Reviewerly/exHarmony huggingface dataset:
The Southern African Heat Low: Structure, Seasonal and Diurnal Variability, and Climatological Trends

Abstract Heat lows are key features of subtropical climates and monsoon systems. In southern Africa, they are pivotal to understanding divergent climate change projections, in particular the veracity of future rainfall decline. Compared to other heat lows, including in West Africa and Australia, the southern African heat low remains poorly documented. Here, we analyze the diurnal cycle, seasonal variability and trends of the heat low in reanalysis data. In ERA5, 462 strong heat low days are detected between September and March from 1990-2019, equating to 7.3% of days sampled. These events feature ascent (exceeding -0.2 Pa/s) at low levels (strongest between 800-600 hPa) and subsidence aloft, generating low-level cyclonic flow with anticyclonic flow above. This flow exhibits strong diurnal variability, with peak windspeeds between 0600-0900 UTC and maximum ascent at ∼2300 UTC. Heat lows form preferentially over Angola in September (∼14°S) and October (15-20°S), and in Namibia from November to March (∼20-26°S). Strongest ascent occurs over areas of high elevation. Finally, we show a rapidly increasing frequency of strong heat low days, with a 175% increase between 1960-1989 and 1990-2019. The greatest increase (459%) has occurred in the early summer months of September and October, consistent with projections of delayed rainfall onset. Strikingly, more strong heat lows are detected in the most recent 5 years of analysis (2014-2019) than in the 30-year period from 1960-1989. These results suggest the heat low is an important feature in determining drying trends over southern Africa and is a vital indicator of climate model accuracy.
Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study

The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home-developed approaches. In this study, both traditional machine learning of K-means clustering versus deep learning of U-Net, U-Net + ResNet18, and U-Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep-learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell-type classification of ALL-IDB2 and PCB-HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.
Gamification in the Metaverse: Affordance, perceived value, flow state, and engagement

Abstract This study aims to empirically validate a theoretical model for the engagement of the performance Metaverse platform with other rapidly developing Metaverse platforms around the world. It innovatively examines the relationship between gamification affordance, perceived value, and flow state in the context of the Metaverse concert platform and the relationship that improves engagement in the tourism and entertainment industries. The proposed model incorporates five stages: gamification, affordance, perceived value, flow state, and engagement. A total of 671 usable questionnaires were obtained, and the results were analysed using confirmatory factor analysis, discriminant validity analysis, and path analysis. Social gain and enjoyment have a positive effect on the personal flow state and are influenced by identity affordance, competition affordance, and self‐expression affordance. Furthermore, the flow state increases engagement. However, the relationship between perceived value (effectiveness, social gain, and enjoyment) and flow state differ according to gender.
Man and the Machine: Effects of AI-assisted Human Labeling on Interactive Annotation of Real-Time Video Streams

AI-assisted interactive annotation is a powerful way to facilitate data annotation – a prerequisite for constructing robust AI models. While AI-assisted interactive annotation has been extensively studied in static settings, less is known about its usage in dynamic scenarios where the annotators operate under time and cognitive constraints, e.g., while detecting suspicious or dangerous activities from real-time surveillance feeds. Understanding how AI can assist annotators in these tasks and facilitate consistent annotation is paramount to ensure high performance for AI models trained on these data. We address this gap in interactive machine learning (IML) research, contributing an extensive investigation of the benefits, limitations, and challenges of AI-assisted annotation in dynamic application use cases. We address both the effects of AI on annotators and the effects of (AI) annotations on the performance of AI models trained on annotated data in real-time video annotations. We conduct extensive experiments that compare annotation performance at two annotator levels (expert and non-expert) and two interactive labelling techniques (with and without AI-assistance). In a controlled study with N = 34 annotators and a follow up study with 51963 images and their annotation labels being input to the AI model, we demonstrate that the benefits of AI-assisted models are greatest for non-expert users and for cases where targets are only partially or briefly visible. The expert users tend to outperform or achieve similar performance as AI model. Labels combining AI and expert annotations result in the best overall performance as the AI reduces overflow and latency in the expert annotations. We derive guidelines for the use of AI-assisted human annotation in real-time dynamic use cases.
“Face Log” (Attendance Management System using Face Recognition)

