Reviewerly/exHarmony huggingface dataset:
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them. The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants' eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

Abstract Projection algorithms such as t‐SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter values is computationally intensive and unintuitive due to the stochastic nature of such methods. In this paper we propose HyperNP, a scalable method that allows for real‐time interactive hyperparameter exploration of projection methods by training neural network approximations. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can compute projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualization systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real‐world settings.
Algebraic Semantics for C++11 Memory Model

The C++11 standard introduced a language level weak memory model (i.e., the C++11 memory model) to improve the performance of the execution of C/C++ programs. Algebra is well-suited for direct use by engineers in symbolic calculation of parameters. It is a challenge to investigate the algebraic semantics for the C++11 memory model. Inspired by the promising semantics, in this paper, we explore the algebraic laws for the C++11 memory model, including a set of sequential and parallel expansion laws. We introduce the concept of guarded choice, and every program under the C++11 memory model can be converted into the head normal form of guarded choice. In addition, the proposed algebraic laws are implemented in the rewriting engine Maude.
Enhanced image super-resolution using hierarchical generative adversarial network

Recently, generative adversarial networks (GAN) have been introduced in single-image super-resolution (SISR) to reconstruct more realistic high-resolution (HR) images. In this paper, we propose an effective SISR method, named super-resolution using hierarchical generative adversarial network (SRHGAN), based on the idea of GAN and the prior knowledge. Different from the existing GANs that focus on the depth of networks, our proposed method considers the prior knowledge in addition. That is, we introduce an edge extraction branch and an edge enhancement branch into GAN for considering the edge information. By means of the added edge loss in the loss function, the edge extraction branch and the edge enhancement branch will be trained to reconstruct the sharp edge well. Experimental results on several datasets illustrate that our reconstructed visual effect images are clearer and sharper than some related SISR methods.
Safety Helmet Wearing Detection Method Based on Improved YOLOX

Aiming at the problem that the current safety helmet detection methods are easy to miss and false detect small objects and occluded objects in crowded scene, a lightweight safety helmet detection method based on YOLOX was proposed. Firstly, the coordinate attention mechanism is added into YOLOX backbone network to improve the network's perception of object position. Secondly, a weighted bidirectional feature pyramid was used to replace the feature fusion part of the neck network to achieve simple and fast multi-scale feature fusion, and depthwise separable convolution is used to reduce the number of parameters of the network and model size. Finally, a new detection scale is added to further strengthen the detection ability of small objects. On the helmet wearing detection dataset SHWD, the experimental results show that the mAP of the improved method reaches 92.2%, which is improved by 2.34% compared with the original algorithm, and the model size is reduced by 16%. The missed detection and false detection of small objects and occluded objects are improved.
UML Statechart Anti-Patterns

UML statechart diagrams are widely used in spec-ification of software behaviour. For various reasons, during statechart design process, detrimental sections can be introduced in the statechart design that has the potential adverse effects in the development lifetime of the software. This study introduces statechart design anti-patterns to give examples to some of those flawed design practices and proposes ideas to repair them.
Multi-domain vision-based sign language recognition based on auto-labeled hand tracking data learning

Remote operating and autonomous systems are widely applied in various fields, and the development of technology for human machine interface and communication is strongly demanded. In order to overcome the limitations of the conventional keyboard and tablet devices, various vision sensors and state-of-the-art artificial intelligence image processing techniques are used to recognize hand gestures. In this study, we propose a method for recognizing a reference sign language using auto labeled AI model training datasets. This study can be applied to the remote control interfaces for drivers to vehicles, person to home appliances, and gamers to entertainment contents and remote character input technology for the metaverse environment.
Locally fitting hyperplanes to high-dimensional data

Abstract Problems such as data compression, pattern recognition and artificial intelligence often deal with a large data sample as observations of an unknown object. An effective method is proposed to fit hyperplanes to data points in each hypercubic subregion of the original data sample. Corresponding to a set of affine linear manifolds, the locally fitted hyperplanes optimally approximate the object in the sense of least squares of their perpendicular distances to the sample points. Its effectiveness and versatility are illustrated through approximation of nonlinear manifolds Möbius strip and Swiss roll, handwritten digit recognition, dimensionality reduction in a cosmological application, inter/extrapolation for a social and economic data set, and prediction of recidivism of criminal defendants. Based on two essential concepts of hyperplane fitting and spatial data segmentation, this general method for unsupervised learning is rigorously derived. The proposed method requires no assumptions on the underlying object and its data sample. Also, it has only two parameters, namely the size of segmenting hypercubes and the number of fitted hyperplanes for user to choose. These make the proposed method considerably accessible when applied to solving various problems in real applications.
Scrum in Classroom Collaborations: A Quasi-Experimental Study

