Reviewerly/exHarmony huggingface dataset:
Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)

Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is a non-communicable diseases (NCDs), which account for 71% of total deaths worldwide; lung cancer is the second most diagnosed cancer after female breast cancer. The cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as an X-ray, CT scan, PET-CT scan, bronchoscopy, or biopsy. However, to know the subtype of lung cancer based on the tissue type, H and E staining is widely used; the staining is done on the tissue aspirated from a biopsy. Studies have reported that the type of histology is associated with prognosis and treatment in lung cancer. Therefore, early and accurate detection of lung cancer histology is an urgent need, and as its treatment is dependent on the type of histology, molecular profile, and stage of the disease, it is essential to analyze the histopathology images of lung cancer. Hence, to speed up the diagnosis of lung cancer and reduce the burden on pathologists, deep learning techniques are used. These techniques have shown improved efficacy in the analysis of histopathology slides of cancer. Several studies reported the importance of convolution neural networks (CNN) in the classification of histopathological pictures of various cancer types such as brain, skin, breast, lung, and colorectal cancer. In this study, tri-category classification of lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma) are carried out by using ResNet 50, VGG-19, Inception_ResNet_V2, and DenseNet for the feature extraction and triplet loss to guide the CNN such that it increases inter-cluster distance and reduces intra-cluster distance.
Shorter Hash-and-Sign Lattice-Based Signatures

Lattice-based digital signature schemes following the hash-and-sign design paradigm of Gentry, Peikert and Vaikuntanathan (GPV) tend to offer an attractive level of efficiency, particularly when instantiated with structured compact trapdoors. In particular, NIST postquantum finalist Falcon is both quite fast for signing and verification and quite compact: NIST notes that it has the smallest bandwidth (as measured in combined size of public key and signature) of all round 2 digital signature candidates. Nevertheless, while Falcon–512, for instance, compares favorably to ECDSA–384 in terms of speed, its signatures are well over 10 times larger. For applications that store large number of signatures, or that require signatures to fit in prescribed packet sizes, this can be a critical limitation. In this paper, we explore several approaches to further improve the size of hash-and-sign lattice-based signatures, particularly instantiated over NTRU lattices like Falcon and its recent variant Mitaka. In particular, while GPV signatures are usually obtained by sampling lattice points according to some spherical discrete Gaussian distribution, we show that it can be beneficial to sample instead according to a suitably chosen ellipsoidal discrete Gaussian: this is because only half of the sampled Gaussian vector is actually output as the signature, while the other half is recovered during verification. Making the half that actually occurs in signatures shorter reduces signature size at essentially no security loss (in a suitable range of parameters). Similarly, we show that reducing the modulus q with respect to which signatures are computed can improve signature size as well as verification key size almost “for free”; this is particularly true for constructions like Falcon and Mitaka that do not make substantial use of NTT-based multiplication (and rely instead on transcendental FFT). Finally, we show that the Gaussian vectors in signatures can be represented in a more compact way with appropriate coding-theoretic techniques, improving signature size by an additional 7 to 14%. All in all, we manage to reduce the size of, e.g., Falcon signatures by 30–40% at the cost of only 4–6 bits of Core-SVP security.
Blockchain-assisted cyber security in medical things using artificial intelligence

