Reviewerly/exHarmony huggingface dataset:
Energy and cost aware workflow scheduling in clouds with deadline constraint

Abstract Cloud computing is a promising platform for executing scientific workflow applications. Commonly, the scientific workflow applications are complex in size and computation intensive. Task scheduling in clouds is a familiar NP‐complete problem. Efficient workflow application scheduling is critical for meeting multi‐objectives in cloud environments. By reason of its pivotal role, this problem has been widely studied, and many approaches have been developed. Most of the algorithms focus on schedule makespan and execution cost. The cloud data centers consume a large amount of energy while running workflow applications due to a lack of efficient scheduling algorithms to perform the task to a virtual machine (VM) mapping. Hence, there is a significant need to address energy utilization in the cloud data center (CDC) for two reasons such as data center operational cost optimization and to improve the environment. This article presents an energy and cost‐aware scheduling (ECWS) approach with the objectives of decreasing energy utilization, execution cost, and maximizing utilization of the resources. The ECWS algorithm is a heterogeneous earliest finish time (HEFT) based energy‐efficient heuristic for cloud scheduler. The ECWS algorithm includes three sub‐algorithms like RE calculation, RE Threshold selection, and slack algorithm. The effectiveness of the proposed algorithm is evaluated on the WorkflowSim tool using distinct workloads from various scientific areas. The experimental outcome exhibit that the proposed algorithm achieved significant energy conservation, maximized resource utilization, and cost‐saving when compared with related well‐known algorithms.
Sarima-Based Cyber-Risk Assessment and Mitigation Model for A Smart City’s Traffic Management Systems (Scram)

A malicious hacker can compromise the integrated traffic management systems (ITMS) speed sensors in a smart city, causing a vehicle to misread the speed and result in collision or congestion on the pathways. Based on protection-motivation theory (PMT), we propose a Time series-based Cyber-risk Assessment and Mitigation for smart cities (SCRAM) model comprising three modules for the smart city administrator. In line with the threat appraisal component of PMT, our cyber-risk assessment (CRA) module uses a Seasonal time series forecasting method, takes ITMS average speed as an input, and outputs the probability of anomalies in the average speed of vehicles. Subsequently, based on the concepts of risk theory, the cyber-risk quantification (CRQ) module calculates the expected loss for a smart city due to collision or congestion on the pathways. In line with the coping appraisal component of PMT, our cyber-risk mitigation (CRM) module proposes strategies for the smart-city administrator to reduce cyber-risk using technological means and pass the residual risk to third-party cyber-insurer.
Public Intellectuals

Abstract The defining characteristics and roles of the public intellectuals in Korea have evolved closely following the vicissitudes of the country’s history. During the post-1945 authoritarian period, the public intellectuals’ pursuit of truth and universal good organically linked them to the democratization movement, leading them to re-evaluate and contest the most foundational and normative ideological underpinnings of the state. The post-1987 institutionalization of liberal democracy, the simultaneous path of neoliberal restructuring, the advent of the internet and the information revolution, as well as the high rate of university graduates transformed the intellectual landscape and consequently the concept and ideals of the public intellectual. While some of the socially conscious celebrity figures emerged to play the role of the public intellectual in the early 2000s, there also emerged a new type of public intellectual who engages with social issues based on and utilizing their professional expertise.
Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targeted navigation task. The challenges reside in the sparseness of positive samples for learning, multi-modal sensor perception with partial observability, the demand for accurate steering maneuvers together with long training cycles. To address these points, we propose NavACL-Q as an automatic curriculum learning method in combination with a distributed version of the soft actor-critic algorithm. The performance of the learning algorithm is evaluated exhaustively in a different warehouse environment to validate both robustness and generalizability of the learned policy. Results in NVIDIA Isaac Sim demonstrates that our trained agent significantly outperforms the map-based navigation pipeline provided by NVIDIA Isaac Sim with an increased agent-goal distance of 3 m and a wider initial relative agent-goal rotation of approximately 45∘. The ablation studies also suggest that NavACL-Q greatly facilitates the whole learning process with a performance gain of roughly 40% compared to training with random starts and a pre-trained feature extractor manifestly boosts the performance by approximately 60%.
Towards cost-aware VM migration to maximize the profit in federated clouds

