Reviewerly/exHarmony huggingface dataset:
Graph Pooling with Representativeness

Graph Neural Networks (GNNs), which extend deep neural networks to graph-structured data, have attracted increasing attention. They have been proven to be powerful for numerous graph related tasks such as graph classification, link prediction, and node classification. To adapt GNNs to graph classification, recent works aim to learn graph-level representation through a hierarchical pooling procedure. One major direction is to select important nodes to hierarchically coarsen the input graph and gradually reduce the information into the graph representation. However, most of the existing methods only select important nodes, which can be redundant and cannot represent the original graph well. Meanwhile, the information of non-selected nodes is often overlooked when generating a new coarser graph, which may lead to the tremendous loss of important structural and node feature information. In this paper, we propose a novel pooling operator RepPool to learn hierarchical graph representations. Specifically, we introduce the concept of representativeness that is combined with the importance for node selection and we provide a learnable way to integrate non-selected nodes. By combining the RepPool operator with conventional GCN convolutional layers, a hierarchical graph classification architecture is developed. Extensive experiments on various public benchmarks have demonstrated the effectiveness of the proposed method. The implementation of the proposed framework is available.
A comparative study between the capabilities of MySQL and ClickHouse in low-performance Linux environment

Fast, reliable, and secure data processing is critical in the Information Technology (IT) sector as it is an indispensable requirement of all the growing technologies. Achieving this requires handling large amounts of data and massive computing power. Emerging technologies use devices under low-performance environments, such as raspberry-pi, which require highly efficient databases under low computing power. Inappropriate selection of databases leads to considerable resource wastage in numerous ways, such as waste of time, cost, and energy. The selection of a reliable, user-friendly database compatible with low-performance environments will downplay the energy requirement and cut down applications' carbon footprint. Thus, this study was conducted to compare the performance and query cost of using ClickHouse and MySQL databases as column-oriented and row-oriented databases in a low-performance environment. The effectiveness of using column-oriented or row-oriented databases with large amounts of data at different platforms was analyzed. The study was conducted by running different queries using an automated PHP script and checking the execution time, processing power, disk I/O, and memory usage for each query. The study was conducted with a virtual Linux environment of 1024MB RAM, 1.8Ghz single processor, and 25Gb storage, representing very low computing power, even lesser than the recommended minimum requirement of ClickHouse and MySQL databases. Both databases performed well in the above mentioned environment. Our experimental results show that ClickHouse execution time and disk write speed is tremendously low compared to MySQL. To obtain this performance, ClickHouse used more CPU processing power and memory utilization than MySQL.
TEM image restoration from fast image streams

Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.
Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement

Variational AutoEncoders (VAEs) provide a means to generate representational latent embeddings. Previous research has highlighted the benefits of achieving representations that are disentangled, particularly for downstream tasks. However, there is some debate about how to encourage disentanglement with VAEs, and evidence indicates that existing implementations do not achieve disentanglement consistently. The evaluation of how well a VAE's latent space has been disentangled is often evaluated against our subjective expectations of which attributes should be disentangled for a given problem. Therefore, by definition, we already have domain knowledge of what should be achieved and yet we use unsupervised approaches to achieve it. We propose a weakly-supervised approach that incorporates any available domain knowledge into the training process to form a Gated-VAE. The process involves partitioning the representational embedding and gating backpropagation. All partitions are utilised on the forward pass but gradients are backpropagated through different partitions according to selected image/target pairings. The approach can be used to modify existing VAE models such as beta-VAE, InfoVAE and DIP-VAE-II. Experiments demonstrate that using gated backpropagation, latent factors are represented in their intended partition. The approach is applied to images of faces for the purpose of disentangling head-pose from facial expression. Quantitative metrics show that using Gated-VAE improves average disentanglement, completeness and informativeness, as compared with un-gated implementations. Qualitative assessment of latent traversals demonstrate its disentanglement of head-pose from expression, even when only weak/noisy supervision is available.
Design and Development of Hand Gesture based Communication Device for Deaf and Mute People

