Reviewerly/exHarmony huggingface dataset:
RETRACTED: Accounting Information Revolution Based on Cloud Computing Technology

Abstract China‘s information technology has been rapid development, cloud computing technology has been widely used in many fields. The informatization level of accounting major is one of the important factors influencing the development of enterprises. There are some problems in the traditional way of accounting work, such as low efficiency, data error and so on. Based on this, this paper puts forward the research of accounting information transformation based on cloud computing technology. This paper first analyzes the existing problems in the development of accounting informatization, and further expounds the advantages of cloud computing technology in the construction of accounting informatization, and then puts forward the transformation scheme of accounting informatization based on cloud computing technology. The results show that the scheme proposed in this paper is helpful to enhance the development strength of the company, and is widely praised by the accountants.
On system sequence descriptions

Context: Use cases (UCs) are widely used to specify the functionality of a SW system. A UC is usually worked out in a Main Success Scenario and several other (Alternative) Scenarios. To make the overall structure of the UC clear and to prepare for the software implementing the UC, these scenarios must be integrated into one structure per UC. Question/problem: How to integrate the different scenarios of a UC into one structure? Our solution: We propose so-called (textual) System Sequence Descriptions (SSDs). We introduce a suitable (context-free) grammar for our SSDs. We can express all usual constructs with our SSDs, such as primary and secondary actors, basic steps, the internal responsibilities of the system, sequential composition, arbitrary order, loops/repetition, conditionals, alternatives, options, choices, definitions, and calls/'Includes'. To support validation of the resulting SSDs (with integrated scenarios) and check it with the users (Requirements quality assessment), we give (inductive) translation rules to translate the SSDs to natural language (Natural Language Generation for RE). Additionally, we give (inductive) rules to generate graphical SSDs (like the more familiar UML-diagrams) from our textual SSDs. This might support validation too. Results: With this new artefact design we can easily integrate the different scenarios of a UC into one clear SSD and also check the result with the users. That integrated SSD clarifies the overall structure of the UC and forms a suitable basis for implementation. With a nontrivial example we illustrate that the proposed grammar is very practical and that the approach scales up easily. Main contribution: The paper describes novel technical solutions for the application of NL-technologies to RE-relevant artefacts. The paper includes additional solutions to the RE-problem of validation.
Performance Comparison of Different Machine Learning Algorithms for Risk Prediction and Diagnosis of Breast Cancer

Breast cancer is the most common life threatening diseases and morbidity is also very high among the women population globally. Manual diagnosis of each image pattern in many cases has not produced any satisfactory outcome as it is time consuming as well as inefficient. In medical field, to classify the data, classification and data mining method play a major role. Diagnosis and analysis process in terms of time and accuracy can be substantially upgraded due to the classifier without human involvement. The main objective of this paper is to evaluate the performance of different machine learning algorithms SVM (Support Vector Machine), K Nearest Neighbors (K-NN), Naive Bayes (NB), CART (Classification and Regression Tree) in terms of efficiency and effectiveness. The two parameters of each algorithm have been measured by accuracy, precession, sensitivity and specificity. The SVM achieved highest accuracy (98.14%) with lower error rate. The valuation process is carried out on the on the Wisconsin Breast Cancer (original) datasets. The aim of this is to evaluate the suitable classifier by comparing the performance of four algorithms. SVM achieved a significant performance in respect of accuracy (98.14%) with lowest error rate.
Brain Storm Optimization Algorithm with Estimation of Distribution

Brain Storm Optimization (BSO) algorithm is a new intelligence optimization algorithm, which is effective to solve the multi-modal, high-dimensional and large-scale optimization problems. However, when the BSO algorithm deals with the complex problems, there are still some disadvantages, such as the slow speed of the search algorithm for the late, premature convergence and easy to fall into local optimal solutions and so on. In order to solve these problems, a BSO algorithm with Estimation of Distribution (EDBSO) is proposed. Similarity as the DMBSO, the EDBSO algorithm is divided the discussion process into two parts, including intra-group discussion and inter-group discussion. The Estimation of Distribution algorithm in continuous domains, that is based on the variables subject to Gaussian distribution, is used to improve the process of inter-group discussion of DMBSO algorithm. In this paper, five benchmark functions are used to evaluate the search performance of EDBSO algorithm. In order to verify the convergence and accuracy of the EDBSO algorithm, the EDBSO algorithm is compared with 4 improved algorithm in different dimensions. The simulation results show that the EDBSO algorithm can effectively avoid to falling into the local optimum and prevent premature convergence of this algorithm, and it can find better optimal solutions stably. With the increase of the problem dimensions, the EDBSO algorithm which has better robustness is suitable for solving complex optimization problems.
ARTIFICIAL INTELLIGENCE IN ONLINE SHOPPING USING NATURAL LANGUAGE PROCESSING (NLP)

