Reviewerly/exHarmony huggingface dataset:
Parameter Learning in ProbLog with Annotated Disjunctions

In parameter learning, a partial interpretation most often contains information about only a subset of the parameters in the program. However, standard EM-based algorithms use all interpretations to learn all parameters, which significantly slows down learning. To tackle this issue, we introduce EMPLiFI, an EM-based parameter learning technique for probabilistic logic programs, that improves the efficiency of EM by exploiting the rule-based structure of logic programs. In addition, EMPLiFI enables parameter learning of multi-head annotated disjunctions in ProbLog programs, which was not yet possible in previous methods. Theoretically, we show that EMPLiFI is correct. Empirically, we compare EMPLiFI to LFI-ProbLog and EMBLEM. The results show that EMPLiFI is the most efficient in learning single-head annotated disjunctions. In learning multi-head annotated disjunctions, EMPLiFI is more accurate than EMBLEM, while LFI-ProbLog cannot handle this task.
Program Synthesis for Cyber-Resilience

Architectural tactics enable stakeholders to achieve cyber-resilience requirements. They permit systems to react, resist, detect, and recover from cyber incidents. This paper presents an approach to generate source code for architectural tactics typically used in safety and mission-critical systems. Our approach extensively relies on the use of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Event-B</small> formal method and the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EventB2Java</small> code generation plugin of the Rodin platform. It leverages the modeling of architectural tactics in the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Event-B</small> formal language and uses a set of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EventB2Java</small> transformation rules to generate certified code implementations for the said tactics. Since resilience requirements are statements about a system over time, and because of the fact that the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Event-B</small> language does not provide (native) support for the writing of temporal specifications, we have implemented a novel Linear Temporal Logic (LTL) extension for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Event-B</small> . We support several architectural tactics for availability, performance, and security. The generated code is certified in the following sense: discharging proof obligations in Rodin - the platform we use for writing the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Event-B</small> models - attests to the soundness of the architectural tactics modelled in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Event-B</small> , and the soundness of the translation encoded by the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EventB2Java</small> tool attests to the code correctness. Finally, we demonstrate the usability of our resilience validation approach with the aid of an Autonomous Vehicle System. It further helped us increase our confidence in the soundness of our Event-B LTL extension.
A ciphertext updatable attribute-based searchable encryption scheme via blockchain

To ensure that multiple data users can access the data and realize the dynamic change of user authority, this paper proposes a ciphertext updatable attribute-based searchable encryption scheme based on blockchain. Combine the blockchain with the cloud server, keyword ciphertext is stored on the blockchain, symmetric key ciphertext and data ciphertext are stored on the cloud server. When the data user wants to change the access policy or revoke the access rights of some users, this scheme uses the proxy re-encryption technology to realize attribute revocation and ciphertext update of the data requester. At the same time, the access policy is hidden, which realizes the user's privacy protection. The security analysis shows that our scheme is safe and effective. In addition, the performance analysis shows that the proposed scheme is efficient and feasible.
A Modified Particle Swarm Optimization for the Applications of Electromagnetic Devices

Particle Swarm Optimization (PSO) is a global search algorithm based on swarm intelligence. The PSO is becoming more popular because of solving complex and complicated benchmark optimization problems. The mathematical model of PSO is easy due to few control parameters. However, the PSO algorithm are stuck due to premature convergence while dealing with high dimensional problems. To handle this issue the current research paper introducing especial best particle that incorporated in the updating equations. Also, the novel best particle control the diversity of the search process and keep a decent balance between the exploration and exploitation searches. The experimental outcomes demonstrates that the novel algorithm performs well as compared to other one.
Reports Aggregation of Crowdsourcing Test Based on Feature Fusion

In recent years, a new testing method based on the concept of crowdsourcing has made great progress. Developers upload the project to the crowdsourcing test platform and recruit a large number of crowdsourcing workers for testing, so that the testing process has higher test adequacy, faster testing speed and lower testing cost. However, the test reports submitted after the test have serious problems such as large quantity and high similarity, resulting in the failure to achieve the expected results. Based on the method of feature fusion, this paper integrates the text description information, bug type information and screenshot information of crowdsourcing test reports, clusters crowdsourcing test reports through the calculation of similarity between reports, and finally achieves better results.
Adversarial attack via dual-stage network erosion

Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, it still leaves a long way to go for generating transferable adversarial examples under the black-box setting. To this end, this paper proposes to improve the transferability of adversarial examples, and applies dual-stage feature-level perturbations to an existing model to implicitly create a set of diverse models. Then these models are fused by the longitudinal ensemble during the iterations. The proposed method is termed Dual-Stage Network Erosion (DSNE). We conduct comprehensive experiments both on non-residual and residual networks, and obtain more transferable adversarial examples with the computational cost similar to the state-of-the-art method. In particular, for the residual networks, the transferability of the adversarial examples can be significantly improved by biasing the residual block information to the skip connections. Our work provides new insights into the architectural vulnerability of neural networks and presents new challenges to the robustness of neural networks.
A Case of Study on Traffic Cone Detection for Autonomous Racing on a Jetson Platform

Autonomous driving is a growing research line since the future of transportation depends to a great extent on it. Driving is highly dependant on the environment sensing system. Over the last decade, several detection architectures based on neural networks and monocular cameras have been proposed to address this task. However, adapting these proposals to a vehicle with limited resources remains a challenging problem. In our study, we propose a lightweight neural network to perform cone detection from a racing car. We also compare its performance against other popular state-of-the-art proposals on a resource constrained system. From the obtained results, we can conclude that our network outperforms the state-of-the-art works for our use case and it is less resource demanding.
Learning Residual Color for Novel View Synthesis

Scene Representation Networks (SRN) have been proven as a powerful tool for novel view synthesis in recent works. They learn a mapping function from the world coordinates of spatial points to radiance color and the scene's density using a fully connected network. However, scene texture contains complex high-frequency details in practice that is hard to be memorized by a network with limited parameters, leading to disturbing blurry effects when rendering novel views. In this paper, we propose to learn 'residual color' instead of 'radiance color' for novel view synthesis, i.e., the residuals between surface color and reference color. Here the reference color is calculated based on spatial color priors, which are extracted from input view observations. The beauty of such a strategy lies in that the residuals between radiance color and reference are close to zero for most spatial points thus are easier to learn. A novel view synthesis system that learns the residual color using SRN is presented in this paper. Experiments on public datasets demonstrate that the proposed method achieves competitive performance in preserving high-resolution details, leading to visually more pleasant results than the state of the arts.
Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification

Network pruning and binarization have been demonstrated to be effective in neural network accelerator design for high speed and energy efficiency. However, most existing pruning approaches achieve a poor tradeoff between accuracy and efficiency, which on the other hand, has limited the progress of neural network accelerators. At the same time, binary networks are highly efficient, however, a large accuracy gap exists between binary networks and their full-precision counterparts. In this article, we investigate the merits of extremely sparse networks with binary connections for image classification through software-hardware codesign. More specifically, we first propose a binary augmented extremely pruning method that can achieve ~98% sparsity with small accuracy degradation. Then we design the hardware architecture based on the resulting sparse and binary networks, which extensively explores the benefits of extreme sparsity with negligible resource consumption introduced by binary branch. Experiments on large-scale ImageNet classification and field-programmable gate array (FPGA) demonstrate that the proposed software-hardware architecture can achieve a prominent tradeoff between accuracy and efficiency.
A Natural Conditionalization of the DWE Framework

Continuing prior work by the author on his DWE (Doing Well Enough) framework, this paper sketches a modest but needed change in the monadic framework, and then a conditionalized extension of that result—the main goal of the paper. It begins with a brief sketch of the DWE framework, and then of the simple modification to that monadic framework. Next I motivate and explore a conditionalized extension of the modified DWE monadic framework. The context is conservative and classical, so this is but an initial installment in answer to the question: what might conditionalized extensions of frameworks intended to accommodate supererogation and kindred concepts look like, and more specifically, are there distinctive resources called for in the conditionalization of the expressively richer set of notions modeled in the DWE framework?
Civil Aviation Data Monitoring System Based on Encryptable Consortium Blockchain

