Reviewerly/exHarmony huggingface dataset:
Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland

Bayesian network (BN) modeling is a rich and flexible analytical framework capable of elucidating complex veterinary epidemiological data. It is a graphical modeling technique that enables visual presentation of multi-dimensional results while retaining statistical rigor in population-level inference. Using previously published case study data about feline calicivirus (FCV) and other respiratory pathogens in cats in Switzerland, a full BN modeling analysis is presented. The analysis shows that reducing the group size and vaccinating animals are the two actionable factors directly associated with the FCV status and are primary targets to control for FCV infection. The presence of gingivostomatitis and \emph{Mycoplasma felis} are also associated with FCV status, but signs of upper respiratory tract disease (URTD) are not. FCV data is particularly well-suited to a network modeling approach as both multiple pathogens and multiple clinical signs per pathogen are involved, along with multiple potentially interrelated risk factors. BN modeling is a holistic approach - all variables of interest may be mutually interdependent - which may help to address issues such as confounding and collinear factors, as well as disentangle directly versus indirectly related variables. We introduce the BN methodology, as an alternative to the classical uni- and multivariable regression approaches commonly used for risk factor analyses. We advice and guide researchers about how to use BNs as an exploratory data tool and demonstrate limitations and practical issues. We present a step-by-step case study using FCV data along with all code necessary to reproduce our analyses using the open source R environment. We compare and contrast the findings of the current case study using BN modeling with previous results which used classical regression techniques, and we highlight new potential insights. Finally, we discuss advanced methods such as Bayesian model averaging, a common way of accounting for model uncertainty in a Bayesian network context.
Digging Into Pseudo Label: A Low-Budget Approach for Semi-Supervised Semantic Segmentation

The capability to understand visual scenes with limited labeled data has been widely concerned in the field of computer vision.Although semi-supervised learning for image classification has been extensively studied in some cases, semantic segmentation with limited data has only recently gained attention.In this work, we follow the standard semi-supervised segmentation pipeline for image classification and propose a two-branch network that can encode strong and pseudo label spaces respectively, extracting reliable supervision information from pseudo-labels to assist in training network with strong labels.Our method outperforms previous semi-supervised methods with limited annotation cost.On standard benchmark PASCAL VOC 2012 for semi-supervised semantic segmentation, the proposed approach gains fresh state-of-the-art performance.
Automated Bangla sign language translation system for alphabets by means of MobileNet

Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.
A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing

This manuscript outlines a viable approach for training and evaluating machine learning systems for high-stakes, human-centered, or regulated applications using common Python programming tools. The accuracy and intrinsic interpretability of two types of constrained models, monotonic gradient boosting machines and explainable neural networks, a deep learning architecture well-suited for structured data, are assessed on simulated data and publicly available mortgage data. For maximum transparency and the potential generation of personalized adverse action notices, the constrained models are analyzed using post-hoc explanation techniques including plots of partial dependence and individual conditional expectation and with global and local Shapley feature importance. The constrained model predictions are also tested for disparate impact and other types of discrimination using measures with long-standing legal precedents, adverse impact ratio, marginal effect, and standardized mean difference, along with straightforward group fairness measures. By combining interpretable models, post-hoc explanations, and discrimination testing with accessible software tools, this text aims to provide a template workflow for machine learning applications that require high accuracy and interpretability and that mitigate risks of discrimination.
Sparse Bayesian Broad Learning System for Probabilistic Estimation of Prediction

Broad learning system (BLS) is proposed as a competitor to deep neural network, which is constructed using only single hidden layer instead of multilayered structure.It also manages incremental learning with intelligible mathematical derivation by pseudoinverse.Despite its empirical success, the limitations of BLS cannot be overlooked, especially for its sensibility to the number of feature nodes.For this reason, large model is required in training, which may easily cause overfitting.In this paper, a probabilistic model called sparse Bayesian broad learning system (SBBLS) is proposed to dispose of this defect by linearly-weighting a small group of basis functions from an enormous number of candidates within Bayesian framework.Outstanding sparsity and generalization can be observed in SBBLS.More distinctively, SBBLS is capable of outputting probabilistic estimation of prediction for further decision analysis while maintaining inherent properties of BLS simultaneously.Experimental results demonstrate that the proposed SBBLS model could achieve comparable performance to BLS both in regression and classification with probabilistic interpretability.
Integrating Security Requirements Engineering into MBSE: Profile and Guidelines

