Reviewerly/exHarmony huggingface dataset:
Do you see what I see?

In this talk we present recent progress on large-scale learning of multimodal video representations. We start by presenting VideoBert, a joint model for video and language, repurposing the Bert model for multimodal data. This model achieves state-of-the-art results on zero shot prediction and video captioning. Next we show how to extend learning from instruction videos to general movies based on cross-modal supervision. We use movie screenplays to learn a speech to action classifiers and use these classifiers to mine video clips from thousands of hours of movies. We demonstrate a performance comparable or better than fully supervised approaches for action classification. Next we present an approach for video question answering which relies on training from instruction videos and cross-modal supervision with a textual question answer module. We show state-of-the-art results for video question answering without any supervision (zero-shot VQA) and demonstrate that our approach obtains competitive results for pre-training and then fine-tuning on video question answering datasets. We conclude our talk by presenting a recent video feature which is fully transformer based. Our Video Vision Transformer (ViViT) is shown to outperform the state-of-the-art on video classification. Furthermore, it is flexible and allows for performance / accuracy trade-off based on several different architectures.
Simulated Annealing Embedded Within Personal Velocity Update of Particle Swarm Optimization

This paper’s focus is on using simulated annealing (SA) to improve basic particle swarm optimization (PSO) by embedding SA in the part of PSO that is responsible for a particle’s inertial force, creating a new hybrid evolutionary algorithm. The paper studies the effect of this addition using two benchmark functions in a continuous solution space as well as the effect of this addition on PSO for the Travelling Salesperson Problem (TSP), which has a discrete solution. Finally, the novel hybrid algorithm is compared against basic PSO to establish a ground-level performance evaluation.
SymphonyDB: A Polyglot Model for Knowledge Graph Query Processing

Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various semantic and other applications requires Data Management Systems (DMSs) to efficiently store and process the content of KGs. However, the increases in the size and variety of KG datasets as well as the growing diversity of KG queries pose efficiency challenges for the current generation of DMSs to the extent that the performance of representative DMSs tends to vary significantly across diverse query types and no single platform dominates performance. We present our extensible prototype, SymphonyDB, as an approach to addressing this problem based on a polyglot model of query processing as part of a multi-database system supported by a unified access layer that can analyze/translate individual queries just-in-time and match each to the likely best-performing DMS among Virtuoso, Blazegraph, RDF-3X, and MongoDB as representative DMSs that are included in our prototype at this time. The results of our experiments with the prototype over wellknown KG benchmark datasets and queries point to the efficiency and consistency of its performance across different query types and datasets.
A Reflection on the Role of Data for Health: COVID-19 and Beyond

As previous chapters in this book have thoughtfully and knowledgeably elaborated, data and digitalization have a profound impact on the social, economic and political fabric of contemporary societies. They can be "a powerful tool in making progress on some of humanity's biggest challenges", as Claudia Juech writes in her chapter. At the same time, they pose new risks and problems, with potentially disruptive, destructive consequences.
Gaussian Process for Trajectories

chapter Share on Gaussian Process for Trajectories Authors: Kien Nguyen Search about this author , John Krumm Search about this author , Cyrus Shahabi Search about this author Authors Info & Claims Spatial Gems, Volume 2January 2024Pages 37–48https://doi.org/10.1145/3617291.3617296Published:25 January 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 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
Autonomy Framing and Cybersecurity Training Completion

ABSTRACT This pilot project conducted an AB test to see whether inclusion of an autonomy framed appeal in announcement of organizational cybersecurity training influenced training completion or time to training completion. The interim results, which include approximately 31% of the population, show that groups receiving the autonomy appeal had a lower training completion rate. The results show no difference in time to training completion.
Quantum Multiplexing

Multiplexing is an extremely well-established technique in the telecommunication industry [1] allowing multiple independent massages generated by multiple users to share a common transmission line. It works by the multiplexer dividing the capacity of the communication channel into a number of ‘logical’ channels which allow each of the users signal to be transmitted before a demultiplexer extracts them. There are various forms of it including space-division, frequency-division, time-division, code-division multiplexing to mention a few. Recently new form of ‘telecommunication’ has arisen. The principle of quantum mechanics and their associated quantum technologies [2] allow new forms of communication governed by superposition/entanglement and it is an open question as to where multiplexing will fit into it. Further those quantum principles may allow new forms of multiplexing unavailable to the classical world.
Modulation Recognition Method Based on Complex Neural Network

