Reviewerly/exHarmony huggingface dataset:
Fault Diagnosis of Electrical Power Systems with Hybrid Quantum-Classical Deep Learning

In this work we develop hybrid QC-based deep learning models for fault diagnosis of electrical power systems that effectively extract suitable features from time-series data using Conditional restricted Boltzmann machine (CRBM) based network. Generatively training the CRBM network using classical learning techniques can be computationally intensive. Therefore, we train the CRBM network with a learning algorithm assisted by a quantum computer that yields better quality of optimal model parameters of the CRBM network. To demonstrate the applicability of the proposed hybrid QC-based deep learning model, we use a standard case study based on IEEE 14-bus that is commonly used to test concepts and methodologies in power systems. The obtained fault diagnosis results show that the proposed hybrid QC-CRBM fault diagnosis model clearly outperforms state-of-the-art classical fault diagnosis models with low missed detection rates and false alarm rates.
Unsupervised, Supervised and Semi‐supervised Dimensionality Reduction by Low‐Rank Regression Analysis

Techniques for dimensionality reduction have attracted much attention in computer vision and pattern recognition. However, for the supervised or unsupervised case, the methods combining regression analysis and spectral graph analysis do not consider the global structure of the subspace; For semi-supervised case, how to use the unlabeled samples more effectively is still an open problem. In this paper, we propose the methods by Low-rank regression analysis (LRRA) to deal with these problems. For supervised or unsupervised dimensionality reduction, combining spectral graph analysis and LRRA can make a global constraint on the subspace. For semi-supervised dimensionality reduction, the proposed method incorporating LRRA can exploit the unlabeled samples more effectively. The experimental results show the effectiveness of our methods.
A Distributed Consensus Protocol for Sustainable Federated Learning

The most significant challenge of our time is global warming, it impacts every area of our lives. This study was motivated by the observation that to train Artificial Intelligence and Machine learning (AI/ML) algorithms result in staggering carbon footprints. Moreover, centralized implementations are becoming a bottleneck of several AI/ML applications that needs frequent retraining and low latency responses. To overcome the limitations of a centralized ML research community has proposed Federated Learning, a technique used to train AI/ML algorithms in a distributed fashion. There has been significant previous work to reduce power consumption by adopting efficient hardware techniques; while such techniques yield large savings, they are not focusing on distributed learning. We propose an Energy-efficient Consensus Protocol (EECP) for sustainable Federated Learning. Our protocol iterates over the bidding phase and agreement (or consensus) phase by only exchanging bids and a few other policy-driven information with neighbor workers. Our simulations show significant energy savings of up to 22.7% with respect to our benchmark.
Visual Prediction Based on Photorealistic Style Transfer

In this study, we explore recent advances in photorealistic style transfer methods to make visual predictions of outdoor scenes. These methods transfer the elements' visual appearance from one photo (style image) to another (content image), maintaining the original composition of the elements in the original image. However, the search for reference images containing the same elements as the content image and presenting all the desired style characteristics makes the process challenging and time-consuming. To overcome this challenge, we propose a dynamic search method based on the transient scene attributes performed in a dataset developed especially for this task. Our team developed a set of 924 3D images divided into six scenario groups, with the main elements found in the outdoor scenes to be used as style images. Each group has stylizations of the four seasons, the time of day, the presence or absence of rain, snow, and cloudy skies. In the end, we measured the similarity of the results obtained with real images. The structural similarity index measure (SSIM) reaches an average score greater than 0.8.
Automatic Classification for Ontology Generation by Pretrained Language Model

In recent years, for systemizing enormous information on the Internet, ontology that organizes knowledge through a hierarchical structure of concepts has received a large amount of attention in spatiotemporal information science. However, constructing ontology manually requires a large amount of time and deep knowledge of the target field. Consequently, automating ontology generation from raw text corpus is required to meet the ontology demand. As an initial attempt of ontology generation with a neural network, a recurrent neural N = network (RNN)-based method is proposed. However, updating the architecture is possible because of the development in natural language processing (NLP). In contrast, the transfer learning of language models trained by a large unlabeled corpus such as bidirectional encoder representations from transformers (BERT) has yielded a breakthrough in NLP. Inspired by these achievements, to apply transfer learning of language models, we propose a novel workflow for ontology generation consisting of two-stage learning. This paper provides a quantitative comparison between the proposed method and the existing methods. Our result showed that our best method improved accuracy by over 12.5%.
RDF knowledge graph keyword type search using frequent patterns

