Reviewerly/exHarmony huggingface dataset:
Speaker identification in courtroom contexts – Part I: Individual listeners compared to forensic voice comparison based on automatic-speaker-recognition technology

Expert testimony is only admissible in common law if it will potentially assist the trier of fact to make a decision that they would not be able to make unaided. The present paper addresses the question of whether speaker identification by an individual lay listener (such as a judge) would be more or less accurate than the output of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology. Listeners listen to and make probabilistic judgements on pairs of recordings reflecting the conditions of the questioned- and known-speaker recordings in an actual case. Reflecting different courtroom contexts, listeners with different language backgrounds are tested: Some are familiar with the language and accent spoken, some are familiar with the language but less familiar with the accent, and others are less familiar with the language. Also reflecting different courtroom contexts: In one condition listeners make judgements based only on listening, and in another condition listeners make judgements based on both listening to the recordings and considering the likelihood-ratio values output by the forensic-voice-comparison system.
Unbiased feature generating for generalized zero-shot learning

Generalized zero-shot learning (GZSL) aims at training a model on seen data to recognize objects from both seen and unseen classes. Existing generated-based methods show encouraging performance by directly generating unseen samples. However, due to insufficient exploration of unseen label space and limited class-wise semantic descriptions, existing methods still face the bias problem. In this paper, we divide the bias problem into seen-biased and neighbor-biased problems and propose a GZSL method named Unbiased Feature Generating. For the seen-biased problem, we train a classifier in complete label space by introducing the discriminative information contained in fake unseen samples. For the neighbor-biased problem, we generate untypical samples and refine the classification boundaries among neighbor classes. The classifier in complete label space and generator are trained in an iterative process to complement each other. The experimental results on four widely used datasets verify our method achieves encouraging performance compared with the state-of-the-art methods.
XOR-Based Meaningful (n, n) Visual Multi-Secrets Sharing Schemes

The basic visual cryptography (VC) model was proposed by Naor and Shamir in 1994. The secret image is encrypted into pieces, called shares, which can be viewed by collecting and directly stacking these shares. Many related studies were subsequently proposed. The most recent advancement in visual cryptography, XOR-based VC, can address the issue of OR-based VC’s poor image quality of the restored image by lowering hardware costs. Simultaneous sharing of multiple secret images can reduce computational costs, while designing shared images into meaningful unrelated images helps avoid attacks and is easier to manage. Both have been topics of interest to many researchers in recent years. This study suggests ways for XOR-based VCS that simultaneously encrypts several secret images and makes each share separately meaningful. Theoretical analysis and experimental results show that our methods are secure and effective. Compared with previous schemes, our scheme has more capabilities.
Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion

Although algorithms make more accurate forecasts than humans in many applications, decision-makers often refuse to resort to their use. In an economic experiment, we examine whether the extent of this phenomenon known as algorithm aversion can be reduced by granting decision-makers the possibility to exert an influence on the configuration of the algorithm (an influence on the algorithmic input). In addition, we replicate the study carried out by Dietvorst et al. (2018). This shows that algorithm aversion recedes significantly if the subjects can subsequently change the results of the algorithm—and even if this is only by a small percentage (an influence on the algorithmic output). The present study confirms that algorithm aversion is reduced significantly when there is such a possibility to influence the algorithmic output. However, exerting an influence on the algorithmic input seems to have only a limited ability to reduce algorithm aversion. A limited opportunity to modify the algorithmic output thus reduces algorithm aversion more effectively than having the ability to influence the algorithmic input.
Speculative approaches in social science and design research: Methodological implications of working in ‘the gap’ of uncertainty

Recent studies in design research and science and technology studies (STS) have investigated how speculative thinking might be applied in empirical contexts. A unifying feature of speculative approaches has been an interest in futures as mediated, shaped and conditioned by science and technology. Yet concrete methodological conditions of speculative research events remain under-explored. There is a need for a more nuanced understanding of speculation ‘as it works’. While speculation does not constitute a unified method or analytical grid with a defined set of elements, speculative research is not innocently playful or free of methodological constraints. Speculation denotes here philosophically driven knowledge production conducted with research participants on science, technology and futures. Based on experiences of two social science and two design research cases in cellular reprogramming and genomic engineering, we illustrate and theorize our methodological observations on what takes place in speculative practices with participants. Drawing on Whitehead’s and Stengers’ conceptual work on experiential practices of knowing, we develop the concept of ‘the gap’ to describe the mode of speculative engagement that shapes concrete relations and positioning in research events. Contingent and situated, the gap of speculative action builds on openness, uncertainty and hesitation. Achieving the gap is the aim of speculative engagement and also a methodologically elusive, risky part of the study process. The concept of the gap helps illustrate what researchers ask from participants in the name of speculative openness, and how participants position themselves in these encounters. It allows us to highlight how participants, in turn, invite researchers to reposition themselves and demand experiential involvement that may reconfigure the course of the study.
Product Authentication Technology Integrating Blockchain and Traceability Structure

