Reviewerly/exHarmony huggingface dataset:
arXiv : Correlating Non-Resonant Di-Electron Searches at the LHC to the Cabibbo-Angle Anomaly and Lepton Flavour Universality Violation

In addition to the existing strong indications for lepton flavour university violation (LFUV) in low energy precision experiments, CMS recently released an analysis of non-resonant di-lepton pairs which could constitute the first sign of LFUV in high-energy LHC searches. In this article we show that the Cabibbo angle anomaly, an (apparent) violation of first row and column CKM unitarity with $\approx3\,\sigma$ significance, and the CMS result can be correlated and commonly explained in a model independent way by the operator $[Q_{\ell q}^{(3)}]_{1111} = (\bar{\ell}_1\gamma^{\mu}\sigma^I\ell_1)(\bar{q}_1\gamma_{\mu}\sigma^Iq_1)$. This is possible without violating the bounds from the non-resonant di-lepton search of ATLAS (which interestingly also observed slightly more events than expected in the electron channel) nor from $R(\pi)=\pi \to\mu\nu/\pi \to e \nu$. We find a combined preference for the new physics hypothesis of $4.5\,\sigma$ and predict $1.0004<R(\pi)<1.0009$ (95\%~CL) which can be tested in the near future with the forthcoming results of the PEN experiment.
The Economic Effect of Bitcoin Halving Events on the U.S. Capital Market

Bitcoin is a digital asset that was first mined in January 2009 after the global financial crisis of 2007–2008. Over a decade later, there is still no consensus across different market regulations on the classification, use cases, policies, and economic implications of bitcoin. However, there is an increasing demand for digital currency, as an alternative to fiat currency which would spur financial innovation and inclusion. This study reviews regulations on digital assets across countries. It further discusses some use cases for bitcoin to reduce financial risk and facilitate cross border transactions. The study also discusses challenges related to bitcoin such as: cryptocurrencies substitution, cross border financing, cyber risk and security, and benefits in terms of the effect of coronavirus on the speed of capital market innovation and hence bitcoin usage. The study concludes by examining the economic effect of bitcoin halving events on the U.S. capital market to better understand the influence of bitcoin on financial markets and key drivers of its intrinsic value. The empirical evidence from this study suggests that bitcoin halving events are associated with significant negative stock market reaction, signaling a trading tradeoff between cryptocurrencies and U.S. stock markets.
Support Vector Machine

This chapter covers another major and powerful machine learning tool which is SVMSupport Vector Machine (SVM) . The chapter begins with the introduction of linear classifierLinear classifier , K-NN classifier, and perceptronPerceptron which are the key to understand discriminative approaches such as SVMSupport Vector Machine (SVM) and ANN. After these preparations, the primal formPrimal form SVMSupport Vector Machine (SVM) is formally introduced. It then goes on to transform primal formPrimal form SVMSupport Vector Machine (SVM) to dual formDual form SVMSupport Vector Machine (SVM) with detailed explanation and justification. In the next, kernelKernel SVMSupport Vector Machine (SVM) is formally defined and techniques of building new kernels are described in details. For computation purpose, the kernel trickKernel trick is introduced and an application of SVMSupport Vector Machine (SVM) using PMK is also demonstrated. The chapter concludes with three fusion techniques to build multi-class SVMSupport Vector Machine (SVM) classifiers.
Characterizing the implicit bias via a primal-dual analysis.

This paper shows that the implicit bias of gradient descent on linearly separable data is exactly characterized by the optimal solution of a dual optimization problem given by a smoothed margin, even for general losses. This is in contrast to prior results, which are often tailored to exponentially-tailed losses. For the exponential loss specifically, with $n$ training examples and $t$ gradient descent steps, our dual analysis further allows us to prove an $O(\ln(n)/\ln(t))$ convergence rate to the $\ell_2$ maximum margin direction, when a constant step size is used. This rate is tight in both $n$ and $t$, which has not been presented by prior work. On the other hand, with a properly chosen but aggressive step size schedule, we prove $O(1/t)$ rates for both $\ell_2$ margin maximization and implicit bias, whereas prior work (including all first-order methods for the general hard-margin linear SVM problem) proved $\widetilde{O}(1/\sqrt{t})$ margin rates, or $O(1/t)$ margin rates to a suboptimal margin, with an implied (slower) bias rate. Our key observations include that gradient descent on the primal variable naturally induces a mirror descent update on the dual variable, and that the dual objective in this setting is smooth enough to give a faster rate.
Decentralized Energy To Power Rural Homes Through Smart Contracts And Carbon Credit

