Reviewerly/exHarmony huggingface dataset:
Survey on Deep Neural Networks in Speech and Vision Systems

This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in speech and vision applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent speech and vision systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent speech and vision systems to date. An overview of large-scale industrial research and development efforts is provided to emphasize future trends and prospects of intelligent speech and vision systems. Robust and efficient intelligent systems demand low-latency and high fidelity in resource-constrained hardware platforms such as mobile devices, robots, and automobiles. Therefore, this survey also provides a summary of key challenges and recent successes in running deep neural networks on hardware-restricted platforms, i.e. within limited memory, battery life, and processing capabilities. Finally, emerging applications of speech and vision across disciplines such as affective computing, intelligent transportation, and precision medicine are discussed. To our knowledge, this paper provides one of the most comprehensive surveys on the latest developments in intelligent speech and vision applications from the perspectives of both software and hardware systems. Many of these emerging technologies using deep neural networks show tremendous promise to revolutionize research and development for future speech and vision systems.
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. In order to ensure that the user's device contains no extra information except what is necessary for the main task and preventing any secondary inference on the data, we introduce Siamese fine-tuning. We evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also assess the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy, and performance.
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection

The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We integrate the features of different modalities through densely connected structures and use their mixed features to generate dynamic filters with receptive fields of different sizes. In the end, we implement a kind of more flexible and efficient multi-scale cross-modal feature processing, i.e. dynamic dilated pyramid module. In order to make the predictions have sharper edges and consistent saliency regions, we design a hybrid enhanced loss function to further optimize the results. This loss function is also validated to be effective in the single-modal RGB SOD task. In terms of six metrics, the proposed method outperforms the existing twelve methods on eight challenging benchmark datasets. A large number of experiments verify the effectiveness of the proposed module and loss function. Our code, model and results are available at https://github.com/lartpang/HDFNet .
Identity-Guided Human Semantic Parsing for Person Re-identification

Existing alignment-based methods have to employ the pre-trained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels. We design the cascaded clustering on feature maps to generate the pseudo-labels of human parts. Specifically, for the pixels of all images of a person, we first group them to foreground or background and then group the foreground pixels to human parts. The cluster assignments are subsequently used as pseudo-labels of human parts to supervise the part estimation and ISP iteratively learns the feature maps and groups them. Finally, local features of both human body parts and personal belongings are obtained according to the self-learned part estimation, and only features of visible parts are utilized for the retrieval. Extensive experiments on three widely used datasets validate the superiority of ISP over lots of state-of-the-art methods. Our code is available at https://github.com/CASIA-IVA-Lab/ISP-reID .
Factors influencing blockchain adoption in supply chain management practices: A study based on the oil industry

Planning to adopt the Blockchain is very active in many industries, especially in supply chains. Researchers believe that the Radio-frequency identification (RFIDs), yesterday’s Blockchain, is now obsolete. The strongest reason that the Blockchain is the tool of this era is its unique features; real-time information sharing, cyber-security, transparency, reliability, traceability, and visibility, all of which boost the supply-chain performance. Despite the extensive literature on Blockchain, in recent years, no clear framework has defined whether a supply chain should implement Blockchain or not. This study attempts to fill this gap by proposing a framework for complex supply chain networks. In doing so, first, we identified the supply-chain practices of the oil industry in Pakistan, then we empirically analyzed the impact of these practices on operational performance. The results show that the supply chain management (SCM) practices positively impact operational performance. On the other hand, with the help of literature, we identified different Blockchain features and their influence on different supply chain practices. This study guides managers and decision-makers to evaluate their current supply-chain practices and understand the relationship between supply-chain practices and Blockchain features, and how different Blockchain features can help improving supply-chain practices and ultimately improving operational performance.
Teacher Digital Literacy: The Indisputable Challenge after COVID-19

The COVID-19 outbreak opened a new scenario where teachers must have adequate digital literacy to teach online and to implement a current and innovative educational model. This paper provides the most relevant results obtained from a quantitative study in which 4883 Spanish teachers of all education levels participated to measure their digital skills, during the last school years. It also proposes a digital skills teacher training plan, taking the joint framework of digital skills of INTEF (Spanish acronym for National Institute of Educational Technologies and Teacher Training) as its reference point. The tool ACDC (Analysis of Common Digital Competences) was used for data collection. The results of descriptive analysis show, overall, the low self-perception that teachers have of their digital skills. In addition, this paper studies the relationship existing between the characteristics that define the population and the teachers’ digital skills level. This relationship is obtained through a multiple linear regression model. The study reveals that digital literacy is not a reality that has favored the teaching–learning process and that a training program is urgently required for teachers to reach optimal levels of digital skills, so as to undergo a true paradigm shift, ultimately combining methodology and educational strategies.
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling

A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community made great advancements in training effective dual-encoder dense retrieval (DR) models recently. A dense text retrieval model uses a single vector representation per query and passage to score a match, which enables low-latency first-stage retrieval with a nearest neighbor search. Increasingly common, training approaches require enormous compute power, as they either conduct negative passage sampling out of a continuously updating refreshing index or require very large batch sizes. Instead of relying on more compute capability, we introduce an efficient topic-aware query and balanced margin sampling technique, called TAS-Balanced. We cluster queries once before training and sample queries out of a cluster per batch. We train our lightweight 6-layer DR model with a novel dual-teacher supervision that combines pairwise and in-batch negative teachers. Our method is trainable on a single consumer-grade GPU in under 48 hours. We show that our TAS-Balanced training method achieves state-of-the-art low-latency (64ms per query) results on two TREC Deep Learning Track query sets. Evaluated on [email protected], we outperform BM25 by 44%, a plainly trained DR by 19%, docT5query by 11%, and the previous best DR model by 5%. Additionally, TAS-Balanced produces the first dense retriever that outperforms every other method on recall at any cutoff on TREC-DL and allows more resource intensive re-ranking models to operate on fewer passages to improve results further.
OCNet: Object Context for Semantic Segmentation

In this paper, we address the semantic segmentation task with a new context aggregation scheme named object context, which focuses on enhancing the role of object information. Motivated by the fact that the category of each pixel is inherited from the object it belongs to, we define the object context for each pixel as the set of pixels that belong to the same category as the given pixel in the image. We use a binary relation matrix to represent the relationship between all pixels, where the value one indicates the two selected pixels belong to the same category and zero otherwise. We propose to use a dense relation matrix to serve as a surrogate for the binary relation matrix. The dense relation matrix is capable to emphasize the contribution of object information as the relation scores tend to be larger on the object pixels than the other pixels. Considering that the dense relation matrix estimation requires quadratic computation overhead and memory consumption w.r.t. the input size, we propose an efficient interlaced sparse self-attention scheme to model the dense relations between any two of all pixels via the combination of two sparse relation matrices. To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling (Zhao et al. 2017) and atrous spatial pyramid pooling (Chen et al. 2018). We empirically show the advantages of our approach with competitive performances on five challenging benchmarks including: Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff.
Diverse Branch Block: Building a Convolution as an Inception-like Unit

We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multiscale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https://github.com/DingXiaoH/DiverseBranchBlock.
Overview: Technology Roadmap of the Future Trend of Metaverse based on IoT, Blockchain, AI Technique, and Medical Domain Metaverse Activity

Metaverse is defined as a collection of technology gadgets and metaverse connected to IoT, Blockchain, Artificial Intelligence, and all the other tech industries including the medical area. IoT and Metaverse are the digital twins, Metaverse is using maximum IoT devices in their virtual workstation. This data has a unique identifying tag and is used as traceable data in the blockchain-based Metaverse. In the Metaverse, such data is becoming a valuable resource for artificial intelligence. Metaverse uses artificial intelligence and blockchain technology to build a digital virtual world where you can safely and freely engage in social and economic activities that transcend the limits of the real world, and the application of these latest technologies will be expedited. In this paper, we are going to describe what technologies metaverse is using and metaverse potentiality in medical healthcare.
A scalable discrete-time survival model for neural networks

There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks, which we refer to as Nnet-survival. The model is trained with the maximum likelihood method using mini-batch stochastic gradient descent (SGD). The use of SGD enables rapid convergence and application to large datasets that do not fit in memory. The model is flexible, so that the baseline hazard rate and the effect of the input data on hazard probability can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrate the performance of the model on both simulated and real data and compare it to existing models Cox-nnet and Deepsurv.
Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs

Deep learning, especially Convolutional Neural Networks (CNNs), has been widely applied in many domains. The large number of parameters in a CNN allow it to learn complex features, however, they may tend to hinder generalization by over-fitting training data. Despite many previously proposed regularization methods, over-fitting is still a problem in training a robust CNN. Among many factors that lead to over-fitting, the numerous parameters of fully-connected layers (FCLs) of a typical CNN should be taken into account. This paper proposes the SparseConnect, a simple idea which alleviates over-fitting by sparsifying connections to FCLs. Experimental results on three benchmark datasets MNIST, CIFAR10 and ImageNet show that the SparseConnect outperforms several state-of-the-art regularization methods.
Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection

Unsupervised feature selection always occupies a key position as a preprocessing in the tasks of classification or clustering due to the existence of extra essential features within high-dimensional data. Although lots of efforts have been made, the existing methods neglect to consider the redundancy of features, and thus select redundant features. In this brief, by virtue of a generalized uncorrelated constraint, we present an improved sparse regression model [generalized uncorrelated regression model (GURM)] for seeking the uncorrelated yet discriminative features. Benefited from this, the structure of data is kept in the Stiefel manifold, which avoids the potential trivial solution triggered by a conventional ridge regression model. Besides that, the uncorrelated constraint equips the model with the closed-form solution. In addition, we also incorporate a graph regularization term based on the principle of maximum entropy into the GURM model (URAFS), so as to embed the local geometric structure of data into the manifold learning. An efficient algorithm is designed to perform URAFS by virtue of the existing generalized powered iteration method. Extensive experiments on eight benchmark data sets among seven state-of-the-art methods on the task of clustering are conducted to verify the effectiveness and superiority of the proposed method.
Towards Robust Image Steganography

Posting images on social network platforms is happening everywhere and every single second. Thus, the communication channels offered by various social networks have a great potential for covert communication. However, images transmitted through such channels will usually be JPEG compressed, which fails most of the existing steganographic schemes. In this paper, we propose a novel image steganography framework that is robust for such channels. In particular, we first obtain the channel compressed version (i.e., the channel output) of the original image. Secret data is embedded into the channel compressed original image by using any of the existing JPEG steganographic schemes, which produces the stego-image after the channel transmission. To generate the corresponding image before the channel transmission (termed the intermediate image), we propose a coefficient adjustment scheme to slightly modify the original image based on the stego-image. The adjustment is done such that the channel compressed version of the intermediate image is exactly the same as the stego-image. Therefore, after the channel transmission, secret data can be extracted from the stego-image with 100% accuracy. Various experiments are conducted to show the effectiveness of the proposed framework for image steganography robust to JPEG compression.
Authentication in cloud-driven IoT-based big data environment: Survey and outlook

The Internet of Things (IoT) is composed of different networked objects (i.e., smart devices) which are interconnected to gather, process, refine, and exchange meaningful data over the Internet. These objects are assigned to their respective IP addresses, and they are able to send and receive data over a network without any human assistance. IoT offers different types of applications, such as, but not limited to, smart traffic monitoring, smart home, smart health care and smart cities, to name a few. In a Cyber-Physical System (CPS), computing elements coordinate and communicate with sensor devices, which monitor cyber and physical indicators, and actuators, and also modify the cyber and physical environment where they run. The synergy of computational as well as physical components, specifically the use of CPSs, led to the advancement of IoT implementations. In a cloud-driven IoT-based big data environment, a cloud-based platform is used to store the data generated by IoT devices (normally by sensor devices) which further can be considered as a big data warehouse. This environment is highly scalable and provides important real-time event processing (for example, in critical scenarios like surveillance and monitoring of an industrial plant). In IoT-based critical applications, the real-time data access is obligatory as and when it is required. Such access is possible if we permit only authorized external users to access the real-time data directly from the IoT sensors. Sometimes authorized user may also request for big data query processing and big data analytics over the data stored in cloud servers to figure out hidden patterns of some phenomena (i.e., chances of fire in an industrial plant in future). Therefore, we need secure authentication schemes for cloud-driven IoT-based big data environment in which a legitimate user and an IoT sensor can mutually authenticate each other and establish a common session key for secure communication. In this context, this paper first discusses the network and threat models of the authentication schemes for cloud-driven IoT-based big data environment. Some security requirements, issues and challenges of this environment are then discussed. A taxonomy of various existing authentication schemes applicable for cloud-driven IoT-based big data environment is also discussed, which covers a comparative study of these schemes. We identify and briefly discuss some future research challenges in designing the authentication schemes and other security protocols for cloud-driven IoT-based big data environment that need to be addressed in the future.
Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

Liver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method. In this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET). We have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process. Our developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.