Reviewerly/exHarmony huggingface dataset:
Time-aware Entity Alignment using Temporal Relational Attention

Knowledge graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration. Temporal KGs (TKGs) extend traditional Knowledge Graphs (KGs) by associating static triples with specific timestamps (e.g., temporal scopes or time points). Moreover, open-world KGs (OKGs) are dynamic with new emerging entities and timestamps. While entity alignment (EA) between KGs has drawn increasing attention from the research community, EA between TKGs and OKGs still remains unexplored. In this work, we propose a novel Temporal Relational Entity Alignment method (TREA) which is able to learn alignment-oriented TKG embeddings and represent new emerging entities. We first map entities, relations and timestamps into an embedding space, and the initial feature of each entity is represented by fusing the embeddings of its connected relations and timestamps as well as its neighboring entities. A graph neural network (GNN) is employed to capture intra-graph information and a temporal relational attention mechanism is utilized to integrate relation and time features of links between nodes. Finally, a margin-based full multi-class log-loss is used for efficient training and a sequential time regularizer is used to model unobserved timestamps. We use three well-established TKG datasets, as references for evaluating temporal and non-temporal EA methods. Experimental results show that our method outperforms the state-of-the-art EA methods.
Satellite Image Classification and Segmentation with Transfer Learning

Deep convolutional neural networks have been successfully used in image classification, image segmentation, and many other recognition tasks in computer vision. Nevertheless, building specialized models for industrial applications, is still a challenging task (as these problems often lack the rich and fully tagged databses, such as the one found in ImageNet). We used the ``Airbus Ship Detection Challenge'' as a case study to demonstrate how transfer learning can aid in building models for image classification and segmentation within an industrial setting. The training set is highly biased as there is only 22% of the satellite images with ships on it. On top of that, images are very noisy, with clouds, haze, ships near coastal lines and ports, etc.. Our proposed solution used two deep neural networks: a convolutional neural network for image classification (selecting images with ships on it) followed by a residual network for image segmentation (returning the ship locations within the image). More specifically, the image classification was done through a VGG16 network while the image segmentation was done through a U-Net (we tested both VGG16 and InceptionV3 as encoders). For this application, our results show that a) using transfer learning from models previously created for ImageNet, we were able to achieve a 30% savings in computational cost associated with training while keeping the same accuracy level, and b) both VGG16 and inception led to accurate U-Net models for image segmentation, even though there is slightly smaller computational cost when using inception.
Impact of feature selection on classification via clustering techniques in software defect prediction

Software testing using software defect prediction aims to detect as many defects as possible in software before the software release. This plays an important role in ensuring quality and reliability. Software defect prediction can be modeled as a classification problem that classifies software modules into two classes: defective and non-defective; and classification algorithms are used for this process. This study investigated the impact of feature selection methods on classification via clustering techniques for software defect prediction. Three clustering techniques were selected; Farthest First Clusterer, K-Means and Make-Density Clusterer, and three feature selection methods: Chi-Square, Clustering Variation, and Information Gain were used on software defect datasets from NASA repository. The best software defect prediction model was farthest-first using information gain feature selection method with an accuracy of 78.69%, precision value of 0.804 and recall value of 0.788. The experimental results showed that the use of clustering techniques as a classifier gave a good predictive performance and feature selection methods further enhanced their performance. This indicates that classification via clustering techniques can give competitive results against standard classification methods with the advantage of not having to train any model using labeled dataset; as it can be used on the unlabeled datasets.Keywords: Classification, Clustering, Feature Selection, Software Defect PredictionVol. 26, No 1, June, 2019
Adaptive Preference-Aware Co-Location for Improving Resource Utilization of Power Constrained Datacenters

