Reviewerly/exHarmony huggingface dataset:
Historical Audio Search and Preservation: Finding Waldo Within the Fearless Steps Apollo 11 Naturalistic Audio Corpus [Applications Corner]

Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal. Manual annotation is costly, so this makes it necessary to determine robust speaker identification and tracking methods. In this study, a subset of 100 h derived from the collective 9,000 h of the Fearless Steps (FSteps) Apollo 11 audio data were investigated, corresponding to three critical mission phases: liftoff, lunar landing, and lunar walk. A speaker recognition assessment is performed on 140 speakers from a collective set of 183 NASA mission specialists who participated, based on sufficient training data obtained from 5 (out of 30) mission channels. We observe that SincNet performs the best in terms of accuracy and F score achieving 78.6% accuracy. Speaker models trained on specific phases are also compared with each other to determine if stress, g-force/atmospheric pressure, acoustic environments, etc., impact the robustness of the models. Higher performance was obtained using i-vector and x-vector systems for phases with limited data, such as liftoff and lunar walk. When provided with a sufficient amount of data (lunar landing phase), SincNet was shown to perform the best. This represents one of the first investigations on speaker recognition for massively large team-based communications involving naturalistic communication data. In addition, we use the concept of “Where’s Waldo?” to identify key speakers of interest (SOIs) and track them over the complete FSteps audio corpus. This additional task provides an opportunity for the research community to transition the FSteps collection as an educational resource while also serving as a tribute to the “heroes behind the heroes of Apollo.”
An efficient revocable identity‐based encryption with ciphertext evolution in the cloud‐assisted system

Abstract Identity‐based encryption (IBE) is a public key cryptosystem on purpose to remove the traditional certificate management. How to realize efficient user revocation in the IBE is of great importance when considering its application. The drawback of most existing works is that the revoked user can still access the ciphertext prior to revocation. One possible solution proposed by Sun et al. is to evolve the ciphertext in the cloud. However, it is unfortunate that the master time key in their scheme is kept by the private key generator (PKG), which means it is sustained extra burden. In this article, we present an efficient revocable IBE with ciphertext evolution in the cloud‐assisted system and the ciphertext remains constant size. The cloud server keeps only one secret master time update key sent by the PKG in a private channel, and thus our scheme offers scalability. In the meantime, our detailed analysis demonstrates that our proposed scheme is semantically secure against ciphertext chosen attacks based on the k‐CAA problem and enjoys better efficiency in computation and communication costs compared with previous works.
Laser Speckle Pattern Stability in Hypersonic Regimes for Experimental Mechanics and Metrological Studies

An empirical study assessing the suitability of laser speckle patterns for use in experimental mechanics research conducted at hypersonic conditions is presented. Speckle patterns generated via coherent optical scattering from a gold-coated graphite surface inserted into a Mach 10 air stream were collected and analyzed using Computer-Aided Speckle Interferometry using two methodologies: a typical spanwise approach involving increasing temporal span between image pairs, and a sequential approach that utilizes a combination of temporal re-referencing and spatial resampling. Results were characterized for spatial-temporal stability and variance. Our observations demonstrate that optical methods relying on stable and properly formed speckle patterns can be used in a hypersonic regime at various Reynolds numbers and common camera frame rates, but the technique is sensitive to the conditions of the airflow and the processing methodology chosen.
Using Graphical Perception in Visualization Recommendation

column Share on Using Graphical Perception in Visualization Recommendation Authors: Zehua Zeng Intel IntelView Profile , Leilani Battle University of Washington University of WashingtonView Profile Authors Info & Claims InteractionsVolume 30Issue 3May + June 2023pp 23–25https://doi.org/10.1145/3588744Published:01 May 2023Publication History 0citation152DownloadsMetricsTotal Citations0Total Downloads152Last 12 Months152Last 6 weeks152 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning

