Reviewerly/exHarmony huggingface dataset:
A secure fish farm platform based on blockchain for agriculture data integrity

Internet of Things (IoT) has opened up a new dimension for smart farming and agriculture because of the natural feature that makes it possible to assign tasks made by a user or that transfers agriculture data obtained through sensors to producers for analysis on various terminal devices. In recent years, heightened interest in agriculture data has arisen since the commercialization of precision agriculture technology. Agriculture data are known to be messy, especially from combine yield monitors, and analysts are concerned with the validity of data, especially given that other people may have impacted data quality at various steps along the data path. The blockchain can be a possible solution to the analyst’s problem of uncertain data quality from prior data manipulation since it ensures data have not been inappropriately manipulated or at the very least documents what changes have been made by specific individuals. This paper proposes a blockchain-based fish farm platform to ensure agriculture data integrity. The designed platform aims to provide fish farmers with secure storage for preserving the large amounts of agriculture data that cannot be tampered with. Diverse processes of the fish farm are executed automatically by using the smart contract to reduce the risk of error or manipulation. A proof of concept that integrates a legacy fish farm system with the Hyperledger Fabric blockchain is implemented on top of the proposed architecture. The efficiency and usability of the proposed platform are demonstrated through a series of experiments using various metrics.
Improving RNN Transducer Modeling for End-to-End Speech Recognition

In the last few years, an emerging trend in automatic speech recognition research is the study of end-to-end (E2E) systems. Connectionist Temporal Classification (CTC), Attention Encoder-Decoder (AED), and RNN Transducer (RNN-T) are the most popular three methods. Among these three methods, RNN-T has the advantages to do online streaming which is challenging to AED and it doesn't have CTC's frame-independence assumption. In this paper, we improve the RNN-T training in two aspects. First, we optimize the training algorithm of RNN-T to reduce the memory consumption so that we can have larger training minibatch for faster training speed. Second, we propose better model structures so that we obtain RNN-T models with the very good accuracy but small footprint. Trained with 30 thousand hours anonymized and transcribed Microsoft production data, the best RNN-T model with even smaller model size (216 Megabytes) achieves up-to 11.8% relative word error rate (WER) reduction from the baseline RNN-T model. This best RNN-T model is significantly better than the device hybrid model with similar size by achieving up-to 15.0% relative WER reduction, and obtains similar WERs as the server hybrid model of 5120 Megabytes in size.
Image encryption and hiding algorithm based on compressive sensing and random numbers insertion

Most current image encryption algorithms encrypt plain images directly into meaningless cipher images. Visually, a few of them are vulnerable to illegal attacks on a few sharing platforms or open channels when being transmitted. Therefore, this paper proposes a new meaningful image encryption algorithm based on compressive sensing and information hiding technology, which hides the existence of the plain image and reduces the possibility of being attacked. Firstly, the discrete wavelet transform (DWT) is employed to sparse the plain image. This is followed by confusion operation on pixel positions, where logistic-tent map is employed to produce a confusion sequence. And then the image is compressed and encrypted by compressive sensing to form an intermediate cipher image. Here, measurement matrix is generated using low-dimension complex tent-sine system. To further enhance recovery quality, we suggest that the intermediate cipher image be filled with random numbers according to the compression ratio and confusing them to obtain the secret image. Finally, two-dimensional (2D) DWT of the carrier image is performed, followed by singular value decomposition. The singular values of the secret image are embedded into the singular values of the carrier image with certain embedding strength to obtain the final visually meaningful encrypted image.
PoBT: A Lightweight Consensus Algorithm for Scalable IoT Business Blockchain

Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.
Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death.Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells.Self-tests and Periodic clinical checks help to early diagnosis and thereby improve the survival chances significantly.The breast cancer classification is a medical method that provides researchers and scientists with a great challenge.Neural networks have recently become a popular tool in cancer data classification.In this paper, Deep Learning assisted Efficient Adaboost Algorithm (DLA-EABA) for breast cancer detection has been mathematically proposed with advanced computational techniques.In addition to traditional computer vision approaches, tumor classification methods using transfers are being actively developed through the use of deep convolutional neural networks (CNNs).This study starts with examining the CNNbased transfer learning to characterize breast masses for different diagnostic, predictive tasks or prognostic or in several imaging modalities, such as Magnetic Resonance Imaging (MRI), Ultrasound (US), digital breast tomosynthesis and mammography.The deep learning framework contains several convolutional layers, LSTM, Max-pooling layers.The classification and error estimation that has been included in a fully connected layer and a softmax layer.This paper focuses on combining these machine learning approaches with the methods of selecting features and extracting them through evaluating their output using classification and segmentation techniques to find the most appropriate approach.The experimental results show that the high accuracy level of 97.2%, Sensitivity 98.3%, and Specificity 96.5% has been compared to other existing systems.
<scp>COVID</scp> ‐19 as an accelerator for digitalization at a German university: Establishing hybrid campuses in times of crisis

