Reviewerly/exHarmony huggingface dataset:
To Recommend or Not? A Model-Based Comparison of Item-Matching Processes

Recommender systems are central to modern online platforms, but a popular concern is that they may be pulling society in dangerous directions (e.g., towards filter bubbles). However, a challenge with measuring the effects of recommender systems is how to compare user outcomes under these systems to outcomes under a credible counterfactual world without such systems. We take a model-based approach to this challenge, introducing a dichotomy of process models that we can compare: (1) a “recommender” model describing a generic item-matching process under a personalized recommender system and (2) an “organic” model describing a baseline counterfactual where users search for items without the mediation of any system. Our key finding is that the recommender and organic models result in dramatically different outcomes at both the individual and societal level, as supported by theorems and simulation experiments with real data. The two process models also induce different trade-offs: regularization improves the mean squared error of matches in both settings, but at the cost of less diverse (less radical) items chosen in the recommender model but more diverse (more radical) items chosen in the organic model. These findings provide a formal framework for how recommender systems may be fundamentally altering how we interact with content, in a world increasingly mediated by such systems.
A Real-Time Scene Parsing Network for Autonomous Maritime Transportation

On the sea, scene parsing, one of the most basic environmental perception methods for autonomous tasks, helps autonomous devices safely navigate and avoid obstacles. However, most of the existing methods lack real-time performance and have difficulty accurately detecting small obstacles and changeable textures. We, therefore, propose a real-time scene-parsing network for autonomous maritime transportation. Specifically, we first design a lightweight model framework and then explore three efficient loss functions, such that a balance between accuracy and real-time performance can be achieved. The first loss function is an obstacle-weighted loss, which improves the extraction of small obstacles by analyzing the distribution law of obstacles at sea. The second one is a detail loss, which optimizes the detail segmentation at complex contours by emphasizing edge features. The last one is an affinity loss, utilizing the context dependency between features to accurately detect reflections, ripples, and other changeable textures. In addition, a new maritime sense-parsing dataset called Greater Bay Area (GBA) dataset is proposed and made publicly available. We tested the proposed model on the GBA dataset and MaSTr1325 dataset, and the experimental results show that the proposed method achieves superior performance in both segmentation and speed, with mean intersection over union (mIOU) of 94.59% and an FPS of 39.44.
An Analysis of Cloud Computing, a New Computing Model

As an approach to hosting and delivering IT resources and services over the internet, "the cloud" is focused on providing dependable, secure, fault-tolerant, sustainable, and scalable systems for hosting and delivering application services over the internet.Businesses find cloud computing appealing because it enables them to start small and scale up as needed in response to an increase in service demand without requiring customers to make advance preparations for provisioning.The purpose of this article is to provide an overview of cloud computing, including its history, service model, and current security challenges.
RRT Trajectory Planning Approach For Automated Semi-trailer truck Parking

The automation of processes, especially in the automotive sector, has experienced an increase in safety and efficiency, and at the same time, it reduces execution times and human errors. Nowadays, conventional logistics has experimented with significant change. Those go from individual transport services using personal vehicles, passing through internal logistics of the warehouse, and finishing with the delivery of goods in different last mile transport and circumstances with mobile robots (e.g. Amazon logistic centers). However, the automation solutions for heavy-duty vehicles during loading and unloading processes are still a research challenge. This work presents a trajectory planning which targets automated semi-trailer trucks. The algorithm considers turning under difficult and tight circumstances, as well as reversing maneuvers. It is based on RRT (Rapidly exploring Random Trees) planning. The validation was done in a simulation environment considering a logistic warehouse. The solution generates the best possible path in a short time (under 500 milliseconds), and it is a promising result for a near-future real-deployment.
A comparative study on image forgery-facial retouching

Forgery with the digital images is being very easy now days due to the very advanced and open source image editing tools, software and devises which supports a high quality of resolutions. Tempering with digital documents for changing identity or sometimes for fun is increasing day by day as the era is of digital world. Detecting clues of tampering and verifying the authenticity of images is an important issues now-a-days and growing research field. The existing research in the area of digital image forgery identification is discussed here. Different types of image forgery attacks along with its detection and classification are discussed too. A general review of facial image authentication is studied and discussed here along with the current methods and techniques for the detection and classification. A comparative analysis is presented here for the researchers as a mind mapping to work in the same field.
Towards Conversational Agents to support Informal Caregivers of People with Dementia: Challenges and Opportunities

