Reviewerly/exHarmony huggingface dataset:
Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks

Developers often introduce the self-admitted technical debt (SATD), i.e., a compromised solution to satisfy the delivery of the current goals, in code comments but do not eliminate them timely in the following software development and maintenance process. Automatically identifying the SATDs to reduce potential harm to software has attracted the attention of researchers. However, existing approaches only identified SATDs at a coarse-grained level, which impacts developers to locate and remove them. This paper proposes a novel model named GCF, which is a deep learning method to enhance the performance of multi-type SATD classification based on generative adversarial network. Method: The GCF model employs the JSD Generative Adversarial Network to solve the imbalance problem, utilizes CodeBERT to fuse information of code snippets and natural language for initializing the instances as embedding vectors, and introduces the feature extraction module to extract the instance features more comprehensively. The experimental results show that, the GCF model obtains better performance compared with the state-of-the-art method. Moreover, experiments on the GCF model variants and others with different GAN models show the superiority of the GCF model. Our proposed GCF model effectively solves the problem of imbalanced types of SATD, fuses the information of code snippets and natural language, and extracts key features to achieve outstanding performance in detecting multi-type SATD. Therefore, the GCF model is an effective method for detecting multi-type SATD.
Agricultural Text Classification Method Based on Dynamic Fusion of Multiple Features

The traditional text classification method, which treats the values in agricultural text as characters, will lose the original semantic expression of numerical features. In order to fully mine the deep latent semantic features in agricultural text, a novel text classification method based on multivariate feature dynamic fusion is proposed. The Bi-directional Long Short Term Memory network (Bi-LSTM) model with attention mechanism was used to extract the global key semantic features of the text; the multiple windows Convolution Neural Network were used to extract the local semantic information of the text at different levels; Numerical value features containing essential semantic expression were extracted by artificial method to construct the numerical value feature vector. By introducing the attention mechanism to dynamically fuse the extracted multiple semantic features, which can further enrich the deep semantic expression of agricultural text and effectively improve the effect of agricultural text classification with phenotypic numerical type.
Toward the Automatic Modulation Classification With Adaptive Wavelet Network

With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment. Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully connected layers for classification. However, existing schemes still are insufficient in utilizing feature maps. To overcome this limitation, a novel adaptive wavelet network (AWN) is proposed, which combines adaptive wavelet decomposition based on the lifting scheme and channel attention mechanism.In contrast to the previous models, the multi-level decomposition of AWN explicitly extracts the features of multiple frequency bands. The channel attention mechanism further selects the optimal frequencies from the candidate frequencies. AWN explores a novel AMC paradigm that efficiently integrates the inherent properties of the signal by introducing prior knowledge in the frequency domain. Simulation results demonstrate that our proposed AMC scheme outperforms the benchmark scheme and has rather low computational complexity.
Adapting Data-Driven Techniques to Improve Surrogate Machine Learning Model Performance

We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions and fine-tuning of pre-trained models. Each of these methods have seen widespread use in the field of machine learning, however, here we apply them specifically to surrogate machine learning model development. The machine learning model that forms the basis behind this work was intended to surrogate a traditional engineering model used in the UK nuclear industry. Previous performance of this model has been hampered by poor performance due to limited training data. Here, we demonstrate that through a combination of additional techniques, model performance can be significantly improved. We show that each of the aforementioned techniques have utility in their own right and in combination with one another. However, we see them best applied when used to fine-tune existing models. Five pre-trained surrogate models produced prior to this study were further trained using an augmented dataset and with our custom loss function. Through the combination of all three techniques, we see an improvement of at least 38% in performance across the five models.
OFCOS: An Oriented Anchor-Free Detector for Ship Detection in Remote Sensing Images

Ship detection is a significant and challenging task in remote sensing. At present, anchor-based ship detectors have achieved remarkable results, but they require introducing additional parameters and their performance is easily affected by the size of anchor boxes. In this paper, we propose an anchor-free rotated detector (OFCOS) for ship detection based on FCOS, which can be trained end-to-end. Specifically, a feature-enhanced feature pyramid network (FE-FPN) is proposed, in which the structure of the feature pyramid is optimized and an attention mechanism is introduced during fusion to enhance the significance of object features. Then, to better describe the orientation of objects, a regression branch with orientation characterization capability is constructed, and a center-to-corner bounding box prediction strategy is used to improve the accuracy of object localization. Moreover, the calculation of center-ness is optimized so that the assignment of center weights is orientation-aware and adaptive to down-weight low-quality predictions. A new remote sensing ship dataset, named RS-Ship, is constructed to further verify the effectiveness and robustness of OFCOS. Our experiments show that OFCOS achieves AP values of 91.07% and 97.05% on the publicly available dataset HRSC2016 and our self-built RS-Ship dataset, respectively, which are 13.01% and 9.84% higher than FCOS. OFCOS outperforms other mainstream detection methods in terms of both detection speed and detection accuracy.
SRL-TR<sup>2</sup>: A Safe Reinforcement Learning Based TRajectory TRacker Framework

