Reviewerly/exHarmony huggingface dataset:
An Effective and Efficient Face Mask Recognition System for Edge Devices

Wearing face masks is a simple but effective measure to mitigate the spread of COVID-19. Instead of manually searching persons without a face mask, designing an intelligent system to automatically detect and recognize them is necessary. The system is usually deployed on portable edge devices in order to be used in complex environments. As these devices have limited memory and computing power, popular large models are not suitable. In this paper, we propose a novel face mask recognition system, which consists of four modules: face detection, mask recognition, liveness detection, and face recognition. Experimental results verify that the proposed system has low latency, low memory burden, and satisfactory performance.
On Controlling Drones for Disaster Relief

Drones or unmanned aerial vehicles (UAVs) are increasingly deployed for disaster relief, from aiding in drug or supply delivery to search and rescue victims. For a large-scale mission, the more drones are used, the more difficult they are to control and manage in a timely manner. Automated control is one of the central techniques used in knowledge-based drone management systems to enhance the quality and efficiency of the disaster relief mission. Although many drone management systems have been developed, automatic control of these systems remains a difficult problem. Many approaches have "human in the loop" that trades the degree of autonomy with the efficiency and perhaps accuracy. Our goal is not to fully automate the system but to reduce user interactions where intelligible (e.g., selecting appropriate drones/actions based on prioritized needs) by means of significant advances in control mechanisms. This paper presents a knowledge-based framework for intelligent drone management by explicitly representing control knowledge for use in selecting appropriate task-specific actions. The paper describes how control decisions are derived using domain knowledge relevant to drones and disaster relief. By employing a blackboard control architecture, our framework provides adaptability for dynamic control behaviors and flexibility to handle unanticipated situations during the drone mission activities. The paper illustrates the use of the framework on specific scenarios.
Blockchain-Based UAV-Assisted Forest Supervision and Data Sharing

Forest supervision is an effective means for preventing forest fires, forest diseases and insect pests, and deforestation. This paper proposes a blockchain-based forest supervision system (BSR) in a multi-party environment to achieve forest supervision, where the collected image data are stored on blockchain and IPFS, and a hybrid encryption of CP-ABE and AES algorithm is employed to realize secure forest data sharing and access control among ground base stations. Theoretical analysis and comparison show that the BSR scheme is effective, and the experimental analysis demonstrates its practicability.
Accelerating Deep Convolutional Neural Networks Using Number Theoretic Transform

Modern deep convolutional neural networks (CNNs) suffer from high computational complexity due to excessive convolution operations. Recently, fast convolution algorithms such as fast Fourier transform (FFT) and Winograd transform have gained attention to address this problem. They reduce the number of multiplications required in the convolution operation by replacing it with element-wise multiplication in the transform domain. However, fast convolution-based CNN accelerators have three major concerns: expensive domain transform, large memory overhead, and limited flexibility in kernel size. In this paper, we present a novel CNN accelerator based on number theoretic transform (NTT), which overcomes the existing limitations. We propose the low-cost NTT and inverse-NTT converter that only use adders and shifters for on-chip domain transform, which solves the inflated bandwidth problem and enables more parallel computations in the accelerator. We also propose the accelerator architecture that includes multiple tile engines with the optimized data flow and mapping. Finally, we implement the proposed NTT-based CNN accelerator on the Xilinx Alveo U50 FPGA and evaluate it for popular deep CNN models. As a result, the proposed accelerator achieves 2859.5, 990.3, and 805.6 GOPS throughput for VGG-16, GoogLeNet, and Darknet-19, respectively. It outperforms the existing fast convolution-based CNN accelerators up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.6\times $ </tex-math></inline-formula> .
Multi-Part Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

In skeleton-based action recognition task, graph convolutional network has attracted widespread attention and achieved remarkable results. However, most of the current methods are performing graph convolution on the entire skeleton graph, ignoring the fact that people are composed of different body parts. In addition, previous work ignores the temporal and spatial independence and relevance of different parts. Thus, to solve these issues, we optimize the representation of the skeleton graph, graph convolution and temporal convolution respectively. In this work, we propose multi-part adaptive graph convolution (MPA-GC) to adaptively learn the topology of each part of the body and dynamically aggregate the relevance between them. Meanwhile, we add a multi-scale temporal convolution module to better obtain temporal dimension features. Ultimately, we develop a powerful graph convolutional network named MPA-GCN, and extensive experiments on two public large-scale datasets NTU-RGB+D and NTU-RGB+D120 demonstrate the effectiveness of our module, which outperforms state-of-the-art methods.
An Efficient Optical Mura Compensation System for Large Liquid-Crystal Display Panels

