Reviewerly/exHarmony huggingface dataset:
Goal Line Technology Using Line axis detection

The exponential growth of football all around the world has caused the exactness of every single innovation included in the game to be of immense value. Basic circumstances emerge when the referee can't separate a goal or no goal by fine margins because of human visual limitations. In the modern era popular football leagues have adopted the use of hawk-eye technology which is unaffordable by the local football leagues. In this project, we have designed and developed a prototype of an efficient and cost-effective goal-line technology system. The proposed framework utilizes object detection techniques i.e. HSV model and contours. We identify the color of the ball and use line-axis detection to reference the position of the ball with respect to the goal line. If the goal is scored, the updated score line is sent to the referee through an email. The result is also broadcasted to the live audience through a speaker system and LCD screen. This system helps in decision-making, in this manner making the framework quicker to help the referees in quick decision-making and keeping up the momentum of the game.
The Effects of Using a Machine Translator on Korean University Students" English Writing

The purpose of this study is to investigate the effects of the use of a machine translator on Korean university students" English writing. 50 non-English major students engaged in the 15-week experiment. To examine the effectiveness of using a machine translator, pre-writing tasks without the help of a machine translator and post-writing tasks with it were performed every week and Grammarly, a writing assistant tool, was employed to rate students" writing proficiency. A perception survey was also conducted to explore students" perceptions and satisfaction toward the use of a machine translator. An SPSS statistical analysis was used for data analysis. Findings indicate that participants improved their writing proficiency, especially in vocabulary and sentence length. The survey resultevealed that participants showed high satisfaction with a machine translator. Based on the findings, using a machine translator in English writing had positive effects on students in terms of writing proficiency, study attitude, and satisfaction.
Research on Accounting Data Quality Detection Method Based on Wavelet Transform

Aiming at the problems of poor detection effect in the existing accounting data quality detection methods, an accounting data quality detection method based on wavelet transform is designed. By determining the accounting data quality detection objectives and setting the corresponding standards for the accounting data quality detection objectives, again, on the basis of this, the hierarchical accounting detection process is designed, the accounting data quality detection model is reconstructed under the ERP environment, and the data quality detection matrix of wavelet transform is established, The selection of the best wavelet decomposition layer is used to detect the quality of accounting data. The experimental results show that the data quality error detected by this method is small and feasible.
A Denoising Method of Hyperspectral Remote Sensing Image Based on Wavelet Transform

Since different types of noise are inevitably introduced in the processes of image formation and transmission, image denoising is a necessary pre-processing process before various image applications. In this paper, a local adaptive wavelet denoising method based on elliptic direction window and edge detection is proposed. The method first performs wavelet decomposition for the image and performs edge detection on the wavelet coefficients. Then, the wavelet coefficients of the image are sampled by the elliptic directional window, and the local threshold of it is calculated. Next, the wavelet coefficients are quantized by the soft threshold function. Finally, the denoised image is obtained by inverse wavelet transformation. In addition, it is noted that weight less than 1 is multiplied to reduce the threshold amplitude as much as possible to preserve the edge features of the image. To validate the performance of the proposed denoising method, four standard gray-scale test images and hyperspectral remote sensing images are employed and the denoising results are compared with the Local Wiener Filtering with Directional Windows (LWFDW). The experimental results show that the method proposed in this paper performs better in the numerical indicators of classification of the hyperspectral image, and has fewer pseudo-Gibbs phenomena in visual than the LWFDW.
Lyapunov stability for detecting adversarial image examples

• Applying chaos theory to detect adversarial examples, following two different approaches. • Our proposed detection method is based on a novel approach, extracting Lyapunov exponents from neural network activations. • Adversarial examples are generated using 8 state-of-the-art methods, in datasets like MNIST and ImageNet. • Two additional adversarial detection methods are trained and compared. • Extensive discussion is performed in comparison to a plenty variety of detection techniques from different approaches. Adversarial examples are a challenging threat to machine learning models in terms of trustworthiness and security. Using small perturbations to manipulate input data, it is possible to drive the decision of a deep learning model into failure, which can be catastrophic in applications like autonomous driving, security-surveillance or other critical systems that increasingly rely on machine learning technologies. On the one hand, a body of research proposes attack techniques to generate adversarial examples from more and more models and datasets. On the other hand, efforts are also being made to defend against adversarial examples. One family of defense methods aims at detecting whether the input sample is adversarial or legit. This works proposes an adversarial example detection method based on the application of chaos theory to evaluate the perturbations that the input introduces in the deep network. The assumption is that the adversarial inputs trigger a chaotic behavior in the network. For this purpose, the Lyapunov exponents are used to evaluate chaoticity in network activations. This allows to detect adversarial perturbations. Adversarial attacks like Carlini and Wagner, Elastic Net or Projected Gradient Descent are used in the experiments, reaching a detection rate that reaches 60% for the most difficult scenarios and up to 100% for most of the combinations of attack, dataset and network tested.
Sensor-Based Human Activity Recognition for Elderly In-patients with a Luong Self-Attention Network

