Reviewerly/exHarmony huggingface dataset:
ElectionBlock: An Electronic Voting System using Blockchain and Fingerprint Authentication

Voting is the method of choice used to make a large number of democratic decisions amongst many groups of people. Regardless of whether the method is used in professional or casual scenarios, it provides a fair and efficient way to determine a decision based on the majority. In smaller groups, keeping track of voter decisions is not a difficult task, however, in situations where there are hundreds of thousands of voters, keeping a precise record of voter decisions becomes important and more difficult. The advancements in blockchain technology provide a potential solution to the record-keeping problem of contemporary voting procedures, as blockchain technology by design, excels in applications where multiple users are working on immutable data. In this paper we discuss the design and development of ElectionBlock, a voting system that provides its own blockchain, running on a centralized network of nodes, with the integration of a biometric scanner, to maintain vote integrity and distinguish between registered and unregistered voters. This scheme allows data immutability while providing the user with security and control over their ballot. Experimental results demonstrate the potential for scalability of the system to handle a high volume of votes from multiple servers while maintaining data integrity, performance, and security. This paper will address the considerations taken to develop and implement the centralized and independent blockchain network for use as a voting platform with the integration of biometrics for the purpose of enhanced user security.
Multistream CNN for Robust Acoustic Modeling

This paper proposes multistream CNN, a novel neural network architecture for robust acoustic modeling in speech recognition tasks. The proposed architecture processes input speech with diverse temporal resolutions by applying different dilation rates to convolutional neural networks across multiple streams to achieve the robustness. The dilation rates are selected from the multiples of a sub-sampling rate of 3 frames. Each stream stacks TDNN-F layers (a variant of 1D CNN), and output embedding vectors from the streams are concatenated then projected to the final layer. We validate the effectiveness of the proposed multistream CNN architecture by showing consistent improvements against Kaldi’s best TDNN-F model across various data sets. Multistream CNN improves the WER of the test-other set in the LibriSpeech corpus by 12% (relative). On custom data from ASAPP’s production ASR system for a contact center, it records a relative WER improvement of 11% for customer channel audio to prove its robustness to data in the wild. In terms of real-time factor, multistream CNN outperforms the baseline TDNN-F by 15%, which also suggests its practicality on production systems. When combined with self-attentive SRU LM rescoring, multistream CNN contributes for ASAPP to achieve the best WER of 1.75% on test-clean in LibriSpeech.
A Divide-and-Conquer Genetic Programming Algorithm With Ensembles for Image Classification

Genetic programming (GP) has been applied to feature learning in image classification and achieved promising results. However, one major limitation of existing GP-based methods is the high computational cost, which may limit their applications on large-scale image classification tasks. To address this, this article develops a divide-and-conquer GP algorithm with knowledge transfer (KT) and ensembles to achieve fast feature learning in image classification. In the new algorithm framework, a divide-and-conquer strategy is employed to split the training data and the population into small subsets or groups to reduce computational time. A new KT method is proposed to improve GP learning performance. A new fitness function based on log loss and a new ensemble formulation strategy are developed to build an effective ensemble for image classification. The performance of the proposed approach has been examined on 12 image classification datasets of varying difficulty. The results show that the new approach achieves better classification performance in significantly less computation time than the baseline GP-based algorithm. The comparisons with state-of-the-art algorithms show that the new approach achieves better or comparable performance in almost all the comparisons. Further analysis demonstrates the effectiveness of ensemble formulation and KT in the proposed approach.
Systematic Literature Review on the Use of Trusted Execution Environments to Protect Cloud/Fog-Based Internet of Things Applications

Trusted Execution Environments have been applied to improve data security in many distinct application scenarios since they enable data processing in a separate and protected region of memory. To investigate how this technology has been applied to the different IoT scenarios, which commonly deal with specific characteristics such as device resource constraints, we carried out a systematic literature review. For this, we selected and analyzed 58 papers from different conferences and journals, identifying the main IoT solutions and scenarios in which TEE has been employed. We also gathered the mentioned TEE advantages and disadvantages as well as the suggestions for future works. This study gives a general overview of the use of TEEs for cloud/fog-based IoT applications, bringing some challenges and directions.
High-order error function designs to compute time-varying linear matrix equations

