Reviewerly/exHarmony huggingface dataset:
Rethinking the framework constructed by counterfactual functional model

The causal inference represented by counterfactual inference technology breathes new life into the current field of artificial intelligence. Although the fusion of causal inference and artificial intelligence has an excellent performance in many various applications, some theoretical justifications have not been well resolved. In this paper, we focus on two fundamental issues in causal inference: probabilistic evaluation of counterfactual queries and the assumptions used to evaluate causal effects. Both of these issues are closely related to counterfactual inference tasks. Among them, counterfactual queries focus on the outcome of the inference task, and the assumptions provide the preconditions for performing the inference task. Counterfactual queries are to consider the question of what kind of causality would arise if we artificially apply the conditions contrary to the facts. In general, to obtain a unique solution, the evaluation of counterfactual queries requires the assistance of a functional model. We analyze the limitations of the original functional model when evaluating a specific query and find that the model arrives at ambiguous conclusions when the unique probability solution is 0. In the task of estimating causal effects, the experiments are conducted under some strong assumptions, such as treatment-unit additivity. However, such assumptions are often insatiable in real-world tasks, and there is also a lack of scientific representation of the assumptions themselves. We propose a mild version of the treatment-unit additivity assumption coined as M-TUA based on the damped vibration equation in physics to alleviate this problem. M-TUA reduces the strength of the constraints in the original assumptions with reasonable formal expression.
Identifying SQL misconceptions of novices

research-article Open Access Share on Identifying SQL misconceptions of novices: findings from a think-aloud study Authors: Daphne Miedema Eindhoven University of Technology Eindhoven University of TechnologyView Profile , Efthimia Aivaloglou Open Universiteit Open UniversiteitView Profile , George Fletcher Eindhoven University of Technology Eindhoven University of TechnologyView Profile Authors Info & Claims ACM InroadsVolume 13Issue 1March 2022 pp 52–65https://doi.org/10.1145/3514214Online:14 February 2022Publication History 0citation245DownloadsMetricsTotal Citations0Total Downloads245Last 12 Months245Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Mining API Constraints from Library and Client to Detect API Misuses

Calling Application Programming Interfaces (APIs) shall follow various constraints (e.g., call orders). If these con-straints are violated, API misuses are introduced to code, and such misuses can cause severe bugs. To effectively detect API misuses, most prior approaches mine constraints from client code, and assume that the violations of constraints are potential misuses. However, as client code only illustrates a small portion of API usages, constraints mined from client code are typically incomplete. As a result, when mined constraints are used to detect bugs, many violations of constraints turn out to be false positives. In this paper, our research purpose is to find more misuses and to reduce false positives. As library code contains many details on APIs, we propose an approach that mines API constraints from both client and library code. From client code, our approach builds API usage graphs and uses a frequent subgraph mining algorithm to mine frequent usage patterns as API constraints. From library code, our approach derives various types of constraints with our predefined strategies. With constraints from both sources, our graph matching algorithm can detect API misuses. As a result, our approach takes advantage from both the comprehensiveness and informativeness of library-based constraints and the accuracy of client-based patterns. We compared our approach with MuDetect on the MuBench dataset. Our results show that it significantly improves the detection effectiveness of MuBench from 39.5&#x0025; to 50.2&#x0025; of the recall, and from 30.6&#x0025; to 41.7&#x0025; of the precision.
A software simulator for noisy quantum circuits

We have developed a software library that simulates noisy quantum logic circuits. We represent quantum states by their density matrices in the Pauli basis, and incorporate possible errors in initialization, logic gates, memory and measurement using simple models. Our quantum simulator is implemented as a new backend on IBM’s open-source Qiskit platform. In this paper, we provide its description, and illustrate it with some simple examples.
Blockchain and IoT for Educational Certificates Generation and Verification

