Reviewerly/exHarmony huggingface dataset:
Recognition of Electromagnetic Signals Based on the Spiking Convolutional Neural Network

Feature extraction and recognition of signals are the bases of a cognitive radio. Traditional manual extraction for signals’ features becomes difficult in the complex electromagnetic environment. Although convolutional neural networks (CNNs) can extract signal features automatically, they have low accuracy in recognizing electromagnetic signals at low signal-to-noise ratios (SNRs) due to the agility of signals. Considering the great potential of spiking neural networks (SNNs) in classification, a spiking convolution neural network (SCNN) for the recognition of electromagnetic signals is proposed in this paper. The SCNN effectively integrates the extraction ability of spatial features in CNNs and temporal features in SNNs. Since the SCNN is difficult to train, the strategy of surrogate gradient is proposed to train it. By taking the 2-dimensional time-frequency distribution of 6 signals as input, the SCNN can effectively identify different signals at low SNRs. The method proposed in this paper contributes to promote the research and application of SNNs in the recognition of electromagnetic signals.
Identifying Features that Characterize Children’s Free-Hand Sketches using Machine Learning

From an early age, children begin developing critical motor skills, such as fine motor control, that contribute significantly to reading, writing, drawing, and more, all of which are important for communication and school readiness. Pediatricians can evaluate a child’s motor skills using activities and questionnaires. Sometimes these involve adults drawing with their child, but it can be difficult to fully evaluate a child’s drawings through a handful of sketches from limited direct assessments. We propose creating a sketching system that will collect free-form drawing data from parents and children that can then automatically differentiate a child’s sketch from an adult’s using only the pen strokes of their drawing. In this paper, we describe our study that collected sketches from 14 children aged 2 to 5 and 25 adults over 18. We contribute a machine learning classifier based on sketch recognition features from free-hand drawings capable of distinguishing children’s sketches from those made by adults with an F-measure of 0.906. These results indicate the potential of creating sketch-based applications for assessing children’s fine motor development.
Accelerating Join of Distributed Datasets by a Given Criterion

This article discusses the operation of joining distributed datasets by a given criterion in distributed systems. A critical analysis of literature data on the architecture of distributed data warehouses and typical methods for joining datasets was carried out, limiting stages that slow down the process were identified. A method for accelerating the operation of data joining according to a given criterion is proposed, on the basis of which an algorithm is developed and implemented in the Apache Spark data processing environment. Experimental studies confirming the efficiency of the developed method were performed. The results of the experiments show that the proposed method can significantly increase speed of the operation compared to existing solutions. From the presented experimental data, it can be seen that for 2 TB data, the algorithm made it possible to perform the merge operation ~ 37% faster than the standard algorithm offered by the Spark SQL library, for 7 TB data it was already ~ 47%.
Design of the Participatory Learning Experience

Design and maker-based approaches in education provide opportunities for younger generations to learn many future competencies, but how these approaches and practices are introduced to students and scaffolded in classrooms is of increasing interest among researchers. This paper aims to describe key elements that were used as the design principles for the Participatory Design (PD) project designed for and implemented for lower-secondary school level students (13-14 years). The project was designed by the researcher in collaboration with the craft teacher. The overall design of the project, learning goals and pedagogical arrangements based on earlier studies, and the researcher's experiences in craft, design, and maker education in Finland, is presented. Selected key elements are reflected based on the first look of the versatile data from the researcher's and teacher's perspectives. Students’ voices are briefly presented through open-ended questionnaire reflections. In future work, our aim is to evaluate in more detail which elements of the learning design supported and enabled the participation of students. These insights might be valuable for others designing PD projects for formal education.
BERT-BiGRU Intelligent Classification of Metro On-Board Equipment Faults Based on Key Layer Fusion

