Reviewerly/exHarmony huggingface dataset:
Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams

Digitization of scanned Piping and Instrumentation diagrams(P&ID), widely used in manufacturing or mechanical industries such as oil and gas over several decades, has become a critical bottleneck in dynamic inventory management and creation of smart P&IDs that are compatible with the latest CAD tools. Historically, P&ID sheets have been manually generated at the design stage, before being scanned and stored as PDFs. Current digitization initiatives involve manual processing and are consequently very time consuming, labour intensive and error-prone.Thanks to advances in image processing, machine and deep learning techniques there are emerging works on P&ID digitization. However, existing solutions face several challenges owing to the variation in the scale, size and noise in the P&IDs, sheer complexity and crowdedness within drawings, domain knowledge required to interpret the drawings. This motivates our current solution called Digitize-PID which comprises of an end-to-end pipeline for detection of core components from P&IDs like pipes, symbols and textual information, followed by their association with each other and eventually, the validation and correction of output data based on inherent domain knowledge. A novel and efficient kernel-based line detection and a two-step method for detection of complex symbols based on a fine-grained deep recognition technique is presented in the paper. In addition, we have created an annotated synthetic dataset, Dataset-P&ID, of 500 P&IDs by incorporating different types of noise and complex symbols which is made available for public use (currently there exists no public P&ID dataset). We evaluate our proposed method on this synthetic dataset and a real-world anonymized private dataset of 12 P&ID sheets. Results show that Digitize-PID outperforms the existing state-of-the-art for P&ID digitization.
Automated assessment of learner text complexity

EFL methodology has always recognized the importance of giving student learners of foreign languages regular and quick feedback on student production, both written and oral. The presented paper describes the decisions taken during the development of an application to measure text complexity, and shows how the results achieved with this application were translated into feedback related to the author’s language proficiency. Along with some standard text complexity features, this tool takes into account those that are significant for Russian learners of English. The application provides students with the statistics of the relevant linguistic features of the text in comparison with texts of the learner essays that were considered the top and the bottom levels in the learner corpus. The paper also points out what text features are especially relevant for the assessment of the essays written in English by Russian students. The choice was made possible after the analysis of 3440 texts from Russian Error-Annotated English Learner Corpus, and after applying methods of machine learning and statistical analysis to predict the grade that could be received for the essay.
The Work of Workplace Disclosure

Health disclosure at work is complicated for people with invisible chronic conditions. Due to the lack of visible symptoms, invisible conditions affect the work life of people in ways that are not obvious to others. This study examines how people disclose and conceal their conditions in the workplace and opens the design space for this topic. In the first phase, we analyzed posts on two subreddit forums, r/migraine and r/fibromyalgia, and found a range of strategies that individuals use to disclose or conceal their conditions. In the second phase, we created five technological design concepts based on these strategies that were shown to eight people with migraines or fibromyalgia in semi-structured interviews. Based on these phases, we contribute understandings of disclosure and concealment of invisible conditions in the workplace for future research, such as potential areas for intervention ranging from individual to societal level efforts, as well as the potential and limitations of relying on empathy from others.
Factors Affecting Internet of Things (IoT) Adoption in the Yemeni Oil and Gas Sector

The Internet of Things (IoT) is defined as a potentially effective means of integrating multiple technologies to support the oil and gas (O&G) sector as a network of physical objects connected to the Internet. In Yemen, O&G organizations can embedded vehicles, equipment, buildings, fire siren and wells with electronics, software, sensors, and network connectivity that is now evolving. It is assumed that in one way or another, everything should be connected to one network to ease processes of business. To make IoT a reality, and not only remain academic, O&G sector must be able to engage in and pick up this technology in its day-to-day activities. Several studies have explored IoT's potential for numerous organizations. However, IoT remains inadequately applied by organizations, including O&G sectors in developing countries. This article attempts to identify the factors that influence users to adopt IoT in the O&G sector in Yemen. Based on a study of prior tests, drivers were identified. Furthermore, a framework is proposed based on information system adoption theory which is the technology-organization-environment (TOE). Partial least squares structural equation modeling (PLS-SEM) has become a popular tool for analyzing such relationships is applied to a survey of 390 IoT users, with results indicating that the factors selected in this study significantly affected the IoT adoption in the Yemeni O&G sector. This study is enabling O&G organizations to understand the factors of IoT adoption, to improve their framework of business and investment in IoT and to inspire researchers to continue research into other IoT adoption or implementation factors.
Efficient Scalable Multi-party Private Set Intersection Using Oblivious PRF