Increased mura defect rates as liquid-crystal display (LCD) sizes and resolutions increase have become an issue of the utmost concern for large LCD panel makers. To address this issue, an efficient optical mura compensation (referred to as a de-mura) system for large LCD panels is proposed in this work. More specifically, the following measures were implemented to ensure the operation of the system with high reliability and efficiency: (1) An elliptic paraboloid (EP) model was proposed to deal with the measurement error caused by the viewing-angle vignetting in the LCD panel during imaging. (2) A time-domain filter and other preprocessing algorithms were built into the firmware of a high-resolution monochrome camera. (3) To accurately compensate for luminance mura, a fourth-order polynomial was applied to generate luminance de-mura data. On this basis, a local linear interpolation model was established to generate the chromaticity de-mura data. (4) The parallel acceleration of the algorithm was achieved by using a combined central processing unit (CPU) multithreading-graphic processing unit (GPU) computing architecture. The experimental results show that the developed system and algorithm perform quite well, in terms of reliability and operational efficiency. The tact time (T.T) for 4K and 8K LCD panels was less than 20 and 25 s, respectively, which fully meets manufacturers’ mass production requirements.
Monocular 3D Object Detection Based on Pseudo-LiDAR Point Cloud for Autonomous Vehicles

Pseudo-LiDAR point clouds are generated from monocular image. Compared with the point cloud from LiDAR, it can provide denser data. Due to the inaccuracy of depth estimation, there is still a performance gap between the pseudo-LiDAR point cloud and the LiDAR one. In this paper, we propose an approach to eliminate the performance degradation caused by deviation of depth estimation and realize 3D object detection based on pseudo-LiDAR point cloud. Comparative results on KITTI 3D benchmark illustrate that in contrast to other methods, our scheme can achieve more reliable performance on both object localization and shape estimation.
Sentimental analysis of Twitter data and Comparison of covid 19 Cases trend Using Machine learning algorithms

Coronavirus was first detected in the year 2019 in Wuhan, China. The disease rapidly spread across the country in a short span of time. The Government had imposed strict rules and restrictions for lockdown and social distancing, work from home, and online classes to prevent the further spread of these covid cases During this phase, the morality of the covid cases was significantly controlled. But the larger population was affected by this. So, the mindset of the people has been changed. Sentimental analysis is an opinion mining approach to NLP which is used to detect and categorize the data as positive, negative, and neutral. In a situation like the COVID pandemic, one must stay in a positive mindset. In our project, we are implementing sentimental analysis using the Random Forest algorithm along with comparing the trend in variation of COVID 19 cases using the LSTM and KNN algorithms.
How Much Does Software Complexity Matter for Maintenance Productivity? The Link Between Team Instability and Diversity

Software complexity decreases maintenance productivity, as do team attributes of instability and knowledge diversity. We know little about the extent to which the two team attributes interact with software complexity and shape productivity across systems of varying complexity. We address this gap by investigating whether and to what degree software complexity moderates the effects of team instability and knowledge diversity on maintenance productivity over the life of a system. We posit, given the exponential growth of code and task dependencies inherent in complex software systems, that system-level complexity has a significant nonlinear amplifying effect on the adverse effects of the two team attributes. To validate the presence of such an effect, we conduct a robust split-sample econometric analysis using three years of maintenance data from 426 mission-critical systems of a Fortune 100 company. The sampled systems vary in size (50KLOC to 2000KLOC, where 20% exceed 500KLOC), with a considerable portion of the sample manifesting “high” to “very high” software complexity. The analysis corroborates the known adverse effects of team instability, team knowledge diversity, and software complexity on maintenance productivity. More importantly, it shows—as theorized—that the adverse effects of the team attributes on maintenance productivity are significantly amplified only when software complexity grows high. We conclude with practical and research implications about how to manage software teams maintaining complex software over the life of a system.
Online Edge Flow Imputation on Networks

An online algorithm for missing data imputation for networks with signals defined on the edges is presented.Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via (i) a sparse line graph identification strategy based on a group-Lasso and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation.The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.
ML-Based Model for Risk Prediction in Software Requirements

