Reviewerly/exHarmony huggingface dataset:
Evolving Local Plasticity Rules for Synergistic Learning in Echo State Networks

Existing synaptic plasticity rules for optimizing the connections between neurons within the reservoir of echo state networks (ESNs) remain to be global in that the same type of plasticity rule with the same parameters is applied to all neurons. However, this is biologically implausible and practically inflexible for learning the structures in the input signals, thereby limiting the learning performance of ESNs. In this paper, we propose to use local plasticity rules that allow different neurons to use different types of plasticity rules and different parameters, which are achieved by optimizing the parameters of the local plasticity rules using the evolution strategy (ES) with covariance matrix adaptation (CMA-ES). We show that evolving neural plasticity will result in a synergistic learning of different plasticity rules, which plays an important role in improving the learning performance. Meanwhile, we show that the local plasticity rules can effectively alleviate synaptic interferences in learning the structure in sensory inputs. The proposed local plasticity rules are compared with a number of the state-of-the-art ESN models and the canonical ESN using a global plasticity rule on a set of widely used prediction and classification benchmark problems to demonstrate its competitive learning performance.
What is Blockchain Technology and its Significance in the Current Healthcare System? A Brief Insight

Background: The promising eventual fate of blockchain in healthcare has a lot more extensive prospect. Blockchain is a novel structure that gives another design to storage and trade of data among different members of a particular network. In case of a hospital, blockchain takes into consideration the creation of a better treatment structure by the expert doctor in order to arrange the meeting based on the symptoms of patients throughout the world by the electronic system. Blockchain technology is crucial for biomedical and human services applications as social insurance has turned out to be a standout among the most essential rising application areas of the blockchain distributed ledger technology. Result: By and large, blockchain is treated as a conveyed record to store social insurance related information for allocation, trading, dissecting, footage, and affirming purposes among accomplices. The advantage of blockchain databases versus traditional dispersed databases is that they are decentralized, permanent, and perfected with advanced digital payment frameworks and hash chain occasion structure. The blockchain code is an unlocked resource and can be utilized, altered, and customized by its clients. Conclusion: Nowadays, blockchain is expected to be almost universally adopted across medical organizations around the world. The purpose of this review article is to comprehend the current explored subjects, difficulties, and future headings in regards to blockchain innovation from the specialized perspective in the health care system.
A Provably Secure Anonymous Biometrics-Based Authentication Scheme for Wireless Sensor Networks Using Chaotic Map

The rapid growth of wireless sensor networks (WSNs) has opened new doors to realize remote monitoring in various areas. However, designing authentication protocol for resource-constrained WSNs is a challenging task. Recently, Aghili et al. introduced an efficient three-factor authentication scheme for WSNs using hash function, and argued that the scheme is immune to known attacks. However, we discover that their scheme suffers from a few serious weaknesses, such as session key disclosure attack, desynchronization attack, sensor node impersonation attack, and session-specific temporary information attack, and does not provide forward secrecy. In order to overcome the deficiency of this scheme, we propose an enhanced biometric-based authentication scheme for WSNs using Chebyshev chaotic map. We demonstrate that the proposed scheme is provably secure in the random oracle model. Furthermore, Burrows-Abadi-Needham logic analysis shows that the proposed scheme achieves mutual authentication and session key agreement. The informal analysis demonstrates that the proposed scheme is resistant to various attacks and has desirable attributes, such as forward secrecy and three-factor secrecy. Finally, the security and performance comparison show that the proposed scheme is more practical.
The FacT

Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regularization imposed by the tree structure. As a result, we are able to track the creation of latent profiles by looking into the path of each factor on regression trees, which thus serves as an explanation for the resulting recommendations. Extensive experiments on two large collections of Amazon and Yelp reviews demonstrate the advantage of our model over several competitive baseline algorithms. Besides, our extensive user study also confirms the practical value of explainable recommendations generated by our model.
An Adaptive Machine Learning Based Approach for Phishing Detection Using Hybrid Features

Nowadays, phishing is one of the most usual web threats with regards to the significant growth of the World Wide Web in volume over time. Phishing attackers always use new (zero-day) and sophisticated techniques to deceive online customers. Hence, it is necessary that the anti-phishing system be real-time and fast and also leverages from an intelligent phishing detection solution. Here, we develop a reliable detection system which can adaptively match the changing environment and phishing websites. Our method is an online and feature-rich machine learning technique to discriminate the phishing and legitimate websites. Since the proposed approach extracts different types of discriminative features from URLs and webpages source code, it is an entirely client-side solution and does not require any service from the third-party. The experimental results highlight the robustness and competitiveness of our anti-phishing system to distinguish the phishing and legitimate websites.
An Efficient Hybrid Recommendation Model With Deep Neural Networks