Model-Based System Engineering (MBSE) provides a number of ways on how to create, validate, and verify the complex system design; unfortunately, the inherent security aspects are addressed neither by the SysML language that is the main MBSE enabler nor by popular MBSE methods. Although there are many common points between MBSE and security requirements engineering, the key advantages of MBSE (such as managed complexity, reduced risk and cost, and improved communication across a multidisciplinary team) have not been exploited enough. This paper reviews security requirements engineering processes and modeling methods and standards and provides the MBSE security profile as well, which is formalized with the UML 2.5 profiling capability. The new UML-based security profile conforms to the ISO/IEC 27001 information security standard. In addition to the MBSE security profile, this paper also presents the security profile application use case and the feasibility study of current status for security and systems engineering processes.
Factors Influencing the Use of Multimedia Technologies in Teaching English Language in Kuwait

The way of learning and teaching has been improved and the term of learning is developed. Generally, many researchers try to identify the important factors that affect the usage of multimedia in teaching and learning. However, there are few studies in this area specifically in Kuwait. Therefore, this study comes to fill this gab and identify the factors that influence the usage of multimedia technologies in teaching English language in Kuwait. Consequently, a research model is developed. To evaluate the proposed model, questionnaire is developed and the empirical data is collected and analyzed. The results reveal that there are seven factors that have an impact in using multimedia in teaching English subject, which are: facilitation, motivation, performance, behavioral, social, pedagogical, and effort factors. Moreover, the study proves that instructors have positive perceptions of multimedia technologies, but still need more supplements and supports.
Reusing Data Technical and Ethical Challenges

Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues.
Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances. However, due to their large quantities, it is impractical to require text labels for the all stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. Two main challenges are confronted in this task. One is to learn semantic meaning of stickers without corresponding text labels. Another challenge is to jointly model the candidate sticker with the multi-turn dialog context. To tackle these challenges, we propose a sticker response selector (SRS) model. Specifically, SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score. To evaluate our proposed method, we collect a large-scale real-world dialog dataset with stickers from one of the most popular online chatting platform. Extensive experiments conducted on this dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the effectiveness of each component of SRS. To facilitate further research in sticker selection field, we release this dataset of 340K multi-turn dialog and sticker pairs1.
Photonic Convolutional Neural Networks Using Integrated Diffractive Optics

With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.
Deep Domain Adaptation With Differential Privacy

Nowadays, it usually requires a massive amount of labeled data to train a deep neural network. When no labeled data is available in some application scenarios, domain adaption can be employed to transfer a learner from one or more source domains with labeled data to a target domain with unlabeled data. However, due to the exposure of the trained model to the target domain, the user privacy may potentially be compromised. Nevertheless, the private information may be encoded into the representations in different stages of the deep neural networks, i.e., hierarchical convolutional feature maps, which poses a great challenge for a full-fledged privacy protection. In this paper, we propose a novel differentially private domain adaptation framework called DPDA to achieve domain adaptation with privacy assurance. Specifically, we perform domain adaptation in an adversarial-learning manner and embed the differentially private design into specific layers and learning processes. Although applying differential privacy techniques directly will undermine the performance of deep neural networks, DPDA can increase the classification accuracy for the unlabeled target data compared to the prior arts. We conduct extensive experiments on standard benchmark datasets, and the results show that our proposed DPDA can indeed achieve high accuracy in many domain adaptation tasks with only a modest privacy loss.
<scp>CPU</scp> usage prediction for cloud resource provisioning based on deep belief network and particle swarm optimization