Software risk prediction is the most sensitive and crucial activity of the SDLC. It may lead to the success or failure of the project. The requirement gathering stage is the most important and challenging stage of the SDLC. The risks should be tackled at this stage and saved to be used in future projects. However, a model is proposed for the prediction of software requirement risks using the requirement risk dataset and ML classification. This research study proposed a model for risk prediction in software requirements that will be evaluated using several evaluation measures (e.g., precision, F-measure, MCC, recall, and accuracy). For the completion of this study, the dataset is taken from Zenodo repository. The model is evaluated using ML techniques. After the finding and analysis of results, DT shows best performance with accuracy of 99%.
Performance Analysis of Resampling and Ensemble Learning Methods on Diabetes Detection as Imbalanced Dataset

Early detection of diabetes is essential to reducing a high mortality rate. Early detection can be made by studying the possibility of diabetes from the variables obtained in the data of diabetes patients. How to diagnose a patient with medical data becomes a challenge because these are usually imbalanced, where negative cases severely outnumber positive cases. For preprocessing the imbalanced data, this paper designs an algorithm using resampling techniques combined with an ensemble learning algorithm. There are some oversampling techniques ADASYN, ROS, and SMOTE. Whereas, the undersampling techniques are RUS, Tomek, and ENN. The combined techniques like SMOTE-ENN and SMOTE-Tomek are also used to handle highly imbalanced dataset diabetes. Then, the ensemble learning algorithm that is used is Random Forest, Bagging, AdaBoost, and XGBoost. Based on the experimental results, the best performance is using SMOTE-ENN with AdaBoost, with a recall score of 0.7330 even though the F1-Score of this model is 0.6459. AdaBoost Classifier also has good and stable results with various types of resampling. By using SMOTE-ENN, the recall score of the model increased by 0.1819 and the F1 score decreased by 0.2000 from the original model result. The higher sensitivity/recall is more important in medical diagnoses to correctly identify patients with disease than the F1 Score.
Concreate: Using Data Physicalization to Increase The Understanding and Inspirational Use of Quantitative Data In Data-Driven Design Scenarios

Abstract In today’s world, the advantages of data-enabled design are undeniable, increasing the performance of organisations drastically by informing and inspiring the design process. While organisations seem to be more experienced with quantitative data for evaluative purposes, they do struggle to use data as creative material to inspire the design process. Choosing the right type of data representation is critical for using data for creative purposes. Data visualization has proven to be highly effective in increasing understanding of data, as it is fast, accurate and flexible. Data physicalization, on the other hand, remains unexplored in comparison, especially its effect on creativity. This paper presents the results of two studies (one preliminary and one follow-up study), which explored the use of data physicalization in creative settings. The preliminary study enabled to collect initial requirements for the development of a physicalization toolkit, while the follow-up study investigated its impact on the design process, in comparison to data visualization. From the studies, we developed Concreate, a collaborative data physicalization toolkit designed to lead to creative insights from quantitative data. Our results show that Concreate can potentially stimulate creative thinking, by encouraging intense, tangible interaction with data leading to increased reflection-in-action and a deeper understanding of data. The two studies and toolkit development were carried out at a multinational automotive company, interested in innovating by incorporating data as creative material. Besides the immediate practical implications, we conclude this paper with a discussion on future recommendations for using data physicalization in the design process.
A Collaborative Access Control Scheme Based on Incentive Mechanisms

Aiming at the problems of the existing attribute-based collaborative decryption schemes exemplified by failures in cloud storage servers and poor enthusiasm of users for collaborative decryption, a collaborative access control scheme based on incentive mechanisms is proposed. In this scheme, users are divided into different groups and only users of the same group can collaborate to decrypt. The data is encrypted with attribute-based encryption (ABE) and is uploaded to Inter-Planetary File System (IPFS), which not only ensures the confidentiality of data but also prevents malfunctions. Through the incentive mechanism of the blockchain, the user’s enthusiasm of decryption is improved. Finally, the theoretical analysis shows that the scheme is secure and efficient.
Can we trust trust-based data governance models?