Aiming at the needs of civil aviation data sharing and a credible system building, this work designs a civil aviation data monitoring system based on an encrypted consortium blockchain. This system realizes data encryption on the basis of the consortium blockchain, which has the advantages of the blockchain while preventing the leakage of sensitive data. The data generated in civil aviation, including flight information, ground passenger services, aircraft maintenance information, and the hash value of monitoring multimedia files, can also be packaged on the chain in real time so that it cannot be tampered with and can be tracked. At the same time, data needs to be checked before uploading to determine whether there is any violations, so as to achieve real-time monitoring. The system can also choose whether to encrypt the data to be uploaded by means of data annotation. Finally, the system has been tested for performance and safety. Experimental results show that the system has low time cost and can resist multiple attacks.
Short Sale Attack: A PoW-blockchain-aimed attacking model via short sale

The 51&#x0025; attack was generally considered to occur at a very small probability when it was first proposed. However, there are more 51&#x0025; attacks happening recently. With the market price of cryptocurrencies continues to hit a new record recent years, we should also pay attention the higher potential risks accompanied. In this paper, we introduce a new kind of potential 51&#x0025; attact risk using short sales on PoW-based blockchains and cryptocurrencies. We come out with a model to estimate the upper limit price for a certain cryptocurrency. In particular, we consider the influence of marketing fluctuation and introduce ARIMA to amend the model. Moreover, we discuss some more threatened scene using this kind of attack. Finally, based on the model, we conduct experiments to estimate the price limit and the risk of cryptocurrencies circulating in the current market. Results show that whether large blockchain system(like Bitcoin) or small one (like Ravencoin), they all have varying degrees of risk of being attacked.
Linear discriminant analysis based stage wise breast cancer identification

One of the most fatal diseases among women is breast cancer. It is also called the second leading cause of death due to delay in the diagnosis of cancerous tissues. The most common symptoms of breast cancer are post- menopause, obesity, family genes, hormonal imbalance, and genetically muted abnormalities. Death rates are still excessive as breast cancer detection in many developing countries is diagnosed during later stages. To reduce the death rates, we need to diagnose it at the early stages. Digital mammogram images are used as input in the proposed method. Our main focus in this paper is differentiating between normal, benign, and malignant cancer. The pre-processing is done using a median filter. The K-means Segmentation is used for the segmentation process. The feature extraction is done using GLCM (Grey Level Co-occurrence Matrix) algorithm. The last step which is object classification is done using LDA (Linear Discriminant Analysis). We have classified them into different stages of cancer using the digital image processing technique and the performance of the classifier is evaluated through a confusion matrix.
Classification of Malignant and Benign Tumors

Machine learning has a long-standing history of application for computer-aided diagnosis (CADx) purposes and discriminating between different types of benign and malignant lesions. In this chapter, we describe the application of machine and deep learning algorithms for development of classifiers of tumors using image features from diagnostic imaging modalities. Examples from our work on mammography using conventional classification approaches, deep neural networks, and advanced methods based on content-based imagine retrieval (CBIR) are presented and discussed.
Microbial Content Generation for Natural Terrains in Computer Games

Procedural content generation (PCG) has been applied since several decades to fulfill various game-related design needs. Besides bio-inspired methods, living (non-human) organisms were used in computer games for various purposes, such as behavior generation, data gathering, and player education. No living organisms were used for the generation of virtual terrains in games. Such an approach to terrain generation could benefit from morphological similarity between natural terrains and colonies of microbial organisms, real-time development of terrains over time, and educational opportunities. We successfully executed an experiment in which we used growing bacterial and fungal cultures for generating naturally appearing virtual terrains in real-time. Concludingly, we confirm the feasibility of using living organisms in real-time non-behavioral PCG and reflect on its potential impact.
The Side-Channel Vulnerability in Network Protocol

Some recent studies have found that there are some side-channel vulnerabilities in the operating system. Attackers would exploit the side-channel vulnerability for malicious purpose, such as hijack connections, denial of service attacks, etc. Currently, most attacks are detected manually. In this paper, we found that the reason for the existence of network protocol side-channel vulnerability is the use of shared resources. Since the state of shared resource affects all connections, when a connection uses a shared resource, information about that connection can be inferred by observing the usage of the shared resource. In order to find the shared resources, we implemented a tool called TASR which is a method of static analysis. The first is to find out what shared resources are available by the definition of shared resources in static analysis. Then, the data packet is used as the taint source to search the tainted shared resources. The second step is to analyze the taint-transmission-path according to the acquired tainted shared variable. Then it can find the side-channel vulnerability. By using this method on TCP, UDP and ICMP protocols, we find the following four shared variables: challenge_count, tcp_memory_allocated, tcp_memory_pressure, sysctl_icmp_msg_per_sec. It is difficult for tcp_memory allocated and tcp_memory pressure to exploit, because they will go through multiple strict checks. Using challenge_count can hijack the connection and inject malicious packets. Using sysctl_icmp_msg_per_sec can assist in DNS cache poisoning attack.
A design of secure supply chain management system with blockchain technology

