Reviewerly/exHarmony huggingface dataset:
A Unifying Framework for Federated Learning

There have been multiple federated learning (FL) algorithms proposed in the FL community during the recent years. However, a thorough comparison of these algorithms has not been done, and our understanding of the theory of FL is still limited. The lack of a unifying view in practice has also led to the reinvention of the same algorithms under different names. Motivated by this gap, we develop a unifying scheme for FL and demonstrate that many of the algorithms that exist in the FL literature are special cases of this scheme. The unification allows us to get a deeper understanding of different FL algorithms, to compare them easier, to improve the previous results for their convergence analysis and to find new FL algorithms. In particular, we demonstrate the important role that step size plays in the convergence of FL algorithms. Further, based on our unifying scheme, we propose an efficient and economic method for accelerating FL algorithms. This streamlined acceleration method does not incur any communication overheads. We evaluate our findings by performing extensive experiments on both nonconvex and convex problems.
Bayesian Multivariate Mixed Poisson Models with Copula-Based Mixture

It is common practice to use multivariate count modeling in actuarial literature when dealing with claim counts from insurance policies with multiple covers. One possible way to construct such a model is to implement copula directly on discrete margins. However, likelihood inference under this construction involves the computation of multidimensional rectangle probabilities, which could be computationally expensive, especially in the elliptical copula case. Another potential approach is based on the multivariate mixed Poisson model. The crucial work under this method is to find an appropriate multivariate continuous distribution for mixing parameters. By virtue of the copula, this issue could be easily addressed. Under such a framework, the Markov chain Monte Carlo (MCMC) method is a feasible strategy for inference. The usefulness of our model is then illustrated through a real-life example. The empirical analysis demonstrates the superiority of adopting a copula-based mixture over other types of mixtures. Finally, we demonstrate how those fitted models can be applied to the insurance ratemaking problem in a Bayesian context.
LENS: Bandwidth-efficient video analytics with adaptive super resolution

Video streaming analytic pipelines are deployed widely nowadays, where in many cases videos are transmitted continuously from cameras to servers. However, video analytic accuracy highly depends on video resolution, and when bandwidth suffers only low-resolution videos can be transmitted which leads to low accuracy. The rapid development of super resolution DNNs sparks the possibility of reconstructing high-resolution videos reliably from low resolution videos, and thus boosts video analytic accuracy. However, pretrained SR models may fail to cope with dynamic video content, while introducing online learning can be efficient under this condition. In this paper, we propose a practical system, LENS, which integrates an online learning period into analytic pipelines. The experimental results show that LENS is capable of responding to video dynamics and retraining SR models for changes quickly. In summary, LENS can save bandwidth consumption by up to 87%, or achieve a higher accuracy by up to 9.6% compared with state-of-the-art methods with slight additional bandwidth.
Deus ex Poiesis: A Manifesto for the End of the World and the Future of Art and Technology

In 2020, the Doomsday Clock advanced to just one hundred seconds before nuclear midnight, the hour at which atomic armageddon would end the world as we know it. Using the Greek theatrical device of deus ex machina and Heidegger's reliance on poiesis as a liberating force from technological enframing, I propose the concept of deus ex poiesis as a potential model for addressing the seemingly insurmountable challenges of the anthropocene, an era in which industrial technologies have instigated global climatic changes on the scale of Earth's five mass extinctions. Drawing on additional theoretical insights from Gregory Bateson, Jack Burnham, Donna Haraway, Alva Noë, Rita Raley and others, my essay discusses two aesthetic approaches: 1) digital artworks that explicitly engage in forms of (h)activism through tactical media; 2 artworks that develop and apply ways of knowing that lie outside of rational scientific models and that explore hybrid forms of perception and expanded states of consciousness. Artists considered include Electronic Disturbance Theater, rTMark, Julian Oliver and Danja Vasiliev, Roy Ascott, Pauline Oliveros, and Kim Jeong-Han.
Simple book keeping assistance for AA Laundry, Yogyakarta

This community service aims to optimize business financial management by assisting partners in creating and implementing simple bookkeeping. This program is carried out with a gradual mentoring method, start from communication with service partners, assisting in making simple bookkeeping, monitoring progress and producing a simple bookkeeping report. The result of this program provides benefits for AA Laundy's business, so that they are able to make simple bookkeeping properly and correctly. It is hoped that the bookkeeping will be able to help the running of AA Laundry's business.
A Sample Efficiency Improved Method via Hierarchical Reinforcement Learning Networks

