Reviewerly/exHarmony huggingface dataset:
AirConstellations: In-Air Device Formations for Cross-Device Interaction via Multiple Spatially-Aware Armatures

AirConstellations supports a unique semi-fixed style of cross-device interactions via multiple self-spatially-aware armatures to which users can easily attach (or detach) tablets and other devices. In particular, AirConstellations affords highly flexible and dynamic device formations where the users can bring multiple devices together in-air — with 2–5 armatures poseable in 7DoF within the same workspace — to suit the demands of their current task, social situation, app scenario, or mobility needs. This affords an interaction metaphor where relative orientation, proximity, attaching (or detaching) devices, and continuous movement into and out of ad-hoc ensembles can drive context-sensitive interactions. Yet all devices remain self-stable in useful configurations even when released in mid-air.
Multi-Head Self-Attention Transformer for Dogecoin Price Prediction

Cryptocurrency market has witnessed a boom during the global pandemic and has proven as a strong investment with a wide institutional adoption. A time-series forecasting solution will play a vital role in analyzing the fluctuation of the bitcoin and altcoin markets. Dogecoin is one such altcoin that is a low-price, high-risk investment option garnering considerable interest this year. The variation of the price trend of this altcoin is studied using the multi-head attention mechanism implemented in a transformer, where the attention heads attend to the tokens that are relevant to each current token based on varying short-term and long-term dependencies. In this paper, a multi-head attention-based transformer encoder-decoder model is applied on the hourly data of the Dogecoin price for its prediction over time. The performance of the model has been evaluated using a number of evaluation metrics including MAE and predictive R-squared value. The model trained over the Dogecoin hourly price variation gave an impressive accuracy of 98.46% and R-squared value of 0.8616 comparable with the existing state-of-the-art cryptocurrency price forecasting models.
Detailed 3D human body reconstruction from multi-view images combining voxel super-resolution and learned implicit representation

Abstract The task of reconstructing detailed 3D human body models from images is interesting but challenging in computer vision due to the high freedom of human bodies. This work proposes a coarse-to-fine method to reconstruct detailed 3D human body from multi-view images combining Voxel Super-Resolution (VSR) based on learning the implicit representation. Firstly, the coarse 3D models are estimated by learning an Pixel-aligned Implicit Function based on Multi-scale Features (MF-PIFu) which are extracted by multi-stage hourglass networks from the multi-view images. Then, taking the low resolution voxel grids which are generated by the coarse 3D models as input, the VSR is implemented by learning an implicit function through a multi-stage 3D convolutional neural network. Finally, the refined detailed 3D human body models can be produced by VSR which can preserve the details and reduce the false reconstruction of the coarse 3D models. Benefiting from the implicit representation, the training process in our method is memory efficient and the detailed 3D human body produced by our method from multi-view images is the continuous decision boundary with high-resolution geometry. In addition, the coarse-to-fine method based on MF-PIFu and VSR can remove false reconstructions and preserve the appearance details in the final reconstruction, simultaneously. In the experiments, our method quantitatively and qualitatively achieves the competitive 3D human body models from images with various poses and shapes on both the real and synthetic datasets.
The Geometry of Two-Weight Codes Over ℤ<sub><i>p</i></sub><sup><i>m</i></sup>

We investigate fat projective linear codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb Z}_{p^{m}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m\geqslant 2$ </tex-math></inline-formula> , with two nonzero homogeneous weights (“two-weight codes”), building on the graph theory approach developed by Delsarte for codes over fields. Our main result is the classification of such codes under the additional assumption that the columns of a generator matrix of the code determine a cap in the projective Hjelmslev geometry <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathop {\mathrm {PHG}}\nolimits (k-1, {\mathbb Z}_{p^{m}})$ </tex-math></inline-formula> . This generalizes a result on projective two weight codes with dual distance at least four (Calderbank, 1982). The proof relies on a careful analysis of a certain strongly regular graph built on the cosets of the dual code, and on an interpretation of its parameters in terms of projective Hjelmslev geometry.
Evaluating the Performance of NIST’s Framework Cybersecurity Controls Through a Constructivist Multicriteria Methodology

This paper aims to show how creating a risk plan can be solved with the help of the constructivist multicriteria method.A case study using Multicriteria Decision Aid Constructivist (MCDA-C) was applied, with cybersecurity framework's controls as a reference.The study was conducted in a large Brazilian bank in Brazil.The relevance of this work is the need to show that the application of multicriteria methods can be applied in the context of information security, which recommends the use of such methods to assist in risk analysis.The methodology used in this study was both quantitative and qualitative, obtaining primary data through brainstorming with decision-makers and forms answered by experts.The secondary data were obtained through the Framework for Improving Critical Infrastructure Cybersecurity, created by NIST -the National Institute of Standards and Technology of the United States.The problem was structured according to the constructivist method, and the data collected were processed and calculated.The study concluded that the category of Security Continuous Monitoring controls stood out compared to other categories.It also shows the importance of applying the constructivist method for the management of cyber risks by unravelling a problem and providing a basis for decision making.Our work contributes to a better understanding of risk management, encouraging the adoption of the constructivist method as a form of risk management best practice.
Quantum-Resistant Cryptography for the Internet of Things Based on Location-Based Lattices

