Reviewerly/exHarmony huggingface dataset:
A Platform to Manage the End-to-End Lifecycle of Batch-Prediction Machine Learning Models

Developing, deploying and monitoring in production setups are all aspects of the end-to-end lifecycle of machine intelligence workflows. Even though recognized to play key roles in this lifecycle, reproducibility and automation are often enough neglected, leading to undesired long-term results. In this paper we outline the typical reproducibility and automation pitfalls, and subsequently introduce the platform we built to address these during training, validation, productionization and operation. Tuned for batch-predictions on large volumes of data, the platform also focuses on zero-rework production deployment and monitoring. We also present the case study of an ensemble model developed and deployed on our platform for a multi-dimensional image analysis problem.
Rectangular surface code under biased noise

Abstract To date, the surface code has become a promising candidate for quantum error correcting codes because it achieves a high threshold and is composed of only the nearest gate operations and low-weight stabilizers. Here, we have exhibited that the logical failure rate can be enhanced by manipulating the lattice size of surface codes that they can show an enormous improvement in the number of physical qubits for a noise model where dephasing errors dominate over relaxation errors. We estimated the logical error rate in terms of the lattice size and physical error rate. When the physical error rate was high, the parameter estimation method was applied, and when it was low, the most frequently occurring logical error cases were considered. By using the minimum weight perfect matching decoding algorithm, we obtained the optimal lattice size by minimizing the number of qubits to achieve the required failure rates when physical error rates and bias are provided .
A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

In recent years, the number of online services has grown rapidly, invoking the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction (PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first uses the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. At the density of 5%–30%, the experimental results on both QoS attribute response time and throughput indicate that PLRes performs better than the existing five state-of-the-art QoS prediction approaches.
Robust and Recursively Feasible Real-Time Trajectory Planning in Unknown Environments

Motion planners for mobile robots in unknown environments face the challenge of simultaneously maintaining both robustness against unmodeled uncertainties and persistent feasibility of the trajectory-finding problem. That is, while dealing with uncertainties, a motion planner must update its trajectory, adapting to the newly revealed environment in real-time; failing to do so may involve unsafe circumstances. Many existing planning algorithms guarantee these by maintaining the clearance needed to perform an emergency brake, which is itself a robust and persistently feasible maneuver. However, such maneuvers are not applicable for systems in which braking is impossible or risky, such as fixed-wing aircraft. To that end, we propose a real-time robust planner that recursively guarantees persistent feasibility without any need of braking. The planner ensures robustness against bounded uncertainties and persistent feasibility by constructing a loop of sequentially composed funnels, starting from the receding horizon local trajectory’s forward reachable set. We implement the proposed algorithm for a robotic car tracking a speed-fixed reference trajectory. The experiment results show that the proposed algorithm can be run at faster than 16 Hz, while successfully keeping the system away from entering any dead end, to maintain safety and feasibility.
Real-Time Implementation of Adaptive Neural Controller by Labview Software

This paper presents one of the practical solutions for the real-time control of nonlinear systems using adaptive neural network-based control theory and shows two possible structures for this theory. The direct adaptive control (DAC) and indirect adaptive control (IDAC). Real-time test results reveal that the adaptive neural control structures suggested are practically feasible. The proposed structures have been implemented in Lab Viewsoftware and tested in real-time using a laboratory position control system.
Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a significant improvement over the traditional algorithm. It also obtained notably better results than differential evolution when applied to problems with high-dimensional spaces relative to the number of available particles.
MegaLite-2: An Extended Bilingual Comparative Literary Corpus

In this paper we introduced an extended bilingual version of the literary corpus MegaLite. This new version contains literary documents in Spanish and French. The motivation is to provide to the community a specialized and free linguistic support for different NLP tasks. The creation of this genre of corpus is very important for designing algorithms of Text Generation, Text Classification and Sentiment Analysis. The corpora contain about 6 500 documents: 1 500 in French (MegaLite-Fr) and near of 5 000 in Spanish (MegaLite-Es), all collected manually into the genres narrative, poetry and plays. A shallow linguistic comparison using the Jensen-Shannon divergences is presented and discussed. The MegaLite-2 corpora will be available to the community as a free resource in several suitable formats.
Verification and Strategy Synthesis for Coalition Announcement Logic

Abstract Coalition announcement logic (CAL) is one of the family of the logics of quantified announcements. It allows us to reason about what a coalition of agents can achieve by making announcements in the setting where the anti-coalition may have an announcement of their own to preclude the former from reaching its epistemic goals. In this paper, we describe a PSPACE-complete model checking algorithm for CAL that produces winning strategies for coalitions. The algorithm is implemented in a proof-of-concept model checker.
Social Media Anatomy of Text and Emoji in Expressions