Blockchain, a revolutionary technology is a buzzword and has gained huge popularity and attention. Blockchain creates an immutable chain of transaction blocks without the intervention of a trusted third party that enables secured trustless environments. Distributed ledger technology such as blockchain has drawn significant interest from government agencies, financial institutions, start-ups, technology enthusiastic, financial institutions, and the academic and research community. This technology can support a wide range of applications from identity management to IoT applications. The energy industry has been consistently catalyzed by innovations like decarbonization, decentralization, and digitization. With numerous organizations developing low-cost renewable energy revolution like solar powers, wind energy, and Energy as a service, our proposed solution would ease the demand for energy supply in rural areas by combining blockchain and IoT. To address the growing demand of the rural electrifying process we propose a Distributed Application (DApp) that can securely record the ownership and consumption of energy generated through solar panels connected through an IoT device. This DApp empowers rural energy supply trading without grids and has the potential to improve the incentives to utility providers by offering carbon credits or renewable energy certificates thus attracting investors.
Sentiment Analysis of Movie Reviews based on Machine Learning

Nowadays, people are overwhelmed by fragmented text information online. It is easy for a person to post comments on a movie without actually watching it. Therefore, analyzing the sentiment of the movie reviews becomes necessary and significant. We first got the data of movie reviews and transformed the text data into vector data by using the TF-IDF algorithm. The total amount of the transformed features was approximately 72,000 to describe the sentiment of movie reviews from different people. Three machine learning models were used to train by these feature vectors, which were Lasso Regression (L1 Logistic Regression), Ridge Regression (L2 Logistic Regression), and CatBoost models. Finally, the evaluation of these three methods showed that the accuracy and the precision of Lasso Regression were only close to 0.8. It was the worst prediction model among these three models while the accuracy and precision of CatBoost were more than 0.85, which was the best. This paper not only helps guests understand the accurate evaluation of movies, but also helps filmmakers predict the box office performance by comparing the performance of these three machine learning models in terms of sentiment analysis.
Shadow detection from images using fuzzy logic and PCPerturNet

Shadow detection is a challenging and essential task for interpreting the scene. Regardless of encouraging findings from current Deep Learning (DL) approaches used for shadow detection, the methods are also dealing with inconsistent situations where the visual representation of non-shadow and shadow regions is equivalent. In this article, a DL based approach is introduced for image pixel-level shadow detection. The proposed CNN-based approach, pattern conserver convolutional neural network (PCPerturNet) profits from a new design where shadow features are defined utilizing an effective skip-connection mapping arrangement. To make PCPerturNet robust from the change in brightness and contrast, several perturbed instances are generated by using a fuzzy-logic based method to train the system. Also, five types of augmentations are applied to images during training to make the system robust from the change in scale, orientation and flip. PCPerturNet derives and conserves shadow patterns in manifold layers and uses those layers progressively in several units to produce the shadow mask. The output of the proposed method is tested on two freely accessible databases and one self-created database where the accuracy rate obtained is 96.4%, 96.8%, and 89.4% which indicates that the proposed method outperforms the other shadow detection approaches used in the literature.
Improved Moth Flame Optimization with Multioperator for solving real-world optimization problems

The Moth flame optimization algorithm (MFO) is a nature-inspired optimization algorithm which imitates moths darts into the fire. Similarly, to other meta-heuristic algorithm, MFO still has the disadvantage of getting into the local best and the convergence rate cannot be satisfying. In this paper a Multi-operator Moth Flame Optimization algorithm (MOMFO) is proposed to improve the MFO algorithm, which combines Elite Search strategy, Adaptive control strategy and Chaos search strategy to enhance MFO.A two-stage strategy is used to balance the global search and local search ability, which makes the algorithm have better search performance and accuracy. The proposed MOMFO is benchmarked on eight well-known test functions and real-world problem, and the results show that the algorithm is effective in solving complex nonlinearity problems.
M-AR: A Visual Representation of Manual Operation Precision in AR Assembly

The research of augmented reality physical tasks for human-computer collaboration is still an attractive dynamic research field. In recent years, AR Instruction supporting collaborative assembly has been developed, which can transfer graphic annotation between man and machine. However, there is little research on high precision manual assembly. This paper proposes a high-precision cognitive AR representation (M-AR) for manual operation guidance, which aims to transform a high-precision assembly relationship into a series of visual information to meet the user’s cognition of high-precision task intention. Using this representation, we conducted a within-subject case study to compare it with original AR representation (original AR) and low-precision cognitive AR representation. The results showed that there were significant differences in execution time and operation errors. The user’s subjective feedback also showed that M-AR supporting cognitive feedback can significantly improve a user’s cognitive experience in a physical task.
Stopping Criteria for Technology Assisted Reviews based on Counting Processes