People who have dementia (PwD) experience deteriorating executive functions, in particular their working memory, and therefore find it hard to complete multistep tasks or activities of daily living. There is no doubt that during the pandemic, PwD and their caregivers were particularly vulnerable, often isolated which affected their mental and physical health. Their ability to live independently was hampered, fomenting depression in the PwD and burnout on informal caregivers. Information technology can support dementia care improving the quality of life of PwD and easing the burden on caregivers. There is an increasing demand to support informal caregivers and improve their well-being by making dementia challenges less severe. This study uses qualitative techniques to design a model with technological strategies based on semi-structured interviews applied to seven informal caregivers from two different countries. Based on these interviews we developed design insights for implementing solutions to help informal caregivers take care of their PwD at home using conversational agents. We hope that the findings presented in this study will help researchers, and developers design solutions that can support PwD and informal caregivers.
Privacy-preserving targeted mobile advertising: A Blockchain-based framework for mobile ads

The targeted advertising is based on preference profiles inferred via relationships among individuals, their monitored responses to previous advertising and temporal activity over the Internet, which has raised critical privacy concerns. In this paper, we present a novel proposal for a Blockchain-based advertising platform that provides: a system for privacy preserving user profiling, privately requesting ads from the advertising system, the billing mechanisms for presented and clicked ads, the advertising system that uploads ads to the cloud according to profiling interests, various types of transactions to enable advertising operations in Blockchain-based network, and the method that allows a cloud system to privately compute the access policies for various resources (such as ads, mobile user profiles). Our main goal is to design a decentralised framework for targeted ads, which enables private delivery of ads to users whose behavioural profiles accurately match the presented ads, defined by the ad system. We implement a POC of our proposed framework i.e. a Bespoke Miner and experimentally evaluate various components of Blockchain-based in-app advertising system, implementing various critical components; such as, evaluating user profiles, implementing access policies, encryption and decryption of users' profiles. We observe that the processing delay for traversing policies of various tree sizes, the encryption/decryption time of user profiling with various key-sizes and user profiles of various interests evaluates to an acceptable amount of processing time as that of the currently implemented ad systems.
An improved grey wolf optimization for solving TSP

An improved grey wolf optimization (IGWO) is proposed for the travel salesman problem. Based on traditional grey wolf optimization, a genetic gene sequence was introduced for the first time to initialize the population to improve population quality. Subsequently, the cosine adaptive function is used to balance the global and local search abilities. Finally, a distance heuristic factor is introduced into the update strategy to enhance the local search capability and improve the robustness of the algorithm. Several cases were randomly selected from the TSPLIB database for the simulation experiments, and it was found that the IGWO has higher stability and accuracy under the same conditions.
A lightweight and style-robust neural network for autonomous driving in end side devices

The autonomous driving algorithm studied in this paper makes a ground vehicle capable of sensing its environment via visual images and moving safely with little or no human input. Due to the limitation of the computing power of end side devices, the autonomous driving algorithm should adopt a lightweight model and have high performance. Conditional imitation learning has been proved an efficient and promising policy for autonomous driving and other applications on end side devices due to its high performance and offline characteristics. In driving scenarios, the images captured in different weathers have different styles, which are influenced by various interference factors, such as illumination, raindrops, etc. These interference factors bring challenges to the perception ability of deep models, thus affecting the decision-making process in autonomous driving. The first contribution of this paper is to investigate the performance gap of driving models under different weather conditions. Following the investigation, we utilise StarGAN-V2 to translate images from source domains into the target clear sunset domain. Based on the images translated by StarGAN-V2, we propose Conditional Imitation Learning with ResNet backbone named Star-CILRS. The proposed method is able to convert an image to multiple styles using only one single model, making our method easier to deploy on end side devices. Visualization results show that Star-CILRS can eliminate some environmental interference factors. Our method outperforms other methods and the success rate values in different tasks are 98%, 74%, and 22%, respectively.
Web User Profile Generation and Discovery Analysis using LSTM Architecture

