Reviewerly/exHarmony huggingface dataset:
Privacy vs Accuracy Trade-Off in Privacy Aware Face Recognition in Smart Systems

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.
MA3: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains

Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to predict. Nevertheless, planning a safely traversable path through such an environment requires the ability to predict the outcomes of these interactions instead of avoiding them. One approach to doing this is to learn the interaction model offline based on collected data. Unfortunately, though, this requires large amounts of data and can often be brittle. Alternatively, models using physics-based simulators can generate large data and provide a reliable prediction. However, they are very slow to query online within the planning loop. This work proposes an algorithmic framework that utilizes the combination of a learned model and a physics-based simulation model for fast planning. Specifically, it uses the learned model as much as possible to accelerate planning while sparsely using the physics-based simulator to verify the feasibility of the planned path. We provide a theoretical analysis of the algorithm and its empirical evaluation showing a significant reduction in planning times.
Efficient real-time portrait video segmentation with temporal guidance

Virtual background has become an increasingly important feature of online video conferencing due to the popularity of remote work in recent years.To enable virtual background, a segmentation mask of the participant needs to be extracted from the real-time video input.Most previous works have focused on image based methods for portrait segmentation.However, portrait video segmentation poses additional challenges due to complicated background, body motion, and inter-frame consistency.In this paper, we utilize temporal guidance to improve video segmentation, and propose several methods to address these challenges including prior mask, optical flow, and visual memory.We leverage an existing portrait segmentation model PortraitNet to incorporate our temporal guided methods.Experimental results show that our methods can achieve improved segmentation performance on portrait videos with minimum latency.
Real-time Feasibility of a Human Intention Method Evaluated Through a Competitive Human-Robot Reaching Game

Predicting human behavior is a necessary robot ability for safe and fluent human-robot collaboration in shared workspaces. Robots should recognize human intended actions by combining information from ongoing movements and other environmental cues. In many cases, visual sensors might be required for obtaining information regarding the human move-ment. While using cameras, especially a single one, offers several benefits, the information captured is noisy and of relative low frequency considering the requirements for intention prediction. The purpose of this study was to evaluate the feasibility of obtaining real-time intention prediction and using it timely for robot action, when human behavior is observed by a single RGB-D camera. Visual information is used to obtain human joint data using Openpose. Based on this we then construct appropriate features and train several Machine Learning models. We evaluate the feasibility of timely robot action using a competitive human-robot game. The results show that a prediction available at about 288ms is early enough to enable timely robot action provided that the robot has to act on objects that are no farther than 10 cm away.
The Effects of Background Noise on User Experience and Performance of Mixed Reality Voice Dictation

From education to manufacturing and medicine, the Microsoft HoloLens 2 mixed reality headset has been increasingly adopted. Text entry is a core functionality of the HoloLens 2, enabling communication and search features. The device tracks hand and finger movements interacting with a virtual keyboard but is slow and cumbersome. Speech-to-text dictation is a promising alternative requiring less physical effort and time. However, the HoloLens 2 dictation performance in environmental settings with varying noise levels is unclear. This study investigated the text input accuracy (WER), speed (WPM), and user perceptions of the HoloLens 2 dictation across three background noise levels (40 dB, 55 dB, and 70 dB). The 70 dB condition resulted in inferior outcomes for text input speed, perceived usability, mental demand, temporal demand, and effort. Users also reported lengthy processing delays in the 70 dB condition, while accuracy remained consistent (2–5% WER) across all noise conditions.
Learner comments-based Recommendation system

Traditional techniques of recommender systems presented low accuracy performance because it suffer from sparsity problem hence. In this work we build a recommender model based on free-text reviews written online by user. The model built on two method recommendation and sentiment analysis. Our recommendation model first predicts new rating from user reviews then detect user requirements and interest by differences analysis between a new rating prediction and original rating. Presented model take in account both explicit original rating and implicit user sentiments, hence that will be roll key to address data sparsity problem. In this model we try to extract feature and opinion by sentiment analysis of text user comments and reviews to get more accurate rating calculation from student reviews and comments written in English. This model tested and trained on online learner comments and reviews on video learning of three courses on coursera learning platform to produce a video learning recommendation system and to validate the accuracy and efficiency of proposed model.
A Study of Search User Interface Design based on Hofstede’s Six Cultural Dimensions

