopted to generate multiple specific embeddings. These embeddings are concatenated into input matrices, and an attention mechanism is integrated into the convolutional operation. Finally, we use a score function to measure the likelihood of candidate information and a cross-entropy loss function to speed up computation by reducing the convolution operations. Using five real-world KBs, the experimental results indicate that the proposed method achieves state-of-the-art performance.
INFORMATION &amp; COMMUNICATION TECHNOLOGIES (ICT) AND ECONOMIC DEVELOPMENT OF THE AZERBAIJAN REPUBLIC

Nowadays, the Republic of Azerbaijan is one of the most developed countries in the Caucasus region. The country’s development is associated with hydrocarbon production; but in recent years, the country’s authorities have taken a course towards the IT sector development. This is an extremely interesting phenomenon, especially considering the low base's effect and the high growth rate of the main indicators which characterize a digital society. Within the article, the authors identify the relationship between the indicators of the digital society development and IT and the growth rate of the country’s economy and then the authors characterize the trends which are characteristic for Azerbaijan’s IT sector. The major challenge of the article is the preparation of the statistics for research, as they are scarce and segmented. There are two main elements of the research novelty: first, the methodology proposed by the authors for assessing the investments' effectiveness in IT by comparing them with the ITU index and, second, the identifying the shortcomings of the IT development system with the use of the digitalization efficiency analysis (from the below). The major contribution of the article is the development of the econometric methods for the estimation of the ICT investments’ effectiveness on the example of the Azerbaijan economy and the proof of the high importance of the sphere for the future of the country economy.
Unveiling the I2P web structure: A connectivity analysis

Web is a primary and essential service to share information among users and organizations at present all over the world. Despite the current significance of such a kind of traffic on the Internet, the so-called Surface Web traffic has been estimated in just about 5% of the total. The rest of the volume of this type of traffic corresponds to the portion of the Web known as Deep Web. These contents are not accessible by usual search engines because they are authentication protected contents or pages only reachable through technologies denoted as darknets. To browse through darknet websites, special authorization or specific software and configurations are needed. TOR is one of the most used darknet nowadays, but there are several other alternatives such as I2P or Freenet, which offer different features for end users. In this work, we perform a connectivity analysis of the websites in the I2P network (named eepsites) aimed to discover if different patterns and relationships from those used in legacy webs are followed in I2P, as well as to get insights about the dimension and structure of this darknet. For that, a novel tool is specifically developed by the authors and deployed on a distributed scenario. Main results conclude the decentralized nature of the I2P network, where there is a structural part of interconnected eepsites while other several nodes are isolated probably due to their intermittent presence in the network.
Audit Analysis of Abnormal Behavior of Social Security Fund Based on Adaptive Spectral Clustering Algorithm

Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is still in the early stage of development. Many existing algorithms are very sensitive to clustering parameters, especially scale parameters, and need to manually input the number of clustering. Therefore, a density-sensitive similarity measure is introduced in this paper, which is obtained by introducing new parameters to transform the Gaussian function. Under this metric, the distance between data points belonging to different classes will be effectively amplified, while the distance between data points belonging to the same class will be reduced, and finally, the distribution of data will be effectively clustered. At the same time, the idea of Eigen gap is introduced into the spectral clustering algorithm, and the verified gap sequence is constructed on the basis of Laplace matrix, so as to solve the problem of the number of initial clustering. The strong global search ability of artificial bee colony algorithm is used to make up for the shortcoming of spectral clustering algorithm that is easy to fall into local optimal. The experimental results show that the adaptive spectral clustering algorithm can better identify the initial clustering center, perform more effective clustering, and detect abnormal behavior more accurately.
Black or White but Never Neutral: How Readers Perceive Identity from Yellow or Skin-toned Emoji