With the increasingly intensive communication environment and the indefinite appearance of signal modulation, it is more and more difficult to identify signal modulation. Modulation recognition technology is very important in both civil and military fields. In order to solve the problems of multiple signal styles and low signal-to-noise ratio, a modulation recognition method based on complex neural network is proposed, which involves the field of wireless communication technology. For the complex signal widely existing in the field of communication, this method proposes a new method, which uses the complex signal received in time domain. It does not need to extract any parameters from the received signal. It only needs to input the complex data into the complex neural network for training, and fully learn the characteristics of the real part and the imaginary part of the data. It can obtain higher accuracy than the traditional high-order accumulation method, the complex number has more abundant expression ability. The complex number neural network can learn the real part and imaginary part of the complex number, which is more suitable for most communication signals in the form of complex number. This method does not need to manually calculate and observe the differences between various signals or eigenvalues, and does not need to manually set the threshold to distinguish the modulation types of signals, so the neural network has the function of classifier.
Infrared object tracking method based on kernel correlation filters

Benefiting from the superiority visual capacity of infrared imaging system, infrared object tracking is widely applied in military, weapon guidance, security protection and other fields. Recently, correlation filter has attracted considerable attention within the tracking community due to its high computational efficiency. However, traditional correlation filter based trackers use the single feature, which has poor stability in low resolution and complex background. Aiming at the low resolution, poor contrast, low signal-to-noise and insufficient texture information of infrared image, we propose an Infrared object tracking method based on Kernel Correlation Filters, named Inf-KCF. We design a Multi-Feature Fusion structure to improve the expression ability for infrared targets, and separate the target from the background well. Experimental results on public infrared dataset show that, the Inf-KCF achieves promising performance.
Statistical Study and Analysis of Polysemy Words in the Kannada Language for Various Text Processing Applications

A language consists of polysemy words that have multiple meanings which have more than one sense associated with the word. Word sense disambiguation (WSD) is the process of recognizing the presence of such terms in a given input text and determining the right sense for the word in the given context. In this project, an effort is made to perform WSD for the polysemy words in the Kannada language. The proposed methodology employs the technique of matching the semantics of the word under consideration such as its gloss, examples, parts of speech (POS), and synsets with the target word. The semantic sense with the most number of matches with the sense of the target word would be considered as the most desirable sense of the target word. Ranks would be assigned to each of the senses. The highest rank would be assigned to such sense which would have maximum overlapping of words with the input context. The sense with the highest rank would be considered as the most appropriate sense of the polysemy word.
An underwater maneuvering target tracking method with compensation mechanism under short-term sensor failure

In the field of underwater maneuvering target tracking, the detection of sensors is not effective in real time. Under man-made or non-man-made interference, the observer only receives the noise value or abnormal value, that is, the sensor will fail for a short time. Once the sensor fails to obtain correct data continuously for a period of time, this is fatal to the filter. Aiming at the short-term failure of sensors, this paper proposes a tracking method for surface targets with compensation mechanism. This method uses the idea of deep learning to extract enough training samples from the observation data, and uses the samples to train the Long Short-Term Memory (LSTM) neural network with time series prediction ability offline. Secondly, the trained neural network model is used to compensate the sensor's observations online, combined with the unscented Kalman filter theory, to further correct the filter gain and covariance, thereby reducing the sensor's uncertainty measurement error and improving the target tracking filter Accuracy. The simulation results show that the unscented Kalman filter based on LSTM neural network compensation can predict the sensor measurement value, greatly reduce the target tracking error caused by the sensor uncertainty measurement problem, and meet the actual requirements.
Implementation and Operation of Blockchain-Based Energy Communities Under the New Legal Framework

The current movement within the energy market caused by the need for climate measures along with emerging new technologies leads to an evolution to a more intelligent, decentralized power network. Energy communities are part of this progress by jointly producing, consuming, storing, and sharing energy to increase the self-consumption of locally generated energy. These energy communities are the focus of this article. Besides the legal framework, in particular, the European Union’s Clean Energy Package, the implementation of energy communities will depend heavily on suitable information and communications technology (ICT) solutions, e.g., the Blockchain technology which again rises legal implications like privacy issues.This article provides an interdisciplinary overview about the legal, economic, and technical questions arising due to the deployment of energy communities in their integration into the existing power system. A concrete implementation of a Renewable Energy Community by utilizing Blockchain technology and the implications regarding privacy issues, energy efficiency as well as profitability aspects are discussed and results of a comprehensive stimulative study on energy savings for community customers are presented.
Dynamic Obstacle Avoidance on Middle Size League Robot Soccer ERSOW Using Subtargets

