Reviewerly/exHarmony huggingface dataset:
Spatial-Temporal Map Vehicle Trajectory Detection Using Dynamic Mode Decomposition and Res-UNet+ Neural Networks

This paper presents a machine-learning-enhanced longitudinal scanline method to extract vehicle trajectories from high-angle traffic cameras. The Dynamic Mode Decomposition (DMD) method is applied to extract vehicle strands by decomposing the Spatial-Temporal Map (STMap) into the sparse foreground and low-rank background. A deep neural network named Res-UNet+ was designed for the semantic segmentation task by adapting two prevalent deep learning architectures. The Res-UNet+ neural networks significantly improve the performance of the STMap-based vehicle detection, and the DMD model provides many interesting insights for understanding the evolution of underlying spatial-temporal structures preserved by STMap. The model outputs were compared with the previous image processing model and mainstream semantic segmentation deep neural networks. After a thorough evaluation, the model is proved to be accurate and robust against many challenging factors. Last but not least, this paper fundamentally addressed many quality issues found in NGSIM trajectory data. The cleaned high-quality trajectory data are published to support future theoretical and modeling research on traffic flow and microscopic vehicle control. This method is a reliable solution for video-based trajectory extraction and has wide applicability.
Checking HateCheck: a cross-functional analysis of behaviour-aware learning for hate speech detection

Behavioural testing -- verifying system capabilities by validating human-designed input-output pairs -- is an alternative evaluation method of natural language processing systems proposed to address the shortcomings of the standard approach: computing metrics on held-out data. While behavioural tests capture human prior knowledge and insights, there has been little exploration on how to leverage them for model training and development. With this in mind, we explore behaviour-aware learning by examining several fine-tuning schemes using HateCheck, a suite of functional tests for hate speech detection systems. To address potential pitfalls of training on data originally intended for evaluation, we train and evaluate models on different configurations of HateCheck by holding out categories of test cases, which enables us to estimate performance on potentially overlooked system properties. The fine-tuning procedure led to improvements in the classification accuracy of held-out functionalities and identity groups, suggesting that models can potentially generalise to overlooked functionalities. However, performance on held-out functionality classes and i.i.d. hate speech detection data decreased, which indicates that generalisation occurs mostly across functionalities from the same class and that the procedure led to overfitting to the HateCheck data distribution.
Zero-shot Cross-lingual Conversational Semantic Role Labeling

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training.To avoid expensive data collection and error-propagation of translation-based methods, we present a simple but effective approach to perform zero-shot cross-lingual CSRL.Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders and elaborately designed pre-training objectives.Experimental results show that our model outperforms all baselines by large margins on two newly collected English CSRL test sets.More importantly, we confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese by incorporating the CSRL information into the downstream conversation-based models.We believe this finding is significant and will facilitate the research of non-Chinese dialogue tasks which suffer the problems of ellipsis and anaphora.
Are Infographics Worth It?: An Assessment of Information Retention in Relation to Information Embedded in Infographics

Infographics are an effective means to communicate with audiences, draw attention, and make concepts digestible in a quick and straightforward way. This study explores students’ opinions on infographics used in library instruction, and provides quantitative data to help librarians determine whether these tools will help students engage with text and retain key concepts. Based on research conducted with undergraduate music students at two universities, DePauw University and the University of Minnesota, Twin Cities, this paper examines how students understand and interact with content embedded in infographics, and measures retention of information presented in either plain-text or infographic form.
A Secured Text Encryption with Near Field Communication (NFC) using Huffman Compression

There are a lot of Challenges raised over the security of information written to the Mifare classic 1k a Radio Frequency Identification card due to the vulnerability status of this card. The card’s information can be traced to another card or an electronic device. These issues allow for unauthorized access to the data on the Mifare classic 1k‐enabled device data which are transmitted between the device and reader. The information produced by a Mifare classic 1k enabled credential system for a stated status is also a concern. The focus of the study is to develop an algorithm to secure information written to the Near Field Communication tag. The performance of the system shows that when n=50, with elapse time of 1.2ms the unique character was 64, likewise at optimal when n=200, the elapse time was 1ms with the unique character of 62. This result shows a decline of the symbol-by-symbol restriction with elapses time which can secure the information of the unique character.
Financial Data Anomaly Detection Method Based on Decision Tree and Random Forest Algorithm

The fast-developing computer network not only brings convenience to people but also brings security problems to people due to the appearance of various abnormal flows. However, various current detection systems for abnormal network flows have more or less flaws, such as the most common intrusion detection system (IDS). Due to the lack of self-learning capabilities of market-oriented IDS, developers and maintenance personnel have to update the virus database of the system in real time to make the system work normally. With the emergence of machine learning and data mining in recent years, new ideas and methods have emerged in the detection of abnormal network flows. In this paper, the random forest algorithm is introduced into the detection of abnormal samples, and the concept of abnormal point scale is proposed to measure the abnormal degree of the sample based on the similarity of the samples, and the abnormal samples are screened out according to this scale. Simulation experiments show that compared with the other two distance-based abnormal sample detection techniques, the random forest-based abnormal sample detection has greater advantages than the other two methods in terms of improving the accuracy of the model and reducing the computing time.
Non-Normal Super-Strict Implications