Metro train operation may result in massive and complex unstructured fault text data. To solve the problem of low classification accuracy and incomplete classification effect of unstructured fault data automatic classification task, a BERT-BiGRU fault text classification model based on key layer fusion is proposed. Firstly, the unstructured text data is processed into word vectors with location information in the word embedding layer and then input into the BERT layer. Based on the traditional 12-layer BERT model, the semantic information is fully obtained by encoding the two-way transformer encoder in layers 2, 4, 6, 8, and 12 for fusion and dimensionality reduction, which is then input into the BiGRU layer to extract the context information to obtain the high-level feature representation of the text. After that, the final classification results are output in the output layer through the full connection layer FC and softmax functions. This model is tested with other mainstream models on the fault text data of metro on-board equipment. The experiment results show that on the same data set, the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -score of this model is 7%~8% higher than that of the current mainstream classification model, that is, about 1~2% higher than that of other PLMs, and the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -score of this model is higher than that of BERT model with different transformer layers, reaching the highest value of 0.9272, and the convergence speed in the training process is fast.
Poster: Leveraging Question Answering to Understand Context Specific Patterns in Fact Checked Articles in the Global South

Propagation of misinformation on various social media platforms is a common occurrence, especially around political events, religious beliefs, and public health. Fact checked articles, which investigate the credibility of dubious claims online, provide a reliable source of debunked misinformation. However, existing (older) fact checked articles remain an underutilized resource for understanding patterns in fake stories. We propose the use of Question Answering (QA) for analysing fact checked articles for systematically extracting metadata, potentially useful for downstream tasks such as misinformation detection, using a range of simple to nuanced questions. We find that the method gives us a context-specific understanding of common patterns and themes in misinformation, which is especially important in the Global South, where misinformation is layered with propagandist underpinning. Our findings suggest that this method can be extended by fine tuning on any event specific data set of fact checked articles to yield more robust and accurate results.
Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

Abstract Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM.
Mobilizing Digital Volunteers to Support Underserved Communities in India During COVID-19 Lockdowns

As community-driven organizations sought to support their constituents through the COVID-19 crisis, many drew on digital volunteers to expand their capacity and reach. However, coordinating the efforts of virtual volunteers is a challenging task with few empirical studies of the associated risks and best practices. In this paper, we report on the activities of CGNet Swara, a citizen journalism platform that published 401 distress calls from vulnerable communities stranded in India due to the imposition of a nationwide lockdown. CGNet mobilized 11 digital volunteers to help these contributors over a period of nearly 2 months. We found that a lack of proper guidance to digital volunteers and outdated organizational policies resulted in demonstrable harms to vulnerable communities. We discuss risks that are inherent in collaborations between organizations extending themselves to crisis response and emergent groups of digital volunteers, and how they can be mitigated by real-time monitoring and development of standard operating procedures relating to impact metrics, verification standards and disclosure policies.
6th KidRec Workshop: Information Retrieval Systems for Children in the COVID-19 Era

The role that technology plays in supporting children at school and at home is more prominent than ever before due to the global COVID-19 pandemic. This has prompted us to focus the 6th International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) workshop on what the lasting changes will be to the design and development of child information retrieval systems. After two years, are information retrieval systems used more in and out of the classroom? Are they more interactive, more or less personalized? What is the impact on the research and business community? Are there long-term and unexpected changes on the design, ethics, and algorithms? The primary goal of our workshop continues to be to build community by bringing together researchers, practitioners, and other stakeholders from various backgrounds and disciplines to understand and advance information retrieval systems for children.
Structured Pruning with Automatic Pruning Rate Derivation for Image Processing Neural Networks

Structured pruning has been proposed for network model compression. Because most of existing structured pruning methods assign pruning rate manually, finding appropriate pruning rate to suppress the degradation of pruned model accuracy is difficult. Although we have been proposed the automatic pruning rate search method, the pruned model performances for complex image processing task such as ImageNet have not been evaluated. In this paper, we demonstrate a performance of the pruned model on ImageNet task using our proposed structured pruning method. Furthermore, we evaluate our pruning method in comparison of the pruned model performance using CIFAR-10 and ImageNet. When using ResNet-34 on ImageNet task, our proposed method reduces model parameters of ResNet-34 by 44.0% with 72.99% accuracy.
Interactive Design and Production of Live-Action Stop-Motion Animation Based on Digital Media