The mobility status of older adults is directly con-nected to their health conditions and physical abilities. Decrease in mobility is a serious issue that leads to adverse health outcomes and declined functional abilities in older adults. Hence, it is highly desirable to assess their mobility status continuously. With the advent of wearable technology, Sensor-based Human Activity Recognition (HAR), which uses measurements from body-worn sensors to gain insights into activities undertaken by individuals, has become an active research field. However, HAR for older adults poses two major challenges: (i) Collecting data from older adults is inherently difficult due to their declining physical abilities, and (ii) Diversity in sensor data distributions is more pronounced across different older adults due to their different mobility statuses. In this paper, we propose an attention-based deep learning model to accurately estimate older adult&#x0027;s mobility through wearable sensor-based HAR. The proposed model achieves a 7.5 &#x0025; and 7.25 &#x0025; improvement over baseline methods in a pooling task learning and meta-learning settings on an in-house IMU dataset collected from in-hospital patients, the first of its kind.
Iterative Dense Network using Laplacian Pyramid Model for Blind Super Resolution

The Deep learning-based approach for solving the ill-posed problem of single image super-resolution reconstruction (SRR) has achieved tremendous success in recent times. However, not much work is carried out in the direction of blind image super-resolution, where the degradation kernel is said to be unknown. This paper addresses the said problem of blind single image super-resolution reconstruction using an alternative learning approach by training two convolutional neural networks. Most of the available model for blind super-resolution considers a fixed degradation kernel for reconstruction, which leads to drop in performance. Therefore a learnable kernel estimation approach is adopted by using a kernel-estimator network. Further, this estimated kernel is used to generate a super-resolution image using a Generator network. To successfully model the reconstruction of vital features like edges and texture and to learn the inter-pixel dependencies between multi-level feature maps, we employ a densely residual Laplacian attention block (DLA-Block). The proposed method is extensively tested on real image and synthetic image datasets. The experimental results have shown out-performance compared to the state-of-the-art in terms of high reconstruction accuracy as well as PSNR and SSIM.
Side-Channel Analysis-Based Model Extraction on Intelligent CPS: An Information Theory Perspective

The intelligent cyber-physical system (CPS) has been applied in various fields, covering multiple critical infras-tructures and human daily life support areas. CPS Security is a major concern and of critical importance, especially the security of the intelligent control component. Side-channel analysis (SCA) is the common threat exploiting the weaknesses in system operation to extract information of the intelligent CPS. However, existing literature lacks the systematic theo-retical analysis of the side-channel attacks on the intelligent CPS, without the ability to quantify and measure the leaked information. To address these issues, we propose the SCA-based model extraction attack on intelligent CPS. First, we design an efficient and novel SCA-based model extraction framework, including the threat model, hierarchical attack process, and the multiple micro-space parallel search enabled weight extraction algorithm. Secondly, an information theory-empowered analy-sis model for side-channel attacks on intelligent CPS is built. We propose a mutual information-based quantification method and derive the capacity of side-channel attacks on intelligent CPS, formulating the amount of information leakage through side channels. Thirdly, we develop the theoretical bounds of the leaked information over multiple attack queries based on the data processing inequality and properties of entropy. These convergence bounds provide theoretical means to estimate the amount of information leaked. Finally, experimental evaluation, including real-world experiments, demonstrates the effective-ness of the proposed SCA-based model extraction algorithm and the information theory-based analysis method in intelligent CPS.
Computer-assisted medical billing information extraction: comparing rule-based and end-to-end transfer learning approaches

Medical billing is important for both healthcare providers and payers, yet filling reimbursement requires tremen-dous effort to process clinical notes, making it labor-intensive and error-prone. This work compares two natural language processing (NLP) approaches to extract patients&#x0027; history information from clinical notes for billing purposes. A rule-based pipeline built on top of a generic clinical NLP tool CLAMP, is compared against an end-to-end deep neural network architecture. We annotate a gold-standard corpus to evaluate the two approaches. Results show information extraction for medical billing is a challenging problem though NLP has great potential to automate the task. Our work is the first academic study using NLP in Evaluation and Management billing.
MuLSA - Multi Linguistic Sentimental Analyzer for Kannada and Malayalam using Deep Learning

