Reviewerly/exHarmony huggingface dataset:
Social Spider and the Prey Search Method for Global Optimization in Hyper Dimensional Search Space

Finding an efficient search strategy to solving complex or difficult problems has been a subject of interest across multiple disciplines particularly in computer science and engineering. In recent times, the applications of metaheuristic algorithms based on evolutionary computation and swarm-based intelligence have demonstrated outstanding performances in search for global optimal solution for any optimization problem. A metaheuristic search algorithm applied in hyper dimensional search space, to find a global optimal solution do not specifically require information about the nature and complexity of the problem because of ease of adapting to some maximum or minimal parameters/constraints of the problem space. In this paper, we present an enhancement to the relatively new developed algorithm called social spider prey (SSP) algorithm for global optimization problem. This algorithm mimics the foraging behavior of social spiders in capturing prey(s) on the social web. The weight of a prey which stimulates the spider’s web and cause vibration is depicted and modelled in SSP to enhance the searching strategy of the algorithm particularly, in a hyperdimensional search space. Thus, for improved global optimization algorithm such as SSP to stand the test of time, it is imperative to have it tested on proven benchmarked test functions which is achieved in this paper. A computational experiment was carried out to ascertain the performance of SSP in dealing with complex optimization problems, and the results were discussed. SSP demonstrated outstanding global optimization performance as shown in all the results in all the test functions converging nearly at the global optimum value 0. This study shows the prospects of this relatively new SSP algorithm to solving complex optimization problems.
Blockchain-Based Software Systems: Taxonomy Development

At an era of new emerging technologies, and spotting lights to blockchain technology specifically; many promises take place. Blockchain has attracted many researchers and practitioners from various professions and disciplines, due to the powerful characteristics behind adopting such technology. And in order to formulate a better understanding of it, many researchers are interested in classifying the Blockchain-Based Software Systems (BBSSs). Unfortunately, none of the available taxonomies in the literature considers the various Software Engineering (SWE) aspects for building a BBSS. A blockchain subject matter expert (SME) may absorb a specific aspect understanding from each of the related taxonomies, and in order to have a complete understanding of the various options available for building a BBSS, they will have to flip from a resource to another in order to collect the scattered information they need. So, this paper aims to assist the blockchain SMEs to articulate a comprehensive understanding of the various and most recent concepts and design decisions available, in order to implement or propose a BBSS solution. The taxonomy is derived from the fundamental knowledge and the major SWE aspects which a BBSS implementer or researcher has to consider, and thus is not biased to a specific SWE aspect.
Semi-supervised LDA and Multi-distance Metric Learning for Person Re-identification

Abstract The problem of person re-identification has attracted a lot of attention in the field of machine vision. In practice, the non-overlapping sample images change drastically and the sample size is small, which makes the metric model overfitting phenomenon. In this paper, based on the k-NN and the sample normality property, we propose a resampling linear discriminant analysis (LDA) algorithm to suppress the local constraints caused by small samples, then train it to obtain the person re-identification metric learning model. A semi-supervised LDA algorithm with semi-supervised characteristics is developed by optimizing the inter-class scatter for weighting. A joint distance metric-based approach is also proposed to learn both the Mahalanobis distance and Euclidean distance. The improved algorithm is tested on the VIPeR and CUHK01 datasets, and the results indicate that, despite the change in the total number of training samples, the algorithm in this paper shows high recognition accuracy.
Data streams classification using deep learning under different speeds and drifts

Abstract Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, much effort has been put into the adaption of complex deep learning (DL) models to streaming tasks by reducing the processing time. The design of the asynchronous dual-pipeline DL framework allows making predictions of incoming instances and updating the model simultaneously, using two separate layers. The aim of this work is to assess the performance of different types of DL architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time series datasets that are simulated as streams at different speeds. In addition, we evaluate how the different architectures react to concept drifts typically found in evolving data streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency, but are also the most sensitive to concept drifts.
Throughput-Efficient Blockchain for Internet-of-Vehicles