With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time.
Meta-heuristic Algorithms for Resource Allocation in Cloud

Abstract Resource allocation in cloud computing is inherently a challenging task due to the increase in the number of cloud users working on multifarious cloud applications in some of the infrastructure. The majority of resource allocation techniques existing till have focus on providing performance driven by the workload of the applications from diverse domains like scientific and business. This paper presents a detailed review of meta-heuristics algorithms for resource allocation in cloud computing environment. The reviewed meta-heuristic algorithms are capable of achieving much higher performance, reduction in a cost, reduction in time, improve utilization of resources, improve energy efficiency while resource allocation in cloud.
A New Image Watermarking Scheme Based on Block Conversion and DWT-SVD Approach

The authors propose a digital image watermarking mechanism to improve data security, robustness, and capacity by combining singular value decomposition with the discrete wavelet transformation. In the proposed approach, an image is decomposed horizontally as well as vertically into their low and high sub-frequency bands, respectively by using DWT. A horizontal low-frequency sub-band is selected and converted into an M × M block size. After that, the watermark images are embedded into each block using the SVD approach. After the completion of this process, where the watermarked images were subjected to various kinds of attacks such as cropping, rotation, sharpness, noise, contrast adjustment, filtering etc., the inserted watermark image was recovered from the cover image. The quality of recovered watermarks was compared based on correlation coefficients. The proposed algorithm’s robustness was found to be comparatively better than the previously existing methods. Tables show the results.
Positive Deviance in the Pedagogical Action of Ethnomodelling

An impasse in mathematics education is related to its often lack of acknowledgment of local mathematical practices in its research theoretical basis. Pedagogical action of ethnomodelling can aid in recording cultural-historical forms of mathematical procedures and practices developed by members of distinct cultural groups. Ethnomodelling adds cultural perspectives to the mathematical modelling process without attempting to replace academic mathematics during the development of this process. Hence, insubordination triggered by ethnomodelling is creative and often evokes a sense of disturbance that causes conscious review of rules and regulations endemic to many curricula contexts. This process enables educators and investigators to use positive deviance to develop pedagogical actions that deal with content often disconnected from the reality of the students.
Investigative Study of Unigram and Bigram Features for Short Message Spam Detection

Nowadays, it is very imperative to maintain a high standard of security for ensuring reliable and trusted communication channel across various organizations. Studies have shown that spammers have used mobile short message service (SMS) and microblogging as a channel of communication for disseminating unsolicited spam messages containing novel words and abbreviation. Over the years, a growing number of research works have been conducted in the area of short message spam detection where many of these approaches yielded promising results. However, investigating the effect of unigram and bigram features for short message spam detection remains an open research issue. This paper proposed unigram and bigram features for short message spam detection. The performance of the proposed features was evaluated using four classification algorithms: Support Vector Machine (SVM), Naïve Bayes, LibSVM and Random Forest. The results were evaluated on two datasets based on Tweets and mobile SMS data. Results obtained were compared for unigram and bigram features based on the individual classifiers. Experimental results showed that unigram features produced the best accuracy of 98.72% with Naïve Bayes algorithm compared to bigram features.
Exploiting texture characteristics and spatial correlations for robustness metric of data hiding with noisy transmission

Data hiding aims to embed a secret message into a digital object such as image by slightly modifying the object content without arousing noticeable artefacts. The resultant object containing hidden information will be sent to a desired receiver via some insecure channels, e.g. images transmitted through noisy channel, social networks are vulnerable to unknown pollution or compression by a third party, which may lead the transmitted objects to be attacked such that the reconstructed message has a significant error rate. It therefore requires us to use robust embedding strategies for data hiding to realise reliable message retrieval. To this end, in this paper, a metric model to estimate the robustness of data hiding for noisy transmission based on the statistical characteristics of cover and embedding operation is presented, the former is mainly reflected by spatial frequency and texture feature, and the latter embedding operation is mainly reflected by embedding modification. The goal is to ensure that both statistical characteristics and embedding operation can be used to maximise the embedding robustness. To the best knowledge, it is the first time to estimate robustness before data hiding by a special metric model. Experimental results show that, by combining the proposed metric model in three classical data hiding methods, i.e. BPS, DE and QIM, the robustness can be significantly improved, which demonstrates its superiority and applicability.
Pyramidal Reservoir Graph Neural Network

