Reviewerly/exHarmony huggingface dataset:
Let's Downgrade Let's Encrypt

Following the recent off-path attacks against PKI, Let's Encrypt deployed in 2020 domain validation from multiple vantage points to ensure security even against the stronger on-path MitM adversaries. The idea behind such distributed domain validation is that even if the adversary can hijack traffic of some vantage points, it will not be able to intercept traffic of all the vantage points to all the nameservers in a domain.
Unclonable Encryption, Revisited

Unclonable encryption, introduced by Broadbent and Lord (TQC'20), is an encryption scheme with the following attractive feature: given a ciphertext, an adversary cannot create two ciphertexts both of which decrypt to the same message as the original ciphertext.
Blockchain-Oriented Software Development Issues: A Literature Review

Blockchain-oriented software is a trend and a hot topic for discussion. The fitness of the common software engineering practices, tools, techniques, and methods for this new type of software has been discussed by many investigators. Furthermore, a new branch of software engineering known as blockchain-oriented software engineering has been announced as a new research field. However, the current issues of Blockchain-oriented software development have not been well defined yet. This study offers a systematic review procedure for software engineering, the stages pursued, the explanation of every one of them, along with its execution employed to the subject of the current issues in blockchain-oriented software development. The results of this study confirm that there 15 issues related to the exceptional properties of blockchain-oriented software affecting its development process.
Understanding and Detecting Mobile Ad Fraud Through the Lens of Invalid Traffic

Along with gaining popularity of Real-Time Bidding (RTB) based programmatic advertising, the click farm based invalid traffic, which leverages massive real smartphones to carry out large-scale ad fraud campaigns, is becoming one of the major threats against online advertisement. In this study, we take an initial step towards the detection and large-scale measurement of the click farm based invalid traffic. Our study begins with a measurement on the device's features using a real-world labeled dataset, which reveals a series of features distinguishing the fraudulent devices from the benign ones. Based on these features, we develop EvilHunter, a system for detecting fraudulent devices through ad bid request logs with a focus on clustering fraudulent devices. EvilHunter functions by 1) building a classifier to distinguish fraudulent and benign devices; 2) clustering devices based on app usage patterns; and 3) relabeling devices in clusters through majority voting. EvilHunter demonstrates 97% precision and 95% recall on a real-world labeled dataset. By investigating a super click farm, we reveal several cheating strategies that are commonly adopted by fraudulent clusters. We further reduce the overhead of EvilHunter and discuss how to deploy the optimized EvilHunter in a real-world system. We are in partnership with a leading ad verification company to integrate EvilHunter into their industrial platform.
Intelligent automation in hospitality: exploring the relative automatability of frontline food service tasks

Purpose Automation poses to change how service work is organized. However, there is a lack of understanding of how automation influences specific sectors, including specific hospitality jobs. Addressing this gap, this paper looks at the relative automatability of jobs and tasks which fall within one specific hospitality context: frontline food service. Design/methodology/approach Study 1 analyzes the UK Office for National Statistics' Standard Occupational Classification (2020) data to determine the degree to which frontline food service jobs consist of tasks requiring mechanical, analytical, intuitive or empathetic intelligence. Study 2 contrasts these findings to current state of intelligent automation technology development through interviews and a focus group with food service technology experts ( n = 13). Findings Of all the tasks listed under food service in the ONS SOC 2020, 58.8% are found to require mechanical, 26.8% analytical, 11.3% intuitive and 3.1% empathetic intelligence. Further, the automatability of these tasks is found to be driven by three streams of technology development in particular: (1) autonomous navigation, (2) object manipulation and (3) natural language processing. Originality/value Hospitality management literature has started to conceptualize a move from mechanical and analytical service tasks to tasks centered around intuition and empathy. While previous studies have adopted a general view to what this might mean for hospitality jobs, this paper develops a novel, task-centric framework for Actioning Intelligent Automation in Frontline Food Service.
An Automated Approach for Bangla Sentence Classification Using Supervised Algorithms

Natural Language Processing (NLP) is one of the greatest familiar field of Artificial Intelligence. Bangla text classification is become a significant problem due to the availability of textual documents. So, it&#x0027;s high time to solve this problem for better information organization and knowledge management. This study identifies the type of Bangla sentence from a human story: exclamatory or interrogative text document. Several machine learning approaches are applying to the dataset and identify the sentence from the text document. Regulating data is collected from various platforms such as story, Bengali&#x0027;s blog, conversation, etc. To get the output, we need to go through hard work as data cleaning, preprocessing, tokenized by the Countervector, etc. After processing data for the feeding model, we conduct various classification approaches to predict the sentence and provide high accuracy for deciding the result. Among all the approaches, Random Forest and XGBoost provide the highest accuracy of 96.39&#x0025; in parallel. This study also provides the advantage of sentimental analysis by detecting the type of document in further implementation.
Metaheuristic minimum dose path planning for nuclear power plant decommissioning

