Reviewerly/exHarmony huggingface dataset:
Parallax Contextual Representations For Stereo Matching

In this work, we study the context aggregation in stereo matching from a new parallax perspective. Unlike previous works, we propose to characterize and augment a pixel with its parallax contextual representation (PCR), which has not been explored before. We also propose a new concept called disparity prototype to describe the overall representation of a disparity plane. Our proposed PCR module consists of three steps: 1) divide disparity planes for a rough estimation of disparity; 2) estimate the disparity prototypes for each disparity plane; 3) derive PCR-augmented representations with disparity prototypes. Extensive experiments on various datasets using different networks validate the effectiveness of our proposal.
Deep Learning Techniques for Social Media Analytics

Machine learning has seized both academia and industry's attention as deep learning (DL) is the frontrunner in data science. In order to construct computational models, DL uses multiple layers to epitomize data theories. A few of the key DL techniques, such as model transfer (MT), convolutional neural networks (CNN), and generative adversarial networks (GAN), have completely altered our understanding of information processing. Indeed, DL's processing power while handling images, text, and speech is truly remarkable. Because of the rapid growth and extensive availability of digitized social media (SM), evaluating these data by employing conventional technologies and tools is complex and unmanageable. These challenges are expected to be well managed through solutions offered by DL methods. Hence, we consider the executed DL methods built-in regard to social media analytics (SMA). However, rather than engaging in technical details, we study domains that pose serious challenges to SM where DL is involved and propose solutions to those challenges. We also present a few case studies.
An information classification model for public sector organizations in Sweden: a case study of a Swedish municipality

Purpose The purpose of this study is to create an information classification model that is tailored to suit the specific needs of public sector organizations in Sweden. Design/methodology/approach To address the purpose of this research, a case study in a Swedish municipality was conducted. Data was collected through a mixture of techniques such as literature, document and website review. Empirical data was collected through interviews with 11 employees working within 7 different sections of the municipality. Findings This study resulted in an information classification model that is tailored to the specific needs of Swedish municipalities. In addition, a set of steps for tailoring an information classification model to suit a specific public organization are recommended. The findings also indicate that for a successful information classification it is necessary to educate the employees about the basics of information security and classification and create an understandable and unified information security language. Practical implications This study also highlights that to have a tailored information classification model, it is imperative to understand the value of information and what kind of consequences a violation of established information security principles could have through the perspectives of the employees. Originality/value It is the first of its kind in tailoring an information classification model to the specific needs of a Swedish municipality. The model provided by this study can be used as a tool to facilitate a common ground for classifying information within all Swedish municipalities, thereby contributing the first step toward a Swedish municipal model for information classification.
An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

In recent years machine learning techniques have been able to perform tasks previously thought impossible or impractical such as image classification and natural language translation, as such this allows for the automation of tasks previously thought only possible by humans. This research work aims to test a naïve post processing grammar correction method using a Long Short Term Memory neural network to rearrange translated sentences from Subject Object Verb to Subject Verb Object. Here machine learning based techniques are used to successfully translate works in an automated fashion rather than manually and post processing translations to increase sentiment and grammar accuracy. The implementation of the proposed methodology uses a bounding box object detection model, optical character recognition model and a natural language processing model to fully translate manga without human intervention. The grammar correction experimentation tries to fix a common problem when machines translate between two natural languages that use different ordering, in this case from Japanese Subject Object Verb to English Subject Verb Object. For this experimentation 2 sequence to sequence Long Short Term Memory neural networks were developed, a character level and a word level model using word embedding to reorder English sentences from Subject Object Verb to Subject Verb Object. The results showed that the methodology works in practice and can automate the translation process successfully.
A Vehicle Detection Method Based on YOLO­V4 Model

Vehicle detection is a basic problem in target detection and has significant application value in fields such as intelligent traffic supervision and automatic driving. In order to improve the vehicle detection accuracy and detection speed, a vehicle detection method based on YOLOv4 is used in this paper. First, manually label the Apollo Scape automatic driving data set to establish a vehicle detection database, and then the YOLOv4 deep learning model is used to extract vehicle features adaptively, which avoiding the artificial subjective selection of features and achieving high precision vehicle target detection. The experimental results show that this model has a significant improvement in detection accuracy. Specifically, the mean average precision (mAP) reaches 92.1%, which is higher than the YOLOv4­Tiny, YOLOv3 and Faster R­CNN models 3.2%, 6.9% and 3.1%, respectively.
Modeling Student Confusion Using Fuzzy Logic in e-Learning Environments