Data-to-text generation plays an important role in natural language processing by processing structured data and helping people understand those data by generating user-friendly descriptive text. It can be applied to news generation, financial report generation, customer service, etc. However, in practice, it needs to adapt to different domains that may lack an annotated training corpus. To alleviate this dataset scarcity problem, distantly-supervised data-to-text generation has emerged, which constructs a training corpus automatically and is more practical to apply to new domains when well-aligned data is expensive to obtain. However, this distant supervision method of training induces an over-generation problem since the automatically aligned text includes hallucination. These expressions cannot be inferred from the data, misguiding the model to produce unfaithful text. To exploit the noisy dataset while maintaining faithfulness, we empower the neural data-to-text model by dynamically increasing the weights of those well-aligned training instances and reducing the weights of the low-quality ones via meta learning. To our best knowledge, we are the first to alleviate the noise in distantly-supervised data-to-text generation via meta learning. In addition, we rewrite those low-quality texts to provide better training instances. Finally, we construct a new distantly-supervised dataset, DIST-ToTTo (abbreviation for Distantly-supervised Table-To-Text), and conduct experiments on both the benchmark WITA (abbreviation for the data source Wikipedia and Wikidata) and DIST-ToTTo datasets. The evaluation results show that our model can improve the state-of-the-art DSG (abbreviation for Distant Supervision Generation) model across all automatic evaluation metrics, with an improvement of 3.72% on the WITA dataset and 3.82% on the DIST-ToTTo dataset in terms of the widely used metric BLEU (abbreviation for BiLingual Evaluation Understudy). Furthermore, based on human evaluation, our model can generate more grammatically correct and more faithful text compared to the state-of-the-art DSG model.
Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain

The problem of digital identity acquires more relevance every day in the eyes of a society that spends more and more time connected to the Internet. It has evolved throughout its history to reach a decentralized model known as Self-Sovereign Identity (SSI), which finds its natural tools in the blockchain technology and Zero-Knowledge Proofs (ZKPs). ZKPs, in this context, allow users to prove that their credentials are legitimate without revealing more information than is strictly necessary, and constitute one of the most promising areas of applied cryptography. In this work, an application is developed for the study of Zero-Knowledge Proof methods and, specifically, in their application for authentication in public-private key encryption systems. It focuses on the study of three ZKP protocols (Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr, which rely on the problems of large number factorizations and discrete logarithms for security) in the practical use-case where a prover wants to demonstrate knowledge of a private key for a public key without revealing the key itself. The application allows the user to modify the necessary parameters in each method to achieve a better understanding of their role in their safety and efficiency. Several types of attacks are carried out against the above-mentioned protocols to analyze their degree of security and what recommendations can be made to improve it.
Sampling by Difference as a Method of Applying the Sampling Kantorovich Model in Digital Image Processing

In this paper, the connections between the Sampling Kantorovich model and the sampling process are highlighted and exploited. Based on the theoretical framework of the Sampling Kantorovich operators, a sampling paradigm, here named Sampling Kantorovich by Difference (SKD), is introduced. In line of principle, SKD allows for overcoming the technical limitation due to the fact that the resolution of a signal/image is strictly connected with the size of the used sensors. We analyze the paradigm in the case of a simulated super resolution type problem. The same mathematical model, being extendable to other signal reconstruction procedures, suggests a theoretical way for new technical solutions in the sampling procedures.
Multi-Attention-Guided Cascading Network for End-to-End Person Search

The key procedure is to accurately identify pedestrians in complex scenes and effectively embed features from multiple vision cues. However, it is still a limitation to coordinate two tasks in the unified framework, thus leading to high computational overhead and unsatisfactory search performance. Furthermore, most methods do not take significant clues and key features of pedestrians into consideration. To remedy these issues, we introduce a novel method named Multi-Attention-Guided Cascading Network (MGCN) in this paper. Specifically, we obtain the trusted bounding box through the detection header as the label information for post-process. Based on the end-to-end network, we demonstrate the advantages of jointly learning to construct the bounding box and attention module by maximizing the complementary information from different attention modules, which can achieve optimized person search performance. Meanwhile, by imposing an aligning module on re-id feature extracted network to locate visual clues with semantic information, which can restrain redundant background information. Extensive experimental results for the two benchmark person search datasets are provided to demonstrate that the proposed MGCN markedly outperforms the state-of-the-art baselines.
Multi-stage information diffusion for joint depth and surface normal estimation