Internet-of-Vehicle (IoV) is empowering smart vehicles with data collection and sharing capabilities, and blockchains have been introduced to manage the IoV data due to many advantages, including decentralization, security, reliability, and scalability. Nevertheless, existing IoV blockchain models suffer from poor security against collusion attacks instigated by malicious blockchain miners typically represented by roadside units (RSUs). To address this problem, additional block verifiers, e.g., vehicles, can be recruited during block verification, which enhances security but also can lead to the reduced throughput. Therefore, in this paper, we propose a resource management scheme for IoV blockchains to enhance the system security while maximizing the throughput by optimizing contributed computing resources from RSUs and recruited vehicles. We show that the optimal strategies of RSUs and vehicles can be found through the Karush-Kuhn-Tucker (KKT) conditions and verify (using simulations) that our scheme achieves the higher throughput with enhanced security compared to the existing IoV blockchains.
LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time

Training neural networks on large distributed clusters has become a common practice due to the size and complexity of recent neural networks. These high-end clusters of advanced computational devices cooperate together to reduce the neural network training duration. In practice, training at linear scalability with respect to the number of devices is difficult, due to communication overheads. These communication overheads often cause long idle times for the computational devices. In this paper, we propose LAGA (Lagged AllReduce with Gradient Accumulation): a hybrid technique that combines the best of synchronous and asynchronous approaches, that scales linearly. LAGA reduces the device idle time by accumulating locally computed gradients and executing the communications in the background. We demonstrate the effectiveness of LAGA in both final accuracy and scalability on the ImageNet dataset, where LAGA achieves a speedup of up to 2. 96x and 5. 24x less idle time. Finally, we provide convergence guarantees for LAGA under the non-convex setting.
Learning Dynamic User Interactions for Online Forum Commenting Prediction

Predicting whether a user would interact with a particular message or topic in online social services is essential for applications related to recommendation systems. Two perspectives are commonly adopted when tackling this problem: the user’s interest in the post content and the social relationship between posters and commenters. However, explicit social relationships might not be available in online forums, e.g., Stack Overflow. This makes it challenging for predictive models to understand the social connections between forum users. In this paper, we propose a novel framework to solve the comment prediction problem in online forums. Specifically, the framework incorporates attention mechanisms to capture users’ interests in the post contents and a stacked graph convolutional network to perceive users’ implicit social relationships from their past temporal interactions. The multi-modal features learned by the framework would be combined together through a fusion layer and used for the final prediction. We verify the effectiveness of our framework from multiple perspectives using real forum datasets. Experimental results show that our framework could achieve better performance than existing state-of-the-art methods.
SYMBIOMON: A High-Performance, Composable Monitoring Service

High-performance computing (HPC) software is evolving to support an increasingly diverse set of applications and heterogeneous hardware architectures. As part of this evolution, the construction of scientific software has shifted from a traditional monolithic message passing interface executable model to a coupled, services-style model in which simulations run alongside a host of distributed HPC data services within the same batch job allocation. Microservices have emerged as a powerful new way to build these distributed data services through a composition model. However, performance analysis of composed microservices is a daunting challenge. It requires collecting, monitoring, aggre-gating, and exporting performance data from multiple sources. To be effective, the design of such a monitoring solution must allow for seamless integration into HPC applications and distributed services alike, be scalable, operate with a low overhead, and take advantage of the HPC platform. We propose SYMBIOMON, a monitoring service that is built by composing high-performance microservices. We describe its design and implementation within the context of the Mochi framework. SYMBIOMON combines a time-series data model with existing Mochi data services to collect, aggregate, and export performance metrics in a distributed manner. SYMBIOMON enables seamless, low-overhead monitoring and analysis of data services and HPC applications alike. Using HEPnOS, a production-quality Mochi data service, we demonstrate the use of SYMBIOMON to identify better service configurations.
Style transfer in NLP: a framework and multilingual analysis with Friends TV series

Style transfer is an important and a rapidly developing of Natural Language Processing. This days more and more methods and models are proposed which allow us to generate text in predefined style. In this paper we propose a framework for style transfer of “Friends” TV series. The trained models are able to mimic one of 6 main characters of this famous TV-series in English and Russian. We also present a dialogue dataset of “Friends” subtitles in English and its Russian automatic translation. In addition to that we perform a multilingual comparison of “Friends” style transfer in the two considered languages.
Knowledge Graph Enhanced Community Consensus: a Scenario-based Knowledge Construction on Buddha Images

