Reviewerly/exHarmony huggingface dataset:
Parallel Image-Based Visual Servoing/Force Control of a Collaborative Delta Robot

In this paper, a parallel Image-based visual servoing/force controller is developed in order to solve the interaction problem between the collaborative robot and the environment so that the robot can track the position trajectory and the desired force at the same time. This control methodology is based on the image-based visual servoing (IBVS) dynamic computed torque control and couples the force control feedback in parallel. Simulations are performed on a collaborative Delta robot and two types of image features are tested to determine which one is better for this parallel IBVS/force controller. The results show the efficiency of this controller.
Investigation of the Effectiveness of Augmented Reality and Modelling Based Teaching in "Solar System and Eclipses" Unit

This study was aimed to investigate the effectiveness of augmented reality and modelling based teaching in the "Solar System and Eclipses" unit. The pretest-posttest quasi-experimental design model was used in this study. For three weeks, the "Solar System and Eclipses" unit of the Science course was taught to the experimental group using modelling based teaching and augmented reality applications, and, in the control group, the existing science curriculum was followed. The study group consists of 22 students who were in the 6th grade in the 2020-2021 academic year in a secondary school affiliated with the Ministry of National Education. Data collection tools in this study were administered “Academic Achievement Test”, “21st-Century Skills Scale”, and “Augmented Reality Applications Attitude Scale”. The application of augmented reality and modelling based teaching in covering the "Solar System and Eclipses" unit was positively affected students' learning and increased their levels of success, improved the 21st-century skills and improved attitudes towards augmented reality applications. Studies on the use of augmented reality applications and modelling based teaching in different Science course units of different grades of secondary school that will contribute positively to the literature are recommended.
An improved secure designated server public key searchable encryption scheme with multi-ciphertext indistinguishability

Abstract In the cloud, users prefer to store their sensitive data in encrypted form. Searching keywords over encrypted data without loss of data confidentiality is an important issue. In 2004, Boneh et al. proposed the first public-key searchable encryption scheme which allows users to search by the private key. However, most existing public-key searchable encryption schemes are vulnerable to keyword guessing attack and can not satisfy multi-ciphertext indistinguishability. In this paper, we construct a secure designated server public-key searchable encryption based on Diffie-Hellman problem. Our security analysis shows that our proposed scheme can resist against keyword guessing attack and provide multi-ciphertext indistinguishability for any adversity. Furthermore, the proposed scheme can achieve multi-trapdoor privacy for external attackers. Moreover, the simulation results between our scheme and previous schemes demonstrate our new scheme is suitable for practical application.
Graph-based active learning for semi-supervised classification of SAR data

We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. Graph-based methods in machine learning are based on a similarity graph constructed from the data. When the data consists of raw images composed of scenes, extraneous information can make the classification task more difficult. In recent years, neural network methods have been shown to provide a promising framework for extracting patterns from SAR images. These methods, however, require ample training data to avoid overfitting. At the same time, such training data are often unavailable for applications of interest, such as automatic target recognition (ATR) and SAR data. We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semi-supervised learning techniques. The CNNVAE feature embedding and graph construction requires no labeled data, which reduces overfitting and improves the generalization performance of graph learning at low label rates. Furthermore, the method easily incorporates a human-in-the-loop for active learning in the data-labeling process. We present promising results and compare them to other standard machine learning methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset for ATR with small amounts of labeled data.
The association of public trust with the utilization of digital contact tracing for COVID-19 in Japan

