Reviewerly/exHarmony huggingface dataset:
How to implement secure cloud file sharing using optimized attribute-based access control with small policy matrix and minimized cumulative errors

The stunning growth of Internet users through Cloud File Sharing (CFS) is raising great concerns about unprecedented cloud security and privacy breach. Also, the recent breakthrough in quantum computing further reinforces this kind of concerns, thus we exploit an efficient solution to guarantee personal privacy and resist quantum attacks in the CFS service. In our solution, we integrate the Attribute-based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model and the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) into the CFS. To improve the performance of CP-ABE, we make use of an optimization method to convert the ABAC/XACML policy into a Small Policy Matrix (SPM). We further prove that this matrix has small coefficients and generates an all-one reconstruction vector, such that it reduces the cumulative error in lattice cryptosystem to the minimum. By using the SPM, we design a new CP-ABE scheme from Lattice (CP-ABE-L) to prevent the enlargement of error bounds. We also give the optimal estimation of system parameters, which satisfy three lattice-generation conditions to implement a valid Error Proportion Allocation (EPA). Our scheme is proved secure against chosen-plaintext attack with a selective attribute set under the Decision Learning with Errors (DLWE) assumption in the standard model. The performance evaluation and analyses illustrate that our scheme not only has short parameters, but also maintains efficient computation and reasonable storage overloads.
Binarization of Stone Inscription Images by Modified Bi-level Entropy Thresholding

India is rich in its heritage and culture. It has many historical monuments and temples where the walls are made of inscribed stones and rocks. The stone inscriptions play a vital role in portraying about the ancient incidents. Hence, the digitization of these stone inscriptions is necessary and contributes much for the epigraphers. Recently, the digitizing of these inscriptions began with the binarization process of stone inscriptions. This process mainly depends on the thresholding technique. In this paper, the binarization of terrestrial and underwater stone inscription images is preceded by a contrast enhancement and succeeded by edge-based filtering that minimizes noise and fine points the edges. A new method called modified bi-level thresholding (MBET) algorithm is proposed and compared with various existing thresholding algorithms namely Otsu method, Niblack method, Sauvola method, Bernsen method and Fuzzy C means method. The obtained results are evaluated with the performance metrics such as peak signal-to-noise ratio (PSNR) and standard deviation (SD). It is observed that the proposed method has an improvement of 49% and 39%, respectively, on an average by the metrics considered.
Investigating the Negative Effects of Emojis in Facebook Sponsored Ads for Establishing Sustainable Marketing in Social Media

Emojis are prevalent in modern social media advertising. Despite this fact, little research can be found on its effects on consumer purchase intentions. This study seeks to examine purchase intentions in the context of Sponsored Ads on Facebook News Feeds, their perceived intrusiveness, and how the added factor of emoji presence can further affect consumer perception in order to suggest a pathway for establishing sustainable marketing strategies. We investigated the effect of emojis on consumers and then the extent to which ad personalization can attenuate intrusiveness to the point of influencing purchase intentions. In the empirical investigations (an online study) conducted, the study revealed several interesting findings. First, the emoji presence in Sponsored Ads on the News Feed did not prompt the users’ perceived intrusiveness. Second, the emoji use led to decreased purchase intentions. Third, the perceived intrusiveness did not mediate the relationship between emoji presence (vs. absence) and purchase intentions. Lastly, the emoji presence decreased perceived intrusiveness and also increased purchase intentions when consumers perceived ads to be less personalized. The findings of this research provide both theoretical and managerial implications of the effects of emojis, and the reasons as to why their usage affects the desired ad goals when used in Sponsored Ads on Facebook from the perspective of sustainable marketing.
Observability in Kubernetes Cluster: Automatic Anomalies Detection using Prometheus

Kubernetes is a portable, extensible, open-source platform for managing containers. It comes with features such as automatic scaling, service discovery, load balancing, fault tolerance, etc. Being such a complex system, which has a lot of internal services and with the ability to manage a lot more user services, Kubernetes comes with a monitoring system, which provides metrics and logs for every service in the cluster. However, most of the time, the monitoring system needs human intervention for detection and troubleshooting defects. Human intervention usually occurs when it is too late, when a defect appears. We think that detecting anomalies in metrics provided by the monitoring system will help to prevent defects. In this paper, we analyze current solutions for automatic anomaly detection and alerting, and also we propose a new solution that will help system administrators to catch and predict anomalies earlier, which may lead to defects. Our solution, which is a technical one, is developed around Prometheus, an open-source monitoring system for metrics.
A Comparative Study on Liver Tumor Detection Using CT Images