According to World Health Organization (WHO), the 5% of world's population is disabled of speaking and hearing. That makes a large number of people who are deaf and mute in whole world and communications between deaf-mute and a normal person has always been a challenging task. We have developed a cheap, reliable and efficient device that would help deaf-mute people to work with other normal people efficiently towards the development of humanity. In this paper, 3-D accelerometer is used to detect the gesture of disable person and based on it customized database is generated which is processed through nodeMCU and Raspberry Pi and displayed the message on LCD screen. The Support Vector Classifier algorithm is used in proposed system. The experimental analysis gives comparison of proposed system with existing machine learning algorithm and shows that our system outperforms well in terms of translating complete sentence instead of single alphabet which resulted into increased accuracy of device.
On linear weak predictability with single point spectrum degeneracy

The paper studies predicting problems for continuous time signals with the Fourier transforms vanishing with a certain rate at a single point. It shows that, with some linear causal predictors, anticausal convolution integrals over future times for these processes can be approximated by causal convolutions over past times. The corresponding predicting kernels are time invariant, and they are presented explicitly in the frequency domain via their transfer functions. These predictors are “universal” meaning that they do not require to know details of the spectrum of the underlying signals; the same predictor can be used for the entire class of signals with a single point spectrum degeneracy. The predictors feature some robustness with respect to noise contamination.
Model-Based Static and Runtime Verification for Ethereum Smart Contracts

Distributed ledger technologies, e.g. blockchains, are an innovative solution to the problem of trust between different parties. Smart contracts, programs executing on these ledgers present new challenges given their non-traditional execution context – blockchains. The immutability of smart contracts once they are deployed makes their pre-deployment correctness essential. This can be achieved through verification methods, which attempt to answer conclusively whether the code respects some specification. Another approach is model-driven development, where the specification is used directly to create a correct-by-const-ruction implementation. A specification may however still need to be verified to ensure it satisfies some properties. Verifying properties pre-deployment is ideal, however it may not always be possible to do completely, depending on the complexity of the smart contract. Traditionally upon failure of a verification attempt the only option is to attempt a different verification method. Recent approaches instead enable the transformation of the verification problem into a smaller problem, reducing the load of subsequent verification attempts. We have previously proposed an automata-theoretic approach to reason systematically about this kind of residual analysis for (co-)safety properties, while we have implemented an intraprocedural data-flow approach for Java programs. In this paper we extend our approach for Solidity smart contracts, present a corresponding tool, evaluate the approach with several new case studies, and compare it with existing approaches.
Summary of the 2nd International Workshop on Bots in Software Engineering (BotSE 2020)

Bots automate many tasks in software engineering projects often in the form of chatbots. Bots have been proposed, for example, for testing, maintenance, or automating bug fixes. Following the success of the first BotSE workshop, we organized this second edition collocated with ICSE 2020 to bring together the research community that investigates bots for software engineering. Specifically, the workshop's goal was to share experiences and challenges, discuss new types of bots, and map out future directions. The workshop program comprised the presentation of 8 papers and 2 keynotes, followed by extensive discussion. Overall, the community matured by discussing how to design, build, and evaluate bots. The community aims to organise a 3rd edition of the workshop. Website: http://botse.org/
Colony: A Privileged Trusted Execution Environment With Extensibility

The code base of system software is growing fast, which results in a large number of vulnerabilities: for example, 296 CVEs have been found in Xen hypervisor and 2195 CVEs in Linux kernel. To reduce the reliance on the trust of system software, many researchers try to provide trusted execution environments (TEEs), which can be categorized into two types: non-privileged TEEs and privileged TEEs. Non-privileged TEEs (e.g., Intel SGX) are extensible, but cannot protect security services like virtual machine introspection (VMI) due to the lack of system-level semantics. On the contrary, privileged TEEs (e.g., the secure world of ARM TrustZone) have system-level semantics, but any additional service implemented in the privileged TEE directly increases the TCB of the entire system. In this article, we propose a new design of TEE to support system-level security services and achieve better extensibility with a small TCB. Each TEE instance of the proposed design is named a <small>Colony</small>. Specifically, we introduce a <i>secure monitor</i> for isolation and capability management. Each <small>Colony</small> is assigned capabilities to access only necessary system-level semantics. We use the new TEE to build four security services, including secure device accessing, VMI tools, a system call tracer, and a much more complex service to virtualize ARM TrustZone with multiple <small>Colonies</small>. We have implemented the system on ARMv7 and ARMv8 platforms, in Xen hypervisor and Linux kernel, and perform a detailed evaluation to show its efficiency.<xref rid="fn1" ref-type="fn"><sup>1</sup></xref><fn id="fn1"><label>1.</label> This paper is an extended version of the conference paper published in USENIX Security&#x2019;17: vTZ: Virtualizing ARM TrustZone <xref ref-type="bibr" rid="ref29">[29]</xref> . A brief summary of differences is in Section<xref ref-type="sec" rid="sec8">8</xref> . </fn>
Hate Speech Prosecution of Politicians and its Effect on Support for the Legal System and Democracy