Blockchain and Internet of Things are the most influential and innovative technologies of the last decade. Blockchain attracts practitioners and researchers due to its inimitable features, including security, data integrity, decentralization, and reliability. Whereas IoT describes the collection of connection objects with each other through wearable devices, and software&#x0027;s to communicate and exchange data with other devices through the internet. To improve the current state of knowledge of blockchain practices in IoT enabled the education sector to utilize the thoroughgoing features to benefit the students, teachers, employers, organizations and lifelong learners. Document verification is a complex domain that involves various challenging and tedious processes to authenticate. For students, educational certificates are the most important documents issued by their universities. However, as the issuing process is not that transparent and verifiable, fake certificates can be easily created. Blockchain based framework for verifying educational certificates focusing on themes including authentication, authorization, confidentiality, privacy and ownership. The most significant accomplishment of this research is the establishment of a framework for the educational system to communicate through IoT devices and utilize blockchain technology for producing certificates and securing other credentials as well.
Aspect-Based Sentiment Analysis Using Interaction Matrix And Global Attention Neural Network

Abstract Aspect-based sentiment analysis aims to identify the sentiment polarity of aspects in a given sentence. Although existing neural network models show promising results, they cannot meet the expectations in the case of a single network structure and limited dataset. When an aspect term composes more than one word, many models use the coarse-grained attention mechanism but lead to the unsatisfactory results. Besides, the relative distance between words in a sentence is always out of consideration. In this paper, we propose a model based on the interaction matrix and global attention mechanism to improve the ability of aspect-based sentiment analysis. First of all, the relative distance features of words in a sentence are initialized to enrich word embedding. Second, classic neural networks are applied to extract the essential features of word embedding in a sentence, such as long short-term memory and convolutional neural network. Third, an interaction matrix and global attention mechanism are combined to calculate weighted scores and measure relationships between aspect terms and context words. Finally, sentiment polarity is represented through a softmax layer. Experimental results on restaurant, laptop and twitter datasets show that the performance of the proposed model is superior to other methods.
Research ‘In the Wild’

Over recent years the term 'in the wild' has increasingly appeared in publications within the field of Human Computer Interaction (HCI). The phrase has become synonymous with a range of approaches that focus upon carrying out research-based studies reporting on user behaviour in 'natural', 'situated' contexts, in distinction to lab-based studies. The objective of this book is to bring together a range of perspectives from a variety of researchers who have carried out studies in the wild. By bringing these together we aim to explore and demonstrate how such studies can support research in different fields and domains. In doing this we wish to help the broader research community understand some of the issues, reasoning, methods and practical matters that are involved in doing research in the wild. This edited collection is part of an ongoing and developing debate, and as such provides both a backdrop and platform that will promote further discussions in this emerging area.
Cybersecurity threats to Healthcare Sectors during Covid-19

This paper has discussed cybersecurity threats and their mitigation of measures to be taken in the healthcare sector. Due to the corona pandemic, the cases of cybersecurity hiked up, bringing havoc to online operators. As many governments were busy combating the pandemic, cybercriminals took advantage primarily to attack healthcare systems globally. Furthermore, the healthcare sector has been prone to attacks due to its vulnerability. It has faced challenges in keeping patient&#x0027;s data confidential, thus making its ng its accessibility easy by attackers. As a result, this paper has highlighted the appropriate measures to be taken by the health care sector in terms of hospital system infrastructures and safety. The paper has also discussed the common cybersecurity threats to the health sector via diverse methodologies as studied in the literature review of this paper.
Network traffic identification in packet sampling environment

With the rapid growth of network bandwidth, traffic identification is currently an important challenge for network management and security. In recent years, packet sampling has been widely used in most network management systems. In this paper, in order to improve the accuracy of network traffic identification, sampled NetFlow data is applied to traffic identification, and the impact of packet sampling on the accuracy of the identification method is studied. This study includes feature selection, a metric correlation analysis for the application behavior, and a traffic identification algorithm. Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment. Meanwhile, in this paper, the correlation analysis results in different trends according to different features. However, as long as the flow number meets the statistical requirement, the feature selection and the correlation degree will be independent of the sampling ratio. While in a high sampling ratio, where the effective information would be less, the identification accuracy is much lower than the unsampled packets. Finally, in order to improve the accuracy of the identification, we propose a Deep Belief Networks Application Identification (DBNAI) method, which can achieve better classification performance than other state-of-the-art methods.
Digital Twins

Following along with the theme of this issue of IEEE Software, this column reports papers about digital twins from the 2021 Empirical Assessment in Software Engineering (EASE’21) conference, the 2021 International Conference on Software Engineering (ICSE 2021), the 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2021), the 2021 European Conference on Software Architecture (ECSA 2021), and the 15th International Workshop on Models@Run.Time, a part of MODELS 2021. Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send us and the authors a note about your experiences.
Development of interactive learning media in computer network using augmented reality technology

