Reviewerly/exHarmony huggingface dataset:
Teachers’ Readiness for Remote Teaching During COVID-19 Pandemic: The Case of Latvia

The state of emergency due to the COVID-19 pandemic influenced educational processes in schools all over the world. Teachers had a very short time to reform all their work and started to use new technologies, which increased their workload and level of stress. Information on factors affecting teachers' readiness for remote teaching is insufficient, and we aimed to investigate it in this study. We performed a population-based cross-sectional study after the first experience of teachers working remotely during the Spring 2020 state of emergency in Latvia. We assessed the self-reported “overall readiness for remote teaching” using five sub-domains of a specific survey. We observed a reversed association between the teachers' “overall readiness for remote teaching” and teaching the subject of sports (ß = -7.25, CI - 10.97; -3.54) and a significant association with the selfassessment of teachers' own digital skills (ß = 6.54, CI 5.25; 7.83). We conclude that special training programs for teachers on the efficacy of remote teaching should be created to help them get over the difficulties of new educational processes and improve their digital skills. We recommend additional support for sports and health teachers as their readiness for remote teaching was significantly lower.
Document-Level Sentiment Analysis through Incorporating Prior Domain Knowledge into Logistic Regression

In this paper, we present a prior domain knowledge-enhanced classification approach for document-level sentiment analysis. The proposed approach is a hybrid category of sentiment classification approaches which uses two types of prior domain knowledge - general sentiment knowledge extracted from a lexicon, and knowledge extracted from unlabeled domain data that we call domain-specific sentiment knowledge. We combine prior domain knowledge with logistic regression to enhance sentiment classification, and use gradient descent approach to optimize the modified logistic regression model. The novelty of our proposed approach lies in incorporating prior domain knowledge directly into the logistic regression model. The proposed approach is empirically evaluated through extensive experiments over a multi-domain sentiment dataset. It is also compared with three baseline methods and performs significantly better.
Learning transferable and discriminative features for unsupervised domain adaptation

Although achieving remarkable progress, it is very difficult to induce a supervised classifier without any labeled data. Unsupervised domain adaptation is able to overcome this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. Transferability and discriminability are two key criteria for characterizing the superiority of feature representations to enable successful domain adaptation. In this paper, a novel method called learning TransFerable and Discriminative Features for unsupervised domain adaptation (TFDF) is proposed to optimize these two objectives simultaneously. On the one hand, distribution alignment is performed to reduce domain discrepancy and learn more transferable representations. Instead of adopting Maximum Mean Discrepancy (MMD) which only captures the first-order statistical information to measure distribution discrepancy, we adopt a recently proposed statistic called Maximum Mean and Covariance Discrepancy (MMCD), which can not only capture the first-order statistical information but also capture the second-order statistical information in the reproducing kernel Hilbert space (RKHS). On the other hand, we propose to explore both local discriminative information via manifold regularization and global discriminative information via minimizing the proposed class confusion objective to learn more discriminative features, respectively. We integrate these two objectives into the Structural Risk Minimization (RSM) framework and learn a domain-invariant classifier. Comprehensive experiments are conducted on five real-world datasets and the results verify the effectiveness of the proposed method.
Hybrid Architecture of Face and Action Recognition Systems for Proctoring on a Graphic Processor

A solution has been developed due to problem of face recognition and actions when we are trying to process images of human faces in real time on the server. Approaches to solving the problem of face recognition and actions on images are considered. The solution of the problem of action's recognition in real time is considered. An action recognition system was used for proctoring.A hybrid architecture was developed for face and action recognition. The FaceNet algorithm was used to implement the system. We used the faces of a person that was filmed to collect the dataset. The results obtained show a high accuracy of the face recognition percentage. Face and motion recognition is implemented in Windows Server 2016. A special web application was developed to test these algorithms.To reduce processing time and recognition algorithms were used like bandwidth, resize images. In addition, the entire process of video processing for proctoring was launched in the graphics server. With the web app, you can see how well the face recognition algorithm and the result of the action recognition algorithm are.
Accurate pattern-based extraction of complex Gleason score expressions from pathology reports

