Reviewerly/exHarmony huggingface dataset:
Creating the Future: Augmented Reality, the next Human-Machine Interface

XR, consisting of Virtual Reality (VR) and Augmented Reality (AR) together, will be the next general computing platform, dominating our relationship with the digital world for the next 50 years, much as personal computing has dominated the last 50. XR will be the way people work, play, and connect. VR headsets will create deeply immersive new experiences and will enable the richest Metaverse experiences. Always-available AR glasses will be with us when we're on the go, letting us act with extremely low latency and friction, allowing us to use virtual entities of all sorts to annotate our world, share with others, and communicate, and extending our perceptions, memory, and cognition with an Artificial Intelligence (AI) assistant that truly understands our context and our personal needs. Both will be centered around people rather than technology, allowing us to connect more strongly than ever before, and providing a contextually personalized interface that is far more intuitive and natural than anything that exists today. However, VR and particularly AR are at a very early stage, and need a great deal of innovation and development, across multiple technologies, before they can offer the level of performance that would cause billions of people to make XR a part of their everyday lives. Those technologies include optics, projectors, display systems, graphics, audio, hand tracking, eye tracking, face tracking, body tracking, world mapping and reconstruction, contextual understanding, sensors, interaction, and AI. VR headsets operate within tight power, weight, and thermal budgets, and AR glasses will need to operate within extraordinarily tight budgets; for AR glasses, the constraints include light weight, very low power, and all-day comfort and all-day operation in a socially acceptable form factor,. Breakthroughs are needed in every one of the areas listed above, and in almost every case, innovative, highly customized semiconductor technologies will be needed to achieve those breakthroughs. This will notably require new materials, miniaturization of the key components, and very-large-scale 3D heterogeneous integration of circuits and systems to create new functions. In addition, specialization at the level of architecture and domain-oriented accelerators, especially with respect to Machine Learning (ML), will be essential, and algorithm optimization will need to evolve in new directions. Finally, the challenges of AR can only be solved by true co-design of hardware and software; technology development will need to be driven from end to end, based on delivering important use cases with a full-system approach. This will create unforeseen opportunities and challenges for semiconductor technologies for decades to come.
Hierarchical Exploration of Continuous Seismograms With Unsupervised Learning

Continuous seismograms contain a wealth of information with a large variety of signals with different origin. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of signals in continuous single-station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two-day-long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic burst that includes around 200 events with similar waveforms and high-frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under-represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data-driven.
Investigating Attention Mechanism in 3D Point Cloud Object Detection

Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality. As the basic format of 3D data, the point cloud can provide detailed geometric information about the objects in the original 3D space. However, due to 3D data s sparsity and unorderedness, specially designed networks and modules are needed to process this type of data. Attention mechanism has achieved impressive performance in diverse computer vision tasks; however, it is unclear how attention modules would affect the performance of 3D point cloud object detection and what sort of attention modules could fit with the inherent properties of 3D data. This work investigates the role of the attention mechanism in 3D point cloud object detection and provides insights into the potential of different attention modules. To achieve that, we comprehensively investigate classical 2D attentions, novel 3D attentions, including the latest point cloud transformers on SUN RGB-D and ScanNetV2 datasets. Based on the detailed experiments and analysis, we conclude the effects of different attention modules. This paper is expected to serve as a reference source for benefiting attention-embedded 3D point cloud object detection. The code and trained models are available at: https://qithub.com/SkiQiu0419/attentions_in_3D_detection.
Untidy Data: The Unreasonable Effectiveness of Tables

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets - the quintessential table tool - remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and "get their hands on" the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.
Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation

Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-specific items has become attainable only due to recent technological progress. We propose that pre-trained causal transformer models can be fine-tuned to achieve this task using implicit parameterization in conjunction with conditional generation. We demonstrate this method in a tutorial-like fashion and finally compare aspects of validity in human- and machine-authored items using empirical data. Our study finds that approximately two-thirds of the automatically generated items show good psychometric properties (factor loadings above .40) and that one-third even have properties equivalent to established and highly curated human-authored items. Our work thus demonstrates the practical use of deep neural networks for non-cognitive automatic item generation.
Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking

Multi-object tracking (MOT) is an essential task in the computer vision field. With the fast development of deep learning technology in recent years, MOT has achieved great improvement. However, some challenges still remain, such as sensitiveness to occlusion, instability under different lighting conditions, and non-robustness to deformable objects, causing incorrect temporal associations. To address such common challenges in most of the existing trackers, in this paper, a tracklet booster (TBooster) algorithm is proposed to correct the association errors resulting from existing trackers. The correction of the association error from TBooster has two folds: split tracklets on potential ID-change positions and then connect multiple tracklets into one if they are from the same object. To achieve this goal, the TBooster consists of two components, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Splitter and Connector. In Splitter, an architecture with stacked temporal dilated convolution blocks is employed for the splitting position prediction via label smoothing strategy with adaptive Gaussian kernels. In Connector, a multi-head self-attention-based encoder is exploited for the tracklet embedding, which is further used to connect tracklets into full tracks. We conduct sufficient experiments on MOT17 and MOT20 benchmark datasets and achieve promising results. Combined with the proposed tracklet booster, existing trackers can achieve large improvements on the IDF1 score, which shows the effectiveness of the proposed TBooster.
GPT2SP: A Transformer-Based Agile Story Point Estimation Approach

Story point estimation is a task to estimate the overall effort required to fully implement a product backlog item. Various estimation approaches (e.g., Planning Poker, Analogy, and expert judgment) are widely-used, yet they are still inaccurate and may be subjective, leading to ineffective sprint planning. Recent work proposed Deep-SE, a deep learning-based Agile story point estimation approach, yet it is still inaccurate, not transferable to other projects, and not interpretable. In this paper, we propose GPT2SP, a Transformer-based Agile Story Point Estimation approach. Our GPT2SP employs a GPT-2 pre-trained language model with a GPT-2 Transformer-based architecture, allowing our GPT2SP models to better capture the relationship among words while considering the context surrounding a given word and its position in the sequence and be transferable to other projects, while being interpretable. Through an extensive evaluation on 23,313 issues that span across 16 open-source software projects with 10 existing baseline approaches for within- and cross-project scenarios, our results show that our GPT2SP approach achieves a median MAE of 1.16, which is (1) 34%-57% more accurate than existing baseline approaches for within-project estimations; (2) 39%-49% more accurate than existing baseline approaches for cross-project estimations. The ablation study also shows that the GPT-2 architecture used in our approach substantially improves Deep-SE by 6%-47%, highlighting the significant advancement of the AI for Agile story point estimation. Finally, we develop a proof-of-concept tool to help practitioners better understand the most important words that contributed to the story point estimation of the given issue with the best supporting examples from past estimates. Our survey study with 16 Agile practitioners shows that the story point estimation task is perceived as an extremely challenging task. In addition, our AI-based story point estimation with explanations is perceived as more useful and trustworthy than without explanations, highlighting the practical need of our Explainable AI-based story point estimation approach.
A Self-Paced Regularization Framework for Partial-Label Learning

Partial-label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true label. Nonetheless, existing algorithms usually treat all labels and instances equally, and the complexities of both labels and instances are not taken into consideration during the learning stage. Inspired by the successful application of a self-paced learning strategy in the machine-learning field, we integrate the self-paced regime into the PLL framework and propose a novel self-paced PLL (SP-PLL) algorithm, which could control the learning process to alleviate the problem by ranking the priorities of the training examples together with their candidate labels during each learning iteration. Extensive experiments and comparisons with other baseline methods demonstrate the effectiveness and robustness of the proposed method.
The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups

Artificial intelligence (AI) provides a powerful tool to extract information from digitised histopathology whole slide images. During the last 5 years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into.To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing subfields of computational pathology with a focus upon solid tumours. The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in subgroups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high throughout subgroups.Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.
Similarity-based integrity protection for deep learning systems

