Reviewerly/exHarmony huggingface dataset:
Smart Real-Time Vehicle Detection and Tracking System Using Road Surveillance Cameras

Several traffic studies necessitate vehicle counting during peak hours and throughout the day, as well as detailed classification and tracking, which consumes human time and effort, particularly at intersections. Manual efforts primarily collect the necessary traffic demand data live in the field or from video recordings using an extensive data manipulation process. Alternative solutions include computer-based systems that perform human tasks more efficiently and with less time and effort, and these systems vary in function and performance. This paper proposes a comprehensive computer-based system that detects, tracks, and computes related statistics in real time while making the best use of available resources, such as public road surveillance cameras. The main contribution of this work is the effectiveness of combining various computer vision algorithms to achieve high-accuracy performance during real-time streaming of road cameras. The experiments confirm the system’s performance by achieving an average success rate of 93.2%. The novel aspect of this work is that detections, point extractions, matching, tracking, and classification were implemented in a single system that ensures real-time execution and high-accuracy output and uses existing infrastructure. The system compensates for the variations in light between day and night and between cloudy and sunny weather. It also recovers hidden vehicles and changes in view for each vehicle as it moves. The proposed method efficiently and partially integrates some mechanisms into a single system.
ShaderTransformer: Predicting Shader Quality via One-shot Embedding for Fast Simplification

Given specific scene configurations and target functions, automatic shader simplification searches for the best simplified shader variant from an optimization space with many candidates. Although various speedup methods have been proposed, there is still a costly render-and-evaluate process to obtain variant’s performance and quality, especially when the scene changes.
Aligning restricted access data with FAIR: a systematic review

Understanding the complexity of restricted research data is vitally important in the current new era of Open Science. While the FAIR Guiding Principles have been introduced to help researchers to make data Findable, Accessible, Interoperable and Reusable, it is still unclear how the notions of FAIR and Openness can be applied in the context of restricted data. Many methods have been proposed in support of the implementation of the principles, but there is yet no consensus among the scientific community as to the suitable mechanisms of making restricted data FAIR. We present here a systematic literature review to identify the methods applied by scientists when researching restricted data in a FAIR-compliant manner in the context of the FAIR principles. Through the employment of a descriptive and iterative study design, we aim to answer the following three questions: (1) What methods have been proposed to apply the FAIR principles to restricted data?, (2) How can the relevant aspects of the methods proposed be categorized?, (3) What is the maturity of the methods proposed in applying the FAIR principles to restricted data?. After analysis of the 40 included publications, we noticed that the methods found, reflect the stages of the Data Life Cycle, and can be divided into the following Classes: Data Collection, Metadata Representation, Data Processing, Anonymization, Data Publication, Data Usage and Post Data Usage. We observed that a large number of publications used 'Access Control' and 'Usage and License Terms' methods, while others such as 'Embargo on Data Release' and the use of 'Synthetic Data' were used in fewer instances. In conclusion, we are presenting the first extensive literature review on the methods applied to confidential data in the context of FAIR, providing a comprehensive conceptual framework for future research on restricted access data.
Filtered Belief Revision: Syntax and Semantics

Abstract In an earlier paper [Rational choice and AGM belief revision, Artificial Intelligence , 2009] a correspondence was established between the set-theoretic structures of revealed-preference theory (developed in economics) and the syntactic belief revision functions of the AGM theory (developed in philosophy and computer science). In this paper we extend the re-interpretation of those structures in terms of one-shot belief revision by relating them to the trichotomous attitude towards information studied in Garapa (Rev Symb Logic, 1–21, 2020) where information may be either (1) fully accepted or (2) rejected or (3) taken seriously but not fully accepted. We begin by introducing the syntactic notion of filtered belief revision and providing a characterization of it in terms of a mixture of both AGM revision and contraction. We then establish a correspondence between the proposed notion of filtered belief revision and the above-mentioned set-theoretic structures, interpreted as semantic partial belief revision structures. We also provide an interpretation of the trichotomous attitude towards information in terms of the degree of implausibility of the information.
StainCUT: Stain Normalization with Contrastive Learning

