Reviewerly/exHarmony huggingface dataset:
Research on the Application of Data Mining Techniques in the Audit Process

How to accurately locate the meaningful audit doubts in the massive data has become the main problem facing the current audit work. Especially in the era of Internet data, data mining technology is developing very fast, and the analysis methods are becoming more mature, which are widely and successfully applied in various fields. Therefore, it is of great practical significance to apply data mining technology to the audit process, which can clearly locate the degree of correlation between different statistical index variables, evaluate certain abnormal data or abnormal correlation performance, and serve as an important supplement to the audit work.
SL3D - Single Look 3D Object Detection based on RGB-D Images

We present SL3D, Single Look 3D object detection approach to detect the 3D objects from the RGB-D image pair. The approach is a proposal free, single-stage 3D object detection method from RGB-D images by leveraging multi-scale feature fusion of RGB and depth feature maps, and multi-layer predictions. The method takes pair of RGB and depth images as an input and outputs predicted 3D bounding boxes. The neural network SL3D, comprises of two modules: multi-scale feature fusion and multi-layer prediction. The multi-scale feature fusion module fuses the multi-scale features from RGB and depth feature maps, which are later used by the multi-layer prediction module for 3D object detection. Each location of prediction layer is attached with a set of predefined 3D prior boxes to account for varying shapes of 3D objects. The output of the network regresses the predicted 3D bounding boxes as an offset to the set of 3D prior boxes and duplicate 3D bounding boxes are removed by applying 3D non-maximum suppression. The network is trained end-to-end on publicly available SUN RGB-D dataset. The SL3D approach with ResNeXt50 achieves 31.77 mAP on SUN RGB-D test dataset with an inference speed of approximately 4 fps, and with MobileNetV2, it achieves approximately 15 fps with a reduction of around 2 mAP. The quantitative results show that the proposed method achieves competitive performance to state-of-the-art methods on SUN RGB-D dataset with near real-time inference speed.
A Cluster-Based Approach for Extracting Redundancy-aware Top-k Co-location Patterns

Spatial co-location pattern mining, which discovers interesting and potentially useful neighboring relationships among spatial features from spatial datasets, plays an important role in spatial data mining. However, as the scale of spatial datasets gradually increases, traditional co-location pattern mining algorithms generate numerous patterns, which may confuse, even mislead the following decision-making of the users. To solve this problem, this paper proposed a cluster-based approach to discover non-redundant top-k co-location patterns, where k is the number of generated patterns. In this proposed approach, first, the spatial data is transformed into transactions to calculate the distance between two co-location patterns, and then the k-means clustering method is adopted to classify the patterns into k clusters, finally, the outputs are generated by selecting one pattern from each cluster. The experimental results on both synthetic and real-world data sets demonstrate that our proposed approach effectively summarizes spatial co-location patterns.
Take Me To Greece: Exploring Intergenerational Interactivity in a Mixed Reality Children's Museum Exhibition

Socializing and interaction are important aspects for families who visit museums. Mixed reality (MR) uses a set of technology that museums employ to encourage this behavior, but they face challenges in maintaining family cohesion with experiences across different exhibitions and kiosks. In this paper, we explore these challenges broadly to provide strategies for MR exhibitions that support better family cohesion, and thus interaction. As part of our exploration, we partnered with the Indianapolis Children's Museum and used its Take Me To Greece exhibition and observed intergenerational family interactions at eight different kiosks with a range of MR technologies using “research in the wild” approach. We present our findings in terms of concerted immersion patterns of families with the kiosks encompassing both real and virtual spaces, while disrupted interactions when family members split from the group to interact with virtual kiosks. We then present design considerations to provide a broad understanding of the aspects of reality and virtuality in a MR children's museum exhibition, and the influence of these MR dimensions on intergenerational family interactions.
Towards the Identification and Visualization of Causal Events to Support the Analysis of Closed-Loop Combat Simulations

Analysis of output data from closed-loop combat simulations can provide insights into the relationships between model inputs and outputs. However, analyses that only consider end-of-run output data may not be sufficient to explain why those relationships exist. In the case of stochastic models, where multiple replications of the same scenario are conducted, the presence of outliers and multi-modal results also needs to be accounted for. In this paper, we use a military case study to explore a range of techniques to interrogate the intra-run event data (i.e. trace logs) generated by a combat simulation in order to help address these two issues. Cumulative event plots and geo-spatial visualization techniques which also incorporate the temporal aspects of the simulation appear best suited to explain the presence of outlier replications and multi-modal results. Exploratory work using hierarchical clustering techniques and temporal decision trees provide a promising step towards better explaining causal events within the combat simulation data.
Keyword-Based Wine and Beer Product Categorization