Liver cancer (LC) is a globally known issue. It is one of the most common cancers that can cause human beings. It is a fatal disease spreading especially in developing countries. Many algorithms have been used to perform the detection of liver cancer with the help of both traditional machine learning classifiers and deep learning classifiers. To analyze the performance of commonly used algorithms, this paper attempts a comparative study on LC detection. It includes both machine learning and deep learning techniques; and several methods for liver and tumor detection from CT images are used. With the advances in Artificial Intelligence (AI) and convolution neural networks algorithms, the methods included in this comparative study achieved great results. The best accuracy among traditional machine learning classifiers reaches 90.46% using Support Vector Machine (RBF). Inception V4 pre-trained model obtained 93.15% in terms of testing accuracy, and it is the best classifier among deep learning models. The performance of deep learning models is very promising to take place in medical decisions.
Logic Design and Simulation of a 128-b AES Encryption Accelerator Based on Rapid Single-Flux-Quantum Circuits

A 128-b rapid single-flux-quantum (RSFQ) Advanced Encryption Standard (AES) encryption accelerator based on bit-slice architecture is proposed for the first time. Unlike the traditional methods, the method of looking up only one 256-B table to complete the entire AES round function is proposed. The proposed method can reduce nearly 50% of the hardware cost compared to the traditional method. The simple lookup table, shift, and xor operations are used in the proposed accelerator where one 256-B table needs to be stored for lookup table operations. The RSFQ logic circuits of the proposed accelerator are designed and simulated at the logic level. It consists of 136 558 JJs based on the Open Dataset of CONNECT Cell Library for AIST ADP2. The simulation results show the proposed accelerator works correctly.
Efficient Hardware Implementation of the LEDAcrypt Decoder

This work describes an efficient implementation of the iterative decoder that is the main part of the decryption stage in the LEDAcrypt cryptosystem, recently proposed for post-quantum cryptography based on low-density parity-check (LDPC) codes. The implementation we present exploits the structure of the variables in order to accelerate the decoding process while keeping the area bounded. In particular, our focus is on the design of an efficient multiplier, the latter being a fundamental component also in view of considering different values of the cryptosystem’s parameters, as it might be required in future applications. We aim to provide an architecture suitable for low cost implementation on both Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) implementations. As for the FPGA, the total execution time is 0.6 ms on the Artix-7 200 platform, employing at most 30% of the total available memory, 15% of the total available Look-up Tables and 3% of the Flip-Flops. The ASIC synthesis has been performed for both STM FDSOI 28 nm and UMC CMOS 65 nm technologies. After logic synthesis with the STM FDSOI 28 nm, the proposed decoder achieves a total latency of 0.15 ms and an area occupation of 0.09 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The post-Place&Route implementation results for the UMC 65 nm show a total execution time of 0.3 ms, with an area occupation of 0.42 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a power consumption of at most 10.5 mW.
Quantization and Knowledge Distillation for Efficient Federated Learning on Edge Devices

Federated learning enables distributed machine learning for decentralized data on edge devices. As communication is a critical bottleneck for federated learning, we utilize model compression techniques for efficient federated learning. First, we propose an adaptive quantized federated average algorithm to reduce the communication cost by dynamically quantizing neural networks' weights. Then, we design a federated knowledge distillation method to achieve high-quality small models with limited labeled data. Adaptive quantized federated learning can significantly speed up model training while retaining model accuracy. With a small fraction of data as labeled data, our federated knowledge distillation can reach a fixed accuracy achieved by supervised learning with the entire labeled data set.
C3VQG

Visual Question Generation (VQG) is the task of generating natural questions based on an image. Popular methods in the past have explored image-to-sequence architectures trained with maximum likelihood which have demonstrated meaningful generated questions given an image and its associated ground-truth answer. VQG becomes more challenging if the image contains rich contextual information describing its different semantic categories. In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers. Our approach solves two major shortcomings of existing VQG systems: (i) minimize the level of supervision and (ii) replace generic questions with category relevant generations. Most importantly, by eliminating expensive answer annotations, the required supervision is weakened. Using different categories enables us to exploit different concepts as the inference requires only the image and the category. Mutual information is maximized between the image, question, and answer category in the latent space of our VAE. A novel category consistent cyclic loss is proposed to enable the model to generate consistent predictions with respect to the answer category, reducing redundancies and irregularities. Additionally, we also impose supplementary constraints on the latent space of our generative model to provide structure based on categories and enhance generalization by encapsulating decorrelated features within each dimension. Through extensive experiments, the proposed model, C3VQG outperforms state-of-the-art VQG methods with weak supervision.
FastVer: Making Data Integrity a Commodity

