Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis

In recent years, it is seen that the opinion-based postings in social media are helping to reshape business and public sentiments, and emotions have an impact on our social and political systems. Opinions are central to mostly all human activities as they are the key influencers of our behaviour. Whenever we need to make a decision, we generally want to know others opinion. Every organization and business always wants to find customer or public opinion about their products and services. Thus, it is necessary to grab and study the opinions on the Web. However, finding and monitoring sites on the web and distilling the reviews remains a big task because each site typically contains a huge volume of opinion text and the average human reader will have difficulty in identifying the polarity of each review and summarizing the opinions in them. Hence, it needs the automated sentiment analysis to find the polarity score and classify the reviews as positive or negative. This article uses NLTK, Text blob and VADER Sentiment analysis tool to classify the movie reviews which are downloaded from the website www.rottentomatoes.com that is provided by the Cornell University, and makes a comparison on these tools to find the efficient one for sentiment classification. The experimental results of this work confirm that VADER outperforms the Text blob.
Does BLE technology contribute towards improving marketing strategies, customers’ satisfaction and loyalty? The role of open innovation

The purpose of this study is to explore the marketing strategies for the introduction of Beacons technology applications (BLE) technology in businesses and how it can convert potential clients into satisfactory and loyal customers. Open innovation is the pathway to taking businesses to the next level with the help of and introduction to BLE technology contributions. Companies are flexible towards adopting new technologies to make sure that their marketing strategies are working positively. Global Village Dubai was the market that we targeted to study the customers’ needs and wants. This was done through 138 questionnaires distributed among 159 stores that met our research criteria. We used ANOVA through the SPSS program for analysis of the data. The results supported all our hypothesis of the study, which means there is a positive and strong relationship of adopting BLE technology on customers’ satisfaction which leads towards customers’ loyalty, making them stick to the brand for long term. The results of our research revealed that Beacons technology applications have positively influenced marketing strategies which in turn have impacted customer satisfaction and loyalty. The proximity marketing is the best strategy developed in lieu of open innovation. Future studies are welcomed for analyzing the same model in perspective of other markets for clearer advocacy of the hypothesis that proximity marketing is the marketing strategy that helps in customers’ happiness leading towards their loyalty.
Feature-level Deeper Self-Attention Network for Sequential Recommendation

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. Specifically, FDSA first integrates various heterogeneous features of items into feature sequences with different weights through a vanilla mechanism. After that, FDSA applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Then, we integrate the outputs of these two blocks to a fully-connected layer for next item recommendation. Finally, comprehensive experimental results demonstrate that considering the transition relationships between features can significantly improve the performance of sequential recommendation.
Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.
Federated Learning in Smart City Sensing: Challenges and Opportunities

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.
A Survey on Biometric Authentication: Toward Secure and Privacy-Preserving Identification

In order to overcome the difficulty of password management and improve the usability of authentication systems, biometric authentication has been widely studied and has attracted special attention in both academia and industry. Many biometric authentication systems have been researched and developed, especially for mobile devices. However, the existing biometric authentication systems still have defects. Some biological features have not been deeply investigated. The existing systems could be vulnerable to attacks, such as replay attack and suffer from user privacy intrusion, which seriously hinder their wide acceptance by end users. The literature still lacks a thorough review on the recent advances of biometric authentication for the purpose of secure and privacy-preserving identification. In this paper, we classify and thoroughly review the existing biometric authentication systems by focusing on the security and privacy solutions. We analyze the threats of biometric authentication and propose a number of criteria with regard to secure and privacy-preserving authentication. We further review the existing works of biometric authentication by analyzing their differences and summarizing the advantages and disadvantages of each based on the proposed criteria. In particular, we discuss the problems of aliveness detection and privacy protection in biometric authentication. Based on our survey, we figure out a number of open research issues and further specify a number of significant research directions that are worth special efforts in future research.
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions

Cross-lingual word embeddings (CLEs) facilitate cross-lingual transfer of NLP models.Despite their ubiquitous downstream usage, increasingly popular projection-based CLE models are almost exclusively evaluated on bilingual lexicon induction (BLI).Even the BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models.In this work, we take the first step towards a comprehensive evaluation of CLE models: we thoroughly evaluate both supervised and unsupervised CLE models, for a large number of language pairs, on BLI and three downstream tasks, providing new insights concerning the ability of cuttingedge CLE models to support cross-lingual NLP.We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI may hurt downstream performance.We indicate the most robust supervised and unsupervised CLE models and emphasize the need to reassess simple baselines, which still display competitive performance across the board.We hope our work catalyzes further research on CLE evaluation and model analysis.
HomeChain: A Blockchain-Based Secure Mutual Authentication System for Smart Homes

