Reviewerly/exHarmony huggingface dataset:
Understanding devops education with grounded theory

DevOps stands for Development-Operations. It arises from the IT industry as a movement aligning development and operations teams. DevOps is broadly recognized as an IT standard, and there is high demand for DevOps practitioners in industry. Since ACM & IEEE suggest that undergraduate computer science curricula "must adequately prepare [students] for the workforce", we studied whether undergraduates acquired adequate DevOps skills to fulfill the demand for DevOps practitioners in industry. We employed Grounded Theory (GT), a social science qualitative research methodology, to study DevOps education from academic and industrial perspectives. In academia, academics were not motivated to learn or adopt DevOps, and we did not find strong evidence of academics teaching DevOps. Academics need incentives to adopt DevOps, in order to stimulate interest in teaching DevOps. In industry, DevOps practitioners lack clearly defined roles and responsibilities, for the DevOps topic is diverse and growing too fast. Therefore, practitioners can only learn DevOps through hands-on working experience. As a result, academic institutions should provide fundamental DevOps education (in culture, procedure, and technology) to prepare students for their future DevOps advancement in industry. Based on our findings, we proposed five groups of future studies to advance DevOps education in academia.
Convolutional Neural Network Based Symbol Detector for Two-Dimensional Magnetic Recording

Data detection in magnetic recording (MR) channels can be viewed as an image processing problem, proceeding from the 2-D image of readback bits, to higher level abstractions of features using convolutional layers that finally allow classification of individual bits. In this work, convolutional neural networks (ConvNets) are employed in place of the typical partial response equalizer and maximum-likelihood detector with noise prediction to directly process the un-equalized readback signals and output soft estimates. Several variations of ConvNets are compared in terms of network complexity and performance. The best performing ConvNet detector with two convolutional layers provides a data storage density of up to 3.7489 Terabits/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> on a low track pitch two-dimensional MR channel simulated with a grain-flipping-probability (GFP) model. An alternate ConvNet architecture reduces the network complexity by about 74%, yet results in only a 2.09% decrease in density compared to the best performing detector.
High-Level Video Event Modeling, Recognition, and Reasoning via Petri Net

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.
A new discrete wavelet transform appropriate for hardware implementation

Summary A new method for computation of discrete wavelet transform is introduced. The impulse response of the finite impulse response (FIR) filter, as the main block in filter bank method, is realized such that orthonormal wavelet transform is achieved without using any multiplier as the most challenging part of the FIR filters. It is proved that the calculated Sobolev regularity of the proposed wavelet is higher than that of Daubechies for the same support width. Compared with most popular known wavelets, simulation results of the newly introduced wavelet for signal denoising prove remarkable advantage of our wavelet especially in terms of complexity and power consumption. The proposed wavelet and well‐known Daubechies wavelet are separately implemented on the Spartan‐6 FPGA (Field Programmable Gate Array) to compare the performance of them. The occupied slices and LUTs (Lookup Table) in realization of the proposed wavelet in comparison with those of Daubechies wavelet are decreased by 50% and 56%, respectively. Its power consumption at 100 MHz is also reduced by 86% in comparison with the Daubechies wavelet, and maximum frequency is increased by 186%. Implementation with standard cells of a 0.18‐μm CMOS (Complementary Metal Oxide Semiconductor Field Effect Transistor) technology shows 75% and 85% reduction in the power and chip area, respectively.
Enhancing Trust-based Medical Smartphone Networks via Blockchain-based Traffic Sampling

With more devices being inter- or intra-connected, Internet of Things (IoT) has gradually been adopted in many disciplines, such as healthcare industry, coined as Internet of Medical Things (IoMT). The purpose of IoMT is to facilitate the efficiency and effectiveness of medical operations, i.e., remotely monitoring the status of patients. In such healthcare environments, smartphones have become an important device to communicate with others and update the information of patients, resulting in a special type of IoMT called Medical Smartphone Networks (MSNs). To reinforce the distributed architecture, trust management schemes are often implemented to defend against insider attacks. However, how to maintain the robustness of trust management in heavy traffic networks still remains a challenge, i.e., COVID-19 incident would cause excessive traffic for healthcare organizations and increase the difficulty of validating trustworthiness among MSN nodes. In this work, we focus on this issue and propose a blockchain-enabled adaptive traffic sampling method to help enhance the robustness of trust management under high traffic environments. The use of blockchain technology aims to build a verified database of malicious traffic among all nodes. The evaluation in a real healthcare environment demonstrates the viability and effectiveness of our approach.
SQL Injection Attacks Detection and Prevention Based on Neuro—Fuzzy Technique