When a nuclear power plant reaches its end of operational life, decommissioning work needs to be carried out. One of the most important decommissioning strategies is to plan an optimal path for workers or robots to move around in a radioactive environment, keeping the radiation exposure as low as possible. This paper develops two bio-inspired metaheuristic methods for minimum dose path planning using a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), respectively. To evaluate the effectiveness of the two metaheuristic methods, two extreme hypothetical environments are simulated. The developed bio-inspired metaheuristic methods are compared with prior grid-based and sampling-based minimum dose path planning algorithms, in terms of cumulative dose, computational time, and distance. The results indicate that PSO outperforms prior grid-based and sampling-based algorithms in cumulative dose and distance. GA outperforms only the grid-based algorithms in cumulative dose and distance.
Time-aware User Modeling with Check-in Time Prediction for Next POI Recommendation

POI (point-of-interest) recommendation as an important type of location-based services has received increasing attention with the rise of location-based social networks. Although significant efforts have been dedicated to learning and recommending users' next POIs based on their historical mobility traces, there still lacks consideration of the discrepancy of users' check-in time preferences and the inherent relationships between POIs and check-in times. To fill this gap, this paper proposes a novel recommendation method which applies multi-task learning over historical user mobility traces known to be sparse. Specifically, we design a cross-graph neural network to obtain time-aware user modeling and control how much information flows across different semantic spaces, which makes up the inadequate representation of existing user modeling methods. In addition, we design a check-in time prediction task to learn users' activities from a time perspective and learn internal patterns between POIs and their check-in times, aiming to reduce the search space to overcome the data sparsity problem. Comprehensive experiments on two real-world public datasets demonstrate that our proposed method outperforms several representative POI recommendation methods with 8.93% to 20.21 % improvement on Recall@1, 5, 10, and 9.25% to 17.56% improvement on Mean Reciprocal Rank.
Medical Prescription and Report Analyzer

More often than not, we find prescriptions are not properly written which are being handed over to patients and the diagnostic reports handed can't be monitored without the doctor's involvement due to lack of medical knowledge. This has resulted in putting the patient's safety at risk, and also sometimes there is an unavailability of doctors for the required time for monitoring of reports because of which there is also a danger to patients' life. The objective of this Medical Prescription and Report Analyzer (MPRA) model is to help patients get an easy understanding of their report and prescribed medicine by addressing the important challenge of extraction of handwritten text data from images, analyzing it using test data then giving a much-refined output. Handwriting Character Recognition (HCR) technique, as well as Printer Character Recognition (PCR) technique, have been used to identify both handwritten as well as printed text in prescription and report, and techniques such as image processing to refine image to give an accurate output.
Expanding the Gordon-Loeb model to cyber-insurance

We present an economic model for decisions on competing cyber-security and cyber-insurance investment based on the Gordon-Loeb model for investment in information security. We consider a one-period scenario in which a firm may invest in information security measures to reduce the probability of a breach, in cyber-insurance or in a combination of both. The optimal combination of investment and insurance under the assumptions of the Gordon-Loeb model is investigated via consideration of the costs and benefits of investment in security alongside purchasing insurance at an independent premium rate. Under both exponential (constant absolute risk aversion) and logarithmic (constant relative risk aversion) utility functions it is found that when the insurance premium is below a certain value, utility is maximised with insurance and security investment. These results suggest that cyber-insurance is a worthwhile undertaking provided it is not overly costly. We believe this model to be the first attempt to integrate the Gordon-Loeb model into a classical microeconomic analysis of insurance, particularly using the Gordon-Loeb security breach functions to determine the probability of an insurance claim. The model follows the tradition of the Gordon-Loeb model in being accessible to practitioners and decision makers in information security.
House price prediction based on machine learning and deep learning methods

House price is closely related to everyone's life, and it is affected by many factors. House price prediction is an important research point for that it can help people to make strategies about house dealing. There are many existing researches about house price prediction. However, existing studies cannot make a comprehensive comparison and analysis of popular housing price forecasting methods. This paper uses five popular machine learning and in-depth learning approaches to predict house prices and evaluate the models. Our methods include Linear Regression (LR), Bayesian, Backpropagation neural network (BP neural network), Support Vector Machine (SVM), Deep Neural Network (DNN). The dataset we used is from the Kaggle platform. Experimental results show that Bayesian, Backpropagation neural networks, and SVM are more suitable for house price prediction.
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning

Federated learning has arisen as a mechanism to allow multiple participants to collaboratively train a model without sharing their data. In these settings, participants (workers) may not trust each other fully; for instance, a set of competitors may collaboratively train a machine learning model to detect fraud. The workers provide local gradients that a central server uses to update a global model. This global model can be corrupted when Byzantine workers send malicious gradients, which necessitates robust methods for aggregating gradients that mitigate the adverse effects of Byzantine inputs. Existing robust aggregation algorithms are often computationally expensive and only effective under strict assumptions. In this paper, we introduce LayerwisE Gradient AggregatTiOn (LEGATO), an aggregation algorithm that is, by contrast, scalable and generalizable. Informed by a study of layer-specific responses of gradients to Byzantine attacks, LEGATO employs a dynamic gradient reweighing scheme that is novel in its treatment of gradients based on layer-specific robustness. We show that LEGATO is more computationally efficient than multiple state-of-the-art techniques and more generally robust across a variety of attack settings in practice. We also demonstrate LEGATO's benefits for gradient descent convergence in the absence of an attack.
Comparative analysis of AVIRIS-NG and Landsat-8 OLI data for lithological mapping in parts of Sittampundi layered complex, Tamil Nadu, India

The present study addresses a comparative analysis of the potentials of Airborne Visible and infrared imaging spectrometer -next generation (AVIRIS-NG) data, with respect to Landsat-8 Operational Land Imager (OLI) data for mapping of sporadic exposures of mafic cumulates within the anorthosite intrusion and delineation of major lithological units of Sittampundi layered complex. AVIRIS-NG and Landsat-8 OLI data were initially compared using scene-based relative signal-to-noise ratio (SNR) analysis. Subsequently, spectral anomaly maps were generated to map different lithological rock units using the constrained energy minimization (CEM) approach. Spectral maps derived using CEM were further filtered by low-pass filtering to control the false-positive result. Spectral angle mapper (SAM) classification technique was applied to analyze the suitability of AVIRIS-NG and Landsat-8 OLI data for lithological mapping. Confusion matrix analysis was carried out to assess the SAM classified maps. The first vertical derivative (FVD) map of magnetic anomaly and field evidence/geological exposures were used to validate the results. SNR analysis indicates the superior signal strength of AVIRIS-NG data than Landsat-8 OLI data. Moreover, combined analysis based on CEM and SAM of remote sensing data and FVD of magnetic anomaly reveals that the false-positive area indicating metagabbro/mafic cumulate is lower in AVIRIS-NG data than Landsat-8 OLI data. This is because of the relatively poor SNR, spectral and spatial resolutions of Landsat-8 OLI data; however, it can be good enough for mapping of metagabbro/mafic cumulate when AVIRIS-NG data is unavailable.
Key Qualities of Conversational Recommender Systems: From Users’ Perspective

An increasing number of recommender systems enable conversational interaction to enhance the system's overall user experience (UX). However, it is unclear what qualities of a conversational recommender system (CRS) are essential to determine the success of a CRS. This paper presents a model to capture the key qualities of conversational recommender systems and their related user experience aspects. Our model incorporates the characteristics of conversations (such as adaptability, understanding, response quality, rapport, humanness, etc.) in four major user experience dimensions of the recommender system: User Perceived Qualities, User Belief, User Attitudes, and Behavioral Intentions. Following the psychometric modeling method, we validate the combined metrics using the data collected from an online user study of a conversational music recommender system. The user study results 1) support the consistency, validity, and reliability of the model that identifies seven key qualities of a CRS; and 2) reveal how conversation constructs interact with recommendation constructs to influence the overall user experience of a CRS. We believe that the key qualities identified in the model help practitioners design and evaluate conversational recommender systems.
On the use of high-order feature propagation in Graph Convolution Networks with Manifold Regularization

Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present a revisited scheme for the new method called ”GCNs with Manifold Regularization” (GCNMR). While manifold regularization can add additional information, the GCN-based semi-supervised classification process cannot consider the full layer-wise structured information. Inspired by graph-based label propagation approaches, we will integrate high-order feature propagation into each GCN layer. High-order feature propagation over the graph can fully exploit the structured information provided by the latter at all the GCN’s layers. It fully exploits the clustering assumption, which is valid for structured data but not well exploited in GCNs. Our proposed scheme would lead to more informative GCNs. Using the revisited model, we will conduct several semi-supervised classification experiments on public image datasets containing objects, faces and digits: Extended Yale, PF01, Caltech101 and MNIST. We will also consider three citation networks. The proposed scheme performs well compared to several semi-supervised methods. With respect to the recent GCNMR approach, the average improvements were 2.2%, 4.5%, 1.0% and 10.6% on Extended Yale, PF01, Caltech101 and MNIST, respectively.
HEA-PAS: A hybrid energy allocation strategy for parallel applications scheduling on heterogeneous computing systems

