Reviewerly/exHarmony huggingface dataset:
PSec: Programming Secure Distributed Systems using Enclaves

We introduce PSec, a domain-specific language for programming secure distributed systems. PSec is a state-machine based programming language with information flow control capabilities that leverages Intel SGX enclaves to provide security guarantees at runtime. Combining state machines and information flow control with hardware enclaves enables programmers to build complex distributed systems without inadvertently leaking sensitive information to adversaries. We formally prove the security properties of PSec and evaluate our work by programming several real-world examples, including One Time Passcode and Secure Electronic Voting systems. We present performance results of PSec systems and show that there is an acceptable performance overhead of approximately 3x for long running systems with a possible minimum of approximately 1.2x, as compared to baseline systems that do not provide any security guarantees.
Diverse and Adjustable Versatile Image Enhancer

Enhancing the quality of photographs is a highly subjective process and depends on users' preferences.Hence, it is often more desired to let users choose their own best from a set of diverse and adjustable enhanced images with astounding quality.However, a system that can satisfy this requirement has not yet been established.While classical algorithms blindly enhance an image by filtering, recent intelligent enhancement systems can only do it with limited styles through learning from a set of single expert-retouched (ER) images.To fill this void, we propose a novel framework, Diverse and adjustable Versatile Image Enhancer (DaVIE), that learns from multiple ER images simultaneously.Thereby, it can output diverse results without being bound to a specific enhancement style while allowing users to freely adjust the level of enhancement.For ease of diversity, we adopt a variational auto-encoder (VAE) that learns stochastic distribution of enhancement styles.By using the VAE, the proposed model provides diversely enhanced images.To establish better control in terms of enhancement level, we propose a more general form of adaptive instance normalization and loss functions, which can afford even extreme image editing.Through rigorous experiments, we demonstrate that the proposed DaVIE framework yields visually pleasing and diverse results.We also show the proposed model quantitatively outperforms existing methods on the MIT-Adobe-5K dataset.Furthermore, through a strict user-study, we show that the users consider the qualities of ER images and machine-retouched images to be similar, with about 35% selection probability for DaVIE enhanced images.
Joint Model for Human Body Part Instance Segmentation and DensePose Estimation

Human body part instance segmentation and human body densepose estimation are new frontier research problems of great significance, they have high practical application and research value. We will propose a new unified algorithm framework that can train these two tasks at the same time. First, we will build a joint model for these two tasks based on the basic framework of MASK-RCNN; In addition, we will propose a new lightweight attention mechanism module LAM, which reduces the amount of parameters and calculations more than ten times compared with the GCE module in Parsing-RCNN; We combine LAM and the improved dense semantic analysis module based on DenseASPP to form deep and dense feature information. The extraction module LADCEM is lighter than the GCE module, but can bring more accuracy improvements; Then we will use anchor freed FCOS of one-stage which is full convolution detection module for the first time to replace the two-stage anchor based RPN, which can significantly improve the accuracy of human detection, both subtasks are greatly improved. In the end, the MobileNet-V3 network is used as the basic Backbone, the overall parameter amount is about 10% of the Backbone based on Rensenet50, making it easier to deploy on mobile device using our model.
Application of Artificial Intelligence and Integrated Development of Engineering Training in Flat Garage

Abstract With the continuous development of economy, private cars have become an important means of transportation for people to travel. When private cars bring convenience to people, parking Spaces in the parking lot can hardly meet people’s demand for parking Spaces. In this paper, the introduction of artificial intelligence and the connotation of engineering training are explained, and the application of the overall development of artificial intelligence and engineering training in flat garage is discussed, for readers’ reference.
DWT Based QR Steganography

Abstract Data security pays more attention in most of the communication fields. Image steganography is one of the leading data security techniques that provides confidentiality with high embedding capacity and high steganography image quality. This work proposes Discrete Haar Wavelet Transform (DWT-H) and bitwise operations-based embedding technique to embed secret QR-code message in the cover image and extraction of QR-code secret message is also carried out by the same methodology. The efficiency of the proposed method is estimated by Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). This technique provides less MSE and PSNR in the range of 31dB to 44dB and extracts QR code image up to version 40. The results of the proposed method provide the practical feasibility for QR-coded-based security applications with less time and high accuracy.
Cloud Computer Research on Table Detection Model Based on the DC-LSTM Model