This paper aims to solve the trajectory tracking control problem for an autonomous vehicle based on reinforcement learning methods. Existing reinforcement learning approaches have found limited successful applications on safety-critical tasks in the real world mainly due to two challenges: 1) sim-to-real transfer; 2) closed-loop stability and safety concern. In this paper, we propose an actor-critic-style framework SRL-TR2, in which the RL-based TRajectory TRackers are trained under the safety constraints and then deployed to a full-size vehicle as the lateral controller. To improve the generalization ability, we adopt a light-weight adapter State and Action Space Alignment (SASA) to establish mapping relations between the simulation and reality. To address the safety concern, we leverage an expert strategy to take over the control when the safety constraints are not satisfied. Hence, we conduct safe explorations during the training process and improve the stability of the policy. The experiments show that our agents can achieve one-shot transfer across simulation scenarios and unseen realistic scenarios, finishing the field tests with average running time less than 10 ms/step and average lateral error less than 0.1 m under the speed ranging from 12 km/h to 18 km/h. A video of the field tests is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/pjWcN_fV24g</uri> .
SDANet: Semantic-Embedded Density Adaptive Network for Moving Vehicle Detection in Satellite Videos

In satellite videos, moving vehicles are extremely small-sized and densely clustered in vast scenes. Anchor-free detectors offer great potential by predicting the keypoints and boundaries of objects directly. However, for dense small-sized vehicles, most anchor-free detectors miss the dense objects without considering the density distribution. Furthermore, weak appearance features and massive interference in the satellite videos limit the application of anchor-free detectors. To address these problems, a novel semantic-embedded density adaptive network (SDANet) is proposed. In SDANet, the cluster-proposals, including a variable number of objects, and centers are generated parallelly through pixel-wise prediction. Then, a novel density matching algorithm is designed to obtain each object via partitioning the cluster-proposals and matching the corresponding centers hierarchically and recursively. Meanwhile, the isolated cluster-proposals and centers are suppressed. In SDANet, the road is segmented in vast scenes and its semantic features are embedded into the network by weakly supervised learning, which guides the detector to emphasize the regions of interest. By this way, SDANet reduces the false detection caused by massive interference. To alleviate the lack of appearance information on small-sized vehicles, a customized bi-directional conv-RNN module extracts the temporal information from consecutive input frames by aligning the disturbed background. The experimental results on Jilin-1 and SkySat satellite videos demonstrate the effectiveness of SDANet, especially for dense objects.
A Novel Team Formation Framework Based on Performance in a Cybersecurity Operations Center

A Cybersecurity Operations Center (CSOC) performs various tasks to protect an organization from cyber threats. Several types of personnel collaborate to function effectively as a team to analyze the threat signals, in the form of alerts, arriving from various sources. Teams are often formed ad hoc, resulting in an imbalance in their performances and thereby increasing the risk associated with the low-performing teams. The current approach taken by behavioral scientists in forming effective teams focuses on first qualitatively assessing individuals such as analysts, who are then grouped into teams based on their credentials and expertise. Our work takes a holistic view of the CSOC by first defining team requirements and then selecting individuals to form several collaborative teams that meet these requirements for every shift of operation. We present a novel team formation framework that integrates optimization, simulation, and scoring methods to form effective teams and introduce a new collaborative score metric that measures their effectiveness. Results from simulated experiments show the formation of effective teams whose collaborative scores are maximized and balanced. Our approach is also able to identify high and low performers within the first few months of implementing the framework.
A Greedy Agglomerative Framework for Clustered Federated Learning