Federated cloud has emerged as a cost-effective and scalable platform for multiple cloud providers to collaborate in serving the growing demand for cloud resources. In a federation, the major concern for each cloud provider is to maximize its own profit while meeting the quality of service requirements of end-users. Virtual machine (VM) migration allows the cloud provider to rent resources across the other cloud providers in a federation to minimize the cost. However, VM migration comes with an additional penalty in terms of time and cost, and it might degrade the performance of the system during migration. We address the problem of cost-aware VM migration in federated clouds to maximize the profit of the cloud provider by minimizing the cost. We analyze the impact of VM migration across the data centers in a federation by modeling migration cost, migration time, and downtime. We formulate an optimization problem to minimize the total cost of operation (TCO) of the cloud provider as well as the migration time. We model the TCO to include the cost of hosted VMs and their migration cost. We propose efficient heuristics to select the source and destination data centers for migration, as well as the VM to be migrated. Based on these heuristics, we design a polynomial-time VM migration algorithm to minimize the TCO and migration time. The proposed algorithm considers the characteristics of the VMs, the bandwidth and the data transfer price across data centers to give the highest benefit in migration. We evaluate the proposed algorithm using CloudSim across different scenarios. Our results demonstrate the advantages of VM migration in a federated cloud for both the cloud provider and the end-user, to lower the TCO and the migration downtime. The proposed algorithm is shown to outperform other baseline approaches, with up to 48% reduction in the TCO along with reducing the migration time, downtime, and number of migrations. We also evaluate the impact of network bandwidth and the page dirty rate of VMs on migration performance.
A systematic survey on embodied cognition: 11 years of research in child–computer interaction

Embodied cognition is a concept that has been extensively explored by scholars within the Child–Computer Interaction community. However, there is a lack of a synthesis of this research to clarify the field’s benefits and drawbacks. This paper presents a survey of articles published between 2010 and 2020 in the Interaction Design and Children (IDC) conference and the International Journal of Child–Computer Interaction (IJCCI). We retrieved 158 papers using the keyword ”embodied cognition” and its derivatives. Further screening narrowed these down to 43. The purpose of this review is to provide an overview of the current landscape of ‘embodied’ research by reporting the most common subject areas of application, forms, and modes of embodiment, and the role of children and adults. Our contribution is twofold: we highlight the main trends around these themes within the field, and we provide eight critical areas of future research. By illustrating new challenges and opportunities, we aim to support the growth of this area of research within the CCI community.
Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules

Machine learning technologies are increasingly being applied in many different domains in the real world. As autonomous machines and black-box algorithms begin making decisions previously entrusted to humans, great academic and public interest has been spurred to provide explanations that allow users to understand the decision-making process of the machine learning model. Besides explanations, Interactive Machine Learning (IML) seeks to leverage user feedback to iterate on an ML solution to correct errors and align decisions with those of the users. Despite the rise in explainable AI (XAI) and Interactive Machine Learning (IML) research, the links between interactivity, explanations, and trust have not been comprehensively studied in the machine learning literature. Thus, in this study, we develop and evaluate an explanation-driven interactive machine learning (XIML) system with the Tic-Tac-Toe game as a use case to understand how a XIML mechanism improves users' satisfaction with the machine learning system. We explore different modalities to support user feedback through visual or rules-based corrections. Our online user study (n = 199) supports the hypothesis that allowing interactivity within this XIML system causes participants to be more satisfied with the system, while visual explanations play a less prominent (and somewhat unexpected) role. Finally, we leverage a user-centric evaluation framework to create a comprehensive structural model to clarify how subjective system aspects, which represent participants' perceptions of the implemented interaction and visualization mechanisms, mediate the influence of these mechanisms on the system's user experience.
Detection of Animals in Thermal Imagery for Surveillance using GAN and Object Detection Framework

