Reviewerly/exHarmony huggingface dataset:
Compressed Gaussian Estimation under Low Precision Numerical Representation

This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy.
Quantum Circuit Template Matching Optimization Method for Constrained Connectivity

The execution of quantum algorithms requires two key considerations. On the one hand, it should meet the connectivity constraint requirements of quantum circuit mapping for quantum architectures, and on the other hand, it needs to consider reducing the probability of errors in the execution of quantum circuits as much as possible. This paper proposes a novel optimization technique based on template matching that to satisfy both requirements. The template matching optimization method can significantly reduce the number of gates in a quantum circuit and further enhance its practicality. It stands as advanced optimization technology available today. Our method optimizes quantum logic circuits mapped onto quantum architecture by initially selecting their linear substructure. We then zone the circuit according to the gate dependency graph and optimize each block through template matching. Finally, we reorganize the circuit to obtain the optimized version as the final result. Our proposed method is amenable to various quantum architectures. To evaluate its efficacy, we conduct a comparative analysis with the t|ket⟩ and Qiskit compiler using a set of benchmark test circuits. Specifically, compare to the t|ket⟩ compiler method, the highest average optimization rate of our method can reach 25.75%. Compare with the Qiskit compiler method, the highest average optimization rate can reach 32.72%. Overall, our approach has significant optimization advantages.
Experiencer-Driven and Knowledge-Aware Graph Model for emotion–cause pair extraction

Numerous approaches have been explored to learn relations between emotion and cause clauses as a key step in the extraction of emotion–cause pairs. Despite their effectiveness, there are some limitations of previous studies: (1) they ignored that the emotion experiencer is an important clue implicating causality between clauses and (2) ignored that causal commonsense as prior knowledge can enhance semantic associations between clauses. In this paper, we propose a novel Experiencer-Driven and Knowledge-Aware Graph Model (EDKA-GM). For the first limitation, we introduce an experiencer identification task and present a document-level heterogeneous graph network for capturing global experiencer information to enrich experiencer-based cross-clause association. For the second, we retrieve the causal commonsense from the ATOMIC knowledge base for each clause and establish a knowledge-aware graph network to further enhance the inter-clause relationship by modeling a full connection graph of clauses with commonsense knowledge. We are the first to explore the influence of experiencer on emotion–cause pair extraction. On a benchmark dataset, our approach performs better than competitive baselines, achieving new state-of-the-art performance.
Emergency Distance Education From the Perspective of a University During the COVID-19 Era

In this chapter, the reflections of the emergency distance education process experienced in a state university in Turkey were presented. The decisions taken during the pandemic period, the triggers of the decisions, the changes made, the problems encountered, the solution methods developed to solve these problems and the lessons learned were discussed. In addition, by associating the inferences obtained with the findings of the studies in the literature, indicators were provided to researchers, practitioners, and policy makers in the field of distance education to determine their future strategies. In this context, it is thought that sharing and criticizing the practices made are important in terms of being a guide for similar situations that may be encountered in the future.
CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back

Representing code changes as numeric feature vectors, i.e., code change representations, is usually an essential step to automate many software engineering tasks related to code changes, e.g., commit message generation and just-in-time defect prediction. Intuitively, the quality of code change representations is crucial for the effectiveness of automated approaches. Prior work on code changes usually designs and evaluates code change representation approaches for a specific task, and little work has investigated code change encoders that can be used and jointly trained on various tasks. To fill this gap, this work proposes a novel Code Change Representation learning approach named CCRep, which can learn to encode code changes as feature vectors for diverse downstream tasks. Specifically, CCRep regards a code change as the combination of its before-change and after-change code, leverages a pre-trained code model to obtain high-quality contextual embeddings of code, and uses a novel mechanism named query back to extract and encode the changed code fragments and make them explicitly interact with the whole code change. To evaluate CCRep and demonstrate its applicability to diverse code-change-related tasks, we apply it to three tasks: commit message generation, patch correctness assessment, and just-in-time defect prediction. Experimental results show that CCRep outperforms the state-of-the-art techniques on each task.
Reusing Deep Neural Network Models through Model Re-engineering