In recent years, numerous deep-learning approaches have been developed for the analysis of histopathology Whole Slide Images (WSI). A recurrent issue is the lack of generalization ability of a model that has been trained with images of one laboratory and then used to analyze images of a different laboratory. This occurs mainly due to the use of different scanners, laboratory procedures, and staining variations. This can produce strong color differences, which change not only the characteristics of the image, such as the contrast, brightness, and saturation, but also create more complex style variations. In this paper, we present a deep-learning solution based on contrastive learning to transfer from one staining style to another: StainCUT. This method eliminates the need to choose a reference frame and does not need paired images with different staining to learn the mapping between the stain distributions. Additionally, it does not rely on the CycleGAN approach, which makes the method efficient in terms of memory consumption and running time. We evaluate the model using two datasets that consist of the same specimens digitized with two different scanners. We also apply it as a preprocessing step for the semantic segmentation of metastases in lymph nodes. The model was trained on data from one of the laboratories and evaluated on data from another. The results validate the hypothesis that stain normalization indeed improves the performance of the model. Finally, we also investigate and compare the application of the stain normalization step during the training of the model and at inference.
Special-Purpose English Teaching Reform and Model Design in the Era of Artificial Intelligence

Research on English teaching reform and model design is the focus of the current society. It is a novel idea to use artificial intelligence algorithm to design an English teaching platform, which combines the current field of English teaching reform with the field of artificial intelligence network. The current method is to use the sincent algorithm in artificial intelligence to design the model. Its defect is that the single network education learning makes the learners feel boring. In order to solve these problems, this paper proposes an English blended education method based on artificial intelligence, which aims to study the integration of the network teaching platform and traditional education. This paper uses the artificial intelligence sincent algorithm to establish a teaching platform framework and simulate application strategies for the development of blended education. Through the investigation process of blended English teaching, the results show that the proportion of students who are generally satisfied with English teaching has reached 53.67% and the proportion of students who feel generally satisfied has reached 27.83%. The results of this survey indicate that the majority of students approve of this type of English blended education method.
Application Analysis of Multimedia Technology in College English Curriculum

Over the past 30 years, neural computational models have achieved great success in the field of language learning, bringing about remarkable improvements in the study of individual differences across languages. However, most of the existing research on neural computational models is about the cognitive development of mother tongue, and there is no correlation model of phonological ability, short-term memory ability, and long-term memory ability with individual differences in the research. Therefore, the intervention suggestions in English learning disabilities cannot be targeted. Through neural network simulation and t-test, it can be seen that the factors influencing the overall English performance of learners are not phonological awareness, but rather short-term memory and long-term memory. It can be seen from the simulated individual ANN that the role of working memory is particularly obvious. This conclusion is consistent with neuroscience theories on bilingual control in the brain. Individual ANN can provide a basis for predicting learners’ learning ability and risk analysis of learning disabilities by simulating the learning path of learners, and at the same time providing more accurate clues and directions for subject teachers to carry out personalized teaching.
LesionTalk: Core Data Extraction and Multi-class Lesion Detection in IoT-based Intelligent Healthcare

With the development of intelligent medicine, lesion detection supported by Internet of Things (IoT), big data, and deep learning has become a hotspot. However, lesion detection technology based on deep learning requires huge amounts of high-quality medical image data, and the data from social IoT has the problems of uneven quality and lack of lesion labeling. Current studies usually ignore the unstable quality of IoT data and the interpretability of diagnostic results, resulting in deeper model layers, larger models, poor real-time performance, and lack of persuasion. To address the problems, this article first proposes a core data extraction method for multi-class lesion detection based on unlabeled medical image from social IoT. Then, we propose an ensemble algorithm based on lightweight models to improve the detection accuracy. Finally, we visualize pathological features to enhance the interpretability of core data and detection results. The experimental results show that our method can effectively extract the core data of multiple lesions from low-quality medical images and improve the accuracy of the lightweight lesion detection model as well as the interpretation of detection results.
Exploring Orientation Invariant Heuristic Features with Variant Window Length of 1D-CNN-LSTM in Human Activity Recognition

Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridization (CNN-LSTM), have been implemented. However, the sensor orientation problem poses challenges in HAR, and the length of windows as inputs for the deep neural networks has mostly been adopted arbitrarily. This paper explores the effect of window lengths with orientation invariant heuristic features on the performance of 1D-CNN-LSTM in recognizing six human activities; sitting, lying, walking and running at three different speeds using data from an accelerometer sensor encapsulated into a smartphone. Forty-two participants performed the six mentioned activities by keeping smartphones in their pants pockets with arbitrary orientation. We conducted an inter-participant evaluation using 1D-CNN-LSTM architecture. We found that the average accuracy of the classifier was saturated to 80 ± 8.07% for window lengths greater than 65 using only four selected simple orientation invariant heuristic features. In addition, precision, recall and F1-measure in recognizing stationary activities such as sitting and lying decreased with increment of window length, whereas we encountered an increment in recognizing the non-stationary activities.
Fuzzy Cluster Analysis for Interval Data Based on the Overlap Distance