Online courses start with high enrollments, but many learners often feel confused and lost while learning on such courses and ultimately resulting in dropping out of the course. Confusion plays a key role in the learning process and bears two folded impacts, both positive and negative. This paper postulates a Fuzzy logic-based Confusion level detection model in a learner using the clickstream data generated during the time learner attempts the quiz type online assessments. In this paper, various factors are used for measuring Confusion in online courses are explored. In this paper, we gathered or used the common clickstream patterns data when a learner attempts the quiz as proposed factors in determining confusion. This paper proposes that the comprehensive Fuzzy logic prediction can be suitable in detecting and measuring epistemic emotion like confusion. Determining the accurate level of confusion level can potentially help the teachers to keep learners engaged for longer duration in online discourse.
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains

It is well known that Neural Network (network) performance often degrades when a network is used in novel operating domains that differ from its training and testing domains. This is a major limitation, as networks are being integrated into safety critical, cyber-physical systems that must work in unconstrained environments, e.g., perception for autonomous vehicles. Training networks that generalize to novel operating domains and that extract robust features is an active area of research, but previous work fails to predict what the network performance will be in novel operating domains. We propose the task Network Generalization Prediction: predicting the expected network performance in novel operating domains. We describe the network performance in terms of an interpretable Context Subspace, and we propose a methodology for selecting the features of the Context Subspace that provide the most information about the network performance. We identify the Context Subspace for a pretrained Faster RCNN network performing pedestrian detection on the Berkeley Deep Drive (BDD) Dataset, and demonstrate Network Generalization Prediction accuracy within 5% of observed performance. We also demonstrate that the Context Subspace from the BDD Dataset is informative for completely unseen datasets, JAAD and Cityscapes, where predictions have a bias of 10% or less.
Analysis of Automated text generation using Deep learning

A chatbot is a computer program that can converse with humans using artificial intelligence in messaging platforms. The goal of the project is to use and optimize deep learning techniques for making an efficient chat bot. Current chatbots are developed using rule-based techniques, basic machine learning algorithms or retrieval-based techniques which don’t generate good results.in this paper, we will be comparing performance of three chatbots build by using RNN, GRU and LSTM. The conversation chatbots are used by different companies, government organizations and more.
Maximal Mixed-Drove Co-Occurrence Patterns

Mining of Mixed-Drove Co-occurrence Patterns can be very costly. Widely used, Apriori-based methods consist in finding spatial co-location patterns in each considered timestamp and filtering out patterns that are not time prevalent. Such an approach can be inefficient, especially for datasets that contain co-locations with a high number of elements. To solve this problem we introduce the concept of Maximal Mixed-Drove Co-occurrence Patterns and present new algorithm MAXMDCOP-Miner for finding such patterns. Our experiments performed on synthetic and real world datasets show that MAXDCOP-Miner offers very high performance when discovering patterns both in dense data and for low values of spatial or time prevalence thresholds.
Facilitating User Involvement in a Large IT Project: A Comparison of Facilitators’ Perspectives on Process, Role and Personal Practice

Studies indicate that the way structured participatory design activities are facilitated significantly effects participation, power relations, and generated output. Although facilitation is always adapted to the conditions of each design project, the way facilitation is enacted primarily depends on the individual facilitator. It is assumed that enactment on an individual level is influenced by how the facilitator conceptualizes both user involvement and their own role. To address these issues, semi-structured interviews were conducted with three facilitators in the same development unit of a large-scale health IT project. The primary focuses were the individual facilitator’s perspectives on the design process, their role, and their practice in the project. An in-depth qualitative content analysis of the transcribed interviews showed that the three facilitators held highly divergent perspectives on the facilitation of participatory design activities. Similarities were found between the facilitators’ perspectives and how user involvement is understood in different system development traditions. Based on the findings, a reflective approach towards facilitation is proposed along with examples of possible co-reflection activities among facilitators.
The on-site detection technique for silicone oil level in high voltage cable terminal using infrared image processing techniques