Supply chain management is an integral part of most businesses because it could affect the sustainability of a business and the customer’s satisfaction. Various computerized SCM system have been proposed in order to built an effective and efficient supply chain management system. However, these existing systems are based on centralized architecture and could not fully integrate the supply chain data from its members. Furthermore, a centralized system architecture has a higher risk to suffer from single-point-of-failure issue. In this paper, a secure blockchain-based SCM framework is proposed. The proposed SCM framework adopts a data verification mechanism through the use of a unique shipment security token. The shipment security token is distributed via 2 channels: the physical and digital token distribution channel. The 2 channels token verification method secures the data stored in the blockchain ledger and makes it easy to trace the product data in the blockchain network. A prototype based on the proposed blockchain-based supply chain model is implemented to show the feasibility of the proposed framework design.
An Empirical Approach for Tuning an Autonomous Mobile Robot in Gazebo

This paper mainly focuses on simulation of an autonomous mobile robot (AMR) based on the robot operating system (ROS) simulated in Gazebo in a Laboratory scene finding the shortest path and hence traversing the coordinate-based checkpoints in the quickest time possible. ROS is an open-source software framework mainly used for software development for robots. Sensing and perception are done by using sensors like light detection and ranging (LIDAR), depth camera, inertial measurement unit (IMU), and encoders. Global planner and Local planner are responsible for determining the path that the AMR will take and traveling in the scenario without any collisions. Simultaneous localization and mapping (SLAM) are launched in a node and for localization adaptive Monte Carlo localization (AMCL) is used. The robot in the simulated environment can be used in real life applications like autonomous cleaner, final stage delivery robots. The ultimate goal is to travel from point to point in the quickest time possible without any crashes.
Identify Spammers in Rating Systems Using Multi-layer Graph Convolutional Network ✱

With the rise of deep learning technology, researches on the application of graph convolutional networks to fraud detection emerge endlessly. However, the graph convolutional network used often has only two layers, which makes us unable to obtain higher-order node information. So we must use a multi-layer graph neural network to get more node information for training in order to get more accurate detection results. Using a multi-layer graph neural network also causes gradient disappearance; that is, the model parameters cannot be updated, and the model is invalid. This work explores the feasibility of using multi-layer GCN to detect fraudsters from the internal structure of GCN, that is, the number of hidden layer neurons and the activation function. Finally, it is tested on real data sets, and the detection accuracy of fraudsters using multi-layer GCN is increased by about 14.6% at most.
Digitizing Historical Balance Sheet Data: A Practitioner's Guide

This paper discusses how to successfully digitize large-scale historical micro-data by augmenting optical character recognition (OCR) engines with pre- and post-processing methods. Although OCR software has improved dramatically in recent years due to improvements in machine learning, off-the-shelf OCR applications still present high error rates which limits their applications for accurate extraction of structured information. Complementing OCR with additional methods can however dramatically increase its success rate, making it a powerful and cost-efficient tool for economic historians. This paper showcases these methods and explains why they are useful. We apply them against two large balance sheet datasets and introduce quipucamayoc, a Python package containing these methods in a unified framework.
Video Super-Resolution Based on Spatial-Temporal Transformer

In this paper, we proposed a Spatial-Temporal Transformer (STTF) algorithm for video super resolution (SR), to solve the problem of blurs or artifacts after super resolve low-resolution (LR) video with traditional super resolution algorithm. Firstly, the algorithm uses residual blocks to extract initial features from video sequences. Secondly, the three-dimensional video features are decomposed into image patches and then are sent to the Spatial-Temporal Transformer network for self-attention among patches where patches can be aligned and fused. Finally, sub-pixel convolution layer and residual layers are applied to up-sampling and reconstruct the high-resolution (HR) video sequences. In order to improve video visual effects, minimum mean square error (MSE) loss function is applied to train the neural network. The experimental results show that the STTF network has a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to traditional super-resolution algorithm.