As a result of the COVID-19 outbreak, teaching in universities needed to be quickly transitioned from regular on-campus classes into technology-enhanced teaching formats. In this article, we present the case study of Chemnitz University of Technology (Germany), where digital classes were introduced in a matter of weeks. By analyzing syllabus data, we found that the use of video and video conferencing is an important current development. Related to these findings, we present evidence from instructional psychology and social media research that can help in the design of teaching during this crisis. We highlight the need for multimodal learning, that is, learning settings that use multiple sensory modalities. Importantly, we present a strategy of hybrid campuses for this and potential future emergencies. This approach describes how the social distancing measures currently in effect can be used to re-think higher education based on a reasonable use of technology. Taken together, the COVID-19 crisis can be a time of major reform in higher education that will accelerate the process of digitalization in an unprecedented way.
Lightening Network for Low-Light Image Enhancement

Low-light image enhancement is a challenging task that has attracted considerable attention. Pictures taken in low-light conditions often have bad visual quality. To address the problem, we regard the low-light enhancement as a residual learning problem that is to estimate the residual between low- and normal-light images. In this paper, we propose a novel Deep Lightening Network (DLN) that benefits from the recent development of Convolutional Neural Networks (CNNs). The proposed DLN consists of several Lightening Back-Projection (LBP) blocks. The LBPs perform lightening and darkening processes iteratively to learn the residual for normal-light estimations. To effectively utilize the local and global features, we also propose a Feature Aggregation (FA) block that adaptively fuses the results of different LBPs. We evaluate the proposed method on different datasets. Numerical results show that our proposed DLN approach outperforms other methods under both objective and subjective metrics.
SRFlow: Learning the Super-Resolution Space with Normalizing Flow

Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep learning based approaches. These methods instead train a deterministic mapping using combinations of reconstruction and adversarial losses. In this work, we therefore propose SRFlow: a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input. Our model is trained in a principled manner using a single loss, namely the negative log-likelihood. SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images. Moreover, we utilize the strong image posterior learned by SRFlow to design flexible image manipulation techniques, capable of enhancing super-resolved images by, e.g., transferring content from other images. We perform extensive experiments on faces, as well as on super-resolution in general. SRFlow outperforms state-of-the-art GAN-based approaches in terms of both PSNR and perceptual quality metrics, while allowing for diversity through the exploration of the space of super-resolved solutions. Code: git.io/Jfpyu.
Distributional Semantics and Linguistic Theory

Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are of relevance for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar-semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The review aims at fostering greater cross-fertilization of theoretical and computational approaches to language, as a means to advance our collective knowledge of how it works.
Targeted Sentiment Classification with Attentional Encoder Network

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.
I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image

Most of the previous image-based 3D human pose and mesh estimation methods estimate parameters of the human mesh model from an input image. However, directly regressing the parameters from the input image is a highly non-linear mapping because it breaks the spatial relationship between pixels in the input image. In addition, it cannot model the prediction uncertainty, which can make training harder. To resolve the above issues, we propose I2L-MeshNet, an image-to-lixel (line+pixel) prediction network. The proposed I2L-MeshNet predicts the per-lixel likelihood on 1D heatmaps for each mesh vertex coordinate instead of directly regressing the parameters. Our lixel-based 1D heatmap preserves the spatial relationship in the input image and models the prediction uncertainty. We demonstrate the benefit of the image-to-lixel prediction and show that the proposed I2L-MeshNet outperforms previous methods. The code is publicly available ( https://github.com/mks0601/I2L-MeshNet_RELEASE ).
Alexa, do voice assistants influence consumer brand engagement? – Examining the role of AI powered voice assistants in influencing consumer brand engagement