Research in sociology and linguistics shows that people use language not only to express their own identity but to understand the identity of others. Recent work established a connection between expression of identity and emoji usage on social media, through use of emoji skin tone modifiers. Motivated by that finding, this work asks if, as with language, readers are sensitive to such acts of self-expression and use them to understand the identity of authors. In behavioral experiments (n=488), where text and emoji content of social media posts were carefully controlled before being presented to participants, we find in the affirmative - emoji are a salient signal of author identity. That signal is distinct from, and complementary to, the one encoded in language. Participant groups (based on self-identified ethnicity) showed no differences in how they perceive this signal, except in the case of the default yellow emoji. While both groups associate this with a White identity, the effect was stronger in White participants. Our finding that emoji can index social variables will have experimental applications for researchers but also implications for designers: supposedly neutral defaults may be more representative of some users than others.
Securing audio transmission based on encoding and steganography

One of the things that must be considered when establishing a data exchange connection is to make that communication confidential and hide the file’s features when the snoopers intercept it. In this work, transformation (encoding) and steganography techniques are invested to produce an efficient system to secure communication for an audio signal by producing an efficient method to transform the signal into a red–green–blue (RGB) image. Subsequently, this image is hidden in a cover audio file by using the least significant bit (LSB) method in the spatial and transform domains using discrete wavelet transform. The audio files of the message and the cover are in *.wav format. The experimental results showed the success of the transformation in concealing audio secret messages, as well the remarkability of the stego signal quality in both techniques. A peak signal-to-noise ratio peak signal-to-noise ratio (PSNR) scored (20-26) dB with wavelet and (81-112) dB with LSB for cover file size 4.96 MB and structural similarity index metric structural similarity index metric (SSIM) has been used to measure the signal quality which gave 1 with LSB while wavelet was (0.9-1), which is satisfactory in all experimented signals with low time consumption. This work also used these metrics to compare the implementation of LSB and WAV.
COVIZ: Visualization of Effects of COVID-19 on New York City Through Socially Impactful Virtual Reality

This work is the product of a collaboration between students studying computer science and social work to visualize the impacts and effects of COVID-19 in New York City in a virtual environment (VE). As a proof of concept, the team chose two datasets from NYC Open Data; COVID-19 infection cases and rates per zip code and vehicular traffic rates within the five boroughs of New York City. To foster unexplored insights into the relationship between these data, we developed a virtual reality application that provides a stronger sense of embodiment and ownership of urban visualization analysis when manipulating 3D virtual maps for comparison in a VE.
Color Correction for Immersive Video Applications

In this paper, the color correction method developed for immersive video systems is presented.The proposed method significantly increases the consistency of color characteristics of multiview sequences, understood both as the temporal and the inter-view consistency, what highly improves the subjective quality of the synthesized virtual views presented to the final user of the immersive video system.Moreover, the proposal allows to significantly increase the quality of the depth maps calculated for natural sequences, e.g., for views with colors inconsistent due to different lighting conditions.It enables more efficient compression of natural multiview video, as the newest encoding standards highly depend on the quality of depth maps.In order to evaluate the performance of the proposal, three experiments were conducted.In the first one, the proposal was compared to state of the art in the typical immersive video applicationcolor correction of a natural multiview sequence.In the second experiment, the performance of the proposal was tested on the Middlebury stereo dataset.In both experiments, the quality of synthesized virtual views was assessed subjectively by a group of 70 naïve viewers.The third experiment assessed the influence of color correction on the quality of estimated depth maps.All the experiments showed that the proposal significantly increases the color consistency of the multiview content.Due to the high usefulness and robustness, the proposed color correction method became the MPEG reference software for color correction.The implementation of the method is available for other researchers on the public repository.
The Space Weather Modeling Framework Goes Open Access

A versatile suite of computational models, already used to forecast magnetic storms and potential power grid and telecommunications disruptions, is preparing to welcome a larger group of users.
Virtual Community Heritage – An Immersive Approach to Community Heritage

