Reviewerly/exHarmony huggingface dataset:
An LSTM Autoencoder-Based Framework for Satellite Telemetry Anomaly Detection

This paper proposes an approach for detecting and identifying anomalies in satellite telemetry based on multi-dimensional time series analysis. Firstly, the long short-term memory-based autoencoder (LSTM-AE) model is established to reconstruct the normal operating data by simultaneously capturing the nonlinear spatial dependency among different telemetry variables and the temporal dependency in each telemetry variable. Then, the anomaly score is derived from the reconstruction residual based on the Mahalanobis distance. An adaptive threshold estimation method based on random forest regression algorithm is developed to identify anomalous telemetry data samples. The effectiveness of the proposed method is verified by a case study using real-world satellite monitoring telemetry variables.
Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021; it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result.
Quantum switching between nonclassical correlated single photons and two-photon bundles in a two-photon Jaynes-Cummings model

We propose a scheme to realize a two-photon Jaynes-Cummings model for a single atom inside an optical cavity. It is shown that the interplay of a laser detuning and atom (cavity) pump (driven) field gives rise to the strong single photon blockade, two-photon bundles, and photon-induced tunneling. With the cavity driven field, strong photon blockade occurs in the weak coupling regime, and switching between single photon blockade and photon-induced tunneling at two-photon resonance are achievable via increasing the driven strength. By turning on the atom pump field, quantum switching between two-photon bundles and photon-induced tunneling at four-photon resonance are realized. More interestingly, the high-quality quantum switching between single photon blockade, two-photon bundles, and photon-induced tunneling at three-photon resonance is achieved with combining the atom pump and cavity driven fields simultaneously. In contrast to the standard two-level Jaynes-Cummings model, our scheme with generating a two-photon (multi-photon) Jaynes-Cummings model reveals a prominent strategy to engineer a series of special nonclassical quantum states, which may pave the way for investigating basic quantum devices to implement in quantum information processing and quantum networks.
Multipartite-entanglement generation in coupled microcavity arrays

We consider photonic arrays made from quantum emitters in optically coupled microcavities as a platform for entanglement generation. These offer a large degree of tunability with the possibility of site-selective optical excitation. Coherent pumping is considered to drive transitions between vacuum and entangled target states, both in a time-dependent manner and in a quantum bath engineering approach to create entanglement in the steady state. We demonstrate a numerical scheme that allows one to generalize the determination of excitation parameters to larger array sizes and different classes of entangled states. This study is a step towards using coupled-cavity arrays as a hardware platform in novel quantum-photonic applications in quantum computing and quantum machine learning.
Cyberphysical Systems and Energy: A Discussion with Reference to an Enhanced Geothermal Process

As cyberphysical systems research advances, energy systems will be able to benefit from the developments. In this section, we discuss several points in two newer areas for cyberphysical systems research in chemical engineering: (1) cybersecurity of process control systems; and (2) quantum computation of control actions. The discussion is presented in a general manner, with relevance to flow systems or under traditional control laws (e.g., proportional control). However, to showcase energy systems relevance, we will provide links, at intervals, between the developments discussed and enhanced geothermal systems with energy production through the organic Rankine cycle.
Subjective video quality for 4K HDR-WCG content using a browser-based approach for &amp;#xC2;&amp;#x201C;at-home&amp;#xC2;&amp;#x201D; testing

The paper describes a design of a subjective experiment for testing the video quality of High Dynamic Range, Wide Color gamut (HDR-WCG) content at 4K resolution. Due to Covid, testing could not use a lab, so an at-home test procedure was developed. To aim for calibration despite not fully controlling the conditions and settings, we limited subjects to those who had a specific TV model, which we had previously calibrated in our labs. Moreover, we performed the experiment in the Dolby Vision mode (where the various enhancements of the TV are turned OFF by default). A browser approach was used which took control of the TV, and ensure the content was viewed at the native resolution of the TV (e.g., dot-on-dot mode). In addition, we know that video imagery is not ergodic, and there is wide variability in types of low levels features (sharpness, noise, motion, color volume, etc.) that affect both TV and visual system performance. So, a large number of test clips was used (30) and the content was specifically chosen to stress key features. The obtained data is qualitatively similar to an in-lab study and is subsequently used to evaluate several existing objective quality metrics.
Intelligent Platform for College English Blended Teaching Based on Mobile Learning

