Reviewerly/exHarmony huggingface dataset:
Abnormal Event Detection and tracking in Surveillance Videos using Neural Networks

In recent years abnormal event detection has become an important research area because of the following reasons : the increase of the applications of it, the increase of the number surveillance cameras in public locations such as museums, markets, airports, stadiums, train stations, etc.
Sentence Processing

The human brain receives massive amounts of information every minute, and working memory limits how much data can be computed in real time. The brain selects what is processed following probabilistic associations based on perceptual salience, redundancy, frequency, and consistency. This chapter reviews cognitive research focusing on how physical, psychological, and experiential factors shape adults’ ability to compute new and enduring L2 probabilistic patterns under the time demands typical of real-time morphological and morphosyntactic processing and prediction. The chapter includes a summary of critical issues in usage-based models, a review of empirical studies, and future directions.
Reflection Removal with Varied Field of View Images

Due to the presence of an additional glass pane between the camera and the scene, an additional reflection scene is captured in the image apart from the desired object sometimes. Images are more often captured from mobile handsets these days which have multiple cameras. This paper gives the advantage of multiple cameras. There exists a disparity and varied field of view when images are captured with multiple cameras. We use these two factors to act as a cue to remove reflection, as reflection intensity across the image pairs change with different field-of-view. The proposed method is robust and convenient to implement as it does not require an additional hardware, for example, light field camera for stereo images. Also, it does not make assumptions about the appearance or intensity of reflection.
Using Corpora in Research on Second Language Psycholinguistics

This chapter is a survey of corpus-based approaches to L2 psycholinguistics with a focus on how corpus data inform/operationalize psycholinguistic notions such as frequency, entrenchment, dispersion, contingency, and surprisal. Following a brief overview of the historical development of corpus-based methodologies, we discuss these in terms of the statistical techniques they associate with, their strengths and limitations, and their relevance for psycholinguistic analyses. We end with a discussion of possible avenues for future corpus work in psycholinguistics.
SELF-REGULATED LEARNING IN FIRST YEAR INDUSTRIAL ENGINEERING STUDENTS, NATIONAL TECHNOLOGICAL INSTITUTE OF MEXICO CAMPUS OAXACA, MEXICO

Self-regulated learning allows teachers to implement an established teaching process and students to master tools that help them be clear about their goals and objectives. The objective of the present work was to analyze the self-regulation strategies of the first-year students of the Industrial Engineering career assigned to the National Technological Institute of Mexico campus Oaxaca, Mexico corresponding to the academic cycle 2022-B. It was a descriptive-cross-sectional study. The academic self-regulation process was determined through the adaptation of the Torre instrument (2007), this instrument has a total of 20 items, divided into four dimensions (active metacognitive awareness, level of control and academic verification, daily effort in performing tasks and active processing during classes). A total of 114 students participated, 67.8% of first-year students consider themselves self-regulators in their learning process. Students are clear about what self-regulated learning is, but they still perceive behavioral strategies. It is the responsibility of the students to improve their training process, it is essential to do this type of detection in the first two years of the degree to strengthen those skills.
A Further Look at Italian &lt;z&gt;

The relationship between the Italian letter &lt;z&gt; and its sound pattern in the language is unlike any other letter. This article focuses closely on that relationship, demonstrating how it is realized in all contexts, and in what manners it is unique, relative to other consonant letters.
Optus breach spurs Australian cybersecurity reform

Significance Penalties for poor corporate cybersecurity will be increased. Regulators will get more powers, reporting requirements for victims will be strengthened, and information-sharing improved. This comes after personal data of 9.8 million people were stolen from Singapore-owned telco Optus. Impacts Jurisdictional conflicts within government and industry will make it difficult to consolidate oversight functions. Resistance from several leading industries could weaken the impact of regulatory reforms. Neglect of training systems and staffing has raised the threat level for businesses.
A Postdigital Response: User Experience Design, Interactive, Immersive, and Mixed Reality Performance

In responding to a diverse field of practice—including work that does not always claim the precise label “experiential,” but may also be identified as immersive, participatory, or interactive—this essay describes how such performance can respond to and act within cultures that have been formed and shaped by technologies of the digital. It considers how such performance might contend with technologies of presentation and consumption that appear to attenuate a valorized sense of immediacy and tracks the ways in which postdigital performance's contradictory histories, impulses, and agendas may seek to empower artists and audiences alike, even as they remain part of a larger economic imperative of brand-living and the commodification of experience.
A Postdigital Response: Performance Technologies and Design Thinking