Social Media is a burgeoning platform where a person or a group can interact to collaborate and share their ideas through online mediums. Nowadays, social media content of Twitter, Facebook, Instagram, WhatsApp, online blogs, forums, etc. consists of text and emoji. Emoji are pictorial representation used in electronic media, which a user can or cannot combine with a text to express their sentiments. However, there is still too little work related to emoji in sentiment analysis. These emoji assist in extensive analysis of the opinions and sentiments of the public by means of Sentiment Analysis. Due to availability of large quantity of opinion-rich digital data, much of the current research is focusing on the area of the sentiment analysis. In this paper, the anatomy of social online media content is analyzed. Here, nearly 1.7 lac comments are analyzed. Also, the sentiments expressed by the end users on various popular products, delight events, etc., are analyzed to determine the usage of text and emoji on social online media like Facebook.
A Post-quantum Certificateless Ring Signature Scheme for Privacy-Preserving of Blockchain Sharing Economy

Classical cryptography is no longer secure under quantum computing background. In traditional sharing economy, the centralized management mode leads to data tampering and privacy leakage problems. Targeting at these problems, in our paper, a novel privacy protection scheme is designed for sharing economy based on blockchain. Firstly, we propose a post-quantum certificateless ring signature algorithm over lattice, which can hide the signer’s identity information and resist quantum computing attacks. Moreover, our proposed scheme can effectively avoid certificate management problem and third-party central key escrow problem. Secondly, we use the principle of bimodal Gaussian rejection sampling to generate signature. It can reduce the sampling times in signature phase and greatly improves the signature efficiency. Notably, the signature distribution is independent of signer’s private key, which can better avoid the disclosure of signer’s private key information. Thirdly, we construct a blockchain sharing economy transaction scheme based on the proposed signature algorithm that can protect the privacy of user. Finally, security and efficiency of our scheme are analyzed that have lower storage cost and time cost than other related schemes under the random oracle model.
An Extreme Learning Machine-Based Model for Cryptocurrencies Prediction

Cryptocurrency, in a short period of time has got wide popularity and considered as an investment asset. Prediction of cryptocurrency is a recent area of research interest and budding fast. The price trend of cryptocurrency behaves arbitrarily and fluctuates like other stock markets due to inherent volatility. Though few computational intelligence methods are available, sophisticated methodologies for accurate prediction of cryptocurrency are still lacking and need to be explored. Extreme learning machine (ELM) is a faster and better learning method for neural networks with solitary hidden layer and has enhanced generalization performance. This study proposes an ELM based approach for prediction of four emerging cryptocurrencies such as Litecoin, Ethereum, Ripple, and Bitcoin. The prediction ability of the proposed approach is compared with few similar methods such as RBFN, SVM, MLP, ARIMA, and LSE. From exhaustive simulation studies and comparative result analysis it is found that the ELM method performed better than others and hence can be suggested as an efficient tool for cryptocurrencies prediction.
Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.
Performance Evaluation of Language Identification on Emotional Speech Corpus of Three Indian Languages

This paper describes the performance evaluation of spoken language identification (SLID) from emotional speech data of three Indian languages, namely Assamese (AS), Bengali (BN), and Santali (SA). A speech corpus containing six basic human emotions (i.e. anger, fear, happy, sad, surprise, and neutral) has been created and used in this study for experimental purposes. Different experiments are carried out to build SLID models for evaluation. In this study, spectral feature like shifted delta cepstral (SDC) is explored for investigating the presence of language-specific information from emotional data. Support vector machine (SVM) and Gaussian mixture model (GMM)-based models are developed to represent the language-specific information captured through the spectral features. Apart from that, to build the modern SLID i-vectors, time delay neural networks (TDNN) and recurrent neural network with long short-term memory (LSTM-RNN) have been considered. For the evaluation of the different emotional speech utterances, equal error rate (EER) and average cost function (Cavg) have been used as a performance matrix of the SLID system. The evaluation of the study on emotional speech utterances indicates that the EER and Cavg of SLID systems of neutral, fear, and sad are better than the anger, happiness, and surprise emotions.
Trusted Digital Asset Copyright Confirmation and Transaction Mechanism Based on Consortium Blockchain

Aiming at the problems of low transaction efficiency, digital asset copyright and transaction privacy in traditional digital asset trading platforms, based on alliance chain technology, this paper proposes a decentralized digital asset trustworthy copyright confirmation and transaction mechanism, which combines The common digital watermark technology in the field of traditional digital asset copyright confirmation and the common homomorphic encryption technology in the field of cloud computing solves the problem of user data privacy protection in the process of embedding digital watermark on the chain. And combined with smart contract technology to solve the problem of security and credibility of digital assets in the process of confirming the chain and transactions, realizing the confirmation of digital copyright and the traceability of transactions on the chain. Finally, experimental simulations prove that the reform mechanism can stably handle the copyright confirmation of digital asset transactions under normal circumstances, and basically meet the digital asset transaction needs of users.
An Efficient Data Auditing Protocol With a Novel Sampling Verification Algorithm