This paper introduces the logics of super-strict implications that are based on C.I. Lewis' non-normal modal logics S2 and S3.The semantics of these logics is based on Kripke's semantics for non-normal modal logics.This solves a question we left open in a previous paper by showing that these logics are weakly connexive.
Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process–Based Latent Factor Model

As a key decision-making process in compensation and benefits (C&amp;B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&amp;B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as “cold start” issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management. Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability.
Advanced Lightweight Encryption Algorithm for Android (IoT) Devices

Security and Controls with Data privacy in Internet of Things (IoT) devices is not only a present and future technology that is projected to connect a multitude of devices, but it is also a critical survival factor for IoT to thrive. As the quantity of communications increases, massive amounts of data are expected to be generated, posing a threat to both physical device and data security. In the Internet of Things architecture, small and low-powered devices are widespread. Due to their complexity, traditional encryption methods and algorithms are computationally expensive, requiring numerous rounds to encrypt and decode, squandering the limited energy available on devices. A simpler cryptographic method, on the other hand, may compromise the intended confidentiality and integrity. This study examines two lightweight encryption algorithms for Android devices: AES and RSA. On the other hand, the traditional AES approach generates preset encryption keys that the sender and receiver share. As a result, the key may be obtained quickly. In this paper, we present an improved AES approach for generating dynamic keys.
The Virtual Human Breathing Coach

Emerging technology such as Virtual Reality has been successfully explored in healthcare, including Virtual Reality Therapy in mindfulness and relaxation. However, most of these explorations lack the stimulating environment that gives users the sense of personal connection through the exchange of bodily cues with another human. We present a breathing relaxation system where a (virtual) human coach guides users&#x0027; breathing to a relaxed state. Our virtual breathing coach is of a realistic human appearance, speaks in a human voice, produces human gesticulation, and displays breathing animations. Here we describe the theoretical background in human-to-human physiological entrainment and the framework of the Virtual Human Breathing Coach where the virtual coach guides users through a set of established &#x201C;Box&#x201D; breathing exercises. Lastly, we will describe how we quantify the success of the system in influencing the users&#x0027; breathing to a level that is prescribed by the virtual breathing coach. The application of our system ranges from remote health monitoring to self-help training that can be accessed from the comfort of our homes.
Evaluation Methodology for Object Detection and Tracking in Bounding Box Based Perception Modules

The aim of this work is to formulate a new metric to be used in the automotive industry for the evaluation process of software used to detect vehicles on video data. To achieve this goal, we have formulated a new concept for measuring the degree of matching between rectangles for industrial use. We propose new measure based on three sub-measures focused on the area of the rectangle, its shape, and distance. These sub-measures are merged into a General similarity measure to avoid problems with poor adaptability of the Jaccard index to practical issues of recognition. Additionally, we create method of calculation of detection quality in the sequence of video frames that summarizes the local quality and adds information about possible late detection. Experiments with real and artificial data have confirmed that we have created flexible tools that can reduce time needed to evaluate detection software efficiently, and provide more detailed information about the quality of detection than the Jaccard index. Their use can significantly speed up data analysis and capture the weaknesses and limitations of the detection system under consideration. Our detection quality assessment method can be of interest to all engineers involved in machine recognition of video data.
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appropriate responses with similar statistical properties. However, dialogue can also be regarded as a goal directed process, where speakers attempt to accomplish a specific task. Reinforcement learning (RL) algorithms are designed specifically for solving such goal-directed problems, but the most direct way to apply RL -- through trial-and-error learning in human conversations, -- is costly. In this paper, we study how offline reinforcement learning can instead be used to train dialogue agents entirely using static datasets collected from human speakers. Our experiments show that recently developed offline RL methods can be combined with language models to yield realistic dialogue agents that better accomplish task goals.
The linear and non-linear interactions between blockchain technology index and the stock market indices: a case study of the UAE banking sector

