Reviewerly/exHarmony huggingface dataset:
DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation

The videos are more popular for sharing content on social media to capture the audience's attention.The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms.Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques.Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users.To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation.Xception Net was originally designed for application on 2D images only.The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data.The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing.Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy.
Towards the implementation of immersive technology in construction – a SWOT Analysis

Whilst research of immersive technology has been growing, substantial efforts thus far have been scattered. Conflicting ideas and arguments from scholars provoke uncertainty and questions about the validity of these outputs in fostering more extensive implementation of immersive technology in the construction industry. Consequently, the direction for further developments in research remains unclear. This study aims to identify the status quo of immersive technology adoption in construction. A systematic review with thematic analysis was conducted. Common themes and concerns about the use of immersive technology in the construction are classified under the framework of SWOT (strengths, weaknesses, opportunities and threats). The results indicate that whilst ‘strengths’ and ‘opportunities’ display a broad array of applications and functions that are derived from real benefits, a substantial amount of opportunities mentioned are driven by optimism. Alternatively, the results from ‘weaknesses’ and ‘opportunities’ identify several limitations involved in the uptake of immersive technology in the construction industry. Findings of this study provide vision that can help direct resources to those measures that can best meet the needs of construction industry. It is suggested that future resources are better to be put on integration with proven effective systems like Building Information Modelling (BIM) and Computer Aided Design (CAD) software. New developments should due consider the real demand of the industry.
Photonic implementation of artificial synapses in ultrafast laser inscribed waveguides in chalcogenide glass

Simple and direct prototyping methods are ideal for large-scale delivery of cognitive photonic hardware. Here, we choose ultrafast laser writing as a direct fabrication technique to later demonstrate all-optical synaptic-like performance along the laser-written waveguides in a chalcogenide glass. Neuronal communication protocols, such as excitatory and inhibitory responses, temporal summations, and spike-timing-dependent plasticity, are shown in the glass chip. This work manifests the potential for large-scale delivery of fully integrated photonic chips based on cognitive principles by single-step fabrication procedures.
PlenoptiCam v1.0: A Light-Field Imaging Framework

Light-field cameras play a vital role for rich 3D information retrieval in narrow range depth sensing applications.The key obstacle in composing light-fields from exposures taken by a plenoptic camera is to computationally calibrate, align and rearrange four-dimensional image data.Several attempts have been proposed to enhance the overall image quality by tailoring pipelines dedicated to particular plenoptic cameras and improving the consistency across viewpoints at the expense of high computational loads.The framework presented herein advances prior outcomes thanks to its novel micro image scale-space analysis for generic camera calibration independent of the lens specifications and its parallax-invariant, cost-effective viewpoint color equalization from optimal transport theory.Artifacts from the sensor and micro lens grid are compensated in an innovative way to enable superior quality in sub-aperture image extraction, computational refocusing and Scheimpflug rendering with sub-sampling capabilities.Benchmark comparisons using established image metrics suggest that our proposed pipeline outperforms state-of-the-art tool chains in the majority of cases.Results from a Wasserstein distance further show that our color transfer outdoes the existing transport methods.Our algorithms are released under an open-source license, offer cross-platform compatibility with few dependencies and different user interfaces.This makes the reproduction of results and experimentation with plenoptic camera technology convenient for peer researchers, developers, photographers, data scientists and others working in this field.
Biomedical Text Summarization: A Graph-Based Ranking Approach

The latest and precise information regarding the biomedical and healthcare domain is required in the current pandemic situation. The world has turned into a small place where everyone wants quick and relevant medical data to prevent contagious diseases. Doctors, nursing staff, medical practitioners, frontline Covid19 epidemic fighters, and even the common man requires updates and summarized biomedical statistics. A study on Graph-based biomedical text summarization with different similarity measures and ranking of sentence embeddings is presented in this paper. Cosine and Dice similarities and the pre-trained BERT model providing context via sentence embeddings are combined with TextRank and PageRank algorithms resulting in an opulent extractive text summarization of biomedical Cord19 Pubmed articles. Rouge-1 and Rouge-L scores are empirically calculated, providing a comparison between the average F-score, precision, and recall values for various graph-based sentence extraction methods. It has been observed that Cosine similarity and BERT sentence embeddings are equally effective when used with graph-based ranking algorithms. The significant contribution is the proposed TextRank with the BERT embedding model, which is evaluated as the preferred choice for short biomedical document summarization. But for large documents, the BERT model behaves heavy and causes latency in execution whereas, LexRank including Cosine measure still works efficiently for mid-size document summarization.
HLE-UPC at SemEval-2021 Task 5: Multi-Depth DistilBERT for Toxic Spans Detection

This paper presents our submission to SemEval-2021 Task 5: Toxic Spans Detection.The purpose of this task is to detect the spans that make a text toxic, which is a complex labour for several reasons.Firstly, because of the intrinsic subjectivity of toxicity, and secondly, due to toxicity not always coming from single words like insults or offends, but sometimes from whole expressions formed by words that may not be toxic individually.Following this idea of focusing on both single words and multi-word expressions, we study the impact of using a multi-depth DistilBERT model, which uses embeddings from different layers to estimate the final per-token toxicity.Our quantitative results show that using information from multiple depths boosts the performance of the model.Finally, we also analyze our best model qualitatively.
Comparing Span Extraction Methods for Semantic Role Labeling