Abstract Technological development affect on aspects of education that impact on learning activities. In computer networks learning, there is still rarely media use which has an impact on students’ understanding of how to install computer networks. Current mobile phones have many features that can be used to play games, watch videos, listen to music, personal assistant, find information, GPS, and many more. One of the interesting technologies that can be developed on mobile phones is Augmented Reality. Augmented Reality (AR) is a technology that combines two-dimensional or three-dimensional virtual objects into a real three-dimensional environment and then projects these virtual objects in real time. This research discusses the development of learning media Augmented reality of computer network installation. The purpose of this research is to develop augmented reality learning media and build media that makes it easier for students to understand the process of installing computer networks that becomes an alternative as a learning media. The method used is Research and Development (R&amp;D).
Photonic synaptic system for MAC operations by interconnected vertical cavity surface emitting lasers

We report experimentally on high-speed, tuneable photonic synaptic architectures realized with vertical cavity surface emitting lasers (VCSELs) connected in series and in parallel configurations. These are able to perform the controlled weighting of fast (150 ps long) and low energy ( µ W peak power) optical pulses (or spikes), and permit high-speed (0.5 GHz) dynamic weight tunability, for the implementation of important spike processing functionalities. These include, for the in-series VCSEL synaptic architecture, the performance of accumulative weighting and, due to amplification, the compensation of losses in sequential neural network layers. Additionally, for the in-parallel VCSEL synaptic architecture, we show the system’s ability to perform key multiply and accumulate operations using fast, low-power optical spiking signals as inputs. Moreover, this work uses off-the-shelf VCSELs operating at key telecom wavelengths (1300 and 1550 nm) thus making our technique fully compatible with optical telecommunication networks and data centre technologies. These results therefore highlight the suitability of our approach for hardware-friendly, low power, high-speed and fast tuning VCSEL-based photonic synaptic architectures with excellent scalability prospects for use in future neuromorphic photonic computing systems.
A Practical Anonymization Approach for Imbalanced Datasets

Person-specific data owned by different data holders is usually anonymized before being shared with researchers or data-miners. Anonymization is a pertinent solution for releasing useful information while ensuring privacy. Many anonymization approaches have been proposed but majority of the existing approaches do not consider the influence of user’s attributes on privacy and utility. Consequently, privacy preservation and utility enhancement become challenging and particularly difficult when anonymizing imbalanced datasets that contain less heterogeneous values. To address these problems for imbalanced datasets, we propose a practical anonymization approach that effectively preserves users’ privacy while maintaining high utility of anonymous data. It quantifies the influence of attributes on the degree of user’s reidentification in order to protect user’s privacy. Data transformation is performed adjustably considering the influence of users’ attributes and their distributions. Experimental results obtained from real-world datasets show the efficacy of our approach and verify the abovementioned assertions.
Using Wavelet Packet Denoising and a Regularized ELM Algorithm Based on the LOO Approach for Transient Electromagnetic Inversion

Transient electromagnetic method (TEM) inversion is a complex nonlinear problem with high dimensionality and ill-posedness. Using traditional neural networks based on a gradient algorithm to solve the TEM inversion problem might result in a slow convergence where the model falls easily into local minima. To solve these problems, a wavelet packet denoising (WPD) technology and a regularized extreme learning machine (ELM) algorithm based on the leave-one-out cross-validation (LOO) approach are proposed in this article. First, the WPD method is based on the hard threshold, the Shannon entropy with a Sym15 wavelet, which is provided to suppress the noise from the TEM data. Moreover, the learning process of the ELM model is optimized by randomly setting the hidden layer parameters instead of updating based on the gradient algorithm, which accelerates the learning speed. Finally, the LOO methodology is introduced to optimize the regularization factor of the ELM to improve the prediction accuracy and generalization ability of the approach. The inversion results of two typical TEM layered geoelectric models, two anomalous body models, and one field example are provided to prove the validity and feasibility of the proposed approach. In addition, compared with other traditional methods [ELM, backpropagation (BP), radial basis function (RBF), and linear support vector machine (LSVM)], the introduced method achieves higher inversion accuracy, better stability, and stronger forward data fitting ability, enabling it to effectively solve the TEM inversion problem.
Using Ant Colony Optimization to Route a Team of UAVs to Fires Based on Known Fire Intensities

