Reviewerly/exHarmony huggingface dataset:
Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.
Managing Technical Debt

“This is an incredibly wise and useful book. The authors have considerable real-world experience in delivering quality systems that matter, and their expertise shines through in these pages. Here you will learn what technical debt is, what is it not, how to manage it, and how to pay it down in responsible ways. This is a book I wish I had when I was just beginning my career. The authors present a myriad of case studies, born from years of experience, and offer a multitude of actionable insights for how to apply it to your project.” –Grady Booch, IBM Fellow Master Best Practices for Managing Technical Debt to Promote Software Quality and Productivity As software systems mature, earlier design or code decisions made in the context of budget or schedule constraints increasingly impede evolution and innovation. This phenomenon is called technical debt, and practical solutions exist. In Managing Technical Debt, three leading experts introduce integrated, empirically developed principles and practices that any software professional can use to gain control of technical debt in any software system. Using real-life examples, the authors explain the forms of technical debt that afflict software-intensive systems, their root causes, and their impacts. They introduce proven approaches for identifying and assessing specific sources of technical debt, limiting new debt, and “paying off” debt over time. They describe how to establish managing technical debt as a core software engineering practice in your organization. Discover how technical debt damages manageability, quality, productivity, and morale–and what you can do about it Clarify root causes of debt, including the linked roles of business goals, source code, architecture, testing, and infrastructure Identify technical debt items, and analyze their costs so you can prioritize action Choose the right solution for each technical debt item: eliminate, reduce, or mitigate Integrate software engineering practices that minimize new debt Managing Technical Debt will be a valuable resource for every software professional who wants to accelerate innovation in existing systems, or build new systems that will be easier to maintain and evolve.
BlenderProc: Reducing the Reality Gap with Photorealistic Rendering

BlenderProc is an open-source and modular pipeline for rendering photorealistic images of procedurally generated 3D scenes which can be used for training data-hungry deep learning models. The presented results on the tasks of instance segmentation and surface normal estimation suggest that our photorealistic training images reduce the gap between the synthetic training and real test domains, compared to less realistic training images combined with domain randomization. BlenderProc can be used to train models for various computer vision tasks such as semantic segmentation or estimation of depth, optical flow, and object pose. By offering standard modules for parameterizing and sampling materials, objects, cameras and lights, BlenderProc can simulate various real-world scenarios and provide means to systematically investigate the essential factors for sim2real transfer.
Research on Personalized Recommendation System Based on Association Rules

Abstract With the rapid development of network technology, the amount of information is rapidly expanding, and the amount of various data has become huge and scattered. Using traditional keywords to retrieve search data has become quite time-consuming and difficult to focus on. Traditional search engines have been unable to help people effectively solve this problem, so personalized recommendation systems have emerged. In view of the user’s behavior and preferences, this article makes a detailed comparison of different recommendation technologies. It focuses on analyzing the applicability of the current popular content-based, association rules, and collaborative filtering recommendation technologies to the current needs. The technical scheme and system structure of recommendation system based on association rules are proposed, and aiming at the shortcomings of traditional recommendation system algorithms (the algorithm’s sparsity problem) and the current cold start problem of the recommendation system. Based on the MovieLens data set, a series of evaluation indicators are set up, comparative experiments are carried out, and the effectiveness of the proposed improved algorithm is analyzed.
Can Users Distinguish Narrative Texts Written by an Artificial Intelligence Writing Tool from Purely Human Text?

The present user study investigates narrative texts generated by an AI-tool (the Generative Pretrained Transformer 2 Model, GPT-2). In particular, we examined whether readers can distinguish texts written mainly by a GPT-2-based interactive interface for creative writing from texts written without this tool. Nine participants with a literature-specific professional background were provided with the first few lines of 18 unfamiliar poems and short stories written by classic authors (two texts per participant). They created two continuations for each of these texts, one without and one with the help of the AI-tool (only minor human edits allowed). In an evaluation phase, they were presented with all the continuations not written by themselves (16 triples of original continuation, human continuation, AI-based continuation) without knowing each continuation’s category. Their task was to assign each continuation to the correct category. Results showed that participants misclassified 18% of the AI-based continuations (14% as written by other participants without AI-tool and 3% as being the original continuation). Additionally, participants misclassified 35% of the purely human continuations written by other participants (13% as being AI-based continuations and 22% as being the original). These findings indicate that even professionals are no longer able to perfectly distinguish between narrative texts mainly written by an AI-writing-tool and purely human text.
Superposition with First-class Booleans and Inprocessing Clausification