The purpose of this project is to build attendance management system based on face recognition; Face recognition is an important operation of Image processing owing to its use in numerous fields. Identification of candidate in an organization, college or school for the purpose of attendance is a use of face recognition. The Face recognition system basically consists of two phases such as namely face detection and face recognition. This Attendance Management System can do the daily activities of attendance marking and analysis with reduced human interactions. User does not wait in queue as attendance of multiple people can be recorded at once by simultaneous recognition of faces of multiple people at a time. According to current situation of covid- 19 we proposed this system for precluding infection due to contact and to maintain social distancing. In this project, by using OpenCV library facial recognition-based attendance management system developed the attendance results will be stored in the database
Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems

Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, both at the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.
Exploring the Facets of the Multiplayer VR Gaming Experience

While the topic of investigating user experience with immersive services, such as Social Virtual Reality (VR), is starting to gain traction in the research community, the unique case of multiplayer VR games requires a more specific approach. Attempts to investigate user experiences with this complex, multidimensional service are hindered by the absence of specific standards and guidelines going beyond what we know about non-immersive gaming. In this article, we present the results of a user study (N = 32) exploring participants’ experience of playing two competitive VR games of different genres (shooter, sports game), as we focus on three distinct facets of multiplayer VR gaming — network, interpersonal competitiveness, and social interaction. Furthermore, approaching the issue from the perspective of Quality of Experience researchers looking to conduct future user studies on the topic, we also present our findings as a way to shed light on factors that need further consideration, especially in the context of participant recruitment and methodology design, such as the choice of access network to be used for testing, and the issue of matchmaking study participants based on skill and prior relationship.
Agriculture Equipment Rental and Product Selling System

Farmers are facing lot of problems, They will cultivate crops and other agricultural products (fruits, flowers, vegetables),They want to sell their products according to the market price but lack of knowledge they will sell their huge amount of products for small amount of money to the brokers available in the local and customers will directly approach to the brokers because of this formers are losing lot of money, they are getting cheated ,Farmers know that they are selling products to broker for small amount of money, but lack of knowledge to the farmer we thought of doing an application that can help farmers can directly sell their own products to customer with no brokers. Customers can directly contact to farmers , Farmers can sell their own products retail or wholesale according to their quantity of production in the farming to the customer directly, To get aware of all these problems and to get knowledge to the farmers this application is needed and, To bring the choice to any kind of farmer to create an environment that will let them buy or sell their agricultural products, Languages used Java, Language available Hindi, English, Technologies using Android Application. Mobile internet will help the farmers to sell their products directly to consumer. This paper provides market information to a farmer using its easy interface on the mobile application. The mobile application is intended to be used for fast and updated information delivering system for farmers. Also, it has native language support to make the transaction easy for farmers. The mobile application treats farmers as a seller and a buyer. The intention behind this paper is to help farmers so they buy or sell their agriculture goods and products. Market prices provided by data.gov.in lets the system to keep the selling and buying prices in control. As the products are to be browsed and there may be plenty of products for the user. To make browsing easy many filters can provide. Farmers face many problems while selling their goods and products, this system promises to provide an easy and recreational way to sell the products. The system lets the farmers to sell goods at a reasonable price and makes business even fair and transparent. Consumers are the opposite side of the same coin. This system lets consumer to choose from a wide variety of products, select the product as per their requirement and also to apply price filters. Location is a one of parameter for consumer and producer while selling or buying their product it will helps the user to get the product nearby their location. The basic Agriculture Equipment Rental &amp; Product Selling System objective of the system is to considers every one need and full fills their requirement with fair and transparent agriculture business
A Liquidity Analysis System for Large-Scale Video Streams in the Oilfield

This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements and network costs, and enables real-time analysis of large-scale video stream on limited edge devices. Second, it designed a set of liquidity information to describe the liquidity status in the oilfield. Finally, it uses object tracking technology to design a counting algorithm for the unique tubing object in the oilfield. We have deployed LinkStream in an oilfield in Iraq. LinkStream can perform realtime inference on over 200 video streams with acceptable resource overhead.
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows

Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such as cooking a four-course meal. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature – the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, next-action recommendation, etc. Existing neural network-based approaches that learn a continuous-time activity sequence (or CTAS) are limited to the presence of only visual data or are designed specifically for a particular task, i.e. , limited to next action or goal prediction. In this paper, we present ProActive , a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems – next action prediction, sequence-goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. Moreover, for time-sensitive prediction, we perform an early detection of sequence goal via a constrained margin-based optimization procedure. This in-turn allows ProActive to predict the sequence goal using a limited number of actions. In addition, we propose a novel addition over the ProActive model, called ProActive++ , that can handle variations in the order of actions, i.e. , different methods of achieving a given goal. We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of our ProActive and ProActive++ over the state-of-the-art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.
Bystanders’ collective responses set the norm against hate speech

