Reviewerly/exHarmony huggingface dataset:
Searchious: Locating missing people using an optimised face recognition algorithm

Importance of face recognition systems has sped up in the last few decades. Face recognition is a part of biometric processing. Everyday hundreds of people go missing throughout the world. These people are either kidnapped, taken as slaves or forced to work underage, or beg on railway platforms, small shops or compelled into prostitution, human-trafficking or various other illegal activities. The posts of missing people are found in our day to day lives on social media platforms, news channels, newspapers etc. This research wok has been proposed to help them locate missing people in a easier way. The proposed tool, "Searchious" tends to reduce the time taken to trace the person and improve the process of finding the missing person. While Searchious can look for people who are missing, if a new face, which is not present in the database occurs, a new case can be filed for the same. Searchious has an Android application for the common citizens as well as a desktop software built using Python for the police stations by also including a face recognition algorithm based on KNN.
Test-Retest Reliability of the Open Card Sorting Method

Open card sorting is a popular method for designing information architectures. However, there is a lack of empirical evidence on the validity and reliability of the method. This paper explores the test-retest reliability of open card sorting. To this end, a within-subjects study was conducted. The same participants performed open card sorts twice with a time interval of 15-20 days. Content from three website domains was used: an eshop, a travel and tourism website, and a university website. Results showed that participants provided highly similar groupings and labels between the two card sorts per domain. A high agreement of the produced navigation structures was also found. These findings provide support for the test-retest reliability of open card sorting.
Weighted pivot coordinates for partial least squares‐based marker discovery in high‐throughput compositional data

Abstract High‐throughput data representing large mixtures of chemical or biological signals are ordinarily produced in the molecular sciences. Given a number of samples, partial least squares (PLS) regression is a well‐established statistical method to investigate associations between them and any continuous response variables of interest. However, technical artifacts generally make the raw signals not directly comparable between samples. Thus, data normalization is required before any meaningful scientific information can be drawn. This often allows to characterize the processed signals as compositional data where the relevant information is contained in the pairwise log‐ratios between the components of the mixture. The (log‐ratio) pivot coordinate approach facilitates the aggregation into single variables of the pairwise log‐ratios of a component to all the remaining components. This simplifies interpretability and the investigation of their relative importance but, particularly in a high‐dimensional context, the aggregated log‐ratios can easily mix up information from different underlaying processes. In this context, we propose a weighting strategy for the construction of pivot coordinates for PLS regression which draws on the correlation between response variable and pairwise log‐ratios. Using real and simulated data sets, we demonstrate that this proposal enhances the discovery of biological markers in high‐throughput compositional data.
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach

Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Meta-learning has been proposed to address this issue, which focuses on quickly adapting a predictor as a base-learner to new tasks, given limited labeled samples. However, a critical challenge for meta-learning is the representation deficiency since it is hard to discover common information from a small number of training samples or even one, as is the representation of key features from such little information. As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks. Existing methods mostly resort to extracting less expressive features so as to avoid the representation deficiency. Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification. In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency. Furthermore, the latent space is established with variational inference, collaborating well with different base-learners, and can be extended to other models. Finally, our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.
Context-Aware Visual Abstraction of Crowded Parallel Coordinates

As the size of dataset increases, it has become a difficult task to explore structures of interest from crowded parallel coordinates due to visual clutter. Numerous methods have been proposed to simplify the visualization of crowded parallel coordinates, such as filtering, bundling and sampling. However, contextual structures are hardly preserved in the course of simplification, which make significant features easily lost in the simplified parallel coordinates. In this paper, we propose a context-aware visual sampling method for the exploration of crowded parallel coordinates. A Doc2Vec model, widely used in the field of Natural Language Processing (NLP), is utilized to represent the contextual structures across a series of attribute axes with quantifiable vectors. Then, an adaptive blue noise sampling model is employed to reduce the size of original dataset in the vectorized space, guarantying that data items with different contextual structures would be retained in the simplified parallel coordinates. A set of meaningful visual interfaces are designed, enabling users to easily capture the contextual features and evaluate the sampled parallel coordinates. Case studies based on real-world datasets and quantitative comparison have demonstrated the effectiveness of our method in the simplification of crowded parallel coordinates and the exploration of large scale multi-dimensional datasets.
The Use of Virtual Reality in the Teaching of Challenging Concepts in Virology, Cell Culture and Molecular Biology