Summary Resource usage prediction is increasingly important in cloud computing environments, and CPU usage prediction is especially helpful for improving the efficiency of resource provisioning and reducing energy consumption of cloud datacenters. However, accurate CPU usage prediction remains a challenge and few works have been done on predicting CPU usage of physical machines in cloud datacenters. In this article, we present a deep belief network (DBN) and particle swarm optimization (PSO) based CPU usage prediction algorithm, which is named DP‐CUPA and aimed to provide more accurate prediction results. The DP‐CUPA consists of three main steps. First, the historic data on CPU usage are preprocessed and normalized. Then, the autoregressive model and grey model are adopted as base prediction models and trained to provide extra input information for training DBN. Finally, the PSO is used to estimate DBN parameters and the DBN neural network is trained to predict CPU usage. The effectiveness of the DP‐CUPA is evaluated by extensive experiments with a real‐world dataset of Google cluster usage trace.
Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.
Teacher educators’ perceptions of their profession in relation to the digitalization of society

&#x0D; &#x0D; &#x0D; This study takes an exploratory approach to investigating Swedish teacher educators’ perceptions regarding their profession in relation to the digitalization of society and education, including higher education. Eighteen semi-structured interviews were analyzed using thematic analysis. Findings show that the teacher educators perceive digitalization on a scale that ranges from simply using tools to being part of a technology-initiated revolution of educational institutions and society. From this range of digital developments emanate individual, group, and organizational requirements/demands, needs, and consequences for being, that is, personal experiences of how digitalization affects the work, and acting, that is, doing something in response to the demands of using and teaching with digital technology. The teacher educator is situated primarily in being with the requirements for working professionally and acting as a teacher, which creates tensions and challenges for the individual and the professional self.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model

The United Arab Emirates (UAE) has recently focused on implementing Artificial Intelligence (AI) projects in the government healthcare sector to help manage chronic diseases and early detection. However, successful AI implementation depends on adoption and acceptance by decision-makers, physicians, nurses, and patients. This paper develops and tests a modified Technology Acceptance Model (TAM) to explore critical success factors (CSFs) for the adoption of AI in the healthcare sector. The most widely used CSF variables for TAM are Perceived Usefulness (PU), Perceived Ease of Use (PEU), Attitudes toward Use (ATU) and Behavioral Intention to Use (BIU). However, a review of 23 qualitative and quantitative studies of TAM literature from 2015 to 2018 suggested that five key external factors should be included in CSF studies using TAM. An extended model was developed (ETAM) and tested using a qualitative study comprising 53 employees working in the Dubai IT and healthcare sectors. The study showed that managerial, organizational, operational and IT infrastructure factors have a positive effect on PU and PEU and, hence, should be included as CSFs in determining the implementation of AI in the healthcare sector.
Mixed Vertical-and-Horizontal-Text Traffic Sign Detection and Recognition for Street-Level Scene

Much effort has been dedicated to text-based traffic sign detection and recognition. However, there are still two problems. First, unlike English traffic signs with only horizontal text, Chinese traffic signs have both horizontal and vertical text. To the best of our knowledge, there is nothing in the literature about simultaneous recognition of both horizontal and vertical text in Chinese text-based traffic signs. Second, most existing methods focus on wild and expressway scenes; few focus on street scenes. To solve these problems, we propose a mixed vertical-and-horizontal-text traffic sign detection and recognition algorithm for street-level scene. First, an effective combination of different red, green and blue components is used to distinguish the traffic signs from many objects of similar color in the very complex street scenes. Second, unlike English letters, the strokes of many Chinese characters are unconnected, which may result in that a character will be detected as two or more characters. Unlike the English text lines, which are only horizontal, the Chinese text lines on text-based traffic signs are usually both in horizontal and vertical directions. Our proposed method uses the position and structural information of the characters to form the text lines. A dataset of Chinese text-based traffic signs is collected. Experimental results indicate the effectiveness of the proposed method.
Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses’ Health Study. A set of 92 WSIs was annotated for acini, TDLUs and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73±0.07, and segmented TDLUs and adipose tissue with Dice scores of 0.84±0.13 and 0.87±0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 0.81 and 0.73, respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80 for number of TDLUs per tissue area, 0.57 for median TDLU span, and 0.80 for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status. We developed a computational pathology method to measure TDLU involution. This technology eliminates the labor-intensiveness and subjectivity of manual TDLU assessment, and can be applied to future breast cancer risk studies.