Reviewerly/exHarmony huggingface dataset:
Theoretical analysis and comparative study of top 10 optimization algorithms with DMS algorithm

The significance of big data are prone to complication in solving optimization issues. In several scenarios, one requires adapting several contradictory goals and satisfies various criterions. This made the research on multi-objective optimization more vital and has become main topic. This paper presents theoretical analysis and comparative study of top ten optimization algorithms with respect to DMS. The performance analysis and study of optimization algorithms in big data streaming are explicated. Here, the top ten algorithms of optimization based on recency and popularity are considered. In addition, the performance analysis based on Efficiency, Reliability, Quality of solution, and superiority of DMS algorithm over other top 10 algorithms are examined. From analysis, the DMS provides better efficiency as it endeavours less computational effort to generate better solution, due to acquisition of both DA and MS algorithm’s benefits and DMS takes less time to process a task. Moreover, the DMS needs less number of iterations in the process of optimization and helps to stop optimization process in local optimum. In addition, the DMS has better reliability as it poses the potential to handle specific level of performance. In addition, the DMS utilizes heuristic information for attaining high reliability. Moreover, the DMS produced high computation accuracy, which reveals its solution quality. From the analysis, it is noted that DMS attained improved outcomes in terms of efficiency, reliability and solution quality in contrast to other top 10 optimization algorithms.
Explaining Digital Technology: Digital Artifact Delineation and Coalescence

The literature pertaining to digital transformation, digital innovation, digital infrastructure, and digital platforms heavily relies on digital technology. Information Systems (IS) literature defines digital technologies as technologies that incorporate bitstrings (digital object). However, we contend that digital technologies are complex, multilayered information systems that surpass the simplicity of bitstrings. Bitstrings have existed since the inception of computers, and the contemporary use of bitstrings in IS literature is inconsistent with Computer Science and Engineering literature. We theorize artifact delineation—the capacity to provide unique interfaces and capabilities to distinct users—and coalescence—the ability to virtually integrate multiple IT architectural layers of complex information systems to conceal the details from users—to extend the core on digital technology, but more importantly to provide a better understanding of the complex nature of digital technologies for IS researchers and other domains that borrow from IS.
RETAD

Due to the rapid advancement of wireless sensor and location technologies, a large amount of mobile agent trajectory data has become available. Intelligent city systems and video surveillance all benefit from trajectory anomaly detection. The authors propose an unsupervised reconstruction error-based trajectory anomaly detection (RETAD) method for vehicles to address the issues of conventional anomaly detection, which include difficulty extracting features, are susceptible to overfitting, and have a poor anomaly detection effect. RETAD reconstructs the original vehicle trajectories through an autoencoder based on recurrent neural networks. The model obtains moving patterns of normal trajectories by eliminating the gap between the reconstruction results and the initial inputs. Anomalous trajectories are defined as those with a reconstruction error larger than anomaly threshold. Experimental results demonstrate that the effectiveness of RETAD in detecting anomalies is superior to traditional distance-based, density-based, and machine learning classification algorithms on multiple metrics.
CamRadar

Hidden cameras in sensitive locations have become an increasing threat to personal privacy all over the world. Because the camera is small and camouflaged, it is difficult to detect the presence of the camera with naked eyes. Existing works on this subject have either only covered using wireless transmission to detect cameras, or using other methods which are cumbersome in practical use. In this paper, we introduce a new direction that leverages the unintentional electromagnetic (EM) emanations of the camera to detect it. We first find that the digital output of the camera's image sensor will be amplitude-modulated to the EM emanations of the camera's clock. Thus, changes in the scope of the camera will directly cause changes in the camera's EM emanations, which constitutes a unique characteristic for a hidden camera. Based on this, we propose a novel camera detection system named CamRadar, which can filter out potential camera EM emanations from numerous EM signals quickly and achieve accurate hidden camera detection. Benefitting from the camera's EM emanations, CamRadar will not be limited by the camera transmission types or the detection angle. Our extensive real-world experiments using CamRadar and 19 hidden cameras show that CamRadar achieves a fast detection (in 16.75s) with a detection rate of 93.23% as well as a low false positive rate of 3.95%.
I Spy You