As digital tools and devices have become common, digital spaces have become commonplace. Responding to ideas from preceding essays, this writing attempts to trace a future trajectory of art making in this postdigital time and (cyber)space. The focus is on the social use of new device technologies, education, and language, as well as how to grow as designers and creators of experiential theatre as we move forward into an ever-integrated digital life.
Double-Reservoir Deep Echo State Network Architecture for short-term Electricity Demand Forecasting

This study presents a novel, streamlined and scalable deep Echo State Network (ESN) architecture for demand forecasting in electricity markets. In contemporary electricity markets, where each new day it becomes more difficult to achieve smooth technical operations and not too much volatile electricity prices, the market stakeholders face the problem of forecasting energy demand with increasingly high accuracy and computational efficiency, because knowing demand in advance helps manage coming problems. Deep ESNs demonstrated themselves to be very fitting for the forecasting endeavor. We hence propose a deep ESN architecture for demand forecasting that has higher efficiency than currently used deep ESNs, while maintaining similar forecasting accuracy. Our design strategy is based on disentangling the ESN readout matrix from the individual reservoirs that make the ESN deep, just maintaining the readout weights from the output layer reservoir only. Furthermore, we use Particle Swarm optimization (PSO) to optimize the inter-reservoir connecting weights. We will prove, by numerical testing, that this allows our architecture to achieve an accuracy close to that of current deep ESNs, while being more scalable and being based on fewer parameters than the standard deep ESNs. Specifically, we evaluate this architecture by forecasting a daily average electricity demand time series from the Spanish electricity market. Our architecture, once optimized by PSO, is shown to improve over some common benchmarks and state-of-the-art methods.
Weighted Coordinates Poset Block Codes

Given $[n]=\{1,2,\ldots,n\}$, a partial order $\preceq$ on $[n]$, a label map $\pi : [n] \rightarrow \mathbb{N}$ defined by $\pi(i) = k_i$ with $\sum_{i=1}^{n}\pi (i) = N$, the direct sum $ \mathbb{F}_{q}^{k_1} \oplus \mathbb{F}_{q}^{k_2}\oplus \ldots \oplus \mathbb{F}_{q}^{k_n} $ of $ \mathbb{F}_q^N $, and a weight function $w$ on $ \mathbb{F}_q $, we define a poset block metric $d_{(P,w,\pi)}$ on $\mathbb{F}_{q}^{N}$ based on the poset $P=([n],\preceq)$. The metric $d_{(P,w,\pi)}$ is said to be weighted coordinates poset block metric ($(P,w,\pi)$-metric). It extends the weighted coordinates poset metric ($(P,w)$-metric) introduced by L. Panek and J. A. Pinheiro and generalizes the poset block metric ($(P,\pi)$-metric) introduced by M. M. S. Alves et al. We determine the complete weight distribution of a $(P,w,\pi)$-space, thereby obtaining it for $(P,w)$-space, $(P,\pi)$-space, $\pi$-space, and $P$-space as special cases. We obtain the Singleton bound for $(P,w,\pi)$-codes and for $(P,w)$-codes as well. In particular, we re-obtain the Singleton bound for any code with respect to $(P,\pi)$-metric and $P$-metric. Moreover, packing radius and Singleton bound for NRT block codes are found.
APPLICATION OF DIGITAL IMAGE CORRELATION IN HYGROTHERMAL-MECHANICAL TESTING OF FIBER REINFORCED COMPOSITES

The Office of Naval Research High-Fidelity Building Block and Validation Protocols for NextGen Composite Methods project aims to develop validation protocols for NextGen structural analysis methods. For this purpose, more experimental validation data is needed in matrix dominated failure events and adhesive bonded joints under the effects of temperature and moisture. Matrix dominated events test articles and adhesive bonded joints were tested at elevated temperature wet (ETW, 180F), room temperature ambient (RTA, 70F), and cold temperature dry (CTD, -65F) conditions. Tests at cold and elevated temperatures are enabled by a custom oven that is designed for hot and cold mechanical tests with noncontact optical tracking of displacements and strains with minimal interference for the purpose of Digital Image Correlation (DIC). This article will discuss the use of DIC at hygrothermal mechanical loading and methods to mitigate interference in the optical measurements. Noise is introduced to the optical signals due to circulating air, moisture, and refraction. This is illustrated in applications in tensile, shear, and fracture specimens.
An appraisal of suitable evaluation methods for turbulence mitigation algorithms

