Reviewerly/exHarmony huggingface dataset:
The effect of organizational culture on the performance of UAE organizations

This study investigates the relationship between organizational culture (OC) and the performance of organizations in the United Arab Emirates (UAE). A theoretical framework to measure this relationship in the UAE is designed. Interviews with experts and questionnaires were conducted to prioritize certain identifiers and indicators of the relationship between organizational culture and performance in the context of the UAE. The OC—performance relationship framework was analyzed and tested. The number of respondents from the UAE organizations is 131. The results of the study showed a positive relationship between organizational culture and performance, and this relationship is especially stronger with job satisfaction. Moreover, the results also indicate that there is no evidence of any difference between the performance of organizations in the public and private sectors. Furthermore, the degree of implementation of the positive OC constructs varies from one construct to another. These positive OC constructs are goal achievement, employee engagement and commitment, and team spirit. This research is one of the few, if not the first, to investigate the relationship between OC and organizational performance in the private and public organizations in the UAE. The study would be useful for managers to concentrate on the cultural dimensions that enhance organizational performance.
Effective prediction of soil organic matter by deep SVD concatenation using FT-NIR spectroscopy

Soil organic matters (SOM), specifically carbon and nitrogen, bring numerous benefits to soil’s physical and chemical properties. In this paper, we employ spectral data obtained by Fourier transform near-infrared (FT-NIR) spectroscopy to predict the content of organic carbon (OC) and total nitrogen (TN) in mineral soils. To address the limitation generated by massive hyperparameters on convolution neural network (CNN), we substitute using a technique named SVD concatenation to learn features. The proposed model combines the layers of fully connected and regression to complete the prediction task. We abbreviate it as SVD-CNN, which is capable provide a multi-tasks output simultaneously. In experiments, we study the prediction performances of SVD-CNN on two datasets of FT-NIR and LUCAS 2009 topsoil. Based on different situations, the highest performance of R2 achieves 0.8891 for OC and 0.9048 for TN on the FT-NIR dataset. Similarly, the most prominent results on the LUCAS 2009 topsoil dataset are R2 = 0.9304, RMSE = 3.6014 for OC and R2 = 0.9319, RMSE = 0.2733 for TN. Furthermore, we also evaluate the results obtained by solely using SVD concatenation, which reveals SVD-CNN performs a better generalization ability.
Distributed Kalman Filters With Random Sensor Activation and Noisy Channels

This paper studies distributed Kalman filtering problems for sensor networks with random sensor activation and noisy channels. Each sensor measures the target state with a random activation and receives state estimates of its neighbor nodes. Communication noises are considered in communication links between sensors and their neighbor nodes. Considering cross-covariance matrices between sensors, an optimal distributed Kalman filter (ODKF) with a given recursive structure is proposed for each sensor, which depends on the knowledge whether a sensor itself is activated to measure the target state or not and whose filter gains and covariance matrices depend on random activation rate of the sensor. In the ODKF algorithm, optimal Kalman filter gain for each sensor and different optimal consensus filter gains for state estimates of its neighbor nodes are designed in the linear unbiased minimum variance (LUMV) sense. To reduce computational cost, a suboptimal distributed Kalman filter (SDKF) is also presented by considering a locally minimum upper bound of the filtering error covariance matrix, where the calculation of cross-covariance matrices among sensor nodes is avoided. The stability and steady-state property of the proposed ODKF and SDKF algorithms are explored. Finally, the proposed methods are also extended to the nonlinear scenarios. Simulation examples show the effectiveness of the proposed algorithms.
Deformable Linear Object Prediction Using Locally Linear Latent Dynamics

We propose a framework for deformable linear object prediction. Prediction of deformable objects (e.g., rope) is challenging due to their non-linear dynamics and infinite-dimensional configuration spaces. By mapping the dynamics from a non-linear space to a linear space, we can use the good properties of linear dynamics for easier learning and more efficient prediction. We learn a locally linear, action-conditioned dynamics model that can be used to predict future latent states. Then, we decode the predicted latent state into the predicted state. We also apply a sampling-based optimization algorithm to select the optimal control action. We empirically demonstrate that our approach can predict the rope state accurately up to ten steps into the future and that our algorithm can find the optimal action given an initial state and a goal state.
A regularization method to improve adversarial robustness of neural networks for ECG signal classification