This article builds the fuzzy clustering algorithm for interval data (FCAI). In the proposed algorithm, we use the overlap distance as a criterion to cluster for interval data. The FCAI can determine not only the suitable number of clusters, the elements in each cluster but also the probability of assigning the elements to the established clusters at the same time. In addition, we also consider the convergence of the proposed algorithm by the theory and illustrated it by the numerical examples. The FCAI is applied well in image recognition, a problem with many challenges nowadays. Using the Grey Level Co-occurrence matrices (GLCMs), we propose a novel texture extraction approach to generate featured intervals. The complex computations of the FCAI can be performed conveniently and efficiently by the established Matlab program. We utilize the corrected rand indexes (CR) to find the suitable number of clusters while a partition entropy (PE) and partition coefficients (PC) are applied to argue the quality of fuzzy clusters. As a result, the experiments on the data sets having different characteristics and elements show the reasonableness of the proposed algorithm and its advantages in comparison to the existing ones. Regarding our best knowledge, it has also shown potential in the real application of this study.
l-LIPs of codes over finite chain rings

In this paper, we first generalize the l-linear intersection pair (l-LIP) of codes over finite fields to the linear l-intersection pair of codes over finite chain rings. Then three criteria of l-linear intersection pair of codes over finite chain rings are obtained. By means of the generator set of cyclic codes, we obtain the generator set of the intersection of two cyclic codes which helps us to get a characterization of l-cyclic intersection pair of codes. As an application, l-cyclic intersection pair of codes over finite chain rings are used to construct new entanglement-assisted quantum error correcting codes. Finally, under suitable conditions, we also obtain a characterization for a l-cyclic intersection pair of codes over principal ideal rings Zk.
Brief Announcement: Asynchronous Verifiable Information Dispersal with Near-Optimal Communication

We present a near-optimal asynchronous verifiable information dispersal (AVID) protocol. The total dispersal cost of our AVID protocol is O(|M| + κ n^2), and the retrieval cost per client is O(|M| + κ n). Unlike prior works, our AVID protocol only assumes the existence of collision-resistant hash functions. Also, in our AVID protocol, the dispersing client incurs a communication cost of O(|M|+κ n) in comparison to O(|M|+κ n łog n) of prior best. Moreover, each node in our AVID protocol incurs a storage cost of O(|M|/n + κ) bits, in comparison to O(|M|/n + κ łog n) bits of prior best. Finally, we present lower bound results on communication cost and show that our AVID protocol has near-optimal communication costs -- only a factor of O(κ) gap from the lower bounds.
A Study on the Adoption of Blockchain for IoT Devices in Supply Chain

The integration of blockchain and IoT enables promising solutions in decentralized environments in contrast with centralized systems. The blockchain brings forth features such as fault tolerance, security, and transparency of the data in IoT devices. As there is requirement of consensus among the network nodes to agree on a single-state-of-ledger, nonetheless, the extensive computational requirement for the consensus protocol becomes a limitation in resource-constrained IoT devices with limited battery, computation, and communication capabilities. This study proposes an empirical approach on the adoption of blockchain in a supply chain environment. Furthermore, a modified version of the Raft consensus protocol is proposed for use in supply-chain environment on the permissioned blockchain Hyperledger. In Raft consensus protocol, each transaction is directed to the leader node that transmits it to the follower nodes, making the leader node the bottleneck thus inhibiting the scalability and throughput of the system. This also results in high latency for the network. The modified RAFT consensus protocol (mRAFT) is based on the idea of utilizing the idle follower nodes in disseminating the vote requests and log replication messages. A detailed empirical evaluation of the solution built on Hyperledger Caliper is performed to demonstrate the applicability of the system. The improved workload division on the peers boosts the throughput and latency of the system in ordering service that enhances the overall efficiency of the system.
How agile coaches create an agile mindset in development teams: Insights from an interview study

