Reviewerly/exHarmony huggingface dataset:
Structural Graph Representations based on Multiscale Local Network Topologies

In many applications, it is required to analyze a graph merely based on its topology. In these cases, nodes can only be distinguished based on their structural neighborhoods and it is common that nodes having the same functionality or role yield similar neighborhood structures. In this work, we investigate two problems: (1) how to create structural node embeddings which describe a node’s role and (2) how important the nodes’ roles are for characterizing entire graphs. To describe the role of a node, we explore the structure within the local neighborhood (or multiple local neighborhoods of various extents) of the node in the vertex domain, compute the visiting probability distribution of nodes in the local neighborhoods and summarize each distribution to a single number by computing its entropy. Furthermore, we argue that the roles of nodes are important to characterize the entire graph. Therefore, we propose to aggregate the role representations to describe whole graphs for graph classification tasks. Our experiments show that our new role descriptors outperform state-of-the-art structural node representations that are usually more expensive to compute. Additionally, we achieve promising results compared to advanced state-of-the-art approaches for graph classification on various benchmark datasets, often outperforming these approaches.
Table-to-Text Generation via Row-Aware Hierarchical Encoder

In this paper, we present a neural model to map structured table into document-scale descriptive texts. Most existing neural network based approaches encode a table record-by-record and generate long summaries by attentional encoder-decoder model, which leads to two problems. (1) portions of the generated texts are incoherent due to the mismatch between the row and corresponding records. (2) a lot of irrelevant information is described in the generated texts due to the incorrect selection of the redundant records. Our approach addresses both problems by modeling the row representation as an intermediate structure of the table. In the encoding phase, we first learn record-level representation via transformer encoder. Afterwards, we obtain each row's representation according to their corresponding records' representation and model row-level dependency via another transformer encoder. In the decoding phase, we first attend to row-level representation to find important rows. Then, we attend to specific records to generate texts. Experiments were conducted on ROTOWIRE, a dataset which aims at producing a document-scale NBA game summary given structured table of game statistics. Our approach improves a strong baseline's BLEU score from 14.19 to 15.65 (+10.29%). Furthermore, three extractive evaluation metrics and human evaluation also show that our model has the ability to select salient records and the generated game summary is more accurate.
Mitigating Byzantine Failures in Multi-agent based Dependable and Adaptable Avionics Software

With increase in air traffic year on year and recent advancements and proliferation of unmanned aircraft systems, there is an increased drive and pressure to provide efficient and collaborative systems that leverage actionable information available on these ariel platforms. Adaptive systems forming the backbone of such platforms and adaptivity engineered through intelligent software agents is an attractive proposition as these ariel platforms operate in increasingly connected environments along with a number of other onboard systems collaboratively. This environment poses risks and a new set of problems wherein the adaptive system engineered with agents could fail, since some of these agents could be malicious. Byzantine faults form an important class of problems, contributing to these failures. Since the domain of avionics encompasses mission, safety, and flight critical systems, we provide one scheme that forms the basic block providing fault tolerance for byzantine failures caused by a single entity. A second scheme uses concepts from the signal-processing domain to over-come byzantine failures that involve multiple masquerading agents. We discuss the incorporation of these schemes in an exemplar adaptive flight planning system and provide reasoning for their correctness.
A Novel Approach to Compression of Satellite Images Using Butterworth Filtering

Compression of images is an important application in the field of satellite image processing as it is suitable for optimization of storage space and sharing over the Internet with optimum bandwidth. In our present communication, a new method for compression of satellite images using Butterworth low-pass filtering has been proposed. Initially, we have transformed the selected satellite image into frequency domain. By varying the cutoff frequency, satellite image has been filtered out. We have used three different cutoff frequencies to extract image from its spectrum. However, to get a reasonably good output, information loss of images should be minimized during compression. To measure the quality of the output images, PSNR values have been calculated in each case.
Use of Expert System in Requirements Engineering Process A Systematic Literature Review

Requirements Engineering (RE) process deals with elicitation, analysis, negotiation, validation, management, and documentation of requirements. Several artificial intelligence (AI) based approaches have been proposed to automate RE activities. However, the requirements engineering community still lacks a comprehensive understanding on how expert system are used in RE process. The objectives of this study are (1) to explore the different AI approaches which are employed in requirements engineering, (2) to identify the main phases addressed by these approaches, (3) to identify AI tools that used in RE process, and (4) we tried to find the contributions and the benefits of applying expert system in RE. We found that expert system can partially facilitate the RE process, however no expert system can fully automate the RE process.
Facial Makeup Detection using the CMYK Color Model and Convolutional Neural Networks