To tackle the problems of questioning the authenticity of products faced in e-commerce live streaming and the low data integrity in the process of product transactions, the concept of traceability and a product authentication scheme that integrates blockchain technology and traceability are proposed. The proposed scheme deploys the product authentication strategy on the Ethereum blockchain in the form of a smart contract and calls the product authentication method on the traceability body by executing the smart contract to realize the authentication of product quality. A combination of on-chain and off-chain is used to store private information to guarantee information integrity. Security analysis results show that the proposed scheme can make the risk of information leakage lower and data security higher under the premise of achieving product certification. A system simulation experiment shows that the solution can meet product certification requirements and have a favorable impact on the live commerce industry.
Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Effective utilization of premium hospital resources such as intensive care unit (ICU), operating theater (OT), mechanical ventilator, endotracheal tube, and so on plays a significant role in providing high-quality care to critically ill patients within reasonable costs. Non-availability of specialized resources can lead to dire consequences for such patients, and in the worst case, may even turn out to be fatal. However, these resources cannot be kept idle, as they are expensive to maintain. Therefore, one of the core functions of hospital management is targeted at planning and managing these critical resources in order to provide efficient and effective health-care services to the end-users. Predictive technologies play a big role in this. In this article, we present methods for predicting the length of stay in ICU as well as the need for critical interventions for a patient based on the vital signs, laboratory measurements, and the nursing notes of the patient prepared within the first 24 h of ICU stay. The model has been built and cross-validated on the publicly available Medical Information Mart for Intensive Care (MIMIC-III v1.4) data set. We show that the proposed model performs way better than most of the earlier models in the prediction of ICU stay, which had used patient vitals primarily. Experimental results also demonstrate the advantage of using a multiobjective model over independent models for the prediction of ICU stay and critical interventions. The proposed model uses Local Interpretable Model-agnostic Explanations (LIME) that help in identifying the features responsible for predictive decisions. This is very useful in building trust and confidence in the prediction model among clinical practitioners.
Dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning

Abstract Sequence-wise recommendation, where recommend exercises to each student step by step, is one of the most exciting tasks in the field of intelligent tutoring systems (ITS). It is important to develop a personalized sequence-wise recommendation framework that immerses students in learning and helps them acquire as much necessary knowledge as possible, rather than merely focusing on providing non-mastered exercises, which is referred to optimize a single objective. However, due to the different knowledge levels of students and the large scale of exercise banks, it is difficult to generate a personalized exercise recommendation for each student. To fully exploit the multifaceted beneficial information collected from e-learning platforms, we design a dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning, i.e., DMoSwR-DRL, which automatically select the most suitable exercises for each student based on the well-designed domain-objective rewards. Within this framework, the interaction between students and exercises can be explicitly modeled by integrating the actor–critic network and the state representation component, which can greatly help the agent perform effective reinforcement learning. Specifically, we carefully design a state representation module with dynamic recurrent mechanism, which integrates concept information and exercise difficulty level, thus generating a continuous state representation of the student. Subsequently, a flexible reward function is designed to simultaneously optimize the four domain-specific objectives of difficulty, novelty, coverage, and diversity, providing the students with a trade-off sequence-wise recommendation. To set up the online evaluation, we test DMoSwR-DRL on a simulated environment which can model qualitative development of knowledge level and predicts their performance for a given exercise. Comprehensive experiments are conducted on four classical exercise-answer datasets, and the results show the effectiveness and advantages of DMoSwR-DRL in terms of recommendation quality.
Multi-scale and multi-patch transformer for sandstorm image enhancement