Abstract In view of the fact that tables are not easy to detect, this paper designs a table detection model based on the DC-LSTM module, which references the Convolutional Long Short-Term Memory (ConvLSTM). The model uses the backbone network of target detection to extract convolution features. The feature pyramid network is used to complete the detection task, and the DC-LSTM module is embedded in a special position in the feature pyramid network. In order to evaluate the performance of the DC-LSTM module, we added the DC-LSTM module to the YOLO v3, SSD network. Specifically, we added the DC-LSTM module to the YOLO v3 network, the new model can achieve an accuracy of more than 98% on the Table Bank data set and own data set. The model in this paper can realize the automatic extraction of table document images, which is of great significance for the realization of automated data collection.
Digital Surveillance Technologies to Combat COVID-19: A Contemporary View

Since the genesis case was confirmed in Wuhan, China in late 2019, the Novel Coronavirus Disease 2019 (COVID-19) has been spreading all over the world at an accelerating rate. Almost immediately, IoT technologies were deployed in various surveillance scenarios as part of an effort to combat the pandemic. Among the emerging solutions, contact tracing mobile applications have been playing an effective role to help stem the spread of the virus by tracking individuals and those they come into exposure with. This paper aims at providing a panoramic view of the digital tracking technologies that have been utilized so far in response to the pandemic. We particularly provide a detailed analysis of 47 contact tracing mobile applications that emerged in response to COVID-19. We accompany our analysis with a discussion on the privacy and the technology / social constraints that may challenge the deployment of these applications as digital surveillance platforms.
Design and application of computer conference+Thai corpus-oriented word segmentation consistency checking system

Abstract The construction of deep processing corpus is one of the hot topics in corpus linguistics and computational linguistics, and the quality of corpus directly affects the subsequent research based on this corpus. It is a very effective way to build a corpus of questions in restricted areas to help understand users’ questions. In view of the inconsistent results of word segmentation and part-of-speech tagging in Thai corpus, this paper puts forward a proofreading method and strategy based on computer conference, and designs a consistent proofreading system. This method extracts grammatical and semantic collocation information between words, judges candidate sequences by using the theory of support vector machine, and gives a segmentation result, thus improving the segmentation accuracy of Chinese corpus.
Fire risk based on building density in dense settlement

Abstract Fire disaster occurs commonly in densely populated settlements mostly due to a large number of inhabitants. Among the various types of disasters, those related to fire outbreak, need to be avoided. Therefore, it is important to determine the building density, and street conditions, with the availability of fire extinguishers to prevent fire disaster mitigation. This research examined the risk of fire disaster based on building density in a dense settlement. The research was carried out in 9 village communities in Kali Anyar Sub-district, using the case study method. Aspects of total area, number of building units, and fire accidents location were used to evaluate the cases. The result showed that the rate of fire outbreak was not always related to the building density. However, physical arrangement related to fire risk in all building is necessary to minimize the possibility of its occurrence.
A Change-Aware Approach for Relative Motion Segmentation

Analysis on changes of image features is an effective means to leverage multi-dimensional information in motion segmentation. Methods without considering temporal perspective are agnostic of motion state, and inherently unsuitable for motion detection. However, the effect of existing spatio-temporal approaches is lagging behind that of spatial methods by a margin. To make better use of temporal information, this paper tackles the task of moving object segmentation by constructing a Change-aware Siamese neural network(ChaSiam) to detect relative foreground and changes. Further, a reference frame update strategy is attached to our network for overcoming the weakness of spatio-temporal approaches in cases with camera ego-motion. Extensive experiments show that our proposed model outperforms previous state-of-the-art spatio-temporal methods on Change Detection dataset, and compared with spatial methods our model has similar performance with better generalization.
Establishment of response evaluation model and empirical study of risk in enterprise threat intelligence

In order to make up for the deficiency of enterprise threat intelligence response mechanism, a scientific enterprise threat intelligence response evaluation model is proposed and the enterprise risk is empirically evaluated and analyzed. The enterprise threat intelligence response model was constructed based on the four main influencing factors of enterprise personnel quality, software/hardware facilities, technical support, and management awareness. The RBF neural network improved by grey Wolf swarm optimization algorithm was used for training and simulation, so as to conduct in-depth analysis and research. The results show that: 1. RBF neural network can make up for the deficiency of RBF neural network by optimizing grey Wolf pack; 2. While focusing on technological innovation and personnel training in the threat intelligence response system, enterprises cannot ignore the importance of management awareness.
Metrics, Platforms, Emulators, and TestNets for Ethereum