Large-scale datacenters often host latency-sensitive services that have stringent Quality-of-Service requirement and experience diurnal load pattern. Co-locating best-effort applications that have no QoS requirement with the latency-sensitive services has been widely used to improve the resource utilization of datacenters with careful shared resource management. However, existing co-location techniques tend to result in the power overload problem on power constrained servers due to the ignorance of the power consumption. To this end, we propose Sturgeon, a runtime system proactively manages resources between co-located applications in a power constrained environment, to ensure the QoS of latency-sensitive services while maximizing the throughput of best-effort applications. Our investigation shows that, at a given load, there are multiple feasible resource configurations to meet both QoS requirement and power budget, while one of them yields the maximum throughput of best-effort applications. To find such a configuration, we establish models to accurately predict the performance and power consumption of the co-located applications. Sturgeon monitors the QoS of the services periodically, in order to eliminate the potential QoS violation caused by the unpredictable interference. Besides, when the datacenter hosts different types of applications to perform co-location, Sturgeon places applications with their preferable candidates to improve the overall throughput. The experimental results show that at server level Sturgeon improves the throughput of the best-effort application by 25.43 percent compared to the state-of-the-art technique, while guaranteeing the 95%-ile latency within the QoS target; at cluster level, Sturgeon improves the overall throughput of best-effort applications by 13.74 percent compared to the baseline.
Time Optimal Motion Planning with ZMP Stability Constraint for Timber Manipulation

This paper presents a dynamic stability-constrained optimal motion planning algorithm developed for a timber harvesting machine working on rough terrain. First, the kinematics model of the machine, and the Zero Moment Point (ZMP) stability measure is presented. Then, an approach to simplify the model to gain insight and achieve a fast solution of the optimization problem is introduced. The performance and computation time of the motion plan obtained with the simplified model is compared against that obtained with the full kinematics model of the machine with the help of MATLAB simulations. The results demonstrate feasibility of fast motion planning while satisfying the dynamic stability constraint.
Measures of Syntactic Complexity and their Change over Time (the Case of Russian)

Syntactic complexity is an important feature of any text, both written and oral. The information about syntactic complexity is crucial for successful solution of many practical NLP tasks starting from intellectual understanding of texts and ending with automatic machine translation. Because of this, syntactic complexity and its measures are in the center of attention of NLP developers. Thus far, quite a series of different measures of syntactic complexity have been developed; in this paper, it is proposed to consider 10 syntactic measures that have been proposed for syntactic stylometric analysis. The pilot experiment described in this paper was made on automatic syntactic text annotation made by UDPipe syntactic parser, which was manually corrected. In our approach, particular attention is paid to the analysis of stability of certain measures of syntactic complexity and the analysis of their variation. Thus, we try to evaluate, which syntactic properties of Russian texts may be considered as inherent for the language as a whole, and which of them undergo some changes. To achieve this task, we analyze the corpus of Russian literary texts for three decades. Due to their high stylistic variability, texts of fiction may be considered as excellent data for assessing different levels of complexity. The obtained results show the effectiveness of different measures for estimating text syntactic complexity and revealing their correlation.
A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation

Precise representation to undirected weighted network (UWN) is the foundation of understanding connection patterns inside a massive node set. It can be addressed via a Symmetric Non-negative Latent Factor (SNLF) model with a non-convex learning objective. However, existing SNLF models commonly adopt a first-order learning algorithm that cannot well handle such a non-convex objective, thereby leading to inaccurate UWN representation. Aiming at addressing this issue, this study incorporates an efficient second-order learning algorithm into an SNLF model, thereby establishing a Second-order Symmetric Non-negative Latent Factor (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NLF) model with two-fold ideas: a) applying the single latent factor-related mapping function to the non-negativity constrained optimization parameters to achieve an unconstrained learning objective, and b) optimizing this learning objective with its optimization parameters through an efficient second-order learning algorithm to achieve accurate representation to the target UWN with affordable computational burden. Empirical studies indicate that owing to its efficient incorporation of the second-order optimization technique, the proposed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NLF model outperforms state-of-the-art SNLF models when they are used to gain highly accurate representation to UWNs emerging from real applications.
VR Experience from Data Science Point of View: How to Measure Inter-subject Dependence in Visual Attention and Spatial Behavior

Any Virtual Reality (VR) immersive experience inherently allows its subjects to choose their own paths of visual attention and/or spatial behavior. If a VR designer employs any system of attentional cues, they might be interested in measuring the system’s effectiveness. Eye tracking (ET) time series data can be used as a visual attention trail and positional time series data can be used as spatial behavior trails. In this paper we are addressing the issue of measuring inter-subject dependence in visual attention and spatial behavior. We are arguing why recently developed distance correlation coefficient [1, 2] might be both a proper and convenient choice to either measure the inter-subject dependence or test for the inter-subject independence in visual and behavioral data recorded during a VR experience.
BASIC APPROACHES TO TEACHING ELEMENTS OF ARTIFICIAL INTELLIGENCE IN THE SCHOOL COURSE OF INFORMATICS