In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL).While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on popular benchmarks, the potential costs and benefits of structured decoding in these models have become less clear.With extensive experiments on PropBank SRL datasets, we find that more structured decoding methods outperform BIO-tagging when using static (word type) embeddings across all experimental settings.However, when used in conjunction with pre-trained contextualized word representations, the benefits are diminished.We also experiment in cross-genre and cross-lingual settings and find similar trends.We further perform speed comparisons and provide analysis on the accuracy-efficiency trade-offs among different decoding methods.
An Immersive Journey through Flawed Technology: Users’ Perceptions of VR in Journalism

Virtual reality (VR) has had the reputation of being a revolutionising technology ever since it emerged in the early 1960s, but virtual is not yet a successful reality in journalistic practice. Examining VR’s current situation and the factors preventing it from reaching its predicted potential in digital journalism, this paper analyses the user comments (n = 770) on 15 journalistic VR apps offered by media organizations, with the help of a qualitative-reductive content analysis. Deductive categories of analysis contain the constructs of immersion, emotion, usability, and utility, which are further specified by inductive subcategories in the course of the analysis. Results show that users positively highlight different aspects of emotion and immersion that the VR apps elicit, and criticize journalistic VR apps for their low levels of utility and usability. Implications for journalistic practice and research are subsequently drawn.
Towards Graph Pooling by Edge Contraction

Graph Neural Networks (GNNs) research has concentrated on improving convolutional layers, with little attention paid to developing graph poolinglayers. Yet pooling layers can enable GNNs toreason over abstracted groups of nodes instead ofsingle nodes, thus increasing their generalization potential. To close this gap, we propose a graph pooling layer relying on the notion of edge con-traction: EdgePool learns a localized and sparse pooling transform. We evaluate it on four datasets, finding that it increases performance on the three largest. We also show that EdgePool can be integrated in existing GNN architectures without adding any additional losses or regularization.
Investigation of the applicability of the convolutional neural network U-Net to a problem of segmentation of aircraft images

The paper investigates the applicability of the convolutional neural network "U-Net" to a problem of segmentation of aircraft images. The neural network image segmentation method is based on the "Carvana" implementation with the "U-Net" architecture. For orientation recognition, a neural network built in the Keras open neural network library based on the pretrained VGG16 neural network is used. The approach considered allows the image segmentation to be conducted. The results of the experiments have shown the possibility of a fairly accurate selection of the object of interest. The resulting binary masks make it possible to visually classify the aircraft in the image.
Classification Functions for Handwritten Digit Recognition

A classification function maps a set of vectors into several classes. A machine learning problem is treated as a design problem for partially defined classification functions. To realize classification functions for MNIST hand written digits, three different architectures are considered: Single-unit realization, 45-unit realization, and 45-unit ×r realization. The 45-unit realization consists of 45 ternary classifiers, 10 counters, and a max selector. Test accuracy of these architectures are compared using MNIST data set.
The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.
Problems of Teaching Foreign Students under Conditions of the Pandemic

The article analyzes a process of teaching foreign students at regional universities under conditions of restrictions associated with the COVID-19 pandemic, using digital technologies. Currently, universities, and especially technical universities, play a leading role in the development and distribution of digital technologies. The authors believe that achieving these goals involves changing the content of academic disciplines and courses, including those aimed at improving digital literacy. The concept of combining a lecture system and e-learning technologies, which are provided by the electronic platform Microsoft Teams at the VSTU, is a modern format of the educational process. With a mixed format of educational process, the modular principle of organizing a curriculum of disciplines seems to be more appropriate. The authors analyze the results of the first stage of educational digitalization (the second semester of the 2019/20 academic year). This analysis is based on the academic performance of students (Russian and foreign) and the results of teachers’ and students’ survey on the effectiveness of distance learning. According to the results of educational analytics, the authors state, firstly, a decrease in the absolute academic performance of students while teaching with a help of the distance education technologies and e-learning, due to the low level of students’ self-organization and the lack of educational material in the digital format; secondly, a decrease in the quality of teaching, which demonstrates the unwillingness of teachers to use the potential of e-learning and, thirdly, the impossibility of a complete transfer of teaching to a digital environment. In addition, the authors describe the problems faced by regional universities in the process of educational digitalization and the implementation of the program of Russian education export.
An Overview of Grammar Translation Method’s Effect on EFL Teaching And Learning

In language teaching and learning, several methods are used for the effectiveness of teaching and learning activity. The researcher implemented the methods to assist the teacher in teaching the language, time after time. The methods have undergone many changes and development based on the attitude and school of thought for teaching and learning a language, so each method has a different character, technique, and implementation. This reflection will expand on the grammatical translation method (GTM) used in the nineteenth century to teach English as a foreign language. This method is based on the goal of grammatical competence. To achieve the goal, this method has several characteristic and technique which will be used in its implementation. The writer attempts to do a review by investigating 5 published articles gathered from internet websites to show the use of the grammar-translation method, its effectiveness, and why it is criticized for teaching and learning English as a foreign language. The writer’s reflection is included too.
A Novel Anatomy Education Method Using a Spatial Reality Display Capable of Stereoscopic Imaging with the Naked Eye

