Reviewerly/exHarmony huggingface dataset:
A Simplified Intelligent Autonomous Obstacle Bypassing Method for Mobile Robots

This paper presents a demo focusing on developing a robot capable of autonomously bypassing obstacles in cluttered environments using a camera as its sole sensing mechanism. The robot is programmed to follow a predetermined path via a line following module while using a custom object detection model to detect and differentiate obstacles on the road from other objects in the environment. The obstacle detection module based on YOLOv5 architecture can accurately detect obstacles from the surrounding. Upon obstacle detection, the robot initiates an obstacle avoidance maneuver by adjusting its steering based on error measurement, allowing it to navigate around the obstacle smoothly. The proposed design is validated with extensive experimentation, demonstrating its ability to navigate cluttered environments while avoiding obstacles.
iSAW: Intelligent Super-Resolution-Assisted Adaptive WebRTC Video Streaming

High quality video can provide viewers with better visual and immersive experience in video streaming systems. However, traditional such systems with adaptive bitrate algorithms can only provide low-resolution videos when the network conditions deteriorate. To break the strong dependency of video transmission on network, we design and demonstrate iSAW, a super-resolution-assisted intelligent adaptive WebRTC video streaming system, by using two mobile devices and a policy server. iSAW performs a lightweight and scalable super-resolution (SR) model on the mobile devices to enhance the quality of received videos, and leverages an learning-based adaptive algorithm on the policy server to jointly adjust the transmitted video resolution and SR reconstructed video resolution according to dynamic network conditions and client computing power. The enhanced real-time video is smooth and the performance is improved obviously.
Taming Event Cameras with Bio-Inspired Architecture and Algorithm: A Case for Drone Obstacle Avoidance

Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presents BioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone is two simple yet effective system design inspired by the mammalian visual system, namely, a chiasm-inspired signal processing pipeline for fast event filtering and obstacle detection, and a lateral geniculate nucleus (LGN)-inspired event matching algorithm for accurate obstacle localization. To make BioDrone a practical solution, we further take significant engineering efforts to deploy the software stack on FPGA through software and hardware co-design. The performance comparison with two state-of-the-art event-based obstacle avoidance systems shows BioDrone achieves a consistently high obstacle detection rate of 96.1%. The average localization error of BioDrone is 6.8cm with a 4.7ms latency, outperforming both baselines by over 40%.
ARCH: Automatic Roaming-contract Handler for Seamless 6G System to Provide Trusted and Highly Available Network Access

It is expected to be pervasive sensors to integrate physical and digital world. Low-latency and wide-covering networks are hence urged, which lead to costly infrastructure. We are proposing ARCH based on smart contract and Self-sovereign Identity (SSI), to provide telecoms a trusted and transparent multilateral platform sharing their infrastructure to decrease the deployment and agency cost, and carbon emission.
AdaptiveNet: Post-deployment Neural Architecture Adaptation for Diverse Edge Environments

Deep learning models are increasingly deployed to edge devices for real-time applications. To ensure stable service quality across diverse edge environments, it is highly desirable to generate tailored model architectures for different conditions. However, conventional pre-deployment model generation approaches are not satisfactory due to the difficulty of handling the diversity of edge environments and the demand for edge information. In this paper, we propose to adapt the model architecture after deployment in the target environment, where the model quality can be precisely measured and private edge data can be retained. To achieve efficient and effective edge model generation, we introduce a pretraining-assisted on-cloud model elastification method and an edge-friendly on-device architecture search method. Model elastification generates a high-quality search space of model architectures with the guidance of a developer-specified oracle model. Each subnet in the space is a valid model with different environment affinity, and each device efficiently finds and maintains the most suitable subnet based on a series of edge-tailored optimizations. Extensive experiments on various edge devices demonstrate that our approach is able to achieve significantly better accuracy-latency tradeoffs (e.g. 46.74% higher on average accuracy with a 60% latency budget) than strong baselines with minimal overhead (13 GPU hours in the cloud and 2 minutes on the edge server).
ARE POLITICAL STABILITY AND DEMOCRACY EFFECTIVE ON FINANCIAL DEVELOPMENT? EVIDENCE FROM SUB-SAHARAN AFRICAN COUNTRIES