Oil-filled high voltage cable terminals are widely used in urban power transmission networks. It is a common issue that the oil leakage poses troubles on the reliability of the cable lines. This paper proposes a technique to evaluate the oil level in the cable terminal using infrared imaging technique and corresponding image processing algorithm. In this paper, Canny operator and the threshold value of the sum of pixels are utilized to extract the region of interest. The peak envelop curve and its gradient curve are obtained to identity the oil level in the terminal. An on-site detection case of an oil-leaked terminal verifies the proposed technique.
3D human pose estimation base on weighted joint loss

research-article 3D human pose estimation base on weighted joint loss Share on Authors: Chao Wang Tsinghua University, China Tsinghua University, ChinaView Profile , Chun Zhang Tsinghua University, China Tsinghua University, ChinaView Profile , Jinghao Wang Beihang University, China Beihang University, ChinaView Profile , Tianyu Fan Tsinghua University, China Tsinghua University, ChinaView Profile , Xiang Xie Tsinghua University, China Tsinghua University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 63Pages 1–5https://doi.org/10.1145/3469213.3470264Online:18 August 2021Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Analysis of Accounting Fraud in Enterprises Based on Game Theory——Take the Case of Geeya Technology Company Limited Fraud as an Example

Based on the game theory, focusing on the analysis of various factors affecting the financial fraud of domestic enterprises and the collusion of auditors make it possible to find out the dominant influencing factors and to put forward suggestions to reduce the possibility of financial fraud of enterprises. The game shows that the probability of fraud or collusion between the enterprise and the auditor mainly stems from the game between the benefit and the cost, which conforms to the "rational person" assumption. From the perspective of enterprises, progressive punishment can be set to increase the cost of fraud, improve the remuneration system and use the reward reporting function of blockchain technology to reduce the motivation of fraud; from the perspective of auditors, the mandatory rotation system can be implemented to increase the cost of non-compliance, enhance the independence of auditors and use the reward mechanism of blockchain audit to reduce the probability of collusion.
Subverting Counter Mode Encryption for Hidden Communication in High-Security Infrastructures

In highly security-critical network environments, it is a popular design decision to offload cryptographic tasks like encryption or signature generation to a dedicated trusted module or key server with paramount security features, we in this paper refer to with the general term Cryptographic Key Management Device (CKMD). While this network design yields several benefits, we demonstrate that the use of popular counter mode encryption modes like CTR or GCM can show substantial shortcomings in terms of security when used in conjunction with this network design. In particular, we show how the use of authenticated encryption using GCM enables the possibility of establishing a subliminal channel by exploiting the authentication information within messages. We show how decoding of hidden information can proceed in addition to decryption of overt information without raising authentication failures.
Computer Vision Based Prototype Model of Face Gesture Controlled Vehicle

Vehicles has been an integral and progressive part of urban life for the past few decades, a lot of new advancement in the field of vehicle mobility and automation is evident. The development in the field of Image Processing and the need for strategic planning in the field of smart vehicle has laid the foundation of the current research. The current work focuses on minimizing driver’s manual control, thus enhancing the existing smart features in the cars. The vehicle motion is proposed to be controlled through face gestures or face movements of the driver. A novel approach using Image Processing and Computer Vision is proposed for developing a prototype model of face gesture controlled vehicle. An algorithm based on the feature extraction technique of the image is used which is capable of matching the real time face movements of the driver and further sending the command signal to the wheels of the vehicle.
Robust Multi-Frame Future Prediction By Leveraging View Synthesis

In this paper, we focus on the problem of video prediction, i.e., future frame prediction. Most state-of-the-art techniques focus on synthesizing a single future frame at each step. However, this leads to utilizing the model’s own predicted frames when synthesizing multi-step prediction, resulting in gradual performance degradation due to accumulating errors in pixels. To alleviate this issue, we propose a model that can handle multi-step prediction. Additionally, we employ techniques to leverage from view synthesis for future frame prediction, where both problems are treated independently in the literature. Our proposed method employs multiview camera pose prediction and depth-prediction networks to project the last available frame to desired future frames via differentiable point cloud renderer. For the synthesis of moving objects, we utilize an additional refinement stage. In experiments, we show that the proposed framework outperforms state-of-theart methods in both KITTI and Cityscapes datasets.
Method for detection the non-soluble deposit density of insulators based on hyperspectral technology