The Internet of Things (IoT) and localization are two important technology enablers. An important IoT technology is the Narrow-Band Internet of Things (NB-IoT), which is a 3GPP standards compliant connectivity solution. Quantum computing, another developing technological paradigm, promises novel computational opportunities, but it has also been used to compromise many cybersecurity procedures. Therefore, improved methods to mitigate such jeopardies are needed. In this research, we propose a cryptographic system that guarantees post-quantum IoT security. The ultimate value of a location-driven cryptosystem is to use geographic location as a player’s identity and credential. Position-driven cryptography using lattices is efficient and lightweight, and it can be used to protect sensitive and confidential data in many critical situations that rely heavily on exchanging confidential data. Based on our best knowledge, this research starts the study of unconditional-quantum-resistant-location-driven cryptography by using the Lattice problem for the Internet of Moving Things (IoMT) in pre-and post-quantum world. Unlike existing schemes, the proposed cryptosystem is the first secure and unrestricted position-based protocol that guards against any number of collusion attackers and against quantum attacks. It has a guaranteed authentication process, solves the problems of distributing public keys by cancelling public key infrastructure (PKI), offers secure NB-IoT without SIM cards, and resists location spoofing attacks. Furthermore, it can be generalized for any network – not just NB-IoT.
Factors affecting the quality of online learning in a task‐based college course

Abstract In the face of the COVID‐19 pandemic, all colleges in Korea were forced to transition to online teaching, as was the case in most of the rest of the world. This situation engendered confusion, frustration, and dissatisfaction among students as well as instructors. The present study examined college students' perceptions of online learning in general and of one purpose‐designed course in particular and compared their responses. The objective was to identify factors that might enhance the quality of online education (OE) and learner satisfaction. The present study applied a mixed method to ensure robust results, using a survey, interviews, students' reflection papers, and the instructor's field notes. The results indicated that the students perceived online learning as less effective than traditional face‐to‐face classes overall but were satisfied with the customized online course, specifically citing the instructor's prompt feedback, interaction among students, and effective design of tasks. Given this outcome, the present study suggests several pedagogical implications for OE in the future.
AMFF: A new attention-based multi-feature fusion method for intention recognition

Intention recognition is based on a dialog between users to identify their real intentions, which plays a key role in the question answering system. However, the content of a dialog is usually in the form of short text. Due to data sparsity, many current classification models show poor performance on short text. To address this issue, we propose AMFF, an attention-based multi-feature fusion method for intention recognition. In this paper, we enrich short text features by fusing features extracted from frequency-inverse document frequency (TF-IDF), convolutional neural networks (CNNs) and long short-term memory (LSTM). For the purpose of measuring the important features, we utilize the attention mechanisms to assign weights for the fusion features. Experimental results on the TREC, SST1 and SST2 datasets demonstrate that the proposed AMFF model outperforms traditional machine learning models and typical deep learning models on short text classification.
Reference-Frame-Independent Design of Phase-Matching Quantum Key Distribution

The recently proposed phase-matching quantum key distribution offers means to overcome the linear key rate-transmittance bound. Since the key information is encoded onto the phases of coherent states, the misalignment between the two remote reference frames would yield errors and significantly degrade the key generation rate from the ideal case. In this work, we propose a reference-frame-independent design of phase-matching quantum key distribution by introducing high-dimensional key encoding space. With encoded phases spanning the unit circle, the error statistics at arbitrary fixed phase reference difference can be recovered and treated separately, from which the misalignment angle can be identified. By naturally extending the binary encoding symmetry and complementarity to high dimensions, we present a security proof of this high-dimensional phase-matching quantum key distribution and demonstrate with simulation that a 17-dimensional protocol is completely immune to any degree of fixed misalignment and robust to slow phase fluctuations. We expect the high-dimensional protocol to be a practical reference-frame-independent design for general phase-encoding schemes where high-dimensional encoding is relatively easy to implement.
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. QML models can be useful in both of these tasks. On one hand, convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoen-coder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.
A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications

The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder's intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this paper, a new method of watermarking is proposed on both standard and medical images. The paper addresses the use of digital rights management in medical field applications such as embedding the watermark in medical images related to neurodegenerative disorders, lung disorders, and heart issues. The various quality parameters are used to figure out the evaluation of the developed method. In addition, the testing of the watermarking scheme is done by applying various signal processing attacks.
Privacy-Preserving Collective Learning With Homomorphic Encryption