Increasingly, governments around the world, particularly in technologically advanced countries, are exploring or implementing smart homes, or the related smart facilities for the benefits of the society. The capability to remotely access and control Internet of Things (IoT) devices (e.g., capturing of images, audios, and other information) is convenient but risky, as vulnerable devices can be exploited to conduct surveillance or perform other nefarious activities on the users and organizations. This highlights the necessity of designing a secure and efficient remote user authentication solution. Most of the existing solutions for this problem are generally based on a single-server architecture, which has limitations in terms of privacy and anonymity (leading to users' daily activities being predicted), and integrity and confidentiality (resulting in an unreliable behavior auditing). While blockchain-based solutions may mitigate these issues, they still face some critical challenges (e.g., providing regulation of behaviors and privacy protection of access policy). Motivated by these facts, in this article, we construct a novel secure mutual authentication system, which can be applied in smart homes and other applications. Specifically, the proposed approach integrates blockchain, group signature, and message authentication code to provide reliable auditing of users' access history, anonymously authenticate group members, and efficiently authenticate home gateway, respectively. We also prove the security and privacy requirements, including anonymity, traceability, and confidentiality, that the proposed system satisfies, with an implementation and evaluation to demonstrate its practicality.
Implementation of quantum key distribution surpassing the linear rate-transmittance bound

Quantum key distribution (QKD)1,2 offers a long-term solution to secure key exchange. Due to photon loss in transmission, it was believed that the repeaterless key rate is bounded by a linear function of the transmittance, O(η) (refs. 3,4), limiting the maximal secure transmission distance5,6. Recently, a novel type of QKD scheme has been shown to beat the linear bound and achieve a key rate performance of $$O(\sqrt{\eta })$$ (refs. 7–9). Here, by employing the laser injection technique and the phase post-compensation method, we match the modes of two independent lasers and overcome the phase fluctuation. As a result, the key rate surpasses the linear bound via 302 km and 402 km commercial-fibre channels, over four orders of magnitude higher than existing results5. Furthermore, our system yields a secret key rate of 0.118 bps with a 502 km ultralow-loss fibre. This new type of QKD pushes forward long-distance quantum communication for the future quantum internet. Phase-matching quantum key distribution is implemented with a 502 km ultralow-loss optical fibre. The fluctuations of the laser initial phases and frequencies are suppressed by the laser injection technique and the phase post-compensation method.
Explainability in deep reinforcement learning

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data. However, assessing how XAI techniques can help understand models beyond classification tasks, e.g. for reinforcement learning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable Reinforcement Learning (XRL), a relatively new subfield of Explainable Artificial Intelligence, intended to be used in general public applications, with diverse audiences, requiring ethical, responsible and trustable algorithms. In critical situations where it is essential to justify and explain the agent’s behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box. We evaluate mainly studies directly linking explainability to RL, and split these into two categories according to the way the explanations are generated: transparent algorithms and post-hoc explainability. We also review the most prominent XAI works from the lenses of how they could potentially enlighten the further deployment of the latest advances in RL, in the demanding present and future of everyday problems.
Universal Dependencies

Abstract Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for crosslinguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.
Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.
See through Gradients: Image Batch Recovery via GradInversion

Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presumed to be safe for privacy-preserving training in joint, collaborative, and federated learning applications. Prior work only showed the possibility of recovering input data given gradients under very restrictive conditions – a single input point, or a network with no non-linearities, or a small 32 × 32 px input batch. Therefore, averaging gradients over larger batches was thought to be safe. In this work, we introduce GradInversion, using which input images from a larger batch (8 – 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224 × 224 px). We formulate an optimization task that converts random noise into natural images, matching gradients while regularizing image fidelity. We also propose an algorithm for target class label recovery given gradients. We further propose a group consistency regularization framework, where multiple agents starting from different random seeds work together to find an enhanced reconstruction of the original data batch. We show that gradients encode a surprisingly large amount of information, such that all the individual images can be recovered with high fidelity via GradInversion, even for complex datasets, deep networks, and large batch sizes.
Cryptographic primitives in blockchains