As a form of animation that is closest to the essence of movies among all kinds of animation, stop-motion animation is deeply loved by people and has a lot of room for development in the new era. The steady progress of science and technology has raised people’s requirements for the presentation form of stop-motion animation. People are no longer satisfied with the design of stop-motion animation, which only unilaterally transmits information to the audience and does not interact with the audience. To meet people’s needs, this paper applied the augmented reality technology of digital media to the design and production of live-action stop-motion animation. It used 3D tracking and registration technology to integrate virtual characters into real-life stop-motion animation, and presented it to the audience through augmented reality equipment to improve the interaction between real-life stop-motion animation and the audience. Finally, this paper conducted an experimental design on the fluency, accuracy, animation playback, and user satisfaction of the real-life stop-motion animation of augmented reality. The results showed that the smoothness and accuracy of the live-action stop-motion animation using the augmented reality technology of digital media have been correspondingly improved, and the user’s satisfaction with the augmented reality live-action stop-motion animation has increased by 3.1%. The augmented reality technology of digital media has greatly improved the interaction between live-action stop-motion animation and the audience and promoted the development of live-action stop-motion animation.
Approaches to embodied interaction in the design of playful environments for young children

The research conducted in this doctoral study will consider how digital technologies can be used in the design of interactive environments that enable open-ended play for children. The practicalities of interactive technology may be interpreted as at odds with the ambiguity necessary to facilitate open-ended play. However, I argue that this remains underexplored and that the lived experience of the COVID-19 pandemic, profoundly blurring the limits of the digital and non-digital, offers an opportunity to explore how technologies may be used in the design of playful spaces where children participate in the creation of the meaning of their interactions. Future work will follow an iterative methodological structure that combines qualitative observational approaches with Research through Design (RtD) methods to answer the defined research questions while designing and deploying workable prototypes of playful interactive environments for children.
Context-Aware Recommendation Via Interactive Conversational Agents: A Case in Business Analytics

In the era of information overload, the ability to access key information instantaneously is extremely important. While technological advances such as keyword search, dashboards, customizable data reports, and notifications have made information access more flexible, the underlying assumption is that the user knows what to look for. However, this assumption may not hold in many situations. For example, identifying needed information and key metrics affecting a business in Human Resource Management Systems (HRMS) can prove to be difficult. Voice assistance and recommendation systems can help improve these issues by allowing users to efficiently reach key insights which are relevant to their needs and their context. This research presents the design and evaluation of a conversational context-aware information recommendation system for business analytics where a conversational voice assistant helps the user specify the information needed for different analytics by suggesting reports and metrics often used by similar users and companies in their industry. Our prototype evaluation results show the potential of such a system to improve the user experience of searching for efficient and meaningful information in an organization using the data available within their HRMS.
Adaptive Clustering Algorithm of Complex Network Based on Fuzzy Neural Networks

Clustering analysis, as one of the important methods in data mining technology, merely provides a method for the research and analysis of large amounts of data. Starting with the most important nodes, this paper divides clustering based on data field characteristics. The sensitivity pruning algorithm is then used to further adjust and optimize the structure of the fuzzy neural network, allowing the network to automatically learn the structure and parameters of the system in different environments and obtain the optimal control rules. Finally, the clustering function brings the algorithm’s output result to a close. The experimental results show that the adaptive clustering algorithm of complex networks presented in this paper can effectively improve network cluster division, reduce algorithm time complexity, and avoid the problem of providing the number of cluster structures in advance. This method’s cluster structure efficiency can reach 97.6 percent, and its highest clustering accuracy can reach 96.8 percent. The adaptive clustering algorithm proposed in this paper not only overcomes the traditional algorithm’s flaws, such as the need to predetermine the number of clusters and the clustering result being dependent on the initial clustering center selection, but also has ideal clustering accuracy. This study introduces a novel and more effective method for addressing the difficult problems of practical complex control systems.
Estimating parameters of mixtures of multivariate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3764" altimg="si4.svg"><mml:mi>t</mml:mi></mml:math>-populations and application to classification of observations

