Reviewerly/exHarmony huggingface dataset:
Railway Driver Behavior Recognition System Based on Deep Learning Algorithm

For guaranteeing of train operation safety, driver's behavior should comply with the requirements of standardized operating procedures. If the driver is off duty, using mobile phone, failing to deal with commanded operation and other violations during the duty, it may cause a major driving accident. Based on artificial intelligence approaches such as object detection, human posture recognition, graph convolutional neural network, and embedded hardware equipment, this paper develops a intelligent recognition system for railway driving behavior, which is important for ensuring the safe operation of trains and promoting the development of intelligent management and control technologies in the railway industry.
Quantum-Inspired Model Based on Convolutional Neural Network for Sentiment Analysis

Sentiment analysis aims to judge the sentiment polarity of various types of text at the document and sentence level. It has important theoretical and practical significance and is a hot topic in natural language processing. Although existing sentiment analysis methods based on sentiment dictionaries and machine learning consider contextual semantic information, they still have the problem of not being able to effectively encode the mixing of semantic subspaces, and therefore ignore the feature interaction between sentiment words. To solve this problem, this article combines quantum mechanics, deep learning and natural language processing technology, and introduces the concept of density matrix into the convolutional neural network. The density matrix can encode more semantic dependencies and can be integrated into the neural network architecture. We proposed a Quantum-inspired Model based on Convolutional Neural Network for Sentiment Analysis (QI-CNN). Experiments on IMDB English dataset and Weibo Chinese dataset verify the effectiveness of our proposed model.
Synchronous Distance Learning: Students and Faculty Experience from a Gender Perspective

The Covid-19 pandemic forced Higher Education classes to shift to online education. This sudden change required universities to respond rapidly, adapting their classes to remote courses maintaining quality educational delivery. In this context, our university implemented the Digital Flexible Model (DFM) to continue with its academic offer. The challenge involved moving 55,000 classes per week to the DFM, attending 90,000 students with 9,400 professors, all in one week. At the end of the semester, a strategic survey was applied to a statistical sample of professors and students from the Engineering School to get feedback about their experience with the DFM. The results highlight takeaways to improve the instructional model and offer interesting reflections by gender from the experience of faculty and students.
Monocular Vision Ranging and Camera Focal Length Calibration

The camera calibration in monocular vision represents the relationship between the pixels’ units which is obtained from a camera and the object in the real world. As an essential procedure, camera calibration calculates the three-dimensional geometric information from the captured two-dimensional images. Therefore, a modified camera calibration method based on polynomial regression is proposed to simplify. In this method, a parameter vector is obtained by pixel coordinates of obstacles and corresponding distance values using polynomial regression. The set of parameter’s vectors can measure the distance between the camera and the ground object in the field of vision under the camera’s posture and position. The experimental results show that the lowest accuracy of this focal length calibration method for measurement is 97.09%, and the average accuracy was 99.02%.
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Experts

Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM). The model disentangles latent time-varying and dynamic factors via the incorporation of hierarchical structure with a dynamic prior and a Mixture of Experts decoder. VDSM learns separate disentangled representations for the identity of the object or person in the video, and for the action being performed. We evaluate VDSM across a range of qualitative and quantitative tasks including identity and dynamics transfer, sequence generation, Fréchet Inception Distance, and factor classification. VDSM achieves state-of-the-art performance and exceeds adversarial methods, even when the methods use additional supervision.
Generative detect for occlusion object based on occlusion generation and feature completing

Detecting the object with external occlusion has always been a hot topic in computer version, while its accuracy is always limited due to the loss of original object information and increase of new occlusion noise. In this paper, we propose a occluded object detection algorithm named GC-FRCN (Generative feature completing Faster RCNN), which consists of the OSGM (Occlusion Sample Generation Module) and OSIM (Occlusion Sample Inpainting Module). Specifically, the OSGM mines and discards the feature points with high category response on the feature map to enhance the richness of occlusion scenes in the training data set. OSIM learns an implicit mapping relationship from occluded feature map to real feature map adversarially, which aims at improving feature quality by repair the noisy object feature. Extensive experiments and ablation studies have been conducted on four different datasets. All the experiments demonstrate the GC-FRCN can effectively detect objects with local external occlusion and has good robustness for occlusion at different scales.
Securing Healthcare Data with Healthcare Cloud and Blockchain

Blockchain has been considered mainly for the financial platforms. The power of blockchain needs to be explored for other domains as well. Blockchain can be used wherever security and integrity is concerned. In our research, we have explored the usage of blockchain in the field of healthcare. Healthcare data is of high criticality and needs to be tackled in the same manner. So, utilizing the concept of healthcare cloud with blockchain would take healthcare data security a new high. We have proposed a novel approach of securing healthcare data by making the use of cloud computing services to create a healthcare cloud. This cloud contains data validated by blockchain technology. The approach brings security as well as integrity to the data stored in a single healthcare cloud.
CameraReady: Assessing the Influence of Display Types and Visualizations on Posture Guidance