Depth and surface normal estimations are important for 3D geometric perception, which has numerous applications including autonomous vehicles and robots. In this paper, we propose a lightweight Multi-stage Information Diffusion Network (MIDNet) for the simultaneous prediction of depth and surface normals from a single RGB image. To obtain semantic and detail-preserving features, we adopt a high-resolution network as our backbone to learn multi-scale features, which are then fused into shared features for the two tasks. To mutually boost each task, a Cross-Correlation Attention Module (CCAM) is proposed to adaptively integrate information for the prediction of the two tasks in multiple stages, including feature-level information interaction and task-level information interaction. Ablation studies show that the proposed multi-stage information diffusion strategy can boost the performance gain for the two tasks at different levels. Compared to current state-of-the-art methods on the NYU Depth V2, Stanford 2D-3D-Semantic and KITTI datasets, our method achieves superior performance for both monocular depth and surface normal estimation.
Upgrades of Genetic Programming for Data-Driven Modeling of Time Series

In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of symbolic regression (SR) via genetic programming (GP) for the investigation of time series, with the objective of extracting mathematical models directly from the available signals. The main task is not simply prediction but consists of identifying interpretable equations, reflecting the nature of the mechanisms generating the signals. The implemented improvements involve almost all aspects of GP, from the knowledge representation and the genetic operators to the fitness function. The unique capabilities of genetic programming, to accommodate prior information and knowledge, are also leveraged effectively. The proposed upgrades cover the most important applications of empirical modeling of time series, ranging from the identification of autoregressive systems and partial differential equations to the search of models in terms of dimensionless quantities and appropriate physical units. Particularly delicate systems to identify, such as those showing hysteretic behavior or governed by delayed differential equations, are also addressed. The potential of the developed tools is substantiated with both a battery of systematic numerical tests with synthetic signals and with applications to experimental data.
Seeing Like a Dataset: Notes on AI Photography

research-article Share on Seeing Like a Dataset: Notes on AI Photography Author: Eryk Salvaggio Rochester Institute of Technology Rochester Institute of TechnologyView Profile Authors Info & Claims InteractionsVolume 30Issue 3May + June 2023pp 34–37https://doi.org/10.1145/3587241Published:01 May 2023Publication History 0citation243DownloadsMetricsTotal Citations0Total Downloads243Last 12 Months243Last 6 weeks243 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Factors Affecting how University Students Comply with Cybersecurity Measures: A Case of South Africa

Universities across the globe are experiencing a surge of cyberattacks due to the increased usage of information communication technologies (ICTs). To counteract cyberattacks, universities have implemented cybersecurity measures to ensure that students and the universities’ critical infrastructures are protected. Unfortunately, universities in developing countries continue to face increased cyberattacks despite implementing cybersecurity measures. This study explores the factors that affect students’ compliance with universities’ cybersecurity measures. The study used a case of the University of Cape Town in South Africa, adopting qualitative research and an interpretive paradigm. We used a deductive approach to theory using Protection Motivation Theory (PMT) as the lens for inquiry. The sample for the study consisted of 40 participants, of which 35 were students and five were staff members of the University. The sample of the study was selected by convenience. We collected empirical data from the participants using semi-structured interviews. The data was then analysed using thematic analysis on NVivo software. The study found that students’ compliance with cybersecurity measures is affected by their perceptions of the seriousness of the threats, the likelihood of the threats happening, their ability to protect themselves against threat, their belief in the effectiveness of the recommended solutions against cyber threats, and the costs associated with compliance to cybersecurity measures. When students perceive the risk as not severe enough to worry about, they do not find it necessary to comply with the University’s cybersecurity measures. Similarly, when the students deem that the recommended compliance actions will not be practical or affordable, they do not adhere to the university cybersecurity measures.
OpenSpeaks before AI: Frameworks for Creating the AI/ML Building Blocks for Low-Resource Languages

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Price-aware matrix factorization model for personalized recommendations