short-paper Share on Using Ant Colony Optimization to Route a Team of UAVs to Fires Based on Known Fire Intensities Author: Karthik Shaji School of Aerospace Engineering, Georgia Institute of Technology, United States School of Aerospace Engineering, Georgia Institute of Technology, United StatesView Profile Authors Info & Claims ISEC 2022: 15th Innovations in Software Engineering ConferenceFebruary 2022 Article No.: 35Pages 1–3https://doi.org/10.1145/3511430.3511462Online:24 February 2022Publication History 0citation26DownloadsMetricsTotal Citations0Total Downloads26Last 12 Months26Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-stream dynamic video Summarization

With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content. Nonetheless, the spread of social and egocentric cameras creates an abundance of sparse scenarios captured by several devices, and ultimately required to be jointly summarized. In this paper, we discuss the problem of summarizing videos recorded independently by several dynamic cameras that intermittently share the field of view. We present a robust framework that (a) identifies a diverse set of important events among moving cameras that often are not capturing the same scene, and (b) selects the most representative view(s) at each event to be included in a universal summary. Due to the lack of an applicable alternative, we collected a new multi-view egocentric dataset, Multi-Ego. Our dataset is recorded simultaneously by three cameras, covering a wide variety of real-life scenarios. The footage is annotated by multiple individuals under various summarization configurations, with a consensus analysis ensuring a reliable ground truth. We conduct extensive experiments on the compiled dataset in addition to three other standard benchmarks that show the robustness and the advantage of our approach in both supervised and unsupervised settings. Additionally, we show that our approach learns collectively from data of varied number-of-views and orthogonal to other summarization methods, deeming it scalable and generic.
Reliability evaluation of FPGA based pruned neural networks

Convolutional Neural Networks (CNNs) are widely used for image classification. To fit the implementation of CNNs on resource-limited systems like FPGAs, pruning is a popular technique to reduce the complexity. In this paper, the robustness of the pruned CNNs against errors on weights and configuration memory of the FPGA accelerator is evaluated with VGG16 as a case study, and two popular pruning methods (magnitude-based and filter pruning) are considered. In particular, the accuracy loss of the original VGG16 and the ones with different pruning rates is tested based on fault injection experiments, and the results show that the effect of errors on weights and configuration memories are different for the two pruning methods. For errors on weights, the networks pruned using both methods demonstrate higher reliability with higher pruning rates, but the ones using filter pruning are relatively less reliable. For errors on configuration memory, errors on about 30% of the configuration bits will affect the CNN operation, and only 14% of them will introduce significant accuracy loss. However, the effect of the same critical bits is different for the two pruning methods. The pruned networks using magnitude-based method are less reliable than the original VGG16, but the ones using filter pruning are more reliable than the original VGG16. The different effects are explained based on the structure of the CNN accelerator and the properties of the two pruning methods. The impact of quantization on the CNN reliability is also evaluated for the magnitude-based pruning method.
Learning Transferable Node Representations for Attribute Extraction from Web Documents

Given a web page, extracting an object along with various attributes of interest (e.g. price, publisher, author, and genre for a book) can facilitate a variety of downstream applications such as large-scale knowledge base construction, e-commerce product search, and personalized recommendation. Prior approaches have either relied on computationally expensive visual feature engineering or required large amounts of training data to get to an acceptable precision. In this paper, we propose a novel method, LeArNing TransfErable node RepresentatioNs for Attribute Extraction (LANTERN), to tackle the problem. We model the problem as a tree node tagging task. The key insight is to learn a contextual representation for each node in the DOM tree where the context explicitly takes into account the tree structure of the neighborhood around the node. Experiments on the SWDE public dataset show that LANTERN outperforms the previous state-of-the-art (SOTA) by 1.44% (F1 score) with a dramatically simpler model architecture. Furthermore, we report that utilizing data from a different domain (for instance, using training data about web pages with cars to extract book objects) is surprisingly useful and helps beat the SOTA by a further 1.37%.