This paper devotes to solving time-varying linear matrix equations (TVLMEs) from the viewpoint of high-order neural networks. For this purpose, high-order zeroing neural network (ZNN) models are designed and studied to solve TVLMEs. Compared with the first-order ZNN model for TVLMEs, the proposed high-order ZNN models are based on the design of the high-order error functions, and different order choices will generate different high-order ZNN models. Two nonlinear activation functions [i.e., tunable activation function (TunAF) and sign-bi-power activation function (SBPAF)] are used to speedup the high-order ZNN models for achieving the finite-time convergence. Furthermore, the strict theoretical analyses are provided to show that high-order ZNN models have better properties (especially in terms of convergence), when the nonlinear activation functions are used. Two numerical simulations are given to reveal the superior convergence property of the proposed high-order ZNN models, as compared to the first-order ZNN model for solving TVLMEs.
On the Gap between Adoption and Understanding in NLP

There are some issues with current research trends in NLP that can hamper the free development of scientific research.We identify five of particular concern: 1) the early adoption of methods without sufficient understanding or analysis; 2) the preference for computational methods regardless of risks associated with their limitations; 3) the resulting bias in the papers we publish; 4) the impossibility of re-running some experiments due to their cost; 5) the dangers of unexplainable methods.If these issues are not addressed, we risk a loss of reproducibility, reputability, and subsequently public trust in our field.In this position paper, we outline each of these points and suggest ways forward.
Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction

An essential operation in web corpus construction consists in retaining the desired content while discarding the rest.Another challenge finding one's way through websites.This article introduces a text discovery and extraction tool published under open-source license.Its installation and use is straightforward, notably from Python and on the command-line.The software allows for main text, comments and metadata extraction, while also providing building blocks for web crawling tasks.A comparative evaluation on real-world data also shows its interest as well as the performance of other available solutions.The contributions of this paper are threefold: it references the software, features a benchmark, and provides a meaningful baseline for similar tasks.The tool performs significantly better than other open-source solutions in this evaluation and in external benchmarks.
Real-Time Super-Resolution System of 4K-Video Based on Deep Learning

Video super-resolution (VSR) technology excels in reconstructing low-quality video, avoiding unpleasant blur effect caused by interpolation-based algorithms. However, vast computation complexity and memory occupation hampers the edge of deplorability and the runtime inference in real-life applications, especially for large-scale VSR task. This paper explores the possibility of real-time VSR system and designs an efficient and generic VSR network, termed EGVSR. The proposed EGVSR is based on spatio-temporal adversarial learning for temporal coherence. In order to pursue faster VSR processing ability up to 4K resolution, this paper tries to choose lightweight network structure and efficient upsampling method to reduce the computation required by EGVSR network under the guarantee of high visual quality. Besides, we implement the batch normalization computation fusion, convolutional acceleration algorithm and other neural network acceleration techniques on the actual hardware platform to optimize the inference process of EGVSR network. Finally, our EGVSR achieves the real-time processing capacity of 4K@29.61FPS. Compared with TecoGAN, the most advanced VSR network at present, we achieve 85.04% reduction of computation density and 7.92× performance speedups. In terms of visual quality, the proposed EGVSR tops the list of most metrics (such as LPIPS, tOF, tLP, etc.) on the public test dataset Vid4 and surpasses other state-of-the-art methods in overall performance score.
Speech Drives Templates: Co-Speech Gesture Synthesis with Learned Templates

Co-speech gesture generation is to synthesize a gesture sequence that not only looks real but also matches with the input speech audio. Our method generates the movements of a complete upper body, including arms, hands, and the head. Although recent data-driven methods achieve great success, challenges still exist, such as limited variety, poor fidelity, and lack of objective metrics. Motivated by the fact that the speech cannot fully determine the gesture, we design a method that learns a set of gesture template vectors to model the latent conditions, which relieve the ambiguity. For our method, the template vector determines the general appearance of a generated gesture sequence, while the speech audio drives subtle movements of the body, both indispensable for synthesizing a realistic gesture sequence. Due to the intractability of an objective metric for gesturespeech synchronization, we adopt the lip-sync error as a proxy metric to tune and evaluate the synchronization ability of our model. Extensive experiments show the superiority of our method in both objective and subjective evaluations on fidelity and synchronization. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Compressing Many-Body Fermion Operators under Unitary Constraints