The rapidly expanding biotechnology sector horizon is expected to create a surge in demand for expertise underpinning cell and gene therapies, which are recognized as the next generation of medicines. New and innovative approaches to implement active and performative learning in the Molecular Life Sciences are required to support this and to address limitations associated with traditional “front of class” lectern delivery of challenging, three dimensional molecular concepts. Therefore, an immediate need exists for the development and implementation of immersive learning approaches in Virology, Cellular Sciences and Molecular Biology to underpin sustainable development of graduate students for academic and industrial research careers. The Covid-19 pandemic has led to significant changes in the delivery of education globally, with online engagement and accelerated uptake of novel teaching and assessment modalities into majority practice within institutions. This development has been driven by externally imposed necessity and it remains to be seen what form teaching and learning will take post-Covid. Irrespective of the pandemic, technologies are available which can serve intrinsically motivated, discipline specific shifts toward enhanced learner experiences and learning outcomes. Immersive virtual reality offers one such approach to open new entry points for student learning of abstract molecular concepts, which will be just as relevant upon our return to face-to-face teaching. Key to delivering this will be engagement and collaboration by disciplinary and technical experts. Here, we discuss global advances in the area of VR and Molecular Science education and assess potential paths forward for teaching and learning impact and innovative education.
Do You Feel Special When an AI Doctor Remembers You? Individuation Effects of AI vs. Human Doctors on User Experience

You may feel special and believe that you are getting personalized care when your doctor remembers your name and your unique medical history. But, what if it is an AI doctor and not human? Since AI systems are driven by personalization algorithms, it is possible to design AI doctors that can individuate patients with great precision. Is this appreciated or perceived as eerie and intrusive, thereby negatively affecting doctor-patient interaction? We decided to find out by designing a healthcare chatbot that identified itself as AI, Human, or Human assisted by AI. In a user study assessing Covid-19 risk, participants interacted twice, 10 days apart, with a bot that either individuated them or not. Data show that individuation by an AI doctor lowers patient compliance. Surprisingly, a majority of participants in the human doctor condition thought that they chatted with an AI doctor. Findings provide implications for design of healthcare chat applications.
Revisiting Optical Flow Estimation in 360 Videos

Nowadays 360 video analysis has become a significant research topic in the field since the appearance of high-quality and low-cost 360 wearable devices. In this paper, we propose a novel LiteFlowNet360 architecture for 360 videos optical flow estimation. We design LiteFlowNet360 as a domain adaptation framework from perspective video domain to 360 video domain. We adapt it from simple kernel transformation techniques inspired by Kernel Transformer Network (KTN) to cope with inherent distortion in 360 videos caused by the sphere-to-plane projection. First, we apply an incremental transformation of convolution layers in feature pyramid network and show that further transformation in inference and regularization layers are not important, hence reducing the network growth in terms of size and computation cost. Second, we refine the network by training with augmented data in a supervised manner. We perform data augmentation by projecting the images in a sphere and re-projecting to a plane. Third, we train LiteFlowNet360 in a self-supervised manner using target domain 360 videos. Experimental results show the promising results of 360 video optical flow estimation using the proposed novel architecture.
On the Energy Costs of Post-Quantum KEMs in TLS-based Low-Power Secure IoT

Recent achievements in designing quantum computers place a serious threat on the security of state-of-the-art public key cryptography and on all communication that relies on it. Meanwhile, security is seen as one of the most critical issues of low-power IoT devices even with pre-quantum public key cryptography since IoT devices have strict energy constraints and limited computational power. Thus, state-of-the-art dedicated hardware accelerators have been deployed to facilitate secure and confidential communication with well established protocols on such devices.
Designing high-power, octave spanning entangled photon sources for quantum spectroscopy