An information seeker's cultural background could influence their preference for search user interface (UI) design.To study cultural influences Geert Hofstede's cultural dimensions have been applied to website design for a number of years.In this paper, we examine if Hofstede's six cultural dimension can be applied to inform the design of search engine user interfaces.The culturally designed search user interfaces have been evaluated in a study with 148 participants of different cultural backgrounds.The results have been analysed to determine if Hofstede's cultural dimensions are appropriate for understanding users' preferences on search user interface design.Whilst the key findings from the study suggest Hofstede cross-cultural dimensions can be used to model users' preferences on search interface design, further work is still needed for particular cultural dimensions to reinforce the conclusions.
Hybrid Metaheuristics Using Rough Sets for QoS-Aware Service Composition

Quality of Service (QoS)-aware service composition plays an increasingly important role in various computational paradigms and delivery models, predominantly cloud computing. The proliferation of services with expanding quality attributes navigates this problem towards big service compositions, which fall under the umbrella of NP-hard. Within the realm of big services, performing composition also became a computationally expensive and challenging task. Since service composition is an NP-hard problem, numerous research aimed to determine optimal or near-optimal solutions within a reasonable budget. A large body of evidence suggests that metaheuristics could realize this goal to some extent. However, the proliferation of services with expanding quality attributes (search dimensions) may fail the most efficient techniques. In order to deal with the problem of big service composition, one trending approach has been devising hybrid metaheuristics methods by incorporating clustering techniques to minimize search space. This paper proposes a hybrid metaheuristic incorporated with a maximal discernibility heuristic based on rough set theory to perform composition in the subset of search space. Moreover, it introduces a parallel architecture and monitoring mechanism to provide immunity against premature convergence when the search space is minimized. The experiment was conducted for 25 datasets generated incrementally from a real-world QWS dataset, where the proposed hybrid solution effectively improves solution quality and reduces execution time with a statistical significance of 99 % confidence interval across diverse metaheuristics and datasets.
2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium

First posted October 3, 2022 For additional information, contact: Director, Earth Resources Observation and Science CenterU.S. Geological Survey47914 252nd StreetSioux Falls, SD 57198Contact Pubs Warehouse The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the third edition of the JACIE compendium, which is planned to be updated and released annually.
Blockchain Education: as a Challenge in the Academic Digitalization of Higher Education

Over time, there haven't been many significant challenges to the higher education model. Even if the organization of lectures and learning experiences has changed due to the incorporation of increasingly sophisticated technologies, study themes are still evolving. The work being given here is an analysis of blockchain technology and its potential applications in the field of education, with a focus on the potential for upending the current value chain of academic institutions. Institutions act as central authority for providing and approving student knowledge as well as as a bridge between professors and those who would benefit from it (students). The main advancement of blockchain technology is the unbreakable digital transaction ledger, which can record almost any type of value exchange. They open up new possibilities when paired with other cutting-edge technologies like the internet of things and big data. They provide new opportunities for the decentralized sharing of education and the recording of knowledge and skills when paired with other cutting-edge technologies, such as the internet of things and big data. The end results include a thorough examination of the advancement of blockchain technology for educational purposes and a forecast of potential disruptions for educational institutions. There are currently apps targeted at decentralizing educational institutions using blockchain technology, which is developing at an ever-increasing rate. In the interim, a wider group of adepts continue to adopt the technique.
Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm

Indoor waste collection that utilizes mobile robots can solve the labor cost and manpower shortage but has the problem of limited energy resources, making it difficult to operate for long periods of time. Therefore, it is important to reduce the energy consumption for efficient waste collection. The waste collection robot can be modeled as a Capacitated Vehicle Routing Problem (CVRP), where heuristics algorithms can be deployed to search for the most energy-efficient path. This paper proposes the Ant Colony Optimization (ACO) algorithm for finding the optimal path of the waste collection robot. Energy consumption of the robot depends not only on the travel path but also on the weight of the waste it carries. Therefore, the proposed ACO algorithm utilizes the path distance and waste weight as the visibility. The travel distance and energy consumption are also used to determine the updated pheromone. Whereas the conventional and adapted ACO algorithms use only either the path distance or the waste weight as the visibility, respectively. The simulation experiments are conducted to compare the travel distance and the energy consumption that the waste collection robot takes by using the conventional, adapted, and proposed ACO algorithms. In the simulation experiments, the number of nodes, the waste weight, and the carrying capacity are used as parameters to verify the performance under the determined environment. The simulation results express that the proposed ACO algorithm provides a better energy optimal path in terms of travel distance and energy consumption than the conventional and adapted ACO algorithms.
Anchor Assisted Experience Replay for Online Class-Incremental Learning

Online class-incremental learning (OCIL) studies the problem of mitigating the phenomenon of catastrophic forgetting while learning new classes from a continuously non-stationary data stream. Existing approaches mainly constrain the updating of parameters to prevent the drift of previous classes that reflects the movement of samples in the embedding space. Although this kind of drift can be relieved to some extent by existing approaches, it is usually inevitable. Therefore, only prevention of drift is not enough, and we also need to further compensate for it. To this end, for each previous class, we exploit the sample with the smallest loss value as its anchor, which can representatively characterize the corresponding class. Based on the assistance of anchors, we present a novel Anchor Assisted Experience Replay (AAER) method that not only prevents the drift but also compensates for the inevitable drift to overcome the catastrophic forgetting. Specifically, we design a Drift-Prevention with Anchor (DPA) operation, which plays a preventive role by reducing the drift implicitly as well as encouraging the samples with the same label cluster tightly. Moreover, we propose a Drift-Compensation with Anchor (DCA) operation that contains two remedy mechanisms: one is Forward-offset which keeps embedding of previous data but estimates new classification centers; the other is just the opposite named Backward-offset, which keeps the old classification centers unchanged but updates the embedding of previous data. We conduct extensive experiments on three real-world datasets, and empirical results consistently demonstrate the superior performance of AAER over various state-of-the-art methods.
Improving the Comprehension of Evolving Graphical Models

Context: Modeling industrial systems is mostly done collaboratively. In such a scenario, a model is modified by multiple people over a possibly long period of time. In consequence, modelers have to be able to understand a model’s evolution and, in particular, what elements of a model have changed, how, why, when, and by whom. Objective: We derive six distinct user goals and systematically design a graphical modeling languageagnostic set of tools that support users in achieving these goals. Method: We implement those tools and integrate them into an existing graphical modeling tool for technology roadmaps. To measure the tools’ usability, we conducted a mixed-methods study with participants of different levels of experience. With the set of developed tools at hand, in different scenarios, participants had to answer several questions regarding the evolution of a model. Results: The SUS score for assessing usability ranged from 80 to 95, indicating good to excellent usability. Task completeness (measured per scenario) ranged from 92% to 100%. Further, participants emphasized the added value of the tools while completing the scenarios. Conclusion: Although participants stated that the set of tools can be classified as expert tools, the developed tools achieve the goal of enabling users to comprehend changes made by others and trace the impacts of operations.
Video captioning using transformer network

Video Captioning is automatic text description generation process of a given video. It is a type of sequence to sequence translation that should consider the spatial and temporal features of input video data. Recurrent Neural Networks which consist of encoder-decoder architecture are generally used for this type of problem. The model which uses Recurrent Neural Networks suffer from high computational cost and the output generation depends upon the final hidden state vector of the encoder which cannot represent the entire video effectively. Incorporating attention mechanism in the video captioning process can improve the efficiency of caption generation. Nowadays focus has been shifted from RNN based networks to Transformer for video captioning problem. This paper introduces transformer based network architecture over LSTM based models for captioning video. This architecture is generally used in language translation models. Transformer network contains multi-head self-attention and the encoder-decoder attention to improve the performance of text description generation. The model shows BLEU score of 42.4 on MSR-VTT and 53.2 on MSVD datasets which is better than state-of-art models.
Real-Time Human Detection and Counting System Using Deep Learning Computer Vision Techniques