Federated learning (FL) has received widespread attention for decentralized training of deep learning models across devices while preserving privacy. Industrial big data in key applications like healthcare, smart manufacturing, autonomous driving, and robotics is inherently multi-source and heterogeneous. Recent studies have shown that the quality of the global FL model deteriorates in the presence of such non-IID data. To address this, we present a novel clustered FL framework called Federated Learning via Agglomerative Client Clustering (FLACC). FLACC greedily agglomerates clients or groups of clients based on their gradient updates while learning the global FL model. Once the clustering is complete, each cluster is separated into its own federation, allowing clients with similar underlying distributions to train together. In contrast with existing methods, FLACC does not require the number of clusters to be specified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> , can handle client fractions, and is robust to hyperparameter tuning. We demonstrate the efficacy of this framework through extensive experiments on three benchmark FL datasets and an FL case study simulated using industrial mixed fault classification data. Qualitative clustering results show that FLACC accurately identifies clusters in the presence of various statistical heterogeneities in the client data. Quantitative results show that FLACC outperforms vanilla FL and state-of-the-art personalized and clustered FL methods, even when the underlying clustering structure is not apparent.
Lightweight Distortion-Aware Network for Salient Object Detection in Omnidirectional Images

Compared with 2D image salient object detection (SOD), SOD in omnidirectional images (or 360° images) usually suffers from geometric distortion. Although existing omnidirectional image SOD (ODI-SOD) methods have improved the detection accuracy obviously, their application may be cumbersome in real scenes due to their high computational cost. To avoid distortion and reduce the computational cost simultaneously in ODI-SOD, we propose a novel lightweight distortion-aware network, named LDNet, in this letter. First, to extract features with less distortion from ODIs, we integrate the distortion-aware convolution and depth-wise separable convolution (DSConv) into distortion-aware DSConv (DDSConv) and replace the regular convolutions in the last two blocks of the ResNet-18 with DDSConvs to obtain our lightweight backbone network (LD-ResNet-18). To enhance spatial information in each channel of the extracted features at each level comprehensively, then, we propose a lightweight distortion-aware channel-wise enhancement (DCE) module (only 0.05M parameters) including DDSConvs with various dilation rates, channel shuffle operation and attention mechanism, and employ a high-to-low dense connection structure to modulate the enhanced multi-level features. Besides, we design a distortion-aware self-correlation (DSC) module (only 0.02M parameters) for mining the contextual dependency of the features via a coarse-fine strategy, and the correlated features are refined by DCE modules and integrated by another dense connection structure. The final saliency map is predicted from the densely integrated features. Compared with 12 state-of-the-art methods on two public datasets, our lightweight LDNet achieves competitive or even better performance with only 2.9M parameters and 3.4G FLOPs, which balances the efficiency and performance.
Prioritizing Non-Functional Requirements in Agile Process Using Multi Criteria Decision Making Analysis

Agile software development does not include elicitation and management of non-functional requirements (NFRs). Several techniques have been proposed by researchers to elicit, manage and prioritize NFRs. The major issue with such techniques is that they involve customer or users to elicit and prioritize NFRs, which becomes problematic because they do not have exposure of NFRs. They may only be familiar with very few common NFRs such as performance, efficiency and security etc. There could be several other NFRs such as confidentiality, scalability, usability, maintainability, portability, reliability and many others which may not be known to the stakeholders. Consequently, some relevant NFRs may be missed and some wrong NFRS may be implemented in the final product which can degrade its overall quality. Secondly, it is very time consuming to elicit and prioritize NFRs from the stakeholders who have very low exposure. It is especially critical in the case of agile software development where product is developed and delivered at a very rapid pace. To overcome the challenge, this study proposed a model to prioritize NFRs for agile software development to produce quality software products. Thus, firstly, an extensive literature review was conducted to identify the NFRs which were further ranked through a survey carried out in agile software companies. Moreover, the NFRs were analyzed and prioritized using multi criteria decision making analysis technique called analytical hierarchical technique (AHP). The evaluation results showed that the NFRs prioritization model reduce the overhead of meetings and discussion required to gather and identify the NFRs common to agile products.
A Framework for Emotion-Oriented Requirements Change Handling in Agile Software Engineering

