Reviewerly/exHarmony huggingface dataset:
A New Method for the Large-Scale Documentation of Pottery Sherds Through Simultaneous Multiple 3D Model Capture Using <i>Structure from Motion</i>: Phoenician Carinated-Shoulder Amphorae from Tell el-Burak (Lebanon) as a Case Study

Abstract This paper presents a new rapid, low-cost method for the large-scale documentation of pottery sherds through simultaneous multiple 3D model capture using Structure from Motion (SfM). The method has great potential to enhance and replace time-consuming and expensive conventional approaches for pottery documentation, i.e., 2D photographs and drawing on paper with subsequent digitization of the drawings. To showcase the method’s effectiveness and applicability, a case study was developed in the context of an investigation of the Phoenician economy at the Lebanese site of Tell el-Burak, which is based on a large collection of amphora sherds. The same set of sherds were drawn by an experienced draftsperson and then documented through SfM using our new workflow to allow for a direct comparison. The results show that the new technique detailed here is accessible, more cost-effective, and allows for the documentation of ceramic data at a far-greater scale, while producing more consistent and reproducible results. We expect that these factors will enable excavators to greatly increase digital access to their material, which will significantly enhance its utility for subsequent research.
Secured Access Control in Security Information and Event Management Systems

Nowadays, Security Information and Event Management (SIEM) is very important in software. SIEM stores and monitors events in software and unauthorized access to logs can prompt different security threats such as information leakage and violation of confidentiality. In this paper, a novel method is suggested for secured and integrated access control in the SIEM. First, the key points where the SIEM accesses the information within the software is specified and integrated policies for access control are developed in them. Accordingly, the threats entered into the access control module embedded in this system are carefully detected. By applying the proposed method, it is possible to provide the secured and integrated access control module for SIEM as well as the security of the access control module significantly increases in these systems. The method is implemented in the three stages of the requirements analysis for the establishment of a secure SIEM system, secure architectural design, and secure coding. The access control module is designed to create a secured SIEM and the test tool module is designed for evaluating the access control module vulnerabilities. Also, to evaluate the proposed method, the dataset is considered with ten thousand records, and the accuracy is calculated. The outcomes show the accuracy of the proposed method is significantly improved. The results of this paper can be used for designing an integrated and secured access control system in SIEM systems.
Estimating Gaze From Head and Hand Pose and Scene Images for Open-Ended Exploration in VR Environments

The widespread utility of eye tracking technology has created a growing demand for more consistent and reliable eye-tracking systems, and there is a need for new and accessible approaches that can enhance the accuracy of eye-tracking data. Previous studies have offered evidence for associations between certain non-eye signals and gaze such as a strong coordination between head motion and gaze shifts. e.g. [3] , hand and eye spatiotemporal statistics, e.g. [7] , and gaze behavior and scene content, e.g. [2] . Previous studies have also shown how various combinations of eye, head, scene, and hand signals can be leveraged for applications such as gaze estimation [5] , [10] , prediction [8] , and classification [6] . Though these previous approaches provide support for the idea that non-eye sensors (i.e. head, hand, and scene) are useful for estimating gaze, they have not yet fully addressed how these signals individually and in combination contribute to gaze estimation.
Rare bioparticle detection <i>via</i> deep metric learning

Conventional deep neural networks use simple classifiers to obtain highly accurate results. However, they have limitations in practical applications. This study demonstrates a robust deep metric neural network model for rare bioparticle detection.
Agile Project Management as an Answer to Changing Environment

The high level of volatility, uncertainty, complexity and ambiguity in business environments lead to the fact that traditional management has been in serious trouble.The required flexibility should provide the agile project management causing a silent revolution of the way projects are organized and executed.Although initially rooted in the software development industry, we can say that agile methodologies are spreading across a broad range of industries.The benefits of applying an agile approach are widely recognized, but there are still various challenges and problems that the organization faces with when adopting an agile practice.
Age, Gender Prediction and Emotion recognition using Convolutional Neural Network

