Reviewerly/exHarmony huggingface dataset:
An Empirical Study on Code Comment Completion

Code comments play a prominent role in program comprehension activities. However, source code is not always documented and code and comments not always co-evolve. To deal with these issues, researchers have proposed techniques to automatically generate comments documenting a given code at hand. The most recent works in the area applied deep learning (DL) techniques to support such a task. Despite the achieved advances, the empirical evaluations of these approaches show that they are still far from a performance level that would make them valuable for developers. We tackle a simpler and related problem: Code comment completion. Instead of generating a comment for a given code from scratch, we investigate the extent to which state-of-the-art techniques can help developers in writing comments faster. We present a large-scale study in which we empirically assess how a simple n-gram model and the recently proposed Text-To-Text Transfer Transformer (T5) architecture can perform in autocompleting a code comment the developer is typing. The achieved results show the superiority of the T5 model, despite the n-gram model being a competitive solution.
Controllable Summarization with Constrained Markov Decision Process

Abstract We study controllable text summarization, which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a novel training framework based on Constrained Markov Decision Process (CMDP), which conveniently includes a reward function along with a set of constraints, to facilitate better summarization control. The reward function encourages the generation to resemble the human-written reference, while the constraints are used to explicitly prevent the generated summaries from violating user-imposed requirements. Our framework can be applied to control important attributes of summarization, including length, covered entities, and abstractiveness, as we devise specific constraints for each of these aspects. Extensive experiments on popular benchmarks show that our CMDP framework helps generate informative summaries while complying with a given attribute’s requirement.1
Dynamics of quantum correlations in a qubit-qutrit spin system under random telegraph noise

Abstract We study the dimensionless time evolution of the logarithmic negativity and geometric quantum discord of a qubit-qutrit XXX spin model under the both Markovian and non-Markovian noise channels. We find that at a special temperature interval the quantum entanglement based on the logarithmic negativity reveals entanglement sudden deaths together with revivals. The revival phenomenon is due to the non-Markovianity resulting from the feedback effect of the environment. At high temperatures, the scenario of death and revival disappears. The geometric quantum discord evolves alternatively versus time elapsing with damped amplitudes until the system reaches steady state. It is demonstrated that the dynamics of entanglement negativity undergoes substantial changes by varying temperature, and it is much more fragile against the temperature rather than the geometric quantum discord. The real complex heterodinuclear [Ni(dpt)(H 2 O)Cu(pba)] · 2H 2 O [with pba = 1,3-propylenebis(oxamato) and dpt = bis-(3-aminopropyl)amine] is an experimental representative of our considered bipartite qubit-qutrit system that may show remarkable entanglement deaths and revivals at relatively high temperatures and high magnetic field that is comparable with the strength of the exchange interaction J between Cu +2 and Ni +2 ions, i.e., k B T ≈ J and μ B B ≈ J .
Explainable Intelligence-Driven Defense Mechanism against Advanced Persistent Threats: A Joint Edge Game and AI Approach

Advanced persistent threats (APT) have novel features such as long-term latency, precision strikes and uncertain strategies. APT poses severe threats to the resource-limited edge devices in advanced networks. Cyber threat intelligence (CTI) conducts data analysis on attack strategies by artificial intelligence (AI) and generates threat intelligence to optimize the detection model and guide defense strategies. However, AI lacks explanations for the decisions and thus reduces the transparency and performance of the detection model. Besides, the tradeoff between the detection accuracy and the computational resource limitation of edge devices needs an optimal and rapid dynamic resource allocation method, which edge game and AI can help. In this paper, we propose an explainable intelligence-driven APT edge defense mechanism. The proposed mechanism provides guidelines and explanations for designing the defense strategy and resource allocation scheme of the edge defender to detect APT. The edge defense strategy model is based on edge Bayesian Stackelberg game and CTI. Meanwhile, we implement a DRL-based resource allocation scheme to meet rapid response requirements at the edges. We demonstrate that the proposed mechanism can improve the protection level of edges and defense capability against APT through extensive experiments.
Unmanned Aerial Vehicles for Crowd Monitoring and Analysis

Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and regulatory bodies to tackle challenges posed by large crowds. Conventional methods of crowd analysis using static cameras are limited due to their low coverage area and non-flexible perspectives and features. Unmanned aerial vehicles have tremendously increased the quality of images obtained for crowd analysis reasons, relieving the relevant authorities of the venues’ inadequacies and of concerns of inaccessible locations and situation. This paper reviews existing literature sources regarding the use of aerial vehicles for crowd monitoring and analysis purposes. Vehicle specifications, onboard sensors, power management, and an analysis algorithm are critically reviewed and discussed. In addition, ethical and privacy issues surrounding the use of this technology are presented.
The Struggling Towards a Transdisciplinary Metaphysics