Scrum is an increasingly important project management framework that has had limited study in technical communication (TC) and TC classrooms. While research has found student collaborations to be both frustrating and challenging, it has found Scrum to be a scaffolding framework that can improve student interactions and outcomes. Therefore, to determine whether Scrum affects the peer assessments of collaborative teams as well as project grades, this quasi-experimental classroom study compares the midproject and postproject peer assessments and grades of advanced TC students who used Scrum as a framework for collaboration against those students who did not use Scrum in their collaborations. The study found that students who used Scrum rated their team members significantly higher on some peer assessment measures and earned significantly higher grades than did those students who did not use Scrum. Additionally, students in the Scrum protocol reported satisfaction with their group experience broadly but did not report satisfaction with Scrum itself.
Comparing Spectro-radiometer Instruments for a Satellite Mission to Detect Chemical Warfare Agents

Previous research has indicated that it is theoretically possible to detect chemical warfare agents, such as sarin and sulphur mustard from space. However, this analysis showed that limitations with spatial resolution mean that the likelihood of real-time detection of chemical warfare agents was low. This leads to this idea for a satellite mission that could achieve this capability. Such a mission would be of value both to the monitoring of chemical releases and to emergency services in responding rapidly to incidents and attacks, especially in remote and inaccessible areas. In this paper, three potential instruments are compared in a trade study, against the Infrared Atmospheric Sounding Interferometer (IASI) instrument on ESA&#x0027;s MetOp satellite as a benchmark. Operational scenarios in which a potential future mission would operate are used to derive requirements on the instruments. First is a Laser Heterodyne Radiometer (LHR), second is the Tropospheric Emission Spectrometer (TES), a spectrometer flown on NASA&#x0027;s AURA mission, and last is the High -resolution Anthropogenic Pollution Imager (HAPI). The instruments&#x0027; parameters such as spatial, spectral, and temporal resolutions as well as revisit time and instrument needs such as Size, Weight and Power (SWaP) were compared against the mission requirements. It was determined that no single instrument was able to meet all of the operational requirements alone, indicating that combinations of instruments may be necessary to produce a mission that can fulfil the requirements, with two basic concepts suggested for further research.
Building Classes for Sentiment of Social Media Content Using WU Semantic Measure

Sentiment classification or opinion mining is conducted to extract the core data representing opinions, and emotions. Motivated by the role of social media on appointing the dimension of the discussions, a solution is proposed to classify users’ sentiments and define the most common topics deliberated by the users in Twitter. The proposed technique classifies user’s sentiments by applying semantic measure to build classifications (classes) from user tweets. The classifications are alternated according to the study domain. This research has picked International Women’s Day as the study domain because of the availability of historical data and the renewability of the topic. The proposed technique is building the classes that define the topics of interest. WuP (Wu and Palmer) measure is used to compute how much the tweets’ words are similar to the classes’ concepts. In the topic domain “International Women’s Day”, 2000 tweets have been used to build the four classes (Activity, Women role, Time, and Surroundings).
Wiggly Models

Recall from Sect. 4.4.2 that a “linear model” does not need to be linear. Mathematically, we say a model is linear if it is a linear function of its coefficients (“something times β 1 plus something times β 2…”) Linear model . But if we include non-linear functions of x as predictors, we can use this framework to fit a non-linear function of x to data.
High Throughput Implementation of Post-quantum Key Encapsulation and Decapsulation on GPU for Internet of Things Applications

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3103956]
Towards digital economy through data literate workforce

∗In today's digital economy, data are part of everyone's work. Not only decision-makers but also average workers are invited to conduct data-based experiments, interpret data, and create innovative data-based products and services. In this endower, the entire workforce needs additional skills to thrive in this world. This type of competence is united by the name data literacy, and as such, it becomes one of the most valuable skills in the labor market. This paper aims to highlight the needs and shortcomings in terms of competencies for working with data as a critical factor in the business of modern companies striving for digital transformation. Through systematic desk research spanning over 15 European countries, this paper sheds light on how data literacy is addressed in European Higher Education and professional training. In addition, our analysis uses results from an online survey conducted in 20 countries in Europe and North Africa. The results show that the most valuable data literacy competence of an employee is the ability to evaluate or reflect data and the skills related to reading or creating data classification.
Secure and Efficient Decentralized Bitcoin Mixing Scheme using Trusted Execution Environment

Mixing schemes have been applied by Bitcoin users to break their payment links in the blockchain to enhance privacy. However, most mixing schemes cannot provide secure mixing service without compromising efficiency since they are relying on complex cryptographic techniques or interactive protocols. To provide secure yet efficient mixing service, researchers introduce Intel SGX enclave, which provides Trusted Execution Environment (TEE) with confidentiality and integrity guarantees to execute mixing operations. Unfortunately, users will lose their mixing funds if a malicious service provider compromises the confidentiality guarantee of his/her enclave. Moreover, the scheme cannot scale to a large number of users in a single mixing round, that is, limited scalability. In this paper, we present a novel decentralized mixing scheme with multiple enclaves run by different service providers, which uses Shamir secret sharing scheme and additive homomorphic property of keys in Elliptic Curve Cryptography to tolerate a subset of enclaves to be compromised. Moreover, our scheme also provides stronger scalability so it achieves anonymity sets by orders of magnitude higher than the existing TEE-based mixing scheme. The experiment shows our scheme can provide stronger security and anonymity guarantees without compromising efficiency which outperforms existing mixing schemes.
Online and Offline Integration of Smart Educational Monitoring System based on Face Recognition Smart Sign-in Devices