Natural language Processing has been always a topic of interest in artificial intelligence. Opinion mining or Sentiment Analysis is an important application of Natural language Processing. Sentiment Analysis of text is to extract the sentiments underlined in the text. In this paper, a multi-linguistic sentimental analyzer (MuLSA), is implemented, a model that would address Malayalam, Kannada and English text. This model explores two languages in three categories of the text, its original script, transliterated script, and the combination of both along with English. Deep Learning, Recurrent Neural Network with LSTM is used as the basis for this model. The model exhibits 82% of prediction accuracy.
Research on Unstructured Data Access Strategy In Cloud Environment

For the problem of low storage efficiency of small files in HDFS (Hadoop Distributed File Systems, HDFS), a small file merging algorithm based on file type is proposed, and the model structure and implementation steps of the algorithm are studied. On the basis of the original architecture, ClientServer is added for data preprocessing. Finally, algorithms are designed for data "write and read" operations.
Consistency Analysis of NLP Approaches for a Conference Reviewer-Manuscript Match-Making System

Peer-review process is an important part of scholarly communication. The quality of a conference also depends on its peer-review process. The selection of a competent reviewer to review a submitted manuscript in a conference, is a crucial facet of a peer-review process. This selection relies on the adopted match-making approach along with the constraint optimization reviewer allocation algorithm. The match-making approach needs to be consistent with its decision of selection and allocation of the reviewers. In this work, we proposed a framework for evaluating the consistency of various standard NLP approaches that are used for match-making process in a conference. The consistency analysis was performed over a real multi-tracked conference organized in 2019. We showed that the Contextual Neural Topic Modeling (CNTM) with word embedding technique was most consistence among all the 13 approaches that we chose to analyze.
Multi-Granularity Annotation of Instantaneous Intelligibility of Learners' Utterances Based on Shadowing Techniques

The practical goal of pronunciation training is to acquire an intelligible enough pronunciation, not a native-like pronunciation. In our studies [1], [2], we proposed a method that can annotate instantaneous intelligibility of a given L2 English utterance by monitoring listeners' listening behaviors. The listeners were asked to shadow the L2 utterance and the degree of being inarticulate in shadowing was automatically quantified to be used as scores of the instantaneous intelligibility, which were shown to be highly correlated with subjective intelligibility scores. In the present paper, we make an objective assessment of the proposed method, where the automatic scores are compared with those calculated objectively based on manual transcripts of the shadowings. Experiments show that the former scores have such a high correlation as 0.935 with the latter scores, which is higher than correlation obtained using another kind of automatic scores calculated by transcribing the shadowings with ASR. Further, since intelligibility is sometimes discussed in pronunciation training in such smaller units as phonemes, our method is experimentally applied to intelligibility annotation with multiple granularity. Experiments show a high validity of our method to calculate instantaneous intelligibility in units of word, syllable, phoneme, and frame.
A Common but Flexible Method for IoT Device Forensics

IoT devices are becoming prevalent and changing the world from many different aspects. From a digital forensic perspective, IoT devices are rich-evidence sources for criminal and civilian investigations because of their ubiquity. However, safely retrieving forensically sound evidence from IoT devices has been facing challenges due to their diversities in shape, size, function, interface, internal circuit density, etc. Forensic investigators are looking forward to a common, cost-effective, easy-to-use process or method for their IoT evidence acquisition. In this paper, we reveal the big issues of traditional data acquisition methods when they are applied in IoT devices forensics. Then, we propose a common forensic process for most of the IoT devices and utilize the up-to-date 3D print technique combined with the PoGo pins and Epoxy to customize a safe evidence acquisition tool for interface-lack IoT devices. Our experiment result proved the feasibility, flexibility, and generality of our process and method.
MetaFlow: A Meta Learning With Normalizing Flows Approach For Few Shots Learning

Humans can generalize and learn new concepts fast. They are able to learn new concepts given only a few examples and quickly adapt to new situations. In order to do so, they conclude knowledge from their prior experiences, as they are able to combine previous observations with small amounts of new evidence to learn fast. On the other hand, in most machine learning systems there are two distinct phases: Training and testing. The training phase consists of updating the model parameters using data, and during testing, the model is deployed as a decision-making engine. Meta learning, or learning to learn, gained a huge rise in interest in recent years. Contrary to traditional approaches to artificial intelligence where a given problem is solved from scratch using a standard learning algorithm, what meta learning aims at is to improve the learning algorithm itself. In this paper, we consider meta learning problems, where there is a distribution of meta learning tasks, and we would like to train a model that does well and learns quickly when presented with unseen tasks sampled from this distribution. We propose an approach to enhance gradient-based algorithms for meta learning, where we achieved a mean accuracy of 62% with only 3 training iterations over tasks sampled from the Omniglot dataset.
Comparing Rubin and Pearl’s causal modelling frameworks: a commentary on Markus (2021)