Learning from demonstration (LfD) approaches have garnered significant interest for teaching social robots a variety of tasks in healthcare, educational, and service domains after they have been deployed. These LfD approaches often require a significant number of demonstrations for a robot to learn a performant model from task demonstrations. However, requiring non-experts to provide numerous demonstrations for a social robot to learn a task is impractical in real-world applications. In this paper, we propose a method to improve the sample efficiency of existing learning from demonstration approaches via data augmentation, dynamic experience replay sizes, and hierarchical Deep Q-Networks (DQN). After validating our methods on two different datasets, results suggest that our proposed hierarchical DQN is effective for improving sample efficiency when learning tasks from demonstration. In the future, such a sample-efficient approach has the potential to improve our ability to apply LfD approaches for social robots to learn tasks in domains where demonstration data is limited, sparse, and imbalanced.
Simple User Assistance Driving Algorithm for Cost-Effective Smart Electric Wheelchair

Wheelchairs are among the most important equipment in terms of facilitating the lives of people with walking disabilities. In particular, the use of electric wheelchairs allows people with walking disabilities to move comfortably without excessively straining their bodies. However, in some cases, a person with a walking disability who uses an electric wheelchair may have difficulties in driving the wheelchair or may experience unintended collisions due to obstacles that are not within the line of sight. In order to prevent such negative events, it is necessary to develop a wheelchair with electric wheels that is produced at a minimum cost, can be easily sold to all kinds of people with disabilities, and will not be difficult to use. In addition, this equipment must include a system designed to provide wheelchair control and driving comfort with maximum efficiency for areas outside of the line of sight, which is one of the features required by all people with walking disabilities. This article explains the design of a prototype that can detect blind spots thanks to sensors and provide driving comfort while a wheelchair driver with walking disabilities uses it. In this paper, it is aimed to create a smart electric wheelchair that can be used by anyone with a walking disability while keeping the costs to a minimum.
Salient Keypoints for Interactive Meta-Learning (SKIML)

Learning to recognize new objects in real time in unconstrained environments presents significant challenges for robotic platforms. We present a meta-learning solution to this problem as well as a registered image and events dataset to facilitate work in this domain. Our solution uses interactive motion to isolate the object, and motion-based saliency (from events) to select relevant keypoints from a high-resolution RGB image. Salient keypoints are then passed to a meta-learner to classify the object type. We show that using our interactive isolation and keypoint selection approach, we outperform existing techniques by 6-20%.
Training on making creative learning media using the canva application for MI and MTs Al-Mu'minin Kendari Teachers

Attractive learning media has become a necessity for teachers at all levels of education. For this reason, educators need to improve their competence in making good learning media. However, there are still many teachers who are not familiar with using technology to support their teaching and learning activities. As felt by MI and MTs AL-Mu'minin Kendari teachers who still find it difficult to make interesting and creative learning media. This service activity aims to improve the ability of teachers to create interesting media using the Canva application. The stages carried out in this activity are (1) coordinating with the school and identifying the needs of the target audience; (2) carry out community service activities with lecture, question and answer methods, and guided practice; and (3) evaluate and assign independent assignments. The results of this program showed an increase in teacher skills in making interesting learning media as seen from the independent assignments that were collected.
Instructive Interaction for Redirection of Customer Attention from Robot to Service

Social robotics recommendations have been studied for a long time, and many existing studies have addressed in-store recommendations using robots. However, it has been pointed out that many studies conducted in "in-the-wild" field environments have only focused on the initial stages of customer purchase behavior, such as stopping and engaging in a conversation, and few have been able to induce an interest in the product and even purchase. One of the causes is that the robot itself attracts most of the customer’s attention, making it difficult for customers to be interested in the robot’s recommendations. To solve this problem, this study examines the inclusion of clear and specific instructions to customers in interactions in which the robot recommends services and products. We conducted a field experiment to confirm the effectiveness of such instructive interactions, and found that customers are more likely to be interested in the content recommended by the robot, rather than in the robot itself, through the instructive interaction.
Push and Pull Feedback in Mobile Robotic Telepresence - A Telecare Case Study

Mobile robotic telepresence (MRP) has emerged as a possible solution for supporting health caregivers in a multitude of tasks such as monitoring, pre-diagnosis, and delivery of items. Improved interaction with the system is an important part of using such MRP systems. The current study compared two feedback types ('push' and 'pull') for controlling mobile robots via telepresence. An experimental system that represented a hospital environment was developed. A remote operator (defined as a user) teleoperated a mobile robot to deliver medication supplies to a patient and receive samples from the patient while attending to a secondary task involving medical records. The influence of the feedback types on different aspects of performance and user perception was investigated. User studies were performed with 20 participants coming from two different types of groups – users with and without technological backgrounds. Results revealed that for both user types, the 'push' feedback enhances performance, situation awareness, and satisfaction compared to the 'pull' feedback. The study highlights the potential of improving the telecare experience with MRPs through different feedback types.
Explainability in Collaborative Robotics: The Effect of Informing the User on Task Performance and Trust