Recently, deep learning has gained great popularity in the area of recommender systems. Various combinations of deep learning, collaborative recommendation and content-based recommendation have occurred. However, as one of the three most significant recommendation techniques, hybrid recommendation has little cooperation with deep learning. Besides, most current deep hybrid models only incorporate two simple recommendation methods together in post-fusion, leaving massive space for further exploration of better combinations. In this paper, we apply deep learning to hybrid recommendation, proposing a deep hybrid recommendation model DMFL (Deep Metric Factorization Learning). In DMFL, we combine deep learning with improved machine learning models to learn the interaction between users and items from multiple perspectives. Such deep hybrid learning helps to reflect the user preference more comprehensively and strengthen model's ability of generalization. We also propose a more accurate method of user feature representation, taking both long-term static characteristics and short-term dynamic interest changes of users into consideration. Furthermore, thorough experiments have been conducted on real-world datasets, which strongly proves the effectiveness and efficiency of the proposed model.
Mixed Reality for Cultural Heritage

In this paper we present two different approaches with Mixed (Virtual and Augmented) Reality to give pupils an understanding of ancient Greek culture and history in a motivating way. We identify the possibilities of AR in a museum for ancient statues to represent virtual information and to guide the visitor. Moreover, we discuss typical issues of mobile and stationary Virtual Reality (VR) systems in the context of public usage within schools and museums. Additionally, a new VR-streaming approach called SaMaXVR is presented that combines the benefits of mobile and stationary consumer ready VR systems in terms of usability, maintenance, safety and graphics-quality by mitigating many of their individual disadvantages when used in daily business. As this streaming solution consists of cost-effective hardware, this approach can be seen as a valuable and affordable alternative for schools, museums and students. To demonstrate its potential, an application was developed that visualizes complex 3D scans of cultural heritage in their former original environment to give users the possibility to travel back in time (and place) to see how the artifacts might have looked like in the past.
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks

Modern machine learning systems are susceptible to adversarial examples; inputs which clearly preserve the characteristic semantics of a given class, but whose classification is (usually confidently) incorrect. Existing approaches to adversarial defense generally rely on modifying the input, e.g. quantization, or the learned model parameters, e.g. via adversarial training. However, recent research has shown that most such approaches succumb to adversarial examples when different norms or more sophisticated adaptive attacks are considered. In this paper, we propose a fundamentally different approach which instead changes the way the output is represented and decoded. This simple approach achieves state-of-the-art robustness to adversarial examples for L 2 and L ∞ based adversarial perturbations on MNIST and CIFAR10. In addition, even under strong white-box attacks, we find that our model often assigns adversarial examples a low probability; those with high probability are usually interpretable, i.e. perturbed towards the perceptual boundary between the original and adversarial class. Our approach has several advantages: it yields more meaningful probability estimates, is extremely fast during training and testing, requires essentially no architectural changes to existing discriminative learning pipelines, is wholly complementary to other defense approaches including adversarial training, and does not sacrifice benign test set performance
Food Supply Chain Traceability Scheme Based on Blockchain and EPC Technology

In order to effectively detect and prevent food safety issues and track responsibilities, it is indispensable to establish a reliable traceable system. Accurate recording, sharing and tracking of specific data in food production, processing, warehousing, transportation and retailing is particularly important throughout the food supply chain. This paper proposes a safe food traceable scheme based on blockchain and EPC technology. Encoding food by EPC technology, using the Ethernet block chain and smart contract to effectively execute transactions, manage all transactions among participants involved in the supply chain ecosystem, to track and trace food in the entire agricultural supply chain. In addition, a data management system structure combining on-chain and off-chain is proposed, which uses IPFS to store data under the chain, to alleviate the data explosion in the block chain of the Internet of Things. Which provide safe, efficient and transparent food traceable scheme.
Deep Learning for Natural Language Parsing