Abstract Markus (2021) argues that the causal modelling frameworks of Pearl and Rubin are not ‘strongly equivalent’, in the sense of saying ‘the same thing in different ways’. Here I rebut Markus’ arguments against strong equivalence. The differences between the frameworks are best illuminated not by appeal to their causal semantics, but rather reflect pragmatic modelling choices.
Design and Implementation of Financial System of Supply Chain Based on Blockchain

The rapid development of the information age has greatly changed people’s lives, greatly changed people’s lifestyles, and effectively promoted the improvement of people’s quality of life and the convenience of people’s life and work. The financing of small and medium-sized enterprises has been significantly improved by the continuous development of supply chain finance, and as an important supplement to the traditional financial industry, supply chain finance has also played a very positive role in the healthy development of the industry. Supply chain finance provides an opportunity for small and medium-sized enterprises to raise funds, but the maximization of each participant’s own interests in business operation makes the supply chain financing service stagnate. By analyzing the development status of supply chain finance and “blockchain+supply chain finance” system, this paper optimizes the design of supply chain finance system based on blockchain technology, so as to make full use of the characteristics of blockchain technology, ensure the authenticity and sharing of transactions and data, expand the service targets of supply chain finance and achieve full coverage of supply chain.
Cell Segmentation in Digitized Pap Smear Images Using an Ensemble of Fully Convolutional Networks

This paper presents a method that provides reliable performance regarding cell segmentation in digitized Pap smear images. Since our final goal is the early detection of cervical cancer using scanned smear images, the proper segmentation of cells is of utmost importance. Our approach uses segmentation predictions from fully convolutional networks (FCNs) in addition to the original scanned image as its input. Our method transforms these input images to a final segmentation using a dedicated FCN architecture. Thus, our approach can be considered an ensemble-based one and outperforms state-of-the-art segmentation algorithms, achieving close to 93% accuracy and a Dice score of more than 69%.
Investigation of Weight Initialization Using Fibonacci Sequence on the Performance of Neural Networks

Initializing weights are important for fast convergence and performance improvement of Artificial Neural Network models. This study proposes a heuristic method to initialize weights for Neural Network with Fibonacci sequence. Experiments have been carried out with different network structures and datasets and results have been compared with other initialization techniques such as Zero, Random, Xavier and He. It has been observed that for small sized datasets, Fibonacci initialization technique reports 94% of test accuracy which is better than Random (85%) and close to Xavier (93%) and He (96%) initialization methods. Also, for medium sized dataset, we have noted that performance of Fibonacci weight initialization method is comparable with the same for Random, Xavier and He initialization techniques.
Early Warning Signs of Resourcing in Global Software Development Projects

The term “Global software development” refers to the process of creating software program which reduces development cost in any company. GSD helps agencies to leverage the blessings of multi-site improvement with respect to time, value and access to skilful aid. Current research shows that half of the businesses which have attempted international software program development have not realized the anticipated results. Software program projects are increasing number from nations with high prices to reduced offshore countries. They continue to delight in poor overall performance issues because of the inherent complexity and unpredictability of computer program jobs. The main motivation to write survey paper is to identify GSD-related issues and also mitigating solutions. This paper discusses the idea of early caution signs (EWS) to improve mission achievement rates, mainly in the precise onshore-offshore undertaking environment, which is exposed to higher task dangers. EWS provides a way to undertake assessment during initial stages. Several techniques and measures are recommended to control the failure of the project. As a result, understanding the potential difficulties and possible mitigation methods for GSD is critical to a project's success. This structure can be used as a starting point for further study of GSD.
Technology of Job Satisfaction for Employees in Private Technical Higher Education Institutions in Jordan

The study assesses Higher-education institutes include public and private sectors, where academic staff and employees in each sector have different challenges and obstacles. In this research, we investigated functionality and its importance on staff evaluation, a comparison between job conditions in public and private systems in Jordan is presented including salaries, promotion systems and sabbatical leave, also a brief description on the institute' employee is presented where challenges for each group is discussed and further recommendation are suggested; groups included lecturers, lab administrators, employees and craftsmen.