There is a large amount of heterogeneous data in the hybrid teaching platform, and the amount of data is large and various, resulting in low efficiency of database retrieval, which in turn affects the response time of platform query. Design an intelligent platform for college English blended teaching based on mobile learning. The hardware part adopts the combination of FPGA and CPU to realize data collection and processing, and the read-write control module sends data to the process control module to complete the read-write. In the software part, an intelligent platform software architecture is established according to teaching needs, a platform database is designed, and data is stored and accessed through relational databases and SQL. Based on mobile learning, the functional modules of the teaching software are implemented. The client directly faces the user. It receives and calculates and processes the user data, and then sends a request to the server. The test results show that the average response time of the English blended teaching intelligent platform designed in this paper is lower than that of the traditional teaching platform, and the overall cost acceleration is lower than that of other platforms.
APRENDO: UNA HERRAMIENTA DE APOYO DOCENTE PARA LA ENSEÑANZA A DISTANCIA

This work presents the results of the feasibility test for the development of the digital tool “Aprendo”, for teaching practice, in the context of remote teaching and connectivism. Through this tool, it is intended to draw an overview of the profile or learning style of the predominant students in the classroom, which can help the teacher to use more specific didactic methodologies and practices, enhancing the results of the teaching-learning process. A qualitative-quantitative analysis was used, through the application of a survey (A) to 80 students of different educational levels and another survey (B) to 28 teachers in general. The data from survey A showed that 67.5% of students learn through examples and 56.3% prefer to correlate different sources of information. Regarding the structure of the classes, in 73.8% of the cases there was a diversity of opinions and 86.3% of the students stated that the participation of another person added something to their knowledge. This corroborates the principle of connectivism about the connection between information from different sources. The results of B indicated a favorable opinion on the development and usefulness of the tool for 100% of the teachers, since 96.4% stated that they would change their pedagogical practice after obtaining the results of the application of the tool, while only 3.6% declared that would not change.
Provably Secure Randomness Generation from Switching Probability of Magnetic Tunnel Junctions

In recent years, true random number generators (TRNGs) based on magnetic tunnelling junction (MTJ) have become increasingly attractive. This is because MTJ-based TRNGs offer some advantages over traditional CMOS-based TRNGs, such as smaller area and simpler structure. However, there has been no work thus far that quantified the quality of the raw output of an MTJ-based TRNG and performed suitable randomness extraction to produce provably-secure random bits, unlike their CMOS-based counterparts. In this work, we implement an MTJ-based TRNG and characterise the entropy of the raw output. Using this information, we perform post-processing to extract a set of random bits which are provably-secure.
Designing user interfaces for content simplification aimed at people with cognitive impairments

Cognitive accessibility aims to make content more accessible for people with cognitive impairments, such as the elderly and people with intellectual and learning disabilities. In this sense, it is possible to design an accessible user interface from a cognitive point of view. As a contribution, this article presents cognitive accessibility design patterns and their application in designing the Easier web system's user interface. The Easier web system provides a tool that assists in the understanding and readability of text content geared towards people with intellectual disabilities. It detects complex words and offers easier replacements and other resources such as a definition of the complex word. In addition to applying the design patterns, user tests with people with intellectual disabilities and older people have been carried out to evaluate the cognitive accessibility of the Easier system's interface. The results indicate that people with cognitive impairments know how to use the interfaces and have a satisfactory experience. In addition, a design proposal to provide a glossary mechanism to be used in web interfaces with simplified texts is presented and validated.
Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning

The objective of few-shot learning is to design a system that can adapt to a given task with only few examples while achieving generalization. Model-agnostic meta-learning (MAML), which has recently gained the popularity for its simplicity and flexibility, learns a good initialization for fast adaptation to a task under few-data regime. However, its performance has been relatively limited especially when novel tasks are different from tasks previously seen during training. In this work, instead of searching for a better initialization, we focus on designing a better fast adaptation process. Consequently, we propose a new task-adaptive weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can generate per-step hyperparameters for each given task: learning rate and weight decay coefficients. The experimental results validate that learning a good weight update rule for fast adaptation is the equally important component that has drawn relatively less attention in the recent few-shot learning approaches. Surprisingly, fast adaptation from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random</i> initialization with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ALFA</b> can already outperform MAML. Furthermore, the proposed weight-update rule is shown to consistently improve the task-adaptation capability of MAML across diverse problem domains: few-shot classification, cross-domain few-shot classification, regression, visual tracking, and video frame interpolation.
Semantic-Oriented Feature Coupling Transformer for Vehicle Re-Identification in Intelligent Transportation System