The interaction between institutional phenomena such as political stability and democracy and the financial system is an important issue that has been researched for many years. For this reason in this study, the effects of political stability and democracy on the financial system in Sub-Saharan African countries during the period from 2002 to 2019 are investigated. In the study, in which the Two-Stage System Generalized Moments Method estimator is used, the dependent variable is the financial institutions development index. The explanatory variables of the study are the indicators of political stability and democracy. The growth in GDP per capita, trade openness, inflation rate, foreign direct investment and urban population ratio are the control variables. The analyzes display that democracy and political stability have positive effects on financial development. In terms of control variables, while the inflation rate has a negative effect on financial development, the economic growth and trade openness affect financial development positively.
Value function optimistic initialization with uncertainty and confidence awareness in lifelong reinforcement learning

We study the case of value function initialization in a reinforcement learning agent that deals with a set of tasks varying in terms of reward and sampled in a lifelong manner. The existing works tackling this setting of transfer reinforcement learning often consider a uniform sampling of tasks in their experiments, while optimistically transferring the knowledge by using the maximum seen outcome. However, due to the uncertainty of the real world, infrequent events cause the distribution to be non-uniform. As a consequence, the optimistic initialization seems impractical because it gives equally high importance to both frequent and infrequent tasks causing sample complexity to increase. We argue that to overcome such limitation, the agent must be able to assess how optimism is influenced by its uncertainty and confidence; two intercorrelated notions that play a crucial role in decision-making. Therefore we propose a novel approach UCOI (Uncertainty and Confidence aware Optimistic Initialization) that applies optimism only in adequate situations and we prove that our approach shows advantageous results over the existing works, especially for tasks coming from a non-uniform distribution.
On critical design and designing for provocation

This week for COPIM we are reading Bardzell, Shaowen, Jeffrey Bardzell, Jodi Forlizzi, John Zimmerman, and John Antanitis, ‘Critical Design and Critical Theory: The Challenge of Designing for Provocation’, in Proceedings of the Designing Interactive Systems Conference, DIS ’12 (Newcastle Upon Tyne, United Kingdom: Association for Computing Machinery, 2012), pp. 288–297 &amp;lt;https://doi.org/10.1145/2317956.2318001&amp;gt;. This paper is on the
Sports video retrieval and classification using focus u-net based squeeze excitation and residual mapping deep learning model

Sports videos are widely used by athletes and coaches for training and match analysis purposes outside the mainstream audience. Sports videos should be effectively classified into different genres to easily retrieve and index them from large video datasets. Manual labelling classification methods may cause errors and have low accuracy. Classification based on video content analysis is challenging for computer vision-based techniques. This work introduces an improved focus-net deep learning (DL) model called the Convolutional squeeze U-Net based encoder-decoder for sports video retrieval and classification. First, the keyframes are extracted from the input sports video using a clustering and optical flow analysis method. In the next stage, the frames are pre-processed using a smoothed shock filtering technique to remove the noise. The process of image segmentation is carried out using a Convolutional squeeze U-Net based encoder-decoder model. Finally, the sports video can be classified using the softmax classifier. A CNN (convolutional neural network) is utilized at the encoder section for extracting the features and fed to the decoder for video classification. The experiments are performed in the UCF101 dataset, and the proposed model achieved an overall accuracy of 99.68%. Hence, it is proven that the proposed focus-net model can be efficiently utilized in sports video classification.
Congratulations! You are severely underweight—Exploring negative consequences of persuasive system design

Personal health and wellbeing have grown as an object of study within HCI research and many individuals undergoing weight loss journeys use persuasive systems, such as weight loss apps, to guide them. These systems rely on various principles to persuade users toward their health goal and have in much research been considered having positive effects. There are however also parallel findings indicating unwanted effects. To explore the effects of persuasive system design, we conduct a longitudinal exploratory expert evaluation of 10 free weight loss tracking apps by emulating drastic weight loss for an extended period of time. The objective of the exploration is to unravel potential negative effects of using such systems. Our findings highlight risks of negative effects as some apps take a passive stance toward drastic weight loss, while others actively encourage it through design. We contribute to a growing strand of health related HCI research by mapping out the relation between design and negative consequences of persuasive system use.
Reading ‘The Democratisation Myth’ and thinking through decolonial OA

This week for COPIM we are reading Knöchelmann, Marcel, The Democratisation Myth: Open Access and the Solidification of Epistemic Injustices (SocArXiv, 9 June 2020) https://doi.org/10.31235/osf.io/hw7at.
Artificial Intelligence Technologies for Clinical Data PharmacoAnalytics Case Studies on Alzheimer’s Disease