Entangled photon spectroscopy is a nascent field that has important implications for measurement and imaging across chemical, biology, and materials fields. Entangled photon spectroscopy potentially offers improved spatial and temporal-frequency resolutions, increased cross sections for multiphoton and nonlinear measurements, and new abilities in inducing or measuring quantum correlations. A critical step in enabling entangled photon spectroscopies is the creation of high-flux entangled sources that can use conventional detectors, as well as provide redundancy for the losses in realistic samples. Here, we report a periodically poled, chirped, lithium tantalate platform that generates entangled photon pairs with a $10^{-7}$ efficiency. For a near watt level diode laser, this results in a near $\mu$W-level flux. The single photon per mode limit that is necessary to maintain non-classical photon behavior is still satisfied by distributing this power over up to an octave-spanning bandwidth. The spectral-temporal photon correlations are observed via a Michelson-type interferometer that measures the broadband Hong-Ou-Mandel two-photon interference. A coherence time of 245 fs for a 10 nm bandwidth in the collinear case and a 62 fs for a 125 nm bandwidth in the non-collinear case is measured using a CW pump laser, and, essentially, collecting the full photon cone. We outline in detail the numerical methods used for designing and tailoring the entangled photons source, such as changing center wavelength or bandwidth, with the ultimate aim of increasing the availability of high-flux UV-Vis entangled photon sources in the optical spectroscopy community.
Self-supervised learning of Dynamic Representations for Static Images

Facial actions are spatio-temporal signals by nature, and therefore their modeling is crucially dependent on the availability of temporal information. In this paper, we focus on inferring such temporal dynamics of facial actions when no explicit temporal information is available, i.e. from still images. We present a novel self-supervised learning approach to capture multiple scales of temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation. In particular: 1. We propose a framework that infers a dynamic representation (DR) from a still image, capturing the bi-directional flow of time within a short time-window centered at the input image; 2. We show that the proposed rank loss can apply facial temporal evolution to self-supervise the training process without using target representations, allowing the network to represent dynamics more broadly; 3. We propose a multiple temporal scale approach that infers DRs for different window lengths (MDR) from a still image. We empirically validate the value of our approach on the task of frame ranking, and show how our proposed MDR attains state of the art results on BP4D for AU intensity estimation and on SEMAINE for dimensional affect estimation, using only still images at test time.
Time Series Forecasting for the Spread of Covid-19 in Indonesia Using Curve Fitting

The spread of Covid-19 has a huge impact on human life around the world, especially in Indonesia.Until now, the estimation of when this pandemic will end is still a big question.Therefore, a prediction is needed as the basis for the policies of the interested parties.In this paper, the Curve Fitting method is used to estimate the period and total cases of the spread of Covid-19 based on time series data for positive case growth without considering the rate of recovery, lockdown policy, and so on.With the proposed method and calculated based on the normal curve, the estimated end of a pandemic is at day seven hundred and fifty or about two years since the start of the pandemic.So that this estimate can be used as a basis by the government to make policies, for example when schools can be conducted face-to-face or other policies related to community needs.
Wide and Deep Graph Neural Networks with Distributed Online Learning

Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oftentimes, this decentralized graph support changes with time due to link failures or topology variations. These changes create a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be used to retrain GNNs at testing time, overcoming this issue. However, most online algorithms are centralized and work on convex problems (which GNNs rarely lead to). This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms. The WD-GNN comprises two components: the wide part is a bank of linear graph filters and the deep part is a GNN. At training time, the joint architecture learns a nonlinear representation from data. At testing time, the deep part (nonlinear) is left unchanged, while the wide part is retrained online, leading to a convex problem. We derive convergence guarantees for this online retraining procedure and further propose a decentralized alternative. Experiments on the robot swarm control for flocking corroborate theory and show potential of the proposed architecture for distributed online learning.
Target Tracking Algorithm for Table Tennis Using Machine Vision

The current table tennis robot system has two common problems. One is the table tennis ball speed, which moves fast, and it is difficult for the robot to react in a short time. The second is that the robot cannot recognize the type of the ball's movement, i.e., rotation, top rotation, no rotation, wait, etc. It is impossible to judge whether the ball is rotating and the direction of rotation, resulting in a single return strategy of the robot with poor adaptability. In this paper, these problems are solved by proposing a target trajectory tracking algorithm for table tennis using machine vision combined with Scaled Conjugate Gradient (SCG). Real human-machine game's data are obtained in the proposed algorithm by extracting ten continuous position information and speed information frames for feature selection. These features are used as input data for the deep neural network and then are normalized to create a deep neural network algorithm model. The model is trained by the position information of the successive 20 frames. During the initial sets of experiments, we found the shortcomings of the original SCG algorithm. By setting the accuracy threshold and offline learning of historical data and saving the hidden layer weight matrix, the SCG algorithm was improved. Finally, experiments verify the improved algorithm's feasibility and applicability and show that the proposed algorithm is more suitable for table tennis robots.
Unsupervised deep learning for text line segmentation