Training deep neural network (DNN) models, which has become an important task in today's software development, is often costly in terms of computational resources and time. With the inspiration of software reuse, building DNN models through reusing existing ones has gained increasing attention recently. Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e.g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack. To solve the above problem, we propose SeaM, a tool that re-engineers a trained DNN model to improve its reusability. Specifically, given a target problem and a trained model, SeaM utilizes a gradient-based search method to search for the model's weights that are relevant to the target problem. The re-engineered model that only retains the relevant weights is then reused to solve the target problem. Evaluation results on widely-used models show that the re-engineered models produced by SeaM only contain 10.11% weights of the original models, resulting 42.41% reduction in terms of inference time. For the target problem, the re-engineered models even outperform the original models in classification accuracy by 5.85%. Moreover, reusing the re-engineered models inherits an average of 57% fewer defects than reusing the entire model. We believe our approach to reducing reuse overhead and defect inheritance is one important step forward for practical model reuse.
Comparison and Evaluation of Clone Detection Techniques with Different Code Representations

As one of bad smells in code, code clones may increase the cost of software maintenance and the risk of vulnerability propagation. In the past two decades, numerous clone detection technologies have been proposed. They can be divided into text-based, token-based, tree-based, and graph-based approaches according to their code representations. Different code representations abstract the code details from different perspectives. However, it is unclear which code representation is more effective in detecting code clones and how to combine different code representations to achieve ideal performance. In this paper, we present an empirical study to compare the clone detection ability of different code representations. Specifically, we reproduce 12 clone detection algorithms and divide them into different groups according to their code representations. After analyzing the empirical results, we find that token and tree representations can perform better than graph representation when detecting simple code clones. However, when the code complexity of a code pair increases, graph representation becomes more effective. To make our findings more practical, we perform manual analysis on open-source projects to seek a possible distribution of different clone types in the open-source community. Through the results, we observe that most clone pairs belong to simple code clones. Based on this observation, we discard heavyweight graph-based clone detection algorithms and conduct combination experiments to find out a suitable combination of token-based and tree-based approaches for achieving scalable and effective code clone detection. We develop the suitable combination into a tool called TACC and evaluate it with other state-of-the-art code clone detectors. Experimental results indicate that TACC performs better and has the ability to detect large-scale code clones.
Predicting Bugs by Monitoring Developers During Task Execution

Knowing which parts of the source code will be defective can allow practitioners to better allocate testing resources. For this reason, many approaches have been proposed to achieve this goal. Most state-of-the-art predictive models rely on product and process metrics, i.e., they predict the defectiveness of a component by considering what developers did. However, there is still limited evidence of the benefits that can be achieved in this context by monitoring how developers complete a development task. In this paper, we present an empirical study in which we aim at understanding whether measuring human aspects on developers while they write code can help predict the introduction of defects. First, we introduce a new developer-based model which relies on behavioral, psychophysical, and control factors that can be measured during the execution of development tasks. Then, we run a controlled experiment involving 20 software developers to understand if our developer-based model is able to predict the introduction of bugs. Our results show that a developer-based model is able to achieve a similar accuracy compared to a state-of-the-art code-based model, i.e., a model that uses only features measured from the source code. We also observed that by combining the models it is possible to obtain the best results (84% accuracy).
Preserving Utility in Fair Top-k Ranking with Intersectional Bias

Ranking is required for many real applications, such as search, personalisation, recommendation, and filtering. Recent research has focused on developing reliable ranking algorithms that maintain fairness in their outcomes. However, only a few consider multiple protected groups since this extension introduces significant challenges. While useful in the research sector, considering only one binary sensitive feature for handling fairness is inappropriate when the algorithm must be deployed responsibly in real-world applications. Our work is built on top of Multinomial FA*IR, a Fair Top-k ranking with multiple protected groups, which we extend to provide users the option to balance fairness and utility, adapting the final ranking accordingly. Our experimental results show that alternative better solutions overlooked by Multinomial FA*IR may be found through our approach without violating fairness boundaries. The code of the implemented solution and the experiments are publicly available to the community as a GitHub repository.
Improving Generalization in Reinforcement Learning Through Forked Agents