Abstract This study examined if and for whom prosecution of politicians for hate speech undermines support for the legal system and democracy. Three research designs were combined to investigate the case of Dutch politician Geert Wilders, who was convicted for hate speech against minorities in 2016. First, an experiment showed that observing a guilty verdict decreased support among ‘assimilationists’ who oppose the multicultural society. This deterioration of support was found among the entire group of assimilationists, regardless of whether they voted for Wilders. Secondly, a quasi-experiment demonstrated that assimilationists who were interviewed after Wilders' conviction indicated less support than those who were interviewed before the verdict and compared to a pre-test. Thirdly, a nine-year panel study suggested that these effects accumulate into long-term discontent. This case therefore demonstrates that hate speech prosecution can damage the democratic system it is intended to defend.
Internet infrastructure and the commons: grassroots knowledge sharing in Barcelona

Ideas of the Internet as a digital commons offer hope that innovations in networked technologies can bring about more just and equitable futures. Focused on purportedly immaterial flows of knowledge, these visions often ignore the material infrastructures on which network connectivity relies, the control exercised through those infrastructures and the knowledges needed to sustain them. Community wireless networks (CWN) offer a strategy for commoning Internet infrastructure. Exploring Guifi.net, a CWN in Barcelona, the paper explores how commoning Internet infrastructure relies on practices of knowledge sharing that complicate imaginaries of free-flowing information while not abandoning the possibilities for Internet-based commons.
Learning Distribution Independent Latent Representation for 3D Face Disentanglement

Learning disentangled 3D face shape representation is beneficial to face attribute transfer, generation and recognition, etc. In this paper, we propose a novel distribution independence-based method to learn to decompose 3D face shapes. Specifically, we design a variational auto-encoder with Graph Convolutional Network (GCN), namely Mesh-Encoder, to model the distributions of identity and expression representations via variational inference. To disentangle facial expression and identity, we eliminate correlation of the two distributions, and enforce them to be independent by adversarial training. Extensive experiments show that the proposed approach can achieve state-of-the-art results in 3D face shape decomposition and expression transfer. Though focusing on disentanglement, our method also achieves the reconstruction accuracies comparable to the state-of-the-art 3D face reconstruction methods.
Facial Expression Recognition Based on Convolutional Neural Network

Abstract At present, facial expression recognition technology is widely used in artificial intelligence, transportation, medical and other aspects, so it has important research value. Traditional facial expression recognition uses manual feature extraction method with low accuracy and weak generalization ability, which is difficult to be applied in real life. With the development of deep learning, convolution neural network appears in people’s vision. Different from traditional manual feature extraction, convolution neural network can learn image features independently, and learn more features. In addition, it has the advantage of sharing the weight with the neural network. Although convolution neural network has multiple advantages, it also has some disadvantages, especially over fitting. In this paper, the model of convolution network is improved based on the classical VGGNet according to the working principle of convolution neural network. In this new model, the number of convolution kernels is reduced in parameters, and the global average pool layer is used to replace the full connection layer in the structure, so as to reduce the degree of over fitting and decrease the operation parameters. Finally, experiments show that the accuracy, generalization and consumption of resource are enhanced in the new model. It is proposed that the new method is better than the traditional convolution network VGGNet.
A deep learning computer vision iPad application for Sales Rep optimization in the field