In today's technology-driven world, a user profile is a virtual representation of each user, containing various user information such as personal, interest and preference data. These profiles are the result of a user profiling process and are essential to personalizing the service. As the amount of information available on the Internet increases and the number of different users, customization becomes a priority. Due to the large amount of information available on the Internet, referral systems that aim to provide relevant information to users are becoming increasingly important and popular. Various methods, methodologies and algorithms have been proposed in the literature for the user analysis process. Creating automated user profiles is a big challenge in creating adaptive customized applications. In this work proposed the method, Long Short-Term Architecture (LSTM) is User profile is an important issue for both information and service customization. Based on the original information, the user's topic preference and text emotional features into attention information and combines various formats and LSTM (Long Short Term Memory) models to describe and predict the elements of informal community clients. At last, the trial consequences of different gatherings show that the concern-based LSTM model proposed can accomplish improved results than the right now regularly involved strategies in recognizing client character qualities, and the model has great speculation, which implies that it has this capacity.
Performance Framework for Virtual Machine Migration in Cloud Computing

In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways, from one data centre to another.This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment.VMs allow cloud customers to store essential data and resources.However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses.Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network bandwidth, memory, and application bandwidth.In multi-cloud, VM migration addresses the performance degradation due to cloud server configuration, unbalanced traffic load, resource load management, and fault situations during data transfer.VM migration speed is influenced by the size of the VM, the dirty rate of the running application, and the latency of migration iterations.As a result, evaluating VM migration performance while considering all of these factors becomes a difficult task.The main effort of this research is to assess migration problems on performance.The simulation results in Matlab show that if the VM size grows, the migration time of VMs and the downtime can be impacted by three orders of magnitude.The dirty page rate decreases, the migration time and the downtime grow, and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VM transfer is completed.All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.
The Role of Distance Learning in Educational Achievement of Zarqa University Students during the Coronavirus Pandemic

This study aimed to investigate the role of distance learning in educational achievement of Zarqa University students during the Coronavirus pandemic. The population of the study consisted of all male and female students at Zarqa University in Jordan. The sample included (120) students representing the population. The study adopted the analytical descriptive approach, and data was collected through questionnaire. Findings of the study included the following: the total score of the questionnaire, from the perspectives of sample members, was rated high; the lack of statistically significant differences at the significance level of (0.05) among the opinions of sample members on the axes of the questionnaire as a whole, as regards the variables of gender and educational level. Recommendations of the study include the following: seeking to exchange international experiences through distance learning media; the necessity of increasing students' desire and motivation for distance learning; and working in spreading technical culture to help establish an electronic community capable of keeping in pace with the developments of the present time.
Lampung Language Online Learning during the Covid-19 Outbreak: How are the Teacher’s TPACK Skills?

The implementation of online learning at the beginning of the covid-19 outbreak brought up some interference for most teachers and students.Technological Pedagogical Content Knowledge (TPACK) is thought to be the answer for teachers in dealing with current online learning problems.This study aims to explore the TPACK skills of Lampung language teachers and the advantages and problems of online Lampung language classes.A survey method was employed to conduct this study in which a questionnaire consisting of 33 statements and 5 open-ended questions was administrated to 138 Lampung language teachers in Lampung, Indonesia.The data were analyzed quantitatively and qualitatively by using factor analysis and the descriptions of teacher explanations.The results showed that Lampung language teachers, in general, have mastered TPACK in their online classes such as the use of various learning media and educational platforms.Giving assignments in creative forms such as making videos of folk songs during the learning process amid the pandemic using local languages can be used by Lampung language teachers to create non-boring learning.This study provides an overview of Lampung language teachers' TPACK skills and some information related to the problems and advantages faced in online Lampung language learning during the covid-19 pandemic.As a consequence, it can be the reference for policymakers to make the best Lampung language learning system.
Quantum jump metrology in a two-cavity network