Artificial intelligence (AI) technologies are used extensively in health science-related fields. In this book chapter, we will briefly introduce AI applications in clinical data studies focusing on the recent works with AI techniques on Alzheimer’s disease-related topics. This chapter will use three case studies as examples of AI applications covering different categories (statistical modeling, machine learning/deep learning, causal analysis/discovery) in Alzheimer’s disease clinical pharmacoanalytics. We will dissect the study design, discuss the significance of the findings, and finally analyze the advantages and drawbacks of each model used in the studies. Further readings and resources are also provided for readers interested in carrying out studies independently. Lastly, we will touch upon the future opportunities and challenges of AI technology application in clinical research and the efforts made to overcome those challenges and difficulties. After reading this chapter, the readers should have a brief idea of the available pharmacoanalytics predicting models, their strengths, and their limitations, thus helping readers decide the appropriate analytics models suitable for further studies.
SonoSAM - Segment Anything on Ultrasound Images

In this paper, we present SonoSAM - a promptable foundational model for segmenting objects of interest on ultrasound images. Fine-tuned exclusively on a rich, diverse set of objects from $$\approx 200$$ k ultrasound image-mask pairs, SonoSAM demonstrates state-of-the-art performance on 8 unseen ultrasound data-sets, outperforming competing methods by a significant margin on all metrics of interest. SonoSAM achieves average dice similarity score of >90% on almost all test data-sets within 2–6 clicks on an average. Further, to increase practical utility of SonoSAM, we propose a two-step process of fine-tuning followed by knowledge distillation to a smaller footprint model without comprising the performance. We present detailed qualitative and quantitative comparisons of SonoSAM with state-of-the-art methods showcasing efficacy of SonoSAM as one of the first reliable, generic foundational model for ultrasound.
Leveraging Self-supervised Learning for Fetal Cardiac Planes Classification Using Ultrasound Scan Videos

Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, adoption of such methods is not explored enough in ultrasound (US) imaging, especially for fetal assessment. We investigate the potential of dual-encoder SSL in utilizing unlabelled US video data to improve the performance of challenging downstream Standard Fetal Cardiac Planes (SFCP) classification using limited labelled 2D US images. We study 7 SSL approaches based on reconstruction, contrastive loss, distillation and information theory, and evaluate them extensively on a large private US dataset. Our observations and finding are consolidated from more than 500 downstream training experiments under different settings. Our primary observation shows that for SSL training, the variance of the dataset is more crucial than its size because it allows the model to learn generalisable representations which improve the performance of downstream tasks. Overall, the BarlowTwins method shows robust performance irrespective of the training settings and data variations, when used as an initialisation for downstream tasks. Notably, full fine-tuning with $$1\%$$ of labelled data outperforms ImageNet initialisation by $$12\%$$ in F1-score and outperforms other SSL initialisations by at least $$4\%$$ in F1-score, thus making it a promising candidate for transfer learning from US video to image data. Our code is available at https://github.com/BioMedIA-MBZUAI/Ultrasound-SSL-FetalCardiacPlanes .
An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery

Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health. Such examination involves several steps that must be performed correctly: identifying suitable sites on the umbilical artery for the measurement, acquiring the blood flow curve in the form of a Doppler spectrum, and ensuring compliance to a set of quality standards. These steps are performed manually and rely heavily on the operator’s skill. In this work, we propose an automated pipeline as an assistive system. By using a modified Faster R-CNN network, we trained a model that can suggest locations suitable for Doppler measurement. Meanwhile, we have also developed a method for assessment of the Doppler spectrum’s quality. The proposed system is validated on 657 images from a national ultrasound screening database, with results demonstrating its potential as a guidance system.
A multicriteria model to support decisions regarding data protection compliance

ABSTRACTWith the advent of the General Personal Data Protection Law (LGPD) in Brazil, companies need to adapt to the law and, therefore, must identify weaknesses in relation to the protection of personal data. Next, actions are defined that include investments in technology, infrastructure and training. These actions require resources, either for the implementation of some technology or readjustment of the system, time for implementation, restructuring of processes, and involvement of key people. Considering these actions can also impact the organizational strategy, this work proposes a model to support the prioritization of actions necessary to comply with the LGPD. The model is based on the FITradeoff (Flexible and Interactive Tradeoff) multicriteria method. The proposed model was applied in a Brazilian organization that is adapting to the LGPD and needed formal support in relation to decisions. The recommendations obtained demonstrated alignment with the company's needs and strategies.KEYWORDS: Data protection; compliancemulticriteria decisionfitradeoff AcknowledgmentsThe authors would like to acknowledge the National Council for the Improvement of Higher Education (CAPES) and the Brazilian Research Council (CNPq) Brazilian National Research Council (CNPq), under Grant 311197/2020-5, for the support received to develop this research.Disclosure statementNo potential conflict of interest was reported by the author(s).
Efficient Transformer for Video Summarization