• First study of regex-based extraction of complex Gleason score expressions in text. • Even complex expressions can be extracted accurately using regexes. • We recommend using regexes because they are accessible and rapid to develop. • Developed regular expressions and other code freely available online. The Gleason score is an important grading factor of prostate cancer. Gleason scores can be extracted from pathology report texts using regular expressions, but previously developed programmes have targeted only relatively simple Gleason score expressions. We developed a programme capable of extracting also complex expressions. The programme is relatively easy to adapt to other languages and datasets. We developed and evaluated our regular expression-based programme using manually processed pathology reports of prostate cancer cases diagnosed in Finland in 2016–2017. Both simple and complex Gleason score expressions were targeted. We measured the performance of our programme using recall, precision, and the F1. The proportion of complex Gleason score expressions was estimated as the complement of the recall when only addition expressions (e.g. “Gleason 3 + 4”) were targeted. The detection of values (scores and score components) is based on mandatory keywords before or after the value. The programme favours precision over recall by primarily allowing for lists of optional expressions between keyword-value pairs and only secondarily allowing for arbitrary expressions. The programme is straightforward to adapt to new datasets by modifying the lists of mandatory and optional expressions. The full and addition-only programmes had 92% (95% CI: [90%, 95%]) and 65% ([61%, 70%]) recall and high precision (98% [97%, 99%] and 100% [99%, 100%]), respectively. The estimated proportion of complex Gleason score expressions was 100–65 = 35%. Even complex Gleason score expressions can be extracted with high recall and precision using regular expressions. We recommend implementing automated Gleason score extraction where possible by adapting our validated programme.
3D reconstruction with auto-selected keyframes based on depth completion correction and pose fusion

Dense 3D reconstruction is required for robots to safely navigate or perform advanced tasks. The accurate depth information of the image and its pose are the basis of 3D reconstruction. The resolution of depth maps obtained by LIDAR and RGB-D cameras is limited, and traditional pose calculation methods are not accurate enough. In addition, if each image is used for dense 3D reconstruction, the dense point clouds will increase the amount of calculation. To address these issues, we propose a 3D reconstruction system. Specifically, we propose a depth network of contour and gradient attention, which is used to complete and correct depth maps to obtain high-resolution and high-quality depth maps. Then, we propose a method of fusion of traditional algorithms and deep learning for pose estimation to obtain accurate localization results. Finally, we adopt the method of autonomous selection of keyframes to reduce the number of keyframes, the surfel-based geometric reconstruction is performed to reconstruct the dense 3D environment. On the TUM RGB-D, ICL-NIUM, and KITTI datasets, our method significantly improves the quality of the depth maps, the localization results, and the effect of 3D reconstruction. At the same time, we have also accelerated the speed of 3D reconstruction.
Machine learning based approach to analyze file meta data for smart phone file triage

With the rapid increase in mobile phone storage capacity and penetration, digital forensic investigators face a significant challenge in quickly identifying relevant examinable files within a plethora of uninteresting OS and application files extracted by forensic tools. This challenge can have serious adverse effects in time critical cases, and can also result in increasing case backlog. A possible solution for this issue is to prioritize digital artifacts. This is referred to as triage. Several digital forensic triage methodologies based on classical automation techniques such as block hash and regular expression matching have been proposed. However, such techniques suffer from the significant limitation of requiring users to know and hardcode data templates and relations of interest. In literature, more flexible machine learning based approaches have been proposed to classify whether a mobile device, rather than a mobile device artifact, is of interest or not based on its usage metrics and file-system metadata. Also, recently an approach has been proposed and tested in triaging data generated and extracted from a computer-based operating system. However, this approach did not cover smart mobile operating system, and it did not consider key steps such as feature engineering, feature selection, and hyper-parameter tuning. Hence, in this paper, we propose a comprehensive machine learning based solutions with features extracted from file metadata to identify possible smart phone files of interest that should be examined. A range of classification algorithms are tested and their performance compared. Our classification models were trained and tested on a dataset consisting of the metadata of nearly 2 million files extracted from devices running Android OS and linked to real terrorism cases. The use of real case data allows obtaining realistic results, and restricting the operating system and case type helps narrow the experimentation scope enough to provide a proof of concept. Through our experiments, a best classifier is also identified.
OmniFlow: Human Omnidirectional Optical Flow

Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: https://mytuc.org/byfs
Augmented Silkscreen: Designing AR Interactions for Debugging Printed Circuit Boards

Debugging printed circuit boards (PCBs) requires frequent context switching and spatial pattern matching between software design files and physical boards. To reduce this overhead, we conduct a series of interviews with electrical engineers to understand their workflows, around which we design a set of AR interaction techniques, we call Augmented Silkscreen, to streamline identification, localization, annotation, and measurement tasks. We then run a set of remote user studies with illustrative video sketches and simulated PCB tasks to compare our interactions with current practices, finding that our techniques reduce completion times. Based on these quantitative results, as well as qualitative feedback from our participants, we offer design recommendations for the implementation of these interactions on a future, deployable AR system.
Privacy

There are significant gaps between legal and technical thinking around data privacy. Technical standards are described using mathematical language whereas legal standards are not rigorous from a mathematical point of view and often resort to concepts which they only partially define. As a result, arguments about the adequacy of technical privacy measures for satisfying legal privacy often lack rigor, and their conclusions are uncertain. The uncertainty is exacerbated by a litany of successful privacy attacks on privacy measures thought to meet legal expectations but then shown to fall short of doing so. As computer systems manipulating individual privacy-sensitive data become integrated in almost every aspect of society, and as such systems increasingly make decisions of legal significance, the need to bridge the diverging, and sometimes conflicting legal and technical approaches becomes urgent. We formulate and prove formal claims -- "legal theorems'' -- addressing legal questions such as whether the use of technological measures satisfies the requirements of a legal privacy standard. In particular, we analyze the notion of singling out from the GDPR and whether technologies such as k-anonymity and differential privacy prevent singling out. Our long-term goal is to develop concepts which are on one hand technical, so they can be integrated in the design of computer systems, and can be used in legal reasoning and for policymaking on the other hand.
Time-preference-based on-spot bundled cloud-service provisioning

The cloud computing spot instance is one offering that vendors are leveraging to provide differentiated service to an expanding pay-per-use computing market. Spot instances have cost advantages, albeit at a trade-off of interruptions that can occur when the user's bid price falls below the spot price. The interruptions are often exacerbated since customers often require resources in bundles. For these reasons, customers might have to wait for a long time before their jobs are completed. In this paper, we propose a behavioral-economic model in the form of time-preference-based bids, wherein users are willing to use and bid for services at other times if the vendor cannot provide the resources at the preferred time. Given such bids, we consider the problem of provisioning for such service requests. We develop a time-preference-based optimization model. Since the optimization model is NP-Hard, we develop rule-based genetic algorithms. We have obtained very encouraging results with respect to standard commercial solver as a benchmark. In turn, our results provide evidence for the viability of our approach for online service-provisioning problems.
ParSetgnostics: Quality Metrics for Parallel Sets

Abstract While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points ‐ analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi‐dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations.
Efficient All-or-Nothing Public Key Encryption With Authenticated Equality Test

Public key encryption with equality test (PKEET for short) is a new cryptographic primitive which allows a proxy to check whether two ciphertexts encrypted under different public keys are of the same plaintext. PKEET has become a prospective candidate for various application scenarios, such as data management on encrypted databases, personal health record systems, spam filtering in encrypted email systems. In this paper, we propose an efficient all-or-nothing public key encryption scheme with authenticated equality test (AoN-PKEAET for short). In comparison with the existing PKEET schemes, the proposed scheme provides a new feature of ciphertext authentication before the plaintext equality test. It ensures that no misjudgement occurs when testing the equality between ciphertexts. Specially, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {Test}$ </tex-math></inline-formula> algorithm in the proposed scheme first authenticates the compared ciphertexts and the equality test is fulfilled only if the ciphertexts are authenticated as valid. With this new feature, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {Test}$ </tex-math></inline-formula> algorithm cannot output 0 (which means that the encrypted messages are different) when the two input ciphertexts are of an identical message, or it cannot output 1 (which means that the encrypted messages are identical) when the two input ciphertexts are of different messages. Under a redefined security model for AoN-PKEAET, we give the detailed security proof of the proposed scheme.
Radio Frequency Fingerprint Recognition Method Based on Generative Adversarial Net