Abstract Since the publication of the agile manifesto in 2001, many companies implement an agile—or at least more agile—software development process. However, only including agile methods or practices in the overall process does not guarantee being agile. The mindset of the people involved in the process, including the development team, the customers, and the management, is of particular importance. As such an agile mindset cannot be enforced, the process of creating a suitable mindset needs to be handled with care. In an interview study with nine agile coaches, we analyzed which aspects they perceive being of particular importance during an agile transformation. One of these aspects is the agile mindset. We figure out how they support the creation of such a mindset. We identify 12 categories related to the process of creating an agile mindset. These categories include the collaboration between the coach and the management as well as the necessity to internalize the agile values. The main factor for succeeding with the creation of an agile mindset, however, can be hardly influenced: The success strongly depends on the personal prerequisites and attitudes of the individuals involved in the process, mainly the development team. We synthesize the results of our study into a timeline describing the process of how an agile coach can support the development team creating an agile mindset as part of the transformation towards an agile development process.
Evaluation and Analysis of Assisted Instruction and Ability Improvement Based on Artificial Intelligence

Teachers are a very important part of university education. They have the responsibility of teaching and educating people, and it is also their unshakable responsibility to train all-round talents for the country. If we want to improve students’ quality, we must improve teachers’ teaching quality and pay attention to the research of teachers’ teaching ability. This paper analyzes the connotation of artificial intelligence-assisted instruction. Then, Bayesian active learning modeling is used. This paper mainly adopts the way of questionnaire and empirical research methods and launches a basic investigation on the teaching ability of university teachers. Through investigation, the following problems are summarized: (1) insufficient self-knowledge reserve and weak teaching theoretical foundation and (2) inaccurate orientation of teaching objectives and single teaching methods. Schools need to enrich training methods, establish multiple effective mechanisms for evaluation, meet the basic requirements of each teacher, and play the role of inspiring teachers. As for teachers, they need to have a good attitude, be full of interest in teaching and educating people, have a strong sense of responsibility, and constantly improve themselves and improve themselves.
Like, Share, Comment, and Repeat: Far-right Messages, Emotions, and Amplification in Social Media

Investigating the role of emotions in online political engagement, we show that far-right parties receive more amplification online than centrist parties because their messages produce stronger emotional reactions. Using a 2 × 3 mixed design survey experiment (N = 303), which compared far-right and centrist messages about three campaign issues in Ukraine, we demonstrate that nationalist appeals evoked hope and enthusiasm, which resulted in more likes and shares. However, far-rights were only successful when addressing their traditional issues, such as national language policy. We conclude with advice on how other parties can harness emotional appeals to encourage political participation when facing competition from far-right parties.
Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using automatic data extractions from the 3D body scans. Tedious extra efforts have to be made to manually fill the missing data for various applications. To tackle this problem, this paper proposes a machine learning (ML)-based approach for 3D body measurement estimation while considering the measurement (feature) importance. The proposed approach selects the most critical features to reduce the algorithm input and to improve the ML method performance. In addition, a Bayesian search is further used in fine-tuning the hyperparameters to minimize the mean square error. Two distinct ML methods, i.e., Random Forest and XGBoost, are used and tested on a real-world dataset that contains 3D body scans of 212 participants in the Kansas-Missouri area of the United States. The results show the effectiveness of the proposed methods with roughly 3% of Mean Absolute Percentage Errors in estimating the missing data. The two ML methods with the proposed hybrid feature selection and the Baysian search are comprehensively compared. The comparative results suggest that the Random Forest method performs better than the XGBoost counterpart in filling missing 3D body measurements.
Romanus

Due to the high performance and safety requirements of self-driving applications, the complexity of modern autonomous driving systems (ADS) has been growing, instigating the need for more sophisticated hardware which could add to the energy footprint of the ADS platform. Addressing this, edge computing is poised to encompass self-driving applications, enabling the compute-intensive autonomy-related tasks to be offloaded for processing at compute-capable edge servers. Nonetheless, the intricate hardware architecture of ADS platforms, in addition to the stringent robustness demands, set forth complications for task offloading which are unique to autonomous driving. Hence, we present $ROMANUS$, a methodology for robust and efficient task offloading for modular ADS platforms with multi-sensor processing pipelines. Our methodology entails two phases: (i) the introduction of efficient offloading points along the execution path of the involved deep learning models, and (ii) the implementation of a runtime solution based on Deep Reinforcement Learning to adapt the operating mode according to variations in the perceived road scene complexity, network connectivity, and server load. Experiments on the object detection use case demonstrated that our approach is 14.99% more energy-efficient than pure local execution while achieving a 77.06% reduction in risky behavior from a robust-agnostic offloading baseline.