Reviewerly/exHarmony huggingface dataset:
Clustered Federated Learning Based on Data Distribution

Federated learning is a distributed machine learning framework where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. Non-independent and identically distributed data across clients is one of the challenges in federated learning applications which leads to a decline in model accuracy and modeling efficiency. We present a clustered federated learning algorithm based on data distribution and conduct an empirical evaluation. To protect the privacy of data in each client, we apply the encrypted distance computing algorithm in data set similarity measurement. The data experiments demonstrate the approach is effective for improving the accuracy and efficiency of federated learning. The AUC values of the clustered model is about 15% higher than the conventional model while the time cost of clustered modeling is less than 1/2 of that of conventional modeling.
Image Segmentation Techniques: A Survey

Image segmentation plays a crucial role in digital image processing. It is the process of dividing an image into various segments called image objects which in turn reduces the complexity of an image to analyze them efficiently. The purpose is to represent an image free from noise and distortion. Moreover, it is used to specify the position of objects and their boundaries. Numerous segmentation techniques have been proposed in the past with varied approaches and little diversity. This paper presents a comprehensive analysis of different segmentation techniques like edge-based segmentation, region-based segmentation, clustering, thresholding, soft computing-based segmentation.
Chapter 7. Fostering (Critical) digital teaching competence through virtual exchange

This chapter reports on findings in relation to one of the sub-questions that the Evaluating and Upscaling Telecollaborative Teacher Education (EVALUATE) project, an Erasmus+ funded European Policy Experimentation (EPE), set out to address. We explore how online (language) teacher trainees are best supported in developing critical digital literacy as framed by Darvin (2017) and Morris (2017) as well as critical digital teaching competence, which remains underexplored in Virtual Exchange (VE)-based teacher education. The insights gained from a close examination of one EVALUATE exchange highlight how crucial systematic reflective evaluation of the processes the trainees are involved in is for competence development in VE contexts.
Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset

Egocentric vision is an emerging field of computer vision characterized by the acquisition video from the first person perspective. Particularly, for evaluating upper extremity rehabilitation, egocentric vision offers the ability to quantitatively measure the function of hands used in physical-based exercises. For such applications, hand detection and tracking are the first requirement. In this work, we develop a fully automatic tracking by detection pipeline that firstly extracts hands positions and then tracks hands in consecutive frames. The proposed framework consists of state of the art detectors such as RCNN and YOLO family models coupled with advanced trackers (e.g., SORT and DeepSORT) for tracking task. This paper explores how performance of the stand alone object detection algorithms correlates with overall performance of a tracking by detection system. The experimental results show that detection highly impacts the overall performance. Moreover, this work also proves that the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase potential of the whole system. We also present challenges for new egocentric hand tracking dataset for future works.
Deep Reinforcement Learning based Aggressive Collision Avoidance with Limited FOV for Unmanned Aerial Vehicles

As the novel lightweight sensors become more capable of providing high-level perception abilities to the small unmanned aerial vehicles for a cluttered environment, they require more capable navigation and control methodologies to push the operational limits. Such methods are bounded due to computational power requirements, limits of conservative deterministic methods, or the algorithms utilizing heuristics built upon specialized cases. Considering this discussion, in this work, we develop a trajectory re-planning algorithm for collision avoidance, combining machine learning and deterministic trajectory generation methodologies allowing the small UAVs to navigate in clutter environments aggressively. First, we utilize the deferentially flat model description of the UAVs and define the output flight trajectories through high-order B-splines. The local support property of the B-spline allows us to generate these evasive maneuvers without regenerating the whole B-spline trajectory, thus granting aggressiveness. Then, assuming the small UAVs have a limited field of view, to provide instantaneous trajectory segment re-planning, we trained deep reinforcement learning agent for optimal control point reallocation through knot insertion to avoid the sensed obstacles, which also considers the feasibility of the newly generated trajectory segment. The deep reinforcement learning agent can generate an optimal solution in 1.2 ms, which offers the fastest solution in the literature and allows it to be utilized on highly agile vehicles. Finally, we focus on a real-time platform implementations of the algorithm in order to show the performance and to build and perform aggressive collision avoidance maneuvers in highly cluttered environments can be seen in the following video link: https://youtu.be/8IiLQFQ3V0E.
AI/ML for Mission Processing Onboard Satellites