A Structured Query Language (SQL) injection attack (SQLIA) is one of most famous code injection techniques that threaten web applications, as it could compromise the confidentiality, integrity and availability of the database system of an online application. Whereas other known attacks follow specific patterns, SQLIAs are often unpredictable and demonstrate no specific pattern, which has been greatly problematic to both researchers and developers. Therefore, the detection and prevention of SQLIAs has been a hot topic. This paper proposes a system to provide better results for SQLIA prevention than previous methodologies, taking in consideration the accuracy of the system and its learning capability and flexibility to deal with the issue of uncertainty. The proposed system for SQLIA detection and prevention has been realized on an Adaptive Neuro-Fuzzy Inference System (ANFIS). In addition, the developed system has been enhanced through the use of Fuzzy C-Means (FCM) to deal with the uncertainty problem associated with SQL features. Moreover, Scaled Conjugate Gradient algorithm (SCG) has been utilized to increase the speed of the proposed system drastically. The proposed system has been evaluated using a well-known dataset, and the results show a significant enhancement in the detection and prevention of SQLIAs.
Deterministic and complete hyperentangled Bell states analysis assisted by frequency and time interval degrees of freedom

Hyperentangled Bell states analysis (HBSA) is an essential building block for certain hyper-parallel quantum information processing. We propose a complete and deterministic HBSA scheme encoded in spatial and polarization degrees of freedom (DOFs) of two-photon system assisted by a fixed frequency-based entanglement and a time interval DOF. The parity information the spatial-based and polarization-based hyper-entanglement can be distinguished by the distinct time intervals of the photon pairs, and the phase information can be distinguished by the detection signature. Compared with previous schemes, the number of the auxiliary entanglements is reduced from two to one by introducing time interval DOF. Moreover, the additional frequency and time interval DOFs suffer less from the collective channel noise.
Quality gatekeepers: investigating the effects of code review bots on pull request activities

Abstract Software bots have been facilitating several development activities in Open Source Software (OSS) projects, including code review. However, these bots may bring unexpected impacts to group dynamics, as frequently occurs with new technology adoption. Understanding and anticipating such effects is important for planning and management. To analyze these effects, we investigate how several activity indicators change after the adoption of a code review bot. We employed a regression discontinuity design on 1,194 software projects from GitHub. We also interviewed 12 practitioners, including open-source maintainers and contributors. Our results indicate that the adoption of code review bots increases the number of monthly merged pull requests, decreases monthly non-merged pull requests, and decreases communication among developers. From the developers’ perspective, these effects are explained by the transparency and confidence the bot comments introduce, in addition to the changes in the discussion focused on pull requests. Practitioners and maintainers may leverage our results to understand, or even predict, bot effects on their projects.
Identification of Attack Paths Using Kill Chain and Attack Graphs

The ever-evolving capabilities of cyber attackers force security administrators to focus on the early identification of emerging threats. Targeted cyber attacks usually consist of several phases, from initial reconnaissance of the network environment to final impact on objectives. This paper investigates the identification of multi-step cyber threat scenarios using kill chain and attack graphs. Kill chain and attack graphs are threat modeling concepts that enable determining weak security defense points. We propose a novel kill chain attack graph that merges kill chain and attack graphs together. This approach determines possible chains of attacker's actions and their materialization within the protected network. The graph generation uses a categorization of threats according to violated security properties. The graph allows determining the kill chain phase the administrator should focus on and applicable countermeasures to mitigate possible cyber threats. We implemented the proposed approach for a predefined range of cyber threats, especially vulnerability exploitation and network threats. The approach was validated on a real-world use case. Publicly available implementation contains a proof-of-concept kill chain attack graph generator.
Narrowing the Gap: Improved Detector Training With Noisy Location Annotations