Recommendation methods aim to assist consumers in their decision-making process to find products that they are quite likely interested in. Current recommendation methods generally use online consumer reviews or ratings to predict consumers’ preferences for products. In e-commerce transactions, price plays a significant role in consumers’ purchase decisions. And each consumer's preference for product prices is specific. In this paper, we propose a novel price-aware recommendation method based on the matrix factorization model which fully considers the effect of price. We distill the price preferences of consumers from ratings and discover consumers’ real preferences for products. We further calculate the price sensitivities of consumers, not only considering the difference in price preference between a consumer and others but also focusing on the consumers’ price preferences in a specific price range. The final predicted ratings are calculated by adding the term of price effect into the matrix factorization framework. The results show that the proposed method has achieved high accuracy and performed better than several existing methods in both rating prediction and Top-N recommendation. And the products we recommend for consumers are more in line with their price preferences. Moreover, we find that, to some extent, the proposed method can solve the long-tail product recommendation problem with the consideration of the price effect.
Towards Energy-Efficient Autonomous Driving: A Multi-Objective Reinforcement Learning Approach

Dear Editor, With the development of automobile industry and artificial intelligence (AI) domains, autonomous vehicles (AVs) are becoming a reality and promise to revolutionize human mobility [1]–[3]. The decision-making system of AVs is crucial, which is typically required to trade off multiple competing objectives. For example, when determining driving policies, autonomous electric vehicles (AEVs) need to consider two conflicting objectives: transport efficiency and electricity consumption. As one of state-of-the-art AI technologies, reinforcement learning (RL) has demonstrated its potential in a series of challenging tasks. Accordingly, RL has attracted considerable attention from global researchers [4].
AI-Based Method for Frame Detection in Engineering Drawings

The paper proposes a method for frame detection in engineering drawings documents. The method is based on the modern AI techniques. We proposed to use YOLOv5 as framework for neural network architecture development for frame detection. We presented a neural network architecture, introduce loss functions we used and show the experiments. We got good results showed that our accuracy is about 99% for frame detection. Sure that we used similar drawings for training and testing dataset. We showed examples in the paper how is the frame detection worked for several examples of drawings.
Digital Assistant to Operator

AI technologies gain value when they become an essential tool for the industry. In this article, we show the result of turning image recognition technology into an industrial tool for vehicle fleet management. The relevance of the drivers' actions monitoring task is substantiated. The choice of a object detection model was carried out among two-stage and single-stage models. R-CNN, Fast R-CNN, Faster R-CNN, R-FCN, SSD, RetinaNet, CenterNet and YOLOv4 were considered. Comparison of the different object detectors specifications is convinced us to choose YOLOv4. This choice is due to a good balance between performance, the ability to control the probability of false alarms, high recognition accuracy, and the presence of a special simplified version. In the paper we presented the results of YOLOv4 and YOLOv4-tiny research. The result is applicable to solving the problem of real-time recognition of driver actions, including on low-power platforms.
Combined Method of Cognitive Assessment of the Specialist Professional Potential

An important issue that arises before every person is the choice of a profession. It is obvious that the potential of success in different areas can be influenced both by the knowledge and skills acquired in the process of study, as well as the character traits of a person. The article proposes an intelligent predictive system that allows to assess the student's capabilities in the area of analytics. The topic relevance is explained by the need to increase the importance of connection between the employer and the university on the formation of knowledge, skills and abilities of the student that are in demand on the labor market. When designing the prognostic structure of the system, a hybrid intellectual approach is used that combines the advantages of known methods. It includes a neural network model and a method of accounting of arguments groups. The most demanded professions in the labor market have been identified. Professional skill maps are compiled, based on the description of the requirements. The training samples of are supplemented with images generated by the Monte Carlo method. Using data on the student's progress in selected key disciplines, as well as other available information, the system offers a numerical equivalent of the potential for the declared professions. Such recommendation allows the student to timely and consciously adjust the orientation in the educational process, which positively affects the competitiveness of the labor resources produced by the higher education institution.
Overview of Scene Graph Generation Approaches in Computer Vision

Scene graph generation, the task of identifying objects in an image and their relationships, has numerous applications in computer vision, including image captioning and object detection. While deep learning has shown promise for this task, hyperparameter tuning remains a challenge. This paper considers the background and overviews for understanding the visual scene, scene graph and Scene Graph Generation. We discuss the visual scene related research area and scene graph concept. Also we follow the chronological and methodological approaches for the scene graph generation. Then we go through the neural network and deep learning approaches to scene graph generation (e.g. human-object/human-human interaction).