Reviewerly/exHarmony huggingface dataset:
Social Capital, Organizational Innovation and Performance of Manufacturing MSMEs in Kenya

Sustained growth and development of economies is mainly attributed to the manufacturing sector, and is often the differentiator between developed and developing countries. In Kenya, the sector has played a crucial role, with a contribution of 10% to gross domestic product for the duration 2008 to 2014. Statistics show that in 2017, 2018 and 2019, the contribution deteriorated to 8.4%, 7.7% and 7.54% respectively, which implies possible de-industrialization. The government’s goal of achieving a robust manufacturing sector through Big Four Agenda may not be fruitful if this trend continues. Informed by the trends, the study sought to establish the mediating role of organizational innovation on the connection between social capital and firm performance among the micro, small and medium manufacturing enterprises. The study is based on existing theories namely social capital theory, behavioral theory of the firm, and Schumpeter theory of innovation. The sample size was 384 licensed manufacturing businesses operating within Nairobi City County, derived from a population of 61,931. The study applied descriptive and explanatory research designs. Primary data was collected and analyzed using descriptive and inferential statistics. The findings indicated that relational and cognitive social capital positively and significantly predicted the performance of micro, small and medium manufacturing enterprises, whereas structural social capital was noted to have a significant effect. The mediating effect of organizational innovation was partial on the relationship between social capital and performance of micro, small and medium manufacturing enterprises. This study recommends that the firms’ management taps into additional and diverse networks to drive innovation and subsequently create a competitive edge for their firms.
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge

In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifically the stuttering sub-challenge. In particular, we exploit the embeddings from the pre-trained Wav2Vec2.0 model for stuttering detection (SD) on the KSoF dataset. After embedding extraction, we benchmark with several methods for SD. Our proposed self-supervised based SD system achieves a UAR of 36.9% and 41.0% on validation and test sets respectively, which is 31.32% (validation set) and 1.49% (test set) higher than the best (DeepSpectrum) challenge baseline (CBL). Moreover, we show that concatenating layer embeddings with Mel-frequency cepstral coefficients (MFCCs) features further improves the UAR of 33.81% and 5.45% on validation and test sets respectively over the CBL. Finally, we demonstrate that the summing information across all the layers of Wav2Vec2.0 surpasses the CBL by a relative margin of 45.91% and 5.69% on validation and test sets respectively.
Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation Control

The proliferation of unmanned aerial vehicles (UAVs) has spawned a variety of intelligent services, where efficient coordination plays a significant role in increasing the effectiveness of cooperative execution. However, due to the limited operational time and range of UAVs, achieving highly efficient coordinated actions is difficult, particularly in unknown dynamic environments. This paper proposes a multiagent deep reinforcement learning (MADRL)-based fusion-multiactor-attention-critic (F-MAAC) model for multiple UAVs’ energy-efficient cooperative navigation control. The proposed model is built on the multiactor-attention-critic (MAAC) model, which offers two significant advances. The first is the sensor fusion layer, which enables the actor network to utilize all required sensor information effectively. Next, a layer that computes the dissimilarity weights of different agents is added to compensate for the information lost through the attention layer of the MAAC model. We utilize the UAV LDS (logistic delivery service) environment created by the Unity engine to train the proposed model and verify its energy efficiency. The feature that measures the total distance traveled by the UAVs is incorporated with the UAV LDS environment to validate the energy efficiency. To demonstrate the performance of the proposed model, the F-MAAC model is compared with several conventional reinforcement learning models with two use cases. First, we compare the F-MAAC model to the DDPG, MADDPG, and MAAC models based on the mean episode rewards for 20k episodes of training. The two top-performing models (F-MAAC and MAAC) are then chosen and retrained for 150k episodes. Our study determines the total amount of deliveries done within the same period and the total amount done within the same distance to represent energy efficiency. According to our simulation results, the F-MAAC model outperforms the MAAC model, making 38% more deliveries in 3000 time steps and 30% more deliveries per 1000 m of distance traveled.
Hybrid Approach Based on Grey Wolf Optimizer for Dropout Regularization in Deep Learning

One of the advanced concepts in artificial intelligence is deep learning, which is a subfield of Machine Learning. Training deep neural networks requires setting optimal hyperparameters. Dropout is a regularization parameter that avoids overfitting when training deep neural networks. Despite the success of this technique, finding the optimal value of the dropout probability with a manual search is a time-consuming process. Therefore, metaheuristic algorithms are the best choice to find this optimal value. In this paper, we propose a hybrid search method based on Gray Wolf Optimizer (GWO) and Multi-Verse Optimizer (MVO) to select the dropout probability rate. The results obtained on the image classification task show clearly the good performance of the proposed method.
Exploration and Practice of Multidisciplinary Integration of Law Teaching under the Background of IOT and Wireless Communication

