Reviewerly/exHarmony huggingface dataset:
<i>X</i>-Secure <i>T</i>-Private Information Retrieval From MDS Coded Storage With Byzantine and Unresponsive Servers

The problem of X-secure T-private information retrieval from MDS coded storage is studied in this paper, where the user wishes to privately retrieve one out of K independent messages that are distributed over N servers according to an MDS code. It is guaranteed that any group of up to X colluding servers learn nothing about the messages and that any group of up to T colluding servers learn nothing about the identity of desired message. A lower bound of achievable rates is proved by presenting a novel scheme based on cross-subspace alignment and a successive decoding with interference cancellation strategy. For large number of messages (K → ∞) the achieved rate, which we conjecture to be optimal, improves upon the best known rates previously reported in the literature by Raviv and Karpuk, and generalizes an achievable rate for MDS-TPIR previously found by Freij-Hollanti et al. that is also conjectured to be asymptotically optimal. The setting is then expanded to allow unresponsive and Byzantine servers. Finally, the scheme is applied to find a new lower convex hull of (download, upload) pairs of secure and private distributed matrix multiplication that generalizes, and in certain asymptotic settings strictly improves upon the best known previous results.
Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion

: Breast cancer is a frequently diagnosed cancer in women, contributing to significant mortality rates. Death rates are relatively higher in developing nations due to the shortage of early detection amenities and constraints on access to technical advances combating this disease. The only way to diagnose cancer with certainty is through biopsy performed by pathologists. Computer-aided diagnostic algorithms can assist pathologists in being more productive, objective and consistent in the diagnostic process. The focus of this work is to develop a reliable automated breast cancer diagnosis method which can operate in the prevailing clinical environment. Nuclei overlap and complex structural organisation of the breast tissue in biopsy images make nuclei segmentation, feature extraction and classification challenging. In this work, a nucleus guided transfer learning (NucTraL) methodology is proposed as a simple and affordable breast tumor classification algorithm. The image feature is represented by fusion of local nuclei features that are extracted using convolutional neural network (CNN) models pretrained on the ImageNet database. The nucleus patch extraction strategy used in this work avoids fine segmentation of the nuclei boundary but provides features with good discriminative power for classification. Classification of the fused features into benign and malignant classes is performed using a support vector machine (SVM) classifier. A belief theory based classifier fusion (BCF) strategy is then employed to combine the outputs arising from the different CNN-SVM combinations to improve accuracy further. Evaluation of results is achieved by executing 100 random trials with 70%–30% train to test division on the publicly available BreaKHis dataset. The proposed framework achieved average accuracy of 96.91%, sensitivity of 97.24% and specificity of 96.18%. It is found that the proposed NucTraL+BCF framework outperforms several recent approaches and achieves results comparable to the state-of-the-art methods even without using high computational power. This qualitative framework based on transfer learning can contribute significantly for developing cost effective and low complexity CAD system for breast cancer diagnosis from histopathological images.
Enabling Execution Assurance of Federated Learning at Untrusted Participants

Federated learning (FL), as a privacy-preserving machine learning framework, draws growing attention in both industry and academia. It obtains a jointly accurate model by distributing training tasks into data owners and aggregating their model updates. However, FL faces new security problems, as it losses direct control to training processes. One fundamental demand is to ensure whether participants execute training tasks as intended.In this paper, we propose TrustFL, a practical scheme that leverages Trusted Execution Environments (TEEs) to build assurance of participants' training executions with high confidence. Specifically, we use TEE to randomly check a small fraction of all training processes for tunable levels of assurance, while all computations are executed on the co-located faster yet insecure processor (e.g., GPU) for efficiency. To prevent various cheating behaviors like only processing TEE-requested computations or uploading old results, we devise a commitment-based method with specific data selection. We prototype TrustFL using GPU and SGX and evaluate its performance. The results show that TrustFL achieves one/two orders of magnitude speedups compared with naive training with SGX, when assuring correct training with a confidence level of 99%.
Twitter Sentiments Analysis Using Machine Learninig Methods

