Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Internet Plus Sports Health Management System

In this paper, the health collection equipment of the Internet of things is extended to residents’ families. The health status of residents is continuously recorded through the mobile Internet. A series of personal health management services such as health status evaluation, management and guidance, health care consultation, education and health risk factor assessment are provided for residents by using the information resources of the regional residents’ health file platform. The collection devices of Internet of things include: watch type wearable health manager, home health management client, home electronic blood pressure device, blood glucose tester and fat detector. Mobile communication devices include: mobile phones for the elderly and dedicated mobile communication gateway.
Fast and explainable clustering based on sorting

We introduce a fast and explainable clustering method called CLASSIX. It consists of two phases, namely a greedy aggregation phase of the sorted data into groups of nearby data points, followed by the merging of groups into clusters. The algorithm is controlled by two scalar parameters, namely a distance parameter for the aggregation and another parameter controlling the minimal cluster size. Extensive experiments are conducted to give a comprehensive evaluation of the clustering performance on synthetic and real-world datasets, with various cluster shapes and low to high feature dimensionality. Our experiments demonstrate that CLASSIX competes with state-of-the-art clustering algorithms. The algorithm has linear space complexity and achieves near linear time complexity on a wide range of problems. Its inherent simplicity allows for the generation of intuitive explanations of the computed clusters.
Interactive Books &amp;#x2013; Status Report

This paper presents a status report on reader and adopter responses to printed interactive books using Ricoh's Clickable Paper application.The paper describes how Ricoh's Clickable Paper app has taken a major step forward with the growth of adoptions of the first book, Introduction to Graphic Communication, the completion of a second book, The Sound of Bamboo, and a third book in process focused on Black history and African American journeys to success.The diversity of these topics shows how Ricoh's Clickable Paper technology is applicable for interdisciplinary fields such as graphic communication, the performing arts, and the social sciences.A survey was taken of user and adopter reactions to the Clickable Paper application, with the major emphasis on the first book, Introduction to Graphic Communication, because it has entered its third year of use with adoptions to date by twenty-four schools.The second book, The Sound of Bamboo, focusing on music, is in its first year of adoption.Reviews to date are included.The third book is presently being written and scheduled for release in 2022.Content Analysis is the research method used to quantify the qualitative responses received.Therefore, all readers can interpret the results in the same way.In sum, the results are highly positive from students, schools, faculty members, and other readers of the Clickable Paper books.The scanning abilities of the Clickable Paper app received the greatest number of positive responses along with the value of the videos, and how the interactive Introduction to Graphic Communication book served an important need during the COVID-19 pandemic.Recommendations, for the most part, were not related to functionality of the Clickable Paper technology, but to cosmetic considerations and operational inconsistencies that some users experienced.The Clickable Paper app design received the greatest number of recommendations for improvement, followed by some discomfort in viewing images on the small screen size of smartphones.Remedies are provided.
Hierarchical Over-the-Air Federated Edge Learning

Federated learning (FL) over wireless communication channels, specifically, over-the-air (OTA) model aggregation framework is considered. In OTA wireless setups, the adverse channel effects can be alleviated by increasing the number of receive antennas at the parameter server (PS), which performs model aggregation. However, the performance of OTA FL is limited by the presence of mobile users (MUs) located far away from the PS. In this paper, to mitigate this limitation, we propose hierarchical over-the-air federated learning (HOTAFL), which utilizes intermediary servers (IS) to form clusters near MUs. We provide a convergence analysis for the proposed setup, and demonstrate through theoretical and experimental results that local aggregation in each cluster before global aggregation leads to a better performance and faster convergence than OTA FL.
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger

In this brief, we show that sequentially learning new information presented to a continual (incremental) learning model introduces new security risks: an intelligent adversary can introduce small amount of misinformation to the model during training to cause deliberate forgetting of a specific task or class at test time, thus creating "false memory" about that task. We demonstrate such an adversary's ability to assume control of the model by injecting "backdoor" attack samples to commonly used generative replay and regularization based continual learning approaches using continual learning benchmark variants of MNIST, as well as the more challenging SVHN and CIFAR 10 datasets. Perhaps most damaging, we show this vulnerability to be very acute and exceptionally effective: the backdoor pattern in our attack model can be imperceptible to human eye, can be provided at any point in time, can be added into the training data of even a single possibly unrelated task and can be achieved with as few as just 1\% of total training dataset of a single task.
Cross-View Panorama Image Synthesis

In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points. Instead of learning cross-view mapping in a feedforward pass, we propose a novel adversarial feedback GAN framework named PanoGAN with two key components: an adversarial feedback module and a dual branch discrimination strategy. First, the aerial image is fed into the generator to produce a target panorama image and its associated segmentation map in favor of model training with layout semantics. Second, the feature responses of the discriminator encoded by our adversarial feedback module are fed back to the generator to refine the intermediate representations, so that the generation performance is continually improved through an iterative generation process. Third, to pursue high-fidelity and semantic consistency of the generated panorama image, we propose a pixel-segmentation alignment mechanism under the dual branch discrimiantion strategy to facilitate cooperation between the generator and the discriminator. Extensive experimental results on two challenging cross-view image datasets show that PanoGAN enables high-quality panorama image generation with more convincing details than state-of-the-art approaches. The source code and trained models are available at \url{https://github.com/sswuai/PanoGAN}.
PENGARUH SISTEM INFORMASI SUMBER DAYA MANUSIA, KOMPETENSI DAN DISIPLIN KERJA TERHADAP KINERJA APARATUR SIPIL NEGARA PADA DINAS PEKERJAAN UMUM DAN PENATAAN RUANG KABUPATEN SITUBONDO

This study aims to (1) to determine the effect of Human resource information system, Competence and Work discipline partially to performance of ASN (2) to determine the effect of Human resource information system, Competence and Work discipline simultaneously to Performance of ASN (3) to find out which one the variables of Human resource information system, Competence and Performance of ASN. The research method used in this thesis is a quantitative method. The result of research in multiple linear regression analysis, namely Y = 6.787 + 0.754X1 - 0.243X2 + 0.359 X3 + e. Partially Human resource information system has significant postive effect with a tcount value of 7,915 ttable of 2.001, Competence has not significant positive effect with a tcount of -1,636 ttable of 2.001 and Work discipline has significant positive effect with a tcount of 2,103 ttable of 2.001. Simultaneously the Human resource information system, Competence and Work discipline affect the performance of ASN with an Fcount of 63,522 Ftable of 2.76. The dominant variable in this study is the Human resource information system with a value of 7,915.
Port Reconfigurable Phase-Change Optical Resonator

Active control and manipulation of electromagnetic waves are highly desirable for advanced photonic device technology, such as optical cloaking, active camouflage and information processing. Designing optical resonators with high ease-of-control and reconfigurability remains a open challenge thus far. Here we propose a novel mechanism to continuously reconfigure an optical resonator between one-port and two-port configurations via \emph{phase-change material} for efficient optical modulation. By incorporating a phase-change material VO$_2$ substrate into a photonic crystal optical resonator, we computationally show that the system behaves as a one-port device with near-perfect absorption and two-port device with high transmission up to 92% when VO$_2$ is in the metallic rutile phase and insulating monoclinic phase, respectively. The optical response can be continuously and reversibly modulated between various intermediate states. More importantly, the proposed device is compatible with wide-angle operation and is robust against structural distortion. Our findings reveal a novel device architecture of \emph{port reconfigurable} optical resonator uniquely enabled by switchable optical properties of phase change material.
Ion Coulomb Crystals in Storage Rings for Quantum Information Science

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes. With computing demands potentially outpacing the supply of high-performance systems, quantum computing could bring innovation and scientific advances to particle physics and other DOE supported programs. Increased support of R$\&$D in large scale ion trap quantum computers would allow the timely exploration of this exciting new scalable quantum computer. The R$\&$D program could start immediately at existing facilities and would include the design and construction of a prototype SRQC. We invite feedback from and collaboration with the particle physics and quantum information science communities.
Quantum invariant-based control of interacting trapped ions

