Reviewerly/exHarmony huggingface dataset:
Detection, Localization, and Tracking of Multiple MAVs With Panoramic Stereo Camera Networks

Malicious use of micro aerial vehicles (MAVs) has become a serious threat to public safety and personal privacy in recent years. Motivated by this problem, we propose a systematic approach to monitor the intrusion of malicious MAVs based on a novel type of panoramic stereo camera networks. Each sensing node of such a network consists of 16 lenses that can form a 360-degree panoramic vision system. The 16 lenses further form 8 pairs of stereo cameras that can directly localize aerial targets. The effective range for a sensing node localizing a MAV like DJI M300 could reach 80 meters, which is much farther than existing commercial stereo cameras. In terms of algorithms, we propose i) a novel visual MAV detection algorithm based primarily on motion features of MAVs, ii) an efficient stereo localization algorithm based on sparse feature points, and iii) robust multi-target tracking and trajectory fusion algorithm to fuse the observations of different sensing nodes. The effectiveness, robustness, and accuracy of the proposed algorithms together with the overall system have been verified by extensive experimental tests. To the best of our knowledge, this is the first systematic approach to detect, localize, and track unknown MAVs in the literature. Our approach provides a scalable solution to securely cover large areas of interest against malicious MAV intrusion. Note to Practitioners—Micro aerial vehicles (MAVs) have been widely used in many domains nowadays. However, they have also brought many safety problems. To monitor the intrusion of malicious MAVs, this paper proposes a novel type of panoramic stereo camera networks that can detect, localize, and track multiple MAVs simultaneously. Such a network consists of a number of sensing nodes and a central node. Each sensing node is able to detect, localize, and track multiple MAV targets. The role of the central node is to fuse the observations from multiple sensing nodes to generate more accurate trajectories of the MAV targets and in the meantime secure a large area in a coordinated way. This paper presents the details of the prototype of the system and the key algorithms therein.
Constructive Approach for Text Summarization Using Advanced Techniques of Deep Learning

Text summarization is one of the popular fields, and a great demand is also associated with text summarization due to a large amount of text which is available with the Internet in the form of various social media sites, blogs, and other Web sites. Therefore, the demand with the shortening the information is increasing for reducing the information for various reasons. Nowadays, there are plenty of resources for the data is available, and also there the number of tools available for reducing the amount of information is increasing due to such a great requirement. This paper also discusses the various types of methods and techniques which are effective in shortening the text or information using the various advanced technology and advanced algorithms such as deep learning, machine learning, and artificial intelligence. The advanced algorithms and technology also work with the other technology to make a great combination of technology which will resolve the various issues regarding the text summarization or in other words reduction of information. The main aspect while reducing the amount of information is that the reduced information must retain the information which is very essential from the user or application point of view and must maintain the consistency in the information which is available with the different sauces.
KPI’s for Evaluation of DevOps Teams

In an era of digital transformation, where in the last decades has increased the need of embrace change rapidly, it’s important that organizations make their transformation in the path of continue being competitive. To this, organizations have started adopting agile methodologies so that they can respond faster to the daily change. DevOps allows companies to gain a leverage in reach their goals and increasing their velocity in react to change. This way, measure DevOps teams’ performance is a bigger and bigger necessity of the organizations so that they can identify improvement points. A Systematic Literature Review was performed and were identified 13 Key Performance Indicators were identified. Among them, those referred to the quality and results of testing are the most referred and implemented.
Prompt-Driven Neural Machine Translation

Neural machine translation (NMT) has obtained significant performance improvement over the recent years.However, NMT models still face various challenges including fragility and lack of style flexibility.Moreover, current methods for instance-level constraints are limited in that they are either constraint-specific or model-specific.To this end, we propose prompt-driven neural machine translation to incorporate prompts for enhancing translation control and enriching flexibility.Empirical results demonstrate the effectiveness of our method in both prompt responding and translation quality.Through human evaluation, we further show the flexibility of prompt control and the efficiency in human-in-the-loop translation.
Synthetic Data of Randomly Piled, Similar Objects for Deep Learning-Based Object Detection

Currently, the best object detection results are achieved by supervised deep learning methods, however, these methods depend on annotated training data. With the synthetic data generation approach, we intend to mimic the real data characteristics and diversify the dataset by a systematic rendering of highly realistic synthetic pictures. We systematically explore how different combinations and portions of real and synthetic datasets affect object detectors performance. The developed synthetic data generation framework shows promising results in deep learning-based object detection tasks and can supplement real data when the variety of real training data is insufficient. However, when synthetic data ratio increases over real data ratio, a decrease in average precision can be observed, which has the most affect on 0.75-0.95 IoU threshold range.
A survey on blockchain security for cloud and IoT environment