With the rise of e-commerce, product categorization has become more important than ever. Stock organization is key to attract more customers to an e-commerce business. The costumer needs to be provided with the tools to filter all the product which s/he does not want by specifying a few categories, being the big reasons why product categorization is so important. Therefore, product categorization is also important to Web marketing. In this work, we intend to categorize alcoholic drinks as wine or beer; for that, we used a method that scans the brand and product name for keywords and tries to categorize the product based on the keyword found. The datasets had also user reviews of the products, so we decided to analyze the most used words by users and which words are more frequent in positive reviews. For each dataset, when the products finished being categorized, we analyzed the average user rating of each one of the words used the reviews and the products, and then stored the information inside two different files. The goal of this work is to build a lightweight, simple categorization system and lay a foundation to a more complex recommendation system.
Neural Network Training in Distribution Computing Method

Abstract This paper raises some method to make neural network training can work on distributed computing clusters well.
Viewers’ Tolerance to Brightness Transitions in Cinema

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recent advances in display technologies enable home and cinema display systems to reach higher peak luminance and perceived full screen white (FSW) levels while also improving on black level capabilities. This next-generation imaging technology will help creators deliver content with higher visual quality, increasing the overall quality of experience (QoE) of viewers. However, there is a growing concern in the film industry that possible “mismanagement” or “misuse” of the new luminance capabilities may result in viewers’ visual discomfort. A typical example of potential “misuse” is abrupt temporal brightness transitions during a scene change or even within the same scene. Prior studies, which were conducted in home environments, reported that displaying a higher amount of light could potentially lead to viewers’ visual discomfort. However, no study has been conducted in a cinema environment. In this work, we quantify the human observer tolerance to temporal brightness transitions in a cinema environment by performing four subjective experiments. The different experiments evaluate the impact of various distributions of light on the screen, namely full screen, highlights, noise, and real images. Results of the experiments indicate that the light distribution, as well as the peak luminance values, has minimum impact on viewers’ tolerance to temporal brightness transitions. Indeed, observers reported visual discomfort only when the total amount of light on screen varied significantly. Specifically, for average on-screen luminance equal to both 0.1 or 1 cd/m<sup>2</sup>, viewers rated as “slightly annoying” and “annoying” transitions to average on-screen luminance as low as 35 and 75 cd/m<sup>2</sup>, respectively. It is to be noted that viewers reported visual discomfort at a similar transition intensity for both saturated colors and white. The results of our study could provide useful insights into content creators, for example, providing a warning that temporal transitions may cause visual discomfort</i> .
W-A net: Leveraging Atrous and Deformable Convolutions for Efficient Text Detection

Scene text detection has been gaining a lots of focus in research. Even though the recent methods are able to detect text in complex background having complex shapes with a fairly good accuracy, they still suffer from issues of limited receptive field. These fail from detecting extremely short or long words hence failing in detecting text words precisely in document text images. We propose a new model which we call W-A net, because of it's W shape with the middle branch being Atrous convolutional layers. Our model predicts a segmentation map which divides the image into word and no word regions and also, a boundary map which helps to segregate closer words from each other. We use Atrous convolutions and Deformable convolutional layers to increase the receptive field which helps to detect long words in an image. We treat text detection problem as a single problem irrespective of the background, making our model suitable of detecting text in scene or document images. We present our findings on two scene text datasets and a receipt dataset. Our results show that our method performs better than recent scene text detection methods which perform poorly on document text images, especially receipt images with short words.
Lightweight and Real-Time Framework for Facial Motion Retargeting

Facial action retargeting has been widely used in the field of film and games, but in order to improve accuracy, a large number of professional equipment is used for assistance, or it consumes huge resources for offline rendering, which is difficult to run on consumer-level equipment in real time. In this paper, we propose a framework that allows facial motion redirecting to run in real-time on devices with limited computing resources, only captured by a monocular RGB camera. We use multi-task learning to learn Identity Shape, expression parameters, and head pose at the same time, and use the Depthwise structure in MobileNet to reduce the amount of calculation and parameter size of the model. For the above three tasks, in addition to using the shared layer to extract the common features, the features of different granularities are extracted separately, and finally their loss functions are weighted and summed. Experiments on representative benchmark datasets demonstrate the effectiveness of our approach.
Interactive teaching materials using learning cycle 5E equipped with augmented reality on human respiratory system