The most efficient known quantum circuits for preparing unitary coupled cluster states and applying Trotter steps of the arbitrary basis electronic structure Hamiltonian involve interleaved sequences of Fermionic Gaussian circuits and Ising interaction-type circuits. These circuits arise from factorizing the two-body operators generating those unitaries as a sum of squared one-body operators that are simulated using product formulas. We introduce a numerical algorithm for performing this factorization that has an iteration complexity no worse than single particle basis transformations of the two-body operators and often results in many times fewer squared one-body operators in the sum of squares, compared to the analytical decompositions. As an application of this numerical procedure, we demonstrate that our protocol can be used to approximate generic unitary coupled cluster operators and prepare the necessary high-quality initial states for techniques (like ADAPT-VQE) that iteratively construct approximations to the ground state.
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels

The success of deep convolutional neural networks (DCNNs) benefits from high volumes of annotated data. However, annotating medical images is laborious, expensive, and requires human expertise, which induces the label scarcity problem. Especially When encountering the domain shift, the problem becomes more serious. Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate the label paucity problem on the target domain, the conventional UDA methods suffer from severe performance degradation when source domain annotations are scarce. In this paper, we explore a challenging UDA setting - limited source domain annotations. We aim to investigate how to efficiently leverage unlabeled data from the source and target domains with limited source annotations for cross-modality image segmentation. To achieve this, we propose a new label-efficient UDA framework, termed MT-UDA, in which the student model trained with limited source labels learns from unlabeled data of both domains by two teacher models respectively in a semi-supervised manner. More specifically, the student model not only distills the intra-domain semantic knowledge by encouraging prediction consistency but also exploits the inter-domain anatomical information by enforcing structural consistency. Consequently, the student model can effectively integrate the underlying knowledge beneath available data resources to mitigate the impact of source label scarcity and yield improved cross-modality segmentation performance. We evaluate our method on MM-WHS 2017 dataset and demonstrate that our approach outperforms the state-of-the-art methods by a large margin under the source-label scarcity scenario.
Constant-Overhead Quantum Error Correction with Thin Planar Connectivity

Quantum low density parity check (LDPC) codes may provide a path to build low-overhead fault-tolerant quantum computers. However, as general LDPC codes lack geometric constraints, naïve layouts couple many distant qubits with crossing connections which could be hard to build in hardware and could result in performance-degrading crosstalk. We propose a 2D layout for quantum LDPC codes by decomposing their Tanner graphs into a small number of planar layers. Each layer contains long-range connections which do not cross. For any Calderbank-Shor-Steane code with a degree-δ Tanner graph, we design stabilizer measurement circuits with depth at most (2δ+2) using at most ⌈δ/2⌉ layers. We observe a circuit-noise threshold of 0.28% for a positive-rate code family using 49 physical qubits per logical qubit. For a physical error rate of 10^{-4}, this family reaches a logical error rate of 10^{-15} using fourteen times fewer physical qubits than the surface code.
SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems

Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are memory-bound and thus could be accelerated by hardware techniques such as Processing-in-Memory (PIM). However, they also come with non-straightforward parallelism and complicated memory access patterns. In this work, we address this problem with a simple yet surprisingly powerful observation: operations on sets of vertices, such as intersection or union, form a large part of many complex graph mining algorithms, and can offer rich and simple parallelism at multiple levels. This observation drives our cross-layer design, in which we (1) expose set operations using a novel programming paradigm, (2) express and execute these operations efficiently with carefully designed set-centric ISA extensions called SISA, and (3) use PIM to accelerate SISA instructions. The key design idea is to alleviate the bandwidth needs of SISA instructions by mapping set operations to two types of PIM: in-DRAM bulk bitwise computing for bitvectors representing high-degree vertices, and near-memory logic layers for integer arrays representing low-degree vertices. Set-centric SISA-enhanced algorithms are efficient and outperform hand-tuned baselines, offering more than 10 × speedup over the established Bron-Kerbosch algorithm for listing maximal cliques. We deliver more than 10 SISA set-centric algorithm formulations, illustrating SISA’s wide applicability.
Detection of Abnormal Vibration Dampers on Transmission Lines in UAV Remote Sensing Images with PMA-YOLO