The amount of user-generated content is increasing daily. That is especially true for video content that became popular with social media like TikTok. Other internet sources keep up and easier the way for video sharing. That is why automatic tools for finding core information of content but decreasing its volume are essential. Video summarization is aimed to help with it. In this work, we propose a transformer-based approach to supervised video summarization. Previous applications of attention architectures either used lighter versions or loaded models with RNN modules, that slower computations. Our proposed framework uses all advantages of transformers. Extensive evaluation on two benchmark datasets showed that the introduced model outperform existed approaches on the SumMe dataset by 3% and shows comparable results on the TVSum dataset.
UNBALANCED MULTICLASS CLASSIFICATION WITH ADAPTIVE SYNTHETIC MULTINOMIAL NAIVE BAYES APPROACH

Opinions related to rising fuel prices need to be seen and analysed. Public opinion is closely related to public policy in Indonesia in the future. Twitter is one of the media that people use to convey their opinions. This study uses sentiment analysis to look at this phenomenon. Sentiment is divided into three categories: positive, neutral, and negative. The methods used in this research are Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours, and Adaptive Synthetic Random Forest. The Adaptive Synthetic method is used to handle unbalanced data. The data used in this study are public arguments per province in Indonesia. The results obtained in this study are negative sentiments that dominate all provinces in Indonesia. There is a relationship between negative sentiment and the level of education, internet use, and the human development index. Adaptive Synthetic Multinomial Naive Bayes performed better than other methods, with an accuracy of 0.882. The highest accuracy of the Adaptive Synthetic Multinomial Naive Bayes method is 0.990 in Papua Barat Province.
Principles for training PR specialists in libraries (new requirements for the training of industry specialists)

The main purpose of the article is to explore the principles of training PR specialist in libraries. Thus, changes in the socio-economic life of modern society require qualitative transformations in the nature and content of labor, an increase in the level of socio-psychological mobility, and the competitiveness of specialists working in various fields of industrial and social activities. Vocational education is designed to meet the need for human resources in the economic and social spheres. Additionally, in the educational practice of Azerbaijan, the concept "specialist" has long been interpreted as a graduate who has achieved knowledge and success as a result of mastering an educational program. In the context of this work, a librarian is considered a practical librarian who has a special education and is competent in the field of professional librarianship, regardless of the training level (bachelor's, master's). Methods: The article is based on the method of comparative analysis. Application importance: the material can be used in lecturers and seminars in higher education institutions (HEIs). Libraries occupy a large place among non-profit organizations involved in PR activities. Primarily because it has a wide structured network throughout the country. Also, the public trusts the sincerity of the activities of this institution as a non-profit enterprise. It is from this point of view that it is important to use PR technologies and methods of image formation in the process of training specialists. When organizing library and information activities, along with the promotion of existing products and enterprise capabilities by means of PR, such tasks as maintaining a reliable image are also solved. The training of highly qualified library and information specialists in this direction is accompanied by an increase in their demand in the labor market. The article analyzes the issues of training highly qualified specialists in the direction of library and information activities, as competent personnel in the field of PR and in the direction of image formation. It is also important to study viewpoints and research of experienced scientists on this topic, as well as to analyze the experience of leading enterprises in the field of industry. To sum up, we would like to draw the attention of industry experts to the concept proposed in the article.
SEGMENTATION OF CANCER MASSES ON BREAST ULTRASOUND IMAGES USING MODIFIED U-NET

Breast cancer causes a huge number of women’s deaths every year. The accurate localization of a breast lesion is a crucial stage. The segmentation of breast ultrasound images participates in the improvement of the process of detection of breast anomalies. An automatic approach of segmentation of breast ultrasound images is presented in this paper, the proposed model is a modified u-net called Attention Residual U-net, designed to help radiologists in their clinical examination to determine adequately the limitation of breast tumors. Attention Residual U-net is a combination of existing models (Convolutional Neural Network U-net, the Attention Gate Mechanism and the Residual Neural Network). Public breast ultrasound images dataset of Baheya hospital in Egypt is used in this work. Dice coefficient, Jaccard index and Accuracy are used to evaluate the performance of the proposed model on the test set. Attention residual u-net can significantly give a dice coefficient = 90%, Jaccard index = 76% and Accuracy = 90%. The proposed model is compared with two other breast segmentation methods on the same dataset. The results show that the modified U-net model was able to achieve accurate segmentation of breast lesions in breast ultrasound images.