Purpose This paper aims to investigate the linear and nonlinear interactions between the blockchain technology index and the UAE stock market index within the context of the Abu Dhabi and Dubai banking sector. Design/methodology/approach In this study, linear analysis was performed using the generalized autoregressive conditional heteroscedasticity model (GARCH) (1,1) model, whereas nonlinear analysis was performed using the wavelet coherence model. Findings Based on the results of the GARCH (1) model, the authors find that the blockchain technology index has a positive significant impact on stock market returns in the Abu Dhabi and Dubai banking sector. In addition, the findings indicate that increasing blockchain integration in the banking industry decreases banks’ stock market volatility and facilitates price stabilization. Additionally, the coherence wavelet analysis reveals that there is a phase relationship between the blockchain technology index and banks’ stock market indices in the banking sector of the UAE. The association was stronger during the global pandemic crisis because they were moving together across different timescales. Practical implications With the help of the linear analysis, this study offers a focal point and valuable insights to policymakers, central banks and commercial banks management on how implementing blockchain technology in the banking industry help boost stock market returns, reduce volatility and facilitate price stability. As a result of the nonlinear analysis of the significant long-term degree of co-movement between blockchain technology and banks’ stock markets in UAE, policymakers or the management of banks in UAE should take the growth of the blockchain technology industry into consideration to ensure the continued development of the banking sector. For investors, the findings provide implications for portfolio managers operating in the UAE who are encouraged to take short-term co-movement into account (1–16-week horizons) through both frequency and time when designing their portfolio while keeping long-horizon periods in mind is not recommended. Originality/value It is a pioneering study that empirically examines the linear and nonlinear nexus between the blockchain technology index and banks’ stock market returns and price stability.
Image Shadow Detection and Removal Based on Region Matching of Intelligent Computing

Shadow detection and removal play an important role in the field of computer vision and pattern recognition. Shadow will cause some loss and interference to the information of moving objects, resulting in the performance degradation of subsequent computer vision tasks such as moving object detection or image segmentation. In this paper, each image is regarded as a small sample, and then a method based on material matching of intelligent computing between image regions is proposed to detect and remove image shadows. In shadow detection, the proposed method can be directly used for detection without training and ensures the consistency of similar regions to a certain extent. In shadow removal, the proposed method can minimize the influence of shadow removal operation on other features in the shadow region. The experiments on the benchmark dataset demonstrate that the proposed approach achieves a promising performance, and its improvement is more than 6% in comparison with several advanced shadow detection methods.
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering

Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking. Conventional ranking-based KBQA models, which rely on a candidate enumeration step to reduce the search space, struggle with flexibility in predicting complicated queries and have impractical running time. In this paper, we present ArcaneQA, a novel generation-based model that addresses both the large search space and the schema linking challenges in a unified framework with two mutually boosting ingredients: dynamic program induction for tackling the large search space and dynamic contextualized encoding for schema linking. Experimental results on multiple popular KBQA datasets demonstrate the highly competitive performance of ArcaneQA in both effectiveness and efficiency.
Classification Based on Machine Learning Methods for Identification of Image Matching Achievements

Classification is one method in image processing. Image processing to search for similar images or with similarity ownership is called image matching or image matching. In the measurement of image matching, the original and fake logo objects are used. Identification of similarity manually with the help of human vision is not necessarily precise and it is difficult to obtain accurate results. Based on this, the identification of image matching of the original and fake logos automatically requires an application, in order to obtain precise and more accurate results. Identification of image suitability is determined through the image segmentation process, and feature extraction is based on the statistics of Red-Green-Blue (RGB), Hue-Saturation-Value (HSV), feature extraction of area, perimeter, eccentricity, and tangent distance measurements. The purpose of this study includes the identification of the achievement of image-matching logo images with comparisons of accuracy between various machine learning methods. The use of machine learning methods in this study includes the k-Nearest Neighbor (kNN), Random Forest (RF), and Multilayer Perceptron (MLP) methods. The use of the dataset includes eighteen training data and eight logo image testing data, divided into genuine and fake classes. The results of the measurement of the accuracy value obtained a value of seventy-five percent with the kNN method or the RF method, while the MLP method obtained an accuracy value of eighty-seven point five percent. Based on these results, it can be concluded that the MLP method with the highest accuracy value was chosen as a classification model from machine learning to identify the achievement of image matching on the original and fake logos. For further development, the system can be developed using other methods or a combination of different methods, in order to obtain better accurate results.
Balancing Realities by Improving Cross-Reality Interactions

Virtual reality (VR) devices have a demonstrated capability to make users feel present in a virtual world. Research has shown that, at times, users desire a less immersive system that provides them aware-ness of and the ability to interact with elements from the real world and with a variety of devices. Understanding such cross-reality interactions is an under-explored research area that will become in-creasingly important as immersive devices become more ubiquitous. In this extended abstract, I provide an overview of my previous PhD research on facilitating cross-reality interactions between VR users and nearby non-VR interrupters. I discuss planned future research to investigate the social norms that are complicated by these interactions and design solutions that lead to meaningful interactions. These topics and questions will be discussed at the IEEE VR 2022 Doctoral Consortium.
A semiotic perspective on polysemy

Abstract This paper extends Semiotic-Conceptual Analysis (SCA) to provide a means for comparing and evaluating semiotic relations (i.e. sets of signs) with respect to their ability of and efficiency of expressing conceptual structures. One contributing factor for efficiency is polysemy which refers to reusing representamens of signs in different contexts. Two different types of polysemy are identified: ambiguous polysemy which encodes a view from part signs to compound signs and simultaneous polysemy for analysing how compound signs simultaneously denote more than one meaning. Two detailed examples are included.