Reviewerly/exHarmony huggingface dataset:
Mixed Reality Improves Education and Training in Assembly Processes

Mixed reality is the outcome of blending the physical world with the digital world, made possible by technological advancement. Mixed reality is the next evolution in human, computer, and environment interaction. Augmented reality (AR) uses a virtual model of the real world, augmented by using a computer to see the real environment through a special display device. Current education and training systems in the engineering maintenance field are still insufficiently directed at the psychomotor skills in learning about machine parts, which makes them less effective for trainees. The oil and gas industry always face problems related to inefficiency due to downtime of critical equipment. This study was conducted at designing and developing a virtual reality (VR) and augmented reality (AR) system as a learning and training platform. This work also reviewed AR applications for machine part maintenance and assembly. An AR system was modelled and developed using the following software: CATIA, Blender, Unity and Vuforia. The effectiveness of using the AR technique in an education and training process was evaluated with 20 respondents among university students. The results showed that using this AR app enhanced the participant's understanding according to certain criteria and can be adopted as a learning method.
Preech: A System for Privacy-Preserving Speech Transcription

New Advances in machine learning have made Automated Speech Recognition (ASR) systems practical and more scalable. These systems, however, pose serious privacy threats as speech is a rich source of sensitive acoustic and textual information. Although offline and open-source ASR eliminates the privacy risks, its transcription performance is inferior to that of cloud-based ASR systems, especially for real-world use cases. In this paper, we propose Pr$\epsilon\epsilon$ch, an end-to-end speech transcription system which lies at an intermediate point in the privacy-utility spectrum. It protects the acoustic features of the speakers' voices and protects the privacy of the textual content at an improved performance relative to offline ASR. Additionally, Pr$\epsilon\epsilon$ch provides several control knobs to allow customizable utility-usability-privacy trade-off. It relies on cloud-based services to transcribe a speech file after applying a series of privacy-preserving operations on the user's side. We perform a comprehensive evaluation of Pr$\epsilon\epsilon$ch, using diverse real-world datasets, that demonstrates its effectiveness. Pr$\epsilon\epsilon$ch provides transcriptions at a 2% to 32.25% (mean 17.34%) relative improvement in word error rate over Deep Speech, while fully obfuscating the speakers' voice biometrics and allowing only a differentially private view of the textual content.
Neural Likelihoods via Cumulative Distribution Functions

We leverage neural networks as universal approximators of monotonic functions to builda parameterization of conditional cumulativedistribution functions (CDFs). By the application of automatic differentiation with respectto response variables and then to parameters ofthis CDF representation, we are able to buildblack box CDF and density estimators. A suiteof families is introduced as alternative constructions for the multivariate case. At one extreme, the simplest construction is a competitive density estimator against state-of-the-artdeep learning methods, although it does notprovide an easily computable representation ofmultivariate CDFs. At the other extreme, wehave a flexible construction from which multivariate CDF evaluations and marginalizationscan be obtained by a simple forward pass ina deep neural net, but where the computationof the likelihood scales exponentially with dimensionality. Alternatives in between the extremes are discussed. We evaluate the different representations empirically on a variety oftasks involving tail area probabilities, tail dependence and (partial) density estimation.
Reaching the Unreachable : A Method for Early Stage Software Startups to Reach Inaccessible Stakeholders within Large Corporation

Bridging the gap in software development from idea to need remains a difficult task for startups, especially when users are unreachable within the black boxes of large corporations. The lack of customer collaboration results in the failure of requirements elicitation and subsequently the failure of software products. In this work, we describe a simple but effective method to enable startups to reach relevant hidden stakeholders within large corporations and elicit requirements from them. AdvisorNet leverages professional networks and social media to find senior domain experts. These carefully selected advisors bridge the gap from the outside world to the hidden corporate structure and social networks in the target corporation and enable highly successful elicitation from stakeholders. We demonstrate AdvisorNet with a real-world case study in which a London-based startup successfully connected with multiple advisors who then introduced previously unreachable users for requirements elicitation and decision-makers to achieve their goal of trialing their product within the large corporations. Finally, we provide suggestions for future research to formalize the method further and enable it to become rigorous and repeatable.
Knowledge Tracking Model Based on Learning Process

Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time.Knowledge tracking can automatically discover students' weak knowledge points, which helps to improve students' self-motivation in learning and realize personalized guidance.The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model.To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Network(MANN).When we model the learning process of students, two additional important factors are considered.One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model.In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand.
Pointer-based model for state reduction in momentum space

We revisit the pointer-based measurement concept of von Neumann which allows us to model a quantum counterpart of the classical time-of-flight (ToF) momentum. Our approach is based on the Hamiltonian for a particle interacting with two quantum pointers serving as basic measurement devices. The corresponding dynamics leads to a pointer-based ToF observable for the operational momentum of the particle. We can consider single measurements of our quantum pointers and show that this process will result in a state reduction for a single particle being downstream of the time-of-flight setup.
Domain Adversarial Fine-Tuning as an Effective Regularizer

In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results.However, standard fine-tuning can degrade the general-domain representations captured during pretraining.To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer.Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective.This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations.Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain.Intuitively, the adversarial classifier acts as a regularizer which prevents the model from overfitting to the task-specific domain.Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.
Probabilistic motional averaging

In a continuous measurement scheme a spin-1/2 particle can be measured and simultaneously driven by an external resonant signal. When the driving is weak, it does not prevent the particle wave-function from collapsing and a detector randomly outputs two responses corresponding to the states of the particle. In contrast, when driving is strong, the detector returns a single response corresponding to the mean of the two single-state responses. This situation is similar to a motional averaging, observed in nuclear magnetic resonance spectroscopy. We study such quantum system, being periodically driven and probed, which consists of a qubit coupled to a quantum resonator. It is demonstrated that the transmission through the resonator is defined by the interplay between driving strength, qubit dissipation, and resonator linewidth. We demonstrate that our experimental results are in good agreement with numerical and analytical calculations.
Federated learning with class imbalance reduction

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged to train and transmit the trained model to centralized server for aggregation. Since the local data distribution varies among all devices, class imbalance problem arises along with the unfavorable client selection, resulting in a slow converge rate of the global model. In this paper, an estimation scheme is designed to reveal the class distribution without the awareness of raw data. Based on the scheme, a device selection algorithm towards minimal class imbalance is proposed, thus can improve the convergence performance of the global model. Simulation results demonstrate the effectiveness of the proposed algorithm.
Sound, heuristic type annotation inference for Ruby

Many researchers have explored retrofitting static type systems to dynamic languages. This raises the question of how to add type annotations to code that was previously untyped. One obvious solution is type inference. However, in complex type systems, in particular those with structural types, type inference typically produces most general types that are large, hard to understand, and unnatural for programmers. To solve this problem, we introduce InferDL, a novel Ruby type inference system that infers sound and useful type annotations by incorporating heuristics that guess types. For example, we might heuristically guess that a parameter whose name ends in “count” is an integer. InferDL works by first running standard type inference and then applying heuristics to any positions for which standard type inference produces overly-general types. Heuristic guesses are added as constraints to the type inference problem to ensure they are consistent with the rest of the program and other heuristic guesses; inconsistent guesses are discarded. We formalized InferDL in a core type and constraint language. We implemented InferDL on top of RDL, an existing Ruby type checker. To evaluate InferDL, we applied it to four Ruby on Rails apps that had been previously type checked with RDL, and hence had type annotations. We found that, when using heuristics, InferDL inferred 22% more types that were as or more precise than the previous annotations, compared to standard type inference without heuristics. We also found one new type error. We further evaluated InferDL by applying it to six additional apps, finding five additional type errors. Thus, we believe InferDL represents a promising approach for inferring type annotations in dynamic languages.
Deep Learning Networks-Based Action Videos Classification and Search