This paper presents iSpyU, a system that shows the feasibility of recognition of natural speech content played on a phone during conference calls (Skype, Zoom, etc) using a fusion of motion sensors such as accelerometer and gyroscope. While microphones require permissions from the user to be accessible by an app developer, the motion sensors are zero-permission sensors, thus accessible by a developer without alerting the user. This allows a malicious app to potentially eavesdrop on sensitive speech content played by the user's phone. In designing the attack, iSpyU tackles a number of technical challenges including: (i) Low sampling rate of motion sensors (500 Hz in comparison to 44 kHz for a microphone). (ii) Lack of availability of large-scale training datasets to train models for Automatic Speech Recognition (ASR) with motion sensors. iSpyU systematically addresses these challenges by a combination of techniques in synthetic training data generation, ASR modeling, and domain adaptation. Extensive measurement studies on modern smartphones show a word level accuracy of 53.3 - 59.9% over a dictionary of 2000-10000 words, and a character level accuracy of 70.0 - 74.8%. We believe such levels of accuracy poses a significant threat when viewed from a privacy perspective.
A Scalable Gaussian Process for Large-Scale Periodic Data

The periodic Gaussian process (PGP) has been increasingly used to model periodic data due to its high accuracy. Yet, computing the likelihood of PGP has a high computational complexity of $\mathcal{O}\left(n^{3}\right)$ ($n$ is the data size), which hinders its wide application. To address this issue, we propose a novel circulant PGP (CPGP) model for large-scale periodic data collected at grids that are commonly seen in signal processing applications. The proposed CPGP decomposes the log-likelihood of PGP into the sum of two computationally scalable composite log-likelihoods, which do not involve any approximations. Computing the likelihood of CPGP requires only $\mathcal{O}\left(p^{2}\right)$ (or $\mathcal{O}\left(p\log p\right)$ in some special cases) time for grid observations, where the segment length $p$ is independent of and much smaller than $n$. Simulations and real case studies are presented to show the superiority of CPGP over some state-of-the-art methods, especially for applications requiring periodicity estimation. This new modeling technique can greatly advance the applicability of PGP in many areas and allow the modeling of many previously intractable problems.
Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features

Deep learning models, with billions of parameters, have a great potential for improving our daily life activities, particularly in assisting the diagnosis of medical images. The detection of plus disease in retinopathy of prematurity is an example at the edge between two treatment strategies where a difficult decision in the diagnosis may make the difference between the risk of blindness and complete recovery. Explaining the complex decision-making of these models is crucial before their incorporation into the clinical workflows. This can guarantee that several aspects are respected, for example the absence of unwanted biases, model fairness, trustworthiness and, most importantly, to ensure that the model reflects the clinical expectations. This chapter begins by clarifying the confusion around the taxonomy related to interpretable AI, adding a rich literature review on explainability methods. To illustrate the use of our recently developed methods for explaining convolutional networks’ predictions, we have applied concept attribution to Retinopathy of prematurity. In collaboration with experts in ophthalmology, we define concepts that (1) are relevant to them for the diagnosis of plus disease and (2) can be extracted automatically from the images as visual features. We then evaluate how the Convolutional Neural Networks (CNN) predictions are influenced by these concepts. The results suggest that the decision-making process of the CNN aligns with that of the ophthalmologists.
LB-Chain: Load-Balanced and Low-Latency Blockchain Sharding via Account Migration

Blockchain sharding has been increasingly used to improve blockchain systems’ performance, in which a blockchain is split into multiple smaller, disjoint shards. In practice, however, sharding can only achieve limited throughput and latency improvement, especially for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user-perceived transaction confirmation delay.</i> The performance degradation is believed to be caused by the cross-shard transactions. However, we show, through comprehensive system deployment and measurement studies, that the main culprit is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">imbalanced transaction load</i> on different blockchain shards. To address this problem, we propose a novel sharding system, called LB-Chain, which <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamically</i> balances the transaction load on different shards by periodically <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">migrating active accounts</i> from heavily-loaded shards to less-loaded ones. We have implemented a prototype of LB-Chain, and evaluated its performance through large-scale blockchain deployment using real-world transaction traces. Extensive experiments confirm that LB-Chain significantly boosts sharding performance, reducing the transaction confirmation delays by up to 90% while increasing the transaction throughput by more than 10%. The delay difference between different accounts is also reduced dramatically, leading to improved fairness in the system.
The contribution of blockchain technologies to anti‐corruption practices: A systematic literature review

Abstract This paper proposes a systematic literature review emphasising scholars' view on the impact of the adoption of blockchain technologies to fight corruption. Providing a deep understanding of the state of the art, the paper drafts implications and valuable insights to address future research. The connection of anticorruption policies and blockchain technologies has been investigated using Scopus database and ABS journal ranking. The importance assumed by anticorruption policies and the innovativeness of the blockchain technology, thanks to its characteristics of immutability, automatic timestamping and distributed architecture, needs to be investigated in an integrated perspective. This study provides for the first time a systematic connection among anticorruption practices and blockchain towards the lens of the business, management and accounting field, providing valuable implications, insights and emerging issues.
No longer invisible, but still mistreated: Trans minors and the Spanish digital press (2006-2020)