The article discusses the feasibility of teaching the elements of artificial intelligence in school course of informatics and identified a problem in determining the main approaches to such training. The purpose of this article is to reveal the possibility of forming the content of school course of informatics in the field of artificial intelligence based on system-activity, fundamental and interdisciplinary approaches. The analysis of the scientific-methodological and educational didactic literature in the field of artificial intelligence, the generalization and systematization of the experience of teaching the elements of artificial intelligence to schoolchildren made it possible to determine the goals of teaching the elements of artificial intelligence in school course of informatics; reveal the requirements for the formation of concepts in the field of artificial intelligence; highlight in course of informatics for basic school a knowledge system on the basis of which it is possible to teach elements of artificial intelligence; to identify intersubject communications, the implementation of which will allow schoolchildren to successfully master knowledge and skills in the field of artificial intelligence. The results of the study are a methodological basis for the formation of the content of training in the field of artificial intelligence and they can be used to develop sections of school course of informatics related to artificial intelligence, as well as elective courses in secondary school. The research materials may be useful to specialists in the field of informatics teaching methods and informatics teachers.
A Universal Low Complexity Compression Algorithm for Sparse Marked Graphs

Many modern applications involve accessing and processing graphical data, i.e. data that is naturally indexed by graphs. Examples come from internet graphs, social networks, genomics and proteomics, and other sources. The typically large size of such data motivates seeking efficient ways for its compression and decompression. The current compression methods are usually tailored to specific models, or do not provide theoretical guarantees. In this paper, we introduce a low-complexity lossless compression algorithm for sparse marked graphs, i.e. graphical data indexed by sparse graphs, which is capable of universally achieving the optimal compression rate in a precisely defined sense. In order to define universality, we employ the framework of local weak convergence, which allows one to make sense of a notion of stochastic processes for graphs. Moreover, we investigate the performance of our algorithm through some experimental results on both synthetic and real-world data.
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus

End-to-end (E2E) automatic speech recognition (ASR) systems lack the distinct language model (LM) component that characterizes traditional speech systems. While this simplifies the model architecture, it complicates the task of incorporating text-only data into training, which is important to the recognition of tail words that do not occur often in audio-text pairs. While shallow fusion has been proposed as a method for incorporating a pre-trained LM into an E2E model at inference time, it has not yet been explored for very large text corpora, and it has been shown to be very sensitive to hyperparameter settings in the beam search. In this work, we apply shallow fusion to incorporate a very large text corpus into a state-of-the-art E2EASR model. We explore the impact of model size and show that intelligent pruning of the training set can be more effective than increasing the parameter count. Additionally, we show that incorporating the LM in minimum word error rate (MWER) fine tuning makes shallow fusion far less dependent on optimal hyperparameter settings, reducing the difficulty of that tuning problem.
A study of Open Access research data repositories developed by BRICS countries

Purpose In the present era, research data is a concern for researchers, as they are trying to find new ways to communicate their research findings and conclusions to other researchers in order to increase visibility and credibility. BRICS nations are fast emerging economies and contribute significantly in research output. This study makes an effort to analyze and explore the role of BRICS nations towards open access research data repository registered with Registry of Research Data Repositories. Design/methodology/approach The data were gathered from re3data repository, and the search was limited to BRICS nations. The data were further analyzed and tabulated as per set parameters, namely, country-wise distribution, types of contents, subject coverage and language diversity. Findings The findings depict that in terms of strength, India has the highest number of data repositories, thereby achieved the first rank among BRICS nations, and South Africa has the least number of data repositories, whereas in terms of content type and subject coverage, India again is leading among BRICS nations. The English language is used by repositories as the main language of the interface. Practical implications The study helps to understand the development of research data repositories by BRICS nations. The study is further beneficial to researchers, as Registry of Research Data Repository provides a single platform to access repositories from various disciplines. Readily available data saves time, money and efforts of researchers and helps the researcher in completing their research activity in a very short span of time. Originality/value The paper has investigated open access data repositories of BRICS nation that has not been attempted earlier. This gives readers comprehensive overview of research data repositories developed in fast emerging economies of the global. The paper can be very helpful for information managers, OA promoters and education and research policy makers to devise plans and policy bearing in mind the evolving research channels in emerging economies.
Cascaded Diffusion Models for High Fidelity Image Generation