This work presents the deep learning networks-based method using fine-tuning for classification and search of a diversity of action videos. First, a 3D convolutional neural networks (3D CNN) model which performs pre-training operation and fine-tuning strategy is employed to extract the spatiotemporal features of videos. It is first pre-trained on UCF-101 datasets to train model with initial parameters. Then, a small new dataset is employed to fine-tune the initial model for the training of the new model. Once features are extracted by the final CNNs model, distance measure can be adopted to calculate the similarities between the query video and the test dataset for the video search. The searched video is returned and ranked according to the priority when it has higher similarity with the query video. The comparison results in the experiment shows that the search method using fine-tuning obtains better performance than the method without using fine-tuning. Second, the classification results based on the 3D CNN model using fine-tuning are also presented for the consideration of a query by keyword. Accuracy result obtained using the model with the help of fine-tuning is approximately 2.8% higher than that without using fine-tuning.
Unsupervised Learning from Videos for Object Discovery in Single Images

This paper proposes a method for discovering the primary objects in single images by learning from videos in a purely unsupervised manner—the learning process is based on videos, but the generated network is able to discover objects from a single input image. The rough idea is that an image typically consists of multiple object instances (like the foreground and background) that have spatial transformations across video frames and they can be sparsely represented. By exploring the sparsity representation of a video with a neural network, one may learn the features of each object instance without any labels, which can be used to discover, recognize, or distinguish object instances from a single image. In this paper, we consider a relatively simple scenario, where each image roughly consists of a foreground and a background. Our proposed method is based on encoder-decoder structures to sparsely represent the foreground, background, and segmentation mask, which further reconstruct the original images. We apply the feed-forward network trained from videos for object discovery in single images, which is different from the previous co-segmentation methods that require videos or collections of images as the input for inference. The experimental results on various object segmentation benchmarks demonstrate that the proposed method extracts primary objects accurately and robustly, which suggests that unsupervised image learning tasks can benefit from the sparsity of images and the inter-frame structure of videos.
MR and ultrasound cardiac image dynamic visualization and synchronization over Internet for distributed heart function diagnosis

Dual-modality 4D cardiac data visualization can convey a significant amount of complementary image information from various sources into a single and meaningful display. Even though there are existing publications on combining multiple medical images into a unique representation, there has been no work on rendering a series of cardiac image sequences, acquired from multiple sources, using web browsers and synchronizing the result over the Internet in real time. The ability to display multi-modality beating heart images using Web-based technology is hampered by the lack of efficient algorithms for fusing and visualizing constantly updated multi-source images and streaming the rendering results using internet protocols. To address this practical issue, in this paper we introduce a new Internet-based algorithm and a software platform running on a Node.js server, where a series of registered cardiac images from both magnetic resonance (MR) and ultrasound are employed to display dynamic fused cardiac structures in web browsers. Taking advantage of the bidirectional WebSocket protocol and WebGL-based graphics acceleration, internal cardiac structures are dynamically displayed, and the results of rendering and data exploration are synchronized among all the connected client computers. The presented research and software have the potential to provide clinicians with comprehensive information and intuitive feedback relating to cardiac behavior and anatomy and could impact areas such as distributed diagnosis of cardiac function and collaborative treatment planning for various heart diseases.
RETRACTED: Design of the online platform of intelligent library based on machine learning and image recognition

The Overview of machine learning Key Branch, and then provide the complete protection of the deep neural network. It covers important critical concepts, testing methods, applications, issues related to assessment level (regression and classification), unattended learning (reduction set and dimension), active learning and semi-tracking (pre-attachment method) cover, and intensive learning. The scope of in-depth neural network communication includes Retrieval Neural Networks (RNNs), and word embedding and related technologies. Discussion issues, the online platform of intelligent library technology and easy relational tools are still so obvious that computer origin is a challenge and is based on the natural visualization of Digital Library (DL) related applications and large data analysis. To search the online platform of intelligent library documents based on the digital library image, recommend designing an assortment that adds descriptions to main images. First, propose a machine learning technique visual description appropriate to the representation of that image. The image is divided into regions based on the type of a particular area and then pointers. Second, propose an image classification method for the freedom of interpretation spaces. This feature is obtained by combining selection and kernel-based method classification.
Authorship Identification Using Stylometry and Document Fingerprinting