Targeting the current Covid 19 pandemic situation, this paper identifies the need of crowd management. Thus, it proposes an effective and efficient real-time human detection and counting solution specifically for shopping malls by producing a system with graphical user interface and management functionalities. Besides, it comprehensively reviews and compares the existing techniques and similar systems to select the ideal solution for this scenario. Specifically, advanced deep learning computer vision techniques are decided by using YOLOv3 for detecting and classifying the human objects with DeepSORT tracking algorithm to track each detected human object and perform counting using intrusion line judgment. Additionally, it converts the pretrained YOLOv3 into TensorFlow format for better and faster real-time computation using graphical processing unit instead of using central processing unit as the traditional target machine. The experimental results have proven this implementation combination to be 91.07% accurate and real-time capable with testing videos from the internet to simulate the shopping mall entrance scenario.
Blockchain integration for hospital information system management

Introduction: A lot of hospital management systems, especially open-source ones, still lack interoperability which is holding back the effectiveness of administration. This happens due to different regulations from hospital organizations. Therefore, there must be a system that facilitates interoperability. Blockchain is a decentralized ledger system, to insert the data from the blockchain all parties must agree that the data is valid thus the data can be inserted so, all the data are immutable thus every change can be audited. Methods: We integrate the Hyperledger fabric network, an open-source modular blockchain platform, into an open-source hospital management system, openEMR. We take the changed data using debezium connect and Kafka and inserted it into the blockchain. Results: Based on our test, we managed to get average read latency at 27ms, average read throughput at 36 Read per Second, average transaction latency at 45ms, average transaction throughput at 22 Transaction per Second, and average integrated system data transfer at 111.36ms. Conclusions: All services deployed successfully at the Kubernetes without any error. All services work as they should be. One service can integrate through the internal network of the Kubernetes and from the outside cluster using ingress. OpenEMR can be used normally as indicated in the official documentation and the data change is stored in the blockchain.
Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning Methods

Elderly activity detection is one of the significant applications in machine learning. A supportive lifestyle can help older people with their daily activities to live their lives easier. But the current system is ineffective, expensive, and impossible to implement. Efficient and cost-effective modern systems are needed to address the problems of aged people and enable them to adopt effective strategies. Though smartphones are easily accessible nowadays, thus a portable and energy-efficient system can be developed using the available resources. This paper is supposed to establish elderly people's activity detection based on available resources in terms of robustness, privacy, and cost-effectiveness. We formulated a private dataset by capturing seven activities, including working, standing, walking, and talking, etc. Furthermore, we performed various preprocessing techniques such as activity labeling, class balancing, and concerning the number of instances. The proposed system describes how to identify and classify the daily activities of older people using a smartphone accelerometer to predict future activities. Experimental results indicate that the highest accuracy rate of 93.16% has been achieved by using the J48 Decision Tree algorithm. Apart from the proposed method, we analyzed the results by using various classifiers such as Naïve Bays (NB), Random Forest (RF), and Multilayer Perceptron (MLP). In the future, various other human activities like opening and closing the door, watching TV, and sleeping can also be considered for the evaluation of the proposed model.
The application of path planning algorithm based on deep reinforcement learning for mobile robots

To meet the need for autonomous route planning for tour guide robots in tourist venues, this paper proposes a path planning algorithm based on deep reinforcement learning. The traditional Deep Q-learning Network (DQN) algorithm two defects - overfitting and overestimation. This paper adopts a method that discards the experience pool and treats behavioural values equally, which not only solves the shortcomings of the traditional method, but also satisfies the need for mobile robots to lead tourists on tours through autonomous learning. The paper analyses the principle and process of the method and compares it with the traditional method through experiments to verify that the method outperforms the traditional method in terms of accuracy and speed.
Visual Perception Deep Drive Model for Self-Driving Car

Self driving cars are the need of future technology, there are many companies that are trying to perfect this particular project but there are still some deficiencies there. Most of the companies are using Expensive sensors like RADAR and LiDAR to get the idea of environment, which are very hard to use and need a lot of processing power. Our project focuses on using only visual aid to drive a car, particularly following the lane of the road. We trained a model using Convolutional Neural Network (CNN), in a simulated environment and tested the model in the same environment.