The exploration and practice of multidisciplinary integration of legal teaching under the background of wireless communication are still at the primary level, facing the situation of low degree of multidisciplinary integration of legal teaching and low contribution of wireless communication technology and Internet of Things technology to multidisciplinary integration of legal teaching. To solve these problems, it is necessary to take wireless communication technology and Internet of Things technology as the internal driving force to promote the development of multidisciplinary integration of legal teaching, so that wireless communication technology and Internet of Things technology depend on the strategy of legal and multidisciplinary integration teaching, hide in the teaching link, and use wireless communication technology to solve the pain points in the multidisciplinary integration of legal teaching, so as to serve teaching.
Rashomon Effect and Consistency in Explainable Artificial Intelligence (XAI)

The consistency of the explainability of artificial intelligence (XAI), especially with regard to the Rashomon effect, is in the focus of the here presented work. Rashomon effect has been named the phenomenon of receiving different machine learning (ML) explanations when employing different models to describe the same data. On the basis of concrete examples, cases of Rashomon effect will be visually demonstrated and discussed to underline the difficulty to practically produce definite and unambiguous machine learning explanations and predictions. Artificial intelligence (AI) presently undergoes a so-called replication and reproducibility crisis which hinders models and techniques from being properly assessed for robustness, fairness, and safety. Studying the Rashomon effect is important for understanding the causes of the unintended variability of results which originate from-* within the models and the XAI methods themselves.
Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching

Abstract The extension of quantile regression to count data raises several issues. We compare the traditional approach, based on transforming the count variable using jittering, with a recently proposed approach in which the coefficients of quantile regression are modelled by parametric functions. We exploit both methods to analyse university students’ data to evaluate the effect of emergency remote teaching due to COVID-19 on the number of credits earned by the students. The coefficients modelling approach performs a smoothing that is especially convenient in the tails of the distribution, preventing abrupt changes in the point estimates and increasing precision. Nonetheless, model selection is challenging because of the wide range of options and the limited availability of diagnostic tools. Thus the jittering approach remains fundamental to guide the choice of the parametric functions.
Efficient Region of Interest Encryption Based on a New Chaotic Map

The recent advances in digital imaging and transmission over network demand a secure and efficient image encryption system. Traditional cryptographic algorithms encrypt full image, however only some part of the image is important and needs complex encryption algorithm to provide security. To address this issue, we have proposed a novel encryption technique based on YOLOv2 object detection and proposed Gauss-Square Cosine (GSC) chaotic map. YOLOv2 is used to divide the image into region of interest (ROI) and noncritical region. SHA2-256 hash of scrambled noncritical region is then used as an initial value of GSC chaotic map to encrypt ROI. ROI encryption technique uses pixel permutation to decorrelate adjacent pixels and random substitution to change pixel values. Differential and statistical analysis demonstrate the complexity of proposed chaotic map and robustness of proposed technique against various security attacks. Thus the proposed encryption technique is secure and efficient for large-sized images.
Examining the role of depth information in contextual cuing using a virtual reality visual search task.

Two experiments examined the role that depth plays in the formation of associations during contextual cuing of visual search. Current associative models make predictions about the spatial constraints placed on learning within two-dimensional procedures, but there exists very little evidence of how these predictions translate to three-dimensional space. A virtual reality procedure was used to project the stimuli in three dimensions. Experiment 1 established a contextual cuing effect using this procedure, while Experiment 2 examined whether the relative distance between repeated distractors and the target or the position of the distractors relative to the observer modulated contextual cuing. It was found that the contextual cuing effect was consistent across these different conditions. As a result, there was no evidence to suggest that depth information forms a significant part of the representations that form during contextual cuing. These data are therefore broadly consistent with the mechanisms of current associative models of contextual cuing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
一个面向原始数据搜寻的快速射电暴数据集