Deep learning models such as long short-term memory (LSTM) are valuable classifiers for time series data like hourly clinical statistics.However, access to health data is challenging due to privacy and legal issues.Homomorphic encryption (HE) offers a potential solution by encrypting confidential data.However, the computational complexity of encrypted operations makes it infeasible for training with gradient descent.Prior works only deal with encrypted procedures during the inference phase of the network.To this end, we design Collective Learning protocol, a secure protocol for sharing classified time-series data within entities to train parameters of the binary classifier model partially.The protocol encrypts each data's feature activations using HE and trains the last layers using encrypted logistic regression.We evaluate our protocol on a benchmark LSTM network trained on the Medical Information Mart for Intensive Care (MIMIC-III) dataset.We selected the Cheon-Kim-Kim-Song (CKKS) encryption scheme because of its ability to work with real numbers using approximate arithmetic.Our protocol improved the area under the precision-recall curve (AUPRC) score of the in-hospital mortality prediction model.The same protocol was reimplemented using secure multi-party computation (MPC) and compared.
Withdrawn as Duplicate: Distant viewing: analyzing large visual corpora

Journal Article Withdrawn as Duplicate: Distant viewing: analyzing large visual corpora Get access Taylor Arnold, Taylor Arnold Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, USA Search for other works by this author on: Oxford Academic Google Scholar Lauren Tilton Lauren Tilton Department of Rhetoric and Communication Studies, University of Richmond, Richmond, VA, USA Search for other works by this author on: Oxford Academic Google Scholar Digital Scholarship in the Humanities, Volume 36, Issue 2, June 2021, Page e1, https://doi.org/10.1093/digitalsh/fqz013 Published: 16 March 2019
Deep3DLayout

Recovering the 3D shape of the bounding permanent surfaces of a room from a single image is a key component of indoor reconstruction pipelines. In this article, we introduce a novel deep learning technique capable to produce, at interactive rates, a tessellated bounding 3D surface from a single 360° image. Differently from prior solutions, we fully address the problem in 3D, significantly expanding the reconstruction space of prior solutions. A graph convolutional network directly infers the room structure as a 3D mesh by progressively deforming a graph-encoded tessellated sphere mapped to the spherical panorama, leveraging perceptual features extracted from the input image. Important 3D properties of indoor environments are exploited in our design. In particular, gravity-aligned features are actively incorporated in the graph in a projection layer that exploits the recent concept of multi head self-attention, and specialized losses guide towards plausible solutions even in presence of massive clutter and occlusions. Extensive experiments demonstrate that our approach outperforms current state of the art methods in terms of accuracy and capability to reconstruct more complex environments.
Distributing DNN training over IoT edge devices based on transfer learning

In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed. The approach results in protecting data privacy on the edge devices and decreasing the load on cloud servers. In addition, the technique may reduce the communication traffic between the cloud and the edge devices. Since the available resources in the edge devices are limited, in the proposed approach, we suggest a heuristic technique for generating a smaller network based on the main network in cloud. Next, by exploiting the knowledge distillation method, the knowledge of the main network is transferred to the generated small network. In this approach, small networks on the edge devices under different datasets are trained where some of their parameters are aggregated for updating the main network parameters on the cloud. The effectiveness of this approach is assessed with some state-of-the-art neural networks. Results show that the approach, the price of preserving the data privacy, is, on average, about 3.5% accuracy loss compared to the case when the network is trained on the cloud and all the datasets of the edge devices are available for training.
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis

Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.
Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal information

Stack Overflow is very helpful for software developers who are seeking answers to programming problems. Previous studies have shown that a growing number of questions are of low quality and thus obtain less attention from potential answerers. Gao et al. proposed an LSTM-based model (i.e., BiLSTM-CC) to automatically generate question titles from the code snippets to improve the question quality. However, only using the code snippets in the question body cannot provide sufficient information for title generation, and LSTMs cannot capture the long-range dependencies between tokens. This paper proposes CCBERT, a deep learning based novel model to enhance the performance of question title generation by making full use of the bi-modal information of the entire question body. CCBERT follows the encoder–decoder paradigm and uses CodeBERT to encode the question body into hidden representations, a stacked Transformer decoder to generate predicted tokens, and an additional copy attention layer to refine the output distribution. Both the encoder and decoder perform the multi-head self-attention operation to better capture the long-range dependencies. This paper builds a dataset containing around 200,000 high-quality questions filtered from the data officially published by Stack Overflow to verify the effectiveness of the CCBERT model. CCBERT outperforms all the baseline models on the dataset. Experiments on both code-only and low-resource datasets show the superiority of CCBERT with less performance degradation. The human evaluation also shows the excellent performance of CCBERT concerning both readability and correlation criteria. CCBERT is capable of automatically capturing the bi-modal semantic information from the entire question body and parsing the long-range dependencies to achieve better performance. Therefore, CCBERT is an effective approach for generating Stack Overflow question titles.
A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors

Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.