The rapid development of agriculture and industry in China leads to a fast increase in the non-soluble deposit density (NSDD) of insulators. At present, the detection of NSDD is concentrated in the use of optical principles, which is difficult to achieve quantitative detection. Hyperspectral technique is a new comprehensive image data technique based on imaging spectroscopy, which has the advantages of multi-band, high resolution. Therefore, a non-contact detection method for detecting NSDD based on hyperspectral technique is proposed. The hyperspectral images of the insulator were obtained and preprocessed with black-and-white correction, which were used to establish the model based on the extreme learning machine with the kernel (KELM). Finally, the detection of NSDD was realized. Consequently, this study can guide the prevention of flash and configuration of external insulation in transmission lines.
Exploring CTC Based End-To-End Techniques for Myanmar Speech Recognition

In this work, we explore a Connectionist Temporal Classification (CTC) based end-to-end Automatic Speech Recognition (ASR) model for the Myanmar language. A series of experiments is presented on the topology of the model in which the convolutional layers are added and dropped, different depths of bidirectional long short-term memory (BLSTM) layers are used and different label encoding methods are investigated. The experiments are carried out in low-resource scenarios using our recorded Myanmar speech corpus of nearly 26 hours. The best model achieves character error rate (CER) of 4.72% and syllable error rate (SER) of 12.38% on the test set.
An Adversarial Network-based Multi-model Black-box Attack

Researches have shown that Deep neural networks (DNNs) are vulnerable to adversarial examples. In this paper, we propose a generative model to explore how to produce adversarial examples that can deceive multiple deep learning models simultaneously. Unlike most of popular adversarial attack algorithms, the one proposed in this paper is based on the Generative Adversarial Networks (GAN). It can quickly produce adversarial examples and perform black-box attacks on multi-model. To enhance the transferability of the samples generated by our approach, we use multiple neural networks in the training process. Experimental results on MNIST showed that our method can efficiently generate adversarial examples. Moreover, it can successfully attack various classes of deep neural networks at the same time, such as fully connected neural networks (FCNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). We performed a black-box attack on VGG16 and the experimental results showed that when the test data classes are ten (0–9), the attack success rate is 97.68%, and when the test data classes are seven (0–6), the attack success rate is up to 98.25%.
Expandable Spherical Projection and Feature Fusion Methods for Object Detection from Fisheye Images

One of the key requirements for enhanced autonomous driving systems is accurate detection of the objects from a wide range of view. Large-angle images from a fisheye lens camera can be an effective solution for automotive applications. However, it comes with the cost of strong radial distortions. In particular, the fisheye camera has a photographic effect of exaggerating the size of objects in central regions of the image, while making objects near the marginal area appear smaller. Therefore, we propose the Expandable Spherical Projection that expands center or margin regions to produce straight edges of de-warped objects with less unwanted background in the bounding boxes. In addition to this, we analyze the influence of multi-scale feature fusion in a real-time object detector, which learns to extract more meaningful information for small objects. We present three different types of concatenated YOLOv3-SPP architectures. Moreover, we demonstrate the effectiveness of our proposed projection and feature-fusion using multiple fisheye lens datasets, which shows up to 4.7% AP improvement compared to fisheye images and baseline model.
Image caption generation method based on an interaction mechanism and scene concept selection module

Image caption generation is the process of converting image content information into a smooth and accurate natural language. In the existing image caption generation methods, a consistent matching problem exists between the semantic information and the image information, and the existing methods encode based on only low-level spatial features or high-level text features, which limits the richness of the resulting image caption. To address this problem, this paper designs an interaction mechanism for image caption generation to achieve the mutual selection of image information (global information, target information) and semantic information in both directions. On the basis of the interaction mechanism, a scene concept selection module is designed, and the extracted scene concept information is selected by the interactive information generated by the interaction mechanism. This approach solves the problem of consistent matching between image information and semantic information and the problem of image caption enrichment. Experiments on MSCOCO data sets show that our model can generate accurate captions that match the scene information and is superior to many existing image caption generation models.