RF fingerprint recognition is an emerging technology for identifying specific hardware features of wireless transmitters. In order to solve the problem of illegal transmitter hazardous wireless communication security, this paper proposes a method of generating RF fingerprint recognition methods based on generative adversarial net (GAN).This article first uses I / Q data through wavelet transform data pre-processing , Since the wavelet transform can describe the features of different frequency signals, the characteristics of radio frequency fingerprint can be highlighted after wavelet transform of I / Q data. Then we have designed a generative adversarial net model, which consists of a generate model and a discriminant model. Generate model input noise to generate a pseudo data distribution, The discriminant model enters the data distribution of the real trusted transmitter and the generated dummy data distribution generated, and the determination result is fed back to the generator, allowing the generator to update to generate more real pseudo data distributions, better The network model is used for radio frequency fingerprint recognition. Based on the above, it is possible to effectively identify rogue radio frequency transmitters to some extent to solve wireless security issues. The experimental results show that the generative adversarial net (GAN) can distinguish between 98.1% accuracy of the credibility transmitter and illegal transmitter, it has higher accuracy than traditional convolutional neural networks (CNN) and full connectivity neural network (DNN).
Temporal consistent portrait video segmentation

• A novel correspondence prediction paradigm for high-quality feature reconstruction. • A PVS framework to exploit inter-frame correspondence through meta-learning. • A PVS dataset serving as a benchmark to promote future research. We explore a new video segmentation task, named portrait video segmentation (PVS), which aims to automatically segment the dominant person throughout a given portrait video. To achieve accurate and temporal-coherent segmentation results, a feature reconstruction based PVS method is developed under the meta-learning framework. Due to the dramatic pose variation and severe occlusion in portrait videos, feature reconstruction using existing optical flow models usually suffers from severe ghosting effects in reconstructed features. We mitigate this issue by presenting a soft correspondence network (SCN), which learns to facilitate feature reconstruction in an unsupervised fashion by softly assigning each pixel displacement probabilities between portrait frames. Based on the proposed SCN, a novel portrait segmentation network (PSN) is further designed, which explores the reconstructed features through feature aggregation blocks (FABs), yielding more reliable segmentation results. To capture temporal and target-specific cues, the parameters of FABs are determined by a meta-updater network which is trained offline in the meta-level. In addition, we introduce a new PVS dataset with high-quality segmentation annotations. Experimental results clearly demonstrate the effectiveness of the proposed PVS method.
Fuzzy reinforced polynomial neural networks constructed with the aid of PNN architecture and fuzzy hybrid predictor based on nonlinear function

In the field of dynamic system identification and prediction, linear models (e.g., autoregressive models), nonlinear models (namely, neural networks models), and hybrid predictors (HPs) that are a hybridization of linear and nonlinear models have been proposed in the past. However, they are not completely free from limitations: they exhibit difficulties to describe high-order nonlinear relations between input and output variables. In this study, we propose fuzzy reinforced polynomial neural networks (FRPNNs), which are polynomial neural network architecture-based on fuzzy reinforced polynomial neurons (FRPNs) to overcome this limitation. The proposed FRPNs that consist of approximation part (AP) and compensation part (CP) arise as novel HPs. Here the CP for modeling nonlinear patterns can be regarded as forming the reinforced part for the AP that aims at capturing linear patterns, while AP is the linear polynomial neuron used in the conventional polynomial neural networks. In some sense, the overall FRPNNs are essentially generalized polynomial neural network architecture with novel HPs. The parameters considered in the design of the proposed fuzzy reinforced polynomial neural networks are optimized with the aid of the particle swarm optimization (PSO). The performance of FRPNNs is discussed involving time series and system identification datasets. Experimental results demonstrate that the proposed FRPNNs achieve at most the accuracy of 43.6% higher in comparison with the accuracy produced by some classical models reported in the literature.