Technology Assisted Review (TAR) aims to minimise the manual judgements required to identify relevant documents. Reductions in workload are dependent on a reviewer being able to make an informed decision about when to stop examining documents. Counting processes offer a theoretically sound approach to creating stopping criteria for TAR approaches that are based on analysis of the rate at which relevant documents are observed. This paper introduces two modifications to existing approaches: application of a Cox Process (a counting process which has not previously been used for this problem) and use of a rate function based on a power law. Experiments on the CLEF 2017 e-Health TAR collection demonstrates that these approaches produces results that are superior to those reported previously.
Analysis and improvement of GSA’s optimization process

Gravitational search algorithm (GSA) is one of the heuristic algorithms proposed in recent years, which is inspired by the law of universal gravitation between masses. However, many practical applications and researches show that when the region affected by global optimum occupies less search space, GSA is prone to fall into local optimum, especially when the optimal value is close to the boundary of the search space and there are sub-optimal solutions in the center of the space. By microscopic analysis of the particle motion process of GSA in the above optimization situation, we find that the Kbest mechanism and the characteristic of central convergence are the two main factors affecting the GSA optimization performance. In this paper, an improved algorithm called Balanced Gravitational Search Algorithm is proposed, in which the balance operator is designed to solve two inherent problems in GSA. Then the proposed method is firstly tested on 10 benchmark functions provided by CEC 2020 compared with the state-of-the-art variant algorithms of the GSA and other typical meta-heuristics. Further, the algorithms are tested and compared on the real-world optimization problems including CEC 2011 real-world optimization problems and the Multi-Layer Neural Network (MLNN) training problem based on wine dataset. The simulation results show that BGSA can significantly improve the optimization performance of GSA and it can be a good choice for solving real-world optimization problems.
TrackSign: Guided Web Tracking Discovery

Current web tracking practices pose a constant threat to the privacy of Internet users.As a result, the research community has recently proposed different tools to combat wellknown tracking methods.However, the early detection of new, previously unseen tracking systems is still an open research problem.In this paper, we present TrackSign, a novel approach to discover new web tracking methods.The main idea behind TrackSign is the use of code fingerprinting to identify common pieces of code shared across multiple domains.To detect tracking fingerprints, TrackSign builds a novel 3-mode network graph that captures the relationship between fingerprints, resources and domains.We evaluated TrackSign with the top-100K most popular Internet domains, including almost 1M web resources from more than 5M HTTP requests.Our results show that our method can detect new web tracking resources with high precision (over 92%).TrackSign was able to detect 30K new trackers, more than 10K new tracking resources and 270K new tracking URLs, not yet detected by most popular blacklists.Finally, we also validate the effectiveness of TrackSign with more than 20 years of historical data from the Internet Archive.
WITHDRAWN: An effective analytics of third party auditing and Trust architectures for integrity in cloud environment

Cloud storage is one of Cloud's infrastructures that preserves, handles, centrally backs up data and makes it available to users through a network (normally the Internet). When data may be assaulted or modified by unauthorised attackers, the cloud owner may be interested in the privacy of data contained within the cloud. A new idea known as Data Auditing is therefore being applied with the assistance of a third party auditor (TPA) firm to check data integrity. The main goal of this work is the creation of a stable and efficient audit scheme based on the trust architecture that guarantees the safety and confidentiality of records, including the security of privacy, public audits and other capable functions. Therefore, all these requirements have been taken up with the new method for auditing. The data owner, TPA and the cloud provider contain three organisations. The data owner performs a variety of operations, such as separating the file into blocks, encryption, hash value, combination and signature. The TPA conducts the core feature of data integrity checks and performs functions, including the generation, concatenating and signature generation of hash values for encrypted blocks retrieved from the cloud server. It then tests whether the data stored in the cloud is corrupted or not by matching all signatures. On demand of customers, it tests the quality of the results. Only encrypted data blocks are processed by the cloud provider. This proposed audit scheme and the confidence architecture facilitates complex encryption of complex, integrity assurance and digital signature computing by means of sophisticated cryptographic algorithms. This article introduces the faith architecture, which is the core basis for complex crypto graphing and forensic analyzer cloud auditing programmes.
High-bypass Learning: Automated Detection of Tumor Cells That Significantly Impact Drug Response

Machine learning in biomedicine is reliant on the availability of large, high-quality data sets. These corpora are used for training statistical or deep learning-based models that can be validated against other data sets and ultimately used to guide decisions. The quality of these data sets is an essential component of the quality of the models and their decisions. Thus, identifying and inspecting outlier data is critical for evaluating, curating, and using biomedical data sets. Many techniques are available to look for outlier data, but it is not clear how to evaluate the impact on highly complex deep learning methods. In this paper, we use deep learning ensembles and workflows to construct a system for automatically identifying data subsets that have a large impact on the trained models. These effects can be quantified and presented to the user for further inspection, which could improve data quality overall. We then present results from running this method on the near-exascale Summit supercomputer.
Optimization of English Online Learning Dictionary System Based on Multiagent Architecture