The accurate detection and timely replacement of abnormal vibration dampers on transmission lines are critical for the safe and stable operation of power systems. Recently, unmanned aerial vehicles (UAVs) have become widely used to inspect transmission lines. In this paper, we constructed a data set of abnormal vibration dampers (DAVDs) on transmission lines in images obtained by UAVs. There are four types of vibration dampers in this data set, and each vibration damper may be rusty, defective, or normal. The challenges in the detection of abnormal vibration dampers on transmission lines in the images captured by UAVs were as following: the images had a high resolution as well as the objects of vibration dampers were relatively small and sparsely distributed, and the backgrounds of cross stage partial networks of the images were complex due to the fact that the transmission lines were erected in a variety of outdoor environments. Existing methods of ground-based object detection significantly reduced the accuracy when dealing with complex backgrounds and small objects of abnormal vibration dampers detection. To address these issues, we proposed an end-to-end parallel mixed attention You Only Look Once (PMA-YOLO) network to improve the detection performance for abnormal vibration dampers. The parallel mixed attention (PMA) module was introduced and integrated into the YOLOv4 network. This module combines a channel attention block and a spatial attention block, and the convolution results of the input feature maps in parallel, allowing the network to pay more attention to critical regions of abnormal vibration dampers in complex background images. Meanwhile, in view of the problem that abnormal vibration dampers are prone to missing detections, we analyzed the scale and ratio of the ground truth boxes and used the K-means algorithm to re-cluster new anchors for abnormal vibration dampers in images. In addition, we introduced a multi-stage transfer learning strategy to improve the efficiency of the original training method and prevent overfitting by the network. The experimental results showed that the mAP@0.5 for PMA-YOLO in the detection of abnormal vibration dampers reached 93.8% on the test set of DAVD, 3.5% higher than that of YOLOv4. When the multi-stage transfer learning strategy was used, the mAP@0.5 was improved by a further 0.2%.
Video Salient Object Detection via Contrastive Features and Attention Modules

Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neural networks or optical flow. However, these approaches require high computational cost, and tend to accumulate inaccuracies over time. In this paper, we propose a network with attention modules to learn contrastive features for video salient object detection without the high computational temporal modeling techniques. We develop a non-local self-attention scheme to capture the global information in the video frame. A co-attention formulation is utilized to combine the low-level and high-level features. We further apply the contrastive learning to improve the feature representations, where foreground region pairs from the same video are pulled together, and foreground-background region pairs are pushed away in the latent space. The intra-frame contrastive loss helps separate the foreground and background features, and the inter-frame contrastive loss improves the temporal consistency. We conduct extensive experiments on several benchmark datasets for video salient object detection and unsupervised video object segmentation, and show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
Product Reviews: A Benefit, a Burden, or a Trifle? How Seller Reputation Affects the Role of Product Reviews

The sales effect of product reviews has been a contentious issue with competing perspectives about when product reviews serve as a benefit, a burden, or a trifle. Unlike previous research that separately investigates the impact of each eWOM system, our study empirically examines the interaction effects of dual eWOM systems, i.e., product reviews and seller reputation. Drawing on reference point theory, we find that seller reputation systems play a reference-point role and determine the efficacy of product reviews. Specifically, negative reviews cause a significant loss in sales for high-reputation sellers but are less detrimental for low-reputation sellers. In contrast, positive reviews can boost sales for low-reputation sellers but are less helpful for high-reputation sellers. These results highlight that seller reputation is a double-edged sword. While a high seller reputation can reduce seller uncertainty and attract more consumers, it may also raise consumers’ expectations and lead to potential negative expectancy violations. Moreover, we explore what strategies may help mitigate the potentially detrimental effect of reference points for high-reputation sellers. Through the lens of restructuring reference points, the reputation reference effect can be adjusted in a more dynamic reputation system (e.g., a reputation badge). Compared to sellers that have never lost their top-rated badge, sellers that have lost their top-rated badge may face an attenuated detrimental impact on sales from the negative expectancy violation due to negative reviews and enjoy a positive impact from positive reviews. We discuss the implications of our findings for both theory and practice.