To examine whether public trust was associated with the utilization of COVID-19 Contact Confirming Application (COCOA) in those who self-reported a history of COVID-19. Cross-sectional study. Data were obtained from the Japan Society and New Tobacco Internet Survey, a nationwide online survey conducted from February to March 2021, which also assessed items related to COVID-19 and public trust. We included 453 participants with a history of COVID-19. Participants' reports of their general trust in the national government and the related policies, attitudes toward COVID-19 vaccination, and the adherence to the preventive measures against SARS-CoV-2 spread were compared between COCOA users and non-users controlling for age, sex, and socioeconomic statuses by analysis of covariance. Mediation analysis was conducted to examine whether public trust mediates the associations of certain participants’ characteristics with COCOA utilization. Seventy-six percent (344/453) reported the COCOA utilization. Compared to non-users, the users were younger, more likely to be men and had a tendency to have higher education. They were more willing to get COVID-19 vaccination, adherent to public health measures against the spread of the SARS-Cov-2, and more likely to express trust in government in general and policies related to COVID-19 independent of age, sex, and the socioeconomic status. Trust in government did not mediate the associations of age and education with COCOA utilization. The utilization of digital contact tracing technology for the health of public during pandemic was related to the degree of trust in the government in Japan.
TextCL: A Python package for NLP preprocessing tasks

Preprocessing text data sets for use in Natural Language Processing tasks is usually a time-consuming and expensive effort.Text data, normally obtained from sources such as, but not limited to, web scraping, scanned documents or PDF files, is typically unstructured and prone to artifacts and other types of noise.The goal of the TextCL package is to simplify this process by providing multiple methods suited for text data preprocessing.It includes functionality for splitting texts into sentences, filtering sentences by language, perplexity filtering, and removing duplicate sentences.Another functionality offered by the TextCL package is the outlier detection module, which allows to identify and filter out texts that are different from the main topic distribution of the data set.This method allows selecting one of several unsupervised outlier detection algorithms, such as TONMF (block coordinate descent framework), RPCA (robust principal component analysis), or SVD (singular value decomposition) and apply it to the text data.
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition

Transformers have recently dominated the ASR field.Although able to yield good performance, they involve an autoregressive (AR) decoder to generate tokens one by one, which is computationally inefficient.To speed up inference, non-autoregressive (NAR) methods, e.g.single-step NAR, were designed, to enable parallel generation.However, due to an independence assumption within the output tokens, performance of single-step NAR is inferior to that of AR models, especially with a largescale corpus.There are two challenges to improving singlestep NAR: Firstly to accurately predict the number of output tokens and extract hidden variables; secondly, to enhance modeling of interdependence between output tokens.To tackle both challenges, we propose a fast and accurate parallel transformer, termed Paraformer.This utilizes a continuous integrate-andfire based predictor to predict the number of tokens and generate hidden variables.A glancing language model (GLM) sampler then generates semantic embeddings to enhance the NAR decoder's ability to model context interdependence.Finally, we design a strategy to generate negative samples for minimum word error rate training to further improve performance.Experiments using the public AISHELL-1, AISHELL-2 benchmark, and an industrial-level 20,000 hour task demonstrate that the proposed Paraformer can attain comparable performance to the state-of-the-art AR transformer, with more than 10x speedup.
Assessing Annotator Identity Sensitivity via Item Response Theory: A Case Study in a Hate Speech Corpus

Content Warning: This paper contains content considered profane, hateful, and offensive.
Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

The current power distribution Internet of Things (PDIoT) lacks security protection terminals and techniques. Network security has a large exposure surface that can be attacked from multiple paths. In addition, there are many network security vulnerabilities and weak security protection capabilities of power distribution Internet of Things terminals. Therefore, it is crucial to conduct a scientific assessment of the security of PDIoT. However, traditional security assessment methods are relatively subjective and ambiguous. To address the problems, we propose to use the entropy-weight method and cloud model theory to assess the security risk of the PDIoT. We first analyze the factors of security risks in PDIoT systems and establish a three-layer PDIoT security evaluation index system, including a perception layer, network layer, and application layer. The index system has three first-level indicators and sixteen second-level indicators. Then, the entropy-weight method is used to optimize the weight of each index. Additionally, the cloud model theory is employed to calculate the affiliation degree and eigenvalue of each evaluation index. Based on a comprehensive analysis of all evaluation indexes, we can achieve the security level of PDIoT. Taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example for empirical testing, the experimental results show that the evaluation results are consistent with the actual situation, which proves that the proposed method is effective and feasible.
Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