Several efforts have been made to use virtual reality (VR) and augmented reality (AR) for medical and dental education and surgical support. The current methods still require users to wear devices such as a head-mounted display (HMD) and smart glasses, which pose challenges in hygiene management and long-term use. Additionally, it is necessary to measure the user’s inter-pupillary distance and to reflect it in the device settings each time to accurately display 3D images. This setting is difficult for daily use. We developed and implemented a novel anatomy education method using a spatial reality display capable of stereoscopic viewing with the naked eye without an HMD or smart glasses. In this study, we developed two new applications: (1) a head and neck anatomy education application, which can display 3D-CG models of the skeleton and blood vessels of the head and neck region using 3D human body data available free of charge from public research institutes, and (2) a DICOM image autostereoscopic viewer, which can automatically convert 2D CT/MRI/CBCT image data into 3D-CG models. In total, 104 students at the School of Dentistry experienced and evaluated the system, and the results suggest its usefulness. A stereoscopic display without a head-mounted display is highly useful and promising for anatomy education.
Transferring quantum entangled states between multiple single-photon-state qubits and coherent-state qubits in circuit QED

We present a way to transfer maximally- or partially-entangled states of n single-photon-state (SPS) qubits onto n coherent-state (CS) qubits, by employing 2n microwave cavities coupled to a superconducting flux qutrit. The two logic states of a SPS qubit here are represented by the vacuum state and the single-photon state of a cavity, while the two logic states of a CS qubit are encoded with two coherent states of a cavity. Because of using only one superconducting qutrit as the coupler, the circuit architecture is significantly simplified. The operation time for the state transfer does not increase with the increasing of the number of qubits. When the dissipation of the system is negligible, the quantum state can be transferred in a deterministic way since no measurement is required. Furthermore, the higher-energy intermediate level of the coupler qutrit is not excited during the entire operation and thus decoherence from the qutrit is greatly suppressed. As a specific example, we numerically demonstrate that the high-fidelity transfer of a Bell state of two SPS qubits onto two CS qubits is achievable within the present-day circuit QED technology. Finally, it is worthy to note that when the dissipation is negligible, entangled states of n CS qubits can be transferred back onto n SPS qubits by performing reverse operations. This proposal is quite general and can be extended to accomplish the same task, by employing a natural or artificial atom to couple 2n microwave or optical cavities.
Learn from Concepts: Towards the Purified Memory for Few-shot Learning

Human beings have a great generalization ability to recognize a novel category by only seeing a few number of samples. This is because humans possess the ability to learn from the concepts that already exist in our minds. However, many existing few-shot approaches fail in addressing such a fundamental problem, {\it i.e.,} how to utilize the knowledge learned in the past to improve the prediction for the new task. In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings. This new memory updating scheme enables the model to purify the information from semantic labels and progressively learn consistent, stable, and expressive concepts when episodes are trained one by one. On its basis, a Graph Augmentation Module (GAM) is introduced to aggregate these concepts and knowledge learned from new tasks via a graph neural network, making the prediction more accurate. Generally, our approach is model-agnostic and computing efficient with negligible memory cost. Extensive experiments performed on several benchmarks demonstrate the proposed method can consistently outperform a vast number of state-of-the-art few-shot learning methods.
Control of the stochastic response of magnetization dynamics in spin-torque oscillator through radio-frequency magnetic fields

Abstract Neuromorphic computing using spintronic devices, such as spin-torque oscillators (STOs), has been intensively studied for energy-efficient data processing. One of the critical issues in this application is stochasticity in magnetization dynamics, which limits the accuracy of computation. Such stochastic behavior, however, plays a key role in stochastic computing and machine learning. It is therefore important to develop methods for both suppressing and enhancing stochastic response in spintronic devices. We report on experimental investigations on control of stochastic quantity, such as the width of a distribution of transient time in magnetization dynamics in vortex-type STO. The spin-transfer effect can suppress stochasticity in transient dynamics from a non-oscillating to oscillating state, whereas an application of a radio-frequency magnetic field is effective in reducing stochasticity on the time evolution of the oscillating state.
TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning

Existing research for image captioning usually represents an image using a scene graph with low-level facts (objects and relations) and fails to capture the high-level semantics. In this paper, we propose a Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics. In practice, we model theme concepts as memory vectors and propose Transformer with Theme Nodes (TTN) to incorporate those vectors for image captioning. Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN. On the vision side, TTN is configured to take both scene graph based features and theme concepts as input for visual representation learning. On the language side, TTN is configured to take both captions and theme concepts as input for text representation re-construction. Both settings aim to generate target captions with the same transformer-based decoder. During the training, we further align representations of theme concepts learned from images and corresponding captions to enforce the cross-modality learning. Experimental results on MS COCO show the effectiveness of our approach compared to some state-of-the-art models.