Natural language processing is a process by which particular software is used to convert the speech and texts. To produce human and natural languages it combines computational linguistics with AI. The entire process can be subdivided as 3 different parts integrated statistical model to run the speech recognition, which converts e natural language into the programming language and the resultants decide what was said. This process is called the speech process for text. Natural language understanding is a domain in which the study is made to understand how a particular Artificial Intelligence can comprehend and interact with a human. Starting from a single word to complex sentences and even in the understanding of various poems and big literature can be achieved by this method. And only after this the various famous product of Apple's Siri and Microsoft's Cortana has come up with.
A Novel IN-Gram Technique for Improving the Hate Speech Detection for Larger Datasets

Hate speech is a type of written or a spoken statement that is used to demean or humiliate a person or a community. In this era of new age socialism, this type of speech is prevalent on social media platforms, where certain groups of people display offensive behaviour towards some people that may be distributed over gender, religion, nationality, etc. These kinds of activities must be avoided or suspended on social media platforms. Therefore, it is necessary to automate the detection of hateful content that gets circulated on the social media. The research work provides an enhanced technique as compared to the existing techniques with improved performance. The proposed model of IN-Gram compares the performance of detection of hateful content on social media with the traditional TF-IDF, N-Gram and PMI techniques. The proposed approach improves the hate speech detection rate by 10–12% for larger datasets as compared to existing approaches.
Defect Prediction of Cross Projects Using PCA and Ensemble Learning Approach

Cross-project defect prediction (CPDP) is a technique of detecting defects in software modules in which the training and the testing projects for the classification model are different. The effective prediction leads to a more reliable software. The merging of dataset from varying sources results to an imbalanced dataset. The complex structure and the imbalance data make it a challenge for an effective cross-project defect prediction. To overcome these issues, in this paper, we propose a cross-project defect prediction framework. In the first stage of this framework, PCA is applied for dimensionality reduction of the dataset into two components. In the second phase, SMOTE technique of data sampling is applied to handle the class imbalance problem. Then the ensemble classifiers random forest and XGBoost are applied for an effective defect-prediction model. We have conducted the experiments on eight open source software projects. The results are compared with few baseline techniques. The results indicate that the proposed framework gave comparable performance of cross-project defect prediction to some baseline methods.
Parameterization of the quality factor for the high throughput JPEG~2000

High Throughput JPEG 2000 (HTJ2K, JPEG 2000 Part 15) is a new part of the JPEG 2000 family of standards. HTJ2K provides an alternate block coding algorithm that can be used in place of the EBCOT algorithm, which is used in the JPEG 2000 Part 1. Similar to JPEG and HEVC-Intra mode, a desired option for the proposed HTJ2K (which is not available in the previous versions of the JPEG 2000) is to have a simplified Quality Factor (QF) to steer the compression process. This way the user can modify the compression level by simply increasing or decreasing the value of the QF without knowing the exact underlying compression ratio or the utilized bitdepth. In this paper, we report the results of a subjective experiment, which was designed and implemented to test a proposed QF function for the new HTJ2K encoder, according to the Q factors of the JPEG encoder. A new test methodology for this purpose is utilized and a new formulation of the Q-factor is derived for HTJ2K based on the experimental results. The statistical analysis of the experimental results beside the procedure for deriving the QF range are provided. The derived QF range in this experiment is planned to be integrated in the implementations of the High Throughput JPEG 2000.
2-way Arabic Sign Language Translator using CNNLSTM Architecture and NLP

Over 466 million (5%) people across the world are suffering from hearing impairment, according to the World Health Organization. There is a great need to bridge the communication gap between the deaf and the general population. In our research work, recent developments such as Natural Language Processing (NLP) and Deep Learning Neural Network (DLNN) are utilized to bridge this gap. We developed a 2-way sign language translator for the Arabic language, which translates text to sign and vice versa. The NLP such as parsing, part of speech tagging, tokenization and translation are developed to achieve text to sign translation. The Convolutional Neural Network (CNN) along with Long Short-Term Memory (LSTM) is used to perform sign to text translation.
Best Practices on Personalization and Adaptive Interaction Techniques in the Scope of Smart Homes and Active Assisted Living