The increase in media attention towards trans issues in the last two decades has contributed to reinforcing its visibility. However, the media coverage of trans minors has hardly been addressed by previous literature. The objective of this study is to analyze the media coverage of trans children and adolescents in the Spanish digital press during the period 2006–2020. To do that, this article has conducted a quantitative content analysis combining manual coding and rule-based text classification. The results confirm that the media coverage of trans minors has increased. Most of the analyzed delegitimization patterns of trans people have decreased over the years, although they remain relatively stable during the last period. Contrary to what could be thought, childhood and adolescence do not seem to act as a protective factor against media delegitimization. These findings suggest that trans children are at the front-page of Spanish public debate. Despite the increasing visibility of trans minors, they continue to be mistreated in the media. In fact, trans children may be even more questioned than adults. All in all, this study underlines the importance of trans minors in the news coverage of trans issues and, in extension, in the fight for trans rights.
A study on constructing an elderly abuse detection system by convolutional neural networks

With population aging accelerating, people are currently facing a new challenge which is until now unexplored: elderly abuse. According to some statistics provided by World Health Organization, one in six elderlies, aged at least 60 years old, is a victim of physical offense by their relatives and caregivers. Elder people face many types of abuse. This work focuses on the physical abuse which is defined by the infliction of pain on a person. Physical abuse can severely damage a person, sometimes leading to long-term psychological consequences, hospitalization, and death. The contribution to solve this problem is as follows. A dataset is first built by collecting elder abuse videos. Second, the dataset are applied over three different networks: the standard 3D convolutional neural network (3D CNN), the 3D residual convolutional neural network (R3DCNN) and the factorized 3D convolutional neural network based on the residual network ‘R(2 + 1)D CNN’. Lastly, this paper introduces a new preprocessing method called the repeated frames extraction that has been shown to be efficient for action recognition. The project has been concluded with satisfying results with accuracies of 99.21%, 84.37%, and 85% for training, validation, and testing, respectively, on the standard 3D convolutional neural network.
Comparison of font size and background color strategies for tag weights on tag maps

Tag weight differences in tag maps are usually reflected by different font sizes. With this strategy, low weighted tags may be ignored and tag sizes may be misjudged due to differing word length, character height and word width. To address these shortcomings, this paper improves the layout method of tag maps by presetting anchor points. We construct weight contours based on corner points and center points of tag outer envelope rectangles and adopt hypsometric tinting as background color to reflect tag weight differences. Finally, we compare and analyze the background color strategy with the font size strategy from the perspectives of tag selection, tag recognition, tag recall, confidence, readability, and preference through eye movement experiments and questionnaire surveys. The results show that both strategies exhibit advantages (when tags are devoid of semantics), with the font size strategy being favored slightly in this abstract case. We provide tag map designers with a new visualization scheme for the expression of tag weight.
Learning rate selection in stochastic gradient methods based on line search strategies

Over the past several decades, clinicians have incorporated several applications of diagnostic point-of-care ultrasound (POCUS) into medical decision-making.Among the applications of POCUS, imaging the inferior vena cava (IVC) is practiced by a wide variety of specialties, such as nephrology, emergency medicine, internal medicine, critical care, anesthesiology, pulmonology, and cardiology.Although each specialty uses IVC data in slightly different ways, most medical specialties, at minimum, attempt to use IVC data to make predictions about intravascular volume status.While the relationship between IVC sonographic data and intravascular volume status is complex and highly context-dependent, all clinicians should collect the sonographic data in standardized ways to ensure repeatability.This paper describes standardized IVC image acquisition including patient positioning, transducer selection, probe placement, image optimization, and the pitfalls and limitations of IVC sonographic imaging.This paper also describes the commonly performed anterior IVC long-axis view and three other views of the IVC that can each provide helpful diagnostic information when the anterior long-axis view is difficult to obtain or interpret.
A novel robust progressive cubature Kalman filter with variable step size for inertial navigationsystem/global navigation satellite system tightly coupled navigation