Abstract Hate speech incidents often occur in social settings, from public transport to football stadiums. To counteract a prevailing passive attitude towards them, governmental authorities, sociologists, and philosophers stress bystanders’ responsibility to oppose or block hate speech. Here, across two online experiments with UK participants using custom visual vignettes, we provide empirical evidence that bystanders’ expression of opposition can affect how harmful these incidents are perceived, but only as part of a collective response: one expressed by a majority of bystanders present. Experiment 1 ( N = 329) shows that the silence or intervention of three bystanders affects the harm caused by hate speech, but one bystander does not. Experiment 2 ( N = 269) shows this is not simply a matter of numbers but rather one of norms: only unanimous opposition reduces the public perception of the damage created by the incident. Based on our results, we advance an empirical norm account: group responses to hate speech modulate its harm by indicating either a permissive or a disapproving social norm. Our account and results, showing the need to consider responses to hate speech at a collective level, have direct implications for social psychology, the philosophy of language and public policies.
Spatial-spectral graph convolutional extreme learning machine for hyperspectral image classification

Hyperspectral image has excellent spectral information and abundant spatial information, and its feature quality is one of the key factors affecting the classification performance. Extreme learning machine (ELM) is widely used in hyperspectral classification problems. However, traditional ELM is usually based on regular Euclidean data, ignoring the inherent structured information between hyperspectral pixels, resulting in poor robustness. In this paper, we propose a new supervised extreme learning machine framework, termed spatial-spectral graph convolutional ELM (SSG-ELM), based on using graph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random graph convolutional layer followed by a graph convolutional regression layer, enabling it to model complex intra class variations. Otherwise, a local spectral-spatial context integration and reshaping mechanism is incorporated into the hidden layer feature representation by using a context-aware bilateral filtering procedure. Experimental results show that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art ELM-based methods.
Digital Skills of the Members of Educational Sommunity as a Factor of Successful Internet Communication: Response to the Book by K.A. Milovanov “IT Training for Teachers” (M.:Publishing books com, 2020. 192 p.)

The digital transformation of the science and higher education field is one of the priority tasks implemented by the Government of the Russian Federation. However, at this stage, there are a number of problems, including inequality of digital skills among members of the educational community, which leads to communication failures in the online space. The peer-reviewed educational publication “IT Training for Teachers” by K.A. Milovanov sets the goal of tamping down inequality in digital competences, knowledge and skills among participants in the educational process. The manual analyzes the issues of distance learning, choosing an online platform for conducting lectures and classes, data backup, and provides recommendations for the use of equipment and special applications for recording videos, making presentations and organizing interactive teaching activities, etc.
College Placement Recruitment system using Android App

The purpose of the "College Placement Recruitment Management System" is to improve and expedite the placement procedure in educational establishments. This project aims to design a complete system that automates and streamlines the recruiting workflow in response to the increasing complexity of handling placement activities. The technology makes it easier for students, recruiters, and administrators to communicate with each other in a smooth and user-friendly manner. The College Placement Recruitment Management System's methodical study, design, and implementation are described in this project report. With capabilities like user registration, job posting, application submission, interview scheduling, and result administration, the system meets the varied needs of its users
A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance

Due to the efficiency of self-attention mechanisms in encoding spatial information, Transformer-based models have recently taken a dominant position among semantic segmentation methods. However, Transformer-based models have the disadvantages of requiring a large amount of computation and lacking attention to detail, so we look back to the CNN model. In this paper, we propose a multi-path semantic segmentation network with convolutional attention guidance (dubbed MCAG). It has a multi-path architecture, and feature guidance from the main path is used in other paths, which forces the model to focus on the object’s boundaries and details. It also explores multi-scale convolutional features through spatial attention. Finally, it captures both local and global contexts in spatial and channel dimensions in an adaptive manner. Extensive experiments were conducted on popular benchmarks, and it was found that MCAG surpasses other SOTA methods by achieving 47.7%, 82.51% and 43.6% mIoU on ADE20K, Cityscapes and COCO-Stuff, respectively. Specifically, the experimental results prove that the proposed model has high segmentation precision for small objects, which demonstrates the effectiveness of convolutional attention mechanisms and multi-path strategies. The results show that the CNN model can achieve good segmentation effects with a lower amount of calculation.