Online and offline integration of smart educational monitoring system based on face recognition smart sign-in devices is studied in the paper. The face image analysis algorithm proposed by us is a data simplification analysis method that commonly used for feature extraction and dimensionality reduction. It can not only concentrate the useful information in multi band images into the new principal component images as few as possible, but also make these principal component images uncorrelated, thus greatly reducing the total amount of data. The application of artificial intelligence technology in the smart classroom check-in system will improve the efficiency of attendance check-in and facilitate teachers' management of the class. Hence, the combination of these 2 technologies have been studied. Through the testing, the performance is validated.
Algorithm for the formation of closed contours of objects represented by small piecewise-discontinuous functions for the tasks of constructing stable features and automated selection of areas of complex shape when processing scenes obtained in the infrared and optical ranges

The paper proposes an algorithm for forming stable features and identifying the boundaries of objects obtained in the analysis of scenes recorded in the infrared and optical ranges. The proposed approach is based on a sequential process and includes the following steps. The analysis of the boundaries of objects fixed in the visible range is carried out. For images obtained in the far-infrared range (thermal imaging), a method of decreasing the dimensionality of the cluster swing is used, which makes it possible to reduce the data bit range while preserving the boundaries of objects. Reducing the ranges is performed using analyzing histograms of color gradients and the absorption of scarcely occurring blocks by neighboring ones. Creation of masks of objects and formed closed contours. Search for intersections of the boundaries of objects (visible range) and formed masks (thermal images). Formation of areas of crossing borders and objects, search for an average line between pairs of generated data. Plotting the resulting curve and the close of curves if they are gap. As a criterion for ruining the curve used for analyzing the distance between pare elements (length of 3 pixels). The construction of the connecting line is made from the edges of the formed boundaries by interpolating a straight line. Pairs of test images obtained by sensors with a resolution of 1024x768 (8 bit, color image, visible range) and 320x240 (8 bit, color, thermal images) are used as test data to assess the effectiveness. Images of simple shapes are used as analyzed objects.
Track Initiation for a Multiple Hypothesis Tracker with ML-PMHT

The Multiple Hypothesis Tracker (MHT) is a pow-erful and widely used algorithm for simultaneously tracking multiple targets. As its name suggests, it reasons over multi-ple measurement-to-track assignments, or hypotheses, and at periodic intervals, outputs the optimal global hypothesis. In an ideal world, an MHT could keep track of all possible hypotheses; however, in the real world, the number of hypotheses rapidly becomes extremely large, so for computation and/or memory considerations, low-probability hypotheses must be continu-ously removed, or pruned. One reason for the large number of hypotheses is that in many MHT implementations, every received measurement is allowed to birth a new track. While this has a good chance of discovering actual new targets, it also definitely creates many false tracks that ultimately need to be pruned. We examine a new technique for track initiation - instead of simply creating a track from every new measurement, we use the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT), a deterministic batch tracker to search for new tracks from recently received measurements. Only new tracks discovered by ML-PMHT will birth tracks considered by the MHT. This has the effect of reducing the high number of low-probability hypotheses that need to be reasoned over by the MHT, ultimately improving its performance.
Mapping Alterations associated with Porphyry Copper Ores using ASTER Multispectral Imaging Based on Deep Learning

Mapping Alterations associated with Porphyry Copper Ores using ASTER Multispectral Imaging Based on Deep Learning
Distance learning, emergency remote teaching and blended learning research methodologies

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
An access control model considering with transitions of access rights based on the blockchain

Leakage of sensitive information concerned with database systems, such as social networking services and online storages, has occurred. One of the causes of such leakage is a covert channel, which is a path of information leakage caused by inconsistencies in access rights on databases. Another cause is an inference attack in which secret information is inferred from the fragments of open data. We previously proposed a hypergraph-based model used to prevent the possibility of information leaks by inference but considered only reading operations. However, writing operations also have to be considered. We also previously proposed covert-channel analysis models with inference rules and reading and writing operations. Management mechanisms for access histories to objects by users are required to analysis the satisfaction of the inference rules. If the accesses histories are altered, information leakage by an inference attack will occur. In this study, we used a blockchain to manage access logs to solve the above problems. A traditional covert channel is a static analysis based on the assumption that the access rights of access control lists do not change. Covert channels exist under certain conditions even if no covert channel is detected from static analysis. We define the authorization quad, which describes the relation among the subject as a user, object as a file, access rights, and timing of transition, to consider the transitions of access rights to analyze a covert channel. We also propose a management scheme for covert-channel analysis with a blockchain considering inference attacks and transitions of access rights.