Blockchain technology is considered the most relevant technology after the internet that revolutionizes many application domains. The first generation of BC technology, BC 1.0, is used for cryptocurrency purposes; the second generation, BC 2.0, as represented by Ethereum, is an open and decentralized platform empowering the running of decentralized applications (DApps) on top of BC as a new computing paradigm. Ethereum as a BC 2.0 leader has a large development community. Its open-source feature leads to the development of several emulation tools, simulators, TestNets, and security verification tools dedicated to Ethereum-based system performance analysis. Making an adequate decision regarding the choice of the most appropriate Ethereum tool responding to the requirement of a specific system or application still requires more investigation from researchers. In this regard, this chapter presents the characteristics of the most-used Ethereum simulators, emulators, and TestNets and provides comparative studies between Ethereum simulators, TestNets, and security verification tools.
Two Dimensional and Gesture Based Medical Visualization Interface and Image Processing Methodologies to Aid and Diagnose of Lung Cancer

This work aims to implement specific image processing methodologies to aid the appropriate diagnosis of lung cancer and provide a sterile environment for interacting with the medical data in operation theatre during treatment. Computer Tomography (CT) gives a good resolution axial slice image of the lung. The tumor’s raw CT data analysis is quite impossible as the tumor’s pixel characteristics would be approximately matching its neighboring pixels. Thus, a basic preprocessing algorithm is required to differentiate target from the background. It is processed with level set segmentation to extract tumor from the sequentially acquired multiple slices. The segmented output of each slice provides the geometric feature of the tumor. As the tumor size and shape in segmented portions are irregular, it seems appropriate to reconstruct a three-dimensional representation of 2D images for tumors’ qualitative information. The volume reconstruction using the ray casting method has been applied to render the volume of tumors from a segmented stack of 2D slices. Then touch-less, computer-aided, gesture-based control of the medical images was attained using Kinect sensor, which is the best tool for human-computer interaction to maintain a sterile environment. The paper elaborates on the gestures, which is employed to interact with the medical images such as selection, drag, and swipe gesture. The selection gesture is used to open and close individual medical images. The drag gesture was used to view the patient data along with the slice image. The swipe gesture used to view various medical data sets like 2D slices, tumor irregularity in segmented slices.
Feature interaction based online streaming feature selection via buffer mechanism

Abstract Feature selection is a nontrivial preprocessing technique in many practical application domains. There are three key challenges with respect to real‐world data. Firstly, the dimensionality of data keeps growing and will achieve hundreds of millions. Secondly, the data has the characteristic of high‐dimensional and small‐size. Thirdly, practical applications need to process each feature in an online manner. However, most of the previous methods only pay much attention to solving the challenges of high dimensionality and online stream. To address all issues above, we propose OFSI, in this article, an nline streaming eature election based on feature nteraction method for feature selection. OFSI can effectively select the streaming features that are strongly related to each other in high‐dimensional and small‐size data, via using feature interaction. Furthermore, to address upcoming features that arrive by groups, we present a new group‐OFSI algorithm for online group feature selection. An extensive experiment using a series of benchmark data sets shows that the proposed two algorithms, OFSI and group‐OFSI, outperform six state‐of‐the‐art online streaming feature selection methods.
Classification of Thesis Topics Based on Informatics Science Using SVM

Abstract Thesis topic is an inseparable part in the world of tertiary education. Determining the thesis topic becomes a problem for students. The determination of the thesis topic leads to the trend of the topic in the development of computer science. The determination of the topic of thesis for students often ignores their ability to process. Ideally in determining the topic of the thesis, the record of student grades can be an important variable in deciding topics for students, where the student’s grade record is contained in the transcript. Therefore, this study uses the Support Vector Machine (SVM) method in recommending thesis topics by classifying selected subject groups that have been taken by students. The Support Vector Machine method is a classification method of supervision because it requires testing data and training data as a training process at the time of prediction. Support Vector Machine provides an optimal model, which provides a solution with a maximum margin to determine the distance of data to the hyperplane. The test results show an accuracy of 80%.
Multi-frame image restoration method for Luojia 1-01 night-light remote sensing images

To address blur and noise issues caused by airlight and atmospheric scattering in nighttime imaging environment, we proposed a multi-frame image restoration method. First, the Luojia 1–01 night-light image degradation model was derived. Thereafter, the APSF (Atmospheric Point Spread Function) for night-light images was estimated. Improved dark channel prior and sparse constraint models were used to eliminate effects ofairlight and atmospheric scattering. Finally, a multi-frame sparse constraint model was established to eliminate image noise. The results show the proposed method is effective as it can reduce the image blur phenomenon, suppress image noise, and improve image quality.
Extending a Hybrid Security Risk Assessment Model with CWSS