&lt;abstract&gt;&lt;p&gt;The Internet of Medical Things (IoMT) significantly impacts our healthcare system because it allows us to track and verify patient medical data before storing it in the cloud for future use. A rapidly expanding platform like IoMT requires high security to keep all data safe. The patient's prescription history and other sensitive information must be encrypted and managed with great care. Nevertheless, it is challenging to determine what data uses are acceptable while protecting patient privacy and security. Understanding the limits of current technologies and envisioning future research paths is crucial for establishing a safe and reliable data environment. An untrustworthy person can communicate with a trustworthy person via blockchain, a decentralized digital ledger that allows for end-to-end communication. Therefore, this research suggests that the healthcare industry with blockchain-integrated cyber-security based on artificial intelligence (BICS-AI) in medical care to preserve medical-related things. Blockchain applications have the potential to consistently identify the most severe, potentially life-threatening mistakes in the medical field. The use of blockchain for decentralized data protection helps to protect patient health records from compromise. With the help of an access control provider (ACP), here came up with a lightweight solution that addresses this issue by allowing the delegating of security operations. Medical data from IoMT and integrated devices can be collected and stored securely and distributed using a conventional in-depth approach combined with blockchain, making it suitable for healthcare professionals such as nursing homes, hospitals, and the healthcare industry where data exchange is required. The research findings indicate that the suggested system is viable and has a 94.84$ \% $ security rate, a security performance of 96.4$ \% $, a success rate of 89.9$ \% $, and a 5.1$ \% $ latency rate compared to traditional methods.&lt;/p&gt;&lt;/abstract&gt;
GateHUB: Gated History Unit with Background Suppression for Online Action Detection

Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and has to solely rely on the history, i.e., the frames observed so far, to make predictions. It is therefore important to accentuate parts of the history that are more informative to the prediction of the current frame. We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction. GateHUB further proposes Future-augmented History (FaH) to make history features more informative by using subsequently observed frames when available. In a single unified framework, GateHUB integrates the transformer's ability of long-range temporal modeling and the recurrent model's capacity to selectively encode relevant information. GateHUB also introduces a background suppression objective to further mitigate false positive background frames that closely resemble the action frames. Extensive validation on three benchmark datasets, THUMOS, TVSeries, and HDD, demonstrates that GateHUB significantly outperforms all existing methods and is also more efficient than the existing best work. Furthermore, a flow free version of GateHUB is able to achieve higher or close accuracy at 2.8× higher frame rate compared to all existing methods that require both RGB and optical flow information for prediction.
BigColor: Colorization Using a Generative Color Prior for Natural Images

For realistic and vivid colorization, generative priors have recently been exploited. However, such generative priors often fail for in-the-wild complex images due to their limited representation space. In this paper, we propose BigColor, a novel colorization approach that provides vivid colorization for diverse in-the-wild images with complex structures. While previous generative priors are trained to synthesize both image structures and colors, we learn a generative color prior to focus on color synthesis given the spatial structure of an image. In this way, we reduce the burden of synthesizing image structures from the generative prior and expand its representation space to cover diverse images. To this end, we propose a BigGAN-inspired encoder-generator network that uses a spatial feature map instead of a spatially-flattened BigGAN latent code, resulting in an enlarged representation space. BigColor enables robust colorization for diverse inputs in a single forward pass, supports arbitrary input resolutions, and provides multi-modal colorization results. We demonstrate that BigColor significantly outperforms existing methods especially on in-the-wild images with complex structures.
Realistic One-Shot Mesh-Based Head Avatars

We present a system for the creation of realistic one-shot mesh-based (ROME) human head avatars. From a single photograph, our system estimates the head mesh (with person-specific details in both the facial and non-facial head parts) as well as the neural texture encoding, local photometric and geometric details. The resulting avatars are rigged and can be rendered using a deep rendering network, which is trained alongside the mesh and texture estimators on a dataset of in-the-wild videos. In the experiments, we observe that our system performs competitively both in terms of head geometry recovery and the quality of renders, especially for cross-person reenactment.
EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation

Abstract Although various methods based on convolutional neural networks have improved the performance of biomedical image segmentation to meet the precision requirements of medical imaging segmentation task, medical image segmentation methods based on deep learning still need to solve the following problems: (1) Difficulty in extracting the discriminative feature of the lesion region in medical images during the encoding process due to variable sizes and shapes; (2) difficulty in fusing spatial and semantic information of the lesion region effectively during the decoding process due to redundant information and the semantic gap. In this paper, we used the attention-based Transformer during the encoder and decoder stages to improve feature discrimination at the level of spatial detail and semantic location by its multihead-based self-attention. In conclusion, we propose an architecture called EG-TransUNet, including three modules improved by a transformer: progressive enhancement module, channel spatial attention, and semantic guidance attention. The proposed EG-TransUNet architecture allowed us to capture object variabilities with improved results on different biomedical datasets. EG-TransUNet outperformed other methods on two popular colonoscopy datasets (Kvasir-SEG and CVC-ClinicDB) by achieving 93.44% and 95.26% on mDice. Extensive experiments and visualization results demonstrate that our method advances the performance on five medical segmentation datasets with better generalization ability.
Rehabilitation Is the Main Topic in Virtual and Augmented Reality and Physical Activity Research: A Bibliometric Analysis

Researchers' interest in finding practical applications for virtual reality (VR) and augmented reality (AR) technologies has increased as new devices have become cheaper and more accessible, being used in entertainment, healthcare, and rehabilitation fields, among others. This study aims to provide an overview of the current state of scientific literature related to VR, AR, and physical activity (PA). A bibliometric analysis of studies published between 1994 and 2022 was conducted using The Web of Science (WoS), applying the traditional bibliometric laws and using the VOSviewer software for data and metadata processing. The results revealed an exponential increase in scientific production between 2009 and 2021 (R2 = 94%). The United States (USA) was the country/region with the most relevant co-authorship networks (72 papers); the most prolific author was Kerstin Witte, and the most prominent was Richard Kulpa. The most productive journal's core was composed of high-impact and open access journals. A great thematic diversity was found according to the most used keywords by the co-authors, highlighting concepts such as rehabilitation, cognition, training, and obesity. Then, the research related to this topic is in an exponential development phase, with great interest in the rehabilitation and sports sciences fields.
QoS Prediction and Adversarial Attack Protection for Distributed Services Under DLaaS

Deep-learning-as-a-service (DLaaS) has received increasing attention because of its novelty as a diagram for deploying deep learning techniques. However, DLaaS still faces performance and security issues, which must be solved urgently. Given the limited computation resources and concern of benefits, distributed DLaaS systems require quality-of-service (QoS) metrics to optimize performance and reliability. However, on the one hand, new users and services are continuously joining and leaving the system, resulting in cold start issues. On the other hand, the increasing demand for robust network connections requires the model to evaluate the uncertainty. To address performance problems, we propose a deep learning-based model called embedding enhanced probability neural network, which can extract information from inside the graph structure and estimate the mean and variance values of the prediction distribution. Meanwhile, adversarial attack is a severe threat to model security under DLaaS. Thus, we investigated the service recommender system's vulnerability and propose adversarial training with uncertainty-aware loss to protect the model in noisy and adversarial environments. Extensive experimental results on a large-scale real-world QoS dataset verify the robustness and effectiveness of our model.
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Synthesizing visual content that meets users’ needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to "drag" any points of the image to precisely reach target points in a user-interactive manner, as shown in Fig.1. To achieve this, we propose DragGAN, which consists of two main components: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative generator features to keep localizing the position of the handle points. Through DragGAN, anyone can deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object’s rigidity. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. We also showcase the manipulation of real images through GAN inversion.
Antecedents of Industry 4.0 capabilities and technological innovation: a dynamic capabilities perspective

Purpose This paper aims to empirically examine the impact of dynamic capabilities on Industry 4.0 capabilities in small and medium enterprises (SMEs) in Jordan. It also aims to examine the potential impact of industry 4.0 capabilities on technological innovation. Design/methodology/approach Data were collected from 210 respondents who work and own SMEs in Jordan. SmartPLS software based on the partial least squares-structural equation modeling approach was used to test hypotheses. Findings The findings reveal the positive effects of the three components of dynamic capabilities, including sensing, seizing and reconfiguring, on Industry 4.0 capabilities. They also confirm the positive effect of Industry 4.0 capabilities on technological innovation. Originality/value This study provides valuable practical implications and enriches the literature on the determinants of Industry 4.0 capabilities and its role in developing the dynamic capabilities of SMEs, such as technological innovation.
RWKV: Reinventing RNNs for the Transformer Era

Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
Cloud Computing in Computer Science and Engineering Education

Cloud Computing opens a new chapter in Information Technology.It has its roots in internet technology, and like the Internet, it is rapidly and forcefully advancing into a large range of applications and services.While standardization of Cloud Computing is ongoing, there is every indication that cloud technology is here to stay and will cover most sectors of the society, including education.This paper discusses the potentials of CloudBased Education (CBE) in STEM areas to better stimulate and engage students in their pursuit of knowledge and learning.This paper introduces the concept of CloudBased Education for Computer Science (CBECS) and discusses how its framework can be achieved.Further, it shows how the platform can be generalized to use in various STEM areas.The authors argue that the potentials in using Cloud Computing for teaching Computer Science courses are extraordinary since CS has an intimate relationship with the cloud infrastructure.Thus, CBECS can greatly facilitate teaching complex underlying organizations of CS courses such as Operating Systems, Communication Networks, Cyberspace Security, WebBased Applications, Database, and High Performance Computing.While other STEM education can extensively benefit from CBE at the Software as a Service (SaaS) level to present meaningful examples in the lectures, CS courses can move deeper and utilize also the lower levels of cloud services.Such capability can deliver valuable examples and laboratories for CS students to better understand large scale applications and their complexities.
Reducing the Conservatism of Stabilization for Discrete-Time Takagi–Sugeno Fuzzy Systems via a New Extended Representation Approach

This paper addresses the problem of reducing the conservatism for stabilization of discrete-time Takagi-Sugeno fuzzy systems via a new extended representation approach. Different from previous slack variable methods, an advanced slack variable method without limitations is proposed for further making good use of available fuzzy weighting functions. Owing to the introduction of extended representations for homogenous polynomials, those strong limitations in recent literature are relaxed and thus the primary and secondary sequences of all normalized fuzzy weighting functions can be mapped to one augmented multi-indexed matrix for each homogeneous polynomial. Therefore, less conservative stabilization conditions can be provided as a result of these effective measures and thus a part of the online computational cost is transferred to the offline implementation. Finally, the benefits of the proposed method are illustrated by means of numerical experiments.
Detecting lateral spear phishing attacks in organisations

Lateral spear phishing attack is a powerful type of social engineering attack carried out using compromised email account(s) within the target organisation. Spear phishing attacks are difficult to detect due to the nature of these attacks. The inclusion of a lateral attack vector makes detection more challenging. The authors present an approach to detect lateral spear phishing attacks in organisations in real-time. Their approach uses features derived from domain knowledge and analysis of characteristics pertaining to such attacks, combined with their scoring technique which works on non-labelled dataset. They evaluate the approach on several years’ worth of real-world email dataset collected from volunteers in their institute. They were able to achieve false positive rate of below 1%, and also detected two instances of compromised accounts which were not known earlier. A comparison of their scoring technique with machine learning based anomaly detection techniques shows the proposed technique to be more suited for practical use. The proposed approach is primarily aimed at complementing existing detection techniques on email servers. However, they also developed a Chrome browser extension to demonstrate that such a system can also be used independently by organisations within their network.
Credit Assignment Techniques in Stochastic Computation Graphs

Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a single principle.However, this estimator often has high variance and requires a full model evaluation per data point, making this algorithm costly in large graphs.In this work, we address these problems by generalizing concepts from the reinforcement learning literature. We introduce the concepts of value functions, baselines and critics for arbitrary SCGs, and show how to use them to derive lower-variance gradient estimates from partial model evaluations, paving the way towards general and efficient credit assignment for gradient-based optimization. In doing so, we demonstrate how our results unify recent advances in the probabilistic inference and reinforcement learning literature.