Summary This article investigates the state estimation problem of the nonlinear system with the large process prior uncertainty but high‐accuracy measurement information, the situation is prone to occur in the inertial navigation system (INS)/global navigation satellite system (GNSS) tightly coupled navigation system. Furthermore, the unknown heavy‐tailed measurement noises induced by the inaccurate prior navigation information and random measurement outliers are also considered. Given existing methods such as progressive cubature Kalman filter (PCKF) cannot effectively solve the above issues, a novel robust PCKF with variable step size (RVSS‐PCKF) is proposed. First, a new Gaussian‐uniform‐mixing inverse Gamma (GUMIG) distribution is presented to model the variable step size and measurement noise. Next, the GUMIG distribution is expressed as a hierarchical Gaussian presentation and then RVSS‐PCKF is derived with the help of the variational Bayesian (VB) inference. In the filter, the state, variable step size and noise statistic are jointly estimated by the fixed‐point iterations. Finally, the simulations and real data of the tightly coupled navigation illustrate the superiority of the filter in terms of accuracy and steady‐state performance.
Disentangled Speech Representation Learning for One-Shot Cross-Lingual Voice Conversion Using ß-VAE

We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation. We apply this method to the challenging one-shot cross-lingual voice conversion task to demonstrate the effectiveness of the disentanglement. Inspired by ß- VAE, we introduce a learning objective that balances between the information captured by the content and speaker representations. In addition, the inductive biases from the architectural design and the training dataset further encourage the desired disentanglement. Both objective and subjective evaluations show the effectiveness of the proposed method in speech disentanglement and in one-shot cross-lingual voice conversion.
N-Best Hypotheses Reranking for Text-to-SQL Systems

Text-to-SQL task maps natural language utterances to structured queries that can be issued to a database. State-of-the-art (SOTA) systems rely on finetuning large, pre-trained language models in conjunction with constrained decoding applying a SQL parser. On the well established Spider dataset, we begin with Oracle studies: specifically, choosing an Oracle hypothesis from a SOTA model's 10-best list, yields a 7.7% absolute improvement in both exact match (EM) and execution (EX) accuracy, showing significant potential improvements with reranking. Identifying coherence and correctness as reranking approaches, we design a model generating a query plan and propose a heuristic schema linking algorithm. Combining both approaches, with T5-Large, we obtain a consistent 1% improvement in EM accuracy, and a 2.5% improvement in EX, establishing a new SOTA for this task. Our comprehensive error studies on DEV data show the underlying difficulty in making progress on this task.
SVLDL: Improved Speaker Age Estimation Using Selective Variance Label Distribution Learning

Estimating age from a single speech is a classic and challenging topic. Although Label Distribution Learning (LDL) can represent adjacent indistinguishable ages well, the uncertainty of the age estimate for each utterance varies from person to person, i.e., the variance of the age distribution is different. To address this issue, we propose selective variance label distribution learning (SVLDL) method to adapt the variance of different age distributions. Furthermore, the model uses WavLM as the speech feature extractor and adds the auxiliary task of gender recognition to further improve the performance. Two tricks are applied on the loss function to enhance the robustness of the age estimation and improve the quality of the fitted age distribution. Extensive experiments show that the model achieves state-of-the-art performance on all aspects of the NIST SRE08-10 and a real-world datasets.
The Clever Hans Effect in Voice Spoofing Detection

Does the model that appear to detect fake voices use cues relevant to the problem? Or is it merely a product of how a dataset was constructed? In this paper, we demonstrate how spurious correlations in training data results in improved voice spoofing detection. A simple framework to identify such effects, also known as the Clever Hans effect in machine learning (ML), is proposed and its efficacy is demonstrated using a popular deep spoofing detector on two anti-spoofing benchmarks: ASVspoof 2017 and ASVspoof 2019 PA. By raising awareness of this effect we hope to increase the credibility and reliability of anti-spoofing solutions on these benchmarks. Furthermore, using a separate deep architecture we demonstrate that such effect is not model specific and that any ML solution may exhibit such behaviour.
Weak-Supervised Dysarthria-Invariant Features for Spoken Language Understanding Using an Fhvae and Adversarial Training

The scarcity of training data and the large speaker variation in dysarthric speech lead to poor accuracy and poor speaker generalization of spoken language understanding systems for dysarthric speech. Through work on the speech features, we focus on improving the model generalization ability with limited dysarthric data. Factorized Hierarchical Variational Auto-Encoders (FHVAE) trained unsupervisedly have shown their advantage in disentangling content and speaker representations. Earlier work showed that the dysarthria shows in both feature vectors. Here, we add adversarial training to bridge the gap between the control and dysarthric speech data domains. We extract dysarthric and speaker invariant features using weak supervision. The extracted features are evaluated on a Spoken Language Understanding task and yield a higher accuracy on unseen speakers with more severe dysarthria compared to features from the basic FHVAE model or plain filterbanks.