In this paper, we present a concretely efficient protocol for private set intersection (PSI) in the multi-party setting using oblivious pseudorandom function (OPRF). In fact, we generalize the approach used in the work of Chase and Miao [CRYPTO 2020] towards deploying a lightweight multi-point OPRF construction for two-party PSI. Our protocol only includes oblivious transfer (OT) extension and garbled Bloom filter as its main ingredients and avoids computationally expensive operations. From a communication pattern perspective, the protocol consists of two types of interactions. The first type is performed over a star-like communication graph in which one designated party interacts with all other parties via performing OTs as the sender. Besides, parties communicate through a path-like communication graph that involves sending a garbled Bloom filter from the first party to its neighboring party following the last one. This design makes our protocol to be highly scalable due to the independence of each party’s complexity from the number of participating parties and thus causes a communication and computation complexities of \(O(n\lambda k)\), where n is the set size, k is the number of hash functions, and \(\lambda \) is the security parameter. Moreover, the asymptotic complexity of the designated party is \(O(tn\lambda )\) which linearly scales with the number of parties t. We prove security of the proposed protocol against semi-honest adversaries.
Detecting Cyberbullying Across Multiple Social Media Platforms Using Deep Learning

In today 's time social media platforms have taken over our lives. The number of people using these platforms keeps on increasing day by day. With the increase of social media usage, the person who is using these platforms become more exposed to the negative effects of using social media. Among many negative effects, cyberbullying is one of the major negative effects of using social media. People online get bullied which affects their mental health in a negative manner. It is an incredibly difficult task to detect cyberbullying on social media platforms especially due to the slangs that people make up regularly but even so this paper suggests a working implementation of an application that detects cyberbullying across multiple social media platforms using the data provided by Twitter, Wikipedia and Formspring. This paper makes use of Deep Learning for the purpose of detecting cyberbullying.
Network Traffic Classification Using Ensemble Learning in Software-Defined Networks

Accurate network traffic classification is essential for network management. However, existing network traffic classification methods cannot meet the demand of real networks in terms of classification performance, user privacy, latency, and control overhead. Thus, a machine learning-based approach has been used for network traffic classification. In this paper, we propose a network traffic classification framework using software-defined network (SDN) architecture. The proposed framework is entirely located in the network controller; thus, we can leverage the superior computational capacity, global visibility, and programmability of the SDN controller to realize real-time, adaptive, and accurate traffic classification. We also apply four ensemble algorithms and analyze their classification performance in terms of accuracy, precision, recall, F1-score, training time, and classification time. The experimental results reveal that ensemble model-based network traffic classifiers outperform other classifiers based on the proposed framework and the real-world network traffic dataset. Notably, the LightGBM model achieves the best classification performance.
Blockchain and tourism: Paradoxes, misconceptions, and a research roadmap

A brief discussion of several paradoxes and misunderstandings regarding blockchain and how tourism researchers can get started. By abstracting from technological specifications and focusing on the core characteristics of blockchain, researchers can create meaningful frameworks, models, and theories that enable incremental research and provide value for the industry.
ShuffleFL

Federated Learning (FL) is a promising approach to privacy-preserving machine learning. However, recent works reveal that gradients can leak private data. Using trusted SGX-processors for this task yields gradient-preserving but requires to prevent exploitation of any side-channel attacks.
Automatic API Usage Scenario Documentation from Technical Q&amp;A Sites

