Reviewerly/exHarmony huggingface dataset:
Enhance tree-seed algorithm using hierarchy mechanism for constrained optimization problems

Tree-Seed Algorithm is a kind of swarm intelligence optimization algorithm. It is used to solve various problems widely, but it still has some shortcomings need to be overcame, such as imbalance between exploration and exploitation, local stagnation, premature convergence, and so on. In this study, an enhanced meta-heuristic optimization algorithm, called Migration Tree-Seed Algorithm (MTSA), is proposed inspiring by Grey Wolf Optimizer (GWO). The hierarchical gravity learning and random-based migration mechanisms are introduced to overcome the intrinsic defects of the basic TSA. Firstly, hierarchy mechanism ensures the tree migration to guide the seed generation avoiding the local stagnation. Secondly, random-based migration mechanism increases the seed diversity to improve the exploration ability. Finally, the coordinated update of the two mechanisms actuate a suitable trade-off between exploration ans exploitation. We use IEEE CEC 2014 benchmark functions to compare MTSA with basic TSA, the TSA variants (STSA, EST-TSA, fb_TSA), GWO, ABC, SCA, BOA, JAYA and RSA. MTSA is subsequently applied to three classical engineering design problems reported in the specialized literature. Both results show that the MTSA is very competitive and effective compared with other well-known meta-heuristics, proving its excellent applicability in real-world challenging problems with unknown search spaces.
A lightweight privacy preserving scheme of charging and discharging for electric vehicles based on consortium blockchain in charging service company

• A privacy preserving scheme for electric vehicles in service companies is proposed. • A pseudo identity algorithm is designed to protect EV users’ privacy information. • A aggregation signcryption algorithm is designed to realize secure communication. • The consortium blockchain is adopted to solve the problems of data tampering. • A consensus mechanism is designed to improve the scheme’s efficiency. Charging service companies (CSC) provide charging and discharging services for electric vehicles (EV) by managing numerous charging piles (CP). At that time, the privacy preservation of EV users has become a problem that must be solved. The lightweight solution has a broad applicated prospect because of its excellent efficiency and security. Simultaneously, the centralized storage of massive EVs’ real-time power information (RPI) may cause some risks, such as data tampering and single point of failure. Therefore, a lightweight privacy preserving scheme for EVs based on consortium blockchain (CB) in CSC is proposed to solve the problems mentioned above. The scheme has the following novelties and contributions: (1) a pseudo identity algorithm is designed to realize EV users to charge, discharge, and enjoy other services with pseudo identity, which can protect EV users’ privacy and security; (2) a certificateless aggregation signcryption algorithm is designed to realize lightweight RPI secure communication: in this algorithm, the function of information signature and encryption in one operation is realized, and the operation steps are simplified; bilinear pairing and exponential operation are not used to improve the efficiency of aggregate signcryption; batch aggregation and verification are designed to ensure the EV’s RPI can be stored in the CB securely and efficiently; (3) the CB technology is adopted to solve the problems of data tampering and single point of failure of the central organization. Moreover, the consensus mechanism in the CB is designed in this scheme to further improve the scheme’s efficiency. Finally, theoretical analysis proves that the scheme can guarantee users’ privacy. Experiments show that for 800 EV users, the time overhead of our scheme is only 1969.5 s, which is lower than other existing schemes.
Practical Attacks of Round-Reduced SIMON Based on Deep Learning

Abstract At CRYPTO’19, Gohr built a bridge between deep learning and cryptanalysis. Based on deep neural networks, he trained neural distinguishers of SPECK32/64. Besides, with the help of neural distinguishers, he attacked 11-round SPECK32/64 using Bayesian optimization. Compared with the traditional attack, its complexity was reduced. Although his work opened a new direction of machine learning aided cryptanalysis, there are still two research gaps that researchers are eager to fill in. (i) Can the attack using neural distinguishers be used to other block ciphers? (ii) Are there effective key recovery attacks on large-size block ciphers adopting neural distinguishers? In this paper, our core target is to propose an effective neural-aided key recovery policy to attack large-size block ciphers. For large-size block ciphers, it costs too much time in pre-computation, especially in wrong key response profile, which is the main reason why there are almost no neural aided attacks on large-size block ciphers. Fortunately, we find that there is a fatal flaw in the wrong key profile. In the some experiments of SIMON32/64 and SIMON48/96, there is a regular of change in response profiles, which implies that we can use partial response instead of the complete response. Based on this, we propose a generic key recovery attack scheme which can attack large-size block ciphers. As an application, we perform a key recovery attack on 13-round SIMON64/128, which is the first practical attack using neural distinguishers to large-size ciphers. In addition, we also attack 13-round SIMON32/64 and SIMON48/96, which also shows that the neural distinguishers can be used to other block ciphers.
SmartAuction: A blockchain-based secure implementation of private data queries