Abstract We present a complete superposition calculus for first-order logic with an interpreted Boolean type. Our motivation is to lay the foundation for refutationally complete calculi in more expressive logics with Booleans, such as higher-order logic, and to make superposition work efficiently on problems that would be obfuscated when using clausification as preprocessing. Working directly on formulas, our calculus avoids the costly axiomatic encoding of the theory of Booleans into first-order logic and offers various ways to interleave clausification with other derivation steps. We evaluate our calculus using the Zipperposition theorem prover, and observe that, with no tuning of parameters, our approach is on a par with the state-of-the-art approach.
A Method of Codec Comparison and Selection for Good Quality Video Transmission Over Limited-Bandwidth Networks

Finding a proper balance between video quality and the required bandwidth is an important issue, especially in networks of limited capacity. The problem of comparing the efficiency of video codecs and choosing the most suitable one in a specific situation has become very important. This paper proposes a method of comparing video codecs while also taking into account objective quality assessment metrics. The author shows the process of preparing video footage, assessing its quality, determining the rate–distortion curves, and calculating the bitrate saving for pairs of examined codecs. Thanks to the use of the spline interpolation method, the obtained results are better than those previously presented in the literature, and more resistant to the quality metric used.
An ensemble method for nuclei detection of overlapping cervical cells

The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988).
Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling

Most prior work on task-oriented dialogue systems are restricted to limited coverage of domain APIs. However, users oftentimes have requests that are out of the scope of these APIs. This work focuses on responding to these beyond-API-coverage user turns by incorporating external, unstructured knowledge sources. Our approach works in a pipelined manner with knowledge-seeking turn detection, knowledge selection, and response generation in sequence. We introduce novel data augmentation methods for the first two steps and demonstrate that the use of information extracted from dialogue context improves the knowledge selection and end-to-end performances. Through experiments, we achieve state-of-the-art performance for both automatic and human evaluation metrics on the DSTC9 Track 1 benchmark dataset, validating the effectiveness of our contributions.
Bayesian Embeddings for Few-Shot Open World Recognition

As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information. This stands in stark contrast to modern machine learning systems that are typically designed with a known set of classes and a large number of examples for each class. In this work we extend embedding-based few-shot learning algorithms to the open-world recognition setting. We combine Bayesian non-parametric class priors with an embedding-based pre-training scheme to yield a highly flexible framework which we refer to as few-shot learning for open world recognition (FLOWR). We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets. Our results show, compared to prior methods, strong classification accuracy performance and up to a 12% improvement in H-measure (a measure of novel class detection) from our non-parametric open-world few-shot learning scheme.
A Recursive Estimator for Pseudolinear Target Motion Analysis Using Multiple Hybrid Sensors

The target motion analysis (TMA) based on multiple hybrid sensors is currently performed with a batch algorithm, which is generally memory-demanding for online applications. This article proposes a recursive estimator for the batch counterpart with sequential calculation for multistatic TMA using hybrid measurements of the angle of arrival, time difference of arrival, and frequency difference of arrival. To be specific, a bias-compensated instrumental variable (IV)-based recursive estimator is derived, for which a weighted pseudolinear estimator with fusion policy (WPLE-F) is given to construct the IV and provide the initial state. The proposed method requires no additional batch estimator for initialization and, hence, is more convenient for applications. Simulations have shown that the proposed estimator achieves a mean-squared-error performance close to the Cramér-Rao lower bound and outperforms the existing batch estimator.
Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge

Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis. However, explanation techniques are still embryotic, and they mainly target ML experts rather than heterogeneous end-users. Furthermore, existing solutions assume data to be centralised, homogeneous, and fully/continuously accessible—circumstances seldom found altogether in practice. Arguably, a system-wide perspective is currently missing. The project named “Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge ” (Expectation) aims at overcoming such limitations. This manuscript presents the overall objectives and approach of the Expectation project, focusing on the theoretical and practical advance of the state of the art of XAI towards the construction of personalised explanations in spite of decentralisation and heterogeneity of knowledge, agents, and explainees (both humans or virtual). To tackle the challenges posed by personalisation, decentralisation, and heterogeneity, the project fruitfully combines abstractions, methods, and approaches from the multi-agent systems, knowledge extraction/injection, negotiation, argumentation, and symbolic reasoning communities.
RoadEdgeNet: Road Edge Detection System Using Surround View Camera Images