An eco-system of agents, each having their own policy with limited generalizability, has proven to be a reliable approach to increase generalization across procedurally generated environments. In such an approach, new agents are regularly added to the eco-system when encountering a new environment that is outside of the scope of the eco-system. The speed of adaptation and general effectiveness of the eco-system approach highly depends on the initialization of new agents. In this paper we propose different initialization techniques, inspired from Deep Neural Network initialization and transfer learning, and study their impact.
Do You MIND? Reflections on the MIND Dataset for Research on Diversity in News Recommendations

The MIND dataset is at the moment of writing the most extensive dataset available for the research and development of news recommender systems. This work analyzes the suitability of the dataset for research on diverse news recommendations. On the one hand we analyze the effect the different steps in the recommendation pipeline have on the distribution of article categories, and on the other hand we check whether the supplied data would be sufficient for more sophisticated diversity analysis. We conclude that while MIND is a great step forward, there is still a lot of room for improvement.
HiMoReNet: A Hierarchical Model for Human Motion Refinement

3D human pose estimation has a broad range of applications, including anomaly detection and animation creation. Despite that significant progress on relative research has been made during the past decades, producing precise and smooth estimations for input videos still remains challenging mainly because of its ill-posed attributes. In this paper, we propose HiMoReNet, a post-processing motion refinement neural network based on an elaborate hierarchical architecture. Firstly, we distinguish characteristic motion patterns of joints at different locations by grouping the joints and employing respective spatiotemporal processing modules for each group. In addition, by mimicking interactions among multiple body parts, global context information is leveraged to further guide the motion refinement. Quantitative and qualitative results on the 3DPW dataset demonstrate that our proposed HiMoReNet achieves the state-of-the-art performance, and excels in jitter removal and precise pose estimation.
Informatization of Education in India

Over the last 5 years, the educational environment in India has undergone significant changes. Information technology in education has begun to play a role that had never played before. This paper is devoted to the organization and development of educational environment, first of all, from the point of view of the application of information technology. The problems of organizing the information environment of the educational process are considered. The questions related to the creating and developing the distributed educational environment are described. The problems of interaction between the individual elements of the unified electronic educational environment were solved. Possible prospects for the development of the distributed electronic educational environment in India are outlined. #CSOC1120.
An Evolutionary Shadow Correction Network and a Benchmark UAV Dataset for Remote Sensing Images

Shadow correction is an important task in the analysis of high-resolution remote sensing images, as the existence of shadows reduces radiometric information and causes changes in the energy distribution. This is especially the case when the spatial resolution is very high, as the shadows disturb the subsequent processing and applications, such as image mosaicking, classification, segmentation, etc. Traditional shadow correction methods are limited by the shadow detection accuracy and the available non-shaded samples in the imagery. In this paper, we propose an evolutionary shadow correction network (ESCNet) and describe how we built a benchmark unmanned aerial vehicle (UAV) image dataset to achieve shadow correction directly, without shadow detection. The proposed ESCNet is made up of two sub-networks with an evolutionary relationship: a shadow removal network (SRNet) followed by a radiation adjustment network (RANet). The shadows are first removed by SRNet trained on the UAV image dataset to achieve the primary shadow-corrected image, and the global radiation is then adjusted to a sunlit-like status by RANet. Shadow detection is not required in the proposed method, which effectively overcomes the error accumulation and shadow edge artifact problem of the traditional methods. Experiments were carried out and the results were compared with those of both traditional and deep learning-based shadow correction methods, for which both qualitative and quantitative evaluations were performed. The results suggest that the proposed method shows obvious advantages in information recovery for shadow regions, and the global brightness of the corrected imagery is consistent with that of sunlit conditions.
Temporal Prediction-Based Temporal Iterative Tracking and Parallel Motion Estimation for a 1-ms Rotation-Robust LK-Based Tracking System