The problem of classifying an observation into mixtures of two multivariate t-distributions is studied when the location parameters and covariance matrices are unknown. For mixtures of two multivariate t-distributions, we propose classification rules based on the maximum likelihood estimators and Bayes estimators of the parameters. The maximum penalized likelihood estimators of the parameters are derived using some informative penalty function. We derive shrinkage estimators of the covariance matrices using a regularized parameter and propose the corresponding classification rule. The kernel density-based rule is proposed considering the diagonal matrix as a bandwidth parameter. A simulation study is carried out to compare the rules in terms of the expected probability of misclassification. Applications of the rules are described using real data sets arising in clinical studies and stock market.
Design of Multimedia Intelligent Teaching Platform for Preschool Curriculum Based on Edge Computing

Intelligent teaching platform integrates the respective advantages of network and traditional teaching and makes the teaching content develop in a diversified direction. However, due to the poor response performance of the platform, a multimedia intelligent teaching platform for children’s courses based on edge computing is designed. Construct the overall framework of the teaching platform under the edge computing, and get the functions that the platform needs to have. Set the authority and role of administrators, teachers, and children’s parents, respectively. Develop Android server and client, determine web server, define main parameters, evaluate the quality of teaching platform, and complete the design of teaching platform. The experimental results show that the platform responds quickly, provides users with good experience effect, and has good application performance.
Exploring Community Needs for Disaster Shelters Using Cultural Probes

During disasters, emergency shelters play a central role in emergency management, providing both a secure environment and centralized sites for the distribution of information, material relief supplies, and access to health and human services. Despite their importance, challenges such as physical access, public awareness, and peoples’ willingness to relocate limit the impact of both shelters managed by emergency responders and informal locations created by affected communities. This paper presents research conducted as part of a long-term project aimed at designing digital tools to assist communities and formal responders plan and manage emergency shelters. Working with partners in Puerto Rico, we developed and distributed cultural probes in three communities with recent experience of hurricanes and earthquakes to better understand the needs and resources of disaster affected people related to shelter. This approach yielded novel insights that challenge and expand traditional views of emergency shelters and identified several areas where HCI research and design can contribute to the sector.
Power-Efficient Deep Neural Network Accelerator Minimizing Global Buffer Access without Data Transfer between Neighboring Multiplier—Accumulator Units

This paper presents a novel method for minimizing the power consumption of weight data movements required by a convolutional operation performed on a two-dimensional multiplier–accumulator (MAC) array of a deep neural-network accelerator. The proposed technique employs a local register file (LRF) at each MAC unit in a manner such that once weight pixels are read from the global buffer into the LRF, they are reused from the LRF as many times as desired instead of being repeatedly fetched from the global buffer in each convolutional operation. One of the most evident merits of the proposed method is that the procedure is completely free from the burden of data transfer between neighboring MAC units. It was found from our simulations that the proposed method provides a power saving of approximately 83.33% and 97.62% compared with the power savings recorded by the conventional methods, respectively, when the dimensions of the input data matrix and weight matrix are 128 × 128 and 5 × 5, respectively. The power savings increase as the dimensions of the input data matrix or weight matrix increase.
Smart Mobile Information Systems on the Key Systems of Blockchain Privacy Protection

In the process of data collaboration, the data source copyright, equity boundaries, and other issues must be ensured to ensure the data owner’s income rights. The data on the blockchain are transaction data. After the data are verified, they are added to the block by the node that has the right to keep accounts. Once the data are added to the blockchain, they cannot be deleted or changed, and only authorized query operations can be performed. The transaction records on the blockchain are completely public, and the fund transactions between accounts can reflect a lot of valuable information, especially some special accounts track the IP of transaction users through methods such as address reuse, taint analysis, and cluster analysis. At the same time, a credit contribution certification mechanism is established to ensure that the contributions are directly proportional to the rewards, and finally, a credit mechanism for data fraud is established. This paper mainly analyzes the key systems of privacy protection through research data and blockchain. The research results show that different companies conduct the Pailler homomorphic encryption of data and use the secure multiparty calculation to obtain the results. The execution of the whole process is controlled by a specific intelligent contract, and the records of the execution process are stored in the blockchain. It can be seen that the blockchain-based data distribution system changes the traditional data distribution mode so that the data source and the data user can interact directly, which promotes the maintenance of the system’s security and stability.