This work presents a facial makeup detection technique using CMYK and Neural Networks. The main goal is to detect facial makeup using the CMYK color model, and analyzing its results by comparing it to the HSV color model, which is widely used in the literature. In the detection process, each image was separated into regions of interest (the eyes and the whole face). Five image databases were chosen, all varying in lighting and environment conditions. In HSV, 91% of accuracy was achieved on the eye region and 92% on the face. In CMYK, the results obtained had 97% of accuracy on the eye region and 95% on the face. Therefore, based on the results achieved, the CMYK color model, even though it is mainly used in Printing, deserves attention in the area of Computer Vision, involving Makeup Detection.
Unbalanced data sentiment classification method based on ensemble learning

Sentiment classification is a hot research direction at present, but most research is based on balanced data sets. In real life, the sample is impossible to balance. For sentiment analysis of unbalanced data, we not only need to pay attention to the overall classification performance, but also need to care about the classification performance of a few classes. How to improve the recognition rate of a few types of samples while improving the overall recognition rate has become a research hotspot. Aiming at this problem, this paper proposes a model based on ensemble learning, extracts features by TF-IDF+SVD, and integrates five base classifiers by stacking to sentiment classification. The experimental results show that it can be more effective in emotional classification on unbalanced data sets than other methods.
Smart RogAgent: Where Agents and Humans Team Up

The increasing diffusion of team building as a means to enhance social relations and define roles within teams, and the cost of setting up a real team building event, raises the pressing need of simulating team building activities in order to assess the effectiveness of different formats, run in different contexts and under different conditions, before one is selected for a particular scenario. The selection of a software platform employing software abstractions which are close to the real entities involved in a team building event, including human beings and their roles, goals, and organisations, paves the way to substituting some simulated entity with its real counterpart, moving from simulation to a hybrid application where real entities and their software "alter egos" can co-exist. We present the design of Smart RogAgent, a JaCaMo multi-agent system aimed at simulating rogaining, a special kind of team building activity. Once fully developed, Smart RogAgent will have the potential to allow artificial intelligent agents to enter human teams, and vice versa, providing the technological support for the creation of hybrid human-agent teams in a principled way.
Part of Speech Tagging for Indonesian Language using Bidirectional Long Short-Term Memory

Part of Speech (POS) is a label to distinguish a word based on its grammatical and morphological form. By providing the POS label, we can get the contextual meaning. This label can be used as contextual features for several computational linguistic research - for example, word sense disambiguation, chunking, machine translation, and sequence classification. Our work is done by using bidirectional long short-term memory to do the part of speech tagging task for Bahasa Indonesia. We use deep learning model for Indonesia language POS tagging because deep learning can achieve excellent performance on it. We could reach 96.92% of F1 Score based on our approach.
A Framework for Detecting Key Topics in Social Networks

Finding the key topics in a large amount of short texts in social networks is a hot research point in data mining. There are a lot of models and algorithms to solve this problem, but they are not designed for social networks where many new words and nonstandard writings, grammars exist. It's more difficult to detect key topics in social networks because of these characteristics. In this paper, we propose a framework for detecting key topics in social networks. First, we get the posts in social networks using a focused crawler. Then we introduce the Word Segment Merging (WSM) method to identify new phrases in short texts and represent a document with the vector space model (VSM). At last, we model the life cycle of topics for clustering and popularity computing. Experiments on three datasets of SINA Weibo show that our method is better than existing state-of-arts models.
Analysis Of Twitter Sentiment Using The Classification Of Naive Bayes Method About Television In Indonesia

Television is one of the most widely used electronic mass media in Indonesia. This is because it is easy and inexpensive to be able to watch television broadcasts. Accompanied by the rapid development of the television industry from day to day, causing each television station to compete to present various kinds of shows. Twitter as a micro blog-based social media, is also used by the television industry for publication media, besides that it is also a mouthpiece for criticism and public advice on the programs being aired. Indonesian society with a large number is directly proportional to the number of tweets about television topics in Indonesia. This can be a difficult problem if the assessment data in the form of tweets from the community is still processed manually to produce information regarding the evaluation of television shows. To that end, the authors propose to analyze the data of tweets using an application with a naive Bayes algorithm to be able to produce information on public sentiment assessment regarding television shows. This application is designed using the Python language and PHP. The accuracy value obtained in this study is 91.67%.
Make the Best of Face Clues in iQIYI Celebrity VideoIdentification Challenge 2019