As new emerging threats require faster than human-in-the-loop response times, next generation defense systems are requiring more autonomy, data processing, and decision making at the edge. These new systems are looking to artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomous command and control. For space systems, onboard processing of advanced AI/ML algorithms, especially deep learning algorithms, requires a multiple magnitude increase in compute capability compared to what is available with legacy, radiation-tolerant, space-grade processors on current space vehicles. The next generation of space processors for AI/ML onboard will likely include a diverse landscape of heterogeneous systems including various combinations of CPUs, GPUs, FPGAs, and purpose-built ASICs. In this manuscript, we identify the driving requirements for AI/ML processing onboard; detail the similarities and differences between the ground, edge, and space environments for AI/ML; define a reference architecture and the services required to provide an end-to-end framework for developing and deploying AI/ML applications for space; and evaluate the hardware landscape for current and next-generation space AI processors.
Perceptual Image Outpainting Assisted by Low-Level Feature Fusion and Multi-Patch Discriminator

Recently, deep learning-based image outpainting has made greatly notable improvements in computer vision field. However, due to the lack of fully extracting image information, the existing methods often generate unnatural and blurry outpainting results in most cases. To solve this issue, we propose a perceptual image outpainting method, which effectively takes the advantage of low-level feature fusion and multi-patch discriminator. Specifically, we first fuse the texture information in the low-level feature map of encoder, and simultaneously incorporate these aggregated features reusability with semantic (or structural) information of deep feature map such that we could utilize more sophisticated texture information to generate more authentic outpainting images. Then we also introduce a multi-patch discriminator to enhance the generated texture, which effectively judges the generated image from the different level features and concurrently impels our network to produce more natural and clearer outpainting results. Moreover, we further introduce perceptual loss and style loss to effectively improve the texture and style of outpainting images. Compared with the existing methods, our method could produce finer outpainting results. Experimental results on Places2 and Paris StreetView datasets illustrated the effectiveness of our method for image outpainting.
Top-k User-Based Collaborative Recommendation System Using MapReduce

Collaborative Filtering (CF) methods are widely used in recommender systems. However, due to the high computational complexity of CF, the existing algorithms do not scale well when the number of users and items grow. In this paper, we develop a new user-based collaborative filtering method using the MapReduce framework to recommend top-k items for each user in a given dataset. Our proposed method requires a dataset that contains item numbers, user ids, and ratings. We applied our proposed method to recommend movies using the MovieLens dataset. We implement our proposed algorithms on Apache Spark running on Amazon Web Services (AWS) clusters. We perform experiments on different cluster sizes to demonstrate the scalability of our proposed method. The experimental results show that the proposed method is highly scalable and exhibits near-linear speed-up when we increase the number of clusters.
Social and Web Analytics: An Analytical Case Study on Twitter Data

There are many fascinating headings that can be investigated. Newspapers, blogs, articles print a lot of content and depict a lot about a person’s positive and negative aspects. In this chapter, we have explained how we can estimate those numbers of positivity, negativity, or neutrality about the product or services provided by an organization. This is the way toward recognizing and arranging sentiments communicated in a bit of content, particularly to decide if an author’s disposition toward a point/item is sure, negative, or impartial. It is utilized to check how positive or how negative an announcement is. This analysis is like manner called sentiment mining or opinion AI, is the path toward deciding if a touch of making is sure, negative, or unprejudiced. A run of the mill use case for this development is to discover how people feel about a subject. Sentiment analysis is broadly connected to audits and internet-based life for an assortment of utilizations. Sentiment analysis can be performed from various perspectives. Numerous brands and advertisers use watchword-based apparatuses that arrange information as positive/negative/impartial. Social media platforms play an important role in providing real time streaming data for such analysis. In this chapter we have tried to provide the complete pathway to do social media analytics.
Enforcing Privacy Preservation on Edge Cameras using Lightweight Video Frame Scrambling

Privacy protecting is a very challenging task in a highly surveilled world with zillions of surveillance cameras deployed. The difficulty mainly lies in the facts: (i) there is not a distinctively defined boundary between usability and privacy, (ii) video frames indiscriminately created and collected by the edge cameras could be abused and intercepted, and (iii) it is difficult to enforce the commonly used compute-intensive standard techniques as-is on the edge cameras because of limited computational resources. In this paper, we propose a lightweight and secure scheme to Enforce Privacy-preservation on Edge Cameras (EnPEC) using deep learning and a sinusoidal chaotic-map. The proposed EnPEC architecture comprises a lightweight frame classifier designed to label frames as offensive and harmless depending on their content to ensure the practice of selective surveillance following a frame approximation process and a novel sinusoidal-map-based chaotic image scrambling technique that enciphers frames color-channel wise to ensure end-to-end privacy of frame contents. The extensive analysis of the functionality, performance and security of the EnPEC scheme, and comparison with related works verify that the EnPEC scheme is more feasible, robust and secure when it runs in real-time on edge cameras equipped with computational power equivalent to the Raspberry PI 4.
The Evaluation of the Impact of a Saharan Event on Particulate Matter Using Compositional Data Analysis