Heterogeneous Computing Systems (HCS) have received widespread attention due to their powerful computing power, low cost, and high scalability. In HCS ranging from small-embedded devices to large data centers, energy consumption is one of the crucial design constraints. Meanwhile, the schedule length (response time) of parallel applications directly affects their Quality of Service (QoS) experience. In this study, we address the problem of minimizing the schedule length of energy-constrained parallel applications (MSLEC) on heterogeneous computing systems. Firstly, we define the concept of task energy demand rate and energy allocation factor to reasonably allocate the allocatable energy. Secondly, We propose a two-stage hybrid energy allocation (HEA) strategy and divide the allocatable energy into two parts according to the energy allocation factor, namely static pre-allocate energy (SAE) and dynamic pre-allocate energy (DAE). In the first stage, we pre-allocate SAE for each task based on the minimum energy demand and energy demand rate before task scheduling. In the second stage, we dynamically allocate DAE to each task during the operation of the scheduling algorithm. Thirdly, We conduct a rigorous mathematical proof of the feasibility of the proposed strategy. Finally, according to the proposed strategy, we design a novel HEA-based parallel application scheduling (HEA-PAS) algorithm, which aims to solve the MSLEC problem. Experiments on real-world and randomly generated parallel applications show that the proposed HEA-PAS algorithm outperforms the state-of-the-art methods in terms of effectiveness.
Enhancing Theory-Informed Dictionary Approaches with “Glass-box” Machine Learning: The Case of Integrative Complexity in Social Media Comments

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Causal Cognitive Architecture 3: A solution to the binding problem

The binding problem is considered in terms of how a brain-inspired cognitive system can recognize multiple sensory features from an object which may be among many objects, process those features individually and then bind the multiple features to the object they belong to. The Causal Cognitive Architecture 3 (CCA3) is a brain-inspired cognitive architecture using a multi-dimensional navigation map as its basic store of information, and capable of pre-causal as well as fully causal behavior. Objects within an input sensory scene are segmented, and sensory features (e.g., visual, auditory, etc.) of each segmented object are spatially mapped onto a variety of navigation maps. It is shown that to provide efficient, flexible, causal solutions to real-world problems, it is not sufficient to bind space (i.e., objects spatially) but it is necessary to also bind time (i.e., change and rate of change of objects within a sensory scene). The CCA3 binds both space and time onto a navigation map as physical features, and is better able to function in real-world environments. As the CCA3 is brain-inspired, the Causal Cognitive Architecture can help to better hypothesize and understand biological mammalian brain function, including solutions to the binding problem. The CCA3 architecture allows it to work in different knowledge domains, possess continual lifelong learning, and demonstrate reasonable explainability.
COPA: A Combined Autoscaling Method for Kubernetes

Autoscaling is one of the major features of Cloud Computing aiming to improve the Quality-of-Service(QoS) in response to fluctuating workloads. Existing state-of-the-art autoscaling methods for Kubernetes focus on single scaling mode, that is, only horizontal scaling and only vertical scaling. For horizontal scaling, a high resource usage rate cannot be guaranteed sometimes; and for vertical scaling, microservice instances appear a performance ceiling that does not grow indefinitely as the supply of resources increases. In this paper, we propose a novel combined scaling method called COPA. Based on the collected microservice performance data, real-time workload, expected response time, and microservice instances scheme at runtime, COPA uses the queuing network model to calculate a combined scaling scheme that aims to minimize the default cost and resource cost. We evaluated our approach in a Kubernetes cluster, and compare it with existing state-of-the-art autoscaling methods under four different workload types. Such experiments show a reduction of ×1.22 for resource cost while ensuring the QoS as compared to the baseline method.
Explaining artificial intelligence with visual analytics in healthcare

Abstract To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well‐considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision‐making through visualizations that facilitate interaction with algorithms. Although many visual analytics systems have been developed for healthcare, a clear overview of their explanation techniques is lacking. Therefore, we review 71 visual analytics systems for healthcare, and analyze how they explain advanced algorithms through visualization, interaction, shepherding, and direct explanation. Based on our analysis, we outline research opportunities and challenges to further guide the exciting rapprochement of visual analytics and healthcare. This article is categorized under: Application Areas &gt; Health Care Fundamental Concepts of Data and Knowledge &gt; Explainable AI Technologies &gt; Visualization