Sandstorm is a meteorological phenomenon common in arid and semi-arid regions. A sandstorm can carry large volumes of sand unexpectedly, which leads to severe color deviations and significantly degraded visibility when an image is taken in such a scenario. However, existing image enhancement methods cannot enhance sandstorm images well due to the challenging degradations and the scarcity of sandstorm training data. In this paper, we propose a Transformer with rotary position embedding to perform sandstorm image enhancement via building multi-scale and multi-patch dependencies. Our key insights in this work are 1) a multi-scale Transformer can globally eliminate the color deviations of sandstorm images via aggregating global information, 2) a multi-patch Transformer can recover local details well via learning the spatial variant degradations, and 3) a U-shape Transformer with rotary position embedding as the core unit of multi-scale and multi-patch Transformer can effectively build the long-range dependencies. We also contribute a real-world Sandstorm Image Enhancement ( SIE ) dataset including 1,400 sandstorm images with different degrees of degradations and various scenes. Experiments performed on synthetic images and real-world sandstorm images demonstrate that our proposed method not only obtains visually pleasing results but also outperforms state-of-the-art methods qualitatively and quantitatively.
Multi-Object Detection in Security Screening Scene Based on Convolutional Neural Network

The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a coupled multi-scale feature extraction and multi-scale attention architecture. We integrate this architecture into the Single Shot MultiBox Detector (SSD) algorithm and find that it can significantly improve the effectiveness of target detection. Firstly, ResNet is used as the backbone network to replace the original VGG network to improve the feature extraction capability of the convolutional neural network for images. Secondly, a multi-scale feature extraction (MSE) structure is designed to enrich the information contained in the multi-stage prediction feature layer. Finally, the multi-scale attention architecture (MSA) is fused onto the prediction feature layer to eliminate the redundant features' interference and extract effective contextual information. In addition, a combination of Adaptive-NMS and Soft-NMS is used to output the final prediction anchor boxes when performing non-maximum suppression. The results of the experiments show that the improved method improves the mean average precision (mAP) value by 7.4% compared to the original approach. New modules make detection much more accurate while keeping the detection speed the same.
LssDet: A Lightweight Deep Learning Detector for SAR Ship Detection in High-Resolution SAR Images

Synthetic aperture radar (SAR) ship detection has been the focus of many previous studies. Traditional SAR ship detectors face challenges in complex environments due to the limitations of manual feature extraction. With the rise of deep learning (DL) techniques, SAR ship detection based on convolutional neural networks (CNNs) has achieved significant achievements. However, research on CNN-based SAR ship detection has mainly focused on improving detection accuracy, and relatively little research has been conducted on reducing computational complexity. Therefore, this paper proposes a lightweight detector, LssDet, for SAR ship detection. LssDet uses Shufflenet v2, YOLOX PAFPN and YOLOX Decopuled Head as the baseline networks, improving based on the cross sidelobe attention (CSAT) module, the lightweight path aggregation feature pyramid network (L-PAFPN) module and the Focus module. Specifically, the CSAT module is an attention mechanism that enhances the model’s attention to the cross sidelobe region and models the long-range dependence between the channel and spatial information. The L-PAFPN module is a lightweight feature fusion network that achieves excellent performance with little computational effort and a low parametric count. The Focus module is a low-loss feature extraction structure. Experiments showed that on the Sar ship detection dataset(SSDD), LssDet’s computational cost was 2.60 GFlops, the model’s volume was 2.25 M and AP@[0.5:0.95] was 68.1%. On the Large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0), LssDet’s computational cost was 4.49 GFlops, the model’s volume was 2.25 M and AP@[0.5:0.95] was 27.8%. Compared to the baseline network, LssDet had a 3.6% improvement in AP@[0.5:0.95] on the SSDD, and LssDet had a 1.5% improvement in AP@[0.5:0.95] on the LS-SSDD-v1.0. At the same time, LssDet reduced Floating-point operations per second (Flops) by 7.1% and Paraments (Params) by 23.2%. Extensive experiments showed that LssDet achieves excellent detection results with minimal computational complexity. Furthermore, we investigated the effectiveness of the proposed module through ablation experiments.
Energy-Based Adversarial Example Detection for SAR Images