The online technical Q&amp;A site Stack Overflow (SO) is popular among developers to support their coding and diverse development needs. To address shortcomings in API official documentation resources, several research works have thus focused on augmenting official API documentation with insights (e.g., code examples) from SO. The techniques propose to add code examples/insights about APIs into its official documentation. Recently, surveys of software developers find that developers in SO consider the combination of code examples and reviews about APIs as a form of API documentation, and that they consider such a combination to be more useful than official API documentation when the official resources can be incomplete, ambiguous, incorrect, and outdated. Reviews are opinionated sentences with positive/negative sentiments. However, we are aware of no previous research that attempts to automatically produce API documentation from SO by considering both API code examples and reviews. In this article, we present two novel algorithms that can be used to automatically produce API documentation from SO by combining code examples and reviews towards those examples. The first algorithm is called statistical documentation, which shows the distribution of positivity and negativity around the code examples of an API using different metrics (e.g., star ratings). The second algorithm is called concept-based documentation, which clusters similar and conceptually relevant usage scenarios. An API usage scenario contains a code example, a textual description of the underlying task addressed by the code example, and the reviews (i.e., opinions with positive and negative sentiments) from other developers towards the code example. We deployed the algorithms in Opiner, a web-based platform to aggregate information about APIs from online forums. We evaluated the algorithms by mining all Java JSON-based posts in SO and by conducting three user studies based on produced documentation from the posts. The first study is a survey, where we asked the participants to compare our proposed algorithms against a Javadoc-syle documentation format (called as Type-based documentation in Opiner). The participants were asked to compare along four development scenarios (e.g., selection, documentation). The participants preferred our proposed two algorithms over type-based documentation. In our second user study, we asked the participants to complete four coding tasks using Opiner and the API official and informal documentation resources. The participants were more effective and accurate while using Opiner. In a subsequent survey, more than 80% of participants asked the Opiner documentation platform to be integrated into the formal API documentation to complement and improve the API official documentation.
Oh, Snap! A Fabrication Pipeline to Magnetically Connect Conventional and 3D-Printed Electronics

3D printing has revolutionized rapid prototyping by speeding up the creation of custom-shaped objects. With the rise of multi-material 3D printers, these custom-shaped objects can now be made interactive in a single pass through passive conductive structures. However, connecting conventional electronics to these conductive structures often still requires time-consuming manual assembly involving many wires, soldering or gluing.
An Approach on Data Visualization and Data Mining with Regression Analysis

Right off the bat, the ideas and arrangement about imagined information mining innovation expounds in this paper, data mining at that point the innovation and regression analysis technology and the visualization data, what's more, are talked about; finally, the use of data mining addressed in combination with sliding bearing wear coefficient testing, regression analysis technology and data visualization. In this paper structure, a framework to actualize data mining technology for visualized data mining under the web condition. Data visualization helps to explore data with some satisfactory results in a powerful way. Also, as a part of the data reduction process by combining data, a significant role has been played by data visualization. The linear and nonlinear regression analyses are used in this method, and application research progresses to proving the factors in our methods.
LSFQ: A Low Precision Full Integer Quantization for High-Performance FPGA-Based CNN Acceleration

Neural network quantization has become an important research area. Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, and can maintain high accuracy. However, few quantization can demonstrate this advantage on hardware platform, because the design of quantization algorithm lacks the consideration of actual hardware implementation. In this paper, we propose an efficient quantization method for hardware implementation, a learnable parameter soft clipping fully integer quantization (LSFQ), which includes weight quantization and activation quantization with learnable clipping parameter method. The quantization parameters are optimized automatically by back propagation to minimize the loss, then the BatchNorm layer and convolutional layer are fused, and the bias and quantization step size are further quantized. In this way, LSFQ accomplishes integer-only-arithmetic. We evaluate the quantization algorithm on a variety of models including VGG7, mobile-net v2 in CIFAR10 and CIFAR100. The results show that when the quantization reaches 3-bit or 4-bit, the accuracy loss of our method is less than 1 % compared with the full-precision network. In addition, we design an accelerator for the quantization algorithm and deploy it to the FPGA platform to verify the hardware-awareness of our method.
MultiEmo: Multilingual, Multilevel, Multidomain Sentiment Analysis Corpus of Consumer Reviews

This article presents MultiEmo, a new benchmark data set for the multilingual sentiment analysis task including 11 languages. The collection contains consumer reviews from four domains: medicine, hotels, products and university. The original reviews in Polish contained 8,216 documents consisting of 57,466 sentences. The reviews were manually annotated with sentiment at the level of the whole document and at the level of a sentence (3 annotators per element). We achieved a high Positive Specific Agreement value of 0.91 for texts and 0.88 for sentences. The collection was then translated automatically into English, Chinese, Italian, Japanese, Russian, German, Spanish, French, Dutch and Portuguese. MultiEmo is publicly available under the MIT Licence. We present the results of the evaluation using the latest cross-lingual deep learning models such as XLM-RoBERTa, MultiFiT and LASER+BiLSTM. We have taken into account 3 aspects in the context of comparing the quality of the models: multilingualism, multilevel and multidomain knowledge transfer ability.
Speeding-up pruning for Artificial Neural Networks: Introducing Accelerated Iterative Magnitude Pruning