iQIYI-VID-2019 is the largest video dataset for multi-modal person identification. It is composed of more than 200k video clips of 10,034 celebrities. Face is a critical clue for person identification when the face is visible in video. However face quality in a video may not always be good, and it also contains a lot of noise caused by detection and feature extraction. Meanwhile, conventional multi-modal person classification methods do not fully exploit the ability of face modality. They do not make full use of face detection confidence and quality evaluation indicators, which are key information in face modality. To address these issues, we develop a quality-based video face feature fusion method in inference with a quality-based face feature denoising and augmentation method in training. Our approach is only based on 512-dimensional face features provided by iQIYI-VID-2019 dataset. Utilizing our proposed novel method, we have achieved the mAP score of 89.83% which is the 4th place in iQIYI Celebrity Video Identification Challenge 2019.
Integrating Taylor–Krill herd‐based SVM to fuzzy‐based adaptive filter for medical image denoising

Medical imaging systems contribute much towards effective decision-making by the physicians, which is highly essential in the day-to-day life of humans. In this study, Taylor–Krill herd (KH)-based support vector machine (SVM) is proposed for medical image denoising. The Taylor–KH-based SVM is the integration of Taylor series in KH optimisation algorithm, which is used for tuning the optimal weights of the SVM classifier. The efficiency of KH is due to two global and two local optimisers, and the adaptive operators ensure the adaptive nature of KH. Above all, KH never uses the derivative information as it employs the stochastic search and thereby, reduces the complexity of the algorithm. The proposed method tunes the hyperplane parameters of SVM optimally so that the optimal identification of the noisy pixels in the image is ensured and replaced with adaptive weights. The proposed method is analysed based on the metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and the comparative analysis is done with existing methods for showing the effectiveness of the proposed method. The simulation result shows that the proposed method acquired a PSNR of 30.36 dB and SSIM of 0.89, respectively.
Faster privacy‐preserving location proximity schemes for circles and polygons

In the last decade, location information became easily obtainable using off-the-shelf mobile devices. This gave momentum to developing location-based services (LBSs) such as location proximity detection, which can be used to find friends or taxis nearby. LBSs can, however, be easily misused to track users, which draws attention to the need for protecting the privacy of these users. In this work, the authors address this issue by designing, implementing, and evaluating multiple algorithms for privacy-preserving location proximity (PPLP) that are based on different secure computation protocols. Their PPLP protocols support both circle and polygon range queries and have runtimes from a few to some hundreds of milliseconds and bandwidth requirements from a few hundreds of bytes to one megabyte. Consequently, they are well suited for different scenarios and offer faster runtimes and savings in bandwidth and computational power as well as security improvements compared to previous PPLP schemes. In addition, the computationally most expensive parts of the PPLP computation can be precomputed in their protocols, such that the input-dependent online phase runs in just a few milliseconds.
On Neural Networks Modeling Based on GA, PSO and GW Optimization Techniques

Training artificial neural networks (ANNs) using global optimization techniques is becoming an attractive area of research. In this paper, combined modeling techniques of ANNs with genetic algorithm (ANN-GA), particles warm optimization (ANN-PSO) and grey wolf optimization (ANN-GWO) will be presented. The performance of these three techniques will be investigated in terms of efficiency and effectiveness for solving practical modeling problem. It has been found the recently developed ANN-GWO has a comparable performance with respect to the other techniques. ANN-PSO showed higher rate of convergence, which makes it more practical for real-time applications.
Monocular Depth Estimation as Regression of Classification using Piled Residual Networks

Predicting depth from single monocular image is a challenging task in scene understanding. Most existing work predicts depth by regression or classification with features extracted from local neighborhood area. However, neither regression nor classification achieves the final satisfying solution and local context can be insufficient to predict the depth. This paper innovatively addresses this problem as regression of class related features on a piled residual convolutional neural network. Our framework works at two stages. First, a well-designed deep convolutional neural network model is employed to classify the depths in difference-scale invariance space. The model utilizes all scales of context though piled residual paths. The deeper layers that capture high-level semantic features with long-range context can be directly refined using fine-grained features with local context from earlier convolutions. We then apply centered information gain loss to the model to produce intra-class compact and inter-class discriminative features. Second, to obtain depths instead of class labels, we infer depth regression with convolutional layers which model the mapping from class discriminative features to continuous depth values. Experiments on the popular indoor and outdoor datasets show competitive results compared with the recent state of the art methods.
RDF Multi-query Optimization Algorithm for Query Rewriting Using Common Subgraphs