A data marketplace provides a platform for data trading by bringing together data owners and data consumers with a data broker. Recent advancements in mechanism design of data marketplaces have introduced a number of private data query mechanisms that facilitate the trading of private data. A critical assumption to ensuring that these mechanisms function as intended in a real-world online platform, which is often overlooked in the mechanism design literature, is that they are implemented in a network infrastructure that guarantees secure and fair trade. Such a network infrastructure should allow data storage, communications and transactions to satisfy confidentiality, data integrity, fair brokerage, and privacy requirements. In this paper, we propose SmartAuction, the first blockchain-based secure implementation of a private data query system. SmartAuction utilises a public-key encryption scheme, a digital signature scheme and two smart contracts to facilitate the four tasks of a data trade: data submission, data request, auction and query, and payment and delivery. Using universal composability (UC) framework, we verify that SmartAuction satisfies the desirable properties mentioned above. We also conduct experiment to show that SmartAuction can be implemented in real Ethereum platforms and it has a broad applicability.
Neutrosophic set based local binary pattern for texture classification

This paper presents novel neutrosophic set-based Completed Local Binary Pattern (CLBP) hybrid methods. These methods first transform the input image into a neutrosophic domain, and the image's texture is characterized by truth and false membership components. Grayscale images are more robust to noise in the neutrosophic domain. The neutrosophic components suppress noise, by this way edges in the images may be detected more precisely. As a result, using neutrosophic truth and false sets with a grayscale input image has resulted in more robust features. Besides, these discriminative features are combined with rotation invariant LBP features with a cross-scale joint coding strategy. The proposed methods can contribute to the classification performance with a reasonable computational cost. When compared to state-of-the-art hand-crafted approaches, experimental findings on state-of-the-art texture databases show that the proposed methods can enhance classification accuracy by roughly 24%. Moreover, the proposed method achieves up to 34% better results than the state-of-the-art deep learning-based methods.
An Efficient Implementation of Convolutional Neural Network With CLIP-Q Quantization on FPGA

Convolutional neural networks (CNNs) have achieved tremendous success in the computer vision domain recently. The pursue for better model accuracy drives the model size and the storage requirements of CNNs as well as the computational complexity. Therefore, Compression Learning by InParallel Pruning-Quantization (CLIP-Q) was proposed to reduce a vast amount of weight storage requirements by using a few quantized segments to represent all weights in a CNN layer. Among various quantization strategies, CLIP-Q is suitable for hardware accelerators because it reduces model size significantly while maintaining the full-precision model accuracy. However, the current CLIP-Q approach did not consider the hardware characteristics and it is not straightforward when mapped to a CNN hardware accelerator. In this work, we propose a software-hardware codesign platform that includes a modified version of CLIP-Q algorithm and a hardware accelerator, which consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> reconfigurable convolutional arrays with input and output channel parallelization. Additionally, the proposed CNN accelerator maintains the same accuracy of a full-precision CNN in Cifar-10 and Cifar-100 datasets.
Current Status and Prospects of Blockchain Security Standardization

In recent years, blockchain technology has become one of the key technical innovation fields in the world. From the simple Bitcoin that can only be transferred at first to the blockchain application ecology that is now blooming, blockchain is gradually building a credible internet of value. However, with the continuous development and application of blockchain, even the blockchain based on cryptography is facing a series of network security problems and has caused great property losses to participants. Therefore, studying blockchain security and accelerating standardization of blockchain security have become the top priority to ensure the orderly and healthy development of blockchain technology. This paper briefly introduces the scope of blockchain security from the perspective of network security, sorts out some existing standards related to blockchain security, and gives some suggestions to promote the development and application of blockchain security standardization.
A Secure Federated Learning Framework using Blockchain and Differential Privacy