Abstract Computer vision is becoming an increasingly critical area of research, and its applications to real-world problems are gaining significance. In this paper, we describe the design, development and evaluation of our computer vision Faster R-CNN iPad App for Sales Representatives in grocery store environments. Our system aims to assist Sales Reps to be more productive, reduce errors, and provide increased efficiencies. We report on the creation of the iPad app, the data capturing guidelines we created for the creation of good classifiers and the results of professional Sales Reps evaluating our system. Our system was tested in a variety of conditions in grocery store environments and has an accuracy of 99%, a System Usability Score usability score of 85 (high). It supports up to 40 classifiers running concurrently to perform product identification in less than 3.8 s. We also created a set of data capturing guidelines that will enable other researchers to create their own classifiers for these types of products in complex environments (e.g., products with very similar packaging located on shelves).
A Malicious URL Detection Method Based on CNN

In recent years, phishing events occur frequently, and the detection of phishing URL has become a common concern in the field of network security. In previous studies, researchers distinguish phishing URLs from normal URLs by the string characteristics. However, this method is difficult to achieve efficient detection accuracy in the case of attacker's forgery of URL features. In this paper, we propose a phishing URL detection method based on URL content. Firstly, the simple features are used for preliminary screening, and then, the URLs that cannot be distinguished by simple features are input into the simulation environment to obtain the page content. Finally, the method based on CNN is used to detect malicious URLs based on the page content. Experiments show that our method can achieve satisfying detection accuracy.
A Proposal for Correlation-based Steganography Using Shamir’s Secret Sharing Scheme and DCT Domain

In recent years, the number of opportunities to exchange information on the Internet has increased, and a method for safely exchanging information is required. In such a situation, steganography, which is a communication technology that embeds information in images, is attracting attention. Image steganography is roughly classified into two types: pixel value replacement and transform domain usage. In most cases, pixel value replacement steganography, which uses the least significant bit (LSB) of the image, is used. However, it is easy to predict when hidden information is embedded in the lower bits of an image. In this study, we propose a correlation-based steganography that embeds secret information encrypted by Shamir's secret sharing scheme using the spread spectrum method and analyze its basic characteristics.
An Analysis of Regularization Methods in Deep Neural Networks

Regularization in Deep Neural Networks for Classification has developed into a separate paradigm as that involves regularization in probability spaces. A big contribution to avoid overfitting in Deep Learning has been Dropout [1]. Dropout however is rarely applied alone for classification tasks and is usually used in conjunction with several other techniques like weight normalization which is equivalent to l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm or batch normalization [2]. The use of these techniques is empirical in nature and often ad hoc and thus, it's difficult to estimate the contribution of each of these techniques to the final outcome. Here we isolate each of the common regularization techniques and use a standard Deep Convolutional Network VGG11 [3] and a standard dataset CIFAR10 [4]. We collect and analyze the results to identify the effect of each of these techniques.
Image Tampering Detection via Semantic Segmentation Network

A large number of image forgery created by image tampering pose a serious threat to the construction of social honesty and content security. There is an urgent requirement to identify tampered images through effective image authentication techniques. In this paper, we propose a new image tampering detection method using the deep learning-based semantic image segmentation network. It works under the observation that tampering regions are typically the semantic object ones in an image. One of the state-of-the-art neural networks for semantic image segment task, namely DeepLab V3+, is used to extract rich and high level features from large amounts of prepared tampering-labeled images. Both spatial regional and boundary tampering artifacts are explored in an encode-decoder network. Experimental results on public image forgery datasets verify the effectiveness of our proposed tampering detection method.
Sinhala Sentiment Lexicon Generation using Word Similarity

Sentiment lexicon plays a vital role in sentiment analysis. A look at the related literature suggests that sentiment lexicon creation is considered as a separate task from sentiment analysis. Recently, semi-supervised methods of lexicon creation have become popular compared to the manual and unsupervised techniques. In the semi-supervised approach, the seed set or the training set, which consists only of a few sentiment tagged lexical units, is expanded using different techniques to generate a sentiment lexicon. A good seed set as well as a good seed set expansion method help to increase the performance of the sentiment lexicon. In this paper, a novel approach is discussed to create a good seed set and to propagate sentiment score using word embedding based similarity measurement, resulting in a highly reliable sentiment lexicon for Sinhala language.