Artificially Intelligent (AI) voice assistants (VAs) are continuing to grow in popularity amongst consumers. While in their infancy, several brands are now utilising VAs such as the Amazon Echo to deliver brand-related information and services. However, despite the increasing use of VAs, we have little understanding of what motivates consumers to use such devices for brand-related information. Focusing on the Amazon Echo in-home VA, and its associated Alexa Skills, this research uncovers the key drivers of consumer brand engagement through VAs. In study 1, through a set of in-depth exploratory interviews with 21 respondents, we established three factors as key drivers of why consumers use VAs to engage with brands: AI attributes, technology attributes, and situational attributes. Study 2 examines these specific drivers via a questionnaire with 724 respondents. The findings outline the VA as an actor in the engagement process and affirm the importance of the VA’s AI attributes of social presence, perceived intelligence, and social attraction in influencing consumer brand engagement. Additionally, technology attributes influence consumer brand engagement, along with the utilitarian benefits derived from interactions with brand-related information. Hedonic benefits do not influence consumer brand engagement via VA technology, while trust concerns play a negative role in brand engagement behaviour. Lastly, the results convey that consumer brand engagement via a VA influences brand usage intention, but in contrast to previous research, does not directly influence future purchase intention.
RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark.
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (e.g., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little is known about its usefulness in 3D point cloud understanding. We see this as an opportunity considering the effort required for annotating data in 3D. In this work, we aim at facilitating research on 3D representation learning. Different from previous works, we focus on high-level scene understanding tasks. To this end, we select a suit of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes. Our findings are extremely encouraging: using a unified triplet of architecture, source dataset, and contrastive loss for pre-training, we achieve improvement over recent best results in segmentation and detection across 6 different benchmarks for indoor and outdoor, real and synthetic datasets – demonstrating that the learned representation can generalize across domains. Furthermore, the improvement was similar to supervised pre-training, suggesting that future efforts should favor scaling data collection over more detailed annotation. We hope these findings will encourage more research on unsupervised pretext task design for 3D deep learning.
Blockchain applications in management: A bibliometric analysis and literature review

Blockchain has gained substantial recognition for its ability to induce transformation and innovation in existing business models and frameworks. Consequently, the application of this technology to the management domain and its processes has attracted increasing interest from academia and industry. Although research addressing the use of blockchain in management has gained momentum, this field presents a discontinuous overview of the current scope and boundary of the knowledge thereon. This study addresses this lacuna using bibliometric analyses to synthesize the prior literature. Data from Scopus 586 articles, entailing contributions from 72 countries, 273 journals, 1016 organizations, and 1284 authors, were analyzed. The findings indicate a maturing research focus on blockchain applications in specific managerial sectors, such as finance and supply chain management. However, this field's conceptual evolution is posited to be in its infancy in other sectors, such as for managing luxury goods and counterfeit products. Further, the thematic classification of the extant literature led to the identification of the following four major themes of research: strategy and regulation, enablement and implication, multi-domain deployment, and the inefficiencies of bitcoin. These findings are used to propose directions for further research in this field, such as the need for methodological advancement and theoretical grounding.
MFDNet: Collaborative Poses Perception and Matrix Fisher Distribution for Head Pose Estimation

Head pose estimation suffers from several problems, including low pose tolerance under different disturbances and ambiguity arising from common head pose representation. In this study, a robust three-branch model with triplet module and matrix Fisher distribution module is proposed to address these problems. Based on metric learning, the triplet module employs triplet architecture and triplet loss. It is implemented to maximize the distance between embeddings with different pose pairs and minimize the distance between embeddings with same pose pairs. It can learn a highly discriminate and robust embedding related to head pose. Moreover, the rotation matrix instead of Euler angle and unit quaternion is utilized to represent head pose. An exponential probability density model based on the rotation matrix (referred to as the matrix Fisher distribution) is developed to model head rotation uncertainty. The matrix Fisher distribution can further analyze the head pose, and its maximum likelihood obtained using singular value decomposition provides enhanced accuracy. Extensive experiments executed over AFLW2000 and BIWI datasets demonstrate that the proposed model achieves state-of-the-art performance in comparison with traditional methods.
QANA: Quantum-based avian navigation optimizer algorithm

Differential evolution is an effective and practical approach that is widely applied for solving global optimization problems. Nevertheless, its effectiveness and scalability are decreased when the problems’ dimension is increased. Hence, this paper is devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer algorithm (QANA) inspired by the extraordinary precision navigation of migratory birds during long-distance aerial paths. In the QANA, the population is distributed by partitioning into multi flocks to explore the search space effectively using proposed self-adaptive quantum orientation and quantum-based navigation consisted of two mutation strategies, DE/quantum/I and DE/quantum/II. Except for the first iteration, each flock is assigned using an introduced success-based population distribution (SPD) policy to one of the quantum mutation strategies. Meanwhile, the information flow is shared through the population using a new communication topology named V-echelon. Furthermore, we introduce two long-term and short-term memories to provide meaningful knowledge for partial landscape analysis and a qubit-crossover operator to generate the next search agents. The effectiveness and scalability of the proposed QANA were extensively evaluated using benchmark functions CEC 2018 and CEC 2013 as LSGO problems. The results were statistically analyzed by the Wilcoxon signed-rank sum test, ANOVA, and mean absolute error tests. Finally, the applicability of the QANA to solve real-world problems was evaluated by four engineering problems. The experimental results and statistical analysis prove that the QANA is superior to the competitor DE and swarm intelligence algorithms in test functions CEC 2018 and CEC 2013, with overall effectiveness of 80.46% and 73.33%, respectively.