Reviewerly/exHarmony huggingface dataset:
The Challenge and Prospect of Scalability of Blockchain Technology

Blockchain is a decentralized technology proposed by Satoshi Nakamoto in 2008, without relying on trust, irrevocable and modified, based on a consensus mechanism. Blockchain technology was not created out of thin air, but was born as the underlying technology of bitcoin, a digital currency. Now it has received widespread attention for its implementation in distributed ledger. However, in the current era, the application of blockchain technology in actual scenarios has not yet matured, mainly due to some characteristics of blockchain technology itself. After weighing its advantages and disadvantages, it is increasingly becoming its limitation. One of the main aspects is the poor scalability of the blockchain, which not only limits the functional expansion of the blockchain and makes it impossible to apply the blockchain technology to a wider range of scenarios, but also limits the throughput of the blockchain system to a certain extent Promotion. Taking into account the characteristics and bottlenecks of the above-mentioned blockchain technology, different from previous studies, this article from the perspective of blockchain expansion, introduces some existing expansion technologies in solving the transaction rate of the blockchain, and analyzes its principles and feasibility. Through the classification and comparison of different expansion technologies, the advantages and possible disadvantages of various expansion technologies are described. Finally, on the basis of fully understanding the existing blockchain expansion technology, the outlook for the future development of blockchain expansion technology is proposed, with a view to providing some suggestions for the development of the blockchain expansion technology.
A Cross-Project Aging-Related Bug Prediction Approach Based on Joint Probability Domain Adaptation and k-means SMOTE

In long-running systems, the phenomenon of performance degradation and failure rate increase caused by Aging-Related Bugs (ARBs) is known as software aging. Because of the low presence and reproducing difficulty of ARBs, collecting enough training data to predict ARBs in a project is not easy. Thus, cross-project ARB prediction has been proposed. There are two main challenges in cross-project ARB prediction, namely distribution differences and severe class imbalance. As for the first challenge, existing cross-project ARB prediction approaches only focus on the transferability between domains while ignoring the discriminability between classes. As for the second challenge, existing approaches only consider the imbalance between the classes while ignoring the within-class imbalance problem. To solve these problems, a cross-project ARB prediction approach based on Joint Probability Domain Adaptation (JPDA) and k- means SMOTE (KS), called JPKS, is proposed. JPDA is used to consider the transferability and discriminability simultaneously, and KS solves the within-class and between-class imbalance problems. Experiments are conducted on two natural software systems to verify the performance of JPKS. The results show that JPKS can improve the performance of ARB prediction.
An Efficient Sampling-Based Attention Network for Semantic Segmentation

Self-attention is widely explored to model long-range dependencies in semantic segmentation. However, this operation computes pair-wise relationships between the query point and all other points, leading to prohibitive complexity. In this paper, we propose an efficient Sampling-based Attention Network which combines a novel sample method with an attention mechanism for semantic segmentation. Specifically, we design a Stochastic Sampling-based Attention Module (SSAM) to capture the relationships between the query point and a stochastic sampled representative subset from a global perspective, where the sampled subset is selected by a Stochastic Sampling Module. Compared to self-attention, our SSAM achieves comparable segmentation performance while significantly reducing computational redundancy. In addition, with the observation that not all pixels are interested in the contextual information, we design a Deterministic Sampling-based Attention Module (DSAM) to sample features from a local region for obtaining the detailed information. Extensive experiments demonstrate that our proposed method can compete or perform favorably against the state-of-the-art methods on the Cityscapes, ADE20K, COCO Stuff, and PASCAL Context datasets.
Digital Humanities Event Horizon

This is an introductory text to a collection of selected papers from the Digital Humanities Workshop (DHW 2021), held in Kyiv, Ukraine, on the December 23, 2021. It consists of short introduction, papers’ review and some observations about the event and its future.
Exploiting Document-Based Features for Clarification in Conversational Search

