Reviewerly/exHarmony huggingface dataset:
Finding Representative Interpretations on Convolutional Neural Networks

Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models. However, the existing methods can only interpret some specific decision logic on individual or a small number of images. To facilitate human understandability and generalization ability, it is important to develop representative interpretations that interpret common decision logics of a CNN on a large group of similar images, which reveal the common semantics data contributes to many closely related predictions. In this paper, we develop a novel unsupervised approach to produce a highly representative interpretation for a large number of similar images. We formulate the problem of finding representative interpretations as a co-clustering problem, and convert it into a submodular cost submodular cover problem based on a sample of the linear decision boundaries of a CNN. We also present a visualization and similarity ranking method. Our extensive experiments demonstrate the excellent performance of our method.
Changing the Online Climate via the Online Students: Effects of Three Curricular Interventions on Online CS Students’ Inclusivity

Motivation: Although CS Education researchers and practitioners have found ways to improve CS classroom inclusivity, few researchers have considered inclusivity of online CS education. We are interested in two such improvements in online CS education—besides being inclusive to each other, online CS students also need to be able to create inclusive technology. Objectives: We have begun developing a new approach that we term "embedded inclusive design" to address both of these goals. The essence of the approach is to integrate elements of inclusive design education into mainstream CS coursework. This paper presents three curricular interventions we have developed in this approach and empirically investigates their efficacy in online CS post-baccalaureate education. Our research questions were: How do these three curricular interventions affect (RQ1) the climate among online CS students and (RQ2) how online CS students honor the diversity of their users in the tech they create? Method: To answer these research questions, we implemented the curricular interventions in four asynchronous online CS classes across two CS courses within Oregon State University's Ecampus and conducted an action research study to investigate the impacts. Results: Online CS students who experienced these interventions reported feeling more included in the major than they had before, reported positive impacts on their team dynamics, increased their interest in accommodating diverse users, and created more inclusive technology designs than they had before. Discussion: These results provide encouraging evidence that embedding elements of inclusive design into mainstream CS coursework, via the interventions presented here, can increase both online CS students' inclusivity toward one another and the inclusivity of the technology these future CS practitioners create.
Research on the Design of Computer Scoring System for Chinese College Students’ English Translation

Abstract In the current field of Chinese institute students’ English computer translation, for the subjective questions in the form of question and answer, the answer is often open and non unique, which makes it difficult for the computer system to accurately identify and judge the answer. The score result still has a large drift from the manual score, so the fitting degree and prediction rate need to be further improved. Based on this, this paper first analyzes the research status and progress of English translation computer scoring, and then studies the construction of the computer scoring system of English translation grammar for Chinese institute students, and gives the logical linear model, the algorithm of feature extraction of translated text and the construction process of the model.
Mobile Apps for 3D Face Scanning

3D scanning is a rapidly growing field, and face scanning is a branch showing a great potential in various applications, but it is still new and little researched. As smartphone availability and functionality grows, the potential for 3D face scanning mobile apps has massively increased as well. Our study is aimed at finding and testing mobile apps that are suitable for 3D face scanning purposes. In our research we scanned two people’s faces using iOS and Android based smartphones. 6 mobile apps were found to be eligible: Bellus 3D, Heges, Scandy Pro, Capture, Trnio, 3D scanner Pro. 3D Scanner Pro were the only Android based app that was found to be suitable for face scanning purposes. Our research shows that based on visual evaluation, the most accurate face 3D models were made by Scandy pro, while Heges was better priced with similar accuracy. In the top three is also Bellus3D, which doesn’t provide the best resemblance to the scanning objects, but it was the easiest to use and process. Our quantitative measurements comparing face proportions show that the best accuracy for male face is obtained by Scandy pro and 3D Scanner Pro, but for female face – Scandy Pro and Capture.
Finite state machine for cloud forensic readiness as a service (<scp>CFRaaS</scp>) events

Abstract The importance of demonstrating the correctness of forensic analysis tools and automated incident management tools reinforces the need for a finite state machine (FSM) engine that can generate automated forensic processes. Hence, in this paper, we present an event‐based FSM representation for Cloud Forensic Readiness as a Service (CFRaaS), where we also show how the FSM's predetermined states and transitions could be used to formulate an automated forensic process and generate a hypothesis for litigation purposes. Specifically, this proposition comprises a two‐step level CFRaaS‐FSM with possible transitions and states. This representation is useful because it can alert digital forensic investigators on how to deduce current and next state of attacks based on transitions and current states.
PR-RRN: Pairwise-Regularized Residual-Recursive Networks for Non-rigid Structure-from-Motion