We present FastVer, a high-performance key-value store with strong data integrity guarantees. FastVer is built as an extension of FASTER, an open-source, high-performance key-value store. It offers the same key-value API as FASTER plus an additional verify() method that detects if an unauthorized attacker tampered with the database and checks whether results of all read operations are consistent with historical updates. FastVer is based on a novel approach that combines the advantages of Merkle trees and deferred memory verification. We show that this approach achieves one to two orders of magnitudes higher throughputs than traditional approaches based on either Merkle trees or memory verification. We have formally proven the correctness of our approach in a proof assistant, ensuring that verify() detects any inconsistencies, except if a collision can be found on a cryptographic hash.
Efficient evolutionary neural architecture search by modular inheritable crossover

Deep neural networks are widely used in the domain of image classification, and a large number of excellent deep neural networks have been proposed in recent years. However, hand-crafted neural networks often require human experts for elaborate designs, which can be time-consuming and error-prone. Hence, neural architecture search (NAS) methods have been proposed to design model architecture automatically. The evolutionary NAS methods have achieved encouraging results due to the global search capability of evolutionary algorithms. Nevertheless, most existing evolutionary NAS methods use only the mutation operator to generate offspring architectures. Consequently, the generated architectures could be pretty different from their parent architectures, failing to inherit the modular information to accelerate the convergence rate. We propose an efficient evolutionary method using a tailored crossover operator to address this deficiency, which enables the offspring architectures to inherit from their parent architectures. Moreover, we combine it with mutation operators under the framework of the evolutionary algorithm. Experimental results on both the CIFAR-10 and CIFAR-100 tasks show that our proposed evolutionary NAS method has achieved state-of-the-art results.
Towards decentralized bicycle insurance system based on blockchain

Having a transparent and convenient insurance system is desired by users, but the insurance industry currently plays differently. For example, when users ask for a fast and simple self-service, the industry may need a long and cumbersome process, and sometimes users are not sure whether they have a fair deal according to their contract. With the aim of enhancing the current bicycle insurance system in Denmark, we investigate the local market and develop BlockCycle, which is a blockchain-based bicycle insurance system. The basic idea is that insurance companies transform their insurance products into a smart contract, including the criteria of applying the product and the eligibility of compensating into program logic. When a smart contract is deployed, the immutability can ensure the deal integrity and protect both users and insurance companies from the threat of unauthorized manipulation. In addition, blockchain can provide a secure and simple way to decentralize data and services, which enables high availability and reliability. Our experimental results indicate the viability of our system.
Towards CRYSTALS-Kyber: A M-LWE Cryptoprocessor with Area-Time Trade-Off

CRYSTALS-Kyber is a quantum-resistant and promising lattice-based cryptography (LBC) in the finalists of the third round post-quantum cryptography (PQC) standardization, which is based on the hardness of Module-Learning with Errors (M-LWE). The variadic parameters make M-LWE obtain a more flexible security-performance trade-off than Ring-LWE. In this paper, we propose a M-LWE cryptoprocessor targeting CRYSTALS-Kyber with area-time trade-off for the first time. This balanced design includes a fast and low-cost Binomial Sampler and vector-polynomials multiplication structure based on pipelined decimation-in-frequency (DIF) based Number Theoretic Transform (NTT) technique. The M-LWE cryptoprocessor achieve 27,708 encryption operations per second using only 690 slices and 106,716 decryption operations per second using only 571 slices. Our proposed design achieved the lowest area-time product (ATP) with at least 2 χ performance improvement than the state-of-the-art LBC designs with a similar security level and complexity of polynomials.
Abuse Resistant Law Enforcement Access Systems

The increasing deployment of end-to-end encrypted communications services has ignited a debate between technology firms and law enforcement agencies over the need for lawful access to encrypted communications. Unfortunately, existing solutions to this problem suffer from serious technical risks, such as the possibility of operator abuse and theft of escrow key material. In this work we investigate the problem of constructing law enforcement access systems that mitigate the possibility of unauthorized surveillance. We first define a set of desirable properties for an abuse-resistant law enforcement access system (ARLEAS), and motivate each of these properties. We then formalize these definitions in the Universal Composability (UC) framework, and present two main constructions that realize this definition. The first construction enables prospective access, allowing surveillance only if encryption occurs after a warrant has been issued and activated. The second, more powerful construction, allows retrospective access to communications that occurred prior to a warrant's issuance. To illustrate the technical challenge of constructing the latter type of protocol, we conclude by investigating the minimal assumptions required to realize these systems.
Motion Consistency Guided Robust Geometric Model Fitting With Severe Outliers