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge number of users. Social media platform like twitter where users can post their tweets in 280 characters. Because of the limited number of characters in tweets, it becomes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data becomes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve Bayes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done by this classification algorithm and what is the accuracy and precision in these cases.
Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images

In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learning-based algorithms bring promising performance due to end-to-end detection and automated feature extraction. However, several challenges still exist: (1) standard deep learning detectors based on anchors have certain unsolved problems, such as tuning of anchor-related parameters, scale-variation and high computational costs. (2) SAR data is huge but the labeled data is relatively small, which may lead to overfitting in training. (3) To improve detection speed, deep learning detectors generally detect targets based on low-resolution features, which may cause missed detections for small targets. In order to address the above problems, an anchor-free convolutional network with dense attention feature aggregation is proposed in this paper. Firstly, we use a lightweight feature extractor to extract multiscale ship features. The inverted residual blocks with depth-wise separable convolution reduce the network parameters and improve the detection speed. Secondly, a novel feature aggregation scheme called dense attention feature aggregation (DAFA) is proposed to obtain a high-resolution feature map with multiscale information. By combining the multiscale features through dense connections and iterative fusions, DAFA improves the generalization performance of the network. In addition, an attention block, namely spatial and channel squeeze and excitation (SCSE) block is embedded in the upsampling process of DAFA to enhance the salient features of the target and suppress the background clutters. Third, an anchor-free detector, which is a center-point-based ship predictor (CSP), is adopted in this paper. CSP regresses the ship centers and ship sizes simultaneously on the high-resolution feature map to implement anchor-free and nonmaximum suppression (NMS)-free ship detection. The experiments on the AirSARShip-1.0 dataset demonstrate the effectiveness of our method. The results show that the proposed method outperforms several mainstream detection algorithms in both accuracy and speed.
DoH Insight

Over the past few years, a new protocol DNS over HTTPS (DoH) has been created to improve users' privacy on the internet. DoH can be used instead of traditional DNS for domain name translation with encryption as a benefit. This new feature also brings some threats because various security tools depend on readable information from DNS to identify, e.g., malware, botnet communication, and data exfiltration. Therefore, this paper focuses on the possibilities of encrypted traffic analysis, especially on the accurate recognition of DoH. The aim is to evaluate what information (if any) can be gained from HTTPS extended IP flow data using machine learning. We evaluated five popular ML methods to find the best DoH classifiers. The experiments show that the accuracy of DoH recognition is over 99.9 %. Additionally, it is also possible to identify the application that was used for DoH communication, since we have discovered (using created datasets) significant differences in the behavior of Firefox, Chrome, and cloudflared. Our trained classifier can distinguish between DoH clients with the 99.9 % accuracy.
A survey of recent interactive image segmentation methods

Abstract Image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground-background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS.
Smart contracts for blockchain-based reputation systems: A systematic literature review

Reputation systems offer a medium where users can quantify the trustworthiness or reliability of individuals providing online services or products. In the past, researchers have used blockchain technology for reputation systems. Smart contracts are computer protocols which have the primary objective to supervise, implement, or validate performances or negotiations of contracts. However, through a systematic literature review, in this paper, we find that the existing literature has not proposed a framework that facilitates the interchangeable use of smart contracts for blockchain-based reputation systems. We adopt a systematic literature review from 30 relevant studies and the data from them were extracted before identifying the research gaps. As a solution to the research gaps, we propose the FarMed framework for creating an intelligent framework that will execute Ethereum smart contact-based reputation systems and develop reliable blockchain-based protocols for transferring reputation values from one provider to another. We briefly explain our proposed framework before concluding with our future work.
Random curiosity-driven exploration in deep reinforcement learning

Reinforcement learning (RL) depends on carefully engineering environment rewards. However, rewards from environments are extremely sparse for many RL tasks, challenging for the agent to learn skills and interact with the environment. One solution to this problem is to create intrinsic rewards for agents and to make rewards dense and more suitable for learning. Recent algorithms, such as curiosity-driven exploration, usually estimate the novelty of the next state through the prediction error of dynamics models. However, these methods are typically limited by the capacity of their dynamics models. In this paper, a random curiosity-driven model using deep reinforcement learning is proposed, which uses a target network with fixed weights to maintain the stability of dynamics models and create more suitable intrinsic rewards. We integrate the parametric exploration method for further promoting sufficient exploration. Besides, a deeper and more closely connected network is utilized for encoding the pixel images for policy-gradient. By comparing our method against the previous approaches in several environments, the experiments show that our method achieves state-of-the-art performance on most but not all of the Atari games.
Restorative experiences and online tourists’ willingness to pay a price premium in an augmented reality environment