In order to mitigate atmospheric turbulence effects such as increased blur, reduced contrast, and image motion as well as geometric deformations, a wide variety of reconstruction techniques has been developed. Such techniques have proven reasonably successful in mitigating one or several turbulence effects but frequently at the cost of introducing unwanted artefacts such as ringing or noise amplification, depending on the algorithm's properties. The application of image quality metrics (IQMs) as a means of comparing the results of various reconstruction algorithms as objectively as possible is a widely used practice. However, added noise and artefacts affect IQMs which rely on information like high frequency components, disproportionately, since noisy results are invariably interpreted as "higher quality". The underlying goal of this article is to define a methodology for comparing the performance of structurally differing algorithms by a combination of select quality metrics. As different metrics will likely yield different ratings for the same algorithm's performance, a combination of suitable metrics is proposed. Therefore the main focus here is foremost on a survey of current methods for assessing image quality in general and on appraising their suitability for evaluating the quality of images processed by turbulence mitigation algorithms in particular.
An improved binary particle swarm optimization algorithm

Binary particle swarm algorithm has been widely used in text clustering, but it still has the disadvantages of slow convergence speed. Based on this, this paper proposes an improved binary particle swarm optimization algorithm, and uses Sigmoid function to update the location so that the algorithm has good global and local search capabilities. The proposed algorithm has been tested on 4 benchmark function sets, and the results show that the improved binary particle swarm optimization algorithm has good performance in global optimization. The experimental results show that the proposed improved binary particle swarm optimization algorithm has good global optimization performance.
Private Cloud Platform Service Capability Test Method

In response to the lack of private cloud evaluation methods used at this stage, the problem of mismatch between supply and demand for private cloud construction. A self-adaptive and scalable method for detecting the capabilities of private cloud systems is proposed. It can evaluate the capabilities of the Infrastructure as a Service (IaaS) cloud computing system. The capabilities of the Open-stack open source private cloud platform were evaluated using the proposed methods. According to the experimental results, the proposed method can accurately obtain the private cloud service capabilities, and the results are consistent with the user's actual test. This method has lower load generation cost and higher test efficiency. Comparing this method with Rally, the component that comes with Open-stack, the scalability and load dynamic simulation have been greatly improved.
Research on Computer Simulation of Opponent's Attacking Route during Volleyball Player's Serving Process

In order to improve the volleyball player's batting posture correction ability, this paper proposes a new AR-based volleyball player's batting posture feature extraction method. Build a volleyball player's offensive route simulation AR image acquisition model. This paper extracts edge contour features and analyzes the action feature quantities of volleyball players' offensive route simulation. We use the AR visual space reconstruction technology to complete the image fusion and feature decomposition of the volleyball player's hitting posture. The experimental results show that the proposed method has high accuracy and good stability of action tracking, which can effectively guide the correction and training of volleyball players' attacking routes.
The Application of Neural Networks to Fraud Detection

Nowadays, with the rapid development of the Internet, social reviews conducted through the Internet have become the main source for people to obtain product information. These reviews help individuals, companies and institutions make decisions. Although social commentary can help people provide more objective and comprehensive information, some individuals or organizations use this method to spread false and untrue information to the outside world, thereby affecting the outside world's judgment on the authenticity of the information, resulting in economic losses. Here is a study of user behavior and comment language to address the difficulties of money fraud. Social fraud detection uses a framework of three key components for review: the review itself, the user performing the review, and the item being reviewed are three key components used by social fraud detection. Under this framework, we do this through appropriate sequence modeling methods, Hidden Markov Models (HMM) and Artificial Neural Networks (ANN) are two examples. By summarizing and expanding the contributions of key persons in the subject of financial fraud, we assist new scholars in the field in providing some theoretical support.
Study of ICU Mortality Prediction and Analysis based on Random Forest

Accurately predicting the mortality of ICU patients is a tricky problem in modern clinical medicine. Promoting the mortality prediction can effectively improve the ICU utilization and allow patients in need to enter the ICU as soon as possible. Due to the incomplete collection of patient vital signs, the prediction of patient death usually involves a large component of manual intervention. For example, doctors need to pre-classify patient background information and manually judge whether the patient will die In the light of their experience, etc. There is no complete set of vector features that can be used. ICU mortality prediction in ICU still lacks a unified vector for feature selection. This paper used random forest to predict and analysed ICU patient death according to the data set downloaded from Kaggle website which emphasis on the chronic condition of diabetes, through data from MIT's GOSSIS (Global Open-Source Severity of Illness Score) initiative. Our model approaches encouraging performance (Accuracy=0.9241, F1-score=0.96, Recall=0.99, Precision=0.93), and the most important features are selected, the feasibility of unified vector modelling is proved.
Occluded Pedestrian Detection and Image Recognition with Multi-Attention Context Networks