Geometric model fitting has been widely applied in the electronic industry. However, it remains as a challenging task when handling the data corrupted by a large number of false matches (i.e., severe outliers) between two-view images. In this article, we propose a novel motion consistency guided fitting method (MCF) to robustly and efficiently estimate the parameters of model instances in data involving severe outliers. Specifically, from input data, we first generate a series of neighborhood sets, in each of which gross outliers that are inconsistent in motions can be effectively filtered, according to motion consistency among true matches (i.e., inliers). Then, we propose an effective sampling algorithm to sample minimal subsets from the generated neighborhood sets. In this way, the model hypotheses computed from the sampled minimal subsets can cover all model instances with a high probability. Furthermore, by taking advantages of the generated hypotheses and neighborhood sets, we propose a novel model selection algorithm to estimate the number and the parameters of model instances. For fitting evaluation, we also build a new dataset, in which the images are collected from a fundus camera. Experiments on a variety of electronic industrial applications show that the proposed MCF achieves higher fitting accuracy at a much lower computational cost than several state-of-the-art fitting methods.
Blockchain-Based Peer-to-Peer Sustainable Energy Trading in Microgrid using Smart Contracts

The adaptation of renewable energy and forsaking the conventional fuels for energy generation would not be feasible solely by the government body; thus, the participation of the consumers is crucial. This paper proposes a blockchain-based Peer-to-Peer power trading system, which provides the opportunity for the consumers to contribute to the grid through renewable energy sources. With the integration of a smart microgrid, the system is intended to remain functional during the isolation from the national grid, while making the system self-sufficient. The contributors will be able to make a profit by sharing the produced energy through a smart contract. Furthermore, all the transitions take place only when the preset conditions are satisfied, besides due to the characteristics of blockchain, no participant can alter the data, making the system more reliable. The smart contract was composed in solidity language and multiple trading was performed in the Binance blockchain platform.
Survey on Detection and Prevention of Phishing Websites using Machine Learning

This research paper discusses on the phishing websites Prevention and Detection. A phishing website is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages. Phishing is the most commonly used social engineering and cyber-attack. Through such attacks, the phisher targets naïve online users by tricking them into revealing confidential information, with the purpose of using it fraudulently. In our paper we will be discussing and listing a few of the artificial intelligence models, that will help us to detect these phishing websites so that in the future these data and techniques can be used in machine learning to make our system better and efficient. The problem of phishing is widespread and there is no particular single solution available to effectively reduce all vulnerabilities, so many techniques are often used to reduce certain attacks. Machine learning is a useful tool used to reduce phishing attacks.As innovation keeps on developing, phishing strategies began to advance quickly several anti-phishing tools are available and have their own disadvantages. The paper concentrates on basic Machine learning supervised classification techniques to seek out an answer to phishing attacks. The Basic principle of this paper is to execute the frameworks with good efficiency, exactness, and cost-effectively. The task is attained by using 4 ML managed classification models. The four classification models are KNN, Kernel-SVM, Random Forest Classifier and Decision tree. The supervised classification contains a labeled dataset that is used to train the models. All the four algorithms used: KNN, Kernel-SVM, Random Forest Classifier and Decision tree are classification models. With machine learning, cybersecurity systems can analyze patterns and learn from them to assist prevent similar attacks and answer changing behavior. It can help users to be more active in preventing threats and respond to active attacks in real-time. So, by using Machine Learning, we could progress towards preventing such attacks.
Attendance Management System

Marking of attendance is one of the oppressive tasks in a lecture. Moreover, it takes a lot of time to mark the attendance of students manually. Some of the problems need to be addressed regarding attendance marking are the possibility of a proxy, the analysis of attendance of a student which could include how frequently one is skipping the classes. In recent days, traditional methods of marking attendance are turning out burdensome tasks. A solution for automating the attendance system efficiently can be done by facial recognition and is also a thriving task in face recognition. However, the automation is done using biometric systems in past, but it is not an efficient way of doing as faking a fingerprint is much easier. And also the authenticity and accuracy signifies the methods to be chosen for this task, which many proposed methods lack. This research mainly focuses on the methods of marking attendance and firmly evaluate and analyze intelligent techniques to mark attendance. In this paper, we propose a novel method of marking attendance using facial recognition. The proposed method uses small and accurate deeply supervised network for recognition of faces in a wild classroom scenario. A web application is developed for easy inference to the users. All the analytics is performed on Amazon Elastic Compute Cloud (Amazon EC2) Instance.