We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by iterating a non-linear map until it converges to a fixed point. The second type of layer implements graph pooling operations, that gradually reduce the support graph and the vertex features, and further improve the computational efficiency of the RC-based GNN. The architecture is, therefore, pyramidal. In the last layer, the features of the remaining vertices are combined into a single vector, which represents the graph embedding. Through a mathematical derivation introduced in this paper, we show formally how graph pooling can reduce the computational complexity of the model and speed-up the convergence of the dynamical updates of the vertex features. Our proposed approach to the design of RC-based GNNs offers an advantageous and principled trade-off between accuracy and complexity, which we extensively demonstrate in experiments on a large set of graph datasets.
Lumen: A software for the interactive visualization of probabilistic models together with data

Research in machine learning and applied statistics has led to the development of a plethora of different types of models.Lumen aims to make a particular yet broad class of models, namely, probabilistic models, more easily accessible to humans.Lumen does so by providing an interactive web application for the visual exploration, comparison, and validation of probabilistic models together with underlying data.As the main feature of Lumen a user can rapidly and incrementally build flexible and potentially complex interactive visualizations of both the probabilistic model and the data that the model was trained on.
Global Rate-distortion Optimization of Video-based Point Cloud Compression with Differential Evolution

In video-based point cloud compression (V-PCC), one geometry video and one color video are generated from a dynamic point cloud. Then, the two videos are compressed independently using a state-of-the-art video coder. In the Moving Picture Experts Group (MPEG) V-PCC test model, the quantization parameters for a given group of frames are constrained according to a fixed offset rule. For example, for the low-delay configuration, the difference between the quantization parameters of the first frame and the quantization parameters of the following frames in the same group is zero by default. We show that the rate-distortion performance of the V-PCC test model can be improved by lifting this constraint and considering the ratedistortion optimization problem as a multi-variable constrained combinatorial optimization problem where the variables are the quantization parameters of all frames. To solve the optimization problem, we use a variant of the differential evolution algorithm. Experimental results for the low-delay configuration show that our method can achieve a Bjøntegaard delta bitrate of up to -43.04% and more accurate rate control (average bitrate error to the target bitrate of 0.45% vs. 10.75%) compared to the state-of- the-art method, which optimizes the rate-distortion performance subject to the test model default offset rule. We also show that our optimization strategy can be used to improve the rate-distortion performance of two-dimensional video coders.
JPEG Image Compression Using Multiple Core Strategy in FPGA achieving High Peak Signal to Noise Ratios

Field Programmable Gate Arrays are increasingly becoming an integral part of modern embedded system designs because of their ease of implementation, flexibility and unparalleled processing power capability. Taking into consideration these capabilities, a compression algorithm has been implemented on an FPGA (Xilinx Spartan 3A 3400 Kit) while the decompression part has been carried out on a Personal Computer (PC). The transmission between FPGA and PC has been done through Ethernet interfacing. Decompressor has been designed in MATLAB to decompress the data acquired through FPGA while the compression algorithm used is based on JPEG. Three different images have been considered for the test case purposes. Likewise, three resolutions of each image have been used for compression in FPGA and subsequent decompression using MATLAB in PC. Multiple cores strategy has been incorporated in the FPGA to speed up the compression process. The pictorial representation of each decompressed image viz-a-viz original image has been carried out. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) have been calculated for each set of resolution for comparing image compression quality. The graphical representation of Mean square Error for all resolutions of each image separately has been depicted.
Comparison of Cross-Lingual Similar Documents Retrieval Methods

In this paper, we compare different methods for cross-lingual similar document retrieval for distant language pair, namely Russian and English languages. We compare various methods among them: classical Cross-Lingual Explicit Semantic Analysis (CL-ESA), machine translation methods and approaches based on cross-lingual embeddings. We introduce two datasets for evaluation of this task: Russian-English aligned Wikipedia articles and automatically translated Paraplag. Conducted experiments show that an approach with inverted index, with an extra step of mapping top keywords from one language to other with the help of cross-lingual word embeddings, achieves better performance in terms of recall and MAP than other methods on both datasets.
Support Technology of Weapon Equipment Selection Based on Question and Answer