Abstract Fiduciary agents and trust-based institutions are increasingly proposed and considered in legal, regulatory, and ethical discourse as an alternative or addition to a control-based model of data management. Instead of leaving it up to the citizen to decide what to do with her data and to ensure that her best interests are met, an independent person or organization will act on her behalf, potentially also taking into account the general interest. By ensuring that these interests are protected, the hope is that citizens’ willingness to share data will increase, thereby allowing for more data-driven projects. Thus, trust-based models are presented as a win–win scenario. It is clear, however, that there are also apparent dangers entailed with trust-based approaches. Especially one model, that of data trusts, may have far-reaching consequences.
Searching for Encrypted Data on Blockchain: An Efficient, Secure and Fair Realization

Searchable symmetric encryption (SSE) is a research hotspot in applied cryptography, with the purpose of protecting outsourced data while enabling querying of encrypted data. However, the majority of current research focuses on the scenario in which data is stored on a single server and disregards the possibility that both the clients and servers are malicious. While several existing blockchain-based SSE schemes provide solutions to the issues above, they do not simultaneously achieve security, fairness, and decentralized storage. In this paper, we explore how to efficiently solve the above problems in the blockchain setting. We build up a decentralized fair SSE framework in a layered fashion. First, we present a practical and efficient method for accessing data on the blockchain. Based on this, we craft a decentralized publicly verifiable SSE scheme in which encrypted indexes are stored on the blockchain and search operations are shifted to be executed off-chain for lightweight decentralized storage and efficient query performance. Then, we use smart contracts to confer fairness to SSE by constructing a game model that makes each party prefer to cooperate. Finally, we implement and evaluate our framework on Ethereum. The experimental results demonstrate that our design is effective and practical.
Data sovereignty for AI pipelines

The establishment of collaborative AI pipelines, in which multiple organizations share their data and models, is often complicated by lengthy data governance processes and legal clarifications. Data sovereignty solutions, which ensure data is being used under agreed terms and conditions, are promising to overcome these problems. However, there is limited research on their applicability in AI pipelines. In this study, we extended an existing AI pipeline at Mondragon Corporation, in which sensor data is collected and subsequently forwarded to a data quality service provider with a data sovereignty component. By systematically reflecting and generalizing our experiences during the twelve-month action research project, we formulated ten lessons learned, four benefits, and three barriers to data-sovereign AI pipelines that can inform further research and custom implementations. Our results show that a data sovereignty component can help reduce existing barriers and increase the success of collaborative data science initiatives.
A Bibliometric Review of Methods and Algorithms for Generating Corpora for Learning Vector Word Embeddings

Natural Language Processing (NLP) problems are among the hardest Machine Learning (ML) problems due to the complex nature of the human language. The introduction of word embeddings improved the performance of ML models on various NLP tasks as text classification, sentiment analysis, machine translation, etc. Word embeddings are real-valued vector representations of words in a specific vector space. Producing quality word embeddings that are then used as input to downstream NLP tasks is important in obtaining a good performance. To accomplish it, corpora of sufficient size is needed. Corpora may be formed in a multitude of ways, including text that was originally electronic, spoken language transcripts, optical character recognition, and synthetically producing text from the available dataset. The study provides the most recent bibliometric analysis on the topic of corpora generation for learning word vector embeddings. The analysis is based on the publication data from 2006 to 2022 retrieved from Scopus scientific database. A descriptive analysis method has been employed to obtain statistical characteristics of the publications in the research area. The systematic analysis results show the field’s evolution over time and highlight influential contributions to the field. It is believed that compiled bibliometric reviews could help researchers gain knowledge of the general state of the scientific knowledge, its descriptive features, patterns, and insights to design their studies systematically.
Study and Comparision of Vectorization Techniques Used in Text Classification

Reviews on products and movies play an important role in predicting and formulating business strategies. Entertainment media, E-commerce, and social media use customers’ reviews to analyze customers’ requirements and level of satisfaction with the product. Business Analyst uses Sentiment Analysis for analyzing the attitude of the users from their reviews. E-commerce websites, entertainment and social media posts, tweets, comments, reviews, status, etc are the major sources of sentiment data (reviews). In the review system, users give the rating on a predefined scale of (1-5) i.e lowest to highest in terms of their satisfaction. As sentiment Analysis is one of the major applications of Machine Learning and machine learning deals with numeric data, so, textual-based review data needs to be converted into numeric data. Conversion of text to numeric form requires a large amount of memory and it is time-consuming also. This paper presents various vectorization techniques and their comparison in terms of memory management to convert text file into a vector file. The comparison shows gensim library-based Doc2Vec approach reduces memory requirements by up to 80%. This will also reduce the time consumption for task analysis and data processing of the model.