This study examined the effects of the restorative experience triggered by empowerment features in augmented reality (AR) on online tourists’ immersion and willingness to pay a price premium in the pre-purchase phase. The study focused in particular on the coherence and compatibility perspectives of the restorative experience. A scenario survey was conducted to validate the research framework. The research findings clarified the psychological mechanism behind the willingness to pay a price premium in AR-empowered online travel experiences. They indicated that the two AR empowerment features namely environmental embedding and simulated physical control generate restorative experience, which further fostered immersion and ultimately resulted in the willingness to pay more. The results may help online tourism services select appropriate AR empowerment features that will incite tourists to pay a premium price and also help create an effective AR tourism destination experience in the pre-purchase phase. This study focused on the attention restoration theory, empowerment paradigms, and AR-based service technology to develop an integrated conceptual framework of the relationship among AR empowerment features, restorative experience, immersion, and pre-purchase decision-making behavior.
COLSHADE for Real-World Single-Objective Constrained optimization Problems

In this paper we present the COLSHADE algorithm for real parameter constrained optimization problems. COLSHADE evolved from the basic L-SHADE algorithm by introducing significant features such as adaptive Lévy flights and dynamic tolerance (included in the constraint handling technique). Lévy flights mainly perform the exploration phase in algorithms such as the Firefly algorithm and Cuckoo search; in COLSHADE, however, the goal of the Lévy flights is to administer the selection pressure exerted over the population as to find the feasible region and keeping diversity. Thus, a new adaptive Lévy flight mutation operator is introduced here and called the levy/1/bin. In many problems the levy/1/bin excels during the exploration phase whilst the exploitation phase is performed by current-to-pbest mutation. However, the adaptive strategy propitiates the emergence of these two mutation approaches at different rates and times. The proposed method is tested on 57 constrained optimization functions of the benchmark provided for the CEC 2020 realworld single-objective constrained optimization competition.
FogAuthChain: A secure location-based authentication scheme in fog computing environments using Blockchain

Fog computing is an emerging computing paradigm which expands cloud-based computing services near the network edge. With this new computing paradigm, new challenges arise in terms of security and privacy. These concerns are due to the distributed ownership of Fog devices. Because of the large scale distributed nature of devices at the Fog layer, secure authentication for communication among these devices is a major challenge. The traditional authentication methods (password-based, certificate-based and biometric-based) are not directly applicable due to the unique architecture and characteristics of the Fog. Moreover, the traditional authentication methods consume significantly more computation power and incur high latency, and this does not meet the key requirements of the Fog. To fill this gap, this article proposes a secure decentralised location-based device to device (D2D) authentication model in which Fog devices can mutually authenticate each other at the Fog layer by using Blockchain. We considered an Ethereum Blockchain platform for the Fog device registration, authentication, attestation and data storage. We presented the overall system architecture, various participants and their transactions and message interaction between the participants. We validated the proposed model by comparing it with the existing method; results showed that the proposed authentication mechanism was efficient and secure. From the performance evaluation, it was found that the proposed method is computationally efficient and secure in a highly distributed Fog network.
Accelerating CNN Inference on ASICs: A Survey

Convolutional neural networks (CNNs) have proven to be a disruptive technology in most vision, speech and image processing tasks. Given their ubiquitous acceptance, the research community is investing a lot of time and resources on deep neural networks. Custom hardware such as ASICs are proving to be extremely worthy platforms for running such programs. However, the ever-increasing complexity of these algorithms poses challenges in achieving real-time performance. Specifically, CNNs have prohibitive costs in terms of computation time, throughput, latency, storage space, memory bandwidth, and power consumption. Hence, in the last 5 years, a lot of work has been done by the scientific community to mitigate these costs. Researchers have primarily focused on reducing the computation time, the number of computations, the memory access time, and the size of the memory footprint. In this survey paper, we propose a novel taxonomy to classify prior work, and describe some of the key contributions in these areas in detail.
Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty

Kuwait has a high potential for utilizing meteorologically driven energy resources such as solar PV. However, understanding the extent to which the distinct climatic conditions in Kuwait, reflected in the ambient temperature and occurrence of sandstorms, affect the variability and uncertainty of solar PV output is crucial. This is because it allows power system planners to adopt appropriate measures to accommodate solar PV generation and thus minimize renewable energy curtailment and consequently opportunity costs. In this study, the performance of a 2000 MW solar PV plant operating under the weather conditions in Kuwait is simulated using a Monte Carlo approach. The results show, on average, the power generation is 13% lower in the summer compared with the spring when the temperature is milder and solar production peaks. However, the certainty of production is higher in the summer. Furthermore, the PV generation variability in the summer is of the same magnitude as the demand variability, whereas during winter months, it is approximately twice the demand variability on any given day of the year. Sandstorms with daily average PM10 concentration of less than 300 ppb lead to little to no reduction in daily total irradiation. However, day-long and severe sandstorms (PM10 2700 ppb or greater) reduce daily total irradiation by 57%. Generally, the largest reductions occur when high particulate matter concentrations coincide with peak hourly solar irradiance.
CGA: a new feature selection model for visual human action recognition

Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel feature selection (FS) model called cooperative genetic algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets named Weizmann, KTH, UCF11, HMDB51, and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering very small fraction of the original feature vector.
RGGNet: Tolerance Aware LiDAR-Camera Online Calibration With Geometric Deep Learning and Generative Model

Accurate LiDAR-camera online calibration is critical for modern autonomous vehicles and robot platforms. Dominant methods heavily rely on hand-crafted features, which are not scalable in practice. With the increasing popularity of deep learning (DL), a few recent efforts have demonstrated the advantages of DL for feature extraction on this task. However, their reported performances are not sufficiently satisfying yet. We believe one improvement can be the problem formulation with proper consideration of the underneath geometry. Besides, existing online calibration methods focus on optimizing the calibration error while overlooking the tolerance within the error bounds. To address the research gap, a DL-based LiDAR-camera calibration method, named as the RGGNet, is proposed by considering the Riemannian geometry and utilizing a deep generative model to learn an implicit tolerance model. When evaluated on KITTI benchmark datasets, the proposed method demonstrates superior performances to the state-of-the-art DL-based methods. The code will be publicly available at https://github.com/KleinYuan/RGGNet.
Efficient Data Security Using Hybrid Cryptography on Cloud Computing

Services are distributed among all servers and between the users and individuals in the cloud environment. Cloud providers have trouble guaranteeing file protection as security is the biggest issue in data handling and transfer as it can be accessed, misused and destroyed the original data form. Cloud security is a big concern in the cloud computing environment. To safeguard the cloud environment, many research works are being proposed. To overcome the security issue and achieve the CIA property (confidentiality, integrity and availability) the cryptography is used. Cryptography is the most useful technique to ensure a high level of data transfer and storage security. In traditional symmetric and asymmetric has some limitations. To solve this we are going to introducing a new hybrid technique to achieve high data security and confidentiality. In this article, we are combing ECC and Blowfish to implement a hybrid algorithm. The performance of the hybrid system is compared with the existing hybrid method and shows that the proposed method provides high security and confidentiality of patient data. The hybrid cryptography is used to defeat the inconveniences of both symmetric and asymmetric.
Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation. However, 3D surface representations are often required for proper analysis. They can be obtained by post-processing the labeled volumes which typically introduces artifacts and prevents end-to-end training. In this paper, we therefore introduce a novel architecture that goes directly from 3D image volumes to 3D surfaces without post-processing and with better accuracy than current methods. We evaluate it on Electron Microscopy and MRI brain images as well as CT liver scans. We will show that it outperforms state-of-the-art segmentation methods.