In traditional Internet of Things (IoT) ecosystems, fog devices transmit data from sensors to a centralized cloud server. Issues with security and upkeep while updating firmware for millions of smart devices, single points of failure, as well as third-party Cloud server administration, the difficulty of frequently updating the firmware on millions of smart devices presents security and maintenance issues as well as a bottleneck in information flows, all raise privacy concerns. Blockchain technology eliminates the need for trusted third parties while also preventing single points of failure and other issues. As a result, academics are looking into the usage of blockchain in the IoT space. The most recent state-of-the-art developments in blockchain for IoT, blockchain for Cloud, blockchain for eHealth, smart cities, transportation, and other programmes are examined in this article.
Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN

Bias in the training data can jeopardize fairness and explainability of deep neural network prediction on test data. We propose a novel bias-tailored data augmentation approach, Counterfactual Interpolation Augmentation (CIA), attempting to debias the training data by d-separating the spurious correlation between the target variable and the sensitive attribute. CIA generates counterfactual interpolations along a path simulating the distribution transitions between the input and its counterfactual example. CIA as a pre-processing approach enjoys two advantages: First, it couples with either plain training or debiasing training to markedly increase fairness over the sensitive attribute. Second, it enhances the explainability of deep neural networks by generating attribution maps via integrating counterfactual gradients. We demonstrate the superior performance of the CIA-trained deep neural network models using qualitative and quantitative experimental results. Our code is available at: https://github.com/qiangyao1988/CIA
Shopping in Reality or Virtuality? A Validation Study of Consumers’ Price Memory in a Virtual vs. Physical Supermarket

This study validates a VR supermarket as a research tool by studying the influence of the food shopping setting on consumers’ price memory—an important antecedent for price comparisons in the purchase situation. In a quasi-experiment, two groups of consumers were given a shopping task in either a physical supermarket or a virtual reality supermarket setting. Upon task completion, participants’ explicit and implicit price memory was measured across three food product categories (pizza sauce, pasta, and dark chocolate). Results revealed no significant difference between the two settings, supporting the comparability between the VR shopping experience and the experience in the physical supermarket. The VR supermarket can therefore be a valid tool for studying consumer food choice behaviour in a shopping context. Further results show that explicit price memory is weaker compared to implicit price memory, that even prices are remembered better than odd prices, and that price memory follows the expected pattern in a VR supermarket as well. Finally, exploratory findings indicate that the feeling of physical presence and self-presence is relatively high for this particular VR supermarket, whereas social presence is weaker.
A Modified Uncertainty Measure of Z-numbers

The Z-number is a more adequate construct for describing real-life information. While considering the uncertainty of the information, it also models the partial reliability of the information. It is a combination of probabilistric restriction and possibilistric restriction. In this paper, we modified the uncertainty measurement of the discrete Z-number and proposed the uncertainty measurement of the continuous Z-number. Some numerical examples are used to illustrate the calculation processes and advantages of the proposed method. An application of journey vehicle selection shows the effectiveness of the proposed uncertainty measurement in determining the weights of criteria.
SARS-CoV-2 Catalonia contact tracing program: evaluation of key performance indicators

Guidance on SARS-CoV-2 contact tracing indicators have been recently revised by international public health agencies. The aim of the study is to describe and analyse contact tracing indicators based on Catalonia's (Spain) real data and proposing to update them according to recommendations.Retrospective cohort analysis including Catalonia's contact tracing dataset from 20 May until 31 December 2020. Descriptive statistics are performed including sociodemographic stratification by age, and differences are assessed over the study period.We analysed 923,072 contacts from 301,522 SARS-CoV-2 cases with identified contacts (67.1% contact tracing coverage). The average number of contacts per case was 4.6 (median 3, range 1-243). A total of 403,377 contacts accepted follow-up through three phone calls over a 14-day quarantine period (84.5% of contacts requiring follow-up). The percentage of new cases declared as contacts 14 days prior to diagnosis evolved from 33.9% in May to 57.9% in November. All indicators significantly improved towards the target over time (p < 0.05 for all four indicators).Catalonia's SARS-CoV-2 contact tracing indicators improved over time despite challenging context. The critical revision of the indicator's framework aims to provide essential information in control policies, new indicators proposed will improve system delay's follow-up. The study provides information on COVID-19 indicators framework experience from country's real data, allowing to improve monitoring tools in 2021-2022. With the SARS-CoV-2 pandemic being so harmful to health systems and globally, is important to analyse and share contact tracing data with the scientific community.
Application of Statistical K-Means Algorithm for University Academic Evaluation