Assistive systems and emerging technologies can support individuals with specific needs and diseases effectively. Nonetheless, in the domain of Smart Homes (SH), the interactions tend to become more complex and difficult to adapt to the users, creating a bottleneck for the efficient use and the acceptability of the interventions. This paper presents best practices on personalization and adaptive interaction techniques in order to homogenize different solutions within the SH, ease the interaction acceptability by adaptation to specific user needs and implement better healthcare interventions able to improve the Quality of Life (QoL). The recommendations arise as a result of previous research studies conducted within the MSCA-ITN project ACROSSING.
A Privacy-Preserving Coalition Loyalty Program

Loyalty programs are marketing strategies implemented by the service provider to establish a lasting relationship with customers. The service provider offers loyalty points, rewards, and other benefits to the customers while the customers will trade some private information to receive more offers that are relevant. With company data breaches on the rise, customers are becoming increasingly wary of sharing personal data within the loyalty program network. The increase in privacy concern causes either the customers refusing to enroll or avoiding using their membership privileges. In this paper, we propose a privacy-preserving loyalty program solution to give anonymity and protection of private information from misuse by third parties. To increase customers' confidence and merchants' trust, our protocol ensures that the manipulation of the point is transparent (i.e., can be verified) and data privacy can be protected.
Phase Difference-3D Coordinate Mapping Model of Structural Light Imaging System Based on Extreme Learning Machine Network

To meet the requirements of high accuracy and high efficiency in three-dimensional (3D) measurement, a phase difference-3D coordinate mapping model is proposed based on extreme learning machine (ELM) network. First, the reconstruction model of the ideal measurement system is set following the geometric structure of the system. Subsequently, by generalizing camera and world coordinates, a generalized measurement model is built. Lastly, ELM network is employed to solve the mapping coefficients. During measurement, only one phase difference map is required to complete the 3D reconstruction of the object, which simplifies the data processing process and saves time. The result indicates that the mean square errors (MSEs) of the X, Y and Z of the testing sample are 3.5955×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> mm, 9.5113×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> mm and 4.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> mm, respectively. Moreover, the reconstruction experiments of objects with different geometric structures are performed to demonstrate the general application of the proposed method.
Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition

Graph signal processing on directed graphs poses theoretical challenges since an eigendecomposition of filters is in general not available. Instead, Fourier analysis requires a Jordan decomposition and the frequency response is given by the Jordan normal form, whose computation is numerically unstable for large sizes. In this paper, we propose to replace a given adjacency shift A by a diagonalizable shift A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> obtained via the Jordan-Chevalley decomposition. This means, as we show, that A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> generates the subalgebra of all diagonalizable filters and is itself a polynomial in A (i.e., a filter). For several synthetic and real-world graphs, we show how A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> adds and removes edges compared to A.
Maximizing the Embedding Efficiency Using Linear Block Codes in Spatial and Transform Domains

Steganographic schemes are used in many fields, as it provides high-security transmission of messages. The strength of the steganographic scheme depends on embedding efficiency. Embedding efficiency is understood as the average number of random data bits that must be embedded in an object per one embedding change. The below-done work introduces an efficient embedding technique using various linear block codes useful to both spatial and frequency domain of digital images. Linear block coding algorithms like binary hamming code, Random linear code, Cyclic code, Reed Solomon code, etc., are applied to embed restricted data inside the image, which improves embedding efficiency. Higher embedding efficiency translates to better steganographic security. The Mean squared error (MSE), Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), Normalized Absolute Error (NAE) and Correlation Coefficient values for each algorithm is calculated and compared. Histogram attack is one of the steganalysis techniques which is used in this paper to confirm the security provided to the restricted data. This comparison shows that Binary Hamming code in the spatial domain and transform domain provides better results as compared to the other coding theory algorithms.
Activity Recognition using Deep Denoising Autoencoder

Existing feature extraction method for activity recognition is time consuming and laborious and prone to error. This paper proposes an unsupervised deep learning method for feature learning in activity recognition using tri-axial accelerometer. The proposed method extracts the relevant features automatically, eliminating the needs of feature extraction and selection stages. We evaluate and compared the proposed method with the conventional method in terms of recognition accuracy on a public dataset with wide range of activities. Results have shown that the proposed method achieved a better performance, improving the recognition accuracy by 0.03.
Sharing research data