Wild animals have been a challenge to farmers worldwide as they are very active during the nighttime. Animals like elephants, deer, monkeys, cows, rats, peacocks, and many cause severe damage to crops by trampling. It is easier to protect crops in daylight, but it is tough for farmers to protect the field at night. Even in the forest, it is hard for zoologists to understand the activity pattern of animals at night. To tackle the challenge of detecting and tracking the animals at night, we propose a model that focuses on animal detection on thermal images. Although object detection is an advanced problem in computer vision, they mainly focus on color images rather than thermal images. Hence, a powerful object detection technique is required to detect and recognize the objects in thermal images. In addition, plenty of datasets are available for normal objects. However, there is a dearth of the thermal for animals to carry out the research. The work aims to create the dataset by collecting thermal images from FLIR videos. In addition, the dataset lacks the training data required for deep learning methods. Hence, the ThermalGAN framework uses color images to convert into thermal images. After that, YOLOv4 is trained to estimate the position of the animal. The proposed model predicts the location of animals with an average precision of 84.77% and an F1-score of 94%.
Intelligent Privacy Protection of End User in Long Distance Education

Long distance education is an important part during the COVID-19 age. An intelligent privacy protection with higher effect for the end users is an urgent problem in long distance education. In view of the risk of privacy disclosure of location, social network and trajectory of end users in the education system, this paper deletes the location information in the location set to protect the privacy of end user by providing the anonymous set to location. Firstly, this paper divides the privacy level of social networks by weighted sensitivity, and collects the anonymous set in social networks according to the level; Secondly, after the best anonymous set is generated by taking the data utility loss function as the standard, it was split to get an anonymous graph to hide the social network information; Finally, the trajectory anonymous set is constructed to hide the user trajectory with the l-difference privacy protection algorithm. Experiments show that the algorithm presented in this paper is superior to other algorithms no matter how many anonymous numbers there are, and the gap between relative anonymity levels is as large as 5.1 and 6.7. In addition, when the privacy protection intensity is 8, the trajectory loss rate presented in this paper tends to be stable, ranging from 0.005 to 0.007, all of which are less than 0.01. Meanwhile, its clustering effect is good. Therefore, the proportion of insecure anonymous sets in the algorithm in this paper is small, the trajectory privacy protection effect is good, and the location, social network and trajectory privacy of distance education end users are effectively protected.
External Validity: From <i>Do</i> -Calculus to Transportability Across Populations

chapter Share on External Validity: From Do-Calculus to Transportability Across Populations Authors: Judea Pearl Search about this author , Elias Bareinboim Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 451–482https://doi.org/10.1145/3501714.3501741Online:04 March 2022Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A novel density peaks clustering algorithm based on Hopkins statistic

Density peaks clustering (DPC) is a promising algorithm due to straightforward and easy implementation. However, most of its improvements still rely on expert, strong prior information, or complex iterations to identify the cluster centers, which inevitably adds subjectivity and instability. Moreover, some crisp and sensitive density metrics will sometimes reduce the representativeness of the center, resulting in poor clustering. To this end, we propose an enhanced algorithm, called Density peaks clustering based on Hopkins Statistic. The main property of the method is to realize the automatic identification of cluster centers without prior information. Specifically, with a two-stage strategy, we first specify some objects as candidate centers by linear regression and residual analysis. Subsequently, inspired by optimization idea we design a novel validity index (AHS) instead of the original decision graph to find the desired centers from the candidates. Another novel part of DPC-AHS is that the proposed adjusted-k-nearest neighbors (A-kNN) dynamically defines the neighbors during the process, which further enhances the robustness against outliers. Finally, we compare performance of DPC-AHS with 7 state-of-the-art methods over synthetic, UCI, and image datasets. Experiments on 25 datasets and in-depth discussion cases from 5 perspectives demonstrate that our algorithm is feasible and effective in clustering and center identification.
Processing IMU action recognition based on brain-inspired computing with microfabricated MEMS resonators

Abstract Reservoir computing (RC) decomposes the recurrent neural network into a fixed network with recursive connections and a trainable linear network. With the advantages of low training cost and easy hardware implementation, it provides a method for the effective processing of time-domain correlation information. In this paper, we build a hardware RC system with a nonlinear MEMS resonator and build an action recognition data set with time-domain correlation. Moreover, two different universal data set are utilized to verify the classification and prediction performance of the RC hardware system. At the same time, the feasibility of the novel data set was validated by three general machine learning approaches. Specifically, the processing of this novel time-domain correlation data set obtained a relatively high success rate. These results, together with the dataset that we build, enable the broad implementation of brain-inspired computing with microfabricated devices, and shed light on the potential for the realization of integrated perception and calculation in our future work.
Attentive Feature Augmentation for Long-Tailed Visual Recognition

