Reviewerly/exHarmony huggingface dataset:
Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

High-dimensional and sparse (HiDS) matrices commonly arise in various industrial applications, e.g., recommender systems (RSs), social networks, and wireless sensor networks. Since they contain rich information, how to accurately represent them is of great significance. A latent factor (LF) model is one of the most popular and successful ways to address this issue. Current LF models mostly adopt L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm-oriented Loss to represent an HiDS matrix, i.e., they sum the errors between observed data and predicted ones with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. Yet L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm is sensitive to outlier data. Unfortunately, outlier data usually exist in such matrices. For example, an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users. To address this issue, this work proposes a smooth L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm-oriented latent factor (SL-LF) model. Its main idea is to adopt smooth L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm rather than L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm to form its Loss, making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix. Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
A DWT-SVD based robust digital watermarking for medical image security

In this work, we propose a blind watermarking approach for medical image protection. In this approach, the watermark will be constituted of the Electronic Patient Record and the image acquisition data. In order to enhance the security and guarantee the data integrity, the Electronic Patient Record hash will be added to the watermark. The integration process is based on a DWT-SVD combination, a DWT is applied to the retinal image, then, an SVD is applied to the LL sub-band. The watermark will be then integrated into the least significant bits of the S component obtained by combining the parity of the successive coefficients. Experimental results for imperceptibility and robustness show that the proposed scheme maintains a high quality watermarked image and remains highly robust against several conventional attacks.
Secure Multifactor Authenticated Key Agreement Scheme for Industrial IoT

The application of Internet of Things (IoT) has generally penetrated into people's life and become popular in recent years. The IoT devices with different functions are integrated and applied to various domains, such as E-health, smart home, Industrial IoT (IIoT), and smart farming. IIoT obtains the general attention among these domains, which allows the authorized user remotely access and control the sensing devices. The user suffices to attain the real-time data collected by sensing devices during the process of production. However, these data is usually transmitted via an insecure channel, which brings the problem of the security and privacy arising from the hostile attacks in IIoT. To resist the hostile attacks by the adversary and protect the security of the transmitted data, we propose a secure multifactor authenticated key agreement scheme for IIoT to support the authorized user remotely accessing the sensing device. The scheme adopts password, biometrics, and smart card to identify the user in the IIoT environment. We employ the secret-sharing technology and Chinese remainder theorem to construct a group key among legitimate sensing devices, and then this group key is utilized to assist in negotiating a secure session key between the user and multiple sensing devices. The proposed scheme is suitable for the resource-constrained IIoT as it only uses hash function, bitwise XOR operation, and symmetric cryptography. The performance analysis indicates that our scheme has less communication and computational costs in contrast to other correlative schemes. Besides, the security analysis indicates that our scheme can withstand many known attacks.
Watermarking Neural Networks With Watermarked Images

Watermarking neural networks is a quite important means to protect the intellectual property (IP) of neural networks. In this paper, we introduce a novel digital watermarking framework suitable for deep neural networks that output images as the results, in which any image outputted from a watermarked neural network must contain a certain watermark. Here, the host neural network to be protected and a watermark-extraction network are trained together, so that, by optimizing a combined loss function, the trained neural network can accomplish the original task while embedding a watermark into the outputted images. This work is totally different from previous schemes carrying a watermark by network weights or classification labels of the trigger set. By detecting watermarks in the outputted images, this technique can be adopted to identify the ownership of the host network and find whether an image is generated from a certain neural network or not. We demonstrate that this technique is effective and robust on a variety of image processing tasks, including image colorization, super-resolution, image editing, semantic segmentation and so on.
Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review

The role of Artificial intelligence (AI) which is defined as the ability of computers to perform tasks that normally require human intelligence is constantly expanding. Medicine was slow to embrace AI. However, the role of AI in medicine is rapidly expanding and promises to revolutionize patient care in the coming years. In addition, it has the ability to democratize high level medical care and make it accessible to all parts of the world.Among specialties of medicine, some like radiology were relatively quick to adopt AI whereas others especially pathology (and surgical pathology in particular) are only just beginning to utilize AI. AI promises to play a major role in accurate diagnosis, prognosis and treatment of cancers. In this paper, the general principles of AI are defined first followed by a detailed discussion of its current role in medicine. In the second half of this comprehensive review, the current and future role of AI in surgical pathology is discussed in detail including an account of the practical difficulties involved and the fear of pathologists of being replaced by computer algorithms. A number of recent studies which demonstrate the usefulness of AI in the practice of surgical pathology are highlighted.AI has the potential to transform the practice of surgical pathology by ensuring rapid and accurate results and enabling pathologists to focus on higher level diagnostic and consultative tasks such as integrating molecular, morphologic and clinical information to make accurate diagnosis in difficult cases, determine prognosis objectively and in this way contribute to personalized care.
The Application of Virtual Reality in Engineering Education