With the advancement of machine leaning technologies, Deep Neural Networks (DNNs) have been utilized for automated interpretation of Electrocardiogram (ECG) signals to identify potential abnormalities in a patient's heart within a second. Studies have shown that the accuracy of DNNs for ECG signal classification could reach human-expert cardiologist level if a sufficiently large training dataset is available. However, it is known that, in the field of computer vision, DNNs are not robust to adversarial noises that may cause DNNs to make wrong class-label predictions. In this work, we confirm that DNNs are not robust to adversarial noises in ECG signal classification applications, and we propose a novel regularization method to improve DNN robustness by minimizing the noise-to-signal ratio. Our method is evaluated on two public datasets: the MIT-BIH dataset and the CPSC2018 dataset, and the evaluation results show that our method can significantly enhance DNN robustness against adversarial noises generated by Projected Gradient Descent (PGD) and Smooth Adversarial Perturbation (SAP) adversarial attacks, with a minimal reduction of accuracy on clean data. Our method may serve as the baseline for designing new methods to defend against adversarial attacks for life-critical applications depending on ECG interpretation. The code of this work is publicly available at github.com/SarielMa/Robust_DNN_for_ECG.
The impact of the distance metric and measure on SMOTE-based techniques in software defect prediction

In software defect prediction, SMOTE-based techniques are widely adopted to alleviate the class imbalance problem. SMOTE-based techniques select instances close in the distance to synthesize minority class instances, ensuring few noise instances are generated. However, recent studies show that selecting instances far away effectively increases the diversity and alleviates the overgeneralization brought by SMOTE-based techniques. To investigate the relationship between the distance of the selected instances and the performances of SMOTE-based techniques, we carry out this study. We first conduct experiments to empirically investigate the impact of the distance between the instances on the performances of three common SMOTE-based techniques. Based on the experimental result, we improve a recently proposed oversampling technique-SMOTUNED. The experimental results on five common classifiers across 30 imbalanced datasets from the PROMISE repository show that (1) the selection of the distance metric has little impact on the performances of SMOTE-based techniques, (2) as long as the number of synthesized noise instances is not beyond the noise-resistant ability of classifiers, the overall performances measured by AUC and balance of SMOTE-based techniques are not significantly affected by the distance between instances, and (3) the probability of detection (pd) and the probability of false alarm (pf) values of SMOTE-based techniques are significantly affected by the distance between the selected instances. The larger the distance between the selected instances is, the lower the pd and pf values SMOTE-based techniques obtain. The performance of the improved SMOTUNED is similar to that of the original SMOTUNED, but the improved SMOTUNED dramatically decreases the execution time of the original SMOTUNED. By controlling the distance, different pd and pf values can be obtained. The diversity of SMOTE-based techniques can be improved, and the overgeneralization can be avoided.
Success indicators for an efficient utilization of cloud computing in healthcare organizations: Saudi healthcare as case study

The population in Saudi Arabia is expected to reach 40 million by 2025. Consequently, healthcare information will become critical to manage. Despite the fact that adopting cloud computing in the Saudi healthcare organizations can facilitate cost reduction, capacity building, institutional interoperability, and get access to data analytics, the adoption rate is very low. Hence, a new model is proposed to adopt cloud computing in the Saudi healthcare organization. The novelty of this work comes from using a quantitative method to test users' attitudes, data security, data control, data privacy, compliance, and reliability influence on the cloud computing adoption intention in the context of Saudi Arabian healthcare organizations. Partial Least Squares (PLS) method based Structural Equation Modeling (SEM) was used for model development. About 160 respondents from the relevant health organizations participated. The result shows that the attitude towards using technology, data security, compliance, and reliability of the cloud computing services are important determining factors in the adoption of cloud computing in Saudi healthcare organizations. However, the distinction in the findings regarding Data privacy and Data control in the Saudi healthcare organizational context is a clear manifestation of the fact that there is a need for policy formation for data privacy, data control, and data protection legislation in Saudi Arabia. Therefore, raising awareness regarding the practice of data privacy and data control policies among IT managers is essential. Future study should use a more holistic and industry-specific framework such as the technology-organization-environment (TOE) framework to find new influencing factors from the domains of technological context, the organizational context, and the environmental context.
Deep unsupervised feature selection by discarding nuisance and correlated features

Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance features can be identified using the Laplacian score criterion, which evaluates the importance of a given feature via its consistency with the Graph Laplacians' leading eigenvectors. We demonstrate that in the presence of large numbers of nuisance features, the Laplacian must be computed on the subset of selected features rather than on the complete feature set. To do this, we propose a fully differentiable approach for unsupervised feature selection, utilizing the Laplacian score criterion to avoid the selection of nuisance features. We employ an autoencoder architecture to cope with correlated features, trained to reconstruct the data from the subset of selected features. Building on the recently proposed concrete layer that allows controlling for the number of selected features via architectural design, simplifying the optimization process. Experimenting on several real-world datasets, we demonstrate that our proposed approach outperforms similar approaches designed to avoid only correlated or nuisance features, but not both. Several state-of-the-art clustering results are reported. Our code is publically available at https://github.com/jsvir/lscae.
Multi-Perspective Video Captioning

This work targets at the problems of comprehensive video captioning and the generation of multiple descriptions from different perspectives, termed asMulti-Perspective Video Captioning. We build and release a dataset named VidOR-MPVC, the first dataset for multi-perspective video captioning, where each video is annotated with multiple descriptions from different perspectives. We also propose a novel model, dubbedperspective-aware captioner (PAC), which is capable of mining the various perspectives in a video and generating a description from each perspective. More specifically, a perspective generator is designed to perceive video content with perspective preferences, and followed by a language generator equipped with perspective-aware attention mechanism. As our new task expects to produce multiple descriptions for a video, existing evaluation metrics are fail to handle this situation. To address this problem, we devise the maximum matching scores based on existing metrics for an overall evaluation which aims to cover the aspects of semantic similarity, completeness and compactness. The experimental results demonstrate that our model is able to describe videos with multiple descriptions from different perspectives.
Using Motion Histories for Eye Contact Detection in Multiperson Group Conversations

Eye contact detection in group conversations is the key to developing artificial mediators that can understand and interact with a group. In this paper, we propose to model a group's appearances and behavioral features to perform eye contact detection for each participant in the conversation. Specifically, we extract the participants' appearance features at the detection moment, and extract the participants' behavioral features based on their motion history image, which is encoded with the participants' body movements within a small time window before the detection moment. In order to attain powerful representative features from these images, we propose to train a Convolutional Neural Network (CNN) to model them. A set of relevant features are obtained from the network, which achieves an accuracy of 0.60 on the validation set in the eye contact detection challenge in ACM MM 2021. Furthermore, our experimental results also demonstrate that making use of both participants' appearance and behavior features can lead to higher accuracy at eye detection than only using one of them.
The design of keyed hash function based on CNN-MD structure

In this paper, we propose a new chaotic neural network called the Merkle–Damgaard (CNN-MD) iterative structure, which we designed from the perspective of resisting attacks, such as length extension, second original image, Joux multiple collision, and intermediate encounter attacks. A new chaotic hash function based on the CNN-MD is presented. First, CNN is used as the compression function of CNN-MD, which ensures both the unidirectionality of data compression and the sensitivity of the hash value. Furthermore, the multi-input single-output construction of the CNN can achieve highly efficient data compression. In addition, in the process of group encryption of data, we use the value padding of coupled lattice mapping to improve the alignment of the plaintext of the MD algorithm. This new alignment can enhance the hash function’s collision resistance and the CNN-MD sensitivity. Experimental and theoretical analyses show that our proposed hashing algorithm can resist attacks, such as second original image, brute force, and multiple collision attacks, better than MD5, SHA-1, and SHA-2 algorithms. In terms of speed, our proposed hashing algorithm can successfully handle existing commercial scenarios better than MD5, SHA-1, SHA-3, and other methods. Therefore, the hash algorithm proposed in this study can be applied to existing e-commerce scenarios.
Adversarial Pixel Masking

Object detection based on pre-trained deep neural networks (DNNs) has achieved impressive performance and enabled many applications. However, DNN-based object detectors are shown to be vulnerable to physical adversarial attacks. Despite that recent efforts have been made to defend against these attacks, they either use strong assumptions or become less effective with pre-trained object detectors. In this paper, we propose adversarial pixel masking (APM), a defense against physical attacks, which is designed specifically for pre-trained object detectors. APM does not require any assumptions beyond the "patch-like" nature of a physical attack and can work with different pre-trained object detectors of different architectures and weights, making it a practical solution in many applications. We conduct extensive experiments, and the empirical results show that APM can significantly improve model robustness without significantly degrading clean performance.
Towards Efficient Verifiable Boolean Search Over Encrypted Cloud Data