The proposed approach based on compositional data analysis was applied on simultaneous measurements of the mineral element concentrations of PM10 and PM2.5 from a typical suburban site with and without a Saharan event. The suburban site is located in the city of Rome. The selected mineral elements were Al, Si, Ca, Fe, Ti, Mg, and Sr. The data relating to these elements are reported in a previous study. The considered elements are mainly related to mineral matter. The proposed approach allows statistically validating that the mineral element concentrations of PM during days with a Saharan event differ from those without a Saharan event in terms of mineral element composition and size distribution. In particular, the results showed that the compositional data analysis applied to simultaneous measurements of mineral element concentrations of PM10 and PM2.5 is a helpful technique that can be used to study environmental sites affected by natural sources such as Saharan events. Moreover, the presented technique can be handy in all those conditions where it is important to discriminate whether the occurrence of an exceedance or a violation of the daily limit value established for PM could also be due to natural sources.
Effort Estimation in Agile Software Development: A Exploratory Study of Practitioners’ Perspective

Software is increasingly important for our society. However, software industry presents flaws to meet market demands in a faster and reliable way. Agile methods are a way to tackle this problem. However, this approach also poses several challenges, including effort estimation as one of them. In this scenario, #NoEstimates and #NoProject movements emerged as another way to solve estimation issues. In this new scenario, this study aims to provide further empirical evidence on agile effort estimation techniques in practice. To do so, an online survey was designed based on a literature review. Researchers gathered 53 valid questionnaires from agile practitioners. Result shows the importance of hybrid software development approaches and mixed effort estimation techniques. However, it is important to note that Story Points and Fibonacci series are often used as well. Moreover, the most perceived benefit of estimation in agile contexts is to drive the team to complete the project successfully. Complexity and uncertainty are perceived as key factors in estimation accuracy. Finally, further research should be conducted to gain a better understanding of #NoEstimates and #NoProject movements.
The Analysis of Time Series Forecasting on Resource Provision of Cloud-based Game Servers

The server workloads of large-scale online video games are elastic and on-demand. The workload can range from tens to thousands of server instances in short periods. In fact, a cloud-based video game ecosystem can reach a workload of millions of players every week. Given such a large scale, even a small portion of over-provisioning leads to a significant amount of resource idling and a high cost of waste. It is essential to define an effective forecasting model on the game session workloads given a time span. The effectiveness shall be measured by metrics representing Service Level Objectives (SLOs). In this work, we analyze time series forecasting models using ARIMA, Prophet, and LSTM to predict the number of virtual machines in need of cloud resource monitoring data. In addition, we define service-level metrics for measuring effectiveness based on factors of over/under provision and ratio of resource waste. We analyze models with 16 fleets with an average of 2754 game servers over a four-month-long period of time in the production environment. We observe that our LSTM model is the most accurate in forecasting the demand of virtual machines in terms of RMSE and MAE. Further analysis using metrics of SLOs, we observe that the LSTM model leads to more cases of under-provisioning than ARIMA and Prophet do. The LSTM model forecasts the demand of virtual machines with less over-provision ratio than ARIMA and Prophet do for 14 out of 16 fleets. Using the LSTM model, we further evaluate the forecasting effect across different time spans of a single fleet and across multiple fleets within the same time span.
Modular ST-MRF Environment for Moving Target Detection and Tracking Under Adverse Local Conditions

This paper primarily encompasses phased-implementation plan where at first it executes ST-MRF model for local conditioning, which is followed by machine learning driven MRF model to perform target detection and tracking. In other words, the proposed model at first performs local conditioning using ST-MRF, where it intends to reduce or eliminate detrimental local conditions such as haziness, fog, smoke, etc. Subsequently, over the conditioned compressed as well as moving target tracking even under adverse operating conditions. Domain video input, it executes ST-MRF (with machine learning and expectation maximization) model to perform moving target detection and tracking. To achieve it, this paper encompasses pre-processing followed by intensity value prior estimation, medium transmission modelling, dark channel estimation and maximum likelihood assisted ST-MRF model for compressed video local conditioning. Here, local conditioning (using ST-MRF) represents a task to alleviate fog, low-light condition, dehazing etc. Once performing local conditioning, the proposed model executes Edge-prior Estimation, followed by K-Means clustering for edge-preserving initial ROI labelling. Subsequently, it applied expectation maximization assisted MRF model to segment moving target and track it over consecutive frames. Thus, the proposed model exhibits both local conditioning.
AI as an Inventor: Has the Federal Court of Australia Erred in DABUS?