Natural language processing problems (such as speech recognition, text-based data mining, and text or speech generation) are becoming increasingly important. Before effectively approaching many of these problems, it is necessary to process the syntactic structures of the sentences. Syntactic parsing is the task of constructing a syntactic parse tree over a sentence which describes the structure of the sentence. Parse trees are used as part of many language processing applications. In this paper, we present a multi-lingual dependency parser. Using advanced deep learning techniques, our parser architecture tackles common issues with parsing such as long-distance head attachment, while using `architecture engineering' to adapt to each target language in order to reduce the feature engineering often required for parsing tasks. We implement a parser based on this architecture to utilize transfer learning techniques to address important issues related with limited-resourced language. We exceed the accuracy of state-of-the-art parsers on languages with limited training resources by a considerable margin. We present promising results for solving core problems in natural language parsing, while also performing at state-of-the-art accuracy on general parsing tasks.
KFU NLP Team at SMM4H 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? BERT to The Rescue

This paper describes a system developed for the Social Media Mining for Health (SMM4H) 2019 shared tasks.Specifically, we participated in three tasks.The goals of the first two tasks are to classify whether a tweet contains mentions of adverse drug reactions (ADR) and extract these mentions, respectively.The objective of the third task is to build an end-toend solution: first, detect ADR mentions and then map these entities to concepts in a controlled vocabulary.We investigate the use of a language representation model BERT trained to obtain semantic representations of social media texts.Our experiments on a dataset of user reviews showed that BERT is superior to state-of-the-art models based on recurrent neural networks.The BERT-based system for Task 1 obtained an F1 of 57.38%, with improvements up to +7.19% F1 over a score averaged across all 43 submissions.The ensemble of neural networks with a voting scheme for named entity recognition ranked first among 9 teams at the SMM4H 2019 Task 2 and obtained a relaxed F1 of 65.8%.The end-to-end model based on BERT for ADR normalization ranked first at the SMM4H 2019 Task 3 and obtained a relaxed F1 of 43.2%.
Identity-Based Dynamic Data Auditing for Big Data Storage

Identity-based remote data auditing schemes can verify data integrity and provide a simple identity authentication and management for multiple users. However, prior works on identity-based remote data auditing lack the support of dynamic operations. In these schemes, tag generation is linked to the index of data block, which is related to update operations such as modification, insertion and deletion. If users perform dynamic operations on a data block, the tags of all subsequent blocks need to be modified. It means that if users want to update data on a big data platform, they have to download the whole file, update the file and send the updated file to the big data platform. Such pattern will bring huge communication overhead. In this paper, we propose an identity-based dynamic data auditing scheme which supports dynamic data operations, including modification, insertion and deletion. As far as we know, there is still no other identity-based data auditing scheme that supports dynamic operations. In particular, to achieve efficient dynamic operations, we use the data structure of Merkle hash tree for block tag authentication, which helps update data with integrity assurance. Analyses of security and performance show that the proposed scheme is efficient and secure.
On the Over-Smoothing Problem of CNN Based Disparity Estimation

Currently, most deep learning based disparity estimation methods have the problem of over-smoothing at boundaries, which is unfavorable for some applications such as point cloud segmentation, mapping, etc. To address this problem, we first analyze the potential causes and observe that the estimated disparity at edge boundary pixels usually follows multimodal distributions, causing over-smoothing estimation. Based on this observation, we propose a single-modal weighted average operation on the probability distribution during inference, which can alleviate the problem effectively. To integrate the constraint of this inference method into training stage, we further analyze the characteristics of different loss functions and found that using cross entropy with gaussian distribution consistently further improves the performance. For quantitative evaluation, we propose a novel metric that measures the disparity error in the local structure of edge boundaries. Experiments on various datasets using various networks show our method's effectiveness and general applicability. Code will be available at https://github.com/chenchr/otosp.
Unsupervised dialectal neural machine translation

In this paper, we present the first work on unsupervised dialectal Neural Machine Translation (NMT), where the source dialect is not represented in the parallel training corpus. Two systems are proposed for this problem. The first one is the Dialectal to Standard Language Translation (D2SLT) system, which is based on the standard attentional sequence-to-sequence model while introducing two novel ideas leveraging similarities among dialects: using common words as anchor points when learning word embeddings and a decoder scoring mechanism that depends on cosine similarity and language models. The second system is based on the celebrated Google NMT (GNMT) system. We first evaluate these systems in a supervised setting (where the training and testing are done using our parallel corpus of Jordanian dialect and Modern Standard Arabic (MSA)) before going into the unsupervised setting (where we train each system once on a Saudi-MSA parallel corpus and once on an Egyptian-MSA parallel corpus and test them on the Jordanian-MSA parallel corpus). The highest BLEU score obtained in the unsupervised setting is 32.14 (by D2SLT trained on Saudi-MSA data), which is remarkably high compared with the highest BLEU score obtained in the supervised setting, which is 48.25.
Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER

BiLSTM has been prevalently used as a core module for NER in a sequence-labeling setup. State-of-the-art approaches use BiLSTM with additional resources such as gazetteers, language-modeling, or multi-task supervision to further improve NER. This paper instead takes a step back and focuses on analyzing problems of BiLSTM itself and how exactly self-attention can bring improvements. We formally show the limitation of (CRF-)BiLSTM in modeling cross-context patterns for each word – the XOR limitation. Then, we show that two types of simple cross-structures – self-attention and Cross-BiLSTM – can effectively remedy the problem. We test the practical impacts of the deficiency on real-world NER datasets, OntoNotes 5.0 and WNUT 2017, with clear and consistent improvements over the baseline, up to 8.7% on some of the multi-token entity mentions. We give in-depth analyses of the improvements across several aspects of NER, especially the identification of multi-token mentions. This study should lay a sound foundation for future improvements on sequence-labeling NER1.
Visualization of Blockchain Data: A Systematic Review

We present a systematic review of visual analytics tools used for the analysis of blockchains-related data. The blockchain concept has recently received considerable attention and spurred applications in a variety of domains. We systematically and quantitatively assessed 76 analytics tools that have been proposed in research as well as online by professionals and blockchain enthusiasts. Our classification of these tools distinguishes (1) target blockchains, (2) blockchain data, (3) target audiences, (4) task domains, and (5) visualization types. Furthermore, we look at which aspects of blockchain data have already been explored and point out areas that deserve more investigation in the future.
Scalable spin–photon entanglement by time-to-polarization conversion

Abstract The realization of quantum networks and quantum computers relies on the scalable generation of entanglement, for which spin-photon interfaces are strong candidates. Current proposals to produce entangled-photon states with such platforms place stringent requirements on the physical properties of the photon emitters, limiting the range and performance of suitable physical systems. We propose a scalable protocol, which significantly reduces the constraints on the emitter. We use only a single optical transition and an asymmetric polarizing interferometer. This device converts the entanglement from the experimentally robust time basis via a path degree of freedom into a polarization basis, where quantum logic operations can be performed. The fundamental unit of the proposed protocol is realized experimentally in this work, using a nitrogen-vacancy center in diamond. This classically assisted protocol greatly widens the set of physical systems suited for scalable entangled-photon generation and enables performance enhancement of existing platforms.
Using Harmony Search Algorithm in Neural Networks to Improve Fraud Detection in Banking System

Financial fraud is among the main problems undermining the confidence of customers in addition to incurring economic losses to banks and financial institutions. In recent years, along with the proliferation of fraud, financial institutions began looking for ways to find a suitable solution in the fight against fraud. Given the advanced and varied changes in methods of fraud, extensive research has been conducted to detect fraud. In this paper, the Artificial Neural Network technique and Harmony Search Algorithm are used to detect fraud. In the proposed method, hidden patterns between normal and fraudulent customers’ information are searched. Given that fraudulent behavior could be detected and stopped before they take place, the results of the proposed system show that it has an acceptable capability in fraud detection.
A Novel Image Encryption Technique Using Arnold Transform and Asymmetric RSA Algorithm

Today, people all over the world want to exchange information, but they are concerned about security aspects while sharing information. The encryption and decryption are the one of the best methods to transfer the information securely. In this process, two types of algorithms are used symmetric and asymmetric algorithm. In the symmetric algorithm, we used the same key to encrypt and decrypt the information and in asymmetric algorithm used the pair of key to encrypt and decrypt the information, one is used for encryption, and another is used for decryption. Hence, asymmetric algorithms are more secured. In the asymmetric algorithm, the RSA is the one of the most popular algorithms. In this paper, present a hybrid technique of image encryption using the Arnold’s transformation and RSA algorithm. In this proposed methodology, first scrambles the digital image using Arnold’s transformation after that encrypts that digital image using an asymmetric RSA algorithm. The proposed method is safer, and the result of the resulting encrypted image is improved and safer as shown in this paper.
Transfer Learning for Punctuation Prediction

The output from most of the Automatic Speech Recognition system is a continuous sequence of words without proper punctuation. This decreases human readability and the performance of downstream natural language processing tasks on ASR text. We treat the punctuation prediction task as a sequence tagging task and propose an architecture that uses pre-trained BERT embeddings. Our model significantly improves the state of art on the IWSLT dataset. We achieve an overall F1 of 81.4% on the joint prediction of period, comma and question mark.