In order to test how explanations affect a user working together with a collaborative robot, we created a test scenario where a user sorts trash together with a collaborative robot. Sometimes the robot is not able to fulfill its part of the task. Different modalities (textual, graphical, both) for explaining this error to the user are tested in a between subjects design and the effects on task performance, cognitive load and trust are analyzed.
Holography: a 60 years journey with outstanding achievements

In this paper we look back to 60 years of holograpy. Special attention is given to its application for non-destructive testing and experimental stress analysis. Across these years both techniques have shown to be versatile tools for the solution of many inspection problems. Their main advantages are the non-contact nature, the non-destructive and areal working principle, the fast response, high sensitivity, resolution and accuracy. In contrast to conventional techniques such as classical interferometry, the holographic principle of wave front storage and reconstruction made it possible to investigate objects with rough surfaces that experience any change by temporal wave front division. The paper reviews the history of holographic metrology, honors the inventors of the main principles, and shows exemplary applications. However, the main focus is on modern developments that are inspired by the rapid technological process in sensing technology and digitization, current applications and future challenges.
Determination of the characteristics of a self-built coherence scanning interferometer with a universal calibration artefact

ISO 25178-601 ff. define specific characteristics of individual measuring principles including coherence scanning interferometry (CSI) in -604. In the present study, we use a previously developed Universal Calibration Artefact to examine which additional information about CSI-specific metrological characteristics can be obtained by the evaluation of its measured data. In doing so, a self-built CSI as an exemplary measuring instrument is examined in a case study to test which of the material measures type ASG, AFL, ARS, ACG, AIR according to ISO 25178-70 and the chirp material measure (CIN) can be used to acquire detailed information about the characteristics that are specific to the optical setup of a CSI and their possible deviations. It can be shown that additional information about many characteristics including the properties of the light source like the wavelength or bandwidth, or information about the optical setup like the numerical aperture, can be extracted from a series of only seven measurements of the Universal Calibration Artefact.
Flat target method: mitigation of errors in quad target method

Quality of focus is a critical requirement for structured light illumination (SLI) systems. We present further development of the quad target method, an alignment technique to linearize defocus for SLI systems, by substituting a flat target continuously toggling between two positions. The flat target method enables granular near-real time projector focus assessment with a more universal target and mitigated errors compared to the quad target method.
Vision ray metrology: a new versatile tool for the metrology and alignment of optics

A novel Vision ray metrology technique is reported that estimates the geometric wavefront of a measurement sample using the sample-induced deflection in the vision rays. Vision ray techniques are known in the vision community to provide image formation models even when conventional camera calibration techniques fail. This work extends the use of vision rays to the area of optical metrology. In contrast to phase measuring deflectometry, this work relies on differential measurements, and hence, the absolute position and orientation between target and camera do not need to be known. This optical configuration significantly reduces the complexity of the reconstruction algorithms. The proposed vision ray metrology system does not require mathematical optimization algorithms for calibration and reconstruction – the vision rays are obtained using a simple 3D fitting of a line.
Joint backward and forward temporal masking for perceptually optimized x265-based video coding

There is a strong and ever-growing demand for higher-resolution video content, such as UltraHD, which requires significantly higher bitrates. Providing such content at scale is a challenge due to limitations of the available last-mile bandwidth and content delivery network (CDN) storage and egress capacity. Lower bitrates are often considered an answer. This way, the high-resolution video content is often compressed with visually perceptible coding artifacts, thereby leading to an inferior user experience. Improved compression efficiency is thus the obvious solution for improving the user experience. However, in order to realize the gain from such efficiency improvements in a large-scale deployment, such improvements need to be applicable to an already deployed ecosystem such as set-top boxes or mobile devices, and SmartTVs, and to have a reasonably low computational complexity. This work proposes a low-complexity joint backward and forward temporal masking for reducing bitrate without perceptibly affecting visual quality. This is achieved by introducing a novel low-complexity scenecut-aware adaptive frame-level quantization framework, which considers temporal distances between frames and the closest scenecuts. The proposed framework has been implemented in the popular x265 open-source HEVC encoder. With that said, the framework is codec-independent and can be applied to other encoders and video coding standards. Different backward and forward masking time periods and quantizer behaviors are investigated to determine exact time periods for which temporal masking does not substantially impact video quality, as perceived by the human visual system (HVS). Extensive subjective quality assessments have been carried out for evaluating the benefits and advantages of the proposed scenecut-aware adaptive quantization framework. The subjective results showed significant bitrate savings of up to about 26%, while maintaining substantially the same perceived visual quality.
An algorithm for a quality-optimized bit rate ladder generation for video streaming services using a neural network