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OAK-D as a Platform for Human Movement Analysis: A Case Study

3D interaction is an essential manner of interaction in VR environments. Many devices enable analysis and use of human inputs in these settings, such as the Kinect™ from Microsoft. One of the latest additions is the OpenCV AI Kit with Depth or OAK-D. This paper analyzes the OAK-D and proposes a case study based on a Motion Analysis framework created for at-home workouts to explore its capabilities in a 3D interaction setting. Our results show that the OAK-D is well suited for this kind of task, achieving 7 FPS with a Raspberry Pi 4 and 15 FPS with a laptop. Furthermore, it could detect and correct wrong movements while the user was doing it and count repetitions for the squat and side lateral raise.
Mask Detection From Face Images Using Deep Learning and Transfer Learning

It is vital important for people to wear masks during the pandemic that affects the whole world. In this study, it was detected whether people wear masks by using convolutional neural networks which is one of the deep learning methods and transfer learning. In the classification carried out using the Resnet-18 architecture, both real-life images obtained with the Huawei M2150 camera and images shared as open source were used. The system, which was trained using 18600 images, was tested with 4540 real-world images and 95.16% sensitivity 96.69% specificity values were obtained. Thus, a model that works with high performance not only on high resolution images taken close up, but also on low resolution images taken from afar was obtained.
Automatic Quality Attribute Scenarios Identification and Generation from Quality Attribute Requirements

Identification and generation of Quality Attribute Scenarios (QASs) from Quality Attribute Requirements (QARs) is a critical software engineering technique for defining system specifications and is helpful in facilitating development of Software Architecture (SA) that meets the expected quality. However, identifying QAS types and extracting their components traditionally is a complex task that consumes time and energy. It also requires high budget and is an error-prone task, especially for inexperienced users. This study aims to develop an automatic QASs identification and generation model that extracts QASs from QARs. We used Natural Language Processing (NLP) to preprocess texts and Machine Learning (ML) approaches to identify QAS types, and we built a Custom Named Entity Recognition (CNER) model to generate QAS components. To evaluate the proposed identification model, we used five algorithms. Both SVM and Scholastic Gradient Descent (SGD) classifier algorithms scored 97.7 % accuracy, while LR, KNN, and NB scored 96%, 91.6 %, and 88.8%, respectively. The CNER model achieved 92.3% recall, 93.3% precision, and 92.8% F1-measure score. The results show that automatic identification of QASs from QARs has a potential to replace time taking and error-prone manual work.
ExgFair: A Crowdsourcing Data Exchange Approach To Fair Human Face Datasets Augmentation

Human face images represent a rich set of visual data information that is utilized by various big data driven human facial applications. However, the performance of these applications is usually biased towards the majority demographic group due to the data imbalance issue. In this paper, we focus on a fair human face data exchange problem where the goal is to exchange visual features of human face images between different human face datasets and obtain a set of augmented datasets that improve the fairness and performance of human facial applications. Our problem is motivated by the limitations of current fairness approaches that only focus on a single human face dataset from a particular application and require a large amount of pre-annotated demographic attribute labels to develop fair human facial models. To address these limitations, we develop ExgFair, a crowdsourcing-based fair data exchange framework to generate a set of augmented fair face image datasets by leveraging the crowdsourced demographic attribute labels of human face images. We evaluate ExgFair using a set of real-world human face image datasets with different demographic distributions. The results show that ExgFair not only reduces demographic biases of the datasets but also improves the accuracy of human facial applications trained on the augmented fair datasets.
Physical Limitation Aware Quantization Model for Photonic Convolutional Neural Network

We propose a physical limitation aware clustered quantization model by considering precision device limitation in the training process of integrated photonic CNN, showing 98.5% recognition accuracy for MNIST handwirtten recognition with 2-bit precision.