Reviewerly/exHarmony huggingface dataset:
Evaluating the Impact of Health Care Data Completeness for Deep Generative Models

Abstract Background Deep generative models (DGMs) present a promising avenue for generating realistic, synthetic data to augment existing health care datasets. However, exactly how the completeness of the original dataset affects the quality of the generated synthetic data is unclear. Objectives In this paper, we investigate the effect of data completeness on samples generated by the most common DGM paradigms. Methods We create both cross-sectional and panel datasets with varying missingness and subset rates and train generative adversarial networks, variational autoencoders, and autoregressive models (Transformers) on these datasets. We then compare the distributions of generated data with original training data to measure similarity. Results We find that increased incompleteness is directly correlated with increased dissimilarity between original and generated samples produced through DGMs. Conclusions Care must be taken when using DGMs to generate synthetic data as data completeness issues can affect the quality of generated data in both panel and cross-sectional datasets.
Contractible Regularization for Federated Learning on Non-IID Data

In the medical domain, gathering all data and training a global supervised model is very difficult due to scattered data from different hospitals and security and privacy concerns. In recent years, several federated learning models have been proposed for training over isolated data. These models usually employ a client-server framework: 1) train local models on clients in parallel; 2) aggregate local models on the server to produce a global one. By iterating the above two steps, federated learning aims to approximate the performance of a model centrally trained on data. However, due to the non-IID data distribution issue, local models could deviate from the optimal model resulting in a biased aggregated global model. To address this problem, we propose a contractible regularization (ConTre) to act on the local model’s latent space. On each client, we first project the input data into a latent space and then pose regularization to avoid converging too fast to bad local optima. The proposed regularization can be easily integrated into existing federated learning frameworks without bringing in additional parameters. According to experimental results on multiple natural and medical image datasets, the proposed ConTre can significantly improve the performance of various federated learning frameworks. Our code is available at https://github.com/czifan/ConTre.pytorch.
Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors

The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations; the role of T, n, RH, wind speed (U2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.
Pandemic Response Officers: Integration Between Medical, Public Health, and Higher Education Systems to Expedite Prevention and Response

Research and policy studies alike have enumerated population and community health benefits of system integration between medical, public health, and social entities. The emergence of the COVID-19 pandemic revealed the necessity of a well-trained and adequately staffed public health and medical workforce in order to process SARS-CoV-2 cases and prevent subsequent transmission. Higher education systems, in particular, represented defined populations of exposure and transmission. Opportunities existed for collaboration and task sharing between institutions of higher education and local public health departments to limit spread and impacts.This article describes the Pandemic Response Officer (PRO) program at Cornell University, a team of staff and students created during the intensity of the pandemic to benefit the Tompkins County and Cornell University communities.The PRO program was formed in January 2021, with an original team of 8 individuals, working iteratively to investigate and support employee cases and exposures. Implementation was motivated by Cornell University's dual responsibility as a large employer that also possessed SARS-CoV-2 test results of employees. PROs loaded case information into a shared HIPPA-compliant electronic record that collected information for case notification, case investigation, isolation support, contact tracing, contact notification, and quarantine support. Over time, the PROs grew to a team of 25, gaining responsibilities as university and public health systems shared roles to maximize resources.From January 1 to December 31, 2021, PROs managed 773 employee and 2943 student cases. During the Omicron surge (November 28-December 31, 2021), PROs saved the public health department an estimated 2797 hours of effort, equating to more than 10 professionals working full-time, evenings and weekends, to process cases and contacts during this interval.By integrating efforts between a university and public health agency, this intervention minimized SARS-CoV-2 transmission via expedient case support and alleviated strain on public health systems by expanding the public health workforce.
The Problem of Monitoring the Psycho-Physical Condition of Seniors during Proposed Activities in Urban Space

The world’s population is rapidly ageing, which places a heavy burden on traditional healthcare systems with increased economic and social costs. Technology can assist in the implementation of strategies that enable active and independent ageing by promoting and motivating health-related behaviours, monitoring, and collecting data on daily life for assessment and for aiding in independent living. ICT (Information and Communication Technology) tools can help prevent cognitive and physical decline and social isolation, and enable elderly people to live independently. In this paper, we introduced a comprehensive tool for guiding seniors along the designed urban health paths employing urban architecture as an impulse to perform physical and cognitive exercises. The behaviour of seniors is monitored during their activities using wearable sensors and mobile application. We distinguished three types of data recipients (seniors, path/exercise designers, and the public), for whom we proposed methods of analysing the obtained data and examples of their use. In this work, a wide range of diverse information was examined from which short- and long-term patterns can be drawn. We have also shown that by fusing sensory data and data from mobile applications, we can give context to sensory data, thanks to which we can formulate more insightful assessments of seniors’ behaviour.
Advances and challenges in sensor-based research in mobility, health, and place