Quantum metrology enhances measurement precision by utilizing the properties of quantum physics. In interferometry, this is typically achieved by evolving highly entangled quantum states before performing single-shot measurements to reveal information about an unknown parameter. While this is often the optimum approach, implementation with all but the smallest states is still extremely challenging. An alternative approach is quantum jump metrology [L. A. Clark et al., Phys. Rev. A 99, 022102 (2019)], which deduces information by continuously monitoring an open quantum system while inducing phase-dependent temporal correlations with the help of quantum feedback. Taking this approach here, we analyze measurements of a relative phase in an optical network of two cavities with quantum feedback in the form of laser pulses. It is shown that the proposed approach can exceed the standard quantum limit without the need for complex quantum states while being scalable and more practical than previous related schemes.
OUTLINE OF A DIDACTIC FRAMEWORK FOR COMBINED DATA LITERACY AND MACHINE TRANSLATION LITERACY TEACHING

This paper outlines a didactic framework for combined data literacy and machine translation (MT) literacy teaching for translation and specialised communication students. The framework is being developed in the context of the DataLitMT project, a publicly funded project at the Institute of Translation and Multilingual Communication at TH Köln – University of Applied Sciences, Germany, which develops didactic resources for teaching data literacy in its translation-specific form of MT literacy to students of BA and MA programmes in translation and specialised communication studies. After discussing the high relevance of machine trans-lation literacy and data literacy in professional translation contexts, the paper introduces the DataLitMT project and discusses a framework of professional MT literacy and an MT-oriented data literacy framework, which form the two theoretical pillars of the project. Also, the interface between MT literacy and data literacy will be illustrated by showing how specific (sub)dimensions of data literacy can be mapped to relevant (sub)dimensions of professional MT literacy. Finally, the paper presents some preliminary didactic resources of the DataLitMT project – concerned with social biases in MT, with MT training data preparation and with automatic MT quality evaluation – and discusses how these resources can be used to teach specific (sub)dimensions of data literacy and professional MT literacy to students in the fields of translation/specialised communication studies. Keywords: data literacy, professional machine translation literacy, neural machine translation, translation didactics, DataLitMT
The Metaverse and Mathematics Education

Digital reality technologies have the potential to transform the fields of education, remote work, marketing and economy, and the entertainment sector and begin to create an information communication paradigm. We can say that the new paradigm has begun to take shape around the Metaverse concept. This study was a literature review that investigated using the Metaverse in the field of mathematics education. The scope of this review was the studies, including the “Metaverse” keyword and “mathematics,” “mathematics education,” “teaching mathematics,” or “mathematics learning” keywords, which include the studies listed in Web of Science, Scopus, Academia, ResearchGate, and Google Scholar. The authors examined the purpose of the studies, the scope of the studies, using technology in the studies, the research method of the studies, and the findings of the studies on teaching mathematics in the Metaverse environment. They presented a discussion of the possible advantages and disadvantages that mathematics education could offer.
Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning

Abstract Objective . Histopathology image segmentation can assist medical professionals in identifying and diagnosing diseased tissue more efficiently. Although fully supervised segmentation models have excellent performance, the annotation cost is extremely expensive. Weakly supervised models are widely used in medical image segmentation due to their low annotation cost. Nevertheless, these weakly supervised models have difficulty in accurately locating the boundaries between different classes of regions in pathological images, resulting in a high rate of false alarms Our objective is to design a weakly supervised segmentation model to resolve the above problems. Approach . The segmentation model is divided into two main stages, the generation of pseudo labels based on class residual attention accumulation network (CRAANet) and the semantic segmentation based on pixel feature space construction network (PFSCNet). CRAANet provides attention scores for each class through the class residual attention module, while the Attention Accumulation (AA) module overlays the attention feature maps generated in each training epoch. PFSCNet employs a network model containing an inflated convolutional residual neural network and a multi-scale feature-aware module as the segmentation backbone, and proposes dense energy loss and pixel clustering modules are based on contrast learning to solve the pseudo-labeling-inaccuracy problem. Main results . We validate our method using the lung adenocarcinoma (LUAD-HistoSeg) dataset and the breast cancer (BCSS) dataset. The results of the experiments show that our proposed method outperforms other state-of-the-art methods on both datasets in several metrics. This suggests that it is capable of performing well in a wide variety of histopathological image segmentation tasks. Significance . We propose a weakly supervised semantic segmentation network that achieves approximate fully supervised segmentation performance even in the case of incomplete labels. The proposed AA and pixel-level contrast learning also make the edges more accurate and can well assist pathologists in their research.