Reviewerly/exHarmony huggingface dataset:
Spam review detection using self attention based CNN and bi-directional LSTM

Opinion reviews are a valuable source of information in e-commerce. Indeed, it benefits users in buying decisions and businesses to enhance their quality. However, various greedy organizations employ spammers to post biased spam reviews to gain an advantage or to degrade the reputation of a competitor. This results in the explosive growth of opinion spamming. Due to its nature and their increasing volume, spam reviews are a fast-growing serious issue on the internet. Until now, researchers have developed many Machine Learning (ML) based methods to identify opinion spam reviews. However, the traditional ML methods cannot effectively detect spam messages due to the limited feature representations and the data manipulations done by spammers to escape from the detection mechanism. As an alternative to ML-based detection, in this paper, we proposed a Deep Learning (DL) based novel framework called Self Attention-based CNN Bi-LSTM (ACB) model to learn document level representation for identifying the spam reviews. Our approach computes the weightage of each word present in the sentence and identifies the spamming clues exists in the document with an attention mechanism. Then the model learns sentence representation by using Convolution Neural Network (CNN) and extracts the higher-level n-gram features. Then finally, sentence vectors are combined using Bi-directional LSTM (Bi-LSTM) as document feature vectors and identify the spam reviews with contextual information. The evaluated experiment results are compared with its variants and the result shows that ACB outperforms other variants in terms of classification accuracy.
9.5 A 6K-MAC Feature-Map-Sparsity-Aware Neural Processing Unit in 5nm Flagship Mobile SoC

On-device machine learning is critical for mobile products as it enables real-time applications (e.g. AI-powered camera applications), which need to be responsive, always available (i.e. do not require network connectivity) and privacy preserving. The platforms used in such situations have limited computing resources, power, and memory bandwidth. Enabling such on-device machine learning has triggered wide development of efficient neural-network accelerators that promise high energy and area efficiency compared to general-purpose processors, such as CPUs. The need to support a comprehensive range of neural networks has been important as well because the field of deep learning is evolving rapidly as depicted in Fig. 9.5.1. Recent work on neural-network accelerators has focused on improving energy efficiency, while obtaining high performance in order to meet the needs of real-time applications. For example, weightzero-skipping and pruning have been deployed in recent accelerators [2] -[7]. SIMD or systolic array-based accelerators [2] -[4], [6] provide flexibility to support various types of compute across a wide range of Deep Neural Network (DNN) models.
Blockchain technology in financial services: a comprehensive review of the literature

Purpose The purpose of this study is to thoroughly review studies that have used blockchain technology in financial services. This study will help provide a holistic framework that would highlight the current state and challenges of the blockchain in the financial services sector. Design/methodology/approach The objective of this study is to systematically examine and organize the current body of research literature that either quantitatively or qualitatively explored the use of blockchain technology in financial services. The study uses PRISMA-guided systematic review along with bibliometric analysis to achieve the purpose. Findings This study contributes to the existing literature by exploring and analyzing systematic studies available on blockchain with special reference to financial services sector. With blockchain based on five principles, namely, computational logic, peer-to-peer transmission, irreversibility of records, distributed database and transparency with pseudonym has immense potential to unleash and transform the financial service industry. With increasing blockchain-based operations of decentralized banking, insurance, trade finance, financial markets and cryptocurrency market, the subject is rapidly growing and seeking considerable contribution from scholars from around the world. Research limitations/implications This study uses systematic literature review approach, which has its own demerits. Like other studies based on Systematic Literature Review, this study also suffers from a certain bias such as sample selection bias, publication bias, data interpretation and the combination of quantitative and qualitative studies in the population. Further, the adoption and resultant benefits of blockchain have not been empirically tested. Practical implications This study can help policymakers and institutions in determining their future course of action, as it highlights the state of research in the area of blockchain technology and financial services. Originality/value Very few studies have done a comprehensive review of literature on blockchain in financial services.
Predicting cybersickness based on user’s gaze behaviors in HMD-based virtual reality