&#x0D; Our relationship with cultural heritage has been transformed by digital technologies. Opportunities have emerged to preserve and access cultural heritage material while engaging an audience at both regional and global level. Accessibility of technology has enabled audiences to participate in digital heritage curation process. Participatory practices and co-production methodologies have created new relationships between museums and communities, as they are engaged to become active participants in the co-design and co-creation of heritage material. Audiences are more interested in experiences vs services nowadays and museums and heritage organisations have potential to entertain while providing engaging experiences beyond their physical walls. Mixed reality is an emerging method of engagement that has allowed enhanced interaction beyond traditional 3D visualisation models into fully immersive worlds. There is potential to transport audiences to past worlds that enhance their experience and understanding of cultural heritage.
Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations

Landslide is a natural disaster that occurs mostly in hill areas. Landslide hazard mapping is used to classify the prone areas to mitigate the risk of landslide hazards. This paper aims to compare spatial landslide prediction performance using an artificial neural network (ANN) model based on different data input configurations, different numbers of hidden neurons, and two types of normalization techniques on the data set of Penang Island, Malaysia. The data set involves twelve landslide influencing factors in which five factors are in continuous values, while the remaining seven are in categorical/discrete values. These factors are considered in three different configurations, i.e., original (OR), frequency ratio (FR), and mixed-type (MT) data, which act as an input to train the ANN model separately. A significant effect on the final output is the number of hidden neurons in the hidden layer. In addition, three data configurations are processed using two different normalization methods, i.e., mean-standard deviation (Mean-SD) and Min-Max. The landslide causative data often consist of correlated information caused by overlapping of input instances. Therefore, the principal component analysis (PCA) technique is used to eliminate the correlated information. The area under the receiver of characteristics (ROC) curve, i.e., AUC is also applied to verify the produced landslide hazard maps. The best result of AUC for both Mean-SD and Min-Max with PCA schemes are 96.72% and 96.38%, respectively. The results show that Mean-SD with PCA of MT data configuration yields the best validation accuracy, AUC, and lowest AIC at 100 number of hidden neurons. MT data configuration with the Mean-SD normalization and PCA scheme is more robust and stable in the MLP model's training for landslide prediction. &#x0D; Keywords: Landslide; ANN; Hidden Neurons; Normalization; PCA; ROC; Hazard map&#x0D; &#x0D; Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember&#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
Playing Games with Tito

This article brings together two distinct, but related perspectives on playful museum experiences: Critical play and hybrid design. The article explores the challenges involved in combining these two perspectives, through the design of two hybrid museum experiences that aimed to facilitate critical play with/in the collections of the Museum of Yugoslavia and the highly contested heritage they represent. Based on reflections from the design process as well as feedback from test users, we describe a series of challenges: Challenging the norms of visitor behaviour, challenging the role of the artefact, and challenging the curatorial authority. In conclusion, we outline some possible design strategies to address these challenges.
Predicting Hourly Bitcoin Prices Based on Long Short-Term Memory Neural Networks

Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52%.
Interpretable predictive maintenance for hard drives

Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high predictive power yet cannot be understood by humans. This limits the ability of humans to use these models to derive insights and understanding of the underlying failure mechanisms, and also limits the degree of confidence that can be placed in such a system to perform well on future data. We consider the task of predicting hard drive failure in a data center using recent algorithms for interpretable machine learning. We demonstrate that these methods provide meaningful insights about short- and long-term drive health, while also maintaining high predictive performance. We also show that these analyses still deliver useful insights even when limited historical data is available, enabling their use in situations where data collection has only recently begun.
A Knowledge Extraction Pipeline between Supervised and Unsupervised Machine Learning Using Gaussian Mixture Models for Anomaly Detection

This paper presents a new approach to design a decision support model with suitability across various contexts, and in particular for the Internet of Things. It provides an anomaly detection-learning model that is adapted to the patient
Measuring inconsistency in some branching time logics

Branching time logics have been studied in computer science since the 1980s primarily to model the tree of computations for discrete transition systems. Inconsistency measures for propositional logic have been studied since the early 2000s by AI researchers. This paper introduces inconsistency measures for three branching time logics: ABTL, BBTL, and CBTL. In order to measure inconsistency properly in branching time logics, the semantics differs from the standard semantics using the construction of a canonical tree. ABTL extends propositional logic by three pairs of unary operators: next time, path, and eventually. BBTL adds a pair of binary Until operators that can be applied only to propositional logic formulas. CBTL adds the propositional connectives to BBTL formulas. Propositional logic inconsistency measures are extended to these logics and examples of the computation are given. These measures are shown to satisfy intuitively the concepts involved in the branching time logic operators unlike direct extensions of propositional logic inconsistency measures.
A novel algorithm for extracting frequent gradual patterns