We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality. A cascaded diffusion model comprises a pipeline of multiple diffusion models that generate images of increasing resolution, beginning with a standard diffusion model at the lowest resolution, followed by one or more super-resolution diffusion models that successively upsample the image and add higher resolution details. We find that the sample quality of a cascading pipeline relies crucially on conditioning augmentation, our proposed method of data augmentation of the lower resolution conditioning inputs to the super-resolution models. Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256, outperforming VQ-VAE-2.
Group Contrastive Self-Supervised Learning on Graphs

We study self-supervised learning on graphs using contrastive methods. A general scheme of prior methods is to optimize two-view representations of input graphs. In many studies, a single graph-level representation is computed as one of the contrastive objectives, capturing limited characteristics of graphs. We argue that contrasting graphs in multiple subspaces enables graph encoders to capture more abundant characteristics. To this end, we propose a group contrastive learning framework in this work. Our framework embeds the given graph into multiple subspaces, of which each representation is prompted to encode specific characteristics of graphs. To learn diverse and informative representations, we develop principled objectives that enable us to capture the relations among both intra-space and inter-space representations in groups. Under the proposed framework, we further develop an attention-based group generator to compute representations that capture different substructures of a given graph. Built upon our framework, we extend two current methods into GroupCL and GroupIG, equipped with the proposed objective. Comprehensive experimental results show our framework achieves a promising boost in performance on a variety of datasets. In addition, our qualitative results show that features generated from our representor successfully capture various specific characteristics of graphs.
High‐throughput FPGA implementation for quadratic unconstrained binary optimization

Abstract Quadratic unconstrained binary optimization (QUBO) is a combinatorial optimization problem. Since various NP‐hard problems such as the traveling salesman problem can be formulated as a QUBO instance, QUBO is used with a wide range of applications. The main contribution of this article is to propose high‐throughput FPGA implementations for the QUBO solver. We perform the local search using different bit‐selection strategies based on the simulated annealing in the proposed implementation. The hardware is a pipeline structure with no pipeline hazards using multiple instances, where the bit‐flip operation is always performed every clock cycle. We implemented the proposed circuit on Xilinx UltraScale+ FPGA VU9P. The implementation result shows that the circuit can search solutions per second. Besides, by sharing the block RAM that stores a weight matrix, we implemented a dual annealer architecture that has two QUBO solvers into the FPGA. As a result, the dual annealer architecture can search solutions per second.
Blockchain based security framework for sharing digital images using reversible data hiding and encryption

Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain based security framework is proposed for sharing digital images in a multi user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.
Quantum algorithms for approximate function loading

Loading classical data into quantum computers represents an essential stage in many relevant quantum algorithms, especially in the field of quantum machine learning. Therefore, the inefficiency of this loading process means a major bottleneck for the application of these algorithms. Here, we introduce two approximate quantum-state preparation methods for the NISQ era inspired by the Grover-Rudolph algorithm, which partially solve the problem of loading real functions. Indeed, by allowing for an infidelity $\epsilon$ and under certain smoothness conditions, we prove that the complexity of the implementation of the Grover-Rudolph algorithm without ancillary qubits, first introduced by M\"ott\"onen $\textit{et al}$, results into $\mathcal{O}(2^{k_0(\epsilon)})$, with $n$ the number of qubits and $k_0(\epsilon)$ asymptotically independent of $n$. This leads to a dramatic reduction in the number of required two-qubit gates. Aroused by this result, we also propose a variational algorithm capable of loading functions beyond the aforementioned smoothness conditions. Our variational Ansatz is explicitly tailored to the landscape of the function, leading to a quasi-optimized number of hyperparameters. This allows us to achieve high fidelity in the loaded state with high speed convergence for the studied examples.