Federated learning (FL) has considerably emerged as a promising solution to enhance user privacy and data security by enabling collaboratively multi-party model learning without exchanging confidential data. Nevertheless, most existing FL approaches still rely on a central server to obtain a global model by collecting all uploaded models from participants, which may lead to several threats from malicious participants and even expose participant privacy. Therefore, to tackle these problems, we proposed a secure FL framework by empowering blockchain to replace the centralized aggregator sever and utilize Differential Privacy (DP) to address various attacks, e.g., membership inference attacks, during the collaborative FL model training process. The proposed framework has been implemented through two scenarios, i.e., blockchain-based FL to form a decentralized system and DP-based FL to construct the randomized privacy protection using the IBM DP Library.
Quantum Federated Learning: Remarks and Challenges

As the development of quantum computing hardware is on the rise, its potential application to various research areas has been investigated, including to machine learning. Recently, there have been several initiatives to expand the work to quantum federated learning (QFL). However, challenges arise due to the fact that quantum computation poses different characteristics from classical computation, giving an even more challenge for a federated setting. In this paper, we present a high-level overview of the current state of research in QFL. Furthermore, we also describe in brief about quantum computation and discuss its present limitations in relation to QFL development. Additionally, possible approaches to deploy QFL are explored. Lastly, remarks and challenges of QFL are also presented.
Logical Foundations of Quantitative Equality

In quantitative reasoning one compares objects by distances, instead of equivalence relations, so that one can measure how much they are similar, rather than just saying whether they are equivalent or not. In this paper we aim at providing a logical ground to quantitative reasoning with distances in Linear Logic, using the categorical language of Lawvere’s doctrines. The key idea is to see distances as equality predicates in Linear Logic. We use graded modalities to write a resource sensitive substitution rule for equality, which allows us to give it a quantitative meaning by distances. We introduce a deductive calculus for (Graded) Linear Logic with quantitative equality and the notion of Lipschitz doctrine to give it a sound and complete categorical semantics. We also describe a universal construction of Lipschitz doctrines, which generates examples based for instance on metric spaces and quantitative realisability.
A Survey on Mobile Learning for Adult Learners: State-of-the-Art, Taxonomy, and Challenges

Adult learner performance has become a significant challenge in the field of education.Adult learners are identified as being of productive age in engaging with multiple commitments, including studying.The multiple engagements make it difficult for them to manage their time and focus, which contributes to a high institutional dropout rate.This situation has led to a reduced number of highly skilled and professional workers in the country because of the failure to meet academic requirements.In the current digital world, every aspect of education includes some elements of technology.However, most adult learners are reluctant to utilise mobile technology for the purpose of learning.Therefore, this review paper aims to provide a state of art regarding mobile learning for adult learners.Considering a set of inclusion and exclusion criteria, 135 related articles from IEEE, Google Scholar, Springer Link, Science Direct, Taylor and Francis, and Wiley digital databases were extracted and reviewed.The state of the art of M-learning were discussed in terms of functions of m-learning, effectiveness, and m-learning as a tool.Keywords from articles were extracted and the taxonomy of M-learning was discussed under five classifications (Behavioural Intention, Technological Support, Educational Content, Learner Coordination, and Instructional Design) were created based on these keywords, which represented the same meaning and features that were discussed in the articles.The findings can raise awareness among mobile learning practitioners to provide more effective services for adult learners.Meanwhile, higher education institutions can be better redesigned in terms of pedagogy to fit the needs of adult learners with mobile learning.
A Sparse Framework for Robust Possibilistic K-Subspace Clustering

Clustering noisy, high-dimensional, and structurally complex data have always been a challenging task. As most existing clustering methods are not able to deal with both the adverse impact of noisy samples and the complex structures of data, in this article, we propose a novel robust and sparse possibilistic K-subspace (RSPKS) clustering algorithm to integrate subspace recovery and possibilistic clustering algorithms under a unified sparse framework. First, the proposed method sparsifies the membership matrix and the subspace projection vector under a dual-sparse framework to handle high-dimensional noisy data. This unifies dimensionality reduction and clustering using one objective function for which the optimization can be realized through synchronous iteration. Second, the reconstruction error of each sample in the local subspace is used as the distance metric for classification. That is, each sample itself is treated as a clustering prototype so as not to be affected by the structure of the overall data distribution. Therefore, the clustering prototype construction problem of the data with complex structures can be better addressed. Finally, to deal with nonlinear regions, our RSPKS method is further extended into a kernelized version, namely the kernelized RSPKS clustering algorithm. The experimental results on both synthetic and real-world datasets demonstrate that our proposed method outperforms state-of-the-art algorithms in terms of clustering accuracy.
Location-Guided LiDAR-Based Panoptic Segmentation for Autonomous Driving