With the globalization of higher education, academic evaluation is increasingly valued by the scientific and educational circles. Although the number of published papers of academic evaluation methods is increasing, previous research mainly focused on the method of assigning different weights for various indicators, which can be subjective and limited. This paper investigates the evaluation of academic performance by using the statistical K-means (SKM) algorithm to produce clusters. The core idea is mapping the evaluation data from Euclidean space to Riemannian space in which the geometric structure can be used to obtain accurate clustering results. The method can adapt to different indicators and make full use of big data. By using the K-means algorithm based on statistical manifolds, the academic evaluation results of universities can be obtained. Furthermore, through simulation experiments on the top 20 universities of China with the traditional K-means, GMM and SKM algorithms, respectively, we analyze the advantages and disadvantages of different methods. We also test the three algorithms on a UCI ML dataset. The simulation results show the advantages of the SKM algorithm.
Training Mode of Innovative Accounting Talents in Colleges Using Artificial Intelligence

With the rise of big data revolution, a new generation of artificial intelligence (AI) is gaining momentum and the AI era will come strongly. However, the accounting profession is also showing a booming trend in colleges and universities, which, as the main base of higher education talent training, also undertake a considerable part of the task of training accounting talents. As traditional accounting is gradually replaced, finance professionals are being forced to gradually change to management talents. In the information era, how to realize the transformation of talent cultivation mode of financial management professionals, how to use modern technology to realize the deep integration of AI and education teaching, and how to realize the innovation of intelligent classroom teaching mode are the challenges facing the innovation of applied talent cultivation mode in colleges and universities. The article combines the changes brought by AI to the accounting industry, analyzes the current situation of accounting talent cultivation mode in colleges and universities, sorts out the challenges brought by AI to college accounting talent cultivation, and proposes that the college accounting talent cultivation mode should be further improved in terms of talent cultivation structure, cultivation plan and goal, and cultivation ideas and methods. The experimental results show that the average application time of AI basic scheduling algorithm is 1.47 h shorter than that without AI basic scheduling algorithm, which can not only increase the total number of tasks in a planned and controlled way but also ensure the effective reduction of the whole task running time in the platform. Therefore, the AI-based innovative accounting talents cultivation model in colleges and universities, exploring an excellent new model for cultivating innovative talents in accounting professions, enables the innovative accounting talents cultivated in schools to meet the needs of society and enhance their irreplaceability.
Medical visual question answering via corresponding feature fusion combined with semantic attention

&lt;abstract&gt; &lt;p&gt;Medical visual question answering (Med-VQA) aims to leverage a pre-trained artificial intelligence model to answer clinical questions raised by doctors or patients regarding radiology images. However, owing to the high professional requirements in the medical field and the difficulty of annotating medical data, Med-VQA lacks sufficient large-scale, well-annotated radiology images for training. Researchers have mainly focused on improving the ability of the model's visual feature extractor to address this problem. However, there are few researches focused on the textual feature extraction, and most of them underestimated the interactions between corresponding visual and textual features. In this study, we propose a corresponding feature fusion (CFF) method to strengthen the interactions of specific features from corresponding radiology images and questions. In addition, we designed a semantic attention (SA) module for textual feature extraction. This helps the model consciously focus on the meaningful words in various questions while reducing the attention spent on insignificant information. Extensive experiments demonstrate that the proposed method can achieve competitive results in two benchmark datasets and outperform existing state-of-the-art methods on answer prediction accuracy. Experimental results also prove that our model is capable of semantic understanding during answer prediction, which has certain advantages in Med-VQA.&lt;/p&gt; &lt;/abstract&gt;
High-Capacity Data Hiding Method Based on Two Subgroup Pixels-Value Adjustment Using Encoding Function