We introduce a machine learning (ML) fast radio burst (FRB) dataset that can train ML algorithms to reach the FRBs in raw data. It has 8020 FRB simulation images, 4010 non-FRB and 4010 RFI simulation images, built from public FRB observations and can be expanded to any number as needed. This work provides an open-source dataset for state-of-the-art AI to compare FRB event recognition algorithms. The dataset includes image and Numpy format files for convolutional neural networks (CNN) and classic machine learning algorithms and can implement FRB/non-FRB or FRB/RFI/Blank classification. In the example, we use 31 classical CNN algorithms with pretrained models. In FRB/non-FRB classification, more than 90% accuracy is achieved in the first training epoch, and 99.8% maximum accuracy is achieved in real FRB dataset testing.
TinyML-CAM

TinyML: Tiny in size, big in impact! This paper presents TinyML-CAM pipeline for real-time and memory-efficient image recognition on IoT boards. TinyML-CAM can be used by developers to implement their customized tasks in ≈ 30 minutes, with minimal code configuration. We evaluated TinyML-CAM by using it to create a RandomForestClassifier (RF) based real-time image recognition system, which ran at 80 FPS and consumed only 1 kB of RAM on ESP32.
Can We Use Common Voice to Train a Multi-Speaker TTS System?

Training of multi-speaker text-to-speech (TTS) systems relies on curated datasets based on high-quality recordings or audiobooks. Such datasets often lack speaker diversity and are expensive to collect. As an alternative, recent studies have leveraged the availability of large, crowdsourced automatic speech recognition (ASR) datasets. A major problem with such datasets is the presence of noisy and/or distorted samples, which degrade TTS quality. In this paper, we propose to automatically select high-quality training samples using a non-intrusive mean opinion score (MOS) estimator, WV-MOS. We show the viability of this approach for training a multi-speaker GlowTTS model on the Common Voice English dataset. Our approach improves the overall quality of generated utterances by 1.26 MOS point with respect to training on all the samples and by 0.35 MOS point with respect to training on the LibriTTS dataset. This opens the door to au-tomatic TTS dataset curation for a wider range of languages.
PubMed Author-assigned Keyword Extraction (PubMedAKE) Benchmark

With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature.
A Comparative Study of Various Book Recommendation Algorithms for Public Libraries

Book recommendation algorithms in public libraries can recommend catalogs based on users’ borrowing records to assist in book retrieval. This paper introduced two recommendation algorithms, collaborative filtering and association rule, combined them into a hybrid recommendation algorithm, and conducted simulation experiments on the three recommendation algorithms. The results showed that the hybrid recommendation algorithm gave more personalized and specific recommendation results; the recommendation accuracy of the hybrid recommendation algorithm was higher than that of the other two algorithms regardless of the book borrowing volume of target users.
8.2: Anatomy Education Method using Autostereoscopic 3D Image Overlay and Mid‐Air Augmented Reality Interaction

The augmented reality (AR) medical education systems offer unique advantages for an interactive and hands‐on integration with the student in surgical skill training. However, there are still major differences in visualization and interaction between real surgical environments and AR‐based medical education systems. In this paper, we propose an anatomy education method using an autostereoscopic 3D image overlay and mid‐air augmented reality interaction with soft tissue deformation. To achieve good depth perception and intuitive interaction, we introduce a model‐based occlusion handling for accurate occlusion processing in real time and a position‐based dynamics (PBD) simulation method for virtual soft tissue deformation in real time. The experimental results showed that our approach could provide 3D visualization and interaction of virtual soft tissues and handle the mutual occlusion automatically in real time. Our proposed medical education system will provide users with a visualization and interaction experience close to real surgical environments.
Cross-Context Accurate English Translation Method Based on the Machine Learning Model

The era of big data and cloud computing has come, communication between different languages is becoming more and more common, and the barriers between languages are becoming more and more prominent. As the most important means to overcome language barriers, machine translation will play an increasingly important role in modern society. The previous machine translation technology has more or less disadvantages. The accuracy of translation is too low, which is a huge bottleneck hindering the further development of machine translation technology. Therefore, based on this, we can consider modeling the cross-context accurate English translation model based on the machine translation model and rely on the working principle of machine learning. This experiment shows that the translation accuracy of our method reaches 94.2%, which is higher than 39.5% of the benchmark method. This shows that the method in this paper can reduce the influence of other factors, ensure the accuracy of cross-context English translation to a certain extent, and meet the performance improvement requirements of the English translation system.
WeDIV – An improved k-means clustering algorithm with a weighted distance and a novel internal validation index

Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k-means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted distance, EP_dis, was designed by considering the relative contribution between Euclidean and Pearson distances with a weighted strategy. This strategy can effectively capture information reflecting the globally spatial correlation and locally variable trend simultaneously in high-dimensional space. The new internal validation index,RCH, inspired by the Calinski-Harabasz (CH) index and the analysis of variance, was developed to automatically estimate the optimal number of clusters. The EP_dis was proved reliable in mathematics and was validated on two simulated datasets. Four simulated datasets representing different properties were used to validate the effectiveness of RCH. Furthermore, We compared the clustering performance of WeDIV with 12 prevailing clustering algorithms on 16 UCI datasets. The results demonstrated that WeDIV outperforms the others regardless of specifying the number of clusters or not.
Pose-guided feature fusion for occluded unsupervised person re-identification

Unsupervised person re-identification (re-ID) aims to identify the same persons' images across different cameras by training on unlabeled data. In which, how to alleviating the occlusion problem in unsupervised person re-ID is a great challenge. Recently, the work on unsupervised re-ID has achieved substantial progress by clustering on the unlabelled target data or unsupervised domain adaptation. Nevertheless, previous re-ID methods either ignored the occlusion problem or solved it based on extreme assumptions. Therefore, in order to develop a kind of more practical and generalized re-ID methods, this paper propose to alleviate the occlusion problem for the unsupervised model. Firstly, we introduce a poseguided branch to extract the key-points information of person. Then, the global feature extracted by backbone and postural feature obtained by pose-guided branch are fused to fed into the unsupervised system. Finally, the experimental results demonstrate that our identification accuracy has achieved strong performance on the three person re-ID dataset.
An Integrated Approach of Blockchain &amp; Big Data in Health Care Sector

Blockchain has many inherent features like decentralized storage, authentication, distributed ledger, immutability and safety, which are more practical in the fields of healthcare. Blockchain applications in the medical sector normally require stronger authentication, sharing of records and interoperability due to stringent legislative requirements, including the 1996 HIPAA Act. Researchers from both the academic and the industry have begun to explore applications ready for use in healthcare, using current technology. These applications include detection of fraud, intelligent contracts and identity validation. In addition to these reforms, technology blockchain has concerns about its particular weaknesses and issues such as core management, mining attacks and incentives. This chapter discusses an overview of the blockchain that includes the Health Care Big Data, as well as blockchain solutions for big data on healthcare. In this chapter, a holistic 184model of the healthcare system, specifically for developing a smart healthcare system, has been proposed.
Estimation of Impact of Bounded Perturbations on Nonlinear Discrete Systems

We consider a class of nonlinear discrete dynamical systems consisting of linear and nonlinear parts, subject to bounded perturbations. For nonlinear functions only, the estimates are given either in the form of multivalued mappings or by the norm bounds.
Sentiment Analysis on COVID-19 Twitter Data

Coronavirus first appeared in December 2019 in Wuhan, China which eventually lead to a catastrophic impact all over the world. The entire world had been fighting this pandemic and expressing their feelings, sharing their opinions on various social media platforms. Substantially Twitter had been the better medium to express their opinion and share updates about the situation. This paper analyzes the sentiments of the public based on positive, negative, and neutral tweets. This analysis eventually helps in the prediction of the covid-19 situation in the world. The dataset was collected from Kaggle which was uploaded by Gabriel Preda containing more than 1,70,000 tweets. Based on these tweets posted on Twitter a sentiment analysis was performed. Data Collecting, Data cleaning (Removing URL, #, @ and various types of punctuation), Tokenization, Stemming, Removing stop- words were performed, and to find the polarity, two types of analyzers were used that is TextBlob and Afinn. 1,79,108 tweets were manually analyzed and comparing it with both the analyzers shows Afinn is more accurate. To evaluate the accuracy a few Machine learning Algorithms had been applied (Logistic Regression, Naive Bayes, Decision Tree, and Linear Regression) for predicting the sentiment of the tweet.
Unpacking lexical intertextuality: Vocabulary shared among texts

This paper focuses on lexical intertextuality, namely the three following intertextual properties: 1) the number of word-types shared by two texts; 2) the number of word-types shared by all texts in a collection; 3) the number of wordtypes shared by equal-sized segments of a collection. We have observed that the relation between the number of texts and the number of shared types follows a power law; similar behavior can be seen if text borders are disregarded and the corpus is artificially divided into equal-sized segments. The number of shared types is proportional to the size of these sequences. We developed baseline models for the number of shared types, i.e. models predicting the number of types shared by texts if all tokens were randomly shuffled and evenly spread among texts. The comparison between the empirical data and the baseline model can be used for contrastive purposes, to compare the number of shared types in corpora of different languages.