For video streaming services, a bit rate ladder is generated by encoding each video signal at various bit rates and associated spatial resolutions. For a bit rate ladder that maximizes the subjective quality at a minimum bit rate, it was found that the VMAF of the highest provided quality should not exceed 95, which is on average associated with the same subjective quality as the original signal. Second, all VMAF differences between adjacent renditions should ideally be not greater than 2 as this guarantees indistinguishable subjective quality on average. The generation of a bit rate ladder fulfilling these constraints faces the difficulties that (i) today’s encoders cannot be instructed to achieve a certain VMAF and (ii) a certain VMAF can be achieved by various combinations of bit rate and spatial resolution. These difficulties result in a content-dependent multidimensional solution space for generating the quality-based bit rate ladder at a minimum bit rate. In this paper, an algorithm is presented which can generate such a bit rate ladder. The algorithm determines the VMAF of nine initial encodings of the signal. Using a specifically designed and trained neural network, the VMAF of 5805 combinations of bit rate and spatial resolution is predicted from the nine initial ones. Based on these predictions, a bit rate ladder is extracted and further refined until all VMAF constraints are fulfilled. Experiments show that the algorithm requires 3.6 encodings per provided VMAF on average. A VMAF of 95.07 is achieved on average for the highest provided quality and a VMAF difference between adjacent renditions of 1.92.
Advanced video quality assessment of leading codecs: AV1, VVC, and their draft successor designs

AV1 and VVC are today top performing video codecs, with VVC holding an edge (in our previous estimates, about 10- 12%). Moreover, both organizations are busy working on advancements of their codecs, respectively. Current work within the ISO/IEC/ITU committees shows updates with improvements of about 15% over VVC on common test data, while the Alliance for Open Media, the source of AV1, is already working on AV2 (called AVM), with an as yet unknown performance gain (but targeting about 20%) over AV1. We compare the performance of these codecs objectively on well-known 1080p test data, restricting to Standard Dynamic Range in this study (we treat HDR in an accompanying paper), not only using PSNR, but more advanced video quality assessment tools such as SSIM, VMAF, and FastVDO’s quality measure, FVQ. We highlight the similarities and difference between the well-worn PSNR, and the more advanced approaches, to develop a nominal performance comparison among these leading video codecs. In this context, we use full reference video quality assessment. We observe that the more advanced metrics may be picking up on differences in quality that PSNR misses.
Driving assistance algorithm for self-driving cars based on semantic segmentation

This paper presents the implementation of a driving assistance algorithm based on semantic segmentation. The proposed implementation uses a convolutional neural network architecture known as U-Net to perform the image segmentation of traffic scenes taken by the self-driving car during the navigation, the segmented image gives to every pixel a specific class. The driving assistance algorithm uses the data retrieved from the semantic segmentation to perform an evaluation of the environment and provide the results to the self-driving car to help it make a decision. The evaluation of the algorithm is based on the frequency of the pixels of each class, and on an equation that calculates the importance weight of a pixel with its own specific position and its respective class. Experimental results are presented to evaluate the feasibility of the proposed implementation.
Single-shot quantitative phase microscopy in common-path configuration

Quantitative phase Microscopy is an advantageous technique to retrieve 3D cell information to estimate cell dry mass and morphology. A popular method is to implement this setup in a common path configuration because it is a system robust to vibrations, and it combines phase contrast imaging with phase shifting techniques to provide high-accuracy measurements. However, this approach requires using a spatial light modulator and at least three images to calculate the phase. In our approach, we leverage the optical anisotropy of liquid crystal materials and a polarized camera to obtain four phase-shifted images simultaneously.
Automating sports broadcasting using ultra-high definition cameras, neural networks, and classical denoising

Algorithms for automated sports broadcasting have been explored since the early 2000s. Systems consider automated control of virtual or physical cameras. However, output picture quality is compromised and capturing “off-the-ball” action remains a challenge for automated systems. In this paper, we present an exploration of the components of a semi-automated, high-quality broadcasting system. We simulate multiple dynamic views from fixed wide-angle cameras using an object detection network adapted to UHD wide-angle content. These views can be selected at the discretion of the director for broadcast. The final selected view then undergoes an enhancement process to address the optical blur and geometric distortion in wide-angle footage. Our overall system, through the combination of a human operator with automated view simulation algorithms, is capable of addressing many of the issues facing fully automated production in the complex environment of sport.