In recent years, Artificial Neural Networks (ANNs) pruning has become the focal point of many researches, due to the extreme overparametrization of such models.This has urged the scientific world to investigate methods for the simplification of the structure of weights in ANNs, mainly in an effort to reduce time for both training and inference.Frankle and Carbin [1], and later Renda, Frankle, and Carbin [2] introduced and refined an iterative pruning method which is able to effectively prune the network of a great portion of its parameters with little to no loss in performance.On the downside, this method requires a large amount of time for its application, since, for each iteration, the network has to be trained for (almost) the same amount of epochs of the unpruned network.In this work, we show that, for a limited setting, if targeting high overall sparsity rates, this time can be effectively reduced for each iteration, save for the last one, by more than 50 %, while yielding a final product (i.e., final pruned network) whose performance is comparable to the ANN obtained using the existing method.
SoK: Auditability and Accountability in Distributed Payment Systems

Enforcement of policy regulations and availability of auditing mechanisms are crucial building blocks for the adoption of distributed payment systems. In this work we review a number of existing proposals for distributed payment systems that offer some form of auditability for regulators. We identify two major distinct lines of work: payment systems that are not privacy-preserving such as Bitcoin, where regulation functionalities are typically tailored for organizations controlling many accounts, and privacy-preserving payment systems where regulation functionalities are typically targeted to user level. We provide a systematization methodology over several axes of characteristics and performance, while highlighting insights and research gaps that we have identified, such as lack of dispute-resolution solutions between the regulator and the entity under audit, and the incompatibility of ledger pruning or off-chain protocols with regulatory requirements. Based on our findings, we propose a number of exciting future research directions.
Automation and smart materials in detecting smart contracts vulnerabilities in Blockchain using deep learning

Smart contracts are a vital component of applications being built by blockchain or distributed ledger technology. Smart contracts consists of computer code that composed set of rules agreed upon by the involved parties. When these predefined conditions or rules are satisfied by the transactions initiated by parties, the smart contract executes itself to complete the transaction. Normally, while performing transactions with agreements involving multiple parties, a third party have to be involve to verify all the information, which makes it complex and time consuming process. Smart contracts simplify this process by eliminating the third party and automating the process, enabling the stakeholders to perform transactions directly with each other. Smart contract itself is replicated among multiple nodes of a blockchain there by giving benefits of immutability, security and permanence. Most smart contracts are written in Solidity programming language due to its simplicity and conciseness. Attackers are attracted by the popularity of Solidity language and its vulnerability possibilities. For example, in the 2016 year, 60 million US dollars was theft by Decentralized Autonomous Organization (DAO) attack due to vulnerabilities present in smart contracts. There are few existing tools and papers on this area to find vulnerabilities on smart contracts but taking more time to predict. Thus, this paper presents different Deep learning techniques with satisfactory results to identify smart contract vulnerabilities which are Re-entrancy, Denial of Service (DOS) and Transaction Origin using binary, multi class and multi label classification techniques.
Coverage hole detection method of wireless sensor network based on clustering algorithm

The traditional method does not calculate the moving distance parameter when detecting coverage vulnerabilities, so its coverage performance is poor. The clustering algorithm can effectively calculate the moving distance parameter. Therefore, a method to detect the coverage hole in wireless sensor network based on clustering algorithm was proposed. Firstly, the parameter of coverage hole in wireless sensor network were calculated, including the moving distance parameters, circular intersection area parameters and redundancy parameters. Secondly, the algorithm of hole edge intersection was used to judge edge nodes of coverage holes in wireless sensor network. By judging whether there was a hole edge intersection on the sensing circle, the edge nodes of coverage holes in wireless sensor network could be determined. Thirdly, the mobile nodes were deployed in the way of airdrop, and then they were distributed evenly. The density of the initially deployed mobile nodes should meet the highest coverage requirement in wireless sensor network. After determining the edge nodes of coverage holes in wireless sensor network, the deployed mobile nodes were used to find the edge nodes of coverage holes in wireless sensor networks by random walk. Meanwhile, the coverage holes were approximated by the set of edge nodes. Finally, it was able to detect the coverage holes in wireless sensor network by clustering algorithm, including the detection of hole shape and the judgment of holes size. In order to verify the gap coverage performance of the coverage hole detection method based on clustering algorithm, the traditional method was compared with the proposed method. Experimental results show that the gap coverage performance of the proposed method is better than that of traditional method. The gap coverage performance of this method is 389 T, while the traditional method is only 351 T. so this method is more suitable for the detection of coverage hole in wireless sensor network.