Blockchain, as one of the crypto-intensive creatures, has become a very hot topic recently. Although many surveys have recently been dedicated to the security and privacy issues of blockchains, there still lacks a systematic examination on the cryptographic primitives in blockchains. To this end, we in this paper conduct a systematic study on the cryptographic primitives in blockchains by comprehensive analysis on top-30 mainstream cryptocurrencies, in terms of the usages, functionalities, and evolutions of these primitives. We hope that it would be helpful for cryptographers who are going to devote themselves to the blockchain research, and the financial engineers/managers who want to evaluate cryptographic solutions for blockchain-based projects.
Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks

Recently, a number of existing blockchain systems have witnessed major bugs and vulnerabilities within smart contracts.Although the literature features a number of proposals for securing smart contracts, these proposals mostly focus on proving the correctness or absence of a certain type of vulnerability within a contract, but cannot protect deployed (legacy) contracts from being exploited.In this paper, we address this problem in the context of re-entrancy exploits and propose a novel smart contract security technology, dubbed Sereum (Secure Ethereum), which protects existing, deployed contracts against re-entrancy attacks in a backwards compatible way based on run-time monitoring and validation.Sereum does neither require any modification nor any semantic knowledge of existing contracts.By means of implementation and evaluation using the Ethereum blockchain, we show that Sereum covers the actual execution flow of a smart contract to accurately detect and prevent attacks with a false positive rate as small as 0.06% and with negligible runtime overhead.As a by-product, we develop three advanced reentrancy attacks to demonstrate the limitations of existing offline vulnerability analysis tools.
Vehicle Re-Identification With Viewpoint-Aware Metric Learning

This paper considers vehicle re-identification (re-ID) problem. The extreme viewpoint variation (up to 180 degrees) poses great challenges for existing approaches. Inspired by the behavior in human's recognition process, we propose a novel viewpoint-aware metric learning approach. It learns two metrics for similar viewpoints and different viewpoints in two feature spaces, respectively, giving rise to viewpoint-aware network (VANet). During training, two types of constraints are applied jointly. During inference, viewpoint is firstly estimated and the corresponding metric is used. Experimental results confirm that VANet significantly improves re-ID accuracy, especially when the pair is observed from different viewpoints. Our method establishes the new state-of-the-art on two benchmarks.
600-km repeater-like quantum communications with dual-band stabilization

Twin-field (TF) quantum key distribution (QKD) fundamentally alters the rate-distance relationship of QKD, offering the scaling of a single-node quantum repeater. Although recent experiments have demonstrated the new opportunities for secure long-distance communications allowed by TF-QKD, formidable challenges remain to unlock its true potential. Previous demonstrations have required intense stabilization signals at the same wavelength as the quantum signals, thereby unavoidably generating Rayleigh scattering noise that limits the distance and bit rate. Here, we introduce a dual-band stabilization scheme that overcomes past limitations and can be adapted to other phase-sensitive single-photon applications. Using two different optical wavelengths multiplexed together for channel stabilization and protocol encoding, we develop a setup that provides repeater-like key rates over communication distances of 555 km and 605 km in the finite-size and asymptotic regimes respectively and increases the secure key rate at long distance by two orders of magnitude to values of practical relevance. Twin-field quantum key distribution over 600 km is demonstrated. The key ingredient for success is the dual-band phase stabilization that dramatically reduce the phase fluctuations on optical fibre by more than four orders of magnitude.
Managing the health of the eSport athlete: an integrated health management model

eSport is a form of electronic gaming, also known as professional or competitive video gaming, and is growing at a rapid pace worldwide. Over 50 US colleges have established varsity gaming teams over the past three years; some colleges offer eSport scholarships as they do for traditional sports. There is little objective research on the health habits of these players who are often placed under the direction of the athletics department on college campuses, and there is currently no health management model on how to treat these new athletes.Anonymous electronic surveys were sent to 65 collegiate eSport players from nine universities across the USA and Canada inquiring about gaming and lifestyle habits, and musculoskeletal complaints due to eSport competition.Players practiced between 3 and 10 hours per day. The most frequently reported complaint was eye fatigue (56%), followed by neck and back pain (42%). eSport athletes reported wrist pain (36%) and hand pain (32%). Forty per cent of participants do not participate in any form of physical exercise. Among the players surveyed, only 2% had sought medical attention.eSport players, just like athletes in traditional sports, are susceptible to overuse injuries. The most common complaint was eye fatigue, followed by neck and back pain. This study shows eSport athletes are also prone to wrist and hand pain. This paper proposes a health management model that offers a comprehensive medical team approach to prevent and treat eSport athletes.