Symmetric Searchable Encryption (SSE) schemes facilitate searching over encrypted data, and have been extensively explored to improve function, efficiency or security. There are, however, additional functions that we need to consider in a real-world setting. For example, forward and backward privacy are required to adequately secure newly added documents and deleted documents in Dynamic SSE (DSSE) schemes, and support boolean search (that allows users to search over encrypted data using basic boolean operations) to achieve improved efficiency and retrieval accuracy. Therefore, in this article we first construct the Verifiable Boolean Search over encrypted data (VBS), and then improve VBS to achieve Forward and Backward privacy (VBS-FB). Finally, we formally prove the security of our proposed schemes, and evaluate their performance using real-world datasets.
Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning

Long-tailed distributions and class imbalance are problems of significant importance in applied deep learning where trained models are exploited for decision support and decision automation in critical areas such as health and medicine, transportation and finance. The challenge of learning deep models from such data remains high, and the state-of-the-art solutions are typically data dependent and primarily focused on images. Important real-world problems, however, are much more diverse thus necessitating a general solution that can be applied to diverse data types. In this paper, we propose ReMix, a training technique that seamlessly leverages batch resampling, instance mixing and soft-labels to efficiently enable the induction of robust deep models from imbalanced and long-tailed datasets. Our results show that fully connected neural networks and Convolutional Neural Networks (CNNs) trained with ReMix generally outperform the alternatives according to the g-mean and are better calibrated according to the balanced Brier score.
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks

In the low-bit quantization field, training Binarized Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts. In this paper, we introduce Sub-bit Neural Networks (SNNs), a new type of binary quantization design tailored to compress and accelerate BNNs. SNNs are inspired by an empirical observation, showing that binary kernels learnt at convolutional layers of a BNN model are likely to be distributed over kernel subsets. As a result, unlike existing methods that binarize weights one by one, SNNs are trained with a kernel-aware optimization framework, which exploits binary quantization in the fine-grained convolutional kernel space. Specifically, our method includes a random sampling step generating layer-specific subsets of the kernel space, and a refinement step learning to adjust these subsets of binary kernels via optimization. Experiments on visual recognition benchmarks and the hardware deployment on FPGA validate the great potentials of SNNs. For instance, on ImageNet, SNNs of ResNet-18/ResNet-34 with 0.56-bit weights achieve 3.13/3.33× runtime speedup and 1.8× compression over conventional BNNs with moderate drops in recognition accuracy. Promising results are also obtained when applying SNNs to binarize both weights and activations. Our code is available at https://github.com/yikaiw/SNN.
A Method for MBTI Classification Based on Impact of Class Components

Predicting the personality type of text authors has a well-known usage in psychology with practical applications in business.From the data science perspective, we can look at this problem as a text classification task that can be tackled using natural language processing (NLP) and deep learning.This paper proposes a method and a novel loss function for multiclass classification using the Myers-Briggs Type Indicator (MBTI) approach for predicting the author's personality type.Furthermore, this paper proposes an approach that improves the current results of the MBTI multiclass classification because it considers components of compound class labels as supportive elements for better classification according to MBTI.As such, it also provides a new perspective on this classification problem.The experimental results on long shortterm memory (LSTM) and convolutional neural network (CNN) models outperform baseline models for multiclass classification, related research on multiclass classification, and most research with four binary approaches to MBTI classification.Moreover, other classification problems that target compound class labels and label parts with binary mutually exclusive values can benefit from this approach.
Binary Neural Networks for Wireless Interference Identification

Wireless interference identification (WII) is one of the most promising technologies in anti-interference communication systems and non-cooperative communication systems. Recently, deep learning (DL) based methods have achieved striking performance for WII. However, existing works have not researched hardware-friendly network quantization for WII, which is essential for deploying model on resource-constrained devices. In this letter, we optimize binarized neural networks (BNNs) for WII, which are the most extreme form of quantization by constraining weights and activations to be binary value. Specifically, to overcome the difficulty in propagating gradient during back-propagation due to the non-differentiable quantization function, we introduce a novel approximation for gradients calculation, which bridges the accuracy gap between the BNN and the full-precision counterpart. Additionally, to solve the bottleneck of serious performance degradation of BNNs, we propose two techniques to minimize quantization noise and create multiple routes to update the parameters of BNNs, resulting in further improvement on performance. Experiments demonstrate that the proposed BNN achieves accuracy near the full-precision counterpart with substantially reduced resource consumption for WII.