Computer-supported posture guidance is used in sports, dance training, expression of art with movements, and learning gestures for interaction. At present, the influence of display types and visualizations have not been investigated in the literature. These factors are important as they directly impact perception and cognitive load, and hence influence the performance of participants. In this paper, we conducted a controlled experiment with 20 participants to compare the use of five display types with different screen sizes: smartphones, tablets, desktop monitors, TVs, and large displays. On each device, we compared three common visualizations for posture guidance: skeletons, silhouettes, and 3d body models. To conduct our assessment, we developed a mobile and cross-platform system that only requires a single camera. Our results show that compared to a smartphone display, larger displays show a lower error (12%). Regarding the choice of visualization, participants rated 3D body models as significantly more usable in comparison to a skeleton visualization.
Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.
Three Gaps for Quantisation in Learned Image Compression

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.
Evaluation of Training Effect of New Professional Farmers Based on BP Neural Network

In order to promote the structural reform of agricultural supply sides, speed up agricultural modernization and improve the cultivation of new professional farmers, it is necessary to comprehensively evaluate the effect of this new professional farmer training. In view of the problem that the traditional evaluation method has great artificial influence when determining the weight in the evaluation process, the nonlinear mapping ability of BP neural network is used to reduce the subjective influence of people giving weight in the evaluation process, and the error is distributed to all units of each layer through gradient descent to complete the weight update. A classification model was created for this experiment, and 156 groups of data were used for training and 40 groups were used for testing. The classification accuracy on the test set reached 94.87%, so the training effect can be effectively evaluated by BP network. By averaging the weights of individual evaluation results obtained by the classification model, the comprehensive evaluation of this training are obtained finally. According to the comprehensive evaluation results, we can promote training institutions to improve relevant measures and promote the education and training of new professional farmers.
Apollo

This paper provides a first presentation of Apollo, a runtime system for serverless function compositions distributed across the cloud-edge-IoT continuum. Apollo's modular design enables a fine-grained decomposition of the runtime implementation(scheduling, data transmission, etc.) of the application, so that each of the numerous implementation decisions can be optimized separately, fully exploiting the potential for the optimization of the overall performance and costs. Apollo features (a) a flexible model of the application and the available resources and (b) an implementation process based on a large set of independent agents. This flexible structure enables distributing not only the processing, but the implementation process itself across a large number of resources, each running an independent Apollo instance. The ability to flexibly determine the placement of implementation actions opens up new optimization opportunities, while at the same time providing access to greater computing power for optimizing challenging decisions such as task scheduling and the placement and routing of data.
HuGE: An Entropy-driven Approach to Efficient and Scalable Graph Embeddings

Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE leverages hybrid-property heuristic random walk to capture node features, which considers both node degree and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE achieves both efficiency and performance advantages over recent popular graph embedding approaches. For link prediction and multi-label classification, our approach not only offers >10% average gains, but also exhibits 22×-126× speedup compared with existing sampling-based methods.
Educating the Commons and Commoning Education: Thinking Radical Education with Radical Technology

Abstract All over the world, education—which could be understood as a universal right and public good—is facing processes of economization and privatization. Technology—which could be understood as a common means of production, collaboratively developed—is taken away from the public and put into corporate hands. This article is designed as a conversation investigating the question of shared and common knowledge from the perspectives of an educator and an engineer, respectively. The dialogue explores necessary convergences in radical practices of commoning, and possible future strategies for education and Open Technology. It asks how new models can challenge the neoliberal agenda and move away from established policies, and how a collective re-appropriation of the means of production could emerge within a post-digital society.
Sentiment Analysis of News Comments Based on Improved Intuitionistic Fuzzy Reasoning

Abstract With the development of the Internet, public opinion monitoring emphasizes the need to obtain a more accurate mood prediction model, so as to grasp the public opinion tendency and thinking process more accurately. In addition, the need to process real-world datasets of sentiment analysis without using sampling techniques can introduce noise into the data being used. In this paper, we propose an extraction method based on compact interval valued intuitionistic fuzzy rules for the classification of news comment pages based on IVFS, for modeling and predicting real-world Sentiment Analysis applications. The web pages are partitioned based on DOM tree, and the emotional tendency of news comments is classified by the improved intuitionistic fuzzy reasoning algorithm. Finally, this method is used to extract news comment web pages. To test the quality of our recommendations, we will present an experimental study that includes a dataset of 9 news pages with comments. We will demonstrate that this method is superior to the original C4.5 decision tree, Type 1 and Region 1, which are preprocessed.5. Decision tree. Type 1 corresponds to the interval value fuzzy and approximates the original fuzzy classifier. Our approach avoids a lot of unnecessary trouble and provides interpretable models that allow for more accurate results.
Morphaces: Exploring Morphable Surfaces for Tangible Sketching in VR

This pictorial documents our inquiry into the design and utility of morphable surfaces to provide tangible feedback while sketching in Virtual Reality (VR). We explored materials and various structures that could enable a surface to morph. We designed and implemented the Morphace ecosystem that includes 3D printed accessories that enable handheld and deskmounted pen-and-surface interaction for the Oculus Quest VR device. We present this preliminary exploration with the hope that this will be explored further by the design and broader HCI community.
Teaching Team Collaboration in Cybersecurity: A Case Study from the Transactive Memory Systems Perspective