Mapping the subsurface temperatures can efficiently lead to identifying the geothermal distribution heat flow and potential hot spots at different depths. In this paper, an advanced adaptive multitask deep learning procedure for 3D spatial mapping of the subsurface temperature was proposed. As a result, predictive 3D spatial subsurface temperatures at different depths were successfully generated using geolocation of 494 exploratory boreholes data in Catalonia (Spain). To increase the accuracy of the achieved results, hybridization with a new modified firefly algorithm was carried out. Subsequently, uncertainty analysis using a novel automated ensemble deep learning approach for the predicted temperatures and generated spatial 3D maps were executed. Comparing the accuracy performances in terms of correct classification rate (CCR) and the area under the precision–recall curves for validation and whole datasets with at least 4.93% and 2.76% improvement indicated for superiority of the hybridized model. According to the results, the efficiency of the proposed hybrid multitask deep learning in 3D geothermal characterization to enhance the understanding and predictability of subsurface spatial distribution of temperatures is inferred. This implies that the applicability and cost effectiveness of the adaptive procedure in producing 3D high resolution depth dependent temperatures can lead to locate prospective geothermally hotspot active regions.
A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning

Trash classification is an effective measure to protect the ecological environment and improve resource utilization. With the development of deep learning, it is possible to use the deep convolutional neural network for trash classification. In order to classify the trash of the TrashNet dataset, which consists of six classes of garbage images, this paper proposes a hybrid deep learning model based on deep transfer learning, which includes upper and lower streams. Firstly, the upper stream divides the input garbage image into category MPP (metal, paper, and plastic class) or category CGT (cardboard, glass, and trash class). Then, the lower stream predicts the exact class of trash according to the results of the upper stream. The proposed hybrid deep learning model achieves the best result with 98.5 % than that of the state-of-the-art approaches. Through the verification of CAM (class activation map), the proposed model can reasonably use the features of the image for classification, which explains the reason for the superior performance of this model.
Critical soft skills for information and knowledge management practitioners in the fourth industrial revolution

Background: Many technology jurisdictions have peddled the narrative that the key determinant for an innovative and sustainable fourth industrial revolution (4IR) environment is possessing hard technical skills. Hard technical skills are important to design the actual 4IR-based applications. Postmodernity demands that appropriate soft skills complement the hard skills to effectively integrate technology into various socio-economic value chains. In fact, soft skills are slowly becoming one of the critical enablers to harness the promise of the 4IR.Objectives: This research article aimed to critically understand the soft skills considered to be essential in the South African context by different information and knowledge management (IKM) practitioners. The aims and objectives of the study were to fill the gap where other disciplines have specified soft skills whilst IKM does not. This study looked to identify soft skills to allow IKM practitioners an opportunity to identify and develop these skills.Method: This research was designed based on the Delphi study principles and further used a systematic and targeted literature review to allow the researchers to make logical conclusions deductively. The authors followed a multimethod approach and analysed data using content analysis.Results: The study results have demonstrated that soft skills are considered significantly more important than hard skills in South Africa. The study identified 57 total skills. However, only 17 had consensus from experts.Conclusion: This study provides insights into the critical success skills needed to harness the socio-economics brought about by the 4IR. Further studies are required in different contextual settings to understand the global skills pertinent to the 4IR.
Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles

We introduce an early-phase bottleneck analysis and characterization model called the F-1 for designing computing systems that target autonomous Unmanned Aerial Vehicles (UAVs). The model provides insights by exploiting the fundamental relationships between various components in the autonomous UAV, such as sensor, compute, and body dynamics. To guarantee safe operation while maximizing the performance (e.g., velocity) of the UAV, the compute, sensor, and other mechanical properties must be carefully selected or designed. The F-1 model provides visual insights that can aid a system architect in understanding the optimal compute design or selection for autonomous UAVs. The model is experimentally validated using real UAVs, and the error is between 5.1% to 9.5% compared to real-world flight tests. An interactive web-based tool for the F-1 model called Skyline is available for free of cost use at: https://bit.ly/skyline-tool
Hybrid Features by Combining Visual and Text Information to Improve Spam Filtering Performance