In order to solve the problem of unfriendly interface of the existing search engines, multi-source heterogeneous and uneven quality of knowledge and multiple selection factors in the process of weapon equipment selection, a question-answer (QA)-based support technology of weapon equipment selection was proposed, improving the timeliness and efficiency of decision-makers to obtain information required and overcoming the inability to guarantee the credibility of the decisions made. The knowledge graph was adopted to deal with the problem of multi-source heterogeneous knowledge in the field of weapon equipment. And through the formulation of 8 categories of question templates and an improved question similarity algorithm that incorporates lexical part-of-speech features, a combination of frequently asked questions (FAQ) queries and template matching based question and answer methods were used to realize a QA system that supports weapon equipment selection. Experiments show that the QA system based on this technology can effectively identify the selection questions raised by the decision-makers, and provide appropriate answers to the decision-makers in a relatively short time under the condition that the knowledge required for the question exists in the weapon equipment knowledge graph, thereby improving The timeliness and convenience of equipment selection decision-makers to make correct decisions.
Best Answerers Prediction With Topic Based GAT In Q&amp;A Sites

Q&A communities are playing an important role in online knowledge sharing, where a large number of users with various knowledge background make tremendous contributions to solving many technical problems based on crowd intelligence. However, as new questions are increasingly posted, it is a non-trivial issue to find a matching answerer for each question. As a result, many questions fail to receive satisfying answers in time. This paper addresses the problem by predicting the best answerer for the new question. Many existing efforts are devoted to predicting the best answerer mainly by calculating the textual similarity between questions and a user's historical post documents. Some works consider other features, such as the similarity of tags between questions and users, the average quality of a user's historical answers, and so on. But few works consider interaction within the community. In recent years, works that take account of the interaction between community items (such as GCN and GAT) have made considerable progress in graph mining tasks like item recommendation, node representation, node classification, and link prediction. This kind of graph mining method can easily leverage interactive information in the community and encode it in an easy-to-use way which is very helpful for downstream tasks such as recommendation. However, questions that need to be recommended to answerers are new coming ones and with no interaction with any other node in the community yet. How to make reasonable use of collaborative information to improve recommendation performance is a real challenge. In this paper, we use the interactive information between candidate answerers and combine text information to make our best answerer recommendation. There are two main parts, LDA(Latent Dirichlet Allocation) topic model is used to capture the text information and graph attention networks (GATs) for interaction. We evaluated our approach on a real dataset from Stack Exchange. The result shows that our approach outperforms all the baseline methods.
Mixed Reality Interface for Load Application in Finite Element Analysis

Finite Element Analysis (FEA) is an integral process in manufacturing design, providing assurance of quality and structural integrity of a manufactured item. FEA involves conducting rigorous stress simulations on a designed model to study its structural behaviour when under specific loading scenarios. Current FEA tools involve viewing and manipulating the 3D model displayed on a monitor screen. A Mixed Reality (MR) user interface allowing for direct manipulation and specification of loading scenarios on a designed model was developed, thus enabling design engineers to find new ways of interacting and visualising FEA results for analysis and thus reducing unnecessary prototyping costs. Three gesture interfaces were developed to allow application of load parameters on a surface of a model, namely: Point-Apply, Point-Hold, and Point-Drag. A pilot evaluation was conducted to study the suitability of each as an interface designed for engineers. Results suggest that Point-Hold is empirically the most usable interface compared to the other interfaces.
Real-Time Data Analytics of COVID Pandemic Using Virtual Reality

Visualizing data effectively is critical for the discovery process in the age of big data. We are exploring the use of immersive virtual reality platforms for scientific data visualization for COVID-19 pandemic. We are interested in finding ways to better understand, perceive and interact with multidimensional data in the field of cognition technology and human-computer interaction. Immersive visualization leads to a better understanding and perception of relationships in the data. This paper presents a data visualization tool for immersive data visualizations based on the Unity development platform. The data visualization tool is capable of visualizing the real-time COVID pandemic data for the fifty states in the USA. Immersion provides a better understanding of the data than traditional desktop visualization tools and leads to more human-centric situational awareness insights. This research effort aims to identify how graphical objects like charts and bar graphs depicted in Virtual Reality tools, developed in accordance with an analyst’s mental model can enhance an analyst’s situation awareness. Our results also suggest that users feel more satisfied when using immersive virtual reality data visualization tools and thus demonstrate the potential of immersive data analytics.