We propose PR-RRN, a novel neural-network based method for Non-rigid Structure-from-Motion (NRSfM). PR-RRN consists of Residual-Recursive Networks (RRN) and two extra regularization losses. RRN is designed to effectively recover 3D shape and camera from 2D keypoints with novel residual-recursive structure. As NRSfM is a highly under-constrained problem, we propose two new pairwise regularization to further regularize the reconstruction. The Rigidity-based Pairwise Contrastive Loss regularizes the shape representation by encouraging higher similarity between the representations of high-rigidity pairs of frames than low-rigidity pairs. We propose minimum singular-value ratio to measure the pairwise rigidity. The Pairwise Consistency Loss enforces the reconstruction to be consistent when the estimated shapes and cameras are exchanged between pairs. Our approach achieves state-of-the-art performance on CMU MOCAP and PASCAL3D+ dataset.
A Subfield-Based Construction of Optimal Linear Codes Over Finite Fields

In this paper, we construct four families of linear codes over finite fields from the complements of either the union of subfields or the union of cosets of a subfield, which can produce infinite families of optimal linear codes, including infinite families of (near) Griesmer codes. We also characterize the optimality of these four families of linear codes with an explicit computable criterion using the Griesmer bound and obtain many distance-optimal linear codes. In addition, by a more in-depth discussion on some special cases of these four families of linear codes, we obtain several classes of (distance-)optimal linear codes with few weights and completely determine their weight distributions. It is shown that most of our linear codes are self-orthogonal or minimal which are useful in applications.
Reasoning about effect interaction by fusion

Effect handlers can be composed by applying them sequentially, each handling some operations and leaving other operations uninterpreted in the syntax tree. However, the semantics of composed handlers can be subtle---it is well known that different orders of composing handlers can lead to drastically different semantics. Determining the correct order of composition is a non-trivial task. To alleviate this problem, this paper presents a systematic way of deriving sufficient conditions on handlers for their composite to correctly handle combinations, such as the sum and the tensor, of the effect theories separately handled. These conditions are solely characterised by the clauses for relevant operations of the handlers, and are derived by fusing two handlers into one using a form of fold/build fusion and continuation-passing style transformation. As case studies, the technique is applied to commutative and distributive interaction of handlers to obtain a series of results about the interaction of common handlers: (a) equations respected by each handler are preserved after handler composition; (b) handling mutable state before any handler gives rise to a semantics in which state operations are commutative with any operations from the latter handler; (c) handling the writer effect and mutable state in either order gives rise to a correct handler of the commutative combination of these two theories.
High-precision U-Pb CA-ID-TIMS calibration of the Permian <i>Lueckisporites</i>-dominated assemblages in westernmost Gondwana: inferences for correlations

Palynology is one of the most useful tools for constructing biostratigraphic charts in the late Paleozoic basins of South America, but the lack of radiometric ages often makes it difficult to establish the precise age of the biozones. This paper focuses on two points: (i) the description of new Argentinian palynological assemblages referred to the Lueckisporites/Weylandites (LW) Biozone and (ii) the application of a new isotopic age that enables more confident large-scale correlations with the neighboring Paraná Basin of Brazil. The palynofloras were recovered from the upper part of the De La Cuesta Formation that crops out in the retroarc basin of the Paganzo Basin, more specifically obtained from mudstones intercalated with limestones, cherts, and tuffs interpreted as being deposited in a shallow water body. The assemblages are characterized by an abundance of bi-taeniate pollen grains of the Lueckisporites complex and multitaeniate pollen grains such as Lunatisporites, Protohaploxypinus, Striatopodocarpidites, Tornopollenites, and Vittatina. Colpate pollen grains are also represented by Marsupipollenites and Pakhapites. A U-Pb CA-ID-TIMS age of 278.84 ± 0.22 Ma, obtained from a tuff level interstratified with the fossiliferous strata, allows the age of the LW Biozone in the Paganzo Basin to be constrained for the first time and the adjustment of the record Lueckisporites virkkiae in Gondwana. Moreover, this date enables precise correlations with other basins, such as the San Rafael Basin (Yacimiento Los Reyunos Formation) in Argentina and the Brazilian Paraná Basin (Iratí Formation).
CSRF protection in JavaScript frameworks and the security of JavaScript applications