One Junior High School in Indonesia, is the student feel disinterested and arduous learning independently in Covid-19 pandemic era to understand the material of human respiratory system. The lack of interactiveness teaching material and the lack of learning method variation are the cause. Visualization respiratory system using interactive multimedia is necessary to learn this material. The Learning Cycle 5E model can facilitate students to construct knowledge independently and can be formulated into teaching materials. The purpose of this research is to produce teaching materials using Learning Cycle 5E equipped with Augmented Reality on human respiratory system which is valid and feasible as teaching materials in Covid-19 pandemic era. The research model was adapted from the William W. Lee & Diana L. Owens model, wich consists of five stages: (a) analysis, (b) design, (c) development, (d) implementation, (e) evaluation. The feasibility test of teaching materials is carried out through material validation, media validation and readability test. The result of these tests contains two types, which are quantitative data and qualitative data. The result showed that the teaching material developed was included in the very feasible category (89%), with a description of obtaining a percentage in (a) material validation, (b) media validation, and (c) readability test sequentially 88%, 87%, and 90%. This teaching material is very appropriate to be used as learning resources in the learning process of science, especially the material of human respiratory system inside and outside the classroom.
Research on Target tracking Based on Improved KCF Algorithm

Aiming at the problem of tracking instability caused by imperfect feature expression and variable scale, an improved KCF algorithm is proposed in this paper. Firstly, HOG and MB-LBP features are extracted to form H-L fusion feature, and Hue color features are extracted too. Then the output response values of extracted features are calculated respectively by position filter. Next, the fusion is carried out by linear weighting at the feature level, to obtain the target's predicted location. And in the tracking process, the scale changes of targets are estimated and adaptively adjusted by the standard deviation of the feature matching corner points of the two frames before and after. Finally the difference of the adjacent two frames is judged by the corner matching condition, and the position filter is updated, to achieve better target tracking effect. The experiments on OTB-50 and OTB100 show that the algorithm not only improves the tracking accuracy by 8.3% and the success rate by 10.5%, achieves the real-time tracking effect.
Classification of Histopathology Images with Random Depthwise Convolutional Neural Networks

The classification of whole slide images plays an important role in understanding and diagnosing cancer. Pathologists typically have to work through numerous pathology images that can be in the order of hundreds or thousands which takes time and is prone to manual error. Here we investigate an automated method based on a random depthwise convolutional neural network (RDCNN). In previous work this network has shown to achieve high accuracies for image similarity. We conjecture that for histopathology images similarity may play an important role in accurate classification of the images. We evaluate RDCNN against trained deep convolutional neural networks VGG16 and ResNet50 on four pathology image datasets. We find RDCNN to give the average highest accuracy across the four datasets. On two datasets RDCNN is significantly higher in accuracy and comparable in the others. We examine top similar images to a randomly selected one in the ISIC and Gleason datasets and see that it indeed most of the similar images belong to the same category as the query in the RDCNN feature space compared to ResNet50 and VGG16. For such histopathology datasets where similarity also implies same class membership we can expect RDCNN to be highly accurate and useful.
Performance Evaluation of speed control using Fuzzy dependent Genetic Algorithm in PMSM

Abstract This paper examines the role of the tuning algorithm for speed regulation of the Permanent Magnet Synchronous Motor (PMSM). The picks of the PID regulator normally provide adequate results in the application of a low-force drive, but for high-power application drives, a self PID controller doesn’t provide any acceptable performance. Such applications require high-precision, superior and adaptable speed regulators and effectiveness in the cycle and execution of the plan. High-performance applications need some capacity based on High-speed high-reliability regulators, adaptability with maximum torque coefficient, higher rating capacity with minimum ripple torque. So many speed controlling mechanisms are available in the quick world, and these methods vary from the choice of regulator used in the PMSM to the method of programming/use of equipment. In this paper, generous examination is taken to control the speed of PMSM with three unique specialists, ABC based speed control drive, ANFIS controller of PMSM drive and Genetic algorithm based fuzzy controller. The planned regulators are tried through the mathematical reproductions in the MATLAB Simulink Platform. The examination between the reproduction aftereffects of execution measures are introduced toward the end. Hereditary calculation based Genetic algorithm based fuzzy controller gives some better outcome appropriate for the superior applications.
Decoupled Metric Learning Model combining Tag Information for Recommendation