Invariant-based inverse engineering is an elegant approach to quantum control with corresponding experimental implementations that perform tasks with applications in quantum information processing such as shuttling trapped ions. We build on recent work to generalise invariant-based inverse engineering to control two coupled harmonic oscillators in any number of spatial dimensions. This may be used to perform experimentally relevant tasks such as separation of trapped ions, which is demonstrated numerically, achieving transfer fidelities of over 96% as well as low motional number excitations.
On Certified Randomness from Quantum Advantage Experiments

Certified randomness has a long history in quantum information, with many potential applications. Recently Aaronson (Aaronson 2018, 2020) proposed a novel certified randomness protocol based on existing random circuit sampling (RCS) experiments -- placing this application within the reach of near-term quantum devices. The security of his protocol, however, relies on non-standard complexity-theoretic conjectures which were not previously studied in the literature. In this work we provide strong complexity-theoretic evidence in support of these conjectures. In particular we prove two versions of Aaronson's conjectures unconditionally in a black-box Fourier sampling setting. This mirrors earlier complexity-theoretic evidence for quantum advantage, bringing the security of Aaronson's protocol up to the same level of evidence we have for quantum advantage from heavy output generation.
Developing cognitive independence in high school students through physical education lessons

The article aims to present the results of experimental work on the implementation of a model for the development of cognitive independence of high school students in physical education lessons. Experimental work involved the development of the substantive foundations of the process of developing cognitive independence of high school students in physical education lessons, for this purpose, a phased implementation of this process was organized. The total participants of the research were 210 students between grades 10-and 11. The effectiveness of the implementation of the constructed model and technology is confirmed by the significant differences in the dynamics of the development of cognitive independence among high school students in control and experimental groups. The revealed differences allow us to state the effectiveness of the conducted experiment on the development of cognitive independence in physical education lessons among high school students in terms of motivational-value, cognitive-reflexive, and creative-activity components of cognitive independence.&#x0D; Keywords: Cognitive independence; high school students; independence; physical education.
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training

Video prediction models often combine three components: an encoder from pixel space to a small latent space, a latent space prediction model, and a generative model back to pixel space. However, the large and unpredictable pixel space makes training such models difficult, requiring many training examples. We argue that finding a predictive latent variable and using it to evaluate the consistency of a future image enables data-efficient predictions because it precludes the necessity of a generative model training. To demonstrate it, we created sequence completion intelligence tests in which the task is to identify a predictably changing feature in a sequence of images and use this prediction to select the subsequent image. We show that a one-dimensional Markov Contrastive Predictive Coding (M-CPC_1D) model solves these tests efficiently, with only five examples. Finally, we demonstrate the usefulness of M-CPC_1D in solving two tasks without prior training: anomaly detection and stochastic movement video prediction.
Face Recognition Based Door Lock System

Today we deal with safety and safety problems in every element. So, we have to deal with these problems by utilizing upgraded innovation. In this job, we are utilizing the Deal with acknowledgment component to catch human pictures and to suit with kept data source pictures. If it suits with the licensed individual after that the system will open the door by an electro-magnetic secure. The require for face acknowledgment system that's quick and precise that constantly enhancing which can spot intruders and limits all unapproved individuals from extremely protected locations and helps in reducing human mistake. Deal with acknowledgment is throughout a one amongst the primary Protected System compared to biometric pattern acknowledgment method which is utilized in a big range of applications. The moment and precision element are considered regarding the primary issue which defines the efficiency of automated deal with acknowledgment in real-time atmospheres. Different services have been suggested utilizing multicore systems. By thinking about today difficulty, this offers the total building develop and suggests an evaluation for a real-time deal with acknowledgment system with Ficherface formula. In this formula, it transforms the picture from shade to greyscale picture and splits into pixels and it'll be assigned throughout a matrix develop and people pictures will be kept in the data source. If a photo is spotted after that microcontroller will send out power to the electric motor chauffeur system after that the electro-magnetic secure will open the door and it'll secure once again when there's no power provide to that system. Lastly, this paper wraps up for the progressed applications accomplished by incorporating installed system designs versus the convention.
Research on Automatic Control Model of Two-wheeled Differential Robot