Deep learning methods require massive of annotated data for optimizing parameters. For example, datasets attached with accurate bounding box annotations are essential for modern object detection tasks. However, labeling with such pixel-wise accuracy is laborious and time-consuming, and elaborate labeling procedures are indispensable for reducing man-made noise, involving annotation review and acceptance testing. In this paper, we focus on the impact of noisy location annotations on the performance of object detection approaches and aim to, on the user side, reduce the adverse effect of the noise. First, noticeable performance degradation is experimentally observed for both one-stage and two-stage detectors when noise is introduced to the bounding box annotations. For instance, our synthesized noise results in performance decrease from 38.9% AP to 33.6% AP for FCOS detector on COCO test split, and 37.8%AP to 33.7%AP for Faster R-CNN. Second, a self-correction technique based on a Bayesian filter for prediction ensemble is proposed to better exploit the noisy location annotations following a Teacher-Student learning paradigm. Experiments for both synthesized and real-world scenarios consistently demonstrate the effectiveness of our approach, e.g., our method increases the degraded performance of the FCOS detector from 33.6% AP to 35.6% AP on COCO.
Quantum simulation in the semi-classical regime

Solving the time-dependent Schr\"odinger equation is an important application area for quantum algorithms. We consider Schr\"odinger's equation in the semi-classical regime. Here the solutions exhibit strong multiple-scale behavior due to a small parameter $\hbar$, in the sense that the dynamics of the quantum states and the induced observables can occur on different spatial and temporal scales. Such a Schr\"odinger equation finds many applications, including in Born-Oppenheimer molecular dynamics and Ehrenfest dynamics. This paper considers quantum analogues of pseudo-spectral (PS) methods on classical computers. Estimates on the gate counts in terms of $\hbar$ and the precision $\varepsilon$ are obtained. It is found that the number of required qubits, $m$, scales only logarithmically with respect to $\hbar$. When the solution has bounded derivatives up to order $\ell$, the symmetric Trotting method has gate complexity $\mathcal{O}\Big({ (\varepsilon \hbar)^{-\frac12} \mathrm{polylog}(\varepsilon^{-\frac{3}{2\ell}} \hbar^{-1-\frac{1}{2\ell}})}\Big),$ provided that the diagonal unitary operators in the pseudo-spectral methods can be implemented with $\mathrm{poly}(m)$ operations. When physical observables are the desired outcomes, however, the step size in the time integration can be chosen independently of $\hbar$. The gate complexity in this case is reduced to $\mathcal{O}\Big({\varepsilon^{-\frac12} \mathrm{polylog}( \varepsilon^{-\frac3{2\ell}} \hbar^{-1} )}\Big),$ with $\ell$ again indicating the smoothness of the solution.
Feature Overcorrelation in Deep Graph Neural Networks

Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the success, oversmoothing has been identified as one of the key issues which limit the performance of deep GNNs. It indicates that the learned node representations are highly indistinguishable due to the stacked aggregators. In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i.e., feature overcorrelation. Through empirical and theoretical study on this matter, we demonstrate the existence of feature overcorrelation in deeper GNNs and reveal potential reasons leading to this issue. To reduce the feature correlation, we propose a general framework DeCorr which can encourage GNNs to encode less redundant information. Extensive experiments have demonstrated that DeCorr can help enable deeper GNNs and is complementary to existing techniques tackling the oversmoothing issue.
Fast and Compute-Efficient Sampling-Based Local Exploration Planning via Distribution Learning

Exploration is a fundamental problem in robotics. While sampling-based planners have shown high performance, they are oftentimes compute intensive and can exhibit high variance. To this end, we propose to directly learn the underlying distribution of informative views based on the spatial context in the robot's map. We further explore a variety of methods to also learn the information gain. We show in thorough experimental evaluation that our proposed system improves exploration performance by up to 28% over classical methods, and find that learning the gains in addition to the sampling distribution can provide favorable performance vs. compute trade-offs for compute-constrained systems. We demonstrate in simulation and on a low-cost mobile robot that our system generalizes well to varying environments.
Adaptive Kernel Graph Neural Network