This paper presents the generation of a virtual obstacle to enhance subtargets algorithm that is implemented on robot soccer. One thing that we need to consider is the behavior of our robot when dealing with obstacles. The most challenging ability that must be owned by the robot is to avoid obstacles on the field as fast as possible. Obstacle avoidance skills must be adaptive to highly dynamic movement of the opponent robot and must have cheap computation. Subtargets algorithm is the right answer with considering the speed of each object and the environment of robot soccer. Speed will be converted to several virtual obstacles using a linear constant velocity method and will be input for subtargets algorithm. Based on the result of the experiments have been done. This algorithm worked great if both our robot speed and the virtual obstacles of the opponent robot speed are not exceeding the limits. Robot speed can reach 70 cm/s. The robot has a 100% rate of success to avoid both multiple static obstacles and single static obstacles flawlessly. In the second experiment, we try with different setpoint speeds of virtual obstacle which is 0.25 m/s, 0.6 m/s, 0.65 m/s and the robot can avoid correctly with a 100% rate of success. Third, we increase the virtual obstacles speed until the limits on 1 m/s, the robot is decreasing the rate of success to 90%. This trend will get worst on the rest of the speed if we keep increasing it.
Application of tools to support Linked Open Data

Purpose These projects aim to improve library services for users in the future by combining Link Open Data (LOD) technology with data visualization. It displays and analyses search results in an intuitive manner. These services are enhanced by integrating various LOD technologies into the authority control system. Design/methodology/approach The technology known as LOD is used to access, recycle, share, exchange and disseminate information, among other things. The applicability of Linked Data technologies for the development of library information services is evaluated in this study. Findings Apache Hadoop is used for rapidly storing and processing massive Linked Data data sets. Apache Spark is a free and open-source data processing tool. Hive is a SQL-based data warehouse that enables data scientists to write, read and manage petabytes of data. Originality/value The distributed large data storage system Apache HBase does not use SQL. This study’s goal is to search the geographic, authority and bibliographic databases for relevant links found on various websites. When data items are linked together, all of the data bits are linked together as well. The study observed and evaluated the tools and processes and recorded each data item’s URL. As a result, data can be combined across silos, enhanced by third-party data sources and contextualized.
Seam-Cutting Based Unmanned Aerial Vehicle Hyperspectral Image Stitching

In this paper, a novel unmanned aerial vehicle (UAV) hyperspectral image stitching framework based on radiation correction and seam-cutting blending is proposed. Firstly, spectral correlation constraints are introduced to eliminate mismatched pairs in the transform matrix estimation step. Then, a spectral correction method based on intrinsic images is proposed to ensure spectral consistency of stitching results. In order to obtain more natural stitching results without edge effect, a seam-cutting and multi-scale blending strategy is adopted in the final blending stage. Experimental results on real unmanned aerial vehicle hyperspectral strip images show that the proposed method is superior to a representative image stitching approach.
The Effective Sample Size of EHR-Derived Cohorts Under Biased Sampling