Asking clarifying questions in order to elicit user's information need is becoming an integral part of modern conversational search systems. Current work heavily relies on pre-collected clarifying questions or large-scale query logs. However, such work is very limited given that collecting all possible clarifying questions on a collection is not feasible. Moreover, modeling clarification based on query reformulation limits a model only to head queries with several occurrences in the log. In this work, we aim to address these limitations by exploiting several document- and ranking-based features to generate clarifying questions. We hypothesise that we can acquire enough evidence about different aspects of a query and extract useful facets to generate clarifying questions about. Specifically, we utilise Part-Of-Speech tagging, entity linking, and topic modelling in order to extract features from the ranked list of documents. Among the extracted features, we then extract potentially useful facets based on three different strategies, aimed to capture feature distinctiveness across documents. We then construct clarifying questions based on the extracted facets that are given to crowdsourcing workers to be evaluated in terms of usefulness. Moreover, our findings show significant improvements (+38% nDCG@3) in document retrieval performance with facet-expanded queries.
A Countermeasure Based on CQT Spectrogram for Deepfake Speech Detection

Nowadays, biometrics like face, voice, fingerprint, and iris are widely used for the identity authentication of individuals. Automatic Speaker Verification (ASV) systems aim to verify the speaker&#x0027;s authenticity, but recent research has shown that they are vulnerable to various types of attacks. A large number of Text-To-Speech (TTS) and Voice Conversion (VC) methods are being used to create the so-called synthetic or deepfake speech. In recent years, numerous works have been proposed to improve the spoofing detection ability to protect ASV systems against these attacks. This work proposes a synthetic speech detection system, which uses the spectrogram of Constant Q Transform (CQT) as its input features. The CQT spectrogram provides a constant Q factor in different frequency regions similar to the human perception system. Also, compared with Short-Term Fourier Transform (STFT), CQT provides higher time resolution at higher frequencies and higher frequency resolution at lower frequencies. Additionally, the CQT spectrogram has brought us low input feature dimensions, which aids with reducing needed computation time. The Constant Q Cepstral Coefficients (CQCC) features, driven from cepstral analysis of the CQT, have been employed in some recent works for voice spoofing detection. However, to the best of our knowledge, ours is the first work using CQT magnitude and power spectrogram directly for voice spoofing detection. We also use a combination of self-attended ResNet and one class learning to provide our model the robustness against unseen attacks. Finally, it is observed that even though using input features with relatively lower dimensions and reducing computation time, we can still obtain EER 3.53&#x0025; and min t-DCF 0.10 on ASVspoof 2019 Logical Access (LA) dataset, which places our model among the top performers in this field.
Deep Partial Multiplex Network Embedding

Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most existing multiplex approaches assume that the data is complete in all views. But in real applications, it is often the case that each view suffers from the missing of some data and therefore results in partial multiplex data.
An Anchor-Free Method Based on Adaptive Feature Encoding and Gaussian-Guided Sampling Optimization for Ship Detection in SAR Imagery

Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships’ small size and confusable detail feature. This article proposes a novel anchor-free detection method composed of two modules to deal with these problems. First, for the lack of detailed information on small ships, we suggest an adaptive feature-encoding module (AFE), which gradually fuses deep semantic features into shallow layers and realizes the adaptive learning of the spatial fusion weights. Thus, it can effectively enhance the external semantics and improve the representation ability of small targets. Next, for the foreground–background imbalance, the Gaussian-guided detection head (GDH) is introduced according to the idea of soft sampling and exploits Gaussian prior to assigning different weights to the detected bounding boxes at different locations in the training optimization. Moreover, the proposed Gauss-ness can down-weight the predicted scores of bounding boxes far from the object center. Finally, the effect of the detector composed of the two modules is verified on the two SAR ship datasets. The results demonstrate that our method can effectively improve the detection performance of small ships in datasets.
Fibonacci tree structure based privacy preserving public auditing for IoT enabled data in cloud environment

Presently, Internet of Things (IoT) adopts cloud storage services to facilitate the efficient integration of IoT devices and the seamless management of collected data. Nevertheless, data owners have no direct control over outsourced data in the cloud storage model, leading to substantial data integrity issues. Earlier, many public auditing models have been introduced to ensure data integrity in the cloud environment. However, these models cannot guarantee - data dynamics with proper retrievability and privacy-preservation against trusted third party auditor (TPA) during auditing procedure. To address the limitations of public auditing models, we introduce a Fibonacci Tree (FT) structure-based efficient public auditing model supporting dynamic data updates with a standard security model. Specifically, Fibonacci Tree (FT) structure is designed to achieve real-time data updates with proper data traceability. The proposed model also includes an extended index-hash-table (E-IHT) located at TPA and records the data block's properties for efficient data dynamics and auditing procedure. The comprehensive security analysis with standard proof guarantees the computational intractability of the proposed model. At last, the experimental analysis demonstrates the feasibility and practicability of the proposed model.
Optimization of K-means image segmentation based on manta ray foraging algorithm