More robust intelligent transportation systems including autonomous driving systems are in full flourish with the revolution of deep learning and the 6G wireless communication network. Vehicle Re-Identification, an indispensable branch of the intelligent transportation system, aims to retrieve specific vehicles captured from non-overlapping cameras. However, this is fundamentally challenging with the substantial inter-class similarity and substantial intra-class divergence. Embedding semantic information into vehicle re-identification task has gained ample interest, but the performance needs to be further improved. This work proposes a semantic-oriented feature coupling transformer (SOFCT) for vehicle re-identification as a solution. Specifically, the knowledge-based transformer is first embedded to model images with discriminative attributes. Second, original patches are divided into five semantic groups via semantics-patches coupling, and the feature extractions for different semantics are performed in the semantic feature extraction (SFE) transformer. Third, patch features are weighted via semantics-patches coupling in the patch feature weighting (PFW) transformer, the weighted feature is fed into subsequent encoders to excavate information. Finally, two groups of learnable semantics are embedded to automatically learn semantic features in the learnable semantic extraction (LSE) transformer. Experiments demonstrate that the proposed SOFCT method surpasses other state-of-the-arts with the mAP/Rank-1 of 80.7%/96.6%, 89.8%/84.5%, 86.4%/80.9%, and 84.3%/78.7% on VeRi776 and VehicleID.
Improving Deep Neural Networks’ Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum

Momentum is crucial in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs). In deep learning practice, the momentum is usually weighted by a well-calibrated constant. However, tuning the hyperparameter for momentum can be a significant computational burden. In this article, we propose a novel adaptive momentum for improving DNNs training; this adaptive momentum, with no momentum-related hyperparameter required, is motivated by the nonlinear conjugate gradient (NCG) method. Stochastic gradient descent (SGD) with this new adaptive momentum eliminates the need for the momentum hyperparameter calibration, allows using a significantly larger learning rate, accelerates DNN training, and improves the final accuracy and robustness of the trained DNNs. For instance, SGD with this adaptive momentum reduces classification errors for training ResNet110 for CIFAR10 and CIFAR100 from 5.25% to 4.64% and 23.75% to 20.03% , respectively. Furthermore, SGD, with the new adaptive momentum, also benefits adversarial training and, hence, improves the adversarial robustness of the trained DNNs.
‘John Ate 5 Apples’ != ‘John Ate Some Apples’: Self-supervised Paraphrase Quality Detection for Algebraic Word Problems

This paper introduces the novel task of scoring paraphrases for Algebraic Word Problems (AWP) and presents a self-supervised method for doing so. In the current online pedagogical setting, paraphrasing these problems is helpful for academicians to generate multiple syntactically diverse questions for assessments. It also helps induce variation to ensure that the student has understood the problem instead of just memorizing it or using unfair means to solve it. The current state-of-the-art paraphrase generation models often cannot effectively paraphrase word problems, losing a critical piece of information (such as numbers or units) which renders the question unsolvable. There is a need for paraphrase scoring methods in the context of AWP to enable the training of good paraphrasers. Thus, we propose ParaQD, a self-supervised paraphrase quality detection method using novel data augmentations that can learn latent representations to separate a high-quality paraphrase of an algebraic question from a poor one by a wide margin. Through extensive experimentation, we demonstrate that our method outperforms existing state-of-the-art self-supervised methods by up to 32% while also demonstrating impressive zero-shot performance.
$$\mathtt {RE\text{- }Tagger}$$: A Light-Weight Real-Estate Image Classifier

Real-estate image tagging is one of the essential use-cases to save efforts involved in manual annotation and enhance the user experience. This paper proposes an end-to-end pipeline (referred to as $$\mathtt {RE\text{- }Tagger}$$ ) for the real-estate image classification problem. We present a two-stage transfer learning approach using custom InceptionV3 architecture to classify images into different categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others). Finally, we released the application as REST API hosted as a web application running on 2 cores machine with 2 GB RAM.
Is This Bug Severe? A Text-Cum-Graph Based Model for Bug Severity Prediction