Automated age and gender detection has been generally used in our daily lives that we come across, majorly in a person to computer interaction, visual surveillance, biometric analysis, electronics and other applications of commercial use. By recognizing the emotions of a person, we can improve the recommendation system. The existing methods have quite satisfying performance on real-world images if facial expressions of input image is neutral or calm, it lacks significantly in age prediction when facial expressions are altered. The dataset was obtained from IMDb-WIKI for age-gender classification and Fer2013 dataset for emotion recognition from kaggle. This project has two models, one for age-gender prediction using wide resnet architecture and the other model is trained for emotion recognition using conventional CNN architecture. An improvement in the performance of these tasks was observed by using the convolutional neural-nets (CNN).The accuracy of the wide-resnet model is 96.26% and for the emotion recognition model, accuracy achieved is 69.2%.
The impact of blockchain on the aviation industry: Findings from a qualitative study

The aviation industry is searching for innovative solutions to boost its business in a sector that operates on a financial knife-edge driven by fierce competition. This paper seeks to identify how blockchain, an immutable and append-only data ledger, can impact the aviation industry. This study presents the results from 18 interviews with industry experts. Text mining and a thematic data analysis were combined to investigate the likelihood of the airline industry adopting a disruptive technology that has the potential to affect various stakeholders and replace current platforms. The connection between the key terms and emergent themes is visualised and discussed in detail. The current attitude as well as the expectations and uncertainties of the aviation industry towards blockchain are systematised. The five core topics determining the adoption of blockchain by the industry are specific adoption factors, the replacement of current centralised platforms, customer loyalty, adoption barriers and a general lack of awareness. All topics are discussed in detail, and concrete propositions for future academic research are put forward.
Student Satisfaction and Perceived Value on Word of Mouth (WOM) During the COVID-19 Pandemic: An Empirical Study in Indonesia

Maintaining distance has become an effective pioneering measure in reducing the spread of the coronavirus outbreak (called COVID-19), so that distance learning has become an option for educators. This study seeks to explain the role of student satisfaction in mediating the effect of perceived value on word of mouth (WOM). The population of this study is students in Bali who had attended online lectures during the COVID-19 pandemic. The sample size consists of 260 people selected with a purposive sampling method. The analysis is using SEM-PLS. The results shows that perceived value had a positive and significant effect on WOM. Perceived value also has a positive and significant effect on student satisfaction and student satisfaction has a positive and significant effect on WOM. There are several variables that need to be added, such as the online system itself, the quality and ease of use of online media. Satisfaction can mediate the effect of perceived value on WOM partially. Therefore, it is important for higher education institutions in Bali to increase the perceived value obtained by students, especially in the functional value, to increase student satisfaction and ultimately have an impact on increasing positive WOM about online lectures.
Improving reasoning with contrastive visual information for visual question answering

Visual Question Answering (VQA) aims to output a correct answer based on cross-modality inputs including question and visual content. In general pipeline, information reasoning plays the key role for a reasonable answer. However, visual information is commonly not fully employed in many popular models nowadays. Facing this challenge, a new strategy is proposed in this work to make the best of visual information during reasoning. In detail, visual information is divided into two subsets: (1) question-relevant visual set, and (2) question-irrelevant visual set. Then, both of these two sets are employed by reasoning to generate reasonable outputs. Experiments are conducted on the benchmark VQAv2 dataset, which demonstrate the effectiveness of the proposed strategy. The project page can be found in https://mic.tongji.edu.cn/e6/8d/c9778a190093/page.htm.
Differentially Private Bayesian Inference for Generalized Linear Models

Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of prior works that investigate GLMs under differential privacy (DP) constraints provide only private point estimates of the regression coefficients, and are not able to quantify parameter uncertainty. In this work, with logistic and Poisson regression as running examples, we introduce a generic noise-aware DP Bayesian inference method for a GLM at hand, given a noisy sum of summary statistics. Quantifying uncertainty allows us to determine which of the regression coefficients are statistically significantly different from zero. We provide a previously unknown tight privacy analysis and experimentally demonstrate that the posteriors obtained from our model, while adhering to strong privacy guarantees, are close to the non-private posteriors.
COVID-19 pandemic: educational technology for post-hematopoietic stem cell transplant patients

to describe the experience of building the technology "Care for the prevention of COVID-19 in post-hematopoietic stem cell transplant patients".experience report conducted in a hematopoietic stem cell transplant outpatient clinic that supported the construction of an educational technology in the form of a booklet for the prevention of COVID-19. In May 2020, a literature review in scientific databases and guidelines of health organizations were used for its development.printed booklet with information on the definition of coronavirus, hand hygiene, use of the mask by the patient and caregiver, isolation and social distancing and general hygiene care.the use of technologies is characterized as a tool for educational actions therefore, nurses, patients and caregivers benefit from guidance processes for health care. The developed technology has the potential to minimize the impact and spread of SARS-CoV-2 among high-risk populations.
Computer Vision-based Survey on Human Activity Recognition System, Challenges and Applications

For surveillance purpose, lots of method were used by the researchers but computer vision based Human Activity Recognition (HAR) technologies/systems received the most interest because they automatically distinguish human behaviour and movements from video data utilizing recorded details from cameras. But the extraction of accurate and opportune information from video of human's activities and behaviours is most important and difficult task in pervasive computing environment. Due to lots of applications of HAR systems like in medical field, security, visual monitoring, video recovery, entertainment and irregular behaviour detection, the accuracy of system is most important factors for researchers. This review article presents a brief survey of the existing video or vision-based HAR system to find out their challenges and applications in three aspects such as recognition of activities, activity analysis, and decision from visual content representation. In many applications, system recognition time and accuracy is most important factor and it is affected due to an increase in the usage of simple or low quality type cameras for automated systems. So, to obtain a better accuracy and fast responses, the usage of demanding and computationally intelligent classification techniques such as deep learning and machine learning is a better option for researchers. In this survey, we addressed numerous computationally intelligent classification techniques-based research for HAR from 2010 to 2020 for a better analysis of the benefits and drawbacks of systems, the challenges faced and applications with future directions for HAR. We also present some accessible problems and ideas that should be discussed in future research for the HAR system utilizing machine learning and deep learning principles due to thPeir strong relevance.
Classification of Daily Irradiance Profiles and the Behaviour of Photovoltaic Plant Elements: The Effects of Cloud Enhancement

In this work, the automatic classification of daily irradiance profiles registered in a photovoltaic installation located in the south of Spain was carried out for a period of nine years, with a sampling frequency of 5 min, and the subsequent analysis of the operation of the elements of the installation on each type of day was also performed. The classification was based on the total daily irradiance values and the fluctuations of this parameter throughout the day. The irradiance profiles were grouped into nine different categories using unsupervised machine learning algorithms for clustering, implemented in Python. It was found that the behaviour of the modules and the inverter of the installation was influenced by the type of day obtained, such that the latter worked with a better average efficiency on days with higher irradiance and lower fluctuations. However, the modules worked with better average efficiency on days with irradiance fluctuations than on clear sky days. This behaviour of the modules may be due to the presence, on days with passing clouds, of the phenomenon known as cloud enhancement, in which, due to reflections of radiation on the edges of the clouds, irradiance values can be higher at certain moments than those that occur on clear sky days, without passing clouds. This is due to the higher energy generated during these irradiance peaks and to the lower temperatures that the module reaches due to the shaded areas created by the clouds, resulting in a reduction in its temperature losses.
Systematic Analysis and Future Research Directions in Artificial Intelligence for Marketing

Disruptive technologies like the internet of things, big data analytics, blockchain, and artificial intelligence have transformed how businesses operate. Artificial intelligence (AI) is the most recent technological disruptor and has enormous marketing transformation potential. Practitioners all over the world are attempting to determine which AI solutions are best suited to their marketing needs. A systematic literature review, on the other hand, can highlight the importance of artificial intelligence (AI) in marketing and point the way for future research. The goal of this study is to provide a comprehensive review of AI in marketing by analysing extant literature published between 1982 and 2020 using bibliometric, conceptual, and intellectual network analysis. The performance of the scientific actors, such as the most relevant authors and sources, was identified through a comprehensive review of 1,584 papers. Furthermore, the conceptual and intellectual network was revealed through co-citation and co-occurrence analysis. The Louvain algorithm was used to cluster data and identify research sub-themes and future research directions in order to expand AI in marketing.
QoS Aware Social Spider Cloud Web Algorithm: Analysis of Resource Placement Approach