Confidential information can be hidden in digital images through data hiding technology. This has practical application value for copyright, intellectual property protection, public information protection, and so on. In recent years, researchers have proposed many schemes of data hiding. However, existed data hiding schemes suffer from low hiding capacity or poor stego-image quality. This paper uses a new method of multiple pixels-value adjustment with encoding function (MPA) to further improve the comprehensive performance, which is well in both hiding capacity and stego-image quality. The main idea is to divide n adjacent cover pixels into two sub-groups and implement multi-bit-based modulus operations in each group, respectively. The efficacy of this proposed is evaluated by peak signal-to-noise ratio (PSNR), embedding payload, structural similarity index (SSIM), and quality index (QI). The recorded PSNR value is 30.01 dB, and embedding payload is 5 bpp (bits per pixel). In addition, the steganalysis tests do not detect this steganography technique.
Acceptance of robots as co-workers: Hotel employees’ perspective

This study aims to develop a model for the acceptance of robots as co-workers from the perspective of hotel employees and uses empirical model testing to validate the findings. Mixed-methods research was conducted by employing a sequential exploratory strategy, whereas qualitative research was conducted using interpretative phenomenological analysis (IPA). The key informants were executives, HR managers, reception managers, and some staff of three hotels in Thailand. Five main themes were uncovered from the IPA: human, robot, organization, human–robot collaboration (HRC), and robot acceptance. Relationships between the themes were established and were promoted as the premise for an initial robot acceptance model. Thereafter, the survey questionnaire was drafted using the instrumental development approach. The model is a good fit with the empirical data. Human, robot, and organizational factors significantly affect robot acceptance and HRC. Meanwhile, HRC plays a mediator role in the relationship of human, robot, and organizational factors with robot acceptance, but in a negative direction. This implies that the respondents generally accept robots. However, the level of acceptance decreases when HRC is involved.
Contrastive Learning for Label-Efficient Semantic Segmentation

Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases. This happens because deep CNNs trained with the de facto cross-entropy loss can easily overfit to small amounts of labeled data. To address this issue, we propose a simple and effective contrastive learning-based training strategy in which we first pretrain the network using a pixel-wise, label-based contrastive loss, and then fine-tune it using the cross-entropy loss. This approach increases intra-class compactness and inter-class separability, thereby resulting in a better pixel classifier. We demonstrate the effectiveness of the proposed training strategy using the Cityscapes and PASCAL VOC 2012 segmentation datasets. Our results show that pretraining with the proposed contrastive loss results in large performance gains (more than 20% absolute improvement in some settings) when the amount of labeled data is limited. In many settings, the proposed contrastive pretraining strategy, which does not use any additional data, is able to match or outperform the widely-used ImageNet pretraining strategy that uses more than a million additional labeled images.
SemEval 2022 Task 10: Structured Sentiment Analysis

In this paper, we introduce the first SemEval shared task on Structured Sentiment Analysis, for which participants are required to predict all sentiment graphs in a text, where a single sentiment graph is composed of a sentiment holder, target, expression and polarity.This new shared task includes two subtracks (monolingual and cross-lingual) with seven datasets available in five languages, namely Norwegian, Catalan, Basque, Spanish and English.Participants submitted their predictions on a held-out test set and were evaluated on Sentiment Graph F 1 .Overall, the task received over 200 submissions from 32 participating teams.We present the results of the 16 teams that provided system descriptions and our own expanded analysis of the test predictions.
A Comprehensive Dataset for German Offensive Language and Conversation Analysis

In this work, we present a publicly available offensive language dataset (DeTox-dataset) containing 10,278 annotated German social media comments collected in the first half of 2021.With twelve different annotation categories annotated by six annotators, it is far more comprehensive than other datasets, and goes beyond just hate speech detection.The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments contexts and do conversation analyses in order to research the contagion of offensive language in conversations.
Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation

Providing effective automatic essay feedback is necessary for offering writing instruction at a massive scale.In particular, feedback for promoting coherent flow of ideas in essays is critical.In this paper we propose a state-of-the-art method for automated analysis of structure and flow of writing, referred to as Rhetorical Structure Theory (RST) parsing.In so doing, we lay a foundation for a generalizable approach to automated writing feedback related to structure and flow.We address challenges in automated rhetorical analysis when applied to student writing and evaluate our novel RST parser model on both a recent student writing dataset and a standard benchmark RST parsing dataset.
Using Natural Sentence Prompts for Understanding Biases in Language Models

Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need of prompt-style dataset to trigger specific behaviors of language models. In this paper, we address this gap by creating a prompt dataset with respect to occupations collected from real-world natural sentences present in Wikipedia.We aim to understand the differences between using template-based prompts and natural sentence prompts when studying gender-occupation biases in language models. We find bias evaluations are very sensitiveto the design choices of template prompts, and we propose using natural sentence prompts as a way of more systematically using real-world sentences to move away from design decisions that may bias the results.