Adversarial examples (AEs) bring increasing concern on the security of deep-learning-based synthetic aperture radar (SAR) target recognition systems. SAR AEs with perturbation constrained to the vicinity of the target have been recently in the spotlight due to the physical realization prospects. However, current adversarial detection methods generally suffer severe performance degradation against SAR AEs with region-constrained perturbation. To solve this problem, we treated SAR AEs as low-probability samples incompatible with the clean dataset. With the help of energy-based models, we captured an inherent energy gap between SAR AEs and clean samples that is robust to the changes of the perturbation region. Inspired by this discovery, we propose an energy-based adversarial detector, which requires no modification to a pretrained model. To better distinguish the clean samples and AEs, energy regularization was adopted to fine-tune the pretrained model. Experiments demonstrated that the proposed method significantly boosts the detection performance against SAR AEs with region-constrained perturbation.
A comparison of automated and manual analyses of syntactic complexity in L2 English writing

Abstract Automated tools for syntactic complexity measurement are increasingly used for analyzing various kinds of second language corpora, even though these tools were originally developed and tested for texts produced by advanced learners. This study investigates the reliability of automated complexity measurement for beginner and lower-intermediate L2 English data by comparing manual and automated analyses of a corpus of 80 texts written by Dutch-speaking learners. Our quantitative and qualitative analyses reveal that the reliability of automated complexity measurement is substantially affected by learner errors, parser errors, and Tregex pattern undergeneration. We also demonstrate the importance of aligning the definitions of analytical units between the computational tool and human annotators. In order to enhance the reliability of automated analyses, it is recommended that certain modifications are made to the system, and non-advanced L2 English data are preprocessed prior to automated analyses.
P‐2.8: Research on multi‐user interaction design in augmented reality

Augmented reality, as an expanded branch of virtual reality, is different from traditional virtual reality technology to achieve full immersion virtual environment, it can superimpose virtual objects into the real environment. The user can wear the display device (HoloLens2) to support the interaction of real and virtual objects. Existing devices can basically realize the three technical requirements of augmented reality technology, namely virtual and real fusion, real‐time interaction. Augmented reality technology has been increasingly applied in the fields of medical treatment, education, entertainment, simulation design and so on. In view of the above characteristics and requirements of multi‐user collaborative interaction, it is not difficult to see that although these principles are not the same, they all emphasize the support of interaction between users.
HVAE: A deep generative model via hierarchical variational auto-encoder for multi-view document modeling

With the widespread development of the internet, multi-view text documents have become increasingly common, which has led to extensive research on multi-view text document modeling. As opposed to traditional single-view document modeling, which treats each document independently and learns each document as a single topic representation, the views of multi-view text documents have complicated correlation relationships that include both the global and local underlying topical information. In this study, we introduce a deep generative model for multi-view document modeling known as Hierarchical Variational Auto-Encoder (HVAE), which combines the advantages of the probability generative model for learning interpretable latent information and the deep neural network for efficient parameter inference. Specifically, a set of hierarchical topic representations is learned for each multi-view document to capture the document-level global topical information and view-level local topical information for each view. A two-level hierarchical topic inference network is investigated as the encoder network of HVAE, which is designed using an aligned variational auto-encoder, to learn the hierarchical topic representations. Subsequently, multi-view documents are generated through a two-layered generation network, considering both the view-level local and document-level global topic representations. Experiments on three real datasets of different scales for various tasks demonstrate the satisfactory results of the proposed method.
Threshold EdDSA Signature for Blockchain-based Decentralized Finance Applications

The threshold digital signature technique is important for decentralized finance (DeFi) applications such as asset custody and cross-chain interoperations. The Edwards-curve digital signature algorithm (EdDSA) is widely used in blockchains, e.g., Libra/Diem; however, no suitable threshold solution exists. Therefore, to bridge this gap, we propose a threshold EdDSA that allows n parties to generate keys in a decentralized and distributed manner. Any t + 1-of-n parties can generate standard EdDSA signatures. This scheme supports an arbitrary threshold (t, n) and has been proven to be secure against at most t malicious adversaries. The theoretical analysis (computation complexity and communication footprints) and experimental results demonstrate that the proposed scheme performs efficiently on cloud servers and embedded devices. Furthermore, the proposed scheme is integrated with Tendermint, a blockchain framework that uses EdDSA, to generate keys and sign transactions in a decentralized manner, which indicates that this scheme is compatible with blockchains for supporting DeFi applications.