research-article Share on Knowledge Graph Enhanced Community Consensus: a Scenario-based Knowledge Construction on Buddha Images Authors: Akkharawoot Takhom Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Thailand Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, ThailandView Profile , Tharathon Utasri Faculty of Science and Technology, Rajamangala University of Technology, Thailand Faculty of Science and Technology, Rajamangala University of Technology, ThailandView Profile , Dhanon Leenoi Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Thailand Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, ThailandView Profile , Pitchaya Soomjinda Faculty of Fine Arts, Chiang Mai University, Thailand Faculty of Fine Arts, Chiang Mai University, ThailandView Profile , Prachya Boonkwan Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Thailand Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, ThailandView Profile , Thepchai Supnithi Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Thailand Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, ThailandView Profile Authors Info & Claims IJCKG'21: The 10th International Joint Conference on Knowledge GraphsDecember 2021 Pages 191–194https://doi.org/10.1145/3502223.3502744Online:24 January 2022Publication History 0citation18DownloadsMetricsTotal Citations0Total Downloads18Last 12 Months18Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-Objective Structure and Parameter Evolution of Neuro-Fuzzy Systems

This paper presents an evolutionary algorithm that allows the adaptation of neuro-fuzzy systems according to conflicting objectives. The adaptation includes both the structures and the parameters of the evolved solutions. The suggested algorithm, which is termed Fuzzy-Evolution of Membership and Structures by Decomposition (FEMS/D). As suggested by its name, the algorithm is based on the decomposition approach, which is a divide-and-conquer technique that transforms the original multi-objective problem into a set of single-objective subproblems. In addition, this paper provides initial demonstrations of the applicability of FEMS/D to the development of nondominated robot motion controllers, which exhibit a range of behaviors between the safest and the fastest ones. Finally, the adaptation of the controllers to an abrupt change in the environment is also demonstrated.
Towards a Pattern Language to Embed UX Information in Agile Software Requirements

The concern on how to handle agile software requirements have been explored from different perspectives. User stories (US) are being used as valuable artifacts to agile teams, being a short requirement description with its details complemented by other artifacts. Recently, agile professionals have demonstrated a great interest in integrating User eXperience (UX) information into agile practices because UX is an important cross-cutting quality requirement. These professionals still face difficulties in organizing and linking the UX information to User Stories which can introduce navigational distance, which is characterized by the effort to reach an information. This paper presents a set of patterns intending to support agile teams to embed the UX information in agile software requirements. Our patterns were proposed based on a case study conducting in an industrial setting. From this study results, we found out that by organizing the requirements and other UX information the agile teams introduced navigational distances between that information and the US’s bringing difficulties to accessing the information in virtual tools. These patterns describe practices to motivate agile teams to rethink their ways of organizing agile software requirements and the UX information in virtual tools to support the teams to find and retrieve the UX and requirement information easily. The practices cover the UX information in most of the abstraction layers, from the UX in more practical activities, for example in tasks, to more general UX information that can be used by several US’s.
Traffic Sign Recognition Method Based on Dual-Channel CNN

Aiming at traffic sign problem, the traditional LeNet-5 network structure has low accuracy of traffic sign recognition, slow identification speed and ignores natural factors such as weather. A convolutional network structure model with two-channel and multi-scale based on LeNet-5 improvement is proposed by convolutional neural network technology. In the dual-channel structure, each channel contains two branching structure, and the number of convolution and image scale of each channel is different, making the feature extraction of different image scales richer. Secondly, the improved network structure greatly increases the number of convolutional kernels compared to the traditional LeNet-5 network structure. Finally, by changing the Sigmoid activation function to the ReLu activation function, changing the stochastic gradient descent algorithm to the Adam algorithm, and adding Dropout layers to prevent overfitting and setting the learning rate, thus increasing the traffic sign recognition rate. The recognition rate of the improved network is 98.6&#x0025;, floating by 0.5&#x0025;, relative to the traditional LeNet-5 network structure, the recognition rate increases by more than 15&#x0025;, verifying that the improved network structure has a certain robustness.
Relation Representation Learning for Special Cargo Ontology