Graph neural networks (GNNs) have demonstrated great success in representation learning for graph-structured data. The layer-wise graph convolution in GNNs is shown to be powerful at capturing graph topology. During this process, GNNs are usually guided by pre-defined kernels such as Laplacian matrix, adjacency matrix, or their variants. However, the adoptions of pre-defined kernels may restrain the generalities to different graphs: mismatch between graph and kernel would entail sub-optimal performance. For example, GNNs that focus on low-frequency information may not achieve satisfactory performance when high-frequency information is significant for the graphs, and vice versa. To solve this problem, in this paper, we propose a novel framework - i.e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to adapt to the optimal graph kernel in a unified manner at the first attempt. In the proposed AKGNN, we first design a data-driven graph kernel learning mechanism, which adaptively modulates the balance between all-pass and low-pass filters by modifying the maximal eigenvalue of the graph Laplacian. Through this process, AKGNN learns the optimal threshold between high and low frequency signals to relieve the generality problem. Later, we further reduce the number of parameters by a parameterization trick and enhance the expressive power by a global readout function. Extensive experiments are conducted on acknowledged benchmark datasets and promising results demonstrate the outstanding performance of our proposed AKGNN by comparison with state-of-the-art GNNs. The source code is publicly available at: https://github.com/jumxglhf/AKGNN.
Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription

Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diarisation and speech recognition using a single model, a tandem multitask training (TMT) method is proposed to fine-tune W2V2. For speaker diarisation, the tasks of voice activity detection (VAD) and speaker classification (SC) are required, and connectionist temporal classification (CTC) is used for ASR. The multitask framework implements VAD, SC, and ASR using an early layer, middle layer, and late layer of W2V2, which coincides with the order of segmenting the audio with VAD, clustering the segments based on speaker embeddings, and transcribing each segment with ASR. Experimental results on the augmented multi-party (AMI) dataset showed that using different W2V2 layers for VAD, SC, and ASR from the earlier to later layers for TMT not only saves computational cost, but also reduces diarisation error rates (DERs). Joint fine-tuning of VAD, SC, and ASR yielded 16%/17% relative reductions of DER with manual/automatic segmentation respectively, and consistent reductions in speaker attributed word error rate, compared to the baseline with separately fine-tuned models.
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models

Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning.In this work, we build upon some of the existing techniques for predicting the zero-shot performance on a task, by modeling it as a multi-task learning problem.We jointly train predictive models for different tasks which helps us build more accurate predictors for tasks where we have test data in very few languages to measure the actual performance of the model.Our approach also lends us the ability to perform a much more robust feature selection, and identify a common set of features that influence zero-shot performance across a variety of tasks.
Understanding Iterative Revision from Human-Written Text

Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human's revision cycles. This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. In particular, IteraTeR is collected based on a new framework to comprehensively model the iterative text revisions that generalize to various domains of formal writing, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations. Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.
Sentimental Analysis of Code-Mixed Hindi Language

Rajalakshmi, Ratnavel Reddy, Preethi Khare, Shreya Ganganwar, VaishaliSentiment analysis is the task of identifying and classifying sentiments expressed in texts. Sentiment analysis of code-mixed data is a huge challenge for the NLP community since it is very different from the traditional structures of standard languages. Code mixing refers to additions of linguistic units like phrases or words of one language to another. The mixing of languages takes place not only on sentence level but also at the word level. It is important to perform sentiment analysis on such code-mixed data for better understanding of the text and for further classification. We have implemented various basic machine learning algorithms, viz. decision tree, linear SVC, logistic regression, multinomial naive Bayes, and SGD classifier for performing sentiment analysis on code-mixed Hinglish dataset. To address the issues of phonetic typing and multilingual words, we have proposed an ensemble-based classifier to identify the sentiment expressed in code-mixed Hinglish tweets. Based on the extensive experimental analysis, we observed that XGBoost performed well in comparison with other machine learning algorithms. With the XGBoost ensemble learning algorithm, we obtained an F1-score of 83.10%, which is significantly better than the existing state-of-the-art works on the Hinglish dataset.