Road Edge is defined as the borderline where there is a change from the road surface to the non-road surface. Most of the currently existing solutions for Road Edge Detection use only a single front camera to capture the input image; hence, the system’s performance and robustness suffer. Our efficient CNN trained on a very diverse dataset yields more than 98% semantic segmentation for the road surface, which is then used to obtain road edge segments for individual camera images. Afterward, the multi-cameras raw road edges are transformed into world coordinates, and RANSAC curve fitting is used to get the final road edges on both sides of the vehicle for driving assistance. The process of road edge extraction is also very computationally efficient as we can use the same generic road segmentation output, which is computed along with other semantic segmentation for driving assistance and autonomous driving. RoadEdgeNet algorithm is designed for automated driving in series production, and we discuss the various challenges and limitations of the current algorithm.
Intelligence in Strategic Games

If an agent, or a coalition of agents, has a strategy, knows that she has a strategy, and knows what the strategy is, then she has a know-how strategy. Several modal logics of coalition power for know-how strategies have been studied before.&#x0D; The contribution of the article is three-fold. First, it proposes a new class of know-how strategies that depend on the intelligence information about the opponents’ actions. Second, it shows that the coalition power modality for the proposed new class of strategies cannot be expressed through the standard know-how modality. Third, it gives a sound and complete logical system that describes the interplay between the coalition power modality with intelligence and the distributed knowledge modality in games with imperfect information.
Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

The use of artificial intelligence technology to analyze human behavior is one of the key research topics in the world. In order to detect and analyze the characteristics of human body behavior after training, a detection model combined with a convolutional neural network (CNN) is proposed. Firstly, the human skeleton suggestion model is established to analyze the driving mode of the human body in motion. Secondly, the number of layers and neurons in CNN are set according to the skeleton feature map. Then, the output information is classified according to the fatigue degree according to the body state after exercise. Finally, the training and performance test of the model are carried out, and the effect of the body behavior feature detection model in use is analyzed. The results show that the CNN designed in the study shows high accuracy and low loss rate in training and testing and also has high accuracy in the practical application of fatigue degree recognition after human training. According to the subjective evaluation of volunteers, the overall average evaluation is more than 9 points. The above results show that the designed convolution neural network-based detection model of body behavior characteristics after training has good performance and is feasible and practical, which has guiding significance for the design of sports training and training schemes.
Hidden Markov chains and fields with observations in Riemannian manifolds

Hidden Markov chain, or Markov field, models, with observations in a Euclidean space, play a major role across signal and image processing. The present work provides a statistical framework which can be used to extend these models, along with related, popular algorithms (such as the Baum-Welch algorithm), to the case where the observations lie in a Riemannian manifold. It is motivated by the potential use of hidden Markov chains and fields, with observations in Riemannian manifolds, as models for complex signals and images.
UTMobileNetTraffic2021: A Labeled Public Network Traffic Dataset

A high-quality network traffic dataset is essential to the development of accurate network traffic classification algorithms. In this work, we present a new labeled public network traffic dataset with realistic mobile traffic from a wide range of popular applications. An automated platform is constructed to generate and collect data traffic from specified applications in a controlled environment. The dataset contains over 21 million packets from more than 29 hours of mobile traffic with application and activity-level labels. We provide an application classification example using machine learning (ML) models trained on the proposed dataset.
Causal Discovery with Multi-Domain LiNGAM for Latent Factors

Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the single-domain data only. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results. The model enriches the causal representation for multi-domain data. We propose an integrated two-phase algorithm to estimate the model. In particular, we first locate the latent factors and estimate the factor loading matrix. Then to uncover the causal structure among shared latent factors of interest, we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multi-domain latent factors and latent factors of interest. We show that the proposed method provides locally consistent estimators. Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach.
Logo Detection With No Priors

In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.
A Federated Interactive Learning IoT-Based Health Monitoring Platform

Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as 'Human-in-the-loop' in an active learning strategy in order to improve the clients' Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model's accuracy is improved while privacy and security of data has been upheld.