The advancement of VR technology through the increase in its processing power and decrease in its cost and form factor induced the research and market interest away from the gaming industry and towards education and training. In this paper, we argue and present evidence from vast research that VR is an excellent tool in engineering education. Through our review, we deduced that VR has positive cognitive and pedagogical benefits in engineering education, which ultimately improves the students’ understanding of the subjects, performance and grades, and education experience. In addition, the benefits extend to the university/institution in terms of reduced liability, infrastructure, and cost through the use of VR as a replacement to physical laboratories. There are added benefits of equal educational experience for the students with special needs as well as distance learning students who have no access to physical labs. Furthermore, recent reviews identified that VR applications for education currently lack learning theories and objectives integration in their design. Hence, we have selected the constructivist and variation learning theories as they are currently successfully implemented in engineering education, and strong evidence shows suitability of implementation in VR for education.
A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature's findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Makespan.
BIM security: A critical review and recommendations using encryption strategy and blockchain

Security of information in collaborative BIM platforms is crucial particularly for critical projects such as that of government buildings, prisons, and power plants. Inappropriate distribution and loss of sensitive information may potentially lead to physical security threats, financial loss, and loss of trust and reputation. Therefore, this paper identifies the information security requirements of collaborative BIM platforms considering the characteristics related to data security of collaborative BIM platforms through a literature review and as a result, identifies seven components of BIM security, based on which defines three levels of BIM security. Existing cybersecurity facilitating technologies such as encryption protocols, distributed database technology, and blockchain technology are reviewed to assess applicability to BIM security. This review shows that although the technologies to support BIM security are available in research and on market, they are not customized in existing collaborative BIM platforms to support BIM security. Therefore, two conceptual frameworks are proposed – (1) an encryption strategy-based framework to facilitate secure storage and distribution of BIM and (2) a blockchain-based framework to record BIM changes in a tamper-proof ledger for the non-trusting environment of construction projects. Discussions on cost and functionality are provided, which will be further extended in the future.
Robust deep<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math>-means: An effective and simple method for data clustering

Clustering aims to partition an input dataset into distinct groups according to some distance or similarity measurements. One of the most widely used clustering method nowadays is the k-means algorithm because of its simplicity and efficiency. In the last few decades, k-means and its various extensions have been formulated to solve the practical clustering problems. However, existing clustering methods are often presented in a single-layer formulation (i.e., shallow formulation). As a result, the mapping between the obtained low-level representation and the original input data may contain rather complex hierarchical information. To overcome the drawbacks of low-level features, deep learning techniques are adopted to extract deep representations and improve the clustering performance. In this paper, we propose a robust deep k-means model to learn the hidden representations associate with different implicit lower-level attributes. By using the deep structure to hierarchically perform k-means, the hierarchical semantics of data can be exploited in a layerwise way. Data samples from the same class are forced to be closer layer by layer, which is beneficial for clustering task. The objective function of our model is derived to a more trackable form such that the optimization problem can be tackled more easily and the final robust results can be obtained. Experimental results over 12 benchmark data sets substantiate that the proposed model achieves a breakthrough in clustering performance, compared with both classical and state-of-the-art methods.
Rainbow Memory: Continual Learning with a Memory of Diverse Samples

Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial. Instead, we focus on ‘blurry’ task boundary; where tasks shares classes and is more realistic and practical. To address such task, we argue the importance of diversity of samples in an episodic memory. To enhance the sample diversity in the memory, we propose a novel memory management strategy based on per-sample classification uncertainty and data augmentation, named Rainbow Memory (RM). With extensive empirical validations on MNIST, CIFAR10, CIFAR100, and ImageNet datasets, we show that the proposed method significantly improves the accuracy in blurry continual learning setups, outperforming state of the arts by large margins despite its simplicity. Code and data splits will be available in https://github.com/clovaai/rainbow-memory.
Analysis of cryptocurrency transactions from a network perspective: An overview