As a universal language in the world, English has become a necessary language communication tool under the globalization of trade. Intelligent, efficient, and reasonable English language-assisted learning system helps to further improve the English ability of language learners. English online learning dictionary, as an important query tool for English learners, is an important part of English online learning. This paper will optimize the design of English online learning dictionary system based on multiagent architecture. Based on the hybrid multiagent cooperative algorithm, this paper will improve the disadvantages of the online English learning dictionary system and propose an appropriate dictionary application evaluation function. At the same time, an improved reinforcement learning algorithm is introduced into the corresponding English online learning dictionary navigation problem so as to improve the efficiency of the online English learning dictionary system. English online learning dictionary is more intelligent and efficient. In this paper, the new online learning dictionary system optimization algorithm is proposed and compared with the traditional system algorithm. The experimental results show that the algorithm proposed in this paper solves the collaborative confusion problem of English learning online dictionary to a certain extent and further solves the corresponding navigation problem so as to improve the efficiency.
Internet of Things in the Railway Domain: Edge Sensing System Based on Solid-State LIDAR and Fuzzy Clustering for Virtual Coupling

Recent advances in wireless communication, sensing and processing technologies are fostering novel research and innovation opportunities in areas such as Industry 4.0, Smart Cities and Intelligent Transportation Systems. In particular, the railway domain is envisioned to have important breakthroughs in terms of cost-efficiency, self-management, and reliability in the operation of the rolling stocks and infrastructures. Some of these key objectives are been addressed by the concept of Railway Virtual Coupling, which is a promising solution where the capacity of the tracks is highly improved by means of reducing the distance between adjacent trains, and the physical connection between train's compositions, through accurate Vehicle-to-Vehicle communication systems. In this work a new approach towards supporting the information dynamically exchanged by the trains is proposed, with the design and implementation of a Solid-State LIDAR based sensing system to provide an accurate, robust and low-latency on-board distance detection system between trains. The combination of a long-range distance sensor, an Internet of Things (IoT) edge hardware platform and a fuzzy clustering approach for distance detection of the object of interest allows obtaining very accurate results to support the virtual coupling maneuvers. The system implementation has been tested in a real railway scenario, where several coupling and distance detection maneuvers have been performed to verify the operation of the proposed system in an actual application context. This represents one of the first dedicated distance detection tests of this kind under real dynamic conditions documented in the literature towards railway virtual coupling.
Digitalization of Land Records using Blockchain Technology

In sectors that require to be shielded from corruption, human error or human intervention, Blockchain features a comprehensive approach. The Land Registry is one of the cases in which many intermediaries use the framework to position their trust. The solutions currently in place are obsolete. When you've got thousands of land records, it's difficult to track who owns which pieces of property. It is very normal to face inconsistencies for example, falsified identities, forged materials and complete record loss inside the paperwork. These situations result in expensive court battles between the parties to the dispute. The transparent nature of the blockchain will make it possible to trace the property's hands. The immutable, auditable and traceable features of Blockchain entice governments round the world to implement decentralized technology within the process of land registration. With the potential of The blockchain to demonstrate authenticity will, without a need for third-party verification, legally pass land ownership to the customer.
Image Denoise Methods Based on Deep Learning

Abstract Image denoising is widely used in image, video, nuclear magnetic imaging and so on. In the application scene, camera jitter, the rapid motion of objects, dark light environment and so on may cause the captured photos to be unclean, so the research of image denoising has essential research value. This paper reviews the related research in this field in recent years, introduces the basic theory of image denoising, lists the common image noise, and then summarizes some classical denoising algorithms from traditional denoising methods. In addition, the shortcomings of traditional methods are analyzed. After that, the image denoising method based on depth learning is summarized, including the image denoising method based on REDNet, DnCNN, CBDNet, GAN, Noise2Noise structure, the principle and structure of various methods are introduced. Finally, the challenges of image denoising are analyzed, and the future research direction has prospected.
Mitigating vulnerability windows with hypervisor transplant

The vulnerability window of a hypervisor regarding a given security flaw is the time between the identification of the flaw and the integration of a correction/patch in the running hypervisor. Most vulnerability windows, regardless of severity, are long enough (several days) that attackers have time to perform exploits. Nevertheless, the number of critical vulnerabilities per year is low enough to allow an exceptional solution. This paper introduces hypervisor transplant, a solution for addressing vulnerability window of critical flaws. It involves temporarily replacing the current datacenter hypervisor (e.g., Xen) which is subject to a critical security flaw, by a different hypervisor (e.g., KVM) which is not subject to the same vulnerability.