What is transdisciplinarity? I don’t know, but I do feel that the majority of those writing about it are not willing to jettison their disciplinary positionality to look for something which cannot be realised from what we currently accept as knowledge. Let me be clearer. Transdisciplinarity by its nature transcends inter-, multi- and inter-disciplinarity, for it seeks to resist the epistemological hegemony of disciplines to be the sole arbitrator of the real. Basically, I propose we can’t start from here seeking a new way of knowing and agency without understanding the metaphysics of the new reality (which may or may not somewhere in between being and non-being) which transcends disciplines. In a postdigital reality (Jandric et al. 2018) where we tend to understand and respond to the unleashed power of technology is a Weltanschauung in the terms Heidegger envisioned it in his essay ‘The Question Concerning Technology’ (1977) of where technology rule humanity rather than harnessed to enrich humanity. Like him, we can enhance our being through and with technology but we need to be in control of it. However, first we need to understand why this is important for our notions of being and I suggest we need to think bravely in the terms of that I develop below for of nature of being (see Fuller and Jandric 2019 for a fuller discussion).
MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation

Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU.
START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

A common performance problem in large-scale cloud systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks impact the system's QoS and the SLA. There is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore volatile task characteristics. We propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. START analyzes all tasks and hosts based on compute and network resource consumption using an Encoder LSTM network to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START and compare it with IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler in terms of QoS parameters. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16%, 19%, compared to the state-of-the-art.
Blockchain-Enabled Security Module for Transforming Conventional Inverters toward Firmware Security-Enhanced Smart Inverters

As the traditional inverters are transforming toward more intelligent inverters with advanced information and communication technologies, the cyber-attack surface has been remarkably expanded. Specifically, securing firmware of smart inverters from cyber-attacks is crucial. This paper provides expanded firmware attack surface targeting smart inverters. Moreover, this paper proposes a security module for transforming a conventional inverter to a firmware security built-in smart inverter by preventing potential malware and unauthorized firmware update attacks as well as fast automated inverter recovery from zero-day attacks. Furthermore, the proposed security module as a client of blockchain is connected to blockchain severs to fully utilize blockchain technologies such as membership service, ledgers, and smart contracts to detect and mitigate the firmware attacks. The proposed security module framework is implemented in an Internet-of-Thing (IoT) device and validated by experiments.
IVKMP: A robust data-driven heterogeneous defect model based on deep representation optimization learning

Heterogeneous defect prediction (HDP) aims to transfer informative knowledge, namely the defect-proneness tendency of software metrics, from a source project to predict potential defects in a target project by matching metrics with similar distributions between different software projects. Nevertheless, the complex internal intrinsic structure hidden behind the defect data makes it difficult for the prior heterogeneous defect models to capture and migrate the most informative software metrics, and severely hinders HDP performance. To address these issues, we propose a robust data-driven HDP model called IVKMP in this study. We firstly adopt an advanced deep generation network – InfoGAN (Information maximizing GANs) for data augmentation, namely simultaneously achieving class balance and generating sufficient defect instances. Secondly, the multi-objective VaEA (Vector angle-based Evolutionary Algorithm) optimization is employed to select the fewest representative metric subsets while achieving the minimum error. Finally, a deep defect predictor for HDP based on the lightweight but effective deep network – PCANet (Principal Component Analysis Network) with the binary hashing and block-wise histogram is built to essentially capture more semantically related robust representations. We compare the IVKMP model with multiple state-of-the-art baseline models across 542 heterogeneous project pairs of 26 software projects. Experimental results demonstrate the superiority and robustness of our IVKMP model.
Expanding Accessibility: Sensory Sensitive Programming for Museums

Abstract Museums in the US have a responsibility to provide accessible facilities stipulated by the American with Disabilities Act (ADA). Unfortunately, programming adjustments beyond physical accessibility is generally an emerging consideration or only implemented in large, established institutions. Although some museums have implemented disability‐specific programming, focusing on more universal challenges of access (e.g., sensory needs) can increase accessibility in a museum of any size and budget. The following manuscript presents an example of a small science museum in a mid‐size city and the development and implementation process of innovative sensory‐sensitive programming for community members with disabilities and their families. By addressing a common characteristic that spans across different disabilities, the Sensory Sensitive Program (SSP) was successfully implemented and attended by a diverse array of community members. Feedback from patrons attending the pilot event is reported and used to inform the iterative development process for the program. Considerations for future implementation of SSP are provided.
Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared.
Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance software such as Aphros, Lammps (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art software frameworks.
Fine-Tuning GPT-3 for Russian Text Summarization