For the complex traffic road scenarios where the occluded pedestrians are difficult to be detected by detectors, Multi-Attention Context Network (MACNet) is proposed, aiming to use contextual information and attention mechanism to handle the occluded pedestrians. Firstly, we add the multi-attention context module to make the detector obtain richer contextual information and use its attention mechanism to learn different occlusion patterns. On this basis, add trainable parameters to combine the global context module with the multi-attention context module to establish an adaptive mutual supervision mechanism to further improve the feature extraction of obscured pedestrians. Finally, unreasonable samples and too small positive samples are ignored in the network training process to reduce the negative impact of such samples on the network training. Experimental results show that the proposed method reduces the detection miss rate in different scenarios, and the improvement of pedestrian detection miss rate in heavy occlusion is more obvious.
Correlation Analysis of Traffic Accident Factors based on Mean Clustering

In order to effectively improve the mining efficiency of traffic accident factors and enhance the clarity of mining results, an association analysis method based on mean clustering is proposed. Firstly, the method generalizes the accident data, extracts the main accident attributes, and uses K-means clustering to classify the accident level according to the number of casualties; Based on different accident levels, the improved Apriori algorithm is used for association analysis to mine the main contributing factors. The experiment uses the British government public data set and multiple data mining algorithms for quantitative and qualitative analysis. The results show that the mining efficiency of the combined algorithm has been significantly improved, and the correlation results can more intuitively reflect the relationship between accident factors and accident severity, which is suitable for traffic accident profile analysis.
Research on Indoor Map Construction and Path Optimization of Epidemic Prevention Robot

At present, the new crown epidemic is still fierce. For the scene of centralized isolation area, this paper studies and designs an indoor map suitable for isolation places and a low-cost mobile robot with low computation and low cost for navigation and obstacle avoidance, so as to reduce doctor-patient contact, reduce the probability of infection, save human resources and improve the efficiency of prevention and control management. This paper first analyzes the map generation algorithm, and uses EKF-slam to realize the construction of the isolated site map. Then the DWA and A * algorithms are used to conduct simulation experiments on the generated map and complete the base map's local area and global path planning. Finally, the indoor base map construction and reproduction accuracy of the robot were accepted through the simulation experiment, and the path optimization effect was compared to obtain the robot's practicability in the actual epidemic prevention process. Meanwhile, with the application of the robot in the actual epidemic prevention work, the convenience and application value of robots will be immediately reflected.
Visuomotor Mismatch Responses as a Hallmark of Explaining Away in Causal Inference

How are visuomotor mismatch responses in primary visual cortex embedded into cortical processing? We here show that mismatch responses can be understood as the result of a cooperation of motor and visual areas to jointly explain optic flow. This cooperation requires that optic flow is not explained redundantly by both areas, meaning that optic flow inputs to V1 that are predictable from motor neurons should be canceled (i.e., explained away). As a result, neurons in V1 represent only external causes of optic flow, which could allow the animal to easily detect movements that are independent of its own locomotion. We implement the proposed model in a spiking neural network, where coding errors are computed in dendrites and synaptic weights are learned with voltage-dependent plasticity rules. We find that both positive and negative mismatch responses arise, providing an alternative to the prevailing idea that visuomotor mismatch responses are linked to dedicated neurons for error computation. These results also provide a new perspective on several other recent observations of cross-modal neural interactions in cortex.
An Image Edge Detection Method Based on Marked Watershed Algorithm

Aiming at the problems of traditional watershed edge detection algorithm in the study of abnormal behavior images of rail transit crowd, such as susceptibility to noise and over-segmentation phenomenon, an image edge detection method based on marked watershed algorithm is proposed. The method first uses a mathematical morphology method for smoothing and edge enhancement, and then uses an improved watershed algorithm based on marker for edge detection to obtain the closed edge of the target image, so as to mark the edge of the target image. The experiments show that compared with the traditional watershed edge detection algorithm, this method can reduce the influence of noise, effectively suppress the over-segmentation phenomenon, improve the edge detection effect, and detect clear and uniform edges, which provides a new image edge detection technique and method for the study of abnormal behavior images of rail transit crowds.
Research on Key Technologies of Human Action Intelligent Recognition based on Virtual Reality

In order for computers to understand the semantics of human actions and give correct feedback, action recognition technology is used in interactive tasks. Unity can easily complete various three-dimensional interactive developments and create virtual simulation content with an active rate of up to 30%, occupying an important position in the virtual reality industry. Therefore, this software was chosen to complete the virtual reality interaction based on gesture recognition application. This article first builds the initial virtual reality interactive platform through Unity software, and then applies the gesture recognition method proposed above to the designed interactive platform, so that the computer can accurately understand the meaning of human actions when people are doing actions. This article mainly introduces the construction of the experimental platform of the entire virtual reality system and the real-time realization of the motion recognition system.