Abstract Cybersickness refers to a group of uncomfortable symptoms experienced in virtual reality (VR). Among several theories of cybersickness, the subjective vertical mismatch (SVM) theory focuses on an individual’s internal model, which is created and updated through past experiences. Although previous studies have attempted to provide experimental evidence for the theory, most approaches are limited to subjective measures or body sway. In this study, we aimed to demonstrate the SVM theory on the basis of the participant’s eye movements and investigate whether the subjective level of cybersickness can be predicted using eye-related measures. 26 participants experienced roller coaster VR while wearing a head-mounted display with eye tracking. We designed four experimental conditions by changing the orientation of the VR scene (upright vs. inverted) or the controllability of the participant’s body (unrestrained vs. restrained body). The results indicated that participants reported more severe cybersickness when experiencing the upright VR content without controllability. Moreover, distinctive eye movements (e.g. fixation duration and distance between the eye gaze and the object position sequence) were observed according to the experimental conditions. On the basis of these results, we developed a regression model using eye-movement features and found that our model can explain 34.8% of the total variance of cybersickness, indicating a substantial improvement compared to the previous work (4.2%). This study provides empirical data for the SVM theory using both subjective and eye-related measures. In particular, the results suggest that participants’ eye movements can serve as a significant index for predicting cybersickness when considering natural gaze behaviors during a VR experience.
Hybrid Trusted/Untrusted Relay-Based Quantum Key Distribution Over Optical Backbone Networks

Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.
Shadow: A Lightweight Block Cipher for IoT Nodes

The advancement of the Internet of Things (IoT) has promoted the rapid development of low-power and multifunctional sensors. However, it is seriously significant to ensure the security of data transmission of these nodes. Meanwhile, sensor nodes have the characteristics of converting analog signals into digital signals for operation processing in wireless sensor networks (WSNs). Given the particularity of Addition or AND, Rotation, and XOR (ARX) operations, its round function can only be based on the Feistel structure or generalized Feistel structure, otherwise, the process of decryption cannot be completed correctly. Furthermore, the existing ARX ciphers have the problems of only changing half of the plaintext block in one round and iterating for many rounds. In this article, a new logical combination method of generalized Feistel structure and ARX operations is proposed to improve the diffusion speed of ARX ciphers, called Shadow. Shadow overcomes the shortcomings of traditional ARX ciphers that only diffuse half of the block in one round. To ensure the efficiency of the encryption hardware circuit while ensuring the security of the physical-layer signal, we studied the round-based hardware architecture and the serial hardware architecture for Shadow cipher. Particularly, we conducted a series of performance tests on Shadow, including the avalanche effect, FPGA implementation, and ASIC implementation. Also, we conducted a security analysis of the Shadow. As shown by our experiments and comparisons, Shadow is compact in IoT nodes and is of high security against cryptanalysis.
Pose-based Sign Language Recognition using GCN and BERT

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between the hearing and vocally impaired community and the rest of the society. Word-level sign language recognition (WSLR) is the first important step towards understanding and interpreting sign language. However, recognizing signs from videos is a challenging task as the meaning of a word depends on a combination of subtle body motions, hand configurations and other movements. Recent pose-based architectures for WSLR either model both the spatial and temporal dependencies among the poses in different frames simultaneously or only model the temporal information without fully utilizing the spatial information.We tackle the problem of WSLR using a novel pose-based approach, which captures spatial and temporal information separately and performs late fusion. Our proposed architecture explicitly captures the spatial interactions in the video using a Graph Convolutional Network (GCN). The temporal dependencies between the frames are captured using Bidirectional Encoder Representations from Transformers (BERT). Experimental results on WLASL, a standard word-level sign language recognition dataset show that our model significantly outperforms the state-of-the-art on pose-based methods by achieving an improvement in the prediction accuracy by up to 5%.
PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form

We present PyQUBO, an open-source Python library for constructing quadratic unconstrained binary optimizations (QUBOs) from the objective functions and the constraints of optimization problems. PyQUBO enables users to prepare QUBOs or Ising models for various combinatorial optimization problems with ease thanks to the abstraction of expressions and the extensibility of the program. QUBOs and Ising models formulated using PyQUBO are solvable by Ising machines, including quantum annealing machines. We introduce the features of PyQUBO with applications in the number partitioning problem, knapsack problem, graph coloring problem, and integer factorization using a binary multiplier. Moreover, we demonstrate how PyQUBO can be applied to production-scale problems through integration with quantum annealing machines. Through its flexibility and ease of use, PyQUBO has the potential to make quantum annealing a more practical tool among researchers.
Attacks and countermeasures on blockchains: A survey from layering perspective

Blockchain is an emerging technology with the characteristics of decentralization and transparency and has received extensive attention and in-depth research for the past few years. With security features such as immutability and decentralization, blockchain is used to ensure the security of other applications and services. However, the security issues of the blockchain itself are always easily overlooked. In this paper, we conducted a comprehensive literature survey on the security issues of the blockchain itself. From the perspective of architectural layering, each layer of the blockchain has its own corresponding security issues. We systematically review and analyze attacks against blockchains based on the basic framework of blockchain and conduct a state-of-the-art investigation of the corresponding countermeasures against those attacks. Furthermore, we conclude our observation and point out the future direction of blockchain security issues. The purpose of this paper is to provide researchers with future development insights of blockchain security and to stimulate more efforts about blockchain security issues.
Learning Graph Convolutional Networks for Multi-Label Recognition and Applications