Authorship identification focuses on finding the particular author of an anonymous or unknown document by extracting various predictive features related to that document. It helps to predict the most probable author of articles, messages, codes, or news. This task is generally viewed as a multi-class, single labeled categorization of text. This topic is important and among the most interesting topics in the field of Natural Language Processing. There are many applications in which this can be applied such as identifying anonymous author, supporting crime investigation and its security, also detecting plagiarism, or finding ghostwriters. Till now, most of the existing works are based on the character n-grams of fixed length and/or variable length to classify authorship. In this work, we tackle this problem at different levels with increasing feature engineering using various text-based models and machine learning algorithms. The propose work analyses various types of stylometric features and define individual features that are high in performance for better model understanding. We evaluate the proposed methodology on a part of Reuters news corpus. It consists of texts of 50 different authors on the same topic. The experimental results suggest that, using document finger printing features enhance the accuracy of classifier. Moreover, PCA (Principal Component Analysis) further improves the results. In addition, we compare the results with other works related to the authorship identification domain.
Nursing care provided to people with covid-19: challenges in the performance of the COFEN/CORENS system/ Enfermería en atención a personas con covid-19: desafíos en el desempeño del sistema COFEN/CORENs/ Enfermagem na atenção às pessoas com covid-19: desafios na atuação do sistema COFEN/CORENs

ABSTRACT Objective: to analyze the challenges of the Federal and Regional Nursing Councils regarding the performance of nursing in providing care to people with COVID-19. Method: a documentary study carried out on the websites of the Federal Nursing Council and respective Regional Councils of Sao Paulo, Rio de Janeiro, Ceara, Amazonas and Pernambuco. Data collection took place in May, encompassing bulletins published from March to April 2020. A total of 149 bulletin items were analyzed through Content Analysis. Results: most of the bulletins published by the Federal Nursing Council cited support for professionals, and health service inspection and task force creation stand out for the Regional Nursing Councils among the included units. The findings were organized into four categories: The nursing professional during the pandemic: the fight against the invisible enemy; Working conditions in providing care to people with COVID-19: barriers and challenges; Professional devaluation x technical responsibility: frontline scenario; Mental health of nursing professionals: living with fear and uncertainty. Conclusion: the challenges of the Federal Nursing Council and the Regional Nursing Councils regarding nursing performance in providing care to people with COVID-19 are directly linked to the supervision and support to the category in the daily exercise of the profession as evidenced by structural difficulties of the working conditions, professional devaluation due to their technical responsibility, inadequate dimensioning of the workforce, overload and problems related to mental health.
Optimal p-ary cyclic codes with two zeros

As a subclass of linear codes, cyclic codes have efficient encoding and decoding algorithms, so they are widely used in many areas such as consumer electronics, data storage systems and communication systems. In this paper, we give a general construction of optimal p-ary cyclic codes which leads to three explicit constructions. In addition, another class of p-ary optimal cyclic codes are presented.
An Uncertainty-Based Traffic Training Approach to Efficiently Identifying Encrypted Proxies

Encrypted proxies, such as Shadowsocks and v2ray, are increasingly used to reserve user privacy and circumvent censorship. However, they are also widely misused by attackers to carry out illegal activities like malware downloading, information theft. Therefore, identifying encrypted proxies is a fundamental task concerning cyber security for network administrators. Existing studies focus on traffic feature engineering and designing the classification model. Although indispensable, they do not consider the training efficiency problem, thereby unable to approach the best possible performance when the number of affordable training samples is limited due to resource constraint. In this paper, we propose an uncertainty-based traffic sample selection strategy to boost traffic training of encrypted proxies. The proposed strategy allows one to use fewer samples to quickly learn diverse traffic characteristics. Through experiments, we demonstrate that our strategy significantly outperforms random sample selection, and hence substantially improves identification performance.
Binocular intelligent following robot based on YOLO-LITE

In order to solve the problem that the deep neural network model is large in scale, the calculation time is too long, and the real-time performance is severely limited when combined with embedded devices, so studied the intelligent follower robot system based on YOLO-LITE algorithm combined with Raspberry Pi 3B+. The system mainly includes camera processing, target detection and other modules. Obtained the internal and external parameters of the camera through calibration, and according to these parameters to correct the binocular camera. Recognized and located the target in each frame of image, calculated the distance from the camera to the target and the center location error, and driven the car to move. The experimental results show that the following car has excellent real-time performance, the average detection frame rate can reach 20Fps, and the average detection accuracy can reach more than 80%.