Non-transparent shipping processes of transporting goods with special handling needs (special cargoes) have resulted in inefficiency in the airfreight industry. Special cargo ontology elicits, structures, and stores domain knowledge and represents the domain concepts and relationship between them in a machine-readable format. In this paper, we proposed an ontology population pipeline for the special cargo domain, and as part of the ontology population task, we investigated how to build an efficient information extraction model from low-resource domains based on available domain data for industry use cases. For this purpose, a model is designed for extracting and classifying instances of different relation types between each concept pair. The model is based on a relation representation learning approach built upon a Hierarchical Attention-based Multi-task architecture in the special cargo domain. The results of experiments show that the model could represent the complex semantic information of the domain, and tasks initialized with these representations achieve promising results.
Automatic Information Extraction for Financial Events by Integrating BiGRU and Attention Mechanism

Abstract In this paper, an information extraction method for financial events written in Chinese is proposed. The core entities of the causes and results, as well as the verbs and conditions are extracted from the financial events reported by websites. The method takes the original words and the part-of-speech of words as two inputs. BERT encoder is utilized to transform the original sentences to word-embedding vectors, which then are send to BiGRU to extract the sematic features. And a full-connected network is overlapped on BiGRU to reduce the impact of “covariate shift”. For the second input, the original sentences are cut by Chinese cut-word tool ‘jieba’ to get the part-of-speech of words, which are then transformed by self-attention mechanism to get global dependencies. The two outputs for word-embedding vectors and part-of-speech of words are combined and then decoded by CRF. Finally, the Viterbi algorithm is utilized to get the best sequences. The experiment results validate the effectiveness of the proposed method.
The Strategy Configuration Problem and How to Solve It

The Strategy pattern allows the developer to implement a family of algorithms that can be used interchangeably and vary independently from the objects that use them. To achieve this, the algorithms are implemented as an hierarchy of respective strategy classes that realize the same interface.
FoodChem: A food-chemical relation extraction model

In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provided in biomedical peer-reviewed scientific literature. The RE task is treated as a binary classification problem, aimed at identifying whether the contains relation exists between a food-chemical entity pair. This is accomplished by fine-tuning BERT, BioBERT and RoBERTa transformer models. For evaluation purposes, a novel dataset with annotated contains relations in food-chemical entity pairs is generated, in a golden and silver version. The models are integrated into a voting scheme in order to produce the silver version of the dataset which we use for augmenting the individual models, while the manually annotated golden version is used for their evaluation. Out of the three evaluated models, the BioBERT model achieves the best results, with a macro averaged F1 score of 0.902 in the unbalanced augmentation setting.
An exploratory study of using speculative artefacts in co-design

This paper presents a case study exploring the use of speculative artefacts framed within the Fictional Inquiry co-design technique. The case is derived from a project investigating current practices and potential future strategies to improve patients’ hand hygiene standards at the Department of Oncology of Aarhus University Hospital. This paper explores how five speculative artefacts were designed and used to inquire into participants’ experiences of their hand hygiene, both concerning current practice and future ideas. Our study demonstrates how the speculative artefacts used in Fictional Inquiry supported participant’s immersion and engagement in co-design sessions and how the artefacts were used to maintain momentum throughout the session.
Federated Learning in Heterogeneous Networks with Unreliable Communication

In federated learning, local workers learn a global model collaboratively using their local collected data without ever uploading them for privacy concerns. Locally trained models are sent to a central server for model aggregation. Locally collected data are usually biased and a training algorithm called Federated Proximal (FedProx) is proposed to handle the statistical and system heterogeneity of local data. Enabling federated learning in a cellular wireless network must consider the large size of the model and limited communication resources. In this work, we for the first time prove the convergence of FedProx in case of transmission packet error, following which we propose a communication efficient client selection and resource allocation strategy to increase the convergence speed under unreliable transmissions in a heterogeneous network. By experiment on a real dataset, the fast convergence of our approach is confirmed.
How to use statistics in quantitative corpus analysis

This chapter surveys statistical applications in contemporary corpus linguistics. It does so from two angles: first and from a corpus-linguistic angle, I discuss the four main corpus-linguistic methods of frequency, dispersion, association/contingency and concordances/context; for each method, I exemplify different measures that have been proposed and some central theoretical or applied applications. Second and from a statistical angle, I discuss the two main statistical approaches that are routinely applied to corpus data, namely regression/classification approaches (often involving hypothesis testing and/or machine learning methods such as regression modeling or tree-/forest-based approaches) and exploratory approaches (usually involving hypothesis-generating methods such as cluster or principal component analysis).