• Mobile sensing has transformed research in Mobility, Health, and Place. • It enables pervasive real-life measurements of personal behavior, context and health. • It enables the development of novel digital biomarkers and personalized intervention. • Classification of mobile sensing technologies and methods is proposed. • Issues remain in data collection and analysis to address uncertainties and biases.
Extended reality in language learning: A cognitive affective model of immersive learning perspective

A surge in the interest and implementation of extended reality (XR)-based lessons in language learning has resulted in many related studies. Recent reviews that summarized these studies and the previous studies focus on the technologies used in language-learning settings or the different ways of incorporating XR tools in language-learning activities. However, less work has been done to synthesize XR-based language-learning studies from a language-learning theory perspective. Thus, this study delineates the contour of scholarly literature on XR in language learning using the Cognitive Affective Model of Immersive Learning (CAMIL). The model contains six affective and cognitive factors that lead to XR-based learning: interest, motivation, self-efficacy, embodiment, cognitive load, and self-regulation. This model was adopted in the current study to systematically synthesize the findings from primary studies published between 2017 and 2022 to construct XR explanations on language learning from a cognitive theory perspective. Studies published in 12 indexed privileged journals in the language education and technology field on XR in language learning were reviewed. The results showed that the factors in the CAMIL led to factual, conceptual, and procedural knowledge acquisition and transfer. This study provides some insights into understanding the cognitive outcomes of XR-based language learning by analyzing the findings from previous studies. Suggestions for future studies are proposed in this study.
ImpDet: Exploring Implicit Fields for 3D Object Detection

Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i.e., localization, dimension, and orientation. Despite its popularity and universality, such a straightforward paradigm is sensitive to slight numerical deviations, especially in localization. By exploiting the property that point clouds are naturally captured on the surface of objects along with accurate location and intensity information, we introduce a new perspective that views bounding box regression as an implicit function. This leads to our proposed framework, termed Implicit Detection or ImpDet, which leverages implicit field learning for 3D object detection. Our ImpDet assigns specific values to points in different local 3D spaces, thereby high-quality boundaries can be generated by classifying points inside or outside the boundary. To solve the problem of sparsity on the object surface, we further present a simple yet efficient virtual sampling strategy to not only fill the empty region, but also learn rich semantic features to help refine the boundaries. Extensive experimental results on KITTI and Waymo benchmarks demonstrate the effectiveness and robustness of unifying implicit fields into object detection.
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow

A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time. Recent methods have proposed to address this problem with test-time training (TTT), where a two-branch model is trained to learn a main classification task and also a self-supervised task used to perform test-time adaptation. However, these techniques require defining a proxy task specific to the target application. To tackle this limitation, we propose TTTFlow: a Y-shaped architecture using an unsupervised head based on Normalizing Flows to learn the nor-mal distribution of latent features and detect domain shifts in test examples. At inference, keeping the unsupervised head fixed, we adapt the model to domain-shifted examples by maximizing the log likelihood of the Normalizing Flow. Our results show that our method can significantly improve the accuracy with respect to previous works.
Rebalancing gradient to improve self-supervised co-training of depth, odometry and optical flow predictions

We present CoopNet, an approach that improves the co-operation of co-trained networks by dynamically adapting the apportionment of gradient, to ensure equitable learning progress. It is applied to motion-aware self-supervised prediction of depth maps, by introducing a new hybrid loss, based on a distribution model of photo-metric reconstruction errors made by, on the one hand the depth + odometry paired networks, and on the other hand the optical flow network. This model essentially assumes that the pixels from moving objects (that must be discarded for training depth and odometry), correspond to those where the two reconstructions strongly disagree. We justify this model by theoretical considerations and experimental evidences. A comparative evaluation on KITTI and CityScapes datasets shows that CoopNet improves or is comparable to the state-of-the-art in depth, odometry and optical flow predictions. Our code is available here: https://github.com/mhariat/CoopNet.
PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI).
Role of frame structure in the development of KRS for learning dialogues.