We present an unsupervised deep learning method for text line segmentation that is inspired by the relative variance between text lines and spaces among text lines. Handwritten text line segmentation is important for the efficiency of further processing. A common method is to train a deep learning network for embedding the document image into an image of blob lines that are tracing the text lines. Previous methods learned such embedding in a supervised manner, requiring the annotation of many document images. This paper presents an unsupervised embedding of document image patches without a need for annotations. The number of foreground pixels over the text lines is relatively different from the number of foreground pixels over the spaces among text lines. Generating similar and different pairs relying on this principle definitely leads to outliers. However, as the results show, the outliers do not harm the convergence and the network learns to discriminate the text lines from the spaces between text lines. Remarkably, with a challenging Arabic handwritten text line segmentation dataset, VML-AHTE, we achieved superior performance over the supervised methods. Additionally, the proposed method was evaluated on the ICDAR 2017 and ICFHR 2010 handwritten text line segmentation datasets.
CBES: Cloud Based Learning management System for Educational Institutions

Recent technologies like the internet, coupled with the software applications, have lately assisted to enrich our contemporary lives. Consequently, countless educational institutions can see the benefit of embracing these apps to attain the edge over. Cloud computing is taking the center stage in academia due to its abundant benefits. It is a growing technology that enables the conventional IT systems to be modified. Dissimilar learning institutions can apply various cloud-based services offered by service providers to guarantee that their students and other consumers can efficiently apply all the instructional activities. This paper emphasizes on the various technologies that have been applied to support the education process such as learning management systems. In the current work, we introduced Cloud-Based Educational System (CBES), which presents a conceptual model of cloud-based learning management system for educational institutions. Furthermore, the paper will demonstrate the expected results of the proposed model, which is aimed to provide a conducive environment to the educators, academic staffs and the administrators as well as dealing with more vital tasks required for e-learning process such as requesting for learning management system (LMS) navigations, text and media learning contents, and video learning contents.
Motion modeling and state estimation in range-Doppler plane

The problem of motion modeling in range-Doppler (R-D) plane as well as range and Doppler estimation is investigated. The time evolving equations of target range and Doppler are formulated, for two common Cartesian motions, nearly constant velocity (NCV) and constant turn (CT). This builds the foundation for target tracking based on multisensor data fusion using only range and Doppler measurements and other applications. The R-D state vectors are constructed by range, Doppler and the derivative of the product of range and Doppler with respect to time. The state equations corresponding to these motions are derived by explicit substitutions according to the relationship between R-D states and Cartesian states. The unscented Kalman filter (UKF) is employed to extract R-D states from range and Doppler measurements. The filter initialization is derived by two-point differencing methodology where the correlation among the state components is handled properly. At last, the posterior Cramer-Rao Lower Bound (PCRLB) for state estimation in R-D plane is provided, and the performance of the proposed method is compared against the PCRLB and the existing methods using approximate models in the numerical experiments. The simulation results demonstrate the effectiveness of the proposed models and filtering methods. The enhancement of estimation accuracy benefits the data fusion using range and/or Dopper estimates.
Blockchain: Application in the System of Teaching Informatization Management of Higher Education

With the development of artificial intelligence and biotechnology, people are very interested in the application of blockchain technology for some fields, such as education. Advantages of blockchain technology including decentralization, non-tampering, anonymity, traceability, etc. which provide the possibility to improve the security of the management of teaching informatization in higher education. We try to explore the application of blockchain in education to improve the practice of the system of teaching informatization management in higher education. This will be reflected in three main aspects: 1) Distributed ledger Technology: it could help the teaching informatization management system to decentralize; 2) Asymmetric encryption algorithm: it could ensure user privacy in teaching informatization management system; 3) Smart Contract: it would improve the credibility of the teaching in-formatization management system. We thought that the security of informatization storage would be ensured, the credibility of data supervision would be enhanced, the cost of management would be reduced, and the efficiency of higher education management is improved substantially if we could use the blockchain technology in the system of teaching informatization management.