The development of information and communication technology has created many positive outcomes, including convenience for people; however, cases of unsolicited communication, such as spam, also occur frequently. Spam is the indiscriminate transmission of unwanted information by anonymous users, called spammers. Spam content is indiscriminately transmitted to users in various forms, such as SMS, e-mail, and social network service posts, causing negative experiences for users of the service, while also creating costs, such as unnecessarily large amounts of network traffic. In addition, spam content includes phishing, hype or false advertising, and illegal content. Recently, spammers have also used images that contain stimulating content to effectively attract users’ curiosity and attention. Image spam contains more complex information than text, making it more difficult to analyze and to generalize its properties compared to text. Therefore, existing text-based spam detectors are vulnerable to spam image attacks, resulting in a decline in service quality. In this paper, a “hybrid features by combining visual and text information to improve spam filtering performance” method is proposed to reduce the occurrence of misclassification. The proposed method employs three sub-models to extract features from spam images and a classifier model to output the results using the features. Each sub-model extracts topic-, word-, and image-embedding-based features from spam images. In addition, the sub-models use optical character recognition, latent Dirichlet allocation, and word2Vec techniques to extract features from images. To evaluate spam image classification performance, the spam classifiers were trained using the extracted features and the results were measured using a confusion matrix. Our model achieved an accuracy of 0.9814 and a macro-F1 score of 0.9813. In addition, the application of OCR evasion techniques resulted in a decrease in recognition performance. Using the proposed model, a mean macro-F1 score of 0.9607 was obtained.
AnomalyKiTS: Anomaly Detection Toolkit for Time Series

This demo paper presents a design and implementation of a system AnomalyKiTS for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning. Given an input time series, AnomalyKiTS provides four categories of model building capabilities followed by an enrichment module that helps to label anomaly. AnomalyKiTS also supports a wide range of execution engines to meet the diverse need of anomaly workloads such as Serveless for CPU intensive work, GPU for deep-learning model training, etc.
Tight Neural Network Verification via Semidefinite Relaxations and Linear Reformulations

We present a novel semidefinite programming (SDP) relaxation that enables tight and efficient verification of neural networks. The tightness is achieved by combining SDP relaxations with valid linear cuts, constructed by using the reformulation-linearisation technique (RLT). The computational efficiency results from a layerwise SDP formulation and an iterative algorithm for incrementally adding RLT-generated linear cuts to the verification formulation. The layer RLT-SDP relaxation here presented is shown to produce the tightest SDP relaxation for ReLU neural networks available in the literature. We report experimental results based on MNIST neural networks showing that the method outperforms the state-of-the-art methods while maintaining acceptable computational overheads. For networks of approximately 10k nodes (1k, respectively), the proposed method achieved an improvement in the ratio of certified robustness cases from 0% to 82% (from 35% to 70%, respectively).
AI Driven Accounts Payable Transformation

Accounts Payable (AP) is a resource-intensive business process in large enterprises for paying vendors within contractual payment deadlines for goods and services procured from them. There are multiple verifications before payment to the supplier/vendor. After the validations, the invoice flows through several steps such as vendor identification, line-item matching for Purchase order (PO) based invoices, Accounting Code identification for Non- Purchase order (Non-PO) based invoices, tax code identification, etc. Currently, each of these steps is mostly manual and cumbersome making it labor-intensive, error-prone, and requiring constant training of agents. Automatically processing these invoices for payment without any manual intervention is quite difficult. To tackle this challenge, we have developed an automated end-to-end invoice processing system using AI-based modules for multiple steps of the invoice processing pipeline. It can be configured to an individual client’s requirements with minimal effort. Currently, the system is deployed in production for two clients. It has successfully processed around ~80k invoices out of which 76% invoices were processed with low or no manual intervention.