1Jönköping University, Jönköping, Sweden 2Northwestern University, Chicago, IL, USA 3La Trobe University, Melbourne, VIC, Australia 4University of Washington, Seattle, WA, USA Nerrolyn Ramstrand, Jönköping University, 551 11 Jönköping, Sweden. Email: [email protected] This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
SIFT in GMS-selected areas: a method for feature point matching between photographs and rendered images

Pose estimation with rendered key images often enables more effective and efficient performance for on-line tasks. However, it is difficult to establish correspondences between photographs and rendered images because of their different sources and features. This paper proposes SIGMA (SIFT in GMS-selected Areas), a method combining high quality detector and descriptor with effective method of removing mismatches. It preforms SIFT locally in the areas selected by GMS, which provides high precision and good distribution of points. Experiments show the precision of SIGMA is 0.7638, higher than SIFT(0.5037) and GMS(0.7206).
W8-Scope: Fine-Grained, Practical Monitoring of Weight Stack-based Exercises

Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope can achieve high accuracy-e.g., identify the weight used with an accuracy of 97.5%, detect commonplace mistakes with 96.7% accuracy and identify the user with 98.7% accuracy. Moreover, by adopting incremental learning techniques, W8- Scope can also accurately track these various facets of exercise over longitudinal periods, in spite of the inherent natural changes in a user's exercising behavior.
Medical Entity Extraction and Knowledge Graph Construction

In recent years, artificial intelligence technology has developed rapidly, and people's lifestyles have changed accordingly. Medical health has always been one of the most concerned issues in people's lives. Under the background of "Internet +" and "AI economy", smart medical care has become the development direction of the future medical industry. This paper uses the medical industry as a background to construct a knowledge graph in the medical field. The graph contains the relationship between hospital departments, diseases, and symptoms. The graph can give the patient's disease and the corresponding departments that need to be hung according to the patient's symptoms.
Self-Evolutionary Pose Distillation

Conventional pose distillation method utilizes the teacher's output and the label to co-supervise the student model. The architecture of the student model is fixed in the training, making it impossible to obtain a compact and powerful model. In this paper, we introduce the theory of evolution to the pose distillation and propose the self-evolutionary pose distillation (SEPD) method which not only improves the performance of the student model but also reduces the size of the student model. Specifically, the SEPD considers the original model as the teacher model and obtains the student model by using the model reduction strategy to shrink the teacher model. The student model is supervised by the teacher's output and the label jointly. After the optimization, the student is treated as the teacher model and the student model is obtained by the model reduction strategy. The student model is optimized again. The compact and strong model is obtained by repeating the procedure above. Experiments on the challenging benchmark validate the effectiveness of our SEPD method.
Application of Rain Classroom to College English Writing

Abstract The purpose of the study is to discuss the process of construct the writing teaching mode based on Rain Classroom and explore the effects of the writing teaching mode based on Rain Classroom on College English teaching. This paper concludes that the main links of the writing teaching mode based on Rain Classroom are: model text analysis, imitation exercise, practical writing, and mutual evaluation and revision. In addition, the writing teaching mode based on Rain Classroom has significant effect on improving college students’ English writing ability.
Using Image Captions and Multitask Learning for Recommending Query Reformulations

Interactive search sessions often contain multiple queries, where the user submits a reformulated version of the previous query in response to the original results. We aim to enhance the query recommendation experience for a commercial image search engine. Our proposed methodology incorporates current state-of-the-art practices from relevant literature – the use of generation-based sequence-to-sequence models that capture session context, and a multitask architecture that simultaneously optimizes the ranking of results. We extend this setup by driving the learning of such a model with captions of clicked images as the target, instead of using the subsequent query within the session. Since these captions tend to be linguistically richer, the reformulation mechanism can be seen as assistance to construct more descriptive queries. In addition, via the use of a pairwise loss for the secondary ranking task, we show that the generated reformulations are more diverse.
Detection of the DEJ and Segmentation of Its Morphological Patterns in RCM Images of Melanocytic Skin Lesions

Reflectance confocal microscopy (RCM) provides a real-time noninvasive ( in-vivo ) proxy for histology. Here, we present machine learning models to delineate skin layers in RCM image stacks and analyze morphological patterns in RCM mosaics of skin.