Recent trends in the cybersecurity workforce have recognized that effective solutions for complex problems require collective efforts from individuals with diverse sets of knowledge, skills, and abilities. Therefore, the growing need to train students in team collaboration skills propelled educators in computer science and engineering to adopt team-based pedagogical strategies. Team-based pedagogy has shown success in enhancing students' knowledge in course subjects and their motivation in learning. However, it is limited in offering concrete frameworks specifically focusing on how to teach team collaboration skills. As part of an interdisciplinary effort, we draw on Transactive Memory Systems Theory-a communication theory that explains how individuals in groups learn who knows what and organize who does what-in developing a Team Knowledge Sharing Assignment as a tool for student teams to structure their team collaboration processes. This paper reports a result of a case study in designing and facilitating the assignment for cybersecurity students enrolled in a scholarship program. Students' evaluations and the instructor's assessment reveal that the assignment made a positive impact on students' team collaboration skills by helping them successfully identify their team members' expertise and capitalize on their team's knowledge resources when delegating functional roles. Based on this case study, we offer practical suggestions on how the assignment could be used for various classes or cybersecurity projects and how instructors could maximize its benefits.
A Novel Spec-CNN-CTC Model for End-to-End Speech Recognition

This paper discusses the application of a special data augmentation approach for end-to-end phone recognition system on the Deep Neural Networks. The system improves the performance of phone recognition and alleviates overfitting during training. Also, it offers a solution to the problem of few public datasets annotated at the phone level. And we propose the CNN-CTC structure as a baseline model. The model is based on Convolutional Neural Networks (CNNs) and Connectionist Temporal Classification (CTC) objective function. Which is an end-to-end structure, and there is no need to force alignment each frame of audio. The SpecAugment approach directly processes the feature of audio, such as the log Mel-spectrogram. In our experiment, the Spec-CNN-CTC system achieves a phone error rate of 16.11% on TIMIT corpus with no prior linguistic information. Which is outperforming the previous work Acoustic-State-Transition Model (ASTM) by 27.63%, the DNN-HMM with MFCC + IFCC features by 16.8%, the RNN-CRF model by 17.3% and the DBM-DNN model by 22.62%.
Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors

Sparse Bayesian learning (SBL) provides state-of-the-art performance in accuracy, sparsity and probabilistic prediction for classification. In SBL, the regularization priors are automatically determined that avoids an exhaustive hyperparameter selection by cross-validation. However, scalability to large problems is a drawback of SBL because of the inversion of a potentially enormous covariance matrix for updating the regularization priors in every iteration. This paper develops an approximate SBL algorithm called ARP-SBL, where the regularization priors are approximated without inverting the covariance matrix. Therefore, our approach can easily scale up to problems with large data size or feature dimension. It alleviates the long training time and high memory complexity in SBL. Based on ARP-SBL, two scalable nonlinear SBL models: scalable relevance vector machine (ARP-RVM) and scalable sparse Bayesian extreme learning machine (ARP-SBELM) are developed for problems of large data size and large feature size respectively. Experiments on a variety of benchmarks have shown that the proposed models are with competitive accuracy compared to existing methods while i) converging faster; ii) requiring thousands of times less memory; iii) without exhaustive regularized hyperparameter selection; and iv) easily scaling up to large data size and high dimensional features.
Future Directions for Regulated Private Wallets and VASP Trust Infrastructures

A true decentralized peer-to-peer electronic cash system requires control of private keys by end-users in private wallets. This necessitates wallet systems to utilize trusted hardware to protect keys. However, trusted hardware alone is not enough. There must be a corresponding attestation infrastructure to support the verification of evidence regarding the state of the trusted hardware, the provenance and the presence of keys, and other related configurations. This permits legitimate external entities to obtain some degree of visibility into the wallet state without access to the private keys. Relying parties, such as crypto-funds insurance providers and auditors of CBDC distributors, require such visibility for risk assessment. New VASPs have the opportunity today to invest in these emerging infrastructures.
Bahasa Indonesia pre-trained word vector generation using word2vec for computer and information technology field

Abstract Words embedding or distributed representations is a popular method for representing words. In this method, the resulting vector value is a set of real values with specific dimensions that are more effective than the Bag of Word (BoW) method. Also, the advantages of distributed representations can produce word vectors that contain semantic and syntactic information, so that word vectors with close meanings will have close word vectors. However, distributed representation requires a huge corpus with a long training time. For this reason, many researchers have created trained word vectors that can be used repeatedly. The problem is that the available trained word vectors are usually general domain word vectors. This study aims to form pre-trained word vectors for specific domains, namely computers and information technology. Researchers used a dataset of student scientific papers from the Universitas Sumatera Utara (USU) repository. Researchers used the word2vec model, where the model has two architectures, namely the Continuous Bag-of-Words (CBOW) and Skip-gram. This research’s result is word2vec model with the CBOW method is more effective than the Skip-gram method.