The ultra-low delay tracking system, which enables seamless actuation in visual feedback applications, draws increasing attention in the fields of robotics and factory automation (FA). Tracking accuracy and rotational robustness are critical in robotics and FA applications. In the real-time scene, the change (e.g. object moving) continues in the real space when tracking processing is performed in the virtual (computing) space. However, existing research focuses primarily on the image-processing accuracy of still images, with few works paying attention to tracking errors introduced by the change that occurs during the processing period. On the other hand, making the algorithm robust to rotation comes with a complex model, preventing the whole system from reaching ultra-low delay. This research aims to develop a 1 ms rotation-robust Lucas Kanade (LK) based tracking algorithm and processing architecture, focusing on minimizing the error between the virtual space’s output and the true state in real space. To achieve the above target, this paper proposes: a) Temporal prediction-based temporal iterative tracking, which compensates for temporal change with temporal prediction, significantly reducing real-time processing error. b) Temporal prediction-based parallel motion estimation, which breaks the data dependency of translation and rotation estimations by temporal prediction, allowing translation and rotation estimation to be performed parallelly with low delay. The proposed methods are implemented as a practical system by integrating a high-speed camera and a field programmable gate array (FPGA). Algorithm evaluation shows that the proposed method outperforms the other five related methods in terms of real-time tracking performance. Hardware evaluation shows that the designed rotation-robust LK-based tracking system supports sensing and processing 1000 fps sequence with a delay of less than 1 ms/frame while costing resources less than 30 %. A video demonstration is available at https://wcms.waseda.jp/em/6422257c9c402.
Improving API Knowledge Discovery with ML: A Case Study of Comparable API Methods

Developers constantly learn new APIs, but often lack necessary information from documentation, resorting instead to popular question-and-answer platforms such as Stack Overflow. In this paper, we investigate how to use recent machine-Iearning-based knowledge extraction techniques to automatically identify pairs of comparable API methods and the sentences describing the comparison from Stack Overflow answers. We first built a prototype that can be stocked with a dataset of comparable API methods and provides tool-tips to users in search results and in API documentation. We conducted a user study with this tool based on a dataset of TensorFlow comparable API methods spanning 198 hand-annotated facts from Stack Overflow posts. This study confirmed that providing comparable API methods can be useful for helping developers understand the design space of APIs: developers using our tool were significantly more aware of the comparable API methods and better understood the differences between them. We then created SOREL, an comparable API methods knowledge extraction tool trained on our hand-annotated corpus, which achieves a 71% precision and 55% recall at discovering our manually extracted facts and discovers 433 pairs of comparable API methods from thousands of unseen Stack Overflow posts. This work highlights the merit of jointly studying programming assistance tools and constructing machine learning techniques to power them.
Using Reactive Synthesis: An End-to-End Exploratory Case Study

Reactive synthesis is an automated procedure to obtain a correct-by-construction reactive system from its temporal logic specification. Despite its attractiveness and major research progress in the past decades, reactive synthesis is still in early-stage and has not gained popularity outside academia. We conducted an exploratory case study in which we followed students in a semester-long university workshop class on their end-to-end use of a reactive synthesizer, from writing the specifications to executing the synthesized controllers. The data we collected includes more than 500 versions of more than 80 specifications, as well as more than 2500 Slack messages, all written by the class participants. Our grounded theory analysis reveals that the use of reactive synthesis has clear benefits for certain tasks and that adequate specification language constructs assist in the specification writing process. However, inherent issues such as unrealizabilty, non-well-separation, the gap of knowledge between the users and the synthesizer, and considerable running times prevent reactive synthesis from fulfilling its promise. Based on our analysis, we propose action items in the directions of language and specification quality, tools for analysis and execution, and process and methodology, all towards making reactive synthesis more applicable for software engineers.
An intelligent network monitoring approach for online classification of Darknet traffic

The Internet plays a crucial role in supporting global applications and businesses, but security remains a major challenge. Within the Internet, there exists a parallel network known as the Darknet, where malicious activities and traffic are present and require real-time classification. Many methods aim to classify this Darknet traffic in real-time due to its significant volume within Internet traffic. However, online Darknet traffic classification faces challenges, particularly in determining the optimal packet sampling amount for achieving a high classification rate in high-performance networks. To address this, our paper presents a novel approach that combines Convolutional Neural Network (CNN) and Reinforcement Learning (RL) techniques for intelligent and adaptive packet sampling rates in high-performance network interfaces. This method reduces overhead on monitored entities, especially in high-speed networks with a high bit rate. Our findings demonstrate a TOR traffic prediction accuracy of 99.84% and successful classification tasks in high-throughput networks using our method.