Deep learning technologies have achieved remarkable success in various tasks, ranging from computer vision, object detection to natural language processing. Unfortunately, state-of-the-art deep learning technologies are vulnerable to adversarial examples and backdoor attacks, where an adversary destroys the model’s integrity. The obstacles have urged intensive research on improving the ability of deep learning technologies to resist integrity attacks. However, existing defense methods are either incomplete (i.e., only a single attack can be detected) or expensive computing resources. It requires the defense method to have universal property, which can effectively and efficiently detect multiple integrity attacks. To this end, we propose a similarity-based integrity protection method for deep learning systems (IPDLS), which is provided with the universal property. IPDLS realizes anomaly detection by measuring the similarity between suspicious samples and samples in a preset verification set. We empirically evaluate IPDLS on the MNIST and CIFAR10 datasets. Experimental results have verified the effectiveness of IPDLS, which can detect adversarial examples and backdoor attacks simultaneously.
Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Different types of pandemics that have appeared from time to time have changed many aspects of daily life.Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown.The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store.A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications.The manual processing and extracting of information from users' reviews is an extremely difficult and time-consuming task.Therefore, the use of intelligent methods is necessary to analyse users' reviews and extract issues that can help in improving these apps.This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods.To the best of our knowledge, this is the first study to explore users' opinions about governmental apps in Saudi Arabia.The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases.A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store.Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.
InterTrust: Towards an Efficient Blockchain Interoperability Architecture with Trusted Services

Blockchain as a potentially disruptive technology can advance many different fields, e.g., cryptocurrencies, supply chains, and the industrial Internet of Things. The next-generation blockchain ecosystem is expected to consist of various homogeneous and heterogeneous distributed ledgers. These ledger systems will inevitably require a certain level of proper cooperation of multiple blockchains to enrich advanced functionalities and enhance interoperable capabilities for future applications. The interoperability among blockchains will revolutionize current blockchain design principles, like the emergence of the Internet. However, the development of cross-blockchain applications involves much complexity regarding the variety of underlying cross-blockchain communication. With that regard, we propose an efficient, interoperable blockchain architecture, InterTrust, to support interoperability and trustworthiness among arbitrary blockchain systems (including homogeneous and heterogeneous blockchains). It consists of an atomic cross-chain communication protocol, which can be considered an agnostic protocol to integrate existing blockchain systems smoothly. InterTrust is powered by two innovative techniques: threshold signature scheme and trusted hardware. The threshold signature scheme guarantees consistency and verifiability in the target blockchain systems, and the trusted hardware guarantees trusted services among distinct blockchain systems. Combining these two techniques provides an efficient cross-chain communication protocol to facilitate atomic swaps and interoperable operations between different blockchain systems. Our interoperable architecture is robust to support arbitrary blockchain systems. We also present the security analysis on the scenarios of integrating our protocol into Byzantine fault tolerance based blockchain systems.
Query-Efficient Black-Box Adversarial Attacks Guided by a Transfer-Based Prior

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates.
Machine Learning-Based Analysis of Cryptocurrency Market Financial Risk Management

Cryptocurrency is one of the famous financial state in all over the world which cause several type of risks that effect on the intrinsic assessment of risk auditors. From the beginning the growth of cryptocurrency gives the financial business with the wide risk in term of presentation of money laundering. In the institution of financial supports such as anti-money laundering, banks and secrecy of banks proceed as a specialist of risk, manager of bank and officer of compliance which has a provocation for the related transaction through cryptocurrency and the users who hide the illegal funds.In this study, the Hierarchical Risk Parity and unsupervised machine learning applied on the cryptocurrency framework. The process of professional accounting in term of inherent risk connected with cryptocurrency regarding the occurrence likelihood and statement of financial impact. Determining cryptocurrency risks comprehended to have a high rate of occurrence likelihood and the access of private key which is unauthorized. The professional cryptocurrency experience in transaction cause the lower risk comparing the less experienced one. The Hierarchical Risk Parity gives the better output in term of returning the adjusted risk tail to get the better risk management result.The result section shows the proposed model is robust to various intervals which are re-balanced and the co-variance window estimation.