As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in the blocks. These transaction records containing rich information and complete traces of financial activities are publicly accessible, thus providing researchers with unprecedented opportunities for data mining and knowledge discovery in this area. Networks are a general language for describing interacting systems in the real world, and a considerable part of existing work on cryptocurrency transactions is studied from a network perspective. This survey aims to analyze and summarize the existing literature on analyzing and understanding cryptocurrency transactions from a network perspective. Aiming to provide a systematic guideline for researchers and engineers, we present the background information of cryptocurrency transaction network analysis and review existing research in terms of three aspects, i.e., network modeling, network profiling, and network-based detection. For each aspect, we introduce the research issues, summarize the methods, and discuss the results and findings given in the literature. Furthermore, we present the main challenges and several future directions in this area.
An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.
Standard model physics and the digital quantum revolution: thoughts about the interface

Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress. Pursuing the vision articulated by Feynman, a concerted effort across many areas of research and development is introducing prototypical digital quantum devices into the computing ecosystem available to domain scientists. Through interactions with these early quantum devices, the abstract vision of exploring classically-intractable quantum systems is evolving toward becoming a tangible reality. Beyond catalyzing these technological advances, entanglement is enabling parallel progress as a diagnostic for quantum correlations and as an organizational tool, both guiding improved understanding of quantum many-body systems and quantum field theories defining and emerging from the standard model. From the perspective of three domain science theorists, this article compilesthoughts about the interfaceon entanglement, complexity, and quantum simulation in an effort to contextualize recent NISQ-era progress with the scientific objectives of nuclear and high-energy physics.
Feature Importance-aware Transferable Adversarial Attacks

Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., black-box attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degrade prediction accuracy in a source model without aware of intrinsic features of objects in the images. We argue that such brute-force degradation would introduce model-specific local optimum into adversarial examples, thus limiting the transferability. By contrast, we propose the Feature Importance-aware Attack (FIA), which disrupts important object-aware features that dominate model decisions consistently. More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image. The gradients will be highly correlated to objects of interest, and such correlation presents invariance across different models. Besides, the random transforms will preserve intrinsic features of objects and suppress model-specific information. Finally, the feature importance guides to search for adversarial examples to-wards disrupting critical features, achieving stronger transferability. Extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed FIA, i.e., improving the success rate by 9.5% against normally trained models and 12.8% against defense models as compared to the state-of-the-art transferable attacks. Code is available at: https://github.com/hcguoO0/FIA
MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference

With the rapid development of face manipulation technology, it is difficult for human eyes to distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) discriminators can quickly reach high accuracy in identifying fake/real face images. In this study, we explore the behavior of CNN models in distinguish fake/real faces. We find multi-scale texture difference information plays an important role in face forgery detection. Motivated by the above observation, we propose a new Multi-scale Texture Difference model coined as MTD-Net for robust face forgery detection, which leverages central difference convolution (CDC) and atrous spatial pyramid pooling (ASPP). CDC combines the pixel intensity information and the pixel gradient information to give a stationary description of texture difference information. Simultaneously, based on the ASPP, multi-scale information fusion can keep the texture features from being destroyed. Experimental results on several databases, Faceforensics++, DeeperForensics-1.0, Celeb-DF and DFDC prove that our MTD-Net outperforms existing approaches. The MTD-Net is more robust to image distortion, e.g., JPEG compression and blur, which is urgently needed in the wild world.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction

Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data. Our main goal is to facilitate advances in this field by collecting real-world data in a magnitude similar to the existing synthetic counterparts. The principal contribution of this work is thus a large-scale dataset, called Common Objects in 3D, with real multi-view images of object categories annotated with camera poses and ground truth 3D point clouds. The dataset contains a total of 1.5 million frames from nearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such, it is significantly larger than alternatives both in terms of the number of categories and objects.We exploit this new dataset to conduct one of the first large-scale "in-the-wild" evaluations of several new-view-synthesis and category-centric 3D reconstruction methods. Finally, we contribute NerFormer - a novel neural rendering method that leverages the powerful Transformer to reconstruct an object given a small number of its views.