Deep neural networks have achieved a great success on many visual recognition tasks.However, training data with a long-tailed distribution dramatically degenerates the performance of recognition models.In order to relieve this imbalance problem, an effective Long-Tailed Visual Recognition (LTVR) framework is proposed based on learned balance and robust features under long-tailed distribution circumstance.In this framework, a plug-and-play Attentive Feature Augmentation (AFA) module is designed to mine class-related and variationrelated features of original samples via attention mechanism.Then, those features are aggregated to synthesize fake features to cope with the imbalance of the original dataset.Moreover, a Lay-Back Learning Schedule (LBLS) is developed to ensure a good initialization of feature embedding.Extensive experiments are conducted with a two-stage training method to verify the effectiveness of the proposed framework on both feature learning and classifier rebalancing in the long-tailed image recognition task.Experimental results show that, when trained with imbalanced datasets, the proposed framework achieves superior performance over the state-of-the-art methods.
Android Based Fall Detection and Tracking App for Aged People

The aspiration of this article is to develop an Android application that can detect potential falls in the elderly. Android is a free and open source operating system. Being an open source platform, it is easy for anyone to create their own application. Falling in the elderly is a major concern for families and health care professionals. Falls is the eighth killer in the United States, experts say. Untreated falls in adults 65 years of age and older can pose serious health risks, as 20% of falls require emergency medical care and one-tenth of falls result in fractures. This is the motivation behind the development of an aged assistance system that detects falls and notifies the caretaker. The application involves tracking the person and alerting the caretaker if something goes wrong. One of the key characteristics of this Android app is that it sends alert messages to the caretaker with all of the required information. The alert messages provide important information such as their geo location and map directions. In the event of a false alarm, the person is given the power to stop it before taking any further action. This project focuses on assisting the elderly. The smartphone application can identify potential falls and provide alerts to families, physicians, and other caretakers via a user-friendly interface.
Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes

This paper examines to what extent the job titles assigned to AI agents can influence the customer’s perception of these agents and ultimately their marketing outcomes such as customer satisfaction, brand attitude, and intention to buy AI-recommended products. Also, this study explores how customers perceive the AI agent as the manager working with either a human or an AI representative. Across three experiments (using a scenario or a combination of a scenario and the real AI chatbot), the study shows that consumers perceive the AI manager more positively in terms of likeability, knowledgeability, and trustworthiness than the AI representative and the human manager. The customers perceive the AI manager more positively when they are transferred to the AI manager from a representative of the same kind (AI) than from a human representative. Further, the job titles given to the AI agents are found to have favorable downstream effects on customer satisfaction, brand attitude, and the customers’ intentions to buy the products recommended during the chat by the AI manager.
Adaptive Multimodal Fusion With Attention Guided Deep Supervision Net for Grading Hepatocellular Carcinoma

Multimodal medical imaging plays a crucial role in the diagnosis and characterization of lesions. However, challenges remain in lesion characterization based on multimodal feature fusion. First, current fusion methods have not thoroughly studied the relative importance of characterization modals. In addition, multimodal feature fusion cannot provide the contribution of different modal information to inform critical decision-making. In this study, we propose an adaptive multimodal fusion method with an attention-guided deep supervision net for grading hepatocellular carcinoma (HCC). Specifically, our proposed framework comprises two modules: attention-based adaptive feature fusion and attention-guided deep supervision net. The former uses the attention mechanism at the feature fusion level to generate weights for adaptive feature concatenation and balances the importance of features among various modals. The latter uses the weight generated by the attention mechanism as the weight coefficient of each loss to balance the contribution of the corresponding modal to the total loss function. The experimental results of grading clinical HCC with contrast-enhanced MR demonstrated the effectiveness of the proposed method. A significant performance improvement was achieved compared with existing fusion methods. In addition, the weight coefficient of attention in multimodal fusion has demonstrated great significance in clinical interpretation.