Cloud resource management is a challenging job based on quality of service requirements. Due to the heterogeneous resources, this challenge is required to be addressed by the existing resource management policies. The resources must be arranged and scheduled as per QoS requirements in order to satisfy cloud client. So resource management policies must include QoS requirements before implementing to the resource placement. In this paper, a resource placement technique, QoS disabled social spider cloud web algorithm (SSCWA) is enhanced with QoS aware SSCWA which caters to place resources as per QoS. Finally, performance evaluation of proposed QoS aware SSCWA is done in CloudSim. Results prove that proposed algorithm outperforms in execution time, response time, throughput and execution cost as QoS parameters.
Data-driven framework for high-accuracy color restoration of RGBN multispectral filter array sensors under extremely low-light conditions

RGBN multispectral filter array provides a cost-effective and one-shot acquisition solution to capture well-aligned RGB and near-infrared (NIR) images which are useful for various optical applications. However, signal responses of the R, G, B channels are inevitably distorted by the undesirable spectral crosstalk of the NIR bands, thus the captured RGB images are adversely desaturated. In this paper, we present a data-driven framework for effective spectral crosstalk compensation of RGBN multispectral filter array sensors. We set up a multispectral image acquisition system to capture RGB and NIR image pairs under various illuminations which are subsequently utilized to train a multi-task convolutional neural network (CNN) architecture to perform simultaneous noise reduction and color restoration. Moreover, we present a technique for generating high-quality reference images and a task-specific joint loss function to facilitate the training of the proposed CNN model. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art color restoration solutions and achieving more accurate color restoration results for desaturated and noisy RGB images captured under extremely low-light conditions.
Differentially Private Clustering via Maximum Coverage

This paper studies the problem of clustering in metric spaces while preserving the privacy of individual data. Specifically, we examine differentially private variants of the k-medians and Euclidean k-means problems. We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. Additionally, our algorithms use a clustering algorithm without differential privacy as a black-box. This allows practitioners to control the trade-off between runtime and approximation factor by choosing a suitable clustering algorithm to use.
Understanding Internet Censorship in Europe: The Case of Spain

European Union (EU) member states consider themselves bulwarks of democracy and freedom of speech. However, there is a lack of empirical studies assessing possible violations of these principles in the EU through Internet censorship. This work starts addressing this research gap by investigating Internet censorship in Spain over 2016-2020, including the controversial 2017 Catalan independence referendum. We focus, in particular, on network interference disrupting the regular operation of Internet services or contents.
Robust Graph Structure Learning for Multimedia Data Analysis

With the rapid development of computer network technology, we can acquire a large amount of multimedia data, and it becomes a very important task to analyze these data. Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph-based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction-based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self-expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>l</mi> </mrow> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </math> -norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.
Variational quantum eigensolver for SU(N) fermions

Variational quantum algorithms aim at harnessing the power of noisy intermediate-scale quantum computers, by using a classical optimizer to train a parameterized quantum circuit to solve tractable quantum problems. The variational quantum eigensolver is one of the aforementioned algorithms designed to determine the ground-state of many-body Hamiltonians. Here, we apply the variational quantum eigensolver to study the ground-state properties of $N$-component fermions. With such knowledge, we study the persistent current of interacting SU($N$) fermions, which is employed to reliably map out the different quantum phases of the system. Our approach lays out the basis for a current-based quantum simulator of many-body systems that can be implemented on noisy intermediate-scale quantum computers.
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios.To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains.Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic "weak" data based on their benefits to the target-domain ranking accuracy of Neu-IR models.Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models.Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and metareweighted data selection.The code and data of this paper can be obtained from https: //github.com/thunlp/MetaAdaptRank.