Abstract Aiming at the problem that the speed control of the traditional two-wheeled differential robot is cumbersome and requires a lot of manpower, the intelligent two-wheeled speed matching and intelligent path finding model of the robot under the known surrounding environment are studied. The extended method is used to process the environment image to solve the volume problem of the robot itself, and the improved PRM algorithm is used to solve the shortest path, and the post-alignment of the shortest path is distinguished. Using the radius of curvature, the speed ratio of the two-wheeled differential robot at each position can be obtained, thus establishing the automatic control of the two-wheeled differential robot. The automatic control model of two-wheeled robot is synthesized. The model can effectively reduce the manpower consumed by the robot in path finding and control, and it has important case reference significance for saving the economic cost of two-wheeled differential robot control.
Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language

Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by testing and reporting on resource-rich languages like English. Applying GCN for multi-task text classification is an unexplored area. Moreover, training a GCN or adopting an English GCN for Indian languages is often limited by data availability, rich morphological variation, syntax, and semantic differences. In this paper, we study the use of GCN for the Telugu language in single and multi-task settings for four natural language processing (NLP) tasks, viz. sentiment analysis (SA), emotion identification (EI), hate-speech (HS), and sarcasm detection (SAR). In order to evaluate the performance of GCN with one of the Indian languages, Telugu, we analyze the GCN based models with extensive experiments on four downstream tasks. In addition, we created an annotated Telugu dataset, TEL-NLP, for the four NLP tasks. Further, we propose a supervised graph reconstruction method, Multi-Task Text GCN (MT-Text GCN) on the Telugu that leverages to simultaneously (i) learn the low-dimensional word and sentence graph embeddings from word-sentence graph reconstruction using graph autoencoder (GAE) and (ii) perform multi-task text classification using these latent sentence graph embeddings. We argue that our proposed MT-Text GCN achieves significant improvements on TEL-NLP over existing Telugu pretrained word embeddings, and multilingual pretrained Transformer models: mBERT, and XLM-R. On TEL-NLP, we achieve a high F1-score for four NLP tasks: SA (0.84), EI (0.55), HS (0.83) and SAR (0.66). Finally, we show our model's quantitative and qualitative analysis on the four NLP tasks in Telugu.
Cross-view Transformers for real-time Map-view Semantic Segmentation

We present cross-view transformers, an efficient attention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration. These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in real-time. The presented architecture performs at state-of-the-art on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://github.com/bradyz/cross_view_transformers.
Effectiveness of Techno-Pedagogical Skills of Secondary School Teachers on Online Teaching During Covid-19 Pandemic

This study investigates the effectiveness of techno-pedagogical skills of secondary school teachers in their online teaching. A sample consists of 42 CBSE and ICSE secondary school teachers. The research adopts a descriptive survey design for collecting data using the “Techno-Pedagogical Skill Assessment Scale” developed by Sibichen (2011). Structured interviews also formed a part of data collection. The results reveal a moderately positive correlation between techno-pedagogical skills and effective online teaching. There is a significant difference between the high level and low level of techno-pedagogical skills of secondary school teachers with their effective online teaching. The positive impact of the COVID-19 pandemic on the techno-pedagogical skill of secondary school teachers is reported in the study.
Reproducibility Beyond the Research Community: Experience from NLP Beginners

As NLP research attracts public attention and excitement, it becomes increasingly important for it to be accessible to a broad audience. As the research community works to democratize NLP, it remains unclear whether beginners to the field can easily apply the latest developments. To understand their needs, we conducted a study with 93 students in an introductory NLP course, where students reproduced results of recent NLP papers. Surprisingly, our results suggest that their technical skill (i.e., programming experience) has limited impact on their effort spent completing the exercise. Instead, we find accessibility efforts by research authors to be key to a successful experience, including thorough documentation and easy access to required models and datasets.