Cybersecurity risk management is the foundation of business and organizational decisions involving digital technology. Various models have been proposed and are in use, but these apply to current technologies and use cases, and none are sufficient to evaluate new technologies. This paper builds upon prior work using CVSS to quantify potential security threats for which information is limited. That prior work merges CVSS data with MITRE’s Common Attack Pattern Enumeration and Classification (CAPEC™) tools to inform a new technology risk scoring system in a Bayesian Belief Network (BBN). This work extends this risk model to incorporate CWSS data to better reflect the environments’ weaknesses that may apply to new technologies. This approach enables a more accurate and trustworthy way of quantitatively estimating risk as a function of the Base Finding Subscore and Attack Surface Subscore for weaknesses most relevant to businesses, missions, and deployed technologies.
Recommending Learning Model for Online Learning Delivery

The rapid shift in online learning delivery increases its demand particularly during the prevalence of the Covid-19 pandemic and being able to determine a valid and reliable criteria in the process of its selection appeals a strong importance. In this study, the researchers determine the factors in choosing the appropriate online learning platform for educational institution between Moodle and Canvas by utilizing the map rule of the Naïve Bayes classification technique.
Active Machine Learning to Test Autonomous Driving

Autonomous driving represents a significant challenge to all software quality assurance techniques, including testing. Generative machine learning (ML) techniques including active ML have considerable potential to generate high quality synthetic test data that can complement and improve on existing techniques such as hardware-in-the-loop and road testing.
A New and Efficient Lattice-Based Online/Offline Signature From Perspective of Abort

Abstract Lattice-based online/offline signature is attractive for the merit of resisting quantum attacks besides the short online response time. Prior to this work, the hash-sign-switch paradigm lattice-based online/offline signatures usually increase the length of each signature, and the Fiat–Shamir candidates are highly inefficient due to multiple aborts in online signing phase. In this work we mainly address its efficient issue and propose a new paradigm of its construction in the perspective of abort. In this paradigm, one tries to remove one or more aborts from online to offline signing phase by $\Gamma $-transformation. Specifically, this work proposes an efficient lattice-based online/offline signature scheme with fewer online aborts and thus allows the signer to obtain a valid signature by fewer online repetitions. Through this way, the resulting scheme can reduce much online signing time with the same signature size. The performance evaluation shows that our scheme is efficient and practical.
Transcript Validation System using biometric characteristics

Recently transcripts and confirmations verification become one of the important issues for universities, institutes and organizations. This problem arises due to the spread out of many programs and modern technologies that allows the forgers to forge transcripts and confirmations which cannot be recognized by registrar persons how work for institutions or other entities. So, it becomes necessary for the entities to insure the validation of transcripts, confirmations or other documents from the original source. In this paper, we design a transcript validation system that can be used by different entities to reduce the time required for transcripts verification based on the information saved in a database. The system has been supported by a Graphic User Interface (GUI) to make the system as easy as possible to be used by users. The system works to verify the existing signatures on the transcript and compare them with the database by using offline signature verification depends on neural network method, also the system verifies the existing photo on the transcript using regression method in order to increase the reliability. The system has proven its reliability through high acceptance ratio of the genuine signatures and high rejection ratio for forged and random signatures.
Mutual Information Preserving Back-propagation

Back-propagation based visualizations have been proposed to interpret deep neural networks (DNNs), some of which produce interpretations with good visual quality. However, there exist doubts about whether these intuitive visualizations are related to network decisions. Recent studies have confirmed this suspicion by verifying that almost all these modified back-propagation visualizations are not faithful to the model's decision-making process. Besides, these visualizations produce vague "relative importance scores", among which low values can't guarantee to be independent of the final prediction. Hence, it's highly desirable to develop a novel back-propagation method that guarantees theoretical faithfulness and produces a quantitative attribution score with a clear understanding. To achieve the goal, we resort to mutual information theory to generate the interpretations, studying how much information of output is encoded in each input neuron. The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal. In addition, we propose a Mutual Information Preserving Inverse Network, termed MIP-IN, in which the parameters of each layer are recursively trained to learn how to invert. During the inversion, forward relu operation is adopted to adapt the general interpretations to the specific input. We then empirically demonstrate that the inverted source signal satisfies completeness and minimality property, which are crucial for a faithful interpretation. Furthermore, the empirical study validates the effectiveness of interpretations generated by MIP-IN.