The application of artificial intelligence in autonomous driving is becoming increasingly extensive. LiDAR-based 3D point cloud panoptic segmentation is one of the most promising and arduous tasks. Although recent methods have produced promising results, most of them ignore the prior distribution of objects in the 3D point clouds. In this paper, we first investigate the distribution of objects around the heading of vehicles and observe that several objects are severely biased. On the basis of this observation, we use the bird's eye view (BEV) representation to project the 3D point clouds into a 2D image and divide the BEV projection into eight areas. For each area, we apply input-dependent convolution kernels to extract the local feature. These local features are concatenated to the panoptic backbone for panoptic segmentation. We validate our method on the validation and test sets of the SemanticKITTI dataset. The proposed method outperforms all state-of-the-art methods based on 2D projection in terms of higher panoptic quality scores.
The effectiveness of flagging content belonging to prominent individuals: The case of Donald Trump on Twitter

Abstract There is vigorous debate as to whether influential social media platforms like Twitter and Facebook should censor objectionable posts by prominent individuals in the United States and elsewhere. A tentative middle ground is employing content moderation to signal to social media audiences that certain posts may contain objectionable information through the mechanism of flagging. Existing studies have mainly examined the effect of flagging regular users' content. To add to this emerging literature stream, we examine the effect of flagging when the underlying content is produced by a prominent individual. Leveraging Twitter's moderation activities on former U.S. President Donald Trump's tweets as our empirical setting, we employ three machine learning algorithms to estimate the effect of flagging Trump's tweets. We explore preliminary evidence as to whether these posts were retweeted less or more than expected. Our results indicate that the flagged tweets were retweeted at higher rates than expected. Our findings suggest that flagging content of prominent individuals on social media might be ineffective or even counterproductive in curbing the spread of content deemed objectionable by social media companies.
Even Faster Conflicts and Lazier Reductions for String Solvers

Abstract In the past decade, satisfiability modulo theories (SMT) solvers have been extended to support the theory of strings and regular expressions. This theory has proven to be useful in a wide range of applications in academia and industry. To accommodate the expressive nature of string constraints used in those applications, string solvers use a multi-layered architecture where extended operators are reduced to a set of core operators. These reductions, however, are often costly to reason about. In this work, we propose new techniques for eagerly discovering conflicts based on equality reasoning and lazily avoiding reductions for certain extended functions based on lightweight reasoning. We present a strategy for integrating and scheduling these techniques in a CDCL $$(T)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -based theory solver for strings and regular expressions. We implement the techniques and the strategy in cvc 5, a state-of-the-art SMT solver, and show that they lead to a significant performance improvement."Image missing"<!-- image only, no MathML or LaTex -->"Image missing"<!-- image only, no MathML or LaTex -->
The MyShake App: User Experience of Early Warning Delivery and Earthquake Shaking

Abstract MyShake is a free citizen science and public safety smartphone application that delivers the United States ShakeAlert program’s Earthquake Early Warning to the public in the states of California, Oregon, and Washington. Although smartphone notifications have long been a component of the ShakeAlert warning delivery plan, very little data has been published on the efficiency and accuracy of such communication. MyShake records timestamps in its alert processing system, including the time it takes for a phone to receive and acknowledge an alert’s delivery. We use data collected for five representative earthquakes—three in urban regions, two rural events—since October 2019 to assess MyShake alert delivery latencies and ground-motion prediction accuracy. For these events, MyShake was capable of efficiently processing and delivering warnings. For the smaller urban events that occur beneath the target population, about half of recipients received a warning before the estimated onset of the S-wave, and up to 90% received an alert prior to experiencing peak shaking. When earthquakes occur further from populated areas, the warning time naturally increases. Smartphone acceleration recordings can also be used to quantify the user experience during earthquakes because they are colocated with people. A review of waveforms collected by MyShake shows a systematic amplification of shaking recorded by smartphones relative to nearby traditional stations. The median amplification calculated using records from all five sample events is a factor of 3.1. Once this correction is applied, MyShake peak accelerations are generally consistent with the distribution of shaking intensities in the U.S. Geological Survey ShakeMap product, whereas also showing some individual sites with substantial amplification and de-amplification. In addition to delivering early warnings, therefore, MyShake provides a waveform observation dataset to densify shaking intensity observations and thereby improve our understanding of earthquake effects and evaluate the accuracy of ShakeAlert’s alerting regions.