With JavaScript being the most popular programming language on the web, several new JavaScript frameworks are released every year. A well designed framework may help developers create secure applications. The goal of our study is to understand how framework developers can best protect applications developed using their framework. In this work we studied how cross-site request forgery vulnerability is mitigated in several server-side JavaScript frameworks: Express.js, Koa.js, Hapi.js, Sails.js, and Meteor.js. We then analyzed open source applications developed with these frameworks using open source and custom written tools for automated static analysis and identified the percentage of protected applications for each framework. We correlated our analysis results to the implementation levels of mitigating controls in each framework and performed statistical analysis of our results to ensure no other confounding factors were involved. Based on the received outcomes we provide recommendations for framework developers on how to create frameworks that produce secure applications.
STC-Flow: Spatio-temporal context-aware optical flow estimation

In this paper, we propose a spatio-temporal contextual network, STC-Flow, for optical flow estimation. Unlike previous optical flow estimation approaches with local pyramid feature extraction and multi-level correlation, we propose a contextual relation exploration architecture by capturing rich long-range dependencies in spatial and temporal dimensions. Specifically, STC-Flow contains three key context modules, i.e., pyramidal spatial context module, temporal context correlation module and recurrent residual contextual upsampling module for the effect of feature extraction, correlation, and flow reconstruction, respectively. Experimental results demonstrate that the proposed scheme achieves the state-of-the-art performance of two-frame based methods on Sintel and KITTI datasets.
An Arabic Translation of the Computer System Usability Questionnaire (CSUQ) with Psychometric Evaluation Using Kuwait University Portal

Computer System Usability Questionnaire (CSUQ) is one of the popular standardized usability tools that measures perceived user satisfaction of a system. This research aims to translate CSUQ to the Arabic language in hope that it would be used as a standard usability measuring tool on native Arabic speakers. The process of the translation followed a rigorous step by step approach. The result of the translation – the Arabic-Computer System Usability Questionnaire (A-CSUQ-19 and A-CSUQ-16) – is then applied on an information system used by Kuwait University students. A psychometric evaluation was performed to measure the reliability and validity of A-CSUQ. The results yielded an acceptable level of reliability with overall Cronbach's Alpha for the 19-item and 16-item versions at 0.97 and 0.964, respectively. As for construct validity and face validity, the results obtained were highly satisfactory.
Rgb-D Fusion For Point-Cloud-Based 3d Human Pose Estimation

3D human pose estimation is an important and challenging task in computer vision. In this paper, we propose a method to estimate 3D human pose from RGB-D images. We adopt a 2D pose estimator to extract color features from the RGB image. The color features are integrated with the depth image in the form of point cloud. To fully exploit geometric information, we design a 3D learning module to extract point-wise features. To take advantage of local information as well as facilitate the convergence of the model, we design a dense prediction module. It estimates the offset vectors and closeness scores from points to target keypoints. The point-wise estimations are weighted and summed up to a final 3D pose. Experimental results show that our method achieves state-of-the-art performance on MHAD and SURREAL datasets.
Face recognition on Raspberry Pi based on MobileNetV2

At present, face recognition has been widely used, and face recognition in mobile devices has a broad application prospect. We try to detect and extract faces by extracting Haar-like features and then put the face images into the Convolutional Neural Network (CNN) for training and recognition. We improve the MobileNetV2, use ArcFace for face recognition and classification, and test the CNN face classification program on Raspberry Pi 4B. The training in Raspberry Pi is based on the idea of transfer learning. The features extraction part of the network loads the weight file, and only the fully connected classification layers are trained. Finally, the accuracy of train set and test set reach 99.8% and 94.4% respectively after training 100 epochs in Raspberry Pi. When epoch=10, the accuracy rate reaches the predetermined value. We can consider training 10 epochs in Raspberry Pi, and the total time is about 1h33min.
Improving Blockchain Consensus Mechanism via Network Clusters