The extraction of frequent gradual pattern is an important problem in computer science and largely studied by the scientist’s community of research in data mining. A frequent gradual pattern translates a recurrent co-variation between the attributes of a database. Many applications issues from many domains, such as economy, health, education, market, bio-informatics, astronomy or web mining, are based on the extraction of frequent gradual patterns. Algorithms to extract frequent gradual patterns in the large databases are greedy in CPU time and memory space. This raises the problem of improving the performances of these algorithms. This paper presents a technique for improving the performance of frequent gradual pattern extraction algorithms. The exploitation of this technique leads to a new, more efficient algorithm called SGrite. The experiments carried out confirm the interest of the proposed technique.
Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics

Yang (2020a) recently showed that the Neural Tangent Kernel (NTK) at initialization has an infinite-width limit for a large class of architectures including modern staples such as ResNet and Transformers. However, their analysis does not apply to training. Here, we show the same neural networks (in the so-called NTK parametrization) during training follow a kernel gradient descent dynamics in function space, where the kernel is the infinite-width NTK. This completes the proof of the *architectural universality* of NTK behavior. To achieve this result, we apply the Tensor Programs technique: Write the entire SGD dynamics inside a Tensor Program and analyze it via the Master Theorem. To facilitate this proof, we develop a graphical notation for Tensor Programs.
Parallelizing Legendre Memory Unit Training

Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. Here we leverage the linear time-invariant (LTI) memory component of the LMU to construct a simplified variant that can be parallelized during training (and yet executed as an RNN during inference), thus overcoming a well known limitation of training RNNs on GPUs. We show that this reformulation that aids parallelizing, which can be applied generally to any deep network whose recurrent components are linear, makes training up to 200 times faster. Second, to validate its utility, we compare its performance against the original LMU and a variety of published LSTM and transformer networks on seven benchmarks, ranging from psMNIST to sentiment analysis to machine translation. We demonstrate that our models exhibit superior performance on all datasets, often using fewer parameters. For instance, our LMU sets a new state-of-the-art result on psMNIST, and uses half the parameters while outperforming DistilBERT and LSTM models on IMDB sentiment analysis.
Intelligent Maintenance of Shield Tunelling Machine based on Knowledge Graph

Shield tunnelling machine is a giant engineering equipment working deep under the ground, whose maintenance is significant in ensuring the continually operation of the machine. However, the traditional regular maintenance by engineers takes long time and plenty of people. In this case, a more intelligent maintenance method is required. To fill this gap, this paper proposes an intelligent maintenance method based on knowledge graph, which captures and reuse the knowledge generated during maintenance process in order to intelligently recommend solutions for maintenance tasks. This method includes three stages, creating knowledge representation model, building knowledge graph, developing collaborative knowledge management system for implementation. A case study on a specific shield tunnelling machine is demonstrated in this paper, with results showing the feasibility and effectiveness of this method.
SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting

Recent advancements in sequencing methods have led to significant increase in sequencing data. Increase in sequencing data leads to research challenges such as storage, transfer, processing, etc. data compression techniques have been opted to cope with the storage of these data. There have been good achievements in compression ratio and execution time. This fast-paced advancement has raised major concerns about the security of data. Confidentiality, integrity, authenticity of data needs to be ensured. This paper presents a novel lossless reference-free algorithm that focuses on data compression along with encryption to achieve security in addition to other parameters. The proposed algorithm uses preprocessing of data before applying general-purpose compression library. Genetic algorithm is used to encrypt the data. The technique is validated with experimental results on benchmark datasets. Comparative analysis with state-of-the-art techniques is presented. The results show that the proposed method achieves better results in comparison to existing methods.