Automatic summarization techniques aim to shorten and generalize information given in the text while preserving its core message and the most relevant ideas. This task can be approached and treated with a variety of methods, however, not many attempts have been made to produce solutions specifically for the Russian language despite existing localizations of the state-of-the-art models. In this paper, we aim to showcase ruGPT3 ability to summarize texts, fine-tuning it on the corpora of Russian news with their corresponding human-generated summaries. Additionally, we employ hyperparameter tuning so that the model’s output becomes less random and more tied to the original text. We evaluate the resulting texts with a set of metrics, showing that our solution can surpass the state-of-the-art model’s performance without additional changes in architecture or loss function. Despite being able to produce sensible summaries, our model still suffers from a number of flaws, namely, it is prone to altering Named Entities present in the original text (such as surnames, places, dates, etc.), deviating from facts stated in the given document, and repeating the information in the summary. #COMESYSO1120.
Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.
Adaptive virtual reality

Virtual reality (VR) technology can be used to induce emotional states during academic studies and immersive experiences. This application of VR can be extended through an adaptive interactive mechanism (AIM), which alters aspects of the virtual environment (VE) in real-time to create personalized experiences calibrated to the individual user. Adaptive VR functions on the basis of a closed-loop design, where behavior, psychophysiology, or neurophysiology is monitored to create a real-time model of the user. This quantification is used to infer the emotional state of the individual user and trigger adaptive changes within the VE during run time. This chapter will describe the rationale underpinning the concept of adaptive VR, how distinct groups of AIMs can be organized into a framework and delineates the closed-loop design underpinning this concept. It is argued that adaptive VR can increase the potency of emotional experiences by allowing the system to model individual variation as users interact within a specific VE. A VR study is described that demonstrates how individual differences can be predicted based on an interaction history in a specific context in VR. Participants were monitored in a VE designed to induce threat using a virtual height scenario. Data were used to predict individual traits such as age, gender, neuroticism, and openness. The same data were utilized to predict the likelihood of subsequent behavioral outcomes. The study demonstrates the potential of implicit monitoring within a VE, which illustrates how adaptive VR can create experiences tailored to the individual.
Robustness, replicability and scalability in topic modelling

Approaches for estimating the similarity between individual publications are an area of long-standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mining approaches based on title and abstract text have appeared more frequently in recent years. In principle, topic models have great potential in this domain. But, in practice, they are often difficult to employ successfully, and are notoriously inconsistent as latent space dimension grows. In this manuscript we identify the three properties all usable topic models should have: robustness, descriptive power and reflection of reality. We develop a novel method for evaluating the robustness of topic models and suggest a metric to assess and benchmark descriptive power as number of topics scale. Employing that procedure, we find that the neural-network-based paragraph embedding approach seems capable of providing statistically robust estimates of the document–document similarities, even for topic spaces far larger than what is usually considered prudent for the most common topic model approaches.
RRSARNet: A Novel Network for Radar Radio Sources Adaptive Recognition

Radar radio source (RRS) recognition plays an important role in the fields of military electronic support systems (ESM) and civilian autonomous driving. The rapid development of machine learning technology, especially deep learning, has effectively and efficiently improved RRS intelligent recognition performances when operating in the increasingly complex electromagnetic environment. However, the data sampling limitation and computation cost are still severe challenges in real RRS recognition scenarios. In this paper, we propose a novel network based on meta-transfer learning, called RRSARNet, to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR). First, by using the short-time Fourier transform, a six-type small samples RRS simulation dataset with different SNR levels is constructed. Then, a novel RRSARNet, based on metric learning, is proposed, which consists of a four-layer embedding module and a four-layer relational module. Finally, the RRS dataset is divided into training, supporting and testing subsets, which are used to train and test the RRSARNet in a meta-transfer learning method. Experiments on the RRS dataset show that the proposed RRSARNet can achieve an overall accuracy (OA) above 96% and 99% when the SNR is above −15 dB and −10 dB, respectively. Even when the SNR is -30 dB, OA can reach more than 70%. For 5-way 1-shot and 5-way 5-shot experiments, the inference time of an image is about 0.043 and 0.140 milliseconds, respectively. Besides, experiments on the RRS simulation dataset and the two benchmark datasets, the RRSARNet performs better or more competitive than many existing state-of-the-art technologies in terms of recognition accuracy.