Electronic Health Records (EHRs) have become a popular data source for conducting observational studies of health outcomes. One advantage of using EHR-derived data for biomedical and epidemiologic research is the ability to efficiently construct large cohorts, providing access to “big data” in healthcare. For example, the U.S. Food and Drug Administration’s Sentinel System, which is composed of EHR and administrative claims data, includes over 100 million people, constituting approximately one-third of the U.S. population. Although the sample size of EHR-derived cohorts can be very large, EHR data arise through a complex, non-random sampling process that can induce bias when using such data to obtain parameter estimates that are meant to be representative of an underlying population. In the U.S.A., where most health insurance is employment-based, insured populations are often non-representative of uninsured populations, and thus, insurance status, as well as health literacy and healthcare-seeking behavior, is associated with representation in EHRs. As a result, the non-random sampling mechanism that gives rise to EHR data can induce significant bias in parameter estimates derived from EHR-based studies relative to the underlying population parameters. Here, we derive formulas for the mean-squared error of an EHR-derived sample as a function of the strength of association between a health outcome of interest, the sampling process, and an underlying unobserved covariate. We also provide a formula for the effective sample size of an EHR-derived cohort defined as the sample size of a simple random sample with equivalent mean-squared error to an EHR-derived sample arising from a biased sampling mechanism. The effective sample size allows for assessment of the advantage of using an EHR-derived sample as opposed to conducting a more traditional, designed observational study, taking into account both the number of patients and the biased sampling mechanism. Through simulation studies, we demonstrate the magnitude of bias induced in EHR-based parameter estimates under varying sample selection mechanisms, and we demonstrate how the effective sample size can be used to compute confidence intervals that account for the biased sampling scheme. We conclude that attention to biased sampling is necessary to avoid erroneous inference due to the large sample size and complex, non-random provenance of EHR-derived data, when the goal of a study is to use EHR-derived data to capture parameter estimates that are representative of an underlying population.
A Language Acquisition Support System that Presents Differences and Distances from Model Speech

It is difficult for language learners to know whether they are speaking well and, if not, to know where their speech differs from that of native speakers and how much their speech differs from that of native speakers. Therefore, we propose a novel language learning system that solves these problems. The system uses self-supervised learning to determine whether the user’s speech is good or not. The system also shows where the user’s speech differs from the native speaker’s speech by highlighting the corresponding places on the user’s speech waveform. It also represents the learner’s speech and the native speaker’s speech as points on a two-dimensional coordinate to show the user how far apart they are. We expect that the learner’s speech will gradually become better by repeatedly modifying the speech to eliminate these differences and bring the distance closer.
Information Processing on Compressed Data

Abstract We survey our recent work related to information processing on compressed strings. Note that a “string” here contains any fixed-length sequence of symbols and therefore includes not only ordinary text but also a wide range of data, such as pixel sequences and time-series data. Over the past two decades, a variety of algorithms and their applications have been proposed for compressed information processing. In this survey, we mainly focus on two problems: recompression and privacy-preserving computation over compressed strings. Recompression is a framework in which algorithms transform a given compressed data into another compressed format without decompression. Recent studies have shown that a higher compression ratio can be achieved at lower cost by using an appropriate recompression algorithm such as preprocessing. Furthermore, various privacy-preserving computation models have been proposed for information retrieval, similarity computation, and pattern mining.
STAMPED: RACISM, ANTIRACISM AND YOU: A REMIX Edited by JasonReynoldsLittle, Brown and Company. ISBN‐13: 9780316453691. Price $18.99 / $23.99 (CAD) (hardcover). 320 pages.

TESOL JournalVolume 12, Issue 3 e579 MATERIALS & MEDIA REVIEW STAMPED: RACISM, ANTIRACISM AND YOU: A REMIX Edited by Reynolds, Jason Little, Brown and Company. ISBN-13: 9780316453691. Price $18.99 / $23.99 (CAD) (hardcover). 320 pages. Lisa Cuthbertson, Corresponding Author Lisa Cuthbertson lcuthbertson@scis-china.org Shanghai Community International SchoolSearch for more papers by this author Lisa Cuthbertson, Corresponding Author Lisa Cuthbertson lcuthbertson@scis-china.org Shanghai Community International SchoolSearch for more papers by this author First published: 12 October 2021 https://doi.org/10.1002/tesj.579Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume12, Issue3September 2021e579 RelatedInformation
Towards Machine Learning Support for Embedded System Tests

The correctness of embedded systems needs to be ensured by a high number of tests. Large amounts of data reflecting the system behavior are collected during these test runs. Automated test evaluations are often limited to checking very specific requirements which can hardly cover all possible kinds of erroneous behaviors. Manual examinations can compensate this by implicit knowledge of experienced test engineers but are very time-consuming and therefore costly.This paper shows how machine learning can support the evaluation of embedded system tests. Assessment of new test runs is based on available data from previous tests and aims at identifying those that deviate from usual behavior. Moreover the paper presents a generic approach that helps to find the most suitable detection algorithm in the given context. A case study proves the effectiveness of this approach. Quantitative comparisons show that our exploration is able to find solutions that outperform state of the art methods.