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important information, we propose graph convolutional networks (GCNs) based models for multi-label image recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier Learning GCN (C-GCN) to map class-level semantic representations (e.g., word embeddings) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-aware features and propose prediction learning GCN (P-GCN) to encode such features into inter-dependent image-level prediction scores. Furthermore, we also present an effective correlation matrix construction approach to capture inter-class relationships and consequently guide information propagation among classes. Empirical results on generic multi-label image recognition demonstrate that both of the proposed models can obviously outperform other existing state-of-the-arts. Moreover, the proposed methods also show advantages in some other multi-label classification related applications.
Minimax Design of Graph Filter Using Chebyshev Polynomial Approximation

In this brief, the minimax design problem of graph filter using Chebyshev polynomial approximation (CPA) is studied. First, conventional CPA graph filter design is investigated to show that it does not provide the minimax design, so the peak error of the spectral response of the designed filter is not minimized. Then, a spectral transformation is used to convert the minimax design problem of CPA graph filter into the one of the type-I linear-phase FIR digital filter such that the Parks-McClellan method can be employed to obtain the optimal filter coefficients of minimax design. Next, using the recurrence relation of Chebyshev polynomials, an implementation structure of CPA graph filter with low computational complexity is presented. Finally, a graph signal denoising application example is illustrated to show the usefulness of the proposed CPA graph filter.
Multilingual Autoregressive Entity Linking

Abstract We present mGENRE, a sequence-to- sequence system for the Multilingual Entity Linking (MEL) problem—the task of resolving language-specific mentions to a multilingual Knowledge Base (KB). For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token in an autoregressive fashion. The autoregressive formulation allows us to effectively cross-encode mention string and entity names to capture more interactions than the standard dot product between mention and entity vectors. It also enables fast search within a large KB even for mentions that do not appear in mention tables and with no need for large-scale vector indices. While prior MEL works use a single representation for each entity, we match against entity names of as many languages as possible, which allows exploiting language connections between source input and target name. Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time. This leads to over 50% improvements in average accuracy. We show the efficacy of our approach through extensive evaluation including experiments on three popular MEL benchmarks where we establish new state-of-the-art results. Source code available at https://github.com/facebookresearch/GENRE.
Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits

Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques. In 2014, Lloyd, Mohseni, and Rebentrost proposed a quantum PCA (qPCA) algorithm [Lloyd, Mohseni, and Rebentrost, Nat. Phys. 10, 631 (2014)] that still lacks experimental demonstration due to the experimental challenges in preparing multiple quantum state copies and implementing quantum phase estimations. Here, we propose a new qPCA algorithm using the hybrid classical-quantum control, where parameterized quantum circuits are optimized with simple measurement observables, which significantly reduces the experimental complexity. As one important PCA application, we implement a human face recognition process using the images from the Yale Face Dataset. By training our quantum processor, the eigenface information in the training dataset is encoded into the parameterized quantum circuit, and the quantum processor learns to recognize new face images from the test dataset with high fidelities. Our work paves a new avenue toward the study of qPCA applications in theory and experiment.
Adaptations of music education in primary and secondary school due to COVID-19: the experience in Spain

COVID-19 caused an essential confinament in order to limit its expansion. Globally, this led to a reconsideration of education processes. The study's purpose is to analyse how compulsory education music teachers in Spain adapted. To gather the data, 335 teachers were surveyed. The participants preferred to continue teaching in most cases. However, this situation forced them into an adaptation in which preference was given to contemplative activities. These adaptations were marked by a lack of methodological and material resources. A common complaint was the lack of specific instructions from government bodies. In addition, a difference was observed between public, private and semi-private schools. Interestingly, the teachers considered that the situation had enabled them to have more contact with students, even though the learning was asynchronous.
Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.
The parity operator: Applications in quantum metrology

In this paper, the authors review the use of parity as a detection observable in quantum metrology and introduce some original findings with regard to measurement resolution in Ramsey spectroscopy and quantum nondemolition measures of atomic parity. Parity was first introduced in the context of Ramsey spectroscopy as an alternative to atomic state detection. It was later adapted for use in quantum optical interferometry where it has been shown to be the optimal detection observable saturating the quantum Cramér–Rao bound for path symmetric states. The authors include a brief review on the basics of phase estimation and the connection between parity-based detection and the quantum Fisher information as it applies to quantum optical interferometry. The authors also discuss the efforts made in experimental methods of measuring photon-number parity and close the paper with a discussion on the use of parity, leading to enhanced measurement resolution in multi-atom spectroscopy. The authors show how this may be of use in the construction of high-precision multi-atom atomic clocks.
Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.