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</b> Requirements Changes (RCs) – the additions/modifications/deletions of functional/non-functional requirements in software products – are challenging for software practitioners to handle. Handling some changes may significantly impact the emotions of the practitioners. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</b> We wanted to know the key challenges that make RC handling difficult, how these impact the emotions of software practitioners, what influences their RC handling, and how RC handling can be made less emotionally challenging. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</b> We followed a mixed-methods approach. We conducted two survey studies, with 40 participants and 201 participants respectively. The presentation of key quantitative data was followed by descriptive statistical analysis, and the qualitative data was analysed using Strauss–Corbinian Grounded Theory, and Socio–Technical Grounded Theory analysis techniques. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</b> We found (1) several key factors that make RC handling an emotional challenge, (2) varying emotions that practitioners feel when it is challenging to handle RCs, (3) how stakeholders, including practitioners themselves, peers, managers and customers, influence the RC handling and how practitioners feel due to the stakeholder influence, and (4) practices that can be used to better handle RCs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</b> Some challenges are technical and some are social which also belong to aspects of agile practice, emotional intelligence, and likely belong to cognitive intelligence. Therefore, to better handle RCs with positive emotions in socio–technical environments, agility, emotional intelligence, and cognitive intelligence need to work in synergy with each other.
Disaggregated Memory in the Datacenter: A Survey

Datacenters of today have maintained the same architecture for decades.The building block of the datacenter remains the server, which tightly couples the necessary compute resources, memory, and storage to run its tasks.However, this traditional approach suffers from under-utilization of its resources, often caused by the over-provisioning of these resources when deploying applications.Datacenter operators allocate the worst-case amount of memory required for each deployed application, which lasts for the entirety of the application's lifetime, even when not actually used.This causes servers to quickly, and falsely, run out of memory before their CPUs have been fully utilized.To address these problems, a new shift in the way datacenters are being built has been gaining more traction.Namely, memory disaggregation.Memory disaggregation can address these problems by decoupling the computational elements from the memory resources, allowing each to be provisioned and utilized separately.While the idea of memory disaggregation is not new, an increasing number of different proposals of memory disaggregation have seen the light in recent years.In this paper, we review many of these recent proposals, and study their architectures, implementations, and requirements.We also categorize them based on their features, and attempt to identify their strengths and shortcomings in an effort to highlight possible directions for future work and provide a reference for the research community.
Generative Inference Network for Imbalanced Domain Generalization

Domain generalization (DG) aims to learn transferable knowledge from multiple source domains and generalize it to the unseen target domain. To achieve such expectation, the intuitive solution is to seek domain-invariant representations via generative adversarial mechanism or minimization of cross-domain discrepancy. However, the widespread imbalanced data scale problem across source domains and category in real-world applications becomes the key bottleneck of improving generalization ability of model due to its negative effect on learning the robust classification model. Motivated by this observation, we first formulate a practical and challenging imbalance domain generalization (IDG) scenario, and then propose a straightforward but effective novel method generative inference network (GINet), which augments reliable samples for minority domain/category to promote discriminative ability of the learned model. Concretely, GINet utilizes the available cross-domain images from the identical category and estimates their common latent variable, which derives to discover domain-invariant knowledge for unseen target domain. According to these latent variables, our GINet further generates more novel samples with optimal transport constraint and deploys them to enhance the desired model with more robustness and generalization ability. Considerable empirical analysis and ablation studies on three popular benchmarks under normal DG and IDG setups suggests the advantage of our method over other DG methods on elevating model generalization. The source code is available in GitHub https://github.com/HaifengXia/IDG.
PetPS: Supporting Huge Embedding Models with Persistent Memory

Embedding models are effective for learning high-dimensional sparse data. Traditionally, they are deployed in DRAM parameter servers (PS) for online inference access. However, the ever-increasing model capacity makes this practice suffer from both high storage costs and long recovery time. Rapidly developing Persistent Memory (PM) offers new opportunities to PSs owing to its large capacity at low costs, as well as its persistence, while the application of PM also faces two challenges including high read latency and heavy CPU burden. To provide a low-cost but still high-performance parameter service for online inferences, we introduce PetPS, the first production-deployed PM parameter server. (1) To escape with high PM latency, PetPS introduces a PM hash index tailored for embedding model workloads, to minimize PM access. (2) To alleviate the CPU burden, PetPS offloads parameter gathering to NICs, to avoid CPU stalls when accessing parameters on PM and thus improve CPU efficiency. Our evaluation shows that PetPS can boost throughput by 1.3 -- 1.7X compared to PSs that use state-of-the-art PM hash indexes, or get 2.9 -- 5.5X latency reduction with the same throughput. Since 2020, PetPS has been deployed in Kuaishou, one world-leading short video company, and successfully reduced TCO by 30% without performance degradation.