The traditional K-means clustering algorithm needs to preset the number of clusters and the initial center is uncertain. These problems lead to the unsatisfactory effect of using it for image segmentation. In view of this situation, this paper proposes to use manta ray foraging optimization algorithm (MRFO) to optimize k-means algorithm for image segmentation, The initial clustering center of K-means is determined by using the characteristics of MRFO to solve the problems that K-means are easy to be affected by noise and fall into local optimal solution. The image segmentation simulation experiments of K-means and MRFO are carried out by MATLAB, and the image segmentation performance is verified by three common image segmentation metrics: PSNR, SSIM and FSIM, Experiments show that using MRFO algorithm to optimize K-means clustering algorithm can achieve excellent results in image segmentation.
An Empirical Study of Solidity Language Features

Solidity, like other programming languages, is evolving with many new features being introduced and deprecated features being removed. However, not every feature benefits developers and sometimes it may do more harm than good. In the hope of knowing the actual use of language features and helping improving them, we summarize 41 common features, discuss their pros and cons, and conduct an empirical study. We design an extensible feature analysis tool to analyze their distribution in 172,645 real-world smart contracts. We further analyze the reason why some features are popular. We invite readers for a case study to understand the usage patterns and how seemingly benign features produce subtle bugs. It turns out that some features like high-level function invocations, visibility, events, and assertions are popular while developers cares little about some other features like function modifier overloading. After summarizing our findings, we give suggestions to different people in Solidity community.
Early detection of digital mammogram using kernel extreme learning machine

Abstract An automated computer‐assisted medical diagnosis that combines latest medical approaches and the advanced machine learning algorithms is a very crucial multidisciplinary technology, generating correct and noninvasive diagnoses of multiple diseases like breast cancer. The work proposed in this article focuses on the development of a biomedical computer‐assisted diagnosis model that can classify digital mammography as normal (healthy) or abnormal, and further, as malignant or benign. The proposed approach employs the discrete Chebyshev transform to extract the features. Then, the kernel principal component analysis technique is used to extract the discriminating features from the original feature vector. Subsequently, an optimized kernel extreme learning machine is proposed as a classifier to detect the tumors present in the mammographic images. Because the efficiency of the proposed classifier depends on its characteristic kernel variable, the main idea of the present work is to choose the most appropriate features from the downsized feature set and simultaneously obtain the optimized value of the aforementioned parameter. To validate the efficiency of the proposed work, the proposed scheme is performed on two publicly available data sets, namely the Mammographic Image Analysis Society data set and the INbreast data set. From the experimental analysis and its results, it is showed that for both normal–abnormal and malignant–benign classification, the proposed approach results in accuracy of 100% for the first data set. However, in the case of malignant–benign classification, the proposed approach gives an accuracy of 99.93% for the second data set. Further, it is also observed that the proposed approach exhibits highest performance as compared to that of the other approaches. Additionally, the ANOVA test is evaluated to demonstrate that the achievement of the proposed approach is significantly good than that of the other existing approaches.
Input-Latency Free Versatile Bit-Serial GF(2<sup> <i>m</i> </sup>) Polynomial Basis Multiplication

Cryptography and error correction codes are widely used for information security and data integrity services in modern digital computing and communications systems. A number of standardized and published cryptography, and error correction algorithms utilize arithmetic operations over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> . The polynomial basis (PB) representation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> elements is suitable for the support of multiple <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> fields (that is, versatility). This article considers the problem of reduced hardware efficiency of versatile <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> PB multipliers as a result of increased loading latency of the inputs and irreducible polynomial. In this context, to the best of our knowledge, this article proposes the first input-latency free versatile bit-serial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {GF}(2^{m})$ </tex-math></inline-formula> multiplier using PB. The superiority of the proposed versatile PB multiplier is demonstrated for the case where the multiplication’s inputs arrive serially in terms of higher output throughput and hardware efficiency compared to existing versatile PB and Gaussian normal basis (GNB) counterparts based on the application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) realizations, respectively. The proposed versatile PB multiplier improves hardware efficiency by factors of 1.8 and 2.5, respectively, compared to the versatile PB and versatile GNB counterparts.
Wi-Phrase: Deep Residual-Multihead Model for WiFi Sign Language Phrase Recognition