In the current trend of rapid development of big data, there are frequent application scenarios of high concurrent query processing on RDF data sets. The multi-query optimization scheme for solving concurrent queries needs to provide a global approximate optimal solution for the query set composed of a set of queries, so as to minimize the overall time cost of the query set. Under the premise of accelerating statistics by RDF storage index and narrowing the scope of semantic pruning, firstly, simplified multiple queries are converted into connection graphs, and then all queries are clustered and grouped. In each group, all common subgraphs of connection graphs are iteratively searched and mapping tables are established. Then, the common subgraph is arranged in descending order by the number of vertices to construct the query rewriting scheme. Finally, for all the rewritten queries, the dynamic programming algorithm based on selection rate estimation is used for secondary optimization. On the one hand, the common subgraph is used to rewrite the query to reduce the number of queries, so as to reduce the cost through the reusable common result set. On the other hand, because of the establishment of RDF storage index, the selection rate can be estimated quickly, and the rewritten queries can be optimized again to improve the overall query efficiency. The experimental results show that the proposed algorithm has better query performance than the existing query schemes, especially when the RDF dataset is large, the number of queries in the query set is large, and the query statements are complex, the multi-query optimization method in this paper works better.
WIP - SKOD: A Framework for Situational Knowledge on Demand

Extracting relevant patterns from heterogeneous data streams poses significant computational and analytical challenges. Further, identifying such patterns and pushing analogous content to interested parties according to mission needs in real-time is a difficult problem. This paper presents the design of SKOD, a novel Situational Knowledge Query Engine that continuously builds a multi-modal relational knowledge base using SQL queries; SKOD pushes dynamic content to relevant users through triggers based on modeling of users’ interests. SKOD is a scalable, real-time, on-demand situational knowledge extraction and dissemination framework that processes streams of multi-modal data utilizing publish/subscribe stream engines. The initial prototype of SKOD uses deep neural networks and natural language processing techniques to extract and model relevant objects from video streams and topics, entities and events from unstructured text resources such as Twitter and news articles. Through its extensible architecture, SKOD aims to provide a high-performance, generic framework for situational knowledge on demand, supporting effective information retrieval for evolving missions.
Weakly Supervised Learning of Dense Semantic Correspondences and Segmentation

Finding semantic correspondences is a challenging problem. With the breakthrough of CNNs stronger features are available for tasks like classification but not specifically for the requirements of semantic matching. In the following we present a weakly supervised learning approach which generates stronger features by encoding far more context than previous methods. First, we generate more suitable training data using a geometrically informed correspondence mining method which is less prone to spurious matches and requires only image category labels as supervision. Second, we introduce a new convolutional layer which is a learned mixture of differently strided convolutions and allows the network to encode much more context while preserving matching accuracy at the same time. The strong geometric encoding on the feature side enables us to learn a semantic flow network, which generates more natural deformations than parametric transformation based models and is able to predict foreground regions at the same time. Our semantic flow network outperforms current state-of-the-art on several semantic matching benchmarks and the learned features show astonishing performance regarding simple nearest neighbor matching.
Our Experience in Using Performance Support Learning in “Thermal Management of Electronic Equipment” Course

This article describes our experience in using performance-support learning to acquire new knowledge and competences by future and current engineers for heat transfer modeling in electronic components and systems. Within the framework of the project "Engineering Education and Science for the Benefit of Business in the Design of Heat Transfer in Electronic Equipment", funded by Plovdiv University in 2019, we developed together with the business a curriculum, a performance-support e-learning course and multimedia resources for thermal management of electronic systems.
Imprecise Risk Assessment of Distribution Network Based on Evidence and Utility Theory

This paper introduces a novel imprecise probabilistic risk assessment method to evaluate distribution network failure probability, which is equivalent to research the opposite side of reliability. First, this paper proposes to analyze the interval probabilistic model of distribution network operation reliability using Evidence theory and Bayesian theory. Then the authors implement a novel algorithm for risk assessment combined with fault severity evaluation mode based on the utility theory. Finally, taking a simplified equivalent distribution network system as study case, the intervals results of imprecise risk assessment show that the proposed risk assessment method can effectively evaluate the quantification grading index of risk under different components combination failure within distribution network.