Blockchain is a decentralized database that stores a growing chain of blocks formed and secured by the consensus of a network node. Blockchain technology is a solution to applications and smart programs so that no third party (peer-to-peer network) is required to store and verify transactions in a safe and reliable manner, such as cryptocurrencies, smart contracts, health care, and the Internet of things (IoT). However, this technology suffers from some limitations, such as network latency and slow transaction validation. In this paper, the proposed method is implemented and discussed by improving the topology of the peer-to-peer blockchain structure to a decentralized topology (structured topology in clusters). This aims to speed up transactions broadcasting time, as well as to reduce the enormous time for nodes consensus mechanism to validate transactions through the intra- and inter-cluster parallel mining in the Proof of Work (PoW) mechanism. The test results demonstrate the feasibility of the proposed method in reducing the consensus time, as well as improve the scalability of the network.
Transformer And Node-Compressed Dnn Based Dual-Path System For Manipulated Face Detection

Deep neural networks (DNNs) have extensively promoted data generation development; the quality of these generated content has achieved an impressive new level. Therefore, manipulated content, especially facial manipulation, is a growing concern for online information legitimacy. Most current deep learning-based methods depend on local features sampled by convolutional kernels and lack knowledge globally. To address the problem, we propose a dual-path pipeline using Neural Ordinary Differential Equations (NODE) based neural network and facial-feature biased transformer to deal with the visual content from a different view. The transformer path could link these landmarks in a long-range, moreover, we adopt an attention guided augmentation based self-ensemble for more robust performance. Extensive experiments show that our system could surpass several commonly used approaches in terms of video-level accuracy and AUC with better interpretability.
Trends in Neural Architecture Search: Towards the Acceleration of Search

In modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications. In this paper, the main research trends of neural architecture search (NAS) are classified as neuro-evolutionary algorithms, reinforcement learning based algorithms, and one-shot architecture search approaches. Furthermore, each research trend is introduced and finally all the major three trends are compared. Lastly, the future research directions of NAS research trends are discussed.
Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates

Improvements in Artificial Intelligence (AI), especially in the area of neural networks, have led to calls to use them also in the context of safety-critical systems. However, current AI-based models are data-driven, so we cannot assure that they will provide the intended outcome for any input. To obtain information about the uncertainty remaining in their outcome, uncertainty estimation capabilities can be integrated during model building. However, the approach of providing accurate outcomes and dependable uncertainty estimates using the same model has limitations. Among others, estimates of such 'in-model' approaches are provided without statistical confidence, tend to be overconfident if not calibrated, and are hard to interpret and review by domain experts. An alternative 'outside-model' approach is the use of model-agnostic uncertainty wrappers (UWs). To investigate how well they perform in comparison to in-model approaches, we benchmarked them against deep ensembles, which can be considered the gold standard for in-model uncertainty estimation, as well as to the softmax outputs of a deep neural network as a baseline. Despite a slightly higher Brier score, the UW provides other benefits that are important in a safety-critical context, like considering a statistical confidence level and providing explainable uncertainty estimates through a decision tree considering human-interpretable semantic factors. Furthermore, in-model uncertainty estimates can be forwarded into an UW, combining advantages of both approaches.
Binary Complex Neural Network Acceleration on FPGA : (Invited Paper)

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today&#x2019;s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision-making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 &#x00D7; pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.
What should be done to develop ICT-based PE class in the era of the Fourth Industrial Revolution?: using AHP and IPA analysis within the Korean educational contexts

This study aims to explore educational and political efforts for PE teachers to implement ICT-based PE class by analyzing three significant phases (PE teacher, the curriculum, and teaching environment). To reach the goal of this study, we conducted AHP and IPA analysis to find out the priorities in order to implement ICT-based PE class. As a result, a total of 27 detailed contents were created by IPA analysis. Based on the IPA results, we interrogated what detailed contents should be firstly applied into actual PE practices by analyzing four dimensions by utilizing IPA. Based on the results, this study concluded with some significance, which are required to establish a better PE environment in the future society.
Him-Net: A New Neural Network Approach for SAR and Optical Image Template Matching<sup>1</sup>

SAR and optical images provide highly complementary information about observed scenes. The integrated use of these two data is desired in many data fusion tasks. However, traditional similarity methods cannot correctly match SAR and optical images due to the significant non-linear radio-metric difference between them. This paper proposed a template matching neural network based on stereo matching for SAR and optical image matching. Unlike the classical template matching methods doing feature extraction and similarity calculating separately, our network is a complete end-to-end approach, which allows optimizing the matching between the SAR and optical images through training. Moreover, a heatmap loss function is designed for image template matching, and better result is obtained. Our experiments confirmed our proposed network advantage over the state-of-the-art similarity approaches (such as NCC, CARMI, DeepMatch, and QATM) and superior matching performance.