Repositories of large software systems have become commonplace. This massive expansion has resulted in the emergence of various problems in these software platforms including identification of (i) bug-prone packages, (ii) critical bugs, and (iii) severity of bugs. One of the important goals would be to mine these bugs and recommend them to the developers to resolve them. The first step to this is that one has to accurately detect the extent of severity of the bugs. In this paper, we take up this task of predicting the severity of bugs in the near future. Contextualized neural models built on the text description of a bug and the user comments about the bug help to achieve reasonably good performance. Further information on how the bugs are related to each other in terms of the ways they affect packages can be summarised in the form of a graph and used along with the text to get additional benefits.
Perspective: Leveraging Human Understanding for Identifying and Characterizing Image Atypicality

High-quality data plays a vital role in developing reliable image classification models. Despite that, what makes an image difficult to classify remains an unstudied topic. This paper provides a first-of-its-kind, model-agnostic characterization of image atypicality based on human understanding. We consider the setting of image classification "in the wild", where a large number of unlabeled images are accessible, and introduce a scalable and effective human computation approach for proactive identification and characterization of atypical images. Our approach consists of i) an image atypicality identification and characterization task that presents to the human worker both a local view of visually similar images and a global view of images from the class of interest and ii) an automatic image sampling method that selects a diverse set of atypical images based on both visual and semantic features. We demonstrate the effectiveness and cost-efficiency of our approach through controlled crowdsourcing experiments and provide a characterization of image atypicality based on human annotations of 10K images. We showcase the utility of the identified atypical images by testing state-of-the-art image classification services against such images and provide an in-depth comparative analysis of the alignment between human- and machine-perceived image atypicality. Our findings have important implications for developing and deploying reliable image classification systems.
To Re-experience the Web: A Framework for the Transformation and Replay of Archived Web Pages

When replaying an archived web page, or memento , the fundamental expectation is that the page should be viewable and function exactly as it did at the archival time. However, this expectation requires web archives upon replay to modify the page and its embedded resources so that all resources and links reference the archive rather than the original server. Although these modifications necessarily change the state of the representation, it is understood that without them the replay of mementos from the archive would not be possible. The process of replaying mementos and the modifications made to the representations by web archives varies between archives. Because of this, there is no standard terminology for describing the replay and needed modifications. In this article, we propose terminology for describing the existing styles of replay and the modifications made on the part of web archives to mementos to facilitate replay. Because of issues discovered with server-side only modifications, we propose a general framework for the auto-generation of client-side rewriting libraries. Finally, we evaluate the effectiveness of using a generated client-side rewriting library to augment the existing replay systems of web archives by crawling mementos replayed from the Internet Archive’s Wayback Machine with and without the generated client-side rewriter. By using the generated client-side rewriter, we were able to decrease the cumulative number of requests blocked by the content security policy of the Wayback Machine for 577 mementos by 87.5% and increased the cumulative number of requests made by 32.8%. We were also able to replay mementos that were previously not replayable from the Internet Archive. Many of the client-side rewriting ideas described in this work have been implemented into Wombat, a client-side URL rewriting system that is used by the Webrecorder, Pywb, and Wayback Machine playback systems.
Development of Practical Statistical Education Learning Program Using Design Thinking Techniques

The class was designed to improve learner-centered self-directed learning ability by using design thinking techniques and to diagnose and solve social problems by collecting data and information necessary to identify social problems. In the process, classes were conducted using service design techniques for real life-oriented statistical classes, and through this, real life-oriented statistical classes and statistical newspaper production were applied to actual classes. When discovering problems and defining problems based on design thinking techniques, it is possible to enhance creative problem-solving skills by establishing user-centered and context-oriented problem-solving goals and using statistics in the process of specifying solutions.&#x0D; This can be seen as meaningful as a teaching method that fits the trend of the times of the “4th Industrial Revolution” by applying new processes such as design thinking techniques, which students feel the need to solve statistical problems in school education. In the future, it is necessary to modify the prototype based on improved design thinking and further research through expansion and evaluation of actual class application.