Dialogues are building blocks of tasks and non-tasks of communication, which happen between objects in the universe. Each dialogue is a source of linguistic knowledge within a natural language that explains and elaborates with frame structure in general. In this paper, it is noticed that various forms like (nouns, pronouns, yes-no questions and deletion) are essential part of each dialogue structure (DSS) in Chandan’s work ਜੜਾਂ/Roots. With the help of frames, knowledge representation system (KRS) is prepared for such dialogues in Punjabi. On the other hand, it is argued that highest numbers of nouns are total 45 in DS2 and only 1 deletion case finds in DS3. While DS1 and DS2 both have similar number of 2-2 cases of yes-no questions. The overall evaluation is successfully matched with proposed an algorithm based on frames.
Noise-Aware and Lightweight LSTM for Keyword Spotting Applications

Keyword spotting (KWS) becomes increasingly prominent in voice-enabled IoT devices. To attain high KWS accuracy, neural network (NN) algorithm is commonly used in existing works which focus on developing NN-based ultra-low power KWS to support always-on feature but do not consider the performance under noisy environments. Thus, this work proposes a noise-robust, lightweight and accurate KWS. A long short term memory (LSTM) network is trained from scratch to adapt to different noisy scenarios. The proposed LSTM model is further pruned and quantized to reduce model size while achieving accuracy of over 75.4% for white noise with signal-tonoise ratio (SNR) from -5 dB to 20 dB. The final LSTM model is mapped to a hardware accelerator synthesized using a 40-nm CMOS technology, consuming only <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.7\mu \mathrm{W}$</tex> at 0.6V.
Analysis of Several Quantum Encoding Methods Implemented on A Quantum Circuit Architecture to Improve Classification Accuracy

Quantum encoding is a process to transform classical information into quantum states. It plays a crucial role in using quantum algorithms to solve classical problems, especially in quantum machine learning tasks. There are many QE methods. It is very difficult to determine which QE method to choose to improve classification accuracy. Therefore, this paper will analyze several QE methods. Training and testing on Iris flower datasets were performed in a architecture quantum circuit and some performances parameters were evaluated. The expected result is that we can compare the classification accuracy of some of these Quantum encodings.
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge

This paper describes the TSUP team’s submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering (SC) based diarization, target-speaker voice activity detection (TS-VAD) and end-to-end neural diarization (EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge.
Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks.Wearing a face mask in public areas protects people from spreading COVID-19.Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways.This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic.The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering.To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement.For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector.In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work.At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model.The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset.The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches.
A Chaotic Encrypted Reliable Image Watermarking Scheme based on Integer Wavelet Transform-Schur Transform and Singular Value Decomposition

In the present era of the internet, image watermarking schemes are used to provide content authentication, security and reliability of various multimedia contents. In this paper image watermarking scheme which utilizes the properties of Integer Wavelet Transform (IWT), Schur decomposition and Singular value decomposition (SVD) based is proposed. In the suggested method, the cover image is subjected to a 3-level Integer wavelet transform (IWT), and the HH3 subband is subjected to Schur decomposition. In order to retrieve its singular values, the upper triangular matrix from the HH3 subband’s Schur decomposition is then subjected to SVD. The watermark image is first encrypted using a chaotic map, followed by the application of a 3-level IWT to the encrypted watermark and the usage of singular values of the LL-subband to embed by manipulating the singular values of the processed cover image. The proposed scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping) and noise (Gaussian, Salt & Pepper Noise). The suggested scheme provides strong robustness against numerous attacks and chaotic encryption provides security to watermark.
HINChip: Heterogeneous Information Network Representation with Community Hierarchy Preserving

Heterogeneous information network (HIN) representation aims to learn low-dimensional representations for multiple types of nodes and edges while preserving rich structural and semantic information. Existing HIN embedding methods mainly utilize meta-path, meta-graph, and network schema to guide the learned representations. However, these methods may ignore the community hierarchy information in the information network, which is commonly available in many applications. In this paper, we propose HINChip, a new approach for learning representations of Heterogeneous Information Networks with Community Hierarchy Preserving, including intra-community structures and inter-community relationships. Specifically, multiple homogeneous sub-networks are first constructed based on multiple semantics. Second, hierarchical networks are built from fine to coarse on each homogeneous sub-network, preserving intra-community structures and inter-community relationships on the corresponding semantics. The nodes learn the corresponding vectors from each level of the hierarchical networks by the existing representation learning method. Third, multi-level-integrated semantic attention is proposed to combine the node vectors of each level and weigh the importance of different homogeneous sub-networks under given semantics. The proposed HINChip considers only the topology of HIN and outperforms methods trained with node attributes and labels in the experiments conducted on four real-world datasets.