Sign language (SL) is used by hearing impaired and deaf people. The WiFi-based sign language recognition (SLR) technology has attracted much attention due to its contactless nature and wide applications. Most previous SLR works are designed to recognize a single isolated sign word in data samples. However, such assumption is not realistic in practical applications, such as, in daily communication, deaf people usually express their mind through a phrase (a.k.a. a group of words) rather than an isolated word. Compared with the previous works, the sign words in a phrase have variety of length, sequential patterns, and combinations in realistic communications between deaf people. Therefore, it is challenging in exploiting the WiFi signals to accurately capture the unique patterns of SL in the previous natural setting. In this article, we propose Wi-Phrase, a multigesture context-awareness SLR system. Wi-Phrase exploits WiFi signals to translate SL to the English phrase. To achieve this, Wi-Phrase employs principal component analysis (PCA) projection to filter out the noise and convert cleaned WiFi signals to spectrogram. Then, we propose a novel Residual-MultiHead model that exploit residual learn structure to obtain local patterns of phrases and adopt multihead block to capture the global context information of phrase. To prove the advanced nature of our model, we design a WiFi-based SL phrase data set of 40 categories for experiments. Our comprehensive evaluation shows that Wi-Phrase achieves an accurate phrase recognition accuracy of 95.03%. In future, we envision Wi-Phrase could be widely used as the phrase command control system for deaf people in IoT devices.
Named Entity Recognition for Addresses: An Empirical Study

In this paper, several model architectures are explored in order to design a high-performing named entity recognition model for addresses which deals with challenges such as diversity, ambiguity and complexity of the address entity. Different types of neural networks are used for training the classifier, including the bidirectional LSTM network in combination with a convolutional layer, a conditional random field layer and different word embeddings. Experiments are conducted on two types of corpora specifically constructed and tagged for tackling this challenge: unstructured and semi-structured datasets. For model evaluation, two versions of the unstructured dataset are used that are tagged differently based on the granularity of address entity: entire address, and address consisting of subparts. For both types of corpora, the best results are achieved on a BiLSTM-CRF architecture model with a single RNN layer trained with BERT embeddings.
Fabrication of surface ion traps with integrated current carrying wires enabling high magnetic field gradients

Abstract A major challenge for quantum computers is the scalable simultaneous execution of quantum gates. One approach to address this in trapped ion quantum computers is the implementation of quantum gates based on static magnetic field gradients and global microwave fields. In this paper, we present the fabrication of surface ion traps with integrated copper current carrying wires embedded inside the substrate below the ion trap electrodes, capable of generating high magnetic field gradients. The copper layer’s measured sheet resistance of 1.12 mΩ/sq at room temperature is sufficiently low to incorporate complex designs, without excessive power dissipation at high currents causing a thermal runaway. At a temperature of 40 K the sheet resistance drops to 20.9 μΩ/sq giving a lower limit for the residual resistance ratio of 100. Continuous currents of 13 A can be applied, resulting in a simulated magnetic field gradient of 144 T m −1 at the ion position, which is 125 μm from the trap surface for the particular anti-parallel wire pair in our design.
Discriminative Learning of Two-Dimensional Probabilistic Context-Free Grammars for Mathematical Expression Recognition and Retrieval

We present a discriminative learning algorithm for the estimation of two-dimensional Probabilistic Context-Free Grammars in the context of Mathematical Expressions Recognition and Retrieval. This algorithm is based on a generalization of the H-criterion, as the objective function, and the growth transformations as the optimization method. In addition, experimental results are reported on the Im2Latex-100k dataset, studying the performance of the estimated models depending on the length of the mathematical expressions and the number of admissible errors in the metric used.
Relational Memory-Augmented Language Models

Abstract We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model and a knowledge graph for more coherent and logical generation.