Metric learning produces distance functions to capture the important relationships among data and has been explored in collaborative recommendations successfully. Existing metric learning recommendation model cannot express the rating preference of users well, and the differences between items will also have a non-negligible influence on the trained metric space. For this, we present a decoupled metric learning model combining tags information (DMLT) for recommendations. DMLT model does well in decoupling the preference of users from rating data with tags information. Experiments on public datasets show that DMLT model is more accurate than the matrix factorization algorithm and other latest metric learning recommendation algorithms.
Heterogeneous Graph Neural Networks Based on Meta-path

Heterogeneous graph representation learning aims to learn meaningful representation vectors from heterogeneous networks in low dimension, so as to realize the extraction of structure and attribute features of the networks. Embedding vector is the basis and key of complex network analysis, which can be used in the downstream tasks. The key points in heterogeneous graph neural networks are: how to define heterogeneous neighbors and how to aggregate them. Although a lot of work has been devoted to homogeneous or heterogeneous network representation, the effective combination of network structure information and node attribute information, especially the effective use of meta-path containing specific semantic information is still rare. In this paper, we propose a meta-path-based heterogeneous graph neural network model. Firstly, we apply meta-path to sample the heterogeneous neighbors of each node in the network, and aggregate the features of the same type of nodes together to form type-related embedding; then, attention mechanism is applied to aggregate the neighbor information of different types of node; finally we train the end-to-end model by reducing the context loss. Experiments proved the validity of the model and significantly improved current results.
RADG Modification with Different Values of States

Abstract Cryptography may be a science of changing messages into unclear kind through the employment of encoding algorithms that is an algorithm that converts the text to a cypher and increased by arithmetic that shield information in powerful ways. A novel keyless security theme Reaction Automata Direct Graph (RADG), that relies on automata direct graph and reaction states. The novelty of RADG lies within the incontrovertible fact that it doesn’t need any key to perform the cryptographically operations therefore creating it a possible scheme for big wireless systems. Chaotic maps represent the backgrounds for chaos based mostly cryptography thanks to the actual fact that chaotic transformations are sensitive to changes within the initial conditions and generate solutions similar to random ones. In this paper a proposed modification to RADG will be performed. The main problem of the RADG is that the values in the states are static. In this paper this problem will be solved using chaos logistic map. The RADG which is used in this paper is key not keyless (by using chaos logistic map).
Research on Plaintext Restoration of Blowfish Based on Neural Network

The application of neural network in cryptographic known plaintext attack is a novel cryptanalysis method. Only with known ciphertext, the restoration of plaintext is a valuable part of cryptanalysis. By putting plaintext and ciphertext into model, the network simulates encryption algorithm without the key and output the corresponding plaintext. In this paper, we focus on the plaintext restoration of Blowfish based on neural network. We design a plaintext restoration scheme and perform the experiment in open source database. The result shows that the neural network simulator could fit the dataset with an error rate lower than 0.04 when the size of dataset exceeds 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> .
ALPhANOV: Immersive photonics lab - Photonics training accessible to anyone, anywhere, anytime!

The Immersive Photonics Lab is an innovative virtual reality solution for the development of professional procedural skills in photonics. The simulations offer real-world experiences and engage the learners in a virtual laboratory, allowing them to improve their technical skills. More information: https://www.youtube.com/watch?v=GB3YPbjRYps
Detection of Fraudulent Alteration of Bank Cheques Using Image Processing Techniques

In today‘s world illegal alteration and illegal modifications of authenticated financial documents is increasing rapidly as a fastest growing crimes around the world. The result of this kind of crimes may result in a huge financial loss. In this paper image processing and document image analysis techniques are used to examine such cases in order to identify the fraudulent bank cheques. However, it is very difficult to detect an alteration made on documents once the printing ink of alike color is employed. In this paper, alterations and modifications caused with handwritten ball point pen strokes are considered and proposed a technique for recognition of such types of corrections by employing standard techniques under Digital image processing and pattern recognition. The results are quite promising during the experiments conducted.
ETINE: Enhanced Textual Information Network Embedding

In many reality networks, nodes contain rich text attribute information that exhibits significant role in describing the properties of them and relationship between them. The integration of structural and textual information is beneficial to downstream network analysis tasks. In this work, we present an Enhanced Textual Information Network Embedding model, called ETINE, for learning network embeddings with not only global structural information but also deep semantic relationship between nodes. Specifically, we formulate the optimization of our proposed structure-based and text-based loss functions as a matrix approximation problem. Moreover, to enhance the efficiency and robustness of the proposed method, we propose to optimize the loss functions with an efficient randomized singular value decomposition (RSVD) method. Extensive experiments on four benchmarks demonstrate that our model outperforms other state-of-the-art baselines in multi-class node classification, network reconstruction and node clustering tasks.