Currently, data auditing is an important means to check the integrity of the data stored on the cloud. Existing data auditing schemes have done a lot of work in terms of functionality, implementation, and security. As we can see, all of these schemes are based on the auditing framework proposed by Ateniese et al., which uses random sampling to probabilistically check whether the data is integrity or not. But multiple repeated sampling may lead to an intersection between the challenge sets selected each time, reducing the probability of detection, and the time for detecting corrupted blocks will be indefinite. If the corrupted data is not found for a long time, the data remedial measures will become invalid, resulting in permanent data loss. To address the above problem, we designed an efficient sampling verification algorithm. Based on this algorithm, the auditing scheme is further optimized, and a dynamic auditing function has been developed for the scheme to facilitate data owners to update data. Through the security analysis, our scheme is soundness. The experiment shows that our sampling verification can detect corrupted blocks faster than other auditing schemes.
Improving Usability of a Gaze-Based Surveillance Support Tool Through User-Centered Design

Security surveillance represents a cognitively challenging task. To support surveillance operators, Thales Research and Technology Canada developed the Scantracker, which relies on real-time oculometric data to trigger notifications that mitigate camera negligence, attentional tunneling and vigilance decrement. Recently, this tool was adapted to augmented reality (AR), thus opening opportunities for design adjustments. This paper documents and demonstrates the utility of adopting a design thinking approach to improve the Scantracker and to adapt AR implementation to users’ needs. Surveillance operators took part in two design thinking workshops to evaluate and suggest improvements to the Scantraker from a user perspective. Then, the tool was adjusted and tested in a security surveillance simulation. Results of the design thinking workshop and the surveillance simulation are presented and discussed to support this user-centered approach for the development of cognitive-support tools for surveillance.
Research and Implementation of Medical Information Protection and Sharing Based on Blockchain

Medical information data is showing massive growth. How to make good use of these medical data and realize data sharing and protection is a hot topic now. Based on the research of traditional medical information system and information security technology, this paper proposes a strategy of combining IPFS, Blockchain and Asymmetric Encryption to realize the protection and sharing of medical information. Since the data on the Blockchain and IPFS are immutable, the operation is traceable, and the encryption operation of Asymmetric Encryption will greatly protect the security of medical information and avoid malicious tampering and malicious denial of medical information problem. For the sharing of medical information, this paper proposes the use of ECC algorithm based on Asymmetric Encryption to realize the sharing of medical records. Through the experiment and test of protection and sharing strategy, we found that the transformation of the traditional medical information system by using Blockchain technology can well realize the security problem of medical information in the process of sharing, and ensure that the information after sharing is safe, credible and not tampered.
Detection of Copy Move Forgery in Medical Images Using Deep Learning

Since the advancements in technology and IT has revolutionized the world, digital images have come out with crucial importance. With the fruitful advancements and purposes, the authenticity and security breaches in digital images are simultaneously increasing because many editing software and tools give easy access to manipulate and temper the images, resulting in the change of complete information. Copy Move Forgery is the simplest way of tempering images in which an object is copied, removed, and replaced in the same image. As the medical field is too sensitive and even a minor manipulation can produce disastrous results, this study proposes an algorithm specifically designed to detect copy move forgery in medical images, especially when the world has gone towards telemedicine due to the outbreak of COVID-19. The proposed algorithm is based on CNN working on the whole image. The algorithm works in three phases, i.e., pre-processing, feature extraction, and classification. The proposed algorithm has given the accuracy of 89 percent on the dataset that has been created due to the publicly non-availability of forged medical images dataset. The dataset includes the images from abdominal, lungs, transverse view of lungs, chest abdominal, lungs transverse, lungs ap, vertebrae, and transverse heart.
Design Reverse Converter for Balanced RNS with Three Low-cost Modules

The development of modern computer technologies makes an urgent task of improving methods of storing, transferring and processing information. One of these methods is the Residue Number System (RNS), based on parallel computing of arithmetic operations [1]-[2].in This Paper, the Analysis of Three-Module Rns Was Carried Out Using a Measure of Rns Balance, the Crtf Constants for Rns {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> -1} Were Calculated, a Device Was Proposed that Implements the Reverse Conversion for Rns {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> -1}, the Simulation Was Carried Out on Fpga, Which Confirmed the Quality of the Proposed Reverse Conversion. the Simulation Results Have Confirmed that the Proposed Reverse Conversion Takes Fewer Hardware Costs By 10-15%and Higher Speed By 2-25%.
Federated learning for privacy-preserving data access

Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets.Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organizations.However, data privacy is becoming increasingly important because of regulations (e.g., EU GDPR) and the need to protect their sensitive and personal data.Companies need to manage data access: firstly within their organizations (so they can control staff access), and secondly protecting raw data when collaborating with third parties.What is more, companies are increasingly looking to 'monetize' the data they've collected.However, under new legislations, utilizing data by different organization is becoming increasingly difficult (Yu, 2016).Federated learning pioneered by Google is the emerging privacy-preserving data technology and also a new class of distributed machine learning models.This paper discusses federated learning as a solution for privacy-preserving data access and distributed machine learning applied to distributed data sets.It also presents a privacy-preserving federated learning infrastructure.